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main.py
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import argparse
from lib.automl import AutoML
def main():
# python main.py --mode regression --train-csv '../sdsj2018_automl_check_datasets/check_1_r/train.csv' --model-dir '../model/'
# python main.py --test-csv '../sdsj2018_automl_check_datasets/check_1_r/test.csv' --prediction-csv '../sdsj2018_automl_check_datasets/check_1_r/prediction.csv' --model-dir '../model/'
parser = argparse.ArgumentParser()
# parser.add_argument('--train-csv'
# , default="../../sdsj2018_automl_check_datasets/check_3_r/train.csv"
# )
# parser.add_argument('--test-csv'
# , default='../../sdsj2018_automl_check_datasets/check_3_r/test.csv'
# )
# parser.add_argument('--prediction-csv'
# , default='../../sdsj2018_automl_check_datasets/check_3_r/prediction.csv'
# )
# parser.add_argument('--mode', choices=['classification', 'regression']
# , default="classification"
# )
# parser.add_argument('--model-dir', default="../../model/")
parser.add_argument('--mode', choices=['classification', 'regression'])
parser.add_argument('--model-dir')
parser.add_argument('--train-csv')
parser.add_argument('--test-csv')
parser.add_argument('--prediction-csv')
parser.add_argument('--verbose', default=2)
args = parser.parse_args()
verbose = int(args.verbose)
if not args.train_csv is None:
params_autoML = \
{ 'memory': {'max_size_mb': 2*1024,
'max_size_train_samples': 100000
},
'field_target_name': 'target',
'pipeline' : {
'rename\nid_columns': {'node':'rename_id_columns',
'parents':None, #'args':{}
},
'check\ncolumns\nexists_start': {'node':'check_columns_exists',
'parents':'rename\nid_columns',
'args':{'key_stage':'preprocess_begin', 'drop_columns_test':True}
},
'drop_columns': {'node':'drop_columns',
'parents':'check\ncolumns\nexists_start', # 'args':{}
},
'fillna': {'node':'fillna',
'parents':'drop_columns', #
'args':{'args':{"number_": {'agg': 'median'},
"datetime_": {'agg': 'median'},
"string_": {'value': ''},
}},
},
'to_int8': {'node':'to_int8',
'parents':'fillna', # 'args':{}
},
'non_negative\ntarget_detect': {'node':'non_negative_target_detect',
'parents':'to_int8', # 'args':{}
},
'subsample0': {'node':'subsample',
'parents':'non_negative\ntarget_detect', # 'args':{}
},
'transform\ndatetime': {'node':'transform_datetime',
'parents':'subsample0', # 'args':{}
},
'transform\ncategorical': {'node':'transform_categorical',
'parents':'transform\ndatetime', # 'args':{}
},
# 'scale': {'node':'scale',
# 'parents':'transform\ncategorical', # 'args':{}
# },
# # # 'feature_generation': {'node':'feature_generation',
# # # 'parents':'scale', # 'args':{}
# # # },
'subsample1': {'node':'subsample',
'parents':'transform\ncategorical', #'scale', # 'args':{}
},
'columns_float64\nto_float32': {'node':'columns_float64_to_32',
'parents':'subsample1', # 'args':{}
},
'check\ncolumns\nexists_end': {'node':'check_columns_exists',
'parents':'columns_float64\nto_float32',
'args':{'key_stage':'preprocess_end', 'drop_columns_test':True}
},
# # 'split_X_y': {'node':'split_X_y',
# # 'parents':'check\ncolumns\nexists_end', # 'args':{}
# # },
# 'bayes': {'node':'model',
# 'parents':'check\ncolumns\nexists_end',
# 'args':{'models': ['bayes']}
# },
'lightgbm\nend': {'node':'model',
'parents':'columns_float64\nto_float32', #'bayes',
'args':{'models': ['lightgbm']}
},
# '': {'node':'',
# 'parents':'', # 'args':{}
# },
# '': {'node':'',
# 'parents':'', # 'args':{}
# },
}
}
automl = AutoML(args.model_dir, params=params_autoML, verbose=verbose)
# automl.pipeline_draw(view=True)
automl.train(args.train_csv, args.mode)
automl.save()
elif args.test_csv is not None:
automl = AutoML(args.model_dir, params={}, verbose=verbose)
