alibi_detect.utils.visualize module
- alibi_detect.utils.visualize.plot_feature_outlier_image(od_preds, X, X_recon=None, instance_ids=None, max_instances=5, outliers_only=False, n_channels=3, figsize=(20, 20))[source]
Plot feature (pixel) wise outlier scores for images.
- Parameters:
od_preds (
Dict) – Output of an outlier detector’s prediction.X (
ndarray) – Batch of instances to apply outlier detection to.X_recon (
Optional[ndarray]) – Reconstructed instances of X.instance_ids (
Optional[list]) – List with indices of instances to display.max_instances (
int) – Maximum number of instances to display.outliers_only (
bool) – Whether to only show outliers or not.n_channels (
int) – Number of channels of the images.figsize (
tuple) – Tuple for the figure size.
- Return type:
- alibi_detect.utils.visualize.plot_feature_outlier_tabular(od_preds, X, X_recon=None, threshold=None, instance_ids=None, max_instances=5, top_n=1000000000000, outliers_only=False, feature_names=None, width=0.2, figsize=(20, 10))[source]
Plot feature wise outlier scores for tabular data.
- Parameters:
od_preds (
Dict) – Output of an outlier detector’s prediction.X (
ndarray) – Batch of instances to apply outlier detection to.X_recon (
Optional[ndarray]) – Reconstructed instances of X.threshold (
Optional[float]) – Threshold used for outlier score to determine outliers.instance_ids (
Optional[list]) – List with indices of instances to display.max_instances (
int) – Maximum number of instances to display.top_n (
int) – Maixmum number of features to display, ordered by outlier score.outliers_only (
bool) – Whether to only show outliers or not.width (
float) – Column width for bar charts.figsize (
tuple) – Tuple for the figure size.
- Return type:
- alibi_detect.utils.visualize.plot_feature_outlier_ts(od_preds, X, threshold, window=None, t=None, X_orig=None, width=0.2, figsize=(20, 8), ylim=(None, None))[source]
Plot feature wise outlier scores for time series data.
- Parameters:
od_preds (
Dict) – Output of an outlier detector’s prediction.X (
ndarray) – Time series to apply outlier detection to.threshold (
Union[float,int,list,ndarray]) – Threshold used to classify outliers or adversarial instances.t (
Optional[ndarray]) – Timesteps.X_orig (
Optional[ndarray]) – Optional original time series without outliers.width (
float) – Column width for bar charts.figsize (
tuple) – Tuple for the figure size.ylim (
tuple) – Min and max y-axis values for the outlier scores.
- Return type:
- alibi_detect.utils.visualize.plot_instance_score(preds, target, labels, threshold, ylim=(None, None))[source]
Scatter plot of a batch of outlier or adversarial scores compared to the threshold.
- Parameters:
preds (
Dict) – Dictionary returned by predictions of an outlier or adversarial detector.target (
ndarray) – Ground truth.labels (
ndarray) – List with names of classification labels.threshold (
float) – Threshold used to classify outliers or adversarial instances.ylim (
tuple) – Min and max y-axis values.
- Return type: