1
$\begingroup$

I want statistics to select the characteristics that have the greatest relationship to the output variable.

Thanks to this article, I learned that the scikit-learn library proposes the SelectKBest class that can be used with a set of different statistical tests to select a specific number of characteristics.

Here is my dataframe:

    Do you agree    Gender  Age     City     Urban/Rural  Output
0   Yes             Female  25-34   Madrid   Urban        Will buy
1   No              Male    18-25   Valencia Rural        Won't
2   ...             ...     ...     ...      ...          Undecided
....

The output is 'Will buy', 'won't' and 'undecided'.

I then tried the chi-square statistical test for non-negative characteristics to select 10 of the best characteristics:

import pandas as pd
import numpy as np
from sklearn.feature_selection import SelectKBest
from sklearn.feature_selection import chi2

data = pd.read_csv("D://Blogs//train.csv")
X = data.iloc[:,0:20]  #independent columns
y = data.iloc[:,-1]    #target column i.e price range#apply SelectKBest class to extract top 10 best features
bestfeatures = SelectKBest(score_func=chi2, k=10)
fit = bestfeatures.fit(X,y)
dfscores = pd.DataFrame(fit.scores_)
dfcolumns = pd.DataFrame(X.columns)
#concat two dataframes for better visualization 
featureScores = pd.concat([dfcolumns,dfscores],axis=1)
featureScores.columns = ['Specs','Score']  #naming the dataframe columns
print(featureScores.nlargest(10,'Score'))  #print 10 best features

But certain columns are 'String'. So, I get the terminal back:

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-59-e64d61febefd> in <module>
      1 bestfeatures = SelectKBest(score_func=chi2,k=10)
----> 2 fit = bestfeatures.fit(X,y)
      3 dfscores = pd.Dataframes(X.columns)
      4 #concat two dataframes for better visualization
      5 featuresScores = pd.concat([dfcolumns,dfscores], axis = 1)

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\feature_selection\univariate_selection.py in fit(self, X, y)
    339         self : object
    340         """
--> 341         X, y = check_X_y(X, y, ['csr', 'csc'], multi_output=True)
    342 
    343         if not callable(self.score_func):

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_X_y(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, warn_on_dtype, estimator)
    754                     ensure_min_features=ensure_min_features,
    755                     warn_on_dtype=warn_on_dtype,
--> 756                     estimator=estimator)
    757     if multi_output:
    758         y = check_array(y, 'csr', force_all_finite=True, ensure_2d=False,

C:\ProgramData\Anaconda3\lib\site-packages\sklearn\utils\validation.py in check_array(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    565         # make sure we actually converted to numeric:
    566         if dtype_numeric and array.dtype.kind == "O":
--> 567             array = array.astype(np.float64)
    568         if not allow_nd and array.ndim >= 3:
    569             raise ValueError("Found array with dim %d. %s expected <= 2."

ValueError: could not convert string to float: 'Yes'
$\endgroup$
0
$\begingroup$

You can only compute chi2 between two numerical arrays. You are getting that error because you are comparing a string. Also I am not sure if it works for multiclassification also.

df = df.apply(LabelEncoder().fit_transform)

This will solve the problem for you. But there are a thousand ways to encode features and for sure other will work better for you.

| improve this answer | |
$\endgroup$
  • $\begingroup$ Thanks for your answer. Do you know how I can handle aTypeError when doing fit_transform? Indeed, while applying it to a larger dataset I got: TypeError: ("'<' not supported between instances of 'NoneType' and 'str'", 'occurred at index Segment Cluster') $\endgroup$ – Revolucion for Monica Feb 5 at 15:28

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Not the answer you're looking for? Browse other questions tagged or ask your own question.