Sklearn | Extract function with TF-IDF

Michael Zippo 18.07.2021

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The tf-idf value increases in proportion to the number of times a word appears in the document, but is often offset by the frequency of the word in the corpus, which helps to correct for the fact that some words appear the most often.
TF-IDF uses two statistical methods , the first of them — Term Frequency and the other — Inverse Document Frequency. Term frequency refers to the total number of times a given term t appears in a doc document, in relation to (per) the total number of all words in the document and the Inverse Document Frequency, which determines how much information a word provides. It measures the weight of a given word throughout the entire document. IDF shows how often or rarely a given word appears in all documents.
TF-IDF can be calculated as tf * idf

Tf * Idf does not directly convert raw data into useful functions. First, it converts raw strings or dataset to vectors, and each word has its own vector. Then we will use a specific method to extract a function like Cosine Similarity that works with vectors, etc. As we know, we cannot directly pass a string to our model. So tf * idf gives us the numeric values ‚Äã‚Äãof the entire document.
To extract elements from the word document, we import —

 from sklearn.feature_extraction.text import TfidfVectorizer

Input:

 1st Sentence - "hello i am pulkit" 2nd Sentence - "your name is akshit"

Code: Python code to find similarity measures

       # importing libraries  
   from   sklearn.feature_extraction.text   import   TfidfVectorizer 
   from   sklearn.metrics.pairwise   import   cosine_similarity  
   from   sklearn.metrics   import   pairwise_distances  
   from   sklearn.metrics.pairwise   import   euclidean_distances 
   from   scipy.spatial   import   distance 
   import   pandas as pd 
   import   numpy as np  
  
  ## Convert 3D array to 1D array  
   def   arr_convert_1d (arr): 
   arr   =   np.array (arr) 
   arr   =   np.concaten ate (arr, axis   =   0  )   
  arr   =   np.concatenate (arr, axis   =   0  )  
   return   arr   
 
  ## Cosine Similarity  
   cos   =   [] 
   def   cosine (trans): 
   cos.append (cosine_similarity (trans [  0  ], trans [  1  ])) 
    
  ## Manhattan Distance  
   manhatten   =   [] 
   def   manhatten_distance (trans): 
   manhatten.append (pairwise_distances (trans [  0  ], trans [  1  ], 
   metric   =  ’ manhattan’  )) 
  
  ## Euclidean distance  
   euclidean   =   [] 
   def   euclidean_function (v ectors): 
   euc   =   euclidean_distances (vectors [  0  ], vectors [  1  ]) 
   euclidean.append (euc) 
    
  # This function finds similarities between the two  
  # suggestions using the above functions.  
  
  ## TF - IDF  
   def   tfidf ( str1, str2): 
   ques   =   [] 
   # You must provide a dataset. Dataset link  
   # is given at the end of this article.  
   # and if you are using a different dataset, adjust  
     # according to columns and rows of your dataset  
     dataset   =   pd.read_csv (  ’C : Users dell Desktop quora_duplicate_questions.tsv’  , 
   delimiter   =   ’’  , encoding   =   ’utf-8’  )  
  
     x   =   dataset.iloc [:,   1  :   5  ]  
   x   =   x.dropna (how   =   ’any’  ) 
    
     for   k   in   range   (  len   (x)): 
   for   j   in   [  2  ,   3  ]: 
     ques.append (x.iloc [k, j]) 
   vect   =   TfidfVectorizer () 
   # Your entire dataset will fit. Ultimately this will  
   # produce vectors based on words in the corpus / dataset 
   vect.fit (ques) 
  
   corpus   =   [str1, str2] 
     trans   =   vect.transform (corpus) 
  
   euclidean_function (trans)  
   cosine (trans) 
     manhatten_distance (trans) 
   return   convert () 
   
  def   convert (): 
   dataf   =   pd.DataFrame ()   
  lis2   =   arr_convert_1d (manhatten) 
   dataf [  ’manhatten’  ]   =   lis2   
  lis2   =   arr_convert_1d (cos) 
   dataf [  ’cos_sim’  ]   =   lis2 
   lis2   =   arr_convert_1d (euclidean)   
  dataf [  ’euclidean’  ]   =   lis2 
   return   dataf 
    
   newData   =   pd.DataFrame (); 
   str1   =   "hello i am pulkit " 
   str2   =  " your name is akshit " 
   newData   =   tfidf (str1 , str2); 
   print   (newData);

Output:

  manhatten cos_sim euclidean 0 2.955813 0.0 1.414214

Dataset: Google Drive link
Note: The dataset is large, so it will take 30-40 seconds to display, and if you are going to work as it is, then it won’t work. This only works when you copy this code into your IDE and provide your dataset in the tfidf function.

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Sklearn | Extract function with TF-IDF del: Questions

__del__

How can I make a time delay in Python?

5 answers

I would like to know how to put a time delay in a Python script.

2973

Answer #1

import time
time.sleep(5)   # Delays for 5 seconds. You can also use a float value.

Here is another example where something is run approximately once a minute:

import time
while True:
    print("This prints once a minute.")
    time.sleep(60) # Delay for 1 minute (60 seconds).

2973

Answer #2

You can use the sleep() function in the time module. It can take a float argument for sub-second resolution.

from time import sleep
sleep(0.1) # Time in seconds

Sklearn | Extract function with TF-IDF del: Questions

__del__

How to delete a file or folder in Python?

5 answers

How do I delete a file or folder in Python?

2639

Answer #1

os.remove() removes a file.
os.rmdir() removes an empty directory.
shutil.rmtree() deletes a directory and all its contents.

Path objects from the Python 3.4+ pathlib module also expose these instance methods:

pathlib.Path.unlink() removes a file or symbolic link.
pathlib.Path.rmdir() removes an empty directory.

We hope this article has helped you to resolve the problem. Apart from Sklearn | Extract function with TF-IDF, check other __del__-related topics.

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Manuel Innsbruck

Warsaw | 2023-02-02

Simply put and clear. Thank you for sharing. Sklearn | Extract function with TF-IDF and other issues with stat Python module was always my weak point 😁. Will use it in my bachelor thesis

Boris Lehnman

Warsaw | 2023-02-02

I was preparing for my coding interview, thanks for clarifying this - Sklearn | Extract function with TF-IDF in Python is not the simplest one. Will use it in my bachelor thesis

Walter Ungerschaft

Tallinn | 2023-02-02

Thanks for explaining! I was stuck with Sklearn | Extract function with TF-IDF for some hours, finally got it done 🤗. Checked yesterday, it works!