def salary_lightgbm(path_model_out="") :
"""
Huber Loss includes L1 regurarlization
We test different features combinaison, default params is optimal
"""
data_name = "salary"
model_class = 'LGBMRegressor'
n_sample = 10**5
def post_process_fun(y):
return y_norm(y, inverse=True, mode='boxcox')
def pre_process_fun(y):
return y_norm(y, inverse=False, mode='boxcox')
model_dict = {'model_pars':
{'model_class': model_class
,'model_path': path_model_out
,'model_pars': {'objective': 'huber',
} # default
,'post_process_fun': copy.deepcopy( post_process_fun)
,'pre_process_pars': {'y_norm_fun' : copy.deepcopy(pre_process_fun) ,
### Pipeline for data processing ##############################
'pipe_list': [
{'uri': 'source/prepro.py::pd_coly', 'pars': {}, 'cols_family': 'coly', 'cols_out': 'coly', 'type': 'coly' },
{'uri': 'source/prepro.py::pd_colnum_bin', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'colnum_bin', 'type': '' },
{'uri': 'source/prepro.py::pd_colnum_binto_onehot', 'pars': {}, 'cols_family': 'colnum_bin', 'cols_out': 'colnum_onehot', 'type': '' },
{'uri': 'source/prepro.py::pd_colcat_bin', 'pars': {}, 'cols_family': 'colcat', 'cols_out': 'colcat_bin', 'type': '' },
{'uri': 'source/prepro.py::pd_colcat_to_onehot', 'pars': {}, 'cols_family': 'colcat_bin', 'cols_out': 'colcat_onehot', 'type': '' },
{'uri': 'source/prepro.py::pd_colcross', 'pars': {}, 'cols_family': 'colcross', 'cols_out': 'colcross_pair_onehot', 'type': 'cross'}
],
}
},
'compute_pars': { 'metric_list': ['root_mean_squared_error', 'mean_absolute_error',
'explained_variance_score', 'r2_score', 'median_absolute_error']
},
'data_pars': {
'cols_input_type' : cols_input_type_1
# cols['cols_model'] = cols["colnum"] + cols["colcat_bin"] # + cols[ "colcross_onehot"]
,'cols_model_group': [ 'colnum', 'colcat_bin']
,'filter_pars': { 'ymax' : 100000.0 ,'ymin' : 0.0 } ### Filter data
}}
################################################################################################
##### Filling Global parameters #############################################################
model_dict = global_pars_update(model_dict, data_name, os_get_function_name() )
return model_dict
def house_price_lightgbm(path_model_out="") :
"""
Huber Loss includes L1 regurarlization
We test different features combinaison, default params is optimal
"""
data_name = 'house_price'
model_name = 'LGBMRegressor'
n_sample = 20000
def post_process_fun(y):
return y_norm(y, inverse=True, mode='norm')
def pre_process_fun(y):
return y_norm(y, inverse=False, mode='norm')
