From 2a39c42b5879c02f400850430f1355be57e9ffbb Mon Sep 17 00:00:00 2001
From: Saulo Martiello Mastelini <mastelini@usp.br>
Date: Thu, 26 Oct 2023 18:34:44 -0300
Subject: [PATCH] streamline test

---
 river/drift/no_drift.py | 34 ++++++++++++++++++----------------
 1 file changed, 18 insertions(+), 16 deletions(-)

diff --git a/river/drift/no_drift.py b/river/drift/no_drift.py
index 1762e665fc..c5173aca04 100644
--- a/river/drift/no_drift.py
+++ b/river/drift/no_drift.py
@@ -9,7 +9,7 @@ class NoDrift(base.DriftDetector):
     It always signals that no concept drift was detected.
     Examples
     --------
-    
+
     >>> from river import drift
     >>> from river import evaluate
     >>> from river import forest
@@ -20,27 +20,17 @@ class NoDrift(base.DriftDetector):
     ...     seed=8,
     ...     position=500,
     ...     width=40,
-    ... )
+    ... ).take(700)
 
     We can turn off the warning detection capabilities of Adaptive Random Forest (ARF) or
     other similar models. Thus, the base models will reset immediately after identifying a drift,
     bypassing the background model building phase:
 
-    >>> model = forest.ARFClassifier(
+    >>> adaptive_model = forest.ARFClassifier(
     ...     leaf_prediction="mc",
     ...     warning_detector=drift.NoDrift(),
     ...     seed=8
     ... )
-    >>> metric = metrics.Accuracy()
-
-    >>> evaluate.progressive_val_score(dataset.take(700), model, metric)
-    Accuracy: 76.25%
-
-    >>> model.n_drifts_detected()
-    2
-
-    >>> model.n_warnings_detected()
-    0
 
     We can also turn off the concept drift handling capabilities completely:
 
@@ -50,10 +40,22 @@ class NoDrift(base.DriftDetector):
     ...     drift_detector=drift.NoDrift(),
     ...     seed=8
     ... )
-    >>> metric = metrics.Accuracy()
 
-    >>> evaluate.progressive_val_score(dataset.take(700), stationary_model, metric)
-    Accuracy: 76.25%
+    Let's put that to test:
+
+    >>> for x, y in dataset:
+    ...     adaptive_model = adaptive_model.learn_one(x, y)
+    ...     stationary_model = stationary_model.learn_one(x, y)
+
+    The adaptive model:
+
+    >>> adaptive_model.n_drifts_detected()
+    2
+
+    >>> adaptive_model.n_warnings_detected()
+    0
+
+    The stationary one:
 
     >>> stationary_model.n_drifts_detected()
     0