117 lines
4.6 KiB
Python
117 lines
4.6 KiB
Python
import pandas as pd
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from datetime import datetime, timedelta
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import numpy as np
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from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score
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from sklearn.preprocessing import MinMaxScaler
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from sklearn.decomposition import PCA
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import lightgbm as lgb
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from tqdm import tqdm
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from collections import defaultdict
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import asyncio
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import aiohttp
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import aiofiles
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import pickle
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import time
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import os
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class ScorePredictor:
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def __init__(self):
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self.scaler = MinMaxScaler()
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self.model = lgb.LGBMClassifier(
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n_estimators=1_000,
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learning_rate=0.001,
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max_depth=10,
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num_leaves=2**10-1,
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n_jobs=10,
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random_state=42
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)
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self.warm_start_model_path = 'ml_models/weights/ai-score/stacking_weights.pkl'
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#self.pca = PCA(n_components=3)
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def preprocess_train_data(self, X):
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X = np.where(np.isinf(X), np.nan, X)
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X = np.nan_to_num(X)
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X = self.scaler.fit_transform(X)
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return X #self.pca.fit_transform(X)
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def preprocess_test_data(self, X):
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X = np.where(np.isinf(X), np.nan, X)
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X = np.nan_to_num(X)
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X = self.scaler.fit_transform(X)
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return X #self.pca.fit_transform(X)
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def warm_start_training(self, X_train, y_train):
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X_train = self.preprocess_train_data(X_train)
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self.model.fit(X_train, y_train)
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pickle.dump(self.model, open(self.warm_start_model_path, 'wb'))
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print("Warm start model saved.")
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def fine_tune_model(self, X_train, y_train):
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X_train = self.preprocess_train_data(X_train)
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if self.model is None:
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self.model = load_model(self.warm_start_model_path, custom_objects={'SelfAttention': SelfAttention})
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early_stopping = EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True)
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reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=4, min_lr=0.0001)
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self.model.fit(X_train, y_train, epochs=100, batch_size=128, validation_split=0.1, callbacks=[early_stopping, reduce_lr])
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print("Model fine-tuned (not saved).")
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def evaluate_model(self, X_test, y_test):
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X_test = self.preprocess_test_data(X_test)
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with open(self.warm_start_model_path, 'rb') as f:
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self.model = pickle.load(f)
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test_predictions = self.model.predict_proba(X_test)
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class_1_probabilities = test_predictions[:, 1]
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binary_predictions = (class_1_probabilities >= 0.5).astype(int)
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# Calculate and print metrics
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test_precision = precision_score(y_test, binary_predictions)
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test_accuracy = accuracy_score(y_test, binary_predictions)
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test_f1_score = f1_score(y_test, binary_predictions)
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test_recall_score = recall_score(y_test, binary_predictions)
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test_roc_auc_score = roc_auc_score(y_test, binary_predictions)
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print(f"Test Precision: {round(test_precision * 100)}%")
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print(f"Test Accuracy: {round(test_accuracy * 100)}%")
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print(f"F1 Score: {round(test_f1_score * 100)}%")
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print(f"Recall: {round(test_recall_score * 100)}%")
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print(f"ROC AUC: {round(test_roc_auc_score * 100)}%")
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last_prediction_prob = class_1_probabilities[-1]
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print(pd.DataFrame({'y_test': y_test, 'y_pred': binary_predictions}))
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print(f"Last prediction probability: {last_prediction_prob}")
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thresholds = [0.8, 0.75, 0.7, 0.6, 0.5, 0.45, 0.4, 0.35, 0.3, 0]
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scores = [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
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score = None
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for threshold, value in zip(thresholds, scores):
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if last_prediction_prob >= threshold:
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score = value
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break
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return {
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'accuracy': round(test_accuracy * 100),
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'precision': round(test_precision * 100),
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'f1_score': round(test_f1_score * 100),
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'recall_score': round(test_recall_score * 100),
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'roc_auc_score': round(test_roc_auc_score * 100),
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'score': score
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}
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def feature_selection(self, X_train, y_train, k=100):
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print('Feature selection:')
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print(f"X_train shape: {X_train.shape}, y_train shape: {y_train.shape}")
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selector = SelectKBest(score_func=f_classif, k=k)
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selector.fit(X_train, y_train)
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selector.transform(X_train)
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selected_features = [col for i, col in enumerate(X_train.columns) if selector.get_support()[i]]
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return selected_features |