49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
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import sys
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import pandas as pd
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import numpy as np
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from sklearn import preprocessing
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from sklearn.linear_model import LinearRegression
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from sklearn.metrics import mean_squared_error
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import tensorflow as tf
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from tensorflow import keras
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from tensorflow.keras.layers import Input, Dense, Activation,Dropout
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from tensorflow.keras.models import Model
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from tensorflow.keras.callbacks import EarlyStopping
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from tensorflow.keras.models import Sequential
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# odczytanie danych z plików
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vgsales_train = pd.read_csv('train.csv')
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vgsales_test = pd.read_csv('test.csv')
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vgsales_dev = pd.read_csv('dev.csv')
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vgsales_train['Nintendo'] = vgsales_train['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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vgsales_test['Nintendo'] = vgsales_test['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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vgsales_dev['Nintendo'] = vgsales_dev['Publisher'].apply(lambda x: 1 if x=='Nintendo' else 0)
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# podzial na X i y
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X_train = vgsales_train.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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y_train = vgsales_train[['Nintendo']]
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X_test = vgsales_test.drop(['Rank','Name','Platform','Year','Genre','Publisher'],axis = 1)
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y_test = vgsales_test[['Nintendo']]
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print(X_train.shape[1])
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# keras model
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model = Sequential()
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model.add(Dense(9, input_dim = X_train.shape[1], kernel_initializer='normal', activation='relu'))
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model.add(Dense(1,kernel_initializer='normal', activation='sigmoid'))
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early_stop = EarlyStopping(monitor="val_loss", mode="min", verbose=1, patience=10)
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# kompilacja
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model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
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# model fit
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epochs = int(sys.argv[1])
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batch_size = int(sys.argv[2])
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# trenowanie modelu
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model.fit(X_train, y_train, epochs=epochs, batch_size=batch_size, validation_data=(X_test, y_test))
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# zapisanie modelu
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model.save('vgsales_model.h5')
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