Selecting 8 Best Predictors for the Model
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
Input In [17], in <cell line: 1>()
----> 1 dom_classif_tp = Dominance(data=df_bug_report, target="Y", objective=0, pseudo_r2="mcfadden")

File C:\VirtualEnvironments\ds386\lib\site-packages\dominance_analysis\dominance.py:45, in Dominance.__init__(self, data, target, top_k, objective, pseudo_r2, data_format)
     43 	self.pseudo_r2=pseudo_r2
     44 	assert (self.top_k >1 ) and (self.top_k<(len(self.data.columns))),"Value of top_k ranges from 1 to n-1 !"
---> 45 self.complete_model_rsquare()

File C:\VirtualEnvironments\ds386\lib\site-packages\dominance_analysis\dominance.py:469, in Dominance.complete_model_rsquare(self)
    467 if(self.data_format==0): #Bala changes
    468 	print("Selecting %s Best Predictors for the Model" %self.top_k)
--> 469 	columns=self.get_top_k()
    470 	print("Selected Predictors : ",columns)
    471 	print()

File C:\VirtualEnvironments\ds386\lib\site-packages\dominance_analysis\dominance.py:253, in Dominance.get_top_k(self)
    251 	columns.remove('intercept')
    252 	top_k_vars=SelectKBest(chi2, k=self.top_k)
--> 253 top_k_vars.fit_transform(self.data[columns], self.data[self.target])
    254 return [columns[i] for i in top_k_vars.get_support(indices=True)]

File C:\VirtualEnvironments\ds386\lib\site-packages\sklearn\base.py:870, in TransformerMixin.fit_transform(self, X, y, **fit_params)
    867     return self.fit(X, **fit_params).transform(X)
    868 else:
    869     # fit method of arity 2 (supervised transformation)
--> 870     return self.fit(X, y, **fit_params).transform(X)

File C:\VirtualEnvironments\ds386\lib\site-packages\sklearn\feature_selection\_univariate_selection.py:471, in _BaseFilter.fit(self, X, y)
    465 if not callable(self.score_func):
    466     raise TypeError(
    467         "The score function should be a callable, %s (%s) was passed."
    468         % (self.score_func, type(self.score_func))
    469     )
--> 471 self._check_params(X, y)
    472 score_func_ret = self.score_func(X, y)
    473 if isinstance(score_func_ret, (list, tuple)):

File C:\VirtualEnvironments\ds386\lib\site-packages\sklearn\feature_selection\_univariate_selection.py:668, in SelectKBest._check_params(self, X, y)
    666 def _check_params(self, X, y):
    667     if not (self.k == "all" or 0 <= self.k <= X.shape[1]):
--> 668         raise ValueError(
    669             "k should be >=0, <= n_features = %d; got %r. "
    670             "Use k='all' to return all features." % (X.shape[1], self.k)
    671         )

ValueError: k should be >=0, <= n_features = 7; got 8. Use k='all' to return all features.