ML之LightGBM：基于titanic数据集利用LightGBM和shap算法实现数据特征的可解释性(量化特征对模型贡献度得分)

港控/mmm° 2022-12-27 12:38 150阅读 0赞

**目录**

基于titanic数据集利用LightGBM和shap算法实现数据特征的可解释性(量化特征对模型贡献度得分)

设计思路

输出结果

核心代码

--------------------

**相关文章**  
[ML之LightGBM：基于titanic数据集利用LightGBM和shap算法实现数据特征的可解释性(量化特征对模型贡献度得分)][ML_LightGBM_titanic_LightGBM_shap]  
[ML之LightGBM：基于titanic数据集利用LightGBM和shap算法实现数据特征的可解释性(量化特征对模型贡献度得分)实现][ML_LightGBM_titanic_LightGBM_shap 1]

# 基于titanic数据集利用LightGBM和shap算法实现数据特征的可解释性(量化特征对模型贡献度得分) #

## 设计思路 ##

更新……

## 输出结果 ##

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 1][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 2][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 3][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 4][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 5][]

![20201211000917985.png][]![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 6][]

![20201211000954836.png][]

## 核心代码 ##

# flake8: noqa
    
    import warnings
    import sys
    
    __version__ = '0.37.0'
    
    # check python version
    if (sys.version_info < (3, 0)):
        warnings.warn("As of version 0.29.0 shap only supports Python 3 (not 2)!")
    
    from ._explanation import Explanation, Cohorts
    
    # explainers
    from .explainers._explainer import Explainer
    from .explainers._kernel import Kernel as KernelExplainer
    from .explainers._sampling import Sampling as SamplingExplainer
    from .explainers._tree import Tree as TreeExplainer
    from .explainers._deep import Deep as DeepExplainer
    from .explainers._gradient import Gradient as GradientExplainer
    from .explainers._linear import Linear as LinearExplainer
    from .explainers._partition import Partition as PartitionExplainer
    from .explainers._permutation import Permutation as PermutationExplainer
    from .explainers._additive import Additive as AdditiveExplainer
    from .explainers import other
    
    # plotting (only loaded if matplotlib is present)
    def unsupported(*args, **kwargs):
        warnings.warn("matplotlib is not installed so plotting is not available! Run `pip install matplotlib` to fix this.")
    
    try:
        import matplotlib
        have_matplotlib = True
    except ImportError:
        have_matplotlib = False
    if have_matplotlib:
        from .plots._beeswarm import summary_legacy as summary_plot
        from .plots._decision import decision as decision_plot, multioutput_decision as multioutput_decision_plot
        from .plots._scatter import dependence_legacy as dependence_plot
        from .plots._force import force as force_plot, initjs, save_html, getjs
        from .plots._image import image as image_plot
        from .plots._monitoring import monitoring as monitoring_plot
        from .plots._embedding import embedding as embedding_plot
        from .plots._partial_dependence import partial_dependence as partial_dependence_plot
        from .plots._bar import bar_legacy as bar_plot
        from .plots._waterfall import waterfall as waterfall_plot
        from .plots._group_difference import group_difference as group_difference_plot
        from .plots._text import text as text_plot
    else:
        summary_plot = unsupported
        decision_plot = unsupported
        multioutput_decision_plot = unsupported
        dependence_plot = unsupported
        force_plot = unsupported
        initjs = unsupported
        save_html = unsupported
        image_plot = unsupported
        monitoring_plot = unsupported
        embedding_plot = unsupported
        partial_dependence_plot = unsupported
        bar_plot = unsupported
        waterfall_plot = unsupported
        text_plot = unsupported
    
    
    # other stuff :)
    from . import datasets
    from . import utils
    from . import links
    
    #from . import benchmark
    
    from .utils._legacy import kmeans
    from .utils import sample, approximate_interactions
    
    
    
    
    
    
    
    # TODO: Add support for hclustering based explanations where we sort the leaf order by magnitude and then show the dendrogram to the left
    def summary_legacy(shap_values, features=None, feature_names=None, max_display=None, plot_type=None,
                     color=None, axis_color="#333333", title=None, alpha=1, show=True, sort=True,
                     color_bar=True, plot_size="auto", layered_violin_max_num_bins=20, class_names=None,
                     class_inds=None,
                     color_bar_label=labels["FEATURE_VALUE"],
                     cmap=colors.red_blue,
                     # depreciated
                     auto_size_plot=None,
                     use_log_scale=False):
        """Create a SHAP beeswarm plot, colored by feature values when they are provided.
    
