diff --git a/.circleci/config.yml b/.circleci/config.yml index 72a5472f2..2fe8d3f1f 100644 --- a/.circleci/config.yml +++ b/.circleci/config.yml @@ -91,6 +91,7 @@ jobs: sbt coverage core/test coverageReport exit - run: name: Run Python tests + no_output_timeout: 30m environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH @@ -133,6 +134,7 @@ jobs: sbt core/test exit - run: name: Run Python tests + no_output_timeout: 30m environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH @@ -175,6 +177,7 @@ jobs: sbt core/test exit - run: name: Run Python tests + no_output_timeout: 30m environment: command: | export PATH=$HOME/conda/envs/glow/bin:$PATH 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/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/build.sbt b/build.sbt index 9b9525d7c..32e28e418 100644 --- a/build.sbt +++ b/build.sbt @@ -205,7 +205,7 @@ lazy val pythonSettings = Seq( val env = if (majorMinorVersion(sparkVersion) >= "3.0") { baseEnv :+ "PYSPARK_ROW_FIELD_SORTING_ENABLED" -> "true" } else { - baseEnv + baseEnv :+ "ARROW_PRE_0_15_IPC_FORMAT" -> "1" } val ret = Process( Seq("pytest") ++ args, diff --git a/conftest.py b/conftest.py index 00d179167..d01acd5b9 100644 --- a/conftest.py +++ b/conftest.py @@ -1,11 +1,28 @@ +# 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 # 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/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..43f807a5a --- /dev/null +++ b/core/src/main/scala/io/projectglow/transformers/blockvariantsandsamples/VariantSampleBlockMaker.scala @@ -0,0 +1,124 @@ +/* + * 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 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, + sampleBlockCount: Int): DataFrame = { + val windowSpec = Window + .partitionBy(contigNameField.name, sampleBlockIdField.name) + .orderBy(startField.name, refAlleleField.name, alternateAllelesField.name) + + val baseDf = variantDf + .withColumn( + sortKeyField.name, + col(startField.name).cast(IntegerType) + ) + .withColumn( + headerField.name, + concat_ws( + ":", + col(contigNameField.name), + col(startField.name), + col(refAlleleField.name), + col(alternateAllelesField.name) + ) + ) + .withColumn( + "stats", + subset_struct( + array_summary_stats( + col(valuesField.name) + ), + "mean", + "stdDev" + ) + ) + .withColumn( + meanField.name, + col("stats.mean") + ) + .withColumn( + stdDevField.name, + col("stats.stdDev") + ) + + makeSampleBlocks(baseDf, sampleBlockCount) + .withColumn( + 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), + 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..4d8b2f68a --- /dev/null +++ b/core/src/test/scala/io/projectglow/transformers/blockvariantsandsamples/BlockVariantsAndSamplesTransformerSuite.scala @@ -0,0 +1,153 @@ +/* + * 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 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 io.projectglow.transformers.blockvariantsandsamples.BlockVariantsAndSamplesTransformer._ + +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"$testFolder/1000G.phase3.broad.withGenotypes.chr20.10100000.100Samples.Blocked.tsv" + + def testBlockedvsExpected( + originalVCFFileName: String, + expectedBlockedFileName: String, + variantsPerBlock: Int, + sampleBlockCount: Int + ): Unit = { + + val options: Map[String, String] = Map( + VARIANTS_PER_BLOCK -> variantsPerBlock.toString, + SAMPLE_BLOCK_COUNT -> sampleBlockCount.toString + ) + + val dfOriginal = spark + .read + .format(sourceName) + .load(originalVCFFileName) + .withColumn( + valuesField.name, + slice( + genotype_states( + col(genotypesFieldName) + ), + 1, + 100 + ).cast(ArrayType(DoubleType)) + ) + + val dfBlocked = Glow + .transform( + TRANSFORMER_NAME, + dfOriginal, + options + ) + .orderBy( + headerField.name, + headerBlockIdField.name, + sampleBlockIdField.name + ) + + val dfExpected = spark + .read + .format("csv") + .options( + Map( + "delimiter" -> "\t", + "header" -> "true" + ) + ) + .schema( + StructType( + Seq( + headerField, + sizeField, + StructField(valuesField.name, StringType), + headerBlockIdField, + sampleBlockIdField, + sortKeyField, + meanField, + stdDevField + ) + ) + ) + .load(testExpectedTsv) + .withColumn( + valuesField.name, + split(col(valuesField.name), ",").cast(ArrayType(DoubleType)) + ) + + assert(dfBlocked.count() == dfExpected.count()) + + dfExpected + .collect + .zip( + dfBlocked.collect + ) + .foreach { + case (rowExp, rowBlocked) => + assert(rowExp.equals(rowBlocked), s"Expected\n$rowExp\nBlocked\n$rowBlocked") + } + } + + test("test blocked vs expected") { + testBlockedvsExpected( + testVcf, + testExpectedTsv, + 20, + 7 + ) + } + + test("test schema") { + val vcfDf = spark + .read + .format("vcf") + .load(testVcf) + .withColumn("values", expr("genotype_states(genotypes)").cast(ArrayType(DoubleType))) + val options = Map(VARIANTS_PER_BLOCK -> "10", SAMPLE_BLOCK_COUNT -> "20") + val testSchema = Glow.transform(TRANSFORMER_NAME, vcfDf, options).schema + + val expectedSchema = spark + .read + .format("parquet") + .load(s"$testDataHome/wgr/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/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/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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\n", + "