model_dict = {'model_pars': { 'model_path' : path_model_out
, 'model_class': model_name ### Actual Class Name
, 'model_pars' : {} # default ones of the model name
, 'post_process_fun' : post_process_fun
, 'pre_process_pars' : {'y_norm_fun' : copy.deepcopy(pre_process_fun),
### Pipeline for data processing.
# 'pipe_list' : [ 'filter', 'label', 'dfnum_bin', 'dfnum_hot', 'dfcat_bin', 'dfcat_hot', 'dfcross_hot', ]
'pipe_list' : [ 'filter', 'label', 'dfcat_bin' ]
}
},
'compute_pars': { 'metric_list': ['root_mean_squared_error', 'mean_absolute_error',
'explained_variance_score', 'r2_score', 'median_absolute_error']
},
'data_pars': {
'cols_input_type' : cols_input_type_1,
# 'cols_model_group': [ 'colnum_onehot', 'colcat_onehot', 'colcross_onehot' ]
'cols_model_group': [ 'colnum', 'colcat_bin' ]
,'filter_pars': { 'ymax' : 1000000.0 ,'ymin' : 0.0 } ### Filter data
}}
################################################################################################
##### Filling Global parameters #############################################################
model_dict = global_pars_update(model_dict, data_name, config_name=os_get_function_name() )
return model_dict
def salary_bayesian_pyro(path_model_out="") :
global model_name
model_name = 'model_bayesian_pyro'
def post_process_fun(y):
return y_norm(y, inverse=True, mode='boxcox')
def pre_process_fun(y):
return y_norm(y, inverse=False, mode='boxcox')
model_dict = {'model_pars': {'model_class': 'model_bayesian_pyro'
, 'model_path': path_model_out
, 'model_pars': {'input_width': 112, } # default
, 'post_process_fun': post_process_fun
, 'pre_process_pars': {'y_norm_fun' : copy.deepcopy(pre_process_fun) ,
### Pipeline for data processing ##############################
'pipe_list': [
{'uri': 'source/prepro.py::pd_coly', 'pars': {}, 'cols_family': 'coly', 'cols_out': 'coly', 'type': 'coly' },
{'uri': 'source/prepro.py::pd_colnum_bin', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'colnum_bin', 'type': '' },
{'uri': 'source/prepro.py::pd_colnum_binto_onehot', 'pars': {}, 'cols_family': 'colnum_bin', 'cols_out': 'colnum_onehot', 'type': '' },
{'uri': 'source/prepro.py::pd_colcat_bin', 'pars': {}, 'cols_family': 'colcat', 'cols_out': 'colcat_bin', 'type': '' },
{'uri': 'source/prepro.py::pd_colcat_to_onehot', 'pars': {}, 'cols_family': 'colcat_bin', 'cols_out': 'colcat_onehot', 'type': '' },
{'uri': 'source/prepro.py::pd_colcross', 'pars': {}, 'cols_family': 'colcross', 'cols_out': 'colcross_pair_onehot', 'type': 'cross'}
],
}
},
'compute_pars': {'compute_pars': {'n_iter': 1200, 'learning_rate': 0.01}
, 'metric_list': ['root_mean_squared_error', 'mean_absolute_error',
'explained_variance_score', 'r2_score', 'median_absolute_error']
, 'max_size': 1000000
, 'num_samples': 300
},
'data_pars': {
'cols_input_type' : cols_input_type_1
,'cols_model_group': [ 'colnum_onehot', 'colcat_onehot' ]
,'filter_pars': { 'ymax' : 100000.0 ,'ymin' : 0.0 } ### Filter data
}}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict, data_name, os_get_function_name() )
return model_dict
def house_price_elasticnetcv(path_model_out=""):
model_name = 'ElasticNetCV'
config_name = 'house_price_elasticnetcv'
n_sample = 1000
def post_process_fun(y):
return y_norm(y, inverse=True, mode='norm')
def pre_process_fun(y):
return y_norm(y, inverse=False, mode='norm')
model_dict = {'model_pars': {'model_class': 'ElasticNetCV'
