Serendipity
ceddbdd559
- Added Lecture4 - ModelFree.pdf (3013 KB) - Added Lecture5 - Control.pdf (2575 KB) - Added Lecture6 - Value.pdf (3320 KB)
463 lines
33 KiB
Plaintext
463 lines
33 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "170d0b4f",
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"metadata": {},
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"source": [
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"# Insurance Premium Risk Classification\n",
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"## DTS304TC Machine Learning - Coursework 1\n",
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"\n",
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"**Student ID**: 1234560 (Last digit = 0)\n",
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"**Compulsory Category**: A - Data Quality & Missingness\n",
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"**Optional Category**: D - Robustness & Soft Voting Ensemble\n",
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"\n",
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"**Primary metric**: macro-F1 (imbalanced dataset)\n",
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"**Secondary metric**: accuracy\n",
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"\n",
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"---\n",
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"\n",
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"## Workflow\n",
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"1. Data loading & EDA\n",
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"2. Leakage feature identification & removal\n",
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"3. Preprocessing pipeline construction\n",
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"4. Baseline model (Logistic Regression)\n",
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"5. Controlled comparison: Random Forest vs XGBoost\n",
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"6. Advanced hyperparameter optimisation (Optuna/TPE)\n",
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"7. Personalised improvement (Category A + Category D)\n",
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"8. K-Means & GMM unsupervised exploration\n",
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"9. Final model selection\n",
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"10. Hidden-test CSV export"
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]
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},
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{
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"cell_type": "markdown",
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"id": "463d3e6d",
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"metadata": {},
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"source": [
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"## Step 1: Setup & Data Loading"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a12f069a",
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"metadata": {},
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"outputs": [],
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"source": [
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"import warnings\nwarnings.filterwarnings('ignore')\nimport os\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.metrics import accuracy_score, f1_score, classification_report, confusion_matrix, ConfusionMatrixDisplay\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.preprocessing import StandardScaler, LabelEncoder\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier, VotingClassifier\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.compose import ColumnTransformer\nfrom sklearn.preprocessing import OneHotEncoder\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.cluster import KMeans\nfrom sklearn.mixture import GaussianMixture\nfrom sklearn.metrics import silhouette_score\nfrom sklearn.decomposition import PCA\nimport xgboost as xgb\nimport optuna\noptuna.logging.set_verbosity(optuna.logging.WARNING)\n\nRANDOM_STATE = 42\nnp.random.seed(RANDOM_STATE)\nplt.rcParams['figure.figsize'] = (10, 6)\nplt.rcParams['font.size'] = 12\nsns.set_style('whitegrid')\nprint('All libraries imported successfully!')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1c4b453a",
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"metadata": {},
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"outputs": [],
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"source": [
