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Commit12755c6

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author
minjk-bl
committed
Fix Model options
1 parent2e369e7 commit12755c6

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2 files changed

+86
-23
lines changed

2 files changed

+86
-23
lines changed

‎js/com/component/ModelEditor.js‎

Lines changed: 79 additions & 18 deletions
Original file line numberDiff line numberDiff line change
@@ -85,26 +85,28 @@ define([
8585
},
8686
'predict':{
8787
name:'predict',
88-
code:'${model}.predict(${featureData})',
88+
code:'${allocatePredict} = ${model}.predict(${featureData})',
8989
description:'Predict the closest target data X belongs to.',
9090
options:[
91-
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X_train'}
91+
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X_test'},
92+
{name:'allocatePredict',label:'Allocate to',component:['input'],placeholder:'New variable',default:'pred'}
9293
]
9394
},
9495
'predict_proba':{
9596
name:'predict_proba',
96-
code:'${model}.predict_proba(${featureData})',
97+
code:'${allocatePredict} = ${model}.predict_proba(${featureData})',
9798
description:'Predict class probabilities for X.',
9899
options:[
99-
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X_train'}
100+
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X_test'},
101+
{name:'allocatePredict',label:'Allocate to',component:['input'],placeholder:'New variable',default:'pred'}
100102
]
101103
},
102104
'transform':{
103105
name:'transform',
104106
code:'${allocateTransform} = ${model}.transform(${featureData})',
105107
description:'Apply dimensionality reduction to X.',
106108
options:[
107-
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X_train'},
109+
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X'},
108110
{name:'allocateTransform',label:'Allocate to',component:['input'],placeholder:'New variable'}
109111
]
110112
}
@@ -113,7 +115,23 @@ define([
113115
switch(category){
114116
case'Data Preparation':
115117
actions={
116-
'fit':defaultActions['fit'],
118+
'fit':{
119+
name:'fit',
120+
code:'${model}.fit(${featureData})',
121+
description:'Fit Encoder/Scaler to X.',
122+
options:[
123+
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X'}
124+
]
125+
},
126+
'fit_transform':{
127+
name:'fit_transform',
128+
code:'${allocateTransform} = ${model}.fit_transform(${featureData})',
129+
description:'Fit Encoder/Scaler to X, then transform X.',
130+
options:[
131+
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X'},
132+
{name:'allocateTransform',label:'Allocate to',component:['input'],placeholder:'New variable'}
133+
]
134+
},
117135
'transform':{
118136
...defaultActions['transform'],
119137
description:'Transform labels to normalized encoding.'
@@ -141,11 +159,31 @@ define([
141159
'predict':defaultActions['predict'],
142160
'predict_proba':defaultActions['predict_proba'],
143161
}
162+
if(['LogisticRegression','SVC','GradientBoostingClassifier'].includes(modelType)){
163+
actions={
164+
...actions,
165+
'decision_function':{
166+
name:'decision_function',
167+
code:'${allocateScore} = ${model}.decision_function(${featureData})',
168+
description:'Compute the decision function of X.',
169+
options:[
170+
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X'},
171+
{name:'allocateScore',label:'Allocate to',component:['input'],placeholder:'New variable'}
172+
]
173+
}
174+
}
175+
}
144176
break;
145177
case'Auto ML':
146178
actions={
147179
'fit':defaultActions['fit'],
148-
'predict':defaultActions['predict'],
180+
'predict':defaultActions['predict']
181+
}
182+
if(modelType=='TPOTClassifier'){
183+
actions={
184+
...actions,
185+
'predict_proba':defaultActions['predict_proba']
186+
}
149187
}
150188
break;
151189
case'Clustering':
@@ -155,10 +193,11 @@ define([
155193
'fit':defaultActions['fit'],
156194
'fit_predict':{
157195
name:'fit_predict',
158-
code:'${model}.fit_predict(${featureData})',
196+
code:'${allocatePredict} = ${model}.fit_predict(${featureData})',
159197
description:'Compute clusters from a data or distance matrix and predict labels.',
160198
options:[
161-
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X_train'}
199+
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X'},
200+
{name:'allocatePredict',label:'Allocate to',component:['input'],placeholder:'New variable',default:'pred'}
162201
]
163202
}
164203
}
@@ -167,6 +206,37 @@ define([
167206
actions={
168207
'fit':defaultActions['fit'],
169208
'predict':defaultActions['predict'],
209+
'fit_predict':{
210+
name:'fit_predict',
211+
code:'${allocatePredict} = ${model}.fit_predict(${featureData})',
212+
description:'Compute cluster centers and predict cluster index for each sample.',
213+
options:[
214+
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X'},
215+
{name:'allocatePredict',label:'Allocate to',component:['input'],placeholder:'New variable',default:'pred'}
216+
]
217+
}
218+
}
219+
if(modelType=='KMeans'){
220+
actions={
221+
...actions,
222+
'fit_transform':{
223+
name:'fit_transform',
224+
code:'${model}.fit_transform(${featureData})',
225+
description:'Compute clustering and transform X to cluster-distance space.',
226+
options:[
227+
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X_train'}
228+
]
229+
},
230+
'transform':{
231+
name:'transform',
232+
code:'${allocateTransform} = ${model}.transform(${featureData})',
233+
description:'Transform X to a cluster-distance space.',
234+
options:[
235+
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X'},
236+
{name:'allocateTransform',label:'Allocate to',component:['input'],placeholder:'New variable'}
237+
]
238+
}
239+
}
170240
}
171241
break;
172242
case'Dimension Reduction':
@@ -303,15 +373,6 @@ define([
303373
options:[
304374
{name:'allocateCenters',label:'Allocate to',component:['input'],placeholder:'New variable'}
305375
]
306-
},
307-
'transform':{
308-
name:'transform',
309-
code:'${allocateTransform} = ${model}.transform(${featureData})',
310-
description:'Transform X to a cluster-distance space.',
311-
options:[
312-
{name:'featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],default:'X'},
313-
{name:'allocateTransform',label:'Allocate to',component:['input'],placeholder:'New variable'}
314-
]
315376
}
316377
}
317378
}