" + ], + "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 +} 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/environment.yml b/python/environment.yml index 65606528c..455161675 100644 --- a/python/environment.yml +++ b/python/environment.yml @@ -6,10 +6,11 @@ dependencies: - bedtools - jinja2 - numpy=1.17.4 + - scipy=1.4.1 - 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 @@ -23,3 +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/__init__.py b/python/glow/__init__.py index 38dc332a9..6bb123213 100644 --- a/python/glow/__init__.py +++ b/python/glow/__init__.py @@ -1,2 +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 435f50c80..e0ea6f572 100644 --- a/python/glow/conftest.py +++ b/python/glow/conftest.py @@ -1,9 +1,23 @@ +# 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 -@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/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/functions.py.TEMPLATE b/python/glow/functions.py.TEMPLATE index 79c622203..ea6f5340f 100644 --- a/python/glow/functions.py.TEMPLATE +++ b/python/glow/functions.py.TEMPLATE @@ -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/__init__.py b/python/glow/tests/__init__.py deleted file mode 100644 index e69de29bb..000000000 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 fc852f647..44b54d7c3 100644 --- a/python/glow/tests/test_transform.py +++ b/python/glow/tests/test_transform.py @@ -1,5 +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 pytest -from pyspark.sql.functions import expr, lit from pyspark.sql.utils import IllegalArgumentException import glow diff --git a/python/glow/wgr/__init__.py b/python/glow/wgr/__init__.py new file mode 100644 index 000000000..04df5e9d9 --- /dev/null +++ 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 new file mode 100644 index 000000000..533415eb8 --- /dev/null +++ b/python/glow/wgr/functions.py @@ -0,0 +1,131 @@ +# 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 +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/wgr/linear_model/__init__.py b/python/glow/wgr/linear_model/__init__.py new file mode 100644 index 000000000..f487a8e1a --- /dev/null +++ b/python/glow/wgr/linear_model/__init__.py @@ -0,0 +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 new file mode 100644 index 000000000..f2563d535 --- /dev/null +++ b/python/glow/wgr/linear_model/functions.py @@ -0,0 +1,230 @@ +# 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 +import pandas as pd +from typeguard import typechecked +from typing import Any, Iterable, List, Tuple + + +@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. + + Adapted 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] + + +@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), + (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] + elif len(key) != 3: + raise ValueError(f'Key must have 3 values, pattern is {key_pattern}') + else: + return key + + +@typechecked +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. + + Args: + 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. + """ + 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.column_stack(pdf['values'].array) + else: + 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) + + if cov_matrix.any(): + return np.column_stack((cov_matrix, X)) + else: + return X + + +@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. + + Args: + 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 sample_block 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, :].to_numpy() + else: + return labeldf[label].loc[sample_list].to_numpy().reshape(-1, 1) + + +@typechecked +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) + 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 +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. + + 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 (alpha_name, label_name) + """ + if label == 'all': + label_names = labeldf.columns + else: + label_names = [label] + + return list(itertools.product(alpha_names, label_names)) + + +@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]" + + 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 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 + 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: + inner_index = tokens[1] + new_header_block = 'all' + elif len(tokens) == 1: + inner_index = 0 + new_header_block = 'all' + else: + inner_index = tokens[3] + new_header_block = f'chr_{tokens[1]}' + + sort_keys, headers = [], [] + 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) + + return new_header_block, sort_keys, headers + + +@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. + + 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/wgr/linear_model/ridge_model.py b/python/glow/wgr/linear_model/ridge_model.py new file mode 100644 index 000000000..d052d6bf4 --- /dev/null +++ b/python/glow/wgr/linear_model/ridge_model.py @@ -0,0 +1,318 @@ +# 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 +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 +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: 