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+++ b/predict.py @@ -1,10 +1,19 @@ import pickle +from typing import re + import numpy as np from sklearn.decomposition import PCA -from linear_regression import create_dictionary from sklearn.feature_extraction.text import TfidfVectorizer from sklearn.decomposition import TruncatedSVD +def create_dictionary(in_path): + tfDict = [] + with open(in_path,encoding='utf-8') as in_file: + for line in in_file: + for word in re.findall(r"[\w]+",line): + tfDict.append(word) + return tfDict + def predict(): input_file = open("l_regression.pkl",'rb') l_regression = pickle.load(input_file) @@ -17,7 +26,7 @@ def predict(): testA_vector = tfidf.fit_transform(testA) #print(testA_vector) - pca = TruncatedSVD(n_components=300) + pca = TruncatedSVD(n_components=100) dev0_pca = pca.fit_transform(dev0_vector) testA_pca = pca.fit_transform(testA_vector) @@ -32,9 +41,4 @@ def predict(): foo = np.array(y_test) np.savetxt(output,foo) - #print(y_test) - - # dev0_vectorizer = - - predict() \ No newline at end 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