linear-regression.py

@@ -1,78 +0,0 @@
-import pandas as pd
-import plotly.express as px
-from pandas import DataFrame
-from sklearn import preprocessing
-from sklearn.linear_model import LinearRegression, Ridge, RidgeCV
-from sklearn.metrics import mean_squared_error
-from sklearn.preprocessing import PolynomialFeatures
-
-col_names = ["price", "mileage", "year", "brand", "engine_type", "engine_cap"]
-col_names_in = ["mileage", "year", "brand", "engine_type", "engine_cap"]
-df_train = pd.read_csv(
-    "train/train.tsv", error_bad_lines=False, header=None, sep="\t", names=col_names
-)
-df = df_train
-dev0 = pd.read_csv(
-    "dev-0/in.tsv", error_bad_lines=False, header=None, sep="\t", names=col_names_in
-)
-testA = pd.read_csv(
-    "test-A/in.tsv", error_bad_lines=False, header=None, sep="\t", names=col_names_in
-)
-
-test = pd.read_csv("dev-0/expected.tsv", error_bad_lines=False, header=None, sep="\t")
-Y_a = test[0]
-
-brands = df.brand.value_counts()[:35].index.tolist()
-
-
-def preprocess_data(df: DataFrame, brands: list) -> DataFrame:
-    """Prepare dataset to linear regression"""
-
-    df.brand = df.brand.apply(lambda x: x if x in brands else "0")
-    df["year"] = df.year / 2000
-    df["mileage"] = df.mileage ** 0.3
-    df["engine_cap"] = df.engine_cap * 0.3
-    df["brand"] = df["brand"].str.lower()
-    scaler = preprocessing.RobustScaler()
-    df = pd.get_dummies(df, columns=["brand", "engine_type"])
-    # takes 1k rmse more ;(
-    df[["mileage", "year", "engine_cap", "year"]] = scaler.fit_transform(
-        df[["mileage", "year", "engine_cap", "year"]]
-    )
-    poly = PolynomialFeatures(2, interaction_only=True)
-    df = poly.fit_transform(df)
-
-    return df
-
-
-indexes = df_train[(df_train.price < 1000) & (df_train.price > 1)].index
-df_train.drop(indexes, inplace=True)
-
-index = df_train[(df_train.mileage > 900000)].index
-df_train.drop(index, inplace=True)
-Y_train = df_train["price"]
-df_train.drop("price", axis=1, inplace=True)
-# df_train = df_train[df_train.price not in range (2, 1000)]
-
-df_train = preprocess_data(df_train, brands)
-dev0 = preprocess_data(dev0, brands)
-testA = preprocess_data(testA, brands)
-
-# fig = px.imshow(df_train.corr())
-# fig.show()
-
-
-lm_model = LinearRegression()
-
-# clf = RidgeCV(alphas=[0.1, 0.01, 0.001, 0.00001,  1e-1], cv=10, fit_intercept=True, normalize=True)
-# clf.fit(df_train, Y_train)
-lm_model.fit(df_train, Y_train)
-
-dev0_predicted = lm_model.predict(dev0)
-testA_predicted = lm_model.predict(testA)
-
-# dev0_predicted2 = clf.predict(dev0)
-
-pd.Series(dev0_predicted).to_csv("dev-0/out.tsv", sep="\t", index=False, header=False)
-pd.Series(testA_predicted).to_csv("test-A/out.tsv", sep="\t", index=False, header=False)
-print(mean_squared_error(Y_a, dev0_predicted, squared=False))