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- import itertools
- from warnings import simplefilter
- import numpy as np
- from sklearn import metrics
- from sklearn.utils.multiclass import unique_labels
- import wandb
- from .. import utils
- # ignore all future warnings
- simplefilter(action="ignore", category=FutureWarning)
- def validate_labels(*args, **kwargs): # FIXME
- raise AssertionError()
- def confusion_matrix(
- y_true=None,
- y_pred=None,
- labels=None,
- true_labels=None,
- pred_labels=None,
- normalize=False,
- ):
- """Compute the confusion matrix to evaluate the performance of a classification.
- Called by plot_confusion_matrix to visualize roc curves. Please use the function
- plot_confusion_matrix() if you wish to visualize your confusion matrix.
- """
- cm = metrics.confusion_matrix(y_true, y_pred)
- if labels is None:
- classes = unique_labels(y_true, y_pred)
- else:
- classes = np.asarray(labels)
- if normalize:
- cm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis]
- cm = np.around(cm, decimals=2)
- cm[np.isnan(cm)] = 0.0
- if true_labels is None:
- true_classes = classes
- else:
- validate_labels(classes, true_labels, "true_labels")
- true_label_indexes = np.in1d(classes, true_labels)
- true_classes = classes[true_label_indexes]
- cm = cm[true_label_indexes]
- if pred_labels is None:
- pred_classes = classes
- else:
- validate_labels(classes, pred_labels, "pred_labels")
- pred_label_indexes = np.in1d(classes, pred_labels)
- pred_classes = classes[pred_label_indexes]
- cm = cm[:, pred_label_indexes]
- table = make_table(cm, pred_classes, true_classes, labels)
- chart = wandb.visualize("wandb/confusion_matrix/v1", table)
- return chart
- def make_table(cm, pred_classes, true_classes, labels):
- data, count = [], 0
- for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
- if labels is not None and (
- isinstance(pred_classes[i], int) or isinstance(pred_classes[0], np.integer)
- ):
- pred = labels[pred_classes[i]]
- true = labels[true_classes[j]]
- else:
- pred = pred_classes[i]
- true = true_classes[j]
- data.append([pred, true, cm[i, j]])
- count += 1
- if utils.check_against_limit(
- count,
- "confusion_matrix",
- utils.chart_limit,
- ):
- break
- table = wandb.Table(columns=["Predicted", "Actual", "Count"], data=data)
- return table
|
