成功解决raise TypeError('Unexpected feature_names type')TypeError: Unexpected feature_names type
目录
raise TypeError('Unexpected feature_names type')TypeError: Unexpected feature_names type
类型错误:意外的 feature_names 类型
经过分析发现,
原函数参数要求类型是list类型,
而当前出错的代码却提供了pandas.core.indexes.base.Index类型
feature_names : list, optional. A list of feature names. It allows to specify feature names when they are not provided by an estimator object. This argument may be supported or not, depending on estimator type.
feature_names :列表,可选。 feature 名称列表。 它允许在估算器对象未提供特征名称时指定它们。 根据估算器类型,可能支持或不支持此参数。
- def show_weights(estimator, **kwargs):
- """ Return an explanation of estimator parameters (weights)
- as an IPython.display.HTML object. Use this function
- to show classifier weights in IPython.
-
- :func:`show_weights` accepts all
- :func:`eli5.explain_weights` arguments and all
- :func:`eli5.formatters.html.format_as_html`
- keyword arguments, so it is possible to get explanation and
- customize formatting in a single call.
-
- Parameters
- ----------
- estimator : object
- Estimator instance. This argument must be positional.
-
- top : int or (int, int) tuple, optional
- Number of features to show. When ``top`` is int, ``top``
- features with
- a highest absolute values are shown. When it is (pos, neg)
- tuple,
- no more than ``pos`` positive features and no more than
- ``neg``
- negative features is shown. ``None`` value means no limit.
-
- This argument may be supported or not, depending on
- estimator type.
-
- target_names : list[str] or {'old_name': 'new_name'} dict,
- optional
- Names of targets or classes. This argument can be used to
- provide
- human-readable class/target names for estimators which
- don't expose
- clss names themselves. It can be also used to rename
- estimator-provided
- classes before displaying them.
-
- This argument may be supported or not, depending on
- estimator type.
-
- targets : list, optional
- Order of class/target names to show. This argument can be
- also used
- to show information only for a subset of classes. It should
- be a list
- of class / target names which match either names provided
- by
- an estimator or names defined in ``target_names``
- parameter.
-
- This argument may be supported or not, depending on
- estimator type.
-
- feature_names : list, optional
- A list of feature names. It allows to specify feature
- names when they are not provided by an estimator object.
-
- This argument may be supported or not, depending on
- estimator type.
-
- feature_re : str, optional
- Only feature names which match ``feature_re`` regex are
- shown
- (more precisely, ``re.search(feature_re, x)`` is checked).
-
- feature_filter : Callable[[str], bool], optional
- Only feature names for which ``feature_filter`` function
- returns True
- are shown.
-
- 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),
- and ``ALL`` (all).
-
- horizontal_layout : bool
- When True, feature weight tables are printed horizontally
- (left to right); when False, feature weight tables are printed
- vertically (top to down). Default is True.
-
- 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.
-
- include_styles : bool
- Most styles are inline, but some are included separately in
- <style> tag;
- you can omit them by passing ``include_styles=False``.
- Default is True.
-
- **kwargs: dict
- Keyword arguments. All keyword arguments are passed to
- concrete explain_weights... implementations.
-
- Returns
- -------
- IPython.display.HTML
- The result is printed in IPython notebook as an HTML
- widget.
- If you need to display several explanations as an output of
- a single
- cell, or if you want to display it from a function then use
- IPython.display.display::
-
- from IPython.display import display
- display(eli5.show_weights(clf1))
- display(eli5.show_weights(clf2))
-
- """
- format_kwargs, explain_kwargs = _split_kwargs(kwargs)
- expl = explain_weights(estimator, **explain_kwargs)
- _set_html_kwargs_defaults(format_kwargs)
- html = format_as_html(expl, **format_kwargs)
- return HTML(html)
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