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"DATA_DIR = r'd:\\Code\\doing_exercises\\programs\\外教作业外快\\强化学习个人课程作业报告\\dataset_final'\nOUTPUT_DIR = r'd:\\Code\\doing_exercises\\programs\\外教作业外快\\强化学习个人课程作业报告\\outputs'\n\ntrain_df = pd.read_csv(os.path.join(DATA_DIR, 'train.csv'))\nval_df = pd.read_csv(os.path.join(DATA_DIR, 'val.csv'))\ntest_df = pd.read_csv(os.path.join(DATA_DIR, 'test_features.csv'))\n\nprint(f'Train shape: {train_df.shape}')\nprint(f'Val shape: {val_df.shape}')\nprint(f'Test shape: {test_df.shape}')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8b8e7ad9",
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"metadata": {},
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"source": [
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"## Step 2: Exploratory Data Analysis (EDA)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "45e520e2",
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"metadata": {},
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"outputs": [],
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"source": [
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"print('=== TARGET DISTRIBUTION (TRAIN) ===')\ntarget_counts = train_df['premium_risk'].value_counts()\nprint(target_counts)\nprint((target_counts / len(train_df) * 100).round(2))\n\nfig, ax = plt.subplots(figsize=(8, 5))\ncolors = ['#4CAF50', '#FFC107', '#F44336']\ntarget_counts.sort_index().plot(kind='bar', ax=ax, color=colors)\nax.set_title('Target Variable Distribution (Train)', fontsize=14)\nax.set_xlabel('Premium Risk')\nax.set_ylabel('Count')\nax.set_xticklabels(ax.get_xticklabels(), rotation=0)\nfor i, (idx, val) in enumerate(target_counts.sort_index().items()):\n ax.text(i, val + 300, f'{val}\\n({val/len(train_df)*100:.1f}%)', ha='center')\nplt.tight_layout()\nplt.savefig(os.path.join(OUTPUT_DIR, 'figures', 'target_distribution.png'), dpi=150)\nplt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9e2428e4",
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"metadata": {},
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"outputs": [],
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"source": [
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"print('=== MISSING VALUES (TRAIN) ===')\nmissing = train_df.isnull().sum()\nmissing = missing[missing > 0].sort_values(ascending=False)\nprint(missing)\n\nfig, ax = plt.subplots(figsize=(12, 6))\nmissing.plot(kind='barh', ax=ax, color='coral')\nax.set_title('Missing Values per Column (Train)', fontsize=14)\nax.set_xlabel('Count')\nplt.tight_layout()\nplt.savefig(os.path.join(OUTPUT_DIR, 'figures', 'missing_values.png'), dpi=150)\nplt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a5cafc5e",
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"metadata": {},
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"outputs": [],
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"source": [
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"noise_cols = [c for c in train_df.columns if 'noise' in c.lower()]\nprint(f'Noise features: {noise_cols}')\n\nprint('\\n=== bureau_risk_index stats ===')\nprint(train_df['bureau_risk_index'].describe())\n\nfig, ax = plt.subplots(figsize=(8, 5))\ntrain_df.boxplot(column='bureau_risk_index', by='premium_risk', ax=ax)\nax.set_title('bureau_risk_index by Premium Risk')\nax.set_xlabel('Premium Risk')\nax.set_ylabel('bureau_risk_index')\nplt.suptitle('')\nplt.tight_layout()\nplt.savefig(os.path.join(OUTPUT_DIR, 'figures', 'bureau_risk_boxplot.png'), dpi=150)\nplt.show()"
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]
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},
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{
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"cell_type": "markdown",
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"id": "4db79797",
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"metadata": {},
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"source": [
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"## Step 3: Leakage Feature Identification & Removal\n",
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"\n",
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"**Strategy**: Train a DecisionTree with each feature individually.\n",
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"Features with abnormally high macro-F1 are suspected leakage."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "fbf59f43",
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"metadata": {},
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"outputs": [],
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"source": [
