70 lines
2.3 KiB
Python
70 lines
2.3 KiB
Python
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import numpy as np
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import pandas as pd
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from keras.models import Sequential
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from keras.layers import Dense
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from keras.optimizers import Adam
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from keras import regularizers
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from sacred import Experiment
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from sacred.observers import MongoObserver, FileStorageObserver
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from sklearn.metrics import mean_squared_error, mean_absolute_error, r2_score
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from helper import prepare_tensors
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ex = Experiment('495719')
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ex.observers.append(MongoObserver(url='mongodb://admin:IUM_2021@tzietkiewicz.vm.wmi.amu.edu.pl:27017'))
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ex.observers.append(FileStorageObserver('my_runs'))
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@ex.config
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def config():
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epochs = 10
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learning_rate = 0.001
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batch_size = 32
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@ex.main
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def main(epochs, learning_rate, batch_size, _run):
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with _run.open_resource("../hp_train.csv") as f:
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hp_train = pd.read_csv(f)
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with _run.open_resource("../hp_dev.csv") as f:
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hp_dev = pd.read_csv(f)
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X_train, Y_train = prepare_tensors(hp_train)
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X_dev, Y_dev = prepare_tensors(hp_dev)
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model = Sequential()
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model.add(Dense(64, input_dim=7, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
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model.add(Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
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model.add(Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
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model.add(Dense(8, activation='relu', kernel_regularizer=regularizers.l2(0.01)))
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model.add(Dense(1, activation='linear'))
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adam = Adam(learning_rate=learning_rate, beta_1=0.9, beta_2=0.999, epsilon=1e-7)
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model.compile(optimizer=adam, loss='mean_squared_error')
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model.fit(X_train, Y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_dev, Y_dev))
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model.save('hp_model.h5')
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ex.add_artifact("hp_model.h5")
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with _run.open_resource("../hp_test.csv") as f:
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hp_test = pd.read_csv(f)
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X_test, Y_test = prepare_tensors(hp_test)
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test_predictions = model.predict(X_test)
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predictions_df = pd.DataFrame(test_predictions, columns=["Predicted_Price"])
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predictions_df.to_csv('hp_test_predictions.csv', index=False)
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rmse = np.sqrt(mean_squared_error(Y_test, test_predictions))
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mae = mean_absolute_error(Y_test, test_predictions)
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r2 = r2_score(Y_test, test_predictions)
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_run.log_scalar("rmse", rmse)
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_run.log_scalar("mae", mae)
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_run.log_scalar("r2", r2)
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if __name__ == '__main__':
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ex.run()
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