From 513b8be9b754884eb0938e05a04d57550d964754 Mon Sep 17 00:00:00 2001 From: Henry D Date: Fri, 15 May 2020 09:58:12 -0700 Subject: [PATCH 01/34] Add Leland's demo notebook --- levels_ridge_regression_tutorial.ipynb | 869 +++++++++++++++++++++++++ 1 file changed, 869 insertions(+) create mode 100644 levels_ridge_regression_tutorial.ipynb diff --git a/levels_ridge_regression_tutorial.ipynb b/levels_ridge_regression_tutorial.ipynb new file mode 100644 index 000000000..35e84897c --- /dev/null +++ b/levels_ridge_regression_tutorial.ipynb @@ -0,0 +1,869 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np\n", + "import pyspark\n", + "from pyspark import SparkContext\n", + "from pyspark.sql import SparkSession\n", + "from glow.levels.linear_model import RidgeReducer, RidgeRegression\n", + "import pyspark.sql.functions as f" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "spark = SparkSession.builder.appName('levels').getOrCreate()\n", + "spark.conf.set('spark.sql.execution.arrow.enabled', 'true')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "test_data_root = '/Users/leland.barnard/glow/glow-wgr/test-data/levels/ridge-regression' #path to glow levels test data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We need three objects to get started:\n", + "* A Spark DataFrame representing the block genotype matrix\n", + "* A Spark DataFrame containing a mapping of sample block ID to corresponding list of sample ids\n", + "* A Pandas DataFrame containing phenotypes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "blockdf_lvl0 = spark.read.parquet(f'{test_data_root}/blockedGT.snappy.parquet') #block genotype matrix\n", + "indexdf = spark.read.parquet(f'{test_data_root}/groupedIDs.snappy.parquet') #sample block ID to sample list mapping\n", + "labeldf = pd.read_csv(f'{test_data_root}/pts.csv').set_index('sample_id') #phenotype data" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The block genotype matrix as a DataFrame\n", + "If we imagine the block genotype matrix conceptually, we think of an *NxM* matrix *X* where each row *n* represents an individual sample, each column *m* represents a variant, and each cell *(n, m)* contains a genotype value for sample *n* at variant *m*. We then imagine laying a coarse grid on top of this matrix such that matrix cells within the same coarse grid cell are all assigned to the same block *x*. Each block *x* is indexed by a sample block ID (corresponding to a list of rows belonging to the block) and a header block ID (corresponding to a list of columns belonging to the block). The sample block IDs are generally just integers 0 through the number of sample blocks. The header block IDs are strings of the form 'chr_C_block_B', which refers to the Bth block on chromosome C. The Spark DataFrame representing this block matrix can be thought of as the transpose of each block *xT* all stacked one atop another. Each row represents the values from a particular column from *X*, for the samples corresponding to a particular sample block. The fields in the DataFrame are:\n", + "* header: Corresponds to a column name in the conceptual matrix *X*.\n", + "* size: If the matrix is sparse (so that genotype values of 0 are implicit), the values for this header are represented as a sparse vector, and this column contains the size of that sparse vector. Corresponds to the number of individuals in the sample block for the row.\n", + "* indices: (Optional, present of the matrix is sparse) Indices of the non-zero entries in the sparse vector for this header in this sample block.\n", + "* values: Genotype values for this header in this sample block. If the matrix is sparse, contains only non-zero values.\n", + "* header_block: An ID assigned to the block *x* containing this header.\n", + "* sample_block: An ID assigned to the block *x* containing the group of samples represented on this row.\n", + "* position: An integer assigned to this header that specifies the correct sort order for the headers in this block.\n", + "* mu: The mean of the genotype calls for this header\n", + "* sig: The standard deviation of the genotype calls for this header" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+---------------+----+--------------------+--------------------+-------------+------------+---------+------------------+------------------+\n", + "| header|size| indices| values| header_block|sample_block| position| mu| sig|\n", + "+---------------+----+--------------------+--------------------+-------------+------------+---------+------------------+------------------+\n", + "|2:231414300:T:C| 9| [0, 1, 6, 7, 8]|[1.0, 1.0, 1.0, 1...|chr_2_block_6| 7|231414300|0.8686868686868686|0.6730002176294544|\n", + "|2:231414300:T:C| 10|[0, 1, 2, 3, 4, 5...|[2.0, 1.0, 1.0, 1...|chr_2_block_6| 1|231414300|0.8686868686868686|0.6730002176294544|\n", + "|2:231414300:T:C| 12|[1, 3, 4, 5, 7, 8...|[2.0, 1.0, 1.0, 1...|chr_2_block_6| 8|231414300|0.8686868686868686|0.6730002176294544|\n", + "|2:231414300:T:C| 13|[0, 1, 2, 3, 4, 5...|[2.0, 1.0, 2.0, 1...|chr_2_block_6| 9|231414300|0.8686868686868686|0.6730002176294544|\n", + "+---------------+----+--------------------+--------------------+-------------+------------+---------+------------------+------------------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "blockdf_lvl0.show(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The sample block mapping\n", + "This is a comparitively simple key-value store where each key is a sample block ID and each value is a list of sample IDs contained in that sample block. As a Spark DataFrame, this is represented as a two column DataFrame with the following fields:\n", + "* sample_block: ID for a sample block\n", + "* sample_ids: Array of sample IDs for the samples in this sample block. The order of these IDs must match the order of the values arrays in the block genotype DataFrame." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+--------------------+\n", + "|sample_block| sample_ids|\n", + "+------------+--------------------+\n", + "| 3|[1008962444, 1035...|\n", + "| 9|[1083737921, 1041...|\n", + "| 7|[1048623585, 1030...|\n", + "| 1|[1073111137, 1082...|\n", + "+------------+--------------------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "indexdf.show(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### The phenotype data\n", + "The phenotype data is represented as a Pandas DataFrame indexed by the sample ID. Each column represents a single phenotype, and it is assumed that there are no missing phenotype values, and that the phenotypes mean centered at 0." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " sim100 sim92 sim58 sim16\n", + "sample_id \n", + "1042204109 -0.905058 -1.171217 -1.437376 -1.703535\n", + "1035505158 -0.616539 -0.411283 -0.206027 -0.000770\n", + "1008166305 -0.946014 -0.482639 -0.019263 0.444112\n", + "1068805020 -1.155375 -0.660005 -0.164634 0.330736\n", + "1095012035 -1.024889 -0.492179 0.040530 0.573240" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labeldf.head()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Reducer model fitting\n", + "The first step in the fitting procedure is to apply a dimensionality reduction to the block matrix *X* using the `RidgeReducer`. This is accomplished by fitting multiple ridge models within each block *x* and producing a new block matrix where each column represents the prediction of one ridge model applied within one block. This approach to model building is generally referred to as **stacking**. We will call the block genotype matrix we started with the **level 0** matrix in the stack *X0*, and the output of the ridge reduction step the **level 1** matrix *X1*. The `RidgeReducer` class is used for this step, which is initiallized with a list of ridge regularization values (referred to here as alpha). Since ridge models are indexed by these alpha values, the `RidgeReducer` will generate one ridge model per value of alpha provided, which in turn will produce one column per block in *X0*, so the final dimensions of matrix *X1* will be *Nx(LxK)*, where *L* is the number of header blocks in *X0* and *K* is the number of alpha values provided to the `RidgeReducer`. In practice, we can estimate a span of alpha values in a reasonable order of magnitude based on guesses at the heritability of the phenotype we are fitting, but here we will just pick some values." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "alphas_lvl0 = np.logspace(2, 5, 10)\n", + "stack_lvl0 = RidgeReducer(alphas_lvl0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "When the `RidgeReducer` is initialized, it will assign names to the provided alphas and store them in a dict accessible as `RidgeReducer.alphas`. This is mostly just to give an easily readable and sortable name to the models produced for each ridge value." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'alpha_0': 100.0,\n", + " 'alpha_1': 215.44346900318845,\n", + " 'alpha_2': 464.15888336127773,\n", + " 'alpha_3': 1000.0,\n", + " 'alpha_4': 2154.4346900318824,\n", + " 'alpha_5': 4641.588833612777,\n", + " 'alpha_6': 10000.0,\n", + " 'alpha_7': 21544.346900318822,\n", + " 'alpha_8': 46415.888336127726,\n", + " 'alpha_9': 100000.0}" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "stack_lvl0.alphas" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `RidgeReducer.fit(blockdf, labeldf, indexdf)` method generates a Spark DataFrame representing the model that we can use to reduce *X0* to *X1*." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "modeldf_lvl0 = stack_lvl0.fit(blockdf_lvl0, labeldf, indexdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "In explicit terms, the reduction of a block *x0* from *X0* to the corresponding block *x1* from *X1* is accomplished by the matrix multiplication *x0 * B = x1*, where *B* is a coefficient matrix of size *mxK*, where *m* is the number of columns in block *x0* and *K* is the number of alpha values used in the reduction. As an added wrinkle, if the ridge reduction is being performed against multiple phenotypes at once, each phenotype will have its own *B*, and for convenience we panel these next to each other in the output into a single matrix, so *B* in that case has dimensions *mx(K*P)* where *P* is the number of phenotypes. Each matrix *B* is specific to a particular block in *X0*, so the Spark DataFrame produced by the `RidgeReducer` can be thought of all of as the matrices *B* from all of the blocks stacked one atop another. The fields in the model DataFrame are:\n", + "* header_block: An ID assigned to the block *x0* corresponding to the coefficients in this row.\n", + "* sample_block: An ID assigned to the block *x0* corresponding to the coefficients in this row.\n", + "* header: The name of a column from the conceptual matrix *X0* that correspond with a particular row from the coefficient matrix *B*.\n", + "* alphas: List of alpha names corresponding to the columns of *B*.\n", + "* labels: List of label (i.e., phenotypes) corresponding to the columns of *B*. \n", + "* coefficients: List of the actual values from a row in *B*" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------+------------+---------------+---------+--------------------+--------------------+--------------------+\n", + "| header_block|sample_block| header| position| alphas| labels| coefficients|\n", + "+-------------+------------+---------------+---------+--------------------+--------------------+--------------------+\n", + "|chr_3_block_8| 0|3:160741710:G:A|160741710|[alpha_0, alpha_1...|[sim100, sim100, ...|[0.07462677364336...|\n", + "|chr_3_block_8| 0|3:175345110:C:T|175345110|[alpha_0, alpha_1...|[sim100, sim100, ...|[0.07834053929928...|\n", + "|chr_3_block_8| 0|3:183469890:A:G|183469890|[alpha_0, alpha_1...|[sim100, sim100, ...|[0.02152237814164...|\n", + "|chr_3_block_8| 0|3:195047160:C:T|195047160|[alpha_0, alpha_1...|[sim100, sim100, ...|[0.01153728383795...|\n", + "+-------------+------------+---------------+---------+--------------------+--------------------+--------------------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "modeldf_lvl0.show(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Reducer transformation\n", + "After fitting, the `RidgeReducer.transform(blockdf, labeldf, modeldf)` method can be used to generate `X1` from `X0`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "blockdf_lvl1 = stack_lvl0.transform(blockdf_lvl0, labeldf, modeldf_lvl0)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The output of the transformation is closely analogous to the block matrix DataFrame we started with. The main difference is that, rather than representing a single block matrix, it really represents multiple block matrices, with one such matrix per label (phenotype). Comparing the schema of this block matrix DataFrame (`blockdf_lvl1`) with the DataFrame we started with (`blockdf_lvl0`), the new columns are:\n", + "* alpha: This is the name of the alpha value used in fitting the model that produced the values in this row.\n", + "* label: This is the label corresponding to the values in this row. Since the genotype block matrix *X0* is phenotype-agnostic, the rows in `blockdf_lvl0` were not restricted to any label/phenotype, but the level 1 block matrix *X1* represents ridge model predictions for the labels the reducer was fit with, so each row is associated with a specific label." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+----+--------------------+------------+------------+--------+--------------------+-------------------+-------+------+\n", + "| header|size| values|header_block|sample_block|position| mu| sig| alpha| label|\n", + "+--------------------+----+--------------------+------------+------------+--------+--------------------+-------------------+-------+------+\n", + "|chr_3_block_8_alp...| 13|[0.08337895454032...| chr_3| 0| 80| 0.04148112816674154|0.19099426058493266|alpha_0|sim100|\n", + "|chr_3_block_8_alp...| 13|[0.04796003873174...| chr_3| 0| 81| 0.02402075708176127|0.11316256614620662|alpha_1|sim100|\n", + "|chr_3_block_8_alp...| 13|[0.02504256254617...| chr_3| 0| 82|0.012596289114544081|0.06030642726717367|alpha_2|sim100|\n", + "|chr_3_block_8_alp...| 13|[0.01234023662311...| chr_3| 0| 83|0.006221371128544...|0.03006776034645892|alpha_3|sim100|\n", + "+--------------------+----+--------------------+------------+------------+--------+--------------------+-------------------+-------+------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "blockdf_lvl1.show(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The headers in the *X1* block matrix are derived from a combination of the source block in *X0*, the alpha value used in fitting the ridge model, and the label they were fit with. These headers are assigned to header blocks that correspond to the chromosome of the source block in *X0*." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+----------------------------------+------------+\n", + "|header |header_block|\n", + "+----------------------------------+------------+\n", + "|chr_3_block_8_alpha_0_label_sim100|chr_3 |\n", + "|chr_3_block_8_alpha_1_label_sim100|chr_3 |\n", + "|chr_3_block_8_alpha_2_label_sim100|chr_3 |\n", + "|chr_3_block_8_alpha_3_label_sim100|chr_3 |\n", + "+----------------------------------+------------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "blockdf_lvl1.select('header', 'header_block').show(4, False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Regression fitting\n", + "The block matrix *X1* can be used to fit a final predictive model that can generate phenotype predictions *y_hat* using the `RidgeRegression` class. As with the `RidgeReducer` class, this class is initialized with a list of alpha values." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "alphas_lvl1 = np.logspace(1, 4, 10)\n", + "estimator_lvl1 = RidgeRegression(alphas_lvl1)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [], + "source": [ + "modeldf_lvl1_est, cvdf_lvl1 = estimator_lvl1.fit(blockdf_lvl1, labeldf, indexdf)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The `RidgeRegression.fit(blockdf, labeldf, indexdf)` works in much the same way as the `RidgeReducer.fit(blockdf, labeldf, indexdf)` method, except that it returns two DataFrames:" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A model DataFrame analogous to the model DataFrame provided by the `RidgeReducer`. An important difference is that the header block ID for all rows will be 'all', indicating that all headers from all blocks have been used in a single fit, rather than fitting within blocks." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+------------+--------------------+--------+--------------------+--------------------+--------------------+\n", + "|header_block|sample_block| header|position| alphas| labels| coefficients|\n", + "+------------+------------+--------------------+--------+--------------------+--------------------+--------------------+\n", + "| all| 1|chr_1_block_0_alp...| 0|[alpha_0, alpha_1...|[sim16, sim16, si...|[0.02787784249249...|\n", + "| all| 1|chr_2_block_0_alp...| 0|[alpha_0, alpha_1...|[sim16, sim16, si...|[-0.0164002560049...|\n", + "| all| 1|chr_3_block_0_alp...| 0|[alpha_0, alpha_1...|[sim16, sim16, si...|[-0.0234168451974...|\n", + "| all| 1|chr_1_block_0_alp...| 1|[alpha_0, alpha_1...|[sim16, sim16, si...|[0.00381390574280...|\n", + "+------------+------------+--------------------+--------+--------------------+--------------------+--------------------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "modeldf_lvl1_est.show(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "A cross validation (cv) report DataFrame, which reports the results of the hyperparameter (i.e., alpha) value optimization routine.\n", + "* label: This is the label corresponding to the cross cv results on the row.\n", + "* alpha: The name of the optimal alpha value\n", + "* r2_mean: The mean out of fold r2 score for the optimal alpha value" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------+-------+--------------------+\n", + "| label| alpha| r2_mean|\n", + "+------+-------+--------------------+\n", + "| sim92|alpha_5| 0.18389799898047948|\n", + "| sim16|alpha_8|-0.22499071350515992|\n", + "| sim58|alpha_6|-0.02504464471643515|\n", + "|sim100|alpha_5| 0.2566748993770534|\n", + "+------+-------+--------------------+\n", + "\n" + ] + } + ], + "source": [ + "cvdf_lvl1.show(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Producing phenotype predictions *y_hat*\n", + "After fitting the `RidgeRegression` model, the model DataFrame and cv DataFrame are used to apply the model to the block matrix DataFrame to produce predictions (*y_hat*) for each label in each sample block using the `RidgeRegression.transform(blockdf, labeldf, modeldf, cvdf)` method" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "y_hat_lvl1 = estimator_lvl1.transform(blockdf_lvl1, labeldf, modeldf_lvl1_est, cvdf_lvl1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The resulting *y_hat* DataFrame has the following fields:\n", + "* sample_block: The sample block ID for the samples corresponding to the *y_hat* values on this row.\n", + "* label: The label corresponding to the *y_hat* values on this row\n", + "* alpha: The name of the alpha value used to fit the model that produced the *y_hat* values on this row.\n", + "* values: The array of *y_hat* values for the samples in the sample block for this row." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+-----+-------+--------------------+\n", + "|sample_block|label| alpha| values|\n", + "+------------+-----+-------+--------------------+\n", + "| 1|sim16|alpha_8|[0.08461773658136...|\n", + "| 4|sim16|alpha_8|[0.08343907935865...|\n", + "| 7|sim16|alpha_8|[-0.0976335915514...|\n", + "| 8|sim16|alpha_8|[-0.0461222342349...|\n", + "+------------+-----+-------+--------------------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "y_hat_lvl1.show(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Fitting a second round of ridge reduction instead of ridge regression\n", + "After fitting the first ridge reduction step and producing *X1* from *X0*, we can go directly into fitting the final ridge regression model, as we have just seen. Alternatively, we can fit a second round of ridge reduction to squeeze *X1* into an even smaller feature matrix, which we will call the **level 2** matrix *X2*. This has some advantages when it comes to generating the leave-one-chromosome-out versions of the *y_hat*s and does not come at much additional cost. The procedure for fitting the second round of ridge reduction is identical to the first (we will reuse the same alphas we chose for the ridge regression fit above):" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "stack_lvl1 = RidgeReducer(alphas_lvl1)" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "modeldf_lvl1 = stack_lvl1.fit(blockdf_lvl1, labeldf, indexdf)\n", + "blockdf_lvl2 = stack_lvl1.transform(blockdf_lvl1, labeldf, modeldf_lvl1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The **level 2** block matrix DataFrame produced here has an identical schema to the **level 1** block matrix. A key difference is that the header block ID for all headers is now \"all\" for all headers, indicating that there are now no more blocks to collapse." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+--------------------+----+--------------------+------------+------------+--------+--------------------+-------------------+-------+-----+\n", + "| header|size| values|header_block|sample_block|position| mu| sig| alpha|label|\n", + "+--------------------+----+--------------------+------------+------------+--------+--------------------+-------------------+-------+-----+\n", + "|all_block_1_alpha...| 13|[-0.0796642628265...| all| 9| 10|-1.49453099468771...| 0.4138556129030118|alpha_0|sim16|\n", + "|all_block_1_alpha...| 13|[-0.0957216101643...| all| 9| 11| 0.0|0.40982580962691184|alpha_1|sim16|\n", + "|all_block_1_alpha...| 13|[-0.1070572367031...| all| 9| 12|5.124106267500723...|0.40028561461234197|alpha_2|sim16|\n", + "|all_block_1_alpha...| 13|[-0.1163419313886...| all| 9| 13|1.708035422500241...| 0.3798792747100316|alpha_3|sim16|\n", + "+--------------------+----+--------------------+------------+------------+--------+--------------------+-------------------+-------+-----+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "blockdf_lvl2.show(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The headers for each column now follow the name convention 'all_block_B_alpha_A_label_L', which refer to the ridge model prediction using alpha A and for label L fit using the features from header block B from block matrix *X1*. Since the blocks in *X1* refer to chromosomes, the block number B here can be interpreted as a chromosome. The 'all' token reflects the fact that we are not assigning the columns in *X2* to any new blocks (i.e, *X2* only has sample blocks, but there is only one header block which encompasses the entire matrix)." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+-------------------------------+\n", + "|header |\n", + "+-------------------------------+\n", + "|all_block_1_alpha_0_label_sim16|\n", + "|all_block_1_alpha_1_label_sim16|\n", + "|all_block_1_alpha_2_label_sim16|\n", + "|all_block_1_alpha_3_label_sim16|\n", + "+-------------------------------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "blockdf_lvl2.select('header').show(4, False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can now fit a ridge regression model as we did above, except that we will use the matrix *X2* instead of *X1*" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "alphas_lvl2 = np.logspace(0, 3, 10)\n", + "estimator_lvl2 = RidgeRegression(alphas_lvl2)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "modeldf_lvl2_est, cvdf_lvl2 = estimator_lvl2.fit(blockdf_lvl2, labeldf, indexdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+------------+--------------------+--------+--------------------+--------------------+--------------------+\n", + "|header_block|sample_block| header|position| alphas| labels| coefficients|\n", + "+------------+------------+--------------------+--------+--------------------+--------------------+--------------------+\n", + "| all| 1|all_block_1_alpha...| 10|[alpha_0, alpha_1...|[sim16, sim16, si...|[-0.0939792871878...|\n", + "| all| 1|all_block_1_alpha...| 11|[alpha_0, alpha_1...|[sim16, sim16, si...|[-0.0788683759104...|\n", + "| all| 1|all_block_1_alpha...| 12|[alpha_0, alpha_1...|[sim16, sim16, si...|[-0.0693010949556...|\n", + "| all| 1|all_block_1_alpha...| 13|[alpha_0, alpha_1...|[sim16, sim16, si...|[-0.0446945065691...|\n", + "+------------+------------+--------------------+--------+--------------------+--------------------+--------------------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "modeldf_lvl2_est.show(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------+-------+--------------------+\n", + "| label| alpha| r2_mean|\n", + "+------+-------+--------------------+\n", + "| sim92|alpha_7| 0.199251090828654|\n", + "| sim16|alpha_9|-0.22903758326079596|\n", + "| sim58|alpha_7|0.005461670993813417|\n", + "|sim100|alpha_8| 0.2314559298409073|\n", + "+------+-------+--------------------+\n", + "\n" + ] + } + ], + "source": [ + "cvdf_lvl2.show(4)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "y_hat_lvl2 = estimator_lvl2.transform(blockdf_lvl2, labeldf, modeldf_lvl2_est, cvdf_lvl2)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+-----+-------+--------------------+\n", + "|sample_block|label| alpha| values|\n", + "+------------+-----+-------+--------------------+\n", + "| 9|sim58|alpha_7|[-0.2126330471314...|\n", + "| 6|sim58|alpha_7|[0.18042213283121...|\n", + "| 5|sim58|alpha_7|[-0.0126226427178...|\n", + "| 2|sim58|alpha_7|[0.00871975701462...|\n", + "+------------+-----+-------+--------------------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "y_hat_lvl2.show(4)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For producing the LOCO versions of the *y_hat* vectors, it is only necessary to filter out rows from `blockdf_lvl2` corresponding to the chromosome we wish to drop before applying the transformation. For example, if we wanted to produce *y_hat* with chromosome 1 left out (recall that the chromosomes constitute the source blocks for the headers in `blockdf_lvl2`, so headers from chromosome 1 will have headers like %block_1%):" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": {}, + "outputs": [], + "source": [ + "y_hat_lvl2_loco1 = estimator_lvl2.transform(blockdf_lvl2.filter(f'header NOT LIKE \"%block_1%\"'), labeldf, modeldf_lvl2_est, cvdf_lvl2)" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "+------------+-----+-------+--------------------+\n", + "|sample_block|label| alpha| values|\n", + "+------------+-----+-------+--------------------+\n", + "| 9|sim58|alpha_7|[-0.1347024836295...|\n", + "| 6|sim58|alpha_7|[0.20213653390706...|\n", + "| 5|sim58|alpha_7|[-0.1602333580401...|\n", + "| 2|sim58|alpha_7|[-0.1511874717623...|\n", + "+------------+-----+-------+--------------------+\n", + "only showing top 4 rows\n", + "\n" + ] + } + ], + "source": [ + "y_hat_lvl2_loco1.show(4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "glow", + "language": "python", + "name": "glow" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} From 41d8fba074469ae9259d93d141c47b203772f8c9 Mon Sep 17 00:00:00 2001 From: Kiavash Kianfar Date: Tue, 19 May 2020 15:05:02 -0700 Subject: [PATCH 02/34] block_variants_and_samples Transformer to create genotype DataFrame for WGR (#2) * blocks Signed-off-by: kianfar77 * test vcf Signed-off-by: kianfar77 * transformer Signed-off-by: kianfar77 * remove extra Signed-off-by: kianfar77 * refactor and conform with ridge namings Signed-off-by: kianfar77 * test Signed-off-by: kianfar77 * test files Signed-off-by: kianfar77 * remove extra file Signed-off-by: kianfar77 * sort_key Signed-off-by: kianfar77 --- .../io.projectglow.DataFrameTransformer | 1 + .../scala/io/projectglow/common/schemas.scala | 10 + .../BlockVariantsAndSamplesTransformer.scala | 58 ++ .../VariantSampleBlockMaker.scala | 119 +++ ...ckVariantsAndSamplesTransformerSuite.scala | 125 ++++ ...ypes.chr20.10100000.100Samples.Blocked.tsv | 687 ++++++++++++++++++ 6 files changed, 1000 insertions(+) create mode 100644 core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformer.scala create mode 100644 core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala create mode 100644 core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala create mode 100644 test-data/variantsampleblockmaker-test/1000G.phase3.broad.withGenotypes.chr20.10100000.100Samples.Blocked.tsv diff --git a/core/src/main/resources/META-INF/services/io.projectglow.DataFrameTransformer b/core/src/main/resources/META-INF/services/io.projectglow.DataFrameTransformer index a04cdd8bd..85cf09bc6 100644 --- a/core/src/main/resources/META-INF/services/io.projectglow.DataFrameTransformer +++ b/core/src/main/resources/META-INF/services/io.projectglow.DataFrameTransformer @@ -1,4 +1,5 @@ io.projectglow.transformers.LiftOverVariantsTransformer +io.projectglow.transformers.blockvariantsandsamples.BlockVariantsAndSamplesTransformer io.projectglow.transformers.normalizevariants.NormalizeVariantsTransformer io.projectglow.transformers.splitmultiallelics.SplitMultiallelicsTransformer io.projectglow.transformers.pipe.PipeTransformer diff --git a/core/src/main/scala/io/projectglow/common/schemas.scala b/core/src/main/scala/io/projectglow/common/schemas.scala index 7f7cf601f..b0a11efd4 100644 --- a/core/src/main/scala/io/projectglow/common/schemas.scala +++ b/core/src/main/scala/io/projectglow/common/schemas.scala @@ -140,6 +140,16 @@ object VariantSchemas { def plinkSchema(hasSampleIds: Boolean): StructType = { StructType(plinkBaseSchema :+ plinkGenotypeSchema(hasSampleIds)) } + + // BlockedGT Fields + val headerField = StructField("header", StringType) + val sizeField = StructField("size", IntegerType) + val valuesField = StructField("values", ArrayType(DoubleType)) + val headerBlockIdField = StructField("header_block", StringType) + val sampleBlockIdField = StructField("sample_block", StringType) + val sortKeyField = StructField("sort_key", LongType) + val meanField = StructField("mu", DoubleType) + val stdDevField = StructField("sig", DoubleType) } object FeatureSchemas { diff --git a/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformer.scala b/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformer.scala new file mode 100644 index 000000000..e153e80a5 --- /dev/null +++ b/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformer.scala @@ -0,0 +1,58 @@ +/* + * Copyright 2019 The Glow Authors + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package io.projectglow.transformers.blockvariantsandsamples + +import io.projectglow.DataFrameTransformer +import io.projectglow.common.logging.HlsUsageLogging + +import org.apache.spark.sql.DataFrame + +/** + * Implements DataFrameTransformer to transform the input DataFrame of variants to Blocked GT + * DataFrame for WGR use + */ +class BlockVariantsAndSamplesTransformer extends DataFrameTransformer with HlsUsageLogging { + + import BlockVariantsAndSamplesTransformer._ + + override def name: String = TRANSFORMER_NAME + + override def transform(df: DataFrame, options: Map[String, String]): DataFrame = { + + val variantsPerBlock = validateIntegerOption(options, VARIANTS_PER_BLOCK) + val sampleBlockCount = validateIntegerOption(options, SAMPLE_BLOCK_COUNT) + + VariantSampleBlockMaker.makeVariantAndSampleBlocks(df, variantsPerBlock, sampleBlockCount) + } +} + +object BlockVariantsAndSamplesTransformer { + val TRANSFORMER_NAME = "block_variants_and_samples" + val VARIANTS_PER_BLOCK = "variants_per_block" + val SAMPLE_BLOCK_COUNT = "sample_block_count" + + def validateIntegerOption(options: Map[String, String], optionName: String): Int = { + try { + (options.get(optionName).get.toInt) + } catch { + case _: Throwable => + throw new IllegalArgumentException( + s"$optionName is not provided or cannot be cast as an integer!" + ) + } + } +} diff --git a/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala b/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala new file mode 100644 index 000000000..46335e525 --- /dev/null +++ b/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala @@ -0,0 +1,119 @@ +/* + * Copyright 2019 The Glow Authors + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package io.projectglow.transformers.blockvariantsandsamples + +import io.projectglow.common.GlowLogging +import io.projectglow.common.VariantSchemas._ +import io.projectglow.functions._ + +import org.apache.spark.sql.DataFrame +import org.apache.spark.sql.expressions.Window +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.types.{ArrayType, IntegerType, StringType} + +private[projectglow] object VariantSampleBlockMaker extends GlowLogging { + + def makeVariantAndSampleBlocks( + variantDf: DataFrame, + variantsPerBlock: Int, + sampleBlockCount: Int): DataFrame = { + val windowSpec = Window + .partitionBy(contigNameField.name) + .orderBy(startField.name, refAlleleField.name, alternateAllelesField.name) + + variantDf + .withColumn( + sortKeyField.name, + col(startField.name) + ) + .withColumn( + headerField.name, + concat_ws( + ":", + col(contigNameField.name), + col(startField.name), + col(refAlleleField.name), + col(alternateAllelesField.name) + ) + ) + .withColumn( + headerBlockIdField.name, + concat_ws( + "_", + lit("chr"), + col(contigNameField.name), + lit("block"), + ((row_number().over(windowSpec) - 1) / variantsPerBlock).cast(IntegerType) + ) + ) + .withColumn( + "stats", + subset_struct( + array_summary_stats( + col(valuesField.name) + ), + "mean", + "stdDev" + ) + ) + .withColumn( + meanField.name, + col("stats.mean") + ) + .withColumn( + stdDevField.name, + col("stats.stdDev") + ) + .withColumn( + sampleBlockIdField.name, + explode( + sequence( + lit(1), + lit(sampleBlockCount) + ).cast(ArrayType(StringType)) + ) + ) + .withColumn( + "fractionalSampleBlockSize", + size(col(valuesField.name)) / sampleBlockCount + ) + .withColumn( + valuesField.name, + expr( + s"""slice( + | ${valuesField.name}, + | round((${sampleBlockIdField.name} - 1) * fractionalSampleBlockSize) + 1, + | round(${sampleBlockIdField.name} * fractionalSampleBlockSize) - round((${sampleBlockIdField.name} - 1) * fractionalSampleBlockSize) + |)""".stripMargin + ) + ) + .withColumn( + sizeField.name, + size(col(valuesField.name)) + ) + .select( + col(headerField.name), + col(sizeField.name), + col(valuesField.name), + col(headerBlockIdField.name), + col(sampleBlockIdField.name), + col(sortKeyField.name), + col(meanField.name), + col(stdDevField.name) + ) + } +} diff --git a/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala b/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala new file mode 100644 index 000000000..b401ed1e6 --- /dev/null +++ b/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala @@ -0,0 +1,125 @@ +/* + * Copyright 2019 The Glow Authors + * + * Licensed under the Apache License, Version 2.0 (the "License"); + * you may not use this file except in compliance with the License. + * You may obtain a copy of the License at + * + * http://www.apache.org/licenses/LICENSE-2.0 + * + * Unless required by applicable law or agreed to in writing, software + * distributed under the License is distributed on an "AS IS" BASIS, + * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + * See the License for the specific language governing permissions and + * limitations under the License. + */ + +package io.projectglow.transformers.blockvariantsandsamples + +import io.projectglow.Glow +import io.projectglow.common.GlowLogging +import io.projectglow.sql.GlowBaseTest +import io.projectglow.common.VariantSchemas._ +import io.projectglow.functions.genotype_states + +import org.apache.spark.sql.functions._ +import io.projectglow.transformers.blockvariantsandsamples.BlockVariantsAndSamplesTransformer._ + +import org.apache.spark.sql.types._ + +class BlockVariantsAndSamplesTransformerSuite extends GlowBaseTest with GlowLogging { + + lazy val sourceName: String = "vcf" + lazy val testFolder: String = s"$testDataHome/variantsampleblockmaker-test" + + lazy val testVcf = + s"$testDataHome/1000G.phase3.broad.withGenotypes.chr20.10100000.vcf" + + lazy val testExpectedTsv = + s"$testFolder/1000G.phase3.broad.withGenotypes.chr20.10100000.100Samples.Blocked.tsv" + + def testBlockedvsExpected( + originalVCFFileName: String, + expectedBlockedFileName: String, + variantsPerBlock: Int, + sampleBlockCount: Int + ): Unit = { + + val options: Map[String, String] = Map( + VARIANTS_PER_BLOCK -> variantsPerBlock.toString, + SAMPLE_BLOCK_COUNT -> sampleBlockCount.toString + ) + + val dfOriginal = spark + .read + .format(sourceName) + .load(originalVCFFileName) + .withColumn( + valuesField.name, + slice( + genotype_states( + col(genotypesFieldName) + ), + 1, + 100 + ).cast(ArrayType(DoubleType)) + ) + + val dfBlocked = Glow + .transform( + TRANSFORMER_NAME, + dfOriginal, + options + ) + + val dfExpected = spark + .read + .format("csv") + .options( + Map( + "delimiter" -> "\t", + "header" -> "true" + ) + ) + .schema( + StructType( + Seq( + headerField, + sizeField, + StructField(valuesField.name, StringType), + headerBlockIdField, + sampleBlockIdField, + sortKeyField, + meanField, + stdDevField + ) + ) + ) + .load(testExpectedTsv) + .withColumn( + valuesField.name, + split(col(valuesField.name), ",").cast(ArrayType(DoubleType)) + ) + + assert(dfBlocked.count() == dfExpected.count()) + + dfExpected + .collect + .zip( + dfBlocked.collect + ) + .foreach { + case (rowExp, rowBlocked) => + assert(rowExp.equals(rowBlocked), s"Expected\n$rowExp\nBlocked\n$rowBlocked") + } + } + + test("test blocked vs expected") { + testBlockedvsExpected( + testVcf, + testExpectedTsv, + 20, + 7 + ) + } +} diff --git a/test-data/variantsampleblockmaker-test/1000G.phase3.broad.withGenotypes.chr20.10100000.100Samples.Blocked.tsv b/test-data/variantsampleblockmaker-test/1000G.phase3.broad.withGenotypes.chr20.10100000.100Samples.Blocked.tsv new file mode 100644 index 000000000..9f483ccc8 --- /dev/null +++ b/test-data/variantsampleblockmaker-test/1000G.phase3.broad.withGenotypes.chr20.10100000.100Samples.Blocked.tsv @@ -0,0 +1,687 @@ +header size values header_block sample_block position mu sig +20:10000053:CTTTG:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 1 10000053 0.010000000000000002 0.1 +20:10000053:CTTTG:C 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 2 10000053 0.010000000000000002 0.1 +20:10000053:CTTTG:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 3 10000053 0.010000000000000002 0.1 +20:10000053:CTTTG:C 14 0,0,0,0,0,0,0,0,0,0,1,0,0,0 chr_20_block_0 4 10000053 0.010000000000000002 0.1 +20:10000053:CTTTG:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 5 10000053 0.010000000000000002 0.1 +20:10000053:CTTTG:C 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 6 10000053 0.010000000000000002 0.1 +20:10000053:CTTTG:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 7 10000053 0.010000000000000002 0.1 +20:10000106:T:C 14 0,0,0,0,0,0,0,0,0,0,1,0,0,0 chr_20_block_0 1 10000106 7.355227538141664E-18 0.14213381090374025 +20:10000106:T:C 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 2 10000106 7.355227538141664E-18 0.14213381090374025 +20:10000106:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 3 10000106 7.355227538141664E-18 0.14213381090374025 +20:10000106:T:C 14 0,0,0,0,0,0,0,0,0,-1,0,0,0,0 chr_20_block_0 4 10000106 7.355227538141664E-18 0.14213381090374025 +20:10000106:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 5 10000106 7.355227538141664E-18 0.14213381090374025 +20:10000106:T:C 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 6 10000106 7.355227538141664E-18 0.14213381090374025 +20:10000106:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 7 10000106 7.355227538141664E-18 0.14213381090374025 +20:10000116:C:T 14 0,1,0,0,1,2,2,1,0,2,1,1,1,0 chr_20_block_0 1 10000116 1.1599999999999997 0.7483314773547883 +20:10000116:C:T 15 1,1,0,2,1,1,0,2,0,2,1,1,1,2,0 chr_20_block_0 2 10000116 1.1599999999999997 0.7483314773547883 +20:10000116:C:T 14 0,2,2,2,1,2,2,2,2,1,1,1,1,1 chr_20_block_0 3 10000116 1.1599999999999997 0.7483314773547883 +20:10000116:C:T 14 1,2,0,1,0,0,1,2,1,2,2,2,1,0 chr_20_block_0 4 10000116 1.1599999999999997 0.7483314773547883 +20:10000116:C:T 14 0,0,1,2,2,2,0,1,1,1,2,1,2,2 chr_20_block_0 5 10000116 1.1599999999999997 0.7483314773547883 +20:10000116:C:T 15 2,2,0,1,2,1,1,1,2,2,1,0,0,1,1 chr_20_block_0 6 10000116 1.1599999999999997 0.7483314773547883 +20:10000116:C:T 14 1,1,2,2,1,1,1,2,2,2,1,2,2,0 chr_20_block_0 7 10000116 1.1599999999999997 0.7483314773547883 +20:10000210:C:T 14 0,1,0,0,1,2,2,1,0,2,1,1,1,0 chr_20_block_0 1 10000210 1.1599999999999997 0.7617099317995139 +20:10000210:C:T 15 0,1,0,2,1,2,0,2,0,2,1,2,1,2,0 chr_20_block_0 2 10000210 1.1599999999999997 0.7617099317995139 +20:10000210:C:T 14 0,2,2,2,1,2,2,2,2,1,1,1,1,1 chr_20_block_0 3 10000210 1.1599999999999997 0.7617099317995139 +20:10000210:C:T 14 1,2,0,1,0,0,1,2,1,2,2,2,1,1 chr_20_block_0 4 10000210 1.1599999999999997 0.7617099317995139 +20:10000210:C:T 14 0,0,1,2,1,2,0,1,2,1,2,1,0,2 chr_20_block_0 5 10000210 1.1599999999999997 0.7617099317995139 +20:10000210:C:T 15 2,2,0,1,2,1,1,1,2,2,1,0,1,1,1 chr_20_block_0 6 10000210 1.1599999999999997 0.7617099317995139 +20:10000210:C:T 14 0,1,2,2,1,1,1,2,2,2,1,2,2,0 chr_20_block_0 7 10000210 1.1599999999999997 0.7617099317995139 +20:10000239:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 1 10000239 0.0 0.0 +20:10000239:C:T 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 2 10000239 0.0 0.0 +20:10000239:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 3 10000239 0.0 0.0 +20:10000239:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 4 10000239 0.0 0.0 +20:10000239:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 5 10000239 0.0 0.0 +20:10000239:C:T 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 6 10000239 0.0 0.0 +20:10000239:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 7 10000239 0.0 0.0 +20:10000352:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 1 10000352 0.0 0.0 +20:10000352:C:T 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 2 10000352 0.0 0.0 +20:10000352:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 3 10000352 0.0 0.0 +20:10000352:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 4 10000352 0.0 0.0 +20:10000352:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 5 10000352 0.0 0.0 +20:10000352:C:T 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 6 10000352 0.0 0.0 +20:10000352:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 7 10000352 0.0 0.0 +20:10000364:A:G 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 1 10000364 -0.010000000000000002 0.1 +20:10000364:A:G 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 2 10000364 -0.010000000000000002 0.1 +20:10000364:A:G 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 3 10000364 -0.010000000000000002 0.1 +20:10000364:A:G 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 4 10000364 -0.010000000000000002 0.1 +20:10000364:A:G 14 0,0,0,0,0,0,0,0,0,0,0,0,-1,0 chr_20_block_0 5 10000364 -0.010000000000000002 0.1 +20:10000364:A:G 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 6 10000364 -0.010000000000000002 0.1 +20:10000364:A:G 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 7 10000364 -0.010000000000000002 0.1 +20:10000438:T:G 14 1,1,2,2,2,2,2,2,0,2,1,1,1,2 chr_20_block_0 1 10000438 1.48 0.7174590404667535 +20:10000438:T:G 15 2,1,1,2,1,2,1,-1,0,2,1,0,2,2,0 chr_20_block_0 2 10000438 1.48 0.7174590404667535 +20:10000438:T:G 14 1,2,2,2,2,2,2,1,2,2,1,1,2,2 chr_20_block_0 3 10000438 1.48 0.7174590404667535 +20:10000438:T:G 14 2,2,1,1,0,2,2,2,2,2,2,2,2,2 chr_20_block_0 4 10000438 1.48 0.7174590404667535 +20:10000438:T:G 14 0,0,2,2,2,2,0,1,2,0,2,1,2,2 chr_20_block_0 5 10000438 1.48 0.7174590404667535 +20:10000438:T:G 15 2,2,0,1,2,1,1,1,2,2,2,1,2,1,1 chr_20_block_0 6 10000438 1.48 0.7174590404667535 +20:10000438:T:G 14 2,2,2,2,2,1,1,2,2,2,1,2,2,1 chr_20_block_0 7 10000438 1.48 0.7174590404667535 +20:10000533:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 1 10000533 -0.020000000000000007 0.1407052941362897 +20:10000533:T:C 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 2 10000533 -0.020000000000000007 0.1407052941362897 +20:10000533:T:C 14 0,-1,0,0,0,0,0,-1,0,0,0,0,0,0 chr_20_block_0 3 10000533 -0.020000000000000007 0.1407052941362897 +20:10000533:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 4 10000533 -0.020000000000000007 0.1407052941362897 +20:10000533:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 5 10000533 -0.020000000000000007 0.1407052941362897 +20:10000533:T:C 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 6 10000533 -0.020000000000000007 0.1407052941362897 +20:10000533:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 7 10000533 -0.020000000000000007 0.1407052941362897 +20:10000557:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 1 10000557 -0.020000000000000004 0.1407052941362897 +20:10000557:G:A 15 0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0 chr_20_block_0 2 10000557 -0.020000000000000004 0.1407052941362897 +20:10000557:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 3 10000557 -0.020000000000000004 0.1407052941362897 +20:10000557:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 4 10000557 -0.020000000000000004 0.1407052941362897 +20:10000557:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 5 10000557 -0.020000000000000004 0.1407052941362897 +20:10000557:G:A 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 6 10000557 -0.020000000000000004 0.1407052941362897 +20:10000557:G:A 14 -1,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 7 10000557 -0.020000000000000004 0.1407052941362897 +20:10000585:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 1 10000585 -0.010000000000000007 0.17378728435078383 +20:10000585:C:T 15 0,0,0,0,0,0,0,-1,0,0,0,0,0,0,0 chr_20_block_0 2 10000585 -0.010000000000000007 0.17378728435078383 +20:10000585:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 3 10000585 -0.010000000000000007 0.17378728435078383 +20:10000585:C:T 14 0,-1,0,0,0,0,0,0,0,0,0,1,0,0 chr_20_block_0 4 10000585 -0.010000000000000007 0.17378728435078383 +20:10000585:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 5 10000585 -0.010000000000000007 0.17378728435078383 +20:10000585:C:T 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 6 10000585 -0.010000000000000007 0.17378728435078383 +20:10000585:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 7 10000585 -0.010000000000000007 0.17378728435078383 +20:10000597:T:A 14 1,1,1,2,2,2,2,2,0,2,1,1,1,2 chr_20_block_0 1 10000597 1.4400000000000002 0.7563869460277671 +20:10000597:T:A 15 2,1,1,2,2,2,1,-1,1,2,1,1,1,2,0 chr_20_block_0 2 10000597 1.4400000000000002 0.7563869460277671 +20:10000597:T:A 14 1,2,2,2,2,2,2,1,2,2,1,1,2,2 chr_20_block_0 3 10000597 1.4400000000000002 0.7563869460277671 +20:10000597:T:A 14 2,-1,0,1,0,2,2,2,2,2,2,2,2,2 chr_20_block_0 4 10000597 1.4400000000000002 0.7563869460277671 +20:10000597:T:A 14 0,0,2,2,2,2,0,1,2,1,2,0,2,2 chr_20_block_0 5 10000597 1.4400000000000002 0.7563869460277671 +20:10000597:T:A 15 2,2,0,0,2,1,1,1,2,2,2,1,1,1,2 chr_20_block_0 6 10000597 1.4400000000000002 0.7563869460277671 +20:10000597:T:A 14 2,2,2,2,2,1,1,2,2,2,1,2,2,1 chr_20_block_0 7 10000597 1.4400000000000002 0.7563869460277671 +20:10000693:G:A 14 1,0,0,1,1,2,2,1,0,2,1,1,1,0 chr_20_block_0 1 10000693 1.13 0.7996842811353426 +20:10000693:G:A 15 2,1,0,2,1,1,0,2,0,2,1,0,1,2,0 chr_20_block_0 2 10000693 1.13 0.7996842811353426 +20:10000693:G:A 14 0,2,2,2,0,2,2,1,2,1,1,1,1,0 chr_20_block_0 3 10000693 1.13 0.7996842811353426 +20:10000693:G:A 14 1,2,0,1,0,0,1,2,2,2,2,2,1,1 chr_20_block_0 4 10000693 1.13 0.7996842811353426 +20:10000693:G:A 14 0,0,1,2,2,2,0,1,2,1,2,0,2,2 chr_20_block_0 5 10000693 1.13 0.7996842811353426 +20:10000693:G:A 15 2,2,0,0,2,1,0,1,2,2,1,0,0,0,1 chr_20_block_0 6 10000693 1.13 0.7996842811353426 +20:10000693:G:A 14 1,1,2,2,1,1,1,2,2,2,1,2,2,0 chr_20_block_0 7 10000693 1.13 0.7996842811353426 +20:10000757:T:A 14 1,1,2,2,2,2,2,2,0,2,1,2,1,2 chr_20_block_0 1 10000757 1.4899999999999998 0.7176701923937661 +20:10000757:T:A 15 2,1,1,2,1,2,1,2,0,2,1,1,1,2,0 chr_20_block_0 2 10000757 1.4899999999999998 0.7176701923937661 +20:10000757:T:A 14 0,2,2,2,2,2,2,1,2,2,1,1,2,2 chr_20_block_0 3 10000757 1.4899999999999998 0.7176701923937661 +20:10000757:T:A 14 2,2,1,1,0,2,2,2,2,2,2,2,2,2 chr_20_block_0 4 10000757 1.4899999999999998 0.7176701923937661 +20:10000757:T:A 14 0,0,2,2,2,2,0,1,-1,1,2,0,2,2 chr_20_block_0 5 10000757 1.4899999999999998 0.7176701923937661 +20:10000757:T:A 15 2,2,0,1,2,1,1,1,2,2,2,1,1,2,2 chr_20_block_0 6 10000757 1.4899999999999998 0.7176701923937661 +20:10000757:T:A 14 2,2,2,2,2,1,1,2,2,2,1,2,2,1 chr_20_block_0 7 10000757 1.4899999999999998 0.7176701923937661 +20:10001018:T:G 14 -1,1,0,0,1,2,-1,1,0,2,1,0,1,0 chr_20_block_0 1 10001018 0.97 0.9369518599580172 +20:10001018:T:G 15 1,1,0,2,-1,1,0,2,0,2,1,1,1,2,0 chr_20_block_0 2 10001018 0.97 0.9369518599580172 +20:10001018:T:G 14 0,2,2,2,1,2,2,1,2,1,1,1,1,0 chr_20_block_0 3 10001018 0.97 0.9369518599580172 +20:10001018:T:G 14 1,2,0,1,0,0,-1,2,0,2,-1,2,0,0 chr_20_block_0 4 10001018 0.97 0.9369518599580172 +20:10001018:T:G 14 0,0,1,2,2,2,0,0,2,1,2,-1,1,2 chr_20_block_0 5 10001018 0.97 0.9369518599580172 +20:10001018:T:G 15 2,2,0,1,-1,1,1,1,2,2,1,0,2,1,1 chr_20_block_0 6 10001018 0.97 0.9369518599580172 +20:10001018:T:G 14 1,1,2,2,1,1,2,2,2,2,1,2,2,0 chr_20_block_0 7 10001018 0.97 0.9369518599580172 +20:10001037:T:C 14 -1,0,0,0,0,0,-1,0,0,0,0,0,0,0 chr_20_block_0 1 10001037 -0.06999999999999999 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0,0,2,2,2,2,0,1,2,0,2,0,0,2 chr_20_block_1 5 10001435 1.3099999999999996 0.837203135988567 +20:10001435:A:AAGGCT 15 2,2,0,1,2,1,1,1,2,2,2,1,2,2,2 chr_20_block_1 6 10001435 1.3099999999999996 0.837203135988567 +20:10001435:A:AAGGCT 14 0,2,2,2,2,1,1,2,2,2,1,2,2,1 chr_20_block_1 7 10001435 1.3099999999999996 0.837203135988567 +20:10001473:C:T 14 1,1,1,2,2,2,2,2,0,2,0,1,0,2 chr_20_block_1 1 10001473 1.4200000000000006 0.7808309482192478 +20:10001473:C:T 15 2,1,1,2,1,2,1,2,0,2,1,1,1,2,0 chr_20_block_1 2 10001473 1.4200000000000006 0.7808309482192478 +20:10001473:C:T 14 1,2,2,2,-1,2,2,2,2,2,1,1,2,2 chr_20_block_1 3 10001473 1.4200000000000006 0.7808309482192478 +20:10001473:C:T 14 2,2,0,1,0,2,2,2,2,2,-1,2,2,2 chr_20_block_1 4 10001473 1.4200000000000006 0.7808309482192478 +20:10001473:C:T 14 0,0,2,2,2,2,0,1,2,1,2,0,1,2 chr_20_block_1 5 10001473 1.4200000000000006 0.7808309482192478 +20:10001473:C:T 15 2,2,0,1,2,1,1,1,2,2,2,1,2,1,2 chr_20_block_1 6 10001473 1.4200000000000006 0.7808309482192478 +20:10001473:C:T 14 2,2,2,2,2,1,1,2,2,2,1,2,2,1 chr_20_block_1 7 10001473 1.4200000000000006 0.7808309482192478 +20:10001486:AG:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 1 10001486 -0.020000000000000004 0.1407052941362897 +20:10001486:AG:A 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 2 10001486 -0.020000000000000004 0.1407052941362897 +20:10001486:AG:A 14 0,0,0,0,-1,0,0,0,0,0,0,0,0,0 chr_20_block_1 3 10001486 -0.020000000000000004 0.1407052941362897 +20:10001486:AG:A 14 0,0,0,0,0,0,0,0,0,0,-1,0,0,0 chr_20_block_1 4 10001486 -0.020000000000000004 0.1407052941362897 +20:10001486:AG:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 5 10001486 -0.020000000000000004 0.1407052941362897 +20:10001486:AG:A 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 6 10001486 -0.020000000000000004 0.1407052941362897 +20:10001486:AG:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 7 10001486 -0.020000000000000004 0.1407052941362897 +20:10001502:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 1 10001502 0.03 0.264193072956086 +20:10001502:G:A 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 2 10001502 0.03 0.264193072956086 +20:10001502:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 3 10001502 0.03 0.264193072956086 +20:10001502:G:A 14 0,0,0,0,0,0,0,0,0,0,-1,0,0,0 chr_20_block_1 4 10001502 0.03 0.264193072956086 +20:10001502:G:A 14 1,1,0,0,0,0,0,0,0,0,0,2,0,0 chr_20_block_1 5 10001502 0.03 0.264193072956086 +20:10001502:G:A 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 6 10001502 0.03 0.264193072956086 +20:10001502:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 7 10001502 0.03 0.264193072956086 +20:10001563:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 1 10001563 0.0 0.0 +20:10001563:T:C 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 2 10001563 0.0 0.0 +20:10001563:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 3 10001563 0.0 0.0 +20:10001563:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_1 4 10001563 0.0 0.0 +20:10001563:T:C 14 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10002457 0.94 0.6639337924884386 +20:10002469:C:T 14 0,1,1,2,2,2,-1,2,0,2,1,1,1,2 chr_20_block_2 1 10002469 1.3799999999999997 0.8382280492958122 +20:10002469:C:T 15 2,1,0,2,1,2,1,2,1,2,1,1,1,2,0 chr_20_block_2 2 10002469 1.3799999999999997 0.8382280492958122 +20:10002469:C:T 14 1,2,2,2,2,2,2,1,2,2,1,1,2,2 chr_20_block_2 3 10002469 1.3799999999999997 0.8382280492958122 +20:10002469:C:T 14 2,2,1,0,0,2,2,2,2,2,2,2,2,-1 chr_20_block_2 4 10002469 1.3799999999999997 0.8382280492958122 +20:10002469:C:T 14 0,0,2,2,2,2,0,0,2,1,2,0,0,2 chr_20_block_2 5 10002469 1.3799999999999997 0.8382280492958122 +20:10002469:C:T 15 2,2,0,1,2,2,0,1,2,2,-1,2,1,1,2 chr_20_block_2 6 10002469 1.3799999999999997 0.8382280492958122 +20:10002469:C:T 14 2,2,2,2,2,1,1,2,2,2,1,2,2,2 chr_20_block_2 7 10002469 1.3799999999999997 0.8382280492958122 +20:10002571:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 1 10002571 -0.010000000000000004 0.09999999999999999 +20:10002571:C:T 15 0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0 chr_20_block_2 2 10002571 -0.010000000000000004 0.09999999999999999 +20:10002571:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 3 10002571 -0.010000000000000004 0.09999999999999999 +20:10002571:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 4 10002571 -0.010000000000000004 0.09999999999999999 +20:10002571:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 5 10002571 -0.010000000000000004 0.09999999999999999 +20:10002571:C:T 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 6 10002571 -0.010000000000000004 0.09999999999999999 +20:10002571:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 7 10002571 -0.010000000000000004 0.09999999999999999 +20:10002584:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 1 10002584 -0.04 0.19694638556693236 +20:10002584:T:C 15 0,0,0,0,0,0,0,0,-1,0,0,0,0,0,0 chr_20_block_2 2 10002584 -0.04 0.19694638556693236 +20:10002584:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 3 10002584 -0.04 0.19694638556693236 +20:10002584:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 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0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 2 10002648 -0.04 0.19694638556693236 +20:10002648:A:G 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 3 10002648 -0.04 0.19694638556693236 +20:10002648:A:G 14 0,0,0,0,0,-1,0,0,0,0,0,0,0,0 chr_20_block_2 4 10002648 -0.04 0.19694638556693236 +20:10002648:A:G 14 0,0,0,0,0,0,0,0,-1,0,0,-1,0,0 chr_20_block_2 5 10002648 -0.04 0.19694638556693236 +20:10002648:A:G 15 0,-1,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 6 10002648 -0.04 0.19694638556693236 +20:10002648:A:G 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 7 10002648 -0.04 0.19694638556693236 +20:10002661:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 1 10002661 -0.06 0.23868325657594208 +20:10002661:C:T 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 2 10002661 -0.06 0.23868325657594208 +20:10002661:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 3 10002661 -0.06 0.23868325657594208 +20:10002661:C:T 14 0,0,0,0,0,0,0,0,0,0,-1,0,-1,0 chr_20_block_2 4 10002661 -0.06 0.23868325657594208 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0.23868325657594208 +20:10002772:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 1 10002772 0.010000000000000002 0.09999999999999999 +20:10002772:T:C 15 0,0,0,0,1,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 2 10002772 0.010000000000000002 0.09999999999999999 +20:10002772:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 3 10002772 0.010000000000000002 0.09999999999999999 +20:10002772:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 4 10002772 0.010000000000000002 0.09999999999999999 +20:10002772:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 5 10002772 0.010000000000000002 0.09999999999999999 +20:10002772:T:C 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 6 10002772 0.010000000000000002 0.09999999999999999 +20:10002772:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 7 10002772 0.010000000000000002 0.09999999999999999 +20:10003020:C:T 14 1,1,1,1,2,2,2,2,0,2,1,1,1,2 chr_20_block_2 1 10003020 1.4499999999999997 0.7703468981143128 +20:10003020:C:T 15 2,1,1,2,2,2,1,2,0,2,1,1,1,2,0 chr_20_block_2 2 10003020 1.4499999999999997 0.7703468981143128 +20:10003020:C:T 14 0,2,2,2,-1,2,2,1,2,2,1,1,2,2 chr_20_block_2 3 10003020 1.4499999999999997 0.7703468981143128 +20:10003020:C:T 14 2,2,1,1,0,2,2,2,2,2,2,2,2,2 chr_20_block_2 4 10003020 1.4499999999999997 0.7703468981143128 +20:10003020:C:T 14 -1,0,2,2,2,2,0,1,2,2,2,-1,1,2 chr_20_block_2 5 10003020 1.4499999999999997 0.7703468981143128 +20:10003020:C:T 15 2,2,0,1,2,1,1,1,2,2,2,1,1,2,2 chr_20_block_2 6 10003020 1.4499999999999997 0.7703468981143128 +20:10003020:C:T 14 2,2,2,2,2,1,1,2,2,2,1,2,2,1 chr_20_block_2 7 10003020 1.4499999999999997 0.7703468981143128 +20:10003121:C:A 14 0,0,0,0,0,0,0,0,-1,0,0,0,0,1 chr_20_block_2 1 10003121 0.010000000000000002 0.3013437917198242 +20:10003121:C:A 15 0,0,0,0,0,0,0,1,0,0,0,0,0,0,0 chr_20_block_2 2 10003121 0.010000000000000002 0.3013437917198242 +20:10003121:C:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 3 10003121 0.010000000000000002 0.3013437917198242 +20:10003121:C:A 14 0,0,0,1,0,0,0,-1,0,0,0,0,-1,0 chr_20_block_2 4 10003121 0.010000000000000002 0.3013437917198242 +20:10003121:C:A 14 0,0,0,0,0,-1,0,0,0,0,0,0,0,0 chr_20_block_2 5 10003121 0.010000000000000002 0.3013437917198242 +20:10003121:C:A 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,1 chr_20_block_2 6 10003121 0.010000000000000002 0.3013437917198242 +20:10003121:C:A 14 0,0,0,0,1,0,0,0,0,0,0,0,0,0 chr_20_block_2 7 10003121 0.010000000000000002 0.3013437917198242 +20:10003187:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 1 10003187 0.0 0.0 +20:10003187:G:A 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 2 10003187 0.0 0.0 +20:10003187:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 3 10003187 0.0 0.0 +20:10003187:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 4 10003187 0.0 0.0 +20:10003187:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 5 10003187 0.0 0.0 +20:10003187:G:A 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 6 10003187 0.0 0.0 +20:10003187:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 7 10003187 0.0 0.0 +20:10003241:C:T 14 0,1,1,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 1 10003241 0.020000000000000014 0.2 +20:10003241:C:T 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 2 10003241 0.020000000000000014 0.2 +20:10003241:C:T 14 0,0,0,0,0,0,0,0,-1,0,0,0,0,0 chr_20_block_2 3 10003241 0.020000000000000014 0.2 +20:10003241:C:T 14 0,0,0,0,1,0,0,0,0,0,0,0,0,0 chr_20_block_2 4 10003241 0.020000000000000014 0.2 +20:10003241:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 5 10003241 0.020000000000000014 0.2 +20:10003241:C:T 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 6 10003241 0.020000000000000014 0.2 +20:10003241:C:T 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 7 10003241 0.020000000000000014 0.2 +20:10003242:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 1 10003242 0.0 0.0 +20:10003242:G:A 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 2 10003242 0.0 0.0 +20:10003242:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 3 10003242 0.0 0.0 +20:10003242:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 4 10003242 0.0 0.0 +20:10003242:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 5 10003242 0.0 0.0 +20:10003242:G:A 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 6 10003242 0.0 0.0 +20:10003242:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_2 7 10003242 0.0 0.0 +20:10003357:A:C 14 1,1,0,0,1,2,2,1,0,2,1,1,1,0 chr_20_block_2 1 10003357 1.13 0.812217316792107 +20:10003357:A:C 15 1,1,0,2,0,2,0,2,0,2,1,1,1,2,0 chr_20_block_2 2 10003357 1.13 0.812217316792107 +20:10003357:A:C 14 0,2,2,2,1,2,2,1,2,1,1,1,1,0 chr_20_block_2 3 10003357 1.13 0.812217316792107 +20:10003357:A:C 14 1,2,0,1,0,0,2,2,1,2,2,2,1,0 chr_20_block_2 4 10003357 1.13 0.812217316792107 +20:10003357:A:C 14 -1,0,1,2,2,2,0,1,1,1,2,1,1,2 chr_20_block_2 5 10003357 1.13 0.812217316792107 +20:10003357:A:C 15 2,2,0,2,2,1,1,1,2,2,1,0,1,1,1 chr_20_block_2 6 10003357 1.13 0.812217316792107 +20:10003357:A:C 14 0,-1,2,2,1,1,1,2,2,2,1,2,2,0 chr_20_block_2 7 10003357 1.13 0.812217316792107 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0.020000000000000007 0.14070529413628968 +20:10004646:G:A 14 0,0,0,0,0,0,1,0,0,0,0,0,0,0 chr_20_block_4 4 10004646 0.020000000000000007 0.14070529413628968 +20:10004646:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_4 5 10004646 0.020000000000000007 0.14070529413628968 +20:10004646:G:A 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_4 6 10004646 0.020000000000000007 0.14070529413628968 +20:10004646:G:A 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_4 7 10004646 0.020000000000000007 0.14070529413628968 +20:10004674:A:G 14 0,0,0,0,0,0,0,0,1,0,0,0,0,0 chr_20_block_4 1 10004674 0.010000000000000002 0.17378728435078386 +20:10004674:A:G 15 0,0,0,0,0,0,0,0,0,0,0,-1,0,0,0 chr_20_block_4 2 10004674 0.010000000000000002 0.17378728435078386 +20:10004674:A:G 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_4 3 10004674 0.010000000000000002 0.17378728435078386 +20:10004674:A:G 14 0,0,0,0,0,0,0,1,0,0,0,0,0,0 chr_20_block_4 4 10004674 0.010000000000000002 0.17378728435078386 +20:10004674:A:G 14 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0.7159792333764037 +20:10004724:A:G 14 0,0,1,1,1,0,0,1,1,2,1,1,1,0 chr_20_block_4 7 10004724 0.6499999999999997 0.7159792333764037 +20:10004768:TAAAACTATGC:T 14 0,0,1,2,1,0,0,1,0,0,0,0,0,2 chr_20_block_4 1 10004768 0.38 0.6159496240715951 +20:10004768:TAAAACTATGC:T 15 1,0,1,0,0,1,1,0,0,0,0,0,0,0,0 chr_20_block_4 2 10004768 0.38 0.6159496240715951 +20:10004768:TAAAACTATGC:T 14 1,0,0,0,1,0,0,0,2,1,0,0,1,1 chr_20_block_4 3 10004768 0.38 0.6159496240715951 +20:10004768:TAAAACTATGC:T 14 1,0,1,0,0,2,0,0,2,0,0,0,1,2 chr_20_block_4 4 10004768 0.38 0.6159496240715951 +20:10004768:TAAAACTATGC:T 14 0,0,1,0,0,0,0,0,2,0,0,1,0,0 chr_20_block_4 5 10004768 0.38 0.6159496240715951 +20:10004768:TAAAACTATGC:T 15 0,0,0,0,0,0,0,0,0,0,1,1,0,0,1 chr_20_block_4 6 10004768 0.38 0.6159496240715951 +20:10004768:TAAAACTATGC:T 14 1,1,0,0,1,0,0,0,0,0,0,0,0,1 chr_20_block_4 7 10004768 0.38 0.6159496240715951 From f5424eed192a2b14b1bdab9b25ef73320b86864e Mon Sep 17 00:00:00 2001 From: Leland Date: Fri, 22 May 2020 13:49:47 -0400 Subject: [PATCH 03/34] feat: ridge models for wgr added (#1) * feat: ridge models for wgr added Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * Doc strings added for levels/functions.py Some typos fixed in ridge_model.py Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * ridge_model and RidgeReducer unit tests added Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * RidgeRegression unit tests added test data README added ridge_udfs.py docstrings added Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * Changes made to accessing the sample ID map and more docstrings The map_normal_eqn and score_models functions previously expected the sample IDs for a given sample block to be found in the Pandas DataFrame, which mean we had to join them on before the .groupBy().apply(). These functions now expect the sample block to sample IDs mapping to be provided separately as a dict, so that the join is no longer required. RidgeReducer and RidgeRegression APIs remain unchanged. docstrings have been added for RidgeReducer and RidgeRegression classes. Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * Refactored object names and comments to reflect new terminology Where 'block' was previously used to refer to the set of columns in a block, we now use 'header_block' Where 'group' was previously used to refer to the set of samples in a block, we now use 'sample_block' Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard --- python/environment.yml | 1 + python/glow/__init__.py | 1 + python/glow/levels/__init__.py | 0 python/glow/levels/linear_model/__init__.py | 1 + python/glow/levels/linear_model/functions.py | 177 +++++++++ .../glow/levels/linear_model/ridge_model.py | 178 +++++++++ python/glow/levels/linear_model/ridge_udfs.py | 371 ++++++++++++++++++ python/glow/levels/tests/__init__.py | 0 .../levels/tests/test_ridge_regression.py | 370 +++++++++++++++++ python/project/build.properties | 1 + python/setup.py | 4 +- test-data/levels/ridge-regression/README.md | 24 ++ test-data/levels/ridge-regression/X0.csv | 101 +++++ test-data/levels/ridge-regression/X1.csv | 101 +++++ test-data/levels/ridge-regression/X2.csv | 101 +++++ .../ridge-regression/blockedGT.snappy.parquet | Bin 0 -> 12735 bytes .../groupedIDs.snappy.parquet | Bin 0 -> 1227 bytes test-data/levels/ridge-regression/pts.csv | 101 +++++ 18 files changed, 1530 insertions(+), 2 deletions(-) create mode 100644 python/glow/levels/__init__.py create mode 100644 python/glow/levels/linear_model/__init__.py create mode 100644 python/glow/levels/linear_model/functions.py create mode 100644 python/glow/levels/linear_model/ridge_model.py create mode 100644 python/glow/levels/linear_model/ridge_udfs.py create mode 100644 python/glow/levels/tests/__init__.py create mode 100644 python/glow/levels/tests/test_ridge_regression.py create mode 100644 python/project/build.properties create mode 100644 test-data/levels/ridge-regression/README.md create mode 100644 test-data/levels/ridge-regression/X0.csv create mode 100644 test-data/levels/ridge-regression/X1.csv create mode 100644 test-data/levels/ridge-regression/X2.csv create mode 100644 test-data/levels/ridge-regression/blockedGT.snappy.parquet create mode 100644 test-data/levels/ridge-regression/groupedIDs.snappy.parquet create mode 100644 test-data/levels/ridge-regression/pts.csv diff --git a/python/environment.yml b/python/environment.yml index 3c6340e43..c7cdc2d90 100644 --- a/python/environment.yml +++ b/python/environment.yml @@ -6,6 +6,7 @@ dependencies: - bedtools - jinja2 - numpy=1.17.4 + - scipy=1.4.1 - nomkl # Skip MKL for local development - pandas=0.25.3 - pip diff --git a/python/glow/__init__.py b/python/glow/__init__.py index 38dc332a9..d87df07dd 100644 --- a/python/glow/__init__.py +++ b/python/glow/__init__.py @@ -1,2 +1,3 @@ from glow.glow import * from glow.functions import * +from glow.levels import * diff --git a/python/glow/levels/__init__.py b/python/glow/levels/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/python/glow/levels/linear_model/__init__.py b/python/glow/levels/linear_model/__init__.py new file mode 100644 index 000000000..4c3cd3dd0 --- /dev/null +++ b/python/glow/levels/linear_model/__init__.py @@ -0,0 +1 @@ +from .ridge_model import * diff --git a/python/glow/levels/linear_model/functions.py b/python/glow/levels/linear_model/functions.py new file mode 100644 index 000000000..dc083cdbf --- /dev/null +++ b/python/glow/levels/linear_model/functions.py @@ -0,0 +1,177 @@ +import pandas as pd +import numpy as np +from scipy.sparse import csr_matrix +import itertools + + +def parse_key(key, key_pattern): + """ + Interprets the key corresponding to a group from a groupBy clause. The key may be of the form: + (header_block, sample_block), + (header_block, sample_block, label), + (header_block, header), + (header_block, header, label), + (sample_block, label) + depending on the context. In each case, a tuple with 3 members is returned, with the missing member filled in by + 'all' where necessary + + Args: + key : key for the group + key_pattern : one of the aforementioned key patterns + + Returns: + tuple of (header_block, sample_block, label) or (header_block, header, label), where header_block or label may be filled with 'all' + depending on context. + """ + if key_pattern == ['header_block', 'sample_block']: + return key[0], key[1], 'all' + elif key_pattern == ['header_block', 'header']: + return key[0], key[1], 'all' + elif key_pattern == ['sample_block', 'label']: + return 'all', key[0], key[1] + else: + return key + + +def assemble_block(n_rows, n_cols, col_order, pdf): + """ + Creates a dense n_rows by n_cols matrix from the array of either sparse or dense vectors in the Pandas DataFrame + corresponding to a group. This matrix represents a block. + + Args: + n_rows : The number of rows in the resulting matrix + n_cols : The number of columns in the resulting matrix + col_order : Array of integers representing the desired ordering of the columns in the output matrix + pdf : Pandas DataFrame corresponding to a group + + Returns: + Dense n_rows by n_columns matrix where the columns have been 0-centered and standard scaled. + """ + mu = pdf['mu'].values + sig = pdf['sig'].values + if 'indices' not in pdf.columns: + X_raw = np.row_stack(pdf['values']).T + else: + X_csr = csr_matrix( + ( + np.concatenate(pdf['values']), + (np.concatenate(pdf['indices']), np.concatenate([np.repeat(i, len(v)) for i, v in enumerate(pdf.indices)])) + ), + shape = (n_rows, n_cols) + ) + X_raw = X_csr.todense() + + return ((X_raw - mu)/sig)[:, col_order] + + +def slice_label_rows(labeldf, label, sample_list): + """ + Selects rows from the Pandas DataFrame of labels corresponding to the samples in a particular sample_block. + + Args: + pdf : Pandas DataFrame for the group + labeldf : Pandas DataFrame containing the labels + label : Header for the particular label to slice. Can be 'all' if all labels are desired. + sample_list : List of sample ids corresponding to the sampleBlock to be sliced out. + + Returns: + Matrix of [number of samples in sample_block] x [number of labels to slice] + """ + if label == 'all': + return labeldf.loc[sample_list, :].values + else: + return labeldf[label].loc[sample_list].values.reshape(-1, 1) + + +def evaluate_coefficients(pdf, row_order, alpha_values): + """ + Solves the system (XTX + Ia)^-1 * XtY for each of the a values in alphas. Returns the resulting coefficients. + + Args: + pdf : Pandas DataFrame for the group + row_order : Array of integers representing the intended row ordering of the matrices XtX and XtY + alpha_values : Array of alpha values (regularization strengths) + + Returns: + Matrix of coefficients of size [number of columns in X] x [number of labels * number of alpha values] + """ + XtX = np.stack(pdf['xtx'].values)[row_order] + XtY = np.stack(pdf['xty'].values)[row_order] + return np.column_stack([(np.linalg.inv(XtX + np.identity(XtX.shape[1])*a)@XtY) for a in alpha_values]) + + +def create_row_indexer(alpha_names, labeldf, label): + """ + Creates an array of tuples used to keep the ordering of the coefficients in the output of evaluate_coefficients in + a consistent order regardless of the order in which the alpha values and labels were provided. + + Args: + alpha_names : List of string identifiers assigned to the values of alpha + labeldf : Pandas DataFrame of labels + label : Label used for this set of coefficients. Can be 'all' if all labels were used. + Returns: + List of tuples of the form (i,(a, l)) where i is the column index in the matrix of coefficients, a is an alpha + name, and l is a label name, sorted by label name, then alpha. + """ + if label == 'all': + label_names = labeldf.columns + else: + label_names = [label] + + return sorted(enumerate(itertools.product(alpha_names, label_names)), key = lambda t: (t[1][1], t[1][0])) + + +def new_headers(header_block, alpha_names, row_indexer): + """ + Creates new headers for the output of a matrix reduction step. Generally produces names like + "block_[header_block_number]_alpha_[alpha_name]_label_[label_name]" + + Args: + header_block : Identifier for a header_block (e.g., 'chr1_block_0') + alpha_names : List of string identifiers for alpha parameters + row_indexer : A list of tuples provided by the create_row_indexer function + + Returns: + new_header_block : A new header_block name, typically the chromosome (e.g. chr1), but might be 'all' if there are no more levels to + reduce over. + positions : Array of sortable integers to specify the ordering of the new matrix headers. + headers : List of new matrix headers. + """ + tokens = header_block.split('_') + + if len(tokens) == 2: + outer_index, inner_index = 'all', tokens[1] + new_header_block = f'{outer_index}' + elif len(tokens) == 1: + outer_index, inner_index = 'all', 0 + new_header_block = f'{outer_index}' + else: + outer_index, inner_index = tokens[1:4:2] + new_header_block = f'chr_{outer_index}' + + positions, headers = [], [] + for i, (a, l) in row_indexer: + position = int(inner_index)*len(alpha_names) + int(a.split('_')[1]) + header = f'{new_header_block}_block_{inner_index}_{a}_label_{l}' + positions.append(position) + headers.append(header) + + return new_header_block, positions, headers + + +def r_squared(XB, Y): + """ + Computes the coefficient of determination (R2) metric between the matrix resulting from X*B and the matrix of labels + Y. + + Args: + XB : Matrix representing the result of the multiplication X*B, where X is a matrix of [number of samples] x + [number of headers] and B is a matrix of [number of headers x number of alphas * number of labels] + Y : Matrix of labels, with [number of samples x number of labels] + + Returns: + Array of [number of alphas * number of labels] + """ + tot = np.power(Y - Y.mean(), 2).sum() + res = np.power(Y - XB, 2).sum(axis = 0) + return 1 - (res/tot) diff --git a/python/glow/levels/linear_model/ridge_model.py b/python/glow/levels/linear_model/ridge_model.py new file mode 100644 index 000000000..115bb7d48 --- /dev/null +++ b/python/glow/levels/linear_model/ridge_model.py @@ -0,0 +1,178 @@ +from .ridge_udfs import * +from pyspark.sql.functions import pandas_udf, PandasUDFType +import pyspark.sql.functions as f +from pyspark.sql.window import Window + + +class RidgeReducer: + """ + The RidgeReducer class is intended to reduce the feature space of an N by M block matrix X to an N by P< Pandas DataFrame transformation, and each is intended to be +used as a Pandas GROUPED_MAP UDF. +''' +normal_eqn_struct = StructType([ + StructField('header_block', StringType()), + StructField('sample_block', IntegerType()), + StructField('label', StringType()), + StructField('header', StringType()), + StructField('position', IntegerType()), + StructField('xtx', ArrayType(DoubleType())), + StructField('xty', ArrayType(DoubleType())) +]) + +model_struct = StructType([ + StructField('header_block', StringType()), + StructField('sample_block', IntegerType()), + StructField('header', StringType()), + StructField('position', IntegerType()), + StructField('alphas', ArrayType(StringType())), + StructField('labels', ArrayType(StringType())), + StructField('coefficients', ArrayType(DoubleType())) +]) + +reduced_matrix_struct = StructType([ + StructField('header', StringType()), + StructField('size', IntegerType()), + StructField('values', ArrayType(DoubleType())), + StructField('header_block', StringType()), + StructField('sample_block', IntegerType()), + StructField('position', IntegerType()), + StructField('mu', DoubleType()), + StructField('sig', DoubleType()), + StructField('alpha', StringType()), + StructField('label', StringType()) +]) + +cv_struct = StructType([ + StructField('sample_block', IntegerType()), + StructField('label', StringType()), + StructField('alpha', StringType()), + StructField('r2', DoubleType()) +]) + + +def map_normal_eqn(key, key_pattern, pdf, labeldf, sample_index): + """ + This function constructs matrices X and Y, and returns X_transpose * X (XtX) and X_transpose * Y (XtY), where X + corresponds to a block from a block matrix. + + Each block X is uniquely identified by a header_block ID, which maps to a set of contiguous columns in the overall block + matrix, and a sample_block ID, which maps to a set of rows in the overall block matrix (and likewise a set of rows in the + label matrix Y). The key that identifies X is therefore of the form (header_blockID, sample_blockID). In some contexts, the + block matrix will be tied to a particular label from Y, in which case the key will be of the form + (header_block, sample_block, label). + + Args: + key : unique key identifying the group of rows emitted by a groupBy statement. + key_pattern : pattern of columns used in the groupBy statement that emitted this group of rows + pdf : starting Pandas DataFrame used to build X and Y for block X identified by :key:. + schema: + |-- header: string + |-- size: integer + |-- indices: array (Required only if the matrix is sparse) + | |-- element: integer + |-- values: array + | |-- element: double + |-- header_block: string + |-- sample_block: integer + |-- position: integer + |-- mu: double + |-- sig: double + |-- alpha: double (Required only if the header is tied to a specific value of alpha) + |-- label: double (Required only if the header is tied to a specific label) + labeldf : Pandas DataFrame containing label values (i. e., the Y in the normal equation above). + + Returns: + transformed Pandas DataFrame containing XtX and XtY corresponding to a particular block X. + schema (specified by the normal_eqn_struct): + |-- header_block: string + |-- sample_block: integer + |-- label: string + |-- header: string + |-- position: integer + |-- xtx: array + | |-- element: double + |-- xty: array + | |-- element: double + """ + header_block, sample_block, label = parse_key(key, key_pattern) + n_rows = pdf['size'][0] + n_cols = len(pdf) + position = pdf['position'] + header = pdf['header'] + col_order = [(t[0]) for t in sorted(list(enumerate(zip(position, header))), key = lambda t: (t[1][0], t[1][1]))] + sample_list = sample_index[sample_block] + X = assemble_block(n_rows, n_cols, col_order, pdf) + Y = slice_label_rows(labeldf, label, sample_list) + XtX = X.T@X + XtY = X.T@Y + + data = { + 'header_block' : [header_block]*n_cols, + 'sample_block' : [sample_block]*n_cols, + 'label' : [label]*n_cols, + 'header' : header[col_order].values, + 'position' : position[col_order].values, + 'xtx' : XtX.tolist(), + 'xty' : XtY.tolist() + } + + return pd.DataFrame(data) + + +def reduce_normal_eqn(key, key_pattern, pdf): + """ + This function constructs lists of rows from the XtX and XtY matrices corresponding to a particular header in X but + evaluated in different sample_blocks, and then reduces those lists by element-wise summation. This reduction is + repeated once for each sample_block, where the contribution of that sample_block is omitted. There is therefore a + one-to-one mapping of the starting lists and the reduced lists, e.g.: + + Input: + List(xtx_sample_block0, xtx_sample_block1, ..., xtx_sample_blockN) + Output: + List(xtx_sum_excluding_sample_block0, xtx_sum_excluding_sample_block1, ..., xtx_sum_excluding_sample_blockN) + + Args: + key : unique key identifying the rows emitted by a groupBy statement + key_pattern : pattern of columns used in the groupBy statement + pdf : starting Pandas DataFrame containing the lists of rows from XtX and XtY for block X identified by :key: + schema (specified by the normal_eqn_struct): + |-- header_block: string + |-- sample_block: integer + |-- label: string + |-- header: string + |-- position: integer + |-- xtx: array + | |-- element: double + |-- xty: array + | |-- element: double + + Returns: + transformed Pandas DataFrame containing the aggregated leave-fold-out rows from XtX and XtY + schema (specified by the normal_eqn_struct): + |-- header_block: string + |-- sample_block: integer + |-- label: string + |-- header: string + |-- position: integer + |-- xtx: array + | |-- element: double + |-- xty: array + | |-- element: double + """ + + header_block, header, label = parse_key(key, key_pattern) + position = pdf['position'][0] + n_sample_blocks = len(pdf) + sample_blocks = enumerate(pdf['sample_block']) + slices = [(g, np.append(np.arange(i), np.arange(i+1, n_sample_blocks))) for i, g in sample_blocks] + xtx_stack = np.stack(pdf['xtx'].values) + xty_stack = np.stack(pdf['xty'].values) + + rows = [[header_block, g, label, header, position, xtx_stack[s, :].sum(axis = 0), xty_stack[s, :].sum(axis = 0)] for g, s in slices] + + return pd.DataFrame(rows, columns = ['header_block', 'sample_block', 'label', 'header', 'position', 'xtx', 'xty']) + + +def solve_normal_eqn(key, key_pattern, pdf, labeldf, alphas): + """ + This function assembles the matrices XtX and XtY for a particular sample_block (where the contribution of that sample_block + has been omitted) and solves the equation [(XtX + I*alpha)]-1 * XtY = B for a list of alpha values, and returns the + coefficient matrix B, where B has 1 row per header in the block X and 1 column per combination of alpha value and + label. + + Args: + key : unique key identifying the group of rows emitted by a groupBy statement + key_pattern : pattern of columns used in the groupBy statement that emitted this group of rows + pdf : starting Pandas DataFrame containing the lists of rows from XtX and XtY for block X identified by :key: + schema (specified by the normal_eqn_struct): + |-- header_block: string + |-- sample_block: integer + |-- label: string + |-- header: string + |-- position: integer + |-- xtx: array + | |-- element: double + |-- xty: array + | |-- element: double + labeldf : Pandas DataFrame containing label values (i. e., the Y in the normal equation above). + alphas : dict of {alphaName : alphaValue} for the alpha values to be used + + Returns: + transformed Pandas DataFrame containing the coefficient matrix B + schema (specified by the normal_eqn_struct): + |-- header_block: string + |-- sample_block: integer + |-- header: string + |-- position: integer + |-- alphas: array + | |-- element: string + |-- labels: array + | |-- element: string + |-- coefficients: array + | |-- element: double + """ + + header_block, sample_block, label = parse_key(key, key_pattern) + position = pdf['position'] + header = pdf['header'] + row_order = [(t[0]) for t in sorted(list(enumerate(zip(position, header))), key = lambda t: (t[1][0], t[1][1]))] + alpha_names, alpha_values = zip(*[(k,v) for k, v in sorted(alphas.items(), key = lambda t: t[0])]) + beta_stack = evaluate_coefficients(pdf, row_order, alpha_values) + row_indexer = create_row_indexer(alpha_names, labeldf, label) + col_order, alpha_row, label_row = zip(*[(i,a,l) for i,(a,l) in row_indexer]) + output_length = len(pdf) + data = { + 'header_block' : [header_block]*output_length, + 'sample_block' : [sample_block]*output_length, + 'header' : header[row_order], + 'position' : position[row_order], + 'alphas' : [list(alpha_row)]*output_length, + 'labels' : [list(label_row)]*output_length, + 'coefficients' : [r[list(col_order)].tolist() for r in beta_stack] + } + + return pd.DataFrame(data) + + +def apply_model(key, key_pattern, pdf, labeldf, alphas): + """ + This function takes a block X and a coefficient matrix B and performs the multiplication X*B. The matrix resulting + from this multiplication represents a block in a new, dimensionally-reduced block matrix. + + Args: + key : unique key identifying the group of rows emitted by a groupBy statement + key_pattern : pattern of columns used in the groupBy statement that emitted this group of rows + pdf : starting Pandas DataFrame containing the lists of rows used to assemble block X and coeffients B + identified by :key: + schema: + |-- header_block: string + |-- sample_block: integer + |-- header: string + |-- size: integer + |-- indices: array + | |-- element: integer + |-- values: array + | |-- element: double + |-- position: integer + |-- mu: double + |-- sig: double + |-- alphas: array + | |-- element: string + |-- labels: array + | |-- element: string + |-- coefficients: array + | |-- element: double + labeldf : Pandas DataFrame containing label values that were used in fitting coefficient matrix B. + alphas : dict of {alphaName : alphaValue} for the alpha values that were used when fitting coefficient matrix B + + Returns: + transformed Pandas DataFrame containing reduced matrix block produced by the multiplication X*B + schema (specified by reduced_matrix_struct): + |-- header: string + |-- size: integer + |-- values: array + | |-- element: double + |-- header_block: string + |-- sample_block: integer + |-- position: integer + |-- mu: double + |-- sig: double + |-- alpha: string + |-- label: string + """ + + header_block, sample_block, label = parse_key(key, key_pattern) + n_rows = pdf['size'][0] + n_cols = len(pdf) + position = pdf['position'] + col_order = [(t[0]) for t in sorted(list(enumerate(position)), key = lambda t: t[1])] + X = assemble_block(n_rows, n_cols, col_order, pdf) + B = np.row_stack(pdf['coefficients'][col_order].values) + XB = np.asarray(X@B) + mu, sig = XB.mean(axis = 0).tolist(), XB.std(axis = 0).tolist() + alpha_names = [k for k, v in sorted(alphas.items(), key = lambda t: t[0])] + row_indexer = create_row_indexer(alpha_names, labeldf, label) + alpha_col, label_col = zip(*[(a, l) for i, (a, l) in row_indexer]) + new_header_block, position_col, header_col = new_headers(header_block, alpha_names, row_indexer) + n_output_rows = len(row_indexer) + + data = { + 'header' : header_col, + 'size' : [X.shape[0]]*n_output_rows, + 'values' : XB.T.tolist(), + 'header_block' : [new_header_block]*n_output_rows, + 'sample_block' : [sample_block]*n_output_rows, + 'position' : position_col, + 'mu' : mu, + 'sig' : sig, + 'alpha' : alpha_col, + 'label' : label_col + } + + return pd.DataFrame(data) + + +def score_models(key, key_pattern, pdf, labeldf, sample_index, alphas): + """ + Similar to apply_model, this function performs the multiplication X*B for a block X and corresponding coefficient + matrix B, however it also evaluates the coefficient of determination (r2) for each of columns in B against the + corresponding label. + + Args: + key : unique key identifying the group of rows emitted by a groupBy statement + key_pattern : pattern of columns used in the groupBy statement that emitted this group of rows + pdf : starting Pandas DataFrame containing the lists of rows used to assemble block X and coeffients B + identified by :key: + schema: + |-- header_block: string + |-- sample_block: integer + |-- header: string + |-- size: integer + |-- indices: array + | |-- element: integer + |-- values: array + | |-- element: double + |-- position: integer + |-- mu: double + |-- sig: double + |-- alphas: array + | |-- element: string + |-- labels: array + | |-- element: string + |-- coefficients: array + | |-- element: double + labeldf : Pandas DataFrame containing label values that were used in fitting coefficient matrix B. + alphas : dict of {alphaName : alphaValue} for the alpha values that were used when fitting coefficient matrix B + + Returns: + Pandas DataFrame containing the r2 scores for each combination of alpha and label + schema: + |-- sample_block: integer + |-- label: string + |-- alpha: string + |-- r2: double + """ + header_block, sample_block, label = parse_key(key, key_pattern) + n_rows = pdf['size'][0] + n_cols = len(pdf) + position = pdf['position'] + col_order = [(t[0]) for t in sorted(list(enumerate(position)), key = lambda t: t[1])] + sample_list = sample_index[sample_block] + X = assemble_block(n_rows, n_cols, col_order, pdf) + B = np.row_stack(pdf['coefficients'][col_order].values) + XB = np.asarray(X@B) + Y = slice_label_rows(labeldf, label, sample_list) + scores = r_squared(XB, Y) + alpha_names = [k for k, v in sorted(alphas.items(), key = lambda t: t[0])] + n_output_rows = len(alpha_names) + + data = { + 'sample_block' : [sample_block]*n_output_rows, + 'label' : [label]*n_output_rows, + 'alpha' : alpha_names, + 'r2' : scores + } + + return pd.DataFrame(data) diff --git a/python/glow/levels/tests/__init__.py b/python/glow/levels/tests/__init__.py new file mode 100644 index 000000000..e69de29bb diff --git a/python/glow/levels/tests/test_ridge_regression.py b/python/glow/levels/tests/test_ridge_regression.py new file mode 100644 index 000000000..444436f92 --- /dev/null +++ b/python/glow/levels/tests/test_ridge_regression.py @@ -0,0 +1,370 @@ +import pytest +import glow +from glow.levels.linear_model import RidgeReducer, RidgeRegression +from glow.levels.linear_model.ridge_model import * + +data_root = 'test-data/levels/ridge-regression' +X0 = pd.read_csv(f'{data_root}/X0.csv').set_index('sample_id') +X1 = pd.read_csv(f'{data_root}/X1.csv').set_index('sample_id') +X2 = pd.read_csv(f'{data_root}/X2.csv').set_index('sample_id') +labeldf = pd.read_csv(f'{data_root}/pts.csv').set_index('sample_id') +alphas = np.array([0.1, 1, 10]) +alphaMap = {f'alpha_{i}' : a for i, a in enumerate(alphas)} + + +def test_map_normal_eqn(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + testGroup = 0 + testBlock = 'chr_1_block_0' + ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + + X_in = X0[headers].loc[ids, :] + Y_in = labeldf.loc[ids, :] + + XtX_in = X_in.values.T@X_in.values + XtY_in = X_in.values.T@Y_in.values + + sample_index = {r.sample_block : r.sample_ids for r in indexdf.collect()} + map_key_pattern = ['header_block', 'sample_block'] + map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + + outdf = blockdf\ + .groupBy(map_key_pattern) \ + .apply(map_udf) \ + .filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ + .orderBy('position') \ + .toPandas() + + XtX_in_lvl = np.stack(outdf['xtx'].values) + XtY_in_lvl = np.stack(outdf['xty'].values) + + assert (np.allclose(XtX_in_lvl, XtX_in) and np.allclose(XtY_in_lvl, XtY_in)) + + +def test_reduce_normal_eqn(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + testGroup = 0 + testBlock = 'chr_1_block_0' + ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + + X_out = X0[headers].drop(ids, axis = 'rows') + Y_out = labeldf.drop(ids, axis = 'rows') + + XtX_out = X_out.values.T@X_out.values + XtY_out = X_out.values.T@Y_out.values + + sample_index = {r.sample_block : r.sample_ids for r in indexdf.collect()} + map_key_pattern = ['header_block', 'sample_block'] + reduce_key_pattern = ['header_block', 'header'] + map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + + mapdf = blockdf\ + .groupBy(map_key_pattern) \ + .apply(map_udf) + + outdf = mapdf.groupBy(reduce_key_pattern) \ + .apply(reduce_udf) \ + .filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ + .orderBy('position') \ + .toPandas() + + XtX_out_lvl = np.stack(outdf['xtx'].values) + XtY_out_lvl = np.stack(outdf['xty'].values) + + assert (np.allclose(XtX_out_lvl, XtX_out) and np.allclose(XtY_out_lvl, XtY_out)) + + +def test_solve_normal_eqn(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + testGroup = 0 + testBlock = 'chr_1_block_0' + ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1], t[1][0]))] + + X_out = X0[headers].drop(ids, axis = 'rows') + Y_out = labeldf.drop(ids, axis = 'rows') + + XtX_out = X_out.values.T@X_out.values + XtY_out = X_out.values.T@Y_out.values + B = np.column_stack([(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1])*a)@XtY_out) for a in alphas])[:, coefOrder_l0] + + sample_index = {r.sample_block : r.sample_ids for r in indexdf.collect()} + map_key_pattern = ['header_block', 'sample_block'] + reduce_key_pattern = ['header_block', 'header'] + map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + model_udf = pandas_udf(lambda key, pdf: solve_normal_eqn(key, reduce_key_pattern, pdf, labeldf, alphaMap), model_struct, PandasUDFType.GROUPED_MAP) + + reducedf = blockdf\ + .groupBy(map_key_pattern) \ + .apply(map_udf).groupBy(reduce_key_pattern) \ + .apply(reduce_udf) + + outdf = reducedf.groupBy(map_key_pattern) \ + .apply(model_udf) \ + .filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ + .toPandas() + + position = outdf['position'] + colOrder = [(t[0]) for t in sorted(list(enumerate(position)), key = lambda t: t[1])] + + B_lvl = np.row_stack(outdf['coefficients'][colOrder].values) + + assert np.allclose(B_lvl, B) + + +def test_apply_model(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + testGroup = 0 + testBlock = 'chr_1_block_0' + ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1],t[1][0]))] + + X_in = X0[headers].loc[ids, :] + X_out = X0[headers].drop(ids, axis = 'rows') + Y_out = labeldf.drop(ids, axis = 'rows') + + XtX_out = X_out.values.T@X_out.values + XtY_out = X_out.values.T@Y_out.values + B = np.column_stack([(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1])*a)@XtY_out) for a in alphas])[:, coefOrder_l0] + X1_in = X_in.values@B + + sample_index = {r.sample_block : r.sample_ids for r in indexdf.collect()} + map_key_pattern = ['header_block', 'sample_block'] + reduce_key_pattern = ['header_block', 'header'] + transform_key_pattern = ['header_block', 'sample_block'] + map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + model_udf = pandas_udf(lambda key, pdf: solve_normal_eqn(key, reduce_key_pattern, pdf, labeldf, alphaMap), model_struct, PandasUDFType.GROUPED_MAP) + transform_udf = pandas_udf(lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, alphaMap), reduced_matrix_struct, PandasUDFType.GROUPED_MAP) + + modeldf = blockdf\ + .groupBy(map_key_pattern) \ + .apply(map_udf).groupBy(reduce_key_pattern) \ + .apply(reduce_udf).groupBy(map_key_pattern) \ + .apply(model_udf) + + outdf = blockdf.join(modeldf.drop('position'), ['header_block', 'sample_block', 'header']) \ + .groupBy(transform_key_pattern) \ + .apply(transform_udf) \ + .filter(f'header LIKE "%{testBlock}%" AND sample_block= {testGroup}').toPandas() + + alphaNames = outdf['alpha'] + labels = outdf['label'] + colOrder = [i for i, (a, l) in sorted(enumerate(zip(alphaNames, labels)), key = lambda t: (t[1][1],t[1][0]))] + X1_in_lvl = np.column_stack(outdf['values'])[:, colOrder] + + assert np.allclose(X1_in_lvl, X1_in) + + +def test_ridge_reducer_fit(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + testGroup = 0 + testBlock = 'chr_1_block_0' + ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1],t[1][0]))] + + X_out = X0[headers].drop(ids, axis = 'rows') + Y_out = labeldf.drop(ids, axis = 'rows') + + XtX_out = X_out.values.T@X_out.values + XtY_out = X_out.values.T@Y_out.values + B = np.column_stack([(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1])*a)@XtY_out) for a in alphas])[:, coefOrder_l0] + + stack = RidgeReducer(alphas) + modeldf = stack.fit(blockdf, labeldf, indexdf) + + outdf = modeldf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').toPandas() + position = outdf['position'] + colOrder = [(t[0]) for t in sorted(list(enumerate(position)), key = lambda t: t[1])] + + B_stack = np.row_stack(outdf['coefficients'][colOrder].values) + + assert np.allclose(B_stack, B) + + +def test_ridge_reducer_transform(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + testGroup = 0 + testBlock = 'chr_1_block_0' + ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1], t[1][0]))] + + X_in = X0[headers].loc[ids, :] + X_out = X0[headers].drop(ids, axis = 'rows') + Y_out = labeldf.drop(ids, axis = 'rows') + + XtX_out = X_out.values.T@X_out.values + XtY_out = X_out.values.T@Y_out.values + B = np.column_stack([(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1])*a)@XtY_out) for a in alphas])[:, coefOrder_l0] + X1_in = X_in.values@B + + stack = RidgeReducer(alphas) + modeldf = stack.fit(blockdf, labeldf, indexdf) + level1df = stack.transform(blockdf, labeldf, modeldf) + + outdf = level1df.filter(f'header LIKE "%{testBlock}%" AND sample_block= {testGroup}').toPandas() + alphaNames = outdf['alpha'] + labels = outdf['label'] + colOrder = [i for i, (a, l) in sorted(enumerate(zip(alphaNames, labels)), key = lambda t: (t[1][1], t[1][0]))] + X1_in_stack = np.column_stack(outdf['values'])[:, colOrder] + + assert np.allclose(X1_in_stack, X1_in) + + +def test_one_level_regression(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + testLabel = 'sim100' + columnIndexer = sorted(enumerate(alphaMap.keys()), key = lambda t: t[1]) + coefOrder = [i for i, a in columnIndexer] + + group2ids = {r.sample_block : r.sample_ids for r in indexdf.collect()} + groups = sorted(group2ids.keys(), key = lambda v: v) + headersToKeep = [c for c in X1.columns if testLabel in c] + + + r2s = [] + for group in groups: + ids = group2ids[group] + X1_in = X1[headersToKeep].loc[ids, :].values + X1_out = X1[headersToKeep].drop(ids, axis = 'rows') + Y_in = labeldf[testLabel].loc[ids].values + Y_out = labeldf[testLabel].loc[X1_out.index].values + X1tX1_out = X1_out.values.T@X1_out.values + X1tY_out = X1_out.values.T@Y_out + B = np.column_stack([(np.linalg.inv(X1tX1_out + np.identity(X1tX1_out.shape[1])*a)@X1tY_out) for a in alphas])[:, coefOrder] + X1B = X1_in@B + r2 = r_squared(X1B, Y_in.reshape(-1,1)) + r2s.append(r2) + + r2_mean = np.row_stack(r2s).mean(axis = 0) + bestAlpha, bestr2 = sorted(zip(alphaMap.keys(), r2_mean), key = lambda t: -t[1])[0] + + y_hat = [] + r2s_pred = [] + for group in groups: + ids = group2ids[group] + X1_in = X1[headersToKeep].loc[ids, :].values + X1_out = X1[headersToKeep].drop(ids, axis = 'rows') + Y_in = labeldf[testLabel].loc[ids].values + Y_out = labeldf[testLabel].loc[X1_out.index].values + X1tX1_out = X1_out.values.T@X1_out.values + X1tY_out = X1_out.values.T@Y_out + b = np.linalg.inv(X1tX1_out + np.identity(X1tX1_out.shape[1])*alphaMap[bestAlpha])@X1tY_out + r2s_pred.append(r_squared(X1_in@b, Y_in)) + y_hat.extend((X1_in@b).tolist()) + + y_hat = np.array(y_hat) + + stack0 = RidgeReducer(alphas) + model0df = stack0.fit(blockdf, labeldf, indexdf) + model0df.cache() + level1df = stack0.transform(blockdf, labeldf, model0df) + + regressor = RidgeRegression(alphas) + model1df, cvdf = regressor.fit(level1df, labeldf, indexdf) + model1df.cache() + cvdf.cache() + yhatdf = regressor.transform(level1df, labeldf, model1df, cvdf) + + bestAlpha_lvl, bestr2_lvl = [(r.alpha, r.r2_mean) for r in cvdf.filter(f'label = "{testLabel}"').collect()][0] + y_hat_lvl = np.concatenate([r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').collect()]) + + assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and np.allclose(y_hat_lvl, np.array(y_hat))) + + +def test_two_level_regression(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + testLabel = 'sim100' + columnIndexer = sorted(enumerate(alphaMap.keys()), key = lambda t: t[1]) + coefOrder = [i for i, a in columnIndexer] + + group2ids = {r.sample_block : r.sample_ids for r in indexdf.collect()} + groups = sorted(group2ids.keys(), key = lambda v: v) + headersToKeep = [c for c in X2.columns if testLabel in c] + + r2s = [] + + for group in groups: + ids = group2ids[group] + X2_in = X2[headersToKeep].loc[ids, :].values + X2_out = X2[headersToKeep].drop(ids, axis = 'rows') + Y_in = labeldf[testLabel].loc[ids].values + Y_out = labeldf[testLabel].loc[X2_out.index].values + X2tX2_out = X2_out.values.T@X2_out.values + X2tY_out = X2_out.values.T@Y_out + B = np.column_stack([(np.linalg.inv(X2tX2_out + np.identity(X2tX2_out.shape[1])*a)@X2tY_out) for a in alphas])[:, coefOrder] + X2B = X2_in@B + r2 = r_squared(X2B, Y_in.reshape(-1,1)) + r2s.append(r2) + + r2_mean = np.row_stack(r2s).mean(axis = 0) + bestAlpha, bestr2 = sorted(zip(alphaMap.keys(), r2_mean), key = lambda t: -t[1])[0] + + y_hat = [] + r2s_pred = [] + + for group in groups: + ids = group2ids[group] + X2_in = X2[headersToKeep].loc[ids, :].values + X2_out = X2[headersToKeep].drop(ids, axis = 'rows') + Y_in = labeldf[testLabel].loc[ids].values + Y_out = labeldf[testLabel].loc[X2_out.index].values + X2tX2_out = X2_out.values.T@X2_out.values + X2tY_out = X2_out.values.T@Y_out + b = np.linalg.inv(X2tX2_out + np.identity(X2tX2_out.shape[1])*alphaMap[bestAlpha])@X2tY_out + r2s_pred.append(r_squared(X2_in@b, Y_in)) + y_hat.extend((X2_in@b).tolist()) + + y_hat = np.array(y_hat) + + stack0 = RidgeReducer(alphas) + model0df = stack0.fit(blockdf, labeldf, indexdf) + model0df.cache() + level1df = stack0.transform(blockdf, labeldf, model0df) + + stack1 = RidgeReducer(alphas) + model1df = stack1.fit(level1df, labeldf, indexdf) + model1df.cache() + level2df = stack1.transform(level1df, labeldf, model1df) + + regressor = RidgeRegression(alphas) + model2df, cvdf = regressor.fit(level2df, labeldf, indexdf) + model2df.cache() + cvdf.cache() + yhatdf = regressor.transform(level2df, labeldf, model2df, cvdf) + + bestAlpha_lvl, bestr2_lvl = [(r.alpha, r.r2_mean) for r in cvdf.filter(f'label = "{testLabel}"').collect()][0] + y_hat_lvl = np.concatenate([r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').collect()]) + + assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and np.allclose(y_hat_lvl, np.array(y_hat))) + + + + + + + diff --git a/python/project/build.properties b/python/project/build.properties new file mode 100644 index 000000000..797e7ccfd --- /dev/null +++ b/python/project/build.properties @@ -0,0 +1 @@ +sbt.version=1.3.10 diff --git a/python/setup.py b/python/setup.py index 75076d940..c92cae7f5 100644 --- a/python/setup.py +++ b/python/setup.py @@ -1,4 +1,4 @@ -from setuptools import setup +from setuptools import setup, setuptools import imp version = imp.load_source('version', 'version.py').VERSION @@ -6,7 +6,7 @@ setup( name='glow.py', version=version, - packages=['glow'], + packages=setuptools.find_packages(), install_requires=[ 'typeguard==2.5.0', ], diff --git a/test-data/levels/ridge-regression/README.md b/test-data/levels/ridge-regression/README.md new file mode 100644 index 000000000..ad634cf92 --- /dev/null +++ b/test-data/levels/ridge-regression/README.md @@ -0,0 +1,24 @@ +####Test data explanation +The data provided for the ridge regression unit tests consist of ~100 variants across 3 chromosomes, ~100 individuals, +and 4 simulated phenotypes. +* **blockedGT.snappy.parquet**: Blocked genotype matrix representing 100 variants across 3 chromosomes. Genotype +data are represented in a sparse format, with size, indices, and values columns. Missing genotype values have been +mean imputed. +* **groupedIDs.snappy.parquet**: File for mapping sample group IDs to lists of sample IDs. Consists of a column +containing group IDs and a column containing an array of sample IDs. This dataset contains 10 groups, with ~10 +samples per group. +* **pts.csv**: File containing simulated phenotypes. Consists of a sample ID column and 4 phenotype columns. Phenotypes +were simulated by randomly generating weights (b) for the (standardized) test genotypes (X) and mixing the resulting +vector with a noise vector (e) with varying proportions (e.g.: phenotype_i = (Xb)*f_i + e*(1-f_i)). Phenotype names +follow a pattern of "simX" where X is an integer representing the explainable variance in the phenotype (e.g. sim100 +is 100% explainable with the underlying genotype data). +* **X0.csv**: File containing the same genotype data contained in blockedGT.snappy.parquet in the flattened and +standardized state (e.g., each row represents an individual, each column represents a standardized variant, where a +standardized variant has been centered at 0 and standard scaled to have variance = 1). +* **X1.csv**: File representing the output of 1 round of ridge reduction, in a flattened and standardized state. +Each row represents an individual, each column represents the output of a ridge model prediction for a particular block +of variants, a regularization (alpha) value, and a phenotype (e.g., the level 1 ridge model). +* **X2.csv**: File representing the output of 2 rounds of ridge reduction, in a flattened and standardized state. +Each row represents an individual, each column represents the output of a level 2 ridge model prediction for a block of +level 1 ridge predictions, a regularization (alpha) value, and a phenotype. At this level, the block represents a +chromosome. \ No newline at end of file diff --git a/test-data/levels/ridge-regression/X0.csv b/test-data/levels/ridge-regression/X0.csv new file mode 100644 index 000000000..c02dc7e5e --- /dev/null +++ b/test-data/levels/ridge-regression/X0.csv @@ -0,0 +1,101 @@ 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diff --git a/test-data/levels/ridge-regression/X1.csv b/test-data/levels/ridge-regression/X1.csv new file mode 100644 index 000000000..5377aee5b --- /dev/null +++ b/test-data/levels/ridge-regression/X1.csv @@ -0,0 +1,101 @@ 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+1084331087,-0.8558903124999222,-0.528590787178397,-0.20129126185687207,0.12600826346465302 +1014891357,2.397574238311788,1.939778074273793,1.4819819102357985,1.0241857461978032 +1093361029,0.8065137337290055,0.8538696263374435,0.9012255189458812,0.9485814115543192 +1023728468,0.4812344032148365,0.6421904263795193,0.8031464495442021,0.9641024727088848 +1079447298,-1.7711055008326917,-1.038614785079056,-0.3061240693254198,0.4263666464282163 From b065560f59f3c0e7447e70643a582b77424dbd35 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Fri, 29 May 2020 11:29:23 -0700 Subject: [PATCH 04/34] [HLS-539] Fix compatibility between blocked GT transformer and WGR (#6) * WIP Signed-off-by: Karen Feng * existing tests pass Signed-off-by: Karen Feng * rename file Signed-off-by: Karen Feng * Add compat test Signed-off-by: Karen Feng * scalafmt Signed-off-by: Karen Feng * collect minimal columns Signed-off-by: Karen Feng * address comments Signed-off-by: Karen Feng * Test fixup Signed-off-by: Karen Feng * Spark 3 needs more recent PyArrow, reduce mem consumption by removing unnecessary caching Signed-off-by: Karen Feng * PyArrow 0.15.1 only with PySpark 3 Signed-off-by: Karen Feng * Don't use toPandas() Signed-off-by: Karen Feng * Upgrade pyarrow Signed-off-by: Karen Feng * Only register once Signed-off-by: Karen Feng * Minimize memory usage Signed-off-by: Karen Feng * Select before head Signed-off-by: Karen Feng * set up/tear down Signed-off-by: Karen Feng * Try limiting pyspark memory Signed-off-by: Karen Feng * No teardown Signed-off-by: Karen Feng * Extend timeout Signed-off-by: Karen Feng --- .circleci/config.yml | 7 + build.sbt | 3 +- conftest.py | 2 + .../VariantSampleBlockMaker.scala | 2 +- ...ckVariantsAndSamplesTransformerSuite.scala | 31 ++++- python/environment.yml | 2 +- python/glow/conftest.py | 2 +- python/glow/levels/linear_model/functions.py | 11 +- .../glow/levels/linear_model/ridge_model.py | 8 +- python/glow/levels/linear_model/ridge_udfs.py | 85 ++++++------ .../tests/test_ridge_regression.py | 127 +++++++++--------- python/glow/levels/tests/__init__.py | 0 python/glow/tests/__init__.py | 0 python/glow/tests/test_transform.py | 1 - .../ridge-regression/blockedGT.snappy.parquet | Bin 12735 -> 12586 bytes .../groupedIDs.snappy.parquet | Bin 1227 -> 1170 bytes 16 files changed, 159 insertions(+), 122 deletions(-) rename python/glow/levels/{ => linear_model}/tests/test_ridge_regression.py (81%) delete mode 100644 python/glow/levels/tests/__init__.py delete mode 100644 python/glow/tests/__init__.py diff --git a/.circleci/config.yml b/.circleci/config.yml index a3eab47a0..e5ad20698 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -87,12 +87,15 @@ jobs: environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH + export ARROW_PRE_0_15_IPC_FORMAT=1 sbt coverage core_2_11/test coverageReport exit - run: name: Run Python tests + no_output_timeout: 30m environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH + export ARROW_PRE_0_15_IPC_FORMAT=1 sbt python_2_11/test exit - run: name: Run docs tests @@ -129,15 +132,18 @@ jobs: sbt core_2_12/test exit - run: name: Run Python tests + no_output_timeout: 30m environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH + export ARROW_PRE_0_15_IPC_FORMAT=1 sbt python_2_12/test exit - run: name: Run docs tests environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH + export ARROW_PRE_0_15_IPC_FORMAT=1 sbt docs_2_12/test exit - *check_clean_repo - store_artifacts: @@ -168,6 +174,7 @@ jobs: sbt core_2_12/test exit - run: name: Run Python tests + no_output_timeout: 30m environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH diff --git a/build.sbt b/build.sbt index b891a19a2..7a0254d85 100644 --- a/build.sbt +++ b/build.sbt @@ -205,7 +205,8 @@ lazy val python = Seq("pytest", "--doctest-modules", "python"), None, "SPARK_CLASSPATH" -> sparkClasspath.value, - "SPARK_HOME" -> sparkHome.value + "SPARK_HOME" -> sparkHome.value, + "PYTHONPATH" -> pythonPath.value ).! require(ret == 0, "Python tests failed") }, diff --git a/conftest.py b/conftest.py index 00d179167..dcc0db228 100644 --- a/conftest.py +++ b/conftest.py @@ -4,8 +4,10 @@ # Set up a new Spark session for each test suite @pytest.fixture(scope="module") def spark(): + print("set up new spark session") sess = SparkSession.builder \ .master("local[2]") \ .config("spark.hadoop.io.compression.codecs", "io.projectglow.sql.util.BGZFCodec") \ + .config("spark.ui.enabled", "false") \ .getOrCreate() return sess.newSession() diff --git a/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala b/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala index 46335e525..9c663627e 100644 --- a/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala +++ b/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala @@ -38,7 +38,7 @@ private[projectglow] object VariantSampleBlockMaker extends GlowLogging { variantDf .withColumn( sortKeyField.name, - col(startField.name) + col(startField.name).cast(IntegerType) ) .withColumn( headerField.name, diff --git a/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala b/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala index b401ed1e6..48305ff2c 100644 --- a/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala +++ b/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala @@ -16,17 +16,17 @@ package io.projectglow.transformers.blockvariantsandsamples +import org.apache.spark.sql.functions._ +import org.apache.spark.sql.SQLUtils +import org.apache.spark.sql.types._ + import io.projectglow.Glow import io.projectglow.common.GlowLogging import io.projectglow.sql.GlowBaseTest import io.projectglow.common.VariantSchemas._ import io.projectglow.functions.genotype_states - -import org.apache.spark.sql.functions._ import io.projectglow.transformers.blockvariantsandsamples.BlockVariantsAndSamplesTransformer._ -import org.apache.spark.sql.types._ - class BlockVariantsAndSamplesTransformerSuite extends GlowBaseTest with GlowLogging { lazy val sourceName: String = "vcf" @@ -122,4 +122,27 @@ class BlockVariantsAndSamplesTransformerSuite extends GlowBaseTest with GlowLogg 7 ) } + + test("test schema") { + val vcfDf = spark + .read + .format("vcf") + .load(testVcf) + .withColumn("values", expr("genotype_states(genotypes)").cast(ArrayType(DoubleType))) + val options = Map(VARIANTS_PER_BLOCK -> "10", SAMPLE_BLOCK_COUNT -> "20") + val testSchema = Glow.transform(TRANSFORMER_NAME, vcfDf, options).schema + + val expectedSchema = spark + .read + .format("parquet") + .load(s"$testDataHome/levels/ridge-regression/blockedGT.snappy.parquet") + .drop("indices") + .schema + + assert(testSchema.length == expectedSchema.length) + testSchema.zip(expectedSchema).foreach { + case (t, e) => + assert(SQLUtils.structFieldsEqualExceptNullability(t, e), s"Expected\n$e\nBlocked\n$t") + } + } } diff --git a/python/environment.yml b/python/environment.yml index c7cdc2d90..987203bea 100644 --- a/python/environment.yml +++ b/python/environment.yml @@ -10,7 +10,7 @@ dependencies: - nomkl # Skip MKL for local development - pandas=0.25.3 - pip - - pyarrow=0.13.0 + - pyarrow=0.15.1 - pyspark=2.4.5 - pytest - pyyaml diff --git a/python/glow/conftest.py b/python/glow/conftest.py index 6f9cd3a01..66eb9d0c8 100644 --- a/python/glow/conftest.py +++ b/python/glow/conftest.py @@ -2,7 +2,7 @@ from pyspark.sql import functions, Row import glow -@pytest.fixture(autouse=True) +@pytest.fixture(autouse=True, scope="module") def add_spark(doctest_namespace, spark): glow.register(spark) doctest_namespace['Row'] = Row diff --git a/python/glow/levels/linear_model/functions.py b/python/glow/levels/linear_model/functions.py index dc083cdbf..0449d1aca 100644 --- a/python/glow/levels/linear_model/functions.py +++ b/python/glow/levels/linear_model/functions.py @@ -1,4 +1,3 @@ -import pandas as pd import numpy as np from scipy.sparse import csr_matrix import itertools @@ -134,7 +133,7 @@ def new_headers(header_block, alpha_names, row_indexer): Returns: new_header_block : A new header_block name, typically the chromosome (e.g. chr1), but might be 'all' if there are no more levels to reduce over. - positions : Array of sortable integers to specify the ordering of the new matrix headers. + sort_keys : Array of sortable integers to specify the ordering of the new matrix headers. headers : List of new matrix headers. """ tokens = header_block.split('_') @@ -149,14 +148,14 @@ def new_headers(header_block, alpha_names, row_indexer): outer_index, inner_index = tokens[1:4:2] new_header_block = f'chr_{outer_index}' - positions, headers = [], [] + sort_keys, headers = [], [] for i, (a, l) in row_indexer: - position = int(inner_index)*len(alpha_names) + int(a.split('_')[1]) + sort_key = int(inner_index)*len(alpha_names) + int(a.split('_')[1]) header = f'{new_header_block}_block_{inner_index}_{a}_label_{l}' - positions.append(position) + sort_keys.append(sort_key) headers.append(header) - return new_header_block, positions, headers + return new_header_block, sort_keys, headers def r_squared(XB, Y): diff --git a/python/glow/levels/linear_model/ridge_model.py b/python/glow/levels/linear_model/ridge_model.py index 115bb7d48..91e62fd1c 100644 --- a/python/glow/levels/linear_model/ridge_model.py +++ b/python/glow/levels/linear_model/ridge_model.py @@ -39,7 +39,7 @@ def fit(self, blockdf, labeldf, indexdf): map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header_block', 'header'] - if 'label'in blockdf.columns: + if 'label' in blockdf.columns: map_key_pattern.append('label') reduce_key_pattern.append('label') @@ -76,7 +76,7 @@ def transform(self, blockdf, labeldf, modeldf): transform_udf = pandas_udf(lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, self.alphas), reduced_matrix_struct, PandasUDFType.GROUPED_MAP) - return blockdf.join(modeldf.drop('position'), ['header_block', 'sample_block', 'header']) \ + return blockdf.join(modeldf.drop('sort_key'), ['header_block', 'sample_block', 'header']) \ .groupBy(transform_key_pattern) \ .apply(transform_udf) @@ -138,7 +138,7 @@ def fit(self, blockdf, labeldf, indexdf): .apply(model_udf) cvdf = blockdf\ - .join(modeldf.drop('header_block', 'position'), ['header', 'sample_block'], 'inner') \ + .join(modeldf.drop('header_block', 'sort_key'), ['header', 'sample_block'], 'inner') \ .groupBy(map_key_pattern) \ .apply(score_udf) \ .groupBy('label', 'alpha').agg(f.mean('r2').alias('r2_mean')) \ @@ -171,7 +171,7 @@ def transform(self, blockdf, labeldf, modeldf, cvdf): transform_udf = pandas_udf(lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, self.alphas), reduced_matrix_struct, PandasUDFType.GROUPED_MAP) - return blockdf.drop('header_block').join(modeldf.drop('header_block', 'position'), ['sample_block', 'header']) \ + return blockdf.drop('header_block').join(modeldf.drop('header_block', 'sort_key'), ['sample_block', 'header']) \ .groupBy(transform_key_pattern) \ .apply(transform_udf) \ .join(cvdf, ['label', 'alpha'], 'inner') \ diff --git a/python/glow/levels/linear_model/ridge_udfs.py b/python/glow/levels/linear_model/ridge_udfs.py index 77d4fd834..43e983585 100644 --- a/python/glow/levels/linear_model/ridge_udfs.py +++ b/python/glow/levels/linear_model/ridge_udfs.py @@ -1,24 +1,27 @@ from .functions import * +import pandas as pd from pyspark.sql.types import ArrayType, IntegerType, FloatType, StructType, StructField, StringType, DoubleType + + ''' Each function in this module performs a Pandas DataFrame => Pandas DataFrame transformation, and each is intended to be used as a Pandas GROUPED_MAP UDF. ''' normal_eqn_struct = StructType([ StructField('header_block', StringType()), - StructField('sample_block', IntegerType()), + StructField('sample_block', StringType()), StructField('label', StringType()), StructField('header', StringType()), - StructField('position', IntegerType()), + StructField('sort_key', IntegerType()), StructField('xtx', ArrayType(DoubleType())), StructField('xty', ArrayType(DoubleType())) ]) model_struct = StructType([ StructField('header_block', StringType()), - StructField('sample_block', IntegerType()), + StructField('sample_block', StringType()), StructField('header', StringType()), - StructField('position', IntegerType()), + StructField('sort_key', IntegerType()), StructField('alphas', ArrayType(StringType())), StructField('labels', ArrayType(StringType())), StructField('coefficients', ArrayType(DoubleType())) @@ -29,8 +32,8 @@ StructField('size', IntegerType()), StructField('values', ArrayType(DoubleType())), StructField('header_block', StringType()), - StructField('sample_block', IntegerType()), - StructField('position', IntegerType()), + StructField('sample_block', StringType()), + StructField('sort_key', IntegerType()), StructField('mu', DoubleType()), StructField('sig', DoubleType()), StructField('alpha', StringType()), @@ -38,7 +41,7 @@ ]) cv_struct = StructType([ - StructField('sample_block', IntegerType()), + StructField('sample_block', StringType()), StructField('label', StringType()), StructField('alpha', StringType()), StructField('r2', DoubleType()) @@ -68,8 +71,8 @@ def map_normal_eqn(key, key_pattern, pdf, labeldf, sample_index): |-- values: array | |-- element: double |-- header_block: string - |-- sample_block: integer - |-- position: integer + |-- sample_block: string + |-- sort_key: integer |-- mu: double |-- sig: double |-- alpha: double (Required only if the header is tied to a specific value of alpha) @@ -80,10 +83,10 @@ def map_normal_eqn(key, key_pattern, pdf, labeldf, sample_index): transformed Pandas DataFrame containing XtX and XtY corresponding to a particular block X. schema (specified by the normal_eqn_struct): |-- header_block: string - |-- sample_block: integer + |-- sample_block: string |-- label: string |-- header: string - |-- position: integer + |-- sort_key: integer |-- xtx: array | |-- element: double |-- xty: array @@ -92,9 +95,9 @@ def map_normal_eqn(key, key_pattern, pdf, labeldf, sample_index): header_block, sample_block, label = parse_key(key, key_pattern) n_rows = pdf['size'][0] n_cols = len(pdf) - position = pdf['position'] + sort_key = pdf['sort_key'] header = pdf['header'] - col_order = [(t[0]) for t in sorted(list(enumerate(zip(position, header))), key = lambda t: (t[1][0], t[1][1]))] + col_order = [(t[0]) for t in sorted(list(enumerate(zip(sort_key, header))), key = lambda t: (t[1][0], t[1][1]))] sample_list = sample_index[sample_block] X = assemble_block(n_rows, n_cols, col_order, pdf) Y = slice_label_rows(labeldf, label, sample_list) @@ -106,7 +109,7 @@ def map_normal_eqn(key, key_pattern, pdf, labeldf, sample_index): 'sample_block' : [sample_block]*n_cols, 'label' : [label]*n_cols, 'header' : header[col_order].values, - 'position' : position[col_order].values, + 'sort_key' : sort_key[col_order].values, 'xtx' : XtX.tolist(), 'xty' : XtY.tolist() } @@ -132,10 +135,10 @@ def reduce_normal_eqn(key, key_pattern, pdf): pdf : starting Pandas DataFrame containing the lists of rows from XtX and XtY for block X identified by :key: schema (specified by the normal_eqn_struct): |-- header_block: string - |-- sample_block: integer + |-- sample_block: string |-- label: string |-- header: string - |-- position: integer + |-- sort_key: integer |-- xtx: array | |-- element: double |-- xty: array @@ -145,10 +148,10 @@ def reduce_normal_eqn(key, key_pattern, pdf): transformed Pandas DataFrame containing the aggregated leave-fold-out rows from XtX and XtY schema (specified by the normal_eqn_struct): |-- header_block: string - |-- sample_block: integer + |-- sample_block: string |-- label: string |-- header: string - |-- position: integer + |-- sort_key: integer |-- xtx: array | |-- element: double |-- xty: array @@ -156,16 +159,16 @@ def reduce_normal_eqn(key, key_pattern, pdf): """ header_block, header, label = parse_key(key, key_pattern) - position = pdf['position'][0] + sort_key = pdf['sort_key'][0] n_sample_blocks = len(pdf) sample_blocks = enumerate(pdf['sample_block']) slices = [(g, np.append(np.arange(i), np.arange(i+1, n_sample_blocks))) for i, g in sample_blocks] xtx_stack = np.stack(pdf['xtx'].values) xty_stack = np.stack(pdf['xty'].values) - rows = [[header_block, g, label, header, position, xtx_stack[s, :].sum(axis = 0), xty_stack[s, :].sum(axis = 0)] for g, s in slices] + rows = [[header_block, g, label, header, sort_key, xtx_stack[s, :].sum(axis = 0), xty_stack[s, :].sum(axis = 0)] for g, s in slices] - return pd.DataFrame(rows, columns = ['header_block', 'sample_block', 'label', 'header', 'position', 'xtx', 'xty']) + return pd.DataFrame(rows, columns = ['header_block', 'sample_block', 'label', 'header', 'sort_key', 'xtx', 'xty']) def solve_normal_eqn(key, key_pattern, pdf, labeldf, alphas): @@ -181,10 +184,10 @@ def solve_normal_eqn(key, key_pattern, pdf, labeldf, alphas): pdf : starting Pandas DataFrame containing the lists of rows from XtX and XtY for block X identified by :key: schema (specified by the normal_eqn_struct): |-- header_block: string - |-- sample_block: integer + |-- sample_block: string |-- label: string |-- header: string - |-- position: integer + |-- sort_key: integer |-- xtx: array | |-- element: double |-- xty: array @@ -196,9 +199,9 @@ def solve_normal_eqn(key, key_pattern, pdf, labeldf, alphas): transformed Pandas DataFrame containing the coefficient matrix B schema (specified by the normal_eqn_struct): |-- header_block: string - |-- sample_block: integer + |-- sample_block: string |-- header: string - |-- position: integer + |-- sort_key: integer |-- alphas: array | |-- element: string |-- labels: array @@ -208,9 +211,9 @@ def solve_normal_eqn(key, key_pattern, pdf, labeldf, alphas): """ header_block, sample_block, label = parse_key(key, key_pattern) - position = pdf['position'] + sort_key = pdf['sort_key'] header = pdf['header'] - row_order = [(t[0]) for t in sorted(list(enumerate(zip(position, header))), key = lambda t: (t[1][0], t[1][1]))] + row_order = [(t[0]) for t in sorted(list(enumerate(zip(sort_key, header))), key = lambda t: (t[1][0], t[1][1]))] alpha_names, alpha_values = zip(*[(k,v) for k, v in sorted(alphas.items(), key = lambda t: t[0])]) beta_stack = evaluate_coefficients(pdf, row_order, alpha_values) row_indexer = create_row_indexer(alpha_names, labeldf, label) @@ -220,7 +223,7 @@ def solve_normal_eqn(key, key_pattern, pdf, labeldf, alphas): 'header_block' : [header_block]*output_length, 'sample_block' : [sample_block]*output_length, 'header' : header[row_order], - 'position' : position[row_order], + 'sort_key' : sort_key[row_order], 'alphas' : [list(alpha_row)]*output_length, 'labels' : [list(label_row)]*output_length, 'coefficients' : [r[list(col_order)].tolist() for r in beta_stack] @@ -241,14 +244,14 @@ def apply_model(key, key_pattern, pdf, labeldf, alphas): identified by :key: schema: |-- header_block: string - |-- sample_block: integer + |-- sample_block: string |-- header: string |-- size: integer |-- indices: array | |-- element: integer |-- values: array | |-- element: double - |-- position: integer + |-- sort_key: integer |-- mu: double |-- sig: double |-- alphas: array @@ -268,8 +271,8 @@ def apply_model(key, key_pattern, pdf, labeldf, alphas): |-- values: array | |-- element: double |-- header_block: string - |-- sample_block: integer - |-- position: integer + |-- sample_block: string + |-- sort_key: integer |-- mu: double |-- sig: double |-- alpha: string @@ -279,8 +282,8 @@ def apply_model(key, key_pattern, pdf, labeldf, alphas): header_block, sample_block, label = parse_key(key, key_pattern) n_rows = pdf['size'][0] n_cols = len(pdf) - position = pdf['position'] - col_order = [(t[0]) for t in sorted(list(enumerate(position)), key = lambda t: t[1])] + sort_key = pdf['sort_key'] + col_order = [(t[0]) for t in sorted(list(enumerate(sort_key)), key = lambda t: t[1])] X = assemble_block(n_rows, n_cols, col_order, pdf) B = np.row_stack(pdf['coefficients'][col_order].values) XB = np.asarray(X@B) @@ -288,7 +291,7 @@ def apply_model(key, key_pattern, pdf, labeldf, alphas): alpha_names = [k for k, v in sorted(alphas.items(), key = lambda t: t[0])] row_indexer = create_row_indexer(alpha_names, labeldf, label) alpha_col, label_col = zip(*[(a, l) for i, (a, l) in row_indexer]) - new_header_block, position_col, header_col = new_headers(header_block, alpha_names, row_indexer) + new_header_block, sort_key_col, header_col = new_headers(header_block, alpha_names, row_indexer) n_output_rows = len(row_indexer) data = { @@ -297,7 +300,7 @@ def apply_model(key, key_pattern, pdf, labeldf, alphas): 'values' : XB.T.tolist(), 'header_block' : [new_header_block]*n_output_rows, 'sample_block' : [sample_block]*n_output_rows, - 'position' : position_col, + 'sort_key' : sort_key_col, 'mu' : mu, 'sig' : sig, 'alpha' : alpha_col, @@ -320,14 +323,14 @@ def score_models(key, key_pattern, pdf, labeldf, sample_index, alphas): identified by :key: schema: |-- header_block: string - |-- sample_block: integer + |-- sample_block: string |-- header: string |-- size: integer |-- indices: array | |-- element: integer |-- values: array | |-- element: double - |-- position: integer + |-- sort_key: integer |-- mu: double |-- sig: double |-- alphas: array @@ -342,7 +345,7 @@ def score_models(key, key_pattern, pdf, labeldf, sample_index, alphas): Returns: Pandas DataFrame containing the r2 scores for each combination of alpha and label schema: - |-- sample_block: integer + |-- sample_block: string |-- label: string |-- alpha: string |-- r2: double @@ -350,8 +353,8 @@ def score_models(key, key_pattern, pdf, labeldf, sample_index, alphas): header_block, sample_block, label = parse_key(key, key_pattern) n_rows = pdf['size'][0] n_cols = len(pdf) - position = pdf['position'] - col_order = [(t[0]) for t in sorted(list(enumerate(position)), key = lambda t: t[1])] + sort_key = pdf['sort_key'] + col_order = [(t[0]) for t in sorted(list(enumerate(sort_key)), key = lambda t: t[1])] sample_list = sample_index[sample_block] X = assemble_block(n_rows, n_cols, col_order, pdf) B = np.row_stack(pdf['coefficients'][col_order].values) diff --git a/python/glow/levels/tests/test_ridge_regression.py b/python/glow/levels/linear_model/tests/test_ridge_regression.py similarity index 81% rename from python/glow/levels/tests/test_ridge_regression.py rename to python/glow/levels/linear_model/tests/test_ridge_regression.py index 444436f92..6f6b04fbb 100644 --- a/python/glow/levels/tests/test_ridge_regression.py +++ b/python/glow/levels/linear_model/tests/test_ridge_regression.py @@ -1,7 +1,6 @@ -import pytest -import glow from glow.levels.linear_model import RidgeReducer, RidgeRegression from glow.levels.linear_model.ridge_model import * +import pandas as pd data_root = 'test-data/levels/ridge-regression' X0 = pd.read_csv(f'{data_root}/X0.csv').set_index('sample_id') @@ -16,10 +15,10 @@ def test_map_normal_eqn(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') - testGroup = 0 + testGroup = '0' testBlock = 'chr_1_block_0' - ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] X_in = X0[headers].loc[ids, :] Y_in = labeldf.loc[ids, :] @@ -27,7 +26,7 @@ def test_map_normal_eqn(spark): XtX_in = X_in.values.T@X_in.values XtY_in = X_in.values.T@Y_in.values - sample_index = {r.sample_block : r.sample_ids for r in indexdf.collect()} + sample_index = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} map_key_pattern = ['header_block', 'sample_block'] map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) @@ -35,11 +34,12 @@ def test_map_normal_eqn(spark): .groupBy(map_key_pattern) \ .apply(map_udf) \ .filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ - .orderBy('position') \ - .toPandas() + .orderBy('sort_key') \ + .select('xtx', 'xty') \ + .collect() - XtX_in_lvl = np.stack(outdf['xtx'].values) - XtY_in_lvl = np.stack(outdf['xty'].values) + XtX_in_lvl = np.array([r.xtx for r in outdf]) + XtY_in_lvl = np.array([r.xty for r in outdf]) assert (np.allclose(XtX_in_lvl, XtX_in) and np.allclose(XtY_in_lvl, XtY_in)) @@ -48,10 +48,10 @@ def test_reduce_normal_eqn(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') - testGroup = 0 + testGroup = '0' testBlock = 'chr_1_block_0' - ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] X_out = X0[headers].drop(ids, axis = 'rows') Y_out = labeldf.drop(ids, axis = 'rows') @@ -59,7 +59,7 @@ def test_reduce_normal_eqn(spark): XtX_out = X_out.values.T@X_out.values XtY_out = X_out.values.T@Y_out.values - sample_index = {r.sample_block : r.sample_ids for r in indexdf.collect()} + sample_index = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header_block', 'header'] map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) @@ -72,11 +72,12 @@ def test_reduce_normal_eqn(spark): outdf = mapdf.groupBy(reduce_key_pattern) \ .apply(reduce_udf) \ .filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ - .orderBy('position') \ - .toPandas() + .orderBy('sort_key') \ + .select('xtx', 'xty') \ + .collect() - XtX_out_lvl = np.stack(outdf['xtx'].values) - XtY_out_lvl = np.stack(outdf['xty'].values) + XtX_out_lvl = np.array([r.xtx for r in outdf]) + XtY_out_lvl = np.array([r.xty for r in outdf]) assert (np.allclose(XtX_out_lvl, XtX_out) and np.allclose(XtY_out_lvl, XtY_out)) @@ -85,10 +86,10 @@ def test_solve_normal_eqn(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') - testGroup = 0 + testGroup = '0' testBlock = 'chr_1_block_0' - ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1], t[1][0]))] X_out = X0[headers].drop(ids, axis = 'rows') @@ -98,7 +99,7 @@ def test_solve_normal_eqn(spark): XtY_out = X_out.values.T@Y_out.values B = np.column_stack([(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1])*a)@XtY_out) for a in alphas])[:, coefOrder_l0] - sample_index = {r.sample_block : r.sample_ids for r in indexdf.collect()} + sample_index = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header_block', 'header'] map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) @@ -110,13 +111,16 @@ def test_solve_normal_eqn(spark): .apply(map_udf).groupBy(reduce_key_pattern) \ .apply(reduce_udf) - outdf = reducedf.groupBy(map_key_pattern) \ + columns = ['sort_key', 'coefficients'] + rows = reducedf.groupBy(map_key_pattern) \ .apply(model_udf) \ .filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ - .toPandas() + .select(*columns) \ + .collect() + outdf = pd.DataFrame(rows, columns=columns) - position = outdf['position'] - colOrder = [(t[0]) for t in sorted(list(enumerate(position)), key = lambda t: t[1])] + sort_key = outdf['sort_key'] + colOrder = [(t[0]) for t in sorted(list(enumerate(sort_key)), key = lambda t: t[1])] B_lvl = np.row_stack(outdf['coefficients'][colOrder].values) @@ -127,10 +131,10 @@ def test_apply_model(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') - testGroup = 0 + testGroup = '0' testBlock = 'chr_1_block_0' - ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1],t[1][0]))] X_in = X0[headers].loc[ids, :] @@ -142,7 +146,7 @@ def test_apply_model(spark): B = np.column_stack([(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1])*a)@XtY_out) for a in alphas])[:, coefOrder_l0] X1_in = X_in.values@B - sample_index = {r.sample_block : r.sample_ids for r in indexdf.collect()} + sample_index = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header_block', 'header'] transform_key_pattern = ['header_block', 'sample_block'] @@ -157,10 +161,14 @@ def test_apply_model(spark): .apply(reduce_udf).groupBy(map_key_pattern) \ .apply(model_udf) - outdf = blockdf.join(modeldf.drop('position'), ['header_block', 'sample_block', 'header']) \ + columns = ['alpha', 'label', 'values'] + rows = blockdf.join(modeldf.drop('sort_key'), ['header_block', 'sample_block', 'header']) \ .groupBy(transform_key_pattern) \ .apply(transform_udf) \ - .filter(f'header LIKE "%{testBlock}%" AND sample_block= {testGroup}').toPandas() + .filter(f'header LIKE "%{testBlock}%" AND sample_block= {testGroup}') \ + .select(*columns) \ + .collect() + outdf = pd.DataFrame(rows, columns=columns) alphaNames = outdf['alpha'] labels = outdf['label'] @@ -174,10 +182,10 @@ def test_ridge_reducer_fit(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') - testGroup = 0 + testGroup = '0' testBlock = 'chr_1_block_0' - ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1],t[1][0]))] X_out = X0[headers].drop(ids, axis = 'rows') @@ -190,9 +198,12 @@ def test_ridge_reducer_fit(spark): stack = RidgeReducer(alphas) modeldf = stack.fit(blockdf, labeldf, indexdf) - outdf = modeldf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').toPandas() - position = outdf['position'] - colOrder = [(t[0]) for t in sorted(list(enumerate(position)), key = lambda t: t[1])] + columns = ['sort_key', 'coefficients'] + rows = modeldf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ + .select(*columns).collect() + outdf = pd.DataFrame(rows, columns=columns) + sort_key = outdf['sort_key'] + colOrder = [(t[0]) for t in sorted(list(enumerate(sort_key)), key = lambda t: t[1])] B_stack = np.row_stack(outdf['coefficients'][colOrder].values) @@ -203,10 +214,10 @@ def test_ridge_reducer_transform(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') - testGroup = 0 + testGroup = '0' testBlock = 'chr_1_block_0' - ids = indexdf.filter(f'sample_block = {testGroup}').collect()[0].sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('position').select('header').collect()] + ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids + headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1], t[1][0]))] X_in = X0[headers].loc[ids, :] @@ -222,7 +233,11 @@ def test_ridge_reducer_transform(spark): modeldf = stack.fit(blockdf, labeldf, indexdf) level1df = stack.transform(blockdf, labeldf, modeldf) - outdf = level1df.filter(f'header LIKE "%{testBlock}%" AND sample_block= {testGroup}').toPandas() + columns = ['alpha', 'label', 'values'] + rows = level1df.filter(f'header LIKE "%{testBlock}%" AND sample_block= {testGroup}') \ + .select(*columns) \ + .collect() + outdf = pd.DataFrame(rows, columns=columns) alphaNames = outdf['alpha'] labels = outdf['label'] colOrder = [i for i, (a, l) in sorted(enumerate(zip(alphaNames, labels)), key = lambda t: (t[1][1], t[1][0]))] @@ -239,7 +254,7 @@ def test_one_level_regression(spark): columnIndexer = sorted(enumerate(alphaMap.keys()), key = lambda t: t[1]) coefOrder = [i for i, a in columnIndexer] - group2ids = {r.sample_block : r.sample_ids for r in indexdf.collect()} + group2ids = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} groups = sorted(group2ids.keys(), key = lambda v: v) headersToKeep = [c for c in X1.columns if testLabel in c] @@ -279,17 +294,15 @@ def test_one_level_regression(spark): stack0 = RidgeReducer(alphas) model0df = stack0.fit(blockdf, labeldf, indexdf) - model0df.cache() level1df = stack0.transform(blockdf, labeldf, model0df) regressor = RidgeRegression(alphas) model1df, cvdf = regressor.fit(level1df, labeldf, indexdf) - model1df.cache() - cvdf.cache() yhatdf = regressor.transform(level1df, labeldf, model1df, cvdf) - bestAlpha_lvl, bestr2_lvl = [(r.alpha, r.r2_mean) for r in cvdf.filter(f'label = "{testLabel}"').collect()][0] - y_hat_lvl = np.concatenate([r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').collect()]) + r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() + bestAlpha_lvl, bestr2_lvl = (r.alpha, r.r2_mean) + y_hat_lvl = np.concatenate([r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').select('values').collect()]) assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and np.allclose(y_hat_lvl, np.array(y_hat))) @@ -302,7 +315,7 @@ def test_two_level_regression(spark): columnIndexer = sorted(enumerate(alphaMap.keys()), key = lambda t: t[1]) coefOrder = [i for i, a in columnIndexer] - group2ids = {r.sample_block : r.sample_ids for r in indexdf.collect()} + group2ids = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} groups = sorted(group2ids.keys(), key = lambda v: v) headersToKeep = [c for c in X2.columns if testLabel in c] @@ -343,28 +356,18 @@ def test_two_level_regression(spark): stack0 = RidgeReducer(alphas) model0df = stack0.fit(blockdf, labeldf, indexdf) - model0df.cache() level1df = stack0.transform(blockdf, labeldf, model0df) stack1 = RidgeReducer(alphas) model1df = stack1.fit(level1df, labeldf, indexdf) - model1df.cache() level2df = stack1.transform(level1df, labeldf, model1df) regressor = RidgeRegression(alphas) model2df, cvdf = regressor.fit(level2df, labeldf, indexdf) - model2df.cache() - cvdf.cache() yhatdf = regressor.transform(level2df, labeldf, model2df, cvdf) - bestAlpha_lvl, bestr2_lvl = [(r.alpha, r.r2_mean) for r in cvdf.filter(f'label = "{testLabel}"').collect()][0] - y_hat_lvl = np.concatenate([r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').collect()]) + r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() + bestAlpha_lvl, bestr2_lvl = (r.alpha, r.r2_mean) + y_hat_lvl = np.concatenate([r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').select('values').collect()]) assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and np.allclose(y_hat_lvl, np.array(y_hat))) - - - - - - - diff --git a/python/glow/levels/tests/__init__.py b/python/glow/levels/tests/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/python/glow/tests/__init__.py b/python/glow/tests/__init__.py deleted file mode 100644 index e69de29bb..000000000 diff --git a/python/glow/tests/test_transform.py b/python/glow/tests/test_transform.py index 5beff8b8c..a68293926 100644 --- a/python/glow/tests/test_transform.py +++ b/python/glow/tests/test_transform.py @@ -1,5 +1,4 @@ import pytest -from pyspark.sql.functions import expr, lit from pyspark.sql.utils import IllegalArgumentException import glow diff --git a/test-data/levels/ridge-regression/blockedGT.snappy.parquet b/test-data/levels/ridge-regression/blockedGT.snappy.parquet index 115efcf49ef928b8608ebf608a83600a9fe3e433..9814225de01270ddd2426844cc1d4e54b985c9e3 100644 GIT binary patch delta 697 zcmYk4L1@!Z7{~Kso1|SQq1}ruOJ~>FtTkCpTAMbuf)g*3!bGpaY}#~|cFwJ>pob|Q z27}!mcqm2mGV4wo-C?IW2g=y=WDyxtM0)TfcoFe1d@-AP_&)f*@5lT9zsGx#U)FoC zj9yis4Q~12d|!6e?Y0A{E!tTCvgjfe#$txrG)0QJ)FZ+9G-fGC@#vGtx1Te>39{`~ zcR#@3JI{9-gS-6=EXuqMTr0rqS=a|{1QBBaK@dq`Clp*MTpF$fuCu_NQO#l8Y_i#6 zkbsXF*kUBO$o}zTc$EkP%?srAzW*eNJwaf_^jsfw;Bf8j;5ia2N9cs9hJ|0I8jd^z zL05?Mx~jRU+ZAI%uT1C5A%cJ;K4hxXv$ct`aTk3%Jp^r(&r97jas^`+lH4N8JqU7d zkRYZ9I$j=-^W4_~44(Ky&`&qTihv=mk7 zqxT(`)99^S1((sN;)@5udc9OB1ac{@P)rpyEgsWVqo}I!ctKWWRWlNDUel9WQq~Q< VphSF4^i+XpMWLA+G(njE^)~~>u$uq? delta 926 zcmZ`&T}TvB7@Zl%nQ>QbQSY?kXzSm!y1O=`tM)Sw2|e{B(L?o8v%6ysuB+{jkOW&) zBC?ot!AeCDEG0^`$%2HESc*s?d=T+LN=(BfO#2Zr=uR7IRKuKy@1Aq+obS7H@4BA3 zQz|QtiGT#pGeJ`tSZrN8FAXQ9#M2Svk;-|n=` zQxa^?=%B!*#l8wo6FCwOSAyf<#mQ3!De`a>W9 z40JPKfC2A&qRYt~h?r166ks?rsoTuD2V*hojtVVYBflgQ`Zf_0PKHfdM`jX1fr-iM zX_7XuX9%VKJ%N+i38+|p*ZDPB(%d-*2^!oQejQ;rtvhcwV(yL1M-lTVe*^hvR-VY5 zHf-(3Ekr(HP7KJk^npjzjHjfJ`C6q#QHkkhX?B_Bm!Q!mIHoMRBp)gE)2h#-x|IOw zRRcj<@hY{7FNhpXhuDn*_EP~r1*>ghQS>MW3^51QAbUgqG|UvU5A*owQ3(#F+Tofl z)v)g4K;o|~&}0YntdV_7@$1lTFFP;N#jy#|In&LEQQ@Gzx>^6ZNwHTu2EzYW Xo{}m-E=cd}tyxZ6-EI^C3x#J>Zxe$ZW`XYjQKQHG6SMQD$Dc(quap`N>b2Yht_#5{n8;Q%iJn zixkRIi;6Sz^ArsA3=Q-Q6*Q7cGjmcD5-luK(k#*}Esc$m%u>_L%#4jw49pD7EK^Ml fk}Z?WEzJ#*Qj<~)H6?Xq7#M(vAr$B;P#6LL;RZ%V delta 381 zcmbQld7866z%j^BltDB`G(q(2;vGxbWJE=PTsBb-83`6n1_lNR79hbO#-M@30}AME zVFHpOjLbk1q>LSi8G)D$h*^Ocq>BTHL8?FiXyz?8(I;%8FD}kL$tWXwf)Q*B+s4#q zjDk$6OcFfBiMa(isqslU`N`Rn($OpKFg%RevDo00y~iXhe?yEgLASvvmxW{$&t*~?3sBbsp+XjlV>r{$%-gQ zEGjHbEz!*_QYcF;D$dN$Q!vysG|)5DGtx~-EJ;i%%1q8KHc`+>D$UGEQ7}kLNi{Gt zG)Yc1F)%YRHBUA+NisAtH#IOwGB!v|Nl7*{NHI<`NYs?nkzrr}B8C#6lR#k(0K Date: Tue, 2 Jun 2020 11:24:20 -0700 Subject: [PATCH 05/34] Simplify ordering logic in levels code (#7) * WIP Signed-off-by: Karen Feng * existing tests pass Signed-off-by: Karen Feng * rename file Signed-off-by: Karen Feng * Add compat test Signed-off-by: Karen Feng * scalafmt Signed-off-by: Karen Feng * collect minimal columns Signed-off-by: Karen Feng * start changing for readability * use input label ordering * rename create_row_indexer * undo column sort * change reduce Signed-off-by: Henry D * further simplify reduce * sorted alpha names * remove ordering * comments Signed-off-by: Henry D * Set arrow env var in build Signed-off-by: Henry D * faster sort * add test file * undo test data change * >= * formatting * empty Co-authored-by: Karen Feng --- .circleci/config.yml | 4 - build.sbt | 14 +- python/glow/conftest.py | 1 + python/glow/levels/linear_model/functions.py | 72 +++-- .../glow/levels/linear_model/ridge_model.py | 46 ++- python/glow/levels/linear_model/ridge_udfs.py | 125 ++++---- .../linear_model/tests/test_functions.py | 33 ++ .../tests/test_ridge_regression.py | 285 +++++++++++------- python/setup.py | 34 +-- 9 files changed, 364 insertions(+), 250 deletions(-) create mode 100644 python/glow/levels/linear_model/tests/test_functions.py diff --git a/.circleci/config.yml b/.circleci/config.yml index a603110b3..e510edcd0 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -87,7 +87,6 @@ jobs: environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH - export ARROW_PRE_0_15_IPC_FORMAT=1 export SCALA_VERSION="2.11.12" sbt coverage core/test coverageReport exit - run: @@ -96,7 +95,6 @@ jobs: environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH - export ARROW_PRE_0_15_IPC_FORMAT=1 export SCALA_VERSION="2.11.12" sbt python/test exit - run: @@ -140,7 +138,6 @@ jobs: environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH - export ARROW_PRE_0_15_IPC_FORMAT=1 export SCALA_VERSION="2.12.8" sbt python/test exit - run: @@ -148,7 +145,6 @@ jobs: environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH - export ARROW_PRE_0_15_IPC_FORMAT=1 export SCALA_VERSION="2.12.8" sbt docs/test exit - *check_clean_repo diff --git a/build.sbt b/build.sbt index 2c4a851c2..bf159905f 100644 --- a/build.sbt +++ b/build.sbt @@ -196,12 +196,20 @@ lazy val pythonSettings = Seq( publish / skip := true, pytest := { val args = spaceDelimited("").parsed - val ret = Process( - Seq("pytest") ++ args, - None, + val baseEnv = Seq( "SPARK_CLASSPATH" -> sparkClasspath.value, "SPARK_HOME" -> sparkHome.value, "PYTHONPATH" -> pythonPath.value + ) + val env = if (majorMinorVersion(sparkVersion) >= "3.0") { + baseEnv + } else { + baseEnv :+ "ARROW_PRE_0_15_IPC_FORMAT" -> "1" + } + val ret = Process( + Seq("pytest") ++ args, + None, + env: _* ).! require(ret == 0, "Python tests failed") } diff --git a/python/glow/conftest.py b/python/glow/conftest.py index 189831067..7163b4f49 100644 --- a/python/glow/conftest.py +++ b/python/glow/conftest.py @@ -2,6 +2,7 @@ from pyspark.sql import functions, Row import glow + @pytest.fixture(autouse=True, scope="module") def add_spark(doctest_namespace, spark): glow.register(spark) diff --git a/python/glow/levels/linear_model/functions.py b/python/glow/levels/linear_model/functions.py index 0449d1aca..33ca1cb53 100644 --- a/python/glow/levels/linear_model/functions.py +++ b/python/glow/levels/linear_model/functions.py @@ -1,8 +1,25 @@ import numpy as np +import pandas as pd from scipy.sparse import csr_matrix import itertools +def sort_in_place(pdf, columns): + """ + A faster alternative to DataFrame.sort_values. Note that this function is less sophisticated + than sort_values and does not allow for control over sort direction or null handling. + + Adapated from https://github.com/pandas-dev/pandas/issues/15389. + + Args: + pdf : The pandas DataFrame to sort + columns : Columns to sort by + """ + order = np.lexsort([pdf[col].array for col in reversed(columns)]) + for col in list(pdf.columns): + pdf[col].array[:] = pdf[col].array[order] + + def parse_key(key, key_pattern): """ Interprets the key corresponding to a group from a groupBy clause. The key may be of the form: @@ -32,7 +49,7 @@ def parse_key(key, key_pattern): return key -def assemble_block(n_rows, n_cols, col_order, pdf): +def assemble_block(n_rows, n_cols, pdf): """ Creates a dense n_rows by n_cols matrix from the array of either sparse or dense vectors in the Pandas DataFrame corresponding to a group. This matrix represents a block. @@ -46,21 +63,19 @@ def assemble_block(n_rows, n_cols, col_order, pdf): Returns: Dense n_rows by n_columns matrix where the columns have been 0-centered and standard scaled. """ - mu = pdf['mu'].values - sig = pdf['sig'].values + mu = pdf['mu'].to_numpy() + sig = pdf['sig'].to_numpy() if 'indices' not in pdf.columns: - X_raw = np.row_stack(pdf['values']).T + X_raw = np.row_stack(pdf['values'].array).T else: X_csr = csr_matrix( - ( - np.concatenate(pdf['values']), - (np.concatenate(pdf['indices']), np.concatenate([np.repeat(i, len(v)) for i, v in enumerate(pdf.indices)])) - ), - shape = (n_rows, n_cols) - ) - X_raw = X_csr.todense() + (np.concatenate(pdf['values'].array), + (np.concatenate(pdf['indices'].array), + np.concatenate([np.repeat(i, len(v)) for i, v in enumerate(pdf.indices.array)]))), + shape=(n_rows, n_cols)) + X_raw = X_csr.todense().A - return ((X_raw - mu)/sig)[:, col_order] + return ((X_raw - mu) / sig) def slice_label_rows(labeldf, label, sample_list): @@ -77,47 +92,46 @@ def slice_label_rows(labeldf, label, sample_list): Matrix of [number of samples in sample_block] x [number of labels to slice] """ if label == 'all': - return labeldf.loc[sample_list, :].values + return labeldf.loc[sample_list, :].to_numpy() else: - return labeldf[label].loc[sample_list].values.reshape(-1, 1) + return labeldf[label].loc[sample_list].to_numpy().reshape(-1, 1) -def evaluate_coefficients(pdf, row_order, alpha_values): +def evaluate_coefficients(pdf, alpha_values): """ Solves the system (XTX + Ia)^-1 * XtY for each of the a values in alphas. Returns the resulting coefficients. Args: pdf : Pandas DataFrame for the group - row_order : Array of integers representing the intended row ordering of the matrices XtX and XtY alpha_values : Array of alpha values (regularization strengths) Returns: Matrix of coefficients of size [number of columns in X] x [number of labels * number of alpha values] """ - XtX = np.stack(pdf['xtx'].values)[row_order] - XtY = np.stack(pdf['xty'].values)[row_order] - return np.column_stack([(np.linalg.inv(XtX + np.identity(XtX.shape[1])*a)@XtY) for a in alpha_values]) + XtX = np.stack(pdf['xtx'].array) + XtY = np.stack(pdf['xty'].array) + return np.column_stack( + [(np.linalg.inv(XtX + np.identity(XtX.shape[1]) * a) @ XtY) for a in alpha_values]) -def create_row_indexer(alpha_names, labeldf, label): +def cross_alphas_and_labels(alpha_names, labeldf, label): """ - Creates an array of tuples used to keep the ordering of the coefficients in the output of evaluate_coefficients in - a consistent order regardless of the order in which the alpha values and labels were provided. + Crosses all label and alpha names. The output tuples appear in the same order as the output of + evaluate_coefficients. Args: alpha_names : List of string identifiers assigned to the values of alpha labeldf : Pandas DataFrame of labels label : Label used for this set of coefficients. Can be 'all' if all labels were used. Returns: - List of tuples of the form (i,(a, l)) where i is the column index in the matrix of coefficients, a is an alpha - name, and l is a label name, sorted by label name, then alpha. + List of tuples of the form (alpha_name, label_name) """ if label == 'all': label_names = labeldf.columns else: label_names = [label] - return sorted(enumerate(itertools.product(alpha_names, label_names)), key = lambda t: (t[1][1], t[1][0])) + return list(itertools.product(alpha_names, label_names)) def new_headers(header_block, alpha_names, row_indexer): @@ -149,8 +163,8 @@ def new_headers(header_block, alpha_names, row_indexer): new_header_block = f'chr_{outer_index}' sort_keys, headers = [], [] - for i, (a, l) in row_indexer: - sort_key = int(inner_index)*len(alpha_names) + int(a.split('_')[1]) + for a, l in row_indexer: + sort_key = int(inner_index) * len(alpha_names) + int(a.split('_')[1]) header = f'{new_header_block}_block_{inner_index}_{a}_label_{l}' sort_keys.append(sort_key) headers.append(header) @@ -172,5 +186,5 @@ def r_squared(XB, Y): Array of [number of alphas * number of labels] """ tot = np.power(Y - Y.mean(), 2).sum() - res = np.power(Y - XB, 2).sum(axis = 0) - return 1 - (res/tot) + res = np.power(Y - XB, 2).sum(axis=0) + return 1 - (res / tot) diff --git a/python/glow/levels/linear_model/ridge_model.py b/python/glow/levels/linear_model/ridge_model.py index 91e62fd1c..3aa19338b 100644 --- a/python/glow/levels/linear_model/ridge_model.py +++ b/python/glow/levels/linear_model/ridge_model.py @@ -11,7 +11,6 @@ class RidgeReducer: block with L columns to begin with will be reduced to a block with K columns, where each column is the prediction of one ridge model for one target label. """ - def __init__(self, alphas): """ RidgeReducer is initialized with a list of alpha values. @@ -19,7 +18,7 @@ def __init__(self, alphas): Args: alphas : array_like of alpha values used in the ridge reduction """ - self.alphas = {f'alpha_{i}' : a for i, a in enumerate(alphas)} + self.alphas = {f'alpha_{i}': a for i, a in enumerate(alphas)} def fit(self, blockdf, labeldf, indexdf): """ @@ -35,7 +34,7 @@ def fit(self, blockdf, labeldf, indexdf): Spark DataFrame containing the model resulting from the fitting routine. """ - sample_index = {r.sample_block : r.sample_ids for r in indexdf.collect()} + sample_index = {r.sample_block: r.sample_ids for r in indexdf.collect()} map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header_block', 'header'] @@ -43,9 +42,14 @@ def fit(self, blockdf, labeldf, indexdf): map_key_pattern.append('label') reduce_key_pattern.append('label') - map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - model_udf = pandas_udf(lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, self.alphas), model_struct, PandasUDFType.GROUPED_MAP) + map_udf = pandas_udf( + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) + model_udf = pandas_udf( + lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, self.alphas), + model_struct, PandasUDFType.GROUPED_MAP) return blockdf\ .groupBy(map_key_pattern) \ @@ -74,7 +78,9 @@ def transform(self, blockdf, labeldf, modeldf): if 'label' in blockdf.columns: transform_key_pattern.append('label') - transform_udf = pandas_udf(lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, self.alphas), reduced_matrix_struct, PandasUDFType.GROUPED_MAP) + transform_udf = pandas_udf( + lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, self.alphas), + reduced_matrix_struct, PandasUDFType.GROUPED_MAP) return blockdf.join(modeldf.drop('sort_key'), ['header_block', 'sample_block', 'header']) \ .groupBy(transform_key_pattern) \ @@ -90,7 +96,6 @@ class RidgeRegression: coefficients. The optimal ridge alpha value is chosen for each label by maximizing the average out of fold r2 score. """ - def __init__(self, alphas): """ RidgeRegression is initialized with a list of alpha values. @@ -98,7 +103,7 @@ def __init__(self, alphas): Args: alphas : array_like of alpha values used in the ridge reduction """ - self.alphas = {f'alpha_{i}' : a for i, a in enumerate(alphas)} + self.alphas = {f'alpha_{i}': a for i, a in enumerate(alphas)} def fit(self, blockdf, labeldf, indexdf): """ @@ -116,18 +121,25 @@ def fit(self, blockdf, labeldf, indexdf): results of the cross validation procedure. """ - sample_index = {r.sample_block : r.sample_ids for r in indexdf.collect()} + sample_index = {r.sample_block: r.sample_ids for r in indexdf.collect()} map_key_pattern = ['sample_block'] reduce_key_pattern = ['header_block', 'header'] - if 'label'in blockdf.columns: + if 'label' in blockdf.columns: map_key_pattern.append('label') reduce_key_pattern.append('label') - map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - model_udf = pandas_udf(lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, self.alphas), model_struct, PandasUDFType.GROUPED_MAP) - score_udf = pandas_udf(lambda key, pdf: score_models(key, map_key_pattern, pdf, labeldf, sample_index, self.alphas), cv_struct, PandasUDFType.GROUPED_MAP) + map_udf = pandas_udf( + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) + model_udf = pandas_udf( + lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, self.alphas), + model_struct, PandasUDFType.GROUPED_MAP) + score_udf = pandas_udf( + lambda key, pdf: score_models(key, map_key_pattern, pdf, labeldf, sample_index, self. + alphas), cv_struct, PandasUDFType.GROUPED_MAP) modeldf = blockdf\ .groupBy(map_key_pattern) \ @@ -169,7 +181,9 @@ def transform(self, blockdf, labeldf, modeldf, cvdf): if 'label' in blockdf.columns: transform_key_pattern.append('label') - transform_udf = pandas_udf(lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, self.alphas), reduced_matrix_struct, PandasUDFType.GROUPED_MAP) + transform_udf = pandas_udf( + lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, self.alphas), + reduced_matrix_struct, PandasUDFType.GROUPED_MAP) return blockdf.drop('header_block').join(modeldf.drop('header_block', 'sort_key'), ['sample_block', 'header']) \ .groupBy(transform_key_pattern) \ diff --git a/python/glow/levels/linear_model/ridge_udfs.py b/python/glow/levels/linear_model/ridge_udfs.py index 43e983585..a7a3e564a 100644 --- a/python/glow/levels/linear_model/ridge_udfs.py +++ b/python/glow/levels/linear_model/ridge_udfs.py @@ -1,8 +1,7 @@ +import itertools from .functions import * import pandas as pd from pyspark.sql.types import ArrayType, IntegerType, FloatType, StructType, StructField, StringType, DoubleType - - ''' Each function in this module performs a Pandas DataFrame => Pandas DataFrame transformation, and each is intended to be used as a Pandas GROUPED_MAP UDF. @@ -93,25 +92,23 @@ def map_normal_eqn(key, key_pattern, pdf, labeldf, sample_index): | |-- element: double """ header_block, sample_block, label = parse_key(key, key_pattern) + sort_in_place(pdf, ['sort_key', 'header']) n_rows = pdf['size'][0] n_cols = len(pdf) - sort_key = pdf['sort_key'] - header = pdf['header'] - col_order = [(t[0]) for t in sorted(list(enumerate(zip(sort_key, header))), key = lambda t: (t[1][0], t[1][1]))] sample_list = sample_index[sample_block] - X = assemble_block(n_rows, n_cols, col_order, pdf) + X = assemble_block(n_rows, n_cols, pdf) Y = slice_label_rows(labeldf, label, sample_list) - XtX = X.T@X - XtY = X.T@Y + XtX = X.T @ X + XtY = X.T @ Y data = { - 'header_block' : [header_block]*n_cols, - 'sample_block' : [sample_block]*n_cols, - 'label' : [label]*n_cols, - 'header' : header[col_order].values, - 'sort_key' : sort_key[col_order].values, - 'xtx' : XtX.tolist(), - 'xty' : XtY.tolist() + 'header_block': header_block, + 'sample_block': sample_block, + 'label': label, + 'header': pdf['header'], + 'sort_key': pdf['sort_key'], + 'xtx': list(XtX), + 'xty': list(XtY) } return pd.DataFrame(data) @@ -157,18 +154,14 @@ def reduce_normal_eqn(key, key_pattern, pdf): |-- xty: array | |-- element: double """ + sum_xtx = pdf['xtx'].sum() + sum_xty = pdf['xty'].sum() - header_block, header, label = parse_key(key, key_pattern) - sort_key = pdf['sort_key'][0] - n_sample_blocks = len(pdf) - sample_blocks = enumerate(pdf['sample_block']) - slices = [(g, np.append(np.arange(i), np.arange(i+1, n_sample_blocks))) for i, g in sample_blocks] - xtx_stack = np.stack(pdf['xtx'].values) - xty_stack = np.stack(pdf['xty'].values) - - rows = [[header_block, g, label, header, sort_key, xtx_stack[s, :].sum(axis = 0), xty_stack[s, :].sum(axis = 0)] for g, s in slices] + # Use numpy broadcast to subtract each row from the sum + pdf['xtx'] = list(sum_xtx - np.vstack(pdf['xtx'].array)) + pdf['xty'] = list(sum_xty - np.vstack(pdf['xty'].array)) - return pd.DataFrame(rows, columns = ['header_block', 'sample_block', 'label', 'header', 'sort_key', 'xtx', 'xty']) + return pdf def solve_normal_eqn(key, key_pattern, pdf, labeldf, alphas): @@ -211,22 +204,20 @@ def solve_normal_eqn(key, key_pattern, pdf, labeldf, alphas): """ header_block, sample_block, label = parse_key(key, key_pattern) - sort_key = pdf['sort_key'] - header = pdf['header'] - row_order = [(t[0]) for t in sorted(list(enumerate(zip(sort_key, header))), key = lambda t: (t[1][0], t[1][1]))] - alpha_names, alpha_values = zip(*[(k,v) for k, v in sorted(alphas.items(), key = lambda t: t[0])]) - beta_stack = evaluate_coefficients(pdf, row_order, alpha_values) - row_indexer = create_row_indexer(alpha_names, labeldf, label) - col_order, alpha_row, label_row = zip(*[(i,a,l) for i,(a,l) in row_indexer]) + sort_in_place(pdf, ['sort_key', 'header']) + alpha_names, alpha_values = zip(*sorted(alphas.items())) + beta_stack = evaluate_coefficients(pdf, alpha_values) + row_indexer = cross_alphas_and_labels(alpha_names, labeldf, label) + alpha_row, label_row = zip(*row_indexer) output_length = len(pdf) data = { - 'header_block' : [header_block]*output_length, - 'sample_block' : [sample_block]*output_length, - 'header' : header[row_order], - 'sort_key' : sort_key[row_order], - 'alphas' : [list(alpha_row)]*output_length, - 'labels' : [list(label_row)]*output_length, - 'coefficients' : [r[list(col_order)].tolist() for r in beta_stack] + 'header_block': header_block, + 'sample_block': sample_block, + 'header': pdf['header'], + 'sort_key': pdf['sort_key'], + 'alphas': [list(alpha_row)] * output_length, + 'labels': [list(label_row)] * output_length, + 'coefficients': list(beta_stack) } return pd.DataFrame(data) @@ -280,31 +271,30 @@ def apply_model(key, key_pattern, pdf, labeldf, alphas): """ header_block, sample_block, label = parse_key(key, key_pattern) + sort_in_place(pdf, ['sort_key']) n_rows = pdf['size'][0] n_cols = len(pdf) sort_key = pdf['sort_key'] - col_order = [(t[0]) for t in sorted(list(enumerate(sort_key)), key = lambda t: t[1])] - X = assemble_block(n_rows, n_cols, col_order, pdf) - B = np.row_stack(pdf['coefficients'][col_order].values) - XB = np.asarray(X@B) - mu, sig = XB.mean(axis = 0).tolist(), XB.std(axis = 0).tolist() - alpha_names = [k for k, v in sorted(alphas.items(), key = lambda t: t[0])] - row_indexer = create_row_indexer(alpha_names, labeldf, label) - alpha_col, label_col = zip(*[(a, l) for i, (a, l) in row_indexer]) + X = assemble_block(n_rows, n_cols, pdf) + B = np.row_stack(pdf['coefficients'].array) + XB = X @ B + mu, sig = XB.mean(axis=0), XB.std(axis=0) + alpha_names = sorted(alphas.keys()) + row_indexer = cross_alphas_and_labels(alpha_names, labeldf, label) + alpha_col, label_col = zip(*row_indexer) new_header_block, sort_key_col, header_col = new_headers(header_block, alpha_names, row_indexer) - n_output_rows = len(row_indexer) data = { - 'header' : header_col, - 'size' : [X.shape[0]]*n_output_rows, - 'values' : XB.T.tolist(), - 'header_block' : [new_header_block]*n_output_rows, - 'sample_block' : [sample_block]*n_output_rows, - 'sort_key' : sort_key_col, - 'mu' : mu, - 'sig' : sig, - 'alpha' : alpha_col, - 'label' : label_col + 'header': header_col, + 'size': X.shape[0], + 'values': list(XB.T), + 'header_block': new_header_block, + 'sample_block': sample_block, + 'sort_key': sort_key_col, + 'mu': mu, + 'sig': sig, + 'alpha': alpha_col, + 'label': label_col } return pd.DataFrame(data) @@ -351,24 +341,17 @@ def score_models(key, key_pattern, pdf, labeldf, sample_index, alphas): |-- r2: double """ header_block, sample_block, label = parse_key(key, key_pattern) + sort_in_place(pdf, ['sort_key']) n_rows = pdf['size'][0] n_cols = len(pdf) - sort_key = pdf['sort_key'] - col_order = [(t[0]) for t in sorted(list(enumerate(sort_key)), key = lambda t: t[1])] sample_list = sample_index[sample_block] - X = assemble_block(n_rows, n_cols, col_order, pdf) - B = np.row_stack(pdf['coefficients'][col_order].values) - XB = np.asarray(X@B) + X = assemble_block(n_rows, n_cols, pdf) + B = np.row_stack(pdf['coefficients'].array) + XB = X @ B Y = slice_label_rows(labeldf, label, sample_list) scores = r_squared(XB, Y) - alpha_names = [k for k, v in sorted(alphas.items(), key = lambda t: t[0])] - n_output_rows = len(alpha_names) + alpha_names = sorted(alphas.keys()) - data = { - 'sample_block' : [sample_block]*n_output_rows, - 'label' : [label]*n_output_rows, - 'alpha' : alpha_names, - 'r2' : scores - } + data = {'sample_block': sample_block, 'label': label, 'alpha': alpha_names, 'r2': scores} return pd.DataFrame(data) diff --git a/python/glow/levels/linear_model/tests/test_functions.py b/python/glow/levels/linear_model/tests/test_functions.py new file mode 100644 index 000000000..30bcb593a --- /dev/null +++ b/python/glow/levels/linear_model/tests/test_functions.py @@ -0,0 +1,33 @@ +from glow.levels.linear_model.functions import * +import numpy as np +import pandas as pd + + +def test_sort_by_numeric(): + nums = np.random.rand(1000) + df = pd.DataFrame({'nums': nums}) + df_copy = df.copy(deep=True) + sort_in_place(df, ['nums']) + df_copy.sort_values('nums', inplace=True) + assert (df['nums'].array == df_copy['nums'].array).all() + + +def test_sort_by_string(): + nums = np.random.rand(1000) + strings = [str(n) for n in nums] + df = pd.DataFrame({'nums': nums, 'strings': strings}) + df_copy = df.copy(deep=True) + sort_in_place(df, ['strings']) + df_copy.sort_values('strings', inplace=True) + assert (df['nums'].array == df_copy['nums'].array).all() + assert (df['strings'].array == df_copy['strings'].array).all() + + +def test_sort_by_multiple_columns(): + nums = np.random.rand(1000) * 10 + df = pd.DataFrame({'nums': nums}) + df['bin'] = df['nums'] // 1 + df_copy = df.copy(deep=True) + sort_in_place(df, ['bin', 'nums']) + df_copy.sort_values(['bin', 'nums'], inplace=True) + assert (df['nums'].array == df_copy['nums'].array).all() diff --git a/python/glow/levels/linear_model/tests/test_ridge_regression.py b/python/glow/levels/linear_model/tests/test_ridge_regression.py index 6f6b04fbb..7f7044215 100644 --- a/python/glow/levels/linear_model/tests/test_ridge_regression.py +++ b/python/glow/levels/linear_model/tests/test_ridge_regression.py @@ -8,7 +8,7 @@ X2 = pd.read_csv(f'{data_root}/X2.csv').set_index('sample_id') labeldf = pd.read_csv(f'{data_root}/pts.csv').set_index('sample_id') alphas = np.array([0.1, 1, 10]) -alphaMap = {f'alpha_{i}' : a for i, a in enumerate(alphas)} +alphaMap = {f'alpha_{i}': a for i, a in enumerate(alphas)} def test_map_normal_eqn(spark): @@ -18,17 +18,26 @@ def test_map_normal_eqn(spark): testGroup = '0' testBlock = 'chr_1_block_0' ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] + headers = [ + r.header + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + orderBy('sort_key').select('header').collect() + ] X_in = X0[headers].loc[ids, :] Y_in = labeldf.loc[ids, :] - XtX_in = X_in.values.T@X_in.values - XtY_in = X_in.values.T@Y_in.values + XtX_in = X_in.values.T @ X_in.values + XtY_in = X_in.values.T @ Y_in.values - sample_index = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} + sample_index = { + r.sample_block: r.sample_ids + for r in indexdf.select('sample_block', 'sample_ids').collect() + } map_key_pattern = ['header_block', 'sample_block'] - map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + map_udf = pandas_udf( + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) outdf = blockdf\ .groupBy(map_key_pattern) \ @@ -51,19 +60,29 @@ def test_reduce_normal_eqn(spark): testGroup = '0' testBlock = 'chr_1_block_0' ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] - - X_out = X0[headers].drop(ids, axis = 'rows') - Y_out = labeldf.drop(ids, axis = 'rows') - - XtX_out = X_out.values.T@X_out.values - XtY_out = X_out.values.T@Y_out.values - - sample_index = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} + headers = [ + r.header + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + orderBy('sort_key').select('header').collect() + ] + + X_out = X0[headers].drop(ids, axis='rows') + Y_out = labeldf.drop(ids, axis='rows') + + XtX_out = X_out.values.T @ X_out.values + XtY_out = X_out.values.T @ Y_out.values + + sample_index = { + r.sample_block: r.sample_ids + for r in indexdf.select('sample_block', 'sample_ids').collect() + } map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header_block', 'header'] - map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + map_udf = pandas_udf( + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) mapdf = blockdf\ .groupBy(map_key_pattern) \ @@ -89,22 +108,34 @@ def test_solve_normal_eqn(spark): testGroup = '0' testBlock = 'chr_1_block_0' ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] - coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1], t[1][0]))] - - X_out = X0[headers].drop(ids, axis = 'rows') - Y_out = labeldf.drop(ids, axis = 'rows') - - XtX_out = X_out.values.T@X_out.values - XtY_out = X_out.values.T@Y_out.values - B = np.column_stack([(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1])*a)@XtY_out) for a in alphas])[:, coefOrder_l0] - - sample_index = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} + headers = [ + r.header + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + orderBy('sort_key').select('header').collect() + ] + + X_out = X0[headers].drop(ids, axis='rows') + Y_out = labeldf.drop(ids, axis='rows') + + XtX_out = X_out.values.T @ X_out.values + XtY_out = X_out.values.T @ Y_out.values + B = np.column_stack( + [(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1]) * a) @ XtY_out) for a in alphas]) + + sample_index = { + r.sample_block: r.sample_ids + for r in indexdf.select('sample_block', 'sample_ids').collect() + } map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header_block', 'header'] - map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - model_udf = pandas_udf(lambda key, pdf: solve_normal_eqn(key, reduce_key_pattern, pdf, labeldf, alphaMap), model_struct, PandasUDFType.GROUPED_MAP) + map_udf = pandas_udf( + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) + model_udf = pandas_udf( + lambda key, pdf: solve_normal_eqn(key, reduce_key_pattern, pdf, labeldf, alphaMap), + model_struct, PandasUDFType.GROUPED_MAP) reducedf = blockdf\ .groupBy(map_key_pattern) \ @@ -120,9 +151,8 @@ def test_solve_normal_eqn(spark): outdf = pd.DataFrame(rows, columns=columns) sort_key = outdf['sort_key'] - colOrder = [(t[0]) for t in sorted(list(enumerate(sort_key)), key = lambda t: t[1])] - B_lvl = np.row_stack(outdf['coefficients'][colOrder].values) + B_lvl = np.row_stack(outdf['coefficients'].values) assert np.allclose(B_lvl, B) @@ -134,26 +164,40 @@ def test_apply_model(spark): testGroup = '0' testBlock = 'chr_1_block_0' ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] - coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1],t[1][0]))] + headers = [ + r.header + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + orderBy('sort_key').select('header').collect() + ] X_in = X0[headers].loc[ids, :] - X_out = X0[headers].drop(ids, axis = 'rows') - Y_out = labeldf.drop(ids, axis = 'rows') - - XtX_out = X_out.values.T@X_out.values - XtY_out = X_out.values.T@Y_out.values - B = np.column_stack([(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1])*a)@XtY_out) for a in alphas])[:, coefOrder_l0] - X1_in = X_in.values@B - - sample_index = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} + X_out = X0[headers].drop(ids, axis='rows') + Y_out = labeldf.drop(ids, axis='rows') + + XtX_out = X_out.values.T @ X_out.values + XtY_out = X_out.values.T @ Y_out.values + B = np.column_stack( + [(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1]) * a) @ XtY_out) for a in alphas]) + X1_in = X_in.values @ B + + sample_index = { + r.sample_block: r.sample_ids + for r in indexdf.select('sample_block', 'sample_ids').collect() + } map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header_block', 'header'] transform_key_pattern = ['header_block', 'sample_block'] - map_udf = pandas_udf(lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - model_udf = pandas_udf(lambda key, pdf: solve_normal_eqn(key, reduce_key_pattern, pdf, labeldf, alphaMap), model_struct, PandasUDFType.GROUPED_MAP) - transform_udf = pandas_udf(lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, alphaMap), reduced_matrix_struct, PandasUDFType.GROUPED_MAP) + map_udf = pandas_udf( + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) + model_udf = pandas_udf( + lambda key, pdf: solve_normal_eqn(key, reduce_key_pattern, pdf, labeldf, alphaMap), + model_struct, PandasUDFType.GROUPED_MAP) + transform_udf = pandas_udf( + lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, alphaMap), + reduced_matrix_struct, PandasUDFType.GROUPED_MAP) modeldf = blockdf\ .groupBy(map_key_pattern) \ @@ -172,8 +216,7 @@ def test_apply_model(spark): alphaNames = outdf['alpha'] labels = outdf['label'] - colOrder = [i for i, (a, l) in sorted(enumerate(zip(alphaNames, labels)), key = lambda t: (t[1][1],t[1][0]))] - X1_in_lvl = np.column_stack(outdf['values'])[:, colOrder] + X1_in_lvl = np.column_stack(outdf['values']) assert np.allclose(X1_in_lvl, X1_in) @@ -185,15 +228,19 @@ def test_ridge_reducer_fit(spark): testGroup = '0' testBlock = 'chr_1_block_0' ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] - coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1],t[1][0]))] + headers = [ + r.header + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + orderBy('sort_key').select('header').collect() + ] - X_out = X0[headers].drop(ids, axis = 'rows') - Y_out = labeldf.drop(ids, axis = 'rows') + X_out = X0[headers].drop(ids, axis='rows') + Y_out = labeldf.drop(ids, axis='rows') - XtX_out = X_out.values.T@X_out.values - XtY_out = X_out.values.T@Y_out.values - B = np.column_stack([(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1])*a)@XtY_out) for a in alphas])[:, coefOrder_l0] + XtX_out = X_out.values.T @ X_out.values + XtY_out = X_out.values.T @ Y_out.values + B = np.column_stack( + [(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1]) * a) @ XtY_out) for a in alphas]) stack = RidgeReducer(alphas) modeldf = stack.fit(blockdf, labeldf, indexdf) @@ -203,9 +250,8 @@ def test_ridge_reducer_fit(spark): .select(*columns).collect() outdf = pd.DataFrame(rows, columns=columns) sort_key = outdf['sort_key'] - colOrder = [(t[0]) for t in sorted(list(enumerate(sort_key)), key = lambda t: t[1])] - B_stack = np.row_stack(outdf['coefficients'][colOrder].values) + B_stack = np.row_stack(outdf['coefficients'].values) assert np.allclose(B_stack, B) @@ -217,17 +263,21 @@ def test_ridge_reducer_transform(spark): testGroup = '0' testBlock = 'chr_1_block_0' ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids - headers = [r.header for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}').orderBy('sort_key').select('header').collect()] - coefOrder_l0 = [i for i, (a, l) in sorted(enumerate(itertools.product(alphaMap.keys(), labeldf.columns)), key = lambda t: (t[1][1], t[1][0]))] + headers = [ + r.header + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + orderBy('sort_key').select('header').collect() + ] X_in = X0[headers].loc[ids, :] - X_out = X0[headers].drop(ids, axis = 'rows') - Y_out = labeldf.drop(ids, axis = 'rows') + X_out = X0[headers].drop(ids, axis='rows') + Y_out = labeldf.drop(ids, axis='rows') - XtX_out = X_out.values.T@X_out.values - XtY_out = X_out.values.T@Y_out.values - B = np.column_stack([(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1])*a)@XtY_out) for a in alphas])[:, coefOrder_l0] - X1_in = X_in.values@B + XtX_out = X_out.values.T @ X_out.values + XtY_out = X_out.values.T @ Y_out.values + B = np.column_stack( + [(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1]) * a) @ XtY_out) for a in alphas]) + X1_in = X_in.values @ B stack = RidgeReducer(alphas) modeldf = stack.fit(blockdf, labeldf, indexdf) @@ -240,8 +290,7 @@ def test_ridge_reducer_transform(spark): outdf = pd.DataFrame(rows, columns=columns) alphaNames = outdf['alpha'] labels = outdf['label'] - colOrder = [i for i, (a, l) in sorted(enumerate(zip(alphaNames, labels)), key = lambda t: (t[1][1], t[1][0]))] - X1_in_stack = np.column_stack(outdf['values'])[:, colOrder] + X1_in_stack = np.column_stack(outdf['values']) assert np.allclose(X1_in_stack, X1_in) @@ -251,44 +300,48 @@ def test_one_level_regression(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') testLabel = 'sim100' - columnIndexer = sorted(enumerate(alphaMap.keys()), key = lambda t: t[1]) - coefOrder = [i for i, a in columnIndexer] + columnIndexer = sorted(enumerate(alphaMap.keys()), key=lambda t: t[1]) - group2ids = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} - groups = sorted(group2ids.keys(), key = lambda v: v) + group2ids = { + r.sample_block: r.sample_ids + for r in indexdf.select('sample_block', 'sample_ids').collect() + } + groups = sorted(group2ids.keys(), key=lambda v: v) headersToKeep = [c for c in X1.columns if testLabel in c] - r2s = [] for group in groups: ids = group2ids[group] X1_in = X1[headersToKeep].loc[ids, :].values - X1_out = X1[headersToKeep].drop(ids, axis = 'rows') + X1_out = X1[headersToKeep].drop(ids, axis='rows') Y_in = labeldf[testLabel].loc[ids].values Y_out = labeldf[testLabel].loc[X1_out.index].values - X1tX1_out = X1_out.values.T@X1_out.values - X1tY_out = X1_out.values.T@Y_out - B = np.column_stack([(np.linalg.inv(X1tX1_out + np.identity(X1tX1_out.shape[1])*a)@X1tY_out) for a in alphas])[:, coefOrder] - X1B = X1_in@B - r2 = r_squared(X1B, Y_in.reshape(-1,1)) + X1tX1_out = X1_out.values.T @ X1_out.values + X1tY_out = X1_out.values.T @ Y_out + B = np.column_stack( + [(np.linalg.inv(X1tX1_out + np.identity(X1tX1_out.shape[1]) * a) @ X1tY_out) + for a in alphas]) + X1B = X1_in @ B + r2 = r_squared(X1B, Y_in.reshape(-1, 1)) r2s.append(r2) - r2_mean = np.row_stack(r2s).mean(axis = 0) - bestAlpha, bestr2 = sorted(zip(alphaMap.keys(), r2_mean), key = lambda t: -t[1])[0] + r2_mean = np.row_stack(r2s).mean(axis=0) + bestAlpha, bestr2 = sorted(zip(alphaMap.keys(), r2_mean), key=lambda t: -t[1])[0] y_hat = [] r2s_pred = [] for group in groups: ids = group2ids[group] X1_in = X1[headersToKeep].loc[ids, :].values - X1_out = X1[headersToKeep].drop(ids, axis = 'rows') + X1_out = X1[headersToKeep].drop(ids, axis='rows') Y_in = labeldf[testLabel].loc[ids].values Y_out = labeldf[testLabel].loc[X1_out.index].values - X1tX1_out = X1_out.values.T@X1_out.values - X1tY_out = X1_out.values.T@Y_out - b = np.linalg.inv(X1tX1_out + np.identity(X1tX1_out.shape[1])*alphaMap[bestAlpha])@X1tY_out - r2s_pred.append(r_squared(X1_in@b, Y_in)) - y_hat.extend((X1_in@b).tolist()) + X1tX1_out = X1_out.values.T @ X1_out.values + X1tY_out = X1_out.values.T @ Y_out + b = np.linalg.inv(X1tX1_out + + np.identity(X1tX1_out.shape[1]) * alphaMap[bestAlpha]) @ X1tY_out + r2s_pred.append(r_squared(X1_in @ b, Y_in)) + y_hat.extend((X1_in @ b).tolist()) y_hat = np.array(y_hat) @@ -302,9 +355,13 @@ def test_one_level_regression(spark): r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() bestAlpha_lvl, bestr2_lvl = (r.alpha, r.r2_mean) - y_hat_lvl = np.concatenate([r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').select('values').collect()]) + y_hat_lvl = np.concatenate([ + r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').select( + 'values').collect() + ]) - assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and np.allclose(y_hat_lvl, np.array(y_hat))) + assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and + np.allclose(y_hat_lvl, np.array(y_hat))) def test_two_level_regression(spark): @@ -312,11 +369,14 @@ def test_two_level_regression(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') testLabel = 'sim100' - columnIndexer = sorted(enumerate(alphaMap.keys()), key = lambda t: t[1]) + columnIndexer = sorted(enumerate(alphaMap.keys()), key=lambda t: t[1]) coefOrder = [i for i, a in columnIndexer] - group2ids = {r.sample_block : r.sample_ids for r in indexdf.select('sample_block', 'sample_ids').collect()} - groups = sorted(group2ids.keys(), key = lambda v: v) + group2ids = { + r.sample_block: r.sample_ids + for r in indexdf.select('sample_block', 'sample_ids').collect() + } + groups = sorted(group2ids.keys(), key=lambda v: v) headersToKeep = [c for c in X2.columns if testLabel in c] r2s = [] @@ -324,18 +384,20 @@ def test_two_level_regression(spark): for group in groups: ids = group2ids[group] X2_in = X2[headersToKeep].loc[ids, :].values - X2_out = X2[headersToKeep].drop(ids, axis = 'rows') + X2_out = X2[headersToKeep].drop(ids, axis='rows') Y_in = labeldf[testLabel].loc[ids].values Y_out = labeldf[testLabel].loc[X2_out.index].values - X2tX2_out = X2_out.values.T@X2_out.values - X2tY_out = X2_out.values.T@Y_out - B = np.column_stack([(np.linalg.inv(X2tX2_out + np.identity(X2tX2_out.shape[1])*a)@X2tY_out) for a in alphas])[:, coefOrder] - X2B = X2_in@B - r2 = r_squared(X2B, Y_in.reshape(-1,1)) + X2tX2_out = X2_out.values.T @ X2_out.values + X2tY_out = X2_out.values.T @ Y_out + B = np.column_stack( + [(np.linalg.inv(X2tX2_out + np.identity(X2tX2_out.shape[1]) * a) @ X2tY_out) + for a in alphas])[:, coefOrder] + X2B = X2_in @ B + r2 = r_squared(X2B, Y_in.reshape(-1, 1)) r2s.append(r2) - r2_mean = np.row_stack(r2s).mean(axis = 0) - bestAlpha, bestr2 = sorted(zip(alphaMap.keys(), r2_mean), key = lambda t: -t[1])[0] + r2_mean = np.row_stack(r2s).mean(axis=0) + bestAlpha, bestr2 = sorted(zip(alphaMap.keys(), r2_mean), key=lambda t: -t[1])[0] y_hat = [] r2s_pred = [] @@ -343,14 +405,15 @@ def test_two_level_regression(spark): for group in groups: ids = group2ids[group] X2_in = X2[headersToKeep].loc[ids, :].values - X2_out = X2[headersToKeep].drop(ids, axis = 'rows') + X2_out = X2[headersToKeep].drop(ids, axis='rows') Y_in = labeldf[testLabel].loc[ids].values Y_out = labeldf[testLabel].loc[X2_out.index].values - X2tX2_out = X2_out.values.T@X2_out.values - X2tY_out = X2_out.values.T@Y_out - b = np.linalg.inv(X2tX2_out + np.identity(X2tX2_out.shape[1])*alphaMap[bestAlpha])@X2tY_out - r2s_pred.append(r_squared(X2_in@b, Y_in)) - y_hat.extend((X2_in@b).tolist()) + X2tX2_out = X2_out.values.T @ X2_out.values + X2tY_out = X2_out.values.T @ Y_out + b = np.linalg.inv(X2tX2_out + + np.identity(X2tX2_out.shape[1]) * alphaMap[bestAlpha]) @ X2tY_out + r2s_pred.append(r_squared(X2_in @ b, Y_in)) + y_hat.extend((X2_in @ b).tolist()) y_hat = np.array(y_hat) @@ -368,6 +431,10 @@ def test_two_level_regression(spark): r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() bestAlpha_lvl, bestr2_lvl = (r.alpha, r.r2_mean) - y_hat_lvl = np.concatenate([r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').select('values').collect()]) + y_hat_lvl = np.concatenate([ + r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').select( + 'values').collect() + ]) - assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and np.allclose(y_hat_lvl, np.array(y_hat))) + assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and + np.allclose(y_hat_lvl, np.array(y_hat))) diff --git a/python/setup.py b/python/setup.py index c92cae7f5..db7e8f413 100644 --- a/python/setup.py +++ b/python/setup.py @@ -3,21 +3,19 @@ version = imp.load_source('version', 'version.py').VERSION -setup( - name='glow.py', - version=version, - packages=setuptools.find_packages(), - install_requires=[ - 'typeguard==2.5.0', - ], - author='The Glow Authors', - description='An open-source toolkit for large-scale genomic analysis', - long_description=open('README.rst').read(), - long_description_content_type='text/x-rst', - license='Apache License 2.0', - classifiers=[ - 'Intended Audience :: Developers', - 'Programming Language :: Python :: 3.7', - ], - url='https://projectglow.io' -) +setup(name='glow.py', + version=version, + packages=setuptools.find_packages(), + install_requires=[ + 'typeguard==2.5.0', + ], + author='The Glow Authors', + description='An open-source toolkit for large-scale genomic analysis', + long_description=open('README.rst').read(), + long_description_content_type='text/x-rst', + license='Apache License 2.0', + classifiers=[ + 'Intended Audience :: Developers', + 'Programming Language :: Python :: 3.7', + ], + url='https://projectglow.io') From 1f32506bcf597368735f062c57f6d61e7c406bbd Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Tue, 2 Jun 2020 13:28:23 -0700 Subject: [PATCH 06/34] Limit Spark memory conf in tests (#9) * yapf Signed-off-by: Karen Feng * yapf transform Signed-off-by: Karen Feng * Set driver memory Signed-off-by: Karen Feng * Try changing spark mem Signed-off-by: Karen Feng * match java tests Signed-off-by: Karen Feng * whoops Signed-off-by: Karen Feng * remove driver memory flag Signed-off-by: Karen Feng --- bin/spark-submit | 2 +- .../levels/linear_model/tests/test_ridge_regression.py | 8 ++++---- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/bin/spark-submit b/bin/spark-submit index fb7b333a5..d4d8a165d 100755 --- a/bin/spark-submit +++ b/bin/spark-submit @@ -2,5 +2,5 @@ # A simple wrapper around the SparkSubmit main class that allows us to run # PySpark unit tests with the same classpath as our Java tests. -HEAPSIZE=${SPARK_MEMORY:-2g} +HEAPSIZE=${SPARK_MEMORY:-1024m} java -Xmx"$HEAPSIZE" -cp "$SPARK_CLASSPATH" org.apache.spark.deploy.SparkSubmit "$@" diff --git a/python/glow/levels/linear_model/tests/test_ridge_regression.py b/python/glow/levels/linear_model/tests/test_ridge_regression.py index 7f7044215..ef0390720 100644 --- a/python/glow/levels/linear_model/tests/test_ridge_regression.py +++ b/python/glow/levels/linear_model/tests/test_ridge_regression.py @@ -39,7 +39,7 @@ def test_map_normal_eqn(spark): lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - outdf = blockdf\ + outdf = blockdf \ .groupBy(map_key_pattern) \ .apply(map_udf) \ .filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ @@ -84,7 +84,7 @@ def test_reduce_normal_eqn(spark): reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - mapdf = blockdf\ + mapdf = blockdf \ .groupBy(map_key_pattern) \ .apply(map_udf) @@ -137,7 +137,7 @@ def test_solve_normal_eqn(spark): lambda key, pdf: solve_normal_eqn(key, reduce_key_pattern, pdf, labeldf, alphaMap), model_struct, PandasUDFType.GROUPED_MAP) - reducedf = blockdf\ + reducedf = blockdf \ .groupBy(map_key_pattern) \ .apply(map_udf).groupBy(reduce_key_pattern) \ .apply(reduce_udf) @@ -199,7 +199,7 @@ def test_apply_model(spark): lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, alphaMap), reduced_matrix_struct, PandasUDFType.GROUPED_MAP) - modeldf = blockdf\ + modeldf = blockdf \ .groupBy(map_key_pattern) \ .apply(map_udf).groupBy(reduce_key_pattern) \ .apply(reduce_udf).groupBy(map_key_pattern) \ From cfc08e601edf851db96878d2b1f851516830d2b5 Mon Sep 17 00:00:00 2001 From: Kiavash Kianfar Date: Fri, 5 Jun 2020 08:16:45 -0700 Subject: [PATCH 07/34] Improve partitioning in block_variants_and_samples transformer (#11) Signed-off-by: kianfar77 --- .../VariantSampleBlockMaker.scala | 30 +++++++++---------- ...ckVariantsAndSamplesTransformerSuite.scala | 5 ++++ 2 files changed, 20 insertions(+), 15 deletions(-) diff --git a/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala b/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala index 9c663627e..0e4994c7f 100644 --- a/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala +++ b/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala @@ -32,7 +32,7 @@ private[projectglow] object VariantSampleBlockMaker extends GlowLogging { variantsPerBlock: Int, sampleBlockCount: Int): DataFrame = { val windowSpec = Window - .partitionBy(contigNameField.name) + .partitionBy(contigNameField.name, sampleBlockIdField.name) .orderBy(startField.name, refAlleleField.name, alternateAllelesField.name) variantDf @@ -50,16 +50,6 @@ private[projectglow] object VariantSampleBlockMaker extends GlowLogging { col(alternateAllelesField.name) ) ) - .withColumn( - headerBlockIdField.name, - concat_ws( - "_", - lit("chr"), - col(contigNameField.name), - lit("block"), - ((row_number().over(windowSpec) - 1) / variantsPerBlock).cast(IntegerType) - ) - ) .withColumn( "stats", subset_struct( @@ -78,6 +68,10 @@ private[projectglow] object VariantSampleBlockMaker extends GlowLogging { stdDevField.name, col("stats.stdDev") ) + .withColumn( + "fractionalSampleBlockSize", + size(col(valuesField.name)) / sampleBlockCount + ) .withColumn( sampleBlockIdField.name, explode( @@ -87,10 +81,6 @@ private[projectglow] object VariantSampleBlockMaker extends GlowLogging { ).cast(ArrayType(StringType)) ) ) - .withColumn( - "fractionalSampleBlockSize", - size(col(valuesField.name)) / sampleBlockCount - ) .withColumn( valuesField.name, expr( @@ -105,6 +95,16 @@ private[projectglow] object VariantSampleBlockMaker extends GlowLogging { sizeField.name, size(col(valuesField.name)) ) + .withColumn( + headerBlockIdField.name, + concat_ws( + "_", + lit("chr"), + col(contigNameField.name), + lit("block"), + ((row_number().over(windowSpec) - 1) / variantsPerBlock).cast(IntegerType) + ) + ) .select( col(headerField.name), col(sizeField.name), diff --git a/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala b/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala index 48305ff2c..76c1e1008 100644 --- a/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala +++ b/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala @@ -71,6 +71,11 @@ class BlockVariantsAndSamplesTransformerSuite extends GlowBaseTest with GlowLogg dfOriginal, options ) + .orderBy( + headerField.name, + headerBlockIdField.name, + sampleBlockIdField.name + ) val dfExpected = spark .read From f2f30c04d3e6ecc691c019eab492940b868cce28 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Fri, 5 Jun 2020 13:17:56 -0700 Subject: [PATCH 08/34] Remove unnecessary header_block grouping (#10) * cleanup Signed-off-by: Karen Feng * whoops Signed-off-by: Karen Feng * cleanup Signed-off-by: Karen Feng --- .gitignore | 3 + python/glow/levels/linear_model/functions.py | 40 +++++------ .../glow/levels/linear_model/ridge_model.py | 27 +++---- python/glow/levels/linear_model/ridge_udfs.py | 11 ++- .../tests/test_ridge_regression.py | 71 +++++++++---------- 5 files changed, 74 insertions(+), 78 deletions(-) diff --git a/.gitignore b/.gitignore index 2c7e0ee78..543b5714c 100644 --- a/.gitignore +++ b/.gitignore @@ -18,6 +18,9 @@ maven-repo/ **/__pycache__ *.pyc +# Jupyter notebook checkpoints +.ipynb_checkpoints/ + # Sphinx documentation docs/build diff --git a/python/glow/levels/linear_model/functions.py b/python/glow/levels/linear_model/functions.py index 33ca1cb53..f9ef5cb08 100644 --- a/python/glow/levels/linear_model/functions.py +++ b/python/glow/levels/linear_model/functions.py @@ -1,5 +1,4 @@ import numpy as np -import pandas as pd from scipy.sparse import csr_matrix import itertools @@ -9,7 +8,7 @@ def sort_in_place(pdf, columns): A faster alternative to DataFrame.sort_values. Note that this function is less sophisticated than sort_values and does not allow for control over sort direction or null handling. - Adapated from https://github.com/pandas-dev/pandas/issues/15389. + Adapted from https://github.com/pandas-dev/pandas/issues/15389. Args: pdf : The pandas DataFrame to sort @@ -25,8 +24,7 @@ def parse_key(key, key_pattern): Interprets the key corresponding to a group from a groupBy clause. The key may be of the form: (header_block, sample_block), (header_block, sample_block, label), - (header_block, header), - (header_block, header, label), + (sample_block), (sample_block, label) depending on the context. In each case, a tuple with 3 members is returned, with the missing member filled in by 'all' where necessary @@ -36,15 +34,17 @@ def parse_key(key, key_pattern): key_pattern : one of the aforementioned key patterns Returns: - tuple of (header_block, sample_block, label) or (header_block, header, label), where header_block or label may be filled with 'all' - depending on context. + tuple of (header_block, sample_block, label), where header_block or label may be filled with 'all' depending on + context. """ if key_pattern == ['header_block', 'sample_block']: return key[0], key[1], 'all' - elif key_pattern == ['header_block', 'header']: - return key[0], key[1], 'all' + elif key_pattern == ['sample_block']: + return 'all', key[0], 'all' elif key_pattern == ['sample_block', 'label']: return 'all', key[0], key[1] + elif len(key) != 3: + raise ValueError(f'Key must have 3 values, pattern is {key_pattern}') else: return key @@ -57,7 +57,6 @@ def assemble_block(n_rows, n_cols, pdf): Args: n_rows : The number of rows in the resulting matrix n_cols : The number of columns in the resulting matrix - col_order : Array of integers representing the desired ordering of the columns in the output matrix pdf : Pandas DataFrame corresponding to a group Returns: @@ -75,7 +74,7 @@ def assemble_block(n_rows, n_cols, pdf): shape=(n_rows, n_cols)) X_raw = X_csr.todense().A - return ((X_raw - mu) / sig) + return (X_raw - mu) / sig def slice_label_rows(labeldf, label, sample_list): @@ -83,10 +82,9 @@ def slice_label_rows(labeldf, label, sample_list): Selects rows from the Pandas DataFrame of labels corresponding to the samples in a particular sample_block. Args: - pdf : Pandas DataFrame for the group labeldf : Pandas DataFrame containing the labels label : Header for the particular label to slice. Can be 'all' if all labels are desired. - sample_list : List of sample ids corresponding to the sampleBlock to be sliced out. + sample_list : List of sample ids corresponding to the sample_block to be sliced out. Returns: Matrix of [number of samples in sample_block] x [number of labels to slice] @@ -140,27 +138,27 @@ def new_headers(header_block, alpha_names, row_indexer): "block_[header_block_number]_alpha_[alpha_name]_label_[label_name]" Args: - header_block : Identifier for a header_block (e.g., 'chr1_block_0') + header_block : Identifier for a header_block (e.g., 'chr_1_block_0') alpha_names : List of string identifiers for alpha parameters row_indexer : A list of tuples provided by the create_row_indexer function Returns: - new_header_block : A new header_block name, typically the chromosome (e.g. chr1), but might be 'all' if there are no more levels to - reduce over. + new_header_block : A new header_block name, typically the chromosome (e.g. chr_1), but might be 'all' if + there are no more levels to reduce over. sort_keys : Array of sortable integers to specify the ordering of the new matrix headers. headers : List of new matrix headers. """ tokens = header_block.split('_') if len(tokens) == 2: - outer_index, inner_index = 'all', tokens[1] - new_header_block = f'{outer_index}' + inner_index = tokens[1] + new_header_block = 'all' elif len(tokens) == 1: - outer_index, inner_index = 'all', 0 - new_header_block = f'{outer_index}' + inner_index = 0 + new_header_block = 'all' else: - outer_index, inner_index = tokens[1:4:2] - new_header_block = f'chr_{outer_index}' + inner_index = tokens[3] + new_header_block = f'chr_{tokens[1]}' sort_keys, headers = [], [] for a, l in row_indexer: diff --git a/python/glow/levels/linear_model/ridge_model.py b/python/glow/levels/linear_model/ridge_model.py index 3aa19338b..2a253272f 100644 --- a/python/glow/levels/linear_model/ridge_model.py +++ b/python/glow/levels/linear_model/ridge_model.py @@ -36,7 +36,7 @@ def fit(self, blockdf, labeldf, indexdf): sample_index = {r.sample_block: r.sample_ids for r in indexdf.collect()} map_key_pattern = ['header_block', 'sample_block'] - reduce_key_pattern = ['header_block', 'header'] + reduce_key_pattern = ['header'] if 'label' in blockdf.columns: map_key_pattern.append('label') @@ -45,13 +45,13 @@ def fit(self, blockdf, labeldf, indexdf): map_udf = pandas_udf( lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), - normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, + PandasUDFType.GROUPED_MAP) model_udf = pandas_udf( lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, self.alphas), model_struct, PandasUDFType.GROUPED_MAP) - return blockdf\ + return blockdf \ .groupBy(map_key_pattern) \ .apply(map_udf) \ .groupBy(reduce_key_pattern) \ @@ -101,7 +101,7 @@ def __init__(self, alphas): RidgeRegression is initialized with a list of alpha values. Args: - alphas : array_like of alpha values used in the ridge reduction + alphas : array_like of alpha values used in the ridge regression """ self.alphas = {f'alpha_{i}': a for i, a in enumerate(alphas)} @@ -123,7 +123,7 @@ def fit(self, blockdf, labeldf, indexdf): sample_index = {r.sample_block: r.sample_ids for r in indexdf.collect()} map_key_pattern = ['sample_block'] - reduce_key_pattern = ['header_block', 'header'] + reduce_key_pattern = ['header'] if 'label' in blockdf.columns: map_key_pattern.append('label') @@ -132,8 +132,8 @@ def fit(self, blockdf, labeldf, indexdf): map_udf = pandas_udf( lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), - normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, + PandasUDFType.GROUPED_MAP) model_udf = pandas_udf( lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, self.alphas), model_struct, PandasUDFType.GROUPED_MAP) @@ -141,7 +141,7 @@ def fit(self, blockdf, labeldf, indexdf): lambda key, pdf: score_models(key, map_key_pattern, pdf, labeldf, sample_index, self. alphas), cv_struct, PandasUDFType.GROUPED_MAP) - modeldf = blockdf\ + modeldf = blockdf \ .groupBy(map_key_pattern) \ .apply(map_udf) \ .groupBy(reduce_key_pattern) \ @@ -149,13 +149,13 @@ def fit(self, blockdf, labeldf, indexdf): .groupBy(map_key_pattern) \ .apply(model_udf) - cvdf = blockdf\ + cvdf = blockdf \ .join(modeldf.drop('header_block', 'sort_key'), ['header', 'sample_block'], 'inner') \ .groupBy(map_key_pattern) \ .apply(score_udf) \ .groupBy('label', 'alpha').agg(f.mean('r2').alias('r2_mean')) \ - .withColumn('modelRank', f.dense_rank().over(Window.partitionBy("label").orderBy(f.desc("r2_mean"))))\ - .filter(f'modelRank = 1')\ + .withColumn('modelRank', f.dense_rank().over(Window.partitionBy("label").orderBy(f.desc("r2_mean")))) \ + .filter('modelRank = 1') \ .drop('modelRank') return modeldf, cvdf @@ -185,7 +185,8 @@ def transform(self, blockdf, labeldf, modeldf, cvdf): lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, self.alphas), reduced_matrix_struct, PandasUDFType.GROUPED_MAP) - return blockdf.drop('header_block').join(modeldf.drop('header_block', 'sort_key'), ['sample_block', 'header']) \ + return blockdf.drop('header_block') \ + .join(modeldf.drop('header_block', 'sort_key'), ['sample_block', 'header']) \ .groupBy(transform_key_pattern) \ .apply(transform_udf) \ .join(cvdf, ['label', 'alpha'], 'inner') \ diff --git a/python/glow/levels/linear_model/ridge_udfs.py b/python/glow/levels/linear_model/ridge_udfs.py index a7a3e564a..ce56e0031 100644 --- a/python/glow/levels/linear_model/ridge_udfs.py +++ b/python/glow/levels/linear_model/ridge_udfs.py @@ -77,6 +77,7 @@ def map_normal_eqn(key, key_pattern, pdf, labeldf, sample_index): |-- alpha: double (Required only if the header is tied to a specific value of alpha) |-- label: double (Required only if the header is tied to a specific label) labeldf : Pandas DataFrame containing label values (i. e., the Y in the normal equation above). + sample_index: dict containing a mapping of sample_block ID to a list of corresponding sample IDs Returns: transformed Pandas DataFrame containing XtX and XtY corresponding to a particular block X. @@ -114,7 +115,7 @@ def map_normal_eqn(key, key_pattern, pdf, labeldf, sample_index): return pd.DataFrame(data) -def reduce_normal_eqn(key, key_pattern, pdf): +def reduce_normal_eqn(pdf): """ This function constructs lists of rows from the XtX and XtY matrices corresponding to a particular header in X but evaluated in different sample_blocks, and then reduces those lists by element-wise summation. This reduction is @@ -127,8 +128,6 @@ def reduce_normal_eqn(key, key_pattern, pdf): List(xtx_sum_excluding_sample_block0, xtx_sum_excluding_sample_block1, ..., xtx_sum_excluding_sample_blockN) Args: - key : unique key identifying the rows emitted by a groupBy statement - key_pattern : pattern of columns used in the groupBy statement pdf : starting Pandas DataFrame containing the lists of rows from XtX and XtY for block X identified by :key: schema (specified by the normal_eqn_struct): |-- header_block: string @@ -231,7 +230,7 @@ def apply_model(key, key_pattern, pdf, labeldf, alphas): Args: key : unique key identifying the group of rows emitted by a groupBy statement key_pattern : pattern of columns used in the groupBy statement that emitted this group of rows - pdf : starting Pandas DataFrame containing the lists of rows used to assemble block X and coeffients B + pdf : starting Pandas DataFrame containing the lists of rows used to assemble block X and coefficients B identified by :key: schema: |-- header_block: string @@ -274,7 +273,6 @@ def apply_model(key, key_pattern, pdf, labeldf, alphas): sort_in_place(pdf, ['sort_key']) n_rows = pdf['size'][0] n_cols = len(pdf) - sort_key = pdf['sort_key'] X = assemble_block(n_rows, n_cols, pdf) B = np.row_stack(pdf['coefficients'].array) XB = X @ B @@ -330,6 +328,7 @@ def score_models(key, key_pattern, pdf, labeldf, sample_index, alphas): |-- coefficients: array | |-- element: double labeldf : Pandas DataFrame containing label values that were used in fitting coefficient matrix B. + sample_index: dict containing a mapping of sample_block ID to a list of corresponding sample IDs alphas : dict of {alphaName : alphaValue} for the alpha values that were used when fitting coefficient matrix B Returns: @@ -344,10 +343,10 @@ def score_models(key, key_pattern, pdf, labeldf, sample_index, alphas): sort_in_place(pdf, ['sort_key']) n_rows = pdf['size'][0] n_cols = len(pdf) - sample_list = sample_index[sample_block] X = assemble_block(n_rows, n_cols, pdf) B = np.row_stack(pdf['coefficients'].array) XB = X @ B + sample_list = sample_index[sample_block] Y = slice_label_rows(labeldf, label, sample_list) scores = r_squared(XB, Y) alpha_names = sorted(alphas.keys()) diff --git a/python/glow/levels/linear_model/tests/test_ridge_regression.py b/python/glow/levels/linear_model/tests/test_ridge_regression.py index ef0390720..6a9ff74e6 100644 --- a/python/glow/levels/linear_model/tests/test_ridge_regression.py +++ b/python/glow/levels/linear_model/tests/test_ridge_regression.py @@ -20,7 +20,7 @@ def test_map_normal_eqn(spark): ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids headers = [ r.header - for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}'). orderBy('sort_key').select('header').collect() ] @@ -42,7 +42,7 @@ def test_map_normal_eqn(spark): outdf = blockdf \ .groupBy(map_key_pattern) \ .apply(map_udf) \ - .filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ + .filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}') \ .orderBy('sort_key') \ .select('xtx', 'xty') \ .collect() @@ -62,7 +62,7 @@ def test_reduce_normal_eqn(spark): ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids headers = [ r.header - for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}'). orderBy('sort_key').select('header').collect() ] @@ -77,12 +77,12 @@ def test_reduce_normal_eqn(spark): for r in indexdf.select('sample_block', 'sample_ids').collect() } map_key_pattern = ['header_block', 'sample_block'] - reduce_key_pattern = ['header_block', 'header'] + reduce_key_pattern = ['header'] map_udf = pandas_udf( lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), - normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, + PandasUDFType.GROUPED_MAP) mapdf = blockdf \ .groupBy(map_key_pattern) \ @@ -90,7 +90,7 @@ def test_reduce_normal_eqn(spark): outdf = mapdf.groupBy(reduce_key_pattern) \ .apply(reduce_udf) \ - .filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ + .filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}') \ .orderBy('sort_key') \ .select('xtx', 'xty') \ .collect() @@ -110,7 +110,7 @@ def test_solve_normal_eqn(spark): ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids headers = [ r.header - for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}'). orderBy('sort_key').select('header').collect() ] @@ -127,31 +127,30 @@ def test_solve_normal_eqn(spark): for r in indexdf.select('sample_block', 'sample_ids').collect() } map_key_pattern = ['header_block', 'sample_block'] - reduce_key_pattern = ['header_block', 'header'] + reduce_key_pattern = ['header'] map_udf = pandas_udf( lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), - normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, + PandasUDFType.GROUPED_MAP) model_udf = pandas_udf( - lambda key, pdf: solve_normal_eqn(key, reduce_key_pattern, pdf, labeldf, alphaMap), + lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, alphaMap), model_struct, PandasUDFType.GROUPED_MAP) reducedf = blockdf \ .groupBy(map_key_pattern) \ - .apply(map_udf).groupBy(reduce_key_pattern) \ + .apply(map_udf) \ + .groupBy(reduce_key_pattern) \ .apply(reduce_udf) - columns = ['sort_key', 'coefficients'] + columns = ['coefficients'] rows = reducedf.groupBy(map_key_pattern) \ .apply(model_udf) \ - .filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ + .filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}') \ .select(*columns) \ .collect() outdf = pd.DataFrame(rows, columns=columns) - sort_key = outdf['sort_key'] - B_lvl = np.row_stack(outdf['coefficients'].values) assert np.allclose(B_lvl, B) @@ -166,7 +165,7 @@ def test_apply_model(spark): ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids headers = [ r.header - for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}'). orderBy('sort_key').select('header').collect() ] @@ -185,15 +184,15 @@ def test_apply_model(spark): for r in indexdf.select('sample_block', 'sample_ids').collect() } map_key_pattern = ['header_block', 'sample_block'] - reduce_key_pattern = ['header_block', 'header'] + reduce_key_pattern = ['header'] transform_key_pattern = ['header_block', 'sample_block'] map_udf = pandas_udf( lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), - normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, + PandasUDFType.GROUPED_MAP) model_udf = pandas_udf( - lambda key, pdf: solve_normal_eqn(key, reduce_key_pattern, pdf, labeldf, alphaMap), + lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, alphaMap), model_struct, PandasUDFType.GROUPED_MAP) transform_udf = pandas_udf( lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, alphaMap), @@ -201,21 +200,21 @@ def test_apply_model(spark): modeldf = blockdf \ .groupBy(map_key_pattern) \ - .apply(map_udf).groupBy(reduce_key_pattern) \ - .apply(reduce_udf).groupBy(map_key_pattern) \ + .apply(map_udf) \ + .groupBy(reduce_key_pattern) \ + .apply(reduce_udf) \ + .groupBy(map_key_pattern) \ .apply(model_udf) - columns = ['alpha', 'label', 'values'] + columns = ['values'] rows = blockdf.join(modeldf.drop('sort_key'), ['header_block', 'sample_block', 'header']) \ .groupBy(transform_key_pattern) \ .apply(transform_udf) \ - .filter(f'header LIKE "%{testBlock}%" AND sample_block= {testGroup}') \ + .filter(f'header LIKE "%{testBlock}%" AND sample_block = {testGroup}') \ .select(*columns) \ .collect() outdf = pd.DataFrame(rows, columns=columns) - alphaNames = outdf['alpha'] - labels = outdf['label'] X1_in_lvl = np.column_stack(outdf['values']) assert np.allclose(X1_in_lvl, X1_in) @@ -230,7 +229,7 @@ def test_ridge_reducer_fit(spark): ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids headers = [ r.header - for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}'). orderBy('sort_key').select('header').collect() ] @@ -245,11 +244,10 @@ def test_ridge_reducer_fit(spark): stack = RidgeReducer(alphas) modeldf = stack.fit(blockdf, labeldf, indexdf) - columns = ['sort_key', 'coefficients'] - rows = modeldf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}') \ + columns = ['coefficients'] + rows = modeldf.filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}') \ .select(*columns).collect() outdf = pd.DataFrame(rows, columns=columns) - sort_key = outdf['sort_key'] B_stack = np.row_stack(outdf['coefficients'].values) @@ -265,7 +263,7 @@ def test_ridge_reducer_transform(spark): ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids headers = [ r.header - for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}'). orderBy('sort_key').select('header').collect() ] @@ -283,13 +281,11 @@ def test_ridge_reducer_transform(spark): modeldf = stack.fit(blockdf, labeldf, indexdf) level1df = stack.transform(blockdf, labeldf, modeldf) - columns = ['alpha', 'label', 'values'] - rows = level1df.filter(f'header LIKE "%{testBlock}%" AND sample_block= {testGroup}') \ + columns = ['values'] + rows = level1df.filter(f'header LIKE "%{testBlock}%" AND sample_block = {testGroup}') \ .select(*columns) \ .collect() outdf = pd.DataFrame(rows, columns=columns) - alphaNames = outdf['alpha'] - labels = outdf['label'] X1_in_stack = np.column_stack(outdf['values']) assert np.allclose(X1_in_stack, X1_in) @@ -300,7 +296,6 @@ def test_one_level_regression(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') testLabel = 'sim100' - columnIndexer = sorted(enumerate(alphaMap.keys()), key=lambda t: t[1]) group2ids = { r.sample_block: r.sample_ids From 1686138d2561da8705809f38639d56acaba250f8 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Wed, 10 Jun 2020 13:37:39 -0700 Subject: [PATCH 09/34] Create sample ID blocking helper functions (#12) * WIP Signed-off-by: Karen Feng * WIP Signed-off-by: Karen Feng * WIP Signed-off-by: Karen Feng * WIP Signed-off-by: Karen Feng * WIP Signed-off-by: Karen Feng * whoops Signed-off-by: Karen Feng * tests Signed-off-by: Karen Feng * simplify tests Signed-off-by: Karen Feng * WIP Signed-off-by: Karen Feng * yapf Signed-off-by: Karen Feng * index map compat Signed-off-by: Karen Feng * Add docs Signed-off-by: Karen Feng * Add more tests Signed-off-by: Karen Feng * pass args as ints Signed-off-by: Karen Feng * Don't roll our own splitter Signed-off-by: Karen Feng * rename sample_index to sample_blocks Signed-off-by: Karen Feng --- .../VariantSampleBlockMaker.scala | 53 ++++---- python/glow/levels/functions.py | 117 ++++++++++++++++++ .../glow/levels/linear_model/ridge_model.py | 16 ++- .../tests/test_ridge_regression.py | 59 ++++----- .../tests/test_block_variants_and_samples.py | 91 ++++++++++++++ .../levels/tests/test_sample_id_extraction.py | 49 ++++++++ 6 files changed, 317 insertions(+), 68 deletions(-) create mode 100644 python/glow/levels/functions.py create mode 100644 python/glow/levels/tests/test_block_variants_and_samples.py create mode 100644 python/glow/levels/tests/test_sample_id_extraction.py diff --git a/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala b/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala index 0e4994c7f..43f807a5a 100644 --- a/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala +++ b/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala @@ -27,6 +27,32 @@ import org.apache.spark.sql.types.