Merge pull request #532 from Valentin-Laurent/WIP-public-api-v1

Reorganized files, cleaned stuff, added 1 integration test

.gitignore

@@ -49,7 +49,7 @@ var/
 pip-log.txt
 pip-delete-this-directory.txt
 
-# Unit test / coverage reports
+# Tests / coverage reports
 htmlcov/
 .tox/
 .coverage
@@ -59,6 +59,7 @@ nosetests.xml
 coverage.xml
 *,cover
 .hypothesis/
+mapie_v1/integration_tests/mapie_v0_package
 
 # Translations
 *.mo

Makefile

@@ -9,6 +9,12 @@ type-check:
 tests:
 	pytest -vs --doctest-modules mapie
 
+integration-tests-v1:
+	@pip install mapie --no-dependencies --target=./mapie_v1/integration_tests/mapie_v0_package >/dev/null 2>&1
+	@mv ./mapie_v1/integration_tests/mapie_v0_package/mapie ./mapie_v1/integration_tests/mapie_v0_package/mapiev0
+	@- export PYTHONPATH="${PYTHONPATH}:./mapie_v1/integration_tests/mapie_v0_package"; pytest -vs mapie_v1/integration_tests/tests
+	@mv ./mapie_v1/integration_tests/mapie_v0_package/mapiev0 ./mapie_v1/integration_tests/mapie_v0_package/mapie
+
 coverage:
 	pytest -vs \
 		--doctest-modules \

public_api_v1_classifier.py → mapie_v1/classification.py

@@ -1,102 +1,85 @@
 from __future__ import annotations
 
-import warnings
-from typing import Any, Iterable, Optional, Tuple, Union, cast, List
+from typing import Optional, Union, List
 
 import numpy as np
-from sklearn.base import BaseEstimator, ClassifierMixin
-from sklearn.model_selection import BaseCrossValidator, BaseShuffleSplit
-from sklearn.preprocessing import LabelEncoder
-from sklearn.utils import check_random_state
-from sklearn.utils.validation import (_check_y, check_is_fitted, indexable)
+from sklearn.base import ClassifierMixin
+from sklearn.model_selection import BaseCrossValidator
 from sklearn.linear_model import LogisticRegression
 
 from mapie._typing import ArrayLike, NDArray
 from mapie.conformity_scores import BaseClassificationScore
-from mapie.conformity_scores.sets.raps import RAPSConformityScore
-from mapie.conformity_scores.sets.lac import LACConformityScore
-
-from mapie.conformity_scores.utils import (
-    check_depreciated_size_raps, check_classification_conformity_score,
-    check_target
-)
-from mapie.estimator.classifier import EnsembleClassifier
-from mapie.utils import (check_alpha, check_alpha_and_n_samples, check_cv,
-                         check_estimator_classification, check_n_features_in,
-                         check_n_jobs, check_null_weight, check_predict_params,
-                         check_verbose)
 
 
 class SplitConformalClassifier:
-    
-    def __init__(   
+
+    def __init__(
         self,
         estimator: ClassifierMixin = LogisticRegression(),
-        conformity_score: Union[str, BaseClassificationScore] = "lac", # Can be a string or a BaseClassificationScore object
-        alpha: Union[float, List[float]] = 0.1,
-        split_method: str = "simple",  # 'simple' (provide test_size in .fit) or 'prefit'. Future API: 'manual' (provide X_calib, Y_calib in .fit) and BaseCrossValidator (restricted to splitters only)
+        conformity_score: Union[str, BaseClassificationScore] = "lac",
+        # Can be a string or a BaseClassificationScore object
+        confidence_level: Union[float, List[float]] = 0.9,
+        split_method: str = "simple",
+        # 'simple' (provide test_size in .fit) or 'prefit'. Future API: 'manual' (provide X_calib, Y_calib in .fit) and BaseCrossValidator (restricted to splitters only)
         n_jobs: Optional[int] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         verbose: int = 0,
     ) -> None:
-
         pass
-    
+
     def fit(
         self,
         X: ArrayLike,
         y: ArrayLike,
         # sample_weight: Optional[ArrayLike] = None, -> in fit_params
         # groups: Optional[ArrayLike] = None,  # Removed, because it is not used in split conformal classifier
-        test_size: Union[int, float] = 0.1,  #  -> In __init__ ?
+        test_size: Union[int, float] = 0.1,  # -> In __init__ ?
         # Future API: X_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
         # Future API:   y_calib: Optional[ArrayLike] = None,  # Must be None if split_method != 'manual'
         fit_params: Optional[dict] = None,  # For example, LBGMClassifier :  {'categorical_feature': 'auto'}
-        predict_params: Optional[dict] = None, # For example, LBGMClassifier :  {'pred_leaf': False}
+        predict_params: Optional[dict] = None,  # For example, LBGMClassifier :  {'pred_leaf': False}
     ) -> SplitConformalClassifier:
-
         return self
 
-    def predict(self, 
+    def predict(self,
                 X: ArrayLike) -> NDArray:
-
         """
         Return
         ----- 
         Return ponctual prediction similar to predict method of scikit-learn classifiers
         Shape (n_samples,)
         """
 
