refactor: change classify_img->predict_img

detectree/classifier.py

@@ -339,7 +339,7 @@ def __init__(
 
         self.pixel_features_builder_kwargs = pixel_features_builder_kwargs
 
-    def _classify_img(self, img_filepath, clf, *, output_filepath=None):
+    def _predict_img(self, img_filepath, clf, *, output_filepath=None):
         # ACHTUNG: Note that we do not use keyword-only arguments in this method because
         # `output_filepath` works as the only "optional" argument
         src = rio.open(img_filepath)
@@ -403,7 +403,7 @@ def _classify_img(self, img_filepath, clf, *, output_filepath=None):
         src.close()
         return y_pred
 
-    def _classify_imgs(self, img_filepaths, clf, output_dir):
+    def _predict_imgs(self, img_filepaths, clf, output_dir):
         pred_imgs_lazy = []
         pred_img_filepaths = []
         for img_filepath in img_filepaths:
@@ -412,7 +412,7 @@ def _classify_imgs(self, img_filepaths, clf, output_dir):
             #     output_dir, f"{filename}-pred{ext}")
             pred_img_filepath = path.join(output_dir, path.basename(img_filepath))
             pred_imgs_lazy.append(
-                dask.delayed(self._classify_img)(
+                dask.delayed(self._predict_img)(
                     img_filepath, clf, output_filepath=pred_img_filepath
                 )
             )
@@ -423,7 +423,7 @@ def _classify_imgs(self, img_filepaths, clf, output_dir):
 
         return pred_img_filepaths
 
-    def classify_img(self, img_filepath, *, img_cluster=None, output_filepath=None):
+    def predict_img(self, img_filepath, *, img_cluster=None, output_filepath=None):
         """
         Use a trained classifier to predict tree pixels in an image.
 
@@ -460,11 +460,11 @@ def classify_img(self, img_filepath, *, img_cluster=None, output_filepath=None):
         # return self._classify_img(
         #     img_filepath, clf, output_filepath=output_filepath
         # )
-        return self._classify_img(
+        return self._predict_img(
             img_filepath, self.clf, output_filepath=output_filepath
         )
 
-    def classify_imgs(self, split_df, output_dir):
+    def predict_imgs(self, split_df, output_dir):
         """
         Use trained classifier(s) to predict tree pixels in multiple images.
 
@@ -484,7 +484,7 @@ def classify_imgs(self, split_df, output_dir):
             File paths of the dumped tiles.
         """
         if hasattr(self, "clf"):
-            return self._classify_imgs(
+            return self._predict_imgs(
                 split_df[~split_df["train"]]["img_filepath"], self.clf, output_dir
             )
         else:
@@ -498,7 +498,7 @@ def classify_imgs(self, split_df, output_dir):
                         f"Classifier for cluster {img_cluster} not found in"
                         " `self.clf_dict`."
                     )
-                pred_imgs[img_cluster] = self._classify_imgs(
+                pred_imgs[img_cluster] = self._predict_imgs(
                     utils.get_img_filepaths(split_df, img_cluster, False),
                     clf,
                     output_dir,

detectree/cli/main.py

@@ -313,7 +313,7 @@ def train_classifiers(
 @click.option("--refine-int-rescale", type=int)
 @click.option("--pixel-features-builder-kwargs", cls=_OptionEatAll)
 @click.option("--output-filepath", type=click.Path())
-def classify_img(
+def predict_img(
     ctx,
     img_filepath,
     clf_filepath,
@@ -354,7 +354,7 @@ def classify_img(
         filename, ext = path.splitext(path.basename(img_filepath))
         output_filepath = f"{filename}-pred{ext}"
 
-    c.classify_img(
+    c.predict_img(
         img_filepath,
         output_filepath=output_filepath,
     )
@@ -373,7 +373,7 @@ def classify_img(
 @click.option("--refine-int-rescale", type=int)
 @click.option("--pixel-features-builder-kwargs", cls=_OptionEatAll)
 @click.option("--output-dir", type=click.Path(exists=True))
-def classify_imgs(
+def predict_imgs(
     ctx,
     split_filepath,
     clf_filepath,
@@ -434,7 +434,7 @@ def classify_imgs(
     if output_dir is None:
         output_dir = ""
 
