From a63c9bad5702222a07ea89050f945fc8e617e8ff Mon Sep 17 00:00:00 2001 From: Zeyu Date: Thu, 29 Aug 2024 17:20:05 +0800 Subject: [PATCH 1/4] [UPDATE] fix some script error --- Agent/workspace/hyperopt/abalone/code/code.py | 8 +- .../workspace/hyperopt/abalone2/code/code.py | 13 +- .../hyperopt/bank-churn2/code/code.py | 8 +- .../hyperopt/digit-recognizer/code/code.py | 219 ++++++++++++ .../code/keras-auto-hypertuning-a-cnn.ipynb | 1 + Agent/workspace/hyperopt/fstp2/code/code.py | 11 +- .../hyperopt/higgs-boson2/code/code.py | 2 +- .../workspace/hyperopt/mercedes2/code/code.py | 6 +- .../hyperopt/nlp-getting-started/code/code.py | 180 ++++++++++ .../code/xgb-svc-nlp-implementation.ipynb | 1 + .../hyperopt/obesity-risk2/code/code.py | 195 ++++++----- Agent/workspace/hyperopt/ogpc/code/code.py | 104 ++++++ .../ogpc/code/otto-simple-lgb-4e8206.ipynb | 1 + Agent/workspace/hyperopt/ogpc2/code/code.py | 69 ++++ .../code/fork-of-lr-gbm-rf-ensemble.ipynb | 1 + Agent/workspace/hyperopt/ps311/code/code.py | 320 ++++++++++++++++++ ...ick-eda-and-simple-baseline-with-xgb.ipynb | 1 + Agent/workspace/hyperopt/ps3112/code/code.py | 307 +++++++++++++++++ ...feature-eng-xgb-cat-ensemble-0-29265.ipynb | 1 + Agent/workspace/hyperopt/rcaf2/code/code.py | 52 +-- Agent/workspace/hyperopt/rrp2/code/code.py | 14 +- .../workspace/hyperopt/scrabble/code/code.py | 6 +- .../workspace/hyperopt/sf-crime/code/code.py | 68 ++-- .../workspace/hyperopt/sf-crime2/code/code.py | 35 +- Agent/workspace/hyperopt/tpsf/code/code.py | 105 ++++++ ...d-feb-tabular-playground-competition.ipynb | 1 + Agent/workspace/hyperopt/tpsf2/code/code.py | 273 +++++++++++++++ .../ensemble-lgb-xgb-catboost-optimized.ipynb | 1 + 28 files changed, 1804 insertions(+), 199 deletions(-) create mode 100644 Agent/workspace/hyperopt/digit-recognizer/code/code.py create mode 100644 Agent/workspace/hyperopt/digit-recognizer/code/keras-auto-hypertuning-a-cnn.ipynb create mode 100644 Agent/workspace/hyperopt/nlp-getting-started/code/code.py create mode 100644 Agent/workspace/hyperopt/nlp-getting-started/code/xgb-svc-nlp-implementation.ipynb create mode 100644 Agent/workspace/hyperopt/ogpc/code/code.py create mode 100644 Agent/workspace/hyperopt/ogpc/code/otto-simple-lgb-4e8206.ipynb create mode 100644 Agent/workspace/hyperopt/ogpc2/code/code.py create mode 100644 Agent/workspace/hyperopt/ogpc2/code/fork-of-lr-gbm-rf-ensemble.ipynb create mode 100644 Agent/workspace/hyperopt/ps311/code/code.py create mode 100644 Agent/workspace/hyperopt/ps311/code/quick-eda-and-simple-baseline-with-xgb.ipynb create mode 100644 Agent/workspace/hyperopt/ps3112/code/code.py create mode 100644 Agent/workspace/hyperopt/ps3112/code/feature-eng-xgb-cat-ensemble-0-29265.ipynb create mode 100644 Agent/workspace/hyperopt/tpsf/code/code.py create mode 100644 Agent/workspace/hyperopt/tpsf/code/get-started-feb-tabular-playground-competition.ipynb create mode 100644 Agent/workspace/hyperopt/tpsf2/code/code.py create mode 100644 Agent/workspace/hyperopt/tpsf2/code/ensemble-lgb-xgb-catboost-optimized.ipynb diff --git a/Agent/workspace/hyperopt/abalone/code/code.py b/Agent/workspace/hyperopt/abalone/code/code.py index 2672087..05f93da 100644 --- a/Agent/workspace/hyperopt/abalone/code/code.py +++ b/Agent/workspace/hyperopt/abalone/code/code.py @@ -105,19 +105,15 @@ # List of models to evaluate catboost_model = CatBoostRegressor(random_state=1, verbose=False) -# lgbm_model = LGBMRegressor(verbose=-1, random_state=1) -# xgb_model = XGBRegressor(verbose=0, random_state=1, enable_categorical=True) + # # Fit the models on the training data # catboost_model.fit(X_train, y_train) -# lgbm_model.fit(X_train, y_train) -# xgb_model.fit(X_train, y_train) # # Evaluate the models # catboost_preds = catboost_model.predict(X_val) -# lgbm_preds = lgbm_model.predict(X_val) -# xgb_preds = xgb_model.predict(X_val) + # final_preds = np.round((catboost_preds + lgbm_preds + xgb_preds) / 3).astype("int") diff --git a/Agent/workspace/hyperopt/abalone2/code/code.py b/Agent/workspace/hyperopt/abalone2/code/code.py index 5fc6edd..4a3a40a 100644 --- a/Agent/workspace/hyperopt/abalone2/code/code.py +++ b/Agent/workspace/hyperopt/abalone2/code/code.py @@ -33,7 +33,7 @@ np.random.seed(RANDOM_SEED) random.seed(RANDOM_SEED) -FILE_PATH = "./workspace/hyperopt/abalone/data/" +FILE_PATH = "./workspace/hyperopt/abalone2/data/" # FILE_PATH="../data/" submmision_file = "submission.csv" train = pd.read_csv(FILE_PATH + "train.csv") @@ -317,14 +317,15 @@ def log_transformation(data, columns): "xgboost_weight": 0.48550637896530635, "catboost_weight": 4.189724537494019, } - -voting_regressor = VotingRegressor( - estimators=cv_estimators, - weights=[ +weights_list=[ weight_best_params["lgbm_weight"], weight_best_params["xgboost_weight"], weight_best_params["catboost_weight"] - ] + +] +voting_regressor = VotingRegressor( + estimators=cv_estimators, + weights=weights_list ) # cv = StratifiedKFold(n_splits=3, shuffle=True, random_state=RANDOM_SEED) diff --git a/Agent/workspace/hyperopt/bank-churn2/code/code.py b/Agent/workspace/hyperopt/bank-churn2/code/code.py index aad81fc..06a1f55 100644 --- a/Agent/workspace/hyperopt/bank-churn2/code/code.py +++ b/Agent/workspace/hyperopt/bank-churn2/code/code.py @@ -110,8 +110,8 @@ def plot_kde_for_all_columns(df): # Below are the parameters for xgboost. xgb_params = {"booster": "gbtree", - "lambda": 0.8611971458776956, - "alpha": 3.3684132992886347e-07, + "reg_lambda": 0.8611971458776956, + "reg_alpha": 3.3684132992886347e-07, "max_depth": 3, "eta": 0.17374299923922656, "gamma": 1.2505690952357777e-06, @@ -144,9 +144,9 @@ def plot_kde_for_all_columns(df): # ## Voting Ensemble - +weight_list=[0.2,0.4,0.4] voter = VotingClassifier(estimators=[("m1", xgb_model), ("m2", lgbm_model), ("m3", cb_model)], voting="soft", - weights=[0.2, 0.4, 0.4]) + weights=weight_list) # voter.fit(X,y) diff --git a/Agent/workspace/hyperopt/digit-recognizer/code/code.py b/Agent/workspace/hyperopt/digit-recognizer/code/code.py new file mode 100644 index 0000000..508fc81 --- /dev/null +++ b/Agent/workspace/hyperopt/digit-recognizer/code/code.py @@ -0,0 +1,219 @@ +#!/usr/bin/env python +# coding: utf-8 + +# get_ipython().system('pip install keras-tuner') + + +import numpy as np # linear algebra +import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) + +import tensorflow as tf +from tensorflow import keras +from tensorflow.keras.utils import to_categorical +from tensorflow.keras.optimizers import Adam +from tensorflow.keras import layers +from tensorflow.keras.datasets import mnist + +# from kerastuner import RandomSearch + +import matplotlib.pyplot as plt +from sklearn.model_selection import train_test_split +# from keras.callbacks import ReduceLROnPlateau +# from keras.optimizers import RMSprop +# Input data files are available in the "../input/" directory. +# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory +from keras.datasets import mnist +FILE_PATH = "./workspace/hyperopt/tspf2/data/" + +# import os +# for dirname, _, filenames in os.walk('/kaggle/input'): +# for filename in filenames: +# print(os.path.join(dirname, filename)) + +# Any results you write to the current directory are saved as output. + + +# Load the data +train = pd.read_csv(FILE_PATH+'train.csv') +labels = train.iloc[:,0].values.astype('int32') + +X_train = (train.iloc[:,1:].values).astype('float32') +X_test = (pd.read_csv(FILE_PATH+'test.csv').values).astype('float32') + +#reshape into images +X_train = X_train.reshape(-1,28,28,1) +X_test = X_test.reshape(-1,28,28,1) + +# one hot encoding +y_train = tf.keras.utils.to_categorical(labels) + +# print("Check data") +# print(labels) +# print(X_train[0].shape) +# print(y_train) + + +# Load Data from Keras MNIST +(train_imagesRaw, train_labelsRaw), (test_imagesRaw, test_labelsRaw) = mnist.load_data() + + +#reshape into images +X_train_keras = train_imagesRaw.reshape(-1,28,28,1) +X_test_keras = test_imagesRaw.reshape(-1,28,28,1) + +# print("X_train_keras",X_train_keras.shape) +# print("X_test_keras",X_test_keras.shape) + +train_labels_keras = tf.keras.utils.to_categorical(train_labelsRaw) +test_labels_keras = tf.keras.utils.to_categorical(test_labelsRaw) +# print("train_labels_keras ",train_labels_keras.shape) +# print("test_labels_keras ", test_labels_keras.shape) + + +# merge datasets + +train_images = np.concatenate((X_train_keras,X_train,X_test_keras), axis=0) +# print("new Concatenated train_images ", train_images.shape) +# print("_"*50) + +train_labels = np.concatenate((train_labels_keras,y_train,test_labels_keras), axis=0) +# print("new Concatenated train_labels ", train_labels.shape) + + +#visualize an image + +# fig = plt.figure() +# plt.imshow(X_train[6][:,:,0], cmap='gray', interpolation='none') +# plt.xticks([]) +# plt.yticks([]) + + +scale = np.max(train_images) +train_images /= scale +X_test /= scale + +#visualize scales + +# print("Max: {}".format(scale)) + + +# X_train, X_val, y_train, y_val = train_test_split(train_images, train_labels, test_size=0.10) + + +# # Here we define the input and output layer sizes +input_size = X_train.shape +n_logits = y_train.shape[1] + +# print("Input: {}".format(input_size)) +# print("Output: {}".format(n_logits)) + +num_layers = 8 #hp.Int('num_layers', min_value=2, max_value=16, step=2) + +lr = 1e-4 #hp.Choice('learning_rate', [1e-3, 5e-4]) +filters = 128 #hp.Int('filters_' + idx, 32, 256, step=32, default=64) +pool_type = 'max' #hp.Choice('pool_' + idx, values=['max', 'avg']) + +inputs = layers.Input(shape=(28, 28, 1)) +x = inputs +for idx in range(num_layers): + idx = str(idx) + x = layers.Conv2D(filters=filters, kernel_size=3, padding='same', + activation='relu')(x) + + # add a pooling layers if needed + if x.shape[1] >= 8: + if pool_type == 'max': + x = layers.MaxPooling2D(2)(x) + elif pool_type == 'avg': + x = layers.AveragePooling2D(2)(x) + +# My dense layer + +x = layers.Flatten()(x) +x = layers.Dense(256, activation='relu')(x) +x = layers.Dense(256, activation='relu')(x) +x = layers.Dense(256, activation='relu')(x) +x = layers.Dropout(0.5)(x) +outputs = layers.Dense(n_logits, activation='softmax')(x) + +# Build model +model = keras.Model(inputs, outputs) +model.compile(optimizer=Adam(lr), + loss='categorical_crossentropy', + metrics=['accuracy']) + + + + +# def build_model(hp): +# """Function that build a TF model based on hyperparameters values. +# Args: +# hp (HyperParameter): hyperparameters values +# Returns: +# Model: Compiled model +# """ +# num_layers = hp.Int('num_layers', min_value=2, max_value=16, step=2) + +# lr = hp.Choice('learning_rate', [1e-3, 5e-4]) + +# inputs = layers.Input(shape=(28, 28, 1)) +# x = inputs + +# for idx in range(num_layers): +# idx = str(idx) + +# filters = hp.Int('filters_' + idx, 32, 256, step=32, default=64) +# x = layers.Conv2D(filters=filters, kernel_size=3, padding='same', +# activation='relu')(x) + +# # add a pooling layers if needed +# if x.shape[1] >= 8: +# pool_type = hp.Choice('pool_' + idx, values=['max', 'avg']) +# if pool_type == 'max': +# x = layers.MaxPooling2D(2)(x) +# elif pool_type == 'avg': +# x = layers.AveragePooling2D(2)(x) + +# # My dense layer + +# x = layers.Flatten()(x) +# x = layers.Dense(256, activation='relu')(x) +# x = layers.Dense(256, activation='relu')(x) +# x = layers.Dense(256, activation='relu')(x) +# x = layers.Dropout(0.5)(x) +# outputs = layers.Dense(n_logits, activation='softmax')(x) + +# # Build model +# model = keras.Model(inputs, outputs) +# model.compile(optimizer=Adam(lr), +# loss='categorical_crossentropy', +# metrics=['accuracy']) +# return model + + +# tuner = RandomSearch( +# build_model, +# objective='val_accuracy', +# max_trials=8, +# executions_per_trial=3, +# directory='my_dir', +# project_name='mnist') + +# tuner.search_space_summary() + + +# tuner.search(X_train, y_train, +# epochs=30, +# validation_data=(X_val, y_val)) + + +# model = tuner.get_best_models(num_models=1)[0] +# model.summary() + + +# # generate predictions +# predictions_vector = model.predict(X_test, verbose=0) +# predictions = np.argmax(predictions_vector,axis=1) + +# pd.DataFrame({"ImageId": list(range(1,len(predictions)+1)), "Label": predictions}).to_csv("preds.csv", index=False, header=True) + diff --git a/Agent/workspace/hyperopt/digit-recognizer/code/keras-auto-hypertuning-a-cnn.ipynb b/Agent/workspace/hyperopt/digit-recognizer/code/keras-auto-hypertuning-a-cnn.ipynb new file mode 100644 index 0000000..e0a143a --- /dev/null +++ b/Agent/workspace/hyperopt/digit-recognizer/code/keras-auto-hypertuning-a-cnn.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":3004,"databundleVersionId":861823,"sourceType":"competition"}],"dockerImageVersionId":29841,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"!pip install keras-tuner","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras.utils import to_categorical\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.datasets import mnist\n\nfrom kerastuner import RandomSearch\n\nimport matplotlib.pyplot as plt\nfrom sklearn.model_selection import train_test_split\nfrom keras.callbacks import ReduceLROnPlateau\nfrom keras.optimizers import RMSprop\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\nfrom keras.datasets import mnist\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# Any results you write to the current directory are saved as output.","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Load the data\ntrain = pd.read_csv('../input/digit-recognizer/train.csv')\nlabels = train.iloc[:,0].values.astype('int32')\n\nX_train = (train.iloc[:,1:].values).astype('float32')\nX_test = (pd.read_csv('../input/digit-recognizer/test.csv').values).astype('float32')\n\n#reshape into images\nX_train = X_train.reshape(-1,28,28,1)\nX_test = X_test.reshape(-1,28,28,1)\n\n# one hot encoding\ny_train = tf.keras.utils.to_categorical(labels) \n\nprint(\"Check data\")\nprint(labels)\nprint(X_train[0].shape)\nprint(y_train)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Load Data from Keras MNIST\n(train_imagesRaw, train_labelsRaw), (test_imagesRaw, test_labelsRaw) = mnist.load_data()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#reshape into images\nX_train_keras = train_imagesRaw.reshape(-1,28,28,1)\nX_test_keras = test_imagesRaw.reshape(-1,28,28,1)\n\nprint(\"X_train_keras\",X_train_keras.shape)\nprint(\"X_test_keras\",X_test_keras.shape)\n\ntrain_labels_keras = tf.keras.utils.to_categorical(train_labelsRaw)\ntest_labels_keras = tf.keras.utils.to_categorical(test_labelsRaw)\nprint(\"train_labels_keras \",train_labels_keras.shape)\nprint(\"test_labels_keras \", test_labels_keras.shape)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# merge datasets\n\ntrain_images = np.concatenate((X_train_keras,X_train,X_test_keras), axis=0)\nprint(\"new Concatenated train_images \", train_images.shape)\nprint(\"_\"*50)\n\ntrain_labels = np.concatenate((train_labels_keras,y_train,test_labels_keras), axis=0)\nprint(\"new Concatenated train_labels \", train_labels.shape)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#visualize an image\n\nfig = plt.figure()\nplt.imshow(X_train[6][:,:,0], cmap='gray', interpolation='none')\nplt.xticks([])\nplt.yticks([])","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"scale = np.max(train_images)\ntrain_images /= scale\nX_test /= scale\n\n#visualize scales\n\nprint(\"Max: {}\".format(scale))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train, X_val, y_train, y_val = train_test_split(train_images, train_labels, test_size=0.10)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Here we define the input and output layer sizes\ninput_size = X_train.shape\nn_logits = y_train.shape[1]\n\nprint(\"Input: {}\".format(input_size))\nprint(\"Output: {}\".format(n_logits))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def build_model(hp):\n \"\"\"Function that build a TF model based on hyperparameters values.\n Args:\n hp (HyperParameter): hyperparameters values\n Returns:\n Model: Compiled model\n \"\"\"\n num_layers = hp.Int('num_layers', min_value=2, max_value=16, step=2)\n \n lr = hp.Choice('learning_rate', [1e-3, 5e-4])\n\n inputs = layers.Input(shape=(28, 28, 1))\n x = inputs\n\n for idx in range(num_layers):\n idx = str(idx)\n\n filters = hp.Int('filters_' + idx, 32, 256, step=32, default=64)\n x = layers.Conv2D(filters=filters, kernel_size=3, padding='same',\n activation='relu')(x)\n\n # add a pooling layers if needed\n if x.shape[1] >= 8:\n pool_type = hp.Choice('pool_' + idx, values=['max', 'avg'])\n if pool_type == 'max':\n x = layers.MaxPooling2D(2)(x)\n elif pool_type == 'avg':\n x = layers.AveragePooling2D(2)(x)\n\n # My dense layer\n \n x = layers.Flatten()(x)\n x = layers.Dense(256, activation='relu')(x)\n x = layers.Dense(256, activation='relu')(x)\n x = layers.Dense(256, activation='relu')(x)\n x = layers.Dropout(0.5)(x)\n outputs = layers.Dense(n_logits, activation='softmax')(x)\n \n # Build model\n model = keras.Model(inputs, outputs)\n model.compile(optimizer=Adam(lr),\n loss='categorical_crossentropy',\n metrics=['accuracy'])\n return model","metadata":{"_uuid":"d629ff2d2480ee46fbb7e2d37f6b5fab8052498a","_cell_guid":"79c7e3d0-c299-4dcb-8224-4455121ee9b0","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tuner = RandomSearch(\n build_model,\n objective='val_accuracy',\n max_trials=8,\n executions_per_trial=3,\n directory='my_dir',\n project_name='mnist')\n\ntuner.search_space_summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"tuner.search(X_train, y_train,\n epochs=30,\n validation_data=(X_val, y_val))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = tuner.get_best_models(num_models=1)[0]\nmodel.summary()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# generate predictions\npredictions_vector = model.predict(X_test, verbose=0)\npredictions = np.argmax(predictions_vector,axis=1)\n\npd.DataFrame({\"ImageId\": list(range(1,len(predictions)+1)), \"Label\": predictions}).to_csv(\"preds.csv\", index=False, header=True)","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/fstp2/code/code.py b/Agent/workspace/hyperopt/fstp2/code/code.py index b2ab6b4..1cdde74 100644 --- a/Agent/workspace/hyperopt/fstp2/code/code.py +++ b/Agent/workspace/hyperopt/fstp2/code/code.py @@ -28,9 +28,9 @@ # ### Import Necessary Libraries and Data Sets. -from subprocess import check_output +# from subprocess import check_output -print(check_output(["ls", "../input"]).decode("utf8")) +# print(check_output(["ls", "../input"]).decode("utf8")) # Import the necessary packages import numpy as np @@ -67,7 +67,7 @@ from sklearn.ensemble import RandomForestClassifier from sklearn.model_selection import StratifiedKFold from sklearn.ensemble import VotingClassifier -from sklearn.metrics import mean_squared_error +from sklearn.metrics import accuracy_score # FILE_PATH = "../data/" FILE_PATH = "./workspace/hyperopt/fstp2/data/" @@ -88,8 +88,11 @@ # Combine train and test sets concat_data = pd.concat((train, test), sort=False).reset_index(drop=True) # Drop the target "Cover_Type" and Id columns +print(concat_data.columns) + concat_data.drop(["Cover_Type"], axis=1, inplace=True) -concat_data.drop(["Id"], axis=1, inplace=True) +# print(concat_data.columns) +# concat_data.drop(["Id"], axis=1, inplace=True) # print("Total size is :",concat_data.shape) diff --git a/Agent/workspace/hyperopt/higgs-boson2/code/code.py b/Agent/workspace/hyperopt/higgs-boson2/code/code.py index fc80ca5..43ea459 100644 --- a/Agent/workspace/hyperopt/higgs-boson2/code/code.py +++ b/Agent/workspace/hyperopt/higgs-boson2/code/code.py @@ -26,7 +26,7 @@ FILE_PATH = "./workspace/hyperopt/higgs-boson2/data/" # FILE_PATH ="../data/" -TARGET = "NObeyesdad" +# TARGET = "NObeyesdad" submission_path = "best_submission.csv" RANDOM_SEED = 73 diff --git a/Agent/workspace/hyperopt/mercedes2/code/code.py b/Agent/workspace/hyperopt/mercedes2/code/code.py index ce948b7..ad278fa 100644 --- a/Agent/workspace/hyperopt/mercedes2/code/code.py +++ b/Agent/workspace/hyperopt/mercedes2/code/code.py @@ -14,13 +14,13 @@ # FILE_PATH = "../data/" FILE_PATH = "./workspace/hyperopt/mercedes2/data/" -TARGET = "NObeyesdad" +# TARGET = "NObeyesdad" submission_path = "ori_submission.csv" n_splits = 9 RANDOM_SEED = 73 -train = pd.read_csv(FILE_PATH + "train.csv") -test = pd.read_csv(FILE_PATH + "test.csv") +train = pd.read_csv(FILE_PATH + "train.csv.zip") +test = pd.read_csv(FILE_PATH + "test.csv.zip") y_train = train["y"].values y_mean = np.mean(y_train) diff --git a/Agent/workspace/hyperopt/nlp-getting-started/code/code.py b/Agent/workspace/hyperopt/nlp-getting-started/code/code.py new file mode 100644 index 0000000..6d6ba1d --- /dev/null +++ b/Agent/workspace/hyperopt/nlp-getting-started/code/code.py @@ -0,0 +1,180 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Natural Langugae Processing with Disaster Tweets: XGBoost and SVC Implementation +# +#
+# House Prices +#
+ +# ## Import Libraries + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +import re +import string +from sklearn.model_selection import train_test_split +from sklearn.feature_extraction.text import TfidfVectorizer +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import classification_report, accuracy_score +from sklearn.pipeline import Pipeline +from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier +from sklearn.svm import SVC +from xgboost import XGBClassifier +from sklearn.model_selection import GridSearchCV +import nltk +from nltk.corpus import stopwords +from nltk.stem import PorterStemmer +nltk.download('stopwords') + + +# ## Loading Data +FILE_PATH = "./workspace/hyperopt/ps311/data/" + +train = pd.read_csv(FILE_PATH+'nlp-getting-started/train.csv') +test = pd.read_csv(FILE_PATH+'nlp-getting-started/test.csv') + +train.head() + + +# ## EDA + +# print(train.isnull().sum()) + + +# print(train.describe()) + + +# # Plot the distribution of the target variable +# plt.figure(figsize=(8, 6)) +# sns.countplot(x='target', data=train, palette='viridis') +# plt.title('Distribution of Target Variable') +# plt.xlabel('Target') +# plt.ylabel('Count') +# plt.show() + + +# Visualize the length of tweets +train['text_length'] = train['text'].apply(len) + +# plt.figure(figsize=(10, 6)) +# sns.histplot(train['text_length'], kde=True, color='purple', bins=30) +# plt.title('Distribution of Tweet Lengths') +# plt.xlabel('Tweet Length') +# plt.ylabel('Frequency') +# plt.show() + + + + + +# Visualize word count distribution +train['word_count'] = train['text'].apply(lambda x: len(x.split())) + +# plt.figure(figsize=(10, 6)) +# sns.histplot(train['word_count'], kde=True, color='blue', bins=30) +# plt.title('Distribution of Word Counts in Tweets') +# plt.xlabel('Word Count') +# plt.ylabel('Frequency') +# plt.show() + + +# Visualize top 20 most frequent words in the tweets (excluding stop words) +from sklearn.feature_extraction.text import CountVectorizer +from collections import Counter + +vectorizer = CountVectorizer(stop_words='english') +word_count_vector = vectorizer.fit_transform(train['text']) +word_counts = word_count_vector.toarray().sum(axis=0) +vocab = vectorizer.get_feature_names_out() + + +common_words_df = pd.DataFrame(sorted(list(zip(vocab, word_counts)), key=lambda x: x[1], reverse=True)[:20], + columns=['Word', 'Count']) + +# plt.figure(figsize=(12, 8)) +# sns.barplot(x='Count', y='Word', data=common_words_df, palette='coolwarm') +# plt.title('Top 20 Most Frequent Words') +# plt.xlabel('Count') +# plt.ylabel('Word') +# plt.show() + + +# ## Preprocess the Text Data + +def preprocess(text): + text = text.lower() # Convert to lowercase + text = re.sub(r'https?://\S+|www\.\S+', '', text) # Remove URLs + text = re.sub(r'<.*?>', '', text) # Remove HTML tags + text = re.sub(r'\d+', '', text) # Remove digits + text = re.sub(f'[{re.escape(string.punctuation)}]', '', text) # Remove punctuation + text = re.sub(r'\n', '', text) # Remove newline characters + text = re.sub(r'\s+', ' ', text).strip() # Remove extra spaces + return text + +train['text'] = train['text'].apply(preprocess) +test['text'] = test['text'].apply(preprocess) + + +# ## Split the Training Data + +# X_train, X_val, y_train, y_val = train_test_split(train['text'], train['target'], test_size=0.2, random_state=42) + + +# ## Vectorize the Text Data Using TF-IDF + +tfidf = TfidfVectorizer(max_features=15000, stop_words=stopwords.words('english')) +X_train_tfidf = tfidf.fit_transform(train['text']) +# X_val_tfidf = tfidf.transform(X_val) +X_test_tfidf = tfidf.transform(test['text']) + + +# ## Build the Model + +# svc = SVC() +# svc.fit(X_train_tfidf, y_train) +# y_pred_svc = svc.predict(X_val_tfidf) + + +xgb = XGBClassifier(use_label_encoder=False, eval_metric='logloss') +# xgb.fit(X_train_tfidf, y_train) +# y_pred_xgb = xgb.predict(X_val_tfidf) + + +# # Assuming you have these models trained already +# models = { +# 'Support Vector Classifier': svc, +# 'XGBoost': xgb +# } + +# # Initialize variables to store the best model and accuracy +# best_model = None +# best_accuracy = 0 + +# # Evaluate each model and store the one with the best accuracy +# for name, model in models.items(): +# predictions = model.predict(X_val_tfidf) +# accuracy = accuracy_score(y_val, predictions) +# print(f'{name} Accuracy: {accuracy}') + +# if accuracy > best_accuracy: +# best_accuracy = accuracy +# best_model = model + +# print(f'\nBest Model: {best_model} with Accuracy: {best_accuracy}') + +# # Make predictions on the test data using the best model +# test_predictions = best_model.predict(X_test_tfidf) + + +# # Create the submission DataFrame +# submission = pd.DataFrame({ +# 'id': test['id'], +# 'target': test_predictions +# }) + +# # Save the submission file +# submission.to_csv('submission.csv', index=False) + diff --git a/Agent/workspace/hyperopt/nlp-getting-started/code/xgb-svc-nlp-implementation.ipynb b/Agent/workspace/hyperopt/nlp-getting-started/code/xgb-svc-nlp-implementation.ipynb new file mode 100644 index 0000000..735e311 --- /dev/null +++ b/Agent/workspace/hyperopt/nlp-getting-started/code/xgb-svc-nlp-implementation.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":17777,"databundleVersionId":869809,"sourceType":"competition"}],"dockerImageVersionId":30746,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Natural Langugae Processing with Disaster Tweets: XGBoost and SVC Implementation\n\n
\n \"House\n
","metadata":{}},{"cell_type":"markdown","source":"## Import Libraries","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport re\nimport string\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import classification_report, accuracy_score\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier, VotingClassifier\nfrom sklearn.svm import SVC\nfrom xgboost import XGBClassifier\nfrom sklearn.model_selection import GridSearchCV\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem import PorterStemmer\nnltk.download('stopwords')\n","metadata":{"execution":{"iopub.status.busy":"2024-08-06T15:00:21.009799Z","iopub.execute_input":"2024-08-06T15:00:21.010228Z","iopub.status.idle":"2024-08-06T15:00:23.511873Z","shell.execute_reply.started":"2024-08-06T15:00:21.01018Z","shell.execute_reply":"2024-08-06T15:00:23.510528Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Loading Data","metadata":{}},{"cell_type":"code","source":"train = pd.read_csv('/kaggle/input/nlp-getting-started/train.csv')\ntest = pd.read_csv('/kaggle/input/nlp-getting-started/test.csv')\n\ntrain.head()","metadata":{"execution":{"iopub.status.busy":"2024-08-06T15:01:12.939165Z","iopub.execute_input":"2024-08-06T15:01:12.939627Z","iopub.status.idle":"2024-08-06T15:01:12.99244Z","shell.execute_reply.started":"2024-08-06T15:01:12.939586Z","shell.execute_reply":"2024-08-06T15:01:12.991334Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## EDA","metadata":{}},{"cell_type":"code","source":"print(train.isnull().sum())\n","metadata":{"execution":{"iopub.status.busy":"2024-08-06T15:01:22.917851Z","iopub.execute_input":"2024-08-06T15:01:22.918252Z","iopub.status.idle":"2024-08-06T15:01:22.926935Z","shell.execute_reply.started":"2024-08-06T15:01:22.918197Z","shell.execute_reply":"2024-08-06T15:01:22.925758Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(train.describe())\n","metadata":{"execution":{"iopub.status.busy":"2024-08-06T15:00:51.926126Z","iopub.execute_input":"2024-08-06T15:00:51.92694Z","iopub.status.idle":"2024-08-06T15:00:51.945183Z","shell.execute_reply.started":"2024-08-06T15:00:51.926907Z","shell.execute_reply":"2024-08-06T15:00:51.94389Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Plot the distribution of the target variable\nplt.figure(figsize=(8, 6))\nsns.countplot(x='target', data=train, palette='viridis')\nplt.title('Distribution of Target Variable')\nplt.xlabel('Target')\nplt.ylabel('Count')\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-08-06T15:00:29.969043Z","iopub.execute_input":"2024-08-06T15:00:29.970094Z","iopub.status.idle":"2024-08-06T15:00:30.232736Z","shell.execute_reply.started":"2024-08-06T15:00:29.970058Z","shell.execute_reply":"2024-08-06T15:00:30.231264Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Visualize the length of tweets\ntrain['text_length'] = train['text'].apply(len)\n\nplt.figure(figsize=(10, 6))\nsns.histplot(train['text_length'], kde=True, color='purple', bins=30)\nplt.title('Distribution of Tweet Lengths')\nplt.xlabel('Tweet Length')\nplt.ylabel('Frequency')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-08-06T15:01:56.023324Z","iopub.execute_input":"2024-08-06T15:01:56.024094Z","iopub.status.idle":"2024-08-06T15:01:56.464228Z","shell.execute_reply.started":"2024-08-06T15:01:56.024057Z","shell.execute_reply":"2024-08-06T15:01:56.462935Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Visualize word count distribution\ntrain['word_count'] = train['text'].apply(lambda x: len(x.split()))\n\nplt.figure(figsize=(10, 6))\nsns.histplot(train['word_count'], kde=True, color='blue', bins=30)\nplt.title('Distribution of Word Counts in Tweets')\nplt.xlabel('Word Count')\nplt.ylabel('Frequency')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-08-06T15:02:15.52682Z","iopub.execute_input":"2024-08-06T15:02:15.527305Z","iopub.status.idle":"2024-08-06T15:02:15.950651Z","shell.execute_reply.started":"2024-08-06T15:02:15.527269Z","shell.execute_reply":"2024-08-06T15:02:15.949644Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Visualize top 20 most frequent words in the tweets (excluding stop words)\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom collections import Counter\n\nvectorizer = CountVectorizer(stop_words='english')\nword_count_vector = vectorizer.fit_transform(train['text'])\nword_counts = word_count_vector.toarray().sum(axis=0)\nvocab = vectorizer.get_feature_names_out() \n","metadata":{"execution":{"iopub.status.busy":"2024-08-06T15:03:32.713472Z","iopub.execute_input":"2024-08-06T15:03:32.713995Z","iopub.status.idle":"2024-08-06T15:03:33.666472Z","shell.execute_reply.started":"2024-08-06T15:03:32.713953Z","shell.execute_reply":"2024-08-06T15:03:33.665225Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"common_words_df = pd.DataFrame(sorted(list(zip(vocab, word_counts)), key=lambda x: x[1], reverse=True)[:20], \n columns=['Word', 'Count'])\n\nplt.figure(figsize=(12, 8))\nsns.barplot(x='Count', y='Word', data=common_words_df, palette='coolwarm')\nplt.title('Top 20 Most Frequent Words')\nplt.xlabel('Count')\nplt.ylabel('Word')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-08-06T15:03:34.670181Z","iopub.execute_input":"2024-08-06T15:03:34.670986Z","iopub.status.idle":"2024-08-06T15:03:35.088412Z","shell.execute_reply.started":"2024-08-06T15:03:34.670952Z","shell.execute_reply":"2024-08-06T15:03:35.087432Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Preprocess the Text Data","metadata":{}},{"cell_type":"code","source":"def preprocess(text):\n text = text.lower() # Convert to lowercase\n text = re.sub(r'https?://\\S+|www\\.\\S+', '', text) # Remove URLs\n text = re.sub(r'<.*?>', '', text) # Remove HTML tags\n text = re.sub(r'\\d+', '', text) # Remove digits\n text = re.sub(f'[{re.escape(string.punctuation)}]', '', text) # Remove punctuation\n text = re.sub(r'\\n', '', text) # Remove newline characters\n text = re.sub(r'\\s+', ' ', text).strip() # Remove extra spaces\n return text\n\ntrain['text'] = train['text'].apply(preprocess)\ntest['text'] = test['text'].apply(preprocess)","metadata":{"execution":{"iopub.status.busy":"2024-08-06T02:35:31.134959Z","iopub.execute_input":"2024-08-06T02:35:31.135397Z","iopub.status.idle":"2024-08-06T02:35:31.490838Z","shell.execute_reply.started":"2024-08-06T02:35:31.135362Z","shell.execute_reply":"2024-08-06T02:35:31.489565Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Split the Training Data","metadata":{}},{"cell_type":"code","source":"X_train, X_val, y_train, y_val = train_test_split(train['text'], train['target'], test_size=0.2, random_state=42)\n","metadata":{"execution":{"iopub.status.busy":"2024-08-06T02:35:32.899099Z","iopub.execute_input":"2024-08-06T02:35:32.899525Z","iopub.status.idle":"2024-08-06T02:35:32.913365Z","shell.execute_reply.started":"2024-08-06T02:35:32.89949Z","shell.execute_reply":"2024-08-06T02:35:32.912155Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Vectorize the Text Data Using TF-IDF","metadata":{}},{"cell_type":"code","source":"tfidf = TfidfVectorizer(max_features=15000, stop_words=stopwords.words('english'))\nX_train_tfidf = tfidf.fit_transform(X_train)\nX_val_tfidf = tfidf.transform(X_val)\nX_test_tfidf = tfidf.transform(test['text'])","metadata":{"execution":{"iopub.status.busy":"2024-08-06T02:35:35.777357Z","iopub.execute_input":"2024-08-06T02:35:35.77777Z","iopub.status.idle":"2024-08-06T02:35:36.201442Z","shell.execute_reply.started":"2024-08-06T02:35:35.777735Z","shell.execute_reply":"2024-08-06T02:35:36.199828Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Build the Model","metadata":{}},{"cell_type":"code","source":"svc = SVC()\nsvc.fit(X_train_tfidf, y_train)\ny_pred_svc = svc.predict(X_val_tfidf)\n","metadata":{"execution":{"iopub.status.busy":"2024-08-06T02:35:38.702731Z","iopub.execute_input":"2024-08-06T02:35:38.703225Z","iopub.status.idle":"2024-08-06T02:35:43.853657Z","shell.execute_reply.started":"2024-08-06T02:35:38.703187Z","shell.execute_reply":"2024-08-06T02:35:43.85231Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"xgb = XGBClassifier(use_label_encoder=False, eval_metric='logloss')\nxgb.fit(X_train_tfidf, y_train)\ny_pred_xgb = xgb.predict(X_val_tfidf)\n","metadata":{"execution":{"iopub.status.busy":"2024-08-06T02:35:44.21812Z","iopub.execute_input":"2024-08-06T02:35:44.218572Z","iopub.status.idle":"2024-08-06T02:35:46.262731Z","shell.execute_reply.started":"2024-08-06T02:35:44.218535Z","shell.execute_reply":"2024-08-06T02:35:46.260742Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n# Assuming you have these models trained already\nmodels = {\n 'Support Vector Classifier': svc,\n 'XGBoost': xgb\n}\n\n# Initialize variables to store the best model and accuracy\nbest_model = None\nbest_accuracy = 0\n\n# Evaluate each model and store the one with the best accuracy\nfor name, model in models.items():\n predictions = model.predict(X_val_tfidf)\n accuracy = accuracy_score(y_val, predictions)\n print(f'{name} Accuracy: {accuracy}')\n \n if accuracy > best_accuracy:\n best_accuracy = accuracy\n best_model = model\n\nprint(f'\\nBest Model: {best_model} with Accuracy: {best_accuracy}')\n\n# Make predictions on the test data using the best model\ntest_predictions = best_model.predict(X_test_tfidf)\n","metadata":{"execution":{"iopub.status.busy":"2024-08-06T02:35:49.592767Z","iopub.execute_input":"2024-08-06T02:35:49.593212Z","iopub.status.idle":"2024-08-06T02:35:52.297672Z","shell.execute_reply.started":"2024-08-06T02:35:49.593179Z","shell.execute_reply":"2024-08-06T02:35:52.296501Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"\n# Create the submission DataFrame\nsubmission = pd.DataFrame({\n 'id': test['id'],\n 'target': test_predictions\n})\n\n# Save the submission file\nsubmission.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2024-08-06T02:35:58.318207Z","iopub.execute_input":"2024-08-06T02:35:58.318591Z","iopub.status.idle":"2024-08-06T02:35:58.3343Z","shell.execute_reply.started":"2024-08-06T02:35:58.318561Z","shell.execute_reply":"2024-08-06T02:35:58.332994Z"},"trusted":true},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/obesity-risk2/code/code.py b/Agent/workspace/hyperopt/obesity-risk2/code/code.py index c589384..2ca429a 100644 --- a/Agent/workspace/hyperopt/obesity-risk2/code/code.py +++ b/Agent/workspace/hyperopt/obesity-risk2/code/code.py @@ -61,11 +61,10 @@ from sklearn.preprocessing import StandardScaler, MinMaxScaler from category_encoders import OneHotEncoder, CatBoostEncoder, MEstimateEncoder from sklearn.model_selection import StratifiedGroupKFold - from xgboost import XGBClassifier from catboost import CatBoostClassifier from lightgbm import LGBMClassifier -from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor, VotingClassifier from sklearn.linear_model import RidgeClassifier, LogisticRegression from sklearn import set_config @@ -114,17 +113,17 @@ def prettify_df(df): print(table) -train.head(10) +# train.head(10) -# Train Data -print("Train Data") -print(f"Total number of rows: {len(train)}") -print(f"Total number of columns: {train.shape[1]}\n") +# # Train Data +# print("Train Data") +# print(f"Total number of rows: {len(train)}") +# print(f"Total number of columns: {train.shape[1]}\n") -# Test Data -print("Test Data") -print(f"Total number of rows: {len(test)}") -print(f"Total number of columns:{test.shape[1]}") +# # Test Data +# print("Test Data") +# print(f"Total number of rows: {len(test)}") +# print(f"Total number of columns:{test.shape[1]}") # check null and unique count # FHWO: family_history_with_overweight @@ -254,44 +253,44 @@ def transform(self, x): skf = StratifiedKFold(n_splits=n_splits) -def cross_val_model(estimators, cv=skf, verbose=True): - """ - estimators : pipeline consists preprocessing, encoder & model - cv : Method for cross validation (default: StratifiedKfold) - verbose : print train/valid score (yes/no) - """ - - X = train.copy() - y = X.pop(TARGET) - - y = y.map(target_mapping) - test_predictions = np.zeros((len(test), 7)) - valid_predictions = np.zeros((len(X), 7)) - - val_scores, train_scores = [], [] - for fold, (train_ind, valid_ind) in enumerate(skf.split(X, y)): - model = clone(estimators) - # define train set - X_train = X.iloc[train_ind] - y_train = y.iloc[train_ind] - # define valid set - X_valid = X.iloc[valid_ind] - y_valid = y.iloc[valid_ind] - - model.fit(X_train, y_train) - if verbose: - print("-" * 100) - print(f"Fold: {fold}") - print(f"Train Accuracy Score:{accuracy_score(y_true=y_train, y_pred=model.predict(X_train))}") - print(f"Valid Accuracy Score:{accuracy_score(y_true=y_valid, y_pred=model.predict(X_valid))}") - print("-" * 100) - - test_predictions += model.predict_proba(test) / cv.get_n_splits() - valid_predictions[valid_ind] = model.predict_proba(X_valid) - val_scores.append(accuracy_score(y_true=y_valid, y_pred=model.predict(X_valid))) - if verbose: - print(f"Average Mean Accuracy Score: {np.array(val_scores).mean()}") - return val_scores, valid_predictions, test_predictions +# def cross_val_model(estimators, cv=skf, verbose=True): +# """ +# estimators : pipeline consists preprocessing, encoder & model +# cv : Method for cross validation (default: StratifiedKfold) +# verbose : print train/valid score (yes/no) +# """ + +# X = train.copy() +# y = X.pop(TARGET) + +# y = y.map(target_mapping) +# test_predictions = np.zeros((len(test), 7)) +# valid_predictions = np.zeros((len(X), 7)) + +# val_scores, train_scores = [], [] +# for fold, (train_ind, valid_ind) in enumerate(skf.split(X, y)): +# model = clone(estimators) +# # define train set +# X_train = X.iloc[train_ind] +# y_train = y.iloc[train_ind] +# # define valid set +# X_valid = X.iloc[valid_ind] +# y_valid = y.iloc[valid_ind] + +# model.fit(X_train, y_train) +# if verbose: +# print("-" * 100) +# print(f"Fold: {fold}") +# print(f"Train Accuracy Score:{accuracy_score(y_true=y_train, y_pred=model.predict(X_train))}") +# print(f"Valid Accuracy Score:{accuracy_score(y_true=y_valid, y_pred=model.predict(X_valid))}") +# print("-" * 100) + +# test_predictions += model.predict_proba(test) / cv.get_n_splits() +# valid_predictions[valid_ind] = model.predict_proba(X_valid) +# val_scores.append(accuracy_score(y_true=y_valid, y_pred=model.predict(X_valid))) +# if verbose: +# print(f"Average Mean Accuracy Score: {np.array(val_scores).mean()}") +# return val_scores, valid_predictions, test_predictions # Combine Orignal & Synthetic Data @@ -389,14 +388,14 @@ def cross_val_model(estimators, cv=skf, verbose=True): ) # Execute Random Forest Pipeline -val_scores, val_predictions, test_predictions = cross_val_model(RFC) +# val_scores, val_predictions, test_predictions = cross_val_model(RFC) # Save train/test predictions in dataframes -for k, v in target_mapping.items(): - oof_list[f"rfc_{k}"] = val_predictions[:, v] +# for k, v in target_mapping.items(): +# oof_list[f"rfc_{k}"] = val_predictions[:, v] -for k, v in target_mapping.items(): - predict_list[f"rfc_{k}"] = test_predictions[:, v] +# for k, v in target_mapping.items(): +# predict_list[f"rfc_{k}"] = test_predictions[:, v] # 0.8975337326149792 # 0.9049682643904575 @@ -486,13 +485,13 @@ def cross_val_model(estimators, cv=skf, verbose=True): # Train LGBM Model -val_scores, val_predictions, test_predictions = cross_val_model(lgbm) +# val_scores, val_predictions, test_predictions = cross_val_model(lgbm) -for k, v in target_mapping.items(): - oof_list[f"lgbm_{k}"] = val_predictions[:, v] +# for k, v in target_mapping.items(): +# oof_list[f"lgbm_{k}"] = val_predictions[:, v] -for k, v in target_mapping.items(): - predict_list[f"lgbm_{k}"] = test_predictions[:, v] +# for k, v in target_mapping.items(): +# predict_list[f"lgbm_{k}"] = test_predictions[:, v] # 0.91420543252078 @@ -585,13 +584,13 @@ def cross_val_model(estimators, cv=skf, verbose=True): XGBClassifier(**best_params, seed=RANDOM_SEED) ) -val_scores, val_predictions, test_predictions = cross_val_model(XGB) +# val_scores, val_predictions, test_predictions = cross_val_model(XGB) -for k, v in target_mapping.items(): - oof_list[f"xgb_{k}"] = val_predictions[:, v] +# for k, v in target_mapping.items(): +# oof_list[f"xgb_{k}"] = val_predictions[:, v] -for k, v in target_mapping.items(): - predict_list[f"xgb_{k}"] = test_predictions[:, v] +# for k, v in target_mapping.items(): +# predict_list[f"xgb_{k}"] = test_predictions[:, v] # 0.90634942296329 # 0.9117093455898445 with rounder @@ -653,12 +652,12 @@ def cross_val_model(estimators, cv=skf, verbose=True): # ) # Train Catboost Model -val_scores, val_predictions, test_predictions = cross_val_model(CB) -for k, v in target_mapping.items(): - oof_list[f"cat_{k}"] = val_predictions[:, v] +# val_scores, val_predictions, test_predictions = cross_val_model(CB) +# for k, v in target_mapping.items(): +# oof_list[f"cat_{k}"] = val_predictions[:, v] -for k, v in target_mapping.items(): - predict_list[f"cat_{k}"] = test_predictions[:, v] +# for k, v in target_mapping.items(): +# predict_list[f"cat_{k}"] = test_predictions[:, v] # best 0.91179835368868 with extract features, n_splits = 10 # best 0.9121046227778054 without extract features, n_splits = 10 @@ -670,21 +669,31 @@ def cross_val_model(estimators, cv=skf, verbose=True): "lgbm_": 3, "xgb_": 1, "cat_": 0} -tmp = oof_list.copy() -for k, v in target_mapping.items(): - tmp[f"{k}"] = (weights["rfc_"] * tmp[f"rfc_{k}"] + - weights["lgbm_"] * tmp[f"lgbm_{k}"] + - weights["xgb_"] * tmp[f"xgb_{k}"] + - weights["cat_"] * tmp[f"cat_{k}"]) -tmp["pred"] = tmp[target_mapping.keys()].idxmax(axis=1) -tmp["label"] = train[TARGET] -print(f"Ensemble Accuracy Scoe: {accuracy_score(train[TARGET], tmp["pred"])}") - -cm = confusion_matrix(y_true=tmp["label"].map(target_mapping), - y_pred=tmp["pred"].map(target_mapping), - normalize="true") - -cm = cm.round(2) + +weight_list=[3,1] +voting_classifier=VotingClassifier( + estimators=[ + # ('rfc',RFC), + ('lgbm',lgbm), + ('xgb',XGB), + # ('cat',CB) + ], + weights=weight_list) +# tmp = oof_list.copy() +# for k, v in target_mapping.items(): +# tmp[f"{k}"] = (weights["rfc_"] * tmp[f"rfc_{k}"] + +# weights["lgbm_"] * tmp[f"lgbm_{k}"] + +# weights["xgb_"] * tmp[f"xgb_{k}"] + +# weights["cat_"] * tmp[f"cat_{k}"]) +# tmp["pred"] = tmp[target_mapping.keys()].idxmax(axis=1) +# tmp["label"] = train[TARGET] +# print(f"Ensemble Accuracy Scoe: {accuracy_score(train[TARGET], tmp["pred"])}") + +# cm = confusion_matrix(y_true=tmp["label"].map(target_mapping), +# y_pred=tmp["pred"].map(target_mapping), +# normalize="true") + +# cm = cm.round(2) # plt.figure(figsize=(8,8)) # disp = ConfusionMatrixDisplay(confusion_matrix = cm, # display_labels = target_mapping.keys()) @@ -705,15 +714,15 @@ def cross_val_model(estimators, cv=skf, verbose=True): # # Final Submission -for k, v in target_mapping.items(): - predict_list[f"{k}"] = (weights["rfc_"] * predict_list[f"rfc_{k}"] + - weights["lgbm_"] * predict_list[f"lgbm_{k}"] + - weights["xgb_"] * predict_list[f"xgb_{k}"] + - weights["cat_"] * predict_list[f"cat_{k}"]) +# for k, v in target_mapping.items(): +# predict_list[f"{k}"] = (weights["rfc_"] * predict_list[f"rfc_{k}"] + +# weights["lgbm_"] * predict_list[f"lgbm_{k}"] + +# weights["xgb_"] * predict_list[f"xgb_{k}"] + +# weights["cat_"] * predict_list[f"cat_{k}"]) -final_pred = predict_list[target_mapping.keys()].idxmax(axis=1) +# final_pred = predict_list[target_mapping.keys()].idxmax(axis=1) -sample_sub[TARGET] = final_pred -sample_sub.to_csv(os.path.join(FILE_PATH, submission_path), index=False) +# sample_sub[TARGET] = final_pred +# sample_sub.to_csv(os.path.join(FILE_PATH, submission_path), index=False) -score = 1 - accuracy_score(train[TARGET], tmp["pred"]) +# score = 1 - accuracy_score(train[TARGET], tmp["pred"]) diff --git a/Agent/workspace/hyperopt/ogpc/code/code.py b/Agent/workspace/hyperopt/ogpc/code/code.py new file mode 100644 index 0000000..8e36cdd --- /dev/null +++ b/Agent/workspace/hyperopt/ogpc/code/code.py @@ -0,0 +1,104 @@ +#!/usr/bin/env python +# coding: utf-8 + +import numpy as np +import pandas as pd +from lightgbm import LGBMClassifier +# import optuna.integration.lightgbm as lgb +from sklearn.metrics import accuracy_score +import seaborn as sns + +FILE_PATH = "./workspace/hyperopt/ogpc/data/" + +train = pd.read_csv(FILE_PATH+'train.csv.zip') +test = pd.read_csv(FILE_PATH+'test.csv.zip') +sample_submit = pd.read_csv(FILE_PATH+'sampleSubmission.csv.zip') + + +# replace 'Class_1' ~ 'Class_9' to 0~8 +train['target'] = train['target'].str.replace('Class_', '') +train['target'] = train['target'].astype(int) - 1 + + +# # Feature Engineering + +data = pd.concat([train, test]) +cols = [c for c in data.columns if c not in ['id', 'target']] + +for col in cols: + dictionary=data[col].value_counts().to_dict() + data['count_'+col]=data[col].map(dictionary) +from sklearn import preprocessing + +data['max_val'] = data[cols].max(axis=1) +data['sum_val'] = data[cols].sum(axis=1) +data['non_zero'] = (data[cols] > 0).sum(axis=1) +data['count_one'] = (data[cols] == 1).sum(axis=1) +data['count_two'] = (data[cols] == 2).sum(axis=1) +data['count_three'] = (data[cols] == 3).sum(axis=1) + +train = data[~data['target'].isnull()].reset_index(drop=True) +test = data[data['target'].isnull()].reset_index(drop=True) + + +# train setting +NFOLDS = 5 +RANDOM_STATE = 871972 + +excluded_column = ['target', 'id'] +cols = [c for c in train.columns if c not in excluded_column] + +# parameter calculated by LGBtuner +# params = { +# 'metric':'multi_logloss','objective': 'multiclass', 'num_class': 9, 'verbosity': 1, +# 'feature_fraction': 0.4, 'num_leaves': 139, 'bagging_fraction': 0.8254401463359962, 'bagging_freq': 3, +# 'lambda_l1': 0.02563829140437355, 'lambda_l2': 9.594334397031103, 'min_child_samples': 100 +# } + + +y_pred_test = np.zeros((len(test), 9)) +oof = np.zeros((len(train), 9)) +score = 0 +feature_importance_df = pd.DataFrame() + +lgb_model=LGBMClassifier() +# for fold_n, (train_index, valid_index) in enumerate(folds.split(train, y = train['target'])): +# print('Fold', fold_n) +# X_train, X_valid = train.iloc[train_index], train.iloc[valid_index] +# y_train, y_valid = X_train['target'].astype(int), X_valid['target'].astype(int) + +# train_data = lgb.Dataset(X_train[cols], label=y_train) +# valid_data = lgb.Dataset(X_valid[cols], label=y_valid) + +# lgb_model = lgb.train(params,train_data,num_boost_round=30000, +# valid_sets = [train_data, valid_data],verbose_eval=300,early_stopping_rounds = 300) + +# y_pred_valid = lgb_model.predict(X_valid[cols], num_iteration=lgb_model.best_iteration) +# oof[valid_index] = y_pred_valid +# score += log_loss(y_valid, y_pred_valid) + +# y_pred_test += lgb_model.predict(test[cols], num_iteration=lgb_model.best_iteration)/NFOLDS + +# fold_importance_df = pd.DataFrame() +# fold_importance_df["feature"] = cols +# fold_importance_df["importance"] = lgb_model.feature_importance(importance_type='gain') +# fold_importance_df["fold"] = fold_n + 1 +# feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0) +# print('valid logloss average:', score/NFOLDS, log_loss(train['target'], oof)) + + +# feature_importance_df[["feature", "importance"]].groupby("feature", as_index=False).mean().sort_values(by="importance", ascending=False).head(20) + + +# submit = pd.concat([sample_submit[['id']], pd.DataFrame(y_pred_test)], axis = 1) +# submit.columns = sample_submit.columns +# submit.to_csv('submit.csv', index=False) + + +# column_name = ['lgb_' + str(i) for i in range(9)] +# pd.DataFrame(oof, columns = column_name).to_csv('oof_lgb.csv', index=False) +# pd.DataFrame(y_pred_test, columns = column_name).to_csv('submit_lgb.csv', index=False) + + + + diff --git a/Agent/workspace/hyperopt/ogpc/code/otto-simple-lgb-4e8206.ipynb b/Agent/workspace/hyperopt/ogpc/code/otto-simple-lgb-4e8206.ipynb new file mode 100644 index 0000000..4d15744 --- /dev/null +++ b/Agent/workspace/hyperopt/ogpc/code/otto-simple-lgb-4e8206.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"language_info":{"codemirror_mode":{"name":"ipython","version":3},"file_extension":".py","mimetype":"text/x-python","name":"python","nbconvert_exporter":"python","pygments_lexer":"ipython3","version":"3.7.6"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":4280,"databundleVersionId":860643,"sourceType":"competition"}],"dockerImageVersionId":29852,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport lightgbm as lgb\n# import optuna.integration.lightgbm as lgb\nfrom sklearn.model_selection import StratifiedKFold\nimport matplotlib.pyplot as plt\n%matplotlib inline\nfrom sklearn.metrics import log_loss\nimport seaborn as sns","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv('../input/otto-group-product-classification-challenge/train.csv')\ntest = pd.read_csv('../input/otto-group-product-classification-challenge/test.csv')\nsample_submit = pd.read_csv('../input/otto-group-product-classification-challenge/sampleSubmission.csv')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# replace 'Class_1' ~ 'Class_9' to 0~8\ntrain['target'] = train['target'].str.replace('Class_', '')\ntrain['target'] = train['target'].astype(int) - 1","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Feature Engineering","metadata":{}},{"cell_type":"code","source":"data = pd.concat([train, test])\ncols = [c for c in data.columns if c not in ['id', 'target']]\n\nfor col in cols:\n dictionary=data[col].value_counts().to_dict()\n data['count_'+col]=data[col].map(dictionary)\nfrom sklearn import preprocessing\n\ndata['max_val'] = data[cols].max(axis=1)\ndata['sum_val'] = data[cols].sum(axis=1)\ndata['non_zero'] = (data[cols] > 0).sum(axis=1)\ndata['count_one'] = (data[cols] == 1).sum(axis=1)\ndata['count_two'] = (data[cols] == 2).sum(axis=1)\ndata['count_three'] = (data[cols] == 3).sum(axis=1)\n\ntrain = data[~data['target'].isnull()].reset_index(drop=True)\ntest = data[data['target'].isnull()].reset_index(drop=True)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# train setting\nNFOLDS = 5\nRANDOM_STATE = 871972\n\nexcluded_column = ['target', 'id']\ncols = [c for c in train.columns if c not in excluded_column]\n\nfolds = StratifiedKFold(n_splits=NFOLDS, shuffle=True, \n random_state=RANDOM_STATE)\n\n# parameter calculated by LGBtuner\nparams = {\n 'metric':'multi_logloss','objective': 'multiclass', 'num_class': 9, 'verbosity': 1,\n 'feature_fraction': 0.4, 'num_leaves': 139, 'bagging_fraction': 0.8254401463359962, 'bagging_freq': 3,\n 'lambda_l1': 0.02563829140437355, 'lambda_l2': 9.594334397031103, 'min_child_samples': 100\n}","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_test = np.zeros((len(test), 9))\noof = np.zeros((len(train), 9))\nscore = 0\nfeature_importance_df = pd.DataFrame()\nfor fold_n, (train_index, valid_index) in enumerate(folds.split(train, y = train['target'])):\n print('Fold', fold_n)\n X_train, X_valid = train.iloc[train_index], train.iloc[valid_index]\n y_train, y_valid = X_train['target'].astype(int), X_valid['target'].astype(int)\n \n train_data = lgb.Dataset(X_train[cols], label=y_train)\n valid_data = lgb.Dataset(X_valid[cols], label=y_valid)\n\n lgb_model = lgb.train(params,train_data,num_boost_round=30000,\n valid_sets = [train_data, valid_data],verbose_eval=300,early_stopping_rounds = 300)\n \n y_pred_valid = lgb_model.predict(X_valid[cols], num_iteration=lgb_model.best_iteration)\n oof[valid_index] = y_pred_valid\n score += log_loss(y_valid, y_pred_valid)\n \n y_pred_test += lgb_model.predict(test[cols], num_iteration=lgb_model.best_iteration)/NFOLDS\n \n fold_importance_df = pd.DataFrame()\n fold_importance_df[\"feature\"] = cols\n fold_importance_df[\"importance\"] = lgb_model.feature_importance(importance_type='gain')\n fold_importance_df[\"fold\"] = fold_n + 1\n feature_importance_df = pd.concat([feature_importance_df, fold_importance_df], axis=0)\nprint('valid logloss average:', score/NFOLDS, log_loss(train['target'], oof))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"feature_importance_df[[\"feature\", \"importance\"]].groupby(\"feature\", as_index=False).mean().sort_values(by=\"importance\", ascending=False).head(20)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submit = pd.concat([sample_submit[['id']], pd.DataFrame(y_pred_test)], axis = 1)\nsubmit.columns = sample_submit.columns\nsubmit.to_csv('submit.csv', index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"column_name = ['lgb_' + str(i) for i in range(9)]\npd.DataFrame(oof, columns = column_name).to_csv('oof_lgb.csv', index=False)\npd.DataFrame(y_pred_test, columns = column_name).to_csv('submit_lgb.csv', index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/ogpc2/code/code.py b/Agent/workspace/hyperopt/ogpc2/code/code.py new file mode 100644 index 0000000..7fe9e15 --- /dev/null +++ b/Agent/workspace/hyperopt/ogpc2/code/code.py @@ -0,0 +1,69 @@ +#!/usr/bin/env python +# coding: utf-8 + +# This Python 3 environment comes with many helpful analytics libraries installed +# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python +# For example, here's several helpful packages to load in + +import pandas as pd +import numpy as np +import xgboost as xgb + +from scipy.optimize import minimize +from sklearn.ensemble import RandomForestClassifier +from sklearn.linear_model import LogisticRegression +from sklearn.metrics import accuracy_score +import os +# Input data files are available in the "../input/" directory. +# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory + +FILE_PATH = "./workspace/hyperopt/ogpc2/data/" + +# Any results you write to the current directory are saved as output. + + +train = pd.read_csv(FILE_PATH+"train.csv.zip") +test = pd.read_csv(FILE_PATH+"test.csv.zip") + + +sample = pd.read_csv(FILE_PATH+'sampleSubmission.csv.zip') + + +train.head() + + +target = train['target'] +train.drop(['id','target'],axis=1,inplace=True) +train.shape + + +testId = test['id'] +test.drop('id',axis=1,inplace=True) + + +rfc = RandomForestClassifier(n_estimators=50,random_state=1412,n_jobs=-1) +# rfc.fit(train,target) +# preds = rfc.predict_proba(test) + + +lr = LogisticRegression() +# lr.fit(train,target) +# lpreds = lr.predict_proba(test) + + +xg = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05) +# xg.fit(train,target) +# xpreds = xg.predict_proba(test) + + +# finalPreds = 0.33*preds+0.33*lpreds+0.33*xpreds +# finalPreds = lpreds + + +# pred = pd.DataFrame(finalPreds, index=sample.id.values, columns=sample.columns[1:]) +# pred.to_csv('onlylr.csv', index_label='id') + +from sklearn.ensemble import VotingClassifier + +voting_classifier=VotingClassifier(estimators=[('rfc',rfc),('lr',lr),('xg',xg)],voting='soft') + diff --git a/Agent/workspace/hyperopt/ogpc2/code/fork-of-lr-gbm-rf-ensemble.ipynb b/Agent/workspace/hyperopt/ogpc2/code/fork-of-lr-gbm-rf-ensemble.ipynb new file mode 100644 index 0000000..645ed0d --- /dev/null +++ b/Agent/workspace/hyperopt/ogpc2/code/fork-of-lr-gbm-rf-ensemble.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"name":"python3","display_name":"Python 3","language":"python"},"language_info":{"name":"python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.3","mimetype":"text/x-python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":4280,"databundleVersionId":860643,"sourceType":"competition"}],"dockerImageVersionId":33,"isInternetEnabled":false,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load in \n\nimport pandas as pd\nimport numpy as np\nimport xgboost as xgb\n\nfrom scipy.optimize import minimize\nfrom sklearn.cross_validation import StratifiedShuffleSplit\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import log_loss\nimport os\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nfrom subprocess import check_output\nprint(check_output([\"ls\", \"../input\"]).decode(\"utf8\"))\n\n# Any results you write to the current directory are saved as output.","metadata":{"_cell_guid":"4ee72f20-039d-4e67-ba50-76a05f57b471","_uuid":"5f4f47ab37f03f10eebd15be84c6984b6ed2b610"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv(\"../input/train.csv\")\ntest = pd.read_csv(\"../input/test.csv\")","metadata":{"_cell_guid":"5af9ded6-19c6-4f48-9233-bb14870ce454","collapsed":true,"_uuid":"f6e0de76d574b89460a1731ceebc1f4a378fbf22","jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample = pd.read_csv('../input/sampleSubmission.csv')\n","metadata":{"_cell_guid":"315b06c4-5f8f-40d0-a8dc-95823cfdbc15","collapsed":true,"_uuid":"eff8a11e0f819cb895d80277c6ff492123e48806","jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"_cell_guid":"50c68421-d0ac-4a87-9951-42c542ec9226","_uuid":"744d1da852671e9e72eead93205c85a4bc6a3b8b"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"target = train['target']\ntrain.drop(['id','target'],axis=1,inplace=True)\ntrain.shape","metadata":{"_cell_guid":"f18cc215-5445-403d-8062-e257cb0e11ae","_uuid":"5cd5f78235476aa26dc813a7c8f13eed9aaec7ea"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"testId = test['id']\ntest.drop('id',axis=1,inplace=True)","metadata":{"_cell_guid":"e8dff637-74cc-48af-bdf5-60d082b48cf0","collapsed":true,"_uuid":"c8f3232c3b4920b8645659f5e40f2951001ef97a","jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"rfc = RandomForestClassifier(n_estimators=50,random_state=1412,n_jobs=-1)\nrfc.fit(train,target)\npreds = rfc.predict_proba(test)\n","metadata":{"_cell_guid":"09249fee-89b6-49f4-adb0-ebae1c88e75f","collapsed":true,"_uuid":"6dc532f4d45cce4ab08e16d31c9f4c98e711d8b8","jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"lr = LogisticRegression()\nlr.fit(train,target)\nlpreds = lr.predict_proba(test)","metadata":{"_cell_guid":"06e313b6-2319-4fdc-87d3-2fb63b991b48","collapsed":true,"_uuid":"8e8ceca4f391ef8ce2472c698ef7c35352400986","jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"xg = xgb.XGBClassifier(max_depth=3, n_estimators=300, learning_rate=0.05)\nxg.fit(train,target)\nxpreds = xg.predict_proba(test)","metadata":{"_cell_guid":"cfb041d8-1be1-4d49-9923-aa5145c2bb48","collapsed":true,"_uuid":"5d373d6fead3ae85965a775a47317b990819fa42","jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# finalPreds = 0.33*preds+0.33*lpreds+0.33*xpreds\nfinalPreds = lpreds","metadata":{"_cell_guid":"599a5230-aec2-4922-8d39-238d010fe114","collapsed":true,"_uuid":"3dd227ac3ed819a49da1ca1e0d1dec8379d0317d","jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"pred = pd.DataFrame(finalPreds, index=sample.id.values, columns=sample.columns[1:])\npred.to_csv('onlylr.csv', index_label='id')","metadata":{"_cell_guid":"9330e80d-c912-4712-92c4-932099f39184","collapsed":true,"_uuid":"34e10a91269320001b53564d15bd6c43f692e355","jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{"_cell_guid":"4ea7ca10-e81e-41e4-85cf-5e50ec6b39a5","collapsed":true,"_uuid":"5e202c60628fe6b2606fdf36b1dd972b612e3155","jupyter":{"outputs_hidden":true}},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/ps311/code/code.py b/Agent/workspace/hyperopt/ps311/code/code.py new file mode 100644 index 0000000..07a8484 --- /dev/null +++ b/Agent/workspace/hyperopt/ps311/code/code.py @@ -0,0 +1,320 @@ +#!/usr/bin/env python +# coding: utf-8 + +# ###