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"def screen_single_feature_leakage(df, target_col, feature_cols, scoring='f1_macro'):\n from sklearn.tree import DecisionTreeClassifier\n results = []\n for col in feature_cols:\n temp_df = df[[col, target_col]].dropna()\n X_temp = temp_df[[col]].values\n y_temp = temp_df[target_col].values\n le = LabelEncoder()\n y_enc = le.fit_transform(y_temp)\n try:\n clf = DecisionTreeClassifier(random_state=RANDOM_STATE, max_depth=3)\n scores = cross_val_score(clf, X_temp, y_enc, cv=3, scoring=scoring)\n results.append({'feature': col, 'mean_f1_macro': scores.mean(), 'std': scores.std()})\n except:\n results.append({'feature': col, 'mean_f1_macro': 0.0, 'std': 0.0})\n return pd.DataFrame(results).sort_values('mean_f1_macro', ascending=False)\n\nfeature_to_test = [c for c in train_df.columns if c not in ['applicant_id', 'customer_key', 'premium_risk']]\nprint('Screening single features for leakage detection (this may take a few minutes)...')\nleakage_results = screen_single_feature_leakage(train_df, 'premium_risk', feature_to_test)\nprint('\\n=== TOP 10 SINGLE-FEATURE F1 MACRO SCORES ===')\nprint(leakage_results.head(10))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "ec03b578",
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"metadata": {},
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"outputs": [],
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"source": [
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"LEAKAGE_THRESHOLD = 0.85\nprint('=== LEAKAGE DETECTION RESULTS ===')\nprint(leakage_results.head(10))\n\nbureau_score = leakage_results[leakage_results['feature'] == 'bureau_risk_index']['mean_f1_macro'].values[0]\nprint(f'\\nbureau_risk_index F1 macro: {bureau_score:.4f}')\n\nif bureau_score > LEAKAGE_THRESHOLD:\n print('\\n*** ALERT: bureau_risk_index shows abnormally high predictive power! ***')\n print('*** This is consistent with a leakage feature. ***')\n print('*** ACTION: bureau_risk_index will be removed from features. ***')\n LEAKAGE_FEATURE = 'bureau_risk_index'\nelse:\n top_feat = leakage_results.iloc[0]['feature']\n top_score = leakage_results.iloc[0]['mean_f1_macro']\n print(f'\\nTop feature: {top_feat} with F1 macro = {top_score:.4f}')\n if top_score > 0.80:\n LEAKAGE_FEATURE = top_feat\n else:\n LEAKAGE_FEATURE = None"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "f01746fe",
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"metadata": {},
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"outputs": [],
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"source": [
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"if LEAKAGE_FEATURE:\n print(f'Removing leakage feature: {LEAKAGE_FEATURE}')\n train_df_clean = train_df.drop(columns=[LEAKAGE_FEATURE])\n val_df_clean = val_df.drop(columns=[LEAKAGE_FEATURE])\n test_df_clean = test_df.drop(columns=[LEAKAGE_FEATURE])\nelse:\n print('No leakage feature to remove.')\n train_df_clean = train_df.copy()\n val_df_clean = val_df.copy()\n test_df_clean = test_df.copy()\n\nprint(f'After removal - Train: {train_df_clean.shape}, Val: {val_df_clean.shape}, Test: {test_df_clean.shape}')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "ed28be55",
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"metadata": {},
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"source": [
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"## Step 4: Preprocessing Pipeline Construction"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "6f56180d",
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"metadata": {},
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"outputs": [],
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"source": [
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"ID_COLS = ['applicant_id', 'customer_key', 'applicant_ref_code']\nNOISE_COLS = ['noise_feature_1', 'noise_feature_2', 'noise_feature_3', 'noise_feature_4', 'noise_feature_5']\nTARGET_COL = 'premium_risk'\n\nall_cols = train_df_clean.columns.tolist()\nfeature_cols_all = [c for c in all_cols if c not in ID_COLS + NOISE_COLS + [TARGET_COL]]\n\nNUMERIC_FEATURES = train_df_clean[feature_cols_all].select_dtypes(include=[np.number]).columns.tolist()\nCATEGORICAL_FEATURES = train_df_clean[feature_cols_all].select_dtypes(include=['object']).columns.tolist()\n\nprint(f'Total features: {len(feature_cols_all)}')\nprint(f'Numeric ({len(NUMERIC_FEATURES)}): {NUMERIC_FEATURES}')\nprint(f'Categorical ({len(CATEGORICAL_FEATURES)}): {CATEGORICAL_FEATURES}')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2fbe754d",
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"metadata": {},
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"outputs": [],
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"source": [
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"numeric_transformer = Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='median')),\n ('scaler', StandardScaler())\n])\n\ncategorical_transformer = Pipeline(steps=[\n ('imputer', SimpleImputer(strategy='most_frequent')),\n ('onehot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))\n])\n\npreprocessor = ColumnTransformer(\n transformers=[\n ('num', numeric_transformer, NUMERIC_FEATURES),\n ('cat', categorical_transformer, CATEGORICAL_FEATURES)\n ],\n remainder='drop'\n)\nprint('Preprocessing pipeline created!')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "0e797d98",
