parameter clean up

src/scimilarity/cell_embedding.py

@@ -8,9 +8,6 @@ def __init__(
         self,
         model_path: str,
         use_gpu: bool = False,
-        parameters: Optional[dict] = None,
-        filenames: Optional[dict] = None,
-        residual: bool = False,
     ):
         """Constructor.
 
@@ -20,12 +17,6 @@ def __init__(
             Path to the directory containing model files.
         use_gpu: bool, default: False
             Use GPU instead of CPU.
-        parameters: dict, optional, default: None
-            Use a dictionary of custom model parameters instead of infering from model files.
-        filenames: dict, optional, default: None
-            Use a dictionary of custom filenames for model files instead default.
-        residual: bool, default: False
-            Use residual connections.
 
         Examples
         --------
@@ -40,44 +31,36 @@ def __init__(
         self.model_path = model_path
         self.use_gpu = use_gpu
 
-        if filenames is None:
-            filenames = {}
-
         self.filenames = {
-            "model": os.path.join(
-                self.model_path, filenames.get("model", "encoder.ckpt")
-            ),
-            "gene_order": os.path.join(
-                self.model_path, filenames.get("gene_order", "gene_order.tsv")
-            ),
+            "model": os.path.join(self.model_path, "encoder.ckpt"),
+            "gene_order": os.path.join(self.model_path, "gene_order.tsv"),
         }
 
         # get gene order
         with open(self.filenames["gene_order"], "r") as fh:
             self.gene_order = [line.strip() for line in fh]
 
-        # get neural network model
-        if parameters is None:  # infer network size if not explicitly given
-            with open(os.path.join(self.model_path, "layer_sizes.json"), "r") as fh:
-                layer_sizes = json.load(fh)
-            # keys: network.1.weight, network.2.weight, ..., network.n.weight
-            layers = [
-                (key, layer_sizes[key])
-                for key in sorted(list(layer_sizes.keys()))
-                if "weight" in key and len(layer_sizes[key]) > 1
-            ]
-            parameters = {
-                "latent_dim": layers[-1][1][0],  # last
-                "hidden_dim": [layer[1][0] for layer in layers][0:-1],  # all but last
-            }
+        # get neural network model and infer network size
+        with open(os.path.join(self.model_path, "layer_sizes.json"), "r") as fh:
+            layer_sizes = json.load(fh)
+        # keys: network.1.weight, network.2.weight, ..., network.n.weight
+        layers = [
+            (key, layer_sizes[key])
+            for key in sorted(list(layer_sizes.keys()))
+            if "weight" in key and len(layer_sizes[key]) > 1
+        ]
+        parameters = {
+            "latent_dim": layers[-1][1][0],  # last
+            "hidden_dim": [layer[1][0] for layer in layers][0:-1],  # all but last
+        }
 
         self.n_genes = len(self.gene_order)
         self.latent_dim = parameters["latent_dim"]
         self.model = Encoder(
             n_genes=self.n_genes,
             latent_dim=parameters["latent_dim"],
             hidden_dim=parameters["hidden_dim"],
-            residual=residual,
+            residual=False,
         )
         if self.use_gpu is True:
             self.model.cuda()

src/scimilarity/cell_search_knn.py

@@ -10,9 +10,6 @@ def __init__(
         self,
         model_path: str,
         use_gpu: bool = False,
-        parameters: Optional[dict] = None,
-        filenames: Optional[dict] = None,
-        residual: bool = False,
     ):
         """Constructor.
 
@@ -22,31 +19,17 @@ def __init__(
             Path to the directory containing model files.
         use_gpu: bool, default: False
             Use GPU instead of CPU.
-        parameters: dict, optional, default: None
-            Use a dictionary of custom model parameters instead of infering from model files.
-        filenames: dict, optional, default: None
-            Use a dictionary of custom filenames for model files instead default.
-            The kNN filenames also need to be specified here.
-        residual: bool, default: False
-            Use residual connections.
 
         Examples
         --------
-        >>> filenames = {"knn": "knn.bin"}
-        >>> cs = CellSearch(model_path="/opt/data/model", filenames=filesnames)
+        >>> cs = CellSearchKNN(model_path="/opt/data/model")
         """
 
         super().__init__(
             model_path=model_path,
             use_gpu=use_gpu,
-            parameters=parameters,
-            filenames=filenames,
-            residual=residual,
         )
 
-        if filenames is None:
-            filenames = {}
-
         self.knn = None
         self.safelist = None
         self.blocklist = None