πŸ“œ Notebook At a Glance

+ +# ![image.png](attachment:7dac6d29-d7ab-4b5c-8a6e-a5b7ce0a1c99.png) + +#
+# +#

πŸ“Š Data description:

+# +# * store_sales(in millions) - store_sales(in million dollars) +# * unit_sales(in millions) - unit_sales(in millions) in stores Quantity +# * Total_children - TOTAL CHILDREN IN HOME +# * avg_cars_at home(approx) - avg_cars_at home(approx) +# * Num_children_at_home - num_children_at_home AS PER CUSTOMERS FILLED DETAILS +# * Gross_weight - gross_weight OF ITEM +# * Recyclable_package - FOOD ITEM IS recyclable_package +# * Low_fat - LOW_FAT FOOD ITEM IS LOW FAT +# * Units_per_case - UNITS/CASE UNITS AVAILABLE IN EACH STORE SHELVES +# * Store_sqft - STORE AREA AVAILABLE IN SQFT +# * Coffee_bar - COFFEE BAR available in store +# * Video_store - VIDEO STORE/gaming store available +# * Salad_bar - SALAD BAR available in store +# * Prepared_food - food prepared available in store +# * Florist - flower shelves available in store +# * Cost - COST ON ACQUIRING A CUSTOMERS in dollars +# +# **Your Task is to devise a Machine Learning Model that helps us predict the cost of media campaigns in the food marts on the basis of the features provided.** + + + + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +import gc + +from tqdm.auto import tqdm +import math +from sklearn.model_selection import KFold, StratifiedKFold, train_test_split, GridSearchCV +import warnings +warnings.filterwarnings('ignore') + + +# from lightgbm import LGBMRegressor +from xgboost import XGBRegressor +# from catboost import CatBoostRegressor + +tqdm.pandas() + +# rc = { +# "axes.facecolor": "#FFF9ED", +# "figure.facecolor": "#FFF9ED", +# "axes.edgecolor": "#000000", +# "grid.color": "#EBEBE7", +# "font.family": "serif", +# "axes.labelcolor": "#000000", +# "xtick.color": "#000000", +# "ytick.color": "#000000", +# "grid.alpha": 0.4 +# } + +# sns.set(rc=rc) + +# # from colorama import Style, Fore +# red = Style.BRIGHT + Fore.RED +# blu = Style.BRIGHT + Fore.BLUE +# mgt = Style.BRIGHT + Fore.MAGENTA +# gld = Style.BRIGHT + Fore.YELLOW +# res = Style.RESET_ALL + +FILE_PATH = "./workspace/hyperopt/ps311/data/" + +train = pd.read_csv(FILE_PATH+"train.csv.zip") +test = pd.read_csv(FILE_PATH+"test.csv.zip") +original = pd.read_csv(FILE_PATH+"train_dataset.csv.zip") + + +# ###

Brief EDA

+ +#
+# +#

πŸ’‘ Summary of EDA:

+# +# * There are 16 X variables and 1 target(y) variable, while 1 variable(id) is extra data +# +# * No missing values on each columns~! +# +# * All variables are float64 type. However please note that some of them are categorical variables! +# +# * The size of synthetic dataset is much bigger than original one. Therefore, no need to add original dataset (but I added...) + +# summary table function + + + +# summary(train) + + +# summary(test) + + +# summary(original) + + +# select numerical and categorical variables respectively. +num_cols = test.select_dtypes(include=['float64','int64']).columns.tolist() +num_cols.remove('id') + + +# sns.displot(train, x="cost", kde=True) + + +# > #### βœ”οΈ target value is not normally distributed. But not skewed at all. + +# > #### πŸ“Š let's check the distribution of each X variables. + +features = num_cols +# n_bins = 50 +# histplot_hyperparams = { +# 'kde':True, +# 'alpha':0.4, +# 'stat':'percent', +# 'bins':n_bins +# } + +# columns = features +# n_cols = 4 +# n_rows = math.ceil(len(columns)/n_cols) +# fig, ax = plt.subplots(n_rows, n_cols, figsize=(20, n_rows*4)) +# ax = ax.flatten() + +# for i, column in enumerate(columns): +# plot_axes = [ax[i]] +# sns.kdeplot( +# train[column], label='Train', +# ax=ax[i], color='#9E3F00' +# ) + +# sns.kdeplot( +# test[column], label='Test', +# ax=ax[i], color='yellow' +# ) + +# # sns.kdeplot( +# # original[column], label='Original', +# # ax=ax[i], color='#20BEFF' +# # ) + +# # titles +# ax[i].set_title(f'{column} Distribution'); +# ax[i].set_xlabel(None) + +# # remove axes to show only one at the end +# plot_axes = [ax[i]] +# handles = [] +# labels = [] +# for plot_ax in plot_axes: +# handles += plot_ax.get_legend_handles_labels()[0] +# labels += plot_ax.get_legend_handles_labels()[1] +# plot_ax.legend().remove() + +# for i in range(i+1, len(ax)): +# ax[i].axis('off') + +# fig.suptitle(f'Numerical Feature Distributions\n\n\n', ha='center', fontweight='bold', fontsize=25) +# fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 0.96), fontsize=25, ncol=3) +# plt.tight_layout() + +# kudos to @jcaliz / +# refer to https://www.kaggle.com/code/sergiosaharovskiy/ps-s3e7-2023-eda-and-submission + + +#
+# +#

πŸ’‘ Insights:

+# +# * There are many ordinal and categorical variables!!! +# +# * 'total_children', 'num_children_at_home', 'avg_cars_at home(approx).1', 'recyclable_package', 'low_fat', 'coffee_bar', 'video_store', 'salad_bar', 'prepared_food','florist' these variables are should be analyzed in a different way. + +# fig, ax = plt.subplots(3,4, figsize=(25,14), dpi=150) +# ax = ax.flatten() +# for i, ft in enumerate(['total_children', 'num_children_at_home', 'avg_cars_at home(approx).1', 'recyclable_package', 'low_fat', +# 'coffee_bar', 'video_store', 'salad_bar', 'prepared_food','florist']): +# sns.histplot( +# data=train, +# x="cost", hue=ft, +# multiple="stack", +# palette="dark:blue", +# edgecolor=".3", +# linewidth=.5, +# log_scale=True, +# ax=ax[i] +# ) +# fig.suptitle(f'Categorical Features and its Price\n\n', ha='center', fontweight='bold', fontsize=24) +# plt.tight_layout() +# plt.show() + + +#
+# +#

πŸ’‘ Insights:

+# +# * All of these variables seems useful as it shows different distributions, which indicates that thses variables have predictive power. + +# def plot_correlation_heatmap(df: pd.core.frame.DataFrame, title_name: str='Train correlation') -> None: +# corr = df.corr() +# fig, axes = plt.subplots(figsize=(20, 10)) +# mask = np.zeros_like(corr) +# mask[np.triu_indices_from(mask)] = True +# sns.heatmap(corr, mask=mask, linewidths=.5, cmap='YlOrRd', annot=True) +# plt.title(title_name) +# plt.show() + +# plot_correlation_heatmap(train, 'Train Dataset Correlation') +# plot_correlation_heatmap(test, 'Test Dataset Correlation') + + +# +# ###

Baseline modeling with XGB

+ +train.drop('id',axis=1, inplace=True) +df = pd.concat([train, original]) + + +# create dummies with categorical variables +X = df.drop('cost',axis=1) +Y = df['cost'] + + +test.set_index('id',inplace=True) + + +#
+# πŸ“Œ  modeling overview:
+# +# * build baseline model without hyperparameter tuning.
+# * 3-fold cross validation methods are used for baseline modeling.
+# * Evalution metric is Root Mean Squared Error
+# +#
+ +from sklearn.metrics import mean_squared_log_error + +cv_scores = list() +importance_xgb = list() +preds = list() +XGB_md = XGBRegressor(tree_method = 'gpu_hist', + colsample_bytree = 0.8, + gamma = 0.8, + learning_rate = 0.01, + max_depth = 6, + min_child_weight = 10, + n_estimators = 1000, + subsample = 0.8) +## Running 3 fold CV +# for i in range(3): +# print(f'{i} fold cv begin') +# skf = KFold(n_splits = 3, random_state = 1004, shuffle = True) + +# for train_ix, test_ix in skf.split(X, Y): + +# ## Splitting the data +# X_train, X_test = X.iloc[train_ix], X.iloc[test_ix] +# Y_train, Y_test = Y.iloc[train_ix], Y.iloc[test_ix] + +# ## Building RF model +# XGB_md = XGBRegressor(tree_method = 'gpu_hist', +# colsample_bytree = 0.8, +# gamma = 0.8, +# learning_rate = 0.01, +# max_depth = 6, +# min_child_weight = 10, +# n_estimators = 1000, +# subsample = 0.8).fit(X_train, Y_train) +# importance_xgb.append(XGB_md.feature_importances_) + +# XGB_pred_1 = XGB_md.predict(X_test) +# XGB_pred_2 = XGB_md.predict(test) + +# # Calculate RMSE +# cv_scores.append(mean_squared_log_error(Y_test, XGB_pred_1, squared = False)) +# preds.append(XGB_pred_2) +# print(f'{i} fold cv done') + +# scores = np.mean(cv_scores) +# print('The average RMSE over 3-folds (run 3 times) is:', scores) + + +# > ##### It is the Root Mean Squared Error of the log-transformed predicted and log-transformed actual values. +# +# > ##### RMSLE adds 1 to both actual and predicted values before taking the natural logarithm to avoid taking the natural log of possible 0 (zero) values. +# +# > ##### As a result, the function can be used if actual or predicted have zero-valued elements. But this function is not appropriate if either is negative valued + +# ![image.png](attachment:f4edccfd-a096-4660-9508-1e03f5b3a9de.png) + +# plt.figure(figsize = (6, 6)) +# pd.DataFrame(importance_xgb, columns = X.columns).apply(np.mean, axis = 0).sort_values().plot(kind = 'barh'); +# plt.xlabel('Feature importance score') +# plt.ylabel('Features') +# plt.show(); + + +#
+# +#

πŸ’‘ Insights:

+# +# * As we skipped feature engineering process, this result might be different once you apply scaling and other feature engineering methods. +# * The average RMSE over 3-folds (run 3 times) is: 0.304 , this is slightly better than benchmark. + +#
+# +#

βœ”οΈ Conclusion:

+# +# 😊 this is a simple baseline for beginners. you can 1) adjust hyper-parameter (HP tuning) ; 2) try different algorithms ; 3) add more feature engineered data to improve the performance. +# diff --git a/Agent/workspace/hyperopt/ps311/code/quick-eda-and-simple-baseline-with-xgb.ipynb b/Agent/workspace/hyperopt/ps311/code/quick-eda-and-simple-baseline-with-xgb.ipynb new file mode 100644 index 0000000..ee90dca --- /dev/null +++ b/Agent/workspace/hyperopt/ps311/code/quick-eda-and-simple-baseline-with-xgb.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":47790,"databundleVersionId":5172264,"sourceType":"competition"},{"sourceId":4449018,"sourceType":"datasetVersion","datasetId":2605336}],"dockerImageVersionId":30408,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"###

πŸ“œ Notebook At a Glance

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"}}},{"cell_type":"markdown","source":"
\n\n

πŸ“Š Data description:

\n\n* store_sales(in millions) - store_sales(in million dollars)\n* unit_sales(in millions) - unit_sales(in millions) in stores Quantity\n* Total_children - TOTAL CHILDREN IN HOME\n* avg_cars_at home(approx) - avg_cars_at home(approx)\n* Num_children_at_home - num_children_at_home AS PER CUSTOMERS FILLED DETAILS\n* Gross_weight - gross_weight OF ITEM\n* Recyclable_package - FOOD ITEM IS recyclable_package\n* Low_fat - LOW_FAT FOOD ITEM IS LOW FAT\n* Units_per_case - UNITS/CASE UNITS AVAILABLE IN EACH STORE SHELVES\n* Store_sqft - STORE AREA AVAILABLE IN SQFT\n* Coffee_bar - COFFEE BAR available in store\n* Video_store - VIDEO STORE/gaming store available\n* Salad_bar - SALAD BAR available in store\n* Prepared_food - food prepared available in store\n* Florist - flower shelves available in store\n* Cost - COST ON ACQUIRING A CUSTOMERS in dollars\n \n**Your Task is to devise a Machine Learning Model that helps us predict the cost of media campaigns in the food marts on the basis of the features provided.**","metadata":{}},{"cell_type":"code","source":"!wget http://bit.ly/3ZLyF82 -O CSS.css -q\n \nfrom IPython.core.display import HTML\nwith open('./CSS.css', 'r') as file:\n custom_css = file.read()\n\nHTML(custom_css)\n\n# Thanks for the idea of CSS @SERGEY SAHAROVSKIY\n# Please refer to https://www.kaggle.com/code/sergiosaharovskiy/ps-s3e7-2023-eda-and-submission","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:44:54.594848Z","iopub.execute_input":"2023-05-01T10:44:54.595619Z","iopub.status.idle":"2023-05-01T10:44:55.960648Z","shell.execute_reply.started":"2023-05-01T10:44:54.595579Z","shell.execute_reply":"2023-05-01T10:44:55.959265Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport gc\n\nfrom tqdm.auto import tqdm\nimport math\nfrom sklearn.model_selection import KFold, StratifiedKFold, train_test_split, GridSearchCV\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\nfrom lightgbm import LGBMRegressor\nfrom xgboost import XGBRegressor\nfrom catboost import CatBoostRegressor\n\ntqdm.pandas()\n\nrc = {\n \"axes.facecolor\": \"#FFF9ED\",\n \"figure.facecolor\": \"#FFF9ED\",\n \"axes.edgecolor\": \"#000000\",\n \"grid.color\": \"#EBEBE7\",\n \"font.family\": \"serif\",\n \"axes.labelcolor\": \"#000000\",\n \"xtick.color\": \"#000000\",\n \"ytick.color\": \"#000000\",\n \"grid.alpha\": 0.4\n}\n\nsns.set(rc=rc)\n\nfrom colorama import Style, Fore\nred = Style.BRIGHT + Fore.RED\nblu = Style.BRIGHT + Fore.BLUE\nmgt = Style.BRIGHT + Fore.MAGENTA\ngld = Style.BRIGHT + Fore.YELLOW\nres = Style.RESET_ALL\n","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:44:55.973271Z","iopub.execute_input":"2023-05-01T10:44:55.977619Z","iopub.status.idle":"2023-05-01T10:45:04.445255Z","shell.execute_reply.started":"2023-05-01T10:44:55.977536Z","shell.execute_reply":"2023-05-01T10:45:04.444244Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv(\"/kaggle/input/playground-series-s3e11/train.csv\")\ntest = pd.read_csv(\"/kaggle/input/playground-series-s3e11/test.csv\")\noriginal = pd.read_csv(\"/kaggle/input/media-campaign-cost-prediction/train_dataset.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:45:04.446911Z","iopub.execute_input":"2023-05-01T10:45:04.447304Z","iopub.status.idle":"2023-05-01T10:45:06.136127Z","shell.execute_reply.started":"2023-05-01T10:45:04.447264Z","shell.execute_reply":"2023-05-01T10:45:06.13503Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###

Brief EDA

","metadata":{}},{"cell_type":"markdown","source":"
\n\n

πŸ’‘ Summary of EDA:

\n\n* There are 16 X variables and 1 target(y) variable, while 1 variable(id) is extra data\n \n* No missing values on each columns~!\n \n* All variables are float64 type. However please note that some of them are categorical variables!\n\n* The size of synthetic dataset is much bigger than original one. Therefore, no need to add original dataset (but I added...)","metadata":{}},{"cell_type":"code","source":"# summary table function\ndef summary(df):\n print(f'data shape: {df.shape}')\n summ = pd.DataFrame(df.dtypes, columns=['data type'])\n summ['#missing'] = df.isnull().sum().values * 100\n summ['%missing'] = df.isnull().sum().values / len(df)\n summ['#unique'] = df.nunique().values\n desc = pd.DataFrame(df.describe(include='all').transpose())\n summ['min'] = desc['min'].values\n summ['max'] = desc['max'].values\n summ['first value'] = df.loc[0].values\n summ['second value'] = df.loc[1].values\n summ['third value'] = df.loc[2].values\n \n return summ","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:45:06.140193Z","iopub.execute_input":"2023-05-01T10:45:06.140498Z","iopub.status.idle":"2023-05-01T10:45:06.14778Z","shell.execute_reply.started":"2023-05-01T10:45:06.140471Z","shell.execute_reply":"2023-05-01T10:45:06.146588Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"summary(train)","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:45:06.149255Z","iopub.execute_input":"2023-05-01T10:45:06.149893Z","iopub.status.idle":"2023-05-01T10:45:06.497992Z","shell.execute_reply.started":"2023-05-01T10:45:06.149855Z","shell.execute_reply":"2023-05-01T10:45:06.496763Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"summary(test)","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:45:06.500901Z","iopub.execute_input":"2023-05-01T10:45:06.501692Z","iopub.status.idle":"2023-05-01T10:45:06.719722Z","shell.execute_reply.started":"2023-05-01T10:45:06.501642Z","shell.execute_reply":"2023-05-01T10:45:06.718614Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"summary(original)","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:45:06.721165Z","iopub.execute_input":"2023-05-01T10:45:06.722188Z","iopub.status.idle":"2023-05-01T10:45:06.821058Z","shell.execute_reply.started":"2023-05-01T10:45:06.72215Z","shell.execute_reply":"2023-05-01T10:45:06.819628Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# select numerical and categorical variables respectively.\nnum_cols = test.select_dtypes(include=['float64','int64']).columns.tolist()\nnum_cols.remove('id')","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:45:06.822938Z","iopub.execute_input":"2023-05-01T10:45:06.823412Z","iopub.status.idle":"2023-05-01T10:45:06.848704Z","shell.execute_reply.started":"2023-05-01T10:45:06.823369Z","shell.execute_reply":"2023-05-01T10:45:06.847402Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.displot(train, x=\"cost\", kde=True)","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:45:06.850672Z","iopub.execute_input":"2023-05-01T10:45:06.851477Z","iopub.status.idle":"2023-05-01T10:45:08.813877Z","shell.execute_reply.started":"2023-05-01T10:45:06.851432Z","shell.execute_reply":"2023-05-01T10:45:08.812781Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> #### βœ”οΈ target value is not normally distributed. But not skewed at all.","metadata":{}},{"cell_type":"markdown","source":"> #### πŸ“Š let's check the distribution of each X variables.","metadata":{}},{"cell_type":"code","source":"\nfeatures = num_cols\nn_bins = 50\nhistplot_hyperparams = {\n 'kde':True,\n 'alpha':0.4,\n 'stat':'percent',\n 'bins':n_bins\n}\n\ncolumns = features\nn_cols = 4\nn_rows = math.ceil(len(columns)/n_cols)\nfig, ax = plt.subplots(n_rows, n_cols, figsize=(20, n_rows*4))\nax = ax.flatten()\n\nfor i, column in enumerate(columns):\n plot_axes = [ax[i]]\n sns.kdeplot(\n train[column], label='Train',\n ax=ax[i], color='#9E3F00'\n )\n \n sns.kdeplot(\n test[column], label='Test',\n ax=ax[i], color='yellow'\n )\n \n# sns.kdeplot(\n# original[column], label='Original',\n# ax=ax[i], color='#20BEFF'\n# )\n \n # titles\n ax[i].set_title(f'{column} Distribution');\n ax[i].set_xlabel(None)\n \n # remove axes to show only one at the end\n plot_axes = [ax[i]]\n handles = []\n labels = []\n for plot_ax in plot_axes:\n handles += plot_ax.get_legend_handles_labels()[0]\n labels += plot_ax.get_legend_handles_labels()[1]\n plot_ax.legend().remove()\n \nfor i in range(i+1, len(ax)):\n ax[i].axis('off')\n \nfig.suptitle(f'Numerical Feature Distributions\\n\\n\\n', ha='center', fontweight='bold', fontsize=25)\nfig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 0.96), fontsize=25, ncol=3)\nplt.tight_layout()\n\n# kudos to @jcaliz / \n# refer to https://www.kaggle.com/code/sergiosaharovskiy/ps-s3e7-2023-eda-and-submission","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:45:08.817557Z","iopub.execute_input":"2023-05-01T10:45:08.817851Z","iopub.status.idle":"2023-05-01T10:45:46.403407Z","shell.execute_reply.started":"2023-05-01T10:45:08.817823Z","shell.execute_reply":"2023-05-01T10:45:46.401669Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
\n\n

πŸ’‘ Insights:

\n\n* There are many ordinal and categorical variables!!! \n \n* 'total_children', 'num_children_at_home', 'avg_cars_at home(approx).1', 'recyclable_package', 'low_fat', 'coffee_bar', 'video_store', 'salad_bar', 'prepared_food','florist' these variables are should be analyzed in a different way.","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(3,4, figsize=(25,14), dpi=150)\nax = ax.flatten()\nfor i, ft in enumerate(['total_children', 'num_children_at_home', 'avg_cars_at home(approx).1', 'recyclable_package', 'low_fat', \n 'coffee_bar', 'video_store', 'salad_bar', 'prepared_food','florist']):\n sns.histplot(\n data=train,\n x=\"cost\", hue=ft,\n multiple=\"stack\",\n palette=\"dark:blue\",\n edgecolor=\".3\",\n linewidth=.5,\n log_scale=True,\n ax=ax[i]\n )\nfig.suptitle(f'Categorical Features and its Price\\n\\n', ha='center', fontweight='bold', fontsize=24)\nplt.tight_layout()\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:45:46.404472Z","iopub.execute_input":"2023-05-01T10:45:46.404914Z","iopub.status.idle":"2023-05-01T10:45:58.481681Z","shell.execute_reply.started":"2023-05-01T10:45:46.404868Z","shell.execute_reply":"2023-05-01T10:45:58.480725Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
\n\n

πŸ’‘ Insights:

