Metrics

doc/api.rst

@@ -68,3 +68,26 @@ API
    ensembles.OOB
    ensembles.UOB
    ensembles.WAE
+
+:mod:`strlearn.metrics`: Metrics
+====================================================
+
+.. automodule:: strlearn.metrics
+  :no-members:
+  :no-inherited-members:
+
+.. currentmodule:: strlearn
+
+.. autosummary::
+  :toctree: generated/
+  :template: function.rst
+
+  metrics.binary_confusion_matrix
+  metrics.specificity
+  metrics.recall
+  metrics.precision
+  metrics.fbeta_score
+  metrics.f1_score
+  metrics.balanced_accuracy_score
+  metrics.geometric_mean_score_1
+  metrics.geometric_mean_score_2

doc/index.rst

@@ -46,7 +46,7 @@ The ``stream-learn`` module is a set of tools necessary for processing data stre
 - `evaluators <evaluators.html>`_ - containing classes for running experiments on stream data in accordance with the Test-Then-Train and Prequential methodology.
 - `classifiers <classifiers.html>`_ - containing sample stream classifiers,
 - `ensembles <ensembles.html>`_ - containing standard team models of stream data classification,
-- `utils <evaluators.html>`_ - containing typical classification quality metrics in data streams.
+- `metrics <evaluators.html>`_ - containing typical classification quality metrics in data streams.
 
 You can read more about each module in the User Guide.
 

examples/plot_basic.py

@@ -19,7 +19,7 @@
 stream = sl.streams.StreamGenerator(n_chunks=30, n_drifts=1)
 
 # Select vector of metrics
-metrics = [sl.metrics.bac, sl.metrics.f1_score]
+metrics = [sl.metrics.balanced_accuracy_score, sl.metrics.f1_score]
 
 # Initialize evaluator with given metrics
 evaluator = sl.evaluators.TestThenTrain(metrics)

strlearn/evaluators/Prequential.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 from sklearn.metrics import accuracy_score
-from ..metrics import bac
+from ..metrics import balanced_accuracy_score
 from sklearn.base import ClassifierMixin
 
 
@@ -44,7 +44,7 @@ class Prequential:
     [0.925      0.92567027 0.9250634  0.92567027 0.92610837]]
     """
 
-    def __init__(self, metrics=(accuracy_score, bac)):
+    def __init__(self, metrics=(accuracy_score, balanced_accuracy_score)):
         if isinstance(metrics, (list, tuple)):
             self.metrics = metrics
         else:

strlearn/evaluators/TestThenTrain.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 from sklearn.metrics import accuracy_score
-from ..metrics import bac
+from ..metrics import balanced_accuracy_score
 from sklearn.base import ClassifierMixin
 
 
@@ -20,7 +20,7 @@ class TestThenTrain:
         The class labels.
     scores_ : array-like, shape (stream.n_chunks, 5)
         Values of accuracy_score, roc_auc_score,
-        geometric_mean_score, bac and f_score for
+        geometric_mean_score, balanced_accuracy_score and f_score for
         each processed data chunk.
 
     Examples
@@ -41,7 +41,7 @@ class TestThenTrain:
     [0.935      0.93569212 0.93540766 0.93569212 0.93467337]]
     """
 
-    def __init__(self, metrics=(accuracy_score, bac)):
+    def __init__(self, metrics=(accuracy_score, balanced_accuracy_score)):
         if isinstance(metrics, (list, tuple)):
             self.metrics = metrics
         else:

strlearn/metrics/__init__.py

@@ -3,7 +3,7 @@
     f1_score,
     recall,
     precision,
-    bac,
+    balanced_accuracy_score,
     geometric_mean_score_1,
     geometric_mean_score_2,
     specificity,

strlearn/metrics/metrics.py

@@ -90,7 +90,7 @@ def f1_score(y_true, y_pred):
     return fbeta_score(y_true, y_pred, 1)
 
 
-def bac(y_true, y_pred):
+def balanced_accuracy_score(y_true, y_pred):
     """
     Calculates the balanced accuracy score.
 
@@ -103,7 +103,7 @@ def bac(y_true, y_pred):
 
     Returns
     -------
-    bac : float
+    balanced_accuracy_score : float
     """
     spe, rec = specificity(y_true, y_pred), recall(y_true, y_pred)
     return np.nan_to_num((rec + spe) / 2)

strlearn/tests/test_evaluators.py

@@ -3,7 +3,7 @@
 import sys
 import strlearn as sl
 from sklearn.metrics import accuracy_score, roc_auc_score
-from ..metrics import bac, f1_score, geometric_mean_score_1
+from ..metrics import balanced_accuracy_score, f1_score, geometric_mean_score_1
 
 sys.path.insert(0, "../..")
 
@@ -24,7 +24,13 @@ def test_TTT_single_clf():
 def test_TTT_custom_metrics():
     stream = get_stream()
     clf = sl.classifiers.AccumulatedSamplesClassifier()
-    metrics = [accuracy_score, roc_auc_score, geometric_mean_score_1, bac, f1_score]
+    metrics = [
+        accuracy_score,
+        roc_auc_score,
+        geometric_mean_score_1,
+        balanced_accuracy_score,
+        f1_score,
+    ]
     evaluator = sl.evaluators.TestThenTrain(metrics=metrics)
     evaluator.process(stream, clf)
 
@@ -46,7 +52,13 @@ def test_TTT_multiple_clfs():
         sl.classifiers.AccumulatedSamplesClassifier(),
         sl.classifiers.AccumulatedSamplesClassifier(),
     ]
-    metrics = [accuracy_score, roc_auc_score, geometric_mean_score_1, bac, f1_score]
+    metrics = [
+        accuracy_score,
+        roc_auc_score,
+        geometric_mean_score_1,
+        balanced_accuracy_score,
+        f1_score,
+    ]
     evaluator = sl.evaluators.TestThenTrain(metrics=metrics)
     evaluator.process(stream, clfs)
 
@@ -59,7 +71,13 @@ def test_P_multiple_clfs():
         sl.classifiers.AccumulatedSamplesClassifier(),
         sl.classifiers.AccumulatedSamplesClassifier(),
     ]
-    metrics = [accuracy_score, roc_auc_score, geometric_mean_score_1, bac, f1_score]
+    metrics = [
+        accuracy_score,
+        roc_auc_score,
+        geometric_mean_score_1,
+        balanced_accuracy_score,
+        f1_score,
+    ]
     evaluator = sl.evaluators.Prequential(metrics=metrics)
     evaluator.process(stream, clfs)