name: Xgboost train regression and calculate metrics on csv
inputs:
- {name: training_data, type: CSV}
- {name: testing_data, type: CSV}
- {name: label_column, type: Integer, default: '0', optional: true}
- {name: objective, type: String, default: 'reg:squarederror', optional: true}
- {name: num_iterations, type: Integer, default: '200', optional: true}
outputs:
- {name: model, type: XGBoostModel}
- {name: mean_absolute_error, type: Float}
- {name: mean_squared_error, type: Float}
- {name: root_mean_squared_error, type: Float}
- {name: metrics, type: JsonObject}
implementation:
  graph:
    tasks:
      Xgboost train:
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          url: 'https://raw.githubusercontent.com/kubeflow/pipelines/567c04c51ff00a1ee525b3458425b17adbe3df61/components/XGBoost/Train/component.yaml'}
        arguments:
          training_data:
            graphInput: {inputName: training_data}
          label_column:
            graphInput: {inputName: label_column}
          num_iterations:
            graphInput: {inputName: num_iterations}
          objective:
            graphInput: {inputName: objective}
      Xgboost predict:
        componentRef: {digest: ecdfaf32cff15b6abc3d0dd80365ce00577f1a19a058fbe201f515431cea1357,
          url: 'https://raw.githubusercontent.com/kubeflow/pipelines/567c04c51ff00a1ee525b3458425b17adbe3df61/components/XGBoost/Predict/component.yaml'}
        arguments:
          data:
            graphInput: {inputName: testing_data}
          model:
            taskOutput: {outputName: model, taskId: Xgboost train, type: XGBoostModel}
          label_column:
            graphInput: {inputName: label_column}
      Pandas Transform DataFrame in CSV format:
        componentRef: {digest: 58dc88349157bf128021708c316ce4eb60bc1de0a5a7dd3af45fabac3276d510,
          url: 'https://raw.githubusercontent.com/kubeflow/pipelines/6162d55998b176b50267d351241100bb0ee715bc/components/pandas/Transform_DataFrame/in_CSV_format/component.yaml'}
        arguments:
          table:
            graphInput: {inputName: testing_data}
          transform_code: df = df[["tips"]]
      Remove header:
        componentRef: {digest: ba35ffea863855b956c3c50aefa0420ba3823949a6c059e6e3971cde960dc5a3,
          url: 'https://raw.githubusercontent.com/kubeflow/pipelines/02c9638287468c849632cf9f7885b51de4c66f86/components/tables/Remove_header/component.yaml'}
        arguments:
          table:
            taskOutput: {outputName: transformed_table, taskId: Pandas Transform DataFrame
                in CSV format, type: CSV}
      Calculate regression metrics from csv:
        componentRef: {digest: e3ecbfeb18032820edfee4255e2fb6d15d15ed224e166519d5e528e12053a995,
          url: 'https://raw.githubusercontent.com/kubeflow/pipelines/7da1ac9464b4b3e7d95919faa2f1107a9635b7e4/components/ml_metrics/Calculate_regression_metrics/from_CSV/component.yaml'}
        arguments:
          true_values:
            taskOutput: {outputName: table, taskId: Remove header}
          predicted_values:
            taskOutput: {outputName: predictions, taskId: Xgboost predict, type: Text}
    outputValues:
      model:
        taskOutput: {outputName: model, taskId: Xgboost train, type: XGBoostModel}
      mean_absolute_error:
        taskOutput: {outputName: mean_absolute_error, taskId: Calculate regression
            metrics from csv, type: Float}
      mean_squared_error:
        taskOutput: {outputName: mean_squared_error, taskId: Calculate regression
            metrics from csv, type: Float}
      root_mean_squared_error:
        taskOutput: {outputName: root_mean_squared_error, taskId: Calculate regression
            metrics from csv, type: Float}
      metrics:
        taskOutput: {outputName: metrics, taskId: Calculate regression metrics from
            csv, type: JsonObject}