Revert "Plotting and Metric for Local classifier 2 sample test"

pyproject.toml

@@ -19,7 +19,6 @@ numpy = "^1.26.4"
 matplotlib = "^3.8.3"
 tarp = "^0.1.1"
 deprecation = "^2.1.0"
-scipy = "1.12.0"
 
 
 [tool.poetry.group.dev.dependencies]

src/client/client.py

@@ -97,15 +97,9 @@ def main():
     plots = config.get_section("plots", raise_exception=False)
 
     for metrics_name, metrics_args in metrics.items():
-        try: 
-            Metrics[metrics_name](model, data, **metrics_args)()
-        except (NotImplementedError, RuntimeError) as error: 
-            print(f"WARNING - skipping metric {metrics_name} due to error: {error}")
+        Metrics[metrics_name](model, data, **metrics_args)()
 
     for plot_name, plot_args in plots.items():
-        try: 
-            Plots[plot_name](model, data, save=True, show=False, out_dir=out_dir)(
-                **plot_args
-            )
-        except (NotImplementedError, RuntimeError) as error: 
-            print(f"WARNING - skipping plot {plot_name} due to error: {error}")
+        Plots[plot_name](model, data, save=True, show=False, out_dir=out_dir)(
+            **plot_args
+        )

src/data/data.py

@@ -125,4 +125,4 @@ def load_prior(self, prior, prior_kwargs):
             return lambda size: choices[prior](**prior_kwargs, size=size)
 
         except KeyError as e:
-            raise RuntimeError(f"Data missing a prior specification - {e}")
+            raise RuntimeError(f"Data missing a prior specification - {e}")

src/data/h5_data.py

@@ -10,8 +10,7 @@
 class H5Data(Data):
     def __init__(self, path: str, simulator: Callable):
         super().__init__(path, simulator)
-        self.theta_true = self.get_theta_true()
-
+
     def _load(self, path):
         assert path.split(".")[-1] == "h5", "File extension must be h5"
         loaded_data = {}

src/metrics/__init__.py

@@ -1,7 +1,4 @@
 from metrics.all_sbc import AllSBC
 from metrics.coverage_fraction import CoverageFraction
-from metrics.local_two_sample import LocalTwoSampleTest
 
-
-_all = [CoverageFraction, AllSBC, LocalTwoSampleTest]
-Metrics = {m.__name__: m for m in _all}
+Metrics = {CoverageFraction.__name__: CoverageFraction, AllSBC.__name__: AllSBC}

src/models/sbi_model.py

@@ -24,8 +24,6 @@ def sample_posterior(self, n_samples: int, y_true):  # TODO typing
     def predict_posterior(self, data):
         posterior_samples = self.sample_posterior(data.y_true)
         posterior_predictive_samples = data.simulator(
-            data.get_theta_true(), posterior_samples
+            data.theta_true(), posterior_samples
         )
         return posterior_predictive_samples
-
-

src/plots/__init__.py

@@ -1,8 +1,11 @@
 from plots.cdf_ranks import CDFRanks
 from plots.coverage_fraction import CoverageFraction
 from plots.ranks import Ranks
-from plots.local_two_sample import LocalTwoSampleTest
 from plots.tarp import TARP
 
-_all = [CoverageFraction, CDFRanks, Ranks, LocalTwoSampleTest, TARP]
-Plots = {m.__name__: m for m in _all}
+Plots = {
+    CDFRanks.__name__: CDFRanks,
+    CoverageFraction.__name__: CoverageFraction,
+    Ranks.__name__: Ranks,
+    TARP.__name__: TARP,
+}