def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
backend = config.backend
work_mode = config.work_mode
param = {
"name": "hetero_feature_binning_0",
"method": "quantile",
"compress_thres": 10000,
"head_size": 10000,
"error": 0.001,
"bin_num": 10,
"bin_indexes": -1,
"bin_names": None,
"category_indexes": None,
"category_names": None,
"adjustment_factor": 0.5,
"local_only": False,
"transform_param": {
"transform_cols": [
0,
1,
2
],
"transform_names": None,
"transform_type": "woe"
}
}
pipeline = common_tools.make_normal_dsl(config, namespace, param)
pipeline.fit(backend=backend, work_mode=work_mode)
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
param = {
"name": "hetero_feature_binning_0",
"method": "quantile",
"optimal_binning_param": {
"metric_method": "iv",
"min_bin_pct": 0.05,
"max_bin_pct": 0.8,
"init_bucket_method": "quantile",
"init_bin_nums": 100,
"mixture": True
},
"compress_thres": 10000,
"head_size": 10000,
"error": 0.001,
"bin_num": 10,
"bin_indexes": -1,
"bin_names": None,
"category_indexes": [
0
],
"category_names": None,
"adjustment_factor": 0.5,
"local_only": False,
"transform_param": {
"transform_cols": -1,
"transform_names": None,
"transform_type": "bin_num"
}
}
pipeline = common_tools.make_normal_dsl(config, namespace, param)
pipeline.fit()
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
param = {
"name": "hetero_feature_binning_0",
"method": "quantile",
"compress_thres": 10000,
"head_size": 10000,
"error": 0.001,
"bin_num": 100,
"bin_indexes": -1,
"bin_names": None,
"category_indexes": None,
"category_names": None,
"adjustment_factor": 0.5,
"local_only": False,
"transform_param": {
"transform_cols": -1,
"transform_names": None,
"transform_type": "bin_num"
}
}
pipeline = common_tools.make_normal_dsl(config,
namespace,
param,
dataset='default_credit')
pipeline.fit()
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
backend = config.backend
work_mode = config.work_mode
param = {
"name": "hetero_feature_binning_0",
"method": "optimal",
"optimal_binning_param": {
"metric_method": "iv",
"min_bin_pct": 0.05,
"max_bin_pct": 0.8,
"init_bucket_method": "quantile",
"init_bin_nums": 100,
"mixture": False
},
"compress_thres": 10000,
"head_size": 10000,
"error": 0.001,
"bin_num": 10,
"bin_indexes": -1,
"bin_names": None,
"category_indexes": None,
"category_names": None,
"adjustment_factor": 0.5,
"local_only": False,
"transform_param": {
"transform_cols": -1,
"transform_names": None,
"transform_type": "bin_num"
}
}
pipeline = common_tools.make_normal_dsl(config,
namespace,
is_multi_host=True,
bin_param=param,
host_dense_output=False)
job_parameters = JobParameters(backend=backend, work_mode=work_mode)
pipeline.fit(job_parameters)
def main(config="../../config.yaml", namespace=""):
# obtain config
if isinstance(config, str):
config = load_job_config(config)
backend = config.backend
work_mode = config.work_mode
param = {
"name": "hetero_feature_binning_0",
"method": "optimal",
"optimal_binning_param": {
"metric_method": "iv",
"min_bin_pct": 0.05,
"max_bin_pct": 0.8,
"init_bucket_method": "quantile",
"init_bin_nums": 100,
"mixture": True
},
"compress_thres": 10000,
"head_size": 10000,
"error": 1e-05,
"bin_num": 10,
"bin_indexes": -1,
"bin_names": None,
"category_indexes": [3, 7],
"category_names": ["x3", "x7"],
"adjustment_factor": 0.5,
"local_only": False,
"transform_param": {
"transform_cols": -1,
"transform_names": None,
"transform_type": "woe"
}
}
pipeline = common_tools.make_normal_dsl(config,
namespace,
param,
dataset='default_credit')
pipeline.fit(backend=backend, work_mode=work_mode)