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. + + 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 containing the model resulting from the fitting routine. + """ + + map_key_pattern = ['header_block', 'sample_block'] + 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, 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, covdf + ), model_struct, PandasUDFType.GROUPED_MAP) + + return blockdf \ + .groupBy(map_key_pattern) \ + .apply(map_udf) \ + .groupBy(reduce_key_pattern) \ + .apply(reduce_udf) \ + .groupBy(map_key_pattern) \ + .apply(model_udf) + + 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. + + 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 + """ + + transform_key_pattern = ['header_block', 'sample_block'] + + 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, sample_blocks, + self.alphas, covdf), reduced_matrix_struct, + PandasUDFType.GROUPED_MAP) + + return joined \ + .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: + """ + The RidgeRegression class is used to fit ridge models against one or labels optimized over a provided list of + ridge alpha parameters. It is similar in function to RidgeReducer except that whereas RidgeReducer attempts to + reduce a starting matrix X to a block matrix of smaller dimension, RidgeRegression is intended to find an optimal + model of the form Y_hat ~ XB, where Y_hat is a matrix of one or more predicted labels and B is a matrix of + coefficients. The optimal ridge alpha value is chosen for each label by maximizing the average out of fold r2 + score. + """ + 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: 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 + Spark DataFrame containing the optimal ridge alpha value for each label. + + 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: + 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', '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, 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, 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, covdf), cv_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) + + # 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))) \ + .groupBy(map_key_pattern) \ + .apply(score_udf) \ + .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 + + def transform(self, + blockdf: DataFrame, + labeldf: pd.DataFrame, + sample_blocks: Dict[str, List[str]], + modeldf: DataFrame, + cvdf: 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 + 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: + 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'] + + transform_udf = pandas_udf( + lambda key, pdf: apply_model(key, transform_key_pattern, pdf, labeldf, sample_blocks, + self.alphas, covdf), reduced_matrix_struct, + PandasUDFType.GROUPED_MAP) + + 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') + + 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 + + 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/wgr/linear_model/ridge_udfs.py b/python/glow/wgr/linear_model/ridge_udfs.py new file mode 100644 index 000000000..2e0d35b3d --- /dev/null +++ b/python/glow/wgr/linear_model/ridge_udfs.py @@ -0,0 +1,425 @@ +# 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 +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. +''' +normal_eqn_struct = StructType([ + StructField('header_block', StringType()), + StructField('sample_block', StringType()), + StructField('label', StringType()), + StructField('header', StringType()), + StructField('sort_key', IntegerType()), + StructField('xtx', ArrayType(DoubleType())), + StructField('xty', ArrayType(DoubleType())) +]) + +model_struct = StructType([ + StructField('header_block', StringType()), + StructField('sample_block', StringType()), + StructField('header', StringType()), + StructField('sort_key', 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', StringType()), + StructField('sort_key', IntegerType()), + StructField('mu', DoubleType()), + StructField('sig', DoubleType()), + StructField('alpha', StringType()), + StructField('label', StringType()) +]) + +cv_struct = StructType([ + StructField('sample_block', StringType()), + StructField('label', StringType()), + StructField('alpha', StringType()), + StructField('r2', DoubleType()) +]) + + +@typechecked +def map_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: 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. + + 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: string + |-- sort_key: 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). + 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. + schema (specified by the normal_eqn_struct): + |-- header_block: string + |-- sample_block: string + |-- label: string + |-- header: string + |-- sort_key: integer + |-- xtx: array + | |-- element: double + |-- xty: array + | |-- 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) + sample_list = sample_index[sample_block] + + 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 + + data = { + 'header_block': header_block, + 'sample_block': sample_block, + 'label': label, + 'header': header_col, + 'sort_key': sort_key_col, + 'xtx': list(XtX), + 'xty': list(XtY) + } + + return