Diagnostics: partitioning filtering and naming

bayesflow/diagnostics/plot_calibration_curves.py

@@ -102,7 +102,7 @@ def plot_calibration_curves(
         axes=plot_data["axes"],
         num_row=plot_data["num_row"],
         num_col=plot_data["num_col"],
-        title=plot_data["names"],
+        title=plot_data["variable_names"],
         xlabel="Predicted Probability",
         ylabel="True Probability",
         title_fontsize=title_fontsize,

bayesflow/diagnostics/plot_recovery.py

@@ -11,6 +11,7 @@
 def plot_recovery(
     post_samples: dict[str, np.ndarray] | np.ndarray,
     prior_samples: dict[str, np.ndarray] | np.ndarray,
+    filter_keys: Sequence[str] = None,
     variable_names: Sequence[str] = None,
     point_agg=np.median,
     uncertainty_agg=median_abs_deviation,
@@ -59,7 +60,7 @@ def plot_recovery(
     """
 
     # Gather plot data and metadata into a dictionary
-    plot_data = preprocess(post_samples, prior_samples, variable_names, num_col, num_row, figsize)
+    plot_data = preprocess(post_samples, prior_samples, filter_keys, variable_names, num_col, num_row, figsize)
     plot_data["post_samples"] = plot_data.pop("post_variables")
     plot_data["prior_samples"] = plot_data.pop("prior_variables")
 
@@ -93,7 +94,7 @@ def plot_recovery(
             corr = np.corrcoef(plot_data["prior_samples"][:, i], point_estimate[:, i])[0, 1]
             add_metric(ax=ax, metric_text="$r$", metric_value=corr, metric_fontsize=metric_fontsize)
 
-        ax.set_title(plot_data["names"][i], fontsize=title_fontsize)
+        ax.set_title(plot_data["variable_names"][i], fontsize=title_fontsize)
 
     # Add custom schmuck
     prettify_subplots(plot_data["axes"], num_subplots=plot_data["num_variables"], tick_fontsize=tick_fontsize)

bayesflow/diagnostics/plot_samples_2d.py

@@ -2,11 +2,14 @@
 import seaborn as sns
 import pandas as pd
 
+from typing import Sequence
 from bayesflow.utils import logging
+from bayesflow.utils.dict_utils import dicts_to_arrays
 
 
 def plot_samples_2d(
-    samples: np.ndarray = None,
+    samples: dict[str, np.ndarray] | np.ndarray = None,
+    filter_keys: Sequence[str] = None,
     context: str = None,
     variable_names: list = None,
     height: float = 2.5,
@@ -41,7 +44,11 @@ def plot_samples_2d(
         Additional keyword arguments passed to the sns.PairGrid constructor
     """
 
-    dim = samples.shape[-1]
+    plot_data = dicts_to_arrays(
+        post_variables=samples, filter_keys=filter_keys, variable_names=variable_names, context=context
+    )
+
+    dim = plot_data["post_variables"].shape[-1]
     if context is None:
         context = "Default"
 
@@ -52,7 +59,10 @@ def plot_samples_2d(
         titles = [f"{context} {p}" for p in variable_names]
 
     # Convert samples to pd.DataFrame
-    data_to_plot = pd.DataFrame(samples, columns=titles)
+    if context == "Posterior":
+        data_to_plot = pd.DataFrame(plot_data["post_variables"][0], columns=titles)
+    else:
+        data_to_plot = pd.DataFrame(plot_data["post_variables"], columns=titles)
 
     # Generate plots
     artist = sns.PairGrid(data_to_plot, height=height, **kwargs)

bayesflow/diagnostics/plot_sbc_ecdf.py

@@ -9,6 +9,7 @@
 def plot_sbc_ecdf(
     post_samples: dict[str, np.ndarray] | np.ndarray,
     prior_samples: dict[str, np.ndarray] | np.ndarray,
+    filter_keys: Sequence[str] = None,
     variable_names: Sequence[str] = None,
     difference: bool = False,
     stacked: bool = False,
@@ -92,7 +93,9 @@ def plot_sbc_ecdf(
     """
 
     # Preprocessing
-    plot_data = preprocess(post_samples, prior_samples, variable_names, num_col, num_row, figsize, stacked=stacked)
+    plot_data = preprocess(
+        post_samples, prior_samples, filter_keys, variable_names, num_col, num_row, figsize, stacked=stacked
+    )
     plot_data["post_samples"] = plot_data.pop("post_variables")
     plot_data["prior_samples"] = plot_data.pop("prior_variables")
 
