49 lines
1.3 KiB
Python
49 lines
1.3 KiB
Python
import pandas as pd
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from sklearn.metrics import mean_absolute_error
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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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movies_train = pd.read_csv("train.csv")
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X_train = movies_train.drop("rating", axis=1)
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Y_train = movies_train["rating"]
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movies_test = pd.read_csv("test.csv")
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X_test = movies_test.drop("rating", axis=1)
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Y_test = movies_test["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,
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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=128,
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epochs=400,
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callbacks=[early_stop],
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)
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# Predict movie ratings
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predictions = model.predict(X_test)
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pd.DataFrame(predictions).to_csv("results.csv")
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# Compare outputs
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for i, score in enumerate(predictions):
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print(f"Original score: {Y_test.iloc[i]} Predicted score: {score} \n")
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print(f"Difference is : {Y_test.iloc[i] - score}")
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# Evaluate
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print(mean_absolute_error(Y_test, predictions))
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