, 'model_path': path_model_out
, 'model_pars': {} # default ones
, 'post_process_fun': post_process_fun
, 'pre_process_pars': {'y_norm_fun' : pre_process_fun,
### Pipeline for data processing.
# 'pipe_list' : [ 'filter', 'label', 'dfnum_bin', 'dfnum_hot', 'dfcat_bin', 'dfcat_hot', 'dfcross_hot', ]
'pipe_list' : [ 'filter', 'label', 'dfcat_hot' ]
}
},
'compute_pars': { 'metric_list': ['root_mean_squared_error', 'mean_absolute_error',
'explained_variance_score', 'r2_score', 'median_absolute_error']
},
'data_pars': {
'cols_input_type' : cols_input_type_1,
# 'cols_model_group': [ 'colnum_onehot', 'colcat_onehot', 'colcross_onehot' ]
'cols_model_group': [ 'colnum', 'colcat_onehot' ]
,'filter_pars': { 'ymax' : 100000.0 ,'ymin' : 0.0 } ### Filter data
}}
################################################################################################
##### Filling Global parameters #############################################################
model_dict = global_pars_update(model_dict, data_name, config_name=os_get_function_name() )
return model_dict
### family of columns used for model input #########################################################
"cols_model_group": [ "colnum", ### numerical continuous
"colcat_bin", ### category
]
### Filter data rows ##################################################################
,"filter_pars": { "ymax" : 2 ,"ymin" : -1 }
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict, data_name, config_name=os_get_function_name() )
return model_dict
###################################################################################
########## Preprocess #############################################################
### def preprocess(config="", nsample=1000):
from core_run import preprocess
"""
def preprocess(config=None, nsample=None):
config_name = config if config is not None else config_default
mdict = globals()[config_name]()
m = mdict["global_pars"]
def titanic1(path_model_out="") :
"""
Contains all needed informations for Light GBM Classifier model,
used for titanic classification task
"""
config_name = os_get_function_name()
data_name = "titanic" ### in data/input/
model_class = 'LGBMClassifier' ### ACTUAL Class name for model_sklearn.py
n_sample = 1000
def post_process_fun(y):
return int(y)
def pre_process_fun(y):
return int(y)
model_dict = {'model_pars': {
### LightGBM API model #######################################
'model_class': model_class
,'model_pars' : {'objective': 'binary', 'n_estimators':10,
}
, 'post_process_fun' : post_process_fun
, 'pre_process_pars' : {'y_norm_fun' : pre_process_fun ,
### Pipeline for data processing ##############################
'pipe_list': [
{'uri': 'source/prepro.py::pd_coly', 'pars': {}, 'cols_family': 'coly', 'cols_out': 'coly', 'type': 'coly' },
{'uri': 'source/prepro.py::pd_colnum_bin', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'colnum_bin', 'type': '' },
# {'uri': 'source/prepro.py::pd_colnum_binto_onehot', 'pars': {}, 'cols_family': 'colnum_bin', 'cols_out': 'colnum_onehot', 'type': '' },
{'uri': 'source/prepro.py::pd_colcat_bin', 'pars': {}, 'cols_family': 'colcat', 'cols_out': 'colcat_bin', 'type': '' },
# {'uri': 'source/prepro.py::pd_colcat_to_onehot', 'pars': {}, 'cols_family': 'colcat_bin', 'cols_out': 'colcat_onehot', 'type': '' },
# {'uri': 'source/prepro.py::pd_colcross', 'pars': {}, 'cols_family': 'colcross', 'cols_out': 'colcross_pair_onehot', 'type': 'cross'},
{'uri': 'source/prepro.py::pd_colcat_minhash', 'pars': {}, 'cols_family': 'colcat', 'cols_out': 'colcat_minhash', 'type': '' },
# {'uri': 'source/prepro.py::pd_coltext_universal_google', 'pars': {}, 'cols_family': 'coltext', 'cols_out': 'coltext_universal_google', 'type': '' },
{'uri': 'source/prepro.py::pd_col_genetic_transform', 'pars': { ## 'pars_genetic' : {}