pd.DataFrame(data) + + +@typechecked +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 + 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: string + |-- label: string + |-- header: string + |-- sort_key: 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: string + |-- label: string + |-- header: string + |-- sort_key: integer + |-- xtx: array + | |-- element: double + |-- xty: array + | |-- element: double + """ + sum_xtx = pdf['xtx'].sum() + sum_xty = pdf['xty'].sum() + + # 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 pdf + + +@typechecked +def solve_normal_eqn(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: 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 + 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: string + |-- label: string + |-- header: string + |-- sort_key: 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 + 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 + schema (specified by the normal_eqn_struct): + |-- header_block: string + |-- sample_block: string + |-- header: string + |-- sort_key: integer + |-- alphas: array + | |-- element: string + |-- labels: array + | |-- element: string + |-- coefficients: array + | |-- element: double + """ + + 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())) + 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) + data = { + '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) + + +@typechecked +def apply_model(key: Tuple, key_pattern: List[str], pdf: pd.DataFrame, labeldf: 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. + + 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 coefficients B + identified by :key: + schema: + |-- header_block: string + |-- sample_block: string + |-- header: string + |-- size: integer + |-- indices: array + | |-- element: integer + |-- values: array + | |-- element: double + |-- sort_key: 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. + 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 + schema (specified by reduced_matrix_struct): + |-- header: string + |-- size: integer + |-- values: array + | |-- element: double + |-- header_block: string + |-- sample_block: string + |-- sort_key: integer + |-- mu: double + |-- sig: double + |-- alpha: string + |-- label: string + """ + + header_block, sample_block, label = parse_key(key, key_pattern) + sort_in_place(pdf, ['sort_key']) + + 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) + 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) + + data = { + '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) + + +@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], + 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 + 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 coefficients B + identified by :key: + schema: + |-- header_block: string + |-- sample_block: string + |-- header: string + |-- size: integer + |-- indices: array + | |-- element: integer + |-- values: array + | |-- element: double + |-- sort_key: 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. + 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 + schema: + |-- sample_block: string + |-- label: string + |-- alpha: string + |-- r2: double + """ + header_block, sample_block, label = parse_key(key, key_pattern) + sort_in_place(pdf, ['sort_key']) + 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 + Y = slice_label_rows(labeldf, label, sample_list) + scores = r_squared(XB, Y) + alpha_names = sorted(alphas.keys()) + + data = {'sample_block': sample_block, 'label': label, 'alpha': alpha_names, 'r2': scores} + + return pd.DataFrame(data) diff --git a/python/glow/wgr/linear_model/tests/test_functions.py b/python/glow/wgr/linear_model/tests/test_functions.py new file mode 100644 index 000000000..487e910a3 --- /dev/null +++ b/python/glow/wgr/linear_model/tests/test_functions.py @@ -0,0 +1,60 @@ +# 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 +import pytest + + +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() + + +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, 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, cov_matrix=np.array([[]])) diff --git a/python/glow/wgr/linear_model/tests/test_ridge_regression.py b/python/glow/wgr/linear_model/tests/test_ridge_regression.py new file mode 100644 index 000000000..a593a9fc7 --- /dev/null +++ b/python/glow/wgr/linear_model/tests/test_ridge_regression.py @@ -0,0 +1,542 @@ +# 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 * + +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) + +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)} +columnIndexer = sorted(enumerate(alphaMap.keys()), key=lambda t: t[1]) +coefOrder = [i for i, a in columnIndexer] + + +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') + 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 + 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.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'] + map_udf = pandas_udf( + 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) \ + .apply(map_udf) \ + .filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}') \ + .orderBy('sort_key') \ + .select('xtx', 'xty') \ + .collect() + + 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)) + + +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}').