run.py

@@ -1,56 +0,0 @@
-#!/usr/bin/env python
-
-import os
-import math
-import pandas as pd
-from sklearn.metrics import mean_squared_error
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.linear_model import LinearRegression
-from sklearn.pipeline import make_pipeline
-from stop_words import get_stop_words
-
-
-def rmse(y_expected, y_predicted):
-    return math.sqrt(mean_squared_error(y_expected, y_predicted))
-
-
-def read_tsv(path, filename, col_names, cutoff=None):
-    i = 0
-    data = []
-    with open(os.path.join(path, filename), 'r', encoding='utf-8') as file:
-        for line in file:
-            if cutoff and i >= cutoff:
-                break
-            data.append(line.strip().split('\t'))
-            i += 1
-
-    df = pd.DataFrame(data, columns=col_names)
-    return df
-
-
-def evaluate_and_save(path, file, model):
-    df = read_tsv(path, file, ['y'])['y'].values
-    y_predicted = model.predict(df)
-    # y_expected = read_tsv(path, 'expected.tsv', ['y'])['y'].values
-    # print('score: ', rmse(y_expected, y_predicted))
-    with open(os.path.join(path, 'out.tsv'), 'w') as f:
-        for value in y_predicted:
-            f.write(f'{value}\n')
-
-
-def main():
-    train_data = read_tsv('train', 'train.tsv', ['date_start', 'date_end', 'x1', 'x2', 'text'], 50000)
-    train_data['date'] = (train_data['date_start'].astype(float) + train_data['date_end'].astype(float)) / 2
-    train_x = train_data['text'].values
-    train_y = train_data['date'].values
-    vectorizer = TfidfVectorizer(stop_words=get_stop_words('polish'))
-    model = LinearRegression()
-    pipe = make_pipeline(vectorizer, model)
-    pipe.fit(train_x, train_y)
-    # print('training done')
-    for path, file in [('dev-0', 'in.tsv'), ('dev-1', 'in.tsv'), ('test-A', 'in.tsv')]:
-        evaluate_and_save(path, file, pipe)
-
-
-if __name__ == '__main__':
-    main()