84 lines
3.3 KiB
Python
84 lines
3.3 KiB
Python
import yfinance as yf
|
|
import pandas as pd
|
|
from datetime import datetime, timedelta
|
|
from sklearn.ensemble import RandomForestClassifier
|
|
import numpy as np
|
|
from xgboost import XGBClassifier
|
|
from sklearn.metrics import precision_score, recall_score, f1_score, roc_auc_score, accuracy_score
|
|
from sklearn.preprocessing import MinMaxScaler
|
|
|
|
from tqdm import tqdm
|
|
from collections import defaultdict
|
|
import asyncio
|
|
import aiohttp
|
|
import aiofiles
|
|
import pickle
|
|
import time
|
|
|
|
class ScorePredictor:
|
|
def __init__(self):
|
|
self.scaler = MinMaxScaler()
|
|
self.warm_start_model_path = 'ml_models/weights/ai-score/warm_start_weights.pkl'
|
|
self.model = XGBClassifier(
|
|
n_estimators=200, # Increased from 100 due to problem complexity
|
|
max_depth=6, # Reduced to prevent overfitting with many features
|
|
learning_rate=0.1, # Added to control the learning process
|
|
colsample_bytree=0.8, # Added to randomly sample columns for each tree
|
|
subsample=0.8, # Added to randomly sample training data
|
|
reg_alpha=1, # L1 regularization to handle many features
|
|
reg_lambda=1, # L2 regularization to handle many features
|
|
random_state=42,
|
|
n_jobs=10
|
|
)
|
|
|
|
def preprocess_data(self, X):
|
|
X = np.where(np.isinf(X), np.nan, X)
|
|
X = np.nan_to_num(X)
|
|
X = self.scaler.fit_transform(X)
|
|
return X
|
|
|
|
def warm_start_training(self, X_train, y_train):
|
|
X_train = self.preprocess_data(X_train)
|
|
self.model.fit(X_train, y_train)
|
|
pickle.dump(self.model, open(f'{self.warm_start_model_path}', 'wb'))
|
|
print("Warm start model saved.")
|
|
|
|
def fine_tune_model(self, X_train, y_train):
|
|
X_train = self.preprocess_data(X_train)
|
|
|
|
with open(f'{self.warm_start_model_path}', 'rb') as f:
|
|
self.model = pickle.load(f)
|
|
|
|
self.model.fit(X_train, y_train)
|
|
print("Model fine-tuned")
|
|
|
|
|
|
def evaluate_model(self, X_test, y_test):
|
|
X_test = self.preprocess_data(X_test)
|
|
|
|
test_predictions = self.model.predict_proba(X_test)
|
|
class_1_probabilities = test_predictions[:, 1]
|
|
binary_predictions = (class_1_probabilities >= 0.5).astype(int)
|
|
#print(test_predictions)
|
|
test_precision = precision_score(y_test, binary_predictions)
|
|
test_accuracy = accuracy_score(y_test, binary_predictions)
|
|
|
|
print("Test Set Metrics:")
|
|
print(f"Precision: {round(test_precision * 100)}%")
|
|
print(f"Accuracy: {round(test_accuracy * 100)}%")
|
|
print(pd.DataFrame({'y_test': y_test, 'y_pred': binary_predictions}))
|
|
thresholds = [0.8, 0.75, 0.7, 0.6, 0.5, 0.45, 0.4, 0.35, 0.3, 0]
|
|
scores = [10, 9, 8, 7, 6, 5, 4, 3, 2, 1]
|
|
|
|
last_prediction_prob = class_1_probabilities[-1]
|
|
score = None
|
|
print(f"Last prediction probability: {last_prediction_prob}")
|
|
|
|
for threshold, value in zip(thresholds, scores):
|
|
if last_prediction_prob >= threshold:
|
|
score = value
|
|
break
|
|
|
|
return {'accuracy': round(test_accuracy * 100),
|
|
'precision': round(test_precision * 100),
|
|
'score': score} |