forked from kubapok/auta-public
41 lines
1.4 KiB
Python
41 lines
1.4 KiB
Python
from sklearn.linear_model import LinearRegression
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import pandas as pd
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import numpy as np
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brands = None
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def get_model():
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global brands
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df = pd.read_csv('./train/train.tsv', sep='\t',
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names=["price", "mileage", "year", "brand", "engine_type", "engine_capacity"])
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X = df.loc[:, df.columns != 'price']
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y = df['price']
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X["age"] = X.year.apply(lambda x: np.sqrt(2017-x))
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X["sqrt_mileage"] = X.mileage.apply(lambda x: np.sqrt(x))
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brands = X.brand.value_counts()[:35].index.tolist()
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X.brand = X.brand.apply(lambda x: x if x in brands else "0")
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X = pd.get_dummies(X)
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regr = LinearRegression()
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return regr.fit(X, y)
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def predict_and_write(path, model):
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global brands
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with open(f'{path}out.tsv', 'w') as out:
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df_dev = pd.read_csv(f'{path}in.tsv', sep='\t',
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names=["mileage", "year", "brand", "engine_type", "engine_capacity"])
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df_dev.brand = df_dev.brand.apply(lambda x: x if x in brands else "0")
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df_dev["age"] = df_dev.year.apply(lambda x: np.sqrt(2017-x))
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df_dev["sqrt_mileage"] = df_dev.mileage.apply(lambda x: np.sqrt(x))
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df_dev = pd.get_dummies(df_dev)
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predictions = model.predict(df_dev).astype(int)
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for prediction in predictions:
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out.write(f"{prediction}\n")
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def main():
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model = get_model()
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predict_and_write('./dev-0/', model)
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predict_and_write('./test-A/', model)
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if __name__ == '__main__':
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main() |