Adding parameter estimate data class

Author: JBris

.github/pull_request_template.md

@@ -23,7 +23,7 @@ Example how to mark a checkbox :-
 
 # How Has This Been Tested?
 
-Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce. 
+Please describe the tests that you ran to verify your changes. Provide instructions so we can reproduce.
 
 # Describe if there is any unusual behaviour of your code(Write `NA` if there isn't)
 

calisim/abc/pyabc_wrapper.py

@@ -14,7 +14,7 @@
 from matplotlib import pyplot as plt
 
 from ..base import CalibrationWorkflowBase
-from ..data_model import ParameterDataType
+from ..data_model import ParameterDataType, ParameterEstimateModel
 
 
 class PyABCApproximateBayesianComputation(CalibrationWorkflowBase):
@@ -183,22 +183,22 @@ def analyze(self) -> None:
 
 		distribution_dfs = pd.concat(distribution_dfs)
 		outfile = self.join(
-			outdir, f"{time_now}-{task}-{experiment_name}-parameters.csv"
+			outdir, f"{time_now}-{task}-{experiment_name}_parameters.csv"
 		)
 		self.append_artifact(outfile)
 		distribution_dfs.to_csv(outfile, index=False)
 
 		populations_df = self.history.get_all_populations()
 		outfile = self.join(
-			outdir, f"{time_now}-{task}-{experiment_name}-populations.csv"
+			outdir, f"{time_now}-{task}-{experiment_name}_populations.csv"
 		)
 		self.append_artifact(outfile)
 		populations_df.to_csv(outfile, index=False)
 
 		population_particles_df = self.history.get_nr_particles_per_population()
 		outfile = self.join(
 			outdir,
-			f"{time_now}-{task}-{experiment_name}-nr_particles_per_population.csv",
+			f"{time_now}-{task}-{experiment_name}_nr_particles_per_population.csv",
 		)
 		self.append_artifact(outfile)
 		population_particles_df.to_csv(outfile, index=False)
@@ -211,7 +211,20 @@ def analyze(self) -> None:
 		distances_df = pd.concat(distances_df)
 
 		outfile = self.join(
-			outdir, f"{time_now}-{task}-{experiment_name}-distances.csv"
+			outdir, f"{time_now}-{task}-{experiment_name}_distances.csv"
 		)
 		self.append_artifact(outfile)
 		distances_df.to_csv(outfile, index=False)
+
+		max_t = distribution_dfs["t"].max()
+		trace_df = distribution_dfs.query(f"t == {max_t}")
+		trace_df = trace_df.drop(columns=["t", "w"])
+
+		for name in trace_df:
+			estimate = trace_df[name].mean()
+			uncertainty = trace_df[name].std()
+
+			parameter_estimate = ParameterEstimateModel(
+				name=name, estimate=estimate, uncertainty=uncertainty
+			)
+			self.add_parameter_estimate(parameter_estimate)

calisim/abc/pymc_wrapper.py

@@ -14,6 +14,7 @@
 from matplotlib import pyplot as plt
 
 from ..base import CalibrationWorkflowBase
+from ..data_model import ParameterEstimateModel
 
 
 class PyMCApproximateBayesianComputation(CalibrationWorkflowBase):
@@ -111,7 +112,7 @@ def analyze(self) -> None:
 
 			if outdir is not None:
 				outfile = self.join(
-					outdir, f"{time_now}-{task}-{experiment_name}-{plot}.png"
+					outdir, f"{time_now}-{task}-{experiment_name}_{plot}.png"
 				)
 				self.append_artifact(outfile)
 				plt.tight_layout()
@@ -125,7 +126,7 @@ def _create_plot(plot_func: Callable, plot_kwargs: dict) -> None:
 			if outdir is not None:
 				outfile = self.join(
 					outdir,
-					f"{time_now}-{task}-{experiment_name}-{plot_func.__name__}.png",
+					f"{time_now}-{task}-{experiment_name}_{plot_func.__name__}.png",
 				)
 				self.append_artifact(outfile)
 				plt.tight_layout()
@@ -156,13 +157,23 @@ def _create_plot(plot_func: Callable, plot_kwargs: dict) -> None:
 			return
 
 		trace_df = self.trace.to_dataframe(include_coords=False, groups="posterior")
-		outfile = self.join(outdir, f"{time_now}-{task}-{experiment_name}-trace.csv")
+		outfile = self.join(outdir, f"{time_now}-{task}-{experiment_name}_trace.csv")
 		self.append_artifact(outfile)
 		trace_df.to_csv(outfile, index=False)
 
-		trace_summary_df = az.summary(self.trace)
+		trace_summary_df = az.summary(self.trace).reset_index()
+		trace_summary_df = trace_summary_df.rename(columns={"index": "name"})
 		outfile = self.join(
-			outdir, f"{time_now}-{task}-{experiment_name}-trace_summary.csv"
+			outdir, f"{time_now}-{task}-{experiment_name}_trace_summary.csv"
 		)
 		self.append_artifact(outfile)
 		trace_summary_df.to_csv(outfile, index=False)
+
+		for row in trace_summary_df.to_dict("records"):
+			name = row["name"]
+			estimate = row["mean"]
+			uncertainty = row["sd"]
+			parameter_estimate = ParameterEstimateModel(
+				name=name, estimate=estimate, uncertainty=uncertainty
+			)
+			self.add_parameter_estimate(parameter_estimate)