\n\n* All of these variables seems useful as it shows different distributions, which indicates that thses variables have predictive power.","metadata":{}},{"cell_type":"code","source":"def plot_correlation_heatmap(df: pd.core.frame.DataFrame, title_name: str='Train correlation') -> None:\n corr = df.corr()\n fig, axes = plt.subplots(figsize=(20, 10))\n mask = np.zeros_like(corr)\n mask[np.triu_indices_from(mask)] = True\n sns.heatmap(corr, mask=mask, linewidths=.5, cmap='YlOrRd', annot=True)\n plt.title(title_name)\n plt.show()\n\nplot_correlation_heatmap(train, 'Train Dataset Correlation')\nplot_correlation_heatmap(test, 'Test Dataset Correlation')","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:45:58.483299Z","iopub.execute_input":"2023-05-01T10:45:58.483981Z","iopub.status.idle":"2023-05-01T10:46:01.256667Z","shell.execute_reply.started":"2023-05-01T10:45:58.48394Z","shell.execute_reply":"2023-05-01T10:46:01.255579Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"\n###

Baseline modeling with XGB

","metadata":{}},{"cell_type":"code","source":"train.drop('id',axis=1, inplace=True)\ndf = pd.concat([train, original])","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:46:01.258489Z","iopub.execute_input":"2023-05-01T10:46:01.258893Z","iopub.status.idle":"2023-05-01T10:46:01.301636Z","shell.execute_reply.started":"2023-05-01T10:46:01.258854Z","shell.execute_reply":"2023-05-01T10:46:01.300522Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create dummies with categorical variables\nX = df.drop('cost',axis=1)\nY = df['cost']","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:46:01.303086Z","iopub.execute_input":"2023-05-01T10:46:01.303916Z","iopub.status.idle":"2023-05-01T10:46:01.3283Z","shell.execute_reply.started":"2023-05-01T10:46:01.303874Z","shell.execute_reply":"2023-05-01T10:46:01.327138Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test.set_index('id',inplace=True)","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:46:01.329854Z","iopub.execute_input":"2023-05-01T10:46:01.330318Z","iopub.status.idle":"2023-05-01T10:46:01.33637Z","shell.execute_reply.started":"2023-05-01T10:46:01.33028Z","shell.execute_reply":"2023-05-01T10:46:01.335088Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
\n πŸ“Œ  modeling overview:
\n \n* build baseline model without hyperparameter tuning.
\n* 3-fold cross validation methods are used for baseline modeling.
\n* Evalution metric is Root Mean Squared Error
\n \n
","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import mean_squared_log_error\n\ncv_scores = list()\nimportance_xgb = list()\npreds = list()\n\n## Running 3 fold CV\nfor i in range(3):\n print(f'{i} fold cv begin')\n skf = KFold(n_splits = 3, random_state = 1004, shuffle = True)\n \n for train_ix, test_ix in skf.split(X, Y):\n \n ## Splitting the data \n X_train, X_test = X.iloc[train_ix], X.iloc[test_ix]\n Y_train, Y_test = Y.iloc[train_ix], Y.iloc[test_ix]\n \n ## Building RF model\n XGB_md = XGBRegressor(tree_method = 'gpu_hist',\n colsample_bytree = 0.8, \n gamma = 0.8, \n learning_rate = 0.01, \n max_depth = 6, \n min_child_weight = 10, \n n_estimators = 1000, \n subsample = 0.8).fit(X_train, Y_train)\n importance_xgb.append(XGB_md.feature_importances_)\n \n XGB_pred_1 = XGB_md.predict(X_test)\n XGB_pred_2 = XGB_md.predict(test)\n \n # Calculate RMSE\n cv_scores.append(mean_squared_log_error(Y_test, XGB_pred_1, squared = False))\n preds.append(XGB_pred_2)\n print(f'{i} fold cv done')\n\nscores = np.mean(cv_scores) \nprint('The average RMSE over 3-folds (run 3 times) is:', scores)","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:46:01.338184Z","iopub.execute_input":"2023-05-01T10:46:01.338937Z","iopub.status.idle":"2023-05-01T10:47:45.160499Z","shell.execute_reply.started":"2023-05-01T10:46:01.338896Z","shell.execute_reply":"2023-05-01T10:47:45.159423Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> ##### It is the Root Mean Squared Error of the log-transformed predicted and log-transformed actual values.\n\n> ##### RMSLE adds 1 to both actual and predicted values before taking the natural logarithm to avoid taking the natural log of possible 0 (zero) values.\n\n> ##### As a result, the function can be used if actual or predicted have zero-valued elements. But this function is not appropriate if either is negative valued","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:f4edccfd-a096-4660-9508-1e03f5b3a9de.png)","metadata":{},"attachments":{"f4edccfd-a096-4660-9508-1e03f5b3a9de.png":{"image/png":"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"}}},{"cell_type":"code","source":"plt.figure(figsize = (6, 6))\npd.DataFrame(importance_xgb, columns = X.columns).apply(np.mean, axis = 0).sort_values().plot(kind = 'barh');\nplt.xlabel('Feature importance score')\nplt.ylabel('Features')\nplt.show(); ","metadata":{"execution":{"iopub.status.busy":"2023-05-01T10:47:45.162242Z","iopub.execute_input":"2023-05-01T10:47:45.164104Z","iopub.status.idle":"2023-05-01T10:47:45.53878Z","shell.execute_reply.started":"2023-05-01T10:47:45.164057Z","shell.execute_reply":"2023-05-01T10:47:45.537726Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
\n\n

πŸ’‘ Insights:

\n\n* As we skipped feature engineering process, this result might be different once you apply scaling and other feature engineering methods.\n* The average RMSE over 3-folds (run 3 times) is: 0.304 , this is slightly better than benchmark.","metadata":{}},{"cell_type":"markdown","source":"
\n\n

βœ”οΈ Conclusion:

\n\n 😊 this is a simple baseline for beginners. you can 1) adjust hyper-parameter (HP tuning) ; 2) try different algorithms ; 3) add more feature engineered data to improve the performance.\n","metadata":{}}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/ps3112/code/code.py b/Agent/workspace/hyperopt/ps3112/code/code.py new file mode 100644 index 0000000..ce2f36a --- /dev/null +++ b/Agent/workspace/hyperopt/ps3112/code/code.py @@ -0,0 +1,307 @@ +#!/usr/bin/env python +# coding: utf-8 + +# ## πŸ›‘If you use this topic as a fork or to submit/ensemble the output, don't forget to **show support** !πŸ›‘ +# ## If you don't understand some of the things I do, check out [this notebook](https://www.kaggle.com/code/janmpia/what-you-need-to-know-about-this-competition) + +# # Imports + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +from xgboost import XGBRegressor +from sklearn.model_selection import KFold, GroupKFold,StratifiedKFold +import matplotlib.pyplot as plt +from sklearn.metrics import log_loss +from sklearn.cluster import KMeans +from sklearn.preprocessing import scale +from sklearn.calibration import CalibrationDisplay +from itertools import combinations +from itertools import permutations +from sklearn.model_selection import train_test_split +from sklearn.ensemble import ExtraTreesClassifier +# import optuna +from xgboost import XGBRegressor +from xgboost import XGBClassifier +import xgboost as xgb +from sklearn.metrics import log_loss +import lightgbm as lgb +from sklearn.ensemble import RandomForestClassifier +import joblib +from sklearn.metrics import mean_squared_error +from catboost import CatBoostRegressor +from sklearn.metrics import mean_squared_log_error +from sklearn.model_selection import cross_validate +from sklearn.model_selection import cross_val_score +from sklearn.model_selection import KFold +from xgboost import XGBRegressor +import xgboost as xgb +from sklearn.metrics import log_loss +# sns.set(style="ticks") + +FILE_PATH = "./workspace/hyperopt/ps311/data/" + +train_total = pd.read_csv(FILE_PATH+"train.csv.zip") +test = pd.read_csv(FILE_PATH+"test.csv.zip") +original = pd.read_csv(FILE_PATH+"train_dataset.csv.zip") + + + +X_train,X_test,Y_train,Y_test = train_test_split(train_total.drop(["cost"],axis=1),train_total["cost"],test_size=0.1,stratify=train_total["cost"], random_state = 100) +train = pd.merge(X_train,Y_train, on =X_train['id']) +hold = pd.merge(X_test,Y_test, on =X_test['id']) +train['id'] = range(0, len(train)) +def empty_callback(env): + pass + + +# # Feature engeneering/selection + +FEATS = ["total_children", "num_children_at_home", "avg_cars_at home(approx).1", + "store_sqft", "coffee_bar", "video_store", 'florist',"prepared_food"] +INIT_FEATS =["total_children", "num_children_at_home", "avg_cars_at home(approx).1",'unit_sales(in millions)','store_sales(in millions)','units_per_case', + "store_sqft", "coffee_bar", "video_store", 'florist',"prepared_food",'average_children','average_units','average_sales','gross_weight'] + +CAT_FEATS = FEATS.copy() +avg_df = pd.DataFrame(index = train.store_sqft.unique()) +avg_df['store_sqft'] = avg_df.index + +avg_df_test = pd.DataFrame(index = test.store_sqft.unique()) +avg_df_test['store_sqft'] = avg_df_test.index + +avg_df_hold = pd.DataFrame(index = hold.store_sqft.unique()) +avg_df_hold['store_sqft'] = avg_df_hold.index + + +avg_df_original = pd.DataFrame(index = original.store_sqft.unique()) +avg_df_original['store_sqft'] = avg_df_original.index + +concat_train_hold_test = pd.concat([train,hold,test],ignore_index=True) +for feature in INIT_FEATS: + if feature in ['units_per_case','store_sales(in millions)','total_children']: + avg_df[f'avg_{feature}'] = concat_train_hold_test.groupby('store_sqft')[feature].mean() + avg_df_test[f'avg_{feature}'] = concat_train_hold_test.groupby('store_sqft')[feature].mean() + avg_df_hold[f'avg_{feature}'] = concat_train_hold_test.groupby('store_sqft')[feature].mean() + avg_df_original[f'avg_{feature}'] = concat_train_hold_test.groupby('store_sqft')[feature].mean() + + CAT_FEATS.append(f'avg_{feature}') + +train = pd.merge(train, avg_df, on='store_sqft', how='left') +hold = pd.merge(hold, avg_df_hold, on='store_sqft', how='left') +test = pd.merge(test, avg_df_test, on='store_sqft', how='left') +original = pd.merge(original, avg_df_original, on='store_sqft', how='left') +avg_df.head() + + +FEATS + + +CAT_FEATS + + +train[FEATS] = train[FEATS].round(2) +original[FEATS] = original[FEATS].round(2) + + +train.head() + + +num_bins =5 +train['bins'] = pd.cut(train['cost'], bins=num_bins).astype('category') +bins = train['bins'].unique() +bin_map = {} +for i,bin in enumerate(bins): + bin_map[bin] = i + +train['bins'] = train['bins'].map(bin_map) + + +# # Train + +ALL_USERS = train.id.unique() +gkf = GroupKFold(n_splits=5) +oof = pd.DataFrame(index=ALL_USERS) + +#hyperparams +cat_params = { + 'iterations': 10000, + 'learning_rate': 0.07, + 'depth': 11, + 'l2_leaf_reg':8 , + 'random_strength':0.5, + 'loss_function': 'RMSE', + 'eval_metric': 'RMSE', + 'border_count': 128, + 'verbose': 1000, + 'early_stopping_rounds': 100, + 'use_best_model': True , + 'random_state': 42, + +} + +xgb_params = { + 'objective': 'reg:squarederror', + 'eval_metric': 'rmse', + 'seed': 42, + 'n_estimators': 1000, + 'learning_rate': 0.14774138317002128, + 'early_stopping_rounds': 1000, + 'max_depth': 11, + 'subsample': 0.90, + 'colsample_bytree': 0.90, + + 'alpha': 4, + 'lambda': 5 +} +from sklearn.metrics import mean_squared_log_error +from sklearn.ensemble import VotingRegressor + +voting_regressor = VotingRegressor(estimators=[('xgb', XGBRegressor(**xgb_params)),('cat', CatBoostRegressor(**cat_params))]) + + + + + +# targets = np.log(train.cost+1) +# n_splits = 5 +# kf = KFold(n_splits=5, shuffle=True, random_state=100) +# skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=100) +# num_trees_cat = [] +# num_trees_xgb = [] + +# for i, (train_index, test_index) in enumerate(skf.split(X=train, y=train['bins'], groups=train.index)): +# print(f'\nFold {i+1}:') + +# train_x = train.iloc[train_index] +# train_x = pd.concat([train_x,original]) +# train_gems = train_x.index.values +# train_y = targets.loc[train_gems] + +# valid_x = train.iloc[test_index] +# valid_gems = valid_x.index.values +# valid_y = targets.loc[valid_gems] + +# model_dict = { +# 'cat': [CatBoostRegressor(**cat_params),{"eval_set":[(valid_x[CAT_FEATS].astype('float32'), valid_y)],'plot':False},CAT_FEATS], +# 'xgb': [XGBRegressor(**xgb_params),{"eval_set":[(valid_x[FEATS].astype('float32'), valid_y)],"verbose":0},FEATS], +# } + +# all_models = model_dict.keys() +# for model in all_models: +# clf = model_dict[model][0] +# clf.fit(train_x[model_dict[model][2]].astype('float32'), train_y, **model_dict[model][1]) + +# oof.loc[valid_gems, f'{model}_prediction'] = np.exp(clf.predict(valid_x[model_dict[model][2]].astype('float32')))-1 +# print(f'model {model} trained') +# if model == 'cat': +# num_trees_cat.append(clf.get_best_iteration()) +# if model == 'xgb': +# num_trees_xgb.append(clf.best_ntree_limit) + + +# # # Stratified CV + +# from sklearn.metrics import mean_squared_log_error +# rmses = {} +# targets = train.cost +# for model in all_models: +# rmses[f"{model}_prediction"] = mean_squared_log_error(targets,oof[f'{model}_prediction'], squared=False) +# print(f'{model} = {rmses[f"{model}_prediction"]}') + +# oof['prediction'] = 0 +# rmse_inv_sum = 0 +# for model in all_models: +# if model in ['xgb','lgb','cat']: +# oof['prediction'] = oof['prediction'] + oof[f'{model}_prediction'] * (1/rmses[f'{model}_prediction']) +# rmse_inv_sum += 1/rmses[f'{model}_prediction'] + +# #print('xgb = 0.2930786590554832') +# oof['prediction'] = oof['prediction'] / rmse_inv_sum +# print(f'ensemble = {mean_squared_log_error(targets,oof[f"prediction"], squared=False)}') + + +# fig, ax = plt.subplots(figsize=(17,8)) +# sns.histplot(data=oof, x="prediction",bins = 100, ax=ax, kde = True) +# ax2 = ax.twinx() +# sns.boxplot(data=oof, x="prediction", ax=ax2,boxprops=dict(alpha=.7)) +# ax2.set(ylim=(-.5, 10)) +# plt.suptitle('Cost countplot and Boxplot for my train predictions', fontsize=20) +# ax.grid(True) +# plt.show() + + +# # # Hold + +# print(f"CAT :{num_trees_cat}; mean : {np.array(num_trees_cat).mean()}") +# print(f"XGB :{num_trees_xgb}; mean : {np.array(num_trees_xgb).mean()}") +# print() +# hold_num_trees_cat = int(np.array(num_trees_cat).mean()*1.25) +# hold_num_trees_xgb = int(np.array(num_trees_xgb).mean()*1.25) +# #cat model +# cat_params2 = cat_params.copy() +# cat_params2.pop('iterations') +# cat_params2.pop('early_stopping_rounds') +# cat_params2.pop('use_best_model') +# new_params = { +# 'iterations': hold_num_trees_cat, +# } + +# hold_cat = CatBoostRegressor(**cat_params2,**new_params) +# hold_cat.fit(train[CAT_FEATS].astype('float32'), np.log(train.cost+1),plot=False) + + +# #xgb model +# xgb_params2 = xgb_params.copy() +# xgb_params2.pop('early_stopping_rounds') +# xgb_params2['n_estimators'] = hold_num_trees_xgb + +# hold_xgb = XGBRegressor(**xgb_params2) +# hold_xgb.fit(train[FEATS].astype('float32'), np.log(train.cost+1)) + +# #hold oof +# hold_pred = pd.DataFrame(index = hold.id) + +# hold_pred['cat_predictions'] = np.exp(hold_cat.predict(hold[CAT_FEATS].astype('float32')))-1 +# hold_pred['xgb_predictions'] = np.exp(hold_xgb.predict(hold[FEATS].astype('float32')))-1 +# hold_pred['predictions'] = (hold_pred['xgb_predictions'] + hold_pred['cat_predictions']) /2 + +# #rmsles +# print(f"\nhold cat prediction: {mean_squared_log_error(hold.cost,hold_pred['cat_predictions'], squared=False)}") +# print(f"hold xgb prediction: {mean_squared_log_error(hold.cost,hold_pred['xgb_predictions'], squared=False)}") +# print(f"\nhold ensemble prediction: {mean_squared_log_error(hold.cost,hold_pred['predictions'], squared=False)}") + + +# hold_pred.head() + + +# # # Inference + +# new_params = { +# 'iterations': int(hold_num_trees_cat*1.25), +# } + +# last_train = pd.concat([train,hold,original], ignore_index=True) + +# test_cat = CatBoostRegressor(**cat_params2,**new_params) + +# xgb_params3 = xgb_params2.copy() +# xgb_params3['n_estimators'] = int(xgb_params3['n_estimators']*1.25) +# test_xgb = XGBRegressor(**xgb_params3) + +# test_xgb.fit(last_train[FEATS].astype('float32'), np.log(last_train.cost+1)) +# test_cat.fit(last_train[CAT_FEATS].astype('float32'), np.log(last_train.cost+1),plot=False) + +# submission = pd.DataFrame(index = test.id) +# submission['id'] = submission.index + +# submission['cost_xgb'] = np.exp(test_xgb.predict(test[FEATS].astype('float32')))-1 +# submission['cost_cat'] = np.exp(test_cat.predict(test[CAT_FEATS].astype('float32')))-1 +# submission['cost'] = (submission['cost_xgb'] + submission['cost_cat']) / 2 + + +# submission.head() + + +# submission[['id','cost']].to_csv('submission.csv', index= False) + diff --git a/Agent/workspace/hyperopt/ps3112/code/feature-eng-xgb-cat-ensemble-0-29265.ipynb b/Agent/workspace/hyperopt/ps3112/code/feature-eng-xgb-cat-ensemble-0-29265.ipynb new file mode 100644 index 0000000..05978b1 --- /dev/null +++ b/Agent/workspace/hyperopt/ps3112/code/feature-eng-xgb-cat-ensemble-0-29265.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":47790,"databundleVersionId":5172264,"sourceType":"competition"},{"sourceId":4449018,"sourceType":"datasetVersion","datasetId":2605336}],"dockerImageVersionId":30446,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"## πŸ›‘If you use this topic as a fork or to submit/ensemble the output, don't forget to **show support** !πŸ›‘\n## If you don't understand some of the things I do, check out [this notebook](https://www.kaggle.com/code/janmpia/what-you-need-to-know-about-this-competition)","metadata":{}},{"cell_type":"markdown","source":"# Imports","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom xgboost import XGBRegressor\nfrom sklearn.model_selection import KFold, GroupKFold,StratifiedKFold\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import log_loss\nfrom sklearn.cluster import KMeans\nfrom sklearn.preprocessing import scale\nfrom sklearn.calibration import CalibrationDisplay\nfrom itertools import combinations\nfrom itertools import permutations\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import ExtraTreesClassifier\nimport optuna\nfrom xgboost import XGBRegressor\nfrom xgboost import XGBClassifier\nimport xgboost as xgb\nfrom sklearn.metrics import log_loss\nimport lightgbm as lgb\nfrom sklearn.ensemble import RandomForestClassifier\nimport joblib\nfrom sklearn.metrics import mean_squared_error\nfrom catboost import CatBoostRegressor\nfrom sklearn.metrics import mean_squared_log_error\nfrom sklearn.model_selection import cross_validate\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.model_selection import KFold\nfrom xgboost import XGBRegressor\nimport xgboost as xgb\nfrom sklearn.metrics import log_loss\nsns.set(style=\"ticks\")","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2023-03-30T14:41:25.151826Z","iopub.execute_input":"2023-03-30T14:41:25.152188Z","iopub.status.idle":"2023-03-30T14:41:30.493113Z","shell.execute_reply.started":"2023-03-30T14:41:25.152156Z","shell.execute_reply":"2023-03-30T14:41:30.492078Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_total = pd.read_csv(\"/kaggle/input/playground-series-s3e11/train.csv\")\ntest = pd.read_csv('/kaggle/input/playground-series-s3e11/test.csv')\noriginal = pd.read_csv('/kaggle/input/media-campaign-cost-prediction/train_dataset.csv')\nX_train,X_test,Y_train,Y_test = train_test_split(train_total.drop([\"cost\"],axis=1),train_total[\"cost\"],test_size=0.2,stratify=train_total[\"cost\"], random_state = 100)\ntrain = pd.merge(X_train,Y_train, on =X_train['id'])\nhold = pd.merge(X_test,Y_test, on =X_test['id'])\ntrain['id'] = range(0, len(train))\ndef empty_callback(env):\n pass","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:41:30.495107Z","iopub.execute_input":"2023-03-30T14:41:30.495552Z","iopub.status.idle":"2023-03-30T14:41:32.433583Z","shell.execute_reply.started":"2023-03-30T14:41:30.495515Z","shell.execute_reply":"2023-03-30T14:41:32.432529Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Feature engeneering/selection","metadata":{}},{"cell_type":"code","source":"FEATS = [\"total_children\", \"num_children_at_home\", \"avg_cars_at home(approx).1\",\n \"store_sqft\", \"coffee_bar\", \"video_store\", 'florist',\"prepared_food\"]\nINIT_FEATS =[\"total_children\", \"num_children_at_home\", \"avg_cars_at home(approx).1\",'unit_sales(in millions)','store_sales(in millions)','units_per_case',\n \"store_sqft\", \"coffee_bar\", \"video_store\", 'florist',\"prepared_food\",'average_children','average_units','average_sales','gross_weight']\n\nCAT_FEATS = FEATS.copy()\navg_df = pd.DataFrame(index = train.store_sqft.unique())\navg_df['store_sqft'] = avg_df.index\n\navg_df_test = pd.DataFrame(index = test.store_sqft.unique())\navg_df_test['store_sqft'] = avg_df_test.index\n\navg_df_hold = pd.DataFrame(index = hold.store_sqft.unique())\navg_df_hold['store_sqft'] = avg_df_hold.index\n\n\navg_df_original = pd.DataFrame(index = original.store_sqft.unique())\navg_df_original['store_sqft'] = avg_df_original.index\n\nconcat_train_hold_test = pd.concat([train,hold,test],ignore_index=True)\nfor feature in INIT_FEATS:\n if feature in ['units_per_case','store_sales(in millions)','total_children']:\n avg_df[f'avg_{feature}'] = concat_train_hold_test.groupby('store_sqft')[feature].mean()\n avg_df_test[f'avg_{feature}'] = concat_train_hold_test.groupby('store_sqft')[feature].mean()\n avg_df_hold[f'avg_{feature}'] = concat_train_hold_test.groupby('store_sqft')[feature].mean()\n avg_df_original[f'avg_{feature}'] = concat_train_hold_test.groupby('store_sqft')[feature].mean()\n \n CAT_FEATS.append(f'avg_{feature}')\n\ntrain = pd.merge(train, avg_df, on='store_sqft', how='left')\nhold = pd.merge(hold, avg_df_hold, on='store_sqft', how='left')\ntest = pd.merge(test, avg_df_test, on='store_sqft', how='left')\noriginal = pd.merge(original, avg_df_original, on='store_sqft', how='left')\navg_df.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:41:32.435263Z","iopub.execute_input":"2023-03-30T14:41:32.435636Z","iopub.status.idle":"2023-03-30T14:41:32.822389Z","shell.execute_reply.started":"2023-03-30T14:41:32.4356Z","shell.execute_reply":"2023-03-30T14:41:32.82124Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"FEATS","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:41:32.825464Z","iopub.execute_input":"2023-03-30T14:41:32.825849Z","iopub.status.idle":"2023-03-30T14:41:32.832137Z","shell.execute_reply.started":"2023-03-30T14:41:32.825812Z","shell.execute_reply":"2023-03-30T14:41:32.831061Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"CAT_FEATS","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:41:32.833808Z","iopub.execute_input":"2023-03-30T14:41:32.834554Z","iopub.status.idle":"2023-03-30T14:41:32.844941Z","shell.execute_reply.started":"2023-03-30T14:41:32.834491Z","shell.execute_reply":"2023-03-30T14:41:32.843942Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train[FEATS] = train[FEATS].round(2)\noriginal[FEATS] = original[FEATS].round(2)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:41:32.84697Z","iopub.execute_input":"2023-03-30T14:41:32.84762Z","iopub.status.idle":"2023-03-30T14:41:32.928735Z","shell.execute_reply.started":"2023-03-30T14:41:32.847584Z","shell.execute_reply":"2023-03-30T14:41:32.92769Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:41:32.930363Z","iopub.execute_input":"2023-03-30T14:41:32.930764Z","iopub.status.idle":"2023-03-30T14:41:32.959817Z","shell.execute_reply.started":"2023-03-30T14:41:32.930725Z","shell.execute_reply":"2023-03-30T14:41:32.958734Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"num_bins =5\ntrain['bins'] = pd.cut(train['cost'], bins=num_bins).astype('category')\nbins = train['bins'].unique()\nbin_map = {}\nfor i,bin in enumerate(bins):\n bin_map[bin] = i\n \ntrain['bins'] = train['bins'].map(bin_map)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:41:32.961405Z","iopub.execute_input":"2023-03-30T14:41:32.961784Z","iopub.status.idle":"2023-03-30T14:41:32.989631Z","shell.execute_reply.started":"2023-03-30T14:41:32.961748Z","shell.execute_reply":"2023-03-30T14:41:32.988671Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Train","metadata":{}},{"cell_type":"code","source":"ALL_USERS = train.id.unique()\ngkf = GroupKFold(n_splits=5)\noof = pd.DataFrame(index=ALL_USERS)\n\n#hyperparams\ncat_params = {\n 'iterations': 10000,\n 'learning_rate': 0.07,\n 'depth': 11,\n 'l2_leaf_reg':8 ,\n 'random_strength':0.5,\n 'loss_function': 'RMSE',\n 'eval_metric': 'RMSE',\n 'task_type': 'GPU',\n 'border_count': 128,\n 'verbose': 1000,\n 'early_stopping_rounds': 100,\n 'use_best_model': True ,\n 'random_state': 42,\n \n}\n\nxgb_params = {\n 'objective': 'reg:squarederror',\n 'tree_method': 'gpu_hist',\n 'eval_metric': 'rmse',\n 'seed': 42,\n 'n_estimators': 1000,\n 'learning_rate': 0.14774138317002128,\n 'early_stopping_rounds': 1000,\n 'max_depth': 11,\n 'subsample': 0.90,\n 'colsample_bytree': 0.90,\n\n 'alpha': 4,\n 'lambda': 5\n}\n\ntargets = np.log(train.cost+1)\nn_splits = 5\nkf = KFold(n_splits=5, shuffle=True, random_state=100)\nskf = StratifiedKFold(n_splits=5, shuffle=True, random_state=100)\nnum_trees_cat = []\nnum_trees_xgb = []\n\nfor i, (train_index, test_index) in enumerate(skf.split(X=train, y=train['bins'], groups=train.index)):\n print(f'\\nFold {i+1}:')\n\n train_x = train.iloc[train_index]\n train_x = pd.concat([train_x,original])\n train_gems = train_x.index.values\n train_y = targets.loc[train_gems]\n\n valid_x = train.iloc[test_index]\n valid_gems = valid_x.index.values\n valid_y = targets.loc[valid_gems]\n \n model_dict = {\n 'cat': [CatBoostRegressor(**cat_params),{\"eval_set\":[(valid_x[CAT_FEATS].astype('float32'), valid_y)],'plot':False},CAT_FEATS],\n 'xgb': [XGBRegressor(**xgb_params),{\"eval_set\":[(valid_x[FEATS].astype('float32'), valid_y)],\"verbose\":0},FEATS],\n }\n \n all_models = model_dict.keys()\n for model in all_models:\n clf = model_dict[model][0]\n clf.fit(train_x[model_dict[model][2]].astype('float32'), train_y, **model_dict[model][1])\n \n oof.loc[valid_gems, f'{model}_prediction'] = np.exp(clf.predict(valid_x[model_dict[model][2]].astype('float32')))-1\n print(f'model {model} trained')\n if model == 'cat':\n num_trees_cat.append(clf.get_best_iteration())\n if model == 'xgb':\n num_trees_xgb.append(clf.best_ntree_limit)\n","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:41:32.991242Z","iopub.execute_input":"2023-03-30T14:41:32.991606Z","iopub.status.idle":"2023-03-30T14:45:30.813651Z","shell.execute_reply.started":"2023-03-30T14:41:32.991569Z","shell.execute_reply":"2023-03-30T14:45:30.812585Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Stratified CV","metadata":{}},{"cell_type":"code","source":"from sklearn.metrics import mean_squared_log_error\nrmses = {}\ntargets = train.cost\nfor model in all_models:\n rmses[f\"{model}_prediction\"] = mean_squared_log_error(targets,oof[f'{model}_prediction'], squared=False)\n print(f'{model} = {rmses[f\"{model}_prediction\"]}')\n \noof['prediction'] = 0\nrmse_inv_sum = 0\nfor model in all_models:\n if model in ['xgb','lgb','cat']:\n oof['prediction'] = oof['prediction'] + oof[f'{model}_prediction'] * (1/rmses[f'{model}_prediction'])\n rmse_inv_sum += 1/rmses[f'{model}_prediction']\n \n#print('xgb = 0.2930786590554832')\noof['prediction'] = oof['prediction'] / rmse_inv_sum\nprint(f'ensemble = {mean_squared_log_error(targets,oof[f\"prediction\"], squared=False)}')","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:45:30.81897Z","iopub.execute_input":"2023-03-30T14:45:30.819281Z","iopub.status.idle":"2023-03-30T14:45:30.87054Z","shell.execute_reply.started":"2023-03-30T14:45:30.819249Z","shell.execute_reply":"2023-03-30T14:45:30.868885Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fig, ax = plt.subplots(figsize=(17,8)) \nsns.histplot(data=oof, x=\"prediction\",bins = 100, ax=ax, kde = True)\nax2 = ax.twinx()\nsns.boxplot(data=oof, x=\"prediction\", ax=ax2,boxprops=dict(alpha=.7))\nax2.set(ylim=(-.5, 10))\nplt.suptitle('Cost countplot and Boxplot for my train predictions', fontsize=20)\nax.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:45:30.873627Z","iopub.execute_input":"2023-03-30T14:45:30.873943Z","iopub.status.idle":"2023-03-30T14:45:32.48187Z","shell.execute_reply.started":"2023-03-30T14:45:30.873908Z","shell.execute_reply":"2023-03-30T14:45:32.480834Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Hold","metadata":{}},{"cell_type":"code","source":"print(f\"CAT :{num_trees_cat}; mean : {np.array(num_trees_cat).mean()}\")\nprint(f\"XGB :{num_trees_xgb}; mean : {np.array(num_trees_xgb).mean()}\")\nprint()\nhold_num_trees_cat = int(np.array(num_trees_cat).mean()*1.25)\nhold_num_trees_xgb = int(np.array(num_trees_xgb).mean()*1.25)\n#cat model\ncat_params2 = cat_params.copy()\ncat_params2.pop('iterations')\ncat_params2.pop('early_stopping_rounds')\ncat_params2.pop('use_best_model')\nnew_params = {\n 'iterations': hold_num_trees_cat,\n}\n\nhold_cat = CatBoostRegressor(**cat_params2,**new_params)\nhold_cat.fit(train[CAT_FEATS].astype('float32'), np.log(train.cost+1),plot=False)\n\n\n#xgb model\nxgb_params2 = xgb_params.copy()\nxgb_params2.pop('early_stopping_rounds')\nxgb_params2['n_estimators'] = hold_num_trees_xgb\n\nhold_xgb = XGBRegressor(**xgb_params2)\nhold_xgb.fit(train[FEATS].astype('float32'), np.log(train.cost+1))\n\n#hold oof\nhold_pred = pd.DataFrame(index = hold.id)\n\nhold_pred['cat_predictions'] = np.exp(hold_cat.predict(hold[CAT_FEATS].astype('float32')))-1\nhold_pred['xgb_predictions'] = np.exp(hold_xgb.predict(hold[FEATS].astype('float32')))-1\nhold_pred['predictions'] = (hold_pred['xgb_predictions'] + hold_pred['cat_predictions']) /2\n\n#rmsles\nprint(f\"\\nhold cat prediction: {mean_squared_log_error(hold.cost,hold_pred['cat_predictions'], squared=False)}\")\nprint(f\"hold xgb prediction: {mean_squared_log_error(hold.cost,hold_pred['xgb_predictions'], squared=False)}\")\nprint(f\"\\nhold ensemble prediction: {mean_squared_log_error(hold.cost,hold_pred['predictions'], squared=False)}\")","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:45:32.482891Z","iopub.execute_input":"2023-03-30T14:45:32.483236Z","iopub.status.idle":"2023-03-30T14:46:06.106509Z","shell.execute_reply.started":"2023-03-30T14:45:32.483202Z","shell.execute_reply":"2023-03-30T14:46:06.105527Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"hold_pred.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:46:06.110829Z","iopub.execute_input":"2023-03-30T14:46:06.113128Z","iopub.status.idle":"2023-03-30T14:46:06.129271Z","shell.execute_reply.started":"2023-03-30T14:46:06.113089Z","shell.execute_reply":"2023-03-30T14:46:06.128016Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Inference","metadata":{}},{"cell_type":"code","source":"new_params = {\n 'iterations': int(hold_num_trees_cat*1.25),\n}\n\nlast_train = pd.concat([train,hold,original], ignore_index=True)\n\ntest_cat = CatBoostRegressor(**cat_params2,**new_params)\n\nxgb_params3 = xgb_params2.copy()\nxgb_params3['n_estimators'] = int(xgb_params3['n_estimators']*1.25)\ntest_xgb = XGBRegressor(**xgb_params3)\n\ntest_xgb.fit(last_train[FEATS].astype('float32'), np.log(last_train.cost+1))\ntest_cat.fit(last_train[CAT_FEATS].astype('float32'), np.log(last_train.cost+1),plot=False)\n\nsubmission = pd.DataFrame(index = test.id)\nsubmission['id'] = submission.index\n\nsubmission['cost_xgb'] = np.exp(test_xgb.predict(test[FEATS].astype('float32')))-1\nsubmission['cost_cat'] = np.exp(test_cat.predict(test[CAT_FEATS].astype('float32')))-1\nsubmission['cost'] = (submission['cost_xgb'] + submission['cost_cat']) / 2","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:46:06.133337Z","iopub.execute_input":"2023-03-30T14:46:06.135552Z","iopub.status.idle":"2023-03-30T14:47:02.285508Z","shell.execute_reply.started":"2023-03-30T14:46:06.135516Z","shell.execute_reply":"2023-03-30T14:47:02.284443Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission.head()","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:47:02.287029Z","iopub.execute_input":"2023-03-30T14:47:02.287404Z","iopub.status.idle":"2023-03-30T14:47:02.301681Z","shell.execute_reply.started":"2023-03-30T14:47:02.287367Z","shell.execute_reply":"2023-03-30T14:47:02.300287Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission[['id','cost']].to_csv('submission.csv', index= False)","metadata":{"execution":{"iopub.status.busy":"2023-03-30T14:47:02.303422Z","iopub.execute_input":"2023-03-30T14:47:02.303865Z","iopub.status.idle":"2023-03-30T14:47:02.74577Z","shell.execute_reply.started":"2023-03-30T14:47:02.303825Z","shell.execute_reply":"2023-03-30T14:47:02.744708Z"},"trusted":true},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/rcaf2/code/code.py b/Agent/workspace/hyperopt/rcaf2/code/code.py index 6237278..c42330f 100644 --- a/Agent/workspace/hyperopt/rcaf2/code/code.py +++ b/Agent/workspace/hyperopt/rcaf2/code/code.py @@ -13,7 +13,7 @@ # FILE_PATH="../data/" FILE_PATH = "./workspace/hyperopt/rcaf2/data/" -TARGET = "NObeyesdad" +# TARGET = "NObeyesdad" submission_path = "ori_submission.csv" n_splits = 9 RANDOM_SEED = 73 @@ -118,27 +118,29 @@ def import_data(file): n_estimators=500, learning_rate=0.2) -clf1.fit(X_train, y_train) -pred1 = clf1.predict(X_test) -print("lgbm1: ", accuracy_score(pred1, y_test)) - -pred = clf1.predict_proba(test) -sub = pd.DataFrame(pred, columns=["A", "B", "C", "D"]) -sub["id"] = test_id -cols = sub.columns.tolist() -cols = cols[-1:] + cols[:-1] -sub = sub[cols] -sub.to_csv("sub_lgb1.csv", index=False) - -clf2.fit(X_train, y_train) -pred2 = clf2.predict(X_test) -print("lgbm2: ", accuracy_score(pred2, y_test)) - -clf3.fit(X_train, y_train) -pred3 = clf3.predict(X_test) -print("lgbm3: ", accuracy_score(pred3, y_test)) - -eclf3 = VotingClassifier(estimators=[ - ("lr", clf1), ("rf", clf2), ("gnb", clf3)], - voting="soft", weights=[2, 1, 1], - flatten_transform=True) +# clf1.fit(X_train, y_train) +# pred1 = clf1.predict(X_test) +# print("lgbm1: ", accuracy_score(pred1, y_test)) + +# pred = clf1.predict_proba(test) +# sub = pd.DataFrame(pred, columns=["A", "B", "C", "D"]) +# sub["id"] = test_id +# cols = sub.columns.tolist() +# cols = cols[-1:] + cols[:-1] +# sub = sub[cols] +# sub.to_csv("sub_lgb1.csv", index=False) + +# clf2.fit(X_train, y_train) +# pred2 = clf2.predict(X_test) +# print("lgbm2: ", accuracy_score(pred2, y_test)) + +# clf3.fit(X_train, y_train) +# pred3 = clf3.predict(X_test) +# print("lgbm3: ", accuracy_score(pred3, y_test)) +voting_classifier_fix_params={ + "estimators": [("lr", clf1), ("rf", clf2), ("gnb", clf3)], + "voting": "soft", + "weights": [2, 1, 1], + "flatten_transform": True +} +eclf3 = VotingClassifier(**voting_classifier_fix_params) diff --git a/Agent/workspace/hyperopt/rrp2/code/code.py b/Agent/workspace/hyperopt/rrp2/code/code.py index 3b27fa5..6268df4 100644 --- a/Agent/workspace/hyperopt/rrp2/code/code.py +++ b/Agent/workspace/hyperopt/rrp2/code/code.py @@ -17,18 +17,18 @@ from sklearn.model_selection import KFold # FILE_PATH="../data/" -FILE_PATH = "./workspace/hyperopt/rrp/data/" +FILE_PATH = "./workspace/hyperopt/rrp2/data/" submission_path = "best_submission.csv" n_splits = 9 RANDOM_SEED = 73 df = pd.read_csv(FILE_PATH + "train.csv.zip") -df.shape +# df.shape # The dataset is quite small so complex models with many parameters should be avoided. Using a complex model for this dataset will cause the model to overfit to the dataset. Regularization techniques will definitely need to be used to prevent the possibility of overfitting. test_df = pd.read_csv(FILE_PATH + "test.csv.zip") -test_df.shape +# test_df.shape # The **MB** Type will be replaced with the **DT** Type in the test set since it"s not available in our training set. The **City** feature is useless since our training set contains **34** unique cities but the test set contains **57** unique cities. @@ -394,10 +394,10 @@ def get_models(): # evaluate a given model using cross-validation -def evaluate_model(model, X, y): - cv = RepeatedKFold(n_splits=10, n_repeats=5, random_state=19) - scores = cross_val_score(model, X, y, scoring="neg_mean_absolute_error", cv=cv, n_jobs=-1, error_score="raise") - return scores +# def evaluate_model(model, X, y): +# cv = RepeatedKFold(n_splits=10, n_repeats=5, random_state=19) +# scores = cross_val_score(model, X, y, scoring="neg_mean_absolute_error", cv=cv, n_jobs=-1, error_score="raise") +# return scores # get the models to evaluate diff --git a/Agent/workspace/hyperopt/scrabble/code/code.py b/Agent/workspace/hyperopt/scrabble/code/code.py index 8a0b906..e537e81 100644 --- a/Agent/workspace/hyperopt/scrabble/code/code.py +++ b/Agent/workspace/hyperopt/scrabble/code/code.py @@ -28,8 +28,8 @@ # # **Load Data file** FILE_PATH = "../data/" -# FILE_PATH= "./workspace/hyperopt/scrabble/data/" -TARGET = "NObeyesdad" +FILE_PATH= "./workspace/hyperopt/scrabble/data/" +# TARGET = "NObeyesdad" submission_path = "ori_submission.csv" n_splits = 9 RANDOM_SEED = 73 @@ -41,7 +41,7 @@ games = pd.read_csv(FILE_PATH + "games.csv") turns = pd.read_csv(FILE_PATH + "turns.csv") -print(train.shape, test.shape, sample.shape, games.shape, turns.shape) +# print(train.shape, test.shape, sample.shape, games.shape, turns.shape) # # **KFold** diff --git a/Agent/workspace/hyperopt/sf-crime/code/code.py b/Agent/workspace/hyperopt/sf-crime/code/code.py index eef0da8..35cadec 100644 --- a/Agent/workspace/hyperopt/sf-crime/code/code.py +++ b/Agent/workspace/hyperopt/sf-crime/code/code.py @@ -9,7 +9,7 @@ from sklearn.compose import ColumnTransformer from sklearn.linear_model import LogisticRegression from sklearn.model_selection import train_test_split -from sklearn.preprocessing import OrdinalEncoder, StandardScaler +from sklearn.preprocessing import OrdinalEncoder, StandardScaler, LabelEncoder # FILE_PATH= "../data/" FILE_PATH = "./workspace/hyperopt/sf-crime/data/" @@ -84,6 +84,12 @@ # df["target"] df.Category.value_counts() +class_counts = df.Category.value_counts() + +# Filter out classes with fewer than 5 instances +valid_classes = class_counts[class_counts >= 5].index +df = df[df.Category.isin(valid_classes)] + df["year"] = df["Dates"].dt.year df["month"] = df["Dates"].dt.month @@ -107,7 +113,7 @@ X_train, X_test, y_train, y_test = train_test_split( df[usecols], df.Category, - test_size=.3, + test_size=.1, random_state=2024, shuffle=True, ) @@ -135,7 +141,7 @@ prep_pipe.fit(X_train, y_train) -from sklearn.metrics import roc_auc_score +from sklearn.metrics import accuracy_score from sklearn.ensemble import RandomForestClassifier # ΠŸΠΎΠ΄Π±ΠΎΡ€ количСства ΠΈΡ‚Π΅Ρ€Π°Ρ†ΠΈΠΉ @@ -145,32 +151,34 @@ train_scores = [] X_test_transformed = prep_pipe.transform(X_test) X_train_transformed = prep_pipe.transform(X_train) - -for n in range(1, 30): - # print(f"step {n}", end="\r") - clf = RandomForestClassifier(n_estimators=n, n_jobs=-1, max_depth=3) - clf.fit(X_train_transformed, y_train) - - test_scores.append( - roc_auc_score( - y_test, - clf.predict_proba(X_test_transformed), - multi_class="ovr" - )) - - train_scores.append( - roc_auc_score( - y_train, - clf.predict_proba(X_train_transformed), - multi_class="ovr" - )) - - x.append(n) - - if abs(test_scores[-1] - train_scores[-1]) > .2: - print(f"n_estimators: {n}") - print(f"scores train/test {train_scores[-1]:.2f}/{test_scores[-1]:2f}") - break +le=LabelEncoder() +y_train_full_encoded = le.fit_transform(y_train) + +# for n in range(1, 30): +# # print(f"step {n}", end="\r") +# clf = RandomForestClassifier(n_estimators=n, n_jobs=-1, max_depth=3) +# clf.fit(X_train_transformed, y_train) + +# test_scores.append( +# roc_auc_score( +# y_test, +# clf.predict_proba(X_test_transformed), +# multi_class="ovr" +# )) + +# train_scores.append( +# roc_auc_score( +# y_train, +# clf.predict_proba(X_train_transformed), +# multi_class="ovr" +# )) + +# x.append(n) + +# if abs(test_scores[-1] - train_scores[-1]) > .2: +# print(f"n_estimators: {n}") +# print(f"scores train/test {train_scores[-1]:.2f}/{test_scores[-1]:2f}") +# break # plt.plot(x, train_scores) # plt.plot(x, test_scores) @@ -182,7 +190,7 @@ n_estimator = [i for i, x in enumerate(test_scores) if x == max(test_scores)] -clf = RandomForestClassifier(n_estimators=n, n_jobs=-1, max_depth=3) +clf = RandomForestClassifier(n_jobs=-1, max_depth=3) # clf.fit(X_train_transformed, y_train) diff --git a/Agent/workspace/hyperopt/sf-crime2/code/code.py b/Agent/workspace/hyperopt/sf-crime2/code/code.py index d9f9576..ee26117 100644 --- a/Agent/workspace/hyperopt/sf-crime2/code/code.py +++ b/Agent/workspace/hyperopt/sf-crime2/code/code.py @@ -91,19 +91,19 @@ # We further split the full training set into train and validation set. We use Stratified sampling to ensure that the training and val set contains a proper representation of the categories present in the total population -import sklearn -from sklearn.model_selection import StratifiedShuffleSplit +# import sklearn +# from sklearn.model_selection import StratifiedShuffleSplit -sss = StratifiedShuffleSplit(n_splits=1, test_size=0.25, random_state=42) +# sss = StratifiedShuffleSplit(n_splits=1, test_size=0.25, random_state=42) -for train_index, test_index in sss.split(X_train_full, y_train_full): - X_train, y_train = X_train_full[train_index], y_train_full[train_index] - X_val, y_val = X_train_full[test_index], y_train_full[test_index] +# for train_index, test_index in sss.split(X_train_full, y_train_full): +# X_train, y_train = X_train_full[train_index], y_train_full[train_index] +# X_val, y_val = X_train_full[test_index], y_train_full[test_index] # Next, lets set a pipeline to preprocess the datasets. StandardScaler() to normalise the numerical atttributes and OneHotEncoder() to convert the categorical attributes to arrays. from sklearn.pipeline import Pipeline -from sklearn.preprocessing import StandardScaler, OneHotEncoder +from sklearn.preprocessing import StandardScaler, OneHotEncoder, LabelEncoder from sklearn.compose import ColumnTransformer num_attribs = [2, 3] @@ -118,8 +118,8 @@ ("cat", OneHotEncoder(), cat_attribs) ]) -X_train_prepared = full_pipeline.fit_transform(X_train) -X_val_prepared = full_pipeline.transform(X_val) +X_train_prepared = full_pipeline.fit_transform(X_train_full) +# X_val_prepared = full_pipeline.transform(X_val) X_test_prepared = full_pipeline.transform(X_test) # # Select and train model @@ -128,28 +128,29 @@ from sklearn.ensemble import RandomForestClassifier, VotingClassifier import xgboost -X_train_prepared.shape - +X_train_prepared = X_train_prepared.toarray() +le = LabelEncoder() +y_train_full_encoded = le.fit_transform(y_train_full) # The train set contains 658 486 rows which lead to slow training time for me. Therefore, we will reduce the training set to 100 000 rows use Stratified Sampling again to get a good representation of the population. -ss = StratifiedShuffleSplit(n_splits=1, train_size=100_000, random_state=42) -for train_index, _ in ss.split(X_train_prepared, y_train): - X_train_prepared_small, y_train_small = X_train_prepared[train_index], y_train[train_index].ravel() +# ss = StratifiedShuffleSplit(n_splits=1, train_size=100_000, random_state=42) +# for train_index, _ in ss.split(X_train_prepared, y_train): +# X_train_prepared_small, y_train_small = X_train_prepared[train_index], y_train[train_index].ravel() -X_train_prepared_small.shape, y_train_small.shape +# X_train_prepared_small.shape, y_train_small.shape # Ensemble Learning aggregates the prediction of a group of predictors. We usually get better results from it compared with just a single best individual predictor. # # From a prior, not very thorough, testing with different classifiers such as LinearSVC, BaggingClassifier, ExtraTreesClassifier. I found that Ensemble learning with XGBoost and RandomForest yield the best results. rf_clf = RandomForestClassifier(max_depth=16, random_state=42, n_jobs=-1, verbose=3) -xg_clf = xgboost.XGBClassifier() +xg_clf = xgboost.XGBClassifier() # reg_alpha, reg_lambda, learning_rate ... estimators = [ ("rf", rf_clf), ("xg", xg_clf) ] - +from sklearn.metrics import accuracy_score voting_clf = VotingClassifier(estimators, n_jobs=-1, voting="soft") # voting_clf.fit(X_train_prepared_small, y_train_small) # voting_clf.score(X_val_prepared, y_val) diff --git a/Agent/workspace/hyperopt/tpsf/code/code.py b/Agent/workspace/hyperopt/tpsf/code/code.py new file mode 100644 index 0000000..c951f4f --- /dev/null +++ b/Agent/workspace/hyperopt/tpsf/code/code.py @@ -0,0 +1,105 @@ +#!/usr/bin/env python +# coding: utf-8 + +# In this notebook, you will learn how to make your first submission to the [Tabular Playground Series - Feb 2021 competition.](https://www.kaggle.com/c/tabular-playground-series-feb-2021) +# +# # Make the most of this notebook! +# +# You can use the "Copy and Edit" button in the upper right of the page to create your own copy of this notebook and experiment with different models. You can run it as is and then see if you can make improvements. + +import numpy as np +import pandas as pd +from pathlib import Path + +# import os +# for dirname, _, filenames in os.walk('/kaggle/input'): +# for filename in filenames: +# print(os.path.join(dirname, filename)) + +import matplotlib.pyplot as plt + +from sklearn.model_selection import train_test_split +from sklearn.metrics import mean_squared_error +from sklearn.preprocessing import LabelEncoder + +from sklearn.ensemble import RandomForestRegressor + +FILE_PATH = "./workspace/hyperopt/tspf/data/" + +# # Read in the data files + +train = pd.read_csv(FILE_PATH + 'train.csv.zip', index_col='id') +# display(train.head()) + + +test = pd.read_csv(FILE_PATH + 'test.csv.zip', index_col='id') +# display(test.head()) + + +submission = pd.read_csv(FILE_PATH + 'sample_submission.csv.zip', index_col='id') +# display(submission.head()) + + +# ## We need to encode the categoricals. +# +# There are different strategies to accomplish this, and different approaches will have different performance when using different algorithms. For this starter notebook, we'll use simple encoding. + +for c in train.columns: + if train[c].dtype=='object': + lbl = LabelEncoder() + lbl.fit(list(train[c].values) + list(test[c].values)) + train[c] = lbl.transform(train[c].values) + test[c] = lbl.transform(test[c].values) + +# display(train.head()) + + +# ## Pull out the target, and make a validation split + +target = train.pop('target') +X_train, X_test, y_train, y_test = train_test_split(train, target, train_size=0.60) + + +# # How well can we do with a completely naive model? +# +# We'll want any of our models to do (hopefully much!) better than this. + +# Let's get a benchmark score + +# print(f'{score_dummy:0.5f}') + + +# # Simple Linear Regression +# +# A simple linear regression doesn't do better than our dummy regressor! (Alghouth, simple categorical encoding really doesn't make sense for this approach!) + +# Simple Linear Regression + +# print(f'{score_simple_linear:0.5f}') + + +# # This seems slow and repetative. Can we automate it a bit? + +# def plot_results(name, y, yhat, num_to_plot=10000, lims=(0,12), figsize=(6,6)): +# plt.figure(figsize=figsize) +# score = mean_squared_error(y, yhat, squared=False) +# plt.scatter(y[:num_to_plot], yhat[:num_to_plot]) +# plt.plot(lims, lims) +# plt.ylim(lims) +# plt.xlim(lims) +# plt.title(f'{name}: {score:0.5f}', fontsize=18) +# plt.show() + + + +# # It look like RandomForest did the best. Let's train it on all the data and make a submission! + +model = RandomForestRegressor(n_estimators=50, n_jobs=-1) +# model.fit(train, target) +# submission['target'] = model.predict(test) +# submission.to_csv('random_forest.csv') + + +# ## Now you should save your Notebook (blue button in the upper right), and then when that's complete go to the notebook viewer and make a submission to the competition. :-) +# +# ## There's lots of room for improvement. What things can you try to get a better score? diff --git a/Agent/workspace/hyperopt/tpsf/code/get-started-feb-tabular-playground-competition.ipynb b/Agent/workspace/hyperopt/tpsf/code/get-started-feb-tabular-playground-competition.ipynb new file mode 100644 index 0000000..4848198 --- /dev/null +++ b/Agent/workspace/hyperopt/tpsf/code/get-started-feb-tabular-playground-competition.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":25225,"databundleVersionId":1923495,"sourceType":"competition"}],"dockerImageVersionId":30055,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"In this notebook, you will learn how to make your first submission to the [Tabular Playground Series - Feb 2021 competition.](https://www.kaggle.com/c/tabular-playground-series-feb-2021)\n\n# Make the most of this notebook!\n\nYou can use the \"Copy and Edit\" button in the upper right of the page to create your own copy of this notebook and experiment with different models. You can run it as is and then see if you can make improvements.","metadata":{}},{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom pathlib import Path\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n \nimport matplotlib.pyplot as plt\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.preprocessing import LabelEncoder\n\nfrom sklearn.dummy import DummyRegressor\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.linear_model import Ridge, Lasso\nfrom sklearn.ensemble import RandomForestRegressor\n \ninput_path = Path('/kaggle/input/tabular-playground-series-feb-2021/')","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Read in the data files","metadata":{}},{"cell_type":"code","source":"train = pd.read_csv(input_path / 'train.csv', index_col='id')\ndisplay(train.head())","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test = pd.read_csv(input_path / 'test.csv', index_col='id')\ndisplay(test.head())","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submission = pd.read_csv(input_path / 'sample_submission.csv', index_col='id')\ndisplay(submission.head())","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## We need to encode the categoricals.\n\nThere are different strategies to accomplish this, and different approaches will have different performance when using different algorithms. For this starter notebook, we'll use simple encoding.","metadata":{}},{"cell_type":"code","source":"for c in train.columns:\n if train[c].dtype=='object': \n lbl = LabelEncoder()\n lbl.fit(list(train[c].values) + list(test[c].values))\n train[c] = lbl.transform(train[c].values)\n test[c] = lbl.transform(test[c].values)\n \ndisplay(train.head())","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Pull out the target, and make a validation split","metadata":{}},{"cell_type":"code","source":"target = train.pop('target')\nX_train, X_test, y_train, y_test = train_test_split(train, target, train_size=0.60)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# How well can we do with a completely naive model?\n\nWe'll want any of our models to do (hopefully much!) better than this.","metadata":{}},{"cell_type":"code","source":"# Let's get a benchmark score\nmodel_dummy = DummyRegressor(strategy='median')\nmodel_dummy.fit(X_train, y_train)\ny_dummy = model_dummy.predict(X_test)\nscore_dummy = mean_squared_error(y_test, y_dummy, squared=False)\nprint(f'{score_dummy:0.5f}')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Simple Linear Regression\n\nA simple linear regression doesn't do better than our dummy regressor! (Alghouth, simple categorical encoding really doesn't make sense for this approach!)","metadata":{}},{"cell_type":"code","source":"# Simple Linear Regression\nmodel_simple_linear = LinearRegression(fit_intercept=True) # data is not centered, we need an intercept!\nmodel_simple_linear.fit(X_train, y_train)\ny_simple_linear = model_simple_linear.predict(X_test)\nscore_simple_linear = mean_squared_error(y_test, y_simple_linear, squared=False)\nprint(f'{score_simple_linear:0.5f}')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# This seems slow and repetative. Can we automate it a bit?","metadata":{}},{"cell_type":"code","source":"def plot_results(name, y, yhat, num_to_plot=10000, lims=(0,12), figsize=(6,6)):\n plt.figure(figsize=figsize)\n score = mean_squared_error(y, yhat, squared=False)\n plt.scatter(y[:num_to_plot], yhat[:num_to_plot])\n plt.plot(lims, lims)\n plt.ylim(lims)\n plt.xlim(lims)\n plt.title(f'{name}: {score:0.5f}', fontsize=18)\n plt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model_names = [\"Dummy Median\", \"Linear\", \"Lasso\", \"Random Forest\"]\n\nmodels = [\n DummyRegressor(strategy='median'),\n LinearRegression(fit_intercept=True),\n Lasso(fit_intercept=True),\n RandomForestRegressor(n_estimators=50, n_jobs=-1)]\n\nfor name, model in zip(model_names, models):\n model.fit(X_train, y_train)\n y_pred = model.predict(X_test)\n plot_results(name, y_test, y_pred)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# It look like RandomForest did the best. Let's train it on all the data and make a submission!","metadata":{}},{"cell_type":"code","source":"model = RandomForestRegressor(n_estimators=50, n_jobs=-1)\nmodel.fit(train, target)\nsubmission['target'] = model.predict(test)\nsubmission.to_csv('random_forest.csv')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"## Now you should save your Notebook (blue button in the upper right), and then when that's complete go to the notebook viewer and make a submission to the competition. :-)\n\n## There's lots of room for improvement. What things can you try to get a better score?","metadata":{}}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/tpsf2/code/code.py b/Agent/workspace/hyperopt/tpsf2/code/code.py new file mode 100644 index 0000000..2e58d63 --- /dev/null +++ b/Agent/workspace/hyperopt/tpsf2/code/code.py @@ -0,0 +1,273 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # Overview +# +# The purpose of this notebook is to predict the target by an Ensemble model composed of tree individual models +# +# - lightgbm +# - xgboost +# - catboost +# +# Feature Engineering followed basic practices that proved to work for GBM-style models for this competition +# +# - label encoding the cat variables +# - standard scaling to numeric variables +# +# Params for *xgboost* and *catboost* have been discovered via hyperparam search, using *hyperopt*. Params for *lightgbm* have been reused from https://www.kaggle.com/hiro5299834/tps-feb-2021-with-single-lgbm-tuned (they appeared to work better vs. the set of parameters I discovered in *hyperopt*-based search). +# +# Weight of lightgbm prediction was set to be a little higher then catboost and xgboost. +# +# The well-thought software design of the Ensembling class was inspired by https://www.kaggle.com/kenkpixdev/ensemble-lgb-xgb-with-hyperopt + +import pandas as pd +import numpy as np +import time +import datetime as dt +from typing import Tuple, List, Dict + +# from hyperopt import STATUS_OK, Trials, fmin, hp, tpe +# from hyperopt.pyll.base import scope +from xgboost import XGBRegressor +from lightgbm import LGBMRegressor +from catboost import CatBoostRegressor + +from sklearn.model_selection import train_test_split, KFold +from sklearn.preprocessing import StandardScaler, LabelEncoder + + +# main flow +start_time = dt.datetime.now() +print("Started at ", start_time) + + +# read data +in_kaggle = True + +FILE_PATH = "./workspace/hyperopt/tspf2/data/" + +def get_data_file_path(is_in_kaggle: bool) -> Tuple[str, str, str]: + train_path = '' + test_path = '' + sample_submission_path = '' + + if is_in_kaggle: + # running in Kaggle, inside the competition + train_path = '../input/tabular-playground-series-feb-2021/train.csv' + test_path = '../input/tabular-playground-series-feb-2021/test.csv' + sample_submission_path = '../input/tabular-playground-series-feb-2021/sample_submission.csv' + else: + # running locally + train_path = 'data/train.csv' + test_path = 'data/test.csv' + sample_submission_path = 'data/sample_submission.csv' + + return train_path, test_path, sample_submission_path + + +# get_ipython().run_cell_magic('time', '', '# get the training set and labels\ntrain_set_path, test_set_path, sample_subm_path = get_data_file_path(in_kaggle)\n\ntrain = pd.read_csv(train_set_path)\ntest = pd.read_csv(test_set_path)\ntarget = train.target\n\nsubm = pd.read_csv(sample_subm_path)\n') + + +train = pd.read_csv(FILE_PATH + 'train.csv.zip') +test = pd.read_csv(FILE_PATH +'test.csv.zip') +target = train.target + +subm = pd.read_csv(FILE_PATH + 'sample_submission.csv.zip') + + +def preprocess(df, encoder=None,scaler=None, cols_to_drop=None,cols_to_encode=None, cols_to_scale=None): + """ + Preprocess input data + :param df: DataFrame with data + :param encoder: encoder object with fit_transform method + :param scaler: scaler object with fit_transform method + :param cols_to_drop: columns to be removed + :param cols_to_encode: columns to be encoded + :param cols_to_scale: columns to be scaled + :return: DataFrame + """ + + if encoder: + for col in cols_to_encode: + df[col] = encoder.fit_transform(df[col]) + + if scaler: + for col in cols_to_scale: + df[col] = scaler.fit_transform(df[col].values.reshape(-1, 1)) + + if cols_to_drop: + df = df.drop(cols_to_drop, axis=1) + + return df + + +cat_cols = ['cat' + str(i) for i in range(10)] +cont_cols = ['cont' + str(i) for i in range(14)] + +train = preprocess(train, encoder=LabelEncoder(), scaler=StandardScaler(), + cols_to_drop=['id', 'target'], cols_to_encode=cat_cols, + cols_to_scale=cont_cols) + +# encoder=LabelEncoder() +test = preprocess(test, encoder=LabelEncoder(), scaler=StandardScaler(), + cols_to_drop=['id'], cols_to_encode=cat_cols, + cols_to_scale=cont_cols) + +# ------------------------------------------------------------------------------ +# Parameters +# ------------------------------------------------------------------------------ +N_FOLDS = 10 +N_ESTIMATORS = 30000 +SEED = 2021 +BAGGING_SEED = 48 + + + +class EnsembleModel: + def __init__(self, params): + """ + LGB + XGB + CatBoost model + """ + self.lgb_params = params['lgb'] + self.xgb_params = params['xgb'] + self.cat_params = params['cat'] + + self.lgb_model = LGBMRegressor(**self.lgb_params, + **{ + 'n_jobs': -1, + 'cat_feature': [x for x in range(len(cat_cols))], + 'bagging_seed': SEED, + 'feature_fraction_seed': SEED, + 'random_state': SEED, + 'metric': 'rmse', + } + + ) + self.xgb_model = XGBRegressor(**self.xgb_params, + **{ 'random_state': SEED, + 'objective': 'reg:squarederror', + 'tree_method': 'gpu_hist', + 'eval_metric': 'rmse', + 'n_jobs': -1 + } + ) + + + + self.cat_model = CatBoostRegressor(**self.cat_params, + **{ + 'random_state': SEED, + 'eval_metric': 'RMSE', + 'leaf_estimation_backtracking': 'AnyImprovement', + + } + + ) + + def fit(self, x, y, *args, **kwargs): + return (self.lgb_model.fit(x, y, *args, **kwargs), + self.xgb_model.fit(x, y, *args, **kwargs), + self.cat_model.fit(x, y, *args, **kwargs)) + + def predict(self, x, weights=[1.0, 1.0, 1.0]): + """ + Generate model predictions + :param x: data + :param weights: weights on model prediction, first one is the weight on lgb model + :return: array with predictions + """ + return (weights[0] * self.lgb_model.predict(x) + + weights[1] * self.xgb_model.predict(x) + + weights[2] * self.cat_model.predict(x)) / 3 + + +since = time.time() +columns = train.columns + + + +# ------------------------------------------------------------------------------ +# LightGBM: training and inference +# ------------------------------------------------------------------------------ +# + +ensemble_params = { + "lgb" : { + 'n_estimators': N_ESTIMATORS, + 'learning_rate': 0.003899156646724397, + 'max_depth': 99, + 'num_leaves': 63, + 'reg_alpha': 9.562925363678952, + 'reg_lambda': 9.355810045480153, + 'colsample_bytree': 0.2256038826485174, + 'min_child_samples': 290, + 'subsample_freq': 1, + 'subsample': 0.8805303688019942, + 'max_bin': 882, + 'min_data_per_group': 127, + 'cat_smooth': 96, + 'cat_l2': 19 + }, + 'xgb': { + 'max_depth': 13, + 'learning_rate': 0.020206705089028228, + 'gamma': 3.5746731812451156, + 'min_child_weight': 564, + 'n_estimators': 8000, + 'colsample_bytree': 0.5015940592112956, + 'subsample': 0.6839489639112909, + 'reg_lambda': 18.085502002853246, + 'reg_alpha': 0.17532087359570606, + }, + 'cat': { + 'depth': 3.0, + 'fold_len_multiplier': 1.1425259013471902, + 'l2_leaf_reg': 7.567589781752637, + 'learning_rate': 0.25121635918496565, + 'max_bin': 107.0, + 'min_data_in_leaf': 220.0, + 'random_strength': 3.2658690042589726, + 'n_estimators': 8000, + + } +} + +preds = np.zeros(test.shape[0]) +kf = KFold(n_splits=N_FOLDS, random_state=22, shuffle=True) +rmse = [] +n = 0 +model = EnsembleModel(ensemble_params) +from sklearn.metrics import mean_squared_error +# for trn_idx, test_idx in kf.split(train[columns], target): + +# X_tr, X_val=train[columns].iloc[trn_idx], train[columns].iloc[test_idx] +# y_tr, y_val=target.iloc[trn_idx], target.iloc[test_idx] + +# model = EnsembleModel(ensemble_params) + +# model.fit(X_tr, y_tr, eval_set=[(X_val, y_val)], early_stopping_rounds=100, verbose=False) + +# preds += model.predict(test[columns], weights=[1.1, 1.0, 0.9]) / kf.n_splits +# rmse.append(mean_squared_error(y_val, model.predict(X_val), squared=False)) + +# print(f"Fold {n+1}, RMSE: {rmse[n]}") +# n += 1 + + +# print("Mean RMSE: ", np.mean(rmse)) +# end_time = time.time() - since +# print('Training complete in {:.0f}m {:.0f}s'.format( +# end_time // 60, end_time % 60)) + + +# # submit prediction +# subm['target'] = preds +# subm.to_csv("ensemble_model_lgb_xgb_cat_other_lgb_params.csv", index=False) + + +# print('We are done. That is all, folks!') +# finish_time = dt.datetime.now() +# print("Finished at ", finish_time) +# elapsed = finish_time - start_time +# print("Elapsed time: ", elapsed) + diff --git a/Agent/workspace/hyperopt/tpsf2/code/ensemble-lgb-xgb-catboost-optimized.ipynb b/Agent/workspace/hyperopt/tpsf2/code/ensemble-lgb-xgb-catboost-optimized.ipynb new file mode 100644 index 0000000..93d3cd1 --- /dev/null +++ b/Agent/workspace/hyperopt/tpsf2/code/ensemble-lgb-xgb-catboost-optimized.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"gpu","dataSources":[{"sourceId":25225,"databundleVersionId":1923495,"sourceType":"competition"}],"dockerImageVersionId":30061,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# Overview\n\nThe purpose of this notebook is to predict the target by an Ensemble model composed of tree individual models\n\n- lightgbm\n- xgboost\n- catboost\n\nFeature Engineering followed basic practices that proved to work for GBM-style models for this competition\n\n- label encoding the cat variables\n- standard scaling to numeric variables\n\nParams for *xgboost* and *catboost* have been discovered via hyperparam search, using *hyperopt*. Params for *lightgbm* have been reused from https://www.kaggle.com/hiro5299834/tps-feb-2021-with-single-lgbm-tuned (they appeared to work better vs. the set of parameters I discovered in *hyperopt*-based search).\n\nWeight of lightgbm prediction was set to be a little higher then catboost and xgboost.\n\nThe well-thought software design of the Ensembling class was inspired by https://www.kaggle.com/kenkpixdev/ensemble-lgb-xgb-with-hyperopt","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport time\nimport datetime as dt\nfrom typing import Tuple, List, Dict\n\nfrom hyperopt import STATUS_OK, Trials, fmin, hp, tpe\nfrom hyperopt.pyll.base import scope\nfrom xgboost import XGBRegressor\nfrom lightgbm import LGBMRegressor\nfrom catboost import CatBoostRegressor\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split, KFold\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder","metadata":{"_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# main flow\nstart_time = dt.datetime.now()\nprint(\"Started at \", start_time)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# read data\nin_kaggle = True\n\n\ndef get_data_file_path(is_in_kaggle: bool) -> Tuple[str, str, str]:\n train_path = ''\n test_path = ''\n sample_submission_path = ''\n\n if is_in_kaggle:\n # running in Kaggle, inside the competition\n train_path = '../input/tabular-playground-series-feb-2021/train.csv'\n test_path = '../input/tabular-playground-series-feb-2021/test.csv'\n sample_submission_path = '../input/tabular-playground-series-feb-2021/sample_submission.csv'\n else:\n # running locally\n train_path = 'data/train.csv'\n test_path = 'data/test.csv'\n sample_submission_path = 'data/sample_submission.csv'\n\n return train_path, test_path, sample_submission_path\n","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\n# get the training set and labels\ntrain_set_path, test_set_path, sample_subm_path = get_data_file_path(in_kaggle)\n\ntrain = pd.read_csv(train_set_path)\ntest = pd.read_csv(test_set_path)\ntarget = train.target\n\nsubm = pd.read_csv(sample_subm_path)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def preprocess(df, encoder=None,\n scaler=None, cols_to_drop=None,\n cols_to_encode=None, cols_to_scale=None):\n \"\"\"\n Preprocess input data\n :param df: DataFrame with data\n :param encoder: encoder object with fit_transform method\n :param scaler: scaler object with fit_transform method\n :param cols_to_drop: columns to be removed\n :param cols_to_encode: columns to be encoded\n :param cols_to_scale: columns to be scaled\n :return: DataFrame\n \"\"\"\n\n if encoder:\n for col in cols_to_encode:\n df[col] = encoder.fit_transform(df[col])\n\n if scaler:\n for col in cols_to_scale:\n df[col] = scaler.fit_transform(df[col].values.reshape(-1, 1))\n\n if cols_to_drop:\n df = df.drop(cols_to_drop, axis=1)\n\n return df","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"cat_cols = ['cat' + str(i) for i in range(10)]\ncont_cols = ['cont' + str(i) for i in range(14)]\n\ntrain = preprocess(train, encoder=LabelEncoder(), scaler=StandardScaler(),\n cols_to_drop=['id', 'target'], cols_to_encode=cat_cols,\n cols_to_scale=cont_cols)\n\n# encoder=LabelEncoder()\ntest = preprocess(test, encoder=LabelEncoder(), scaler=StandardScaler(),\n cols_to_drop=['id'], cols_to_encode=cat_cols,\n cols_to_scale=cont_cols)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"class EnsembleModel:\n def __init__(self, params):\n \"\"\"\n LGB + XGB + CatBoost model\n \"\"\"\n self.lgb_params = params['lgb']\n self.xgb_params = params['xgb']\n self.cat_params = params['cat']\n\n self.lgb_model = LGBMRegressor(**self.lgb_params)\n self.xgb_model = XGBRegressor(**self.xgb_params)\n self.cat_model = CatBoostRegressor(**self.cat_params)\n\n def fit(self, x, y, *args, **kwargs):\n return (self.lgb_model.fit(x, y, *args, **kwargs),\n self.xgb_model.fit(x, y, *args, **kwargs),\n self.cat_model.fit(x, y, *args, **kwargs))\n\n def predict(self, x, weights=[1.0, 1.0, 1.0]):\n \"\"\"\n Generate model predictions\n :param x: data\n :param weights: weights on model prediction, first one is the weight on lgb model\n :return: array with predictions\n \"\"\"\n return (weights[0] * self.lgb_model.predict(x) +\n weights[1] * self.xgb_model.predict(x) +\n weights[2] * self.cat_model.predict(x)) / 3","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"since = time.time()\ncolumns = train.columns\n\n# ------------------------------------------------------------------------------\n# Parameters\n# ------------------------------------------------------------------------------\nN_FOLDS = 10\nN_ESTIMATORS = 30000\nSEED = 2021\nBAGGING_SEED = 48\n\n# ------------------------------------------------------------------------------\n# LightGBM: training and inference\n# ------------------------------------------------------------------------------\nlgb_params = {'random_state': SEED,\n 'metric': 'rmse',\n 'n_estimators': N_ESTIMATORS,\n 'n_jobs': -1,\n 'cat_feature': [x for x in range(len(cat_cols))],\n 'bagging_seed': SEED,\n 'feature_fraction_seed': SEED,\n 'learning_rate': 0.003899156646724397,\n 'max_depth': 99,\n 'num_leaves': 63,\n 'reg_alpha': 9.562925363678952,\n 'reg_lambda': 9.355810045480153,\n 'colsample_bytree': 0.2256038826485174,\n 'min_child_samples': 290,\n 'subsample_freq': 1,\n 'subsample': 0.8805303688019942,\n 'max_bin': 882,\n 'min_data_per_group': 127,\n 'cat_smooth': 96,\n 'cat_l2': 19\n }\n\nensemble_params = {\n \"lgb\" : lgb_params,\n 'xgb': {\n 'random_state': SEED,\n 'max_depth': 13,\n 'learning_rate': 0.020206705089028228,\n 'gamma': 3.5746731812451156,\n 'min_child_weight': 564,\n 'n_estimators': 8000,\n 'colsample_bytree': 0.5015940592112956,\n 'subsample': 0.6839489639112909,\n 'reg_lambda': 18.085502002853246,\n 'reg_alpha': 0.17532087359570606,\n 'objective': 'reg:squarederror',\n 'tree_method': 'gpu_hist',\n 'eval_metric': 'rmse',\n 'n_jobs': -1\n },\n 'cat': {\n 'random_state': SEED,\n 'depth': 3.0,\n 'fold_len_multiplier': 1.1425259013471902,\n 'l2_leaf_reg': 7.567589781752637,\n 'leaf_estimation_backtracking': 'AnyImprovement',\n 'learning_rate': 0.25121635918496565,\n 'max_bin': 107.0,\n 'min_data_in_leaf': 220.0,\n 'random_strength': 3.2658690042589726,\n 'n_estimators': 8000,\n 'eval_metric': 'RMSE',\n }\n}\n \npreds = np.zeros(test.shape[0])\nkf = KFold(n_splits=N_FOLDS, random_state=22, shuffle=True)\nrmse = []\nn = 0\n\nfor trn_idx, test_idx in kf.split(train[columns], target):\n\n X_tr, X_val=train[columns].iloc[trn_idx], train[columns].iloc[test_idx]\n y_tr, y_val=target.iloc[trn_idx], target.iloc[test_idx]\n\n model = EnsembleModel(ensemble_params)\n\n model.fit(X_tr, y_tr, eval_set=[(X_val, y_val)], early_stopping_rounds=100, verbose=False)\n\n preds += model.predict(test[columns], weights=[1.1, 1.0, 0.9]) / kf.n_splits\n rmse.append(mean_squared_error(y_val, model.predict(X_val), squared=False))\n \n print(f\"Fold {n+1}, RMSE: {rmse[n]}\")\n n += 1\n\n\nprint(\"Mean RMSE: \", np.mean(rmse))\nend_time = time.time() - since\nprint('Training complete in {:.0f}m {:.0f}s'.format(\n end_time // 60, end_time % 60))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# submit prediction\nsubm['target'] = preds\nsubm.to_csv(\"ensemble_model_lgb_xgb_cat_other_lgb_params.csv\", index=False)","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print('We are done. That is all, folks!')\nfinish_time = dt.datetime.now()\nprint(\"Finished at \", finish_time)\nelapsed = finish_time - start_time\nprint(\"Elapsed time: \", elapsed)","metadata":{"trusted":true},"execution_count":null,"outputs":[]}]} \ No newline at end of file From 36880037876c7db316b55970aafd764788313256 Mon Sep 17 00:00:00 2001 From: Zeyu Ba <72795264+ZeyuBa@users.noreply.github.com> Date: Thu, 29 Aug 2024 17:25:00 +0800 Subject: [PATCH 2/4] Update code.py --- Agent/workspace/hyperopt/nlp-getting-started/code/code.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Agent/workspace/hyperopt/nlp-getting-started/code/code.py b/Agent/workspace/hyperopt/nlp-getting-started/code/code.py index 6d6ba1d..63f0020 100644 --- a/Agent/workspace/hyperopt/nlp-getting-started/code/code.py +++ b/Agent/workspace/hyperopt/nlp-getting-started/code/code.py @@ -31,7 +31,7 @@ # ## Loading Data -FILE_PATH = "./workspace/hyperopt/ps311/data/" +FILE_PATH = "./workspace/hyperopt/nlp-getting-started/data/" train = pd.read_csv(FILE_PATH+'nlp-getting-started/train.csv') test = pd.read_csv(FILE_PATH+'nlp-getting-started/test.csv') From 2f4a7834de728eacaf4b4f7f230a902d4a594472 Mon Sep 17 00:00:00 2001 From: Zeyu Ba <72795264+ZeyuBa@users.noreply.github.com> Date: Thu, 29 Aug 2024 17:25:45 +0800 Subject: [PATCH 3/4] Update code.py --- Agent/workspace/hyperopt/digit-recognizer/code/code.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Agent/workspace/hyperopt/digit-recognizer/code/code.py b/Agent/workspace/hyperopt/digit-recognizer/code/code.py index 508fc81..f378a77 100644 --- a/Agent/workspace/hyperopt/digit-recognizer/code/code.py +++ b/Agent/workspace/hyperopt/digit-recognizer/code/code.py @@ -23,7 +23,7 @@ # Input data files are available in the "../input/" directory. # For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory from keras.datasets import mnist -FILE_PATH = "./workspace/hyperopt/tspf2/data/" +FILE_PATH = "./workspace/hyperopt/digit-recognizer/data/" # import os # for dirname, _, filenames in os.walk('/kaggle/input'): From 9bb486c9afbbf074a3dbf102b342daf6d81b76f3 Mon Sep 17 00:00:00 2001 From: Zeyu Date: Tue, 3 Sep 2024 17:27:36 +0800 Subject: [PATCH 4/4] [UPDATE] add some new tasks --- .../hyperopt/{ps3112 => ps311_2}/code/code.py | 0 ...feature-eng-xgb-cat-ensemble-0-29265.ipynb | 0 Agent/workspace/hyperopt/ps314/code/code.py | 312 +++++++++++ .../simple-eda-and-baseline-in-2mintues.ipynb | 1 + Agent/workspace/hyperopt/ps314_2/code/code.py | 382 +++++++++++++ ...-eda-fe-models-ensemble-for-starters.ipynb | 1 + Agent/workspace/hyperopt/ps315/code/code.py | 198 +++++++ .../simple-solution-with-random-forest.ipynb | 1 + Agent/workspace/hyperopt/ps315_2/code/code.py | 508 ++++++++++++++++++ .../s3-e15-eda-w-imputation-xgb-lgbm.ipynb | 1 + Agent/workspace/hyperopt/ps821/code/code.py | 97 ++++ ...simple-histgradientboosting-baseline.ipynb | 1 + Agent/workspace/hyperopt/ps821_2/code/code.py | 266 +++++++++ ...-aug-21-ensemble-stackingcvregressor.ipynb | 1 + 14 files changed, 1769 insertions(+) rename Agent/workspace/hyperopt/{ps3112 => ps311_2}/code/code.py (100%) rename Agent/workspace/hyperopt/{ps3112 => ps311_2}/code/feature-eng-xgb-cat-ensemble-0-29265.ipynb (100%) create mode 100644 Agent/workspace/hyperopt/ps314/code/code.py create mode 100644 Agent/workspace/hyperopt/ps314/code/simple-eda-and-baseline-in-2mintues.ipynb create mode 100644 Agent/workspace/hyperopt/ps314_2/code/code.py create mode 100644 Agent/workspace/hyperopt/ps314_2/code/ps3e14-eda-fe-models-ensemble-for-starters.ipynb create mode 100644 Agent/workspace/hyperopt/ps315/code/code.py create mode 100644 Agent/workspace/hyperopt/ps315/code/simple-solution-with-random-forest.ipynb create mode 100644 Agent/workspace/hyperopt/ps315_2/code/code.py create mode 100644 Agent/workspace/hyperopt/ps315_2/code/s3-e15-eda-w-imputation-xgb-lgbm.ipynb create mode 100644 Agent/workspace/hyperopt/ps821/code/code.py create mode 100644 Agent/workspace/hyperopt/ps821/code/tps-08-2021-simple-histgradientboosting-baseline.ipynb create mode 100644 Agent/workspace/hyperopt/ps821_2/code/code.py create mode 100644 Agent/workspace/hyperopt/ps821_2/code/tps-aug-21-ensemble-stackingcvregressor.ipynb diff --git a/Agent/workspace/hyperopt/ps3112/code/code.py b/Agent/workspace/hyperopt/ps311_2/code/code.py similarity index 100% rename from Agent/workspace/hyperopt/ps3112/code/code.py rename to Agent/workspace/hyperopt/ps311_2/code/code.py diff --git a/Agent/workspace/hyperopt/ps3112/code/feature-eng-xgb-cat-ensemble-0-29265.ipynb b/Agent/workspace/hyperopt/ps311_2/code/feature-eng-xgb-cat-ensemble-0-29265.ipynb similarity index 100% rename from Agent/workspace/hyperopt/ps3112/code/feature-eng-xgb-cat-ensemble-0-29265.ipynb rename to Agent/workspace/hyperopt/ps311_2/code/feature-eng-xgb-cat-ensemble-0-29265.ipynb diff --git a/Agent/workspace/hyperopt/ps314/code/code.py b/Agent/workspace/hyperopt/ps314/code/code.py new file mode 100644 index 0000000..c3495fd --- /dev/null +++ b/Agent/workspace/hyperopt/ps314/code/code.py @@ -0,0 +1,312 @@ +#!/usr/bin/env python +# coding: utf-8 + +# ###

