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jblitzar
/
github-python

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kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4),
),
Dropout(0.2), # Add dropout layer
LSTM(units=32, kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)),
Dropout(0.2), # Add dropout layer
Dense(
units=16, activation="relu", kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)
),
Dense(units=1),
]
)
model.compile(optimizer="adam", loss="mean_squared_error")
return model
# Create a GRU model for time series prediction
def create_gru_model(input_shape):
model = Sequential(
[
GRU(
units=64,
return_sequences=True,
input_shape=input_shape,
kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4),
),
Dropout(0.2), # Add dropout layer
GRU(units=32, kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)),
Dropout(0.2), # Add dropout layer
Dense(
units=16, activation="relu", kernel_regularizer=l1_l2(l1=1e-5, l2=1e-4)
),
Dense(units=1),
]
)
model.compile(optimizer="adam", loss="mean_squared_error")
return model
# Train and evaluate a model using time series cross-validation
def train_and_evaluate_model(model, X, y, n_splits=5, model_name="Model"):
tscv = TimeSeriesSplit(n_splits=n_splits)
all_predictions = []
all_true_values = []
with tqdm(total=n_splits, desc=f"Training {model_name}", leave=False) as pbar:
for train_index, test_index in tscv.split(X):
X_train, X_test = X[train_index], X[test_index]
y_train, y_test = y[train_index], y[test_index]
if isinstance(model, (RandomForestRegressor, XGBRegressor)):
X_train_2d = X_train.reshape(X_train.shape[0], -1)
X_test_2d = X_test.reshape(X_test.shape[0], -1)
model.fit(X_train_2d, y_train)
predictions = model.predict(X_test_2d)
elif isinstance(model, Sequential):
early_stopping = EarlyStopping(
monitor="val_loss", patience=10, restore_best_weights=True
)
with tqdm(total=100, desc="Epochs", leave=False) as epoch_pbar:
class EpochProgressCallback(Callback):
def on_epoch_end(self, epoch, logs=None):
epoch_pbar.update(1)
model.fit(
X_train,
y_train,
epochs=100,
batch_size=32,
verbose=0,
validation_split=0.2,
callbacks=[early_stopping, EpochProgressCallback()],
)
predictions = model.predict(X_test, verbose=0).flatten()
all_predictions.extend(predictions)
all_true_values.extend(y_test)
pbar.update(1)
score = r2_score(all_true_values, all_predictions)
return score, 0, score, np.array(all_predictions)
# Make predictions using an ensemble of models
def ensemble_predict(models, X):
predictions = []
for model in models:
if isinstance(model, (RandomForestRegressor, XGBRegressor)):
pred = model.predict(X.reshape(X.shape[0], -1))
else:
pred = model.predict(X)
predictions.append(pred.flatten()) # Flatten the predictions
return np.mean(predictions, axis=0)
def weighted_ensemble_predict(models, X, weights):
predictions = []
for model, weight in zip(models, weights):
if isinstance(model, (RandomForestRegressor, XGBRegressor)):
pred = model.predict(X.reshape(X.shape[0], -1))