fix config

linreg.py

@@ -1,56 +0,0 @@
-import numpy as np
-import pandas as pd
-import sys
-from sklearn .linear_model import LinearRegression
-
-TRAIN_FILE_PATH = 'train/train.tsv'
-
-DEV0_IN = 'dev-0/in.tsv'
-DEV0_OUT = 'dev-0/out.tsv'
-
-TEST_A_IN = 'test-A/in.tsv'
-TEST_A_OUT = 'test-A/out.tsv'
-
-
-def read_data_file(filepath, x_index, y_index):
-    df = pd.read_csv(filepath, sep='\t', header=None, index_col=None)
-    x = df[x_index].tolist() if x_index is not None else None
-    y = df[y_index].tolist() if y_index is not None else None
-
-    return {'x': x, 'y': y}
-
-
-def to_numpy_2d(lst):
-    return np.array(lst).reshape(-1, 1)
-
-
-def get_trained_linreg_model(train_data):
-    x = to_numpy_2d(train_data.get('x'))
-    y = to_numpy_2d(train_data.get('y'))
-
-    model = LinearRegression()
-    model.fit(x, y)
-
-    return model
-
-
-def make_predictions(model, in_file, out_file):
-    input = read_data_file(in_file, 1, None)
-    input_x = to_numpy_2d(input.get('x'))
-    pred_y = model.predict(input_x)
-
-    with open(out_file, 'w') as f:
-        for pred in pred_y:
-            f.write(str(pred[0]) + '\n')
-
-
-def main():
-    train_data = read_data_file(TRAIN_FILE_PATH, 2, 0)
-    model = get_trained_linreg_model(train_data)
-
-    make_predictions(model, DEV0_IN, DEV0_OUT)
-    make_predictions(model, TEST_A_IN, TEST_A_OUT)
-
-
-if __name__ == '__main__':
-    main()