tesi updated

TODO.md

@@ -61,3 +61,6 @@ multiclass:
 - [x] aggiungere supporto a multiclass in dataset.py
 - [x] aggiungere group_false in ExtensionPolicy
 - [ ] modificare BQAE in modo che i quantifier si adattino alla casistica(binary/multi in base a group_false)
+
+fix:
+- [ ] make quantifiers predict 0 prevalence for classes for which we have 0 samples

quacc/evaluation/estimators.py

@@ -88,7 +88,8 @@ _renames = {
     "d_bin_sld_rbf": "(2x2)_SLD_RBF",
     "d_mul_sld_rbf": "(1x4)_SLD_RBF",
     "d_m3w_sld_rbf": "(1x3)_SLD_RBF",
-    "sld_lr_gs": "MS_SLD_LR",
+    # "sld_lr_gs": "MS_SLD_LR",
+    "sld_lr_gs": "QuAcc(SLD)",
     "bin_kde_lr": "(2x2)_KDEy_LR",
     "mul_kde_lr": "(1x4)_KDEy_LR",
     "m3w_kde_lr": "(1x3)_KDEy_LR",
@@ -98,8 +99,10 @@ _renames = {
     "bin_cc_lr": "(2x2)_CC_LR",
     "mul_cc_lr": "(1x4)_CC_LR",
     "m3w_cc_lr": "(1x3)_CC_LR",
-    "kde_lr_gs": "MS_KDEy_LR",
+    # "kde_lr_gs": "MS_KDEy_LR",
-    "cc_lr_gs": "MS_CC_LR",
+    "kde_lr_gs": "QuAcc(KDEy)",
+    # "cc_lr_gs": "MS_CC_LR",
+    "cc_lr_gs": "QuAcc(CC)",
     "atc_mc": "ATC",
     "doc": "DoC",
     "mandoline": "Mandoline",

quacc/evaluation/method.py

@@ -493,9 +493,9 @@ __cc_lr_set = [
 ]
 
 __ms_set = [
+    E("cc_lr_gs"),
     E("sld_lr_gs"),
     E("kde_lr_gs"),
-    E("cc_lr_gs"),
     E("QuAcc"),
 ]
 

quacc/evaluation/report.py

@@ -447,6 +447,7 @@ class DatasetReport:
         "delta_test",
         "stdev_test",
         "test_table",
+        "diagonal",
         "stats_table",
         "fit_scores",
     ]
@@ -745,6 +746,25 @@ class DatasetReport:
                 base_path=base_path,
                 backend=backend,
             )
+        elif mode == "diagonal":
+            f_data = self.data(metric=metric + "_score", estimators=estimators)
+            if f_data.empty:
+                return None
+
+            ref: pd.Series = f_data.loc[:, "ref"]
+            f_data.drop(columns=["ref"], inplace=True)
+            return plot.plot_diagonal(
+                reference=ref.to_numpy(),
+                columns=f_data.columns.to_numpy(),
+                data=f_data.T.to_numpy(),
+                metric=metric,
+                name=conf,
+                # train_prev=self.train_prev,
+                fixed_lim=True,
+                save_fig=save_fig,
+                base_path=base_path,
+                backend=backend,
+            )
 
     def to_md(
         self,

quacc/plot/base.py

@@ -17,6 +17,7 @@ class BasePlot:
         title="default",
         x_label="true",
         y_label="estim.",
+        fixed_lim=False,
         legend=True,
     ):
         ...

quacc/plot/plot.py

@@ -77,6 +77,7 @@ def plot_diagonal(
     metric="acc",
     name="default",
     train_prev=None,
+    fixed_lim=False,
     legend=True,
     save_fig=False,
     base_path=None,
@@ -103,6 +104,7 @@ def plot_diagonal(
         title=title,
         x_label=x_label,
         y_label=y_label,
+        fixed_lim=fixed_lim,
         legend=legend,
     )
 

quacc/plot/plotly.py

@@ -184,13 +184,20 @@ class PlotlyPlot(BasePlot):
         title="default",
         x_label="true",
         y_label="estim.",
+        fixed_lim=False,
         legend=True,
     ) -> go.Figure:
         fig = go.Figure()
         x = reference
         line_colors = self.get_colors(len(columns))
 
-        _edges = (np.min([np.min(x), np.min(data)]), np.max([np.max(x), np.max(data)]))
+        if fixed_lim:
+            _lims = np.array([[0.0, 1.0], [0.0, 1.0]])
+        else:
+            _edges = (
+                np.min([np.min(x), np.min(data)]),
+                np.max([np.max(x), np.max(data)]),
+            )
             _lims = np.array([[_edges[0], _edges[1]], [_edges[0], _edges[1]]])
 
         named_data = {c: d for c, d in zip(columns, data)}
@@ -201,7 +208,7 @@ class PlotlyPlot(BasePlot):
             r_name = r_columns[name]
             color = next(line_colors)
             slope, interc = np.polyfit(x, val, 1)
-            y_lr = np.array([slope * _x + interc for _x in _lims[0]])
+            # y_lr = np.array([slope * _x + interc for _x in _lims[0]])
             fig.add_traces(
                 [
                     go.Scatter(
@@ -210,17 +217,25 @@ class PlotlyPlot(BasePlot):
                         customdata=np.stack((val - x,), axis=-1),
                         mode="markers",
                         name=r_name,
-                        line=dict(color=self.hex_to_rgb(color, t=0.5)),
+                        marker=dict(color=self.hex_to_rgb(color, t=0.5)),
                         hovertemplate="true acc: %{x:,.4f}<br>estim. acc: %{y:,.4f}<br>acc err.: %{customdata[0]:,.4f}",
+                        # showlegend=False,
                     ),
-                    go.Scatter(
+                    # go.Scatter(
-                        x=_lims[0],
+                    #     x=[x[-1]],
-                        y=y_lr,
+                    #     y=[val[-1]],
-                        mode="lines",
+                    #     mode="markers",
-                        name=name,
+                    #     marker=dict(color=self.hex_to_rgb(color), size=8),
-                        line=dict(color=self.hex_to_rgb(color), width=3),
+                    #     name=r_name,
-                        showlegend=False,
+                    # ),
-                    ),
+                    # go.Scatter(
+                    #     x=_lims[0],
+                    #     y=y_lr,
+                    #     mode="lines",
+                    #     name=name,
+                    #     line=dict(color=self.hex_to_rgb(color), width=3),
+                    #     showlegend=False,
+                    # ),
                 ]
             )
         fig.add_trace(
@@ -235,7 +250,14 @@ class PlotlyPlot(BasePlot):
         )
 
         self.update_layout(fig, title, x_label, y_label)
-        fig.update_layout(yaxis_scaleanchor="x", yaxis_scaleratio=1.0)
+        fig.update_layout(
+            autosize=False,
+            width=1300,
+            height=1000,
+            yaxis_scaleanchor="x",
+            yaxis_scaleratio=1.0,
+            yaxis_range=[-0.1, 1.1],
+        )
         return fig
 
     def plot_shift(