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Merge pull request #40 from NGO-Algorithm-Audit/feature/demo-data-tabel-info
table with demo data info (translatable)
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‎src/assets/synthetic-data.tsx‎

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@@ -137,6 +137,41 @@ def run():
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'type': 'text',
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'key': 'syntheticData.demo.description'
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}))
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demoData = {
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"syntheticData.demo.data.column.Variable_name": ["syntheticData.demo.data.sex", "syntheticData.demo.data.race1", "syntheticData.demo.data.ugpa", "syntheticData.demo.data.bar"],
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"syntheticData.demo.data.column.Description": [
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"syntheticData.demo.data.column.Description.gender_of_students",
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"syntheticData.demo.data.column.Description.race_of_students",
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"syntheticData.demo.data.column.Description.undergraduate_GPA_student",
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"syntheticData.demo.data.column.Description.Ground_truth_label"
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],
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"syntheticData.demo.data.column.Values": [
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"syntheticData.demo.data.column.Values.sex",
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"syntheticData.demo.data.column.Values.race",
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"syntheticData.demo.data.column.Values.ugpa",
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"syntheticData.demo.data.column.Values.bar"
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]
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}
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# Create the DataFrame
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dfDemoData = pd.DataFrame(demoData)
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# Display the DataFrame
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print(dfDemoData)
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setResult(json.dumps({
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'type': 'table',
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'showIndex': False,
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'translate': True,
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'data': dfDemoData.to_json(orient="records")
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}))
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setResult(json.dumps({
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'type': 'text',
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'key': 'syntheticData.demo.post.description'
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}))
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else:
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admissions_df.reset_index(drop=True, inplace=True)
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admissions_sub = admissions_df

‎src/components/SimpleTable.tsx‎

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@@ -13,10 +13,12 @@ export default function SimpleTable({
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title,
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data,
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showIndex,
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translate,
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}: {
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showIndex: boolean;
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title?: string;
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data: Record<string, string | number>[];
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translate?: boolean;
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}) {
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const { t } = useTranslation();
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// limit data to the first 100 rows.
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{showIndex && <TableHead></TableHead>}
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{Object.keys(data[0]).map(key => (
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<TableHead key={key} className="text-black">
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{key}
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{translate ? t(key) : key}
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</TableHead>
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))}
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</TableRow>
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<TableRow key={i}>
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{showIndex && <TableCell>{i}</TableCell>}
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{Object.values(row).map((value, i) => (
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<TableCell key={i}>{value}</TableCell>
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<TableCell key={i}>
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{translate && typeof value !== 'number'
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? t(value)
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: value}
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</TableCell>
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))}
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</TableRow>
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))}

‎src/components/componentMapper.tsx‎

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@@ -57,6 +57,7 @@ export default function ComponentMapper({
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data={JSON.parse(resultItem.data)}
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title={t(resultItem.title)}
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showIndex={resultItem.showIndex ?? false}
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translate={resultItem.translate ?? false}
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/>
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);
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‎src/locales/en.json‎

