华为云AI开发平台ModelArts梯度提升树分类特征重要性_云淘科技
概述
采用梯度提升树分类算法计算数据集特征的特征重要性。
输入
|
参数 |
子参数 |
参数说明 |
|---|---|---|
|
inputs |
dataframe |
参数必选，表示输入的数据集；如果没有pipeline_model和gbt_classify_model参数，表示直接根据数据集训练gbdt分类模型得到特征重要性 |
|
pipeline_model |
参数可选，如果含有该参数，表示根据上游的pyspark pipeline模型对象pipeline_model来计算特征重要性 |
|
|
gbt_classify_model |
参数可选，如果含有该参数，表示根据上游的gbt_classify_model对象来计算特征重要性 |
输出
特征重要性结果数据集
参数说明
|
参数 |
子参数 |
参数说明 |
|---|---|---|
|
input_columns_str |
– |
数据集的特征列名组成的格式化字符串，例如: “column_a” “column_a,column_b” |
|
label_col |
– |
目标列名 |
|
model_input_features_col |
– |
特征向量的列名 |
|
classifier_label_index_col |
– |
将目标列按照标签编码后的列名，默认为”label_index” |
|
prediction_index_col |
– |
训练模型时，预测结果对应标签的列名，默认为”prediction_index” |
|
prediction_col |
– |
训练模型时，预测结果对应的列名，默认为”prediction” |
|
max_depth |
– |
树的最大深度 |
|
max_bins |
– |
特征分裂时的最大分箱个数 |
|
min_instances_per_node |
– |
树分裂时要求每个节点必须包含的实例数目，默认为1 |
|
min_info_gain |
– |
最小信息增益 |
|
max_iter |
– |
最大迭代次数 |
|
step_size |
– |
步长 |
|
subsampling_rate |
– |
训练每棵树时，对训练集的抽样率 |
样例
inputs = {
"dataframe": None, # @input {"label":"dataframe","type":"DataFrame"}
"pipeline_model": None, # @input {"label":"pipeline_model","type":"PipelineModel"}
"gbt_classify_model": None
}
params = {
"inputs": inputs,
"input_columns_str": "", # @param {"label": "input_columns_str", "type": "string", "required": "false", "helpTip": ""}
"label_col": "", # @param {"label": "label_col", "type": "string", "required": "true", "helpTip": ""}
"model_input_features_col": "model_features", # @param {"label": "model_input_features_col", "type": "string", "required": "false", "helpTip": ""}
"classifier_label_index_col": "label_index", # @param {"label": "classifier_label_index_col", "type": "string", "required": "false", "helpTip": ""}
"prediction_index_col": "prediction_index", # @param {"label": "prediction_index_col", "type": "string", "required": "false", "helpTip": ""}
"prediction_col": "prediction", # @param {"label": "prediction_col", "type": "string", "required": "false", "helpTip": ""}
"max_depth": 5, # @param {"label": "max_depth", "type": "integer", "required": "false","range":"(0,2147483647]", "helpTip": ""}
"max_bins": 32, # @param {"label": "max_bins", "type": "integer", "required": "false","range":"(0,2147483647]", "helpTip": ""}
"min_instances_per_node": 1, # @param {"label": "min_instances_per_node", "type": "integer", "required": "false","range":"(0,2147483647]", "helpTip": ""}
"min_info_gain": 0.0, # @param {"label": "min_info_gain", "type": "number", "required": "false", "helpTip": ""}
"loss_type": "logistic",
"max_iter": 20, # @param {"label": "max_iter", "type": "integer", "required": "false","range":"(0,2147483647]", "helpTip": ""}
"step_size": 0.1, # @param {"label": "step_size", "type": "number", "required": "false", "helpTip": ""}
"subsampling_rate": 1.0 # @param {"label": "subsampling_rate", "type": "number", "required": "false", "helpTip": ""}
}
gbt_classifier_feature_importance____id___ = MLSGBTClassifierFeatureImportance(**params)
gbt_classifier_feature_importance____id___.run()
# @output {"label":"dataframe","name":"gbt_classifier_feature_importance____id___.get_outputs()['output_port_1']","type":"DataFrame"}
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