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Commitea072f2

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Merge pull request#126 from minjk-bl/devops
Fix Clustering, Dimension Reduction options
2 parents9b94b21 +6462bda commitea072f2

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1 file changed

+155
-10
lines changed

1 file changed

+155
-10
lines changed

‎js/com/component/ModelEditor.js‎

Lines changed: 155 additions & 10 deletions
Original file line numberDiff line numberDiff line change
@@ -135,7 +135,7 @@ define([
135135
description:'Fit Encoder/Scaler to X, then transform X.',
136136
options:[
137137
{name:'fit_trans_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
138-
{name:'fit_trans_allocate',label:'Allocate to',component:['input'],placeholder:'New variable'}
138+
{name:'fit_trans_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'trans'}
139139
]
140140
},
141141
'transform':{
@@ -154,7 +154,7 @@ define([
154154
description:'Transform binary labels back to multi-class labels.',
155155
options:[
156156
{name:'inverse_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
157-
{name:'inverse_allocate',label:'Allocate to',component:['input'],placeholder:'New variable'}
157+
{name:'inverse_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'inv_trans'}
158158
]
159159
}
160160
}
@@ -209,7 +209,15 @@ define([
209209
if(modelType=='AgglomerativeClustering'
210210
||modelType=='DBSCAN'){
211211
actions={
212-
'fit':defaultActions['fit'],
212+
'fit':{
213+
name:'fit',
214+
label:'Fit',
215+
code:'${model}.fit(${fit_featureData})',
216+
description:'Perform clustering from features, or distance matrix.',
217+
options:[
218+
{name:'fit_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'}
219+
]
220+
},
213221
'fit_predict':{
214222
name:'fit_predict',
215223
label:'Fit and predict',
@@ -224,8 +232,25 @@ define([
224232
break;
225233
}
226234
actions={
227-
'fit':defaultActions['fit'],
228-
'predict':defaultActions['predict'],
235+
'fit':{
236+
name:'fit',
237+
label:'Fit',
238+
code:'${model}.fit(${fit_featureData})',
239+
description:'Compute clustering.',
240+
options:[
241+
{name:'fit_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'}
242+
]
243+
},
244+
'predict':{
245+
name:'predict',
246+
label:'Predict',
247+
code:'${pred_allocate} = ${model}.predict(${pred_featureData})',
248+
description:'Predict the closest target data X belongs to.',
249+
options:[
250+
{name:'pred_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
251+
{name:'pred_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'pred'}
252+
]
253+
},
229254
'fit_predict':{
230255
name:'fit_predict',
231256
label:'Fit and predict',
@@ -246,7 +271,7 @@ define([
246271
code:'${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData})',
247272
description:'Compute clustering and transform X to cluster-distance space.',
248273
options:[
249-
{name:'fit_trans_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X_train'},
274+
{name:'fit_trans_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
250275
{name:'fit_trans_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'trans'}
251276
]
252277
},
@@ -266,23 +291,114 @@ define([
266291
case'Dimension Reduction':
267292
if(modelType=='TSNE'){
268293
actions={
269-
'fit':defaultActions['fit'],
294+
'fit':{
295+
name:'fit',
296+
label:'Fit',
297+
code:'${model}.fit(${fit_featureData})',
298+
description:'Fit X into an embedded space.',
299+
options:[
300+
{name:'fit_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'}
301+
]
302+
},
270303
'fit_transform':{
271304
name:'fit_transform',
272305
label:'Fit and transform',
273306
code:'${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData})',
274307
description:'Fit X into an embedded space and return that transformed output.',
275308
options:[
276-
{name:'fit_trans_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X_train'},
309+
{name:'fit_trans_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
277310
{name:'fit_trans_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'trans'}
278311
]
279312
}
280313
}
281314
break;
282315
}
316+
if(modelType=='LinearDiscriminantAnalysis'){// LDA
317+
actions={
318+
'fit':{
319+
name:'fit',
320+
label:'Fit',
321+
code:'${model}.fit(${fit_featureData}, ${fit_targetData})',
322+
description:'Fit the Linear Discriminant Analysis model.',
323+
options:[
324+
{name:'fit_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
325+
{name:'fit_targetData',label:'Target Data',component:['var_select'],var_type:['DataFrame','Series'],value:'y'}
326+
]
327+
},
328+
'fit_transform':{
329+
name:'fit_transform',
330+
label:'Fit and transform',
331+
code:'${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData}${fit_trans_targetData})',
332+
description:'Fit to data, then transform it.',
333+
options:[
334+
{name:'fit_trans_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
335+
{name:'fit_trans_targetData',label:'Target Data',component:['var_select'],var_type:['DataFrame','Series'],value:'y'},
336+
{name:'fit_trans_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'trans'}
337+
]
338+
},
339+
'predict':{
340+
name:'predict',
341+
label:'Predict',
342+
code:'${pred_allocate} = ${model}.predict(${pred_featureData})',
343+
description:'Predict class labels for samples in X.',
344+
options:[
345+
{name:'pred_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
346+
{name:'pred_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'pred'}
347+
]
348+
},
349+
'transform':{
350+
name:'transform',
351+
label:'Transform',
352+
code:'${trans_allocate} = ${model}.transform(${trans_featureData})',
353+
description:'Project data to maximize class separation.',
354+
options:[
355+
{name:'trans_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
356+
{name:'trans_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'trans'}
357+
]
358+
}
359+
}
360+
break;
361+
}
283362
actions={
284-
'fit':defaultActions['fit'],
285-
'transform':defaultActions['transform'],
363+
'fit':{
364+
name:'fit',
365+
label:'Fit',
366+
code:'${model}.fit(${fit_featureData})',
367+
description:'Fit X into an embedded space.',
368+
options:[
369+
{name:'fit_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'}
370+
]
371+
},
372+
'fit_transform':{
373+
name:'fit_transform',
374+
label:'Fit and transform',
375+
code:'${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData})',
376+
description:'Fit the model with X and apply the dimensionality reduction on X.',
377+
options:[
378+
{name:'fit_trans_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
379+
{name:'fit_trans_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'trans'}
380+
]
381+
},
382+
'inverse_transform':{
383+
name:'inverse_transform',
384+
label:'Inverse transform',
385+
code:'${inverse_allocate} = ${model}.inverse_transform(${inverse_featureData})',
386+
description:'Transform data back to its original space.',
387+
options:[
388+
{name:'inverse_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
389+
{name:'inverse_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'inv_trans'}
390+
]
391+
},
392+
'transform':{
393+
name:'transform',
394+
label:'Transform',
395+
code:'${trans_allocate} = ${model}.transform(${trans_featureData})',
396+
description:'Apply dimensionality reduction to X.',
397+
options:[
398+
{name:'trans_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
399+
{name:'trans_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'trans'}
400+
]
401+
}
286402
}
287403
break;
288404
}
@@ -533,6 +649,22 @@ define([
533649
}
534650
break;
535651
case'Dimension Reduction':
652+
if(modelType=='LDA'){
653+
infos={
654+
'score':{
655+
name:'score',
656+
label:'Score',
657+
code:'${score_allocate} = ${model}.score(${score_featureData}, ${score_targetData})',
658+
description:'Return the average log-likelihood of all samples.',
659+
options:[
660+
{name:'score_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
661+
{name:'score_targetData',label:'Target Data',component:['var_select'],var_type:['DataFrame','Series'],value:'y'},
662+
{name:'score_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'scores'}
663+
]
664+
}
665+
}
666+
break;
667+
}
536668
if(modelType=='PCA'){
537669
infos={
538670
'explained_variance_ratio_':{
@@ -546,6 +678,19 @@ define([
546678
}
547679
}
548680
}
681+
infos={
682+
...infos,
683+
'score':{
684+
name:'score',
685+
label:'Score',
686+
code:'${score_allocate} = ${model}.score(${score_featureData})',
687+
description:'Return the average log-likelihood of all samples.',
688+
options:[
689+
{name:'score_featureData',label:'Feature Data',component:['var_select'],var_type:['DataFrame','Series'],value:'X'},
690+
{name:'score_allocate',label:'Allocate to',component:['input'],placeholder:'New variable',value:'scores'}
691+
]
692+
}
693+
}
549694
break;
550695
}
551696
returninfos;

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