ENH: Add contribution/ random state

AUTHORS.rst

@@ -30,5 +30,6 @@ Contributors
 * Arnaud Capitaine <[email protected]>
 * Tarik Tazi <[email protected]>
 * Daniel Herbst <[email protected]>
+* Sofiane Ziane <[email protected]>
 
 To be continued ...

HISTORY.rst

@@ -10,6 +10,12 @@ History
 * Add split conformal option for regression and classification
 * Update check method for calibration
 
+
+0.6.4(2023-07-11)
+------------------
+
+* Add LTT for multilabel-classification
+
 0.6.4 (2023-04-05)
 ------------------
 
@@ -34,7 +40,7 @@ History
 0.6.0 (2023-01-19)
 ------------------
 
-* Add RCPS and CRC for multilabel-classifcation
+* Add RCPS and CRC for multilabel-classification
 * Add Top-Label calibration
 * Fix bug for classification with very low scores
 

doc/theoretical_description_multilabel_classification.rst

@@ -32,7 +32,7 @@ on the recall. RCPS, LTT and CRC give three slightly different guarantees:
 .. math::
  \mathbb{P}(R(\mathcal{T}_{\lambda_{\lambda\in\hat{\Lambda}}) \leq \alpha ) \geq 1 - \delta
 
-Notice that at the opposite of the other two methods, LTT allows to control any non-monotone loss. In Mapie for multilabel classification,
+Notice that at the opposite of the other two methods, LTT allows to control any non-monotone loss. In MAPIE for multilabel classification,
 we use CRC and RCPS for recall control and LTT for precision control.
 
 1. Risk-Controlling Prediction Sets

mapie/control_risk/risks.py

@@ -12,7 +12,7 @@ def _compute_risk_recall(
  y: NDArray
 ) -> NDArray:
  """
- In `MapieMultiLabelClassifier` when`metric_control=recall`,
+ In `MapieMultiLabelClassifier` when `metric_control=recall`,
  compute the recall per observation for each different
  thresholds lambdas.
 

mapie/tests/test_multi_label_classification.py

@@ -29,6 +29,7 @@
 METHODS = ["crc", "rcps", "ltt"]
 METRICS = ['recall', 'precision']
 BOUNDS = ["wsr", "hoeffding", "bernstein"]
+random_state = 42
 
 WRONG_METHODS = ["rpcs", "rcr", "test", "llt"]
 WRONG_BOUNDS = ["wrs", "hoeff", "test", "", 1, 2.5, (1, 2)]
@@ -40,39 +41,39 @@
  Params(
  method="crc",
  bound=None,
- random_state=42,
+ random_state=random_state,
  metric_control="recall"
  ),
  ),
  "rcps_wsr": (
  Params(
  method="rcps",
  bound="wsr",
- random_state=42,
+ random_state=random_state,
  metric_control='recall'
  ),
  ),
  "rcps_hoeffding": (
  Params(
  method="rcps",
  bound="hoeffding",
- random_state=42,
+ random_state=random_state,
  metric_control='recall'
  ),
  ),
  "rcps_bernstein": (
  Params(
  method="rcps",
  bound="bernstein",
- random_state=42,
+ random_state=random_state,
  metric_control='recall'
  ),
  ),
  "ltt": (
  Params(
  method="ltt",
  bound=None,
- random_state=42,
+ random_state=random_state,
  metric_control='precision'
  ),
  ),
@@ -218,17 +219,20 @@ def test_valid_method() -> None:
 def test_valid_metric_method(strategy: str) -> None:
  """Test that valid metric raise no errors"""
  args = STRATEGIES[strategy][0]
- mapie_clf = MapieMultiLabelClassifier(random_state=42,
- metric_control=args["metric_control"]
- )
+ mapie_clf = MapieMultiLabelClassifier(
+ random_state=42,
+ metric_control=args["metric_control"]
+ )
  mapie_clf.fit(X_toy, y_toy)
  check_is_fitted(mapie_clf, mapie_clf.fit_attributes)
 
 
 @pytest.mark.parametrize("bound", BOUNDS)
 def test_valid_bound(bound: str) -> None:
  """Test that valid methods raise no errors."""
- mapie_clf = MapieMultiLabelClassifier(random_state=42, method="rcps")
+ mapie_clf = MapieMultiLabelClassifier(
+ random_state=random_state, method="rcps"
+ )
  mapie_clf.fit(X_toy, y_toy)
  mapie_clf.predict(X_toy, bound=bound, delta=.1)
  check_is_fitted(mapie_clf, mapie_clf.fit_attributes)