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"metadata": {},
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"outputs": [],
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"source": [
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"X_train = train_df_clean[feature_cols_all]\ny_train = train_df_clean[TARGET_COL]\nX_val = val_df_clean[feature_cols_all]\ny_val = val_df_clean[TARGET_COL]\nX_test = test_df_clean[feature_cols_all]\n\nle_target = LabelEncoder()\ny_train_enc = le_target.fit_transform(y_train)\ny_val_enc = le_target.transform(y_val)\n\nprint(f'Classes: {le_target.classes_}')\nprint(f'X_train: {X_train.shape} | X_val: {X_val.shape} | X_test: {X_test.shape}')"
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]
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},
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{
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"cell_type": "markdown",
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"id": "481e4b48",
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"metadata": {},
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"source": [
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"## Step 5: Baseline Model - Logistic Regression"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "1a900d26",
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"metadata": {},
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"outputs": [],
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"source": [
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"def evaluate_model(pipeline, X_tr, y_tr, X_v, y_v, le, model_name='Model'):\n y_tr_pred = pipeline.predict(X_tr)\n y_v_pred = pipeline.predict(X_v)\n results = {\n 'model': model_name,\n 'train_accuracy': accuracy_score(y_tr, y_tr_pred),\n 'val_accuracy': accuracy_score(y_v, y_v_pred),\n 'train_f1_macro': f1_score(y_tr, y_tr_pred, average='macro'),\n 'val_f1_macro': f1_score(y_v, y_v_pred, average='macro'),\n }\n f1_per_class = f1_score(y_v, y_v_pred, average=None)\n for i, cls in enumerate(le.classes_):\n results[f'val_f1_{cls}'] = f1_per_class[i]\n return results\n\ndef plot_confusion_matrix(pipeline, X_v, y_v, le, title, save_path):\n y_pred = pipeline.predict(X_v)\n fig, ax = plt.subplots(figsize=(8, 6))\n cm = confusion_matrix(y_v, y_pred)\n disp = ConfusionMatrixDisplay(confusion_matrix=cm, display_labels=le.classes_)\n disp.plot(ax=ax, cmap='Blues', values_format='d')\n ax.set_title(title, fontsize=14)\n plt.tight_layout()\n plt.savefig(save_path, dpi=150)\n plt.show()\n return cm"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "c8992d98",
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"metadata": {},
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"outputs": [],
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"source": [
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"print('Training Baseline: Logistic Regression...')\nbaseline_pipeline = Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('classifier', LogisticRegression(class_weight='balanced', max_iter=1000, random_state=RANDOM_STATE, n_jobs=-1))\n])\nbaseline_pipeline.fit(X_train, y_train_enc)\n\nbaseline_results = evaluate_model(baseline_pipeline, X_train, y_train_enc, X_val, y_val_enc, le_target, 'Baseline_LR')\n\nprint('\\n=== BASELINE MODEL RESULTS ===')\nfor k, v in baseline_results.items():\n if k != 'model':\n print(f'{k}: {v:.4f}')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "5ff29071",
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_confusion_matrix(baseline_pipeline, X_val, y_val_enc, le_target,\n 'Baseline: Logistic Regression - Confusion Matrix',\n os.path.join(OUTPUT_DIR, 'figures', 'baseline_confusion_matrix.png'))\n\nprint('\\n=== CLASSIFICATION REPORT (VAL) ===')\ny_val_pred = baseline_pipeline.predict(X_val)\nprint(classification_report(y_val_enc, y_val_pred, target_names=le_target.classes_))\n\nall_results = [baseline_results]\npd.DataFrame(all_results).to_csv(\n os.path.join(OUTPUT_DIR, 'tables', 'model_comparison_summary.csv'), index=False)"
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]
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},
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{
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"cell_type": "markdown",
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"id": "8675fd8e",
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"metadata": {},
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"source": [
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"## Step 6: Controlled Comparison - Random Forest vs XGBoost"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "30cd02ce",
