39 lines
1.2 KiB
Python
39 lines
1.2 KiB
Python
import numpy as np
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from sklearn.naive_bayes import MultinomialNB
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from sklearn.feature_extraction.text import TfidfVectorizer
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from sklearn import preprocessing
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from sklearn.pipeline import make_pipeline
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import csv
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prep = preprocessing.LabelEncoder()
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with open("train/train.tsv") as file_train:
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csv_input = csv.reader(file_train, delimiter='\t')
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X = []
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Y = []
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for line in csv_input:
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Y.append(line[0])
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X.append(line[1])
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Y = prep.fit_transform(Y)
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with open("test-A/in.tsv") as file_in:
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work_file_lines = file_in.readlines()
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MNB = make_pipeline(TfidfVectorizer(use_idf = True), MultinomialNB())
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model = MNB.fit(X,Y)
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y_predict = model.predict(work_file_lines)
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y_predict = np.array(y_predict)
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np.set_printoptions(threshold=np.inf)
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labels = np.array2string(y_predict.flatten(), separator='\n', suppress_small=True)
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file_out = open("test-A/out.tsv", 'w')
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file_out.write(labels[1:-1])
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with open("test-A/out.tsv", 'r') as fix_space:
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lines = fix_space.readlines()
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lines = [line.replace(' ', '') for line in lines]
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with open("test-A/out.tsv", 'w') as fix_space:
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fix_space.writelines(lines) |