Fix tests

README.md

@@ -74,10 +74,10 @@ For further examples check out the <a href="https://online-ml.github.io/deep-riv
 
 >>> for x, y in dataset:
 ...     y_pred = model_pipeline.predict_one(x)  # make a prediction
-...     metric = metric.update(y, y_pred)  # update the metric
-...     model_pipeline = model_pipeline.learn_one(x, y)  # make the model learn
+...     metric.update(y, y_pred)  # update the metric
+...     model_pipeline.learn_one(x, y)  # make the model learn
 >>> print(f"Accuracy: {metric.get():.4f}")
-Accuracy: 0.6728
+Accuracy: 0.6736
 
 ```
 ### Multi Target Regression 
@@ -115,7 +115,7 @@ Accuracy: 0.6728
 >>> metric = metrics.multioutput.MicroAverage(metrics.MAE())
 >>> ev = evaluate.progressive_val_score(dataset, model, metric)
 >>> print(f"MicroAverage(MAE): {metric.get():.2f}")
-MicroAverage(MAE): 28.36
+MicroAverage(MAE): 34.31
 
 ```
 
@@ -153,11 +153,11 @@ MicroAverage(MAE): 28.36
 
 >>> for x, y in dataset:
 ...     score = model.score_one(x)
-...     model = model.learn_one(x=x)
-...     metric = metric.update(y, score)
+...     model.learn_one(x=x)
+...     metric.update(y, score)
 ...
 >>> print(f"ROCAUC: {metric.get():.4f}")
-ROCAUC: 0.7447
+ROCAUC: 0.9017
 
 ```
 

deep_river/anomaly/ae.py

@@ -89,11 +89,11 @@ class Autoencoder(DeepEstimator, AnomalyDetector):
 
     >>> for x, y in dataset:
     ...    score = model.score_one(x)
-    ...    model = model.learn_one(x=x)
-    ...    metric = metric.update(y, score)
+    ...    model.learn_one(x=x)
+    ...    metric.update(y, score)
     ...
     >>> print(f"ROCAUC: {metric.get():.4f}")
-    ROCAUC: 0.7447
+    ROCAUC: 0.9017
     """
 
     def __init__(

deep_river/anomaly/probability_weighted_ae.py

@@ -83,8 +83,8 @@ class ProbabilityWeightedAutoencoder(ae.Autoencoder):
 
     >>> for x, y in dataset:
     ...    score = model.score_one(x)
-    ...    model = model.learn_one(x=x)
-    ...    metric = metric.update(y, score)
+    ...    model.learn_one(x=x)
+    ...    metric.update(y, score)
     ...
     >>> print(f"ROCAUC: {metric.get():.4f}")
     ROCAUC: 0.8599

deep_river/classification/classifier.py

@@ -116,11 +116,11 @@ class Classifier(DeepEstimator, base.MiniBatchClassifier):
 
     >>> for x, y in dataset:
     ...     y_pred = model_pipeline.predict_one(x)  # make a prediction
-    ...     metric = metric.update(y, y_pred)  # update the metric
-    ...     model_pipeline = model_pipeline.learn_one(x,y)
+    ...     metric.update(y, y_pred)  # update the metric
+    ...     model_pipeline.learn_one(x,y)
 
     >>> print(f'Accuracy: {metric.get()}')
-    Accuracy: 0.6728
+    Accuracy: 0.6736
     """
 
     def __init__(

deep_river/classification/rolling_classifier.py

@@ -119,10 +119,10 @@ class RollingClassifier(Classifier, RollingDeepEstimator):
 
     >>> for x, y in dataset.take(5000):
     ...     y_pred = model_pipeline.predict_one(x)  # make a prediction
-    ...     metric = metric.update(y, y_pred)  # update the metric
-    ...     model = model_pipeline.learn_one(x, y)  # make the model learn
+    ...     metric.update(y, y_pred)  # update the metric
+    ...     model_pipeline.learn_one(x, y)  # make the model learn
     >>> print(f'Accuracy: {metric.get()}')
-    Accuracy: 0.4552
+    Accuracy: 0.4548
     """
 
     def __init__(

deep_river/classification/zoo.py

@@ -60,8 +60,8 @@ class LogisticRegression(Classifier):
 
     >>> for x, y in dataset:
     ...     y_pred = model_pipeline.predict_one(x) # make a prediction
-    ...     metric = metric.update(y, y_pred) # update the metric
-    ...     model_pipeline = model_pipeline.learn_one(x, y) # update the model
+    ...     metric.update(y, y_pred) # update the metric
+    ...     model_pipeline.learn_one(x, y) # update the model
 
     >>> print(f"Accuracy: {metric.get():.2f}")
     Accuracy: 0.44
@@ -181,8 +181,8 @@ class MultiLayerPerceptron(Classifier):
 
     >>> for x, y in dataset:
     ...     y_pred = model_pipeline.predict_one(x) # make a prediction
-    ...     metric = metric.update(y, y_pred) # update the metric
-    ...     model_pipeline = model_pipeline.learn_one(x, y) # update the model
+    ...     metric.update(y, y_pred) # update the metric
+    ...     model_pipeline.learn_one(x, y) # update the model
 
     >>> print(f"Accuracy: {metric.get():.2f}")
     Accuracy: 0.44

deep_river/regression/multioutput.py

@@ -82,7 +82,7 @@ class MultiTargetRegressor(RiverMultiTargetRegressor, DeepEstimator):
     >>> metric = metrics.multioutput.MicroAverage(metrics.MAE())
     >>> ev = evaluate.progressive_val_score(dataset, model, metric)
     >>> print(f"MicroAverage(MAE): {metric.get():.2f}")
-    MicroAverage(MAE): 28.36
+    MicroAverage(MAE): 34.31
 
     """