πŸ“œ Notebook At a Glance

+ +# ![image.png](attachment:741aaf1f-6ef0-4f05-854d-baf287a2c15f.png) + +#
+# +#

πŸ“Š Data description:

+# +# * Clonesize m2 The average blueberry clone size in the field +# * Honeybee bees/m2/min Honeybee density in the field +# * Bumbles bees/m2/min Bumblebee density in the field +# * Andrena bees/m2/min Andrena bee density in the field +# * Osmia bees/m2/min Osmia bee density in the field +# * MaxOfUpperTRange ℃ The highest record of the upper band daily air temperature during the bloom season +# * MinOfUpperTRange ℃ The lowest record of the upper band daily air temperature +# * AverageOfUpperTRange ℃ The average of the upper band daily air temperature +# * MaxOfLowerTRange ℃ The highest record of the lower band daily air temperature +# * MinOfLowerTRange ℃ The lowest record of the lower band daily air temperature +# * AverageOfLowerTRange ℃ The average of the lower band daily air temperature +# * RainingDays Day The total number of days during the bloom season, each of which has precipitation larger than zero +# * AverageRainingDays Day The average of raining days of the entire bloom season +# +# * yield - Target variable +# +# ** there's no descriptions of fruits-related variables T_T ** +# from IPython.core.display import HTML +# with open('./CSS.css', 'r') as file: +# custom_css = file.read() + +# HTML(custom_css) + + +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +import seaborn as sns +import gc + +from tqdm.auto import tqdm +import math +from sklearn.model_selection import KFold, StratifiedKFold, train_test_split, GridSearchCV +import warnings +warnings.filterwarnings('ignore') + + +from lightgbm import LGBMRegressor +from xgboost import XGBRegressor +from catboost import CatBoostRegressor + +# tqdm.pandas() + +# rc = { +# "axes.facecolor": "#FFF9ED", +# "figure.facecolor": "#FFF9ED", +# "axes.edgecolor": "#000000", +# "grid.color": "#EBEBE7", +# "font.family": "serif", +# "axes.labelcolor": "#000000", +# "xtick.color": "#000000", +# "ytick.color": "#000000", +# "grid.alpha": 0.4 +# } + +# sns.set(rc=rc) + +from colorama import Style, Fore +red = Style.BRIGHT + Fore.RED +blu = Style.BRIGHT + Fore.BLUE +mgt = Style.BRIGHT + Fore.MAGENTA +gld = Style.BRIGHT + Fore.YELLOW +res = Style.RESET_ALL + +FILE_PATH = "./workspace/hyperopt/ps314/data/" + +train = pd.read_csv(FILE_PATH+"train.csv.zip") +test = pd.read_csv(FILE_PATH+"test.csv.zip") + + +# ###

Brief EDA

+ +# # summary table function +# def summary(df): +# print(f'data shape: {df.shape}') +# summ = pd.DataFrame(df.dtypes, columns=['data type']) +# summ['#missing'] = df.isnull().sum().values +# summ['%missing'] = df.isnull().sum().values / len(df)* 100 +# summ['#unique'] = df.nunique().values +# desc = pd.DataFrame(df.describe(include='all').transpose()) +# summ['min'] = desc['min'].values +# summ['max'] = desc['max'].values +# summ['first value'] = df.loc[0].values +# summ['second value'] = df.loc[1].values +# summ['third value'] = df.loc[2].values + +# return summ + + +# summary(train) + + +#
+# +#

πŸ’‘ Summary of EDA:

+# +# * There are 16 X variables and 1 target(y) variable, while 1 variable(id) is extra data +# +# * No missing values on each columns~! +# +# * All variables are float64 type. + +# select numerical and categorical variables respectively. +num_cols = test.select_dtypes(include=['float64','int64']).columns.tolist() +num_cols.remove('id') + + +# sns.displot(train, x="yield") + + +# # > #### βœ”οΈ target value is normally distributed + +# # kudos to @jcaliz / +# # refer to https://www.kaggle.com/code/sergiosaharovskiy/ps-s3e7-2023-eda-and-submission +# features = num_cols +# n_bins = 50 +# histplot_hyperparams = { +# 'kde':True, +# 'alpha':0.4, +# 'stat':'percent', +# 'bins':n_bins +# } + +# columns = features +# n_cols = 4 +# n_rows = math.ceil(len(columns)/n_cols) +# fig, ax = plt.subplots(n_rows, n_cols, figsize=(20, n_rows*4)) +# ax = ax.flatten() + +# for i, column in enumerate(columns): +# plot_axes = [ax[i]] +# sns.kdeplot( +# train[column], label='Train', +# ax=ax[i], color='#9E3F00' +# ) + +# sns.kdeplot( +# test[column], label='Test', +# ax=ax[i], color='yellow' +# ) + +# # sns.kdeplot( +# # original[column], label='Original', +# # ax=ax[i], color='#20BEFF' +# # ) + +# # titles +# ax[i].set_title(f'{column} Distribution'); +# ax[i].set_xlabel(None) + +# # remove axes to show only one at the end +# plot_axes = [ax[i]] +# handles = [] +# labels = [] +# for plot_ax in plot_axes: +# handles += plot_ax.get_legend_handles_labels()[0] +# labels += plot_ax.get_legend_handles_labels()[1] +# plot_ax.legend().remove() + +# for i in range(i+1, len(ax)): +# ax[i].axis('off') + +# fig.suptitle(f'Numerical Feature Distributions\n\n\n', ha='center', fontweight='bold', fontsize=25) +# fig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 0.96), fontsize=25, ncol=3) +# plt.tight_layout() + + +#
+# +#

πŸ’‘ Insights:

+# +# * Distiribution between train and test dataset is almost the same. +# +# * As all variables are numerical, you need to scale it if you want to use linear methods. + +# def plot_correlation_heatmap(df: pd.core.frame.DataFrame, title_name: str='Train correlation') -> None: +# corr = df.corr() +# fig, axes = plt.subplots(figsize=(20, 10)) +# mask = np.zeros_like(corr) +# mask[np.triu_indices_from(mask)] = True +# sns.heatmap(corr, mask=mask, linewidths=.5, cmap='YlOrRd', annot=True) +# plt.title(title_name) +# plt.show() + +# # plot_correlation_heatmap(original, 'Original Dataset Correlation') +# plot_correlation_heatmap(train, 'Train Dataset Correlation') +# plot_correlation_heatmap(test, 'Test Dataset Correlation') + + +#
+# +#

πŸ’‘ Insights:

+# +# * TRange variables are highly correlated. +# * fruitmass and fruitset , fruitmass and seed are highly correlated with each other. + +# +# ###

