tesi updated
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Lorenzo Volpi
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527598bbf5
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3
TODO.md
3
TODO.md
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@ -61,3 +61,6 @@ multiclass:
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- [x] aggiungere supporto a multiclass in dataset.py
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- [x] aggiungere group_false in ExtensionPolicy
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- [ ] modificare BQAE in modo che i quantifier si adattino alla casistica(binary/multi in base a group_false)
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fix:
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- [ ] make quantifiers predict 0 prevalence for classes for which we have 0 samples
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@ -88,7 +88,8 @@ _renames = {
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"d_bin_sld_rbf": "(2x2)_SLD_RBF",
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"d_mul_sld_rbf": "(1x4)_SLD_RBF",
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"d_m3w_sld_rbf": "(1x3)_SLD_RBF",
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"sld_lr_gs": "MS_SLD_LR",
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# "sld_lr_gs": "MS_SLD_LR",
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"sld_lr_gs": "QuAcc(SLD)",
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"bin_kde_lr": "(2x2)_KDEy_LR",
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"mul_kde_lr": "(1x4)_KDEy_LR",
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"m3w_kde_lr": "(1x3)_KDEy_LR",
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@ -98,8 +99,10 @@ _renames = {
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"bin_cc_lr": "(2x2)_CC_LR",
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"mul_cc_lr": "(1x4)_CC_LR",
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"m3w_cc_lr": "(1x3)_CC_LR",
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"kde_lr_gs": "MS_KDEy_LR",
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"cc_lr_gs": "MS_CC_LR",
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# "kde_lr_gs": "MS_KDEy_LR",
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"kde_lr_gs": "QuAcc(KDEy)",
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# "cc_lr_gs": "MS_CC_LR",
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"cc_lr_gs": "QuAcc(CC)",
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"atc_mc": "ATC",
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"doc": "DoC",
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"mandoline": "Mandoline",
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@ -493,9 +493,9 @@ __cc_lr_set = [
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]
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__ms_set = [
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E("cc_lr_gs"),
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E("sld_lr_gs"),
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E("kde_lr_gs"),
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E("cc_lr_gs"),
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E("QuAcc"),
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]
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@ -447,6 +447,7 @@ class DatasetReport:
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"delta_test",
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"stdev_test",
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"test_table",
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"diagonal",
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"stats_table",
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"fit_scores",
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]
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@ -745,6 +746,25 @@ class DatasetReport:
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base_path=base_path,
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backend=backend,
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)
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elif mode == "diagonal":
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f_data = self.data(metric=metric + "_score", estimators=estimators)
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if f_data.empty:
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return None
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ref: pd.Series = f_data.loc[:, "ref"]
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f_data.drop(columns=["ref"], inplace=True)
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return plot.plot_diagonal(
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reference=ref.to_numpy(),
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columns=f_data.columns.to_numpy(),
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data=f_data.T.to_numpy(),
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metric=metric,
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name=conf,
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# train_prev=self.train_prev,
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fixed_lim=True,
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save_fig=save_fig,
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base_path=base_path,
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backend=backend,
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)
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def to_md(
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self,
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@ -17,6 +17,7 @@ class BasePlot:
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title="default",
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x_label="true",
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y_label="estim.",
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fixed_lim=False,
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legend=True,
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):
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...
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@ -77,6 +77,7 @@ def plot_diagonal(
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metric="acc",
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name="default",
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train_prev=None,
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fixed_lim=False,
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legend=True,
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save_fig=False,
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base_path=None,
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@ -103,6 +104,7 @@ def plot_diagonal(
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title=title,
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x_label=x_label,
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y_label=y_label,
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fixed_lim=fixed_lim,
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legend=legend,
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)
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@ -184,14 +184,21 @@ class PlotlyPlot(BasePlot):
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title="default",
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x_label="true",
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y_label="estim.",
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fixed_lim=False,
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legend=True,
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) -> go.Figure:
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fig = go.Figure()
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x = reference
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line_colors = self.get_colors(len(columns))
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_edges = (np.min([np.min(x), np.min(data)]), np.max([np.max(x), np.max(data)]))
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_lims = np.array([[_edges[0], _edges[1]], [_edges[0], _edges[1]]])
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if fixed_lim:
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_lims = np.array([[0.0, 1.0], [0.0, 1.0]])
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else:
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_edges = (
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np.min([np.min(x), np.min(data)]),
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np.max([np.max(x), np.max(data)]),
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)
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_lims = np.array([[_edges[0], _edges[1]], [_edges[0], _edges[1]]])
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named_data = {c: d for c, d in zip(columns, data)}
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r_columns = {c: r for c, r in zip(columns, self.rename_plots(columns))}
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@ -201,7 +208,7 @@ class PlotlyPlot(BasePlot):
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r_name = r_columns[name]
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color = next(line_colors)
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slope, interc = np.polyfit(x, val, 1)
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y_lr = np.array([slope * _x + interc for _x in _lims[0]])
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# y_lr = np.array([slope * _x + interc for _x in _lims[0]])
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fig.add_traces(
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[
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go.Scatter(
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@ -210,17 +217,25 @@ class PlotlyPlot(BasePlot):
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customdata=np.stack((val - x,), axis=-1),
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mode="markers",
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name=r_name,
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line=dict(color=self.hex_to_rgb(color, t=0.5)),
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marker=dict(color=self.hex_to_rgb(color, t=0.5)),
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hovertemplate="true acc: %{x:,.4f}<br>estim. acc: %{y:,.4f}<br>acc err.: %{customdata[0]:,.4f}",
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# showlegend=False,
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),
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go.Scatter(
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x=_lims[0],
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y=y_lr,
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mode="lines",
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name=name,
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line=dict(color=self.hex_to_rgb(color), width=3),
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showlegend=False,
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),
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# go.Scatter(
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# x=[x[-1]],
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# y=[val[-1]],
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# mode="markers",
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# marker=dict(color=self.hex_to_rgb(color), size=8),
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# name=r_name,
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# ),
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# go.Scatter(
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# x=_lims[0],
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# y=y_lr,
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# mode="lines",
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# name=name,
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# line=dict(color=self.hex_to_rgb(color), width=3),
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# showlegend=False,
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# ),
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]
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)
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fig.add_trace(
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@ -235,7 +250,14 @@ class PlotlyPlot(BasePlot):
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)
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self.update_layout(fig, title, x_label, y_label)
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fig.update_layout(yaxis_scaleanchor="x", yaxis_scaleratio=1.0)
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fig.update_layout(
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autosize=False,
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width=1300,
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height=1000,
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yaxis_scaleanchor="x",
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yaxis_scaleratio=1.0,
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yaxis_range=[-0.1, 1.1],
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)
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return fig
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def plot_shift(
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