mlm_insights.core.sdcs.confusion_matrix package¶

Submodules¶

mlm_insights.core.sdcs.confusion_matrix.multi_class_confusion_matrix module¶

class mlm_insights.core.sdcs.confusion_matrix.multi_class_confusion_matrix.ConfusionMatrixCounts(true_positive_counts: numpy.ndarray[Any, numpy.dtype[numpy.int32]], false_positive_counts: numpy.ndarray[Any, numpy.dtype[numpy.int32]], true_negative_counts: numpy.ndarray[Any, numpy.dtype[numpy.int32]], false_negative_counts: numpy.ndarray[Any, numpy.dtype[numpy.int32]])¶

Bases: object

false_negative_counts: ndarray[Any, dtype[int32]]¶

false_positive_counts: ndarray[Any, dtype[int32]]¶

true_negative_counts: ndarray[Any, dtype[int32]]¶

true_positive_counts: ndarray[Any, dtype[int32]]¶

class mlm_insights.core.sdcs.confusion_matrix.multi_class_confusion_matrix.MultiClassConfusionMatrix(class_map: Dict[str, int], matrix: List[List[int]])¶

Bases: object

This class stores the confusion matrix and provides simple APIs.

class_map: Dict[str, int] = Field(name=None,type=None,default=<dataclasses._MISSING_TYPE object>,default_factory=<class 'dict'>,init=True,repr=True,hash=None,compare=True,metadata=mappingproxy({}),_field_type=None)¶

get_average_by_weights(numerator: Any, denominator: ndarray[Any, dtype[int32]]) → Any¶

Helper method to get the weighted average

Parameter¶

numerator: Any

Numerator array

denominator: npt.NDArray[np.int32]

Denominator array

Returns¶

Any: Weighted average

get_classes() → List[str]¶

Get the list of the classes in confusion matrix

Returns¶

List[str]:

A list of string representing the classes used in confusion matrix. The classes will be sorted in lexicographical order.

get_counts() → ConfusionMatrixCounts¶

Get True Positive, False Positive, True Negative, False Negative per class.

Returns¶

ConfusionMatrixCounts: An object that contain TP, FP, TN, and FN per class.

get_encoded_value(label: str | int) → int¶: Get the encoded value of a class label. The returned value represents the index of class label in confusion matrix.

Returns¶

int: Index of class label.

get_false_negative_counts() → ndarray[Any, dtype[int32]]¶

This method returns False Negative (FN) count per class.

Returns¶

npt.NDArray: One dimensional numpy array contains the FN per class

get_false_positive_counts() → ndarray[Any, dtype[int32]]¶

This method returns False Positive (FP) count per class.

Returns¶

npt.NDArray: One dimensional numpy array contains the FP per class

get_number_of_classes() → int¶

Get the total count of the classes in confusion matrix

Returns¶

int: The total count of the classes in confusion matrix

get_precision_scores_per_class() → Any¶: Get the Precision score per class.

Returns¶

Any: List of precision score for each class

get_recall_scores_per_class() → Any¶: Get the Recall score per class.

Returns¶

Any: List of recall score for each class

get_true_negative_counts() → ndarray[Any, dtype[int32]]¶

This method returns True Negative (TN) count per class.

Returns¶

npt.NDArray: One dimensional numpy array contains the TN per class

get_true_positive_counts() → ndarray[Any, dtype[int32]]¶

This method returns True Positive (TP) count per class.

Returns¶

npt.NDArray: One dimensional numpy array contains the TP per class

is_binary_classes() → bool¶

matrix: ndarray[Any, dtype[int32]]¶

mlm_insights.core.sdcs.confusion_matrix.multi_threshold_metric_summary module¶

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class mlm_insights.core.sdcs.confusion_matrix.multi_threshold_metric_summary.MultiThresholdMetricSummary(tp: ndarray[Any, dtype[Any]], fp: ndarray[Any, dtype[Any]], tn: ndarray[Any, dtype[Any]], fn: ndarray[Any, dtype[Any]], thresholds: ndarray[Any, dtype[Any]])¶

Bases: object

Store Required multi threshold metrics.

tp: True Positive for multiple thresholds. fp: False Positive for multiple thresholds. tn: True Negative for multiple thresholds. fn: False Negative for multiple thresholds. thresholds: Thresholds.

fn: ndarray[Any, dtype[Any]]¶

fp: ndarray[Any, dtype[Any]]¶

get_precision_recall_curve() → Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]]¶

Compute precision-recall pairs for different probability thresholds.

Part of the implementation is taken from sklearn: https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/metrics/_ranking.py Returns

predictions with score >= thresholds[i] and the last element is 1.

recallndarray of shape (n_thresholds + 1,): Decreasing recall values such that element i is the recall of predictions with score >= thresholds[i] and the last element is 0.
thresholdsndarray of shape (n_thresholds,): Increasing thresholds on the decision function used to compute precision and recall.

get_roc_curve() → Tuple[ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]], ndarray[Any, dtype[Any]]]¶

Compute Receiver operating characteristic (ROC).

Part of the implementation is taken from sklearn: https://github.com/scikit-learn/scikit-learn/blob/main/sklearn/metrics/_ranking.py

Returns¶

fprndarray of shape (>2,): Increasing false positive rates such that element i is the false positive rate of predictions with score >= thresholds[i].
tprndarray of shape (>2,): Increasing true positive rates such that element i is the true positive rate of predictions with score >= thresholds[i].
thresholdsndarray of shape = (n_thresholds,): Decreasing thresholds on the decision function used to compute fpr and tpr.

thresholds: ndarray[Any, dtype[Any]]¶

tn: ndarray[Any, dtype[Any]]¶

tp: ndarray[Any, dtype[Any]]¶