Karolina Oparczyk
ba9b25f4c3
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46 lines
1.5 KiB
Python
46 lines
1.5 KiB
Python
import pandas as pd
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import numpy as np
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from tensorflow import keras
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device = 'cpu'
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data = pd.read_csv("data_train", sep=',', error_bad_lines=False).dropna()
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X = data.loc[:,data.columns == "2805317"].astype(int)
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y = data.loc[:,data.columns == "198909"].astype(int)
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def NormalizeData(data):
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return (data - np.min(data)) / (np.max(data) - np.min(data))
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X = NormalizeData(X)
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y = NormalizeData(y)
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model = keras.Sequential([
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keras.layers.Dense(512,input_dim = X.shape[1],kernel_initializer='normal', activation='relu'),
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keras.layers.Dense(512,kernel_initializer='normal', activation='relu'),
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keras.layers.Dense(256,kernel_initializer='normal', activation='relu'),
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keras.layers.Dense(256,kernel_initializer='normal', activation='relu'),
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keras.layers.Dense(128,kernel_initializer='normal', activation='relu'),
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keras.layers.Dense(1,kernel_initializer='normal', activation='linear'),
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])
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model.compile(loss='mean_absolute_error', optimizer='adam', metrics=['mean_absolute_error'])
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model.fit(X, y, epochs=30, validation_split = 0.3)
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data = pd.read_csv("data_test", sep=',', error_bad_lines=False).dropna()
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X_test = data.loc[:,data.columns == "2805317"].astype(int)
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y_test = data.loc[:,data.columns == "198909"].astype(int)
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X_test = NormalizeData(X_test)
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y_test = NormalizeData(y_test)
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prediction = model.predict(X_test)
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f = open("predictions.txt", "w")
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for (pred, test) in zip(prediction, y_test.values):
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f.write("predicted: %s expected: %s\n" % (str(pred), str(test)))
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