{ArrayType, IntegerType, StringType} private[projectglow] object VariantSampleBlockMaker extends GlowLogging { + def makeSampleBlocks(df: DataFrame, sampleBlockCount: Int): DataFrame = { + df.withColumn( + "fractionalSampleBlockSize", + size(col(valuesField.name)) / sampleBlockCount + ) + .withColumn( + sampleBlockIdField.name, + explode( + sequence( + lit(1), + lit(sampleBlockCount) + ).cast(ArrayType(StringType)) + ) + ) + .withColumn( + valuesField.name, + expr( + s"""slice( + | ${valuesField.name}, + | round((${sampleBlockIdField.name} - 1) * fractionalSampleBlockSize) + 1, + | round(${sampleBlockIdField.name} * fractionalSampleBlockSize) - round((${sampleBlockIdField.name} - 1) * fractionalSampleBlockSize) + |)""".stripMargin + ) + ) + } + def makeVariantAndSampleBlocks( variantDf: DataFrame, variantsPerBlock: Int, @@ -35,7 +61,7 @@ private[projectglow] object VariantSampleBlockMaker extends GlowLogging { .partitionBy(contigNameField.name, sampleBlockIdField.name) .orderBy(startField.name, refAlleleField.name, alternateAllelesField.name) - variantDf + val baseDf = variantDf .withColumn( sortKeyField.name, col(startField.name).cast(IntegerType) @@ -68,29 +94,8 @@ private[projectglow] object VariantSampleBlockMaker extends GlowLogging { stdDevField.name, col("stats.stdDev") ) - .withColumn( - "fractionalSampleBlockSize", - size(col(valuesField.name)) / sampleBlockCount - ) - .withColumn( - sampleBlockIdField.name, - explode( - sequence( - lit(1), - lit(sampleBlockCount) - ).cast(ArrayType(StringType)) - ) - ) - .withColumn( - valuesField.name, - expr( - s"""slice( - | ${valuesField.name}, - | round((${sampleBlockIdField.name} - 1) * fractionalSampleBlockSize) + 1, - | round(${sampleBlockIdField.name} * fractionalSampleBlockSize) - round((${sampleBlockIdField.name} - 1) * fractionalSampleBlockSize) - |)""".stripMargin - ) - ) + + makeSampleBlocks(baseDf, sampleBlockCount) .withColumn( sizeField.name, size(col(valuesField.name)) diff --git a/python/glow/levels/functions.py b/python/glow/levels/functions.py new file mode 100644 index 000000000..5c0b16f2b --- /dev/null +++ b/python/glow/levels/functions.py @@ -0,0 +1,117 @@ +from glow import glow +from pyspark import SparkContext +from pyspark.sql import DataFrame, Row, SQLContext +from typeguard import check_argument_types, check_return_type +from typing import Dict, List + + +def __validate_sample_ids(sample_ids: List[str]): + """" + Validates that a set of sample IDs are valid (non-empty and unique). + """ + assert check_argument_types() + if any(not s for s in sample_ids): + raise Exception("Cannot have empty sample IDs.") + if len(sample_ids) != len(set(sample_ids)): + raise Exception("Cannot have duplicated sample IDs.") + + +def __get_index_map(sample_ids: List[str], sample_block_count: int, + sql_ctx: SQLContext) -> Dict[str, List[str]]: + """ + Creates an index mapping from sample blocks to a list of corresponding sample IDs. Uses the same sample-blocking + logic as the blocked GT matrix transformer. + + Requires that: + - Each variant row has the same number of values + - The number of values per row matches the number of sample IDs + + Args: + sample_ids : The list of sample ID strings + sample_block_count : The number of sample blocks + + Returns: + index mapping from sample block IDs to a list of sample IDs + """ + + assert check_argument_types() + + sample_id_df = sql_ctx.createDataFrame([Row(values=sample_ids)]) + make_sample_blocks_fn = SparkContext._jvm.io.projectglow.transformers.blockvariantsandsamples.VariantSampleBlockMaker.makeSampleBlocks + output_jdf = make_sample_blocks_fn(sample_id_df._jdf, sample_block_count) + output_df = DataFrame(output_jdf, sql_ctx) + output_df.printSchema() + index_map = {r.sample_block: r.values for r in output_df.collect()} + + assert check_return_type(index_map) + return index_map + + +def get_sample_ids(data: DataFrame) -> List[str]: + """ + Extracts sample IDs from a variant DataFrame, such as one read from PLINK files. + + Requires that the sample IDs: + - Are in `genotype.sampleId` + - Are the same across all the variant rows + - Are a list of strings + - Are non-empty + - Are unique + + Args: + data : The variant DataFrame containing sample IDs + + Returns: + list of sample ID strings + """ + assert check_argument_types() + distinct_sample_id_sets = data.selectExpr("genotypes.sampleId as sampleIds").distinct() + if distinct_sample_id_sets.count() != 1: + raise Exception("Each row must have the same set of sample IDs.") + sample_ids = distinct_sample_id_sets.head().sampleIds + __validate_sample_ids(sample_ids) + assert check_return_type(sample_ids) + return sample_ids + + +def block_variants_and_samples(variant_df: DataFrame, sample_ids: List[str], + variants_per_block: int, + sample_block_count: int) -> (DataFrame, Dict[str, List[str]]): + """ + Creates a blocked GT matrix and index mapping from sample blocks to a list of corresponding sample IDs. Uses the + same sample-blocking logic as the blocked GT matrix transformer. + + Requires that: + - Each variant row has the same number of values + - The number of values per row matches the number of sample IDs + + Args: + variant_df : The variant DataFrame + sample_ids : The list of sample ID strings + variants_per_block : The number of variants per block + sample_block_count : The number of sample blocks + + Returns: + tuple of (blocked GT matrix, index mapping) + """ + assert check_argument_types() + distinct_num_values = variant_df.selectExpr("size(values) as numValues").distinct() + distinct_num_values_count = distinct_num_values.count() + if distinct_num_values_count == 0: + raise Exception("DataFrame has no values.") + if distinct_num_values_count > 1: + raise Exception("Each row must have the same number of values.") + num_values = distinct_num_values.head().numValues + if num_values != len(sample_ids): + raise Exception("Number of values does not match between DataFrame and sample ID list.") + __validate_sample_ids(sample_ids) + + blocked_gt = glow.transform("block_variants_and_samples", + variant_df, + variants_per_block=variants_per_block, + sample_block_count=sample_block_count) + index_map = __get_index_map(sample_ids, sample_block_count, variant_df.sql_ctx) + + output = blocked_gt, index_map + assert check_return_type(output) + return output diff --git a/python/glow/levels/linear_model/ridge_model.py b/python/glow/levels/linear_model/ridge_model.py index 2a253272f..463b89de8 100644 --- a/python/glow/levels/linear_model/ridge_model.py +++ b/python/glow/levels/linear_model/ridge_model.py @@ -20,7 +20,7 @@ def __init__(self, alphas): """ self.alphas = {f'alpha_{i}': a for i, a in enumerate(alphas)} - def fit(self, blockdf, labeldf, indexdf): + def fit(self, blockdf, labeldf, sample_blocks): """ Fits a ridge reducer model, represented by a Spark DataFrame containing coefficients for each of the ridge alpha parameters, for each block in the starting matrix, for each label in the target labels. @@ -28,13 +28,12 @@ def fit(self, blockdf, labeldf, indexdf): Args: blockdf : Spark DataFrame representing the beginning block matrix X labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models - indexdf : Spark DataFrame containing a mapping of sample_block ID to a list of corresponding sample IDs + sample_blocks : Dict containing a mapping of sample_block ID to a list of corresponding sample IDs Returns: Spark DataFrame containing the model resulting from the fitting routine. """ - sample_index = {r.sample_block: r.sample_ids for r in indexdf.collect()} map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header'] @@ -43,7 +42,7 @@ def fit(self, blockdf, labeldf, indexdf): reduce_key_pattern.append('label') map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), normal_eqn_struct, PandasUDFType.GROUPED_MAP) reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) @@ -105,7 +104,7 @@ def __init__(self, alphas): """ self.alphas = {f'alpha_{i}': a for i, a in enumerate(alphas)} - def fit(self, blockdf, labeldf, indexdf): + def fit(self, blockdf, labeldf, sample_blocks): """ Fits a ridge regression model, represented by a Spark DataFrame containing coefficients for each of the ridge alpha parameters, for each block in the starting matrix, for each label in the target labels, as well as a @@ -114,14 +113,13 @@ def fit(self, blockdf, labeldf, indexdf): Args: blockdf : Spark DataFrame representing the beginning block matrix X labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models - indexdf : Spark DataFrame containing a mapping of sample_block ID to a list of corresponding sample IDs + sample_blocks : Dict containing a mapping of sample_block ID to a list of corresponding sample IDs Returns: Two Spark DataFrames, one containing the model resulting from the fitting routine and one containing the results of the cross validation procedure. """ - sample_index = {r.sample_block: r.sample_ids for r in indexdf.collect()} map_key_pattern = ['sample_block'] reduce_key_pattern = ['header'] @@ -130,7 +128,7 @@ def fit(self, blockdf, labeldf, indexdf): reduce_key_pattern.append('label') map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), normal_eqn_struct, PandasUDFType.GROUPED_MAP) reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) @@ -138,7 +136,7 @@ def fit(self, blockdf, labeldf, indexdf): lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, self.alphas), model_struct, PandasUDFType.GROUPED_MAP) score_udf = pandas_udf( - lambda key, pdf: score_models(key, map_key_pattern, pdf, labeldf, sample_index, self. + lambda key, pdf: score_models(key, map_key_pattern, pdf, labeldf, sample_blocks, self. alphas), cv_struct, PandasUDFType.GROUPED_MAP) modeldf = blockdf \ diff --git a/python/glow/levels/linear_model/tests/test_ridge_regression.py b/python/glow/levels/linear_model/tests/test_ridge_regression.py index 6a9ff74e6..9f39756c6 100644 --- a/python/glow/levels/linear_model/tests/test_ridge_regression.py +++ b/python/glow/levels/linear_model/tests/test_ridge_regression.py @@ -11,6 +11,13 @@ alphaMap = {f'alpha_{i}': a for i, a in enumerate(alphas)} +def __get_sample_blocks(indexdf): + return { + r.sample_block: r.sample_ids + for r in indexdf.select('sample_block', 'sample_ids').collect() + } + + def test_map_normal_eqn(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') @@ -30,13 +37,10 @@ def test_map_normal_eqn(spark): XtX_in = X_in.values.T @ X_in.values XtY_in = X_in.values.T @ Y_in.values - sample_index = { - r.sample_block: r.sample_ids - for r in indexdf.select('sample_block', 'sample_ids').collect() - } + sample_blocks = __get_sample_blocks(indexdf) map_key_pattern = ['header_block', 'sample_block'] map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), normal_eqn_struct, PandasUDFType.GROUPED_MAP) outdf = blockdf \ @@ -72,14 +76,11 @@ def test_reduce_normal_eqn(spark): XtX_out = X_out.values.T @ X_out.values XtY_out = X_out.values.T @ Y_out.values - sample_index = { - r.sample_block: r.sample_ids - for r in indexdf.select('sample_block', 'sample_ids').collect() - } + sample_blocks = __get_sample_blocks(indexdf) map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header'] map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), normal_eqn_struct, PandasUDFType.GROUPED_MAP) reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) @@ -122,14 +123,11 @@ def test_solve_normal_eqn(spark): B = np.column_stack( [(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1]) * a) @ XtY_out) for a in alphas]) - sample_index = { - r.sample_block: r.sample_ids - for r in indexdf.select('sample_block', 'sample_ids').collect() - } + sample_blocks = __get_sample_blocks(indexdf) map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header'] map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), normal_eqn_struct, PandasUDFType.GROUPED_MAP) reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) @@ -179,15 +177,12 @@ def test_apply_model(spark): [(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1]) * a) @ XtY_out) for a in alphas]) X1_in = X_in.values @ B - sample_index = { - r.sample_block: r.sample_ids - for r in indexdf.select('sample_block', 'sample_ids').collect() - } + sample_blocks = __get_sample_blocks(indexdf) map_key_pattern = ['header_block', 'sample_block'] reduce_key_pattern = ['header'] transform_key_pattern = ['header_block', 'sample_block'] map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_index), + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), normal_eqn_struct, PandasUDFType.GROUPED_MAP) reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, PandasUDFType.GROUPED_MAP) @@ -242,7 +237,7 @@ def test_ridge_reducer_fit(spark): [(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1]) * a) @ XtY_out) for a in alphas]) stack = RidgeReducer(alphas) - modeldf = stack.fit(blockdf, labeldf, indexdf) + modeldf = stack.fit(blockdf, labeldf, __get_sample_blocks(indexdf)) columns = ['coefficients'] rows = modeldf.filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}') \ @@ -278,7 +273,7 @@ def test_ridge_reducer_transform(spark): X1_in = X_in.values @ B stack = RidgeReducer(alphas) - modeldf = stack.fit(blockdf, labeldf, indexdf) + modeldf = stack.fit(blockdf, labeldf, __get_sample_blocks(indexdf)) level1df = stack.transform(blockdf, labeldf, modeldf) columns = ['values'] @@ -297,10 +292,7 @@ def test_one_level_regression(spark): blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') testLabel = 'sim100' - group2ids = { - r.sample_block: r.sample_ids - for r in indexdf.select('sample_block', 'sample_ids').collect() - } + group2ids = __get_sample_blocks(indexdf) groups = sorted(group2ids.keys(), key=lambda v: v) headersToKeep = [c for c in X1.columns if testLabel in c] @@ -341,11 +333,11 @@ def test_one_level_regression(spark): y_hat = np.array(y_hat) stack0 = RidgeReducer(alphas) - model0df = stack0.fit(blockdf, labeldf, indexdf) + model0df = stack0.fit(blockdf, labeldf, group2ids) level1df = stack0.transform(blockdf, labeldf, model0df) regressor = RidgeRegression(alphas) - model1df, cvdf = regressor.fit(level1df, labeldf, indexdf) + model1df, cvdf = regressor.fit(level1df, labeldf, group2ids) yhatdf = regressor.transform(level1df, labeldf, model1df, cvdf) r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() @@ -367,10 +359,7 @@ def test_two_level_regression(spark): columnIndexer = sorted(enumerate(alphaMap.keys()), key=lambda t: t[1]) coefOrder = [i for i, a in columnIndexer] - group2ids = { - r.sample_block: r.sample_ids - for r in indexdf.select('sample_block', 'sample_ids').collect() - } + group2ids = __get_sample_blocks(indexdf) groups = sorted(group2ids.keys(), key=lambda v: v) headersToKeep = [c for c in X2.columns if testLabel in c] @@ -413,15 +402,15 @@ def test_two_level_regression(spark): y_hat = np.array(y_hat) stack0 = RidgeReducer(alphas) - model0df = stack0.fit(blockdf, labeldf, indexdf) + model0df = stack0.fit(blockdf, labeldf, group2ids) level1df = stack0.transform(blockdf, labeldf, model0df) stack1 = RidgeReducer(alphas) - model1df = stack1.fit(level1df, labeldf, indexdf) + model1df = stack1.fit(level1df, labeldf, group2ids) level2df = stack1.transform(level1df, labeldf, model1df) regressor = RidgeRegression(alphas) - model2df, cvdf = regressor.fit(level2df, labeldf, indexdf) + model2df, cvdf = regressor.fit(level2df, labeldf, group2ids) yhatdf = regressor.transform(level2df, labeldf, model2df, cvdf) r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() diff --git a/python/glow/levels/tests/test_block_variants_and_samples.py b/python/glow/levels/tests/test_block_variants_and_samples.py new file mode 100644 index 000000000..894b756fa --- /dev/null +++ b/python/glow/levels/tests/test_block_variants_and_samples.py @@ -0,0 +1,91 @@ +from glow import glow +from glow.levels import functions +import pytest +from pyspark.sql import Row +from pyspark.sql.functions import expr +from pyspark.sql.utils import AnalysisException + + +def __construct_row(values): + return Row(contigName="chr21", + start=100, + referenceAllele="A", + alternateAlleles=["T", "C"], + values=values) + + +def test_block_variants_and_samples(spark): + variant_df = spark.read.format("vcf") \ + .load("test-data/combined.chr20_18210071_18210093.g.vcf") \ + .withColumn("values", expr("genotype_states(genotypes)")) + sample_ids = ["HG00096", "HG00268", "NA19625"] + block_gt, index_map = functions.block_variants_and_samples(variant_df, + sample_ids, + variants_per_block=10, + sample_block_count=2) + expected_block_gt = glow.transform("block_variants_and_samples", + variant_df, + variants_per_block=10, + sample_block_count=2) + assert block_gt.collect() == expected_block_gt.collect() + assert index_map == {"1": ["HG00096", "HG00268"], "2": ["NA19625"]} + + +def test_missing_values(spark): + variant_df = spark.read.format("vcf").load("test-data/combined.chr20_18210071_18210093.g.vcf") + sample_ids = ["HG00096", "HG00268", "NA19625"] + with pytest.raises(AnalysisException): + functions.block_variants_and_samples(variant_df, + sample_ids, + variants_per_block=10, + sample_block_count=2) + + +def test_no_values(spark): + variant_df = spark.createDataFrame([__construct_row([0, 1])]).limit(0) + sample_ids = ["a", "b"] + with pytest.raises(Exception): + functions.block_variants_and_samples(variant_df, + sample_ids, + variants_per_block=10, + sample_block_count=2) + + +def test_inconsistent_num_values(spark): + variant_df = spark.createDataFrame([__construct_row([0, 1]), __construct_row([1, 1, 2])]) + sample_ids = ["a", "b", "c"] + with pytest.raises(Exception): + functions.block_variants_and_samples(variant_df, + sample_ids, + variants_per_block=10, + sample_block_count=2) + + +def test_mismatch_num_values_sample_ids(spark): + variant_df = spark.createDataFrame([__construct_row([0, 1]), __construct_row([1, 1])]) + sample_ids = ["a", "b", "c"] + with pytest.raises(Exception): + functions.block_variants_and_samples(variant_df, + sample_ids, + variants_per_block=10, + sample_block_count=2) + + +def test_missing_sample_ids(spark): + variant_df = spark.createDataFrame([__construct_row([0, 1]), __construct_row([1, 1])]) + sample_ids = ["a", ""] + with pytest.raises(Exception): + functions.block_variants_and_samples(variant_df, + sample_ids, + variants_per_block=10, + sample_block_count=2) + + +def test_duplicated_sample_ids(spark): + variant_df = spark.createDataFrame([__construct_row([0, 1]), __construct_row([1, 1])]) + sample_ids = ["a", "a"] + with pytest.raises(Exception): + functions.block_variants_and_samples(variant_df, + sample_ids, + variants_per_block=10, + sample_block_count=2) diff --git a/python/glow/levels/tests/test_sample_id_extraction.py b/python/glow/levels/tests/test_sample_id_extraction.py new file mode 100644 index 000000000..d6b26103b --- /dev/null +++ b/python/glow/levels/tests/test_sample_id_extraction.py @@ -0,0 +1,49 @@ +import pytest +from pyspark.sql import Row +from pyspark.sql.utils import AnalysisException +from glow.levels import functions + + +def __construct_row(sample_id_1, sample_id_2): + return Row(contigName="chr21", + genotypes=[ + Row(sampleId=sample_id_1, calls=[1, 1]), + Row(sampleId=sample_id_2, calls=[0, 1]) + ]) + + +def test_get_sample_ids(spark): + df = spark.read.format("vcf").load("test-data/combined.chr20_18210071_18210093.g.vcf") + sample_ids = functions.get_sample_ids(df) + assert (sample_ids == ["HG00096", "HG00268", "NA19625"]) + + +def test_missing_sample_id_field(spark): + df = spark.read.format("vcf").option("includeSampleIds", "false") \ + .load("test-data/combined.chr20_18210071_18210093.g.vcf") + with pytest.raises(AnalysisException): + functions.get_sample_ids(df) + + +def test_inconsistent_sample_ids(spark): + df = spark.createDataFrame([__construct_row("a", "b"), __construct_row("a", "c")]) + with pytest.raises(Exception): + functions.get_sample_ids(df) + + +def test_incorrectly_typed_sample_ids(spark): + df = spark.createDataFrame([__construct_row(1, 2)]) + with pytest.raises(Exception): + functions.get_sample_ids(df) + + +def test_empty_sample_ids(spark): + df = spark.createDataFrame([__construct_row("a", "")]) + with pytest.raises(Exception): + functions.get_sample_ids(df) + + +def test_duplicated_sample_ids(spark): + df = spark.createDataFrame([__construct_row("a", "a")]) + with pytest.raises(Exception): + functions.get_sample_ids(df) From 6bfad34ea85be7636b254162f56a5933bb3d44e8 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Fri, 12 Jun 2020 11:25:11 -0700 Subject: [PATCH 10/34] Add type-checking to WGR APIs (#14) * Add type-checking to APIs Signed-off-by: Karen Feng * Check valid alphas Signed-off-by: Karen Feng * check 0 sig Signed-off-by: Karen Feng * Add to install_requires list Signed-off-by: Karen Feng * cleanup comments Signed-off-by: Karen Feng --- python/environment.yml | 1 + python/glow/levels/linear_model/functions.py | 38 +++++++++++++----- .../glow/levels/linear_model/ridge_model.py | 26 +++++++++--- python/glow/levels/linear_model/ridge_udfs.py | 24 ++++++++--- .../linear_model/tests/test_functions.py | 13 ++++++ .../tests/test_ridge_regression.py | 4 ++ python/setup.py | 3 ++ .../groupedIDs.snappy.parquet | Bin 1170 -> 1735 bytes 8 files changed, 87 insertions(+), 22 deletions(-) diff --git a/python/environment.yml b/python/environment.yml index 4bbadebbd..455161675 100644 --- a/python/environment.yml +++ b/python/environment.yml @@ -24,3 +24,4 @@ dependencies: - sphinx-tabs==1.1.13 # Code tabs (Python/Scala) - sybil==1.2.0 # Automatic doctest - yapf==0.30.0 + - nptyping==1.1.0 diff --git a/python/glow/levels/linear_model/functions.py b/python/glow/levels/linear_model/functions.py index f9ef5cb08..d31a9bd42 100644 --- a/python/glow/levels/linear_model/functions.py +++ b/python/glow/levels/linear_model/functions.py @@ -1,9 +1,14 @@ +import itertools +from nptyping import Int, Float, NDArray import numpy as np +import pandas as pd from scipy.sparse import csr_matrix -import itertools +from typeguard import typechecked +from typing import Iterable, List, Tuple -def sort_in_place(pdf, columns): +@typechecked +def sort_in_place(pdf: pd.DataFrame, columns: List[str]) -> None: """ A faster alternative to DataFrame.sort_values. Note that this function is less sophisticated than sort_values and does not allow for control over sort direction or null handling. @@ -19,7 +24,8 @@ def sort_in_place(pdf, columns): pdf[col].array[:] = pdf[col].array[order] -def parse_key(key, key_pattern): +@typechecked +def parse_key(key: Tuple, key_pattern: List[str]) -> Tuple[str, str, str]: """ Interprets the key corresponding to a group from a groupBy clause. The key may be of the form: (header_block, sample_block), @@ -49,7 +55,8 @@ def parse_key(key, key_pattern): return key -def assemble_block(n_rows, n_cols, pdf): +@typechecked +def assemble_block(n_rows: Int, n_cols: Int, pdf: pd.DataFrame) -> NDArray[Float]: """ Creates a dense n_rows by n_cols matrix from the array of either sparse or dense vectors in the Pandas DataFrame corresponding to a group. This matrix represents a block. @@ -64,6 +71,10 @@ def assemble_block(n_rows, n_cols, pdf): """ mu = pdf['mu'].to_numpy() sig = pdf['sig'].to_numpy() + + if 0 in sig: + raise ValueError(f'Standard deviation cannot be 0.') + if 'indices' not in pdf.columns: X_raw = np.row_stack(pdf['values'].array).T else: @@ -77,7 +88,8 @@ def assemble_block(n_rows, n_cols, pdf): return (X_raw - mu) / sig -def slice_label_rows(labeldf, label, sample_list): +@typechecked +def slice_label_rows(labeldf: pd.DataFrame, label: str, sample_list: List[str]) -> NDArray[Float]: """ Selects rows from the Pandas DataFrame of labels corresponding to the samples in a particular sample_block. @@ -95,7 +107,8 @@ def slice_label_rows(labeldf, label, sample_list): return labeldf[label].loc[sample_list].to_numpy().reshape(-1, 1) -def evaluate_coefficients(pdf, alpha_values): +@typechecked +def evaluate_coefficients(pdf: pd.DataFrame, alpha_values: Iterable[Float]) -> NDArray[Float]: """ Solves the system (XTX + Ia)^-1 * XtY for each of the a values in alphas. Returns the resulting coefficients. @@ -112,7 +125,9 @@ def evaluate_coefficients(pdf, alpha_values): [(np.linalg.inv(XtX + np.identity(XtX.shape[1]) * a) @ XtY) for a in alpha_values]) -def cross_alphas_and_labels(alpha_names, labeldf, label): +@typechecked +def cross_alphas_and_labels(alpha_names: Iterable[str], labeldf: pd.DataFrame, + label: str) -> List[Tuple[str, str]]: """ Crosses all label and alpha names. The output tuples appear in the same order as the output of evaluate_coefficients. @@ -132,7 +147,9 @@ def cross_alphas_and_labels(alpha_names, labeldf, label): return list(itertools.product(alpha_names, label_names)) -def new_headers(header_block, alpha_names, row_indexer): +@typechecked +def new_headers(header_block: str, alpha_names: Iterable[str], + row_indexer: List[Tuple[str, str]]) -> Tuple[str, List[int], List[str]]: """ Creates new headers for the output of a matrix reduction step. Generally produces names like "block_[header_block_number]_alpha_[alpha_name]_label_[label_name]" @@ -140,7 +157,7 @@ def new_headers(header_block, alpha_names, row_indexer): Args: header_block : Identifier for a header_block (e.g., 'chr_1_block_0') alpha_names : List of string identifiers for alpha parameters - row_indexer : A list of tuples provided by the create_row_indexer function + row_indexer : A list of tuples provided by the cross_alphas_and_labels function Returns: new_header_block : A new header_block name, typically the chromosome (e.g. chr_1), but might be 'all' if @@ -170,7 +187,8 @@ def new_headers(header_block, alpha_names, row_indexer): return new_header_block, sort_keys, headers -def r_squared(XB, Y): +@typechecked +def r_squared(XB: NDArray[Float], Y: NDArray[Float]): """ Computes the coefficient of determination (R2) metric between the matrix resulting from X*B and the matrix of labels Y. diff --git a/python/glow/levels/linear_model/ridge_model.py b/python/glow/levels/linear_model/ridge_model.py index 463b89de8..47867ecc2 100644 --- a/python/glow/levels/linear_model/ridge_model.py +++ b/python/glow/levels/linear_model/ridge_model.py @@ -1,9 +1,15 @@ from .ridge_udfs import * +from nptyping import Float, NDArray +import pandas as pd +from pyspark.sql import DataFrame from pyspark.sql.functions import pandas_udf, PandasUDFType import pyspark.sql.functions as f from pyspark.sql.window import Window +from typeguard import typechecked +from typing import Any, Dict, List +@typechecked class RidgeReducer: """ The RidgeReducer class is intended to reduce the feature space of an N by M block matrix X to an N by P< None: """ RidgeReducer is initialized with a list of alpha values. Args: alphas : array_like of alpha values used in the ridge reduction """ + if not (alphas >= 0).all(): + raise Exception('Alpha values must all be non-negative.') self.alphas = {f'alpha_{i}': a for i, a in enumerate(alphas)} - def fit(self, blockdf, labeldf, sample_blocks): + def fit(self, blockdf: DataFrame, labeldf: pd.DataFrame, + sample_blocks: Dict[str, List[str]]) -> DataFrame: """ Fits a ridge reducer model, represented by a Spark DataFrame containing coefficients for each of the ridge alpha parameters, for each block in the starting matrix, for each label in the target labels. @@ -58,7 +67,7 @@ def fit(self, blockdf, labeldf, sample_blocks): .groupBy(map_key_pattern) \ .apply(model_udf) - def transform(self, blockdf, labeldf, modeldf): + def transform(self, blockdf: DataFrame, labeldf: pd.DataFrame, modeldf: DataFrame) -> DataFrame: """ Transforms a starting block matrix to the reduced block matrix, using a reducer model produced by the RidgeReducer fit method. @@ -86,6 +95,7 @@ def transform(self, blockdf, labeldf, modeldf): .apply(transform_udf) +@typechecked class RidgeRegression: """ The RidgeRegression class is used to fit ridge models against one or labels optimized over a provided list of @@ -95,16 +105,19 @@ class RidgeRegression: coefficients. The optimal ridge alpha value is chosen for each label by maximizing the average out of fold r2 score. """ - def __init__(self, alphas): + def __init__(self, alphas: NDArray[(Any, ), Float]) -> None: """ RidgeRegression is initialized with a list of alpha values. Args: alphas : array_like of alpha values used in the ridge regression """ + if not (alphas >= 0).all(): + raise Exception('Alpha values must all be non-negative.') self.alphas = {f'alpha_{i}': a for i, a in enumerate(alphas)} - def fit(self, blockdf, labeldf, sample_blocks): + def fit(self, blockdf: DataFrame, labeldf: pd.DataFrame, + sample_blocks: Dict[str, List[str]]) -> (DataFrame, DataFrame): """ Fits a ridge regression model, represented by a Spark DataFrame containing coefficients for each of the ridge alpha parameters, for each block in the starting matrix, for each label in the target labels, as well as a @@ -158,7 +171,8 @@ def fit(self, blockdf, labeldf, sample_blocks): return modeldf, cvdf - def transform(self, blockdf, labeldf, modeldf, cvdf): + def transform(self, blockdf: DataFrame, labeldf: pd.DataFrame, modeldf: DataFrame, + cvdf: DataFrame) -> DataFrame: """ Generates predictions for the target labels in the provided label DataFrame by applying the model resulting from the RidgeRegression fit method to the starting block matrix. diff --git a/python/glow/levels/linear_model/ridge_udfs.py b/python/glow/levels/linear_model/ridge_udfs.py index ce56e0031..85fc716ae 100644 --- a/python/glow/levels/linear_model/ridge_udfs.py +++ b/python/glow/levels/linear_model/ridge_udfs.py @@ -1,7 +1,9 @@ -import itertools from .functions import * +from nptyping import Float import pandas as pd from pyspark.sql.types import ArrayType, IntegerType, FloatType, StructType, StructField, StringType, DoubleType +from typeguard import typechecked +from typing import Dict, List ''' Each function in this module performs a Pandas DataFrame => Pandas DataFrame transformation, and each is intended to be used as a Pandas GROUPED_MAP UDF. @@ -47,7 +49,9 @@ ]) -def map_normal_eqn(key, key_pattern, pdf, labeldf, sample_index): +@typechecked +def map_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: pd.DataFrame, + sample_index: Dict[str, List[str]]) -> pd.DataFrame: """ This function constructs matrices X and Y, and returns X_transpose * X (XtX) and X_transpose * Y (XtY), where X corresponds to a block from a block matrix. @@ -115,7 +119,8 @@ def map_normal_eqn(key, key_pattern, pdf, labeldf, sample_index): return pd.DataFrame(data) -def reduce_normal_eqn(pdf): +@typechecked +def reduce_normal_eqn(pdf: pd.DataFrame): """ This function constructs lists of rows from the XtX and XtY matrices corresponding to a particular header in X but evaluated in different sample_blocks, and then reduces those lists by element-wise summation. This reduction is @@ -163,7 +168,9 @@ def reduce_normal_eqn(pdf): return pdf -def solve_normal_eqn(key, key_pattern, pdf, labeldf, alphas): +@typechecked +def solve_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: pd.DataFrame, + alphas: Dict[str, Float]) -> pd.DataFrame: """ This function assembles the matrices XtX and XtY for a particular sample_block (where the contribution of that sample_block has been omitted) and solves the equation [(XtX + I*alpha)]-1 * XtY = B for a list of alpha values, and returns the @@ -205,6 +212,7 @@ def solve_normal_eqn(key, key_pattern, pdf, labeldf, alphas): header_block, sample_block, label = parse_key(key, key_pattern) sort_in_place(pdf, ['sort_key', 'header']) alpha_names, alpha_values = zip(*sorted(alphas.items())) + beta_stack = evaluate_coefficients(pdf, alpha_values) row_indexer = cross_alphas_and_labels(alpha_names, labeldf, label) alpha_row, label_row = zip(*row_indexer) @@ -222,7 +230,9 @@ def solve_normal_eqn(key, key_pattern, pdf, labeldf, alphas): return pd.DataFrame(data) -def apply_model(key, key_pattern, pdf, labeldf, alphas): +@typechecked +def apply_model(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: pd.DataFrame, + alphas: Dict[str, Float]) -> pd.DataFrame: """ This function takes a block X and a coefficient matrix B and performs the multiplication X*B. The matrix resulting from this multiplication represents a block in a new, dimensionally-reduced block matrix. @@ -298,7 +308,9 @@ def apply_model(key, key_pattern, pdf, labeldf, alphas): return pd.DataFrame(data) -def score_models(key, key_pattern, pdf, labeldf, sample_index, alphas): +@typechecked +def score_models(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: pd.DataFrame, + sample_index: Dict[str, List[str]], alphas: Dict[str, Float]) -> pd.DataFrame: """ Similar to apply_model, this function performs the multiplication X*B for a block X and corresponding coefficient matrix B, however it also evaluates the coefficient of determination (r2) for each of columns in B against the diff --git a/python/glow/levels/linear_model/tests/test_functions.py b/python/glow/levels/linear_model/tests/test_functions.py index 30bcb593a..21796785e 100644 --- a/python/glow/levels/linear_model/tests/test_functions.py +++ b/python/glow/levels/linear_model/tests/test_functions.py @@ -1,6 +1,7 @@ from glow.levels.linear_model.functions import * import numpy as np import pandas as pd +import pytest def test_sort_by_numeric(): @@ -31,3 +32,15 @@ def test_sort_by_multiple_columns(): sort_in_place(df, ['bin', 'nums']) df_copy.sort_values(['bin', 'nums'], inplace=True) assert (df['nums'].array == df_copy['nums'].array).all() + + +def test_assemble_block(): + df = pd.DataFrame({'mu': [0.2], 'sig': [0.1], 'values': [[0.1, 0.3]]}) + block = assemble_block(n_rows=1, n_cols=2, pdf=df) + assert np.allclose(block, np.array([[-1.], [1.]])) + + +def test_assemble_block_zero_sig(): + df = pd.DataFrame({'mu': [0.2, 0], 'sig': [0.1, 0], 'values': [[0.1, 0.3], [0, 0]]}) + with pytest.raises(ValueError): + assemble_block(n_rows=2, n_cols=2, pdf=df) diff --git a/python/glow/levels/linear_model/tests/test_ridge_regression.py b/python/glow/levels/linear_model/tests/test_ridge_regression.py index 9f39756c6..f04ef19d7 100644 --- a/python/glow/levels/linear_model/tests/test_ridge_regression.py +++ b/python/glow/levels/linear_model/tests/test_ridge_regression.py @@ -4,9 +4,13 @@ data_root = 'test-data/levels/ridge-regression' X0 = pd.read_csv(f'{data_root}/X0.csv').set_index('sample_id') +X0.index = X0.index.astype(str, copy=False) X1 = pd.read_csv(f'{data_root}/X1.csv').set_index('sample_id') +X1.index = X1.index.astype(str, copy=False) X2 = pd.read_csv(f'{data_root}/X2.csv').set_index('sample_id') +X2.index = X2.index.astype(str, copy=False) labeldf = pd.read_csv(f'{data_root}/pts.csv').set_index('sample_id') +labeldf.index = labeldf.index.astype(str, copy=False) alphas = np.array([0.1, 1, 10]) alphaMap = {f'alpha_{i}': a for i, a in enumerate(alphas)} diff --git a/python/setup.py b/python/setup.py index db7e8f413..b2e2e94dd 100644 --- a/python/setup.py +++ b/python/setup.py @@ -7,6 +7,9 @@ version=version, packages=setuptools.find_packages(), install_requires=[ + 'nptyping==1.1.0', + 'numpy>=1.17.4', + 'pandas>=0.25.3', 'typeguard==2.5.0', ], author='The Glow Authors', diff --git a/test-data/levels/ridge-regression/groupedIDs.snappy.parquet b/test-data/levels/ridge-regression/groupedIDs.snappy.parquet index 31ef2d690a3ea6a3c76fee6f71a6633979a49135..0d6bc4bbf53331115436a75e4abbeb44cdcff01c 100644 GIT binary patch delta 1142 zcmaJ>&5K+`5Pvfp*L~~sWFEZPwD1HMOpJpAT_4?51zvJ+Kro;(1iXj|EI~+uv*w&3 zwZtn6yw50yV=Z#w=*{{Q3WYYwa`(CPE0q5ebPk z!>p%OGaFCh^z%EYZ$gmN~&hftOYi|GQJVX4N6ezS;RsFP{Lu0 zCIW6i1jy(x;YMUhOvvF0(o6lNE490^nLub>fu$t;I?OPL&^>0()C zcQw)U+nBsD2{=p!AZESG^@Hd|En9&zEh}Qm&37X%Pzt=$x+1Uy_hV5_IpLayn`cNt z2Udi4rM?tR*iY9iAfYkpnzvVCNX=xl7PL%o6vdf|Na$KdGq>os7D-SUt~h;QZ|?LU z5@ab-!kw7ZNl==)9WLGNbxW1qtE7HPP7uh#`)y`ZDiF8W;Nq_Lf|2~2Jl)43u~}Fi zM>SOCWPHj9|AL$^<%LtK?$A?>(@#3H1-beuy$A_)YRWV!adp>8w#gi7ibf!07A7m(abLhF^153@L z2-$0ZOKg3&0Mq&AxYBbsD`@USCGzQq@Te{7tEY>vPpJ0(I{)LqS1H{+JH79hq>y}t z^W>ZjGW@f?K;oNP(kk~~8xBtQE;L5_uspAdG&bX!>tQ``o_7p;BU3b@X`PP delta 598 zcmX@kJBf2bSp6+F(I;#=q9+(d8ARDc*<>VGx*pAQlwdhyx5iqGK?8}$z`(GU`O6wc zAs~bO-|u<>Ms6T^|Gp3-^FA>~W}xT^M&_PwWgE7>9kzW{M{JTqn(be4BsyNQd1YJW z*l)wcAMOA$obmWJ2gmjq4oZ_m?CLX4%&}*>|HGlAr`T|CEIe?)fKe-yMBu=U>@r*L_mKuD<@M{RNA+4jLN+Y(=l#uxpLgcFf=T-QGzf z+dhj|)Xsi&v^~czXNT#gZ|&uGSUTQKNt$5Cf6mPD?u6j9{?`9Ft>N4H<7u?q{9Lo|#vYnx0xT*@&%|BNQkd I;22~G0MTRLk^lez From afaa6df8f15713c9de79ec3c353a15030ad0acb1 Mon Sep 17 00:00:00 2001 From: Leland Date: Fri, 12 Jun 2020 16:35:21 -0400 Subject: [PATCH 11/34] Add covariate support (#13) * Added necessary modifications to accomodate covariates in model fitting. The initial formulation of the WGR model assumed a form y ~ Xb, however in general we would like to use a model of the form y ~ Ca + Xb, where C is some matrix of covariates that are separate from the genomic features X. This PR makes numerous changes to accomodate covariate matrix C. Adding covariates required the following breaking changes to the APIs: * indexdf is now a required argument for RidgeReducer.transform() and RidgeRegression.transform(): * RidgeReducer.transform(blockdf, labeldf, modeldf) -> RidgeReducer.transform(blockdf, labeldf, indexdf, modeldf) * RidgeRegression.transform(blockdf, labeldf, model, cvdf) -> RidgeRegression.transform(blockdf, labeldf, indexdf, model, cvdf) Additionally, the function signatures for the fit and transform methods of RidgeReducer and RidgeRegression have all been updated to accomodate an optional covariate DataFrame as the final argument. Two new tests have been added to test_ridge_regression.py to test run modes with covariates: * test_ridge_reducer_transform_with_cov * test_two_level_regression_with_cov Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * Cleaned up one unnecessary Pandas import Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * Small changes for clarity and consistence with the rest of the code. Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * Forgot one usage of coalesce Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * Added a couple of comments to explain logic and replaced usages of .values with .array Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * Fixed one instance of the change .values -> .array where it was made in error. Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * Typo in test_ridge_regression.py. Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard * Style auto-updates with yapfAll Signed-off-by: Leland Barnard (leland.barnard@gmail.com) Signed-off-by: Leland Barnard Co-authored-by: Leland Barnard Co-authored-by: Karen Feng --- python/glow/levels/linear_model/functions.py | 50 +++-- .../glow/levels/linear_model/ridge_model.py | 112 ++++++---- python/glow/levels/linear_model/ridge_udfs.py | 81 +++++-- .../linear_model/tests/test_functions.py | 4 +- .../tests/test_ridge_regression.py | 199 +++++++++++++++--- 5 files changed, 335 insertions(+), 111 deletions(-) diff --git a/python/glow/levels/linear_model/functions.py b/python/glow/levels/linear_model/functions.py index d31a9bd42..8e6fd4e52 100644 --- a/python/glow/levels/linear_model/functions.py +++ b/python/glow/levels/linear_model/functions.py @@ -1,10 +1,9 @@ import itertools -from nptyping import Int, Float, NDArray +from nptyping import Float, Int, NDArray import numpy as np import pandas as pd -from scipy.sparse import csr_matrix from typeguard import typechecked -from typing import Iterable, List, Tuple +from typing import Any, Iterable, List, Tuple @typechecked @@ -30,7 +29,8 @@ def parse_key(key: Tuple, key_pattern: List[str]) -> Tuple[str, str, str]: Interprets the key corresponding to a group from a groupBy clause. The key may be of the form: (header_block, sample_block), (header_block, sample_block, label), - (sample_block), + (header_block, header), + (header_block, header, label), (sample_block, label) depending on the context. In each case, a tuple with 3 members is returned, with the missing member filled in by 'all' where necessary @@ -40,13 +40,13 @@ def parse_key(key: Tuple, key_pattern: List[str]) -> Tuple[str, str, str]: key_pattern : one of the aforementioned key patterns Returns: - tuple of (header_block, sample_block, label), where header_block or label may be filled with 'all' depending on - context. + tuple of (header_block, sample_block, label) or (header_block, header, label), where header_block or label may be filled with 'all' + depending on context. """ if key_pattern == ['header_block', 'sample_block']: return key[0], key[1], 'all' - elif key_pattern == ['sample_block']: - return 'all', key[0], 'all' + elif key_pattern == ['header_block', 'header']: + return key[0], key[1], 'all' elif key_pattern == ['sample_block', 'label']: return 'all', key[0], key[1] elif len(key) != 3: @@ -56,7 +56,8 @@ def parse_key(key: Tuple, key_pattern: List[str]) -> Tuple[str, str, str]: @typechecked -def assemble_block(n_rows: Int, n_cols: Int, pdf: pd.DataFrame) -> NDArray[Float]: +def assemble_block(n_rows: Int, n_cols: Int, pdf: pd.DataFrame, + cov_matrix: NDArray[(Any, Any), Float]) -> NDArray[Float]: """ Creates a dense n_rows by n_cols matrix from the array of either sparse or dense vectors in the Pandas DataFrame corresponding to a group. This matrix represents a block. @@ -65,6 +66,8 @@ def assemble_block(n_rows: Int, n_cols: Int, pdf: pd.DataFrame) -> NDArray[Float n_rows : The number of rows in the resulting matrix n_cols : The number of columns in the resulting matrix pdf : Pandas DataFrame corresponding to a group + cov_matrix: 2D numpy array representing covariate columns that should be prepended to matrix X from the block. Can be + empty if covariates are not being applied. Returns: Dense n_rows by n_columns matrix where the columns have been 0-centered and standard scaled. @@ -76,16 +79,18 @@ def assemble_block(n_rows: Int, n_cols: Int, pdf: pd.DataFrame) -> NDArray[Float raise ValueError(f'Standard deviation cannot be 0.') if 'indices' not in pdf.columns: - X_raw = np.row_stack(pdf['values'].array).T + X_raw = np.column_stack(pdf['values'].array) else: - X_csr = csr_matrix( - (np.concatenate(pdf['values'].array), - (np.concatenate(pdf['indices'].array), - np.concatenate([np.repeat(i, len(v)) for i, v in enumerate(pdf.indices.array)]))), - shape=(n_rows, n_cols)) - X_raw = X_csr.todense().A + X_raw = np.zeros([n_rows, n_cols]) + for column, row in enumerate(pdf[['indices', 'values']].itertuples()): + X_raw[row.indices, column] = row.values + + X = ((X_raw - mu) / sig) - return (X_raw - mu) / sig + if cov_matrix.any(): + return np.column_stack((cov_matrix, X)) + else: + return X @typechecked @@ -108,21 +113,26 @@ def slice_label_rows(labeldf: pd.DataFrame, label: str, sample_list: List[str]) @typechecked -def evaluate_coefficients(pdf: pd.DataFrame, alpha_values: Iterable[Float]) -> NDArray[Float]: +def evaluate_coefficients(pdf: pd.DataFrame, alpha_values: Iterable[Float], + n_cov: int) -> NDArray[Float]: """ Solves the system (XTX + Ia)^-1 * XtY for each of the a values in alphas. Returns the resulting coefficients. Args: pdf : Pandas DataFrame for the group alpha_values : Array of alpha values (regularization strengths) + n_cov: Number of covariate columns on the left-most side of matrix X. These are regularized with a constant + value of alpha = 1, regardless of the alpha value being used for the rest of the matrix X. Returns: Matrix of coefficients of size [number of columns in X] x [number of labels * number of alpha values] """ XtX = np.stack(pdf['xtx'].array) XtY = np.stack(pdf['xty'].array) - return np.column_stack( - [(np.linalg.inv(XtX + np.identity(XtX.shape[1]) * a) @ XtY) for a in alpha_values]) + diags = [ + np.concatenate([np.ones(n_cov), np.ones(XtX.shape[1] - n_cov) * a]) for a in alpha_values + ] + return np.column_stack([(np.linalg.inv(XtX + np.diag(d)) @ XtY) for d in diags]) @typechecked diff --git a/python/glow/levels/linear_model/ridge_model.py b/python/glow/levels/linear_model/ridge_model.py index 47867ecc2..532d9490d 100644 --- a/python/glow/levels/linear_model/ridge_model.py +++ b/python/glow/levels/linear_model/ridge_model.py @@ -28,8 +28,12 @@ def __init__(self, alphas: NDArray[(Any, ), Float]) -> None: raise Exception('Alpha values must all be non-negative.') self.alphas = {f'alpha_{i}': a for i, a in enumerate(alphas)} - def fit(self, blockdf: DataFrame, labeldf: pd.DataFrame, - sample_blocks: Dict[str, List[str]]) -> DataFrame: + def fit( + self, + blockdf: DataFrame, + labeldf: pd.DataFrame, + sample_blocks: Dict[str, List[str]], + covdf: pd.DataFrame = pd.DataFrame({})) -> DataFrame: """ Fits a ridge reducer model, represented by a Spark DataFrame containing coefficients for each of the ridge alpha parameters, for each block in the starting matrix, for each label in the target labels. @@ -38,26 +42,28 @@ def fit(self, blockdf: DataFrame, labeldf: pd.DataFrame, blockdf : Spark DataFrame representing the beginning block matrix X labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models sample_blocks : Dict containing a mapping of sample_block ID to a list of corresponding sample IDs + covdf : Pandas DataFrame containing covariates to be included in every model in the stacking + ensemble (optional). Returns: Spark DataFrame containing the model resulting from the fitting routine. """ map_key_pattern = ['header_block', 'sample_block'] - reduce_key_pattern = ['header'] + reduce_key_pattern = ['header_block', 'header'] if 'label' in blockdf.columns: map_key_pattern.append('label') reduce_key_pattern.append('label') map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), - normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, - PandasUDFType.GROUPED_MAP) + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks, covdf + ), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) model_udf = pandas_udf( - lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, self.alphas), - model_struct, PandasUDFType.GROUPED_MAP) + lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, self.alphas, covdf + ), model_struct, PandasUDFType.GROUPED_MAP) return blockdf \ .groupBy(map_key_pattern) \ @@ -67,7 +73,12 @@ def fit(self, blockdf: DataFrame, labeldf: pd.DataFrame, .groupBy(map_key_pattern) \ .apply(model_udf) - def transform(self, blockdf: DataFrame, labeldf: pd.DataFrame, modeldf: DataFrame) -> DataFrame: + def transform(self, + blockdf: DataFrame, + labeldf: pd.DataFrame, + sample_blocks: Dict[str, List[str]], + modeldf: DataFrame, + covdf: pd.DataFrame = pd.DataFrame({})) -> DataFrame: """ Transforms a starting block matrix to the reduced block matrix, using a reducer model produced by the RidgeReducer fit method. @@ -75,7 +86,10 @@ def transform(self, blockdf: DataFrame, labeldf: pd.DataFrame, modeldf: DataFram Args: blockdf : Spark DataFrame representing the beginning block matrix labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models + sample_blocks: Dict containing a mapping of sample_block ID to a list of corresponding sample IDs modeldf : Spark DataFrame produced by the RidgeReducer fit method, representing the reducer model + covdf : Pandas DataFrame containing covariates to be included in every model in the stacking + ensemble (optional). Returns: Spark DataFrame representing the reduced block matrix @@ -85,12 +99,18 @@ def transform(self, blockdf: DataFrame, labeldf: pd.DataFrame, modeldf: DataFram if 'label' in blockdf.columns: transform_key_pattern.append('label') + joined = blockdf.drop('sort_key').join(modeldf, ['header_block', 'sample_block', 'header'], 'right') \ + .withColumn('label', f.coalesce(f.col('label'), f.col('labels').getItem(0))) + else: + joined = blockdf.drop('sort_key').join( + modeldf, ['header_block', 'sample_block', 'header'], 'right') transform_udf = pandas_udf( - lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, self.alphas), - reduced_matrix_struct, PandasUDFType.GROUPED_MAP) + lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, sample_blocks, + self.alphas, covdf), reduced_matrix_struct, + PandasUDFType.GROUPED_MAP) - return blockdf.join(modeldf.drop('sort_key'), ['header_block', 'sample_block', 'header']) \ + return joined \ .groupBy(transform_key_pattern) \ .apply(transform_udf) @@ -116,8 +136,13 @@ def __init__(self, alphas: NDArray[(Any, ), Float]) -> None: raise Exception('Alpha values must all be non-negative.') self.alphas = {f'alpha_{i}': a for i, a in enumerate(alphas)} - def fit(self, blockdf: DataFrame, labeldf: pd.DataFrame, - sample_blocks: Dict[str, List[str]]) -> (DataFrame, DataFrame): + def fit( + self, + blockdf: DataFrame, + labeldf: pd.DataFrame, + sample_blocks: Dict[str, List[str]], + covdf: pd.DataFrame = pd.DataFrame({}) + ) -> (DataFrame, DataFrame): """ Fits a ridge regression model, represented by a Spark DataFrame containing coefficients for each of the ridge alpha parameters, for each block in the starting matrix, for each label in the target labels, as well as a @@ -127,30 +152,28 @@ def fit(self, blockdf: DataFrame, labeldf: pd.DataFrame, blockdf : Spark DataFrame representing the beginning block matrix X labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models sample_blocks : Dict containing a mapping of sample_block ID to a list of corresponding sample IDs + covdf : Pandas DataFrame containing covariates to be included in every model in the stacking + ensemble (optional). Returns: Two Spark DataFrames, one containing the model resulting from the fitting routine and one containing the results of the cross validation procedure. """ - map_key_pattern = ['sample_block'] - reduce_key_pattern = ['header'] - - if 'label' in blockdf.columns: - map_key_pattern.append('label') - reduce_key_pattern.append('label') + map_key_pattern = ['sample_block', 'label'] + reduce_key_pattern = ['header_block', 'header', 