},
'cols_family': 'colgen', 'cols_out': 'col_genetic', 'type': 'add_coly' },
{'uri': 'source/prepro.py::pd_colnum_quantile_norm', 'pars': {'colsparse' : [] },
'cols_family': 'colnum', 'cols_out': 'colnum_quantile_norm', 'type': '' },
],
}
},
'compute_pars': { 'metric_list': ['accuracy_score','average_precision_score']
},
'data_pars': { 'n_sample' : n_sample,
'cols_input_type' : cols_input_type_2,
### family of columns for MODEL #########################################################
# "colnum", "colnum_bin", "colnum_onehot", "colnum_binmap", #### Colnum columns
# "colcat", "colcat_bin", "colcat_onehot", "colcat_bin_map", #### colcat columns
# 'colcross_single_onehot_select', "colcross_pair_onehot", 'colcross_pair', #### colcross columns
# 'coldate',
# 'coltext',
'cols_model_group': [ 'colnum', ### should be optional 'colcat'
'colcat_bin',
# 'colcat_bin',
# 'colnum_onehot',
#'colcat_minhash',
# 'colcat_onehot',
# 'coltext_universal_google'
'colcat_minhash',
'col_genetic',
'colnum_quantile_norm'
]
### Filter data rows ##################################################################
,'filter_pars': { 'ymax' : 2 ,'ymin' : -1 }
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict, data_name, config_name )
return model_dict
def config3(path_model_out=""):
"""
Contains all needed informations
"""
config_name = os_get_function_name()
data_name = "titanic" ### in data/input/
model_class = 'LGBMClassifier' ### ACTUAL Class name for model_sklearn.py
n_sample = 1000
def post_process_fun(y):
return int(y)
def pre_process_fun(y):
return int(y)
model_dict = {
'model_pars': {
### LightGBM API model #######################################
'model_class': model_class,
'model_pars': {
'objective': 'binary',
'n_estimators': 5,
},
'post_process_fun': post_process_fun,
'pre_process_pars': {
'y_norm_fun':
pre_process_fun,
### Pipeline for data processing ##############################
'pipe_list': [
### coly encoding
{
'uri': 'source/prepro.py::pd_coly',
'pars': {
'ymin': -9999999999.0,
'ymax': 999999999.0,
'y_norm_fun': None
},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
},
{
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {
'path_pipeline': False
},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
}
#### Data Over/Under sampling, New data
#,{'uri': 'source/prepro_sampler.py::pd_sample_imblearn' ,
# 'pars': {"model_name": 'SMOTEENN',
# 'pars_resample': {'sampling_strategy' : 'auto', 'random_state':0},
# "coly": "Survived"} ,
# 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': 'add_coly' }
# ,{'uri': 'source/prepro_sampler.py::pd_filter_rows' , 'pars': {'ymin': -9999999999.0, 'ymax': 999999999.0} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#,{'uri': 'source/prepro_sampler.py::pd_augmentation_sdv' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
],
}
},
'compute_pars': {
'metric_list': ['accuracy_score', 'average_precision_score']
},
'data_pars': {
'n_sample': n_sample,
#### columns as raw data input
'cols_input_type': cols_input_type_2,
### columns for model input ############################################################
'cols_model_group': [ # 'colnum',
'colcat_bin',
],
#### Separate Category Sparse from Continuous (DLearning input)
'cols_model_type': {
'continuous': [
'colnum',
],
'discreate': [
'colcat_bin',
]
}
### Filter data rows ###################################################################
,
'filter_pars': {
'ymax': 2,
'ymin': -1
}
}
}
##### Filling Global parameters #########################################################
model_dict = global_pars_update(model_dict, data_name, config_name)
return model_dict
def config4(path_model_out=""):
"""
"""
config_name = os_get_function_name()
data_name = "titanic" ### in data/input/