-    def predict_sets(self, 
+    def predict_sets(self,
                      X: ArrayLike,
-                     conformoty_score_params: Optional[dict] = None, # Parameters specific to conformal method, For example: include_last_label
+                     conformity_score_params: Optional[dict] = None,
+                     # Parameters specific to conformal method, For example: include_last_label
                      ) -> NDArray:
-
         """
         Return
         -----
         An array containing the prediction sets 
-        Shape (n_samples, n_classes) if alpha is float,
-        Shape (n_samples, n_classes, alpha) if alpha is a list of floats
+        Shape (n_samples, n_classes) if confidence_level is float,
+        Shape (n_samples, n_classes, confidence_level) if confidence_level is a list of floats
         """
 
         pass
 
+
 class CrossConformalClassifier:
-    
+
     def __init__(
         self,
         estimator: ClassifierMixin = LogisticRegression(),
         conformity_score: Union[str, BaseClassificationScore] = 'lac',
-        cross_val : Union[BaseCrossValidator, str] = 5,
-        alpha: Union[float, List[float]] = 0.1,
+        cross_val: Union[BaseCrossValidator, str] = 5,
+        confidence_level: Union[float, List[float]] = 0.9,
         n_jobs: Optional[int] = None,
         random_state: Optional[Union[int, np.random.RandomState]] = None,
         verbose: int = 0,
 
     ) -> None:
-
-        pass 
+        pass
 
     def fit(
         self,
@@ -105,13 +88,12 @@ def fit(
         # sample_weight: Optional[ArrayLike] = None, -> in fit_params
         # groups: Optional[ArrayLike] = None,  
         fit_params: Optional[dict] = None,  # For example, LBGMClassifier :  {'categorical_feature': 'auto'}
-        predict_params: Optional[dict] = None,  
+        predict_params: Optional[dict] = None,
     ) -> CrossConformalClassifier:
-
         pass
 
-    def predict(self, 
-                X: ArrayLike): # Parameters specific to conformal method, For example: include_last_label) -> ArrayLike:
+    def predict(self,
+                X: ArrayLike):  # Parameters specific to conformal method, For example: include_last_label) -> ArrayLike:
 
         """
         Return 
@@ -123,14 +105,14 @@ def predict(self,
 
     def predict_sets(self,
                      X: ArrayLike,
-                     agg_scores: Optional[str] = "mean", # how to aggregate the scores by the estimators on test data
-                     conformoty_score_params: Optional[dict] = None,): # Parameters specific to conformal method, For example: include_last_label) -> NDArray
-
+                     agg_scores: Optional[str] = "mean",  # how to aggregate the scores by the estimators on test data
+                     conformity_score_params: Optional[
+                         dict] = None, ):  # Parameters specific to conformal method, For example: include_last_label) -> NDArray
+
         """
         Return
         -----
         An array containing the prediction sets 
-        Shape (n_samples, n_classes) if alpha is float,
-        Shape (n_samples, n_classes, alpha) if alpha is a list of floats
+        Shape (n_samples, n_classes) if confidence_level is float,
+        Shape (n_samples, n_classes, confidence_level) if confidence_level is a list of floats
         """
-

mapie_v1/integration_tests/tests/test_regression.py

@@ -0,0 +1,28 @@
+import numpy as np
+from sklearn.model_selection import train_test_split
+
+from mapie_v1.regression import SplitConformalRegressor
+from mapie.tests.test_regression import X_toy, y_toy
+from mapiev0.regression import MapieRegressor as MapieRegressorV0  # noqa
+
+
+def test_dummy():
+    test_size = 0.5
+    alpha = 0.5
+    confidence_level = 1 - alpha
+    random_state = 42
+
+    v0 = MapieRegressorV0(cv="split", test_size=test_size, random_state=random_state)
+    v0.fit(X_toy, y_toy)
+    v0_preds = v0.predict(X_toy)
+    v0_pred_intervals = v0.predict(X_toy, alpha=alpha)
+
+    X_train, y_train, X_conf, y_conf = train_test_split(
+        X_toy, y_toy, test_size=test_size, random_state=random_state
+    )
+    v1 = SplitConformalRegressor(confidence_level=confidence_level, random_state=random_state)
+    v1.fit_conformalize(X_train, y_train, X_conf, y_conf)
+    v1_preds = v1.predict(X_toy)
+    v1_pred_intervals = v1.predict_set(X_toy)
+    np.testing.assert_array_equal(v1_preds, v0_preds)
+    np.testing.assert_array_equal(v1_pred_intervals, v0_pred_intervals)

public_api_v1_regression.py → mapie_v1/regression.py

@@ -1,4 +1,5 @@
-from typing import Optional, Union, Self, List
+from typing import Optional, Union, List
+from typing_extensions import Self
 
 import numpy as np
 from sklearn.linear_model import LinearRegression, QuantileRegressor
@@ -206,6 +207,19 @@ def predict(
         """
         pass
 
+    def fit_conformalize(
+        self,
+        X_train: ArrayLike,
+        y_train: ArrayLike,
+        X_conf: ArrayLike,
+        y_conf: ArrayLike,
+        fit_params: Optional[dict] = None,
+        predict_params: Optional[dict] = None,
+    ) -> Self:
+        """
+        Dummy method to fit and conformalize in one step for testing purposes.
+        """
+        pass
 
 class CrossConformalRegressor:
     """