automl.load()
_, score = automl.predict(args.test_csv, args.prediction_csv)
if verbose: print('score', score)
else:
exit(1)
# %%
if __name__ == '__main__':
main()
# # %%
# params_autoML = \
# { 'model_dir' : 'args.model_dir',
# 'memory': {'max_size_mb': 11, # * 1024 * 1024,
# 'max_size_train_samples': 100000
# },
# 'field_target_name': 'target',
# 'pipeline' : {
# 'check\ncolumns\nexists_start': {'node':'check_columns_exists',
# 'parents':None,
# 'args':{'key_stage':'preprocess_begin', 'drop_columns_test':True}
# },
# 'drop\ncolumns': {'node':'drop_columns',
# 'parents':'check\ncolumns\nexists_start', # 'args':{}
# },
# 'fillna': {'node':'fillna',
# 'parents':'drop\ncolumns', # 'args':{}
# },
# 'to_int8': {'node':'to_int8',
# 'parents':'fillna', # 'args':{}
# },
# 'transform\ndatetime': {'node':'transform_datetime',
# 'parents':'to_int8', # 'args':{}
# },
# 'transform\ncategorical': {'node':'transform_categorical',
# 'parents':'transform\ndatetime', # 'args':{}
# },
# 'scale': {'node':'scale',
# 'parents':'transform\ncategorical', # 'args':{}
# },
# 'float64\nto_float32': {'node':'columns_float64_to_32',
# 'parents':'scale', # 'args':{}
# },
# 'subsample0': {'node':'subsample',
# 'parents':'float64\nto_float32', # 'args':{}
# },
# 'non_negative\ntarget_detect': {'node':'non_negative_target_detect',
# 'parents':'subsample0', # 'args':{}
# },
# # 'feature_generation': {'node':'feature_generation',
# # 'parents':'scale', # 'args':{}
# # },
# # 'subsample1': {'node':'subsample',
# # 'parents':'scale', # 'args':{}
# # },
# 'check\ncolumns\nexists_end': {'node':'check_columns_exists',
# 'parents':'non_negative\ntarget_detect',
# 'args':{'key_stage':'preprocess_end', 'drop_columns_test':True}
# },
# # 'split_X_y': {'node':'split_X_y',
# # 'parents':'check\ncolumns\nexists_end', # 'args':{}
# # },
# 'bayes': {'node':'model',
# 'parents':'check\ncolumns\nexists_end',
# 'args':{'models': ['bayes']}
# },
# 'lightgbm\nend': {'node':'model',
# 'parents':'bayes',
# 'args':{'models': ['lightgbm']}
# },
# # '': {'node':'',
# # 'parents':'', # 'args':{}
# # },
# # '': {'node':'',
# # 'parents':'', # 'args':{}
# # },
# }
# }
# params_autoML = \
# { 'model_dir' : 'args.model_dir',
# 'memory': {'max_size_mb': 11, # * 1024 * 1024,
# 'max_size_train_samples': 100000
# },
# 'field_target_name': 'target',
# 'pipeline' : {
# 'check\ncolumns\nexists_start': {'node':'check_columns_exists',
# 'parents':None,
# 'args':{'key_stage':'preprocess_begin', 'drop_columns_test':True}
# },
# 'drop\ncolumns': {'node':'drop_columns',
# 'parents':'check\ncolumns\nexists_start', # 'args':{}
# },
# 'fillna': {'node':'fillna',
# 'parents':'drop\ncolumns', # 'args':{}
# },
# 'to_int8': {'node':'to_int8',
# 'parents':'fillna', # 'args':{}
# },
# 'transform\ndatetime': {'node':'transform_datetime',
# 'parents':'fillna', # 'args':{}
# },
# 'transform\ncategorical': {'node':'transform_categorical',
# 'parents':'fillna', # 'args':{}
# },
# 'stack\ndata': {'node':'transform_categorical',
# 'parents':['to_int8', 'transform\ndatetime', 'transform\ncategorical'], # 'args':{}
# },
# 'scale': {'node':'scale',
# 'parents':'scale', # 'args':{}
# },
# 'float64\nto_float32': {'node':'columns_float64_to_32',
# 'parents':'stack\ndata', # 'args':{}
# },
# 'subsample0': {'node':'subsample',
# 'parents':'float64\nto_float32', # 'args':{}
# },
# 'non_negative\ntarget_detect': {'node':'non_negative_target_detect',
# 'parents':'subsample0', # 'args':{}
# },
# # 'feature_generation': {'node':'feature_generation',
# # 'parents':'scale', # 'args':{}
# # },
# # 'subsample1': {'node':'subsample',
# # 'parents':'scale', # 'args':{}
# # },
# 'check\ncolumns\nexists_end': {'node':'check_columns_exists',
# 'parents':'non_negative\ntarget_detect',