Baseline modeling with XGB

+ +#
+# πŸ“Œ  modeling overview:
+# +# * build baseline model without hyperparameter tuning.
+# * 3-fold cross validation methods are used for baseline modeling.
+# * Evalution metric is mean absolute error
+# +#
+ +# ![image.png](attachment:ce26852c-7576-474a-bf51-986efcc5cbfa.png) + +train.drop('id',axis=1, inplace=True) + + +X = train.drop('yield',axis=1) +Y = train['yield'] + + +test.set_index('id',inplace=True) + + +from sklearn.metrics import mean_absolute_error + +cv_scores = list() +importance_xgb = list() +preds = list() +XGB_md = XGBRegressor(tree_method = 'gpu_hist', + objective = 'reg:squarederror', + colsample_bytree = 0.8, + gamma = 0.8, + learning_rate = 0.01, + max_depth = 5, + min_child_weight = 10, + n_estimators = 1000, + subsample = 0.8) +## Running 3 fold CV +# for i in range(3): +# print(f'{i} fold cv begin') +# skf = KFold(n_splits = 3, random_state = 1004, shuffle = True) + +# for train_ix, test_ix in skf.split(X, Y): + +# ## Splitting the data +# X_train, X_test = X.iloc[train_ix], X.iloc[test_ix] +# Y_train, Y_test = Y.iloc[train_ix], Y.iloc[test_ix] + +# ## Building RF model +# XGB_md = XGBRegressor(tree_method = 'gpu_hist', +# objective = 'reg:squarederror', +# colsample_bytree = 0.8, +# gamma = 0.8, +# learning_rate = 0.01, +# max_depth = 5, +# min_child_weight = 10, +# n_estimators = 1000, +# subsample = 0.8).fit(X_train, Y_train) +# importance_xgb.append(XGB_md.feature_importances_) + +# XGB_pred_1 = XGB_md.predict(X_test) +# XGB_pred_2 = XGB_md.predict(test) + +# # Calculate RMSE +# cv_scores.append(mean_absolute_error(Y_test, XGB_pred_1)) +# preds.append(XGB_pred_2) +# print(f'{i} fold cv done') + +# scores = np.mean(cv_scores) +# print('The average RMSE over 3-folds (run 3 times) is:', scores) + + +# ![image.png](attachment:3344c953-8391-4bbf-b050-49d62f4e2315.png) + +# > #### MAE is a popular metric to use as the error value is easily interpreted. This is because the value is on the same scale as the target you are predicting for. +# > #### Comparing with RMSE ! +# > ##### RMSE is more sensitive to outliers +# > ##### RMSE penalises large errors more than MAE due to the fact that errors are squared initially +# > ##### MAE returns values that are more interpretable as it is simply the average of absolute error + +# plt.figure(figsize = (8, 8)) +# pd.DataFrame(importance_xgb, columns = X.columns).apply(np.mean, axis = 0).sort_values().plot(kind = 'barh'); +# plt.xlabel('Feature importance score') +# plt.ylabel('Features') +# plt.show(); + + +#
+# +#

πŸ’‘ Insights: :

+# +# * fruitset and seed are two most important features. +# +# * however, these variables are highly correlated, as you know. +# +# + +#
+# +#

βœ”οΈ Conclusion:

+# +# * As we skipped feature engineering process, this result might be different once you apply scaling and other feature engineering methods. +# * The average MAE over 3-folds (run 3 times) is: 352.1 , this is slightly better than benchmark. +# * 😊 this is a simple baseline for beginners. you can 1) adjust hyper-parameter (HP tuning) ; 2) try different algorithms ; 3) add more feature engineered data to improve the performance. diff --git a/Agent/workspace/hyperopt/ps314/code/simple-eda-and-baseline-in-2mintues.ipynb b/Agent/workspace/hyperopt/ps314/code/simple-eda-and-baseline-in-2mintues.ipynb new file mode 100644 index 0000000..697fadb --- /dev/null +++ b/Agent/workspace/hyperopt/ps314/code/simple-eda-and-baseline-in-2mintues.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"nvidiaTeslaT4","dataSources":[{"sourceId":51959,"databundleVersionId":5624004,"sourceType":"competition"}],"dockerImageVersionId":30476,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":true}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"###

πŸ“œ Notebook At a Glance

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"}}},{"cell_type":"markd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\n\n

πŸ“Š Data description:

\n\n* Clonesize m2 The average blueberry clone size in the field\n* Honeybee bees/m2/min Honeybee density in the field\n* Bumbles bees/m2/min Bumblebee density in the field\n* Andrena bees/m2/min Andrena bee density in the field\n* Osmia bees/m2/min Osmia bee density in the field\n* MaxOfUpperTRange ℃ The highest record of the upper band daily air temperature during the bloom season\n* MinOfUpperTRange ℃ The lowest record of the upper band daily air temperature\n* AverageOfUpperTRange ℃ The average of the upper band daily air temperature\n* MaxOfLowerTRange ℃ The highest record of the lower band daily air temperature\n* MinOfLowerTRange ℃ The lowest record of the lower band daily air temperature\n* AverageOfLowerTRange ℃ The average of the lower band daily air temperature\n* RainingDays Day The total number of days during the bloom season, each of which has precipitation larger than zero\n* AverageRainingDays Day The average of raining days of the entire bloom season\n \n* yield - Target variable\n \n** there's no descriptions of fruits-related variables T_T **","metadata":{}},{"cell_type":"code","source":"!wget http://bit.ly/3ZLyF82 -O CSS.css -q\n \nfrom IPython.core.display import HTML\nwith open('./CSS.css', 'r') as file:\n custom_css = file.read()\n\nHTML(custom_css)","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:02:07.640165Z","iopub.execute_input":"2023-05-02T02:02:07.640407Z","iopub.status.idle":"2023-05-02T02:02:09.116322Z","shell.execute_reply.started":"2023-05-02T02:02:07.640381Z","shell.execute_reply":"2023-05-02T02:02:09.115212Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport gc\n\nfrom tqdm.auto import tqdm\nimport math\nfrom sklearn.model_selection import KFold, StratifiedKFold, train_test_split, GridSearchCV\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\nfrom lightgbm import LGBMRegressor\nfrom xgboost import XGBRegressor\nfrom catboost import CatBoostRegressor\n\ntqdm.pandas()\n\nrc = {\n \"axes.facecolor\": \"#FFF9ED\",\n \"figure.facecolor\": \"#FFF9ED\",\n \"axes.edgecolor\": \"#000000\",\n \"grid.color\": \"#EBEBE7\",\n \"font.family\": \"serif\",\n \"axes.labelcolor\": \"#000000\",\n \"xtick.color\": \"#000000\",\n \"ytick.color\": \"#000000\",\n \"grid.alpha\": 0.4\n}\n\nsns.set(rc=rc)\n\nfrom colorama import Style, Fore\nred = Style.BRIGHT + Fore.RED\nblu = Style.BRIGHT + Fore.BLUE\nmgt = Style.BRIGHT + Fore.MAGENTA\ngld = Style.BRIGHT + Fore.YELLOW\nres = Style.RESET_ALL","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:02:09.120916Z","iopub.execute_input":"2023-05-02T02:02:09.12306Z","iopub.status.idle":"2023-05-02T02:02:13.549633Z","shell.execute_reply.started":"2023-05-02T02:02:09.123022Z","shell.execute_reply":"2023-05-02T02:02:13.548715Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv(\"/kaggle/input/playground-series-s3e14/train.csv\")\ntest = pd.read_csv(\"/kaggle/input/playground-series-s3e14/test.csv\")","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:03:04.230355Z","iopub.execute_input":"2023-05-02T02:03:04.230745Z","iopub.status.idle":"2023-05-02T02:03:04.328728Z","shell.execute_reply.started":"2023-05-02T02:03:04.230713Z","shell.execute_reply":"2023-05-02T02:03:04.327829Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###

Brief EDA

","metadata":{}},{"cell_type":"code","source":"# summary table function\ndef summary(df):\n print(f'data shape: {df.shape}')\n summ = pd.DataFrame(df.dtypes, columns=['data type'])\n summ['#missing'] = df.isnull().sum().values \n summ['%missing'] = df.isnull().sum().values / len(df)* 100\n summ['#unique'] = df.nunique().values\n desc = pd.DataFrame(df.describe(include='all').transpose())\n summ['min'] = desc['min'].values\n summ['max'] = desc['max'].values\n summ['first value'] = df.loc[0].values\n summ['second value'] = df.loc[1].values\n summ['third value'] = df.loc[2].values\n \n return summ","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:03:26.231981Z","iopub.execute_input":"2023-05-02T02:03:26.232699Z","iopub.status.idle":"2023-05-02T02:03:26.239211Z","shell.execute_reply.started":"2023-05-02T02:03:26.232661Z","shell.execute_reply":"2023-05-02T02:03:26.238025Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"summary(train)","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:03:26.789386Z","iopub.execute_input":"2023-05-02T02:03:26.789722Z","iopub.status.idle":"2023-05-02T02:03:26.864455Z","shell.execute_reply.started":"2023-05-02T02:03:26.789693Z","shell.execute_reply":"2023-05-02T02:03:26.863513Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
\n\n

πŸ’‘ Summary of EDA:

\n\n* There are 16 X variables and 1 target(y) variable, while 1 variable(id) is extra data\n \n* No missing values on each columns~!\n \n* All variables are float64 type. ","metadata":{}},{"cell_type":"code","source":"# select numerical and categorical variables respectively.\nnum_cols = test.select_dtypes(include=['float64','int64']).columns.tolist()\nnum_cols.remove('id')","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:03:49.014184Z","iopub.execute_input":"2023-05-02T02:03:49.014549Z","iopub.status.idle":"2023-05-02T02:03:49.023503Z","shell.execute_reply.started":"2023-05-02T02:03:49.01452Z","shell.execute_reply":"2023-05-02T02:03:49.022069Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.displot(train, x=\"yield\")","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:04:02.383825Z","iopub.execute_input":"2023-05-02T02:04:02.384174Z","iopub.status.idle":"2023-05-02T02:04:03.020289Z","shell.execute_reply.started":"2023-05-02T02:04:02.384145Z","shell.execute_reply":"2023-05-02T02:04:03.019385Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"> #### βœ”οΈ target value is normally distributed","metadata":{}},{"cell_type":"code","source":"# kudos to @jcaliz / \n# refer to https://www.kaggle.com/code/sergiosaharovskiy/ps-s3e7-2023-eda-and-submission\nfeatures = num_cols\nn_bins = 50\nhistplot_hyperparams = {\n 'kde':True,\n 'alpha':0.4,\n 'stat':'percent',\n 'bins':n_bins\n}\n\ncolumns = features\nn_cols = 4\nn_rows = math.ceil(len(columns)/n_cols)\nfig, ax = plt.subplots(n_rows, n_cols, figsize=(20, n_rows*4))\nax = ax.flatten()\n\nfor i, column in enumerate(columns):\n plot_axes = [ax[i]]\n sns.kdeplot(\n train[column], label='Train',\n ax=ax[i], color='#9E3F00'\n )\n \n sns.kdeplot(\n test[column], label='Test',\n ax=ax[i], color='yellow'\n )\n \n# sns.kdeplot(\n# original[column], label='Original',\n# ax=ax[i], color='#20BEFF'\n# )\n \n # titles\n ax[i].set_title(f'{column} Distribution');\n ax[i].set_xlabel(None)\n \n # remove axes to show only one at the end\n plot_axes = [ax[i]]\n handles = []\n labels = []\n for plot_ax in plot_axes:\n handles += plot_ax.get_legend_handles_labels()[0]\n labels += plot_ax.get_legend_handles_labels()[1]\n plot_ax.legend().remove()\n \nfor i in range(i+1, len(ax)):\n ax[i].axis('off')\n \nfig.suptitle(f'Numerical Feature Distributions\\n\\n\\n', ha='center', fontweight='bold', fontsize=25)\nfig.legend(handles, labels, loc='upper center', bbox_to_anchor=(0.5, 0.96), fontsize=25, ncol=3)\nplt.tight_layout()","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:04:22.003429Z","iopub.execute_input":"2023-05-02T02:04:22.003895Z","iopub.status.idle":"2023-05-02T02:04:29.750407Z","shell.execute_reply.started":"2023-05-02T02:04:22.003862Z","shell.execute_reply":"2023-05-02T02:04:29.749617Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
\n\n

πŸ’‘ Insights:

\n\n* Distiribution between train and test dataset is almost the same.\n\n* As all variables are numerical, you need to scale it if you want to use linear methods.","metadata":{}},{"cell_type":"code","source":"def plot_correlation_heatmap(df: pd.core.frame.DataFrame, title_name: str='Train correlation') -> None:\n corr = df.corr()\n fig, axes = plt.subplots(figsize=(20, 10))\n mask = np.zeros_like(corr)\n mask[np.triu_indices_from(mask)] = True\n sns.heatmap(corr, mask=mask, linewidths=.5, cmap='YlOrRd', annot=True)\n plt.title(title_name)\n plt.show()\n\n# plot_correlation_heatmap(original, 'Original Dataset Correlation')\nplot_correlation_heatmap(train, 'Train Dataset Correlation')\nplot_correlation_heatmap(test, 'Test Dataset Correlation')","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:05:12.950231Z","iopub.execute_input":"2023-05-02T02:05:12.950692Z","iopub.status.idle":"2023-05-02T02:05:14.780365Z","shell.execute_reply.started":"2023-05-02T02:05:12.950655Z","shell.execute_reply":"2023-05-02T02:05:14.779456Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
\n\n

πŸ’‘ Insights:

\n\n* TRange variables are highly correlated.\n* fruitmass and fruitset , fruitmass and seed are highly correlated with each other.","metadata":{}},{"cell_type":"markdown","source":"\n###

Baseline modeling with XGB

","metadata":{}},{"cell_type":"markdown","source":"
\n πŸ“Œ  modeling overview:
\n \n* build baseline model without hyperparameter tuning.
\n* 3-fold cross validation methods are used for baseline modeling.
\n* Evalution metric is mean absolute error
\n \n
","metadata":{}},{"cell_type":"markdown","source":"![image.png](attachment:ce26852c-7576-474a-bf51-986efcc5cbfa.png)","metadata":{},"attachments":{"ce26852c-7576-474a-bf51-986efcc5cbfa.png":{"image/png":"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"}}},{"cell_type":"code","source":"train.drop('id',axis=1, inplace=True)","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:13:30.439758Z","iopub.execute_input":"2023-05-02T02:13:30.44013Z","iopub.status.idle":"2023-05-02T02:13:30.448769Z","shell.execute_reply.started":"2023-05-02T02:13:30.440099Z","shell.execute_reply":"2023-05-02T02:13:30.447678Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X = train.drop('yield',axis=1)\nY = train['yield']","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:13:57.233696Z","iopub.execute_input":"2023-05-02T02:13:57.234111Z","iopub.status.idle":"2023-05-02T02:13:57.245091Z","shell.execute_reply.started":"2023-05-02T02:13:57.234079Z","shell.execute_reply":"2023-05-02T02:13:57.24416Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test.set_index('id',inplace=True)","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:14:01.954163Z","iopub.execute_input":"2023-05-02T02:14:01.954512Z","iopub.status.idle":"2023-05-02T02:14:01.962412Z","shell.execute_reply.started":"2023-05-02T02:14:01.954483Z","shell.execute_reply":"2023-05-02T02:14:01.961298Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.metrics import mean_absolute_error\n\ncv_scores = list()\nimportance_xgb = list()\npreds = list()\n\n## Running 3 fold CV\nfor i in range(3):\n print(f'{i} fold cv begin')\n skf = KFold(n_splits = 3, random_state = 1004, shuffle = True)\n \n for train_ix, test_ix in skf.split(X, Y):\n \n ## Splitting the data \n X_train, X_test = X.iloc[train_ix], X.iloc[test_ix]\n Y_train, Y_test = Y.iloc[train_ix], Y.iloc[test_ix]\n \n ## Building RF model\n XGB_md = XGBRegressor(tree_method = 'gpu_hist',\n objective = 'reg:squarederror',\n colsample_bytree = 0.8, \n gamma = 0.8, \n learning_rate = 0.01, \n max_depth = 5, \n min_child_weight = 10, \n n_estimators = 1000, \n subsample = 0.8).fit(X_train, Y_train)\n importance_xgb.append(XGB_md.feature_importances_)\n \n XGB_pred_1 = XGB_md.predict(X_test)\n XGB_pred_2 = XGB_md.predict(test)\n \n # Calculate RMSE\n cv_scores.append(mean_absolute_error(Y_test, XGB_pred_1))\n preds.append(XGB_pred_2)\n print(f'{i} fold cv done')\n\nscores = np.mean(cv_scores) \nprint('The average RMSE over 3-folds (run 3 times) is:', scores)","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:33:31.478689Z","iopub.execute_input":"2023-05-02T02:33:31.479478Z","iopub.status.idle":"2023-05-02T02:33:47.131735Z","shell.execute_reply.started":"2023-05-02T02:33:31.479444Z","shell.execute_reply":"2023-05-02T02:33:47.130671Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"![image.png](attachment:3344c953-8391-4bbf-b050-49d62f4e2315.png)","metadata":{},"attachments":{"3344c953-8391-4bbf-b050-49d62f4e2315.png":{"image/png":"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"}}},{"cell_type":"markdown","source":"> #### MAE is a popular metric to use as the error value is easily interpreted. This is because the value is on the same scale as the target you are predicting for.\n> #### Comparing with RMSE !\n> ##### RMSE is more sensitive to outliers\n> ##### RMSE penalises large errors more than MAE due to the fact that errors are squared initially\n> ##### MAE returns values that are more interpretable as it is simply the average of absolute error","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (8, 8))\npd.DataFrame(importance_xgb, columns = X.columns).apply(np.mean, axis = 0).sort_values().plot(kind = 'barh');\nplt.xlabel('Feature importance score')\nplt.ylabel('Features')\nplt.show(); ","metadata":{"execution":{"iopub.status.busy":"2023-05-02T02:15:59.061026Z","iopub.execute_input":"2023-05-02T02:15:59.061376Z","iopub.status.idle":"2023-05-02T02:15:59.434777Z","shell.execute_reply.started":"2023-05-02T02:15:59.061347Z","shell.execute_reply":"2023-05-02T02:15:59.433869Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"
\n\n

πŸ’‘ Insights: :

\n\n* fruitset and seed are two most important features.\n \n* however, these variables are highly correlated, as you know.\n\n ","metadata":{}},{"cell_type":"markdown","source":"
\n\n

βœ”οΈ Conclusion:

