Michał Zaręba
2caa596dce
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50 lines
1.3 KiB
Python
50 lines
1.3 KiB
Python
import string
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import pandas as pd
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from sklearn.model_selection import train_test_split
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from sklearn import preprocessing
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import wget
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import numpy as np
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Dense
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from tensorflow.keras.optimizers import Adam
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from tensorflow.keras.layers import Dropout
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from tensorflow.keras.callbacks import EarlyStopping
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from sklearn.metrics import mean_squared_error, mean_absolute_error
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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=0.2, random_state=42
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)
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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=128,
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epochs=400,
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callbacks=[early_stop],
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)
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model.save('model_movies') |