refactor: kws->kwargs

detectree/classifier.py

@@ -29,7 +29,7 @@ def __init__(
         tree_val=None,
         nontree_val=None,
         classifier_class=None,
-        **classifier_kws,
+        **classifier_kwargs,
     ):
         """
         Initialize the classifier.
@@ -83,27 +83,27 @@ def __init__(
             `predict_proba` methods and that can be saved to and loaded from memory
             using skops. If no value is provided, the value set in `settings.CLF_CLASS`
             is used.
-        classifier_kws : key-value pairings, optional
+        classifier_kwargs : key-value pairings, optional
             Keyword arguments that will be passed to the initialization of
             `classifier_class`. If no value is provided, the value set in
-            `settings.CLF_KWS` is used.
+            `settings.CLF_KWARGS` is used.
         """
-        self.pixel_features_builder_kws = dict(
+        self.pixel_features_builder_kwargs = dict(
             sigmas=sigmas,
             num_orientations=num_orientations,
             neighborhood=neighborhood,
             min_neighborhood_range=min_neighborhood_range,
             num_neighborhoods=num_neighborhoods,
         )
-        self.pixel_response_builder_kws = dict(
+        self.pixel_response_builder_kwargs = dict(
             tree_val=tree_val, nontree_val=nontree_val
         )
         if classifier_class is None:
             classifier_class = settings.CLF_CLASS
         self.classifier_class = classifier_class
-        if classifier_kws == {}:
-            classifier_kws = settings.CLF_KWS
-        self.classifier_kws = classifier_kws
+        if classifier_kwargs == {}:
+            classifier_kwargs = settings.CLF_KWARGS
+        self.classifier_kwargs = classifier_kwargs
 
     def train_classifier(
         self,
@@ -179,7 +179,7 @@ def train_classifier(
             ]
 
         X = pixel_features.PixelFeaturesBuilder(
-            **self.pixel_features_builder_kws
+            **self.pixel_features_builder_kwargs
         ).build_features(
             split_df=split_df,
             img_filepaths=img_filepaths,
@@ -190,7 +190,7 @@ def train_classifier(
         )
 
         y = pixel_response.PixelResponseBuilder(
-            **self.pixel_response_builder_kws
+            **self.pixel_response_builder_kwargs
         ).build_response(
             split_df=split_df,
             response_img_dir=response_img_dir,
@@ -200,7 +200,7 @@ def train_classifier(
             img_cluster=img_cluster,
         )
 
-        clf = self.classifier_class(**self.classifier_kws)
+        clf = self.classifier_class(**self.classifier_kwargs)
         clf.fit(X, y)
 
         return clf
@@ -258,7 +258,7 @@ def __init__(
         refine=None,
         refine_beta=None,
         refine_int_rescale=None,
-        **pixel_features_builder_kws,
+        **pixel_features_builder_kwargs,
     ):
         """
         Initialize the classifier instance.
@@ -287,7 +287,7 @@ def __init__(
             transformation of float to integer edge weights, required for the employed
             graph cuts algorithm. Larger values lead to greater precision. If no value
             is provided, the value set in `settings.CLF_REFINE_INT_RESCALE` is used.
-        pixel_features_builder_kws : dict, optional
+        pixel_features_builder_kwargs : dict, optional
             Keyword arguments that will be passed to `detectree.PixelFeaturesBuilder`,
             which customize how the pixel features are built.
         """
@@ -310,7 +310,7 @@ def __init__(
         self.refine_beta = refine_beta
         self.refine_int_rescale = refine_int_rescale
 
-        self.pixel_features_builder_kws = pixel_features_builder_kws
+        self.pixel_features_builder_kwargs = pixel_features_builder_kwargs
 
     def classify_img(self, img_filepath, clf, *, output_filepath=None):
         """
@@ -342,7 +342,7 @@ def classify_img(self, img_filepath, clf, *, output_filepath=None):
         img_shape = src.shape
 
         X = pixel_features.PixelFeaturesBuilder(
-            **self.pixel_features_builder_kws
+            **self.pixel_features_builder_kwargs
         ).build_features_from_filepath(img_filepath)
 
         if not self.refine:

detectree/cli/main.py

@@ -55,14 +55,14 @@ def parser_process(value, state):
         return retval
 
 
-def _dict_from_kws(kws):
+def _dict_from_kwargs(kwargs):
     # Multiple key:value pair arguments in click, see https://bit.ly/32BaES3
-    if kws is not None:
-        kws = dict(kw.split(":") for kw in kws)
+    if kwargs is not None:
+        kwargs = dict(kwarg.split(":") for kwarg in kwargs)
     else:
-        kws = {}
+        kwargs = {}
 
