def test_calibration_display_non_binary(pyplot, iris_data, constructor_name):
X, y = iris_data
clf = DecisionTreeClassifier()
clf.fit(X, y)
y_prob = clf.predict_proba(X)
if constructor_name == "from_estimator":
msg = "to be a binary classifier, but got"
with pytest.raises(ValueError, match=msg):
CalibrationDisplay.from_estimator(clf, X, y)
else:
msg = "y should be a 1d array, got an array of shape"
with pytest.raises(ValueError, match=msg):
CalibrationDisplay.from_predictions(y, y_prob)
def multi_class_calibration(y_true,
y_prob,
n_bins=5,
strategy='uniform',
names=None,
ref_line=True,
ax=None,
**kwargs):
"""
Displays a multi-class Calibration Curve (one line per class in one-v-rest setup)
:param y_true: True Labels: note that labels must be sequential starting from 0
:param y_prob: Predicted Probabilities
:param n_bins: Number of bins to use (see CalibrationDisplay.from_predictions)
:param strategy: Strategy for bins (see CalibrationDisplay.from_predictions)
:param names: Class names to use
:param ref_line: Whether to plot reference line (see CalibrationDisplay.from_predictions)
:param ax: Axes to draw on (see CalibrationDisplay.from_predictions)
:param kwargs: Keyword arguments passed on to plot
:return: Dict of Calibration Displays (by name)
"""
# Iterate over classes
names = utils.default(names, np.arange(y_prob.shape[1]))
displays = {}
for cls, name in zip(range(y_prob.shape[1]), names):
_y_true = (y_true == cls).astype(
int) # Get positive class for this label
_y_prob = y_prob[:, cls] # Get probability assigned to this class
displays[name] = CalibrationDisplay.from_predictions(_y_true,
_y_prob,
n_bins=n_bins,
strategy=strategy,
name=name,
ref_line=ref_line,
ax=ax,
**kwargs)
return displays