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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Set the path to the `xls` file"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"source": [
"training_file = \"../TrainDataset2024.xls\"\n",
"# training_file = \"/kaggle/input/dataset/TrainDataset2024.xls\""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Import libraries"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"2024-12-07 16:54:09.391306: I tensorflow/core/util/port.cc:153] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n",
"2024-12-07 16:54:09.571207: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:477] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
"E0000 00:00:1733590449.640280 43324 cuda_dnn.cc:8310] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
"E0000 00:00:1733590449.660207 43324 cuda_blas.cc:1418] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
"2024-12-07 16:54:09.824402: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
}
],
"source": [
"import sys\n",
"import os\n",
"\n",
"# Add the parent directory to the system path\n",
"sys.path.append(os.path.abspath('../')) # Adjust the path as needed\n",
"\n",
"from my_util import df_to_corr_matrix, remove_outliers\n",
"\n",
"import tensorflow as tf\n",
"import pandas as pd\n",
"import numpy as np\n",
"import seaborn as sns\n",
"import matplotlib.pyplot as plt\n",
"import plotly.graph_objects as go\n",
"\n",
"from matplotlib.colors import Normalize\n",
"from sklearn.decomposition import PCA\n",
"from sklearn.model_selection import train_test_split, KFold, cross_val_score, GridSearchCV, cross_val_predict, StratifiedKFold\n",
"from sklearn.preprocessing import StandardScaler, RobustScaler\n",
"from sklearn.tree import DecisionTreeClassifier\n",
"from sklearn.metrics import classification_report, confusion_matrix, precision_score, recall_score, accuracy_score, f1_score, make_scorer, balanced_accuracy_score, r2_score\n",
"from sklearn.metrics import mean_absolute_error\n",
"from sklearn.svm import SVC\n",
"from sklearn.feature_selection import SelectKBest, f_classif, chi2, mutual_info_classif\n",
"from sklearn.impute import KNNImputer\n",
"\n",
"\n",
"from imblearn.over_sampling import SMOTE\n",
"from imblearn.pipeline import Pipeline\n",
"\n",
"from joblib import Parallel, delayed\n",
"\n",
"import xgboost as xgb\n",
"from xgboost import XGBClassifier, XGBRegressor\n",
"\n",
"from pickle import dump , load\n",
"\n",
"import warnings"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loaded '../FeatureSelection/pkl/regression_features_corr_25_selected_features.pkl' to selected_feature\n",
"(400, 25) (400,)\n",
"['original_firstorder_InterquartileRange', 'original_firstorder_Kurtosis', 'TumourStage', 'original_shape_MajorAxisLength', 'original_firstorder_90Percentile', 'ChemoGrade', 'HER2', 'original_shape_Maximum2DDiameterRow', 'original_shape_LeastAxisLength', 'original_shape_Maximum2DDiameterColumn', 'original_glszm_SmallAreaEmphasis', 'Age', 'original_shape_Sphericity', 'original_firstorder_10Percentile', 'original_glszm_SizeZoneNonUniformityNormalized', 'original_gldm_DependenceEntropy', 'original_ngtdm_Busyness', 'original_glcm_Imc1', 'Gene', 'original_gldm_SmallDependenceEmphasis', 'original_glszm_GrayLevelNonUniformityNormalized', 'PgR', 'TrippleNegative', 'original_shape_Elongation', 'original_glcm_Correlation']\n"
]
}
],
"source": [
"NUM_OF_SELECTED_FEATURES = \"regression_features_corr_25\"\n",
"\n",
"data = pd.read_excel(training_file)\n",
"data.replace(999, np.nan, inplace=True)\n",
"\n",
"data.drop([\"ID\", \"pCR (outcome)\"], axis=1, inplace=True)\n",