-    return kws
+    return kwargs
 
 
 def _init_classifier_trainer(
@@ -72,11 +72,11 @@ def _init_classifier_trainer(
     num_neighborhoods,
     tree_val,
     nontree_val,
-    classifier_kws,
+    classifier_kwargs,
 ):
     # pixel_features_builder_kws = _dict_from_kws(pixel_features_builder_kws)
     # pixel_response_builder_kws = _dict_from_kws(pixel_response_builder_kws)
-    classifier_kws = _dict_from_kws(classifier_kws)
+    classifier_kwargs = _dict_from_kwargs(classifier_kwargs)
 
     return dtr.ClassifierTrainer(
         sigmas=sigmas,
@@ -85,7 +85,7 @@ def _init_classifier_trainer(
         num_neighborhoods=num_neighborhoods,
         tree_val=tree_val,
         nontree_val=nontree_val,
-        **classifier_kws,
+        **classifier_kwargs,
     )
 
 
@@ -155,16 +155,16 @@ def train_test_split(
     else:
         logger.info("Loaded %d images", num_imgs)
 
-    tts_kws = {}
+    tts_kwargs = {}
     if method is not None:
-        tts_kws["method"] = method
+        tts_kwargs["method"] = method
     if num_components is not None:
-        tts_kws["num_components"] = num_components
+        tts_kwargs["num_components"] = num_components
     if num_img_clusters is not None:
-        tts_kws["num_img_clusters"] = num_img_clusters
+        tts_kwargs["num_img_clusters"] = num_img_clusters
     if train_prop is not None:
-        tts_kws["train_prop"] = train_prop
-    df, evr = ts.train_test_split(return_evr=True, **tts_kws)
+        tts_kwargs["train_prop"] = train_prop
+    df, evr = ts.train_test_split(return_evr=True, **tts_kwargs)
     logger.info("Variance ratio explained by PCA: %f", evr)
 
     if output_filepath is None:
@@ -190,7 +190,7 @@ def train_test_split(
 @click.option("--num-neighborhoods", type=int)
 @click.option("--tree-val", type=int)
 @click.option("--nontree-val", type=int)
-@click.option("--classifier-kws", cls=_OptionEatAll)
+@click.option("--classifier-kwargs", cls=_OptionEatAll)
 @click.option("--output-filepath", type=click.Path())
 def train_classifier(
     ctx,
@@ -208,7 +208,7 @@ def train_classifier(
     num_neighborhoods,
     tree_val,
     nontree_val,
-    classifier_kws,
+    classifier_kwargs,
     output_filepath,
 ):
     """Train a tree/non-tree pixel classifier."""
@@ -227,7 +227,7 @@ def train_classifier(
         num_neighborhoods=num_neighborhoods,
         tree_val=tree_val,
         nontree_val=nontree_val,
-        classifier_kws=classifier_kws,
+        classifier_kwargs=classifier_kwargs,
     )
     clf = ct.train_classifier(
         split_df=split_df,
@@ -256,7 +256,7 @@ def train_classifier(
 @click.option("--num-neighborhoods", type=int)
 @click.option("--tree-val", type=int)
 @click.option("--nontree-val", type=int)
-@click.option("--classifier-kws", cls=_OptionEatAll)
+@click.option("--classifier-kwargs", cls=_OptionEatAll)
 @click.option("--output-dir", type=click.Path(exists=True))
 def train_classifiers(
     ctx,
@@ -268,7 +268,7 @@ def train_classifiers(
     num_neighborhoods,
     tree_val,
     nontree_val,
-    classifier_kws,
+    classifier_kwargs,
     output_dir,
 ):
     """Train tree/non-tree pixel classifier(s) for a given train/test split."""
@@ -287,7 +287,7 @@ def train_classifiers(
         num_neighborhoods=num_neighborhoods,
         tree_val=tree_val,
         nontree_val=nontree_val,
-        classifier_kws=classifier_kws,
+        classifier_kwargs=classifier_kwargs,
     )
     clfs_dict = ct.train_classifiers(split_df, response_img_dir)
 
