39 lines
1.1 KiB
Python
39 lines
1.1 KiB
Python
import pandas as pd
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from sklearn.linear_model import LinearRegression
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from sklearn.feature_extraction.text import TfidfVectorizer
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def linear_regression(train_path, predict_path, out_path):
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print("Wczytywanie zbioru treningowego")
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train = pd.read_csv(train_path, sep='\t', header=None)
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X_train = train[4]
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Y_train = train[0]
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print("Wczytywanie pliku do predykcji")
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pred_x = []
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with open(predict_path, encoding='utf-8') as f:
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for line in f:
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pred_x.append(line)
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print("Wektoryzacja")
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vectorizer = TfidfVectorizer()
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X_train = vectorizer.fit_transform(X_train)
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pred_x = vectorizer.transform(pred_x)
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print("Uczenie modelu")
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model = LinearRegression()
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model.fit(X_train, Y_train)
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print("Predykcja wyników")
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pred_y = model.predict(pred_x)
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print("Zapis do pliku")
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pd.DataFrame(pred_y).to_csv(out_path, header=False, index=None)
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linear_regression("train/train.tsv", "dev-0/in.tsv", "dev-0/out.tsv")
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linear_regression("train/train.tsv", "dev-1/in.tsv", "dev-1/out.tsv")
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linear_regression("train/train.tsv", "test-A/in.tsv", "test-A/out.tsv")
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