38 lines
1.4 KiB
Python
38 lines
1.4 KiB
Python
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import pandas as pd
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import numpy as np
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from sklearn.metrics import mean_squared_error
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from keras.models import Sequential
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from keras.layers import Dense
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in_columns = ['id_stacji', 'nazwa_stacji', 'typ_zbioru', 'rok', 'miesiąc']
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df = pd.read_csv('train/in.tsv', names=in_columns, sep='\t')
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df_test = pd.read_csv('dev-0/in.tsv', names=in_columns, sep='\t')
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df = pd.concat([df,df_test])
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df = df.drop(['nazwa_stacji','typ_zbioru'], axis=1)
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x = pd.get_dummies(df,columns = ['id_stacji','rok','miesiąc'])
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x = x.iloc[:-600]
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y = pd.read_csv('train/expected.tsv', sep='\t', names=['rainfall'])
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# Keras
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model = Sequential()
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model.add(Dense(16, input_dim=73, activation= "relu"))
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model.add(Dense(32, activation= "relu"))
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model.add(Dense(64, activation= "relu"))
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model.add(Dense(32, activation= "relu"))
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model.add(Dense(16, activation= "relu"))
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model.add(Dense(1))
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model.compile(loss= "mean_squared_error" , optimizer="adam", metrics=["mean_squared_error"])
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model.fit(x, y, epochs=40)
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#Final Test
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x_test = pd.read_csv('test-A/in.tsv', sep='\t', names=in_columns)
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df_train = pd.read_csv('train/in.tsv', names=in_columns, sep='\t')
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x_test = pd.concat([x_test,df_train])
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x_test = x_test.drop(['nazwa_stacji', 'typ_zbioru'],axis=1)
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x_test = pd.get_dummies(x_test,columns = ['id_stacji','rok','miesiąc'])
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x_test = x_test.iloc[:-8760]
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pred= model.predict(x_test)
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out = pd.DataFrame(pred)
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out.to_csv('test-A/out.tsv',sep='\t',header=False, index=False)
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