diff --git a/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression-cv.handler.spec.ts b/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression-cv.handler.spec.ts
index a2388797da8531010fbbd958421719dcdb8a87e1..728ecb5da4bc104ddfa5987d180d6118077df58e 100644
--- a/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression-cv.handler.spec.ts
+++ b/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression-cv.handler.spec.ts
@@ -83,9 +83,6 @@ describe('Linear regression CV result handler', () => {
const dataPoints = experiment.results[0] as TableResult;
const scoresData = experiment.results[1] as TableResult;
- console.log(JSON.stringify(dataPoints));
- console.log(JSON.stringify(scoresData));
-
expect(dataPoints.data).toStrictEqual(expectedDataPoints);
expect(scoresData.data).toStrictEqual(expectedScoresData);
diff --git a/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression.handler.spec.ts b/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression.handler.spec.ts
index 04e9c0f20e4f16a0b2d571de4e991ece91101099..b4dee3ed2cc6b8b67d5ad4aea07effe2439e7f08 100644
--- a/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression.handler.spec.ts
+++ b/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression.handler.spec.ts
@@ -1,4 +1,4 @@
-import { Domain } from 'src/engine/models/domain.model';
+import { Domain } from '../../../../models/domain.model';
import { Experiment } from '../../../../models/experiment/experiment.model';
import LinearRegressionHandler from './linear-regression.handler';
diff --git a/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression.handler.ts b/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression.handler.ts
index b58526d32447c3040571da65a6f6f138156efc00..d871b1fe34752454720e984510bf7b6f3dea8e86 100644
--- a/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression.handler.ts
+++ b/api/src/engine/connectors/exareme/handlers/algorithms/linear-regression.handler.ts
@@ -1,5 +1,5 @@
-import { Domain } from 'src/engine/models/domain.model';
-import { Variable } from 'src/engine/models/variable.model';
+import { Domain } from '../../../../models/domain.model';
+import { Variable } from '../../../../models/variable.model';
import { isNumber } from '../../../../../common/utils/shared.utils';
import { Experiment } from '../../../../models/experiment/experiment.model';
import {
@@ -31,7 +31,7 @@ const lookupDict = {
export default class LinearRegressionHandler extends BaseHandler {
private getModel(data: any): TableResult | undefined {
- const excepts = ['n_obs'];
+ const exclude = ['n_obs'];
const tableModel: TableResult = {
name: 'Model',
tableStyle: TableStyle.DEFAULT,
@@ -48,7 +48,7 @@ export default class LinearRegressionHandler extends BaseHandler {
'f_pvalue',
].map((name) => [
lookupDict[name],
- isNumber(data[name]) && !excepts.includes(name)
+ isNumber(data[name]) && !exclude.includes(name)
? data[name].toPrecision(NUMBER_PRECISION)
: data[name],
]),
diff --git a/api/src/engine/connectors/exareme/handlers/algorithms/logistic-regression.handler.spec.ts b/api/src/engine/connectors/exareme/handlers/algorithms/logistic-regression.handler.spec.ts
new file mode 100644
index 0000000000000000000000000000000000000000..617f12859d60c04985bcbea78cfd31dc2737346b
--- /dev/null
+++ b/api/src/engine/connectors/exareme/handlers/algorithms/logistic-regression.handler.spec.ts
@@ -0,0 +1,81 @@
+import { Domain } from '../../../../models/domain.model';
+import { Experiment } from '../../../../models/experiment/experiment.model';
+import LogisticRegressionHandler from './logistic-regression.handler';
+
+const data = [
+ {
+ dependent_var: 'ppmicategory',
+ indep_vars: ['Intercept', 'righthippocampus'],
+ summary: {
+ n_obs: 714,
+ coefficients: [2.6245273240614644, -1.392061918138407],
+ stderr: [1.3675179284969878, 0.41070496937876727],
+ lower_ci: [-0.05575856400545254, -2.197028866392417],
+ upper_ci: [5.304813212128382, -0.5870949698843975],
+ z_scores: [1.9191904320742843, -3.389445032145682],
+ pvalues: [0.05496023796478015, 0.0007003424825299484],
+ df_model: 1,
+ df_resid: 712,
+ r_squared_cs: 0.016553598383202917,
+ r_squared_mcf: 0.02359922514841184,
+ ll0: -252.5122763647322,
+ ll: -246.55318230206288,
+ aic: 497.10636460412576,
+ bic: 506.24813052880495,
+ },
+ },
+];
+
+const domain: Domain = {
+ id: 'dummy-id',
+ groups: [],
+ rootGroup: {
+ id: 'dummy-id',
+ },
+ datasets: [{ id: 'desd-synthdata', label: 'Dead Synthdata' }],
+ variables: [
+ { id: 'ppmicategory', label: 'PPMI Category' },