\n\n* As we skipped feature engineering process, this result might be different once you apply scaling and other feature engineering methods.\n* The average MAE over 3-folds (run 3 times) is: 352.1 , this is slightly better than benchmark.\n* 😊 this is a simple baseline for beginners. you can 1) adjust hyper-parameter (HP tuning) ; 2) try different algorithms ; 3) add more feature engineered data to improve the performance.","metadata":{}}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/ps314_2/code/code.py b/Agent/workspace/hyperopt/ps314_2/code/code.py new file mode 100644 index 0000000..095e8aa --- /dev/null +++ b/Agent/workspace/hyperopt/ps314_2/code/code.py @@ -0,0 +1,382 @@ +#!/usr/bin/env python +# coding: utf-8 + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns + + +from sklearn.ensemble import HistGradientBoostingRegressor, VotingRegressor, StackingRegressor + +from sklearn.pipeline import Pipeline, make_pipeline +from sklearn.preprocessing import FunctionTransformer, StandardScaler, MinMaxScaler +from lightgbm import LGBMRegressor +from catboost import CatBoostRegressor + +# sns.set_theme(style = 'white', palette = 'viridis') +# pal = sns.color_palette('viridis') + +pd.set_option('display.max_rows', 100) + +FILE_PATH = "./workspace/hyperopt/ps314_2/data/" + +train = pd.read_csv(FILE_PATH+'train.csv.zip') +test_1 = pd.read_csv(FILE_PATH+'test.csv.zip') +# orig_train = pd.read_csv(r'../input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv') + +train.drop('id', axis = 1, inplace = True) +test = test_1.drop('id', axis = 1) +# orig_train.drop('Row#', axis = 1, inplace = True) + + +# # Knowing Your Data +# +# ## Descriptive Statistics + +# train.head(10) + + +# desc = train.describe().T +# desc['nunique'] = train.nunique() +# desc['%unique'] = desc['nunique'] / len(train) * 100 +# desc['null'] = train.isna().sum() +# desc['type'] = train.dtypes +# desc + + +# desc = test.describe().T +# desc['nunique'] = test.nunique() +# desc['%unique'] = desc['nunique'] / len(train) * 100 +# desc['null'] = test.isna().sum() +# desc['type'] = test.dtypes +# desc + + +# desc = orig_train.describe().T +# desc['nunique'] = orig_train.nunique() +# desc['%unique'] = desc['nunique'] / len(orig_train) * 100 +# desc['null'] = orig_train.isna().sum() +# desc['type'] = orig_train.dtypes +# desc + + +# # Duplicates + +# print(f'There are {train.duplicated(subset = list(train)[0:-1]).value_counts()[0]} non-duplicate values out of {train.count()[0]} rows in train dataset') +# print(f'There are {test.duplicated().value_counts()[0]} non-duplicate values out of {test.count()[0]} rows in test dataset') +# print(f'There are {orig_train.duplicated(subset = list(train)[0:-1]).value_counts()[0]} non-duplicate values out of {orig_train.count()[0]} rows in original train dataset') + + +# # **Key point**: There are row duplicates in train and test dataset. We can remove it from our train dataset, though it may have no effect due to how few they are. + +# # # Adversarial Validation + +# def adversarial_validation(dataset_1 = train, dataset_2 = test, label = 'Train-Test'): + +# adv_train = dataset_1.drop('yield', axis = 1) +# adv_test = dataset_2.copy() + +# adv_train['is_test'] = 0 +# adv_test['is_test'] = 1 + +# adv = pd.concat([adv_train, adv_test], ignore_index = True) + +# adv_shuffled = adv.sample(frac = 1) + +# adv_X = adv_shuffled.drop('is_test', axis = 1) +# adv_y = adv_shuffled.is_test + +# skf = StratifiedKFold(n_splits = 5, random_state = 42, shuffle = True) + +# val_scores = [] +# predictions = np.zeros(len(adv)) + +# for fold, (train_idx, val_idx) in enumerate(skf.split(adv_X, adv_y)): + +# adv_lr = XGBClassifier(random_state = 42) +# adv_lr.fit(adv_X.iloc[train_idx], adv_y.iloc[train_idx]) + +# val_preds = adv_lr.predict_proba(adv_X.iloc[val_idx])[:,1] +# predictions[val_idx] = val_preds +# val_score = roc_auc_score(adv_y.iloc[val_idx], val_preds) +# val_scores.append(val_score) + +# fpr, tpr, _ = roc_curve(adv['is_test'], predictions) + +# plt.figure(figsize = (10, 10), dpi = 300) +# sns.lineplot(x=[0, 1], y=[0, 1], linestyle="--", label="Indistinguishable Datasets") +# sns.lineplot(x=fpr, y=tpr, label="Adversarial Validation Classifier") +# plt.title(f'{label} Validation = {np.mean(val_scores):.5f}', weight = 'bold', size = 17) +# plt.xlabel('False Positive Rate') +# plt.ylabel('True Positive Rate') +# plt.show() + + +# adversarial_validation() +# adversarial_validation(pd.concat([train, orig_train]), test, 'Combo Train-Test Validation') + + +# # **Key points:** +# # 1. Train and test datasets validation results in ROC score of close to .5, therefore **we can trust our cross-validation.** +# # 2. Combined train and test datasets validation results in ROC score of close to .5, which is very far from competition dataset. Therefore, **we can include it in our training**. + +# # # Distribution + +# fig, ax = plt.subplots(4, 4, figsize = (10, 10), dpi = 300) +# ax = ax.flatten() + +# for i, column in enumerate(test.columns): +# sns.kdeplot(train[column], ax=ax[i], color=pal[0]) +# sns.kdeplot(test[column], ax=ax[i], color=pal[2]) + +# ax[i].set_title(f'{column} Distribution', size = 7) +# ax[i].set_xlabel(None) + +# fig.suptitle('Distribution of Feature\nper Dataset\n', fontsize = 24, fontweight = 'bold') +# fig.legend(['Train', 'Test']) +# plt.tight_layout() + + +# # **Key points:** +# # 1. All features have similar distribution between training and test dataset. +# # 2. 13 out of 16 features are categorical + +# plt.figure(figsize = (10, 6), dpi = 300) +# sns.kdeplot(data = train, x = 'yield') +# plt.title('Target Distribution', weight = 'bold', size = 20) +# plt.show() + + +# # **Key point**: It looks like we are having relatively normal distribution here. + +# # # Correlation + +# def heatmap(dataset, label = None): +# corr = dataset.corr(method = 'spearman') +# plt.figure(figsize = (14, 10), dpi = 300) +# mask = np.zeros_like(corr) +# mask[np.triu_indices_from(mask)] = True +# sns.heatmap(corr, mask = mask, cmap = 'viridis', annot = True, annot_kws = {'size' : 7}) +# plt.title(f'{label} Dataset Correlation Matrix\n', fontsize = 25, weight = 'bold') +# plt.show() + + +# heatmap(train, 'Train') +# heatmap(test, 'Test') + + +# # **Key point**: There are so many features with very strong correlation that some of them are practically duplicates. We can remove them to make our model better. Let's try to see it with hierarchy tree this time. + +# def distance(data, label = ''): +# #thanks to @sergiosaharovsky for the fix +# corr = data.corr(method = 'spearman') +# dist_linkage = linkage(squareform(1 - abs(corr)), 'complete') + +# plt.figure(figsize = (10, 8), dpi = 300) +# dendro = dendrogram(dist_linkage, labels=data.columns, leaf_rotation=90) +# plt.title(f'Feature Distance in {label} Dataset', weight = 'bold', size = 22) +# plt.show() + + +# distance(train, 'Train') + + +# **Key points:** +# 1. `MinOfUpperTRange`, `AverageOfUpperTRange`, `AverageOfLowerTRange`, `MaxOfLowerTRange`, `MaxOfUpperTRange`, and `MinOfLowerTRange` are practically duplicates so you can just keep one of them. +# 2. `RainingDays` and `AverageRainingDays` are almost duplicate so you may also drop one of them. + +# # Preparation + +X = train.copy() +y = X.pop('yield') + +seed = 42 +# splits = 5 +# k = KFold(n_splits = splits, random_state = seed, shuffle = True) + +np.random.seed(seed) + + +# # # Base Models + +# def cross_val_score(model, cv = k, label = ''): + +# X = train.copy() +# y = X.pop('yield') + +# #initiate prediction arrays and score lists +# val_predictions = np.zeros((len(train))) +# train_predictions = np.zeros((len(train))) +# train_mae, val_mae = [], [] + +# #training model, predicting prognosis probability, and evaluating log loss +# for fold, (train_idx, val_idx) in enumerate(cv.split(X, y)): + +# model.fit(X.iloc[train_idx], y.iloc[train_idx]) + +# train_preds = model.predict(X.iloc[train_idx]) +# val_preds = model.predict(X.iloc[val_idx]) + +# train_predictions[train_idx] += train_preds +# val_predictions[val_idx] += val_preds + +# train_score = mean_absolute_error(y.iloc[train_idx], train_preds) +# val_score = mean_absolute_error(y.iloc[val_idx], val_preds) + +# train_mae.append(train_score) +# val_mae.append(val_score) + +# print(f'Val MAE: {np.mean(val_mae):.5f} Β± {np.std(val_mae):.5f} | Train MAE: {np.mean(train_mae):.5f} Β± {np.std(train_mae):.5f} | {label}') + +# return val_mae + + +# mae_list = pd.DataFrame() + +# models = [ +# ('linear', LinearRegression()), +# ('ridge', Ridge(random_state = seed)), +# ('lasso', Lasso(random_state = seed, max_iter = 1000000)), +# ('elastic', ElasticNet(random_state = seed, max_iter = 1000000)), +# ('huber', HuberRegressor(max_iter = 1000000)), +# ('ard', ARDRegression()), +# ('passive', PassiveAggressiveRegressor(random_state = seed)), +# ('theilsen', TheilSenRegressor(random_state = seed)), +# ('linearsvm', LinearSVR(random_state = seed, max_iter = 1000000)), +# ('mlp', MLPRegressor(random_state = seed, max_iter = 1000000)), +# ('et', ExtraTreesRegressor(random_state = seed)), +# ('rf', RandomForestRegressor(random_state = seed)), +# ('xgb', XGBRegressor(random_state = seed, eval_metric = 'mae')), +# ('lgb', LGBMRegressor(random_state = seed, objective = 'mae')), +# ('dart', LGBMRegressor(random_state = seed, boosting_type = 'dart')), +# ('cb', CatBoostRegressor(random_state = seed, objective = 'MAE', verbose = 0)), +# ('gb', GradientBoostingRegressor(random_state = seed, loss = 'absolute_error')), +# ('hgb', HistGradientBoostingRegressor(random_state = seed, loss = 'absolute_error')), +# ('knn', KNeighborsRegressor()) +# ] + +# for (label, model) in models: +# mae_list[label] = cross_val_score(model, label = label) + + +# plt.figure(figsize = (8, 4), dpi = 300) +# sns.barplot(data = mae_list.reindex((mae_list).mean().sort_values().index, axis = 1), palette = 'viridis', orient = 'h') +# plt.title('MAE Comparison', weight = 'bold', size = 20) +# plt.show() + + +# # **Key points**; +# # 1. Linear regression can work as well as tree-based models here. +# # 2. Some tree-based models, especially non-gradient boosting ones, have a lot of overfitting. +# # 3. `CatBoostRegressor` gives the best result. + +# # # Base Models 2.0 (With Post-Processing) +# # +# # @mattop has pointed out in [this topic](https://www.kaggle.com/competitions/playground-series-s3e14/discussion/407327) that we can post-process our prediction to make it consistent with the unique values of the `yield`. + +# def postprocessor(prediction): +# #thanks to @mattop +# unique_targets = np.unique(train['yield']) +# return [min(unique_targets, key = lambda x: abs(x - pred)) for pred in prediction] + + +# def cross_val_score_2(model, cv = k, label = ''): + +# X = train.copy() +# y = X.pop('yield') + +# #initiate prediction arrays and score lists +# val_predictions = np.zeros((len(train))) +# train_predictions = np.zeros((len(train))) +# train_mae, val_mae = [], [] + +# #training model, predicting prognosis probability, and evaluating log loss +# for fold, (train_idx, val_idx) in enumerate(cv.split(X, y)): + +# model.fit(X.iloc[train_idx], y.iloc[train_idx]) + +# train_preds = postprocessor(model.predict(X.iloc[train_idx])) +# val_preds = postprocessor(model.predict(X.iloc[val_idx])) + +# train_predictions[train_idx] += train_preds +# val_predictions[val_idx] += val_preds + +# train_score = mean_absolute_error(y.iloc[train_idx], train_preds) +# val_score = mean_absolute_error(y.iloc[val_idx], val_preds) + +# train_mae.append(train_score) +# val_mae.append(val_score) + +# print(f'Val MAE: {np.mean(val_mae):.5f} Β± {np.std(val_mae):.5f} | Train MAE: {np.mean(train_mae):.5f} Β± {np.std(train_mae):.5f} | {label}') + +# return val_mae + + +# for (label, model) in models: +# mae_list[label] = cross_val_score_2( +# model, +# label = label +# ) + + +# plt.figure(figsize = (8, 4), dpi = 300) +# sns.barplot(data = mae_list.reindex((mae_list).mean().sort_values().index, axis = 1), palette = 'viridis', orient = 'h') +# plt.title('MAE Comparison', weight = 'bold', size = 20) +# plt.show() + + +# # **Key point:** It seems that we have a miniscule, but consistent improvement across our models on the MAE score. + +# # # Base Model 3.0 (Postprocessing + Scaling) + +# for (label, model) in models: +# mae_list[label] = cross_val_score_2( +# Pipeline([ +# ('scale', StandardScaler()), +# (label, model)]), +# label = label +# ) + + +# plt.figure(figsize = (8, 4), dpi = 300) +# sns.barplot(data = mae_list.reindex((mae_list).mean().sort_values().index, axis = 1), palette = 'viridis', orient = 'h') +# plt.title('MAE Comparison', weight = 'bold', size = 20) +# plt.show() + + +# **Key point**: There is consistent improvement after we scale the features, especially on non-tree-based models. + +# # Ensemble +# +# Now let's try to build a simple average ensemble. For simplicity, we will only use LightGBM and CatBoost, which are the best 2 models here. +from sklearn.metrics import mean_absolute_error + +ensemble_models = [ + ('lgb', LGBMRegressor(random_state = seed, objective = 'mae')), + ('cb', CatBoostRegressor(random_state = seed, objective = 'MAE', verbose = 0)) +] + +voter = Pipeline([('scale', StandardScaler()), ('vote',VotingRegressor(ensemble_models))]) + +# _ = cross_val_score_2(voter, label = 'Voting Ensemble') + + +# # **Key point**: It looks like our score has improved with the ensemble, from **343.11222** as the best score from our baseline CatBoost, to **341.60802** from simple average ensemble with scaling and post-processing. + +# # # Modeling + +# voter.fit(X, y) +# prediction = postprocessor(voter.predict(test)) + + +# # # Submission + +# test_1.drop(list(test_1.drop('id', axis = 1)), axis = 1, inplace = True) + + +# test_1['yield'] = prediction +# test_1.to_csv('submission.csv', index = False) + + +# Thank you for reading! diff --git a/Agent/workspace/hyperopt/ps314_2/code/ps3e14-eda-fe-models-ensemble-for-starters.ipynb b/Agent/workspace/hyperopt/ps314_2/code/ps3e14-eda-fe-models-ensemble-for-starters.ipynb new file mode 100644 index 0000000..17b6677 --- /dev/null +++ b/Agent/workspace/hyperopt/ps314_2/code/ps3e14-eda-fe-models-ensemble-for-starters.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":51959,"databundleVersionId":5624004,"sourceType":"competition"},{"sourceId":2462316,"sourceType":"datasetVersion","datasetId":1490445}],"dockerImageVersionId":30474,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom category_encoders import OneHotEncoder, MEstimateEncoder, GLMMEncoder, OrdinalEncoder\nfrom sklearn.model_selection import RepeatedStratifiedKFold, StratifiedKFold, KFold\nfrom sklearn.ensemble import ExtraTreesRegressor, RandomForestRegressor, GradientBoostingRegressor\nfrom sklearn.ensemble import HistGradientBoostingRegressor, VotingRegressor, StackingRegressor\nfrom sklearn.svm import SVR, LinearSVR\nfrom sklearn.neighbors import KNeighborsRegressor\nfrom sklearn.linear_model import LinearRegression, Ridge, Lasso, ElasticNet\nfrom sklearn.linear_model import PassiveAggressiveRegressor, ARDRegression\nfrom sklearn.linear_model import TheilSenRegressor, HuberRegressor\nfrom sklearn.neural_network import MLPRegressor\nfrom sklearn.metrics import mean_absolute_error, roc_auc_score, roc_curve\nfrom sklearn.metrics.pairwise import euclidean_distances\nfrom sklearn.pipeline import Pipeline, make_pipeline\nfrom sklearn.base import BaseEstimator, TransformerMixin\nfrom sklearn.preprocessing import FunctionTransformer, StandardScaler, MinMaxScaler\nfrom sklearn.compose import ColumnTransformer\nfrom scipy.cluster.hierarchy import dendrogram, linkage\nfrom scipy.spatial.distance import squareform\nfrom xgboost import XGBRegressor, XGBClassifier\nfrom lightgbm import LGBMRegressor\nfrom catboost import CatBoostRegressor\n\nsns.set_theme(style = 'white', palette = 'viridis')\npal = sns.color_palette('viridis')\n\npd.set_option('display.max_rows', 100)","metadata":{"_kg_hide-input":true,"_kg_hide-output":true,"execution":{"iopub.status.busy":"2023-05-12T22:20:01.349785Z","iopub.execute_input":"2023-05-12T22:20:01.350259Z","iopub.status.idle":"2023-05-12T22:20:01.364009Z","shell.execute_reply.started":"2023-05-12T22:20:01.350224Z","shell.execute_reply":"2023-05-12T22:20:01.363183Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = pd.read_csv(r'../input/playground-series-s3e14/train.csv')\ntest_1 = pd.read_csv(r'../input/playground-series-s3e14/test.csv')\norig_train = pd.read_csv(r'../input/wild-blueberry-yield-prediction-dataset/WildBlueberryPollinationSimulationData.csv')\n\ntrain.drop('id', axis = 1, inplace = True)\ntest = test_1.drop('id', axis = 1)\norig_train.drop('Row#', axis = 1, inplace = True)","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.042847,"end_time":"2023-04-14T03:31:18.325186","exception":false,"start_time":"2023-04-14T03:31:18.282339","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-05-12T22:20:02.006359Z","iopub.execute_input":"2023-05-12T22:20:02.006752Z","iopub.status.idle":"2023-05-12T22:20:02.123347Z","shell.execute_reply.started":"2023-05-12T22:20:02.006722Z","shell.execute_reply":"2023-05-12T22:20:02.122096Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Knowing Your Data\n\n## Descriptive Statistics","metadata":{"papermill":{"duration":0.007001,"end_time":"2023-04-14T03:31:18.340047","exception":false,"start_time":"2023-04-14T03:31:18.333046","status":"completed"},"tags":[]}},{"cell_type":"code","source":"train.head(10)","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.034425,"end_time":"2023-04-14T03:31:18.381669","exception":false,"start_time":"2023-04-14T03:31:18.347244","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-05-12T22:20:03.123559Z","iopub.execute_input":"2023-05-12T22:20:03.12396Z","iopub.status.idle":"2023-05-12T22:20:03.163563Z","shell.execute_reply.started":"2023-05-12T22:20:03.12393Z","shell.execute_reply":"2023-05-12T22:20:03.161657Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"desc = train.describe().T\ndesc['nunique'] = train.nunique()\ndesc['%unique'] = desc['nunique'] / len(train) * 100\ndesc['null'] = train.isna().sum()\ndesc['type'] = train.dtypes\ndesc","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.056827,"end_time":"2023-04-14T03:31:18.507672","exception":false,"start_time":"2023-04-14T03:31:18.450845","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-05-12T22:20:03.166899Z","iopub.execute_input":"2023-05-12T22:20:03.168012Z","iopub.status.idle":"2023-05-12T22:20:03.282703Z","shell.execute_reply.started":"2023-05-12T22:20:03.167938Z","shell.execute_reply":"2023-05-12T22:20:03.281031Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"desc = test.describe().T\ndesc['nunique'] = test.nunique()\ndesc['%unique'] = desc['nunique'] / len(train) * 100\ndesc['null'] = test.isna().sum()\ndesc['type'] = test.dtypes\ndesc","metadata":{"execution":{"iopub.status.busy":"2023-05-12T22:20:03.284898Z","iopub.execute_input":"2023-05-12T22:20:03.285311Z","iopub.status.idle":"2023-05-12T22:20:03.375563Z","shell.execute_reply.started":"2023-05-12T22:20:03.285277Z","shell.execute_reply":"2023-05-12T22:20:03.374112Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"desc = orig_train.describe().T\ndesc['nunique'] = orig_train.nunique()\ndesc['%unique'] = desc['nunique'] / len(orig_train) * 100\ndesc['null'] = orig_train.isna().sum()\ndesc['type'] = orig_train.dtypes\ndesc","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.049846,"end_time":"2023-04-14T03:31:18.616805","exception":false,"start_time":"2023-04-14T03:31:18.566959","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-05-12T22:20:03.693257Z","iopub.execute_input":"2023-05-12T22:20:03.693682Z","iopub.status.idle":"2023-05-12T22:20:03.783589Z","shell.execute_reply.started":"2023-05-12T22:20:03.693649Z","shell.execute_reply":"2023-05-12T22:20:03.782233Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Duplicates","metadata":{"papermill":{"duration":0.008253,"end_time":"2023-04-14T03:31:18.634556","exception":false,"start_time":"2023-04-14T03:31:18.626303","status":"completed"},"tags":[]}},{"cell_type":"code","source":"print(f'There are {train.duplicated(subset = list(train)[0:-1]).value_counts()[0]} non-duplicate values out of {train.count()[0]} rows in train dataset')\nprint(f'There are {test.duplicated().value_counts()[0]} non-duplicate values out of {test.count()[0]} rows in test dataset')\nprint(f'There are {orig_train.duplicated(subset = list(train)[0:-1]).value_counts()[0]} non-duplicate values out of {orig_train.count()[0]} rows in original train dataset')","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.029537,"end_time":"2023-04-14T03:31:18.67261","exception":false,"start_time":"2023-04-14T03:31:18.643073","status":"completed"},"tags":[],"execution":{"iopub.status.busy":"2023-05-12T22:20:05.169639Z","iopub.execute_input":"2023-05-12T22:20:05.170069Z","iopub.status.idle":"2023-05-12T22:20:05.727335Z","shell.execute_reply.started":"2023-05-12T22:20:05.170035Z","shell.execute_reply":"2023-05-12T22:20:05.725858Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Key point**: There are row duplicates in train and test dataset. We can remove it from our train dataset, though it may have no effect due to how few they are.","metadata":{}},{"cell_type":"markdown","source":"# Adversarial Validation","metadata":{}},{"cell_type":"code","source":"def adversarial_validation(dataset_1 = train, dataset_2 = test, label = 'Train-Test'):\n\n adv_train = dataset_1.drop('yield', axis = 1)\n adv_test = dataset_2.copy()\n\n adv_train['is_test'] = 0\n adv_test['is_test'] = 1\n\n adv = pd.concat([adv_train, adv_test], ignore_index = True)\n\n adv_shuffled = adv.sample(frac = 1)\n\n adv_X = adv_shuffled.drop('is_test', axis = 1)\n adv_y = adv_shuffled.is_test\n\n skf = StratifiedKFold(n_splits = 5, random_state = 42, shuffle = True)\n\n val_scores = []\n predictions = np.zeros(len(adv))\n\n for fold, (train_idx, val_idx) in enumerate(skf.split(adv_X, adv_y)):\n \n adv_lr = XGBClassifier(random_state = 42) \n adv_lr.fit(adv_X.iloc[train_idx], adv_y.iloc[train_idx])\n \n val_preds = adv_lr.predict_proba(adv_X.iloc[val_idx])[:,1]\n predictions[val_idx] = val_preds\n val_score = roc_auc_score(adv_y.iloc[val_idx], val_preds)\n val_scores.append(val_score)\n \n fpr, tpr, _ = roc_curve(adv['is_test'], predictions)\n \n plt.figure(figsize = (10, 10), dpi = 300)\n sns.lineplot(x=[0, 1], y=[0, 1], linestyle=\"--\", label=\"Indistinguishable Datasets\")\n sns.lineplot(x=fpr, y=tpr, label=\"Adversarial Validation Classifier\")\n plt.title(f'{label} Validation = {np.mean(val_scores):.5f}', weight = 'bold', size = 17)\n plt.xlabel('False Positive Rate')\n plt.ylabel('True Positive Rate')\n plt.show()","metadata":{"execution":{"iopub.status.busy":"2023-05-12T22:20:06.640735Z","iopub.execute_input":"2023-05-12T22:20:06.641554Z","iopub.status.idle":"2023-05-12T22:20:06.654984Z","shell.execute_reply.started":"2023-05-12T22:20:06.641512Z","shell.execute_reply":"2023-05-12T22:20:06.653754Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"adversarial_validation()\nadversarial_validation(pd.concat([train, orig_train]), test, 'Combo Train-Test Validation')","metadata":{"_kg_hide-input":true,"execution":{"iopub.status.busy":"2023-05-12T22:20:07.056079Z","iopub.execute_input":"2023-05-12T22:20:07.05734Z","iopub.status.idle":"2023-05-12T22:24:11.589528Z","shell.execute_reply.started":"2023-05-12T22:20:07.057282Z","shell.execute_reply":"2023-05-12T22:24:11.588235Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Key points:**\n1. Train and test datasets validation results in ROC score of close to .5, therefore **we can trust our cross-validation.**\n2. Combined train and test datasets validation results in ROC score of close to .5, which is very far from competition dataset. Therefore, **we can include it in our training**.","metadata":{}},{"cell_type":"markdown","source":"# Distribution","metadata":{}},{"cell_type":"code","source":"fig, ax = plt.subplots(4, 4, figsize = (10, 10), dpi = 300)\nax = ax.flatten()\n\nfor i, column in enumerate(test.columns):\n sns.kdeplot(train[column], ax=ax[i], color=pal[0]) \n sns.kdeplot(test[column], ax=ax[i], color=pal[2])\n \n ax[i].set_title(f'{column} Distribution', size = 7)\n ax[i].set_xlabel(None)\n \nfig.suptitle('Distribution of Feature\\nper Dataset\\n', fontsize = 24, fontweight = 'bold')\nfig.legend(['Train', 'Test'])\nplt.tight_layout()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Key points:** \n1. All features have similar distribution between training and test dataset.\n2. 13 out of 16 features are categorical","metadata":{}},{"cell_type":"code","source":"plt.figure(figsize = (10, 6), dpi = 300)\nsns.kdeplot(data = train, x = 'yield')\nplt.title('Target Distribution', weight = 'bold', size = 20)\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Key point**: It looks like we are having relatively normal distribution here.","metadata":{}},{"cell_type":"markdown","source":"# Correlation","metadata":{"papermill":{"duration":0.031508,"end_time":"2023-04-14T03:31:26.00169","exception":false,"start_time":"2023-04-14T03:31:25.970182","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def heatmap(dataset, label = None):\n corr = dataset.corr(method = 'spearman')\n plt.figure(figsize = (14, 10), dpi = 300)\n mask = np.zeros_like(corr)\n mask[np.triu_indices_from(mask)] = True\n sns.heatmap(corr, mask = mask, cmap = 'viridis', annot = True, annot_kws = {'size' : 7})\n plt.title(f'{label} Dataset Correlation Matrix\\n', fontsize = 25, weight = 'bold')\n plt.show()","metadata":{"_kg_hide-input":true,"papermill":{"duration":0.044524,"end_time":"2023-04-14T03:31:26.079498","exception":false,"start_time":"2023-04-14T03:31:26.034974","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"heatmap(train, 'Train')\nheatmap(test, 'Test')","metadata":{"_kg_hide-input":true,"papermill":{"duration":2.669877,"end_time":"2023-04-14T03:31:28.781856","exception":false,"start_time":"2023-04-14T03:31:26.111979","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Key point**: There are so many features with very strong correlation that some of them are practically duplicates. We can remove them to make our model better. Let's try to see it with hierarchy tree this time.","metadata":{}},{"cell_type":"code","source":"def distance(data, label = ''):\n #thanks to @sergiosaharovsky for the fix\n corr = data.corr(method = 'spearman')\n dist_linkage = linkage(squareform(1 - abs(corr)), 'complete')\n \n plt.figure(figsize = (10, 8), dpi = 300)\n dendro = dendrogram(dist_linkage, labels=data.columns, leaf_rotation=90)\n plt.title(f'Feature Distance in {label} Dataset', weight = 'bold', size = 22)\n plt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"distance(train, 'Train')","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Key points:** \n1. `MinOfUpperTRange`, `AverageOfUpperTRange`, `AverageOfLowerTRange`, `MaxOfLowerTRange`, `MaxOfUpperTRange`, and `MinOfLowerTRange` are practically duplicates so you can just keep one of them.\n2. `RainingDays` and `AverageRainingDays` are almost duplicate so you may also drop one of them.","metadata":{}},{"cell_type":"markdown","source":"# Preparation","metadata":{"papermill":{"duration":0.039506,"end_time":"2023-04-14T03:31:29.257052","exception":false,"start_time":"2023-04-14T03:31:29.217546","status":"completed"},"tags":[]}},{"cell_type":"code","source":"X = train.copy()\ny = X.pop('yield')\n\nseed = 42\nsplits = 5\nk = KFold(n_splits = splits, random_state = seed, shuffle = True)\n\nnp.random.seed(seed)","metadata":{"_kg_hide-input":false,"papermill":{"duration":0.049654,"end_time":"2023-04-14T03:31:29.612968","exception":false,"start_time":"2023-04-14T03:31:29.563314","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Base Models","metadata":{"papermill":{"duration":0.040817,"end_time":"2023-04-14T03:31:29.86419","exception":false,"start_time":"2023-04-14T03:31:29.823373","status":"completed"},"tags":[]}},{"cell_type":"code","source":"def cross_val_score(model, cv = k, label = ''):\n \n X = train.copy()\n y = X.pop('yield')\n \n #initiate prediction arrays and score lists\n val_predictions = np.zeros((len(train)))\n train_predictions = np.zeros((len(train)))\n train_mae, val_mae = [], []\n \n #training model, predicting prognosis probability, and evaluating log loss\n for fold, (train_idx, val_idx) in enumerate(cv.split(X, y)):\n \n model.fit(X.iloc[train_idx], y.iloc[train_idx])\n\n train_preds = model.predict(X.iloc[train_idx])\n val_preds = model.predict(X.iloc[val_idx])\n \n train_predictions[train_idx] += train_preds\n val_predictions[val_idx] += val_preds\n \n train_score = mean_absolute_error(y.iloc[train_idx], train_preds)\n val_score = mean_absolute_error(y.iloc[val_idx], val_preds)\n \n train_mae.append(train_score)\n val_mae.append(val_score)\n \n print(f'Val MAE: {np.mean(val_mae):.5f} Β± {np.std(val_mae):.5f} | Train MAE: {np.mean(train_mae):.5f} Β± {np.std(train_mae):.5f} | {label}')\n \n return val_mae","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mae_list = pd.DataFrame()\n\nmodels = [\n ('linear', LinearRegression()),\n ('ridge', Ridge(random_state = seed)),\n ('lasso', Lasso(random_state = seed, max_iter = 1000000)),\n ('elastic', ElasticNet(random_state = seed, max_iter = 1000000)),\n ('huber', HuberRegressor(max_iter = 1000000)),\n ('ard', ARDRegression()),\n ('passive', PassiveAggressiveRegressor(random_state = seed)),\n ('theilsen', TheilSenRegressor(random_state = seed)),\n ('linearsvm', LinearSVR(random_state = seed, max_iter = 1000000)),\n ('mlp', MLPRegressor(random_state = seed, max_iter = 1000000)),\n ('et', ExtraTreesRegressor(random_state = seed)),\n ('rf', RandomForestRegressor(random_state = seed)),\n ('xgb', XGBRegressor(random_state = seed, eval_metric = 'mae')),\n ('lgb', LGBMRegressor(random_state = seed, objective = 'mae')),\n ('dart', LGBMRegressor(random_state = seed, boosting_type = 'dart')),\n ('cb', CatBoostRegressor(random_state = seed, objective = 'MAE', verbose = 0)),\n ('gb', GradientBoostingRegressor(random_state = seed, loss = 'absolute_error')),\n ('hgb', HistGradientBoostingRegressor(random_state = seed, loss = 'absolute_error')),\n ('knn', KNeighborsRegressor())\n]\n\nfor (label, model) in models:\n mae_list[label] = cross_val_score(model, label = label)","metadata":{"_kg_hide-output":false,"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize = (8, 4), dpi = 300)\nsns.barplot(data = mae_list.reindex((mae_list).mean().sort_values().index, axis = 1), palette = 'viridis', orient = 'h')\nplt.title('MAE Comparison', weight = 'bold', size = 20)\nplt.show()","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Key points**;\n1. Linear regression can work as well as tree-based models here.\n2. Some tree-based models, especially non-gradient boosting ones, have a lot of overfitting.\n3. `CatBoostRegressor` gives the best result.","metadata":{}},{"cell_type":"markdown","source":"# Base Models 2.0 (With Post-Processing)\n\n@mattop has pointed out in [this topic](https://www.kaggle.com/competitions/playground-series-s3e14/discussion/407327) that we can post-process our prediction to make it consistent with the unique values of the `yield`.","metadata":{}},{"cell_type":"code","source":"def postprocessor(prediction):\n #thanks to @mattop\n unique_targets = np.unique(train['yield'])\n return [min(unique_targets, key = lambda x: abs(x - pred)) for pred in prediction]","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"def cross_val_score_2(model, cv = k, label = ''):\n \n X = train.copy()\n y = X.pop('yield')\n \n #initiate prediction arrays and score lists\n val_predictions = np.zeros((len(train)))\n train_predictions = np.zeros((len(train)))\n train_mae, val_mae = [], []\n \n #training model, predicting prognosis probability, and evaluating log loss\n for fold, (train_idx, val_idx) in enumerate(cv.split(X, y)):\n \n model.fit(X.iloc[train_idx], y.iloc[train_idx])\n\n train_preds = postprocessor(model.predict(X.iloc[train_idx]))\n val_preds = postprocessor(model.predict(X.iloc[val_idx]))\n \n train_predictions[train_idx] += train_preds\n val_predictions[val_idx] += val_preds\n \n train_score = mean_absolute_error(y.iloc[train_idx], train_preds)\n val_score = mean_absolute_error(y.iloc[val_idx], val_preds)\n \n train_mae.append(train_score)\n val_mae.append(val_score)\n \n print(f'Val MAE: {np.mean(val_mae):.5f} Β± {np.std(val_mae):.5f} | Train MAE: {np.mean(train_mae):.5f} Β± {np.std(train_mae):.5f} | {label}')\n \n return val_mae","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"for (label, model) in models:\n mae_list[label] = cross_val_score_2(\n model,\n label = label\n )","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize = (8, 4), dpi = 300)\nsns.barplot(data = mae_list.reindex((mae_list).mean().sort_values().index, axis = 1), palette = 'viridis', orient = 'h')\nplt.title('MAE Comparison', weight = 'bold', size = 20)\nplt.show()","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Key point:** It seems that we have a miniscule, but consistent improvement across our models on the MAE score.","metadata":{}},{"cell_type":"markdown","source":"# Base Model 3.0 (Postprocessing + Scaling)","metadata":{}},{"cell_type":"code","source":"for (label, model) in models:\n mae_list[label] = cross_val_score_2(\n Pipeline([\n ('scale', StandardScaler()),\n (label, model)]),\n label = label\n )","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize = (8, 4), dpi = 300)\nsns.barplot(data = mae_list.reindex((mae_list).mean().sort_values().index, axis = 1), palette = 'viridis', orient = 'h')\nplt.title('MAE Comparison', weight = 'bold', size = 20)\nplt.show()","metadata":{"_kg_hide-input":true,"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Key point**: There is consistent improvement after we scale the features, especially on non-tree-based models.","metadata":{}},{"cell_type":"markdown","source":"# Ensemble\n\nNow let's try to build a simple average ensemble. For simplicity, we will only use LightGBM and CatBoost, which are the best 2 models here.","metadata":{}},{"cell_type":"code","source":"ensemble_models = [\n ('lgb', LGBMRegressor(random_state = seed, objective = 'mae')),\n ('cb', CatBoostRegressor(random_state = seed, objective = 'MAE', verbose = 0))\n]\n\nvoter = Pipeline([('scale', StandardScaler()), ('vote',VotingRegressor(ensemble_models))])\n\n_ = cross_val_score_2(voter, label = 'Voting Ensemble')","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"**Key point**: It looks like our score has improved with the ensemble, from **343.11222** as the best score from our baseline CatBoost, to **341.60802** from simple average ensemble with scaling and post-processing.","metadata":{}},{"cell_type":"markdown","source":"# Modeling","metadata":{}},{"cell_type":"code","source":"voter.fit(X, y)\nprediction = postprocessor(voter.predict(test))","metadata":{"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# Submission","metadata":{"papermill":{"duration":0.043219,"end_time":"2023-04-14T03:33:59.11743","exception":false,"start_time":"2023-04-14T03:33:59.074211","status":"completed"},"tags":[]}},{"cell_type":"code","source":"test_1.drop(list(test_1.drop('id', axis = 1)), axis = 1, inplace = True)","metadata":{"papermill":{"duration":0.053599,"end_time":"2023-04-14T03:33:59.213081","exception":false,"start_time":"2023-04-14T03:33:59.159482","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_1['yield'] = prediction\ntest_1.to_csv('submission.csv', index = False)","metadata":{"papermill":{"duration":0.06033,"end_time":"2023-04-14T03:33:59.316152","exception":false,"start_time":"2023-04-14T03:33:59.255822","status":"completed"},"tags":[],"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"Thank you for reading!","metadata":{"papermill":{"duration":0.04172,"end_time":"2023-04-14T03:33:59.400672","exception":false,"start_time":"2023-04-14T03:33:59.358952","status":"completed"},"tags":[]}}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/ps315/code/code.py b/Agent/workspace/hyperopt/ps315/code/code.py new file mode 100644 index 0000000..444b5af --- /dev/null +++ b/Agent/workspace/hyperopt/ps315/code/code.py @@ -0,0 +1,198 @@ +#!/usr/bin/env python +# coding: utf-8 + +import numpy as np +import pandas as pd +# from ydata_profiling import ProfileReport +from sklearn.model_selection import train_test_split +from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor +from sklearn.metrics import roc_auc_score + +FILE_PATH = "./workspace/hyperopt/ps315/data/" + +data = pd.read_csv(FILE_PATH+'data.csv.zip') + + +# ProfileReport(data) + + +# data.columns + + +data.columns = ['id', 'author', 'geometry', 'pressure', 'mass_flux', 'x_e_out', 'D_e', 'D_h', 'length', 'chf_exp'] + + +# data + + +train = data[~data.x_e_out.isna()] +test = data[data.x_e_out.isna()] + + +# train.describe() + + +# test.describe() + + +# ### Sanity check + +# tmp = data.drop(['id', 'x_e_out'], axis=1) +# tmp_target = data.x_e_out.isna() * 1.0 + + +# X_tmp_train, X_tmp_test, y_tmp_train, y_tmp_test = train_test_split(tmp, tmp_target) + + +# X_tmp_train.isna().sum() + + +# fill_num = X_tmp_train.median() + + +# X_tmp_train.fillna(fill_num).isna().sum() + + +# X_tmp_train['author'].value_counts() + + +# X_tmp_train['geometry'].value_counts() + + +# X_tmp_train = X_tmp_train.fillna(fill_num) +# X_tmp_test = X_tmp_test.fillna(fill_num) + + +# X_tmp_train[['author', 'geometry']] = X_tmp_train[['author', 'geometry']].fillna({'author': 'Thompson', 'geometry': 'tube'}) +# X_tmp_test[['author', 'geometry']] = X_tmp_test[['author', 'geometry']].fillna({'author': 'Thompson', 'geometry': 'tube'}) + + +# X_tmp_train + + +# geometry_encoder = pd.DataFrame({ +# 'geometry': X_tmp_train.geometry, +# 'target': y_tmp_train +# }).groupby('geometry').target.mean() + +# author_encoder = pd.DataFrame({ +# 'author': X_tmp_train.author, +# 'target': y_tmp_train +# }).groupby('author').target.mean() + + +# X_tmp_train.author + + +# X_tmp_train.author = X_tmp_train.author.map(author_encoder) +# X_tmp_train.geometry = X_tmp_train.geometry.map(geometry_encoder) + +# X_tmp_test.author = X_tmp_test.author.map(author_encoder) +# X_tmp_test.geometry = X_tmp_test.geometry.map(geometry_encoder) + + +# X_tmp_train + + +# clf = RandomForestClassifier(max_depth=3) +# clf.fit(X_tmp_train, y_tmp_train) + + +# print(clf.score(X_tmp_train, y_tmp_train)) +# print(clf.score(X_tmp_test, y_tmp_test)) + + +# y_tmp_train.mean() + + +# 1-y_tmp_train.mean() + + +# train_proba = clf.predict_proba(X_tmp_train)[:,1] +# train_proba.mean(), train_proba.std() + + +# roc_auc_score(y_tmp_train, train_proba) + + +# roc_auc_score(y_tmp_test, clf.predict_proba(X_tmp_test)[:,1]) + + +# ### Conclusion +# Train and test are not different from each other. +# They came out of the same general distribution. +# We can say that the omissions occurred by accident. + +# ## Train the main model +X_train=train.drop(['id', 'x_e_out'],axis=1) +y_train=train.x_e_out +# X_train, X_val, y_train, y_val = train_test_split(train.drop(['id', 'x_e_out'],axis=1), train.x_e_out) + + +# Fix NA + +numeric_cols = X_train.select_dtypes(include='number').columns +non_numeric_cols = X_train.select_dtypes(exclude='number').columns + +# Compute median for numeric columns +fill_num = X_train[numeric_cols].median() + +# Fill missing values in numeric columns +X_train[numeric_cols] = X_train[numeric_cols].fillna(fill_num) + +# Fill missing values in non-numeric columns +X_train[['author', 'geometry']] = X_train[['author', 'geometry']].fillna({'author': 'Thompson', 'geometry': 'tube'}) +# X_val[['author', 'geometry']] = X_val[['author', 'geometry']].fillna({'author': 'Thompson', 'geometry': 'tube'}) + + +geometry_encoder = pd.DataFrame({ + 'geometry': X_train.geometry, + 'target': y_train +}).groupby('geometry').target.mean() + +author_encoder = pd.DataFrame({ + 'author': X_train.author, + 'target': y_train +}).groupby('author').target.mean() + +X_train.author = X_train.author.map(author_encoder) +X_train.geometry = X_train.geometry.map(geometry_encoder) + +# X_val.author = X_val.author.map(author_encoder) +# X_val.geometry = X_val.geometry.map(geometry_encoder) + + +# X_train.isna().sum() + + +# X_val.isna().sum() + +from sklearn.metrics import mean_squared_error +model = RandomForestRegressor(max_depth=5) +# model.fit(X_train, y_train) + + +# np.mean((model.predict(X_train) - y_train)**2)**0.5 + + +# np.mean((model.predict(X_val) - y_val)**2)**0.5 + + +# X_test = test.drop(['id', 'x_e_out'],axis=1) +# X_test[['author', 'geometry']] = X_test[['author', 'geometry']].fillna({'author': 'Thompson', 'geometry': 'tube'}) +# X_test = X_test.fillna(fill_num) +# X_test.author = X_test.author.map(author_encoder) +# X_test.geometry = X_test.geometry.map(geometry_encoder) + + +# model.predict(X_test) + + +# submit = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv') + + +# submit['x_e_out [-]'] = model.predict(X_test) + + +# submit.to_csv('random_forest.csv', index=False) + diff --git a/Agent/workspace/hyperopt/ps315/code/simple-solution-with-random-forest.ipynb b/Agent/workspace/hyperopt/ps315/code/simple-solution-with-random-forest.ipynb new file mode 100644 index 0000000..541aa41 --- /dev/null +++ b/Agent/workspace/hyperopt/ps315/code/simple-solution-with-random-forest.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":51982,"databundleVersionId":5760919,"sourceType":"competition"}],"dockerImageVersionId":30497,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"import numpy as np\nimport pandas as pd\nfrom ydata_profiling import ProfileReport\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\nfrom sklearn.metrics import roc_auc_score","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:12:30.60615Z","iopub.execute_input":"2023-05-27T16:12:30.606571Z","iopub.status.idle":"2023-05-27T16:12:30.613155Z","shell.execute_reply.started":"2023-05-27T16:12:30.60654Z","shell.execute_reply":"2023-05-27T16:12:30.611339Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data = pd.read_csv(r'/kaggle/input/playground-series-s3e15/data.csv')","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:05.5841Z","iopub.execute_input":"2023-05-27T15:47:05.585242Z","iopub.status.idle":"2023-05-27T15:47:05.641976Z","shell.execute_reply.started":"2023-05-27T15:47:05.585196Z","shell.execute_reply":"2023-05-27T15:47:05.640772Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ProfileReport(data)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:05.643936Z","iopub.execute_input":"2023-05-27T15:47:05.644416Z","iopub.status.idle":"2023-05-27T15:47:05.649745Z","shell.execute_reply.started":"2023-05-27T15:47:05.644377Z","shell.execute_reply":"2023-05-27T15:47:05.648488Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.columns","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:11.979735Z","iopub.execute_input":"2023-05-27T15:47:11.980159Z","iopub.status.idle":"2023-05-27T15:47:11.987748Z","shell.execute_reply.started":"2023-05-27T15:47:11.980126Z","shell.execute_reply":"2023-05-27T15:47:11.986572Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data.columns = ['id', 'author', 'geometry', 'pressure', 'mass_flux', 'x_e_out', 'D_e', 'D_h', 'length', 'chf_exp']","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:12.153123Z","iopub.execute_input":"2023-05-27T15:47:12.154303Z","iopub.status.idle":"2023-05-27T15:47:12.160202Z","shell.execute_reply.started":"2023-05-27T15:47:12.15426Z","shell.execute_reply":"2023-05-27T15:47:12.159098Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"data","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:12.340397Z","iopub.execute_input":"2023-05-27T15:47:12.341607Z","iopub.status.idle":"2023-05-27T15:47:12.367129Z","shell.execute_reply.started":"2023-05-27T15:47:12.341574Z","shell.execute_reply":"2023-05-27T15:47:12.36606Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train = data[~data.x_e_out.isna()]\ntest = data[data.x_e_out.isna()]","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:12.464475Z","iopub.execute_input":"2023-05-27T15:47:12.464883Z","iopub.status.idle":"2023-05-27T15:47:12.475577Z","shell.execute_reply.started":"2023-05-27T15:47:12.464854Z","shell.execute_reply":"2023-05-27T15:47:12.474576Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.describe()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:12.609911Z","iopub.execute_input":"2023-05-27T15:47:12.611224Z","iopub.status.idle":"2023-05-27T15:47:12.663285Z","shell.execute_reply.started":"2023-05-27T15:47:12.611176Z","shell.execute_reply":"2023-05-27T15:47:12.661966Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test.describe()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:12.728275Z","iopub.execute_input":"2023-05-27T15:47:12.730363Z","iopub.status.idle":"2023-05-27T15:47:12.774916Z","shell.execute_reply.started":"2023-05-27T15:47:12.730314Z","shell.execute_reply":"2023-05-27T15:47:12.773586Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Sanity check","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:39:11.501677Z","iopub.execute_input":"2023-05-27T15:39:11.502223Z","iopub.status.idle":"2023-05-27T15:39:11.508237Z","shell.execute_reply.started":"2023-05-27T15:39:11.502187Z","shell.execute_reply":"2023-05-27T15:39:11.506946Z"}}},{"cell_type":"code","source":"tmp = data.drop(['id', 'x_e_out'], axis=1)\ntmp_target = data.x_e_out.isna() * 1.0","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:12.977534Z","iopub.execute_input":"2023-05-27T15:47:12.978042Z","iopub.status.idle":"2023-05-27T15:47:12.988023Z","shell.execute_reply.started":"2023-05-27T15:47:12.977982Z","shell.execute_reply":"2023-05-27T15:47:12.986669Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_tmp_train, X_tmp_test, y_tmp_train, y_tmp_test = train_test_split(tmp, tmp_target)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:13.117737Z","iopub.execute_input":"2023-05-27T15:47:13.118562Z","iopub.status.idle":"2023-05-27T15:47:13.132814Z","shell.execute_reply.started":"2023-05-27T15:47:13.118527Z","shell.execute_reply":"2023-05-27T15:47:13.131402Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_tmp_train.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:13.266346Z","iopub.execute_input":"2023-05-27T15:47:13.267565Z","iopub.status.idle":"2023-05-27T15:47:13.298417Z","shell.execute_reply.started":"2023-05-27T15:47:13.267519Z","shell.execute_reply":"2023-05-27T15:47:13.297201Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"fill_num = X_tmp_train.median()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:13.380488Z","iopub.execute_input":"2023-05-27T15:47:13.380866Z","iopub.status.idle":"2023-05-27T15:47:13.409709Z","shell.execute_reply.started":"2023-05-27T15:47:13.380838Z","shell.execute_reply":"2023-05-27T15:47:13.40875Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_tmp_train.fillna(fill_num).isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:13.876396Z","iopub.execute_input":"2023-05-27T15:47:13.876805Z","iopub.status.idle":"2023-05-27T15:47:13.910443Z","shell.execute_reply.started":"2023-05-27T15:47:13.876774Z","shell.execute_reply":"2023-05-27T15:47:13.909292Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_tmp_train['author'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:54:26.458833Z","iopub.execute_input":"2023-05-27T15:54:26.459254Z","iopub.status.idle":"2023-05-27T15:54:26.469774Z","shell.execute_reply.started":"2023-05-27T15:54:26.459224Z","shell.execute_reply":"2023-05-27T15:54:26.468502Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_tmp_train['geometry'].value_counts()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:14.431924Z","iopub.execute_input":"2023-05-27T15:47:14.432969Z","iopub.status.idle":"2023-05-27T15:47:14.443899Z","shell.execute_reply.started":"2023-05-27T15:47:14.432931Z","shell.execute_reply":"2023-05-27T15:47:14.443081Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_tmp_train = X_tmp_train.fillna(fill_num)\nX_tmp_test = X_tmp_test.fillna(fill_num)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:14.566248Z","iopub.execute_input":"2023-05-27T15:47:14.566923Z","iopub.status.idle":"2023-05-27T15:47:14.581677Z","shell.execute_reply.started":"2023-05-27T15:47:14.56688Z","shell.execute_reply":"2023-05-27T15:47:14.580736Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_tmp_train[['author', 'geometry']] = X_tmp_train[['author', 'geometry']].fillna({'author': 'Thompson', 'geometry': 'tube'})\nX_tmp_test[['author', 'geometry']] = X_tmp_test[['author', 'geometry']].fillna({'author': 'Thompson', 'geometry': 'tube'})","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:14.947549Z","iopub.execute_input":"2023-05-27T15:47:14.948302Z","iopub.status.idle":"2023-05-27T15:47:14.972165Z","shell.execute_reply.started":"2023-05-27T15:47:14.948268Z","shell.execute_reply":"2023-05-27T15:47:14.970923Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_tmp_train","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:47:32.970094Z","iopub.execute_input":"2023-05-27T15:47:32.970496Z","iopub.status.idle":"2023-05-27T15:47:32.99694Z","shell.execute_reply.started":"2023-05-27T15:47:32.970465Z","shell.execute_reply":"2023-05-27T15:47:32.996094Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"geometry_encoder = pd.DataFrame({\n 'geometry': X_tmp_train.geometry,\n 'target': y_tmp_train\n}).groupby('geometry').target.mean()\n\nauthor_encoder = pd.DataFrame({\n 'author': X_tmp_train.author,\n 'target': y_tmp_train\n}).groupby('author').target.mean()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:52:06.617583Z","iopub.execute_input":"2023-05-27T15:52:06.618047Z","iopub.status.idle":"2023-05-27T15:52:06.637481Z","shell.execute_reply.started":"2023-05-27T15:52:06.617989Z","shell.execute_reply":"2023-05-27T15:52:06.635974Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_tmp_train.author","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:52:32.486697Z","iopub.execute_input":"2023-05-27T15:52:32.487128Z","iopub.status.idle":"2023-05-27T15:52:32.498893Z","shell.execute_reply.started":"2023-05-27T15:52:32.487097Z","shell.execute_reply":"2023-05-27T15:52:32.496531Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_tmp_train.author = X_tmp_train.author.map(author_encoder)\nX_tmp_train.geometry = X_tmp_train.geometry.map(geometry_encoder)\n\nX_tmp_test.author = X_tmp_test.author.map(author_encoder)\nX_tmp_test.geometry = X_tmp_test.geometry.map(geometry_encoder)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:53:46.849576Z","iopub.execute_input":"2023-05-27T15:53:46.850146Z","iopub.status.idle":"2023-05-27T15:53:46.878507Z","shell.execute_reply.started":"2023-05-27T15:53:46.850102Z","shell.execute_reply":"2023-05-27T15:53:46.877425Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_tmp_train","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:53:49.629003Z","iopub.execute_input":"2023-05-27T15:53:49.629462Z","iopub.status.idle":"2023-05-27T15:53:49.658Z","shell.execute_reply.started":"2023-05-27T15:53:49.629428Z","shell.execute_reply":"2023-05-27T15:53:49.656472Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"clf = RandomForestClassifier(max_depth=3)\nclf.fit(X_tmp_train, y_tmp_train)\n","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:59:49.75535Z","iopub.execute_input":"2023-05-27T15:59:49.755732Z","iopub.status.idle":"2023-05-27T15:59:50.764437Z","shell.execute_reply.started":"2023-05-27T15:59:49.755704Z","shell.execute_reply":"2023-05-27T15:59:50.763371Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print(clf.score(X_tmp_train, y_tmp_train))\nprint(clf.score(X_tmp_test, y_tmp_test))","metadata":{"execution":{"iopub.status.busy":"2023-05-27T15:59:50.76586Z","iopub.execute_input":"2023-05-27T15:59:50.766589Z","iopub.status.idle":"2023-05-27T15:59:50.996987Z","shell.execute_reply.started":"2023-05-27T15:59:50.766558Z","shell.execute_reply":"2023-05-27T15:59:50.995787Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_tmp_train.mean()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:04:31.13771Z","iopub.execute_input":"2023-05-27T16:04:31.138137Z","iopub.status.idle":"2023-05-27T16:04:31.147951Z","shell.execute_reply.started":"2023-05-27T16:04:31.138105Z","shell.execute_reply":"2023-05-27T16:04:31.146923Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"1-y_tmp_train.mean()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:01:24.136206Z","iopub.execute_input":"2023-05-27T16:01:24.136591Z","iopub.status.idle":"2023-05-27T16:01:24.144366Z","shell.execute_reply.started":"2023-05-27T16:01:24.13655Z","shell.execute_reply":"2023-05-27T16:01:24.143173Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_proba = clf.predict_proba(X_tmp_train)[:,1]\ntrain_proba.mean(), train_proba.std()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:04:23.651611Z","iopub.execute_input":"2023-05-27T16:04:23.651973Z","iopub.status.idle":"2023-05-27T16:04:23.813461Z","shell.execute_reply.started":"2023-05-27T16:04:23.651944Z","shell.execute_reply":"2023-05-27T16:04:23.812325Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"roc_auc_score(y_tmp_train, train_proba)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:05:00.686523Z","iopub.execute_input":"2023-05-27T16:05:00.686913Z","iopub.status.idle":"2023-05-27T16:05:00.713282Z","shell.execute_reply.started":"2023-05-27T16:05:00.686886Z","shell.execute_reply":"2023-05-27T16:05:00.712142Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"roc_auc_score(y_tmp_test, clf.predict_proba(X_tmp_test)[:,1])","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:05:26.163224Z","iopub.execute_input":"2023-05-27T16:05:26.163634Z","iopub.status.idle":"2023-05-27T16:05:26.242134Z","shell.execute_reply.started":"2023-05-27T16:05:26.163603Z","shell.execute_reply":"2023-05-27T16:05:26.241051Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Conclusion\nTrain and test are not different from each other.\nThey came out of the same general distribution.\nWe can say that the omissions occurred by accident.","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:05:46.510694Z","iopub.execute_input":"2023-05-27T16:05:46.511139Z","iopub.status.idle":"2023-05-27T16:05:46.516175Z","shell.execute_reply.started":"2023-05-27T16:05:46.511108Z","shell.execute_reply":"2023-05-27T16:05:46.515189Z"}}},{"cell_type":"markdown","source":"## Train the main model","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:21:29.032953Z","iopub.execute_input":"2023-05-27T16:21:29.033426Z","iopub.status.idle":"2023-05-27T16:21:29.038927Z","shell.execute_reply.started":"2023-05-27T16:21:29.033392Z","shell.execute_reply":"2023-05-27T16:21:29.037429Z"}}},{"cell_type":"code","source":"X_train, X_val, y_train, y_val = train_test_split(train.drop(['id', 'x_e_out'],axis=1), train.x_e_out)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:11:02.125267Z","iopub.execute_input":"2023-05-27T16:11:02.125688Z","iopub.status.idle":"2023-05-27T16:11:02.14005Z","shell.execute_reply.started":"2023-05-27T16:11:02.125659Z","shell.execute_reply":"2023-05-27T16:11:02.139036Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Fix NA\n\nfill_num = X_train.median()\nX_train = X_train.fillna(fill_num)\nX_val = X_val.fillna(fill_num)\n\n\nX_train[['author', 'geometry']] = X_train[['author', 'geometry']].fillna({'author': 'Thompson', 'geometry': 'tube'})\nX_val[['author', 'geometry']] = X_val[['author', 'geometry']].fillna({'author': 'Thompson', 'geometry': 'tube'})","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:11:02.255152Z","iopub.execute_input":"2023-05-27T16:11:02.255751Z","iopub.status.idle":"2023-05-27T16:11:02.297906Z","shell.execute_reply.started":"2023-05-27T16:11:02.255715Z","shell.execute_reply":"2023-05-27T16:11:02.297109Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"geometry_encoder = pd.DataFrame({\n 'geometry': X_train.geometry,\n 'target': y_train\n}).groupby('geometry').target.mean()\n\nauthor_encoder = pd.DataFrame({\n 'author': X_train.author,\n 'target': y_train\n}).groupby('author').target.mean()\n\nX_train.author = X_train.author.map(author_encoder)\nX_train.geometry = X_train.geometry.map(geometry_encoder)\n\nX_val.author = X_val.author.map(author_encoder)\nX_val.geometry = X_val.geometry.map(geometry_encoder)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:11:43.136037Z","iopub.execute_input":"2023-05-27T16:11:43.136482Z","iopub.status.idle":"2023-05-27T16:11:43.164534Z","shell.execute_reply.started":"2023-05-27T16:11:43.136442Z","shell.execute_reply":"2023-05-27T16:11:43.163217Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_train.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:12:02.562531Z","iopub.execute_input":"2023-05-27T16:12:02.563461Z","iopub.status.idle":"2023-05-27T16:12:02.576399Z","shell.execute_reply.started":"2023-05-27T16:12:02.563416Z","shell.execute_reply":"2023-05-27T16:12:02.574788Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_val.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:12:07.75162Z","iopub.execute_input":"2023-05-27T16:12:07.752037Z","iopub.status.idle":"2023-05-27T16:12:07.762438Z","shell.execute_reply.started":"2023-05-27T16:12:07.751987Z","shell.execute_reply":"2023-05-27T16:12:07.76113Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model = RandomForestRegressor(max_depth=5)\nmodel.fit(X_train, y_train)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:15:19.370233Z","iopub.execute_input":"2023-05-27T16:15:19.370643Z","iopub.status.idle":"2023-05-27T16:15:20.866585Z","shell.execute_reply.started":"2023-05-27T16:15:19.370612Z","shell.execute_reply":"2023-05-27T16:15:20.865481Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.mean((model.predict(X_train) - y_train)**2)**0.5","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:15:20.868327Z","iopub.execute_input":"2023-05-27T16:15:20.868661Z","iopub.status.idle":"2023-05-27T16:15:20.967227Z","shell.execute_reply.started":"2023-05-27T16:15:20.868633Z","shell.execute_reply":"2023-05-27T16:15:20.965997Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.mean((model.predict(X_val) - y_val)**2)**0.5","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:15:20.968749Z","iopub.execute_input":"2023-05-27T16:15:20.969283Z","iopub.status.idle":"2023-05-27T16:15:21.017484Z","shell.execute_reply.started":"2023-05-27T16:15:20.969244Z","shell.execute_reply":"2023-05-27T16:15:21.01637Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X_test = test.drop(['id', 'x_e_out'],axis=1)\nX_test[['author', 'geometry']] = X_test[['author', 'geometry']].fillna({'author': 'Thompson', 'geometry': 'tube'})\nX_test = X_test.fillna(fill_num)\nX_test.author = X_test.author.map(author_encoder)\nX_test.geometry = X_test.geometry.map(geometry_encoder)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:18:22.668076Z","iopub.execute_input":"2023-05-27T16:18:22.668606Z","iopub.status.idle":"2023-05-27T16:18:22.693132Z","shell.execute_reply.started":"2023-05-27T16:18:22.66856Z","shell.execute_reply":"2023-05-27T16:18:22.692268Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"model.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:18:23.49306Z","iopub.execute_input":"2023-05-27T16:18:23.493654Z","iopub.status.idle":"2023-05-27T16:18:23.568974Z","shell.execute_reply.started":"2023-05-27T16:18:23.493623Z","shell.execute_reply":"2023-05-27T16:18:23.567564Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submit = pd.read_csv('/kaggle/input/playground-series-s3e15/sample_submission.csv')","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:18:46.667326Z","iopub.execute_input":"2023-05-27T16:18:46.667777Z","iopub.status.idle":"2023-05-27T16:18:46.685403Z","shell.execute_reply.started":"2023-05-27T16:18:46.667745Z","shell.execute_reply":"2023-05-27T16:18:46.684085Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submit['x_e_out [-]'] = model.predict(X_test)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:19:06.559494Z","iopub.execute_input":"2023-05-27T16:19:06.559912Z","iopub.status.idle":"2023-05-27T16:19:06.634971Z","shell.execute_reply.started":"2023-05-27T16:19:06.559881Z","shell.execute_reply":"2023-05-27T16:19:06.63342Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"submit.to_csv('random_forest.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2023-05-27T16:19:39.538642Z","iopub.execute_input":"2023-05-27T16:19:39.539081Z","iopub.status.idle":"2023-05-27T16:19:39.595667Z","shell.execute_reply.started":"2023-05-27T16:19:39.539047Z","shell.execute_reply":"2023-05-27T16:19:39.59452Z"},"trusted":true},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/ps315_2/code/code.py b/Agent/workspace/hyperopt/ps315_2/code/code.py new file mode 100644 index 0000000..dbb8348 --- /dev/null +++ b/Agent/workspace/hyperopt/ps315_2/code/code.py @@ -0,0 +1,508 @@ +#!/usr/bin/env python +# coding: utf-8 + +# # πŸ“‚ Imports πŸ“‚ + +# imports +import pandas as pd +import numpy as np +import seaborn as sns +import matplotlib.pyplot as plt +# import optuna +from sklearn.preprocessing import StandardScaler, QuantileTransformer, LabelEncoder, OneHotEncoder +from sklearn.ensemble import RandomForestRegressor, VotingRegressor, StackingRegressor, BaggingRegressor +from sklearn.linear_model import LinearRegression, BayesianRidge +from sklearn.compose import ColumnTransformer + +from sklearn.impute import SimpleImputer, KNNImputer +from sklearn.base import BaseEstimator, TransformerMixin + +from xgboost import XGBRegressor, plot_importance +from catboost import CatBoostRegressor +from lightgbm import LGBMRegressor + +from imblearn.pipeline import Pipeline + +from itertools import combinations + + +# # πŸ“ˆ Exploratory Data Analysis πŸ“Š + +# ### Importing Data + a High-Level Look at the Data + +# import the data +FILE_PATH = "./workspace/hyperopt/ps315_2/data/" + +all_data = pd.read_csv(FILE_PATH+'data.csv.zip') + +# separate data into train and submission sets based on blank target values +train = all_data[all_data['x_e_out [-]'].isna() == False] +submission = all_data[all_data['x_e_out [-]'].isna() == True] + +# # get length of train and test datasets +# print(f'\nTrain dataset length: {train.shape[0]}') +# print(f'Submission dataset length: {submission.shape[0]}\n') + +# # check for missing values +# print(f'There are {int(train.isna().sum().sum())} missing feature values in the train set.') +# print(f'There are {int(submission.isna().sum().sum())} missing feature values in the submission set.\n') + +# # check for duplicate rows +# n_duplicate_rows = len(train) - len(train.drop_duplicates()) +# print(f'There are {int(n_duplicate_rows)} duplicate rows in the train dataset.