eli5.formatters¶
This module holds functions that convert Explanation
objects
(returned by eli5.explain_weights()
and eli5.explain_prediction()
)
into HTML, text, dict/JSON or pandas DataFrames. The following functions are
also available in eli5
namespace (e.g. eli5.format_as_html
):
eli5.formatters.html.format_as_html()
eli5.formatters.html.format_html_styles()
eli5.formatters.text.format_as_text()
eli5.formatters.as_dict.format_as_dict()
eli5.formatters.as_dataframe.explain_weights_df()
eli5.formatters.as_dataframe.explain_weights_dfs()
eli5.formatters.as_dataframe.explain_prediction_df()
eli5.formatters.as_dataframe.explain_prediction_dfs()
eli5.formatters.as_dataframe.format_as_dataframe()
eli5.formatters.as_dataframe.format_as_dataframes()
eli5.formatters.image.format_as_image()
eli5.formatters.html¶
-
format_as_html
(explanation, include_styles=True, force_weights=True, show=('method', 'description', 'transition_features', 'targets', 'feature_importances', 'decision_tree'), preserve_density=None, highlight_spaces=None, horizontal_layout=True, show_feature_values=False)[source]¶ Format explanation as html. Most styles are inline, but some are included separately in <style> tag, you can omit them by passing
include_styles=False
and callformat_html_styles
to render them separately (or just omit them). Withforce_weights=False
, weights will not be displayed in a table for predictions where it is possible to show feature weights highlighted in the document. Ifhighlight_spaces
is None (default), spaces will be highlighted in feature names only if there are any spaces at the start or at the end of the feature. Setting it to True forces space highlighting, and setting it to False turns it off. Ifhorizontal_layout
is True (default), multiclass classifier weights are laid out horizontally. Ifshow_feature_values
is True, feature values are shown if present. Default is False.
-
format_html_styles
()[source]¶ Format just the styles, use with
format_as_html(explanation, include_styles=False)
.
-
remaining_weight_color_hsl
(ws, weight_range, pos_neg)[source]¶ Color for “remaining” row. Handles a number of edge cases: if there are no weights in ws or weight_range is zero, assume the worst (most intensive positive or negative color).
eli5.formatters.text¶
-
format_as_text
(expl, show=('method', 'description', 'transition_features', 'targets', 'feature_importances', 'decision_tree'), highlight_spaces=None, show_feature_values=False)[source]¶ Format explanation as text.
Parameters: expl (eli5.base.Explanation) – Explanation returned by
eli5.explain_weights
oreli5.explain_prediction
functions.highlight_spaces (bool or None, optional) – Whether to highlight spaces in feature names. This is useful if you work with text and have ngram features which may include spaces at left or right. Default is None, meaning that the value used is set automatically based on vectorizer and feature values.
show_feature_values (bool) – When True, feature values are shown along with feature contributions. Default is False.
show (List[str], optional) – List of sections to show. Allowed values:
- ‘targets’ - per-target feature weights;
- ‘transition_features’ - transition features of a CRF model;
- ‘feature_importances’ - feature importances of a decision tree or an ensemble-based estimator;
- ‘decision_tree’ - decision tree in a graphical form;
- ‘method’ - a string with explanation method;
- ‘description’ - description of explanation method and its caveats.
eli5.formatters.fields
provides constants that cover common cases:INFO
(method and description),WEIGHTS
(all the rest), andALL
(all).
eli5.formatters.as_dict¶
eli5.formatters.as_dataframe¶
-
explain_prediction_df
(estimator, doc, **kwargs)[source]¶ Explain prediction and export explanation to
pandas.DataFrame
All keyword arguments are passed toeli5.explain_prediction()
. Weights of all features are exported by default.
-
explain_prediction_dfs
(estimator, doc, **kwargs)[source]¶ Explain prediction and export explanation to a dict with
pandas.DataFrame
values (aseli5.formatters.as_dataframe.format_as_dataframes()
does). All keyword arguments are passed toeli5.explain_prediction()
. Weights of all features are exported by default.
-
explain_weights_df
(estimator, **kwargs)[source]¶ Explain weights and export them to
pandas.DataFrame
. All keyword arguments are passed toeli5.explain_weights()
. Weights of all features are exported by default.
-
explain_weights_dfs
(estimator, **kwargs)[source]¶ Explain weights and export them to a dict with
pandas.DataFrame
values (aseli5.formatters.as_dataframe.format_as_dataframes()
does). All keyword arguments are passed toeli5.explain_weights()
. Weights of all features are exported by default.