-    pred_imgs = c.classify_imgs(
+    pred_imgs = c.predict_imgs(
         split_df,
         output_dir,
     )

tests/test_detectree.py

@@ -585,11 +585,11 @@ def test_classifier(self):
         ]:
             img_filepath = self.split_i_df.iloc[0]["img_filepath"]
             # test that `classify_img` returns a ndarray
-            self.assertIsInstance(c.classify_img(img_filepath), np.ndarray)
+            self.assertIsInstance(c.predict_img(img_filepath), np.ndarray)
             # test that `classify_img` with `output_filepath` returns a ndarray and
             # dumps it
             output_filepath = path.join(self.tmp_output_dir, "foo.tif")
-            y_pred = c.classify_img(img_filepath, output_filepath=output_filepath)
+            y_pred = c.predict_img(img_filepath, output_filepath=output_filepath)
             self.assertIsInstance(y_pred, np.ndarray)
             self.assertTrue(os.path.exists(output_filepath))
             # remove it so that the output dir is clean in the tests below
@@ -599,7 +599,7 @@ def test_classifier(self):
             # dumped. This works regardless of whether a "img_cluster" column is present
             # in the split data frame - since it is ignored for "cluster-I"
             for split_df in [self.split_i_df, self.split_ii_df]:
-                pred_imgs = c.classify_imgs(split_df, self.tmp_output_dir)
+                pred_imgs = c.predict_imgs(split_df, self.tmp_output_dir)
                 self.assertIsInstance(pred_imgs, list)
                 self._test_imgs_exist_and_rm(pred_imgs)
 
@@ -610,22 +610,22 @@ def test_classifier(self):
         ]:
             img_filepath = self.split_i_df.iloc[0]["img_filepath"]
             # test that `classify_img` returns a ndarray
-            self.assertIsInstance(c.classify_img(img_filepath), np.ndarray)
+            self.assertIsInstance(c.predict_img(img_filepath), np.ndarray)
 
         # "cluster-II"
         c = dtr.Classifier(clf_dict=self.clf_dict)
         # `classify_imgs` should raise a `KeyError` if `split_df` doesn't have a
         # "img_cluster" column
         self.assertRaises(
-            KeyError, c.classify_imgs, self.split_i_df, self.tmp_output_dir
+            KeyError, c.predict_imgs, self.split_i_df, self.tmp_output_dir
         )
         # otherwise it should return a list and dump the images (regardless of the
         # `refine` value
         for c in [
             dtr.Classifier(clf_dict=self.clf_dict, refine=refine)
             for refine in [True, False]
         ]:
-            pred_imgs = c.classify_imgs(self.split_ii_df, self.tmp_output_dir)
+            pred_imgs = c.predict_imgs(self.split_ii_df, self.tmp_output_dir)
             self.assertIsInstance(pred_imgs, dict)
             for img_cluster in pred_imgs:
                 self._test_imgs_exist_and_rm(pred_imgs[img_cluster])
@@ -787,9 +787,9 @@ def test_train_classifiers(self):
         )
         self.assertEqual(result.exit_code, 0)
 
-    def test_classify_img(self):
+    def test_predict_img(self):
         base_args = [
-            "classify-img",
+            "predict-img",
             glob.glob(path.join(self.img_dir, "*.tif"))[0],
             "--output-filepath",
             path.join(self.tmp_dir, "foo.tif"),
@@ -798,8 +798,8 @@ def test_classify_img(self):
             result = self.runner.invoke(main.cli, base_args + extra_args)
             self.assertEqual(result.exit_code, 0)
 
-    def test_classify_imgs(self):
-        base_args = ["classify-imgs", self.split_ii_filepath]
+    def test_predict_imgs(self):
+        base_args = ["predict-imgs", self.split_ii_filepath]
         final_args = [
             "--output-dir",
             self.tmp_dir,