\n') + +# # quick high-level overview of dataset +# pd.set_option('display.expand_frame_repr', False) # need this because there are so many features +# pd.set_option('display.max_columns', None) +# pd.set_option('display.max_rows', None) +# display(train.head()) +# print('\n\n') +# display(train.describe().round(decimals=2)) + + +# #### πŸ’‘Insights: First Glance +# - Train dataset is approximately twice as large as the submission dataset +# - The train dataset is missing a significant amount of data, averaging over one missing feature value per data point +# - The submission dataset is missing nearly twice as much data, averaging nearly two missing feature values per data point +# - There are no duplicate rows in the training dataset +# - D_h and chf_exp seem to have some significant outliers on the upper end + +# ### Renaming Featurse + Creating lists of columns by feature type + +# renaming columns to something more succinct and readable +column_renaming_dict = {'pressure [MPa]': 'pressure', + 'mass_flux [kg/m2-s]': 'mass_flux', + 'x_e_out [-]': 'x_e_out', + 'D_e [mm]': 'D_e', + 'D_h [mm]': 'D_h', + 'length [mm]': 'length', + 'chf_exp [MW/m2]': 'chf_exp'} + +train = train.rename(columns=column_renaming_dict) +submission = submission.rename(columns=column_renaming_dict) +# display(train.head()) + +# creating groups by feature type +features = {'continuous': ['pressure', 'mass_flux', 'D_e', 'D_h', 'length', 'chf_exp'], + 'categorical': ['author', 'geometry']} + + +# ### Checking target distribution + +# fix, ax = plt.subplots(figsize=(6, 6)) +# sns.kdeplot(data=train, x='x_e_out', fill=True, ax=ax).set_title('Target Distribution on Train Set'); +# ax = np.ravel(ax) +# ax[0].grid(visible=True) + + +# creating a log transformation of the target +transformed_target = np.power(10, train[['x_e_out']]) - 1 + +# plotting distribution +# fix, ax = plt.subplots(figsize=(6, 6)) +# sns.kdeplot(data=transformed_target, x='x_e_out', fill=True, ax=ax).set_title('Transformed Target Distribution on Train Set [10^x - 1]'); +# ax = np.ravel(ax) +# ax[0].grid(visible=True) + +# adding this to the dataframe +train['log_x_e_out'] = transformed_target + + +# #### πŸ’‘Insights: Target Distribution +# - The target distribution on the training data has some left skewness, but it does not seem too severe +# - Transforming the target with 10^x seems to make the distribution much more Gaussian. It may be worth looking into this to see if it helps the predictions + +# ### Checking Feature Distribution + +# plotting distribution of each continuous feature in train and test datasets +# fig, ax = plt.subplots(2, 3, figsize=(20, 10)) +# ax = np.ravel(ax) +# palette = sns.color_palette('coolwarm', 2) + +# for i, col in enumerate(features['continuous']): +# sns.kdeplot(data=train, x=train[col], ax=ax[i], label='Train', color=palette[0], fill=True) +# sns.kdeplot(data=submission, x=submission[col], ax=ax[i], label='Test', color=palette[1], fill=True) +# ax[i].set_title(f'{col}', fontsize=12) +# ax[i].legend(title='Dataset', loc='upper right', labels=['Train', 'Test']) + +# fig.suptitle('Continuous Feature Distributions (Train & Test)', fontsize=20); +# fig.tight_layout(pad=3) + + +# creating function to create a distribution histogram for each discrete value +# def create_dist_barplot(train_df, test_df, feature_name, ax): +# train_value_counts = pd.DataFrame(train_df.value_counts(feature_name, normalize=True)) +# train_value_counts['Distribution'] = ['Train'] * train_value_counts.shape[0] +# test_value_counts = pd.DataFrame(test_df.value_counts(feature_name, normalize=True)) +# test_value_counts['Distribution'] = ['Test'] * test_value_counts.shape[0] +# barplot_df = pd.concat([train_value_counts, test_value_counts], axis=0) +# barplot_df = barplot_df.rename(columns={'proportion': 'Density'}) +# barplot_df = barplot_df.reset_index() +# sns.barplot(data=barplot_df, x=feature_name, y='Density', hue='Distribution', ax=ax, palette='coolwarm') + +# # plotting distribution of each integer feature in train and test datasets +# fig, ax = plt.subplots(1, 2, figsize=(20, 8)) +# ax = np.ravel(ax) +# palette = sns.color_palette('coolwarm', 2) + +# for i, col in enumerate(features['categorical']): +# create_dist_barplot(train, submission, col, ax[i]) +# ax[i].set_title(f'{col}', fontsize=14) + +# fig.suptitle('Categorical Feature Distributions (Train & Test)', fontsize=20); +# fig.tight_layout(pad=1) + + +# #### πŸ’‘Insights: Feature Distributions: +# - The feature distributions between the train and test datasets seem to be extremely similar for both categorical and numerical features + +# adjusting distributions of skewed features +train['D_e'] = np.log1p(train['D_e']) +train['D_h'] = np.log1p(train['D_h']) +train['length'] = np.log1p(train['length']) +train['chf_exp'] = np.log1p(train['chf_exp']) + +submission['D_e'] = np.log1p(submission['D_e']) +submission['D_h'] = np.log1p(submission['D_h']) +submission['length'] = np.log1p(submission['length']) +submission['chf_exp'] = np.log1p(submission['chf_exp']) + +# plotting distribution of each continuous feature in train and test datasets +# fig, ax = plt.subplots(2, 3, figsize=(20, 10)) +# ax = np.ravel(ax) +# palette = sns.color_palette('coolwarm', 2) + +# for i, col in enumerate(features['continuous']): +# sns.kdeplot(data=train, x=train[col], ax=ax[i], label='Train', color=palette[0], fill=True) +# sns.kdeplot(data=submission, x=submission[col], ax=ax[i], label='Test', color=palette[1], fill=True) +# ax[i].set_title(f'{col}', fontsize=12) +# ax[i].legend(title='Dataset', loc='upper right', labels=['Train', 'Test']) + +# fig.suptitle('Continuous Feature Distributions (Train & Test)', fontsize=20); +# fig.tight_layout(pad=3) + + +# ### Examining Feature Correlation + +# calculating the raw correlation matrix +raw_correlation = train[features['continuous'] + ['x_e_out']].corr() + +# only keeping the lower diagonal +correlation = raw_correlation.copy() +mask = np.zeros_like(correlation, dtype=bool) +mask[np.triu_indices_from(mask)] = True +correlation[mask] = np.nan + +# plotting +fig, ax = plt.subplots(figsize=(10, 8)) +sns.heatmap(correlation, annot=True, cmap='coolwarm', xticklabels=True, yticklabels=True, ax=ax, vmin=-1, vmax=1).set_title('Correlation Matrix', fontsize=20); + + +# showing pairplot for continuous features +pairplot = sns.pairplot(data=train, vars=features['continuous'], diag_kind='kde'); +pairplot.fig.suptitle('Pairplot for Continuous Features on Train Data', y=1.03, fontsize=20); + + +# #### πŸ’‘Insights: Feature Correlation +# - After transforming some of the features, *D_e* and *D_h* are highly correlated. Before transformation, there weren't any highly correlated features +# - *D_h* is somewhat negatively correlated to pressure and positively correlated to *D_e* +# - *D_e* is also somewhat negatively correlated to pressure, which makes sense considering it is positively correlated with *D_h* +# - Looking at the pairplots, there is a strange correlation between *D_e* and *D_h*. They appear to have a perfect linear correlation with some random noise sprinkled in... + +# # πŸ“ Feature Engineering πŸ“ + +# ### Imputing missing categorical feature values and testing encoding technique + +# add missing labels using the mode for each the categories +train_imputed = train.copy(deep=True) +train_imputed['author'] = train_imputed['author'].replace(np.nan, 'Thompson') +train_imputed['geometry'] = train_imputed['geometry'].replace(np.nan, 'tube') + +submission_imputed = submission.copy(deep=True) +submission_imputed['author'] = submission_imputed['author'].replace(np.nan, 'Thompson') +submission_imputed['geometry'] = submission_imputed['geometry'].replace(np.nan, 'tube') + +# show the difference +# display(train.head()) +# display(train_imputed.head()) + +# create a categorical one-hot encoder +categorical_transformer = ColumnTransformer(transformers=[('onehot1', OneHotEncoder(sparse_output=False), ['author']), + ('onehot2', OneHotEncoder(sparse_output=False), ['geometry']), + ('passthrough', 'passthrough', features['continuous'])], + verbose_feature_names_out=False) +categorical_transformer.set_output(transform='pandas') + +# pass the data through the encoder +train_cat_onehot_test = categorical_transformer.fit_transform(train_imputed) +# display(train_cat_onehot_test.head()) + + +# ### Creating Linear Regression based Imputer for D_e and D_h since they are highly correlated + +# creating a custom transformer +class D_transformer(BaseEstimator, TransformerMixin): + def __init__(self): + self.D_e_regressor = LinearRegression() + self.D_h_regressor = LinearRegression() + + def _impute_D_e(self, row): + D_e = row['D_e'] + D_h = row['D_h'] + if np.isnan(D_e) and np.isnan(D_h)==False: + D_e = self.D_e_regressor.predict(np.reshape(np.array(D_h), (-1, 1))) + return float(D_e) + + def _impute_D_h(self, row): + D_e = row['D_e'] + D_h = row['D_h'] + if np.isnan(D_h) and np.isnan(D_e)==False: + D_h = self.D_h_regressor.predict(np.reshape(np.array(D_e), (-1, 1))) + return float(D_h) + + def fit(self, X, y=None): + # gathering D_e and D_h data where both are not NaN + complete_D_data = X[['D_h', 'D_e']] + filtered_D_data = complete_D_data[complete_D_data.isna().T.any() == False] + + D_e_array = np.reshape(np.array(filtered_D_data['D_e']), (-1, 1)) + D_h_array = np.reshape(np.array(filtered_D_data['D_h']), (-1, 1)) + + # fitting regressors for each based on complete data + self.D_e_regressor.fit(D_h_array, D_e_array) + self.D_h_regressor.fit(D_e_array, D_h_array) + + return self + + def transform(self, X, y=None): + X['D_e'] = X.apply(lambda row: self._impute_D_e(row), axis=1) + X['D_h'] = X.apply(lambda row: self._impute_D_h(row), axis=1) + + return X + + +# ### Splitting "train" data into Train and Test Sets + +target_name = 'x_e_out' +features_to_include = features['continuous'] + features['categorical'] +X_train = train_imputed[features_to_include] +y_train = train_imputed[target_name] + +# splitting training data into train and test sets +# X_train, X_test, y_train, y_test = train_test_split(train_imputed[features_to_include], +# train_imputed[target_name], +# train_size=0.80, +# shuffle=True, +# random_state=1) + +# print(f'Size of X_train: {X_train.shape}\nSize of y_train: {y_train.shape}') +# print(f'Size of X_test: {X_test.shape}\nSize of y_test: {y_test.shape}') + + +# ### Building a baseline model and assessing features + +# start with XGBoost +X_baseline = X_train +y_baseline = y_train + +# create a categorical one-hot encoder +categorical_transformer = ColumnTransformer(transformers=[('onehot1', OneHotEncoder(sparse_output=False), ['author']), + ('onehot2', OneHotEncoder(sparse_output=False), ['geometry']), + ('passthrough', 'passthrough', features['continuous'])], + verbose_feature_names_out=False) +categorical_transformer.set_output(transform='pandas') + +# create a regression imputer for D_h and D_e +D_imputer = D_transformer() + +# create an imputer +# simple_imputer = SimpleImputer(strategy='median', copy=True) +# simple_imputer.set_output(transform='pandas') + +# # create a baseline model to compare with +# baseline_model = XGBRegressor(gamma=0.04, reg_lambda=0.04) # quickly add in some regularization by trial and error to prevent extreme overfitting + +# # create the pipeline +# baseline_pipeline = Pipeline([('cat_transformer', categorical_transformer), +# ('D_imputer', D_imputer), +# ('imputer', simple_imputer), +# ('regressor', baseline_model)]) +# cv_results = cross_validate(baseline_pipeline, X_baseline, y_baseline, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True) +# mean_test_score = np.mean(cv_results['test_score']) +# train_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance + +# # print scoring metrics +# print('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3))) +# print('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3))) +# print(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}') +# print(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}') + + +# add a feature of random noise to help judge feature importances +X_baseline['random_noise'] = np.random.normal(size=X_baseline.shape[0]) + +# create a categorical one-hot encoder that includes random noise +categorical_transformer_noise = ColumnTransformer(transformers=[('onehot1', OneHotEncoder(sparse_output=False), ['author']), + ('onehot2', OneHotEncoder(sparse_output=False), ['geometry']), + ('passthrough', 'passthrough', features['continuous'] + ['random_noise'])], + verbose_feature_names_out=False) +categorical_transformer_noise.set_output(transform='pandas') + + + +# #### πŸ’‘Insights: Baseline Model +# - Imputing missing categorical features with the mode seemed to work well +# - Imputing missing continuous features with the median seemed to work well also +# - The baseline XGBoost score seemed to perform fairly well and had much less variance than expected +# - The most important feature by far seems to be *chf_exp*, which out of all features was highest correlated to the target. *pressure*, *D_e*, *D_h*, *length*, and some *author* one-hots follow. +# - A random noise feature was added to use as a reference for how useful features are. All of the continuous features were above this threshold, but some *author* one-hots and all of the *geometry* one-hots were below the threshold. +# - *geometry* may be a good candidate as a feature to drop +# - The ICE plots show some odd behavior with the *D_e* and *D_h* features and their effect on the target. +# - *chf_exp* and *pressure* seem to have a visible negative correlation with the target. *length* seems to have a less apparent positive correlation. This matches the Pearson coefficients calculated earlier + +# ### Testing alternative imputation techniques + +# create a categorical one-hot encoder +categorical_transformer = ColumnTransformer(transformers=[('onehot1', OneHotEncoder(sparse_output=False), ['author']), + ('onehot2', OneHotEncoder(sparse_output=False), ['geometry']), + ('passthrough', 'passthrough', features['continuous'])], + verbose_feature_names_out=False) +categorical_transformer.set_output(transform='pandas') + +# create an imputer +knn_imputer = KNNImputer(n_neighbors=3, weights='uniform', copy=True) +knn_imputer.set_output(transform='pandas') +# iterative_imputer = IterativeImputer(max_iter=10, imputation_order='descending') + +# create an imputer +imputer = SimpleImputer(strategy='median', copy=True) +imputer.set_output(transform='pandas') + + + +# create the XGBoost object +xgb_final_1 = XGBRegressor(n_estimators=267, + max_depth=7, + min_child_weight=5.93313, + gamma=0.002317, + learning_rate=0.034267, + subsample=0.60232, + colsample_bytree=0.62122, + reg_lambda=1.39154) + +# # create the pipeline + + +# create the XGBoost object +xgb_final_2 = XGBRegressor(n_estimators=245, + max_depth=7, + min_child_weight=5.88154, + gamma=0.0024124, + learning_rate=0.035098, + subsample=0.62636, + colsample_bytree=0.61926, + reg_lambda=1.30091) + +# # create the pipeline +# xgb_pipeline_2 = Pipeline([('cat_transformer', categorical_transformer), +# ('D_imputer', D_imputer), +# ('imputer', imputer), +# ('regressor', xgb_final_2)]) +# + +# create the LightGBM object +lgbm_final_1 = LGBMRegressor(n_estimators=176, + learning_rate=0.031217, + num_leaves=2681, + max_depth=11, + min_child_weight=0.03876, + min_child_samples=47, + subsample=0.61635, + colsample_bytree=0.510339, + reg_lambda=0.0082346) + +# # create the pipeline + +# create the LightGBM object +lgbm_final_2 = LGBMRegressor(n_estimators=223, + learning_rate=0.029477, + num_leaves=2618, + max_depth=10, + min_child_weight=0.028960, + min_child_samples=49, + subsample=0.63466, + colsample_bytree=0.52098, + reg_lambda=0.007196) + +# # create the pipeline + +# ### Creating an ensemble model +from sklearn.metrics import mean_squared_error +# creating a voting ensemble from the models +voting_model = VotingRegressor(estimators=[('xgb_1', xgb_final_1), ('xgb_2', xgb_final_2), ('lgbm_1', lgbm_final_1), ('lgbm_2', lgbm_final_2)]) +voting_pipeline = Pipeline([('cat_transformer', categorical_transformer), + ('D_imputer', D_imputer), + ('imputer', imputer), + ('regressor', voting_model)]) + +# # begin cross-validation +# cv_results = cross_validate(voting_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True) +# mean_test_score = np.mean(cv_results['test_score']) +# train_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance + +# # print scoring metrics +# print('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3))) +# print('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3))) +# print(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}') +# print(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}') + +# # fit the pipeline to the training data and report score on test data +# voting_pipeline.fit(X_train, y_train) +# y_test_pred = voting_pipeline.predict(X_test) +# model_score = mean_squared_error(y_test, y_test_pred, squared=False) +# print(f'Test Data Score: {np.round(model_score, decimals=5)}') + + +# # creating a stacked ensemble from the models +# stacked_model = StackingRegressor(estimators=[('xgb_1', xgb_final_1), ('xgb_2', xgb_final_2), ('lgbm_1', lgbm_final_1), ('lgbm_2', lgbm_final_2)], final_estimator=BayesianRidge()) +# stacked_pipeline = Pipeline([('cat_transformer', categorical_transformer), +# ('D_imputer', D_imputer), +# ('imputer', imputer), +# ('regressor', stacked_model)]) + +# begin cross-validation +# cv_results = cross_validate(stacked_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True) +# mean_test_score = np.mean(cv_results['test_score']) +# train_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance + +# # print scoring metrics +# print('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3))) +# print('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3))) +# print(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}') +# print(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}') + +# fit the pipeline to the training data and report score on test data +# stacked_pipeline.fit(X_train, y_train) +# y_test_pred = stacked_pipeline.predict(X_test) +# model_score = mean_squared_error(y_test, y_test_pred, squared=False) +# print(f'Test Data Score: {np.round(model_score, decimals=5)}') + + +# # # πŸ“¦ Submission πŸ“¦ + +# # ### Make Predictions + +# # make predictions +# X_submission = submission_imputed[features['continuous'] + features['categorical']] +# y_submission_pred = voting_pipeline.predict(X_submission) + +# # formatting predictions for submission file output +# submission_df = pd.DataFrame({'id': submission_imputed['id'], 'x_e_out [-]': y_submission_pred}) +# display(submission_df.head()) + +# # saving predictions to .csv file for submission +# submission_df.to_csv('submission.csv', header=True, index=False) + + + + diff --git a/Agent/workspace/hyperopt/ps315_2/code/s3-e15-eda-w-imputation-xgb-lgbm.ipynb b/Agent/workspace/hyperopt/ps315_2/code/s3-e15-eda-w-imputation-xgb-lgbm.ipynb new file mode 100644 index 0000000..3eb0ca6 --- /dev/null +++ b/Agent/workspace/hyperopt/ps315_2/code/s3-e15-eda-w-imputation-xgb-lgbm.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":51982,"databundleVersionId":5760919,"sourceType":"competition"}],"dockerImageVersionId":30474,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"# πŸ“‚ Imports πŸ“‚","metadata":{}},{"cell_type":"code","source":"# imports\nimport pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport optuna\n\nfrom sklearn.model_selection import train_test_split, cross_validate, GridSearchCV, StratifiedKFold\nfrom sklearn.preprocessing import StandardScaler, QuantileTransformer, LabelEncoder, OneHotEncoder\nfrom sklearn.ensemble import RandomForestRegressor, VotingRegressor, StackingRegressor, BaggingRegressor\nfrom sklearn.linear_model import LinearRegression, BayesianRidge\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.metrics import mean_squared_error, make_scorer\nfrom sklearn.decomposition import PCA\nfrom sklearn.pipeline import FeatureUnion\nfrom sklearn.inspection import PartialDependenceDisplay\nfrom sklearn.experimental import enable_iterative_imputer\nfrom sklearn.impute import SimpleImputer, KNNImputer, IterativeImputer\nfrom sklearn.base import BaseEstimator, TransformerMixin\n\nfrom xgboost import XGBRegressor, plot_importance\nfrom catboost import CatBoostRegressor\nfrom lightgbm import LGBMRegressor\n\nfrom imblearn.pipeline import Pipeline\n\nfrom itertools import combinations","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# πŸ“ˆ Exploratory Data Analysis πŸ“Š","metadata":{}},{"cell_type":"markdown","source":"### Importing Data + a High-Level Look at the Data","metadata":{}},{"cell_type":"code","source":"# import the data\nall_data = pd.read_csv('data.csv')\n\n# separate data into train and submission sets based on blank target values\ntrain = all_data[all_data['x_e_out [-]'].isna() == False]\nsubmission = all_data[all_data['x_e_out [-]'].isna() == True]\n\n# get length of train and test datasets\nprint(f'\\nTrain dataset length: {train.shape[0]}')\nprint(f'Submission dataset length: {submission.shape[0]}\\n')\n\n# check for missing values\nprint(f'There are {int(train.isna().sum().sum())} missing feature values in the train set.')\nprint(f'There are {int(submission.isna().sum().sum())} missing feature values in the submission set.\\n')\n\n# check for duplicate rows\nn_duplicate_rows = len(train) - len(train.drop_duplicates())\nprint(f'There are {int(n_duplicate_rows)} duplicate rows in the train dataset.\\n')\n\n# quick high-level overview of dataset\npd.set_option('display.expand_frame_repr', False) # need this because there are so many features\npd.set_option('display.max_columns', None)\npd.set_option('display.max_rows', None)\ndisplay(train.head())\nprint('\\n\\n')\ndisplay(train.describe().round(decimals=2))","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### πŸ’‘Insights: First Glance\n- Train dataset is approximately twice as large as the submission dataset\n- The train dataset is missing a significant amount of data, averaging over one missing feature value per data point\n- The submission dataset is missing nearly twice as much data, averaging nearly two missing feature values per data point\n- There are no duplicate rows in the training dataset\n- D_h and chf_exp seem to have some significant outliers on the upper end","metadata":{}},{"cell_type":"markdown","source":"### Renaming Featurse + Creating lists of columns by feature type","metadata":{}},{"cell_type":"code","source":"# renaming columns to something more succinct and readable\ncolumn_renaming_dict = {'pressure [MPa]': 'pressure',\n 'mass_flux [kg/m2-s]': 'mass_flux',\n 'x_e_out [-]': 'x_e_out',\n 'D_e [mm]': 'D_e',\n 'D_h [mm]': 'D_h',\n 'length [mm]': 'length',\n 'chf_exp [MW/m2]': 'chf_exp'}\n\ntrain = train.rename(columns=column_renaming_dict)\nsubmission = submission.rename(columns=column_renaming_dict)\ndisplay(train.head())\n \n# creating groups by feature type\nfeatures = {'continuous': ['pressure', 'mass_flux', 'D_e', 'D_h', 'length', 'chf_exp'],\n 'categorical': ['author', 'geometry']}","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Checking target distribution","metadata":{}},{"cell_type":"code","source":"fix, ax = plt.subplots(figsize=(6, 6))\nsns.kdeplot(data=train, x='x_e_out', fill=True, ax=ax).set_title('Target Distribution on Train Set');\nax = np.ravel(ax)\nax[0].grid(visible=True)","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# creating a log transformation of the target\ntransformed_target = np.power(10, train[['x_e_out']]) - 1\n\n# plotting distribution\nfix, ax = plt.subplots(figsize=(6, 6))\nsns.kdeplot(data=transformed_target, x='x_e_out', fill=True, ax=ax).set_title('Transformed Target Distribution on Train Set [10^x - 1]');\nax = np.ravel(ax)\nax[0].grid(visible=True)\n\n# adding this to the dataframe\ntrain['log_x_e_out'] = transformed_target","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### πŸ’‘Insights: Target Distribution\n- The target distribution on the training data has some left skewness, but it does not seem too severe\n- Transforming the target with 10^x seems to make the distribution much more Gaussian. It may be worth looking into this to see if it helps the predictions","metadata":{}},{"cell_type":"markdown","source":"### Checking Feature Distribution","metadata":{}},{"cell_type":"code","source":"# plotting distribution of each continuous feature in train and test datasets\nfig, ax = plt.subplots(2, 3, figsize=(20, 10))\nax = np.ravel(ax)\npalette = sns.color_palette('coolwarm', 2)\n\nfor i, col in enumerate(features['continuous']):\n sns.kdeplot(data=train, x=train[col], ax=ax[i], label='Train', color=palette[0], fill=True)\n sns.kdeplot(data=submission, x=submission[col], ax=ax[i], label='Test', color=palette[1], fill=True)\n ax[i].set_title(f'{col}', fontsize=12)\n ax[i].legend(title='Dataset', loc='upper right', labels=['Train', 'Test'])\n \nfig.suptitle('Continuous Feature Distributions (Train & Test)', fontsize=20);\nfig.tight_layout(pad=3)","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# creating function to create a distribution histogram for each discrete value\ndef create_dist_barplot(train_df, test_df, feature_name, ax):\n train_value_counts = pd.DataFrame(train_df.value_counts(feature_name, normalize=True))\n train_value_counts['Distribution'] = ['Train'] * train_value_counts.shape[0]\n test_value_counts = pd.DataFrame(test_df.value_counts(feature_name, normalize=True))\n test_value_counts['Distribution'] = ['Test'] * test_value_counts.shape[0]\n barplot_df = pd.concat([train_value_counts, test_value_counts], axis=0)\n barplot_df = barplot_df.rename(columns={'proportion': 'Density'})\n barplot_df = barplot_df.reset_index()\n sns.barplot(data=barplot_df, x=feature_name, y='Density', hue='Distribution', ax=ax, palette='coolwarm')\n\n# plotting distribution of each integer feature in train and test datasets\nfig, ax = plt.subplots(1, 2, figsize=(20, 8))\nax = np.ravel(ax)\npalette = sns.color_palette('coolwarm', 2)\n\nfor i, col in enumerate(features['categorical']):\n create_dist_barplot(train, submission, col, ax[i])\n ax[i].set_title(f'{col}', fontsize=14)\n \nfig.suptitle('Categorical Feature Distributions (Train & Test)', fontsize=20);\nfig.tight_layout(pad=1)","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### πŸ’‘Insights: Feature Distributions:\n- The feature distributions between the train and test datasets seem to be extremely similar for both categorical and numerical features","metadata":{}},{"cell_type":"code","source":"# adjusting distributions of skewed features\ntrain['D_e'] = np.log1p(train['D_e'])\ntrain['D_h'] = np.log1p(train['D_h'])\ntrain['length'] = np.log1p(train['length'])\ntrain['chf_exp'] = np.log1p(train['chf_exp'])\n\nsubmission['D_e'] = np.log1p(submission['D_e'])\nsubmission['D_h'] = np.log1p(submission['D_h'])\nsubmission['length'] = np.log1p(submission['length'])\nsubmission['chf_exp'] = np.log1p(submission['chf_exp'])\n\n# plotting distribution of each continuous feature in train and test datasets\nfig, ax = plt.subplots(2, 3, figsize=(20, 10))\nax = np.ravel(ax)\npalette = sns.color_palette('coolwarm', 2)\n\nfor i, col in enumerate(features['continuous']):\n sns.kdeplot(data=train, x=train[col], ax=ax[i], label='Train', color=palette[0], fill=True)\n sns.kdeplot(data=submission, x=submission[col], ax=ax[i], label='Test', color=palette[1], fill=True)\n ax[i].set_title(f'{col}', fontsize=12)\n ax[i].legend(title='Dataset', loc='upper right', labels=['Train', 'Test'])\n \nfig.suptitle('Continuous Feature Distributions (Train & Test)', fontsize=20);\nfig.tight_layout(pad=3)","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Examining Feature Correlation","metadata":{"tags":[]}},{"cell_type":"code","source":"# calculating the raw correlation matrix\nraw_correlation = train[features['continuous'] + ['x_e_out']].corr()\n\n# only keeping the lower diagonal\ncorrelation = raw_correlation.copy()\nmask = np.zeros_like(correlation, dtype=bool)\nmask[np.triu_indices_from(mask)] = True\ncorrelation[mask] = np.nan\n\n# plotting\nfig, ax = plt.subplots(figsize=(10, 8))\nsns.heatmap(correlation, annot=True, cmap='coolwarm', xticklabels=True, yticklabels=True, ax=ax, vmin=-1, vmax=1).set_title('Correlation Matrix', fontsize=20);","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# showing pairplot for continuous features\npairplot = sns.pairplot(data=train, vars=features['continuous'], diag_kind='kde');\npairplot.fig.suptitle('Pairplot for Continuous Features on Train Data', y=1.03, fontsize=20);","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### πŸ’‘Insights: Feature Correlation\n- After transforming some of the features, *D_e* and *D_h* are highly correlated. Before transformation, there weren't any highly correlated features\n- *D_h* is somewhat negatively correlated to pressure and positively correlated to *D_e*\n- *D_e* is also somewhat negatively correlated to pressure, which makes sense considering it is positively correlated with *D_h*\n- Looking at the pairplots, there is a strange correlation between *D_e* and *D_h*. They appear to have a perfect linear correlation with some random noise sprinkled in...","metadata":{}},{"cell_type":"markdown","source":"# πŸ“ Feature Engineering πŸ“","metadata":{}},{"cell_type":"markdown","source":"### Imputing missing categorical feature values and testing encoding technique","metadata":{}},{"cell_type":"code","source":"# add missing labels using the mode for each the categories\ntrain_imputed = train.copy(deep=True)\ntrain_imputed['author'] = train_imputed['author'].replace(np.nan, 'Thompson')\ntrain_imputed['geometry'] = train_imputed['geometry'].replace(np.nan, 'tube')\n\nsubmission_imputed = submission.copy(deep=True)\nsubmission_imputed['author'] = submission_imputed['author'].replace(np.nan, 'Thompson')\nsubmission_imputed['geometry'] = submission_imputed['geometry'].replace(np.nan, 'tube')\n\n# show the difference\ndisplay(train.head())\ndisplay(train_imputed.head())\n\n# create a categorical one-hot encoder\ncategorical_transformer = ColumnTransformer(transformers=[('onehot1', OneHotEncoder(sparse_output=False), ['author']),\n ('onehot2', OneHotEncoder(sparse_output=False), ['geometry']),\n ('passthrough', 'passthrough', features['continuous'])],\n verbose_feature_names_out=False)\ncategorical_transformer.set_output(transform='pandas')\n\n# pass the data through the encoder\ntrain_cat_onehot_test = categorical_transformer.fit_transform(train_imputed)\ndisplay(train_cat_onehot_test.head())","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Creating Linear Regression based Imputer for D_e and D_h since they are highly correlated","metadata":{}},{"cell_type":"code","source":"# creating a custom transformer\nclass D_transformer(BaseEstimator, TransformerMixin):\n def __init__(self):\n self.D_e_regressor = LinearRegression()\n self.D_h_regressor = LinearRegression()\n \n def _impute_D_e(self, row):\n D_e = row['D_e']\n D_h = row['D_h']\n if np.isnan(D_e) and np.isnan(D_h)==False:\n D_e = self.D_e_regressor.predict(np.reshape(np.array(D_h), (-1, 1)))\n return float(D_e)\n\n def _impute_D_h(self, row):\n D_e = row['D_e']\n D_h = row['D_h']\n if np.isnan(D_h) and np.isnan(D_e)==False:\n D_h = self.D_h_regressor.predict(np.reshape(np.array(D_e), (-1, 1)))\n return float(D_h)\n \n def fit(self, X, y=None):\n # gathering D_e and D_h data where both are not NaN\n complete_D_data = X[['D_h', 'D_e']]\n filtered_D_data = complete_D_data[complete_D_data.isna().T.any() == False]\n\n D_e_array = np.reshape(np.array(filtered_D_data['D_e']), (-1, 1))\n D_h_array = np.reshape(np.array(filtered_D_data['D_h']), (-1, 1))\n\n # fitting regressors for each based on complete data\n self.D_e_regressor.fit(D_h_array, D_e_array)\n self.D_h_regressor.fit(D_e_array, D_h_array)\n \n return self\n \n def transform(self, X, y=None):\n X['D_e'] = X.apply(lambda row: self._impute_D_e(row), axis=1)\n X['D_h'] = X.apply(lambda row: self._impute_D_h(row), axis=1)\n \n return X","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Splitting \"train\" data into Train and Test Sets","metadata":{}},{"cell_type":"code","source":"target_name = 'x_e_out'\nfeatures_to_include = features['continuous'] + features['categorical']\n\n# splitting training data into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(train_imputed[features_to_include],\n train_imputed[target_name],\n train_size=0.80,\n shuffle=True,\n random_state=1)\n\nprint(f'Size of X_train: {X_train.shape}\\nSize of y_train: {y_train.shape}')\nprint(f'Size of X_test: {X_test.shape}\\nSize of y_test: {y_test.shape}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Building a baseline model and assessing features","metadata":{}},{"cell_type":"code","source":"# start with XGBoost\nX_baseline = X_train\ny_baseline = y_train\n\n# create a categorical one-hot encoder\ncategorical_transformer = ColumnTransformer(transformers=[('onehot1', OneHotEncoder(sparse_output=False), ['author']),\n ('onehot2', OneHotEncoder(sparse_output=False), ['geometry']),\n ('passthrough', 'passthrough', features['continuous'])],\n verbose_feature_names_out=False)\ncategorical_transformer.set_output(transform='pandas')\n\n# create a regression imputer for D_h and D_e\nD_imputer = D_transformer()\n\n# create an imputer\nsimple_imputer = SimpleImputer(strategy='median', copy=True)\nsimple_imputer.set_output(transform='pandas')\n\n# create a baseline model to compare with\nbaseline_model = XGBRegressor(gamma=0.04, reg_lambda=0.04) # quickly add in some regularization by trial and error to prevent extreme overfitting\n\n# create the pipeline\nbaseline_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', simple_imputer),\n ('regressor', baseline_model)])\ncv_results = cross_validate(baseline_pipeline, X_baseline, y_baseline, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# add a feature of random noise to help judge feature importances\nX_baseline['random_noise'] = np.random.normal(size=X_baseline.shape[0])\n\n# create a categorical one-hot encoder that includes random noise\ncategorical_transformer_noise = ColumnTransformer(transformers=[('onehot1', OneHotEncoder(sparse_output=False), ['author']),\n ('onehot2', OneHotEncoder(sparse_output=False), ['geometry']),\n ('passthrough', 'passthrough', features['continuous'] + ['random_noise'])],\n verbose_feature_names_out=False)\ncategorical_transformer_noise.set_output(transform='pandas')\n\n# create a baseline model to compare with\nfeature_importance_model = XGBRegressor(gamma=0.04, reg_lambda=0.04) # quickly add in some regularization by trial and error to prevent extreme overfitting\n\n# create the pipeline\nfeature_importance_pipeline = Pipeline([('cat_transformer', categorical_transformer_noise),\n ('imputer', simple_imputer),\n ('regressor', feature_importance_model)])\n\n# plotting feature importances\nfig, ax = plt.subplots(figsize=(10, 7))\nfeature_importance_pipeline.fit(X_baseline, y_baseline)\nplot_importance(feature_importance_model, ax=ax, importance_type='gain', show_values=False, xlabel='Gain', max_num_features=30);","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create a partial dependence plot figure\nn_cols = 3\nn_rows = int(np.ceil(len(features['continuous']) / 3))\nfig, ax = plt.subplots(n_rows, n_cols, figsize=(20, 5 * n_rows))\nax = np.ravel(ax)\nfor i in range(len(ax)): # hiding any unused axes\n if i >= len(features_to_include):\n ax[i].set_visible(False)\nax = ax[0:len(features_to_include)]\n\n# transforming data\npdp_plot_data = categorical_transformer.fit_transform(X_train)\npdp_plot_data = simple_imputer.fit_transform(pdp_plot_data)\n\n# adding title\nfig.suptitle('Individual Conditional Expectation Plots for Features', fontsize=20);\nfig.tight_layout(pad=3)\n\n# plot PDP's and ICE's\nbaseline_pipeline.fit(X_baseline, y_baseline)\nPartialDependenceDisplay.from_estimator(baseline_model, pdp_plot_data, features['continuous'], n_cols=n_cols, kind='both', subsample=100, ax=ax)\n\n# adjusting y-axis values\nfor axis in ax:\n axis.set_ylim([-0.15, 0.15])","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### πŸ’‘Insights: Baseline Model\n- Imputing missing categorical features with the mode seemed to work well\n- Imputing missing continuous features with the median seemed to work well also\n- The baseline XGBoost score seemed to perform fairly well and had much less variance than expected\n- The most important feature by far seems to be *chf_exp*, which out of all features was highest correlated to the target. *pressure*, *D_e*, *D_h*, *length*, and some *author* one-hots follow.\n- A random noise feature was added to use as a reference for how useful features are. All of the continuous features were above this threshold, but some *author* one-hots and all of the *geometry* one-hots were below the threshold.\n- *geometry* may be a good candidate as a feature to drop\n- The ICE plots show some odd behavior with the *D_e* and *D_h* features and their effect on the target.\n- *chf_exp* and *pressure* seem to have a visible negative correlation with the target. *length* seems to have a less apparent positive correlation. This matches the Pearson coefficients calculated earlier","metadata":{}},{"cell_type":"markdown","source":"### Testing alternative imputation techniques","metadata":{}},{"cell_type":"code","source":"# create a categorical one-hot encoder\ncategorical_transformer = ColumnTransformer(transformers=[('onehot1', OneHotEncoder(sparse_output=False), ['author']),\n ('onehot2', OneHotEncoder(sparse_output=False), ['geometry']),\n ('passthrough', 'passthrough', features['continuous'])],\n verbose_feature_names_out=False)\ncategorical_transformer.set_output(transform='pandas')\n\n# create an imputer\nknn_imputer = KNNImputer(n_neighbors=3, weights='uniform', copy=True)\nknn_imputer.set_output(transform='pandas')\niterative_imputer = IterativeImputer(max_iter=10, imputation_order='descending')\niterative_imputer.set_output(transform='pandas')\n\n# create a baseline model to compare with\nimpute_trial_model = XGBRegressor(gamma=0.04, reg_lambda=0.04) # quickly add in some regularization by trial and error to prevent extreme overfitting\n\n# create the pipeline\nimpute_trial_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', knn_imputer),\n ('regressor', impute_trial_model)])\ncv_results = cross_validate(impute_trial_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# trying XGBoost native missing values functionality\nimpute_trial_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('regressor', impute_trial_model)])\ncv_results = cross_validate(impute_trial_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### πŸ’‘Insights: Alternative Imputation Techniques\n- KNN Imputing seems to result in better performance for some values of K. The training takes significantly longer, though (on the order of 5-10 times as long). Running this through a hyperparameter optimizer may be problematic.\n- Iterative Imputing does not improve results after some experimentation with hyperparameters\n- XGBoost native functionality for missing values performed better than the baseline with statistical imputation techniques","metadata":{}},{"cell_type":"markdown","source":"### Exploring effect of dropping feature","metadata":{}},{"cell_type":"code","source":"# dropping any features that are not needed\nfeatures_to_exclude = ['geometry']\nall_features = features['continuous'] + features['categorical']\nfeatures_to_include = list(set(all_features) - set(features_to_exclude))\n\n# splitting training data into train and test sets\nX_train_drop, X_test_drop, y_train_drop, y_test_drop = train_test_split(train[features_to_include],\n train[target_name],\n train_size=0.80,\n shuffle=True,\n random_state=1)\n\nprint(f'Size of X_train: {X_train.shape}\\nSize of y_train: {y_train.shape}')\nprint(f'Size of X_test: {X_test.shape}\\nSize of y_test: {y_test.shape}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create a categorical one-hot encoder\ncategorical_transformer_drop = ColumnTransformer(transformers=[('onehot1', OneHotEncoder(sparse_output=False), ['author']),\n ('passthrough', 'passthrough', features['continuous'])],\n verbose_feature_names_out=False)\ncategorical_transformer_drop.set_output(transform='pandas')\n\n# create a baseline model to compare with\ndrop_model = XGBRegressor(gamma=0.04, reg_lambda=0.04) # quickly add in some regularization by trial and error to prevent extreme overfitting\n\n# create the pipeline\ndrop_pipeline = Pipeline([('cat_transformer', categorical_transformer_drop),\n ('D_imputer', D_imputer),\n ('imputer', simple_imputer),\n ('regressor', drop_model)])\ncv_results = cross_validate(drop_pipeline, X_train_drop, y_train_drop, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# plotting feature importances\nfig, ax = plt.subplots(figsize=(10, 7))\ndrop_pipeline.fit(X_train_drop, y_train_drop)\nplot_importance(drop_model, ax=ax, importance_type='gain', show_values=False, xlabel='Gain', max_num_features=30);","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### πŸ’‘Insights: Feature Refinement\n- Removing *geometry* feature slightly improved both score and variance. It is likely beneficial to completely remove this feature","metadata":{}},{"cell_type":"markdown","source":"### Trial to explore the results of using a transformed target","metadata":{}},{"cell_type":"code","source":"# transforming the target\ntransformed_target = np.power(10, train['x_e_out']) - 1\n\n# dropping any features that are not needed\nfeatures_to_include = features['continuous'] + features['categorical']\n\n# splitting training data into train and test sets\nX_train_trans, X_test_trans, y_train_trans, y_test_trans = train_test_split(train_imputed[features_to_include],\n transformed_target,\n train_size=0.80,\n shuffle=True,\n random_state=1)\n\nprint(f'Size of X_train: {X_train.shape}\\nSize of y_train: {y_train.shape}')\nprint(f'Size of X_test: {X_test.shape}\\nSize of y_test: {y_test.shape}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# creating a function for the scoring metric (not an sklearn out-of-the-box metric)\ndef transformed_root_mean_squared_error(y_true, y_pred):\n y_true_rev_trans = np.log10(y_true + 1)\n y_pred_rev_trans = np.log10(y_pred + 1)\n score = mean_squared_error(y_true_rev_trans, y_pred_rev_trans, squared=False)\n \n return score\n\n# create a scorer object to use in sklearn functions\ntransformed_rmse = make_scorer(score_func=transformed_root_mean_squared_error, greater_is_better=False)","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create a categorical one-hot encoder\ncategorical_transformer = ColumnTransformer(transformers=[('onehot1', OneHotEncoder(sparse_output=False), ['author']),\n ('onehot2', OneHotEncoder(sparse_output=False), ['geometry']),\n ('passthrough', 'passthrough', features['continuous'])],\n verbose_feature_names_out=False)\ncategorical_transformer.set_output(transform='pandas')\n\n# create an imputer\nimputer = SimpleImputer(strategy='median', copy=True)\nimputer.set_output(transform='pandas')\n\n# create a baseline model to compare with\ntrans_model = XGBRegressor(gamma=0.07, reg_lambda=0.07) # quickly add in some regularization by trial and error to prevent extreme overfitting\n\n# create the pipeline\ntrans_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', simple_imputer),\n ('regressor', trans_model)])\ncv_results = cross_validate(trans_pipeline, X_train_trans, y_train_trans, cv=10, scoring=transformed_rmse, return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### πŸ’‘Insights: Transformed Target\n- Transforming the target did seem to help the score slightly, although it was marginal and the regularization parameters had to be adjusted for the score to improve.\n- Might try investigating later","metadata":{}},{"cell_type":"markdown","source":"### Training CatBoost, Random Forest, and LightGBM models with baseline imputer to see how the performance compares","metadata":{}},{"cell_type":"code","source":"# resetting the categorical_transformer and imputer (getting rid of random noise feature and geometry)\ncategorical_transformer = ColumnTransformer(transformers=[('onehot1', OneHotEncoder(sparse_output=False), ['author']),\n ('passthrough', 'passthrough', features['continuous'])],\n verbose_feature_names_out=False);\ncategorical_transformer.set_output(transform='pandas');","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create a catboost model to compare with\ncb_model = CatBoostRegressor(silent=True, allow_writing_files=False)\n\n# create the pipeline\ncb_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', simple_imputer),\n ('regressor', cb_model)])\n\ncv_results = cross_validate(cb_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create a random forest model to compare with\nrf_model = RandomForestRegressor()\n\n# create the pipeline\nrf_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', simple_imputer),\n ('regressor', rf_model)])\n\ncv_results = cross_validate(rf_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create a LightGBM to compare with\nlgbm_model = LGBMRegressor()\n\n# create the pipeline\nlgbm_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', simple_imputer),\n ('regressor', lgbm_model)])\n\ncv_results = cross_validate(lgbm_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### πŸ’‘Insights: Alternative Models\n- With default parameters, the CatBoost model had a better score and a comparable variance to the baseline model\n- With default parameters, the Random Forest model had much worse score, but a lower variance than the baseline model\n- With default parameters, the LightGBM model had an excellent score and a slightly worse variance than the baseline model\n- All of these models are good contenders for the final model, and could be used for an ensemble. Random forest might not make the cut","metadata":{}},{"cell_type":"markdown","source":"### Training CatBoost and LightGBM models with native functionality for missing values to see how the performance compares","metadata":{}},{"cell_type":"code","source":"# create the pipeline\ncb_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('regressor', cb_model)])\n\ncv_results = cross_validate(cb_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create the pipeline\nlgbm_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('regressor', lgbm_model)])\n\ncv_results = cross_validate(lgbm_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"#### πŸ’‘Insights: Native Functionality for Missing Values\n- The CatBoost model had a comparable score and a better variance than the simple imputation model\n- The LightGBM model had a slightly better score and a better variance than the simple imputation model\n- Both of these boosting models should use their native functionality as it improves their performance","metadata":{}},{"cell_type":"markdown","source":"# πŸ”§ Building and Optimizing an ML Model πŸ”¨","metadata":{}},{"cell_type":"markdown","source":"### Optimizing Hyperparameters with Bayesian Optimization","metadata":{}},{"cell_type":"code","source":"# optimize XGBoost hyperparameters with optuna\ndef objective(trial):\n # create the regressor object\n regressor = XGBRegressor(n_estimators=trial.suggest_int('n_estimators', 70, 300),\n max_depth=trial.suggest_int('max_depth', 2, 8),\n min_child_weight=trial.suggest_float('min_child_weight', 0, 6),\n gamma=trial.suggest_float('gamma', 0.001, 6, log=True),\n learning_rate=trial.suggest_float('learning_rate', 0.001, 0.3, log=True),\n subsample=trial.suggest_float('subsample', 0.50, 1),\n colsample_bytree=trial.suggest_float('colsample_bytree', 0.5, 1),\n reg_lambda=trial.suggest_float('reg_lambda', 0.001, 5, log=True))\n\n # create lists to store scores\n train_scores = []\n test_scores = []\n \n # create the pipeline\n cv_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', simple_imputer),\n ('regressor', regressor)])\n \n # begin cross-validation\n cv_results = cross_validate(cv_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\n mean_test_score = np.mean(cv_results['test_score'])\n train_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n return mean_test_score\n\n# begin optimization\noptuna.logging.set_verbosity(optuna.logging.WARNING) # won't print progress for every single trial\ncv_study = optuna.create_study(directions=['maximize'])\ncv_study.optimize(objective, n_trials=20)\n \n# get the n best trials\nn = 10\nstudy_results_zipped = [(t.values[0], t.params) for t in cv_study.get_trials()]\nordered_study_results = sorted(study_results_zipped, key=lambda x: x[0], reverse=True)\nfor i, t in enumerate(ordered_study_results):\n if i < n:\n print(f'Trial {i} Results:')\n print(f'Mean Test Score: {np.round(t[0], decimals=5)}')\n print(t[1])\n print('\\n')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# optimize CatBoost hyperparameters with optuna\ndef objective(trial):\n # create the regressor object\n regressor = CatBoostRegressor(iterations=trial.suggest_int('iterations', 100, 300),\n depth=trial.suggest_int('depth', 4, 10),\n l2_leaf_reg=trial.suggest_float('l2_leaf_reg', 0.001, 0.1, log=True),\n random_strength=trial.suggest_float('random_strength', 0.0001, 1, log=True),\n bagging_temperature=trial.suggest_float('bagging_temperature', 0, 1),\n min_data_in_leaf=trial.suggest_int('min_data_in_leaf', 1, 100),\n silent=True,\n allow_writing_files=False)\n\n # create lists to store scores\n train_scores = []\n test_scores = []\n \n # create imputer for catboost\n cb_imputer = ColumnTransformer(transformers=[('passthrough', 'passthrough', ['author']),\n ('imputer', simple_imputer, features['continuous'])],\n verbose_feature_names_out=False);\n cb_imputer.set_output(transform='pandas');\n\n # create the pipeline\n cv_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', simple_imputer),\n ('regressor', regressor)])\n \n # begin cross-validation\n cv_results = cross_validate(cv_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\n mean_test_score = np.mean(cv_results['test_score'])\n train_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n return mean_test_score\n\n# begin optimization\noptuna.logging.set_verbosity(optuna.logging.WARNING) # won't print progress for every single trial\ncv_study = optuna.create_study(directions=['maximize'])\ncv_study.optimize(objective, n_trials=20)\n\n# get the n best trials\nn = 10\nstudy_results_zipped = [(t.values[0], t.params) for t in cv_study.get_trials()]\nordered_study_results = sorted(study_results_zipped, key=lambda x: x[0], reverse=True)\nfor i, t in enumerate(ordered_study_results):\n if i < n:\n print(f'Trial {i} Results:')\n print(f'Mean Test Score: {np.round(t[0], decimals=5)}')\n print(t[1])\n print('\\n')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# optimize Random Forest hyperparameters with optuna\ndef objective(trial):\n # create the regressor object\n regressor = RandomForestRegressor(n_estimators=trial.suggest_int('n_estimators', 60, 250),\n max_depth=trial.suggest_categorical('max_depth', [10, 12, 15, 18, 20, 30, 40, 50, 70, 100, None]),\n min_samples_leaf=trial.suggest_int('min_samples_leaf', 1, 30),\n min_samples_split=trial.suggest_int('min_samples_split', 2, 20),\n n_jobs=-1)\n\n # create lists to store scores\n train_scores = []\n test_scores = []\n \n # create the pipeline\n cv_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', simple_imputer),\n ('regressor', regressor)])\n \n # begin cross-validation\n cv_results = cross_validate(cv_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\n mean_test_score = np.mean(cv_results['test_score'])\n train_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n return mean_test_score\n\n# begin optimization\noptuna.logging.set_verbosity(optuna.logging.WARNING) # won't print progress for every single trial\ncv_study = optuna.create_study(directions=['maximize'])\ncv_study.optimize(objective, n_trials=20)\n\n# get the n best trials\nn = 10\nstudy_results_zipped = [(t.values[0], t.params) for t in cv_study.get_trials()]\nordered_study_results = sorted(study_results_zipped, key=lambda x: x[0], reverse=True)\nfor i, t in enumerate(ordered_study_results):\n if i < n:\n print(f'Trial {i} Results:')\n print(f'Mean Test Score: {np.round(t[0], decimals=5)}')\n print(t[1])\n print('\\n')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# optimize LightGBM hyperparameters with optuna\ndef objective(trial):\n # create the regressor object\n regressor = LGBMRegressor(n_estimators=trial.suggest_int('n_estimators', 70, 300),\n learning_rate=trial.suggest_float('learning_rate', 0.001, 0.3, log=True),\n num_leaves=trial.suggest_int('num_leaves', 20, 3000),\n max_depth=trial.suggest_int('max_depth', 3, 12),\n min_child_weight=trial.suggest_float('min_child_weight', 0.0005, 0.1, log=True),\n min_child_samples=trial.suggest_int('min_child_samples', 5, 50),\n subsample=trial.suggest_float('subsample', 0.5, 1),\n colsample_bytree=trial.suggest_float('colsample_bytree', 0.5, 1),\n reg_lambda=trial.suggest_float('reg_lambda', 0.001, 0.1, log=True))\n\n # create lists to store scores\n train_scores = []\n test_scores = []\n \n # create the pipeline\n cv_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', simple_imputer),\n ('regressor', regressor)])\n \n # begin cross-validation\n cv_results = cross_validate(cv_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\n mean_test_score = np.mean(cv_results['test_score'])\n train_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n return mean_test_score\n\n# begin optimization\noptuna.logging.set_verbosity(optuna.logging.WARNING) # won't print progress for every single trial\ncv_study = optuna.create_study(directions=['maximize'])\ncv_study.optimize(objective, n_trials=20)\n\n# get the n best trials\nn = 10\nstudy_results_zipped = [(t.values[0], t.params) for t in cv_study.get_trials()]\nordered_study_results = sorted(study_results_zipped, key=lambda x: x[0], reverse=True)\nfor i, t in enumerate(ordered_study_results):\n if i < n:\n print(f'Trial {i} Results:')\n print(f'Mean Test Score: {np.round(t[0], decimals=5)}')\n print(t[1])\n print('\\n')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Creating finalized models with optimized hyperparameters and checking CV scores","metadata":{}},{"cell_type":"code","source":"# create the XGBoost object\nxgb_final_1 = XGBRegressor(n_estimators=267,\n max_depth=7,\n min_child_weight=5.93313,\n gamma=0.002317,\n learning_rate=0.034267,\n subsample=0.60232,\n colsample_bytree=0.62122,\n reg_lambda=1.39154)\n\n# create the pipeline\nxgb_pipeline_1 = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', imputer),\n ('regressor', xgb_final_1)])\ncv_results = cross_validate(xgb_pipeline_1, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create the XGBoost object\nxgb_final_2 = XGBRegressor(n_estimators=245,\n max_depth=7,\n min_child_weight=5.88154,\n gamma=0.0024124,\n learning_rate=0.035098,\n subsample=0.62636,\n colsample_bytree=0.61926,\n reg_lambda=1.30091)\n\n# create the pipeline\nxgb_pipeline_2 = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', imputer),\n ('regressor', xgb_final_2)])\ncv_results = cross_validate(xgb_pipeline_2, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create a CatBoost object\ncb_final = CatBoostRegressor(iterations=500,\n depth=10,\n l2_leaf_reg=0.0718249,\n silent=True,\n allow_writing_files=False)\n\n# cb_final = CatBoostRegressor(silent=True,\n# allow_writing_files=False)\n\n# create the pipeline\ncb_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', imputer),\n ('regressor', cb_final)])\ncv_results = cross_validate(cb_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create a random forest object\nrf_final = RandomForestRegressor(n_estimators=141, \n max_depth=15,\n min_samples_leaf=12,\n min_samples_split=5,\n n_jobs=-1)\n\n# create the pipeline\nrf_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', imputer),\n ('regressor', rf_final)])\ncv_results = cross_validate(rf_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create the LightGBM object\nlgbm_final_1 = LGBMRegressor(n_estimators=176,\n learning_rate=0.031217,\n num_leaves=2681,\n max_depth=11,\n min_child_weight=0.03876,\n min_child_samples=47,\n subsample=0.61635,\n colsample_bytree=0.510339,\n reg_lambda=0.0082346)\n\n# create the pipeline\nlgbm_pipeline_1 = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', imputer),\n ('regressor', lgbm_final_1)])\ncv_results = cross_validate(lgbm_pipeline_1, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# create the LightGBM object\nlgbm_final_2 = LGBMRegressor(n_estimators=223,\n learning_rate=0.029477,\n num_leaves=2618,\n max_depth=10,\n min_child_weight=0.028960,\n min_child_samples=49,\n subsample=0.63466,\n colsample_bytree=0.52098,\n reg_lambda=0.007196)\n\n# create the pipeline\nlgbm_pipeline_2 = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', imputer),\n ('regressor', lgbm_final_2)])\ncv_results = cross_validate(lgbm_pipeline_2, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"### Creating an ensemble model","metadata":{"tags":[]}},{"cell_type":"code","source":"# creating a voting ensemble from the models\nvoting_model = VotingRegressor(estimators=[('xgb_1', xgb_final_1), ('xgb_2', xgb_final_2), ('lgbm_1', lgbm_final_1), ('lgbm_2', lgbm_final_2)])\nvoting_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', imputer),\n ('regressor', voting_model)])\n\n# begin cross-validation\ncv_results = cross_validate(voting_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')\n\n# fit the pipeline to the training data and report score on test data\nvoting_pipeline.fit(X_train, y_train)\ny_test_pred = voting_pipeline.predict(X_test)\nmodel_score = mean_squared_error(y_test, y_test_pred, squared=False)\nprint(f'Test Data Score: {np.round(model_score, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# creating a stacked ensemble from the models\nstacked_model = StackingRegressor(estimators=[('xgb_1', xgb_final_1), ('xgb_2', xgb_final_2), ('lgbm_1', lgbm_final_1), ('lgbm_2', lgbm_final_2)], final_estimator=BayesianRidge())\nstacked_pipeline = Pipeline([('cat_transformer', categorical_transformer),\n ('D_imputer', D_imputer),\n ('imputer', imputer),\n ('regressor', stacked_model)])\n\n# begin cross-validation\ncv_results = cross_validate(stacked_pipeline, X_train, y_train, cv=10, scoring='neg_root_mean_squared_error', return_train_score=True)\nmean_test_score = np.mean(cv_results['test_score'])\ntrain_test_score_rmse = np.std(cv_results['test_score']) # helpful to measure variance\n\n# print scoring metrics\nprint('Test Scores on K-Folds: ' + str(np.round(cv_results['test_score'], decimals=3)))\nprint('Train Scores on K-Folds: ' + str(np.round(cv_results['train_score'], decimals=3)))\nprint(f'Mean Test Score: {np.round(mean_test_score, decimals=5)}')\nprint(f'Test Score K-Fold Std: {np.round(train_test_score_rmse, decimals=5)}')\n\n# fit the pipeline to the training data and report score on test data\nstacked_pipeline.fit(X_train, y_train)\ny_test_pred = stacked_pipeline.predict(X_test)\nmodel_score = mean_squared_error(y_test, y_test_pred, squared=False)\nprint(f'Test Data Score: {np.round(model_score, decimals=5)}')","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"# πŸ“¦ Submission πŸ“¦","metadata":{}},{"cell_type":"markdown","source":"### Make Predictions","metadata":{}},{"cell_type":"code","source":"# make predictions\nX_submission = submission_imputed[features['continuous'] + features['categorical']]\ny_submission_pred = voting_pipeline.predict(X_submission)\n\n# formatting predictions for submission file output\nsubmission_df = pd.DataFrame({'id': submission_imputed['id'], 'x_e_out [-]': y_submission_pred})\ndisplay(submission_df.head())\n\n# saving predictions to .csv file for submission\nsubmission_df.to_csv('submission.csv', header=True, index=False)","metadata":{"tags":[]},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/ps821/code/code.py b/Agent/workspace/hyperopt/ps821/code/code.py new file mode 100644 index 0000000..321bdf7 --- /dev/null +++ b/Agent/workspace/hyperopt/ps821/code/code.py @@ -0,0 +1,97 @@ +#!/usr/bin/env python +# coding: utf-8 + +# This Python 3 environment comes with many helpful analytics libraries installed +# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python +# For example, here's several helpful packages to load + +import numpy as np # linear algebra +import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv) +from sklearn.experimental import enable_hist_gradient_boosting +from sklearn.ensemble import HistGradientBoostingRegressor +from sklearn.model_selection import KFold + + +# Input data files are available in the read-only "../input/" directory +# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory + +# import os +# for dirname, _, filenames in os.walk('/kaggle/input'): +# for filename in filenames: +# print(os.path.join(dirname, filename)) + +# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using "Save & Run All" +# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session + +FILE_PATH = "./workspace/hyperopt/ps821/data/" + +sample_submission = pd.read_csv(FILE_PATH+'sample_submission.csv.zip') +train = pd.read_csv(FILE_PATH+'train.csv.zip') +test = pd.read_csv(FILE_PATH+'test.csv.zip') + + +sample_submission.head() + + +train.head() + + +test.head() + + +columns = test.columns[1:] +columns + + +X = train[columns].values +X_test = test[columns].values +target = train['loss'].values.reshape(-1,1) + + + +train_oof = np.zeros((train.shape[0],)) +test_preds = np.zeros((test.shape[0],)) +train_oof.shape + + +test_preds.shape + + +n_splits = 5 +n_seeds = 16 +from sklearn.metrics import mean_squared_error + +model = HistGradientBoostingRegressor(max_iter=8700, learning_rate=0.01, early_stopping=False, max_depth=22) + +# for seed in range(n_seeds): +# kf = KFold(n_splits=n_splits, random_state=2*seed**3+137, shuffle=True) + +# for jj, (train_index, val_index) in enumerate(kf.split(train)): +# print("Fitting fold", jj+1) +# train_features = X[train_index] +# train_target = target[train_index] + + +# val_features = X[val_index] +# val_target = target[val_index] + + +# model = HistGradientBoostingRegressor(max_iter=8700, learning_rate=0.01, early_stopping=False, max_depth=22) +# model.fit(train_features, train_target) +# val_pred = model.predict(val_features) +# train_oof[val_index] += val_pred.flatten()/n_seeds +# test_preds += model.predict(X_test).flatten()/(n_splits*n_seeds) +# mean_squared_error(target,train_oof, squared=False) + + +# np.save('train_oof', train_oof) +# np.save('test_preds', test_preds) + + +# sample_submission['loss'] = test_preds +# sample_submission.to_csv('submission.csv', index=False) +# sample_submission.head() + + + + diff --git a/Agent/workspace/hyperopt/ps821/code/tps-08-2021-simple-histgradientboosting-baseline.ipynb b/Agent/workspace/hyperopt/ps821/code/tps-08-2021-simple-histgradientboosting-baseline.ipynb new file mode 100644 index 0000000..5aaec91 --- /dev/null +++ b/Agent/workspace/hyperopt/ps821/code/tps-08-2021-simple-histgradientboosting-baseline.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":28008,"databundleVersionId":2519205,"sourceType":"competition"}],"dockerImageVersionId":30120,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"code","source":"# This Python 3 environment comes with many helpful analytics libraries installed\n# It is defined by the kaggle/python Docker image: https://github.com/kaggle/docker-python\n# For example, here's several helpful packages to load\n\nimport numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nfrom sklearn.experimental import enable_hist_gradient_boosting \nfrom sklearn.ensemble import HistGradientBoostingRegressor\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import KFold\n\n\n# Input data files are available in the read-only \"../input/\" directory\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\nimport os\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n for filename in filenames:\n print(os.path.join(dirname, filename))\n\n# You can write up to 20GB to the current directory (/kaggle/working/) that gets preserved as output when you create a version using \"Save & Run All\" \n# You can also write temporary files to /kaggle/temp/, but they won't be saved outside of the current session","metadata":{"_uuid":"8f2839f25d086af736a60e9eeb907d3b93b6e0e5","_cell_guid":"b1076dfc-b9ad-4769-8c92-a6c4dae69d19","execution":{"iopub.status.busy":"2021-08-12T20:47:53.374256Z","iopub.execute_input":"2021-08-12T20:47:53.374813Z","iopub.status.idle":"2021-08-12T20:47:53.384267Z","shell.execute_reply.started":"2021-08-12T20:47:53.374764Z","shell.execute_reply":"2021-08-12T20:47:53.382966Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_submission = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/sample_submission.csv')\ntrain = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')\ntest = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:45:39.133118Z","iopub.execute_input":"2021-08-12T20:45:39.133448Z","iopub.status.idle":"2021-08-12T20:45:48.676728Z","shell.execute_reply.started":"2021-08-12T20:45:39.133419Z","shell.execute_reply":"2021-08-12T20:45:48.675672Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_submission.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:45:53.843709Z","iopub.execute_input":"2021-08-12T20:45:53.844032Z","iopub.status.idle":"2021-08-12T20:45:53.86855Z","shell.execute_reply.started":"2021-08-12T20:45:53.844005Z","shell.execute_reply":"2021-08-12T20:45:53.867556Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:45:57.267023Z","iopub.execute_input":"2021-08-12T20:45:57.267364Z","iopub.status.idle":"2021-08-12T20:45:57.296831Z","shell.execute_reply.started":"2021-08-12T20:45:57.267335Z","shell.execute_reply":"2021-08-12T20:45:57.295894Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:45:58.076126Z","iopub.execute_input":"2021-08-12T20:45:58.076483Z","iopub.status.idle":"2021-08-12T20:45:58.102853Z","shell.execute_reply.started":"2021-08-12T20:45:58.076446Z","shell.execute_reply":"2021-08-12T20:45:58.10183Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"columns = test.columns[1:]\ncolumns","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:45:59.460992Z","iopub.execute_input":"2021-08-12T20:45:59.461343Z","iopub.status.idle":"2021-08-12T20:45:59.46832Z","shell.execute_reply.started":"2021-08-12T20:45:59.461308Z","shell.execute_reply":"2021-08-12T20:45:59.467388Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"X = train[columns].values\nX_test = test[columns].values\ntarget = train['loss'].values.reshape(-1,1)","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:46:01.579918Z","iopub.execute_input":"2021-08-12T20:46:01.580265Z","iopub.status.idle":"2021-08-12T20:46:01.810551Z","shell.execute_reply.started":"2021-08-12T20:46:01.580233Z","shell.execute_reply":"2021-08-12T20:46:01.809702Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"target.min()\n\n","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:46:03.150617Z","iopub.execute_input":"2021-08-12T20:46:03.150974Z","iopub.status.idle":"2021-08-12T20:46:03.157383Z","shell.execute_reply.started":"2021-08-12T20:46:03.150943Z","shell.execute_reply":"2021-08-12T20:46:03.156478Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"target.max()\n\n","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:46:04.159747Z","iopub.execute_input":"2021-08-12T20:46:04.160074Z","iopub.status.idle":"2021-08-12T20:46:04.165621Z","shell.execute_reply.started":"2021-08-12T20:46:04.160047Z","shell.execute_reply":"2021-08-12T20:46:04.164893Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"train_oof = np.zeros((train.shape[0],))\ntest_preds = np.zeros((test.shape[0],))\ntrain_oof.shape","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:46:06.110773Z","iopub.execute_input":"2021-08-12T20:46:06.111391Z","iopub.status.idle":"2021-08-12T20:46:06.117279Z","shell.execute_reply.started":"2021-08-12T20:46:06.111343Z","shell.execute_reply":"2021-08-12T20:46:06.116639Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"test_preds.shape\n","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:46:07.884629Z","iopub.execute_input":"2021-08-12T20:46:07.884994Z","iopub.status.idle":"2021-08-12T20:46:07.891204Z","shell.execute_reply.started":"2021-08-12T20:46:07.884964Z","shell.execute_reply":"2021-08-12T20:46:07.890126Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"%%time\nn_splits = 5\nn_seeds = 16\n\n\nfor seed in range(n_seeds):\n kf = KFold(n_splits=n_splits, random_state=2*seed**3+137, shuffle=True)\n\n for jj, (train_index, val_index) in enumerate(kf.split(train)):\n print(\"Fitting fold\", jj+1)\n train_features = X[train_index]\n train_target = target[train_index]\n\n\n val_features = X[val_index]\n val_target = target[val_index]\n\n\n model = HistGradientBoostingRegressor(max_iter=8700, learning_rate=0.01, early_stopping=False, max_depth=22)\n model.fit(train_features, train_target)\n val_pred = model.predict(val_features)\n train_oof[val_index] += val_pred.flatten()/n_seeds\n test_preds += model.predict(X_test).flatten()/(n_splits*n_seeds)\n ","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:46:37.620735Z","iopub.execute_input":"2021-08-12T20:46:37.62107Z","iopub.status.idle":"2021-08-12T20:47:31.014391Z","shell.execute_reply.started":"2021-08-12T20:46:37.621042Z","shell.execute_reply":"2021-08-12T20:47:31.013552Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"mean_squared_error(target,train_oof, squared=False)","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:47:59.436399Z","iopub.execute_input":"2021-08-12T20:47:59.4368Z","iopub.status.idle":"2021-08-12T20:47:59.444915Z","shell.execute_reply.started":"2021-08-12T20:47:59.436734Z","shell.execute_reply":"2021-08-12T20:47:59.443985Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"np.save('train_oof', train_oof)\nnp.save('test_preds', test_preds)","metadata":{},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sample_submission['loss'] = test_preds\nsample_submission.to_csv('submission.csv', index=False)\nsample_submission.head()","metadata":{"execution":{"iopub.status.busy":"2021-08-12T20:48:01.309567Z","iopub.execute_input":"2021-08-12T20:48:01.309972Z","iopub.status.idle":"2021-08-12T20:48:01.683157Z","shell.execute_reply.started":"2021-08-12T20:48:01.309938Z","shell.execute_reply":"2021-08-12T20:48:01.682285Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"","metadata":{},"execution_count":null,"outputs":[]}]} \ No newline at end of file diff --git a/Agent/workspace/hyperopt/ps821_2/code/code.py b/Agent/workspace/hyperopt/ps821_2/code/code.py new file mode 100644 index 0000000..6838db6 --- /dev/null +++ b/Agent/workspace/hyperopt/ps821_2/code/code.py @@ -0,0 +1,266 @@ +#!/usr/bin/env python +# coding: utf-8 + +# ![Pink and Purple Patterns General LinkedIn Banner .jpg](attachment:c37ae679-b723-4c9e-901c-58fbd8537c71.jpg) + +#
πŸ“ŒThis TPS dataset has 100 features i.e f0 to f99. +# The target variable 'loss' ranges from 0 to 42 i.e. 43 unique values in the outcome column. +# The train dataset contains 250000 rows with 102 columns and test dataset contains 150000 rows with 101 columns.
+ +# ###

