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Commit112792c

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author
minjk-bl
committed
Edit permutation importances and add plot
1 parent8001e7c commit112792c

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3 files changed

+67
-4
lines changed

3 files changed

+67
-4
lines changed

‎visualpython/js/com/component/ModelEditor.js‎

Lines changed: 16 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -479,16 +479,30 @@ define([
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name:'permutation_importance',
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label:'Permutation importance',
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import:'from sklearn.inspection import permutation_importance',
482-
code:'${importance_allocate} =permutation_importance(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${random_state}${etc})',
482+
code:'${importance_allocate} =vp_create_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort})',
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description:'Permutation importance for feature evaluation.',
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options:[
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{name:'importance_featureData',label:'Feature Data',component:['data_select'],var_type:['DataFrame','Series','ndarray','list','dict'],value:'X_train'},
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{name:'importance_targetData',label:'Target Data',component:['data_select'],var_type:['DataFrame','Series','ndarray','list','dict'],value:'y_train'},
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{name:'scoring',component:['input'],usePair:true},
488-
{name:'random_state',component:['input_number'],placeholder:'123',usePair:true},
488+
{name:'sort',label:'Sort data',component:['bool_checkbox'],value:true,usePair:true},
489489
{name:'importance_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'importances'}
490490
]
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},
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'plot_permutation_importance':{
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name:'plot_permutation_importance',
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label:'Plot permutation importance',
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import:'from sklearn.inspection import permutation_importance',
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code:'vp_plot_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort}${top_count})',
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description:'Permutation importance for feature evaluation.',
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options:[
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{name:'importance_featureData',label:'Feature Data',component:['data_select'],var_type:['DataFrame','Series','ndarray','list','dict'],value:'X_train'},
500+
{name:'importance_targetData',label:'Target Data',component:['data_select'],var_type:['DataFrame','Series','ndarray','list','dict'],value:'y_train'},
501+
{name:'scoring',component:['input'],usePair:true},
502+
{name:'sort',label:'Sort data',component:['bool_checkbox'],value:true,usePair:true},
503+
{name:'top_count',label:'Top count',component:['input_number'],min:0,usePair:true}
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]
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},
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'feature_importances':{
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name:'feature_importances',
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label:'Feature importances',

‎visualpython/js/m_ml/ModelInfo.js‎

Lines changed: 16 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -409,16 +409,30 @@ define([
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name:'permutation_importance',
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label:'Permutation importance',
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import:'from sklearn.inspection import permutation_importance',
412-
code:'${importance_allocate} =permutation_importance(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${random_state}${etc})',
412+
code:'${importance_allocate} =vp_create_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort})',
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description:'Permutation importance for feature evaluation.',
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options:[
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{name:'importance_featureData',label:'Feature Data',component:['data_select'],var_type:['DataFrame','Series','ndarray','list','dict'],value:'X_train'},
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{name:'importance_targetData',label:'Target Data',component:['data_select'],var_type:['DataFrame','Series','ndarray','list','dict'],value:'y_train'},
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{name:'scoring',component:['input'],usePair:true},
418-
{name:'random_state',component:['input_number'],placeholder:'123',usePair:true},
418+
{name:'sort',label:'Sort data',component:['bool_checkbox'],value:true,usePair:true},
419419
{name:'importance_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'importances'}
420420
]
421421
},
422+
'plot_permutation_importance':{
423+
name:'plot_permutation_importance',
424+
label:'Plot permutation importance',
425+
import:'from sklearn.inspection import permutation_importance',
426+
code:'vp_plot_permutation_importances(${model}, ${importance_featureData}, ${importance_targetData}${scoring}${sort}${top_count})',
427+
description:'Permutation importance for feature evaluation.',
428+
options:[
429+
{name:'importance_featureData',label:'Feature Data',component:['data_select'],var_type:['DataFrame','Series','ndarray','list','dict'],value:'X_train'},
430+
{name:'importance_targetData',label:'Target Data',component:['data_select'],var_type:['DataFrame','Series','ndarray','list','dict'],value:'y_train'},
431+
{name:'scoring',component:['input'],usePair:true},
432+
{name:'sort',label:'Sort data',component:['bool_checkbox'],value:true,usePair:true},
433+
{name:'top_count',label:'Top count',component:['input_number'],min:0,usePair:true}
434+
]
435+
},
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'feature_importances':{
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name:'feature_importances',
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label:'Feature importances',

‎visualpython/python/userCommand.py‎

Lines changed: 35 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -123,6 +123,41 @@ def vp_plot_feature_importances(model, X_train=None, sort=False, top_count=0):
123123

124124
_vp_plt.show()
125125
######
126+
# Visual Python: Machine Learning > Model Info
127+
######
128+
defvp_create_permutation_importances(model,X_train,y_train,scoring=None,sort=False):
129+
fromsklearn.inspectionimportpermutation_importance
130+
ifisinstance(X_train,_vp_pd.core.frame.DataFrame):
131+
feature_names=X_train.columns
132+
else:
133+
feature_names= ['X{}'.format(i)foriinrange(len(model.feature_importances_)) ]
134+
135+
imp=permutation_importance(model,X_train,y_train,scoring)
136+
137+
df_i=_vp_pd.DataFrame(imp['importances_mean'],index=feature_names,columns=['Feature_importance'])
138+
df_i['Percentage']=100*df_i['Feature_importance']
139+
ifsort:df_i.sort_values(by='Feature_importance',ascending=False,inplace=True)
140+
df_i=df_i.round(2)
141+
142+
returndf_i
143+
######
144+
# Visual Python: Machine Learning > Model Info
145+
######
146+
defvp_plot_permutation_importances(model,X_train,y_train,scoring=None,sort=False,top_count=0):
147+
df_i=vp_create_permutation_importances(model,X_train,y_train,scoring,sort)
148+
149+
ifsort:
150+
iftop_count>0:
151+
df_i['Percentage'].sort_values().tail(top_count).plot(kind='barh')
152+
else:
153+
df_i['Percentage'].sort_values().plot(kind='barh')
154+
else:
155+
df_i['Percentage'].plot(kind='barh')
156+
_vp_plt.xlabel('Feature importance Percentage')
157+
_vp_plt.ylabel('Features')
158+
159+
_vp_plt.show()
160+
######
126161
# Visual Python: Visualization > Seaborn
127162
######
128163
defvp_seaborn_show_values(axs,precision=1,space=0.01):

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