refactor predict_proba_many and tensor_conversion.py

online-ml · Dec 23, 2022 · ad452fa · ad452fa
1 parent e872e11
commit ad452fa
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Showing 3 changed files with 21 additions and 15 deletions.
diff --git a/deep_river/classification/rolling_classifier.py b/deep_river/classification/rolling_classifier.py
@@ -333,7 +333,7 @@ def predict_proba_many(self, X: pd.DataFrame) -> pd.DataFrame:
             probas = [default_proba] * len(X)
         return pd.DataFrame(probas)
 
-    def _get_default_proba(self)-> List[Dict[ClfTarget, float]]:
+    def _get_default_proba(self) -> List[Dict[ClfTarget, float]]:
         if len(self.observed_classes) > 0:
             mean_proba = (
                 1 / len(self.observed_classes)

diff --git a/deep_river/utils/tensor_conversion.py b/deep_river/utils/tensor_conversion.py
@@ -1,4 +1,4 @@
-from typing import Collection, Deque, Dict, Optional, Union
+from typing import Deque, Dict, List, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -146,7 +146,7 @@ def labels2onehot(
 
 def output2proba(
     preds: torch.Tensor, classes: OrderedSet, with_logits=False
-) -> Collection[Dict[ClfTarget, float]]:
+) -> List[Dict[ClfTarget, float]]:
     if with_logits:
         if preds.shape[-1] >= 1:
             preds = torch.softmax(preds, dim=-1)
@@ -168,4 +168,4 @@ def output2proba(
         if preds_np.shape[0] == 1
         else [dict(zip(classes, pred)) for pred in preds_np]
     )
-    return [probas] if isinstance(probas, dict) else probas
+    return [probas] if isinstance(probas, dict) else list(probas)
diff --git a/deep_river/utils/test_tensor_conversion.py b/deep_river/utils/test_tensor_conversion.py
@@ -80,7 +80,9 @@ def test_output2proba():
     def assert_dicts_almost_equal(d1, d2):
         for i in range(len(d1)):
             for k in d1[i]:
-                assert np.isclose(d1[i][k], d2[i][k]), f"{d1[i][k]} != {d2[i][k]}"
+                assert np.isclose(
+                    d1[i][k], d2[i][k]
+                ), f"{d1[i][k]} != {d2[i][k]}"
 
     y = torch.tensor([[0.1, 0.2, 0.7]])
     classes = ["first class", "second class", "third class"]
@@ -92,20 +94,24 @@ def assert_dicts_almost_equal(d1, d2):
     classes = ["first class"]
     assert_dicts_almost_equal(
         output2proba(y, classes),
-        [dict(
-            zip(
-                ["first class", "unobserved 0"],
-                np.array([0.6, 0.4], dtype=np.float32),
+        [
+            dict(
+                zip(
+                    ["first class", "unobserved 0"],
+                    np.array([0.6, 0.4], dtype=np.float32),
+                )
             )
-        )],
+        ],
     )
     y = torch.tensor([[0.6, 0.4, 0.0]])
     assert_dicts_almost_equal(
         output2proba(y, classes),
-        [dict(
-            zip(
-                ["first class", "unobserved 0", "unobserved 1"],
-                np.array([0.6, 0.4, 0.0], dtype=np.float32),
+        [
+            dict(
+                zip(
+                    ["first class", "unobserved 0", "unobserved 1"],
+                    np.array([0.6, 0.4, 0.0], dtype=np.float32),
+                )
             )
-        )]
+        ],
     )