# 'args':{'key_stage':'preprocess_end', 'drop_columns_test':True}
# },
# # 'split_X_y': {'node':'split_X_y',
# # 'parents':'check\ncolumns\nexists_end', # 'args':{}
# # },
# 'bayes': {'node':'model',
# 'parents':'check\ncolumns\nexists_end',
# 'args':{'models': ['bayes']}
# },
# 'lightgbm\nend': {'node':'model',
# 'parents':'bayes',
# 'args':{'models': ['lightgbm']}
# },
# # '': {'node':'',
# # 'parents':'', # 'args':{}
# # },
# # '': {'node':'',
# # 'parents':'', # 'args':{}
# # },
# }
# }
# params_autoML = \
# { 'model_dir' : 'args.model_dir',
# 'memory': {'max_size_mb': 11, # * 1024 * 1024,
# 'max_size_train_samples': 100000
# },
# 'field_target_name': 'target',
# 'pipeline' : {
# 'check\ncolumns\nexists_start': {'node':'check_columns_exists',
# 'parents':None,
# 'args':{'key_stage':'preprocess_begin', 'drop_columns_test':True}
# },
# 'drop\ncolumns': {'node':'drop_columns',
# 'parents':'check\ncolumns\nexists_start', # 'args':{}
# },
# 'fillna': {'node':'fillna',
# 'parents':'drop\ncolumns', # 'args':{}
# },
# 'to_int8': {'node':'to_int8',
# 'parents':'fillna', # 'args':{}
# },
# 'transform\ndatetime': {'node':'transform_datetime',
# 'parents':'fillna', # 'args':{}
# },
# 'transform\ncategorical': {'node':'transform_categorical',
# 'parents':'fillna', # 'args':{}
# },
# 'stack\ndata': {'node':'transform_categorical',
# 'parents':['to_int8', 'transform\ndatetime', 'transform\ncategorical'], # 'args':{}
# },
# 'scale': {'node':'scale',
# 'parents':'scale', # 'args':{}
# },
# 'float64\nto_float32': {'node':'columns_float64_to_32',
# 'parents':'stack\ndata', # 'args':{}
# },
# 'subsample0': {'node':'subsample',
# 'parents':'float64\nto_float32', # 'args':{}
# },
# 'non_negative\ntarget_detect': {'node':'non_negative_target_detect',
# 'parents':'subsample0', # 'args':{}
# },
# # 'feature_generation': {'node':'feature_generation',
# # 'parents':'scale', # 'args':{}
# # },
# # 'subsample1': {'node':'subsample',
# # 'parents':'scale', # 'args':{}
# # },
# 'check\ncolumns\nexists_end': {'node':'check_columns_exists',
# 'parents':'non_negative\ntarget_detect',
# 'args':{'key_stage':'preprocess_end', 'drop_columns_test':True}
# },
# # 'split_X_y': {'node':'split_X_y',
# # 'parents':'check\ncolumns\nexists_end', # 'args':{}
# # },
# 'vw': {'node':'model',
# 'parents':'check\ncolumns\nexists_end',
# 'args':{'models': ['vw']}
# },
# 'rf': {'node':'model',
# 'parents':'check\ncolumns\nexists_end',
# 'args':{'models': ['rf']}
# },
# 'lcv': {'node':'model',
# 'parents':'check\ncolumns\nexists_end',
# 'args':{'models': ['lcv']}
# },
# 'stack\ndata&models': {'node':'transform_categorical',
# 'parents':['vw', 'rf', 'lcv', 'check\ncolumns\nexists_end'], # 'args':{}
# },
# 'h2o\nend': {'node':'model',
# 'parents':'stack\ndata&models',
# 'args':{'models': ['h2o']}
# },
# # '': {'node':'',
# # 'parents':'', # 'args':{}
# # },
# # '': {'node':'',
# # 'parents':'', # 'args':{}
# # },
# }
# }
# automl = AutoML("../model/", params=params_autoML)
# # # %%
# automl.pipeline_draw()
# # # %%
# # automl.train("../sdsj2018_automl_check_datasets/check_1_r/train.csv", 'regression')
# # # automl.config.data
# # # automl.config
# # # dir(automl.config.params) #.pipeline
# # os.exit(0)
# # # %%
# # _, score = automl.predict('../sdsj2018_automl_check_datasets/check_1_r/test.csv',
# # '../sdsj2018_automl_check_datasets/check_1_r/prediction.csv')
# # print(score)
# # # python main.py --mode regression --train-csv '../sdsj2018_automl_check_datasets/check_1_r/train.csv' --model-dir '../model/'
# # # python main.py --test-csv '../sdsj2018_automl_check_datasets/check_1_r/test.csv' --prediction-csv '../sdsj2018_automl_check_datasets/check_1_r/prediction.csv' --model-dir '../model/'
# #