diff --git a/api/src/engine/connectors/datashield/datashield.connector.ts b/api/src/engine/connectors/datashield/datashield.connector.ts
index d09e8f8dfbfa9995ad94384b79b9248bf17f0263..05c7acdca7f1f8067c901ccafa8aa2e1d03c318e 100644
--- a/api/src/engine/connectors/datashield/datashield.connector.ts
+++ b/api/src/engine/connectors/datashield/datashield.connector.ts
@@ -319,21 +319,27 @@ export default class DataShieldConnector implements Connector {
const coVariable =
experiment.variables.length > 0 ? experiment.variables[0] : undefined;
+ const expToInput = {
+ coVariable,
+ variables: experiment.coVariables,
+ algorithm: {
+ id: experiment.algorithm.name,
+ },
+ datasets: experiment.datasets,
+ };
+
+ experiment.algorithm.parameters.forEach((param) => {
+ if (!expToInput.algorithm[param.name]) {
+ // FIXME: the parameter should be added in a specific key entry (e.g. expToInput.algorithm.parameters')
+ // Should be fixed inside the Datashield API
+ expToInput.algorithm[param.name] = param.value;
+ }
+ });
+
const result = await firstValueFrom(
- this.httpService.post(
- path.href,
- {
- coVariable,
- variables: experiment.coVariables,
- algorithm: {
- id: experiment.algorithm.name,
- },
- datasets: experiment.datasets,
- },
- {
- headers: { cookie, 'Content-Type': 'application/json' },
- },
- ),
+ this.httpService.post(path.href, expToInput, {
+ headers: { cookie, 'Content-Type': 'application/json' },
+ }),
);
handlers(experiment, result.data, vars);
diff --git a/api/src/engine/connectors/datashield/handlers/algorithms/linear-regression.handler.ts b/api/src/engine/connectors/datashield/handlers/algorithms/linear-regression.handler.ts
index 623ca2190b5db5cf08c78e98f51ba76dcac0761e..96d2a3a0970e576c4ed6d47229afd645867697b4 100644
--- a/api/src/engine/connectors/datashield/handlers/algorithms/linear-regression.handler.ts
+++ b/api/src/engine/connectors/datashield/handlers/algorithms/linear-regression.handler.ts
@@ -23,6 +23,8 @@ const properties = [
'high0.95CI',
];
+const summaryProps = ['iter', 'Nvalid', 'Ntotal', 'df'];
+
export default class LinearRegressionHandler extends BaseHandler {
canHandle(algorithm: string, data: any): boolean {
return (
@@ -30,6 +32,19 @@ export default class LinearRegressionHandler extends BaseHandler {
);
}
+ getSummaryTable(data: any): TableResult {
+ const summaryTable: TableResult = {
+ name: 'Summary',
+ headers: [
+ { name: 'Name', type: 'string' },
+ { name: 'Value', type: 'string' },
+ ],
+ data: summaryProps.map((prop) => [lookupDict[prop], data[prop]]),
+ };
+
+ return summaryTable;
+ }
+
private getTableResult(data: any, vars: Variable[]): TableResult {
return {
name: 'Results',
@@ -50,9 +65,11 @@ export default class LinearRegressionHandler extends BaseHandler {
return this.next?.handle(experiment, data, vars);
const tableResult = this.getTableResult(data, vars);
+ const summaryResult = this.getSummaryTable(data);
+
+ if (summaryResult) experiment.results.push(summaryResult);
+ if (tableResult) experiment.results.push(tableResult);
- if (tableResult) {
- experiment.results.push(tableResult);
- }
+ this.next?.handle(experiment, data, vars);
}
}
diff --git a/api/src/engine/connectors/datashield/handlers/algorithms/logistic-regression.handler.spec.ts b/api/src/engine/connectors/datashield/handlers/algorithms/logistic-regression.handler.spec.ts
new file mode 100644
index 0000000000000000000000000000000000000000..b6e9dfb0273359a141a01662711b353f360ad1bc
--- /dev/null
+++ b/api/src/engine/connectors/datashield/handlers/algorithms/logistic-regression.handler.spec.ts
@@ -0,0 +1,90 @@
+import { Experiment } from '../../../../models/experiment/experiment.model';
+import LogisticRegressionHandler from './logistic-regression.handler';
+
+const data = {
+ Nvalid: 214,
+ Nmissing: 0,
+ Ntotal: 214,
+ 'disclosure.risk': [[0], [0]],
+ errorMessage: [['No errors'], ['No errors']],
+ nsubs: 214,
+ iter: 6,
+ formula:
+ 'race ~ Urea.nitrogen..Mass.volume..in.Serum.or.Plasma + Albumin..Mass.volume..in.Serum.or.Plasma',
+ coefficients: [
+ {
+ Estimate: -4.53,
+ 'Std. Error': 3.4497,
+ 'z-value': -1.3132,
+ 'p-value': 0.1891,
+ 'low0.95CI.LP': -11.2914,
+ 'high0.95CI.LP': 2.2313,
+ P_OR: 0.0107,
+ 'low0.95CI.P_OR': 0,