        Parameters
        ----------
        shap_values : numpy.array
            For single output explanations this is a matrix of SHAP values (# samples x # features).
            For multi-output explanations this is a list of such matrices of SHAP values.
    
        features : numpy.array or pandas.DataFrame or list
            Matrix of feature values (# samples x # features) or a feature_names list as shorthand
    
        feature_names : list
            Names of the features (length # features)
    
        max_display : int
            How many top features to include in the plot (default is 20, or 7 for interaction plots)
    
        plot_type : "dot" (default for single output), "bar" (default for multi-output), "violin",
            or "compact_dot".
            What type of summary plot to produce. Note that "compact_dot" is only used for
            SHAP interaction values.
    
        plot_size : "auto" (default), float, (float, float), or None
            What size to make the plot. By default the size is auto-scaled based on the number of
            features that are being displayed. Passing a single float will cause each row to be that 
            many inches high. Passing a pair of floats will scale the plot by that
            number of inches. If None is passed then the size of the current figure will be left
            unchanged.
        """
    
        # support passing an explanation object
        if str(type(shap_values)).endswith("Explanation'>"):
            shap_exp = shap_values
            base_value = shap_exp.base_value
            shap_values = shap_exp.values
            if features is None:
                features = shap_exp.data
            if feature_names is None:
                feature_names = shap_exp.feature_names
            # if out_names is None: # TODO: waiting for slicer support of this
            #     out_names = shap_exp.output_names
    
        # deprecation warnings
        if auto_size_plot is not None:
            warnings.warn("auto_size_plot=False is deprecated and is now ignored! Use plot_size=None instead.")
    
        multi_class = False
        if isinstance(shap_values, list):
            multi_class = True
            if plot_type is None:
                plot_type = "bar" # default for multi-output explanations
            assert plot_type == "bar", "Only plot_type = 'bar' is supported for multi-output explanations!"
        else:
            if plot_type is None:
                plot_type = "dot" # default for single output explanations
            assert len(shap_values.shape) != 1, "Summary plots need a matrix of shap_values, not a vector."
    
        # default color:
        if color is None:
            if plot_type == 'layered_violin':
                color = "coolwarm"
            elif multi_class:
                color = lambda i: colors.red_blue_circle(i/len(shap_values))
            else:
                color = colors.blue_rgb

[ML_LightGBM_titanic_LightGBM_shap]: https://yunyaniu.blog.csdn.net/article/details/111026534
[ML_LightGBM_titanic_LightGBM_shap 1]: https://yunyaniu.blog.csdn.net/article/details/111346850
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70]: /images/20221120/207f039300ed4b768b221970da115005.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 1]: /images/20221120/492518d786234ec999b7532693e3b789.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 2]: /images/20221120/fde5da4970ae481082533b3f89902254.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 3]: /images/20221120/e3eb45c5eaa84dcb873d46a3cbaa6876.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 4]: /images/20221120/67ece84be95e4c1ba61149ee16c4cd4b.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 5]: /images/20221120/f9a40674a34245578a7d248e089c65f5.png
[20201211000917985.png]: https://img-blog.csdnimg.cn/20201211000917985.png
[watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxXzQxMTg1ODY4_size_16_color_FFFFFF_t_70 6]: /images/20221120/2e7065d2a1a1464381b071b2316d57e7.png
[20201211000954836.png]: /images/20221120/cefc3f918272425e924a0aa15e44c42a.png