AI开发平台ModelArts-线性回归:样例

时间：2023-11-01 16:20:21

AI开发平台ModelArts

样例

inputs = {    "dataframe": None  # @input {"label":"dataframe","type":"DataFrame"}}params = {    "inputs": inputs,    "b_output_action": True,    "b_use_default_encoder": True,  # @param {"label": "b_use_default_encoder", "type": "boolean", "required": "true", "helpTip": ""}    "input_features_str": "",  # @param {"label": "input_features_str", "type": "string", "required": "false", "helpTip": ""}    "outer_pipeline_stages": None,    "label_col": "",  # @param {"label": "label_col", "type": "string", "required": "true", "helpTip": "target label column"}    "regressor_feature_vector_col": "model_features",  # @param {"label": "regressor_feature_vector_col", "type": "string", "required": "true", "helpTip": ""}    "max_iter": 100,  # @param {"label": "max_iter", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""}    "reg_param": 0.0,  # @param {"label": "reg_param", "type": "number", "required": "true", "range": "[0.0,none)", "helpTip": ""}    "elastic_net_param": 0.0,  # @param {"label": "elastic_net_param", "type": "number", "required": "true", "range": "[0.0,none)", "helpTip": ""}    "tol": 1e-6,  # @param {"label": "tol"", "type": "number", "required": "true", "range": "[0.0,none)", "helpTip": ""}    "fit_intercept": True,  # @param {"label": "fit_intercept"", "type": "boolean", "required": "true", "helpTip": ""}    "standardization": True,  # @param {"label": "standardization"", "type": "boolean", "required": "true", "helpTip": ""}    "solver": "auto",  # @param {"label": "solver", "type": "enum", "required": "true", "options": "l-bfgs,normal,auto", "helpTip": ""}    "aggregation_depth": 2,  # @param {"label": "aggregation_depth", "type": "integer", "required": "true", "range": "(0,2147483647]", "helpTip": ""}    "loss": "squaredError",  # @param {"label": "loss", "type": "enum", "required": "true", "options": "squaredError,huber", "helpTip": ""}    "epsilon": 1.35  # @param {"label": "epsilon", "type": "number", "required": "true", "range": "(1.0,none)", "helpTip": ""}}linear_regression____id___ = MLSLinearRegression(**params)linear_regression____id___.run()# @output {"label":"pipeline_model","name":"linear_regression____id___.get_outputs()['output_port_1']","type":"PipelineModel"}

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