switch from nn to xgb
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MuslemRahimi
parent
699594cb64
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75b9cdc2b1
@ -332,19 +332,39 @@ async def chunked_gather(tickers, con, start_date, end_date, chunk_size=10):
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async def warm_start_training(tickers, con):
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start_date = datetime(1995, 1, 1).strftime("%Y-%m-%d")
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end_date = datetime.today().strftime("%Y-%m-%d")
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df_train = pd.DataFrame()
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df_test = pd.DataFrame()
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test_size = 0.2
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dfs = await chunked_gather(tickers, con, start_date, end_date, chunk_size=10)
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df_train = pd.concat(dfs, ignore_index=True)
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df_train = df_train.sample(frac=1).reset_index(drop=True)
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train_list = []
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test_list = []
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for df in dfs:
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try:
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split_size = int(len(df) * (1 - test_size))
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train_data = df.iloc[:split_size]
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test_data = df.iloc[split_size:]
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# Append to the lists
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train_list.append(train_data)
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test_list.append(test_data)
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except:
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pass
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# Concatenate all at once outside the loop
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df_train = pd.concat(train_list, ignore_index=True)
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df_test = pd.concat(test_list, ignore_index=True)
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print('======Warm Start Train Set Datapoints======')
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df_train = df_train.sample(frac=1).reset_index(drop=True) #df_train.reset_index(drop=True)
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print(len(df_train))
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predictor = ScorePredictor()
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selected_features = [col for col in df_train if col not in ['price', 'date', 'Target']]
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predictor.warm_start_training(df_train[selected_features], df_train['Target'])
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predictor.evaluate_model(df_train[selected_features], df_train['Target'])
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predictor.evaluate_model(df_test[selected_features], df_test['Target'])
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return predictor
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@ -369,7 +389,7 @@ async def fine_tune_and_evaluate(ticker, con, start_date, end_date):
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data = predictor.evaluate_model(test_data[selected_features], test_data['Target'])
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if len(data) != 0:
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if data['precision'] >= 50 and data['accuracy'] >= 50:
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if data['precision'] >= 60 and data['accuracy'] >= 60 and data['accuracy'] < 100 and data['precision'] < 100:
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res = {'score': data['score']}
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await save_json(ticker, res)
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print(f"Saved results for {ticker}")
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@ -389,23 +409,23 @@ async def run():
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if train_mode:
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# Warm start training
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 10E9 AND symbol NOT LIKE '%.%' AND symbol NOT LIKE '%-%'")
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 300E9 AND symbol NOT LIKE '%.%' AND symbol NOT LIKE '%-%'")
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warm_start_symbols = [row[0] for row in cursor.fetchall()]
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print('Warm Start Training for:', warm_start_symbols)
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predictor = await warm_start_training(warm_start_symbols, con)
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else:
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# Fine-tuning and evaluation for all stocks
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cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 1E9 AND symbol NOT LIKE '%.%'")
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stock_symbols = [row[0] for row in cursor.fetchall()]
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stock_symbols = ['GME'] #[row[0] for row in cursor.fetchall()]
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print(f"Total tickers for fine-tuning: {len(stock_symbols)}")
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start_date = datetime(1995, 1, 1).strftime("%Y-%m-%d")
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end_date = datetime.today().strftime("%Y-%m-%d")
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tasks = []
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for ticker in tqdm(stock_symbols):
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tasks.append(fine_tune_and_evaluate(ticker, con, start_date, end_date))
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await fine_tune_and_evaluate(ticker, con, start_date, end_date)
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await asyncio.gather(*tasks)
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#await asyncio.gather(*tasks)
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con.close()
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@ -5,18 +5,7 @@ from sklearn.ensemble import RandomForestClassifier
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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.model_selection import train_test_split
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from sklearn.preprocessing import MinMaxScaler, StandardScaler
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from keras.models import Sequential, Model
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from keras.layers import Input, Multiply, Reshape, LSTM, Dense, Dropout, BatchNormalization, GlobalAveragePooling1D, MaxPooling1D, Bidirectional
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from keras.optimizers import Adam
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from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
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from keras.models import load_model
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from sklearn.feature_selection import SelectKBest, f_classif
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from tensorflow.keras.backend import clear_session
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from keras import regularizers