model_class = 'LGBMClassifier' ### ACTUAL Class name for model_sklearn.py
n_sample = 1000
def post_process_fun(y):
return int(y)
def pre_process_fun(y):
return int(y)
model_dict = {
'model_pars': {
### LightGBM API model #######################################
'model_class': model_class,
'model_pars': {
'objective': 'binary',
'n_estimators': 5,
},
'post_process_fun': post_process_fun,
'pre_process_pars': {
'y_norm_fun':
pre_process_fun,
### Pipeline for data processing ##############################
'pipe_list': [
### coly encoding
{
'uri': 'source/prepro.py::pd_coly',
'pars': {
'ymin': -9999999999.0,
'ymax': 999999999.0,
'y_norm_fun': None
},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
},
{
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {
'path_pipeline': False
},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
}
#### Time Series
#,{'uri': 'source/prepro_tseries.py::pd_ts_autoregressive' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#,{'uri': 'source/prepro_tseries.py::pd_ts_basic' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#,{'uri': 'source/prepro_tseries.py::pd_ts_date' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#,{'uri': 'source/prepro_tseries.py::pd_ts_detrend' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#,{'uri': 'source/prepro_tseries.py::pd_ts_generic' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#,{'uri': 'source/prepro_tseries.py::pd_ts_groupby' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#,{'uri': 'source/prepro_tseries.py::pd_ts_identity' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#,{'uri': 'source/prepro_tseries.py::pd_ts_lag' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#,{'uri': 'source/prepro_tseries.py::pd_ts_onehot' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#,{'uri': 'source/prepro_tseries.py::pd_ts_rolling' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#,{'uri': 'source/prepro_tseries.py::pd_ts_template' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
],
}
},
'compute_pars': {
'metric_list': ['accuracy_score', 'average_precision_score']
},
'data_pars': {
'n_sample': n_sample,
#### columns as raw data input
'cols_input_type': cols_input_type_2,
### columns for model input #########################################################
'cols_model_group': [ # 'colnum',
'colcat_bin',
],
#### Separate Category Sparse from Continuous (DLearning input)
'cols_model_type': {
'continuous': [
'colnum',
],
'discreate': ['colcat_bin']
}
### Filter data rows ###################################################################
,
'filter_pars': {
'ymax': 2,
'ymin': -1
}
}
}
##### Filling Global parameters #########################################################
model_dict = global_pars_update(model_dict, data_name, config_name)
return model_dict
def config1(path_model_out=""):
"""
Contains all needed informations
"""
config_name = os_get_function_name()
data_name = "titanic" ### in data/input/
model_class = 'LGBMClassifier' ### ACTUAL Class name for model_sklearn.py
n_sample = 1000
def post_process_fun(y):
return int(y)
def pre_process_fun(y):
return int(y)
model_dict = {
'model_pars': {
### LightGBM API model #######################################
'model_class': model_class,
'model_pars': {
'objective': 'binary',
'n_estimators': 3,
},
'post_process_fun': post_process_fun,
'pre_process_pars': {
'y_norm_fun':
pre_process_fun,
### Pipeline for data processing ##############################
'pipe_list': [
### Filter rows
#,{'uri': 'source/prepro.py::pd_filter_rows' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
### coly processing
{
'uri': 'source/prepro.py::pd_coly',
'pars': {
'ymin': -9999999999.0,
'ymax': 999999999.0,
'y_norm_fun': None
},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
},
{
