Fix minor bugs

pertpy/tools/_augur.py

@@ -543,10 +543,10 @@ def run_cross_validation(
         # standardized coefficients with Agresti method
         # cf. https://think-lab.github.io/d/205/#3
         if isinstance(self.estimator, LogisticRegression):
-            for fold, self.estimator in list(zip(range(len(results["estimator"])), results["estimator"], strict=False)):
+            for fold, estimator in list(zip(range(len(results["estimator"])), results["estimator"], strict=False)):
                 feature_importances["genes"].extend(x.columns.tolist())
                 feature_importances["feature_importances"].extend(
-                    (self.estimator.coef_ * self.estimator.coef_.std()).flatten().tolist()
+                    (estimator.coef_ * estimator.coef_.std()).flatten().tolist()
                 )
                 feature_importances["subsample_idx"].extend(len(x.columns) * [subsample_idx])
                 feature_importances["fold"].extend(len(x.columns) * [fold])

pertpy/tools/_dialogue.py

@@ -192,7 +192,7 @@ def _get_cor_zscores(self, estimate: pd.Series, p_val: pd.Series) -> pd.DataFram
         Returns:
             A DataFrame containing the zscores indexed by the estimates.
         """
-        p_val.replace(0, min(p for p in p_val if p is not None and p > 0))
+        p_val = p_val.replace(0, min(p for p in p_val if p is not None and p > 0))
 
         # check for all (negative) estimate values if >0 then divide p_value by 2 at same index else substract the p_value/2 from 1
         # pos_est and neg_est differ in calculation for values as negative estimation is used in neg_est

pertpy/tools/_perturbation_space/_clustering.py

@@ -78,7 +78,9 @@ def evaluate_clustering(
 
                 if "metric" not in kwargs:
                     kwargs["metric"] = "euclidean"
-                if "distances" not in kwargs:
+                if "distances" in kwargs:
+                    distances = kwargs["distances"]
+                else:
                     distances = pairwise_distances(self.X, metric=kwargs["metric"])
                 if "sample_size" not in kwargs:
                     kwargs["sample_size"] = None

pertpy/tools/_perturbation_space/_comparison.py

@@ -107,6 +107,6 @@ def compare_knn(
         uq, uq_counts = np.unique(labels[indices], return_counts=True)
         uq_counts_norm = uq_counts / uq_counts.sum()
         counts = dict(zip(label_groups, [0.0] * len(label_groups), strict=False))
-        counts = dict(zip(uq, uq_counts_norm, strict=False))
+        counts.update(zip(uq, uq_counts_norm, strict=False))
 
         return counts

pertpy/tools/_perturbation_space/_discriminator_classifiers.py

@@ -56,7 +56,7 @@ def compute(
             >>> rcs = pt.tl.LRClassifierSpace()
             >>> pert_embeddings = rcs.compute(adata, embedding_key="X_pca", target_col="perturbation_name")
         """
-        if layer_key is not None and layer_key not in adata.obs.columns:
+        if layer_key is not None and layer_key not in adata.layers:
             raise ValueError(f"Layer key {layer_key} not found in adata.")
 
         if embedding_key is not None and embedding_key not in adata.obsm:
@@ -343,7 +343,7 @@ def compute(
             >>> dcs = pt.tl.MLPClassifierSpace()
             >>> cell_embeddings = dcs.compute(adata, target_col="perturbation_name")
         """
-        if layer_key is not None and layer_key not in adata.obs.columns:
+        if layer_key is not None and layer_key not in adata.layers:
             raise ValueError(f"Layer key {layer_key} not found in adata.")
 
         if target_col not in adata.obs:

pertpy/tools/_perturbation_space/_perturbation_space.py

@@ -105,7 +105,7 @@ def compute_control_diff(  # type: ignore
                 elif num_control > 1:
                     control_expression = np.mean(adata.obsm[embedding_key][(control_mask & mask), :], axis=0)
                 else:
-                    control_expression = np.zeros((1, adata.n_vars))
+                    control_expression = np.zeros((1, adata.obsm[embedding_key].shape[1]))
                 adata.obsm[new_embedding_key][mask, :] = adata.obsm[embedding_key][mask, :] - control_expression
 
         if (not layer_key and not embedding_key) or all_data:
@@ -193,10 +193,9 @@ def add(
         else:
             adata = self.compute_control_diff(adata, copy=True, all_data=True, target_col=target_col)
 