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"syntheticData": {
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"demo": {
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"heading": "Information about demo dataset",
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"description": "A subset of the [Law School Admission Bar](https://www.kaggle.com/datasets/danofer/law-school-admissions-bar-passage)* dataset is used as a demo. Synthetic data will be generated for the following columns:\n \n&nbsp;&nbsp;\n- sex: student gender, i.e. 1 (male), 2 (female);\n- race1: race of student, i.e., asian, black, hispanic, white, other;\n- ugpa: undergraduate GPA of student (average course grades), continous variable;\n- bar: Ground truth label indicating whether or not the student passed the bar, i.e., passed 1st time, passed 2nd time, failed, non-graduated.\n \n&nbsp;&nbsp;\n\nThe CART method is used to generate the synthetic data. CART generally produces higher quality synthetic datasets, but might not work well on datasets with categorical variables with 20+ categories. Use Gaussian Copula in those cases.\n \n&nbsp;&nbsp;\n\n*The original paper can be found [here](https://files.eric.ed.gov/fulltext/ED469370.pdf)\n \n&nbsp;&nbsp;\n"
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"description": "A subset of the [Law School Admission Bar](https://www.kaggle.com/datasets/danofer/law-school-admissions-bar-passage)* dataset is used as a demo. Synthetic data will be generated for the following columns:\n \n&nbsp;&nbsp;\n",
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"post.description": "The CART method is used to generate the synthetic data. CART generally produces higher quality synthetic datasets, but might not work well on datasets with categorical variables with 20+ categories. Use Gaussian Copula in those cases.\n \n&nbsp;&nbsp;\n\n*The original paper can be found [here](https://files.eric.ed.gov/fulltext/ED469370.pdf)\n \n&nbsp;&nbsp;\n",
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"data.column.Variable_name": "Variable name",
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"data.sex": "sex",
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"data.race1": "race1",
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"data.ugpa": "ugpa",
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"data.bar": "bar",
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"data.column.Description": "Description",
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"data.column.Description.gender_of_students": "gender of students",
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"data.column.Description.race_of_students": "race of students",
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"data.column.Description.undergraduate_GPA_student": "undergraduate GPA of student (average course grades)",
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"data.column.Description.Ground_truth_label": "Ground truth label indicating whether or not the student passed the bar",
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"data.column.Values": "Values",
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"data.column.Values.sex": "1 (male), 2 (female)",
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"data.column.Values.race": "asian, black, hispanic, white, other",
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"data.column.Values.ugpa": "1-4",
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"data.column.Values.bar": "passed 1st time, passed 2nd time, failed, non-graduated"
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},
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"exportToPDF": "Download evaluation report as pdf",
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"exportToJSON": "Download synthetic data as json",

‎src/locales/nl.json‎

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"syntheticData": {
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"demo": {
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"heading": "Informatie over demodataset",
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"description": "Een subset van de [Law School Admission Bar](https://www.kaggle.com/datasets/danofer/law-school-admissions-bar-passage)* dataset wordt gebruikt als demo. Synthetische data worden gegenereerd voor de volgende kolommen:\n \n&nbsp;&nbsp;\n\n- sex: geslacht van de student, zijnde 1 (man), 2 (vrouw);\n- race1: etniciteit van de student, d.w.z. aziatisch, zwart, hispanic, wit, anders\n- ugpa: Het GPA van de student tijdens de undergraduate-opleiding, continue variabele;\n- bar: Waarheidslabel dat aangeeft of de student is geslaagd voor de bar, d.w.z. geslaagd 1e keer, geslaagd 2e keer, gezakt, niet-afgestudeerd\n \n&nbsp;&nbsp;\n\nDe CART-methode zal worden gebruikt om de verschillen in distributie en correlatie tussen de echte en synthetische gegevens te evalueren.\n \n&nbsp;&nbsp;\n\n*Het oorspronkelijke artikel is [hier](https://files.eric.ed.gov/fulltext/ED469370.pdf) te vinden."
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"description": "Een subset van de [Law School Admission Bar](https://www.kaggle.com/datasets/danofer/law-school-admissions-bar-passage)* dataset wordt gebruikt als demo. Synthetische data worden gegenereerd voor de volgende kolommen:\n \n&nbsp;&nbsp;\n\n",
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"post.description": "De CART-methode zal worden gebruikt om de verschillen in distributie en correlatie tussen de echte en synthetische gegevens te evalueren.\n \n&nbsp;&nbsp;\n\n*Het oorspronkelijke artikel is [hier](https://files.eric.ed.gov/fulltext/ED469370.pdf) te vinden.",
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"data.column.Variable_name": "Variabele name",
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"data.sex": "sex",
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"data.race1": "race1",
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"data.ugpa": "ugpa",
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"data.bar": "bar",
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"data.column.Description": "Omschrijving",
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"data.column.Description.gender_of_students": "geslacht van de student",
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"data.column.Description.race_of_students": "etniciteit van de student",
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"data.column.Description.undergraduate_GPA_student": "het GPA van de student tijdens de undergraduate-opleiding",
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"data.column.Description.Ground_truth_label": "Waarheidslabel dat aangeeft of de student is geslaagd voor de bar",
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"data.column.Values": "Waardes",
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"data.column.Values.sex": "1 (man), 2 (vrouw)",
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"data.column.Values.race": "aziatisch, zwart, hispanic, wit, anders",
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"data.column.Values.ugpa": "1-4",
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"data.column.Values.bar": "geslaagd 1e keer, geslaagd 2e keer, gezakt, niet-afgestudeerd"
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},
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"exportToPDF": "Download evulatie rapport als pdf",
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"exportToJSON": "Download synthetische data als json",

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