Merge pull request #489 from qiboteam/fix_classification

Fix classification after #447
qiboteam · Aug 28, 2023 · 5fb3700 · 5fb3700
2 parents 91b8ba1 + 7527474
commit 5fb3700
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Showing 2 changed files with 31 additions and 34 deletions.
diff --git a/src/qibocal/fitting/classifier/qubit_fit.py b/src/qibocal/fitting/classifier/qubit_fit.py
@@ -75,13 +75,12 @@ def fit(self, iq_coordinates: list, states: list):
         iq_state0 = iq_coordinates[(states == 0)]
         self.iq_mean0 = np.mean(iq_state0, axis=0)
         self.iq_mean1 = np.mean(iq_state1, axis=0)
-        # translate
-        iq_coordinates_translated = self.translate(iq_coordinates)
-        iq_state1_trans = self.translate(self.iq_mean1)
-        self.angle = -1 * atan2(iq_state1_trans[1], iq_state1_trans[0])
+
+        vector01 = self.iq_mean1 - self.iq_mean0
+        self.angle = -1 * atan2(vector01[1], vector01[0])
 
         # rotate
-        iq_coord_rot = self.rotate(iq_coordinates_translated)
+        iq_coord_rot = self.rotate(iq_coordinates)
 
         x_values_state0 = np.sort(iq_coord_rot[(states == 0)][:, 0])
         x_values_state1 = np.sort(iq_coord_rot[(states == 1)][:, 0])
@@ -110,15 +109,11 @@ def rotate(self, v):
         rot = np.array([[c, -s], [s, c]])
         return v @ rot.T
 
-    def translate(self, v):
-        return v - self.iq_mean0
-
     def predict(self, inputs: npt.NDArray):
         r"""Classify the `inputs`.
 
         Returns:
             List of predictions.
         """
-        translated = self.translate(inputs)
-        rotated = self.rotate(translated)
+        rotated = self.rotate(inputs)
         return (rotated[:, 0] > self.threshold).astype(int)
diff --git a/src/qibocal/protocols/characterization/classification.py b/src/qibocal/protocols/characterization/classification.py
@@ -351,17 +351,17 @@ def _plot(
     fig = make_subplots(
         rows=1,
         cols=len(models_name),
-        horizontal_spacing=SPACING * 3 / len(models_name),
+        horizontal_spacing=SPACING * 3 / len(models_name) * 3,
         vertical_spacing=SPACING,
         subplot_titles=[run.pretty_name(model) for model in models_name],
         column_width=[COLUMNWIDTH] * len(models_name),
     )
-    fig_roc = go.Figure()
-    fig_roc.add_shape(
-        type="line", line=dict(dash="dash"), x0=0.0, x1=1.0, y0=0.0, y1=1.0
-    )
 
     if len(models_name) != 1:
+        fig_roc = go.Figure()
+        fig_roc.add_shape(
+            type="line", line=dict(dash="dash"), x0=0.0, x1=1.0, y0=0.0, y1=1.0
+        )
         fig_benchmarks = make_subplots(
             rows=1,
             cols=3,
@@ -381,20 +381,6 @@ def _plot(
         predictions = np.round(
             np.reshape(fit.models[qubit][i].predict(grid), q_values.shape)
         ).astype(np.int64)
-        # Evaluate the ROC curve
-        fpr, tpr, _ = roc_curve(y_test, y_pred)
-        auc_score = roc_auc_score(y_test, y_pred)
-        model = run.pretty_name(model)
-        name = f"{model} (AUC={auc_score:.2f})"
-        fig_roc.add_trace(
-            go.Scatter(
-                x=fpr,
-                y=tpr,
-                name=name,
-                mode="lines",
-                marker=dict(size=3, color=get_color_state0(i)),
-            )
-        )
 
         max_x = max(grid[:, 0])
         max_y = max(grid[:, 1])
@@ -490,6 +476,20 @@ def _plot(
             col=i + 1,
         )
         if len(models_name) != 1:
+            # Evaluate the ROC curve
+            fpr, tpr, _ = roc_curve(y_test, y_pred)
+            auc_score = roc_auc_score(y_test, y_pred)
+            model = run.pretty_name(model)
+            name = f"{model} (AUC={auc_score:.2f})"
+            fig_roc.add_trace(
+                go.Scatter(
+                    x=fpr,
+                    y=tpr,
+                    name=name,
+                    mode="lines",
+                    marker=dict(size=3, color=get_color_state0(i)),
+                )
+            )
             fig_benchmarks.add_trace(
                 go.Scatter(
                     x=[model],
@@ -568,6 +568,13 @@ def _plot(
                 font=dict(size=LEGEND_FONT_SIZE),
             ),
         )
+
+    fitting_report = "No fitting data" if fitting_report == "" else fitting_report
+
+    figures.append(fig)
+    if len(models_name) != 1:
+        figures.append(fig_roc)
+        figures.append(fig_benchmarks)
         fig_roc.update_layout(
             width=ROC_WIDTH,
             height=ROC_LENGHT,
@@ -582,11 +589,6 @@ def _plot(
             title_text="True Positive Rate",
             range=[0, 1],
         )
-    fitting_report = "No fitting data" if fitting_report == "" else fitting_report
-    figures.append(fig_roc)
-    figures.append(fig)
-    if len(models_name) != 1:
-        figures.append(fig_benchmarks)
     return figures, fitting_report