'uri': 'source/prepro.py::pd_coly_clean',
'pars': {
'y_norm_fun': None
},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
}
### colnum : continuous
,
{
'uri': 'source/prepro.py::pd_colnum_quantile_norm',
'pars': {
'colsparse': []
},
'cols_family': 'colnum',
'cols_out': 'colnum_quantile_norm',
'type': ''
},
{
'uri': 'source/prepro.py::pd_colnum_binto_onehot',
'pars': {
'path_pipeline': False
},
'cols_family': 'colnum',
'cols_out': 'colnum_onehot',
'type': ''
},
{
'uri': 'source/prepro.py::pd_colnum_bin',
'pars': {
'path_pipeline': False
},
'cols_family': 'colnum',
'cols_out': 'colnum_bin',
'type': ''
}
### colcat :Category
,
{
'uri': 'source/prepro.py::pd_colcat_to_onehot',
'pars': {},
'cols_family': 'colcat',
'cols_out': 'colcat_onehot',
'type': ''
},
{
'uri': 'source/prepro.py::pd_colcat_minhash',
'pars': {},
'cols_family': 'colcat',
'cols_out': 'colcat_minhash',
'type': ''
},
{
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {
'path_pipeline': False
},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
}
#### Bug in NA values
,
{
'uri': 'source/prepro.py::pd_colcat_encoder_generic',
'pars': {
'model_name': 'HashingEncoder',
'model_pars': {
'verbose': 1,
'return_df': True
}
},
'cols_family': 'colcat',
'cols_out': 'colcat_encoder2',
'type': ''
}
### colcat, colnum cross-features
,
{
'uri': 'source/prepro.py::pd_colcross',
'pars': {},
'cols_family': 'colcross',
'cols_out': 'colcross_pair_onehot',
'type': 'cross'
}
### New Features
,
{
'uri': 'source/prepro.py::pd_col_genetic_transform',
### Issue with Binary 1 or 0 : need to pass with Logistic
'pars': {
'pars_generic': {
'metric': 'spearman',
'generations': 2,
'population_size':
10, ### Higher than nb_features
'tournament_size': 10,
'stopping_criteria': 1.0,
'const_range': (-1., 1.),
'p_crossover': 0.9,
'p_subtree_mutation': 0.01,
'p_hoist_mutation': 0.01,
'p_point_mutation': 0.01,
'p_point_replace': 0.05,
'parsimony_coefficient':
0.0005, #### 0.00005 Control Complexity
'max_samples': 0.9,
'verbose': 1,
'random_state': 0,
'n_jobs': 4,
#'n_components' ### 'metric': 'spearman', Control number of outtput features : n_components
}
},
'cols_family': 'colgen',
'cols_out': 'col_genetic',
'type': 'add_coly' #### Need to add target coly
}
#### Date
#,{'uri': 'source/prepro.py::pd_coldate' , 'pars': {} , 'cols_family': 'colnum' , 'cols_out': 'colnum_out' , 'type': '' }
#### Example of Custom processor
,
{
"uri": THIS_FILEPATH + "::pd_col_amyfun",
"pars": {},
"cols_family": "colnum",
"cols_out": "col_myfun",
"type": ""
},
],
}
},
'compute_pars': {
'metric_list': ['accuracy_score', 'average_precision_score']
},
'data_pars': {
'n_sample':
n_sample,
#### columns as raw data input
'cols_input_type':
cols_input_type_2,
### columns for model input #########################################################
# "colnum", "colnum_bin", "colnum_onehot", #### Colnum columns
# "colcat", "colcat_bin", "colcat_onehot", "colcat_bin_map", #### colcat columns
# 'colcross', "colcross_pair_onehot" #### colcross columns
'cols_model_group': [ # 'colnum',
'colnum_bin',
'colnum_onehot',
'colnum_quantile_norm',
'colcat_bin',
'colcat_onehot',
'colcat_minhash',
],
#### Separate Category Sparse from Continuous (DLearning input)
'cols_model_type': {
'continuous': [
'colnum',
],
'discreate': [
'colcat_bin',
'colnum_bin',
]
}
### Filter data rows ###################################################################
,
'filter_pars': {
'ymax': 2,
'ymin': -1
}
}
}
##### Filling Global parameters #########################################################
model_dict = global_pars_update(model_dict, data_name, config_name)
return model_dict
def airbnb_lightgbm(path_model_out=""):
"""
"""
data_name = "airbnb" ###in data/
model_name = 'LGBMRegressor'
def post_process_fun(y):