'label'] map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), - normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, - PandasUDFType.GROUPED_MAP) + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks, covdf + ), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) model_udf = pandas_udf( - lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, self.alphas), - model_struct, PandasUDFType.GROUPED_MAP) + lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, self.alphas, covdf + ), model_struct, PandasUDFType.GROUPED_MAP) score_udf = pandas_udf( lambda key, pdf: score_models(key, map_key_pattern, pdf, labeldf, sample_blocks, self. - alphas), cv_struct, PandasUDFType.GROUPED_MAP) + alphas, covdf), cv_struct, PandasUDFType.GROUPED_MAP) modeldf = blockdf \ .groupBy(map_key_pattern) \ @@ -160,19 +183,25 @@ def fit(self, blockdf: DataFrame, labeldf: pd.DataFrame, .groupBy(map_key_pattern) \ .apply(model_udf) - cvdf = blockdf \ - .join(modeldf.drop('header_block', 'sort_key'), ['header', 'sample_block'], 'inner') \ + cvdf = blockdf.drop('header_block', 'sort_key') \ + .join(modeldf, ['header', 'sample_block'], 'right') \ + .withColumn('label', f.coalesce(f.col('label'), f.col('labels').getItem(0)))\ .groupBy(map_key_pattern) \ .apply(score_udf) \ .groupBy('label', 'alpha').agg(f.mean('r2').alias('r2_mean')) \ .withColumn('modelRank', f.dense_rank().over(Window.partitionBy("label").orderBy(f.desc("r2_mean")))) \ - .filter('modelRank = 1') \ + .filter(f'modelRank = 1') \ .drop('modelRank') return modeldf, cvdf - def transform(self, blockdf: DataFrame, labeldf: pd.DataFrame, modeldf: DataFrame, - cvdf: DataFrame) -> DataFrame: + def transform(self, + blockdf: DataFrame, + labeldf: pd.DataFrame, + sample_blocks: Dict[str, List[str]], + modeldf: DataFrame, + cvdf: DataFrame, + covdf: pd.DataFrame = pd.DataFrame({})) -> DataFrame: """ Generates predictions for the target labels in the provided label DataFrame by applying the model resulting from the RidgeRegression fit method to the starting block matrix. @@ -180,25 +209,26 @@ def transform(self, blockdf: DataFrame, labeldf: pd.DataFrame, modeldf: DataFram Args: blockdf : Spark DataFrame representing the beginning block matrix X labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models + sample_blocks: Dict containing a mapping of sample_block ID to a list of corresponding sample IDs modeldf : Spark DataFrame produced by the RidgeRegression fit method, representing the reducer model cvdf : Spark DataFrame produced by the RidgeRegression fit method, containing the results of the cross validation routine. + covdf : Pandas DataFrame containing covariates to be included in every model in the stacking + ensemble (optional). Returns: Spark DataFrame containing prediction y_hat values for each sample_block of samples for each label """ - transform_key_pattern = ['sample_block'] - - if 'label' in blockdf.columns: - transform_key_pattern.append('label') + transform_key_pattern = ['sample_block', 'label'] transform_udf = pandas_udf( - lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, self.alphas), - reduced_matrix_struct, PandasUDFType.GROUPED_MAP) + lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, sample_blocks, + self.alphas, covdf), reduced_matrix_struct, + PandasUDFType.GROUPED_MAP) - return blockdf.drop('header_block') \ - .join(modeldf.drop('header_block', 'sort_key'), ['sample_block', 'header']) \ + return blockdf.drop('header_block', 'sort_key').join(modeldf.drop('header_block'), ['sample_block', 'header'], 'right') \ + .withColumn('label', f.coalesce(f.col('label'), f.col('labels').getItem(0))) \ .groupBy(transform_key_pattern) \ .apply(transform_udf) \ .join(cvdf, ['label', 'alpha'], 'inner') \ diff --git a/python/glow/levels/linear_model/ridge_udfs.py b/python/glow/levels/linear_model/ridge_udfs.py index 85fc716ae..583362fde 100644 --- a/python/glow/levels/linear_model/ridge_udfs.py +++ b/python/glow/levels/linear_model/ridge_udfs.py @@ -51,7 +51,7 @@ @typechecked def map_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: pd.DataFrame, - sample_index: Dict[str, List[str]]) -> pd.DataFrame: + sample_index: Dict[str, List[str]], covdf: pd.DataFrame) -> pd.DataFrame: """ This function constructs matrices X and Y, and returns X_transpose * X (XtX) and X_transpose * Y (XtY), where X corresponds to a block from a block matrix. @@ -81,7 +81,8 @@ def map_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeld |-- alpha: double (Required only if the header is tied to a specific value of alpha) |-- label: double (Required only if the header is tied to a specific label) labeldf : Pandas DataFrame containing label values (i. e., the Y in the normal equation above). - sample_index: dict containing a mapping of sample_block ID to a list of corresponding sample IDs + sample_index : sample_index: dict containing a mapping of sample_block ID to a list of corresponding sample IDs + covdf: Pandas DataFrame containing covariates that should be included with every block X above (can be empty). Returns: transformed Pandas DataFrame containing XtX and XtY corresponding to a particular block X. @@ -101,7 +102,19 @@ def map_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeld n_rows = pdf['size'][0] n_cols = len(pdf) sample_list = sample_index[sample_block] - X = assemble_block(n_rows, n_cols, pdf) + + if covdf.empty: + header_col = pdf['header'] + sort_key_col = pdf['sort_key'] + X = assemble_block(n_rows, n_cols, pdf, np.array([])) + else: + cov_matrix = slice_label_rows(covdf, 'all', sample_list) + n_cov = len(covdf.columns) + header_col = np.concatenate([covdf.columns, pdf['header']]) + #Add new sort_keys for covariates, starting from -n_cov up to 0 to ensure they come ahead of the headers. + sort_key_col = np.concatenate((np.arange(-n_cov, 0), pdf['sort_key'])) + X = assemble_block(n_rows, n_cols, pdf, cov_matrix) + Y = slice_label_rows(labeldf, label, sample_list) XtX = X.T @ X XtY = X.T @ Y @@ -110,8 +123,8 @@ def map_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeld 'header_block': header_block, 'sample_block': sample_block, 'label': label, - 'header': pdf['header'], - 'sort_key': pdf['sort_key'], + 'header': header_col, + 'sort_key': sort_key_col, 'xtx': list(XtX), 'xty': list(XtY) } @@ -120,7 +133,7 @@ def map_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeld @typechecked -def reduce_normal_eqn(pdf: pd.DataFrame): +def reduce_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame): """ This function constructs lists of rows from the XtX and XtY matrices corresponding to a particular header in X but evaluated in different sample_blocks, and then reduces those lists by element-wise summation. This reduction is @@ -133,6 +146,8 @@ def reduce_normal_eqn(pdf: pd.DataFrame): List(xtx_sum_excluding_sample_block0, xtx_sum_excluding_sample_block1, ..., xtx_sum_excluding_sample_blockN) Args: + key : unique key identifying the rows emitted by a groupBy statement + key_pattern : pattern of columns used in the groupBy statement pdf : starting Pandas DataFrame containing the lists of rows from XtX and XtY for block X identified by :key: schema (specified by the normal_eqn_struct): |-- header_block: string @@ -170,7 +185,7 @@ def reduce_normal_eqn(pdf: pd.DataFrame): @typechecked def solve_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: pd.DataFrame, - alphas: Dict[str, Float]) -> pd.DataFrame: + alphas: Dict[str, Float], covdf: pd.DataFrame) -> pd.DataFrame: """ This function assembles the matrices XtX and XtY for a particular sample_block (where the contribution of that sample_block has been omitted) and solves the equation [(XtX + I*alpha)]-1 * XtY = B for a list of alpha values, and returns the @@ -193,6 +208,7 @@ def solve_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labe | |-- element: double labeldf : Pandas DataFrame containing label values (i. e., the Y in the normal equation above). alphas : dict of {alphaName : alphaValue} for the alpha values to be used + covdf: Pandas DataFrame containing covariates that should be included with every block X above (can be empty). Returns: transformed Pandas DataFrame containing the coefficient matrix B @@ -212,8 +228,10 @@ def solve_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labe header_block, sample_block, label = parse_key(key, key_pattern) sort_in_place(pdf, ['sort_key', 'header']) alpha_names, alpha_values = zip(*sorted(alphas.items())) - - beta_stack = evaluate_coefficients(pdf, alpha_values) + if covdf.empty: + beta_stack = evaluate_coefficients(pdf, alpha_values, 0) + else: + beta_stack = evaluate_coefficients(pdf, alpha_values, len(covdf.columns)) row_indexer = cross_alphas_and_labels(alpha_names, labeldf, label) alpha_row, label_row = zip(*row_indexer) output_length = len(pdf) @@ -232,7 +250,8 @@ def solve_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labe @typechecked def apply_model(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: pd.DataFrame, - alphas: Dict[str, Float]) -> pd.DataFrame: + sample_index: Dict[str, List[str]], alphas: Dict[str, Float], + covdf: pd.DataFrame) -> pd.DataFrame: """ This function takes a block X and a coefficient matrix B and performs the multiplication X*B. The matrix resulting from this multiplication represents a block in a new, dimensionally-reduced block matrix. @@ -261,7 +280,9 @@ def apply_model(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: |-- coefficients: array | |-- element: double labeldf : Pandas DataFrame containing label values that were used in fitting coefficient matrix B. + sample_index : sample_index: dict containing a mapping of sample_block ID to a list of corresponding sample IDs alphas : dict of {alphaName : alphaValue} for the alpha values that were used when fitting coefficient matrix B + covdf: Pandas DataFrame containing covariates that should be included with every block X above (can be empty). Returns: transformed Pandas DataFrame containing reduced matrix block produced by the multiplication X*B @@ -281,9 +302,18 @@ def apply_model(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: header_block, sample_block, label = parse_key(key, key_pattern) sort_in_place(pdf, ['sort_key']) - n_rows = pdf['size'][0] - n_cols = len(pdf) - X = assemble_block(n_rows, n_cols, pdf) + + if covdf.empty: + n_rows = pdf['size'][0] + n_cols = len(pdf) + X = assemble_block(n_rows, n_cols, pdf, np.array([])) + else: + sample_list = sample_index[sample_block] + n_rows = int(pdf[~pdf['values'].isnull()]['size'].array[0]) + n_cols = len(pdf[~pdf['values'].isnull()]) + cov_matrix = slice_label_rows(covdf, 'all', sample_list) + X = assemble_block(n_rows, n_cols, pdf[~pdf['values'].isnull()], cov_matrix) + B = np.row_stack(pdf['coefficients'].array) XB = X @ B mu, sig = XB.mean(axis=0), XB.std(axis=0) @@ -310,7 +340,8 @@ def apply_model(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: @typechecked def score_models(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: pd.DataFrame, - sample_index: Dict[str, List[str]], alphas: Dict[str, Float]) -> pd.DataFrame: + sample_index: Dict[str, List[str]], alphas: Dict[str, Float], + covdf: pd.DataFrame) -> pd.DataFrame: """ Similar to apply_model, this function performs the multiplication X*B for a block X and corresponding coefficient matrix B, however it also evaluates the coefficient of determination (r2) for each of columns in B against the @@ -340,8 +371,9 @@ def score_models(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: |-- coefficients: array | |-- element: double labeldf : Pandas DataFrame containing label values that were used in fitting coefficient matrix B. - sample_index: dict containing a mapping of sample_block ID to a list of corresponding sample IDs + sample_index : sample_index: dict containing a mapping of sample_block ID to a list of corresponding sample IDs alphas : dict of {alphaName : alphaValue} for the alpha values that were used when fitting coefficient matrix B + covdf: Pandas DataFrame containing covariates that should be included with every block X above (can be empty). Returns: Pandas DataFrame containing the r2 scores for each combination of alpha and label @@ -353,12 +385,23 @@ def score_models(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: """ header_block, sample_block, label = parse_key(key, key_pattern) sort_in_place(pdf, ['sort_key']) - n_rows = pdf['size'][0] - n_cols = len(pdf) - X = assemble_block(n_rows, n_cols, pdf) + sample_list = sample_index[sample_block] + + if covdf.empty: + n_rows = pdf['size'][0] + n_cols = len(pdf) + X = assemble_block(n_rows, n_cols, pdf, np.array([])) + else: + # If there is a covdf, we will have null 'values' entries in pdf arising from the right join of blockdf + # to modeldf, so we will filter those rows out before assembling the block. + sample_list = sample_index[sample_block] + n_rows = int(pdf[~pdf['values'].isnull()]['size'].array[0]) + n_cols = len(pdf[~pdf['values'].isnull()]) + cov_matrix = slice_label_rows(covdf, 'all', sample_list) + X = assemble_block(n_rows, n_cols, pdf[~pdf['values'].isnull()], cov_matrix) + B = np.row_stack(pdf['coefficients'].array) XB = X @ B - sample_list = sample_index[sample_block] Y = slice_label_rows(labeldf, label, sample_list) scores = r_squared(XB, Y) alpha_names = sorted(alphas.keys()) diff --git a/python/glow/levels/linear_model/tests/test_functions.py b/python/glow/levels/linear_model/tests/test_functions.py index 21796785e..2ea634038 100644 --- a/python/glow/levels/linear_model/tests/test_functions.py +++ b/python/glow/levels/linear_model/tests/test_functions.py @@ -36,11 +36,11 @@ def test_sort_by_multiple_columns(): def test_assemble_block(): df = pd.DataFrame({'mu': [0.2], 'sig': [0.1], 'values': [[0.1, 0.3]]}) - block = assemble_block(n_rows=1, n_cols=2, pdf=df) + block = assemble_block(n_rows=1, n_cols=2, pdf=df, cov_matrix=np.array([[]])) assert np.allclose(block, np.array([[-1.], [1.]])) def test_assemble_block_zero_sig(): df = pd.DataFrame({'mu': [0.2, 0], 'sig': [0.1, 0], 'values': [[0.1, 0.3], [0, 0]]}) with pytest.raises(ValueError): - assemble_block(n_rows=2, n_cols=2, pdf=df) + assemble_block(n_rows=2, n_cols=2, pdf=df, cov_matrix=np.array([[]])) diff --git a/python/glow/levels/linear_model/tests/test_ridge_regression.py b/python/glow/levels/linear_model/tests/test_ridge_regression.py index f04ef19d7..4e9b56c54 100644 --- a/python/glow/levels/linear_model/tests/test_ridge_regression.py +++ b/python/glow/levels/linear_model/tests/test_ridge_regression.py @@ -1,16 +1,25 @@ from glow.levels.linear_model import RidgeReducer, RidgeRegression from glow.levels.linear_model.ridge_model import * -import pandas as pd data_root = 'test-data/levels/ridge-regression' + X0 = pd.read_csv(f'{data_root}/X0.csv').set_index('sample_id') X0.index = X0.index.astype(str, copy=False) + X1 = pd.read_csv(f'{data_root}/X1.csv').set_index('sample_id') X1.index = X1.index.astype(str, copy=False) + X2 = pd.read_csv(f'{data_root}/X2.csv').set_index('sample_id') X2.index = X2.index.astype(str, copy=False) + labeldf = pd.read_csv(f'{data_root}/pts.csv').set_index('sample_id') labeldf.index = labeldf.index.astype(str, copy=False) + +n_cov = 2 +cov_matrix = np.random.randn(*(labeldf.shape[0], n_cov)) +covdf = pd.DataFrame(data=cov_matrix, columns=['cov1', 'cov2'], index=labeldf.index) +covdf_empty = pd.DataFrame({}) + alphas = np.array([0.1, 1, 10]) alphaMap = {f'alpha_{i}': a for i, a in enumerate(alphas)} @@ -44,8 +53,8 @@ def test_map_normal_eqn(spark): sample_blocks = __get_sample_blocks(indexdf) map_key_pattern = ['header_block', 'sample_block'] map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), - normal_eqn_struct, PandasUDFType.GROUPED_MAP) + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks, + covdf_empty), normal_eqn_struct, PandasUDFType.GROUPED_MAP) outdf = blockdf \ .groupBy(map_key_pattern) \ @@ -82,12 +91,12 @@ def test_reduce_normal_eqn(spark): sample_blocks = __get_sample_blocks(indexdf) map_key_pattern = ['header_block', 'sample_block'] - reduce_key_pattern = ['header'] + reduce_key_pattern = ['header_block', 'header'] map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), - normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, - PandasUDFType.GROUPED_MAP) + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks, + covdf_empty), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) mapdf = blockdf \ .groupBy(map_key_pattern) \ @@ -129,15 +138,15 @@ def test_solve_normal_eqn(spark): sample_blocks = __get_sample_blocks(indexdf) map_key_pattern = ['header_block', 'sample_block'] - reduce_key_pattern = ['header'] + reduce_key_pattern = ['header_block', 'header'] map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), - normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, - PandasUDFType.GROUPED_MAP) + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks, + covdf_empty), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) model_udf = pandas_udf( - lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, alphaMap), - model_struct, PandasUDFType.GROUPED_MAP) + lambda key, pdf: solve_normal_eqn(key, reduce_key_pattern, pdf, labeldf, alphaMap, + covdf_empty), model_struct, PandasUDFType.GROUPED_MAP) reducedf = blockdf \ .groupBy(map_key_pattern) \ @@ -183,19 +192,20 @@ def test_apply_model(spark): sample_blocks = __get_sample_blocks(indexdf) map_key_pattern = ['header_block', 'sample_block'] - reduce_key_pattern = ['header'] + reduce_key_pattern = ['header_block', 'header'] transform_key_pattern = ['header_block', 'sample_block'] map_udf = pandas_udf( - lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks), - normal_eqn_struct, PandasUDFType.GROUPED_MAP) - reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(pdf), normal_eqn_struct, - PandasUDFType.GROUPED_MAP) + lambda key, pdf: map_normal_eqn(key, map_key_pattern, pdf, labeldf, sample_blocks, + covdf_empty), normal_eqn_struct, PandasUDFType.GROUPED_MAP) + reduce_udf = pandas_udf(lambda key, pdf: reduce_normal_eqn(key, reduce_key_pattern, pdf), + normal_eqn_struct, PandasUDFType.GROUPED_MAP) model_udf = pandas_udf( - lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, alphaMap), + lambda key, pdf: solve_normal_eqn(key, map_key_pattern, pdf, labeldf, alphaMap, covdf_empty), model_struct, PandasUDFType.GROUPED_MAP) transform_udf = pandas_udf( - lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, alphaMap), - reduced_matrix_struct, PandasUDFType.GROUPED_MAP) + lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, sample_blocks, + alphaMap, covdf_empty), reduced_matrix_struct, + PandasUDFType.GROUPED_MAP) modeldf = blockdf \ .groupBy(map_key_pattern) \ @@ -278,7 +288,7 @@ def test_ridge_reducer_transform(spark): stack = RidgeReducer(alphas) modeldf = stack.fit(blockdf, labeldf, __get_sample_blocks(indexdf)) - level1df = stack.transform(blockdf, labeldf, modeldf) + level1df = stack.transform(blockdf, labeldf, __get_sample_blocks(indexdf), modeldf) columns = ['values'] rows = level1df.filter(f'header LIKE "%{testBlock}%" AND sample_block = {testGroup}') \ @@ -290,6 +300,51 @@ def test_ridge_reducer_transform(spark): assert np.allclose(X1_in_stack, X1_in) +def test_ridge_reducer_transform_with_cov(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + testGroup = '0' + testBlock = 'chr_1_block_0' + ids = indexdf.filter(f'sample_block = {testGroup}').select('sample_ids').head().sample_ids + sample_blocks = __get_sample_blocks(indexdf) + headers = [ + r.header + for r in blockdf.filter(f'header_block = "{testBlock}" AND sample_block= {testGroup}'). + orderBy('sort_key').select('header').collect() + ] + + C_in = covdf.loc[ids, :].values + X_in = X0[headers].loc[ids, :].values + X_in_cov = np.column_stack([C_in, X_in]) + C_out = covdf.drop(ids, axis='rows').values + X_out = X0[headers].drop(ids, axis='rows').values + X_out_cov = np.column_stack((C_out, X_out)) + Y_out = labeldf.drop(ids, axis='rows').values + + XtX_out_cov = X_out_cov.T @ X_out_cov + XtY_out_cov = X_out_cov.T @ Y_out + diags_cov = [ + np.concatenate([np.ones(n_cov), np.ones(XtX_out_cov.shape[1] - n_cov) * a]) for a in alphas + ] + B_cov = np.column_stack( + [(np.linalg.inv(XtX_out_cov + np.diag(d)) @ XtY_out_cov) for d in diags_cov]) + X1_in_cov = X_in_cov @ B_cov + + stack = RidgeReducer(alphas) + modeldf_cov = stack.fit(blockdf, labeldf, sample_blocks, covdf) + level1df_cov = stack.transform(blockdf, labeldf, sample_blocks, modeldf_cov, covdf) + + columns = ['alpha', 'label', 'values'] + rows_cov = level1df_cov.filter(f'header LIKE "%{testBlock}%" AND sample_block= {testGroup}') \ + .select(*columns) \ + .collect() + outdf_cov = pd.DataFrame(rows_cov, columns=columns) + X1_in_stack_cov = np.column_stack(outdf_cov['values']) + + assert np.allclose(X1_in_stack_cov, X1_in_cov) + + def test_one_level_regression(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') @@ -338,11 +393,11 @@ def test_one_level_regression(spark): stack0 = RidgeReducer(alphas) model0df = stack0.fit(blockdf, labeldf, group2ids) - level1df = stack0.transform(blockdf, labeldf, model0df) + level1df = stack0.transform(blockdf, labeldf, group2ids, model0df) regressor = RidgeRegression(alphas) model1df, cvdf = regressor.fit(level1df, labeldf, group2ids) - yhatdf = regressor.transform(level1df, labeldf, model1df, cvdf) + yhatdf = regressor.transform(level1df, labeldf, group2ids, model1df, cvdf) r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() bestAlpha_lvl, bestr2_lvl = (r.alpha, r.r2_mean) @@ -407,15 +462,101 @@ def test_two_level_regression(spark): stack0 = RidgeReducer(alphas) model0df = stack0.fit(blockdf, labeldf, group2ids) - level1df = stack0.transform(blockdf, labeldf, model0df) + level1df = stack0.transform(blockdf, labeldf, group2ids, model0df) stack1 = RidgeReducer(alphas) model1df = stack1.fit(level1df, labeldf, group2ids) - level2df = stack1.transform(level1df, labeldf, model1df) + level2df = stack1.transform(level1df, labeldf, group2ids, model1df) regressor = RidgeRegression(alphas) model2df, cvdf = regressor.fit(level2df, labeldf, group2ids) - yhatdf = regressor.transform(level2df, labeldf, model2df, cvdf) + yhatdf = regressor.transform(level2df, labeldf, group2ids, model2df, cvdf) + + r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() + bestAlpha_lvl, bestr2_lvl = (r.alpha, r.r2_mean) + y_hat_lvl = np.concatenate([ + r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').select( + 'values').collect() + ]) + + assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and + np.allclose(y_hat_lvl, np.array(y_hat))) + + +def test_two_level_regression_with_cov(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + testLabel = 'sim100' + columnIndexer = sorted(enumerate(alphaMap.keys()), key=lambda t: t[1]) + coefOrder = [i for i, a in columnIndexer] + + group2ids = { + r.sample_block: r.sample_ids + for r in indexdf.select('sample_block', 'sample_ids').collect() + } + groups = sorted(group2ids.keys(), key=lambda v: v) + headersToKeep = [c for c in X2.columns if testLabel in c] + + r2s = [] + + for group in groups: + ids = group2ids[group] + C_in = covdf.loc[ids, :].values + X2_in = np.column_stack([C_in, X2[headersToKeep].loc[ids, :].values]) + C_out = covdf.loc[X2[headersToKeep].drop(ids, axis='rows').index].values + X2_out = np.column_stack([C_out, X2[headersToKeep].drop(ids, axis='rows').values]) + Y_in = labeldf[testLabel].loc[ids].values + Y_out = labeldf[testLabel].loc[X2[headersToKeep].drop(ids, axis='rows').index].values + X2tX2_out = X2_out.T @ X2_out + X2tY_out = X2_out.T @ Y_out + n_cov = len(covdf.columns) + diags = [ + np.concatenate([np.ones(n_cov), np.ones(X2tX2_out.shape[1] - n_cov) * a]) + for a in alphas + ] + B = np.column_stack( + [(np.linalg.inv(X2tX2_out + np.diag(d)) @ X2tY_out) for d in diags])[:, coefOrder] + X2B = X2_in @ B + r2 = r_squared(X2B, Y_in.reshape(-1, 1)) + r2s.append(r2) + + r2_mean = np.row_stack(r2s).mean(axis=0) + bestAlpha, bestr2 = sorted(zip(alphaMap.keys(), r2_mean), key=lambda t: -t[1])[0] + + y_hat = [] + r2s_pred = [] + + for group in groups: + ids = group2ids[group] + C_in = covdf.loc[ids, :].values + X2_in = np.column_stack([C_in, X2[headersToKeep].loc[ids, :].values]) + C_out = covdf.loc[X2[headersToKeep].drop(ids, axis='rows').index].values + X2_out = np.column_stack([C_out, X2[headersToKeep].drop(ids, axis='rows').values]) + Y_in = labeldf[testLabel].loc[ids].values + Y_out = labeldf[testLabel].loc[X2[headersToKeep].drop(ids, axis='rows').index].values + X2tX2_out = X2_out.T @ X2_out + X2tY_out = X2_out.T @ Y_out + d = np.concatenate( + [np.ones(n_cov), + np.ones(X2tX2_out.shape[1] - n_cov) * alphaMap[bestAlpha]]) + b = np.linalg.inv(X2tX2_out + np.diag(d)) @ X2tY_out + r2s_pred.append(r_squared(X2_in @ b, Y_in)) + y_hat.extend((X2_in @ b).tolist()) + + y_hat = np.array(y_hat) + + stack0 = RidgeReducer(alphas) + model0df = stack0.fit(blockdf, labeldf, group2ids) + level1df = stack0.transform(blockdf, labeldf, group2ids, model0df) + + stack1 = RidgeReducer(alphas) + model1df = stack1.fit(level1df, labeldf, group2ids) + level2df = stack1.transform(level1df, labeldf, group2ids, model1df) + + regressor = RidgeRegression(alphas) + model2df, cvdf = regressor.fit(level2df, labeldf, group2ids, covdf) + yhatdf = regressor.transform(level2df, labeldf, group2ids, model2df, cvdf, covdf) r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() bestAlpha_lvl, bestr2_lvl = (r.alpha, r.r2_mean) From cd6c6a13fb02218e808f246159be8dace368d590 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 15 Jun 2020 11:12:20 -0700 Subject: [PATCH 12/34] Flatten estimated phenotypes (#15) * WIP Signed-off-by: Karen Feng * Clean up tests Signed-off-by: Karen Feng * WIP Signed-off-by: Karen Feng * Order to match labeldf Signed-off-by: Karen Feng * Check we tie-break Signed-off-by: Karen Feng * cleanup Signed-off-by: Karen Feng * tests Signed-off-by: Karen Feng * test var name Signed-off-by: Karen Feng * clean up tests Signed-off-by: Karen Feng * Clean up docs Signed-off-by: Karen Feng --- .../glow/levels/linear_model/ridge_model.py | 55 ++-- python/glow/levels/linear_model/ridge_udfs.py | 2 +- .../tests/test_ridge_regression.py | 242 ++++++------------ 3 files changed, 123 insertions(+), 176 deletions(-) diff --git a/python/glow/levels/linear_model/ridge_model.py b/python/glow/levels/linear_model/ridge_model.py index 532d9490d..ab90cfb7e 100644 --- a/python/glow/levels/linear_model/ridge_model.py +++ b/python/glow/levels/linear_model/ridge_model.py @@ -1,7 +1,7 @@ from .ridge_udfs import * from nptyping import Float, NDArray import pandas as pd -from pyspark.sql import DataFrame +from pyspark.sql import DataFrame, Row from pyspark.sql.functions import pandas_udf, PandasUDFType import pyspark.sql.functions as f from pyspark.sql.window import Window @@ -99,11 +99,12 @@ def transform(self, if 'label' in blockdf.columns: transform_key_pattern.append('label') - joined = blockdf.drop('sort_key').join(modeldf, ['header_block', 'sample_block', 'header'], 'right') \ + joined = blockdf.drop('sort_key') \ + .join(modeldf, ['header_block', 'sample_block', 'header'], 'right') \ .withColumn('label', f.coalesce(f.col('label'), f.col('labels').getItem(0))) else: - joined = blockdf.drop('sort_key').join( - modeldf, ['header_block', 'sample_block', 'header'], 'right') + joined = blockdf.drop('sort_key') \ + .join(modeldf, ['header_block', 'sample_block', 'header'], 'right') transform_udf = pandas_udf( lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, sample_blocks, @@ -149,7 +150,7 @@ def fit( Spark DataFrame containing the optimal ridge alpha value for each label. Args: - blockdf : Spark DataFrame representing the beginning block matrix X + blockdf : Spark DataFrame representing the reduced block matrix X labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models sample_blocks : Dict containing a mapping of sample_block ID to a list of corresponding sample IDs covdf : Pandas DataFrame containing covariates to be included in every model in the stacking @@ -183,14 +184,20 @@ def fit( .groupBy(map_key_pattern) \ .apply(model_udf) + # Break ties in favor of the larger alpha + alpha_df = blockdf.sql_ctx \ + .createDataFrame([Row(alpha=k, alpha_value=float(v)) for k, v in self.alphas.items()]) + window_spec = Window.partitionBy('label').orderBy(f.desc('r2_mean'), f.desc('alpha_value')) + cvdf = blockdf.drop('header_block', 'sort_key') \ .join(modeldf, ['header', 'sample_block'], 'right') \ - .withColumn('label', f.coalesce(f.col('label'), f.col('labels').getItem(0)))\ + .withColumn('label', f.coalesce(f.col('label'), f.col('labels').getItem(0))) \ .groupBy(map_key_pattern) \ .apply(score_udf) \ - .groupBy('label', 'alpha').agg(f.mean('r2').alias('r2_mean')) \ - .withColumn('modelRank', f.dense_rank().over(Window.partitionBy("label").orderBy(f.desc("r2_mean")))) \ - .filter(f'modelRank = 1') \ + .join(alpha_df, ['alpha']) \ + .groupBy('label', 'alpha', 'alpha_value').agg(f.mean('r2').alias('r2_mean')) \ + .withColumn('modelRank', f.row_number().over(window_spec)) \ + .filter('modelRank = 1') \ .drop('modelRank') return modeldf, cvdf @@ -201,15 +208,15 @@ def transform(self, sample_blocks: Dict[str, List[str]], modeldf: DataFrame, cvdf: DataFrame, - covdf: pd.DataFrame = pd.DataFrame({})) -> DataFrame: + covdf: pd.DataFrame = pd.DataFrame({})) -> pd.DataFrame: """ Generates predictions for the target labels in the provided label DataFrame by applying the model resulting from the RidgeRegression fit method to the starting block matrix. Args: - blockdf : Spark DataFrame representing the beginning block matrix X + blockdf : Spark DataFrame representing the reduced block matrix X labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models - sample_blocks: Dict containing a mapping of sample_block ID to a list of corresponding sample IDs + sample_blocks : Dict containing a mapping of sample_block ID to a list of corresponding sample IDs modeldf : Spark DataFrame produced by the RidgeRegression fit method, representing the reducer model cvdf : Spark DataFrame produced by the RidgeRegression fit method, containing the results of the cross validation routine. @@ -217,7 +224,8 @@ def transform(self, ensemble (optional). Returns: - Spark DataFrame containing prediction y_hat values for each sample_block of samples for each label + Pandas DataFrame containing prediction y_hat values. The shape and order match labeldf such that the + rows are indexed by sample ID and the columns by label. The column types are float64. """ transform_key_pattern = ['sample_block', 'label'] @@ -227,9 +235,24 @@ def transform(self, self.alphas, covdf), reduced_matrix_struct, PandasUDFType.GROUPED_MAP) - return blockdf.drop('header_block', 'sort_key').join(modeldf.drop('header_block'), ['sample_block', 'header'], 'right') \ + blocked_prediction_df = blockdf.drop('header_block', 'sort_key') \ + .join(modeldf.drop('header_block'), ['sample_block', 'header'], 'right') \ .withColumn('label', f.coalesce(f.col('label'), f.col('labels').getItem(0))) \ .groupBy(transform_key_pattern) \ .apply(transform_udf) \ - .join(cvdf, ['label', 'alpha'], 'inner') \ - .select('sample_block', 'label', 'alpha', 'values') + .join(cvdf, ['label', 'alpha'], 'inner') + + sample_block_df = blockdf.sql_ctx \ + .createDataFrame(sample_blocks.items(), ['sample_block', 'sample_ids']) \ + .selectExpr('sample_block', 'posexplode(sample_ids) as (idx, sample_id)') + + flattened_prediction_df = blocked_prediction_df \ + .selectExpr('sample_block', 'label', 'posexplode(values) as (idx, value)') \ + .join(sample_block_df, ['sample_block', 'idx'], 'inner') \ + .select('sample_id', 'label', 'value') + + pivoted_df = flattened_prediction_df.toPandas() \ + .pivot(index='sample_id', columns='label', values='value') \ + .reindex(index=labeldf.index, columns=labeldf.columns) + + return pivoted_df diff --git a/python/glow/levels/linear_model/ridge_udfs.py b/python/glow/levels/linear_model/ridge_udfs.py index 583362fde..e40bb72a7 100644 --- a/python/glow/levels/linear_model/ridge_udfs.py +++ b/python/glow/levels/linear_model/ridge_udfs.py @@ -350,7 +350,7 @@ def score_models(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: Args: key : unique key identifying the group of rows emitted by a groupBy statement key_pattern : pattern of columns used in the groupBy statement that emitted this group of rows - pdf : starting Pandas DataFrame containing the lists of rows used to assemble block X and coeffients B + pdf : starting Pandas DataFrame containing the lists of rows used to assemble block X and coefficients B identified by :key: schema: |-- header_block: string diff --git a/python/glow/levels/linear_model/tests/test_ridge_regression.py b/python/glow/levels/linear_model/tests/test_ridge_regression.py index 4e9b56c54..24c9f5374 100644 --- a/python/glow/levels/linear_model/tests/test_ridge_regression.py +++ b/python/glow/levels/linear_model/tests/test_ridge_regression.py @@ -22,6 +22,8 @@ alphas = np.array([0.1, 1, 10]) alphaMap = {f'alpha_{i}': a for i, a in enumerate(alphas)} +columnIndexer = sorted(enumerate(alphaMap.keys()), key=lambda t: t[1]) +coefOrder = [i for i, a in columnIndexer] def __get_sample_blocks(indexdf): @@ -47,8 +49,8 @@ def test_map_normal_eqn(spark): X_in = X0[headers].loc[ids, :] Y_in = labeldf.loc[ids, :] - XtX_in = X_in.values.T @ X_in.values - XtY_in = X_in.values.T @ Y_in.values + XtX_in = X_in.to_numpy().T @ X_in.to_numpy() + XtY_in = X_in.to_numpy().T @ Y_in.to_numpy() sample_blocks = __get_sample_blocks(indexdf) map_key_pattern = ['header_block', 'sample_block'] @@ -86,8 +88,8 @@ def test_reduce_normal_eqn(spark): X_out = X0[headers].drop(ids, axis='rows') Y_out = labeldf.drop(ids, axis='rows') - XtX_out = X_out.values.T @ X_out.values - XtY_out = X_out.values.T @ Y_out.values + XtX_out = X_out.to_numpy().T @ X_out.to_numpy() + XtY_out = X_out.to_numpy().T @ Y_out.to_numpy() sample_blocks = __get_sample_blocks(indexdf) map_key_pattern = ['header_block', 'sample_block'] @@ -131,8 +133,8 @@ def test_solve_normal_eqn(spark): X_out = X0[headers].drop(ids, axis='rows') Y_out = labeldf.drop(ids, axis='rows') - XtX_out = X_out.values.T @ X_out.values - XtY_out = X_out.values.T @ Y_out.values + XtX_out = X_out.to_numpy().T @ X_out.to_numpy() + XtY_out = X_out.to_numpy().T @ Y_out.to_numpy() B = np.column_stack( [(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1]) * a) @ XtY_out) for a in alphas]) @@ -162,7 +164,7 @@ def test_solve_normal_eqn(spark): .collect() outdf = pd.DataFrame(rows, columns=columns) - B_lvl = np.row_stack(outdf['coefficients'].values) + B_lvl = np.row_stack(outdf['coefficients'].to_numpy()) assert np.allclose(B_lvl, B) @@ -184,11 +186,11 @@ def test_apply_model(spark): X_out = X0[headers].drop(ids, axis='rows') Y_out = labeldf.drop(ids, axis='rows') - XtX_out = X_out.values.T @ X_out.values - XtY_out = X_out.values.T @ Y_out.values + XtX_out = X_out.to_numpy().T @ X_out.to_numpy() + XtY_out = X_out.to_numpy().T @ Y_out.to_numpy() B = np.column_stack( [(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1]) * a) @ XtY_out) for a in alphas]) - X1_in = X_in.values @ B + X1_in = X_in.to_numpy() @ B sample_blocks = __get_sample_blocks(indexdf) map_key_pattern = ['header_block', 'sample_block'] @@ -245,8 +247,8 @@ def test_ridge_reducer_fit(spark): X_out = X0[headers].drop(ids, axis='rows') Y_out = labeldf.drop(ids, axis='rows') - XtX_out = X_out.values.T @ X_out.values - XtY_out = X_out.values.T @ Y_out.values + XtX_out = X_out.to_numpy().T @ X_out.to_numpy() + XtY_out = X_out.to_numpy().T @ Y_out.to_numpy() B = np.column_stack( [(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1]) * a) @ XtY_out) for a in alphas]) @@ -258,7 +260,7 @@ def test_ridge_reducer_fit(spark): .select(*columns).collect() outdf = pd.DataFrame(rows, columns=columns) - B_stack = np.row_stack(outdf['coefficients'].values) + B_stack = np.row_stack(outdf['coefficients'].to_numpy()) assert np.allclose(B_stack, B) @@ -280,11 +282,11 @@ def test_ridge_reducer_transform(spark): X_out = X0[headers].drop(ids, axis='rows') Y_out = labeldf.drop(ids, axis='rows') - XtX_out = X_out.values.T @ X_out.values - XtY_out = X_out.values.T @ Y_out.values + XtX_out = X_out.to_numpy().T @ X_out.to_numpy() + XtY_out = X_out.to_numpy().T @ Y_out.to_numpy() B = np.column_stack( [(np.linalg.inv(XtX_out + np.identity(XtX_out.shape[1]) * a) @ XtY_out) for a in alphas]) - X1_in = X_in.values @ B + X1_in = X_in.to_numpy() @ B stack = RidgeReducer(alphas) modeldf = stack.fit(blockdf, labeldf, __get_sample_blocks(indexdf)) @@ -345,51 +347,60 @@ def test_ridge_reducer_transform_with_cov(spark): assert np.allclose(X1_in_stack_cov, X1_in_cov) -def test_one_level_regression(spark): - - indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') - blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') - testLabel = 'sim100' - - group2ids = __get_sample_blocks(indexdf) +def __calculate_y_hat(X_base, group2ids, testLabel, cov=covdf_empty): groups = sorted(group2ids.keys(), key=lambda v: v) - headersToKeep = [c for c in X1.columns if testLabel in c] + cov_X = pd.concat([cov, X_base], axis=1, sort=True) + headersToKeep = list(cov.columns) + [c for c in X_base.columns if testLabel in c] + n_cov = len(cov.columns) r2s = [] for group in groups: ids = group2ids[group] - X1_in = X1[headersToKeep].loc[ids, :].values - X1_out = X1[headersToKeep].drop(ids, axis='rows') - Y_in = labeldf[testLabel].loc[ids].values - Y_out = labeldf[testLabel].loc[X1_out.index].values - X1tX1_out = X1_out.values.T @ X1_out.values - X1tY_out = X1_out.values.T @ Y_out + X_in = cov_X[headersToKeep].loc[ids, :].to_numpy() + X_out = cov_X[headersToKeep].drop(ids, axis='rows') + Y_in = labeldf[testLabel].loc[ids].to_numpy() + Y_out = labeldf[testLabel].loc[X_out.index].to_numpy() + XtX_out = X_out.to_numpy().T @ X_out.to_numpy() + XtY_out = X_out.to_numpy().T @ Y_out + diags = [ + np.concatenate([np.ones(n_cov), np.ones(XtX_out.shape[1] - n_cov) * a]) for a in alphas + ] B = np.column_stack( - [(np.linalg.inv(X1tX1_out + np.identity(X1tX1_out.shape[1]) * a) @ X1tY_out) - for a in alphas]) - X1B = X1_in @ B - r2 = r_squared(X1B, Y_in.reshape(-1, 1)) + [(np.linalg.inv(XtX_out + np.diag(d)) @ XtY_out) for d in diags])[:, coefOrder] + XB = X_in @ B + r2 = r_squared(XB, Y_in.reshape(-1, 1)) r2s.append(r2) - r2_mean = np.row_stack(r2s).mean(axis=0) + bestAlpha, bestr2 = sorted(zip(alphaMap.keys(), r2_mean), key=lambda t: -t[1])[0] - y_hat = [] - r2s_pred = [] + y_hat = pd.Series(index=labeldf.index) for group in groups: ids = group2ids[group] - X1_in = X1[headersToKeep].loc[ids, :].values - X1_out = X1[headersToKeep].drop(ids, axis='rows') - Y_in = labeldf[testLabel].loc[ids].values - Y_out = labeldf[testLabel].loc[X1_out.index].values - X1tX1_out = X1_out.values.T @ X1_out.values - X1tY_out = X1_out.values.T @ Y_out - b = np.linalg.inv(X1tX1_out + - np.identity(X1tX1_out.shape[1]) * alphaMap[bestAlpha]) @ X1tY_out - r2s_pred.append(r_squared(X1_in @ b, Y_in)) - y_hat.extend((X1_in @ b).tolist()) - - y_hat = np.array(y_hat) + X_in = cov_X[headersToKeep].loc[ids, :].to_numpy() + X_out = cov_X[headersToKeep].drop(ids, axis='rows') + Y_out = labeldf[testLabel].loc[X_out.index].to_numpy() + XtX_out = X_out.to_numpy().T @ X_out.to_numpy() + XtY_out = X_out.to_numpy().T @ Y_out + d = np.concatenate( + [np.ones(n_cov), + np.ones(XtX_out.shape[1] - n_cov) * alphaMap[bestAlpha]]) + b = np.linalg.inv(XtX_out + np.diag(d)) @ XtY_out + group_y_hats = X_in @ b + for s, y in zip(ids, group_y_hats): + y_hat[s] = y + + return bestAlpha, bestr2, y_hat.to_numpy() + + +def test_one_level_regression(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + testLabel = 'sim100' + + group2ids = __get_sample_blocks(indexdf) + bestAlpha, bestr2, y_hat = __calculate_y_hat(X1, group2ids, testLabel) stack0 = RidgeReducer(alphas) model0df = stack0.fit(blockdf, labeldf, group2ids) @@ -401,10 +412,7 @@ def test_one_level_regression(spark): r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() bestAlpha_lvl, bestr2_lvl = (r.alpha, r.r2_mean) - y_hat_lvl = np.concatenate([ - r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').select( - 'values').collect() - ]) + y_hat_lvl = np.array(yhatdf[testLabel]) assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and np.allclose(y_hat_lvl, np.array(y_hat))) @@ -415,50 +423,9 @@ def test_two_level_regression(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') testLabel = 'sim100' - columnIndexer = sorted(enumerate(alphaMap.keys()), key=lambda t: t[1]) - coefOrder = [i for i, a in columnIndexer] group2ids = __get_sample_blocks(indexdf) - groups = sorted(group2ids.keys(), key=lambda v: v) - headersToKeep = [c for c in X2.columns if testLabel in c] - - r2s = [] - - for group in groups: - ids = group2ids[group] - X2_in = X2[headersToKeep].loc[ids, :].values - X2_out = X2[headersToKeep].drop(ids, axis='rows') - Y_in = labeldf[testLabel].loc[ids].values - Y_out = labeldf[testLabel].loc[X2_out.index].values - X2tX2_out = X2_out.values.T @ X2_out.values - X2tY_out = X2_out.values.T @ Y_out - B = np.column_stack( - [(np.linalg.inv(X2tX2_out + np.identity(X2tX2_out.shape[1]) * a) @ X2tY_out) - for a in alphas])[:, coefOrder] - X2B = X2_in @ B - r2 = r_squared(X2B, Y_in.reshape(-1, 1)) - r2s.append(r2) - - r2_mean = np.row_stack(r2s).mean(axis=0) - bestAlpha, bestr2 = sorted(zip(alphaMap.keys(), r2_mean), key=lambda t: -t[1])[0] - - y_hat = [] - r2s_pred = [] - - for group in groups: - ids = group2ids[group] - X2_in = X2[headersToKeep].loc[ids, :].values - X2_out = X2[headersToKeep].drop(ids, axis='rows') - Y_in = labeldf[testLabel].loc[ids].values - Y_out = labeldf[testLabel].loc[X2_out.index].values - X2tX2_out = X2_out.values.T @ X2_out.values - X2tY_out = X2_out.values.T @ Y_out - b = np.linalg.inv(X2tX2_out + - np.identity(X2tX2_out.shape[1]) * alphaMap[bestAlpha]) @ X2tY_out - r2s_pred.append(r_squared(X2_in @ b, Y_in)) - y_hat.extend((X2_in @ b).tolist()) - - y_hat = np.array(y_hat) + bestAlpha, bestr2, y_hat = __calculate_y_hat(X2, group2ids, testLabel) stack0 = RidgeReducer(alphas) model0df = stack0.fit(blockdf, labeldf, group2ids) @@ -474,10 +441,7 @@ def test_two_level_regression(spark): r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() bestAlpha_lvl, bestr2_lvl = (r.alpha, r.r2_mean) - y_hat_lvl = np.concatenate([ - r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').select( - 'values').collect() - ]) + y_hat_lvl = np.array(yhatdf[testLabel]) assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and np.allclose(y_hat_lvl, np.array(y_hat))) @@ -488,63 +452,9 @@ def test_two_level_regression_with_cov(spark): indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') testLabel = 'sim100' - columnIndexer = sorted(enumerate(alphaMap.keys()), key=lambda t: t[1]) - coefOrder = [i for i, a in columnIndexer] - group2ids = { - r.sample_block: r.sample_ids - for r in indexdf.select('sample_block', 'sample_ids').collect() - } - groups = sorted(group2ids.keys(), key=lambda v: v) - headersToKeep = [c for c in X2.columns if testLabel in c] - - r2s = [] - - for group in groups: - ids = group2ids[group] - C_in = covdf.loc[ids, :].values - X2_in = np.column_stack([C_in, X2[headersToKeep].loc[ids, :].values]) - C_out = covdf.loc[X2[headersToKeep].drop(ids, axis='rows').index].values - X2_out = np.column_stack([C_out, X2[headersToKeep].drop(ids, axis='rows').values]) - Y_in = labeldf[testLabel].loc[ids].values - Y_out = labeldf[testLabel].loc[X2[headersToKeep].drop(ids, axis='rows').index].values - X2tX2_out = X2_out.T @ X2_out - X2tY_out = X2_out.T @ Y_out - n_cov = len(covdf.columns) - diags = [ - np.concatenate([np.ones(n_cov), np.ones(X2tX2_out.shape[1] - n_cov) * a]) - for a in alphas - ] - B = np.column_stack( - [(np.linalg.inv(X2tX2_out + np.diag(d)) @ X2tY_out) for d in diags])[:, coefOrder] - X2B = X2_in @ B - r2 = r_squared(X2B, Y_in.reshape(-1, 1)) - r2s.append(r2) - - r2_mean = np.row_stack(r2s).mean(axis=0) - bestAlpha, bestr2 = sorted(zip(alphaMap.keys(), r2_mean), key=lambda t: -t[1])[0] - - y_hat = [] - r2s_pred = [] - - for group in groups: - ids = group2ids[group] - C_in = covdf.loc[ids, :].values - X2_in = np.column_stack([C_in, X2[headersToKeep].loc[ids, :].values]) - C_out = covdf.loc[X2[headersToKeep].drop(ids, axis='rows').index].values - X2_out = np.column_stack([C_out, X2[headersToKeep].drop(ids, axis='rows').values]) - Y_in = labeldf[testLabel].loc[ids].values - Y_out = labeldf[testLabel].loc[X2[headersToKeep].drop(ids, axis='rows').index].values - X2tX2_out = X2_out.T @ X2_out - X2tY_out = X2_out.T @ Y_out - d = np.concatenate( - [np.ones(n_cov), - np.ones(X2tX2_out.shape[1] - n_cov) * alphaMap[bestAlpha]]) - b = np.linalg.inv(X2tX2_out + np.diag(d)) @ X2tY_out - r2s_pred.append(r_squared(X2_in @ b, Y_in)) - y_hat.extend((X2_in @ b).tolist()) - - y_hat = np.array(y_hat) + group2ids = __get_sample_blocks(indexdf) + bestAlpha, bestr2, y_hat = __calculate_y_hat(X2, group2ids, testLabel, covdf) stack0 = RidgeReducer(alphas) model0df = stack0.fit(blockdf, labeldf, group2ids) @@ -560,10 +470,24 @@ def test_two_level_regression_with_cov(spark): r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() bestAlpha_lvl, bestr2_lvl = (r.alpha, r.r2_mean) - y_hat_lvl = np.concatenate([ - r.values for r in yhatdf.filter(f'label = "{testLabel}"').orderBy('sample_block').select( - 'values').collect() - ]) + y_hat_lvl = np.array(yhatdf[testLabel]) assert (bestAlpha_lvl == bestAlpha and np.isclose(bestr2_lvl, bestr2) and np.allclose(y_hat_lvl, np.array(y_hat))) + + +def test_tie_break(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + + group2ids = __get_sample_blocks(indexdf) + + stack0 = RidgeReducer(alphas) + model0df = stack0.fit(blockdf, labeldf, group2ids) + level1df = stack0.transform(blockdf, labeldf, group2ids, model0df) + + regressor = RidgeRegression(np.array([0.1, 0.2, 0.1, 0.2])) + _, cvdf = regressor.fit(level1df, labeldf, group2ids) + + assert cvdf.count() == len(labeldf.columns) From 5944b8446a8d099fc6e0806452cd94c8f54efdf8 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 15 Jun 2020 15:28:24 -0700 Subject: [PATCH 13/34] WIP Signed-off-by: Karen Feng --- .../tertiary/whole-genome-regression.rst | 145 ++++++++++++++++++ 1 file changed, 145 insertions(+) create mode 100644 docs/source/tertiary/whole-genome-regression.rst diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst new file mode 100644 index 000000000..b38dda5cd --- /dev/null +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -0,0 +1,145 @@ +======================= +Whole-Genome Regression +======================= + +.. invisible-code-block: python + + import glow + glow.register(spark) + + genotypes_vcf = 'test-data/gwas/genotypes.vcf.gz' + covariates_csv = 'test-data/gwas/covariates.csv.gz' + continuous_phenotypes_csv = 'test-data/gwas/continuous-phenotypes.csv.gz' + +Glow contains functions for performing Whole Genome Regression (WGR). +WGR has two primary stages: reduction and regression. Both stages are regularized using ridge regression. +WGR operates on block genotype matrices. These can be created with our variant and sample blocking helper functions. + +.. code-block:: python + + variants_per_block = 1000 + sample_block_count = 10 + variants = spark.read.format('vcf').load(genotypes_vcf) + genotypes = glow.transform('split_multiallelics', variants) \ + .withColumn('values', glow.mean_substitute(glow.genotype_states(col('genotypes')))) \ + .filter('size(array_distinct(values)) > 1') \ + .cache() + sample_ids = get_sample_ids(genotypes) + block_df_lvl0, sample_blocks = block_variants_and_samples( + genotypes, sample_ids, variants_per_block, sample_block_count) + + covariates = pd.read_csv(covariates_csv, index_col='sample_id') + covariates['intercept'] = 1. + +Linear model +============ + +The phenotypes must be mean-centered at 0. + +.. code-block:: python + + label_df = pd.read_csv(continuous_phenotypes_csv, index_col='sample_id') \ + .apply(lambda x: x-x.mean())[['Trait_1', 'Trait_2']] + alphas_lvl0 = np.logspace(2, 5, 10) + alphas_lvl1 = np.logspace(1, 4, 10) + alphas_lvl2 = np.logspace(0, 3, 10) + +Reduction and regression +------------------------ + +Calculate the WGR-estimated phenotypes with a single round of reduction before the regression stage. + +.. code-block:: python + + stack_lvl0 = RidgeReducer(alphas_lvl0) + model_df_lvl0 = stack_lvl0.fit(block_df_lvl0, label_df, sample_blocks, covariates) + block_df_lvl1 = stack_lvl0.transform(block_df_lvl0, label_df, sample_blocks, model_df_lvl0, covariates) + + estimator_lvl1 = RidgeRegression(alphas_lvl1) + model_df_lvl1_est, cv_df_lvl1 = estimator_lvl1.fit(block_df_lvl1, label_df, sample_blocks, covariates) + y_hat_one_round = estimator_lvl1.transform(block_df_lvl1, label_df, sample_blocks, model_df_lvl1_est, cv_df_lvl1, covariates) + +Two rounds of reduction and regression +-------------------------------------- + +Calculate the WGR-estimated phenotypes with two rounds of reduction before the regression stage. + +.. code-block:: python + + stack_lvl1 = RidgeReducer(alphas_lvl1) + model_df_lvl1 = stack_lvl1.fit(block_df_lvl1, label_df, sample_blocks, covariates) + block_df_lvl2 = stack_lvl1.transform(block_df_lvl1, label_df, sample_blocks, model_df_lvl1, covariates) + + estimator_lvl2 = RidgeRegression(alphas_lvl2) + model_df_lvl2_est, cv_df_lvl2 = estimator_lvl2.fit(block_df_lvl2, label_df, sample_blocks, covariates) + y_hat_two_rounds = estimator_lvl2.transform(block_df_lvl2, label_df, sample_blocks, model_df_lvl2_est, cv_df_lvl2, covariates) + +Two rounds of reduction and leave-one-chromosome-out regression +--------------------------------------------------------------- + +The Pandas DataFrame output by leave-one-chromosome-out (LOCO) regression is shaped differently. As the phenotype is +estimated on a per-chromosome basis, the DataFrame contains an additional column representing the chromosome. Also, the +number of rows is multiplied by the number of chromosomes. + +.. code-block:: python + + all_contigs = [r.header_block for r in block_df_lvl1.select('header_block').distinct().collect()] + y_hat_two_rounds_loco = pd.DataFrame() + for contig in all_contigs: + loco_block = block_df_lvl2.filter(f'header NOT LIKE "%block_{contig}%"') + loco_df = estimator_lvl2.transform(loco_block, label_df, sample_blocks, model_df_lvl2_est, cv_df_lvl2, covariates) + loco_df['contigName'] = contig.split('_')[1] + y_hat_two_rounds_loco = y_hat_two_rounds_loco.append(loco_df) + +GWAS +---- + +Use the estimated phenotypic values from WGR to adjust the phenotypes before running GWAS. + +To perform GWAS with WGR-estimated phenotypes calculated by standard regression, subtract the estimated phenotypes from +the input phenotypes. The adjusted phenotypes hold across all sites, so perform a cross-join with the genotypes. + +.. code-block:: python + + pdf = (label_df - y_hat_two_rounds).T + apdf = pd.DataFrame() + apdf['pt'] = pdf.values.tolist() + apdf['trait'] = pdf.index + adjusted_two_rounds = spark.createDataFrame(apdf) + genotypes.crossJoin(adjusted_two_rounds).select( + 'contigName', + 'start', + 'names', + 'trait', + expand_struct(linear_regression_gwas( + col('values'), + col('pt'), + lit(covariates.to_numpy()) + ))) + + +To perform GWAS with WGR-estimated phenotypes calculated by LOCO regression, subtract the estimated phenotypes from +the input phenotypes across all chromosomes. The adjusted phenotypes hold on a per-chromosome basis, so perform an +inner join with the genotypes based on chromosome name. + +.. code-block:: python + + pdf = (label_df - y_hat_two_rounds_loco.reset_index().set_index(['contigName', 'sample_id'])) + apdf = pdf.reset_index('contigName') \ + .melt(id_vars=['contigName']) \ + .groupby(['contigName', 'variable']) \ + .aggregate(lambda x: list(x)) \ + .reset_index() \ + .rename(columns={'variable': 'trait', 'value': 'pt'}) + adjusted_two_rounds_loco = spark.createDataFrame(apdf) + genotypes.join(adjusted_two_rounds_loco, ['contigName']).select( + 'contigName', + 'start', + 'names', + 'trait', + expand_struct(linear_regression_gwas( + col('values'), + col('pt'), + lit(covariates.to_numpy()) + ))) + From e29ebfed36ff9db89b0489b36c019a97b91363ae Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Tue, 16 Jun 2020 17:20:24 -0700 Subject: [PATCH 14/34] tests Signed-off-by: Karen Feng --- .../tertiary/whole-genome-regression.rst | 43 +- test-data/gwas/binary-phenotypes.csv.gz | Bin 11736 -> 11754 bytes test-data/gwas/continuous-phenotypes.csv | 2505 +++++++++++++++++ test-data/gwas/continuous-phenotypes.csv.gz | Bin 218668 -> 218684 bytes test-data/gwas/covariates.csv.gz | Bin 55475 -> 55473 bytes test-data/gwas/genotypes.vcf | 261 ++ 6 files changed, 2799 insertions(+), 10 deletions(-) create mode 100644 test-data/gwas/continuous-phenotypes.csv create mode 100644 test-data/gwas/genotypes.vcf diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index b38dda5cd..e710a56b7 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -12,12 +12,21 @@ Whole-Genome Regression continuous_phenotypes_csv = 'test-data/gwas/continuous-phenotypes.csv.gz' Glow contains functions for performing Whole Genome Regression (WGR). -WGR has two primary stages: reduction and regression. Both stages are regularized using ridge regression. -WGR operates on block genotype matrices. These can be created with our variant and sample blocking helper functions. + +WGR consists of two stages: reduction and regression. Both stages are regularized using ridge regression. + +The WGR stages operate on block genotype matrices, which are based on genotype data blocked across samples and variants. +Glow contains variant and sample blocking helper functions to facilitate blocking. .. code-block:: python - variants_per_block = 1000 + from glow.levels.linear_model import RidgeReducer, RidgeRegression + from glow.levels.functions import block_variants_and_samples, get_sample_ids + import numpy as np + import pandas as pd + from pyspark.sql.functions import col, lit + + variants_per_block = 5 sample_block_count = 10 variants = spark.read.format('vcf').load(genotypes_vcf) genotypes = glow.transform('split_multiallelics', variants) \ @@ -27,7 +36,6 @@ WGR operates on block genotype matrices. These can be created with our variant a sample_ids = get_sample_ids(genotypes) block_df_lvl0, sample_blocks = block_variants_and_samples( genotypes, sample_ids, variants_per_block, sample_block_count) - covariates = pd.read_csv(covariates_csv, index_col='sample_id') covariates['intercept'] = 1. @@ -39,7 +47,7 @@ The phenotypes must be mean-centered at 0. .. code-block:: python label_df = pd.read_csv(continuous_phenotypes_csv, index_col='sample_id') \ - .apply(lambda x: x-x.mean())[['Trait_1', 'Trait_2']] + .apply(lambda x: x-x.mean())[['Continuous_Trait_1', 'Continuous_Trait_2']] alphas_lvl0 = np.logspace(2, 5, 10) alphas_lvl1 = np.logspace(1, 4, 10) alphas_lvl2 = np.logspace(0, 3, 10) @@ -59,6 +67,12 @@ Calculate the WGR-estimated phenotypes with a single round of reduction before t model_df_lvl1_est, cv_df_lvl1 = estimator_lvl1.fit(block_df_lvl1, label_df, sample_blocks, covariates) y_hat_one_round = estimator_lvl1.transform(block_df_lvl1, label_df, sample_blocks, model_df_lvl1_est, cv_df_lvl1, covariates) +.. invisible-code-block: python + + import math + + assert math.isclose(y_hat_one_round.at['HG00096','Continuous_Trait_1'], -0.37493755917205657) + Two rounds of reduction and regression -------------------------------------- @@ -74,6 +88,10 @@ Calculate the WGR-estimated phenotypes with two rounds of reduction before the r model_df_lvl2_est, cv_df_lvl2 = estimator_lvl2.fit(block_df_lvl2, label_df, sample_blocks, covariates) y_hat_two_rounds = estimator_lvl2.transform(block_df_lvl2, label_df, sample_blocks, model_df_lvl2_est, cv_df_lvl2, covariates) +.. invisible-code-block: python + + assert math.isclose(y_hat_two_rounds.at['HG00096','Continuous_Trait_1'], -0.3738198784282588) + Two rounds of reduction and leave-one-chromosome-out regression --------------------------------------------------------------- @@ -84,12 +102,17 @@ number of rows is multiplied by the number of chromosomes. .. code-block:: python all_contigs = [r.header_block for r in block_df_lvl1.select('header_block').distinct().collect()] - y_hat_two_rounds_loco = pd.DataFrame() + loco_dfs = pd.DataFrame() for contig in all_contigs: loco_block = block_df_lvl2.filter(f'header NOT LIKE "%block_{contig}%"') loco_df = estimator_lvl2.transform(loco_block, label_df, sample_blocks, model_df_lvl2_est, cv_df_lvl2, covariates) loco_df['contigName'] = contig.split('_')[1] - y_hat_two_rounds_loco = y_hat_two_rounds_loco.append(loco_df) + loco_dfs = loco_dfs.append(loco_df) + y_hat_two_rounds_loco = loco_dfs.reset_index().set_index(['contigName', 'sample_id']) + +.. invisible-code-block: python + + assert math.isclose(y_hat_two_rounds_loco.at[('22','HG00096'),'Continuous_Trait_1'], -0.3738198784282588) GWAS ---- @@ -111,7 +134,7 @@ the input phenotypes. The adjusted phenotypes hold across all sites, so perform 'start', 'names', 'trait', - expand_struct(linear_regression_gwas( + glow.expand_struct(glow.linear_regression_gwas( col('values'), col('pt'), lit(covariates.to_numpy()) @@ -124,7 +147,7 @@ inner join with the genotypes based on chromosome name. .. code-block:: python - pdf = (label_df - y_hat_two_rounds_loco.reset_index().set_index(['contigName', 'sample_id'])) + pdf = label_df - y_hat_two_rounds_loco apdf = pdf.reset_index('contigName') \ .melt(id_vars=['contigName']) \ .groupby(['contigName', 'variable']) \ @@ -137,7 +160,7 @@ inner join with the genotypes based on chromosome name. 