forked from kubapok/retroc2
39 lines
1.3 KiB
Python
39 lines
1.3 KiB
Python
import pandas as pd
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import numpy as np
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import csv
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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.feature_extraction.text import TfidfVectorizer
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train = pd.read_csv("train/train.tsv", names = ['start_date', 'end_date', 'title', 'sort_title', 'data'], sep = "\t")
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vectorizer = TfidfVectorizer(stop_words=get_stop_words('polish'))
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linear_reg = LinearRegression()
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date = (train['start_date'] + train['end_date']) / 2
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train_vec = vectorizer.fit_transform(train['data'])
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linear_reg.fit(train_vec, date)
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dev_0 = pd.read_csv("dev-0/in.tsv", error_bad_lines = False, header = None, sep = "\t", quoting=csv.QUOTE_NONE)
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pred_dev_0 = linear_reg.predict(vectorizer.transform(dev_0[0]))
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pd.DataFrame(pred_dev_0).to_csv('dev-0/out.tsv', sep = "\t", index = False, header = False)
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dev_1 = pd.read_csv("dev-1/in.tsv", error_bad_lines = False, header = None, sep = "\t", quoting=csv.QUOTE_NONE)
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pred_dev_1 = linear_reg.predict(vectorizer.transform(dev_1[0]))
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pd.DataFrame(pred_dev_1).to_csv('dev-1/out.tsv', sep = "\t", index = False, header = False)
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test = pd.read_csv("test-A/in.tsv", names = ['data'], sep = "\t")
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pred_test = linear_reg.predict(vectorizer.transform(test['data']))
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pd.DataFrame(pred_test).to_csv('test-A/out.tsv', sep = "\t", index = False, header = False)
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#./geval -t dev-0
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#21.8069
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#./geval -t dev-1
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#22.0247
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