chore: code qual

src/sasctl/core.py

@@ -571,11 +571,11 @@ def username(self):
     def hostname(self):
         return self._settings.get("domain")
 
-    def send(self, request, **kwargs):
+    def send(self, req, **kwargs):
         if self.message_log.isEnabledFor(logging.DEBUG):
-            r = copy.deepcopy(request)
-            for filter in self.filters:
-                r = filter(r)
+            r = copy.deepcopy(req)
+            for filter_ in self.filters:
+                r = filter_(r)
 
             self.message_log.debug(
                 "HTTP/1.1 {verb} {url}\n{headers}\nBody:\n{body}".format(
@@ -588,14 +588,14 @@ def send(self, request, **kwargs):
                 )
             )
         else:
-            self.message_log.info("HTTP/1.1 %s %s", request.method, request.url)
+            self.message_log.info("HTTP/1.1 %s %s", req.method, req.url)
 
-        response = super(Session, self).send(request, **kwargs)
+        response = super(Session, self).send(req, **kwargs)
 
         if self.message_log.isEnabledFor(logging.DEBUG):
             r = copy.deepcopy(response)
-            for filter in self.filters:
-                r = filter(r)
+            for filter_ in self.filters:
+                r = filter_(r)
 
             self.message_log.debug(
                 "HTTP {status} {url}\n{headers}\nBody:\n{body}".format(

src/sasctl/pzmm/write_json_files.py

@@ -6,7 +6,6 @@
 import importlib
 import json
 
-# import math #not used
 import pickle
 import pickletools
 import sys
@@ -1449,13 +1448,6 @@ def stat_dataset_to_dataframe(
             Raised if an improper data format is provided.
 
         """
-        # If numpy inputs are supplied, then assume numpy is installed
-        try:
-            # noinspection PyPackageRequirements
-            import numpy as np
-        except ImportError:
-            np = None
-
         # Convert target_value to numeric for creating binary probabilities
         if isinstance(target_value, str):
             target_value = float(target_value)

src/sasctl/utils/model_info.py

@@ -63,7 +63,7 @@ def get_model_info(model, X, y=None):
 
     # Most PyTorch models are actually subclasses of torch.nn.Module, so checking module
     # name alone is not sufficient.
-    elif torch and isinstance(model, torch.nn.Module):
+    if torch and isinstance(model, torch.nn.Module):
         return PyTorchModelInfo(model, X, y)
 
     raise ValueError(f"Unrecognized model type {type(model)} received.")
@@ -200,7 +200,8 @@ class OnnxModelInfo(ModelInfo):
     def __init__(self, model, X, y=None):
         if onnx is None:
             raise RuntimeError(
-                "The onnx package must be installed to work with ONNX models.  Please `pip install onnx`."
+                "The onnx package must be installed to work with ONNX models.  "
+                "Please `pip install onnx`."
             )
 
         self._model = model
@@ -214,38 +215,19 @@ def __init__(self, model, X, y=None):
 
         if len(inputs) > 1:
             warnings.warn(
-                f"The ONNX model has {len(inputs)} inputs but only the first input will be captured in Model Manager."
+                f"The ONNX model has {len(inputs)} inputs but only the first input "
+                f"will be captured in Model Manager."
             )
 
         if len(outputs) > 1:
             warnings.warn(
-                f"The ONNX model has {len(outputs)} outputs but only the first input will be captured in Model Manager."
+                f"The ONNX model has {len(outputs)} outputs but only the first output "
+                f"will be captured in Model Manager."
             )
 
         self._X_df = inputs[0]
         self._y_df = outputs[0]
 
-        # initializer (static params)
-
-        # for field in model.ListFields():
-        # doc_string
-        # domain
-        # metadata_props
-        # model_author
-        # model_license
-        # model_version
-        # producer_name
-        # producer_version
-        # training_info
-
-        # irVersion
-        # producerName
-        # producerVersion
-        # opsetImport
-
-        # # list of (FieldDescriptor, value)
-        # fields = model.ListFields()
-
     @staticmethod
     def _tensor_to_dataframe(tensor):
         """
@@ -272,7 +254,7 @@ def _tensor_to_dataframe(tensor):
         name = tensor.get("name", "Var")
         type_ = tensor["type"]
 
-        if not "tensorType" in type_:
+        if "tensorType" not in type_:
             raise ValueError(f"Received an unexpected ONNX input type: {type_}.")
 
         dtype = onnx.helper.tensor_dtype_to_np_dtype(type_["tensorType"]["elemType"])
@@ -374,8 +356,6 @@ def __init__(self, model, X, y=None):
                 raise ValueError(
                     f"Expected input data to be a numpy array or PyTorch tensor, received {type(X)}."
                 )
-        # if X.ndim != 2:
-        #     raise ValueError(f"Expected input date with shape (n_samples, n_dim), received shape {X.shape}.")
 
         # Ensure each input is a PyTorch Tensor
         X = tuple(x if isinstance(x, torch.Tensor) else torch.tensor(x) for x in X)
@@ -395,8 +375,6 @@ def __init__(self, model, X, y=None):
             )
 
         self._model = model
-
-        # TODO: convert X and y to DF with arbitrary names
         self._X = X
         self._y = y