fix hovertemplate in sensitivity plot

Author: SvenKlaassen

doubleml/_utils_plots.py

@@ -63,9 +63,10 @@ def _sensitivity_contour_plot(x,
     if benchmarks is not None:
         fig.add_trace(go.Scatter(x=benchmarks['cf_d'],
                                  y=benchmarks['cf_y'],
+                                 customdata=benchmarks['value'].reshape(-1, 1),
                                  mode="markers+text",
                                  marker=dict(size=10, color='red', line=dict(width=2, color=text_col)),
-                                 hovertemplate=hov_temp + f': {benchmarks["value"]}' + '</b>',
+                                 hovertemplate=hov_temp + ': %{customdata[0]:.3f}' + '</b>',
                                  name="Benchmarks",
                                  textfont=dict(color=text_col, size=14),
                                  text=list(map(lambda s: "<b>" + s + "</b>", benchmarks['name'])),

doubleml/double_ml.py

@@ -1849,13 +1849,13 @@ def sensitivity_plot(self, idx_treatment=0, value='theta', include_scenario=True
         benchmark_dict = copy.deepcopy(benchmarks)
         if benchmarks is not None:
             n_benchmarks = len(benchmarks['name'])
-            benchmark_values = n_benchmarks * [np.nan]
+            benchmark_values = np.full(shape=(n_benchmarks,), fill_value=np.nan)
             for benchmark_idx in range(len(benchmarks['name'])):
                 sens_dict_bench = self._calc_sensitivity_analysis(cf_y=benchmarks['cf_y'][benchmark_idx],
                                                                   cf_d=benchmarks['cf_y'][benchmark_idx],
                                                                   rho=self.sensitivity_params['input']['rho'],
                                                                   level=self.sensitivity_params['input']['level'])
-                benchmark_values[benchmark_idx] = round(sens_dict_bench[value][bound][idx_treatment], 3)
+                benchmark_values[benchmark_idx] = sens_dict_bench[value][bound][idx_treatment]
             benchmark_dict['value'] = benchmark_values
         fig = _sensitivity_contour_plot(x=cf_d_vec,
                                         y=cf_y_vec,
@@ -1868,7 +1868,7 @@ def sensitivity_plot(self, idx_treatment=0, value='theta', include_scenario=True
                                         benchmarks=benchmark_dict,
                                         fill=fill)
         return fig
-
+    
     def sensitivity_benchmark(self, benchmarking_set):
         """
         Computes a benchmark for a given set of features.