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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

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.

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).
```

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

How to delete a file or folder in Python?

5 answers

How do I delete a file or folder in Python?

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.

Want to excel in Python? See our review of the best Python online courses 2023. If you are interested in Data Science, check also how to learn programming in R.

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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

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!