"data.dropna(subset=[\"RelapseFreeSurvival (outcome)\"], inplace=True)\n",
"\n",
"with open(f'../FeatureSelection/pkl/{NUM_OF_SELECTED_FEATURES}_selected_features.pkl', mode='rb') as file:\n",
" selected_features = load(file)\n",
" print(f\"Loaded '{file.name}' to selected_feature\")\n",
"\n",
"X = data[selected_features]\n",
"y = data[\"RelapseFreeSurvival (outcome)\"]\n",
"print(X.shape, y.shape)\n",
"\n",
"print(selected_features)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Splited the data into train and test. The test will not be used in the training, but just for test the xgb. \n",
"The training data has 320 data. The testing data has 80 data. \n",
"RandomState = 7\n"
]
}
],
"source": [
"import random\n",
"\n",
"randomstate = random.randint(0, 1000)\n",
"randomstate = 7\n",
"X_train_full, X_test_reserved, y_train_full, y_test_reserved = train_test_split(X, y, test_size=0.2, random_state=randomstate) # similar distribution of 1 and 0\n",
"\n",
"X_train_full.reset_index(drop=True, inplace=True)\n",
"X_test_reserved.reset_index(drop=True, inplace=True)\n",
"y_train_full.reset_index(drop=True, inplace=True)\n",
"y_test_reserved.reset_index(drop=True, inplace=True)\n",
"\n",
"\n",
"print(\"Splited the data into train and test. The test will not be used in the training, but just for test the xgb. \")\n",
"print(f\"The training data has {len(X_train_full)} data. The testing data has {len(X_test_reserved)} data. \")\n",
"print(f\"RandomState = {randomstate}\")\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([0.00499132, 0.00434028, 0.01540799, 0.01236979, 0.01388889,\n",
" 0.00976562, 0.00499132, 0.0015191 , 0.00065104, 0.0015191 ]),\n",
" array([ 0. , 14.4, 28.8, 43.2, 57.6, 72. , 86.4, 100.8, 115.2,\n",
" 129.6, 144. ]),\n",
" <BarContainer object of 10 artists>)"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(y, density=True, alpha=0.5)\n",
"plt.hist(y_train_full, density=True, alpha=0.5)"
]
},
{
"cell_type": "code",
"execution_count": 103,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Fitting 5 folds for each of 69120 candidates, totalling 345600 fits\n",
"Best parameter: {'gamma': 0, 'learning_rate': 0.1, 'max_bin': 3, 'max_delta_step': 0, 'max_depth': 2, 'max_leaves': 3, 'min_child_weight': 0.001, 'n_estimators': 100}\n",
"Best score: -20.039419563611347\n"
]
}
],
"source": [
"model = XGBRegressor(objective=\"reg:absoluteerror\")\n",
"\n",
"param_grid = {\n",
" \"gamma\": [0, 0.1, 0.2],\n",
" \"learning_rate\": [0.01, 0.1, 0.2, 0.3],\n",
" \"max_bin\": [2, 3, 4, 5, 10],\n",
" \"max_delta_step\": [0, 1, 2],\n",
" \"max_depth\": [1, 2, 4, 6],\n",
" \"max_leaves\": [0, 1, 2, 3, 4, 5],\n",
" \"min_child_weight\": [0.001, 0.01, 0.1, 0.5],\n",
" \"n_estimators\": [10, 50, 100, 200],\n",
"}\n",
"\n",
"# Set up the GridSearchCV\n",
"grid_search = GridSearchCV(\n",
" estimator=model,\n",
" param_grid=param_grid,\n",
" scoring='neg_mean_absolute_error', \n",
" cv=5,\n",
" verbose=1,\n",
" n_jobs=-1,\n",
" return_train_score=True,\n",
")\n",
"\n",
"grid_search.fit(X_train_full, y_train_full)\n",
"\n",
"print(f\"Best parameter: {grid_search.best_params_}\")\n",
"print(f\"Best score: {grid_search.best_score_}\")"
]
},
{
"cell_type": "code",
"execution_count": 104,
"metadata": {},
"outputs": [],
"source": [
"pd.DataFrame(grid_search.cv_results_).to_csv(\"output.csv\")"
]
},
{
"cell_type": "code",
"execution_count": 109,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'gamma': 0, 'learning_rate': 0.1, 'max_bin': 3, 'max_delta_step': 0, 'max_depth': 2, 'max_leaves': 3, 'min_child_weight': 0.001, 'n_estimators': 100}\n",
"-20.039419563611347\n",
"20.508699798583983\n"
]