‎js/m_ml/evaluation.js‎

Lines changed: 7 additions & 5 deletions
Original file line numberDiff line numberDiff line change
@@ -161,11 +161,13 @@ define([
161161
code.appendLine("# ROC Curve");
162162
code.appendFormatLine("fpr, tpr, thresholds = roc_curve({0}, svc.decision_function({1}}))",predictData,targetData);
163163
code.appendLine("plt.plot(fpr, tpr, label='ROC Curve')");
164-
code.appendLine("plt.xlabel('Sensitivity') ");
165-
code.appendLine("plt.ylabel('Specificity') ")
164+
code.appendLine("plt.xlabel('Sensitivity') ");
165+
code.appendLine("plt.ylabel('Specificity') ")
166166
}
167167
if(auc){
168-
// FIXME:
168+
code.appendLine("# AUC");
169+
code.appendFormatLine("fpr, tpr, thresholds = roc_curve({0}, svc.decision_function({1}}))",predictData,targetData);
170+
code.appendLine("metrics.auc(fpr, tpr)");
169171
}
170172
}
171173

@@ -221,11 +223,11 @@ define([
221223
code.appendFormatLine("print(f'Silhouette score: {metrics.cluster.silhouette_score({0}, {1})}')",targetData,predictData);
222224
}
223225
if(ari){
224-
code.appendLine("# ARI");// FIXME:
226+
code.appendLine("# ARI");
225227
code.appendFormatLine("print(f'ARI: {metrics.cluster.adjusted_rand_score({0}, {1})}')",targetData,predictData);
226228
}
227229
if(nm){
228-
code.appendLine("# NM");// FIXME:
230+
code.appendLine("# NM");
229231
code.appendFormatLine("print(f'NM: {metrics.cluster.normalized_mutual_info_score({0}, {1})}')",targetData,predictData);
230232
}
231233
}

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