Michał Zaręba
25cddff6f0
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s430705-evaluation/pipeline/head This commit looks good
81 lines
2.3 KiB
Python
81 lines
2.3 KiB
Python
from datetime import datetime
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import pandas as pd
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from sacred import Experiment
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from sacred.observers import MongoObserver
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from sklearn.metrics import mean_squared_error
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from sklearn.model_selection import train_test_split
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from tensorflow.keras.callbacks import EarlyStopping
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from tensorflow.keras.layers import Dense, Dropout
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from tensorflow.keras.models import Sequential
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ex = Experiment("file_observer", interactive=False, save_git_info=False)
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ex.observers.append(MongoObserver(url='mongodb://mongo_user:mongo_password_IUM_2021@172.17.0.1:27017',
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db_name='sacred'))
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@ex.config
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def my_config():
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train_size_param = 0.8
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test_size_param = 0.2
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epochs = 400
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batch_size = 128
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@ex.capture
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def prepare_model(train_size_param, test_size_param, epochs, batch_size, _run):
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_run.info["prepare_model_ts"] = str(datetime.now())
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movies_data = pd.read_csv("train.csv", error_bad_lines=False)
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movies_data.drop(movies_data.columns[0], axis=1, inplace=True)
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movies_data.dropna(inplace=True)
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X = movies_data.drop("rating", axis=1)
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Y = movies_data["rating"]
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print(X, Y.values)
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# Split set to train/test 8:2 ratio
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X_train, X_test, Y_train, Y_test = train_test_split(
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X, Y, test_size=test_size_param, random_state=42
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)
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test_df = pd.read_csv("test.csv")
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test_df.drop(test_df.columns[0], axis=1, inplace=True)
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x_test = test_df.drop("rating", axis=1)
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y_test = test_df["rating"]
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# Set up model
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model = Sequential()
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model.add(Dense(8, activation="relu"))
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model.add(Dropout(0.5))
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model.add(Dense(3, activation="relu"))
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model.add(Dropout(0.5))
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model.add(Dense(1))
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model.compile(optimizer="adam", loss="mse")
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early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
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model.fit(
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x=X_train.values,
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y=Y_train.values,
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validation_data=(X_test, Y_test.values),
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batch_size=batch_size,
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epochs=epochs,
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callbacks=[early_stop],
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)
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y_pred = model.predict(x_test.values)
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rmse = mean_squared_error(y_test, y_pred)
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_run.info["Final Results: "] = rmse
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model.save("model_movies")
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return rmse
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@ex.automain
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def my_main(train_size_param, test_size_param, epochs, batch_size):
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print(prepare_model())
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r = ex.run()
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ex.add_artifact("model_movies/saved_model.pb")
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