always return 0.45

run.py

@@ -1,40 +1,7 @@
-from stop_words import get_stop_words
+for dataset in 'dev-0', 'dev-1', 'test-A':
-from sklearn.feature_extraction.text import TfidfVectorizer
+    with open(f'{dataset}/in.tsv') as f_in, open(f'{dataset}/out.tsv','w') as f_out:
-from sklearn.linear_model import LogisticRegression
+        for line in f_in:
-import pandas as pd
+            f_out.write('0.45\n')
-import lzma
 
 
-def read_file(f_name, f_ext=""):
-    if f_ext == "xz":
-        with lzma.open(f"{f_name}.{f_ext}") as file:
-            return [line.strip().decode("utf-8") for line in file.readlines()]
-    with open(f_name, encoding="utf-8") as file:
-        return [line.strip() for line in file.readlines()]
 
-
-def write_file(dir_name, preds):
-    with open(f"{dir_name}/out.tsv", "a", encoding="utf-8") as file_out:
-        for val in preds:
-            file_out.writelines(f"{str(val)}")
-
-
-x_data = read_file("train/in.tsv", "xz")
-print(x_data[:5])
-y_data = read_file("train/expected.tsv")
-print(y_data[:5])
-
-tfidf_vec = TfidfVectorizer(
-    use_idf=True, max_df=0.95, stop_words=get_stop_words("pl"))
-x_vectorized = tfidf_vec.fit_transform(x_data)
-
-model = LogisticRegression(max_iter=1000)
-model.fit(x_vectorized, y_data)
-
-# * Predictions
-for data_dir in ["dev-0", "dev-1", "test-A"]:
-    print(f"running for the __{data_dir}__")
-    x_data = read_file(f"{data_dir}/in.tsv")
-    x_vectorized = tfidf_vec.transform(x_data)
-    y_predicted = model.predict(x_vectorized)
-    write_file(data_dir, y_predicted)