-        data: dict[str, np.array] = {}
+        data: dict[str, dict[str, np.ndarray]] = {"layers": {}, "embeddings": {}}
 
         for local_layer_key in adata.layers:
-            data["layers"] = {}
             control_local = adata[reference_key].layers[local_layer_key].copy()
             for perturbation in perturbations:
                 control_local += adata[perturbation].layers[local_layer_key]
@@ -205,7 +204,6 @@ def add(
             data["layers"][local_layer_key] = new_data
 
         for local_embedding_key in adata.obsm:
-            data["embeddings"] = {}
             control_local = adata[reference_key].obsm[local_embedding_key].copy()
             for perturbation in perturbations:
                 control_local += adata[perturbation].obsm[local_embedding_key]
@@ -234,19 +232,13 @@ def add(
         new_obs.loc[new_pert_name[:-1]] = new_pert_obs_series
         new_perturbation.obs = new_obs
 
-        if "layers" in data:
-            for key in data["layers"]:
-                key_name = key
-                if key.endswith("_control_diff"):
-                    key_name = key.removesuffix("_control_diff")
-                new_perturbation.layers[key_name] = data["layers"][key]
+        for key, value in data["layers"].items():
+            key_name = key.removesuffix("_control_diff") if key.endswith("_control_diff") else key
+            new_perturbation.layers[key_name] = value
 
-        if "embeddings" in data:
-            key_name = key
-            for key in data["embeddings"]:
-                if key.endswith("_control_diff"):
-                    key_name = key.removesuffix("_control_diff")
-                new_perturbation.obsm[key_name] = data["embeddings"][key]
+        for key, value in data["embeddings"].items():
+            key_name = key.removesuffix("_control_diff") if key.endswith("_control_diff") else key
+            new_perturbation.obsm[key_name] = value
 
         new_perturbation.obs[target_col] = new_perturbation.obs_names.astype("category")
 
@@ -304,10 +296,9 @@ def subtract(
         else:
             adata = self.compute_control_diff(adata, copy=True, all_data=True, target_col=target_col)
 
-        data: dict[str, np.array] = {}
+        data: dict[str, dict[str, np.ndarray]] = {"layers": {}, "embeddings": {}}
 
         for local_layer_key in adata.layers:
-            data["layers"] = {}
             control_local = adata[reference_key].layers[local_layer_key].copy()
             for perturbation in perturbations:
                 control_local -= adata[perturbation].layers[local_layer_key]
@@ -316,7 +307,6 @@ def subtract(
             data["layers"][local_layer_key] = new_data
 
         for local_embedding_key in adata.obsm:
-            data["embeddings"] = {}
             control_local = adata[reference_key].obsm[local_embedding_key].copy()
             for perturbation in perturbations:
                 control_local -= adata[perturbation].obsm[local_embedding_key]
@@ -345,19 +335,13 @@ def subtract(
         new_obs.loc[new_pert_name[:-1]] = new_pert_obs_series
         new_perturbation.obs = new_obs
 
-        if "layers" in data:
-            for key in data["layers"]:
-                key_name = key
-                if key.endswith("_control_diff"):
-                    key_name = key.removesuffix("_control_diff")
-                new_perturbation.layers[key_name] = data["layers"][key]
-
-        if "embeddings" in data:
-            key_name = key
-            for key in data["embeddings"]:
-                if key.endswith("_control_diff"):
-                    key_name = key.removesuffix("_control_diff")
-                new_perturbation.obsm[key_name] = data["embeddings"][key]
+        for key, value in data["layers"].items():
+            key_name = key.removesuffix("_control_diff") if key.endswith("_control_diff") else key
+            new_perturbation.layers[key_name] = value
+
+        for key, value in data["embeddings"].items():
+            key_name = key.removesuffix("_control_diff") if key.endswith("_control_diff") else key
+            new_perturbation.obsm[key_name] = value
 
         new_perturbation.obs[target_col] = new_perturbation.obs_names.astype("category")