contextual-ai follows simple inversion of control (IoC) programming principle to
allowing user to customize and create use-case-specific explainability report.
This is to increase the modularity of contextual-ai lib and make it easy for extensible.
Example
The following examples can give you an impression of what the package can do:
Titanic Dataset
Automobile Dataset
Supported Format
The supported external configuration format are:
Json - Javascript Object Notation
Yaml - Rhymes with Camel (converted to Json when loaded)
Validation
Validate with Json Schema
The external configuration MUST follow the defined schema bvelow:
{ "definitions": {
"section": {
"type": "object",
"properties": {
"title": { "type": "string" },
"desc": { "type": "string" },
"sections":
{
"type": "array",
"items": { "$ref": "#/definitions/section" },
"default": []
},
"component": { "$ref": "#/definitions/component" }
},
"required": ["title"]
},
"component": {
"type": "object",
"properties": {
"package": { "type": "string" },
"module": { "type": "string" },
"class": { "type": "string" },
"attr": {
"type": "object"
}
},
"required": ["class"]
}
},
"type": "object",
"properties": {
"name": { "type" : "string" },
"overview": {" type": "boolean" },
"content_table": { "type": "boolean" },
"contents":
{
"type": "array",
"items": {"$ref": "#/definitions/section"},
"default": []
},
"writers":
{
"type": "array",
"items": {"$ref": "#/definitions/component"}
}
},
"required": ["name", "content_table", "contents", "writers"]
}
Example in Json
{
"name": "Report for Feature Importance Ranking",
"overview": true,
"content_table": true,
"contents": [
{
"title": "Feature Importance Ranking",
"desc": "This section provides the analysis on feature",
"sections": [
{
"title": "Feature Importance Analysis with Breast Cancer data-set",
"desc": "Model and train data from Breast Cancer",
"sections": [
{
"title": "SHAP analysis with csv (with header)",
"component": {
"_comment": "refer to document section xxxx",
"class": "FeatureImportanceRanking",
"attr": {
"trained_model": "./sample_input/breast_cancer/model.pkl",
"train_data": "./sample_input/breast_cancer/train_data.csv",
"method": "shap"
}
}
}
]
},
{
"title": "Feature Importance Analysis with Titanic data-set",
"desc": "Model and train data from Titanic",
"sections": [
{
"title": "Default analysis with csv (with header)",
"component": {
"_comment": "refer to document section xxxx",
"class": "FeatureImportanceRanking",
"attr": {
"trained_model": "./sample_input/titanic/model.pkl",
"train_data": "./sample_input/titanic/train_data.csv"
}
}
}
]
}
]
}
],
"writers": [
{
"class": "Pdf",
"attr": {
"name": "feature-importance-report",
"path": "./sample_output"
}
},
{
"class": "Html",
"attr": {
"name": "feature-importance-report",
"path": "./sample_output"
}
}
]
}
Example in Yaml
name: Report for Feature Importance Ranking
overview: true
content_table: true
contents:
- title: Feature Importance Ranking
desc: This section provides the analysis on feature
sections:
- title: Feature Importance Analysis with Breast Cancer data-set
desc: Model and train data from Breast Cancer
sections:
- title: SHAP analysis with csv (with header)
component:
_comment: refer to document section xxxx1
class: FeatureImportanceRanking
attr:
trained_model: ./sample_input/breast_cancer/model.pkl
train_data: ./sample_input/breast_cancer/train_data.csv
method: shap
- title: Default analysis with csv (with header)
component:
_comment: refer to document section xxxx
class: FeatureImportanceRanking
attr:
trained_model: ./sample_input/breast_cancer/model.pkl
train_data: ./sample_input/breast_cancer/train_data.csv
- title: SHAP analysis with numpy input file
component:
_comment: refer to document section xxxx2
class: FeatureImportanceRanking
attr:
trained_model: ./sample_input/breast_cancer/model.pkl
train_data: ./sample_input/breast_cancer/train_data.npy
feature_names: ./sample_input/breast_cancer/feature_names.npy
method: shap
- title: Feature Importance Analysis with Titanic data-set
desc: Model and train data from Titanic
sections:
- title: Default analysis with csv (with header)
component:
_comment: refer to document section xxxx
class: FeatureImportanceRanking
attr:
trained_model: ./sample_input/titanic/model.pkl
train_data: ./sample_input/titanic/train_data.csv
writers:
- class: Pdf
attr:
name: feature-importance-yml-report
path: ./sample_output
- class: Html
attr:
name: feature-importance-yml-report
path: ./sample_output