37 lines
966 B
Python
37 lines
966 B
Python
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import pickle
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import sys
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import math
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import fileinput
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model = pickle.load(open("model.pkl", "rb"))
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word_index, vocabulary, weights, words_count = model
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def predict():
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output = []
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for line in fileinput.input(openhook=fileinput.hook_encoded("utf-8")):
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line = line.rstrip()
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fields = line.split('\t')
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label = fields[0].strip()
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document = fields[0]
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terms = document.split(' ')
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for term in terms:
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if term in words_count:
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words_count[term] += 1
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else:
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words_count[term] = 1
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expected = weights[0]
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for t in terms:
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if t in vocabulary:
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expected +=(words_count[t]/len(words_count)*(weights[word_index[t]]))
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if expected > 0.9:
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output.append(1)
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else:
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output.append(0)
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with open("out.tsv", "w") as out:
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for val in output:
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out.write(str(val)+"\n")
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predict()
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