成功解决raise TypeError('Unexpected feature_names type')TypeError: Unexpected feature_names type

解决问题

解决思路

解决方法

源代码解析

解决问题

raise TypeError('Unexpected feature_names type')TypeError: Unexpected feature_names type

解决思路

类型错误：意外的 feature_names 类型

解决方法

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。

源代码解析

def show_weights(estimator, **kwargs):""" Return an explanation of estimator parameters (weights)as an IPython.display.HTML object. Use this functionto 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 andcustomize formatting in a single call.Parameters----------estimator : objectEstimator instance. This argument must be positional.top : int or (int, int) tuple, optionalNumber of features to show. When ``top`` is int, ``top`` features witha 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, optionalNames of targets or classes. This argument can be used to providehuman-readable class/target names for estimators which don't exposeclss names themselves. It can be also used to rename estimator-providedclasses before displaying them.This argument may be supported or not, depending on estimator type.targets : list, optionalOrder of class/target names to show. This argument can be also usedto show information only for a subset of classes. It should be a listof class / target names which match either names provided byan estimator or names defined in ``target_names`` parameter.This argument may be supported or not, depending on estimator type.feature_names : list, optionalA list of feature names. It allows to specify featurenames when they are not provided by an estimator object.This argument may be supported or not, depending on estimator type.feature_re : str, optionalOnly feature names which match ``feature_re`` regex are shown(more precisely, ``re.search(feature_re, x)`` is checked).feature_filter : Callable[[str], bool], optionalOnly feature names for which ``feature_filter`` function returns Trueare shown.show : List[str], optionalList 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 oran 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 : boolWhen True, feature weight tables are printed horizontally(left to right); when False, feature weight tables are printedvertically (top to down). Default is True.highlight_spaces : bool or None, optionalWhether to highlight spaces in feature names. This is useful ifyou work with text and have ngram features which may include spacesat left or right. Default is None, meaning that the value usedis set automatically based on vectorizer and feature values.include_styles : boolMost styles are inline, but some are included separately in <style> tag;you can omit them by passing ``include_styles=False``. Default is True.**kwargs: dictKeyword arguments. All keyword arguments are passed toconcrete explain_weights... implementations.Returns-------IPython.display.HTMLThe result is printed in IPython notebook as an HTML widget.If you need to display several explanations as an output of a singlecell, or if you want to display it from a function then useIPython.display.display::from IPython.display import displaydisplay(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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成功解决raise TypeError(‘Unexpected feature_names type‘)TypeError: Unexpected feature_names type

解决问题

解决思路

解决方法

源代码解析

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