+ { id: 'righthippocampus', label: 'Right Hippo Campus' },
+ ],
+};
+
+const createExperiment = (): Experiment => ({
+ id: 'dummy-id',
+ name: 'Testing purpose',
+ algorithm: {
+ name: 'LOGISTIC_REGRESSION',
+ },
+ datasets: ['desd-synthdata'],
+ domain: 'dementia',
+ variables: ['ppmicategory'],
+ coVariables: ['righthippocampus'],
+ results: [],
+});
+
+describe('Logistic Regression result Handler', () => {
+ let logisticHandler: LogisticRegressionHandler;
+ let experiment: Experiment;
+
+ beforeEach(() => {
+ logisticHandler = new LogisticRegressionHandler();
+ experiment = createExperiment();
+ });
+
+ describe('handle', () => {
+ it('with standard logistic algo data', () => {
+ logisticHandler.handle(experiment, data, domain);
+
+ const json = JSON.stringify(experiment.results);
+
+ expect(json.includes(domain.variables[0].label)).toBeTruthy();
+ expect(experiment.results.length).toBe(2);
+ });
+
+ it('Should be empty with another algo', () => {
+ experiment.algorithm.name = 'dummy_algo';
+ logisticHandler.handle(experiment, data, domain);
+
+ expect(experiment.results.length).toBe(0);
+ });
+ });
+});
diff --git a/api/src/engine/connectors/exareme/handlers/algorithms/logistic-regression.handler.ts b/api/src/engine/connectors/exareme/handlers/algorithms/logistic-regression.handler.ts
new file mode 100644
index 0000000000000000000000000000000000000000..a36e0b921a6cc36d28d82a7558d953e250d6a9a6
--- /dev/null
+++ b/api/src/engine/connectors/exareme/handlers/algorithms/logistic-regression.handler.ts
@@ -0,0 +1,117 @@
+import { isNumber } from '../../../../../common/utils/shared.utils';
+import { Domain } from '../../../../models/domain.model';
+import { Experiment } from '../../../../models/experiment/experiment.model';
+import {
+ TableResult,
+ TableStyle,
+} from '../../../../models/result/table-result.model';
+import BaseHandler from '../base.handler';
+
+const lookupDict = {
+ dependent_var: 'Dependent variable',
+ indep_vars: 'Independent variables',
+ n_obs: 'Number of observations',
+ df_resid: 'Residual degrees of freedom',
+ df_model: 'Model degrees of freedom',
+ coefficients: 'Coefficients',
+ stderr: 'Std.Err.',
+ z_scores: 'z-scores',
+ pvalues: 'P{>|z|}',
+ lower_ci: 'Lower 95% c.i.',
+ upper_ci: 'Upper 59% c.i.',
+ r_squared_mcf: 'McFadden R^2',
+ r_squared_cs: 'Cox-Snell R^2',
+ ll0: 'log(L) of null-model',
+ ll: 'log(L)',
+ aic: 'AIC',
+ bic: 'BIC',
+};
+const EXCLUDE_NUMBER_LIST = ['n_obs', 'df_resid', 'df_model'];
+const NUMBER_PRECISION = 4;
+const roundNumber = (val: any, name: string) =>
+ isNumber(val) && !EXCLUDE_NUMBER_LIST.includes(name)
+ ? val.toPrecision(NUMBER_PRECISION)
+ : val;
+
+export default class LogisticRegressionHandler extends BaseHandler {
+ private getModel(data: any): TableResult | undefined {
+ return {
+ name: 'Model',
+ tableStyle: TableStyle.DEFAULT,
+ headers: ['Name', 'Value'].map((name) => ({ name, type: 'string' })),
+ data: [
+ 'dependent_var',
+ 'n_obs',
+ 'df_resid',
+ 'df_model',
+ 'df_model',
+ 'r_squared_mcf',
+ 'r_squared_cs',
+ 'll0',
+ 'll',
+ 'aic',
+ 'bic',
+ ].map((name) => [lookupDict[name], roundNumber(data[name], name)]),
+ };
+ }
+
+ private getCoefficients(data: any): TableResult | undefined {
+ const fields = [
+ 'indep_vars',
+ 'coefficients',
+ 'stderr',
+ 'z_scores',
+ 'pvalues',
+ 'lower_ci',
+ 'upper_ci',
+ ];
+
+ return {
+ name: 'Coefficients',
+ tableStyle: TableStyle.DEFAULT,
+ headers: fields.map((name) => ({
+ name: lookupDict[name],
+ type: 'string',
+ })),
+ data: [fields.map((name) => roundNumber(data[name], name))],
+ };
+ }
+
+ canHandle(exp: Experiment, data: any): boolean {
+ return (
+ exp.algorithm.name.toLowerCase() === 'logistic_regression' &&
+ !!data &&
+ !!data[0] &&
+ !!data[0]['summary']
+ );
+ }
+
+ handle(experiment: Experiment, data: any, domain?: Domain): void {
+ if (!this.canHandle(experiment, data))
+ return super.handle(experiment, data, domain);
+
+ const extractedData = {
+ ...data[0],
+ ...data[0]['summary'],
+ summary: undefined,
+ };
+
+ const varIds = [...experiment.variables, ...(experiment.coVariables ?? [])];
+ const variables = domain.variables.filter((v) => varIds.includes(v.id));
+
+ let jsonData = JSON.stringify(extractedData);
+
+ variables.forEach((v) => {
+ const regEx = new RegExp(v.id, 'gi');
+ jsonData = jsonData.replaceAll(regEx, v.label);
+ });
+
+ const improvedData = JSON.parse(jsonData);
+
+ const model = this.getModel(improvedData);
+ if (model) experiment.results.push(model);
+
+ const coefs = this.getCoefficients(improvedData);
+ if (coefs) experiment.results.push(coefs);
+ }
+}