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Visualizing decision tree with feature names

0

$begingroup$

from scipy.sparse import hstack
X_tr1 = hstack((X_train_cc_ohe, X_train_csc_ohe, X_train_grade_ohe,X_train_price_norm, X_train_tnppp_norm, X_train_essay_bow, X_train_pt_bow)).tocsr()
X_te1 = hstack((X_test_cc_ohe, X_test_csc_ohe, X_test_grade_ohe, X_test_price_norm, X_test_tnppp_norm, X_test_essay_bow, X_test_pt_bow)).tocsr()

X_train_cc_ohe and all are vectorized categorical data, and X_train_pt_bow is bag of words vectorized text data.

Now i applied decision tree classifier on this model, i got this.


i took max_depth as 3 just for visualization purpose.

my question is i want to get feature names in my output instead of index as X2599, X4 etc.

i know i can do it by vect.get_feature_names() as input to export_graphviz, vect is object of CountVectorizer(), since i have already merged this vectorized data using hstack, now how to get feature names in this decision tree.

visualization decision-trees

share|improve this question

asked Mar 23 at 16:45

torBhakttorBhakt

1

$endgroup$

add a comment | 

0

$begingroup$

from scipy.sparse import hstack
X_tr1 = hstack((X_train_cc_ohe, X_train_csc_ohe, X_train_grade_ohe,X_train_price_norm, X_train_tnppp_norm, X_train_essay_bow, X_train_pt_bow)).tocsr()
X_te1 = hstack((X_test_cc_ohe, X_test_csc_ohe, X_test_grade_ohe, X_test_price_norm, X_test_tnppp_norm, X_test_essay_bow, X_test_pt_bow)).tocsr()

X_train_cc_ohe and all are vectorized categorical data, and X_train_pt_bow is bag of words vectorized text data.

Now i applied decision tree classifier on this model, i got this.


i took max_depth as 3 just for visualization purpose.

my question is i want to get feature names in my output instead of index as X2599, X4 etc.

i know i can do it by vect.get_feature_names() as input to export_graphviz, vect is object of CountVectorizer(), since i have already merged this vectorized data using hstack, now how to get feature names in this decision tree.

visualization decision-trees

share|improve this question

asked Mar 23 at 16:45

torBhakttorBhakt

1

$endgroup$

add a comment | 

$begingroup$

from scipy.sparse import hstack
X_tr1 = hstack((X_train_cc_ohe, X_train_csc_ohe, X_train_grade_ohe,X_train_price_norm, X_train_tnppp_norm, X_train_essay_bow, X_train_pt_bow)).tocsr()
X_te1 = hstack((X_test_cc_ohe, X_test_csc_ohe, X_test_grade_ohe, X_test_price_norm, X_test_tnppp_norm, X_test_essay_bow, X_test_pt_bow)).tocsr()

X_train_cc_ohe and all are vectorized categorical data, and X_train_pt_bow is bag of words vectorized text data.

Now i applied decision tree classifier on this model, i got this.


i took max_depth as 3 just for visualization purpose.

my question is i want to get feature names in my output instead of index as X2599, X4 etc.

i know i can do it by vect.get_feature_names() as input to export_graphviz, vect is object of CountVectorizer(), since i have already merged this vectorized data using hstack, now how to get feature names in this decision tree.

visualization decision-trees

share|improve this question

asked Mar 23 at 16:45

torBhakttorBhakt

1

$endgroup$

from scipy.sparse import hstack
X_tr1 = hstack((X_train_cc_ohe, X_train_csc_ohe, X_train_grade_ohe,X_train_price_norm, X_train_tnppp_norm, X_train_essay_bow, X_train_pt_bow)).tocsr()
X_te1 = hstack((X_test_cc_ohe, X_test_csc_ohe, X_test_grade_ohe, X_test_price_norm, X_test_tnppp_norm, X_test_essay_bow, X_test_pt_bow)).tocsr()

share|improve this question

asked Mar 23 at 16:45

torBhakttorBhakt

1

share|improve this question

asked Mar 23 at 16:45

torBhakttorBhakt

1

asked Mar 23 at 16:45

torBhakttorBhakt

1

asked Mar 23 at 16:45

torBhakttorBhakt

1

1 Answer
1

active

oldest

votes

0

$begingroup$

You can use graphviz instead. and use the following code to view the decision tree with feature names.

import pydotplus
import sklearn.tree as tree
from IPython.display import Image

dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot', 
 feature_names=dt_feature_names, class_names=dt_target_names,
 filled=True) 
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())

This will display feature names with values, gini coefficient, sample, value and class

share|improve this answer

answered Mar 23 at 22:09

Cini09Cini09

166

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  This won't work, I have main problem in collecting feature names, feature names are build when the model is fitted, in my case I have total 6689 features. I got to know exact number when it throwed an error. Now I don't know how to extract features
  $endgroup$
  – torBhakt
  Mar 24 at 10:57
  

  
  
  
  

add a comment | 

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0

$begingroup$

You can use graphviz instead. and use the following code to view the decision tree with feature names.

import pydotplus
import sklearn.tree as tree
from IPython.display import Image

dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot', 
 feature_names=dt_feature_names, class_names=dt_target_names,
 filled=True) 
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())

This will display feature names with values, gini coefficient, sample, value and class

share|improve this answer

answered Mar 23 at 22:09

Cini09Cini09

166

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  This won't work, I have main problem in collecting feature names, feature names are build when the model is fitted, in my case I have total 6689 features. I got to know exact number when it throwed an error. Now I don't know how to extract features
  $endgroup$
  – torBhakt
  Mar 24 at 10:57
  

  
  
  
  

add a comment | 

0

$begingroup$

You can use graphviz instead. and use the following code to view the decision tree with feature names.

import pydotplus
import sklearn.tree as tree
from IPython.display import Image

dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot', 
 feature_names=dt_feature_names, class_names=dt_target_names,
 filled=True) 
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())

This will display feature names with values, gini coefficient, sample, value and class

share|improve this answer

answered Mar 23 at 22:09

Cini09Cini09

166

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  This won't work, I have main problem in collecting feature names, feature names are build when the model is fitted, in my case I have total 6689 features. I got to know exact number when it throwed an error. Now I don't know how to extract features
  $endgroup$
  – torBhakt
  Mar 24 at 10:57
  

  
  
  
  

add a comment | 

$begingroup$

You can use graphviz instead. and use the following code to view the decision tree with feature names.

import pydotplus
import sklearn.tree as tree
from IPython.display import Image

dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot', 
 feature_names=dt_feature_names, class_names=dt_target_names,
 filled=True) 
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())

This will display feature names with values, gini coefficient, sample, value and class

share|improve this answer

answered Mar 23 at 22:09

Cini09Cini09

166

$endgroup$

import pydotplus
import sklearn.tree as tree
from IPython.display import Image

dt_feature_names = list(X.columns)
dt_target_names = [str(s) for s in Y.unique()]
tree.export_graphviz(dt, out_file='tree.dot', 
 feature_names=dt_feature_names, class_names=dt_target_names,
 filled=True) 
graph = pydotplus.graph_from_dot_file('tree.dot')
Image(graph.create_png())

share|improve this answer

answered Mar 23 at 22:09

Cini09Cini09

166

answered Mar 23 at 22:09

Cini09Cini09

166

answered Mar 23 at 22:09

Cini09Cini09

166