main.py

@@ -1,71 +0,0 @@
-import pandas
-import os
-import sys
-from sklearn.linear_model import LinearRegression
-
-IN_FILE_NAME = "in.tsv"
-OUT_FILE_NAME = "out.tsv"
-MAIN_DIR = "."
-NAMES_FILE_NAME = "names"
-TRAIN_DIR = "train"
-TRAIN_FILE_NAME = "train.tsv"
-VALUE_SEP = "\t"
-LINE_SEP = "\n"
-CATEGORY_TYPE = "category"
-
-
-def main(dirname: str):
-    names = get_names()
-
-    X, Y = get_train_data(names)
-
-    clf = LinearRegression().fit(X, Y)
-
-    clf.predict(get_input_data(dirname, names)).tofile(os.path.join(
-        dirname, OUT_FILE_NAME), sep=LINE_SEP)
-
-
-def get_train_data(names: list):
-    train_path = os.path.join(MAIN_DIR, TRAIN_DIR, TRAIN_FILE_NAME)
-    check_file(train_path)
-    train_data = process_input(pandas.read_csv(
-        train_path, header=None, sep=VALUE_SEP, names=names), names)
-
-    train_data = train_data.loc[(train_data[names[0]] > 1000)]
-
-    X = train_data.loc[:, train_data.columns != names[0]]
-    Y = train_data[names[0]]
-
-    return X, Y
-
-
-def get_input_data(dirname, names):
-    in_path = os.path.join(dirname, IN_FILE_NAME)
-    check_file(in_path)
-    return process_input(pandas.read_csv(in_path, header=None, sep=VALUE_SEP, names=names[1:]), names)
-
-
-def process_input(df, names):
-    df = df.drop([names[4]], axis=1)
-    for c in df.select_dtypes(include=object).columns.values:
-        df[c] = df[c].astype(CATEGORY_TYPE).cat.codes
-    return df
-
-
-def get_names() -> list:
-    names_path = os.path.join(MAIN_DIR, NAMES_FILE_NAME)
-    check_file(names_path)
-
-    with open(names_path) as f_names:
-        return f_names.read().strip().split(VALUE_SEP)
-
-
-def check_file(filename: str):
-    if not os.path.exists(filename):
-        raise FileNotFoundError(filename)
-
-
-if __name__ == "__main__":
-    if len(sys.argv) < 2:
-        raise Exception("Name of working dir not specified!")
-    main(sys.argv[1])