36 lines
879 B
Python
36 lines
879 B
Python
from sklearn.linear_model import LinearRegression
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from sklearn.feature_extraction.text import TfidfVectorizer
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import pandas as pd
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def regresja(path):
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# dnae do modelu
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train = pd.read_csv('train/train.tsv', sep='\t', header=None)
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y_train = train[0]
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x_train = train[4]
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# dane do predykcji
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x_predict = []
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with open(f"{path}/in.tsv", encoding='utf-8') as f:
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for line in f:
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x_predict.append(line)
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# tfidf
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vectorizer = TfidfVectorizer()
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x_train = vectorizer.fit_transform(x_train)
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x_predict = vectorizer.transform(x_predict)
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# model regresji
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model = LinearRegression()
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model.fit(x_train, y_train)
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# przewidywanie wyniku
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y_predict = model.predict(x_predict)
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pd.DataFrame(y_predict).to_csv(f"{path}/out.tsv", header=False, index=None)
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regresja("dev-0")
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regresja("dev-1")
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regresja("test-A")
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