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0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 1|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 0|0 From d558115db57c7a29fa4324662b456fb9c4486e21 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Wed, 17 Jun 2020 14:10:55 -0700 Subject: [PATCH 16/34] Add fit_transform function to models (#17) Signed-off-by: Karen Feng --- .../glow/levels/linear_model/ridge_model.py | 50 ++++++++++++++++++- .../tests/test_ridge_regression.py | 35 +++++++++++++ 2 files changed, 83 insertions(+), 2 deletions(-) diff --git a/python/glow/levels/linear_model/ridge_model.py b/python/glow/levels/linear_model/ridge_model.py index ab90cfb7e..2d9d4387f 100644 --- a/python/glow/levels/linear_model/ridge_model.py +++ b/python/glow/levels/linear_model/ridge_model.py @@ -115,6 +115,29 @@ def transform(self, .groupBy(transform_key_pattern) \ .apply(transform_udf) + def fit_transform( + self, + blockdf: DataFrame, + labeldf: pd.DataFrame, + sample_blocks: Dict[str, List[str]], + covdf: pd.DataFrame = pd.DataFrame({})) -> DataFrame: + """ + Fits a ridge reducer model with a block matrix, then transforms the matrix using the model. + + Args: + blockdf : Spark DataFrame representing the beginning block matrix X + labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models + sample_blocks : Dict containing a mapping of sample_block ID to a list of corresponding sample IDs + covdf : Pandas DataFrame containing covariates to be included in every model in the stacking + ensemble (optional). + + Returns: + Spark DataFrame representing the reduced block matrix + """ + + modeldf = self.fit(blockdf, labeldf, sample_blocks, covdf) + return self.transform(blockdf, labeldf, sample_blocks, modeldf, covdf) + @typechecked class RidgeRegression: @@ -150,7 +173,7 @@ def fit( Spark DataFrame containing the optimal ridge alpha value for each label. Args: - blockdf : Spark DataFrame representing the reduced block matrix X + blockdf : Spark DataFrame representing the beginning block matrix X labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models sample_blocks : Dict containing a mapping of sample_block ID to a list of corresponding sample IDs covdf : Pandas DataFrame containing covariates to be included in every model in the stacking @@ -214,7 +237,7 @@ def transform(self, the RidgeRegression fit method to the starting block matrix. Args: - blockdf : Spark DataFrame representing the reduced block matrix X + blockdf : Spark DataFrame representing the beginning block matrix X labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models sample_blocks : Dict containing a mapping of sample_block ID to a list of corresponding sample IDs modeldf : Spark DataFrame produced by the RidgeRegression fit method, representing the reducer model @@ -256,3 +279,26 @@ def transform(self, .reindex(index=labeldf.index, columns=labeldf.columns) return pivoted_df + + def fit_transform( + self, + blockdf: DataFrame, + labeldf: pd.DataFrame, + sample_blocks: Dict[str, List[str]], + covdf: pd.DataFrame = pd.DataFrame({})) -> pd.DataFrame: + """ + Fits a ridge regression model with a block matrix, then transforms the matrix using the model. + + Args: + blockdf : Spark DataFrame representing the beginning block matrix X + labeldf : Pandas DataFrame containing the target labels used in fitting the ridge models + sample_blocks : Dict containing a mapping of sample_block ID to a list of corresponding sample IDs + covdf : Pandas DataFrame containing covariates to be included in every model in the stacking + ensemble (optional). + + Returns: + Pandas DataFrame containing prediction y_hat values. The shape and order match labeldf such that the + rows are indexed by sample ID and the columns by label. The column types are float64. + """ + modeldf, cvdf = self.fit(blockdf, labeldf, sample_blocks, covdf) + return self.transform(blockdf, labeldf, sample_blocks, modeldf, cvdf, covdf) diff --git a/python/glow/levels/linear_model/tests/test_ridge_regression.py b/python/glow/levels/linear_model/tests/test_ridge_regression.py index 24c9f5374..0730b615e 100644 --- a/python/glow/levels/linear_model/tests/test_ridge_regression.py +++ b/python/glow/levels/linear_model/tests/test_ridge_regression.py @@ -491,3 +491,38 @@ def test_tie_break(spark): _, cvdf = regressor.fit(level1df, labeldf, group2ids) assert cvdf.count() == len(labeldf.columns) + + +def test_reducer_fit_transform(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + + group2ids = __get_sample_blocks(indexdf) + + stack0 = RidgeReducer(alphas) + model0df = stack0.fit(blockdf, labeldf, group2ids) + level1df = stack0.transform(blockdf, labeldf, group2ids, model0df) + fit_transform_df = stack0.fit_transform(blockdf, labeldf, group2ids) + + assert fit_transform_df.subtract(level1df).count() == 0 + assert level1df.subtract(fit_transform_df).count() == 0 + + +def test_regression_fit_transform(spark): + + indexdf = spark.read.parquet(f'{data_root}/groupedIDs.snappy.parquet') + blockdf = spark.read.parquet(f'{data_root}/blockedGT.snappy.parquet') + + group2ids = __get_sample_blocks(indexdf) + + stack0 = RidgeReducer(alphas) + model0df = stack0.fit(blockdf, labeldf, group2ids) + level1df = stack0.transform(blockdf, labeldf, group2ids, model0df) + + regressor = RidgeRegression(alphas) + model1df, cvdf = regressor.fit(level1df, labeldf, group2ids) + yhatdf = regressor.transform(level1df, labeldf, group2ids, model1df, cvdf) + fit_transform_df = regressor.fit_transform(level1df, labeldf, group2ids) + + assert fit_transform_df.equals(yhatdf) From 6ffd77a6ff44a67489019625c3804b7fc06f1dd7 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Fri, 19 Jun 2020 15:28:39 -0700 Subject: [PATCH 17/34] WIP Signed-off-by: Karen Feng --- .../tertiary/whole-genome-regression.rst | 143 +++++------------- 1 file changed, 42 insertions(+), 101 deletions(-) diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index e710a56b7..7887d79ef 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -11,12 +11,12 @@ Whole-Genome Regression covariates_csv = 'test-data/gwas/covariates.csv.gz' continuous_phenotypes_csv = 'test-data/gwas/continuous-phenotypes.csv.gz' -Glow contains functions for performing Whole Genome Regression (WGR). +Glow supports Whole Genome Regression (WGR) as GlowGR, a parallelized version of the regenie method. -WGR consists of two stages: reduction and regression. Both stages are regularized using ridge regression. - -The WGR stages operate on block genotype matrices, which are based on genotype data blocked across samples and variants. -Glow contains variant and sample blocking helper functions to facilitate blocking. +GlowGR consists of the following stages: +- Blocking the genotype matrix across samples and variants. +- Performing dimension reduction with ridge regression. +- Estimating phenotypic values with ridge regression. .. code-block:: python @@ -34,7 +34,7 @@ Glow contains variant and sample blocking helper functions to facilitate blockin .filter('size(array_distinct(values)) > 1') \ .cache() sample_ids = get_sample_ids(genotypes) - block_df_lvl0, sample_blocks = block_variants_and_samples( + block_df, sample_blocks = block_variants_and_samples( genotypes, sample_ids, variants_per_block, sample_block_count) covariates = pd.read_csv(covariates_csv, index_col='sample_id') covariates['intercept'] = 1. @@ -42,120 +42,62 @@ Glow contains variant and sample blocking helper functions to facilitate blockin Linear model ============ -The phenotypes must be mean-centered at 0. - -.. code-block:: python - - label_df = pd.read_csv(continuous_phenotypes_csv, index_col='sample_id') \ - .apply(lambda x: x-x.mean())[['Continuous_Trait_1', 'Continuous_Trait_2']] - alphas_lvl0 = np.logspace(2, 5, 10) - alphas_lvl1 = np.logspace(1, 4, 10) - alphas_lvl2 = np.logspace(0, 3, 10) - -Reduction and regression ------------------------- +Estimate phenotypic values +-------------------------- -Calculate the WGR-estimated phenotypes with a single round of reduction before the regression stage. +If the alpha hyperparameter values for ridge reduction and regression are not provided, they will be generated based on +the unique number of headers in the blocked genotype matrix `v`, and a set of heritability values. -.. code-block:: python +.. math:: - stack_lvl0 = RidgeReducer(alphas_lvl0) - model_df_lvl0 = stack_lvl0.fit(block_df_lvl0, label_df, sample_blocks, covariates) - block_df_lvl1 = stack_lvl0.transform(block_df_lvl0, label_df, sample_blocks, model_df_lvl0, covariates) + \vec{\alpha} = v / 0.01, 0.25, 0.50, 0.75, 0.99] - estimator_lvl1 = RidgeRegression(alphas_lvl1) - model_df_lvl1_est, cv_df_lvl1 = estimator_lvl1.fit(block_df_lvl1, label_df, sample_blocks, covariates) - y_hat_one_round = estimator_lvl1.transform(block_df_lvl1, label_df, sample_blocks, model_df_lvl1_est, cv_df_lvl1, covariates) +.. warning:: -.. invisible-code-block: python - - import math - - assert math.isclose(y_hat_one_round.at['HG00096','Continuous_Trait_1'], -0.37493755917205657) - -Two rounds of reduction and regression --------------------------------------- - -Calculate the WGR-estimated phenotypes with two rounds of reduction before the regression stage. + The phenotypes must be mean-centered at 0. The generated alpha values are only sensible if the phenotypes are also + on the scale of one. .. code-block:: python - stack_lvl1 = RidgeReducer(alphas_lvl1) - model_df_lvl1 = stack_lvl1.fit(block_df_lvl1, label_df, sample_blocks, covariates) - block_df_lvl2 = stack_lvl1.transform(block_df_lvl1, label_df, sample_blocks, model_df_lvl1, covariates) - - estimator_lvl2 = RidgeRegression(alphas_lvl2) - model_df_lvl2_est, cv_df_lvl2 = estimator_lvl2.fit(block_df_lvl2, label_df, sample_blocks, covariates) - y_hat_two_rounds = estimator_lvl2.transform(block_df_lvl2, label_df, sample_blocks, model_df_lvl2_est, cv_df_lvl2, covariates) - -.. invisible-code-block: python - - assert math.isclose(y_hat_two_rounds.at['HG00096','Continuous_Trait_1'], -0.3738198784282588) - -Two rounds of reduction and leave-one-chromosome-out regression ---------------------------------------------------------------- + label_df = pd.read_csv(continuous_phenotypes_csv, index_col='sample_id') \ + .apply(lambda x: x-x.mean())[['Continuous_Trait_1', 'Continuous_Trait_2']] + alphas_reducer = np.logspace(2, 5, 10) + alphas_regression = np.logspace(1, 4, 10) -The Pandas DataFrame output by leave-one-chromosome-out (LOCO) regression is shaped differently. As the phenotype is -estimated on a per-chromosome basis, the DataFrame contains an additional column representing the chromosome. Also, the -number of rows is multiplied by the number of chromosomes. + reducer = RidgeReducer(alphas_reducer) + reduced_block_df = reducer.fit_transform(block_df, label_df, sample_blocks, covariates) -.. code-block:: python - - all_contigs = [r.header_block for r in block_df_lvl1.select('header_block').distinct().collect()] - loco_dfs = pd.DataFrame() + regression = RidgeRegression(alphas_regression) + model_df, cv_df = regression.fit(reduced_block_df, label_df, sample_blocks, covariates) + all_contigs = [r.header_block for r in reduced_block_df.select('header_block').distinct().collect()] + y_hat = pd.DataFrame() for contig in all_contigs: - loco_block = block_df_lvl2.filter(f'header NOT LIKE "%block_{contig}%"') - loco_df = estimator_lvl2.transform(loco_block, label_df, sample_blocks, model_df_lvl2_est, cv_df_lvl2, covariates) - loco_df['contigName'] = contig.split('_')[1] - loco_dfs = loco_dfs.append(loco_df) - y_hat_two_rounds_loco = loco_dfs.reset_index().set_index(['contigName', 'sample_id']) + loco_block_df = reduced_block_df.filter(col('header_block') != lit(contig)) + loco_model_df = model_df.filter(col('header_block') != lit(contig)) + loco_y_hat_df = regression.transform(loco_block_df, label_df, sample_blocks, loco_model_df, cv_df, covariates) + loco_y_hat_df['contigName'] = contig.split('_')[1] + y_hat = y_hat.append(loco_df) + y_hat.reset_index(inplace=True).set_index(['contigName', 'sample_id'], inplace=True) .. invisible-code-block: python - assert math.isclose(y_hat_two_rounds_loco.at[('22','HG00096'),'Continuous_Trait_1'], -0.3738198784282588) + import math -GWAS ----- + assert math.isclose(y_hat.at[('22', 'HG00096'),'Continuous_Trait_1'], -0.37493755917205657) -Use the estimated phenotypic values from WGR to adjust the phenotypes before running GWAS. +Run linear regression +--------------------- -To perform GWAS with WGR-estimated phenotypes calculated by standard regression, subtract the estimated phenotypes from -the input phenotypes. The adjusted phenotypes hold across all sites, so perform a cross-join with the genotypes. +To perform GWAS adjusted with WGR, subtract the estimated phenotypes from the input phenotypes. .. code-block:: python - pdf = (label_df - y_hat_two_rounds).T - apdf = pd.DataFrame() - apdf['pt'] = pdf.values.tolist() - apdf['trait'] = pdf.index - adjusted_two_rounds = spark.createDataFrame(apdf) - genotypes.crossJoin(adjusted_two_rounds).select( - 'contigName', - 'start', - 'names', - 'trait', - glow.expand_struct(glow.linear_regression_gwas( - col('values'), - col('pt'), - lit(covariates.to_numpy()) - ))) - - -To perform GWAS with WGR-estimated phenotypes calculated by LOCO regression, subtract the estimated phenotypes from -the input phenotypes across all chromosomes. The adjusted phenotypes hold on a per-chromosome basis, so perform an -inner join with the genotypes based on chromosome name. - -.. code-block:: python - - pdf = label_df - y_hat_two_rounds_loco - apdf = pdf.reset_index('contigName') \ - .melt(id_vars=['contigName']) \ - .groupby(['contigName', 'variable']) \ - .aggregate(lambda x: list(x)) \ - .reset_index() \ - .rename(columns={'variable': 'trait', 'value': 'pt'}) - adjusted_two_rounds_loco = spark.createDataFrame(apdf) - genotypes.join(adjusted_two_rounds_loco, ['contigName']).select( + pdf = label_df - y_hat + apdf = pdf.T + apdf['values'] = list(apdf.drop(['contigName', 'trait'], axis=1).to_numpy()) + apdf.show() + adjusted_phenotypes = spark.createDataFrame(apdf) + genotypes.join(adjusted_phenotypes, ['contigName']).select( 'contigName', 'start', 'names', @@ -165,4 +107,3 @@ inner join with the genotypes based on chromosome name. col('pt'), lit(covariates.to_numpy()) ))) - From 5ccc005cfb2cd81f8921c9e2e5877bbcbc1df87e Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Fri, 19 Jun 2020 16:17:28 -0700 Subject: [PATCH 18/34] WIP Signed-off-by: Karen Feng --- .../tertiary/whole-genome-regression.rst | 4 ++-- test-data/gwas/README | 2 +- test-data/gwas/genotypes.vcf.gz | Bin 9778 -> 10233 bytes 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index 7887d79ef..3bf1d5e8f 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -73,7 +73,7 @@ the unique number of headers in the blocked genotype matrix `v`, and a set of he y_hat = pd.DataFrame() for contig in all_contigs: loco_block_df = reduced_block_df.filter(col('header_block') != lit(contig)) - loco_model_df = model_df.filter(col('header_block') != lit(contig)) + loco_model_df = model_df.filter(~col('header_block').startswith(contig)) loco_y_hat_df = regression.transform(loco_block_df, label_df, sample_blocks, loco_model_df, cv_df, covariates) loco_y_hat_df['contigName'] = contig.split('_')[1] y_hat = y_hat.append(loco_df) @@ -94,7 +94,7 @@ To perform GWAS adjusted with WGR, subtract the estimated phenotypes from the in pdf = label_df - y_hat apdf = pdf.T - apdf['values'] = list(apdf.drop(['contigName', 'trait'], axis=1).to_numpy()) + apdf['values'] = list(pdf.drop(['contigName', 'trait'], axis=1).to_numpy()) apdf.show() adjusted_phenotypes = spark.createDataFrame(apdf) genotypes.join(adjusted_phenotypes, ['contigName']).select( diff --git a/test-data/gwas/README b/test-data/gwas/README index 1f8e70202..6cb56dc18 100644 --- a/test-data/gwas/README +++ b/test-data/gwas/README @@ -1,4 +1,4 @@ -The genotypes are sampled from the Thousand Genomes Project Phase 3 release chr22 VCF +The genotypes are sampled from the Thousand Genomes Project Phase 3 release chr21 and chr22 VCFs (ALL.chr22.phase3_shapeit2_mvncall_integrated_v5a.20130502.genotypes.vcf). 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.../tertiary/whole-genome-regression.rst | 41 ++++++------------- 1 file changed, 12 insertions(+), 29 deletions(-) diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index 3bf1d5e8f..cda773e68 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -70,40 +70,23 @@ the unique number of headers in the blocked genotype matrix `v`, and a set of he regression = RidgeRegression(alphas_regression) model_df, cv_df = regression.fit(reduced_block_df, label_df, sample_blocks, covariates) all_contigs = [r.header_block for r in reduced_block_df.select('header_block').distinct().collect()] - y_hat = pd.DataFrame() + all_y_hat_df = pd.DataFrame() + + reduced_block_df.show() + model_df.show() + model_df.filter(~col('header').startswith('chr_22')).show() + for contig in all_contigs: - loco_block_df = reduced_block_df.filter(col('header_block') != lit(contig)) - loco_model_df = model_df.filter(~col('header_block').startswith(contig)) - loco_y_hat_df = regression.transform(loco_block_df, label_df, sample_blocks, loco_model_df, cv_df, covariates) + loco_reduced_block_df = reduced_block_df.filter(col('header_block') != lit(contig)) + loco_model_df = model_df #.filter(~col('header_block').startswith(contig)) + loco_y_hat_df = regression.transform(loco_reduced_block_df, label_df, sample_blocks, loco_model_df, cv_df, covariates) loco_y_hat_df['contigName'] = contig.split('_')[1] - y_hat = y_hat.append(loco_df) - y_hat.reset_index(inplace=True).set_index(['contigName', 'sample_id'], inplace=True) + all_y_hat_df = all_y_hat_df.append(loco_y_hat_df) + y_hat_df = all_y_hat_df.reset_index().set_index(['contigName', 'sample_id']) .. invisible-code-block: python import math + print(y_hat_df) assert math.isclose(y_hat.at[('22', 'HG00096'),'Continuous_Trait_1'], -0.37493755917205657) - -Run linear regression ---------------------- - -To perform GWAS adjusted with WGR, subtract the estimated phenotypes from the input phenotypes. - -.. code-block:: python - - pdf = label_df - y_hat - apdf = pdf.T - apdf['values'] = list(pdf.drop(['contigName', 'trait'], axis=1).to_numpy()) - apdf.show() - adjusted_phenotypes = spark.createDataFrame(apdf) - genotypes.join(adjusted_phenotypes, ['contigName']).select( - 'contigName', - 'start', - 'names', - 'trait', - glow.expand_struct(glow.linear_regression_gwas( - col('values'), - col('pt'), - lit(covariates.to_numpy()) - ))) From e920d06b539d5719fe87ed3a935e679f6544614a Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 08:06:59 -0700 Subject: [PATCH 20/34] Rename levels (#20) * Rename levels to wgr Signed-off-by: Karen Feng * rename test files Signed-off-by: Karen Feng --- .../BlockVariantsAndSamplesTransformerSuite.scala | 2 +- python/glow/__init__.py | 2 +- python/glow/{levels => wgr}/__init__.py | 0 python/glow/{levels => wgr}/functions.py | 0 .../glow/{levels => wgr}/linear_model/__init__.py | 0 .../glow/{levels => wgr}/linear_model/functions.py | 0 .../{levels => wgr}/linear_model/ridge_model.py | 0 .../glow/{levels => wgr}/linear_model/ridge_udfs.py | 0 .../linear_model/tests/test_functions.py | 2 +- .../linear_model/tests/test_ridge_regression.py | 6 +++--- .../tests/test_block_variants_and_samples.py | 2 +- .../tests/test_sample_id_extraction.py | 2 +- .../{levels => wgr}/ridge-regression/README.md | 0 test-data/{levels => wgr}/ridge-regression/X0.csv | 0 test-data/{levels => wgr}/ridge-regression/X1.csv | 0 test-data/{levels => wgr}/ridge-regression/X2.csv | 0 .../ridge-regression/blockedGT.snappy.parquet | Bin .../ridge-regression/groupedIDs.snappy.parquet | Bin test-data/{levels => wgr}/ridge-regression/pts.csv | 0 19 files changed, 8 insertions(+), 8 deletions(-) rename python/glow/{levels => wgr}/__init__.py (100%) rename python/glow/{levels => wgr}/functions.py (100%) rename python/glow/{levels => wgr}/linear_model/__init__.py (100%) rename python/glow/{levels => wgr}/linear_model/functions.py (100%) rename python/glow/{levels => wgr}/linear_model/ridge_model.py (100%) rename python/glow/{levels => wgr}/linear_model/ridge_udfs.py (100%) rename python/glow/{levels => wgr}/linear_model/tests/test_functions.py (96%) rename python/glow/{levels => wgr}/linear_model/tests/test_ridge_regression.py (99%) rename python/glow/{levels => wgr}/tests/test_block_variants_and_samples.py (99%) rename python/glow/{levels => wgr}/tests/test_sample_id_extraction.py (97%) rename test-data/{levels => wgr}/ridge-regression/README.md (100%) rename test-data/{levels => wgr}/ridge-regression/X0.csv (100%) rename test-data/{levels => wgr}/ridge-regression/X1.csv (100%) rename test-data/{levels => wgr}/ridge-regression/X2.csv (100%) rename test-data/{levels => wgr}/ridge-regression/blockedGT.snappy.parquet (100%) rename test-data/{levels => wgr}/ridge-regression/groupedIDs.snappy.parquet (100%) rename test-data/{levels => wgr}/ridge-regression/pts.csv (100%) diff --git a/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala b/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala index 76c1e1008..4d8b2f68a 100644 --- a/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala +++ b/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala @@ -140,7 +140,7 @@ class BlockVariantsAndSamplesTransformerSuite extends GlowBaseTest with GlowLogg val expectedSchema = spark .read .format("parquet") - .load(s"$testDataHome/levels/ridge-regression/blockedGT.snappy.parquet") + .load(s"$testDataHome/wgr/ridge-regression/blockedGT.snappy.parquet") .drop("indices") .schema diff --git a/python/glow/__init__.py b/python/glow/__init__.py index d87df07dd..81ad4755b 100644 --- a/python/glow/__init__.py +++ b/python/glow/__init__.py @@ -1,3 +1,3 @@ from glow.glow import * from glow.functions import * -from glow.levels import * +from glow.wgr import * diff --git a/python/glow/levels/__init__.py b/python/glow/wgr/__init__.py similarity index 100% rename from python/glow/levels/__init__.py rename to python/glow/wgr/__init__.py diff --git a/python/glow/levels/functions.py b/python/glow/wgr/functions.py similarity index 100% rename from python/glow/levels/functions.py rename to python/glow/wgr/functions.py diff --git a/python/glow/levels/linear_model/__init__.py b/python/glow/wgr/linear_model/__init__.py similarity index 100% rename from python/glow/levels/linear_model/__init__.py rename to python/glow/wgr/linear_model/__init__.py diff --git a/python/glow/levels/linear_model/functions.py b/python/glow/wgr/linear_model/functions.py similarity index 100% rename from python/glow/levels/linear_model/functions.py rename to python/glow/wgr/linear_model/functions.py diff --git a/python/glow/levels/linear_model/ridge_model.py b/python/glow/wgr/linear_model/ridge_model.py similarity index 100% rename from python/glow/levels/linear_model/ridge_model.py rename to python/glow/wgr/linear_model/ridge_model.py diff --git a/python/glow/levels/linear_model/ridge_udfs.py b/python/glow/wgr/linear_model/ridge_udfs.py similarity index 100% rename from python/glow/levels/linear_model/ridge_udfs.py rename to python/glow/wgr/linear_model/ridge_udfs.py diff --git a/python/glow/levels/linear_model/tests/test_functions.py b/python/glow/wgr/linear_model/tests/test_functions.py similarity index 96% rename from python/glow/levels/linear_model/tests/test_functions.py rename to python/glow/wgr/linear_model/tests/test_functions.py index 2ea634038..d1c13560b 100644 --- a/python/glow/levels/linear_model/tests/test_functions.py +++ b/python/glow/wgr/linear_model/tests/test_functions.py @@ -1,4 +1,4 @@ -from glow.levels.linear_model.functions import * +from glow.wgr.linear_model.functions import * import numpy as np import pandas as pd import pytest diff --git a/python/glow/levels/linear_model/tests/test_ridge_regression.py b/python/glow/wgr/linear_model/tests/test_ridge_regression.py similarity index 99% rename from python/glow/levels/linear_model/tests/test_ridge_regression.py rename to python/glow/wgr/linear_model/tests/test_ridge_regression.py index 0730b615e..56c8f4bd5 100644 --- a/python/glow/levels/linear_model/tests/test_ridge_regression.py +++ b/python/glow/wgr/linear_model/tests/test_ridge_regression.py @@ -1,7 +1,7 @@ -from glow.levels.linear_model import RidgeReducer, RidgeRegression -from glow.levels.linear_model.ridge_model import * +from glow.wgr.linear_model import RidgeReducer, RidgeRegression +from glow.wgr.linear_model.ridge_model import * -data_root = 'test-data/levels/ridge-regression' +data_root = 'test-data/wgr/ridge-regression' X0 = pd.read_csv(f'{data_root}/X0.csv').set_index('sample_id') X0.index = X0.index.astype(str, copy=False) diff --git a/python/glow/levels/tests/test_block_variants_and_samples.py b/python/glow/wgr/tests/test_block_variants_and_samples.py similarity index 99% rename from python/glow/levels/tests/test_block_variants_and_samples.py rename to python/glow/wgr/tests/test_block_variants_and_samples.py index 894b756fa..e5c92d2c1 100644 --- a/python/glow/levels/tests/test_block_variants_and_samples.py +++ b/python/glow/wgr/tests/test_block_variants_and_samples.py @@ -1,5 +1,5 @@ from glow import glow -from glow.levels import functions +from glow.wgr import functions import pytest from pyspark.sql import Row from pyspark.sql.functions import expr diff --git a/python/glow/levels/tests/test_sample_id_extraction.py b/python/glow/wgr/tests/test_sample_id_extraction.py similarity index 97% rename from python/glow/levels/tests/test_sample_id_extraction.py rename to python/glow/wgr/tests/test_sample_id_extraction.py index d6b26103b..a0452de8a 100644 --- a/python/glow/levels/tests/test_sample_id_extraction.py +++ b/python/glow/wgr/tests/test_sample_id_extraction.py @@ -1,7 +1,7 @@ import pytest from pyspark.sql import Row from pyspark.sql.utils import AnalysisException -from glow.levels import functions +from glow.wgr import functions def __construct_row(sample_id_1, sample_id_2): diff --git a/test-data/levels/ridge-regression/README.md b/test-data/wgr/ridge-regression/README.md similarity index 100% rename from test-data/levels/ridge-regression/README.md rename to test-data/wgr/ridge-regression/README.md diff --git a/test-data/levels/ridge-regression/X0.csv b/test-data/wgr/ridge-regression/X0.csv similarity index 100% rename from test-data/levels/ridge-regression/X0.csv rename to test-data/wgr/ridge-regression/X0.csv diff --git a/test-data/levels/ridge-regression/X1.csv b/test-data/wgr/ridge-regression/X1.csv similarity index 100% rename from test-data/levels/ridge-regression/X1.csv rename to test-data/wgr/ridge-regression/X1.csv diff --git a/test-data/levels/ridge-regression/X2.csv b/test-data/wgr/ridge-regression/X2.csv similarity index 100% rename from test-data/levels/ridge-regression/X2.csv rename to test-data/wgr/ridge-regression/X2.csv diff --git a/test-data/levels/ridge-regression/blockedGT.snappy.parquet b/test-data/wgr/ridge-regression/blockedGT.snappy.parquet similarity index 100% rename from test-data/levels/ridge-regression/blockedGT.snappy.parquet rename to test-data/wgr/ridge-regression/blockedGT.snappy.parquet diff --git a/test-data/levels/ridge-regression/groupedIDs.snappy.parquet b/test-data/wgr/ridge-regression/groupedIDs.snappy.parquet similarity index 100% rename from test-data/levels/ridge-regression/groupedIDs.snappy.parquet rename to test-data/wgr/ridge-regression/groupedIDs.snappy.parquet diff --git a/test-data/levels/ridge-regression/pts.csv b/test-data/wgr/ridge-regression/pts.csv similarity index 100% rename from test-data/levels/ridge-regression/pts.csv rename to test-data/wgr/ridge-regression/pts.csv From 939e9bb5dcc7cbe0c71b7072d5be4c9d75ffdad0 Mon Sep 17 00:00:00 2001 From: Henry Davidge Date: Mon, 22 Jun 2020 09:12:45 -0700 Subject: [PATCH 21/34] Add license headers (#21) * headers * executable * fix template rendering * yapf --- conftest.py | 15 +++++++++++++++ docs/extensions/notebook.py | 15 +++++++++++++++ docs/source/conf.py | 15 +++++++++++++++ docs/source/conftest.py | 15 +++++++++++++++ pyspark-setup.py | 15 +++++++++++++++ python/glow/__init__.py | 14 ++++++++++++++ python/glow/conftest.py | 14 ++++++++++++++ python/glow/conversions.py | 14 ++++++++++++++ python/glow/functions.py | 15 +++++++++++++++ python/glow/glow.py | 14 ++++++++++++++ python/glow/tests/test_conversions.py | 14 ++++++++++++++ python/glow/tests/test_register.py | 14 ++++++++++++++ python/glow/tests/test_transform.py | 14 ++++++++++++++ python/glow/wgr/__init__.py | 13 +++++++++++++ python/glow/wgr/functions.py | 14 ++++++++++++++ python/glow/wgr/linear_model/__init__.py | 14 ++++++++++++++ python/glow/wgr/linear_model/functions.py | 14 ++++++++++++++ python/glow/wgr/linear_model/ridge_model.py | 14 ++++++++++++++ python/glow/wgr/linear_model/ridge_udfs.py | 14 ++++++++++++++ .../glow/wgr/linear_model/tests/test_functions.py | 14 ++++++++++++++ .../linear_model/tests/test_ridge_regression.py | 14 ++++++++++++++ .../wgr/tests/test_block_variants_and_samples.py | 14 ++++++++++++++ .../glow/wgr/tests/test_sample_id_extraction.py | 14 ++++++++++++++ python/render_template.py | 14 ++++++++++++++ python/setup.py | 14 ++++++++++++++ python/test_render_template.py | 14 ++++++++++++++ python/version.py | 14 ++++++++++++++ test-data/vcf/scripts/gwas-region.py | 15 +++++++++++++++ 28 files changed, 398 insertions(+) mode change 100755 => 100644 test-data/vcf/scripts/gwas-region.py diff --git a/conftest.py b/conftest.py index dcc0db228..d01acd5b9 100644 --- a/conftest.py +++ b/conftest.py @@ -1,3 +1,18 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + from pyspark.sql import SparkSession import pytest diff --git a/docs/extensions/notebook.py b/docs/extensions/notebook.py index f8ca4c967..23b218dff 100644 --- a/docs/extensions/notebook.py +++ b/docs/extensions/notebook.py @@ -1,3 +1,18 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + from docutils import nodes from docutils.parsers import rst from docutils.parsers.rst import directives diff --git a/docs/source/conf.py b/docs/source/conf.py index 154fa6c19..56feafd8a 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -1,3 +1,18 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + # -*- coding: utf-8 -*- # # Configuration file for the Sphinx documentation builder. diff --git a/docs/source/conftest.py b/docs/source/conftest.py index 7be12820f..b357521b9 100644 --- a/docs/source/conftest.py +++ b/docs/source/conftest.py @@ -1,3 +1,18 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + from sybil import Sybil from sybil.parsers.codeblock import CodeBlockParser from pandas.testing import assert_series_equal diff --git a/pyspark-setup.py b/pyspark-setup.py index 95d36d138..ce1617512 100644 --- a/pyspark-setup.py +++ b/pyspark-setup.py @@ -1,3 +1,18 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + # Minimal setup.py for PySpark for Python and docs testing from setuptools import setup import sys diff --git a/python/glow/__init__.py b/python/glow/__init__.py index 81ad4755b..6bb123213 100644 --- a/python/glow/__init__.py +++ b/python/glow/__init__.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from glow.glow import * from glow.functions import * from glow.wgr import * diff --git a/python/glow/conftest.py b/python/glow/conftest.py index 7163b4f49..e0ea6f572 100644 --- a/python/glow/conftest.py +++ b/python/glow/conftest.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import pytest from pyspark.sql import functions, Row import glow diff --git a/python/glow/conversions.py b/python/glow/conversions.py index 077d45c61..2339c65ce 100644 --- a/python/glow/conversions.py +++ b/python/glow/conversions.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import numpy as np from py4j.java_collections import JavaArray from pyspark import SparkContext diff --git a/python/glow/functions.py b/python/glow/functions.py index 0565c924d..abfb03b7c 100644 --- a/python/glow/functions.py +++ b/python/glow/functions.py @@ -1,3 +1,18 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + # The Glow Python functions # Note that this file is generated from the definitions in functions.yml. diff --git a/python/glow/glow.py b/python/glow/glow.py index b05ee64ff..2d5b21b5b 100644 --- a/python/glow/glow.py +++ b/python/glow/glow.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from glow.conversions import OneDimensionalDoubleNumpyArrayConverter, TwoDimensionalDoubleNumpyArrayConverter from py4j import protocol from py4j.protocol import register_input_converter diff --git a/python/glow/tests/test_conversions.py b/python/glow/tests/test_conversions.py index 3a8457fbf..e267f2cb8 100644 --- a/python/glow/tests/test_conversions.py +++ b/python/glow/tests/test_conversions.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from glow.conversions import OneDimensionalDoubleNumpyArrayConverter, TwoDimensionalDoubleNumpyArrayConverter from importlib import reload import numpy as np diff --git a/python/glow/tests/test_register.py b/python/glow/tests/test_register.py index 73a80a5ff..077cef178 100644 --- a/python/glow/tests/test_register.py +++ b/python/glow/tests/test_register.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import pytest from pyspark.sql import Row from pyspark.sql.utils import AnalysisException diff --git a/python/glow/tests/test_transform.py b/python/glow/tests/test_transform.py index 661fa10fc..44b54d7c3 100644 --- a/python/glow/tests/test_transform.py +++ b/python/glow/tests/test_transform.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import pytest from pyspark.sql.utils import IllegalArgumentException import glow diff --git a/python/glow/wgr/__init__.py b/python/glow/wgr/__init__.py index e69de29bb..04df5e9d9 100644 --- a/python/glow/wgr/__init__.py +++ b/python/glow/wgr/__init__.py @@ -0,0 +1,13 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. diff --git a/python/glow/wgr/functions.py b/python/glow/wgr/functions.py index 5c0b16f2b..533415eb8 100644 --- a/python/glow/wgr/functions.py +++ b/python/glow/wgr/functions.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from glow import glow from pyspark import SparkContext from pyspark.sql import DataFrame, Row, SQLContext diff --git a/python/glow/wgr/linear_model/__init__.py b/python/glow/wgr/linear_model/__init__.py index 4c3cd3dd0..f487a8e1a 100644 --- a/python/glow/wgr/linear_model/__init__.py +++ b/python/glow/wgr/linear_model/__init__.py @@ -1 +1,15 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from .ridge_model import * diff --git a/python/glow/wgr/linear_model/functions.py b/python/glow/wgr/linear_model/functions.py index 8e6fd4e52..f2563d535 100644 --- a/python/glow/wgr/linear_model/functions.py +++ b/python/glow/wgr/linear_model/functions.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import itertools from nptyping import Float, Int, NDArray import numpy as np diff --git a/python/glow/wgr/linear_model/ridge_model.py b/python/glow/wgr/linear_model/ridge_model.py index 2d9d4387f..d052d6bf4 100644 --- a/python/glow/wgr/linear_model/ridge_model.py +++ b/python/glow/wgr/linear_model/ridge_model.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from .ridge_udfs import * from nptyping import Float, NDArray import pandas as pd diff --git a/python/glow/wgr/linear_model/ridge_udfs.py b/python/glow/wgr/linear_model/ridge_udfs.py index e40bb72a7..2e0d35b3d 100644 --- a/python/glow/wgr/linear_model/ridge_udfs.py +++ b/python/glow/wgr/linear_model/ridge_udfs.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from .functions import * from nptyping import Float import pandas as pd diff --git a/python/glow/wgr/linear_model/tests/test_functions.py b/python/glow/wgr/linear_model/tests/test_functions.py index d1c13560b..487e910a3 100644 --- a/python/glow/wgr/linear_model/tests/test_functions.py +++ b/python/glow/wgr/linear_model/tests/test_functions.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from glow.wgr.linear_model.functions import * import numpy as np import pandas as pd diff --git a/python/glow/wgr/linear_model/tests/test_ridge_regression.py b/python/glow/wgr/linear_model/tests/test_ridge_regression.py index 56c8f4bd5..a593a9fc7 100644 --- a/python/glow/wgr/linear_model/tests/test_ridge_regression.py +++ b/python/glow/wgr/linear_model/tests/test_ridge_regression.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from glow.wgr.linear_model import RidgeReducer, RidgeRegression from glow.wgr.linear_model.ridge_model import * diff --git a/python/glow/wgr/tests/test_block_variants_and_samples.py b/python/glow/wgr/tests/test_block_variants_and_samples.py index e5c92d2c1..4c082fceb 100644 --- a/python/glow/wgr/tests/test_block_variants_and_samples.py +++ b/python/glow/wgr/tests/test_block_variants_and_samples.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from glow import glow from glow.wgr import functions import pytest diff --git a/python/glow/wgr/tests/test_sample_id_extraction.py b/python/glow/wgr/tests/test_sample_id_extraction.py index a0452de8a..d9265f97b 100644 --- a/python/glow/wgr/tests/test_sample_id_extraction.py +++ b/python/glow/wgr/tests/test_sample_id_extraction.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import pytest from pyspark.sql import Row from pyspark.sql.utils import AnalysisException diff --git a/python/render_template.py b/python/render_template.py index 86660ce8a..d91a0ccfd 100755 --- a/python/render_template.py +++ b/python/render_template.py @@ -1,5 +1,19 @@ #!/usr/bin/env python +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + # A script that turns a YAML file in the format of functions.yml into language specific clients # using jinja2 templates. diff --git a/python/setup.py b/python/setup.py index b2e2e94dd..44588ee59 100644 --- a/python/setup.py +++ b/python/setup.