+ 'high0.95CI.P_OR': 0.903,
+ _row: '(Intercept)',
+ },
+ {
+ Estimate: 0.0598,
+ 'Std. Error': 0.0663,
+ 'z-value': 0.9023,
+ 'p-value': 0.3669,
+ 'low0.95CI.LP': -0.0701,
+ 'high0.95CI.LP': 0.1898,
+ P_OR: 1.0617,
+ 'low0.95CI.P_OR': 0.9323,
+ 'high0.95CI.P_OR': 1.209,
+ _row: 'Urea.nitrogen..Mass.volume..in.Serum.or.Plasma',
+ },
+ {
+ Estimate: 0.4569,
+ 'Std. Error': 0.7518,
+ 'z-value': 0.6078,
+ 'p-value': 0.5433,
+ 'low0.95CI.LP': -1.0166,
+ 'high0.95CI.LP': 1.9304,
+ P_OR: 1.5792,
+ 'low0.95CI.P_OR': 0.3618,
+ 'high0.95CI.P_OR': 6.8926,
+ _row: 'Albumin..Mass.volume..in.Serum.or.Plasma',
+ },
+ ],
+ dev: 172.4603,
+ df: 211,
+ 'output.information':
+ 'SEE TOP OF OUTPUT FOR INFORMATION ON MISSING DATA AND ERROR MESSAGES',
+};
+
+const createExperiment = (): Experiment => ({
+ id: 'dummy-id',
+ name: 'Testing purpose',
+ algorithm: {
+ name: 'logistic-regression',
+ parameters: [
+ {
+ name: 'pos-level',
+ value: 'White',
+ },
+ ],
+ },
+ datasets: ['sophia.db'],
+ domain: 'dementia',
+ variables: ['race'],
+ coVariables: [
+ 'Urea.nitrogen..Mass.volume..in.Serum.or.Plasma',
+ 'Albumin..Mass.volume..in.Serum.or.Plasma',
+ ],
+ results: [],
+});
+
+describe('Logistic Regression Handler', () => {
+ describe('Normal usage', () => {
+ it('should return the correct results', () => {
+ const experiment = createExperiment();
+ const handler = new LogisticRegressionHandler();
+ handler.handle(experiment, data, []);
+
+ expect(experiment.results).toHaveLength(1);
+ });
+ });
+});
diff --git a/api/src/engine/connectors/datashield/handlers/algorithms/logistic-regression.handler.ts b/api/src/engine/connectors/datashield/handlers/algorithms/logistic-regression.handler.ts
new file mode 100644
index 0000000000000000000000000000000000000000..bd0771deeaad1e497fe93fb5549fc98887aa5f84
--- /dev/null
+++ b/api/src/engine/connectors/datashield/handlers/algorithms/logistic-regression.handler.ts
@@ -0,0 +1,83 @@
+import { Experiment } from '../../../../models/experiment/experiment.model';
+import { TableResult } from '../../../../models/result/table-result.model';
+import { Variable } from '../../../../models/variable.model';
+import BaseHandler from '../base.handler';
+
+const lookupDict = {
+ Estimate: 'Estimate',
+ 'Std. Error': 'Std. Error',
+ 'z-value': 'Z value',
+ 'p-value': 'P value',
+ 'low0.95CI.LP': 'Low 95% CI',
+ 'high0.95CI': 'High 95% CI',
+ P_OR: 'P OR',
+ 'low0.95CI.P_OR': 'Low 95% CI P_OR',
+ 'high0.95CI.P_OR': 'High 95% CI P OR',
+ _row: '',
+ iter: 'Iteration(s)',
+ Nvalid: 'Valid observations',
+ Ntotal: 'Total observations',
+ df: 'Degrees of freedom',
+};
+
+const properties = [
+ '_row',
+ 'Estimate',
+ 'Std. Error',
+ 'z-value',
+ 'p-value',
+ 'low0.95CI.LP',
+ 'high0.95CI.LP',
+ 'P_OR',
+ 'low0.95CI.P_OR',
+ 'high0.95CI.P_OR',
+];
+
+const summaryProps = ['iter', 'Nvalid', 'Ntotal', 'df'];
+
+export default class LogisticRegressionHandler extends BaseHandler {
+ canHandle(algorithm: string, data: any): boolean {
+ return (
+ algorithm.toLowerCase() === 'logistic-regression' && data['coefficients']
+ );
+ }
+
+ getSummaryTable(data: any): TableResult {
+ return {
+ name: 'Summary',
+ headers: [
+ { name: 'Name', type: 'string' },
+ { name: 'Value', type: 'string' },
+ ],
+ data: summaryProps.map((prop) => [lookupDict[prop], data[prop]]),
+ };
+ }
+
+ getTableResult(data: unknown, vars: Variable[]): TableResult {
+ return {
+ name: 'Results',
+ headers: properties.map((name) => ({
+ name: lookupDict[name],
+ type: 'string',
+ })),
+ data: data['coefficients'].map((row: any) => {
+ const variable = vars.find((v) => v.id === row['_row']);
+ if (variable) row['_row'] = variable.label ?? variable.id;
+ return properties.map((name) => row[name]);
+ }),
+ };
+ }
+
+ handle(experiment: Experiment, data: unknown, vars: Variable[]): void {
+ if (!this.canHandle(experiment.algorithm.name, data))
+ return this.next?.handle(experiment, data, vars);
+
+ const tableResult = this.getTableResult(data, vars);
+ const summaryTable = this.getSummaryTable(data);
+
+ if (tableResult) experiment.results.push(tableResult);
+ if (summaryTable) experiment.results.push(summaryTable);
+
+ this.next?.handle(experiment, data, vars);
+ }
+}