diff --git a/python/ppc_dev/result/model_result.py b/python/ppc_dev/result/model_result.py index f1cc4e07..5930e45c 100644 --- a/python/ppc_dev/result/model_result.py +++ b/python/ppc_dev/result/model_result.py @@ -12,14 +12,14 @@ class ModelResult(BaseResult): FEATURE_BIN_FILE = "feature_bin.json" MODEL_DATA_FILE = utils.XGB_TREE_PERFIX + '.json' - TEST_MODEL_OUTPUT_FILE = "xgb_output.csv" - TRAIN_MODEL_OUTPUT_FILE = "xgb_train_output.csv" + TEST_MODEL_OUTPUT_FILE = "test_output.csv" + TRAIN_MODEL_OUTPUT_FILE = "train_output.csv" def __init__(self, dataset: DataContext, job_id: str, job_type: str): super().__init__(dataset.ctx) self.job_id = job_id - + participant_id_list = [] for dataset in self.dataset.datasets: participant_id_list.append(dataset.agency.agency_id) @@ -32,8 +32,10 @@ def _xgb_train_result(self): # train_praba, test_praba, train_y, test_y, feature_importance, split_xbin, trees, params # 从hdfs读取结果文件信息，构造为属性 - train_praba_path = os.path.join(self.job_id, self.TRAIN_MODEL_OUTPUT_FILE) - test_praba_path = os.path.join(self.job_id, self.TEST_MODEL_OUTPUT_FILE) + train_praba_path = os.path.join( + self.job_id, self.TRAIN_MODEL_OUTPUT_FILE) + test_praba_path = os.path.join( + self.job_id, self.TEST_MODEL_OUTPUT_FILE) train_output = HDFSApi.download(train_praba_path) test_output = HDFSApi.download(test_praba_path) self.train_praba = train_output['class_pred'].values diff --git a/python/ppc_model/common/base_context.py b/python/ppc_model/common/base_context.py index 9b16680b..bd19d2c7 100644 --- a/python/ppc_model/common/base_context.py +++ b/python/ppc_model/common/base_context.py @@ -17,10 +17,10 @@ class BaseContext: MODEL_DATA_FILE = utils.XGB_TREE_PERFIX + '.json' TEST_MODEL_RESULT_FILE = "model_result.csv" # TEST_MODEL_OUTPUT_FILE = "model_output.csv" - TEST_MODEL_OUTPUT_FILE = "xgb_output.csv" + TEST_MODEL_OUTPUT_FILE = "test_model_output.csv" TRAIN_MODEL_RESULT_FILE = "train_model_result.csv" # TRAIN_MODEL_OUTPUT_FILE = "train_model_output.csv" - TRAIN_MODEL_OUTPUT_FILE = "xgb_train_output.csv" + TRAIN_MODEL_OUTPUT_FILE = "train_model_output.csv" MODEL_FILE = "model.kpl" MODEL_ENC_FILE = "model_enc.kpl" diff --git a/python/ppc_model/model_result/task_result_handler.py b/python/ppc_model/model_result/task_result_handler.py index 935446d1..8c5d87de 100644 --- a/python/ppc_model/model_result/task_result_handler.py +++ b/python/ppc_model/model_result/task_result_handler.py @@ -2,6 +2,7 @@ from ppc_common.ppc_utils.utils import PpcException, PpcErrorCode from ppc_common.ppc_utils import utils from ppc_model.common.protocol import ModelTask +from ppc_model.common.base_context import BaseContext from ppc_common.ppc_ml.model.algorithm_info import ClassificationType from ppc_model.common.model_result import ResultFileHandling from ppc_common.ppc_ml.model.algorithm_info import EvaluationType @@ -194,7 +195,7 @@ def to_dict(self): return self.result -class XGBJobResult: +class ModelJobResult: DEFAULT_PROPERTY_NAME = "outputModelResult" MODEL_RESULT = "ModelResult" MODEL_RESULT_PATH = "modelResultPath" @@ -202,8 +203,9 @@ class XGBJobResult: TEST_RESULT_PATH = "testResultPath" WOE_RESULT_PATH = "woeIVResultPath" - def __init__(self, job_id, components, property_name=DEFAULT_PROPERTY_NAME): + def __init__(self, xgb_job, job_id, components, property_name=DEFAULT_PROPERTY_NAME): self.job_id = job_id + self.xgb_job = xgb_job self.components = components self.logger = components.logger() self.property_name = property_name @@ -212,12 +214,14 @@ def __init__(self, job_id, components, property_name=DEFAULT_PROPERTY_NAME): self.model_result_path = None self.train_result_path = None self.woe_iv_result_path = None - self.xgb_result_path = None + self.model_result_path_dict = None self.evaluation_table = None self.feature_importance_table = None self.iteration_metrics = None - def fetch_model_result(self): + def fetch_xgb_model_result(self): + if not self.xgb_job: + return self.model_result_list = [] i = 0 # while True: @@ -240,25 +244,25 @@ def load_result(self, result_path, result_property): self.job_result = job_result_object.to_dict() def load_model_result_path(self, predict: bool): - self.xgb_result_path = dict() + self.model_result_path_dict = dict() self.model_result_path = ResultFileHandling.get_remote_path( self.components, self.job_id, BaseContext.MODEL_DATA_FILE) - self.xgb_result_path.update( - {XGBJobResult.MODEL_RESULT_PATH: self.model_result_path}) + self.model_result_path_dict.update( + {ModelJobResult.MODEL_RESULT_PATH: self.model_result_path}) self.train_result_path = ResultFileHandling.get_remote_path( self.components, self.job_id, BaseContext.TRAIN_MODEL_OUTPUT_FILE) - self.xgb_result_path.update( - {XGBJobResult.TRAIN_RESULT_PATH: self.train_result_path}) + self.model_result_path_dict.update( + {ModelJobResult.TRAIN_RESULT_PATH: self.train_result_path}) - self.xgb_result_path.update( - {XGBJobResult.TEST_RESULT_PATH: ResultFileHandling.get_remote_path( + self.model_result_path_dict.update( + {ModelJobResult.TEST_RESULT_PATH: ResultFileHandling.get_remote_path( self.components, self.job_id, BaseContext.TEST_MODEL_OUTPUT_FILE)}) self.woe_iv_result_path = ResultFileHandling.get_remote_path( self.components, self.job_id, BaseContext.WOE_IV_FILE) - self.xgb_result_path.update( - {XGBJobResult.WOE_RESULT_PATH: self.woe_iv_result_path}) + self.model_result_path_dict.update( + {ModelJobResult.WOE_RESULT_PATH: self.woe_iv_result_path}) def load_evaluation_table(self, evaluation_path, property): evaluation_table_object = TableResult(self.components, @@ -267,12 +271,16 @@ def load_evaluation_table(self, evaluation_path, property): type=DataType.TABLE).to_dict()} def load_feature_importance_table(self, feature_importance_path, property): + if not self.xgb_job: + return feature_importance_table = TableResult(self.components, self.job_id, ResultFileMeta(feature_importance_path)) self.feature_importance_table = {property: DataItem(name=property, data=feature_importance_table.to_dict(), type=DataType.TABLE).to_dict()} def load_iteration_metrics(self, iteration_path, property): + if not self.xgb_job: + return try: iteration_metrics_data = DataItem(data=ResultFileHandling.make_graph_data(self.components, self.job_id, utils.METRICS_OVER_ITERATION_FILE), name='iteration_metrics', name_property="ModelPlotName", data_property="ModelPlotData", @@ -295,9 +303,9 @@ def to_dict(self): result.update(self.feature_importance_table) if self.iteration_metrics is not None: result.update({self.iteration_property: self.iteration_metrics}) - if self.xgb_result_path is not None: + if self.model_result_path_dict is not None: result.update( - {XGBJobResult.MODEL_RESULT: self.xgb_result_path}) + {ModelJobResult.MODEL_RESULT: self.model_result_path_dict}) return result @@ -308,8 +316,11 @@ def __init__(self, task_result_request: TaskResultRequest, components): self.logger = components.logger() self.result_list = [] self.predict = False - if self.task_result_request.task_type == ModelTask.XGB_PREDICTING.name: + self.xgb_job = False + if self.task_result_request.task_type == ModelTask.XGB_PREDICTING.name or self.task_result_request.task_type == ModelTask.LR_PREDICTING.name: self.predict = True + if self.task_result_request.task_type == ModelTask.XGB_PREDICTING.name or self.task_result_request.task_type == ModelTask.XGB_TRAINING.name: + self.xgb_job = True self.logger.info( f"Init jobResultHandler for: {self.task_result_request.job_id}") self._get_evaluation_result() @@ -323,7 +334,7 @@ def get_response(self): return utils.make_response(PpcErrorCode.SUCCESS.get_code(), PpcErrorCode.SUCCESS.get_msg(), response) def _get_evaluation_result(self): - if self.task_result_request.task_type == ModelTask.XGB_TRAINING.name: + if not self.predict: # the train evaluation result self.train_evaluation_result = JobEvaluationResult( property_name="outputMetricsGraphs", @@ -347,18 +358,18 @@ def _get_evaluation_result(self): "mpc_metric_ks.csv", "KSTable") self.result_list.append(self.validation_evaluation_result) - self.xgb_model = XGBJobResult( - self.task_result_request.job_id, self.components, XGBJobResult.DEFAULT_PROPERTY_NAME) - self.xgb_model.fetch_model_result() + self.model = ModelJobResult(self.xgb_job, + self.task_result_request.job_id, self.components, ModelJobResult.DEFAULT_PROPERTY_NAME) + self.model.fetch_xgb_model_result() # the ks-auc table - self.xgb_model.load_evaluation_table( + self.model.load_evaluation_table( utils.MPC_XGB_EVALUATION_TABLE, "EvaluationTable") # the feature-importance table - self.xgb_model.load_feature_importance_table( + self.model.load_feature_importance_table( utils.XGB_FEATURE_IMPORTANCE_TABLE, "FeatureImportance") - self.result_list.append(self.xgb_model) + self.result_list.append(self.model) # the metrics iteration graph - self.xgb_model.load_iteration_metrics( + self.model.load_iteration_metrics( utils.METRICS_OVER_ITERATION_FILE, "IterationGraph") if self.predict: @@ -374,13 +385,13 @@ def _get_evaluation_result(self): "mpc_eval_metric_ks.csv", "KSTable") self.result_list.append(self.predict_evaluation_result) - # load xgb_result - self.xgb_result = XGBJobResult( - self.task_result_request.job_id, self.components, XGBJobResult.DEFAULT_PROPERTY_NAME) - self.xgb_result.load_result( - "xgb_train_output.csv", "outputTrainPreview") - self.xgb_result.load_model_result_path(self.predict) - self.result_list.append(self.xgb_result) + # load model_result + self.model_result = ModelJobResult(self.xgb_job, + self.task_result_request.job_id, self.components, ModelJobResult.DEFAULT_PROPERTY_NAME) + self.model_result.load_result( + BaseContext.TRAIN_MODEL_OUTPUT_FILE, "outputTrainPreview") + self.model_result.load_model_result_path(self.predict) + self.result_list.append(self.model_result) def _get_feature_processing_result(self): self.feature_processing_result = FeatureProcessingResult(