},
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>0</th>\n",
" <th>1</th>\n",
" <th>2</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>43.000000</td>\n",
" <td>37.987656</td>\n",
" <td>5.012344</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>9.000000</td>\n",
" <td>53.990318</td>\n",
" <td>-44.990318</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>73.000000</td>\n",
" <td>59.660213</td>\n",
" <td>13.339787</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>16.000000</td>\n",
" <td>53.314426</td>\n",
" <td>-37.314426</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>59.000000</td>\n",
" <td>60.072800</td>\n",
" <td>-1.072800</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75</th>\n",
" <td>53.000000</td>\n",
" <td>55.507435</td>\n",
" <td>-2.507435</td>\n",
" </tr>\n",
" <tr>\n",
" <th>76</th>\n",
" <td>93.000000</td>\n",
" <td>59.416786</td>\n",
" <td>33.583214</td>\n",
" </tr>\n",
" <tr>\n",
" <th>77</th>\n",
" <td>82.416667</td>\n",
" <td>47.146587</td>\n",
" <td>35.270079</td>\n",
" </tr>\n",
" <tr>\n",
" <th>78</th>\n",
" <td>89.000000</td>\n",
" <td>57.640259</td>\n",
" <td>31.359741</td>\n",
" </tr>\n",
" <tr>\n",
" <th>79</th>\n",
" <td>88.000000</td>\n",
" <td>54.131992</td>\n",
" <td>33.868008</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>80 rows × 3 columns</p>\n",
"</div>"
],
"text/plain": [
" 0 1 2\n",
"0 43.000000 37.987656 5.012344\n",
"1 9.000000 53.990318 -44.990318\n",
"2 73.000000 59.660213 13.339787\n",
"3 16.000000 53.314426 -37.314426\n",
"4 59.000000 60.072800 -1.072800\n",
".. ... ... ...\n",
"75 53.000000 55.507435 -2.507435\n",
"76 93.000000 59.416786 33.583214\n",
"77 82.416667 47.146587 35.270079\n",
"78 89.000000 57.640259 31.359741\n",
"79 88.000000 54.131992 33.868008\n",
"\n",
"[80 rows x 3 columns]"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"print(grid_search.best_params_)\n",
"print(grid_search.best_score_)\n",
"\n",
"model = grid_search.best_estimator_\n",
"\n",
"y_pred = model.predict(X_test_reserved)\n",
"\n",
"print(mean_absolute_error(y_test_reserved, y_pred))\n",
"\n",
"l1 = np.array(list(y_test_reserved))\n",
"l2 = np.array(list(y_pred))\n",
"l3 = l1 - l2\n",
"\n",
"display(pd.DataFrame([l1, l2, l3]).T)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"CV MAE and R2\n",
"20.039419563611347\n",
"0.05608801676895068\n",
"\n",
"Training set MAE and R2\n",
"17.971198264757792\n",
"0.1730067350076071\n",
"\n",
"Test set MAE and R2\n",
"20.508699798583983\n",
"0.029032418854204156\n"
]
}
],
"source": [
"param = {'gamma': 0, 'learning_rate': 0.1, 'max_bin': 3, 'max_delta_step': 0, 'max_depth': 2, 'max_leaves': 3, 'min_child_weight': 0.001, 'n_estimators': 100, \"objective\":\"reg:absoluteerror\"}\n",
"\n",
"model = XGBRegressor(**param)\n",
"\n",
"model.fit(X_train_full, y_train_full)\n",
"\n",
"print(\"CV MAE and R2\")\n",
"print(-np.mean(cross_val_score(model, X_train_full, y_train_full, scoring='neg_mean_absolute_error')))\n",
"print(np.mean(cross_val_score(model, X_train_full, y_train_full, scoring='r2')))\n",
"\n",
"y_pred_train = model.predict(X_train_full)\n",
"print(\"\\nTraining set MAE and R2\")\n",
"print(mean_absolute_error(y_train_full, y_pred_train))\n",
"print(r2_score(y_train_full, y_pred_train))\n",
"\n",
"y_pred_test = model.predict(X_test_reserved)\n",
"print(\"\\nTest set MAE and R2\")\n",
"print(mean_absolute_error(y_test_reserved, y_pred_test))\n",
"print(r2_score(y_test_reserved, y_pred_test))\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "MLEAsm",
"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": "3.10.15"
}
},
"nbformat": 4,
"nbformat_minor": 2
}