TFIDF proj commit

linear_regression.py

@@ -26,7 +26,7 @@ def train():
     tfidf = TfidfVectorizer(stop_words='english', ngram_range=(1,1)) #Konwertuje tekst w dokumencie do macierzy tfidf , ngram_range - lb słów w sekwencji
     x = tfidf.fit_transform(created_dictionary)
     #PCA - principal component analysis
-    pca = TruncatedSVD(n_components=100) # Liniowa redukcja wymiarów , n_components - Pożądana wymiarowość danych wyjściowych
+    pca = TruncatedSVD(n_components=120) # Liniowa redukcja wymiarów , n_components - Pożądana wymiarowość danych wyjściowych
     x_pca = pca.fit_transform(x)
     l_regression = LinearRegression()
     l_regression.fit(x_pca,expected_dictionary)

predict.py

@@ -13,20 +13,23 @@ def predict():
     input_file = open("l_regression.pkl",'rb')
     l_regression = pickle.load(input_file)
     input_file = open("tfidf_model.pkl",'rb')
-    tfidf = pickle.load(input_file)
+    tfidf = pickle.load(input_file,encoding='UTF-8')
 
-    dev0 = pd.read_csv("dev-0/in.tsv", delimiter="\t", header=None, names=["txt"], quoting=csv.QUOTE_NONE, error_bad_lines=False)
-    testA = pd.read_csv("test-A/in.tsv", delimiter="\t", header=None, names=["txt"], quoting=csv.QUOTE_NONE, error_bad_lines=False)
-    devtxt = dev0["txt"]
-    testAtxt = testA["txt"]
+    dev0 = pd.read_csv("dev-0/in.tsv", delimiter="\t", header=None, names=["txt"], quoting=csv.QUOTE_NONE, error_bad_lines=False, skip_blank_lines=False)
+    testA = pd.read_csv("test-A/in.tsv", delimiter="\t", header=None, names=["txt"], quoting=csv.QUOTE_NONE, error_bad_lines=False, skip_blank_lines=False )
+    #devtxt = dev0["txt"]
+    #testAtxt = testA["txt"]
 
-    print(testAtxt)
-
-    dev0_vector = tfidf.fit_transform(devtxt)
-    testA_vector = tfidf.fit_transform(testAtxt)
+    #print(testAtxt)
 
+    dev0_vector = tfidf.fit_transform(dev0['txt'].apply(lambda dev0_vector: np.str_(dev0_vector)))
+    testA_vector = tfidf.fit_transform(testA['txt'].apply(lambda testA_vector: np.str_(testA_vector)))
+    #dev0_vector = tfidf.fit_transform(dev0['txt'].values.astype('U'))
+    #testA_vector = tfidf.fit_transform(testA['txt'].values.astype('U'))
+    #dev0_vector = tfidf.fit_transform(dev0['txt'],y=None)
+    #testA_vector = tfidf.fit_transform(testA['txt'],y=None)
     #print(testA_vector)
-    pca = TruncatedSVD(n_components=100)
+    pca = TruncatedSVD(n_components=120)
 
     dev0_pca = pca.fit_transform(dev0_vector)
     testA_pca = pca.fit_transform(testA_vector)