108 lines
4.3 KiB
Python
108 lines
4.3 KiB
Python
import yfinance as yf
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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 xgboost import XGBClassifier
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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 # 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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class ScorePredictor:
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def __init__(self):
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self.scaler = MinMaxScaler()
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self.pca = PCA(n_components=5)
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self.warm_start_model_path = 'ml_models/weights/ai-score/warm_start_weights.pkl'
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self.model = lgb.LGBMClassifier(
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n_estimators=20_000, # If you want to use a larger model we've found 20_000 trees to be better
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learning_rate=0.01, # and a learning rate of 0.001
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max_depth=20, # and max_depth=6
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num_leaves=2**6-1, # and num_leaves of 2**6-1
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colsample_bytree=0.1
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)
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'''
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XGBClassifier(
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n_estimators=200,
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max_depth=5,
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learning_rate=0.1,
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random_state=42,
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n_jobs=10
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)
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'''
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def preprocess_train_data(self, X):
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"""Preprocess training data by scaling and applying PCA."""
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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) # Transform using the fitted scaler
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return X#self.pca.fit_transform(X) # Fit PCA and transform
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def preprocess_test_data(self, X):
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"""Preprocess test data by scaling and applying PCA."""
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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.transform(X) # Transform using the fitted scaler
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return X#self.pca.transform(X) # Transform using the fitted PCA
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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(f'{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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with open(f'{self.warm_start_model_path}', 'rb') as f:
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self.model = pickle.load(f)
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self.model.fit(X_train, y_train)
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print("Model fine-tuned")
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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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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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#print(test_predictions)
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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("Test Set Metrics:")
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print(f"Precision: {round(test_precision * 100)}%")
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print(f"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 Score: {round(test_recall_score * 100)}%")
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print(f"ROC AUC Score: {round(test_roc_auc_score * 100)}%")
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print(pd.DataFrame({'y_test': y_test, 'y_pred': binary_predictions}))
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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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last_prediction_prob = class_1_probabilities[-1]
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score = None
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print(f"Last prediction probability: {last_prediction_prob}")
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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 {'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} |