# alibi\_detect.utils.visualize ## Functions ### `plot_feature_outlier_image` ```python plot_feature_outlier_image(od_preds: Dict, X: numpy.ndarray, X_recon: Optional[numpy.ndarray] = None, instance_ids: Optional[list] = None, max_instances: int = 5, outliers_only: bool = False, n_channels: int = 3, figsize: tuple = (20, 20)) -> None ``` Plot feature (pixel) wise outlier scores for images. | Name | Type | Default | Description | | --------------- | ------------------------- | ---------- | ------------------------------------------------- | | `od_preds` | `Dict` | | Output of an outlier detector's prediction. | | `X` | `numpy.ndarray` | | Batch of instances to apply outlier detection to. | | `X_recon` | `Optional[numpy.ndarray]` | `None` | Reconstructed instances of X. | | `instance_ids` | `Optional[list]` | `None` | List with indices of instances to display. | | `max_instances` | `int` | `5` | Maximum number of instances to display. | | `outliers_only` | `bool` | `False` | Whether to only show outliers or not. | | `n_channels` | `int` | `3` | Number of channels of the images. | | `figsize` | `tuple` | `(20, 20)` | Tuple for the figure size. | **Returns** * Type: `None` ### `plot_feature_outlier_tabular` ```python plot_feature_outlier_tabular(od_preds: Dict, X: numpy.ndarray, X_recon: Optional[numpy.ndarray] = None, threshold: Optional[float] = None, instance_ids: Optional[list] = None, max_instances: int = 5, top_n: int = 1000000000000, outliers_only: bool = False, feature_names: Optional[list] = None, width: float = 0.2, figsize: tuple = (20, 10)) -> None ``` Plot feature wise outlier scores for tabular data. | Name | Type | Default | Description | | --------------- | ------------------------- | --------------- | ---------------------------------------------------------------- | | `od_preds` | `Dict` | | Output of an outlier detector's prediction. | | `X` | `numpy.ndarray` | | Batch of instances to apply outlier detection to. | | `X_recon` | `Optional[numpy.ndarray]` | `None` | Reconstructed instances of X. | | `threshold` | `Optional[float]` | `None` | Threshold used for outlier score to determine outliers. | | `instance_ids` | `Optional[list]` | `None` | List with indices of instances to display. | | `max_instances` | `int` | `5` | Maximum number of instances to display. | | `top_n` | `int` | `1000000000000` | Maixmum number of features to display, ordered by outlier score. | | `outliers_only` | `bool` | `False` | Whether to only show outliers or not. | | `feature_names` | `Optional[list]` | `None` | List with feature names. | | `width` | `float` | `0.2` | Column width for bar charts. | | `figsize` | `tuple` | `(20, 10)` | Tuple for the figure size. | **Returns** * Type: `None` ### `plot_feature_outlier_ts` ```python plot_feature_outlier_ts(od_preds: Dict, X: numpy.ndarray, threshold: Union[float, int, list, numpy.ndarray], window: Optional[tuple] = None, t: Optional[numpy.ndarray] = None, X_orig: Optional[numpy.ndarray] = None, width: float = 0.2, figsize: tuple = (20, 8), ylim: tuple = (None, None)) -> None ``` Plot feature wise outlier scores for time series data. | Name | Type | Default | Description | | ----------- | ---------------------------------------- | -------------- | ------------------------------------------------------------- | | `od_preds` | `Dict` | | Output of an outlier detector's prediction. | | `X` | `numpy.ndarray` | | Time series to apply outlier detection to. | | `threshold` | `Union[float, int, list, numpy.ndarray]` | | Threshold used to classify outliers or adversarial instances. | | `window` | `Optional[tuple]` | `None` | Start and end timestep to plot. | | `t` | `Optional[numpy.ndarray]` | `None` | Timesteps. | | `X_orig` | `Optional[numpy.ndarray]` | `None` | Optional original time series without outliers. | | `width` | `float` | `0.2` | Column width for bar charts. | | `figsize` | `tuple` | `(20, 8)` | Tuple for the figure size. | | `ylim` | `tuple` | `(None, None)` | Min and max y-axis values for the outlier scores. | **Returns** * Type: `None` ### `plot_instance_score` ```python plot_instance_score(preds: Dict, target: numpy.ndarray, labels: numpy.ndarray, threshold: float, ylim: tuple = (None, None)) -> None ``` Scatter plot of a batch of outlier or adversarial scores compared to the threshold. | Name | Type | Default | Description | | ----------- | --------------- | -------------- | ------------------------------------------------------------------------- | | `preds` | `Dict` | | Dictionary returned by predictions of an outlier or adversarial detector. | | `target` | `numpy.ndarray` | | Ground truth. | | `labels` | `numpy.ndarray` | | List with names of classification labels. | | `threshold` | `float` | | Threshold used to classify outliers or adversarial instances. | | `ylim` | `tuple` | `(None, None)` | Min and max y-axis values. | **Returns** * Type: `None` ### `plot_roc` ```python plot_roc(roc_data: Dict[str, Dict[str, numpy.ndarray]], figsize: tuple = (10, 5)) -> None ``` Plot ROC curve. | Name | Type | Default | Description | | ---------- | ------------------------------------- | --------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `roc_data` | `Dict[str, Dict[str, numpy.ndarray]]` | | Dictionary with as key the label to show in the legend and as value another dictionary with as keys `scores` and `labels` with respectively the outlier scores and outlier labels. | | `figsize` | `tuple` | `(10, 5)` | Figure size. | **Returns** * Type: `None` --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. 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