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Fix Clustering, Dimension Reduction options #126

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Apr 9, 2022
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Fix Clustering, Dimension Reduction options #126

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165 changes: 155 additions & 10 deletions js/com/component/ModelEditor.js
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Original file line number Diff line number Diff line change
Expand Up @@ -135,7 +135,7 @@ define([
description: 'Fit Encoder/Scaler to X, then transform X.',
options: [
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable' }
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
'transform': {
Expand All @@ -154,7 +154,7 @@ define([
description: 'Transform binary labels back to multi-class labels.',
options: [
{ name: 'inverse_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'inverse_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable' }
{ name: 'inverse_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'inv_trans' }
]
}
}
Expand Down Expand Up @@ -209,7 +209,15 @@ define([
if (modelType == 'AgglomerativeClustering'
|| modelType == 'DBSCAN') {
actions = {
'fit': defaultActions['fit'],
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData})',
description: 'Perform clustering from features, or distance matrix.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' }
]
},
'fit_predict': {
name: 'fit_predict',
label: 'Fit and predict',
Expand All @@ -224,8 +232,25 @@ define([
break;
}
actions = {
'fit': defaultActions['fit'],
'predict': defaultActions['predict'],
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData})',
description: 'Compute clustering.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' }
]
},
'predict': {
name: 'predict',
label: 'Predict',
code: '${pred_allocate} = ${model}.predict(${pred_featureData})',
description: 'Predict the closest target data X belongs to.',
options: [
{ name: 'pred_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'pred_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
},
'fit_predict': {
name: 'fit_predict',
label: 'Fit and predict',
Expand All @@ -246,7 +271,7 @@ define([
code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData})',
description: 'Compute clustering and transform X to cluster-distance space.',
options: [
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X_train' },
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
Expand All @@ -266,23 +291,114 @@ define([
case 'Dimension Reduction':
if (modelType == 'TSNE') {
actions = {
'fit': defaultActions['fit'],
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData})',
description: 'Fit X into an embedded space.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' }
]
},
'fit_transform': {
name: 'fit_transform',
label: 'Fit and transform',
code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData})',
description: 'Fit X into an embedded space and return that transformed output.',
options: [
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X_train' },
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
}
break;
}
if (modelType == 'LinearDiscriminantAnalysis') { // LDA
actions = {
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData}, ${fit_targetData})',
description: 'Fit the Linear Discriminant Analysis model.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'fit_targetData', label: 'Target Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'y' }
]
},
'fit_transform': {
name: 'fit_transform',
label: 'Fit and transform',
code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData}${fit_trans_targetData})',
description: 'Fit to data, then transform it.',
options: [
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'fit_trans_targetData', label: 'Target Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'y' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
'predict': {
name: 'predict',
label: 'Predict',
code: '${pred_allocate} = ${model}.predict(${pred_featureData})',
description: 'Predict class labels for samples in X.',
options: [
{ name: 'pred_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'pred_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'pred' }
]
},
'transform': {
name: 'transform',
label: 'Transform',
code: '${trans_allocate} = ${model}.transform(${trans_featureData})',
description: 'Project data to maximize class separation.',
options: [
{ name: 'trans_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
}
break;
}
actions = {
'fit': defaultActions['fit'],
'transform': defaultActions['transform'],
'fit': {
name: 'fit',
label: 'Fit',
code: '${model}.fit(${fit_featureData})',
description: 'Fit X into an embedded space.',
options: [
{ name: 'fit_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' }
]
},
'fit_transform': {
name: 'fit_transform',
label: 'Fit and transform',
code: '${fit_trans_allocate} = ${model}.fit_transform(${fit_trans_featureData})',
description: 'Fit the model with X and apply the dimensionality reduction on X.',
options: [
{ name: 'fit_trans_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'fit_trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
},
'inverse_transform': {
name: 'inverse_transform',
label: 'Inverse transform',
code: '${inverse_allocate} = ${model}.inverse_transform(${inverse_featureData})',
description: 'Transform data back to its original space.',
options: [
{ name: 'inverse_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'inverse_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'inv_trans' }
]
},
'transform': {
name: 'transform',
label: 'Transform',
code: '${trans_allocate} = ${model}.transform(${trans_featureData})',
description: 'Apply dimensionality reduction to X.',
options: [
{ name: 'trans_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'trans_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'trans' }
]
}
}
break;
}
Expand Down Expand Up @@ -533,6 +649,22 @@ define([
}
break;
case 'Dimension Reduction':
if (modelType == 'LDA') {
infos = {
'score': {
name: 'score',
label: 'Score',
code: '${score_allocate} = ${model}.score(${score_featureData}, ${score_targetData})',
description: 'Return the average log-likelihood of all samples.',
options: [
{ name: 'score_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'score_targetData', label: 'Target Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'y' },
{ name: 'score_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'scores' }
]
}
}
break;
}
if (modelType == 'PCA') {
infos = {
'explained_variance_ratio_': {
Expand All @@ -546,6 +678,19 @@ define([
}
}
}
infos = {
...infos,
'score': {
name: 'score',
label: 'Score',
code: '${score_allocate} = ${model}.score(${score_featureData})',
description: 'Return the average log-likelihood of all samples.',
options: [
{ name: 'score_featureData', label: 'Feature Data', component: ['var_select'], var_type: ['DataFrame', 'Series'], value: 'X' },
{ name: 'score_allocate', label: 'Allocate to', component: ['input'], placeholder: 'New variable', value: 'scores' }
]
}
}
break;
}
return infos;
Expand Down