more bandit tests

river/bandit/exp3.py

@@ -98,6 +98,8 @@ def update(self, arm_id, *reward_args, **reward_kwargs):
 
  @classmethod
  def _unit_test_params(cls):
- yield {"gamma": 0.0}
+ yield {"gamma": 0}
+ yield {"gamma": 0.1}
  yield {"gamma": 0.5}
- yield {"gamma": 1.0}
+ yield {"gamma": 0.9}
+ yield {"gamma": 1}

river/model_selection/bandit.py

@@ -131,7 +131,7 @@ def learn_one(self, x, y):
  for arm_id in self._pick_arms():
  model = self[arm_id]
  y_pred = model.predict_one(x)
- self.policy.update(arm_id, y_true=y, y_pred=y_pred)
+ self.policy.update(arm_id, y, y_pred)
  model.learn_one(x, y)
 
  return self

river/model_selection/test_bandit.py

@@ -0,0 +1,118 @@
+from __future__ import annotations
+
+import importlib
+import inspect
+
+import pytest
+
+from river import (
+ bandit,
+ datasets,
+ evaluate,
+ linear_model,
+ metrics,
+ model_selection,
+ optim,
+ preprocessing,
+)
+
+
+def test_1259():
+ """
+
+ https://github.com/online-ml/river/issues/1259
+
+ >>> from river import bandit
+ >>> from river import datasets
+ >>> from river import evaluate
+ >>> from river import linear_model
+ >>> from river import metrics
+ >>> from river import model_selection
+ >>> from river import optim
+ >>> from river import preprocessing
+
+ >>> models = [
+ ... linear_model.LogisticRegression(optimizer=optim.SGD(lr=lr))
+ ... for lr in [0.0001, 0.001, 1e-05, 0.01]
+ ... ]
+
+ >>> dataset = datasets.Phishing()
+ >>> model = (
+ ... preprocessing.StandardScaler() |
+ ... model_selection.BanditClassifier(
+ ... models,
+ ... metric=metrics.Accuracy(),
+ ... policy=bandit.Exp3(
+ ... gamma=0.5,
+ ... seed=42
+ ... )
+ ... )
+ ... )
+ >>> metric = metrics.Accuracy()
+
+ >>> evaluate.progressive_val_score(dataset, model, metric)
+ Accuracy: 87.20%
+
+ """
+
+
+@pytest.mark.parametrize(
+ "policy",
+ [
+ pytest.param(
+ policy(**params),
+ id=f"{policy.__name__}",
+ )
+ for _, policy in inspect.getmembers(
+ importlib.import_module("river.bandit"),
+ lambda obj: inspect.isclass(obj) and issubclass(obj, bandit.base.Policy),
+ )
+ for params in policy._unit_test_params()
+ if policy.__name__ != "ThompsonSampling"
+ ],
+)
+def test_bandit_classifier_with_each_policy(policy):
+ models = [
+ linear_model.LogisticRegression(optimizer=optim.SGD(lr=lr))
+ for lr in [0.0001, 0.001, 1e-05, 0.01]
+ ]
+
+ dataset = datasets.Phishing()
+ model = preprocessing.StandardScaler() | model_selection.BanditClassifier(
+ models, metric=metrics.Accuracy(), policy=policy
+ )
+ metric = metrics.Accuracy()
+
+ score = evaluate.progressive_val_score(dataset, model, metric)
+ assert score.get() > 0.5
+
+
+@pytest.mark.parametrize(
+ "policy",
+ [
+ pytest.param(
+ policy(**params),
+ id=f"{policy.__name__}",
+ )
+ for _, policy in inspect.getmembers(
+ importlib.import_module("river.bandit"),
+ lambda obj: inspect.isclass(obj) and issubclass(obj, bandit.base.Policy),
+ )
+ for params in policy._unit_test_params()
+ if policy.__name__ not in {"ThompsonSampling", "Exp3"}
+ ],
+)
+def test_bandit_regressor_with_each_policy(policy):
+ models = [
+ linear_model.LinearRegression(optimizer=optim.SGD(lr=lr))
+ for lr in [0.0001, 0.001, 1e-05, 0.01]
+ ]
+
+ dataset = datasets.TrumpApproval()
+ model = preprocessing.StandardScaler() | model_selection.BanditRegressor(
+ models, metric=metrics.MSE(), policy=policy
+ )
+ metric = metrics.MSE()
+
+ score = evaluate.progressive_val_score(dataset, model, metric)
+ assert score.get() < 300