2020-05-05 14:17:49 +02:00
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import pickle
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import numpy as np
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from sklearn.decomposition import PCA
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from linear_regression import create_dictionary
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn.decomposition import TruncatedSVD
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def predict():
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input_file = open("l_regression.pkl",'rb')
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l_regression = pickle.load(input_file)
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input_file = open("tfidf_model.pkl",'rb')
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tfidf = pickle.load(input_file)
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dev0 = create_dictionary("dev-0/in.tsv")
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testA = create_dictionary("test-A/in.tsv")
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dev0_vector = tfidf.fit_transform(dev0)
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testA_vector = tfidf.fit_transform(testA)
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#print(testA_vector)
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2020-05-05 14:52:12 +02:00
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pca = TruncatedSVD(n_components=300)
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2020-05-05 14:17:49 +02:00
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dev0_pca = pca.fit_transform(dev0_vector)
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testA_pca = pca.fit_transform(testA_vector)
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output= open("dev-0/out.tsv","w+",encoding="UTF-8")
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y_dev = l_regression.predict(dev0_pca)
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print(y_dev)
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foo = np.array(y_dev)
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print(foo)
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np.savetxt(output,foo)
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output = open("test-A/out.tsv", "w+", encoding="UTF-8")
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y_test = l_regression.predict(testA_pca)
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foo = np.array(y_test)
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np.savetxt(output,foo)
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#print(y_test)
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# dev0_vectorizer =
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predict()
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