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program.py
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program.py
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import numpy as np
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from sklearn import preprocessing
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from sklearn.naive_bayes import GaussianNB
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.pipeline import make_pipeline
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from sklearn.feature_extraction.text import TfidfVectorizer
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le=preprocessing.LabelEncoder()
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with open("train/in.tsv") as f:
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data = f.readlines()
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vectorizer = TfidfVectorizer(ngram_range=(1,2), use_idf = False)
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vectorizer = TfidfVectorizer()
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x = vectorizer.fit_transform(data)
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X=x.toarray()
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X = f.readlines()
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with open("train/expected.tsv") as ff:
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Y = ff.readlines()
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Y= le.fit_transform(Y)
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with open("dev-0/in.tsv") as d:
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fil = d.readlines()
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vectorizer = TfidfVectorizer(ngram_range=(1,2), use_idf = False)
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vectorizer = TfidfVectorizer()
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r=vectorizer.fit_transform(fil)
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r=r.toarray()
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r=r.reshape(-1,1)
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gnb = GaussianNB()
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with open("test-A/in.tsv") as d:
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r = d.readlines()
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gnb = make_pipeline(TfidfVectorizer(),MultinomialNB())
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model=gnb.fit(X, Y)
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y_pred=model.predict(X)
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print(y_pred)
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y_pred=model.predict(r)
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y_pred=np.array(y_pred)
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t=np.array2string(y_pred, precision=2, separator='\n',suppress_small=True)
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f = open("dev-0/out.tsv", "a")
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np.set_printoptions(threshold=np.inf)
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t=np.array2string(y_pred.flatten(), precision=2, separator='\n',suppress_small=True)
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f = open("test-A/out.tsv", "a")
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f.write(t)
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