Uploaded Test files

venv/Lib/site-packages/sklearn/metrics/_plot/roc_curve.py

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+from .. import auc
+from .. import roc_curve
+
+from .base import _check_classifer_response_method
+from ...utils import check_matplotlib_support
+from ...base import is_classifier
+from ...utils.validation import _deprecate_positional_args
+
+
+class RocCurveDisplay:
+    """ROC Curve visualization.
+
+    It is recommend to use :func:`~sklearn.metrics.plot_roc_curve` to create a
+    visualizer. All parameters are stored as attributes.
+
+    Read more in the :ref:`User Guide <visualizations>`.
+
+    Parameters
+    ----------
+    fpr : ndarray
+        False positive rate.
+
+    tpr : ndarray
+        True positive rate.
+
+    roc_auc : float, default=None
+        Area under ROC curve. If None, the roc_auc score is not shown.
+
+    estimator_name : str, default=None
+        Name of estimator. If None, the estimator name is not shown.
+
+    Attributes
+    ----------
+    line_ : matplotlib Artist
+        ROC Curve.
+
+    ax_ : matplotlib Axes
+        Axes with ROC Curve.
+
+    figure_ : matplotlib Figure
+        Figure containing the curve.
+
+    Examples
+    --------
+    >>> import matplotlib.pyplot as plt  # doctest: +SKIP
+    >>> import numpy as np
+    >>> from sklearn import metrics
+    >>> y = np.array([0, 0, 1, 1])
+    >>> pred = np.array([0.1, 0.4, 0.35, 0.8])
+    >>> fpr, tpr, thresholds = metrics.roc_curve(y, pred)
+    >>> roc_auc = metrics.auc(fpr, tpr)
+    >>> display = metrics.RocCurveDisplay(fpr=fpr, tpr=tpr, roc_auc=roc_auc,\
+                                          estimator_name='example estimator')
+    >>> display.plot()  # doctest: +SKIP
+    >>> plt.show()      # doctest: +SKIP
+    """
+    def __init__(self, *, fpr, tpr, roc_auc=None, estimator_name=None):
+        self.fpr = fpr
+        self.tpr = tpr
+        self.roc_auc = roc_auc
+        self.estimator_name = estimator_name
+
+    @_deprecate_positional_args
+    def plot(self, ax=None, *, name=None, **kwargs):
+        """Plot visualization
+
+        Extra keyword arguments will be passed to matplotlib's ``plot``.
+
+        Parameters
+        ----------
+        ax : matplotlib axes, default=None
+            Axes object to plot on. If `None`, a new figure and axes is
+            created.
+
+        name : str, default=None
+            Name of ROC Curve for labeling. If `None`, use the name of the
+            estimator.
+
+        Returns
+        -------
+        display : :class:`~sklearn.metrics.plot.RocCurveDisplay`
+            Object that stores computed values.
+        """
+        check_matplotlib_support('RocCurveDisplay.plot')
+        import matplotlib.pyplot as plt
+
+        if ax is None:
+            fig, ax = plt.subplots()
+
+        name = self.estimator_name if name is None else name
+
+        line_kwargs = {}
+        if self.roc_auc is not None and name is not None:
+            line_kwargs["label"] = f"{name} (AUC = {self.roc_auc:0.2f})"
+        elif self.roc_auc is not None:
+            line_kwargs["label"] = f"AUC = {self.roc_auc:0.2f}"
+        elif name is not None:
+            line_kwargs["label"] = name
+
+        line_kwargs.update(**kwargs)
+
+        self.line_ = ax.plot(self.fpr, self.tpr, **line_kwargs)[0]
+        ax.set_xlabel("False Positive Rate")
+        ax.set_ylabel("True Positive Rate")
+
+        if "label" in line_kwargs:
+            ax.legend(loc='lower right')
+
+        self.ax_ = ax
+        self.figure_ = ax.figure
+        return self
+
+
+@_deprecate_positional_args
+def plot_roc_curve(estimator, X, y, *, sample_weight=None,
+                   drop_intermediate=True, response_method="auto",
+                   name=None, ax=None, **kwargs):
+    """Plot Receiver operating characteristic (ROC) curve.
+
+    Extra keyword arguments will be passed to matplotlib's `plot`.
+
+    Read more in the :ref:`User Guide <visualizations>`.
+
+    Parameters
+    ----------
+    estimator : estimator instance
+        Fitted classifier or a fitted :class:`~sklearn.pipeline.Pipeline`
+        in which the last estimator is a classifier.
+
+    X : {array-like, sparse matrix} of shape (n_samples, n_features)
+        Input values.
+
+    y : array-like of shape (n_samples,)
+        Target values.
+
+    sample_weight : array-like of shape (n_samples,), default=None
+        Sample weights.
+
+    drop_intermediate : boolean, default=True
+        Whether to drop some suboptimal thresholds which would not appear
+        on a plotted ROC curve. This is useful in order to create lighter
+        ROC curves.
+
+    response_method : {'predict_proba', 'decision_function', 'auto'} \
+    default='auto'
+        Specifies whether to use :term:`predict_proba` or
+        :term:`decision_function` as the target response. If set to 'auto',
+        :term:`predict_proba` is tried first and if it does not exist
+        :term:`decision_function` is tried next.
+
+    name : str, default=None
+        Name of ROC Curve for labeling. If `None`, use the name of the
+        estimator.
+
+    ax : matplotlib axes, default=None
+        Axes object to plot on. If `None`, a new figure and axes is created.
+
+    Returns
+    -------
+    display : :class:`~sklearn.metrics.RocCurveDisplay`
+        Object that stores computed values.
+
+    Examples
+    --------
+    >>> import matplotlib.pyplot as plt  # doctest: +SKIP
+    >>> from sklearn import datasets, metrics, model_selection, svm
+    >>> X, y = datasets.make_classification(random_state=0)
+    >>> X_train, X_test, y_train, y_test = model_selection.train_test_split(\
+            X, y, random_state=0)
+    >>> clf = svm.SVC(random_state=0)
+    >>> clf.fit(X_train, y_train)
+    SVC(random_state=0)
+    >>> metrics.plot_roc_curve(clf, X_test, y_test)  # doctest: +SKIP
+    >>> plt.show()                                   # doctest: +SKIP
+    """
+    check_matplotlib_support('plot_roc_curve')
+
+    classification_error = (
+        "{} should be a binary classifier".format(estimator.__class__.__name__)
+    )
+    if not is_classifier(estimator):
+        raise ValueError(classification_error)
+
+    prediction_method = _check_classifer_response_method(estimator,
+                                                         response_method)
+    y_pred = prediction_method(X)
+
+    if y_pred.ndim != 1:
+        if y_pred.shape[1] != 2:
+            raise ValueError(classification_error)
+        else:
+            y_pred = y_pred[:, 1]
+
+    pos_label = estimator.classes_[1]
+    fpr, tpr, _ = roc_curve(y, y_pred, pos_label=pos_label,
+                            sample_weight=sample_weight,
+                            drop_intermediate=drop_intermediate)
+    roc_auc = auc(fpr, tpr)
+    name = estimator.__class__.__name__ if name is None else name
+    viz = RocCurveDisplay(
+        fpr=fpr, tpr=tpr, roc_auc=roc_auc, estimator_name=name
+    )
+    return viz.plot(ax=ax, name=name, **kwargs)