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + from setuptools import setup, setuptools import imp diff --git a/python/test_render_template.py b/python/test_render_template.py index d4b0b2387..2d0ca9f66 100644 --- a/python/test_render_template.py +++ b/python/test_render_template.py @@ -1,3 +1,17 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + import pytest import render_template as rt diff --git a/python/version.py b/python/version.py index 5052c1fd1..09deb0d4b 100644 --- a/python/version.py +++ b/python/version.py @@ -1 +1,15 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + VERSION = '0.4.0' diff --git a/test-data/vcf/scripts/gwas-region.py b/test-data/vcf/scripts/gwas-region.py old mode 100755 new mode 100644 index 1b567186a..6215bab36 --- a/test-data/vcf/scripts/gwas-region.py +++ b/test-data/vcf/scripts/gwas-region.py @@ -1,3 +1,18 @@ +# Copyright 2019 The Glow Authors +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + import sys # For each region in a space-separated file containing genes and associated variants, prints an association result From db5058419b9a110d5974afa079d1ceae5cae638f Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 09:18:10 -0700 Subject: [PATCH 22/34] WIP Signed-off-by: Karen Feng --- .../tertiary/whole-genome-regression.rst | 111 ++++++++++++++---- 1 file changed, 87 insertions(+), 24 deletions(-) diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index cda773e68..c8e115961 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -11,17 +11,74 @@ Whole-Genome Regression covariates_csv = 'test-data/gwas/covariates.csv.gz' continuous_phenotypes_csv = 'test-data/gwas/continuous-phenotypes.csv.gz' -Glow supports Whole Genome Regression (WGR) as GlowGR, a parallelized version of the regenie method. +Glow supports Whole Genome Regression (WGR) as GlowGR, a parallelized version of the +`regenie ` method. GlowGR consists of the following stages: - Blocking the genotype matrix across samples and variants. - Performing dimension reduction with ridge regression. - Estimating phenotypic values with ridge regression. +------------------------ +Blocking genotype matrix +------------------------ + +``glow.wgr.functions.block_variants_and_samples`` creates two objects: a block genotype matrix and a sample block +mapping. + +Parameters +========== + +- ``genotypes``: Genotype DataFrame created by reading from any variant datasource supported by Glow, such as VCF. Must + also include a column ``values`` containing a numeric representation of each genotype, which cannot be the same + across all samples in a variant. +- ``sample_ids``: List of sample IDs. Can be created by applying ``glow.wgr.functions.get_sample_ids`` to a genotype + DataFrame. +- ``variants_per_block``: Number of variants to include per block. +- ``sample_block_count``: Number of sample blocks to create. + +Return +====== + +The function returns a block genotype matrix and a sample block mapping. + +Block genotype matrix +--------------------- + +If we imagine the block genotype matrix conceptually, we think of an *NxM* matrix *X* where each row *n* represents an +individual sample, each column *m* represents a variant, and each cell *(n, m)* contains a genotype value for sample *n* +at variant *m*. We then imagine laying a coarse grid on top of this matrix such that matrix cells within the same +coarse grid cell are all assigned to the same block *x*. Each block *x* is indexed by a sample block ID (corresponding +to a list of rows belonging to the block) and a header block ID (corresponding to a list of columns belonging to the +block). The sample block IDs are generally just integers 0 through the number of sample blocks. The header block IDs +are strings of the form 'chr_C_block_B', which refers to the Bth block on chromosome C. The Spark DataFrame +representing this block matrix can be thought of as the transpose of each block *xT* all stacked one atop another. Each +row represents the values from a particular column from *X*, for the samples corresponding to a particular sample block. +The fields in the DataFrame are: + +- ``header``: A column name in the conceptual matrix *X*. +- ``size``: The number of individuals in the sample block for the row. +- ``values``: Genotype values for this header in this sample block. If the matrix is sparse, contains only non-zero values. +- ``header_block``: An ID assigned to the block *x* containing this header. +- ``sample_block``: An ID assigned to the block *x* containing the group of samples represented on this row. +- ``position``: An integer assigned to this header that specifies the correct sort order for the headers in this block. +- ``mu``: The mean of the genotype calls for this header. +- ``sig``: The standard deviation of the genotype calls for this header. + +Sample block mapping +-------------------- + +The sample block mapping consists of key-value pairs, where each key is a sample block ID and each value is a list of +sample IDs contained in that sample block. The order of these IDs match the order of the ``values`` arrays in the block +genotype DataFrame. + +Example +======= + .. code-block:: python - from glow.levels.linear_model import RidgeReducer, RidgeRegression - from glow.levels.functions import block_variants_and_samples, get_sample_ids + from glow.wgr.linear_model import RidgeReducer, RidgeRegression + from glow.wgr.functions import block_variants_and_samples, get_sample_ids import numpy as np import pandas as pd from pyspark.sql.functions import col, lit @@ -36,29 +93,25 @@ GlowGR consists of the following stages: sample_ids = get_sample_ids(genotypes) block_df, sample_blocks = block_variants_and_samples( genotypes, sample_ids, variants_per_block, sample_block_count) - covariates = pd.read_csv(covariates_csv, index_col='sample_id') - covariates['intercept'] = 1. - -Linear model -============ - -Estimate phenotypic values --------------------------- - -If the alpha hyperparameter values for ridge reduction and regression are not provided, they will be generated based on -the unique number of headers in the blocked genotype matrix `v`, and a set of heritability values. -.. math:: +------------------------ +Dimensionality reduction +------------------------ - \vec{\alpha} = v / 0.01, 0.25, 0.50, 0.75, 0.99] +``RidgeReducer`` performs dimensionality reduction on the blocked genotype matrix. -.. warning:: +- ``fit`` +- ``transform`` +- ``fit_transform`` - The phenotypes must be mean-centered at 0. The generated alpha values are only sensible if the phenotypes are also - on the scale of one. +Example +======= .. code-block:: python + covariates = pd.read_csv(covariates_csv, index_col='sample_id') + covariates['intercept'] = 1. + label_df = pd.read_csv(continuous_phenotypes_csv, index_col='sample_id') \ .apply(lambda x: x-x.mean())[['Continuous_Trait_1', 'Continuous_Trait_2']] alphas_reducer = np.logspace(2, 5, 10) @@ -67,18 +120,28 @@ the unique number of headers in the blocked genotype matrix `v`, and a set of he reducer = RidgeReducer(alphas_reducer) reduced_block_df = reducer.fit_transform(block_df, label_df, sample_blocks, covariates) +-------------------------- +Estimate phenotypic values +-------------------------- + +``RidgeRegression `` finds and applies an optimal model to calculate estimated phenotypic values. +- ``fit`` +- ``transform`` +- ``fit_transform`` uses the same blocked genotype matrix, phenotype DataFrame, sample block mapping, and covariates + +Example +======= + +.. code-block:: python + regression = RidgeRegression(alphas_regression) model_df, cv_df = regression.fit(reduced_block_df, label_df, sample_blocks, covariates) all_contigs = [r.header_block for r in reduced_block_df.select('header_block').distinct().collect()] all_y_hat_df = pd.DataFrame() - reduced_block_df.show() - model_df.show() - model_df.filter(~col('header').startswith('chr_22')).show() - for contig in all_contigs: loco_reduced_block_df = reduced_block_df.filter(col('header_block') != lit(contig)) - loco_model_df = model_df #.filter(~col('header_block').startswith(contig)) + loco_model_df = model_df.filter(~col('header').startswith(contig)) loco_y_hat_df = regression.transform(loco_reduced_block_df, label_df, sample_blocks, loco_model_df, cv_df, covariates) loco_y_hat_df['contigName'] = contig.split('_')[1] all_y_hat_df = all_y_hat_df.append(loco_y_hat_df) From d3a882e1be8500b06a337228033bd44748e829c9 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 09:33:23 -0700 Subject: [PATCH 23/34] WIP Signed-off-by: Karen Feng --- .../tertiary/whole-genome-regression.rst | 84 ++++++++++++++++--- python/glow/functions.py | 15 ---- 2 files changed, 73 insertions(+), 26 deletions(-) diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index c8e115961..02f193a71 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -98,11 +98,76 @@ Example Dimensionality reduction ------------------------ -``RidgeReducer`` performs dimensionality reduction on the blocked genotype matrix. +The first step in the fitting procedure is to apply a dimensionality reduction to the block matrix *X* using the +`RidgeReducer`. This is accomplished by fitting multiple ridge models within each block *x* and producing a new block +matrix where each column represents the prediction of one ridge model applied within one block. This approach to model +building is generally referred to as **stacking**. We will call the block genotype matrix we started with the +**level 0** matrix in the stack *X0*, and the output of the ridge reduction step the **level 1** matrix *X1*. The +`RidgeReducer` class is used for this step, which is initiallized with a list of ridge regularization values (referred +to here as alpha). Since ridge models are indexed by these alpha values, the `RidgeReducer` will generate one ridge +model per value of alpha provided, which in turn will produce one column per block in *X0*, so the final dimensions of +matrix *X1* will be *Nx(LxK)*, where *L* is the number of header blocks in *X0* and *K* is the number of alpha values +provided to the `RidgeReducer`. In practice, we can estimate a span of alpha values in a reasonable order of magnitude +based on guesses at the heritability of the phenotype we are fitting, but here we will just pick some values. + +Initialization +============== + +When the `RidgeReducer` is initialized, it will assign names to the provided alphas and store them in a dictionary +accessible as `RidgeReducer.alphas`. -- ``fit`` -- ``transform`` -- ``fit_transform`` +Parameters +---------- + +Alphas... + +Example +------- + +.. code-block:: python + + alphas_reducer = np.logspace(2, 5, 10) + reducer = RidgeReducer(alphas_reducer) + +Model fitting +============= + +The RidgeReducer.fit(blockdf, labeldf, indexdf) method generates a Spark DataFrame representing the model that we can +use to reduce *X0* to *X1*. + +In explicit terms, the reduction of a block x0 from X0 to the corresponding block x1 from X1 is accomplished by the matrix multiplication x0 * B = x1, where B is a coefficient matrix of size mxK, where m is the number of columns in block x0 and K is the number of alpha values used in the reduction. As an added wrinkle, if the ridge reduction is being performed against multiple phenotypes at once, each phenotype will have its own B, and for convenience we panel these next to each other in the output into a single matrix, so B in that case has dimensions mx(KP)* where P is the number of phenotypes. Each matrix B is specific to a particular block in X0, so the Spark DataFrame produced by the RidgeReducer can be thought of all of as the matrices B from all of the blocks stacked one atop another. The fields in the model DataFrame are: + +header_block: An ID assigned to the block x0 corresponding to the coefficients in t + +Parameters +---------- + +- ``block_df``: Blocked genotype matrix. +- ``label_df``: Pandas DataFrame of phenotypic data, indexed by sample ID. Each column represents a single phenotype. + We assume that there are no missing phenotype values, and that the phenotypes are mean centered at 0. +- ``sample_blocks``: Sample block mapping. +- ``covariates``: Optional Pandas DataFrame containing covariate data, indexed by sample ID. + +Return +------ + +The ``fit`` functions returns a model DataFrame with the following fields: + +- ``header_block``: An ID assigned to the block x0 corresponding to the coefficients in this row. +- ``sample_block``: An ID assigned to the block x0 corresponding to the coefficients in this row. +- ``header``: The name of a column from the conceptual matrix X0 that correspond with a particular row from the coefficient matrix B. +- ``alphas``: List of alpha names corresponding to the columns of B. +- ``labels``: List of label (i.e., phenotypes) corresponding to the columns of B. +- ``coefficients``: List of the actual values from a row in B + +Model transformation +==================== + +Parameters +---------- + +Return +------ Example ======= @@ -114,10 +179,6 @@ Example label_df = pd.read_csv(continuous_phenotypes_csv, index_col='sample_id') \ .apply(lambda x: x-x.mean())[['Continuous_Trait_1', 'Continuous_Trait_2']] - alphas_reducer = np.logspace(2, 5, 10) - alphas_regression = np.logspace(1, 4, 10) - - reducer = RidgeReducer(alphas_reducer) reduced_block_df = reducer.fit_transform(block_df, label_df, sample_blocks, covariates) -------------------------- @@ -134,6 +195,8 @@ Example .. code-block:: python + alphas_regression = np.logspace(1, 4, 10) + regression = RidgeRegression(alphas_regression) model_df, cv_df = regression.fit(reduced_block_df, label_df, sample_blocks, covariates) all_contigs = [r.header_block for r in reduced_block_df.select('header_block').distinct().collect()] @@ -150,6 +213,5 @@ Example .. invisible-code-block: python import math - - print(y_hat_df) - assert math.isclose(y_hat.at[('22', 'HG00096'),'Continuous_Trait_1'], -0.37493755917205657) + print(y_hat_df.at[('22', 'HG00096'),'Continuous_Trait_1']) + assert math.isclose(y_hat_df.at[('22', 'HG00096'),'Continuous_Trait_1'], -0.48094813262232955) diff --git a/python/glow/functions.py b/python/glow/functions.py index abfb03b7c..0565c924d 100644 --- a/python/glow/functions.py +++ b/python/glow/functions.py @@ -1,18 +1,3 @@ -# Copyright 2019 The Glow Authors -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - - # The Glow Python functions # Note that this file is generated from the definitions in functions.yml. From f9212b0572f6becfb6a9215c08118a1dd84b3819 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 09:55:41 -0700 Subject: [PATCH 24/34] More work Signed-off-by: Karen Feng --- .../tertiary/whole-genome-regression.rst | 137 ++++++++++++++---- 1 file changed, 109 insertions(+), 28 deletions(-) diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index 02f193a71..d422d36ac 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -116,14 +116,6 @@ Initialization When the `RidgeReducer` is initialized, it will assign names to the provided alphas and store them in a dictionary accessible as `RidgeReducer.alphas`. -Parameters ----------- - -Alphas... - -Example -------- - .. code-block:: python alphas_reducer = np.logspace(2, 5, 10) @@ -132,26 +124,28 @@ Example Model fitting ============= -The RidgeReducer.fit(blockdf, labeldf, indexdf) method generates a Spark DataFrame representing the model that we can -use to reduce *X0* to *X1*. - -In explicit terms, the reduction of a block x0 from X0 to the corresponding block x1 from X1 is accomplished by the matrix multiplication x0 * B = x1, where B is a coefficient matrix of size mxK, where m is the number of columns in block x0 and K is the number of alpha values used in the reduction. As an added wrinkle, if the ridge reduction is being performed against multiple phenotypes at once, each phenotype will have its own B, and for convenience we panel these next to each other in the output into a single matrix, so B in that case has dimensions mx(KP)* where P is the number of phenotypes. Each matrix B is specific to a particular block in X0, so the Spark DataFrame produced by the RidgeReducer can be thought of all of as the matrices B from all of the blocks stacked one atop another. The fields in the model DataFrame are: - -header_block: An ID assigned to the block x0 corresponding to the coefficients in t +In explicit terms, the reduction of a block *x0* from *X0* to the corresponding block *x1* from *X1* is accomplished by +the matrix multiplication *x0 * B = x1*, where *B* is a coefficient matrix of size *mxK*, where *m* is the number of +columns in block *x0* and *K* is the number of alpha values used in the reduction. As an added wrinkle, if the ridge +reduction is being performed against multiple phenotypes at once, each phenotype will have its own *B*, and for +convenience we panel these next to each other in the output into a single matrix, so *B* in that case has dimensions +*mx(K*P)* where *P* is the number of phenotypes. Each matrix *B* is specific to a particular block in *X0*, so the +Spark DataFrame produced by the `RidgeReducer` can be thought of all of as the matrices *B* from all of the blocks +stacked one atop another. Parameters ---------- -- ``block_df``: Blocked genotype matrix. -- ``label_df``: Pandas DataFrame of phenotypic data, indexed by sample ID. Each column represents a single phenotype. - We assume that there are no missing phenotype values, and that the phenotypes are mean centered at 0. -- ``sample_blocks``: Sample block mapping. -- ``covariates``: Optional Pandas DataFrame containing covariate data, indexed by sample ID. +- ``block_df``: Spark DataFrame representing the beginning block matrix. +- ``label_df``: Pandas DataFrame containing the target labels used in fitting the ridge models. +- ``sample_blocks``: Dictionary containing a mapping of sample block IDs to a list of corresponding sample IDs. +- ``covariates``: Pandas DataFrame containing covariates to be included in every model in the stacking + ensemble (optional). Return ------ -The ``fit`` functions returns a model DataFrame with the following fields: +The fields in the model DataFrame are: - ``header_block``: An ID assigned to the block x0 corresponding to the coefficients in this row. - ``sample_block``: An ID assigned to the block x0 corresponding to the coefficients in this row. @@ -163,15 +157,41 @@ The ``fit`` functions returns a model DataFrame with the following fields: Model transformation ==================== +After fitting, the `RidgeReducer.transform` method can be used to generate `X1` from `X0`. + Parameters ---------- +- ``block_df``: Spark DataFrame representing the beginning block matrix. +- ``label_df``: Pandas DataFrame containing the target labels used in fitting the ridge models. +- ``sample_blocks``: Dictionary containing a mapping of sample block IDs to a list of corresponding sample IDs. +- ``model_df``: Spark DataFrame produced by the RidgeReducer fit method, representing the reducer model. +- ``covariates``: Pandas DataFrame containing covariates to be included in every model in the stacking + ensemble (optional). + Return ------ +The output of the transformation is closely analogous to the block matrix DataFrame we started with. The main +difference is that, rather than representing a single block matrix, it really represents multiple block matrices, with +one such matrix per label (phenotype). Comparing the schema of this block matrix DataFrame (`reduced_block_df`) with +the DataFrame we started with (`block_df`), the new columns are: +- ``alpha``: This is the name of the alpha value used in fitting the model that produced the values in this row. +- ``label``: This is the label corresponding to the values in this row. Since the genotype block matrix *X0* is + phenotype-agnostic, the rows in `block_df` were not restricted to any label/phenotype, but the level 1 block + matrix *X1* represents ridge model predictions for the labels the reducer was fit with, so each row is associated with + a specific label. + +The headers in the *X1* block matrix are derived from a combination of the source block in *X0*, the alpha value used in +fitting the ridge model, and the label they were fit with. These headers are assigned to header blocks that correspond +to the chromosome of the source block in *X0*. + Example ======= +Use the ``fit_transform`` function if the block genotype matrix, phenotype DataFrame, sample block mapping, and +covariates are constant for both the model fitting and transformation. + .. code-block:: python covariates = pd.read_csv(covariates_csv, index_col='sample_id') @@ -185,19 +205,81 @@ Example Estimate phenotypic values -------------------------- -``RidgeRegression `` finds and applies an optimal model to calculate estimated phenotypic values. -- ``fit`` -- ``transform`` -- ``fit_transform`` uses the same blocked genotype matrix, phenotype DataFrame, sample block mapping, and covariates +The block matrix *X1* can be used to fit a final predictive model that can generate phenotype predictions *y_hat* using +the `RidgeRegression` class. -Example -======= +Initialization +============== + +As with the `RidgeReducer` class, this class is initialized with a list of alpha values. .. code-block:: python alphas_regression = np.logspace(1, 4, 10) - regression = RidgeRegression(alphas_regression) + +Model fitting +============= + +This works much in the same way as the ridge reducer fitting, except that it returns two DataFrames. + +Parameters +---------- + +- ``block_df``: Spark DataFrame representing the beginning block matrix. +- ``label_df``: Pandas DataFrame containing the target labels used in fitting the ridge models. +- ``sample_blocks``: Dictionary containing a mapping of sample block IDs to a list of corresponding sample IDs. +- ``covariates``: Pandas DataFrame containing covariates to be included in every model in the stacking + ensemble (optional). + +Return +------ + +The first output is a model DataFrame analogous to the model DataFrame provided by the `RidgeReducer`. An important +difference is that the header block ID for all rows will be 'all', indicating that all headers from all blocks have been +used in a single fit, rather than fitting within blocks. + +The second output is a cross validation report DataFrame, which reports the results of the hyperparameter (i.e., alpha) +value optimization routine. +- ``label``: This is the label corresponding to the cross cv results on the row. +- ``alpha``: The name of the optimal alpha value +- ``r2_mean``: The mean out of fold r2 score for the optimal alpha value + +Model transformation +==================== + +After fitting the `RidgeRegression` model, the model DataFrame and cross validation DataFrame are used to apply the +model to the block matrix DataFrame to produce predictions (*y_hat*) for each label in each sample block using the +`RidgeRegression.transform` method. + +Parameters +---------- + +- ``block_df``: Spark DataFrame representing the beginning block matrix. +- ``label_df``: Pandas DataFrame containing the target labels used in fitting the ridge models. +- ``sample_blocks``: Dictionary containing a mapping of sample block IDs to a list of corresponding sample IDs. +- ``model_df``: Spark DataFrame produced by the ``RidgeRegression.fit`` method, representing the reducer model +- ``cvdf``: Spark DataFrame produced by the ``RidgeRegression.fit`` method, containing the results of the cross + validation routine. +- ``covariates``: Pandas DataFrame containing covariates to be included in every model in the stacking + ensemble (optional). + +Return +------ + +The resulting *y_hat* DataFrame has the following fields: +- `sample_block`: The sample block ID for the samples corresponding to the *y_hat* values on this row. +- `label`: The label corresponding to the *y_hat* values on this row +- `alpha`: The name of the alpha value used to fit the model that produced the *y_hat* values on this row. +- `values`: The array of *y_hat* values for the samples in the sample block for this row. + +Example +======= + +We can produce the leave one chromosome out (LOCO) version of the *y_hat* values by filtering out rows that correspond +to the chromosome we wish to drop before applying the transformation. + +.. code-block:: python model_df, cv_df = regression.fit(reduced_block_df, label_df, sample_blocks, covariates) all_contigs = [r.header_block for r in reduced_block_df.select('header_block').distinct().collect()] all_y_hat_df = pd.DataFrame() @@ -213,5 +295,4 @@ Example .. invisible-code-block: python import math - print(y_hat_df.at[('22', 'HG00096'),'Continuous_Trait_1']) assert math.isclose(y_hat_df.at[('22', 'HG00096'),'Continuous_Trait_1'], -0.48094813262232955) From 2a50994abe8a78f4b3f06ac31b6279af93755088 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 10:17:09 -0700 Subject: [PATCH 25/34] More cleanup Signed-off-by: Karen Feng --- docs/source/_static/images/wgr_runtime.png | Bin 0 -> 53621 bytes docs/source/tertiary/index.rst | 1 + 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zJ!`jY;k85LeeG{ISJy6Nfs#{Fpa-79kHBs7n46o=ojZ4}q@+D{#hvE?%<}RDa3?kY-R~GC`~}Hhu5hhO@Ite5bK`PzSKt^8jg9R+d2-Vz zZMsmNdvVRX9?RIqIMUQMAmHX74TYo+?&7?KVl1p^&mQ{=ujI)a*P|^)4v;r`#Gn}d e%O82~b$_nX=dp|hBM56LntKoI$xyYp@ZSKi!YI`M literal 0 HcmV?d00001 diff --git a/docs/source/tertiary/index.rst b/docs/source/tertiary/index.rst index 04be9657f..adecbdc12 100644 --- a/docs/source/tertiary/index.rst +++ b/docs/source/tertiary/index.rst @@ -13,3 +13,4 @@ Perform population-scale statistical analyses of genetic variants. pipe-transformer pandas-udf regression-tests + whole-genome-regression diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index d422d36ac..8adee0fb0 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -12,15 +12,19 @@ Whole-Genome Regression continuous_phenotypes_csv = 'test-data/gwas/continuous-phenotypes.csv.gz' Glow supports Whole Genome Regression (WGR) as GlowGR, a parallelized version of the -`regenie ` method. +`regenie `_ method. + +.. image:: ../_static/images/wgr_runtime.png + :scale: 50 % GlowGR consists of the following stages: + - Blocking the genotype matrix across samples and variants. - Performing dimension reduction with ridge regression. - Estimating phenotypic values with ridge regression. ------------------------ -Blocking genotype matrix +Genotype matrix blocking ------------------------ ``glow.wgr.functions.block_variants_and_samples`` creates two objects: a block genotype matrix and a sample block @@ -30,8 +34,8 @@ Parameters ========== - ``genotypes``: Genotype DataFrame created by reading from any variant datasource supported by Glow, such as VCF. Must - also include a column ``values`` containing a numeric representation of each genotype, which cannot be the same - across all samples in a variant. + also include a column ``values`` containing a numeric representation of each genotype, which cannot be the same for + all samples in a variant. - ``sample_ids``: List of sample IDs. Can be created by applying ``glow.wgr.functions.get_sample_ids`` to a genotype DataFrame. - ``variants_per_block``: Number of variants to include per block. @@ -99,22 +103,22 @@ Dimensionality reduction ------------------------ The first step in the fitting procedure is to apply a dimensionality reduction to the block matrix *X* using the -`RidgeReducer`. This is accomplished by fitting multiple ridge models within each block *x* and producing a new block +``RidgeReducer``. This is accomplished by fitting multiple ridge models within each block *x* and producing a new block matrix where each column represents the prediction of one ridge model applied within one block. This approach to model building is generally referred to as **stacking**. We will call the block genotype matrix we started with the **level 0** matrix in the stack *X0*, and the output of the ridge reduction step the **level 1** matrix *X1*. The -`RidgeReducer` class is used for this step, which is initiallized with a list of ridge regularization values (referred -to here as alpha). Since ridge models are indexed by these alpha values, the `RidgeReducer` will generate one ridge +``RidgeReducer`` class is used for this step, which is initialized with a list of ridge regularization values (referred +to here as alpha). Since ridge models are indexed by these alpha values, the ``RidgeReducer`` will generate one ridge model per value of alpha provided, which in turn will produce one column per block in *X0*, so the final dimensions of matrix *X1* will be *Nx(LxK)*, where *L* is the number of header blocks in *X0* and *K* is the number of alpha values -provided to the `RidgeReducer`. In practice, we can estimate a span of alpha values in a reasonable order of magnitude -based on guesses at the heritability of the phenotype we are fitting, but here we will just pick some values. +provided to the ``RidgeReducer``. In practice, we can estimate a span of alpha values in a reasonable order of +magnitude based on guesses at the heritability of the phenotype we are fitting. Initialization ============== -When the `RidgeReducer` is initialized, it will assign names to the provided alphas and store them in a dictionary -accessible as `RidgeReducer.alphas`. +When the ``RidgeReducer`` is initialized, it will assign names to the provided alphas and store them in a dictionary +accessible as ``RidgeReducer.alphas``. .. code-block:: python @@ -130,7 +134,7 @@ columns in block *x0* and *K* is the number of alpha values used in the reductio reduction is being performed against multiple phenotypes at once, each phenotype will have its own *B*, and for convenience we panel these next to each other in the output into a single matrix, so *B* in that case has dimensions *mx(K*P)* where *P* is the number of phenotypes. Each matrix *B* is specific to a particular block in *X0*, so the -Spark DataFrame produced by the `RidgeReducer` can be thought of all of as the matrices *B* from all of the blocks +Spark DataFrame produced by the ``RidgeReducer`` can be thought of all of as the matrices *B* from all of the blocks stacked one atop another. Parameters @@ -157,7 +161,7 @@ The fields in the model DataFrame are: Model transformation ==================== -After fitting, the `RidgeReducer.transform` method can be used to generate `X1` from `X0`. +After fitting, the ``RidgeReducer.transform`` method can be used to generate *X1* from *X0*. Parameters ---------- @@ -174,11 +178,12 @@ Return The output of the transformation is closely analogous to the block matrix DataFrame we started with. The main difference is that, rather than representing a single block matrix, it really represents multiple block matrices, with -one such matrix per label (phenotype). Comparing the schema of this block matrix DataFrame (`reduced_block_df`) with -the DataFrame we started with (`block_df`), the new columns are: +one such matrix per label (phenotype). Comparing the schema of this block matrix DataFrame (``reduced_block_df``) with +the DataFrame we started with (``block_df``), the new columns are: + - ``alpha``: This is the name of the alpha value used in fitting the model that produced the values in this row. - ``label``: This is the label corresponding to the values in this row. Since the genotype block matrix *X0* is - phenotype-agnostic, the rows in `block_df` were not restricted to any label/phenotype, but the level 1 block + phenotype-agnostic, the rows in ``block_df`` were not restricted to any label/phenotype, but the level 1 block matrix *X1* represents ridge model predictions for the labels the reducer was fit with, so each row is associated with a specific label. @@ -206,12 +211,12 @@ Estimate phenotypic values -------------------------- The block matrix *X1* can be used to fit a final predictive model that can generate phenotype predictions *y_hat* using -the `RidgeRegression` class. +the ``RidgeRegression`` class. Initialization ============== -As with the `RidgeReducer` class, this class is initialized with a list of alpha values. +As with the ``RidgeReducer`` class, this class is initialized with a list of alpha values. .. code-block:: python @@ -235,12 +240,13 @@ Parameters Return ------ -The first output is a model DataFrame analogous to the model DataFrame provided by the `RidgeReducer`. An important +The first output is a model DataFrame analogous to the model DataFrame provided by the ``RidgeReducer``. An important difference is that the header block ID for all rows will be 'all', indicating that all headers from all blocks have been used in a single fit, rather than fitting within blocks. The second output is a cross validation report DataFrame, which reports the results of the hyperparameter (i.e., alpha) value optimization routine. + - ``label``: This is the label corresponding to the cross cv results on the row. - ``alpha``: The name of the optimal alpha value - ``r2_mean``: The mean out of fold r2 score for the optimal alpha value @@ -248,9 +254,9 @@ value optimization routine. Model transformation ==================== -After fitting the `RidgeRegression` model, the model DataFrame and cross validation DataFrame are used to apply the +After fitting the ``RidgeRegression`` model, the model DataFrame and cross validation DataFrame are used to apply the model to the block matrix DataFrame to produce predictions (*y_hat*) for each label in each sample block using the -`RidgeRegression.transform` method. +``RidgeRegression.transform`` method. Parameters ---------- @@ -268,10 +274,11 @@ Return ------ The resulting *y_hat* DataFrame has the following fields: -- `sample_block`: The sample block ID for the samples corresponding to the *y_hat* values on this row. -- `label`: The label corresponding to the *y_hat* values on this row -- `alpha`: The name of the alpha value used to fit the model that produced the *y_hat* values on this row. -- `values`: The array of *y_hat* values for the samples in the sample block for this row. + +- ``sample_block``: The sample block ID for the samples corresponding to the *y_hat* values on this row. +- ``label``: The label corresponding to the *y_hat* values on this row +- ``alpha``: The name of the alpha value used to fit the model that produced the *y_hat* values on this row. +- ``values``: The array of *y_hat* values for the samples in the sample block for this row. Example ======= @@ -280,6 +287,7 @@ We can produce the leave one chromosome out (LOCO) version of the *y_hat* values to the chromosome we wish to drop before applying the transformation. .. code-block:: python + model_df, cv_df = regression.fit(reduced_block_df, label_df, sample_blocks, covariates) all_contigs = [r.header_block for r in reduced_block_df.select('header_block').distinct().collect()] all_y_hat_df = pd.DataFrame() From bf0963ae4e69f2f40204d372d78961dc55ae7177 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 10:23:17 -0700 Subject: [PATCH 26/34] Fix docs tests Signed-off-by: Karen Feng --- docs/source/tertiary/regression-tests.rst | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/docs/source/tertiary/regression-tests.rst b/docs/source/tertiary/regression-tests.rst index c6bcfda79..edba86802 100644 --- a/docs/source/tertiary/regression-tests.rst +++ b/docs/source/tertiary/regression-tests.rst @@ -81,7 +81,7 @@ Example standardError=0.1783963733160434, pValue=0.44349953631952943 ) - assert_rows_equal(lin_reg_df.head(), expected_lin_reg_row) + assert_rows_equal(lin_reg_df.filter('contigName = 22 and start = 16050114').head(), expected_lin_reg_row) Parameters ---------- @@ -191,7 +191,7 @@ Example waldConfidenceInterval=[0.7813704896767115, 3.247273366082802], pValue=0.19572327843236637 ) - assert_rows_equal(lrt_log_reg_df.head(), expected_lrt_log_reg_row) + assert_rows_equal(lrt_log_reg_df.filter('contigName = 22 and start = 16050114').head(), expected_lrt_log_reg_row) expected_firth_log_reg_row = Row( contigName='22', @@ -202,7 +202,7 @@ Example waldConfidenceInterval=[0.7719062301156017, 3.2026291934794795], pValue=0.20086839802280376 ) - assert_rows_equal(firth_log_reg_df.head(), expected_firth_log_reg_row) + assert_rows_equal(firth_log_reg_df.filter('contigName = 22 and start = 16050114').head(), expected_firth_log_reg_row) Parameters ---------- From 86e12a60440cb7e72521eac032b37df594862f5e Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 11:46:55 -0700 Subject: [PATCH 27/34] address comments Signed-off-by: Karen Feng --- .../tertiary/whole-genome-regression.rst | 86 +++++++++++++++---- 1 file changed, 68 insertions(+), 18 deletions(-) diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index 8adee0fb0..da33bf6d7 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -1,6 +1,6 @@ -======================= -Whole-Genome Regression -======================= +=============================== +GlowGR: Whole-Genome Regression +=============================== .. invisible-code-block: python @@ -12,17 +12,79 @@ Whole-Genome Regression continuous_phenotypes_csv = 'test-data/gwas/continuous-phenotypes.csv.gz' Glow supports Whole Genome Regression (WGR) as GlowGR, a parallelized version of the -`regenie `_ method. +`regenie `_ method (see the +`preprint `_). .. image:: ../_static/images/wgr_runtime.png :scale: 50 % -GlowGR consists of the following stages: +GlowGR consists of the following stages. - Blocking the genotype matrix across samples and variants. - Performing dimension reduction with ridge regression. - Estimating phenotypic values with ridge regression. +---------------- +Data preparation +---------------- + +GlowGR uses three basic input datasources. + +Genotype data +============= + +The genotype data may be read from any variant datasource supported by Glow, such as VCF, BGEN or PLINK. The DataFrame +must also include a column ``values`` containing a numeric representation of each genotype. The genotypic values may +not be missing, or equal for every sample in a variant. + +Example +------- + +When loading in the variants, perform the following transformations: + +- Split multiallelic variants with the ``split_multiallelics`` transformer. +- Calculate the number of alternate alleles for biallelic variants with ``glow.genotype_states``. +- Replace any missing values with the mean of the non-missing values using ``glow.mean_substitute``. +- Filter out all homozygous SNPs. + +.. code-block:: python + + from pyspark.sql.functions import col, lit + + variants = spark.read.format('vcf').load(genotypes_vcf) + genotypes = glow.transform('split_multiallelics', variants) \ + .withColumn('values', glow.mean_substitute(glow.genotype_states(col('genotypes')))) \ + .filter('size(array_distinct(values)) > 1') \ + +Phenotype data +============== + +The phenotype data is represented as a Pandas DataFrame indexed by the sample ID. Each column represents a single +phenotype. It is assumed that there are no missing phenotype values, and that the phenotypes are mean centered at 0. + +Example +------- + +.. code-block:: python + + import pandas as pd + + label_df = pd.read_csv(continuous_phenotypes_csv, index_col='sample_id') \ + .apply(lambda x: x-x.mean())[['Continuous_Trait_1', 'Continuous_Trait_2']] + +Covariate data +============== + +The covariate data is represented as a Pandas DataFrame indexed by the sample ID. + +Example +------- + +.. code-block:: python + + covariates = pd.read_csv(covariates_csv, index_col='sample_id') + covariates['intercept'] = 1. + ------------------------ Genotype matrix blocking ------------------------ @@ -34,8 +96,7 @@ Parameters ========== - ``genotypes``: Genotype DataFrame created by reading from any variant datasource supported by Glow, such as VCF. Must - also include a column ``values`` containing a numeric representation of each genotype, which cannot be the same for - all samples in a variant. + also include a column ``values`` containing a numeric representation of each genotype. - ``sample_ids``: List of sample IDs. Can be created by applying ``glow.wgr.functions.get_sample_ids`` to a genotype DataFrame. - ``variants_per_block``: Number of variants to include per block. @@ -84,16 +145,10 @@ Example from glow.wgr.linear_model import RidgeReducer, RidgeRegression from glow.wgr.functions import block_variants_and_samples, get_sample_ids import numpy as np - import pandas as pd from pyspark.sql.functions import col, lit variants_per_block = 5 sample_block_count = 10 - variants = spark.read.format('vcf').load(genotypes_vcf) - genotypes = glow.transform('split_multiallelics', variants) \ - .withColumn('values', glow.mean_substitute(glow.genotype_states(col('genotypes')))) \ - .filter('size(array_distinct(values)) > 1') \ - .cache() sample_ids = get_sample_ids(genotypes) block_df, sample_blocks = block_variants_and_samples( genotypes, sample_ids, variants_per_block, sample_block_count) @@ -199,11 +254,6 @@ covariates are constant for both the model fitting and transformation. .. code-block:: python - covariates = pd.read_csv(covariates_csv, index_col='sample_id') - covariates['intercept'] = 1. - - label_df = pd.read_csv(continuous_phenotypes_csv, index_col='sample_id') \ - .apply(lambda x: x-x.mean())[['Continuous_Trait_1', 'Continuous_Trait_2']] reduced_block_df = reducer.fit_transform(block_df, label_df, sample_blocks, covariates) -------------------------- From 418d714de4a645f2d16861189b075f77ef4f5b37 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 11:52:36 -0700 Subject: [PATCH 28/34] fix regression fit description Signed-off-by: Karen Feng --- docs/source/tertiary/whole-genome-regression.rst | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index da33bf6d7..1b57614c8 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -305,7 +305,7 @@ Model transformation ==================== After fitting the ``RidgeRegression`` model, the model DataFrame and cross validation DataFrame are used to apply the -model to the block matrix DataFrame to produce predictions (*y_hat*) for each label in each sample block using the +model to the block matrix DataFrame to produce predictions (*y_hat*) for each label and sample using the ``RidgeRegression.transform`` method. Parameters @@ -323,12 +323,8 @@ Parameters Return ------ -The resulting *y_hat* DataFrame has the following fields: - -- ``sample_block``: The sample block ID for the samples corresponding to the *y_hat* values on this row. -- ``label``: The label corresponding to the *y_hat* values on this row -- ``alpha``: The name of the alpha value used to fit the model that produced the *y_hat* values on this row. -- ``values``: The array of *y_hat* values for the samples in the sample block for this row. +The resulting *y_hat* Pandas DataFrame is shaped like ``label_df``, indexed by the sample ID with each column +representing a single phenotype. Example ======= From 48943cfc7aece6341c65973ce249ca0c95b75e7d Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 15:10:30 -0700 Subject: [PATCH 29/34] fix capitalization Signed-off-by: Karen Feng --- docs/source/tertiary/whole-genome-regression.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index 1b57614c8..d5be65a01 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -1,5 +1,5 @@ =============================== -GlowGR: Whole-Genome Regression +GloWGR: Whole-Genome Regression =============================== .. invisible-code-block: python @@ -11,14 +11,14 @@ GlowGR: Whole-Genome Regression covariates_csv = 'test-data/gwas/covariates.csv.gz' continuous_phenotypes_csv = 'test-data/gwas/continuous-phenotypes.csv.gz' -Glow supports Whole Genome Regression (WGR) as GlowGR, a parallelized version of the +Glow supports Whole Genome Regression (WGR) as GloWGR, a parallelized version of the `regenie `_ method (see the `preprint `_). .. image:: ../_static/images/wgr_runtime.png :scale: 50 % -GlowGR consists of the following stages. +GloWGR consists of the following stages. - Blocking the genotype matrix across samples and variants. - Performing dimension reduction with ridge regression. @@ -28,7 +28,7 @@ GlowGR consists of the following stages. Data preparation ---------------- -GlowGR uses three basic input datasources. +GloWGR accepts three input datasources. Genotype data ============= From 39601e97ce72116b2eafbf41eec5840d153a0562 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 16:01:15 -0700 Subject: [PATCH 30/34] address some comments Signed-off-by: Karen Feng --- .../tertiary/whole-genome-regression.rst | 81 ++++++++++++------- 1 file changed, 52 insertions(+), 29 deletions(-) diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index d5be65a01..990d9b0f5 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -20,9 +20,9 @@ Glow supports Whole Genome Regression (WGR) as GloWGR, a parallelized version of GloWGR consists of the following stages. -- Blocking the genotype matrix across samples and variants. -- Performing dimension reduction with ridge regression. -- Estimating phenotypic values with ridge regression. +- Block the genotype matrix across samples and variants. +- Perform dimensionality reduction with ridge regression. +- Estimate phenotypic values with ridge regression. ---------------- Data preparation @@ -35,7 +35,7 @@ Genotype data The genotype data may be read from any variant datasource supported by Glow, such as VCF, BGEN or PLINK. The DataFrame must also include a column ``values`` containing a numeric representation of each genotype. The genotypic values may -not be missing, or equal for every sample in a variant. +not be missing, or equal for every sample in a variant (eg. all samples are homozygous reference). Example ------- @@ -134,8 +134,9 @@ Sample block mapping -------------------- The sample block mapping consists of key-value pairs, where each key is a sample block ID and each value is a list of -sample IDs contained in that sample block. The order of these IDs match the order of the ``values`` arrays in the block -genotype DataFrame. +sample IDs contained in that sample block. + +The order of these IDs match the order of the ``values`` arrays in the block genotype DataFrame. Example ======= @@ -144,7 +145,6 @@ Example from glow.wgr.linear_model import RidgeReducer, RidgeRegression from glow.wgr.functions import block_variants_and_samples, get_sample_ids - import numpy as np from pyspark.sql.functions import col, lit variants_per_block = 5 @@ -158,16 +158,18 @@ Dimensionality reduction ------------------------ The first step in the fitting procedure is to apply a dimensionality reduction to the block matrix *X* using the -``RidgeReducer``. This is accomplished by fitting multiple ridge models within each block *x* and producing a new block -matrix where each column represents the prediction of one ridge model applied within one block. This approach to model -building is generally referred to as **stacking**. We will call the block genotype matrix we started with the -**level 0** matrix in the stack *X0*, and the output of the ridge reduction step the **level 1** matrix *X1*. The -``RidgeReducer`` class is used for this step, which is initialized with a list of ridge regularization values (referred -to here as alpha). Since ridge models are indexed by these alpha values, the ``RidgeReducer`` will generate one ridge -model per value of alpha provided, which in turn will produce one column per block in *X0*, so the final dimensions of -matrix *X1* will be *Nx(LxK)*, where *L* is the number of header blocks in *X0* and *K* is the number of alpha values -provided to the ``RidgeReducer``. In practice, we can estimate a span of alpha values in a reasonable order of -magnitude based on guesses at the heritability of the phenotype we are fitting. +``RidgeReducer``. + +This is accomplished by fitting multiple ridge models within each block *x* and producing a new block matrix where each +column represents the prediction of one ridge model applied within one block. This approach to model building is +generally referred to as **stacking**. We will call the block genotype matrix we started with the **level 0** matrix in +the stack *X0*, and the output of the ridge reduction step the **level 1** matrix *X1*. The ``RidgeReducer`` class is +used for this step, which is initialized with a list of ridge regularization values (referred to here as alpha). Since +ridge models are indexed by these alpha values, the ``RidgeReducer`` will generate one ridge model per value of alpha +provided, which in turn will produce one column per block in *X0*, so the final dimensions of matrix *X1* will be +*Nx(LxK)*, where *L* is the number of header blocks in *X0* and *K* is the number of alpha values provided to the +``RidgeReducer``. In practice, we can estimate a span of alpha values in a reasonable order of magnitude based on +guesses at the heritability of the phenotype we are fitting. Initialization ============== @@ -175,10 +177,20 @@ Initialization When the ``RidgeReducer`` is initialized, it will assign names to the provided alphas and store them in a dictionary accessible as ``RidgeReducer.alphas``. +Example +------- + +If alpha values are not provided, they will be generated during ``RidgeReducer.fit`` based on the unique number of +headers *h* in the blocked genotype matrix *X0*, and a set of heritability values. These are only sensible if the +phenotypes are on the scale of one. + +.. math:: + + \vec{\alpha} = h / [0.01, 0.25, 0.50, 0.75, 0.99] + .. code-block:: python - alphas_reducer = np.logspace(2, 5, 10) - reducer = RidgeReducer(alphas_reducer) + reducer = RidgeReducer() Model fitting ============= @@ -206,12 +218,13 @@ Return The fields in the model DataFrame are: -- ``header_block``: An ID assigned to the block x0 corresponding to the coefficients in this row. -- ``sample_block``: An ID assigned to the block x0 corresponding to the coefficients in this row. -- ``header``: The name of a column from the conceptual matrix X0 that correspond with a particular row from the coefficient matrix B. -- ``alphas``: List of alpha names corresponding to the columns of B. -- ``labels``: List of label (i.e., phenotypes) corresponding to the columns of B. -- ``coefficients``: List of the actual values from a row in B +- ``header_block``: An ID assigned to the block *x0* corresponding to the coefficients in this row. +- ``sample_block``: An ID assigned to the block *x0* corresponding to the coefficients in this row. +- ``header``: The name of a column from the conceptual matrix *X0* that correspond with a particular row from the + coefficient matrix *B*. +- ``alphas``: List of alpha names corresponding to the columns of *B*. +- ``labels``: List of label (i.e., phenotypes) corresponding to the columns of *B*. +- ``coefficients``: List of the actual values from a row in *B*. Model transformation ==================== @@ -268,10 +281,20 @@ Initialization As with the ``RidgeReducer`` class, this class is initialized with a list of alpha values. +Example +------- + +If alpha values are not provided, they will be generated during ``RidgeRegression.fit`` based on the unique number of +headers *h* in the blocked genotype matrix *X1*, and a set of heritability values. These are only sensible if the +phenotypes are on the scale of one. + +.. math:: + + \vec{\alpha} = h / [0.01, 0.25, 0.50, 0.75, 0.99] + .. code-block:: python - alphas_regression = np.logspace(1, 4, 10) - regression = RidgeRegression(alphas_regression) + regression = RidgeRegression() Model fitting ============= @@ -315,7 +338,7 @@ Parameters - ``label_df``: Pandas DataFrame containing the target labels used in fitting the ridge models. - ``sample_blocks``: Dictionary containing a mapping of sample block IDs to a list of corresponding sample IDs. - ``model_df``: Spark DataFrame produced by the ``RidgeRegression.fit`` method, representing the reducer model -- ``cvdf``: Spark DataFrame produced by the ``RidgeRegression.fit`` method, containing the results of the cross +- ``cv_df``: Spark DataFrame produced by the ``RidgeRegression.fit`` method, containing the results of the cross validation routine. - ``covariates``: Pandas DataFrame containing covariates to be included in every model in the stacking ensemble (optional). @@ -349,4 +372,4 @@ to the chromosome we wish to drop before applying the transformation. .. invisible-code-block: python import math - assert math.isclose(y_hat_df.at[('22', 'HG00096'),'Continuous_Trait_1'], -0.48094813262232955) + assert math.isclose(y_hat_df.at[('22', 'HG00096'),'Continuous_Trait_1'], -0.4973672436810818) From 4c8aac18f3915a706b4f031c386366ff98a3442c Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 16:15:20 -0700 Subject: [PATCH 31/34] more cleanup Signed-off-by: Karen Feng --- docs/source/etl/vcf2delta.rst | 2 ++ docs/source/tertiary/whole-genome-regression.rst | 15 +++++++++------ 2 files changed, 11 insertions(+), 6 deletions(-) diff --git a/docs/source/etl/vcf2delta.rst b/docs/source/etl/vcf2delta.rst index 3db8d592f..7e69e5d63 100644 --- a/docs/source/etl/vcf2delta.rst +++ b/docs/source/etl/vcf2delta.rst @@ -1,3 +1,5 @@ +.. _vcf2delta: + ============================ Create a Genomics Delta Lake ============================ diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index 990d9b0f5..285cd0e96 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -33,9 +33,12 @@ GloWGR accepts three input datasources. Genotype data ============= -The genotype data may be read from any variant datasource supported by Glow, such as VCF, BGEN or PLINK. The DataFrame -must also include a column ``values`` containing a numeric representation of each genotype. The genotypic values may -not be missing, or equal for every sample in a variant (eg. all samples are homozygous reference). +The genotype data may be read from any variant datasource supported by Glow, such as one read from +:ref:`VCF, BGEN or PLINK `. For scalability, recommend ingesting flat genotype files into +:ref:`Delta tables `. + +The DataFrame must also include a column ``values`` containing a numeric representation of each genotype. The genotypic +values may not be missing, or equal for every sample in a variant (eg. all samples are homozygous reference). Example ------- @@ -45,7 +48,7 @@ When loading in the variants, perform the following transformations: - Split multiallelic variants with the ``split_multiallelics`` transformer. - Calculate the number of alternate alleles for biallelic variants with ``glow.genotype_states``. - Replace any missing values with the mean of the non-missing values using ``glow.mean_substitute``. -- Filter out all homozygous SNPs. +- Filter out variants whose values are equal across all samples. .. code-block:: python @@ -218,8 +221,8 @@ Return The fields in the model DataFrame are: -- ``header_block``: An ID assigned to the block *x0* corresponding to the coefficients in this row. -- ``sample_block``: An ID assigned to the block *x0* corresponding to the coefficients in this row. +- ``header_block``: An ID assigned to the header block *x0* corresponding to the coefficients in this row. +- ``sample_block``: An ID assigned to the sample block *x0* corresponding to the coefficients in this row. - ``header``: The name of a column from the conceptual matrix *X0* that correspond with a particular row from the coefficient matrix *B*. - ``alphas``: List of alpha names corresponding to the columns of *B*. From 2d9409c25d824f8dddcd4c37f02ef3f65a6593a7 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 16:53:51 -0700 Subject: [PATCH 32/34] More cleanup Signed-off-by: Karen Feng --- docs/source/tertiary/whole-genome-regression.rst | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index 285cd0e96..fa9e09ba1 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -24,6 +24,10 @@ GloWGR consists of the following stages. - Perform dimensionality reduction with ridge regression. - Estimate phenotypic values with ridge regression. +.. note:: + + GloWGR currently supports only quantitative phenotypes. + ---------------- Data preparation ---------------- @@ -102,8 +106,8 @@ Parameters also include a column ``values`` containing a numeric representation of each genotype. - ``sample_ids``: List of sample IDs. Can be created by applying ``glow.wgr.functions.get_sample_ids`` to a genotype DataFrame. -- ``variants_per_block``: Number of variants to include per block. -- ``sample_block_count``: Number of sample blocks to create. +- ``variants_per_block``: Number of variants to include per block. We recommend 1000. +- ``sample_block_count``: Number of sample blocks to create. We recommend 10. Return ====== @@ -150,7 +154,7 @@ Example from glow.wgr.functions import block_variants_and_samples, get_sample_ids from pyspark.sql.functions import col, lit - variants_per_block = 5 + variants_per_block = 1000 sample_block_count = 10 sample_ids = get_sample_ids(genotypes) block_df, sample_blocks = block_variants_and_samples( @@ -375,4 +379,4 @@ to the chromosome we wish to drop before applying the transformation. .. invisible-code-block: python import math - assert math.isclose(y_hat_df.at[('22', 'HG00096'),'Continuous_Trait_1'], -0.4973672436810818) + assert math.isclose(y_hat_df.at[('22', 'HG00096'),'Continuous_Trait_1'], -0.5203890988445584) From e899e55acaed785b8ef281401941cf77a4f7e7b1 Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 17:19:33 -0700 Subject: [PATCH 33/34] add notebook Signed-off-by: Karen Feng --- .../_static/notebooks/tertiary/glowgr.html | 42 +++++++++++++++++++ .../tertiary/whole-genome-regression.rst | 6 +++ 2 files changed, 48 insertions(+) create mode 100644 docs/source/_static/notebooks/tertiary/glowgr.html diff --git a/docs/source/_static/notebooks/tertiary/glowgr.html b/docs/source/_static/notebooks/tertiary/glowgr.html new file mode 100644 index 000000000..308115c1d --- /dev/null +++ b/docs/source/_static/notebooks/tertiary/glowgr.html @@ -0,0 +1,42 @@ + + + + +GloWGR - Databricks + + + + + + + + + + + + + + + + + + + diff --git a/docs/source/tertiary/whole-genome-regression.rst b/docs/source/tertiary/whole-genome-regression.rst index fa9e09ba1..5970af5ff 100644 --- a/docs/source/tertiary/whole-genome-regression.rst +++ b/docs/source/tertiary/whole-genome-regression.rst @@ -380,3 +380,9 @@ to the chromosome we wish to drop before applying the transformation. import math assert math.isclose(y_hat_df.at[('22', 'HG00096'),'Continuous_Trait_1'], -0.5203890988445584) + +Example notebook +---------------- + +.. notebook:: .. tertiary/glowgr.html + :title: GloWGR notebook From 75ffd4c0b41421aa35955996c8867c8f1c4861ca Mon Sep 17 00:00:00 2001 From: Karen Feng Date: Mon, 22 Jun 2020 17:27:38 -0700 Subject: [PATCH 34/34] update notebook Signed-off-by: Karen Feng --- docs/source/_static/notebooks/tertiary/glowgr.html | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/_static/notebooks/tertiary/glowgr.html b/docs/source/_static/notebooks/tertiary/glowgr.html index 308115c1d..2f75b89dd 100644 --- a/docs/source/_static/notebooks/tertiary/glowgr.html +++ b/docs/source/_static/notebooks/tertiary/glowgr.html @@ -14,7 +14,7 @@ - +