43 lines
1.3 KiB
Python
43 lines
1.3 KiB
Python
import csv
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import pickle
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from typing import re
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import numpy as np
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import pandas as pd
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from sklearn.decomposition import PCA
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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 = pd.read_csv("dev-0/in.tsv", delimiter="\t", header=None, names=["txt"], quoting=csv.QUOTE_NONE)
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testA = pd.read_csv("test-A/in.tsv", delimiter="\t", header=None, names=["txt"], quoting=csv.QUOTE_NONE)
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devtxt = dev0["txt"]
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testAtxt = testA["txt"]
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print(testAtxt)
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dev0_vector = tfidf.fit_transform(devtxt)
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testA_vector = tfidf.fit_transform(testAtxt)
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#print(testA_vector)
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pca = TruncatedSVD(n_components=100)
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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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predict() |