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.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'] + reduce_key_pattern = ['header_block', 'header'] + map_udf = pandas_udf( + 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) \ + .apply(map_udf) + + outdf = mapdf.groupBy(reduce_key_pattern) \ + .apply(reduce_udf) \ + .filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}') \ + .orderBy('sort_key') \ + .select('xtx', 'xty') \ + .collect() + + 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)) + + +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}').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.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]) + + sample_blocks = __get_sample_blocks(indexdf) + 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_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, reduce_key_pattern, pdf, labeldf, alphaMap, + covdf_empty), model_struct, PandasUDFType.GROUPED_MAP) + + reducedf = blockdf \ + .groupBy(map_key_pattern) \ + .apply(map_udf) \ + .groupBy(reduce_key_pattern) \ + .apply(reduce_udf) + + columns = ['coefficients'] + rows = reducedf.groupBy(map_key_pattern) \ + .apply(model_udf) \ + .filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}') \ + .select(*columns) \ + .collect() + outdf = pd.DataFrame(rows, columns=columns) + + B_lvl = np.row_stack(outdf['coefficients'].to_numpy()) + + 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}').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, :] + X_out = X0[headers].drop(ids, axis='rows') + Y_out = labeldf.drop(ids, axis='rows') + + 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.to_numpy() @ B + + sample_blocks = __get_sample_blocks(indexdf) + 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_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, covdf_empty), + model_struct, PandasUDFType.GROUPED_MAP) + transform_udf = pandas_udf( + 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) \ + .apply(map_udf) \ + .groupBy(reduce_key_pattern) \ + .apply(reduce_udf) \ + .groupBy(map_key_pattern) \ + .apply(model_udf) + + 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}') \ + .select(*columns) \ + .collect() + outdf = pd.DataFrame(rows, columns=columns) + + X1_in_lvl = np.column_stack(outdf['values']) + + 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}').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.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]) + + stack = RidgeReducer(alphas) + modeldf = stack.fit(blockdf, labeldf, __get_sample_blocks(indexdf)) + + columns = ['coefficients'] + rows = modeldf.filter(f'header_block = "{testBlock}" AND sample_block = {testGroup}') \ + .select(*columns).collect() + outdf = pd.DataFrame(rows, columns=columns) + + B_stack = np.row_stack(outdf['coefficients'].to_numpy()) + + 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}').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, :] + X_out = X0[headers].drop(ids, axis='rows') + Y_out = labeldf.drop(ids, axis='rows') + + 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.to_numpy() @ B + + stack = RidgeReducer(alphas) + modeldf = stack.fit(blockdf, labeldf, __get_sample_blocks(indexdf)) + level1df = stack.transform(blockdf, labeldf, __get_sample_blocks(indexdf), modeldf) + + columns = ['values'] + rows = level1df.filter(f'header LIKE "%{testBlock}%" AND sample_block = {testGroup}') \ + .select(*columns) \ + .collect() + outdf = pd.DataFrame(rows, columns=columns) + X1_in_stack = np.column_stack(outdf['values']) + + 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 __calculate_y_hat(X_base, group2ids, testLabel, cov=covdf_empty): + groups = sorted(group2ids.keys(), key=lambda v: v) + 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] + 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(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 = pd.Series(index=labeldf.index) + for group in groups: + ids = group2ids[group] + 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) + 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) + + r = cvdf.filter(f'label = "{testLabel}"').select('alpha', 'r2_mean').head() + bestAlpha_lvl, bestr2_lvl = (r.alpha, r.r2_mean) + 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_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' + + group2ids = __get_sample_blocks(indexdf) + bestAlpha, bestr2, y_hat = __calculate_y_hat(X2, group2ids, testLabel) + + 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) + 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.