update model back to lgm classifier
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MuslemRahimi
parent
0bdc818d6b
commit
3f99870301
@ -190,8 +190,8 @@ async def download_data(ticker, con, start_date, end_date, skip_downloading, sav
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# Compute combinations for each group of columns
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#compute_column_ratios(fundamental_columns, df_combined, new_columns)
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compute_column_ratios(stats_columns, df_combined, new_columns)
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compute_column_ratios(ta_columns, df_combined, new_columns)
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#compute_column_ratios(stats_columns, df_combined, new_columns)
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#compute_column_ratios(ta_columns, df_combined, new_columns)
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# Concatenate the new ratio columns with the original DataFrame
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df_combined = pd.concat([df_combined, pd.DataFrame(new_columns, index=df_combined.index)], axis=1)
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@ -242,7 +242,7 @@ async def warm_start_training(tickers, con, skip_downloading, save_data):
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end_date = datetime.today().strftime("%Y-%m-%d")
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test_size = 0.2
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dfs = await chunked_gather(tickers, con, start_date, end_date, skip_downloading, save_data, chunk_size=300)
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dfs = await chunked_gather(tickers, con, start_date, end_date, skip_downloading, save_data, chunk_size=100)
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train_list = []
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test_list = []
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@ -307,6 +307,9 @@ async def fine_tune_and_evaluate(ticker, con, start_date, end_date, skip_downloa
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except Exception as e:
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print(f"Error processing {ticker}: {e}")
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finally:
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gc.collect() # Force the garbage collector to release unreferenced memory
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async def run():
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train_mode = False # Set this to False for fine-tuning and evaluation
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skip_downloading = False
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@ -318,9 +321,10 @@ async def run():
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if train_mode:
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# Warm start training
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warm_start_symbols = list(set(['CB','LOW','PFE','RTX','DIS','MS','BHP','BAC','PG','BABA','ACN','TMO','LLY','XOM','JPM','UNH','COST','HD','ASML','BRK-A','BRK-B','CAT','TT','SAP','APH','CVS','NOG','DVN','COP','OXY','MRO','MU','AVGO','INTC','LRCX','PLD','AMT','JNJ','ACN','TSM','V','ORCL','MA','BAC','BA','NFLX','ADBE','IBM','GME','NKE','ANGO','PNW','SHEL','XOM','WMT','BUD','AMZN','PEP','AMD','NVDA','AWR','TM','AAPL','GOOGL','META','MSFT','LMT','TSLA','DOV','PG','KO']))
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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, skip_downloading, save_data)
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stock_symbols = cursor.execute("SELECT DISTINCT symbol FROM stocks WHERE marketCap >= 500E6 AND symbol NOT LIKE '%.%'") #list(set(['CB','LOW','PFE','RTX','DIS','MS','BHP','BAC','PG','BABA','ACN','TMO','LLY','XOM','JPM','UNH','COST','HD','ASML','BRK-A','BRK-B','CAT','TT','SAP','APH','CVS','NOG','DVN','COP','OXY','MRO','MU','AVGO','INTC','LRCX','PLD','AMT','JNJ','ACN','TSM','V','ORCL','MA','BAC','BA','NFLX','ADBE','IBM','GME','NKE','ANGO','PNW','SHEL','XOM','WMT','BUD','AMZN','PEP','AMD','NVDA','AWR','TM','AAPL','GOOGL','META','MSFT','LMT','TSLA','DOV','PG','KO']))
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stock_symbols = [row[0] for row in cursor.fetchall()]
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print('Training for:', stock_symbols)
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predictor = await warm_start_training(stock_symbols, con, skip_downloading, save_data)
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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 >= 500E6 AND symbol NOT LIKE '%.%'")
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@ -1,25 +1,10 @@
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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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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, Conv1D, Dropout, BatchNormalization, GlobalAveragePooling1D, MaxPooling1D, Bidirectional
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from keras.optimizers import AdamW
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from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
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from tensorflow.keras.activations import gelu
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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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import tensorflow as tf
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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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@ -28,85 +13,39 @@ 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 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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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 = None
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self.warm_start_model_path = 'ml_models/weights/ai-score/warm_start_weights.keras'
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self.pca = PCA(n_components=3)
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def build_model(self):
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clear_session()
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inputs = Input(shape=(3,))
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x = Dense(512, activation=gelu)(inputs) # Using GELU activation
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x = Dropout(0.5)(x)
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x = BatchNormalization()(x)
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for units in [64, 32]:
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x = Dense(units, activation=gelu)(x) # Using GELU activation
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x = Dropout(0.2)(x)
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x = BatchNormalization()(x)
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x = Reshape((32, 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 = AdamW(learning_rate=0.001, weight_decay=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.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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)
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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 self.pca.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 self.pca.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 = 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=20, restore_best_weights=True)
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reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.5, patience=10, min_lr=0.001)
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self.model.fit(X_train, y_train, epochs=100_000, batch_size=256, 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(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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@ -124,12 +63,12 @@ class ScorePredictor:
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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 tf.device('/CPU:0'):
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# Load model and make predictions
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self.model = load_model(self.warm_start_model_path, custom_objects={'SelfAttention': SelfAttention})
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test_predictions = self.model.predict(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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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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