@@ -129,7 +132,7 @@ def plot_sbc_ecdf(
         ylab = "ECDF"
 
     # Add simultaneous bounds
-    titles = plot_data["names"] if not stacked else ["Stacked ECDFs"]
+    titles = plot_data["variable_names"] if not stacked else ["Stacked ECDFs"]
     for ax, title in zip(plot_data["axes"].flat, titles):
         ax.fill_between(z, L, H, color=fill_color, alpha=0.2, label=rf"{int((1-alpha) * 100)}$\%$ Confidence Bands")
         ax.legend(fontsize=legend_fontsize)

bayesflow/diagnostics/plot_sbc_histograms.py

@@ -12,6 +12,7 @@
 def plot_sbc_histograms(
     post_samples: dict[str, np.ndarray] | np.ndarray,
     prior_samples: dict[str, np.ndarray] | np.ndarray,
+    filter_keys: Sequence[str] = None,
     variable_names: Sequence[str] = None,
     figsize: Sequence[float] = None,
     num_bins: int = 10,
@@ -71,11 +72,11 @@ def plot_sbc_histograms(
     """
 
     # Preprocessing
-    plot_data = preprocess(post_samples, prior_samples, num_col, num_row, variable_names, figsize)
+    plot_data = preprocess(post_samples, prior_samples, filter_keys, variable_names, num_col, num_row, figsize=figsize)
     plot_data["post_samples"] = plot_data.pop("post_variables")
     plot_data["prior_samples"] = plot_data.pop("prior_variables")
 
-    # Determine the ratio of simulations to prior draws
+    # Determine the ratio of simulations to prior draw
     # num_params = plot_data['num_variables']
     num_sims = plot_data["post_samples"].shape[0]
     num_draws = plot_data["post_samples"].shape[1]
@@ -119,7 +120,7 @@ def plot_sbc_histograms(
         axes=plot_data["axes"],
         num_row=plot_data["num_row"],
         num_col=plot_data["num_col"],
-        title=plot_data["names"],
+        title=plot_data["variable_names"],
         xlabel="Rank statistic",
         ylabel="",
         title_fontsize=title_fontsize,

bayesflow/diagnostics/plot_z_score_contraction.py

@@ -9,6 +9,7 @@
 def plot_z_score_contraction(
     post_samples: dict[str, np.ndarray] | np.ndarray,
     prior_samples: dict[str, np.ndarray] | np.ndarray,
+    filter_keys: Sequence[str] = None,
     variable_names: Sequence[str] = None,
     figsize: Sequence[int] = None,
     label_fontsize: int = 16,
@@ -84,7 +85,7 @@ def plot_z_score_contraction(
     """
 
     # Preprocessing
-    plot_data = preprocess(post_samples, prior_samples, variable_names, num_col, num_row, figsize)
+    plot_data = preprocess(post_samples, prior_samples, filter_keys, variable_names, num_col, num_row, figsize)
     plot_data["post_samples"] = plot_data.pop("post_variables")
     plot_data["prior_samples"] = plot_data.pop("prior_variables")
 
@@ -98,7 +99,7 @@ def plot_z_score_contraction(
 
     # Compute contraction and z-score
     contraction = 1 - (post_vars / prior_vars)
-    z_score = (post_means - prior_samples) / post_stds
+    z_score = (post_means - plot_data["prior_samples"]) / post_stds
 
     # Loop and plot
     for i, ax in enumerate(plot_data["axes"].flat):
@@ -115,7 +116,7 @@ def plot_z_score_contraction(
         axes=plot_data["axes"],
         num_row=plot_data["num_row"],
         num_col=plot_data["num_col"],
-        title=plot_data["names"],
+        title=plot_data["variable_names"],
         xlabel="Posterior contraction",
         ylabel="Posterior z-score",
         title_fontsize=title_fontsize,

bayesflow/utils/dict_utils.py

@@ -107,36 +107,56 @@ def split_tensors(data: Mapping[any, Tensor], axis: int = -1) -> Mapping[any, Te
 
 def dicts_to_arrays(
     post_variables: dict[str, np.ndarray] | np.ndarray,
-    prior_variables: dict[str, np.ndarray] | np.ndarray,
-    names: Sequence[str] = None,
+    prior_variables: dict[str, np.ndarray] | np.ndarray = None,
+    filter_keys: Sequence[str] | None = None,
+    variable_names: Sequence[str] = None,
     context: str = None,
 ):
-    """Utility to optionally convert dicts as returned from approximators and adapters into arrays."""
+    """
+    # TODO
+    """
 
-    if type(post_variables) is not type(prior_variables):
-        raise ValueError("You should either use dicts or tensors, but not separate types for your inputs.")
+    # Ensure that posterior and prior variables have the same type
+    if prior_variables is not None:
+        if type(post_variables) is not type(prior_variables):
+            raise ValueError("You should either use dicts or tensors, but not separate types for your inputs.")
 
