TFIDF proj commit

linear_regression.py

@@ -1,3 +1,4 @@
+import csv
 import pickle
 
 from sklearn.decomposition import TruncatedSVD
@@ -11,22 +12,16 @@ import re
 from sklearn.linear_model import LinearRegression
 
 
-def create_dictionary(in_path):
-    tfDict = []
-    i=0;
-    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)
-                i+=1
-            if(i>=50054):
-                break
-    return tfDict
-##
-
 def train():
-    created_dictionary=create_dictionary("train/in.tsv")
+    created_dictionary=pd.read_csv("train/in.tsv", delimiter="\t", header=None, names=["txt"], quoting=csv.QUOTE_NONE)
-    expected_dictionary=create_dictionary("train/expected.tsv");
+    #Pandas - służy do analizy danych (wszystko to co umożliwia SQL, Excel itd.)
+    #delimiter - alternatywny argument do separacji, header - numer wiersza, który ma być traktowany jako nazwa
+    #names - określenie nazw kolumn jak ich nie ma dajemy None
+    #quoting - służy do interpretacji pól
+    created_dictionary = created_dictionary["txt"][:100000]
+    print(created_dictionary)
+    expected_dictionary=pd.read_csv("train/expected.tsv", header=None)
+    expected_dictionary = expected_dictionary[:100000]
     #tfidf = TfidfVectorizer(min_df=1,stop_words='english')
     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)
@@ -41,4 +36,5 @@ def train():
     with open('tfidf_model.pkl', 'wb') as f:
         pickle.dump(tfidf,f)
 
+
 train()

predict.py

@@ -1,29 +1,28 @@
+import csv
 import pickle
 from typing import re
 
 import numpy as np
+import pandas as pd
 from sklearn.decomposition import PCA
 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)
     input_file = open("tfidf_model.pkl",'rb')
     tfidf = pickle.load(input_file)
 
-    dev0 = create_dictionary("dev-0/in.tsv")
+    dev0 = pd.read_csv("dev-0/in.tsv", delimiter="\t", header=None, names=["txt"], quoting=csv.QUOTE_NONE)
-    testA = create_dictionary("test-A/in.tsv")
+    testA = pd.read_csv("test-A/in.tsv", delimiter="\t", header=None, names=["txt"], quoting=csv.QUOTE_NONE)
-    dev0_vector = tfidf.fit_transform(dev0)
+
-    testA_vector = tfidf.fit_transform(testA)
+    devtxt = dev0["txt"]
+    testAtxt = testA["txt"]
+    print(testAtxt)
+
+    dev0_vector = tfidf.fit_transform(devtxt)
+    testA_vector = tfidf.fit_transform(testAtxt)
 
     #print(testA_vector)
     pca = TruncatedSVD(n_components=100)