Importing Libraries & Packages πŸ“š

+ +import pandas as pd +import numpy as np +import matplotlib.pyplot as plt +plt.style.use('fivethirtyeight') +import seaborn as sns + +import warnings +warnings.filterwarnings('ignore') +FILE_PATH = "./workspace/hyperopt/ps821_2/data/" + + +# ###

Importing & Reading the dataset πŸ“

+ +df_train= pd.read_csv(FILE_PATH+"train.csv.zip") +df_test= pd.read_csv(FILE_PATH+"test.csv.zip") +df_subm= pd.read_csv(FILE_PATH+"sample_submission.csv.zip") + + +# df_train_row_count, df_train_column_count=df_train.shape +# print('Total number of rows:', df_train_row_count) +# print('Total number of columns:', df_train_column_count) + + +# df_test_row_count, df_test_column_count=df_test.shape +# print('Total number of rows:', df_test_row_count) +# print('Total number of columns:', df_test_column_count) + + +# df_train.head() + + +# df_train.describe().T + + +# df_train.info() + + +# df_test.describe().T + + +# # ###

Checking for missing values ✏️

+ +# df_train.isna().sum() + + +# df_test.isna().sum() + + +# print ("Unique values are:\n",df_train.nunique()) + + +# sns.distplot(df_train['loss']) + + +# plt.figure(figsize = (10,8)) +# sns.countplot(data = df_train, x ='loss',palette='icefire'); + + +# plt.figure(figsize=(11,11)) +# corr=df_train.corr() +# mask = np.triu(np.ones_like(corr, dtype=bool)) +# sns.heatmap(corr, mask=mask, cmap='twilight_r', robust=True, center=0,square=True, linewidths=.6) +# plt.title('Correlation') +# plt.show() + + +# Finding correlations +# correlations_data = df_train.corr()['loss'].sort_values() +# print(correlations_data.head(20),'\n') +# print(correlations_data.tail(20),'\n') + + +# corr_loss = df_train.corr() +# plt.figure(figsize=(24,8)) +# corr_loss["loss"][:-1].plot(kind="bar",grid=True) +# plt.title("Features correlation") + + +df_train.drop(columns = 'id', inplace = True) +df_test.drop(columns = 'id', inplace = True) + + +#fig = plt.figure(figsize = (12,45)) +#for i in range(len(df_train.columns.tolist()[:100])): + #plt.subplot(25,4,i+1) + #plt.title(df_train.columns.tolist()[:100][i]) + #a = sns.kdeplot(df_train[df_train.columns.tolist()[:100][i]]) +#plt.tight_layout() +#plt.show() + + +# df = pd.concat([df_train.drop(["loss"], axis=1)]) +# df = df_train.columns[0:100] +# plt.subplots(figsize=(20,160)) +# length = len(df) +# for i, j in zip(df, range(length)): +# fig = plt.subplot((length/2), 3, j+1) +# plt.subplots_adjust(wspace=.25, hspace=.6) +# plt.yticks([]) +# sns.histplot(x=df_train[i], alpha=0.5,edgecolor="black",color='#3e3b92') +# sns.histplot(x=df_test[i], alpha=0.5,edgecolor="black",color='#00ee6e') +# fig.legend(labels=('Train','Test')) + + +# ###

Dataset split ⏳

+ +# define dataset +X_train = df_train.drop('loss', axis=1) +y_train = df_train['loss'] + + +from sklearn.model_selection import train_test_split + +# creating dataset split for prediction +# X_train, X_test , y_train , y_test = train_test_split(X,y,test_size=0.2,random_state=42) # 80-20 split + +# Checking split +# print('X_train:', X_train.shape) +# print('y_train:', y_train.shape) +# print('X_test:', X_test.shape) +# print('y_test:', y_test.shape) + + +from sklearn.preprocessing import StandardScaler +sc=StandardScaler() +sc.fit(X_train) +X_train=sc.transform(X_train) +# X_test=sc.transform(X_test) + + +# ###

CatBoost ✏️

+ +from catboost import CatBoostRegressor +model1 = CatBoostRegressor(random_state=42,iterations = 5000,learning_rate=0.005, + early_stopping_rounds=50,task_type="GPU") +# model1.fit(X,y, verbose=0) + + +# from sklearn.metrics import mean_absolute_error +# predicted1 = model1.predict(X) + +# mae = metrics.mean_absolute_error(y, predicted1) +# mse = metrics.mean_squared_error(y, predicted1) +# rmse = np.sqrt(mse) +# r2 = metrics.r2_score(y,predicted1) +# print("CatBoost Metrics:") +# print("mae:",mae) +# print("mse:", mse) +# print("rmse:", rmse) +# print("r2:", r2) + + +# y_pred1 = model1.predict(df_test) + + +# import shap +# explainer = shap.Explainer(model1) +# shap_values = explainer(X) +# shap.plots.beeswarm(shap_values,max_display=20) + + +# ###

LightGBM ✏️

+ +from lightgbm import LGBMRegressor +model2 = LGBMRegressor(random_state=42,n_estimators= 500,learning_rate=0.005, + objective='regression', max_depth=5, n_jobs = -1) +# model2.fit(X,y, verbose=1) + + +# from sklearn import metrics +# predicted2 = model2.predict(X) + +# mae = metrics.mean_absolute_error(y, predicted2) +# mse = metrics.mean_squared_error(y, predicted2) +# rmse = np.sqrt(mse) +# r2 = metrics.r2_score(y,predicted2) + +# print("LightGBM Metrics:") +# print("mae:",mae) +# print("mse:", mse) +# print("rmse:", rmse) +# print("r2:", r2) + + +# y_pred2 = model2.predict(df_test) + + +# ###

XGBoost ✏️

+ +from xgboost import XGBRegressor +model3 = XGBRegressor(random_state=42,n_estimators= 500,learning_rate=0.05, + max_depth=8,booster='gbtree',verbosity=0,tree_method = 'gpu_hist',task_type="GPU") +# model3.fit(X,y) + + +# from sklearn import metrics +# predicted3 = model3.predict(X) + +# mae = metrics.mean_absolute_error(y, predicted3) +# mse = metrics.mean_squared_error(y, predicted3) +# rmse = np.sqrt(mse) +# r2 = metrics.r2_score(y,predicted3) + +# print("XGBoost Metrics:") +# print("mae:",mae) +# print("mse:", mse) +# print("rmse:", rmse) +# print("r2:", r2) + + +# y_pred3 = model3.predict(df_test) + + +# ensembled = y_pred1*0.5 + y_pred2*0.25 + y_pred3*0.25 + + +# ###

StackingCVRegressor ✏️

+ +# More information about StackingCVRegressor: https://rasbt.github.io/mlxtend/user_guide/regressor/StackingCVRegressor/ + +from mlxtend.regressor import StackingCVRegressor +from sklearn.metrics import mean_squared_error, mean_absolute_error +# from sklearn.model_selection import KFold +# kfold = KFold(n_splits=10,random_state=42) +regr_models = (model1,model2,model3) +model_stack = StackingCVRegressor(regressors=regr_models, meta_regressor=model1, + use_features_in_secondary=True,shuffle=False,random_state=42) +# model_stack.fit(X, y) + + +# predicted_st = model_stack.predict(X) +# from sklearn import metrics + +# mae = metrics.mean_absolute_error(y, predicted_st) +# mse = metrics.mean_squared_error(y, predicted_st) +# rmse = np.sqrt(mse) +# r2 = metrics.r2_score(y,predicted_st) + +# print("mae:",mae) +# print("mse:", mse) +# print("rmse:", rmse) +# print("r2:", r2) + + +# y_pred_stack = model_stack.predict(df_test) + + +# df_subm['loss'] = y_pred_stack +# df_subm + + +# df_subm.to_csv('submission.csv', index=False) + + +# ###

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diff --git a/Agent/workspace/hyperopt/ps821_2/code/tps-aug-21-ensemble-stackingcvregressor.ipynb b/Agent/workspace/hyperopt/ps821_2/code/tps-aug-21-ensemble-stackingcvregressor.ipynb new file mode 100644 index 0000000..9893b38 --- /dev/null +++ b/Agent/workspace/hyperopt/ps821_2/code/tps-aug-21-ensemble-stackingcvregressor.ipynb @@ -0,0 +1 @@ +{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"pygments_lexer":"ipython3","nbconvert_exporter":"python","version":"3.6.4","file_extension":".py","codemirror_mode":{"name":"ipython","version":3},"name":"python","mimetype":"text/x-python"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":28008,"databundleVersionId":2519205,"sourceType":"competition"}],"dockerImageVersionId":30120,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"![Pink and Purple Patterns General 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πŸ“ŒThis TPS dataset has 100 features i.e f0 to f99. \n The target variable 'loss' ranges from 0 to 42 i.e. 43 unique values in the outcome column.\n The train dataset contains 250000 rows with 102 columns and test dataset contains 150000 rows with 101 columns.
","metadata":{}},{"cell_type":"markdown","source":"###

Importing Libraries & Packages πŸ“š

","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nplt.style.use('fivethirtyeight')\nimport seaborn as sns\n\nimport warnings\nwarnings.filterwarnings('ignore')","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:33.511246Z","iopub.execute_input":"2022-02-19T11:26:33.511555Z","iopub.status.idle":"2022-02-19T11:26:34.268223Z","shell.execute_reply.started":"2022-02-19T11:26:33.511481Z","shell.execute_reply":"2022-02-19T11:26:34.267394Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###

Importing & Reading the dataset πŸ“

","metadata":{}},{"cell_type":"code","source":"df_train= pd.read_csv(\"../input/tabular-playground-series-aug-2021/train.csv\")\ndf_test= pd.read_csv(\"../input/tabular-playground-series-aug-2021/test.csv\")\ndf_subm= pd.read_csv(\"../input/tabular-playground-series-aug-2021/sample_submission.csv\")","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:34.26951Z","iopub.execute_input":"2022-02-19T11:26:34.269826Z","iopub.status.idle":"2022-02-19T11:26:44.261811Z","shell.execute_reply.started":"2022-02-19T11:26:34.269793Z","shell.execute_reply":"2022-02-19T11:26:44.260983Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train_row_count, df_train_column_count=df_train.shape\nprint('Total number of rows:', df_train_row_count)\nprint('Total number of columns:', df_train_column_count)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:44.263643Z","iopub.execute_input":"2022-02-19T11:26:44.263998Z","iopub.status.idle":"2022-02-19T11:26:44.272102Z","shell.execute_reply.started":"2022-02-19T11:26:44.263962Z","shell.execute_reply":"2022-02-19T11:26:44.271129Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test_row_count, df_test_column_count=df_test.shape\nprint('Total number of rows:', df_test_row_count)\nprint('Total number of columns:', df_test_column_count)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:44.273497Z","iopub.execute_input":"2022-02-19T11:26:44.273841Z","iopub.status.idle":"2022-02-19T11:26:44.281167Z","shell.execute_reply.started":"2022-02-19T11:26:44.273815Z","shell.execute_reply":"2022-02-19T11:26:44.280053Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.head()","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:44.282975Z","iopub.execute_input":"2022-02-19T11:26:44.283885Z","iopub.status.idle":"2022-02-19T11:26:44.325255Z","shell.execute_reply.started":"2022-02-19T11:26:44.283847Z","shell.execute_reply":"2022-02-19T11:26:44.324343Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.describe().T","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:44.326597Z","iopub.execute_input":"2022-02-19T11:26:44.326954Z","iopub.status.idle":"2022-02-19T11:26:45.25955Z","shell.execute_reply.started":"2022-02-19T11:26:44.326918Z","shell.execute_reply":"2022-02-19T11:26:45.258527Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.info()","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:45.260947Z","iopub.execute_input":"2022-02-19T11:26:45.261449Z","iopub.status.idle":"2022-02-19T11:26:45.278542Z","shell.execute_reply.started":"2022-02-19T11:26:45.261411Z","shell.execute_reply":"2022-02-19T11:26:45.27774Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test.describe().T","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:45.281441Z","iopub.execute_input":"2022-02-19T11:26:45.281776Z","iopub.status.idle":"2022-02-19T11:26:45.929294Z","shell.execute_reply.started":"2022-02-19T11:26:45.281745Z","shell.execute_reply":"2022-02-19T11:26:45.928228Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###

Checking for missing values ✏️

","metadata":{}},{"cell_type":"code","source":"df_train.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:45.93163Z","iopub.execute_input":"2022-02-19T11:26:45.932004Z","iopub.status.idle":"2022-02-19T11:26:45.985678Z","shell.execute_reply.started":"2022-02-19T11:26:45.931965Z","shell.execute_reply":"2022-02-19T11:26:45.98489Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_test.isna().sum()","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:45.986841Z","iopub.execute_input":"2022-02-19T11:26:45.987193Z","iopub.status.idle":"2022-02-19T11:26:46.02134Z","shell.execute_reply.started":"2022-02-19T11:26:45.98716Z","shell.execute_reply":"2022-02-19T11:26:46.020416Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"print (\"Unique values are:\\n\",df_train.nunique())","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:46.022563Z","iopub.execute_input":"2022-02-19T11:26:46.022893Z","iopub.status.idle":"2022-02-19T11:26:47.522047Z","shell.execute_reply.started":"2022-02-19T11:26:46.022859Z","shell.execute_reply":"2022-02-19T11:26:47.52122Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"sns.distplot(df_train['loss'])","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:47.523261Z","iopub.execute_input":"2022-02-19T11:26:47.523573Z","iopub.status.idle":"2022-02-19T11:26:49.104877Z","shell.execute_reply.started":"2022-02-19T11:26:47.523544Z","shell.execute_reply":"2022-02-19T11:26:49.104075Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize = (10,8))\nsns.countplot(data = df_train, x ='loss',palette='icefire');","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:49.106109Z","iopub.execute_input":"2022-02-19T11:26:49.106604Z","iopub.status.idle":"2022-02-19T11:26:49.59477Z","shell.execute_reply.started":"2022-02-19T11:26:49.106564Z","shell.execute_reply":"2022-02-19T11:26:49.593868Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"plt.figure(figsize=(11,11))\ncorr=df_train.corr()\nmask = np.triu(np.ones_like(corr, dtype=bool))\nsns.heatmap(corr, mask=mask, cmap='twilight_r', robust=True, center=0,square=True, linewidths=.6)\nplt.title('Correlation')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:49.596061Z","iopub.execute_input":"2022-02-19T11:26:49.596581Z","iopub.status.idle":"2022-02-19T11:26:56.569756Z","shell.execute_reply.started":"2022-02-19T11:26:49.596539Z","shell.execute_reply":"2022-02-19T11:26:56.568918Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"# Finding correlations \ncorrelations_data = df_train.corr()['loss'].sort_values()\nprint(correlations_data.head(20),'\\n')\nprint(correlations_data.tail(20),'\\n')","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:26:56.570997Z","iopub.execute_input":"2022-02-19T11:26:56.571365Z","iopub.status.idle":"2022-02-19T11:27:03.351387Z","shell.execute_reply.started":"2022-02-19T11:26:56.57133Z","shell.execute_reply":"2022-02-19T11:27:03.350541Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"corr_loss = df_train.corr()\nplt.figure(figsize=(24,8))\ncorr_loss[\"loss\"][:-1].plot(kind=\"bar\",grid=True)\nplt.title(\"Features correlation\")","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:27:03.352678Z","iopub.execute_input":"2022-02-19T11:27:03.353011Z","iopub.status.idle":"2022-02-19T11:27:10.620774Z","shell.execute_reply.started":"2022-02-19T11:27:03.352974Z","shell.execute_reply":"2022-02-19T11:27:10.619918Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_train.drop(columns = 'id', inplace = True)\ndf_test.drop(columns = 'id', inplace = True)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:27:10.622143Z","iopub.execute_input":"2022-02-19T11:27:10.622467Z","iopub.status.idle":"2022-02-19T11:27:10.718877Z","shell.execute_reply.started":"2022-02-19T11:27:10.622433Z","shell.execute_reply":"2022-02-19T11:27:10.717962Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"#fig = plt.figure(figsize = (12,45))\n#for i in range(len(df_train.columns.tolist()[:100])):\n #plt.subplot(25,4,i+1)\n #plt.title(df_train.columns.tolist()[:100][i])\n #a = sns.kdeplot(df_train[df_train.columns.tolist()[:100][i]])\n#plt.tight_layout()\n#plt.show()","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:27:10.720206Z","iopub.execute_input":"2022-02-19T11:27:10.720705Z","iopub.status.idle":"2022-02-19T11:27:10.727512Z","shell.execute_reply.started":"2022-02-19T11:27:10.720666Z","shell.execute_reply":"2022-02-19T11:27:10.726667Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df = pd.concat([df_train.drop([\"loss\"], axis=1)])\ndf = df_train.columns[0:100]\nplt.subplots(figsize=(20,160))\nlength = len(df)\nfor i, j in zip(df, range(length)):\n fig = plt.subplot((length/2), 3, j+1)\n plt.subplots_adjust(wspace=.25, hspace=.6)\n plt.yticks([])\n sns.histplot(x=df_train[i], alpha=0.5,edgecolor=\"black\",color='#3e3b92')\n sns.histplot(x=df_test[i], alpha=0.5,edgecolor=\"black\",color='#00ee6e')\n fig.legend(labels=('Train','Test'))","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:27:10.728835Z","iopub.execute_input":"2022-02-19T11:27:10.729439Z","iopub.status.idle":"2022-02-19T11:29:25.700749Z","shell.execute_reply.started":"2022-02-19T11:27:10.729401Z","shell.execute_reply":"2022-02-19T11:29:25.699965Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###

Dataset split ⏳

","metadata":{}},{"cell_type":"code","source":"# define dataset\nX = df_train.drop('loss', axis=1)\ny = df_train['loss']","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:29:25.701901Z","iopub.execute_input":"2022-02-19T11:29:25.702354Z","iopub.status.idle":"2022-02-19T11:29:25.768964Z","shell.execute_reply.started":"2022-02-19T11:29:25.70232Z","shell.execute_reply":"2022-02-19T11:29:25.768108Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.model_selection import train_test_split\n\n# creating dataset split for prediction\nX_train, X_test , y_train , y_test = train_test_split(X,y,test_size=0.2,random_state=42) # 80-20 split\n\n# Checking split \nprint('X_train:', X_train.shape)\nprint('y_train:', y_train.shape)\nprint('X_test:', X_test.shape)\nprint('y_test:', y_test.shape)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:29:25.770248Z","iopub.execute_input":"2022-02-19T11:29:25.770798Z","iopub.status.idle":"2022-02-19T11:29:26.13556Z","shell.execute_reply.started":"2022-02-19T11:29:25.770753Z","shell.execute_reply":"2022-02-19T11:29:26.134694Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn.preprocessing import StandardScaler\nsc=StandardScaler()\nsc.fit(X_train)\nX_train=sc.transform(X_train)\nX_test=sc.transform(X_test)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:29:26.136854Z","iopub.execute_input":"2022-02-19T11:29:26.137217Z","iopub.status.idle":"2022-02-19T11:29:26.679334Z","shell.execute_reply.started":"2022-02-19T11:29:26.137181Z","shell.execute_reply":"2022-02-19T11:29:26.678452Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###

CatBoost ✏️

","metadata":{}},{"cell_type":"code","source":"from catboost import CatBoostRegressor\nmodel1 = CatBoostRegressor(random_state=42,iterations = 5000,learning_rate=0.005,\n early_stopping_rounds=50,task_type=\"GPU\")\nmodel1.fit(X,y, verbose=0)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:29:26.683077Z","iopub.execute_input":"2022-02-19T11:29:26.683341Z","iopub.status.idle":"2022-02-19T11:31:03.92943Z","shell.execute_reply.started":"2022-02-19T11:29:26.683315Z","shell.execute_reply":"2022-02-19T11:31:03.928676Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn import metrics\npredicted1 = model1.predict(X)\n\nmae = metrics.mean_absolute_error(y, predicted1)\nmse = metrics.mean_squared_error(y, predicted1)\nrmse = np.sqrt(mse) \nr2 = metrics.r2_score(y,predicted1)\nprint(\"CatBoost Metrics:\")\nprint(\"mae:\",mae)\nprint(\"mse:\", mse)\nprint(\"rmse:\", rmse)\nprint(\"r2:\", r2)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:31:03.933286Z","iopub.execute_input":"2022-02-19T11:31:03.935023Z","iopub.status.idle":"2022-02-19T11:31:05.433416Z","shell.execute_reply.started":"2022-02-19T11:31:03.934987Z","shell.execute_reply":"2022-02-19T11:31:05.432516Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred1 = model1.predict(df_test)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:31:05.434661Z","iopub.execute_input":"2022-02-19T11:31:05.435169Z","iopub.status.idle":"2022-02-19T11:31:06.167017Z","shell.execute_reply.started":"2022-02-19T11:31:05.435119Z","shell.execute_reply":"2022-02-19T11:31:06.16612Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"import shap\nexplainer = shap.Explainer(model1)\nshap_values = explainer(X)\nshap.plots.beeswarm(shap_values,max_display=20)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:31:06.168301Z","iopub.execute_input":"2022-02-19T11:31:06.169415Z","iopub.status.idle":"2022-02-19T11:34:58.549643Z","shell.execute_reply.started":"2022-02-19T11:31:06.169374Z","shell.execute_reply":"2022-02-19T11:34:58.548771Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###

LightGBM ✏️

","metadata":{}},{"cell_type":"code","source":"from lightgbm import LGBMRegressor\nmodel2 = LGBMRegressor(random_state=42,n_estimators= 500,learning_rate=0.005,\n objective='regression', max_depth=5, n_jobs = -1)\nmodel2.fit(X,y, verbose=1)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:34:58.550871Z","iopub.execute_input":"2022-02-19T11:34:58.551339Z","iopub.status.idle":"2022-02-19T11:35:42.234878Z","shell.execute_reply.started":"2022-02-19T11:34:58.551302Z","shell.execute_reply":"2022-02-19T11:35:42.233974Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn import metrics\npredicted2 = model2.predict(X)\n\nmae = metrics.mean_absolute_error(y, predicted2)\nmse = metrics.mean_squared_error(y, predicted2)\nrmse = np.sqrt(mse) \nr2 = metrics.r2_score(y,predicted2)\n\nprint(\"LightGBM Metrics:\")\nprint(\"mae:\",mae)\nprint(\"mse:\", mse)\nprint(\"rmse:\", rmse)\nprint(\"r2:\", r2)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:35:42.236198Z","iopub.execute_input":"2022-02-19T11:35:42.236574Z","iopub.status.idle":"2022-02-19T11:35:49.498427Z","shell.execute_reply.started":"2022-02-19T11:35:42.236535Z","shell.execute_reply":"2022-02-19T11:35:49.497507Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred2 = model2.predict(df_test)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:35:49.501846Z","iopub.execute_input":"2022-02-19T11:35:49.502207Z","iopub.status.idle":"2022-02-19T11:35:53.754291Z","shell.execute_reply.started":"2022-02-19T11:35:49.502174Z","shell.execute_reply":"2022-02-19T11:35:53.753407Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###

XGBoost ✏️

","metadata":{}},{"cell_type":"code","source":"from xgboost import XGBRegressor\nmodel3 = XGBRegressor(random_state=42,n_estimators= 500,learning_rate=0.05,\n max_depth=8,booster='gbtree',verbosity=0,tree_method = 'gpu_hist',task_type=\"GPU\")\nmodel3.fit(X,y)","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-02-19T11:35:53.755557Z","iopub.execute_input":"2022-02-19T11:35:53.755897Z","iopub.status.idle":"2022-02-19T11:36:07.155749Z","shell.execute_reply.started":"2022-02-19T11:35:53.755863Z","shell.execute_reply":"2022-02-19T11:36:07.154871Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"from sklearn import metrics\npredicted3 = model3.predict(X)\n\nmae = metrics.mean_absolute_error(y, predicted3)\nmse = metrics.mean_squared_error(y, predicted3)\nrmse = np.sqrt(mse) \nr2 = metrics.r2_score(y,predicted3)\n\nprint(\"XGBoost Metrics:\")\nprint(\"mae:\",mae)\nprint(\"mse:\", mse)\nprint(\"rmse:\", rmse)\nprint(\"r2:\", r2)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:36:07.15713Z","iopub.execute_input":"2022-02-19T11:36:07.157509Z","iopub.status.idle":"2022-02-19T11:36:11.648772Z","shell.execute_reply.started":"2022-02-19T11:36:07.157471Z","shell.execute_reply":"2022-02-19T11:36:11.64804Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred3 = model3.predict(df_test)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:36:11.651946Z","iopub.execute_input":"2022-02-19T11:36:11.652218Z","iopub.status.idle":"2022-02-19T11:36:13.879214Z","shell.execute_reply.started":"2022-02-19T11:36:11.652191Z","shell.execute_reply":"2022-02-19T11:36:13.878402Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"ensembled = y_pred1*0.5 + y_pred2*0.25 + y_pred3*0.25 ","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:36:13.882871Z","iopub.execute_input":"2022-02-19T11:36:13.884544Z","iopub.status.idle":"2022-02-19T11:36:13.89052Z","shell.execute_reply.started":"2022-02-19T11:36:13.884509Z","shell.execute_reply":"2022-02-19T11:36:13.889867Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###

StackingCVRegressor ✏️

","metadata":{}},{"cell_type":"markdown","source":"More information about StackingCVRegressor: https://rasbt.github.io/mlxtend/user_guide/regressor/StackingCVRegressor/","metadata":{}},{"cell_type":"code","source":"from mlxtend.regressor import StackingCVRegressor\nfrom sklearn.model_selection import KFold\nkfold = KFold(n_splits=10,random_state=42)\nregr_models = (model1,model2,model3)\nmodel_stack = StackingCVRegressor(regressors=regr_models, meta_regressor=model1, \n use_features_in_secondary=True,shuffle=False,cv=kfold,random_state=42)\nmodel_stack.fit(X, y)","metadata":{"_kg_hide-output":true,"execution":{"iopub.status.busy":"2022-02-19T11:36:13.892937Z","iopub.execute_input":"2022-02-19T11:36:13.893346Z","iopub.status.idle":"2022-02-19T11:54:13.749322Z","shell.execute_reply.started":"2022-02-19T11:36:13.893311Z","shell.execute_reply":"2022-02-19T11:54:13.748476Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"predicted_st = model_stack.predict(X)\nfrom sklearn import metrics\n\nmae = metrics.mean_absolute_error(y, predicted_st)\nmse = metrics.mean_squared_error(y, predicted_st)\nrmse = np.sqrt(mse) \nr2 = metrics.r2_score(y,predicted_st)\n\nprint(\"mae:\",mae)\nprint(\"mse:\", mse)\nprint(\"rmse:\", rmse)\nprint(\"r2:\", r2)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:54:13.750652Z","iopub.execute_input":"2022-02-19T11:54:13.751006Z","iopub.status.idle":"2022-02-19T11:54:37.927612Z","shell.execute_reply.started":"2022-02-19T11:54:13.750967Z","shell.execute_reply":"2022-02-19T11:54:37.926728Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"y_pred_stack = model_stack.predict(df_test)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:54:37.928929Z","iopub.execute_input":"2022-02-19T11:54:37.929463Z","iopub.status.idle":"2022-02-19T11:54:52.221201Z","shell.execute_reply.started":"2022-02-19T11:54:37.929424Z","shell.execute_reply":"2022-02-19T11:54:52.220347Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_subm['loss'] = y_pred_stack\ndf_subm","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:54:52.222418Z","iopub.execute_input":"2022-02-19T11:54:52.222753Z","iopub.status.idle":"2022-02-19T11:54:52.238776Z","shell.execute_reply.started":"2022-02-19T11:54:52.22272Z","shell.execute_reply":"2022-02-19T11:54:52.237747Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"code","source":"df_subm.to_csv('submission.csv', index=False)","metadata":{"execution":{"iopub.status.busy":"2022-02-19T11:54:52.240388Z","iopub.execute_input":"2022-02-19T11:54:52.240983Z","iopub.status.idle":"2022-02-19T11:54:52.789304Z","shell.execute_reply.started":"2022-02-19T11:54:52.240914Z","shell.execute_reply":"2022-02-19T11:54:52.788412Z"},"trusted":true},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":"###

If you found this notebook useful, please Upvote. Thanks!

","metadata":{}}]} \ No newline at end of file