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"metadata": {},
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"outputs": [],
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"source": [
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"import time\n\nprint('Training Random Forest...')\nstart = time.time()\nrf_pipeline = Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('classifier', RandomForestClassifier(n_estimators=200, class_weight='balanced', random_state=RANDOM_STATE, n_jobs=-1))\n])\nrf_pipeline.fit(X_train, y_train_enc)\nrf_time = time.time() - start\n\nrf_results = evaluate_model(rf_pipeline, X_train, y_train_enc, X_val, y_val_enc, le_target, 'RandomForest')\nrf_results['train_time'] = rf_time\n\nprint('Training XGBoost...')\nstart = time.time()\nxgb_pipeline = Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('classifier', xgb.XGBClassifier(n_estimators=200, learning_rate=0.1, max_depth=6,\n objective='multi:softmax', num_class=3,\n tree_method='gpu_hist', device='cuda', random_state=RANDOM_STATE, verbosity=0))\n])\nxgb_pipeline.fit(X_train, y_train_enc)\nxgb_time = time.time() - start\n\nxgb_results = evaluate_model(xgb_pipeline, X_train, y_train_enc, X_val, y_val_enc, le_target, 'XGBoost')\nxgb_results['train_time'] = xgb_time\n\nprint(f'RF time: {rf_time:.2f}s | XGB time: {xgb_time:.2f}s')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "814e6787",
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"metadata": {},
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"outputs": [],
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"source": [
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"all_results.append(rf_results)\nall_results.append(xgb_results)\nresults_df = pd.DataFrame(all_results)\n\nprint('\\n=== MODEL COMPARISON SUMMARY ===')\ndisplay_cols = ['model', 'train_accuracy', 'val_accuracy', 'train_f1_macro', 'val_f1_macro', 'train_time']\nprint(results_df[display_cols].round(4).to_string(index=False))\n\nprint('\\n=== CLASS-WISE F1 (VAL) ===')\nclass_cols = [c for c in results_df.columns if c.startswith('val_f1_') and c != 'val_f1_macro']\nprint(results_df[['model'] + class_cols].round(4).to_string(index=False))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "704d4061",
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"metadata": {},
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"outputs": [],
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"source": [
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"fig, axes = plt.subplots(1, 2, figsize=(14, 5))\nmodels = results_df['model'].tolist()\nval_f1 = results_df['val_f1_macro'].tolist()\nval_acc = results_df['val_accuracy'].tolist()\n\nbars1 = axes[0].bar(models, val_f1, color=['#2196F3', '#4CAF50', '#FF9800'])\naxes[0].set_title('Validation Macro-F1 Comparison', fontsize=13)\naxes[0].set_ylabel('Macro-F1')\naxes[0].set_ylim(0, 1)\nfor bar, val in zip(bars1, val_f1):\n axes[0].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, f'{val:.4f}', ha='center')\n\nbars2 = axes[1].bar(models, val_acc, color=['#2196F3', '#4CAF50', '#FF9800'])\naxes[1].set_title('Validation Accuracy Comparison', fontsize=13)\naxes[1].set_ylabel('Accuracy')\naxes[1].set_ylim(0, 1)\nfor bar, val in zip(bars2, val_acc):\n axes[1].text(bar.get_x() + bar.get_width()/2, bar.get_height() + 0.01, f'{val:.4f}', ha='center')\n\nplt.tight_layout()\nplt.savefig(os.path.join(OUTPUT_DIR, 'figures', 'model_comparison.png'), dpi=150)\nplt.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "89747cf4",
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"metadata": {},
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"outputs": [],
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"source": [
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"plot_confusion_matrix(rf_pipeline, X_val, y_val_enc, le_target,\n 'Random Forest - Confusion Matrix',\n os.path.join(OUTPUT_DIR, 'figures', 'rf_confusion_matrix.png'))\n\nplot_confusion_matrix(xgb_pipeline, X_val, y_val_enc, le_target,\n 'XGBoost - Confusion Matrix',\n os.path.join(OUTPUT_DIR, 'figures', 'xgb_confusion_matrix.png'))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d9e3d57d",
|
|
"metadata": {},
|
|
"source": [
|
|