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from keras.layers import Layer
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from tensorflow.keras import backend as K
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from sklearn.preprocessing import MinMaxScaler
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from tqdm import tqdm
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from collections import defaultdict
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@ -26,62 +15,11 @@ import aiofiles
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import pickle
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import time
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class SelfAttention(Layer):
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def __init__(self, **kwargs):
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super(SelfAttention, self).__init__(**kwargs)
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def build(self, input_shape):
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self.W = self.add_weight(name='attention_weight', shape=(input_shape[-1], 1),
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initializer='random_normal', trainable=True)
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super(SelfAttention, self).build(input_shape)
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def call(self, x):
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# Alignment scores. Pass them through tanh function
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e = K.tanh(K.dot(x, self.W))
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# Remove dimension of size 1
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e = K.squeeze(e, axis=-1)
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# Compute the weights
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alpha = K.softmax(e)
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# Reshape to tensor of same shape as x for multiplication
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alpha = K.expand_dims(alpha, axis=-1)
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# Compute the context vector
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context = x * alpha
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context = K.sum(context, axis=1)
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return context, alpha
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def compute_output_shape(self, input_shape):
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return (input_shape[0], input_shape[-1]), (input_shape[0], input_shape[1])
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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 = None
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self.warm_start_model_path = 'ml_models/weights/ai-score/warm_start_weights.keras'
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def build_model(self):
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clear_session()
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inputs = Input(shape=(231,))
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x = Dense(128, activation='leaky_relu')(inputs)
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x = BatchNormalization()(x)
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x = Dropout(0.2)(x)
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for units in [64,32,16]:
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x = Dense(units, activation='leaky_relu')(x)
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x = BatchNormalization()(x)
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x = Dropout(0.2)(x)
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x = Reshape((16, 1))(x)
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x, _ = SelfAttention()(x)
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outputs = Dense(2, activation='softmax')(x)
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model = Model(inputs=inputs, outputs=outputs)
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optimizer = Adam(learning_rate=0.01, clipnorm=1.0)
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model.compile(optimizer=optimizer, loss='sparse_categorical_crossentropy', metrics=['accuracy'])
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return model
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self.warm_start_model_path = 'ml_models/weights/ai-score/warm_start_weights.pkl'
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self.model = XGBClassifier(n_estimators=100, max_depth = 10, min_samples_split=5, random_state=42, n_jobs=10)
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def preprocess_data(self, X):
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X = np.where(np.isinf(X), np.nan, X)
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@ -91,37 +29,24 @@ class ScorePredictor:
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def warm_start_training(self, X_train, y_train):
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X_train = self.preprocess_data(X_train)
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self.model = self.build_model()
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checkpoint = ModelCheckpoint(self.warm_start_model_path, save_best_only=True, save_freq=1, monitor='val_loss', mode='min')
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early_stopping = EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True)
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reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=30, min_lr=0.001)
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self.model.fit(X_train, y_train, epochs=100_000, batch_size=32, validation_split=0.1, callbacks=[checkpoint, early_stopping, reduce_lr])
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self.model.save(self.warm_start_model_path)
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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_data(X_train)
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#batch_size = min(64, max(16, len(X_train) // 10))
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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=20, restore_best_weights=True)
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#reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=10, min_lr=0.01)
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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, epochs=150, batch_size=16, validation_split=0.1)
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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_data(X_test)
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if self.model is None:
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raise ValueError("Model has not been trained or fine-tuned. Call warm_start_training or fine_tune_model first.")
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test_predictions = self.model.predict(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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@ -146,15 +71,4 @@ class ScorePredictor:
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return {'accuracy': round(test_accuracy * 100),
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'precision': round(test_precision * 100),
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'score': score}
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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
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'score': score}
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