-
format_as_dataframe
(explanation)[source]¶ Export an explanation to a single
pandas.DataFrame
. In case several dataframes could be exported byeli5.formatters.as_dataframe.format_as_dataframes()
, a warning is raised. If no dataframe can be exported,None
is returned. This function also accepts some components of the explanation as arguments: feature importances, targets, transition features. Note thateli5.explain_weights()
limits number of features by default. If you need all features, passtop=None
toeli5.explain_weights()
, or useexplain_weights_df()
.
-
format_as_dataframes
(explanation)[source]¶ Export an explanation to a dictionary with
pandas.DataFrame
values and string keys that correspond to explanation attributes. Use this method if several dataframes can be exported from a single explanation (e.g. for CRF explanation with has both feature weights and transition matrix). Note thateli5.explain_weights()
limits number of features by default. If you need all features, passtop=None
toeli5.explain_weights()
, or useexplain_weights_dfs()
.
eli5.formatters.image¶
-
expand_heatmap
(heatmap, image, resampling_filter=<Mock spec='type' id='140653902993552'>)[source]¶ Resize the
heatmap
image array to fit over the originalimage
, using the specifiedresampling_filter
method. The heatmap is converted to an image in the process.Parameters: heatmap (numpy.ndarray) – Heatmap that is to be resized, as an array.
image (PIL.Image.Image) – The image whose dimensions will be resized to.
resampling_filter (int or None) – Interpolation to use when resizing.
See
eli5.format_as_image()
for more details on the resampling_filter parameter.
Raises: TypeError – if
image
is not a Pillow image instance.Returns: resized_heatmap (PIL.Image.Image) – The heatmap, resized, as a PIL image.
-
format_as_image
(expl, resampling_filter=Image.LANCZOS, colormap=matplotlib.cm.viridis, alpha_limit=0.65)[source]¶ Format a
eli5.base.Explanation
object as an image.Note that this formatter requires
matplotlib
andPillow
optional dependencies.Parameters: - expl (Explanation) –
eli5.base.Explanation
object to be formatted. It must have animage
attribute with a Pillow image that will be overlaid. It must have atargets
attribute, a list ofeli5.base.TargetExplanation
instances that contain the attributeheatmap
, a rank 2 numpy array with float values in the interval [0, 1]. Currentlytargets
must be length 1 (only one target is supported).raises TypeError: if heatmap
is not a numpy array.raises ValueError: if heatmap
does not contain values as floats in the interval [0, 1].raises TypeError: if image
is not a Pillow image. - resampling_filter (int, optional) –
Interpolation ID or Pillow filter to use when resizing the image.
- Example filters from PIL.Image
NEAREST
BOX
BILINEAR
HAMMING
BICUBIC
LANCZOS
See also https://pillow.readthedocs.io/en/stable/handbook/concepts.html#filters.
Note that these attributes are integer values.
Default is
PIL.Image.LANCZOS
. - colormap (callable, optional) –
Colormap scheme to be applied when converting the heatmap from grayscale to RGB. Either a colormap from matplotlib.cm, or a callable that takes a rank 2 array and returns the colored heatmap as a [0, 1] RGBA numpy array.
- Example colormaps from matplotlib.cm
viridis
jet
binary
See also https://matplotlib.org/gallery/color/colormap_reference.html.
Default is
matplotlib.cm.viridis
(green/blue to yellow). - alpha_limit (float or int, optional) –
Maximum alpha (transparency / opacity) value allowed for the alpha channel pixels in the RGBA heatmap image.
Between 0.0 and 1.0.
Useful when laying the heatmap over the original image, so that the image can be seen over the heatmap.
Default is 0.65.
raises ValueError: if alpha_limit
is outside the [0, 1] interval.raises TypeError: if alpha_limit
is not float, int, or None.
Returns: overlay (PIL.Image.Image) – PIL image instance of the heatmap blended over the image.
- expl (Explanation) –
-
heatmap_to_image
(heatmap)[source]¶ Convert the numpy array
heatmap
to a Pillow image.Parameters: heatmap (numpy.ndarray) – Rank 2 grayscale (‘L’) array or rank 3 coloured (‘RGB’ or RGBA’) array, with values in interval [0, 1] as floats.
Raises: - TypeError – if
heatmap
is not a numpy array. - ValueError – if
heatmap
does not contain values as floats in the interval [0, 1]. - ValueError – if
heatmap
rank is neither 2 nor 3. - ValueError – if rank 3
heatmap
does not have 4 (RGBA) or 3 (RGB) channels.
Returns: heatmap_image (PIL.Image.Image) – Heatmap as an image with a suitable mode.
- TypeError – if