+    # Filtering
     if isinstance(post_variables, dict):
-        if post_variables.keys() != prior_variables.keys():
-            raise ValueError("Keys in your posterior / prior arrays should match.")
+        # Ensure that the keys of selected posterior and prior variables match
+        if prior_variables is not None:
+            if not (set(post_variables) <= set(prior_variables)):
+                raise ValueError("Keys in your posterior / prior arrays should match.")
 
-        # Use user-provided names instead of inferred ones
-        names = list(post_variables.keys()) if names is None else names
+        # If they match, users can further select the variables by using filter keys
+        filter_keys = list(post_variables.keys()) if filter_keys is None else filter_keys
 
-        post_variables = np.concatenate([v for k, v in post_variables.items() if k in names], axis=-1)
-        prior_variables = np.concatenate([v for k, v in prior_variables.items() if k in names], axis=-1)
+        # The variables will then be overridden with the filtered keys
+        post_variables = np.concatenate([v for k, v in post_variables.items() if k in filter_keys], axis=-1)
+        if prior_variables is not None:
+            prior_variables = np.concatenate([v for k, v in prior_variables.items() if k in filter_keys], axis=-1)
 
+    # Naming or Renaming
     elif isinstance(post_variables, np.ndarray):
-        if names is not None:
-            if post_variables.shape[-1] != len(names) or prior_variables.shape[-1] != len(names):
-                raise ValueError("The length of the names list should match the number of target variables.")
-        else:
-            if context is not None:
-                names = [f"${context}_{{{i}}}$" for i in range(post_variables.shape[-1])]
+        # If there are filter_keys, check if their number is the same as that of the variables.
+        # If it does, check if there are sufficient variable names.
+        # If there are, then the variable names are adopted.
+        if variable_names is not None:
+            if post_variables.shape[-1] != len(variable_names) or prior_variables.shape[-1] != len(variable_names):
+                raise ValueError("The number of variable names should match the number of target variables.")
+
+        else:  # Otherwise, we would assume that all variables are used for plotting.
+            if context is None:
+                if variable_names is None:
+                    variable_names = [f"$\\theta_{{{i}}}$" for i in range(post_variables.shape[-1])]
             else:
-                names = [f"$\\theta_{{{i}}}$" for i in range(post_variables.shape[-1])]
-
+                variable_names = [f"${context}_{{{i}}}$" for i in range(post_variables.shape[-1])]
     else:
         raise TypeError("Only dicts and tensors are supported as arguments.")
 
-    return dict(post_variables=post_variables, prior_variables=prior_variables, names=names, num_variables=len(names))
+    return dict(
+        post_variables=post_variables,
+        prior_variables=prior_variables,
+        variable_names=variable_names,
+        num_variables=len(variable_names),
+    )

bayesflow/utils/plot_utils.py

@@ -9,9 +9,10 @@
 
 
 def preprocess(
-    post_variables: dict[str, np.ndarray],
-    prior_variables: dict[str, np.ndarray],
-    names: Sequence[str] = None,
+    post_variables: dict[str, np.ndarray] | np.ndarray,
+    prior_variables: dict[str, np.ndarray] | np.ndarray,
+    filter_keys: Sequence[str] = None,
+    variable_names: Sequence[str] = None,
     context: str = None,
     num_col: int = None,
     num_row: int = None,
@@ -43,7 +44,13 @@ def preprocess(
         Whether the plots are stacked horizontally
     """
 
-    plot_data = dicts_to_arrays(post_variables, prior_variables, names, context)
+    plot_data = dicts_to_arrays(
+        post_variables=post_variables,
+        prior_variables=prior_variables,
+        filter_keys=filter_keys,
+        variable_names=variable_names,
+        context=context,
+    )
     check_posterior_prior_shapes(plot_data["post_variables"], plot_data["prior_variables"])
 
     # Configure layout