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_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' + + 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) + 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) + 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) + + +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) diff --git a/python/glow/wgr/tests/test_block_variants_and_samples.py b/python/glow/wgr/tests/test_block_variants_and_samples.py new file mode 100644 index 000000000..4c082fceb --- /dev/null +++ b/python/glow/wgr/tests/test_block_variants_and_samples.py @@ -0,0 +1,105 @@ +# 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 +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/wgr/tests/test_sample_id_extraction.py b/python/glow/wgr/tests/test_sample_id_extraction.py new file mode 100644 index 000000000..d9265f97b --- /dev/null +++ b/python/glow/wgr/tests/test_sample_id_extraction.py @@ -0,0 +1,63 @@ +# 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 +from glow.wgr 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) 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/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 c8eb0eb80..44588ee59 100644 --- a/python/setup.py +++ b/python/setup.py @@ -1,12 +1,29 @@ -from setuptools import setup +# 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 version = imp.load_source('version', 'version.py').VERSION setup(name='glow.py', version=version, - packages=['glow'], + 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/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/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 0.2931886695621029 +20:10001037:T:C 15 0,0,0,0,-1,0,0,0,0,-1,0,0,0,0,0 chr_20_block_0 2 10001037 -0.06999999999999999 0.2931886695621029 +20:10001037:T:C 14 0,-1,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 3 10001037 -0.06999999999999999 0.2931886695621029 +20:10001037:T:C 14 0,0,0,0,0,0,-1,1,0,0,-1,0,0,0 chr_20_block_0 4 10001037 -0.06999999999999999 0.2931886695621029 +20:10001037:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 5 10001037 -0.06999999999999999 0.2931886695621029 +20:10001037:T:C 15 0,0,0,0,-1,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 6 10001037 -0.06999999999999999 0.2931886695621029 +20:10001037:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_0 7 10001037 -0.06999999999999999 0.2931886695621029 +20:10001297:T:A 14 0,1,1,2,2,2,2,2,0,2,1,1,1,2 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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 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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 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chr_20_block_3 5 10003650 1.4899999999999998 0.6589707309451794 +20:10003650:T:C 15 2,2,0,1,2,1,1,1,2,2,2,1,1,1,2 chr_20_block_3 6 10003650 1.4899999999999998 0.6589707309451794 +20:10003650:T:C 14 2,2,2,2,2,1,1,2,2,2,2,2,2,1 chr_20_block_3 7 10003650 1.4899999999999998 0.6589707309451794 +20:10003679:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_3 1 10003679 0.0 0.0 +20:10003679:T:C 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_3 2 10003679 0.0 0.0 +20:10003679:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_3 3 10003679 0.0 0.0 +20:10003679:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_3 4 10003679 0.0 0.0 +20:10003679:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_3 5 10003679 0.0 0.0 +20:10003679:T:C 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_3 6 10003679 0.0 0.0 +20:10003679:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_3 7 10003679 0.0 0.0 +20:10003691:A:G 14 1,1,1,2,2,2,2,2,0,2,1,1,1,2 chr_20_block_3 1 10003691 1.4800000000000002 0.6739001514626733 +20:10003691:A:G 15 2,1,1,2,0,2,1,2,1,2,1,1,1,2,0 chr_20_block_3 2 10003691 1.4800000000000002 0.6739001514626733 +20:10003691:A:G 14 1,2,2,2,2,2,2,1,2,2,1,1,2,2 chr_20_block_3 3 10003691 1.4800000000000002 0.6739001514626733 +20:10003691:A:G 14 2,2,1,1,0,2,2,2,2,2,2,1,2,2 chr_20_block_3 4 10003691 1.4800000000000002 0.6739001514626733 +20:10003691:A:G 14 0,0,2,2,2,2,0,0,2,1,2,1,1,2 chr_20_block_3 5 10003691 1.4800000000000002 0.6739001514626733 +20:10003691:A:G 15 2,2,0,1,2,1,1,1,2,2,2,1,0,2,2 chr_20_block_3 6 10003691 1.4800000000000002 0.6739001514626733 +20:10003691:A:G 14 2,2,2,2,2,1,1,2,2,2,1,2,2,1 chr_20_block_3 7 10003691 1.4800000000000002 0.6739001514626733 +20:10003726:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_3 1 10003726 -0.010000000000000002 0.1 +20:10003726:T:C 15 0,0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_3 2 10003726 -0.010000000000000002 0.1 +20:10003726:T:C 14 0,0,0,0,0,0,0,0,0,0,0,0,0,0 chr_20_block_3 3 10003726 -0.010000000000000002 0.1 +20:10003726:T:C 14 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chr_20_block_4 7 10004768 0.38 0.6159496240715951 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 diff --git a/test-data/wgr/ridge-regression/README.md b/test-data/wgr/ridge-regression/README.md new file mode 100644 index 000000000..ad634cf92 --- /dev/null +++ b/test-data/wgr/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/wgr/ridge-regression/X0.csv b/test-data/wgr/ridge-regression/X0.csv new file mode 100644 index 000000000..c02dc7e5e --- /dev/null +++ b/test-data/wgr/ridge-regression/X0.csv @@ -0,0 +1,101 @@ 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diff --git a/test-data/wgr/ridge-regression/X1.csv b/test-data/wgr/ridge-regression/X1.csv new file mode 100644 index 000000000..5377aee5b --- /dev/null +++ b/test-data/wgr/ridge-regression/X1.csv @@ -0,0 +1,101 @@ 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