First stage

mian.py

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+import gensim as gensim
+import smart_open
+from sklearn.feature_extraction.text import TfidfVectorizer
+from sklearn.linear_model import LinearRegression
+from stop_words import get_stop_words
+from sklearn.cluster import KMeans
+from gensim.models.doc2vec import Doc2Vec
+import os
+import pandas as pd
+
+def read_train_file(inDirectory):
+    colnames = ['start_date', 'end_date', 'title', 'sort_title', 'data']
+    df_train = pd.read_csv(inDirectory, sep="\t", names=colnames)
+    return df_train[:5000]
+
+def read_evaluate_file(inDirectory):
+    colnames = ['data']
+    df_train = pd.read_csv(inDirectory, sep="\t", names=colnames)
+    return df_train[:5000]
+
+def train_date_mean(df):
+    date_mean = (df['start_date'] + df['end_date']) / 2
+    return date_mean
+
+def preper_data(df):
+        document_list = list()
+        for line in df:
+            tokens = gensim.utils.simple_preprocess(line, min_len=2, max_len=15)
+            filtered_sentence = []
+            for word in tokens:
+                if word not in get_stop_words('polish'):
+                    filtered_sentence.append(word)
+            document_list.append(filtered_sentence)
+        return document_list
+
+df = read_train_file('train/train.tsv')
+date_mean_df = train_date_mean(df)[:5000]
+vectorizer = TfidfVectorizer(stop_words=get_stop_words('polish'))
+train_vectorized_corpus = vectorizer.fit_transform(df['data'])
+reg = LinearRegression().fit(train_vectorized_corpus, date_mean_df)
+
+df_evaluate = read_evaluate_file('dev-0/in.tsv')
+evaluate_vectorized_corpus = vectorizer.transform(df_evaluate['data'])
+evaluate = reg.predict(evaluate_vectorized_corpus)
+with open("dev-0/out.tsv", 'w') as file:
+    for e in evaluate:
+        file.write("%i\n" % e)