DOC: revert v1-related README changes, to hide them before actual v1 release (#690)

Author: Valentin-Laurent

README.rst

@@ -58,14 +58,14 @@ Here's a quick instantiation of MAPIE models for regression and classification p
 .. code:: python
 
     # Uncertainty quantification for regression problem
-    from mapie.regression import SplitConformalRegressor
-    mapie_regressor = SplitConformalRegressor(estimator=regressor)
+    from mapie.regression import MapieRegressor
+    mapie_regressor = MapieRegressor(estimator=regressor, method='plus', cv=5)
 
 .. code:: python
 
     # Uncertainty quantification for classification problem
-    from mapie.classification import _MapieClassifier
-    mapie_classifier = _MapieClassifier(estimator=classifier, method='score', cv=5)
+    from mapie.classification import MapieClassifier
+    mapie_classifier = MapieClassifier(estimator=classifier, method='score', cv=5)
 
 Implemented methods in **MAPIE** respect three fundamental pillars:
 
@@ -79,17 +79,8 @@ Implemented methods in **MAPIE** respect three fundamental pillars:
 🔗 Requirements
 ===============
 
-**MAPIE** runs on:
-
-- Python >=3.9, <3.12
-- NumPy >=1.23
-- scikit-learn >=1.4
-
-Note that even though we're not officially supporting and testing it, **MAPIE** may run using either:
-
-- Python >=3.12, without using multi-processing (ie, ``n_jobs=-1``)
-- Python <3.9
-- scikit-learn <1.4, provided SciPy <=1.10
+- **MAPIE** runs on Python 3.7+.
+- **MAPIE** stands on the shoulders of giants. Its only internal dependencies are `scikit-learn <https://scikit-learn.org/stable/>`_ and `numpy=>1.21 <https://numpy.org/>`_.
 
 
 🛠 Installation
@@ -121,21 +112,19 @@ As **MAPIE** is compatible with the standard scikit-learn API, you can see that
     from sklearn.linear_model import LinearRegression
     from sklearn.datasets import make_regression
     from sklearn.model_selection import train_test_split
-    from mapie.regression import SplitConformalRegressor
 
-    X, y = make_regression(n_samples=500, n_features=1, noise=20, random_state=59)
-    X_train_conformalize, X_test, y_train_conformalize, y_test = train_test_split(X, y, test_size=0.5)
-    X_train, X_conformalize, y_train, y_conformalize = train_test_split(X_train_conformalize, y_train_conformalize, test_size=0.5)
+    from mapie.regression import MapieRegressor
+
+
+    X, y = make_regression(n_samples=500, n_features=1)
+    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
 
     regressor = LinearRegression()
-    regressor.fit(X_train, y_train)
-    mapie_regressor = SplitConformalRegressor(
-        regressor,
-        confidence_level=[0.95, 0.68],
-    )
-    mapie_regressor.conformalize(X_conformalize, y_conformalize)
 
-    y_pred, y_pred_intervals = mapie_regressor.predict_interval(X_test)
+    mapie_regressor = MapieRegressor(estimator=regressor, method='plus', cv=5)
+
+    mapie_regressor = mapie_regressor.fit(X_train, y_train)
+    y_pred, y_pis = mapie_regressor.predict(X_test, alpha=[0.05, 0.32])
 
 .. code:: python
 
@@ -145,15 +134,15 @@ As **MAPIE** is compatible with the standard scikit-learn API, you can see that
     from sklearn.datasets import make_blobs
     from sklearn.model_selection import train_test_split
 
-    from mapie.classification import _MapieClassifier
+    from mapie.classification import MapieClassifier
 
 
     X, y = make_blobs(n_samples=500, n_features=2, centers=3)
     X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5)
 
     classifier = LogisticRegression()
 
-    mapie_classifier = _MapieClassifier(estimator=classifier, method='score', cv=5)
+    mapie_classifier = MapieClassifier(estimator=classifier, method='score', cv=5)
 
     mapie_classifier = mapie_classifier.fit(X_train, y_train)
     y_pred, y_pis = mapie_classifier.predict(X_test, alpha=[0.05, 0.32])