script.py

@@ -1,47 +0,0 @@
-from sklearn.linear_model import LinearRegression
-from sklearn.preprocessing import PolynomialFeatures
-import pandas as pd
-import numpy as np
-
-brands = None
-
-def process_data(df):
-    df["age"] = 2018 - df["year"]
-    df["sqrt_age"] = df.age**0.7
-    df["sqrt_mileage"] = df.mileage ** 0.7
-    df["sqrt_engine_capacity"] = df.engine_capacity ** 0.7
-    global brands
-    if not brands:
-        brands = df.brand.value_counts()[:35].index.tolist()
-    df.brand = df.brand.apply(lambda x: x if x in brands else "0")
-    df = pd.get_dummies(df)
-    poly = PolynomialFeatures(2, interaction_only=True)
-    df  = poly.fit_transform(df)
-    return df
-
-def get_model():
-    df = pd.read_csv('./train/train.tsv', sep='\t',
-                 names=["price", "mileage", "year", "brand", "engine_type", "engine_capacity"])
-    X = df.loc[:, df.columns != 'price']
-    y = df['price']
-    X  = process_data(X)
-    regr = LinearRegression()
-    return regr.fit(X, y)
-
-
-def predict_and_write(path, model):
-    with open(f'{path}out.tsv', 'w') as out:
-        df_dev = pd.read_csv(f'{path}in.tsv', sep='\t',
-                            names=["mileage", "year", "brand", "engine_type", "engine_capacity"])
-        df_dev = process_data(df_dev)
-        predictions = model.predict(df_dev).astype(int)
-        for prediction in predictions:
-            out.write(f"{prediction}\n")
-
-def main():
-    model = get_model()
-    predict_and_write('./dev-0/', model)
-    predict_and_write('./test-A/', model)
-
-if __name__ == '__main__':
-    main()