forked from kubapok/retroc2
47 lines
1.7 KiB
Python
47 lines
1.7 KiB
Python
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import gensim as gensim
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import smart_open
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.linear_model import LinearRegression
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from stop_words import get_stop_words
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from sklearn.cluster import KMeans
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from gensim.models.doc2vec import Doc2Vec
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import os
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import pandas as pd
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def read_train_file(inDirectory):
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colnames = ['start_date', 'end_date', 'title', 'sort_title', 'data']
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df_train = pd.read_csv(inDirectory, sep="\t", names=colnames)
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return df_train[:5000]
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def read_evaluate_file(inDirectory):
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colnames = ['data']
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df_train = pd.read_csv(inDirectory, sep="\t", names=colnames)
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return df_train[:5000]
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def train_date_mean(df):
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date_mean = (df['start_date'] + df['end_date']) / 2
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return date_mean
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def preper_data(df):
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document_list = list()
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for line in df:
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tokens = gensim.utils.simple_preprocess(line, min_len=2, max_len=15)
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filtered_sentence = []
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for word in tokens:
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if word not in get_stop_words('polish'):
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filtered_sentence.append(word)
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document_list.append(filtered_sentence)
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return document_list
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df = read_train_file('train/train.tsv')
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date_mean_df = train_date_mean(df)[:5000]
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vectorizer = TfidfVectorizer(stop_words=get_stop_words('polish'))
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train_vectorized_corpus = vectorizer.fit_transform(df['data'])
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reg = LinearRegression().fit(train_vectorized_corpus, date_mean_df)
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df_evaluate = read_evaluate_file('dev-0/in.tsv')
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evaluate_vectorized_corpus = vectorizer.transform(df_evaluate['data'])
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evaluate = reg.predict(evaluate_vectorized_corpus)
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with open("dev-0/out.tsv", 'w') as file:
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for e in evaluate:
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file.write("%i\n" % e)
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