"### Bagging vs Boosting Analysis"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "81508463",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print('=== BAGGING VS BOOSTING ANALYSIS ===')\nrf_val_f1 = rf_results['val_f1_macro']\nrf_train_f1 = rf_results['train_f1_macro']\nrf_gap = rf_train_f1 - rf_val_f1\n\nxgb_val_f1 = xgb_results['val_f1_macro']\nxgb_train_f1 = xgb_results['train_f1_macro']\nxgb_gap = xgb_train_f1 - xgb_val_f1\n\nprint(f'Random Forest - val_f1_macro: {rf_val_f1:.4f}, overfitting gap: {rf_gap:.4f}')\nprint(f'XGBoost - val_f1_macro: {xgb_val_f1:.4f}, overfitting gap: {xgb_gap:.4f}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "de4a5bc9",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Step 7: Advanced Hyperparameter Optimisation (Optuna)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e6361576",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"def objective(trial):\n params = {\n 'n_estimators': trial.suggest_int('n_estimators', 100, 500),\n 'max_depth': trial.suggest_int('max_depth', 3, 10),\n 'learning_rate': trial.suggest_float('learning_rate', 0.01, 0.3, log=True),\n 'min_child_weight': trial.suggest_int('min_child_weight', 1, 10),\n 'subsample': trial.suggest_float('subsample', 0.5, 1.0),\n 'colsample_bytree': trial.suggest_float('colsample_bytree', 0.5, 1.0),\n 'gamma': trial.suggest_float('gamma', 0, 5),\n 'reg_alpha': trial.suggest_float('reg_alpha', 1e-4, 10.0, log=True),\n 'reg_lambda': trial.suggest_float('reg_lambda', 1e-4, 10.0, log=True),\n 'objective': 'multi:softmax',\n 'num_class': 3,\n 'random_state': RANDOM_STATE,\n 'tree_method': 'gpu_hist', 'device': 'cuda',\n 'verbosity': 0\n }\n pipeline = Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('classifier', xgb.XGBClassifier(**params))\n ])\n pipeline.fit(X_train, y_train_enc)\n y_pred = pipeline.predict(X_val)\n score = f1_score(y_val_enc, y_pred, average='macro')\n return score\n\nprint('Starting Optuna hyperparameter optimisation (30 trials)...')\nstudy = optuna.create_study(direction='maximize', sampler=optuna.samplers.TPESampler(seed=RANDOM_STATE))\nstudy.optimize(objective, n_trials=30, show_progress_bar=False)\n\nprint(f'Best trial: {study.best_trial.number} | Best macro-F1: {study.best_value:.4f}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "f7ba4f2a",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print('\\n=== BEST HYPERPARAMETERS ===')\nbest_params = study.best_params\nfor k, v in best_params.items():\n print(f' {k}: {v}')\n\nfig = optuna.visualization.matplotlib.plot_optimization_history(study)\nplt.title('Optuna Optimization History')\nplt.tight_layout()\nplt.savefig(os.path.join(OUTPUT_DIR, 'figures', 'optuna_optimization_history.png'), dpi=150)\nplt.show()\n\nfig = optuna.visualization.matplotlib.plot_param_importances(study)\nplt.title('Hyperparameter Importance')\nplt.tight_layout()\nplt.savefig(os.path.join(OUTPUT_DIR, 'figures', 'optuna_param_importance.png'), dpi=150)\nplt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "640263ea",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"best_xgb_params = {\n **study.best_params,\n 'objective': 'multi:softmax',\n 'num_class': 3,\n 'random_state': RANDOM_STATE,\n 'tree_method': 'gpu_hist', 'device': 'cuda',\n 'verbosity': 0\n}\n\nprint('Training tuned XGBoost...')\nimport time\nstart = time.time()\ntuned_xgb_pipeline = Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('classifier', xgb.XGBClassifier(**best_xgb_params))\n])\ntuned_xgb_pipeline.fit(X_train, y_train_enc)\ntuned_time = time.time() - start\n\ntuned_results = evaluate_model(tuned_xgb_pipeline, X_train, y_train_enc, X_val, y_val_enc, le_target, 'XGBoost_Tuned')\ntuned_results['train_time'] = tuned_time\n\nprint('\\n=== TUNED XGBOOST RESULTS ===')\nfor k, v in tuned_results.items():\n if k != 'model':\n print(f'{k}: {v:.4f}')\n\nprint(f'\\nTuning improvement (macro-F1): +{tuned_results[\"val_f1_macro\"] - xgb_results[\"val_f1_macro\"]:.4f}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "19742e63",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"all_results.append(tuned_results)\nresults_df = pd.DataFrame(all_results)\n\nprint('\\n=== BEFORE VS AFTER TUNING ===')\nprint(results_df[['model', 'val_f1_macro', 'val_accuracy', 'train_time']].round(4).to_string(index=False))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "d01bcca7",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Step 8: Personalised Improvement (Category A + Category D)\n",
|
|
"\n",
|
|
"**Student ID last digit = 0 → Category A (Compulsory) + Category D (Optional)**\n",
|
|
"\n",
|
|
"- **Category A** (Data Quality & Missingness): Add missing value indicator features\n",
|
|
"- **Category D** (Robustness & Ensemble): Soft Voting Ensemble (RF + XGBoost)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "1f662833",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print('=== CATEGORY A: IMPROVED MISSING VALUE HANDLING ===')\n\nMISSING_COLS = ['net_monthly_income_gbp', 'avg_payment_delay_days', 'monthly_investment_gbp',\n 'prior_debt_products', 'account_tenure']\n\nfor col in MISSING_COLS:\n missing_col_name = f'{col}_missing'\n train_df_clean[missing_col_name] = train_df_clean[col].isnull().astype(int)\n val_df_clean[missing_col_name] = val_df_clean[col].isnull().astype(int)\n test_df_clean[missing_col_name] = test_df_clean[col].isnull().astype(int)\n print(f'Added missing indicator: {missing_col_name}')\n\nfeature_cols_catA = feature_cols_all + [f'{c}_missing' for c in MISSING_COLS]\nprint(f'\\nFeature columns after adding indicators: {len(feature_cols_catA)}')\n\nX_train_A = train_df_clean[feature_cols_catA]\nX_val_A = val_df_clean[feature_cols_catA]\nX_test_A = test_df_clean[feature_cols_catA]\n\nNUMERIC_FEATURES_A = X_train_A.select_dtypes(include=[np.number]).columns.tolist()\nCATEGORICAL_FEATURES_A = X_train_A.select_dtypes(include=['object']).columns.tolist()\n\npreprocessor_A = ColumnTransformer(\n transformers=[\n ('num', numeric_transformer, NUMERIC_FEATURES_A),\n ('cat', categorical_transformer, CATEGORICAL_FEATURES_A)\n ],\n remainder='drop'\n)\n\ncatA_pipeline = Pipeline(steps=[\n ('preprocessor', preprocessor_A),\n ('classifier', xgb.XGBClassifier(**best_xgb_params))\n])\ncatA_pipeline.fit(X_train_A, y_train_enc)\n\ncatA_results = evaluate_model(catA_pipeline, X_train_A, y_train_enc, X_val_A, y_val_enc, le_target, 'XGB_CatA_MissingHandling')\n\nprint('\\n=== CATEGORY A RESULTS ===')\nprint(f'val_f1_macro: {catA_results[\"val_f1_macro\"]:.4f}')\nprint(f'val_accuracy: {catA_results[\"val_accuracy\"]:.4f}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "0069e9d5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print('=== CATEGORY D: SOFT VOTING ENSEMBLE ===')\nprint('Training Soft Voting Ensemble (RF + XGBoost)...')\n\nrf_clf = RandomForestClassifier(n_estimators=200, class_weight='balanced', random_state=RANDOM_STATE, n_jobs=-1)\nxgb_clf = xgb.XGBClassifier(**best_xgb_params)\n\nvoting_clf = VotingClassifier(\n estimators=[\n ('rf', rf_clf),\n ('xgb', xgb_clf)\n ],\n voting='soft',\n n_jobs=-1\n)\n\nensemble_pipeline = Pipeline(steps=[\n ('preprocessor', preprocessor),\n ('classifier', voting_clf)\n])\nensemble_pipeline.fit(X_train, y_train_enc)\n\nensemble_results = evaluate_model(ensemble_pipeline, X_train, y_train_enc, X_val, y_val_enc, le_target, 'Ensemble_SoftVoting')\n\nprint(f'Ensemble val_f1_macro: {ensemble_results[\"val_f1_macro\"]:.4f}')\nprint(f'Ensemble val_accuracy: {ensemble_results[\"val_accuracy\"]:.4f}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "c95e0008",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"all_results.append(catA_results)\nall_results.append(ensemble_results)\nresults_df = pd.DataFrame(all_results)\n\nprint('\\n=== PERSONALISED IMPROVEMENT SUMMARY ===')\nprint(results_df[['model', 'val_f1_macro', 'val_accuracy']].round(4).to_string(index=False))\n\nresults_df.to_csv(\n os.path.join(OUTPUT_DIR, 'tables', 'personalised_improvement_summary.csv'), index=False)\n\nimprove_A = catA_results['val_f1_macro'] - tuned_results['val_f1_macro']\nimprove_D = ensemble_results['val_f1_macro'] - tuned_results['val_f1_macro']\nprint(f'\\nCategory A improvement (vs Tuned): +{improve_A:.4f}')\nprint(f'Category D improvement (vs Tuned): +{improve_D:.4f}')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "df4d2cc2",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Step 9: K-Means & GMM Unsupervised Exploration"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "5ddfd4d3",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print('=== K-MEANS & GMM CLUSTERING ===')\n\npreprocessor_eval = ColumnTransformer(\n transformers=[\n ('num', numeric_transformer, NUMERIC_FEATURES),\n ('cat', categorical_transformer, CATEGORICAL_FEATURES)\n ],\n remainder='drop'\n)\n\nX_train_scaled = preprocessor_eval.fit_transform(X_train)\nprint(f'Scaled training data shape: {X_train_scaled.shape}')\n\npca = PCA(n_components=2, random_state=RANDOM_STATE)\nX_train_pca = pca.fit_transform(X_train_scaled)\nprint(f'PCA explained variance: {pca.explained_variance_ratio_.sum():.4f}')\n\nk_range = range(2, 9)\nkmeans_results = []\ngmm_results = []\n\nfor k in k_range:\n print(f' Running k={k}...')\n \n km = KMeans(n_clusters=k, random_state=RANDOM_STATE, n_init=10)\n km_labels = km.fit_predict(X_train_scaled)\n sil_km = silhouette_score(X_train_scaled, km_labels)\n \n gmm_model = GaussianMixture(n_components=k, random_state=RANDOM_STATE, n_init=5)\n gmm_labels = gmm_model.fit_predict(X_train_scaled)\n sil_gmm = silhouette_score(X_train_scaled, gmm_labels)\n \n kmeans_results.append({\n 'k': k,\n 'inertia': km.inertia_,\n 'silhouette_x': sil_km\n })\n gmm_results.append({\n 'k': k,\n 'log_likelihood': gmm_model.score(X_train_scaled) * X_train_scaled.shape[0],\n 'bic': gmm_model.bic(X_train_scaled),\n 'aic': gmm_model.aic(X_train_scaled),\n 'silhouette_y': sil_gmm\n })\n\nkm_df = pd.DataFrame(kmeans_results)\ngmm_df = pd.DataFrame(gmm_results)\ncluster_df = km_df.merge(gmm_df, on='k')\nprint('\\n=== CLUSTERING COMPARISON ===')\nprint(cluster_df.round(4).to_string(index=False))\n\ncluster_df.to_csv(os.path.join(OUTPUT_DIR, 'tables', 'clustering_comparison.csv'), index=False)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "d438c228",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"fig, axes = plt.subplots(1, 3, figsize=(15, 4))\n\naxes[0].plot(cluster_df['k'], cluster_df['inertia'], 'bo-', label='K-Means Inertia', linewidth=2)\naxes[0].set_xlabel('k')\naxes[0].set_ylabel('Inertia')\naxes[0].set_title('K-Means: Elbow Method')\naxes[0].grid(True)\n\naxes[1].plot(cluster_df['k'], cluster_df['bic'], 'g^-', label='BIC', linewidth=2)\naxes[1].plot(cluster_df['k'], cluster_df['aic'], 'rs--', label='AIC', linewidth=2)\naxes[1].set_xlabel('k')\naxes[1].set_ylabel('Score')\naxes[1].set_title('GMM: BIC & AIC (lower is better)')\naxes[1].legend()\naxes[1].grid(True)\n\naxes[2].plot(cluster_df['k'], cluster_df['silhouette_x'], 'bo-', label='K-Means', linewidth=2)\naxes[2].plot(cluster_df['k'], cluster_df['silhouette_y'], 'g^-', label='GMM', linewidth=2)\naxes[2].set_xlabel('k')\naxes[2].set_ylabel('Silhouette Score')\naxes[2].set_title('Silhouette Score Comparison (higher is better)')\naxes[2].legend()\naxes[2].grid(True)\n\nplt.tight_layout()\nplt.savefig(os.path.join(OUTPUT_DIR, 'figures', 'clustering_comparison.png'), dpi=150)\nplt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "08ba45ed",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"best_k = cluster_df.loc[cluster_df['silhouette_x'].idxmax(), 'k']\nprint(f'Best K for K-Means (by silhouette): {best_k}')\n\nkm_best = KMeans(n_clusters=int(best_k), random_state=RANDOM_STATE, n_init=10)\nkm_best_labels = km_best.fit_predict(X_train_scaled)\n\nfig, ax = plt.subplots(figsize=(8, 6))\nscatter = ax.scatter(X_train_pca[:, 0], X_train_pca[:, 1],\n c=km_best_labels, cmap='viridis', alpha=0.5, s=10)\nax.set_xlabel('PC1')\nax.set_ylabel('PC2')\nax.set_title(f'K-Means Clustering (k={best_k}) - PCA Visualization')\nplt.colorbar(scatter, ax=ax, label='Cluster')\nplt.tight_layout()\nplt.savefig(os.path.join(OUTPUT_DIR, 'figures', 'clustering_visualization.png'), dpi=150)\nplt.show()"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"id": "48c4ad67",
|
|
"metadata": {},
|
|
"source": [
|
|
"## Step 10: Final Model Selection & Hidden-Test Export"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "34692aa5",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print('=== FINAL MODEL SELECTION ===')\nprint('Based on val_f1_macro (primary metric):')\nfinal_model_name = results_df.loc[results_df['val_f1_macro'].idxmax(), 'model']\nprint(f'Selected model: {final_model_name} (val_f1_macro = {results_df[\"val_f1_macro\"].max():.4f})')\n\nif final_model_name == 'XGB_CatA_MissingHandling':\n final_pipeline = catA_pipeline\n X_test_final = X_test_A\nelif final_model_name == 'Ensemble_SoftVoting':\n final_pipeline = ensemble_pipeline\n X_test_final = X_test\nelse:\n final_pipeline = tuned_xgb_pipeline\n X_test_final = X_test\n\ny_val_final_pred = final_pipeline.predict(X_test_final if final_model_name == 'XGBoost_Tuned' else X_test)\ny_val_final_decoded = le_target.inverse_transform(y_val_final_pred)\n\nplot_confusion_matrix(final_pipeline, X_val_A if final_model_name == 'XGB_CatA_MissingHandling' else X_val,\n y_val_enc, le_target,\n f'Final Model: {final_model_name} - Confusion Matrix',\n os.path.join(OUTPUT_DIR, 'figures', 'final_model_confusion_matrix.png'))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "e5d2526b",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"print('\\n=== FINAL CLASSIFICATION REPORT (VAL) ===')\ny_val_pred_final = final_pipeline.predict(X_val_A if final_model_name == 'XGB_CatA_MissingHandling' else X_val)\nprint(classification_report(y_val_enc, y_val_pred_final, target_names=le_target.classes_))"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"id": "89d712c0",
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"STUDENT_ID = '1234560'\n\nif final_model_name == 'XGB_CatA_MissingHandling':\n y_test_pred = final_pipeline.predict(X_test_A)\nelif final_model_name == 'Ensemble_SoftVoting':\n y_test_pred = final_pipeline.predict(X_test)\nelse:\n y_test_pred = final_pipeline.predict(X_test)\n\ny_test_labels = le_target.inverse_transform(y_test_pred)\n\nsubmission_df = pd.DataFrame({\n 'applicant_id': test_df['applicant_id'],\n 'customer_key': test_df['customer_key'],\n 'premium_risk': y_test_labels\n})\n\nprint('=== SUBMISSION CSV VALIDATION ===')\nprint(f'Shape: {submission_df.shape}')\nprint(f'Columns: {list(submission_df.columns)}')\nprint(submission_df.head())\n\nprint('\\nPrediction counts:')\nprint(submission_df['premium_risk'].value_counts())\n\ncsv_path = os.path.join(OUTPUT_DIR, 'predictions', f'test_result_{STUDENT_ID}.csv')\nsubmission_df.to_csv(csv_path, index=False)\nprint(f'\\n*** CSV saved to: {csv_path} ***')"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "my_env",
|
|
"language": "python",
|
|
"name": "python3"
|
|
},
|
|
"language_info": {
|
|
"name": "python",
|
|
"version": "3.10.18"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 5
|
|
} |