[FIX] charts: non-invertible matrix returns NaN as predicted data

Task Description When trying to make a trending line, almost all model use matrix computation to get the predicted data, with some matrix multiplications and inversions. When the matrixes aren't invertible the user get a traceback, which is not fine. This PR aims to fix these issues by returning an array of NaN when there is an error in the computation of the predicted dataset. Related Task closes #5289 Task: 4328743 X-original-commit: 02b6618 Signed-off-by: Lucas Lefèvre (lul) <[email protected]> Signed-off-by: Hendrickx Anthony (anhe) <[email protected]>
odoo · Dec 3, 2024 · 12c4cf8 · 12c4cf8
1 parent a3c357b
commit 12c4cf8
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Showing 3 changed files with 55 additions and 34 deletions.
diff --git a/...mponents/side_panel/chart/building_blocks/series_design/series_with_axis_design_editor.ts b/...mponents/side_panel/chart/building_blocks/series_design/series_with_axis_design_editor.ts
@@ -107,7 +107,7 @@ export class SeriesWithAxisDesignEditor extends Component<Props, SpreadsheetChil
       case "polynomial":
         config = {
           type: "polynomial",
-          order: type === "linear" ? 1 : 2,
+          order: type === "linear" ? 1 : this.getMaxPolynomialDegree(index),
         };
         break;
       case "exponential":

diff --git a/src/helpers/figures/charts/runtime/chart_data_extractor.ts b/src/helpers/figures/charts/runtime/chart_data_extractor.ts
@@ -339,44 +339,51 @@ function interpolateData(
   const labelRange = labelMax - labelMin;
   const normalizedLabels = labels.map((v) => (v - labelMin) / labelRange);
   const normalizedNewLabels = newLabels.map((v) => (v - labelMin) / labelRange);
-  switch (config.type) {
-    case "polynomial": {
-      const order = config.order ?? 2;
-      if (order === 1) {
-        return predictLinearValues([values], [normalizedLabels], [normalizedNewLabels], true)[0];
+  try {
+    switch (config.type) {
+      case "polynomial": {
+        const order = config.order;
+        if (!order) {
+          return Array.from({ length: newLabels.length }, () => NaN);
+        }
+        if (order === 1) {
+          return predictLinearValues([values], [normalizedLabels], [normalizedNewLabels], true)[0];
+        }
+        const coeffs = polynomialRegression(values, normalizedLabels, order, true).flat();
+        return normalizedNewLabels.map((v) => evaluatePolynomial(coeffs, v, order));
       }
-      const coeffs = polynomialRegression(values, normalizedLabels, order, true).flat();
-      return normalizedNewLabels.map((v) => evaluatePolynomial(coeffs, v, order));
-    }
-    case "exponential": {
-      const positiveLogValues: number[] = [];
-      const filteredLabels: number[] = [];
-      for (let i = 0; i < values.length; i++) {
-        if (values[i] > 0) {
-          positiveLogValues.push(Math.log(values[i]));
-          filteredLabels.push(normalizedLabels[i]);
+      case "exponential": {
+        const positiveLogValues: number[] = [];
+        const filteredLabels: number[] = [];
+        for (let i = 0; i < values.length; i++) {
+          if (values[i] > 0) {
+            positiveLogValues.push(Math.log(values[i]));
+            filteredLabels.push(normalizedLabels[i]);
+          }
+        }
+        if (!filteredLabels.length) {
+          return Array.from({ length: newLabels.length }, () => NaN);
         }
+        return expM(
+          predictLinearValues([positiveLogValues], [filteredLabels], [normalizedNewLabels], true)
+        )[0];
       }
-      if (!filteredLabels.length) {
-        return [];
+      case "logarithmic": {
+        return predictLinearValues(
+          [values],
+          logM([normalizedLabels]),
+          logM([normalizedNewLabels]),
+          true
+        )[0];
       }
-      return expM(
-        predictLinearValues([positiveLogValues], [filteredLabels], [normalizedNewLabels], true)
-      )[0];
-    }
-    case "logarithmic": {
-      return predictLinearValues(
-        [values],
-        logM([normalizedLabels]),
-        logM([normalizedNewLabels]),
-        true
-      )[0];
-    }
-    case "trailingMovingAverage": {
-      return getMovingAverageValues(values, config.window);
+      case "trailingMovingAverage": {
+        return getMovingAverageValues(values, config.window);
+      }
+      default:
+        return Array.from({ length: newLabels.length }, () => NaN);
     }
-    default:
-      return [];
+  } catch (e) {
+    return Array.from({ length: newLabels.length }, () => NaN);
   }
 }
 

diff --git a/tests/figures/chart/chart_plugin.test.ts b/tests/figures/chart/chart_plugin.test.ts
@@ -3344,6 +3344,20 @@ describe("trending line", () => {
     }
   });
 
+  test("non-invertible matrix doesn't throw error", () => {
+    // prettier-ignore
+    setGrid(model, {
+      A1: "label",
+      A2: "0",
+      A3: "1",
+    });
+    updateChart(model, "1", {
+      dataSets: [{ dataRange: "A1:A3", trend: { display: true, type: "polynomial", order: 2 } }],
+    });
+    const runtime = model.getters.getChartRuntime("1") as LineChartRuntime;
+    expect(runtime.chartJsConfig.data.datasets[1].data.every((x) => isNaN(Number(x)))).toBeTruthy();
+  });
+
   test("trend line works with real date values as labels", () => {
     setGrid(model, {
       B1: "1",