@@ -311,7 +311,7 @@ def train_classifiers(
 @click.option("--refine", is_flag=True)
 @click.option("--refine-beta", type=int)
 @click.option("--refine-int-rescale", type=int)
-@click.option("--pixel-features-builder-kws", cls=_OptionEatAll)
+@click.option("--pixel-features-builder-kwargs", cls=_OptionEatAll)
 @click.option("--output-filepath", type=click.Path())
 def classify_img(
     ctx,
@@ -322,22 +322,22 @@ def classify_img(
     refine,
     refine_beta,
     refine_int_rescale,
-    pixel_features_builder_kws,
+    pixel_features_builder_kwargs,
     output_filepath,
 ):
     """Use a trained classifier to predict tree pixels in an image."""
     logger = ctx.obj["LOGGER"]
 
     logger.info("Classifying %s with classifier of %s", img_filepath, clf_filepath)
 
-    pixel_features_builder_kws = _dict_from_kws(pixel_features_builder_kws)
+    pixel_features_builder_kwargs = _dict_from_kwargs(pixel_features_builder_kwargs)
     c = dtr.Classifier(
         tree_val=tree_val,
         nontree_val=nontree_val,
         refine=refine,
         refine_beta=refine_beta,
         refine_int_rescale=refine_int_rescale,
-        **pixel_features_builder_kws,
+        **pixel_features_builder_kwargs,
     )
 
     if output_filepath is None:
@@ -364,7 +364,7 @@ def classify_img(
 @click.option("--refine", is_flag=True)
 @click.option("--refine-beta", type=int)
 @click.option("--refine-int-rescale", type=int)
-@click.option("--pixel-features-builder-kws", cls=_OptionEatAll)
+@click.option("--pixel-features-builder-kwargs", cls=_OptionEatAll)
 @click.option("--output-dir", type=click.Path(exists=True))
 def classify_imgs(
     ctx,
@@ -378,7 +378,7 @@ def classify_imgs(
     refine,
     refine_beta,
     refine_int_rescale,
-    pixel_features_builder_kws,
+    pixel_features_builder_kwargs,
     output_dir,
 ):
     """Use trained classifier(s) to predict tree pixels in multiple images."""
@@ -404,15 +404,15 @@ def classify_imgs(
                 settings.SKOPS_TRUSTED,
             )
 
-    pixel_features_builder_kws = _dict_from_kws(pixel_features_builder_kws)
+    pixel_features_builder_kwargs = _dict_from_kwargs(pixel_features_builder_kwargs)
 
     c = dtr.Classifier(
         tree_val=tree_val,
         nontree_val=nontree_val,
         refine=refine,
         refine_beta=refine_beta,
         refine_int_rescale=refine_int_rescale,
-        **pixel_features_builder_kws,
+        **pixel_features_builder_kwargs,
     )
 
     if output_dir is None:

detectree/lidar.py

@@ -119,7 +119,7 @@ def to_canopy_mask(
         output_filepath=None,
         postprocess_func=None,
         postprocess_func_args=None,
-        postprocess_func_kws=None,
+        postprocess_func_kwargs=None,
     ):
         """
         Transform a LiDAR file into a canopy mask.
@@ -144,7 +144,7 @@ def to_canopy_mask(
             ndarray.
         postprocess_func_args : list-like, optional
             Arguments to be passed to `postprocess_func`.
-        postprocess_func_kws : dict, optional
+        postprocess_func_kwargs : dict, optional
             Keyword arguments to be passed to `postprocess_func`.
 
         Returns
@@ -168,7 +168,7 @@ def to_canopy_mask(
         canopy_arr = lidar_arr >= self.tree_threshold
         if postprocess_func is not None:
             canopy_arr = postprocess_func(
-                canopy_arr, *postprocess_func_args, **postprocess_func_kws
+                canopy_arr, *postprocess_func_args, **postprocess_func_kwargs
             )
         canopy_arr = np.where(
             canopy_arr, self.output_tree_val, self.output_nodata

detectree/settings.py

@@ -22,7 +22,7 @@
 
 # classifier
 CLF_CLASS = lgb.LGBMClassifier
-CLF_KWS = {"n_estimators": 200}
+CLF_KWARGS = {"n_estimators": 200}
 CLF_TREE_VAL = 255
 CLF_NONTREE_VAL = 0
 CLF_REFINE = True

tests/test_detectree.py

@@ -649,7 +649,7 @@ def test_lidar_to_canopy(self):
             self.ref_img_filepath,
             postprocess_func=ndi.binary_dilation,
             postprocess_func_args=[ndi.generate_binary_structure(2, 2)],
-            postprocess_func_kws={"border_value": 0},
+            postprocess_func_kwargs={"border_value": 0},
         )
         # test that `to_canopy_mask` with `output_filepath` returns a ndarray and dumps
         # it