File size: 3,952 Bytes
fc0f7bd | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 | import React from 'react';
import { FairnessWizard } from 'fairlearn-dashboard';
import { binaryClassifier } from '../__mock-data/binaryClassifier';
import {regression} from "../__mock-data/regression";
import { probit } from "../__mock-data/probit";
class App extends React.Component {
constructor(props) {
super(props);
this.state = {value: 0};
this.handleChange = this.handleChange.bind(this);
this.generateRandomScore = this.generateRandomScore.bind(this);
}
static choices = [
{label: 'binaryClassifier', data: binaryClassifier},
{label: 'regression', data: regression},
{label: "probit", data: probit}
]
messages = {
'LocalExpAndTestReq': [{displayText: 'LocalExpAndTestReq'}],
'LocalOrGlobalAndTestReq': [{displayText: 'LocalOrGlobalAndTestReq'}],
'TestReq': [{displayText: 'TestReq'}],
'PredictorReq': [{displayText: 'PredictorReq'}]
}
handleChange(event){
this.setState({value: event.target.value});
}
generateRandomScore(data) {
return Promise.resolve(data.map(x => Math.random()));
}
generateRandomMetrics(data, signal) {
const binSize = Math.max(...data.binVector);
const bins = new Array(binSize + 1).fill(0).map(x => Math.random())
bins[2] = undefined
let promise = new Promise((resolve, reject) => {
let timeout = setTimeout(() => {resolve({
global: Math.random(),
bins
})}, 300);
if (signal) {
signal.addEventListener('abort', () => {
clearTimeout(timeout);
reject(new DOMException('Aborted', 'AbortError'));
});
}
});
return promise;
}
generateExplanatins(explanations, data, signal) {
let promise = new Promise((resolve, reject) => {
let timeout = setTimeout(() => {resolve(explanations)}, 300);
signal.addEventListener('abort', () => {
clearTimeout(timeout);
reject(new DOMException('Aborted', 'AbortError'));
});
});
return promise;
}
render() {
const data = _.cloneDeep(App.choices[this.state.value].data);
return (
<div style={{backgroundColor: 'grey', height:'100%'}}>
<label>
Select dataset:
</label>
<select value={this.state.value} onChange={this.handleChange}>
{App.choices.map((item, index) => <option key={item.label} value={index}>{item.label}</option>)}
</select>
<div style={{ width: '80vw', backgroundColor: 'white', margin:'50px auto'}}>
<div style={{ width: '940px'}}>
<FairnessWizard
modelInformation={{modelClass: 'blackbox'}}
dataSummary={{featureNames: data.featureNames, classNames: data.classNames}}
testData={data.augmentedData}
predictedY={data.predictedYs}
trueY={data.trueY}
supportedBinaryClassificationAccuracyKeys={["accuracy_score", "balanced_accuracy_score","precision_score", "recall_score"]}
supportedRegressionAccuracyKeys={["mean_absolute_error", "r2_score", "mean_squared_error", "root_mean_squared_error"]}
supportedProbabilityAccuracyKeys={["auc", "root_mean_squared_error", "balanced_root_mean_squared_error", "r2_score", "mean_squared_error", "mean_absolute_error"]}
stringParams={{contextualHelp: this.messages}}
requestMetrics={this.generateRandomMetrics.bind(this)}
key={new Date()}
/>
</div>
</div>
</div>
);
}
}
export default App; |