return y_norm(y, inverse=True, mode='norm')
def pre_process_fun(y):
return y_norm(y, inverse=False, mode='norm')
#############################################################################
model_dict = {
'model_pars': {
'model_class': model_name,
'model_path': path_model_out,
'model_pars': {
'objective': 'huber',
} # lightgbm one
,
'post_process_fun': post_process_fun,
'pre_process_pars': {
'y_norm_fun':
copy.deepcopy(pre_process_fun),
### Pipeline for data processing ########################
'pipe_list': [{
'uri': 'source/prepro.py::pd_coly',
'pars': {},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
}, {
'uri': 'source/prepro.py::pd_colnum_bin',
'pars': {},
'cols_family': 'colnum',
'cols_out': 'colnum_bin',
'type': ''
}, {
'uri': 'source/prepro.py::pd_colnum_binto_onehot',
'pars': {},
'cols_family': 'colnum_bin',
'cols_out': 'colnum_onehot',
'type': ''
}, {
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
}, {
'uri': 'source/prepro.py::pd_colcat_to_onehot',
'pars': {},
'cols_family': 'colcat_bin',
'cols_out': 'colcat_onehot',
'type': ''
}, {
'uri': 'source/prepro.py::pd_coltext',
'pars': {},
'cols_family': 'coltext',
'cols_out': 'coltext_svd',
'type': ''
}, {
'uri': 'source/prepro.py::pd_coldate',
'pars': {},
'cols_family': 'coldate',
'cols_out': 'coldate',
'type': ''
}, {
'uri': 'source/prepro.py::pd_colcross',
'pars': {},
'cols_family': 'colcross',
'cols_out': 'colcross_pair_onehot',
'type': 'cross'
}],
}
},
'compute_pars': {
'metric_list': [
'root_mean_squared_error',
'mean_absolute_error', #### sklearm names
'explained_variance_score',
'r2_score',
'median_absolute_error'
]
},
'data_pars': {
'cols_input_type': cols_input_type_1
# "colnum", "colnum_bin", "colnum_onehot", "colnum_binmap", #### Colnum columns
# "colcat", "colcat_bin", "colcat_onehot", "colcat_bin_map", #### colcat columns
# 'colcross_single_onehot_select', "colcross_pair_onehot", 'colcross_pair', #### colcross columns
# 'coldate', #'coltext', 'coltext_svd'
,
'cols_model_group': ['colnum', 'colcat_bin', 'coltext_svd'],
'filter_pars': {
'ymax': 100000.0,
'ymin': 0.0
} ### Filter data
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict, data_name,
os_get_function_name())
return model_dict
def multi_lightgbm():
"""
multiclass
"""
data_name = f"multiclass" ### in data/input/
model_name = 'LGBMClassifier'
n_sample = 6000
def post_process_fun(y): ### After prediction is done
return int(y)
def pre_process_fun_multi(y): ### Before the prediction is done
return int(y)
model_dict = {
'model_pars': {
#'model_path' : path_model_out
### LightGBM API model ########################################
'model_class':
model_name ## ACTUAL Class name for model_sklearn.py
,
'model_pars': {
'objective': 'multiclass',
'num_class': 4,
'metric': 'multi_logloss',
'learning_rate': 0.03,
'boosting_type': 'gbdt',
"n_estimators": 3,
}
### After prediction ##########################################
,
'post_process_fun': post_process_fun,
'pre_process_pars': {
'y_norm_fun':
pre_process_fun_multi,
### Pipeline for data processing.
'pipe_list': [{
'uri': 'source/prepro.py::pd_coly',
'pars': {},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
}, {
'uri': 'source/prepro.py::pd_colnum_bin',
'pars': {},
'cols_family': 'colnum',
'cols_out': 'colnum_bin',
'type': ''
}, {
'uri': 'source/prepro.py::pd_colnum_binto_onehot',
'pars': {},
'cols_family': 'colnum_bin',
'cols_out': 'colnum_onehot',
'type': ''
}, {
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
}, {
'uri': 'source/prepro.py::pd_colcat_to_onehot',
'pars': {},
'cols_family': 'colcat_bin',
'cols_out': 'colcat_onehot',
'type': ''
}, {
'uri': 'source/prepro.py::pd_colcross',
'pars': {},
'cols_family': 'colcross',
'cols_out': 'colcross_pair_onehot',
'type': 'cross'
}],
},
},
'compute_pars': {
'metric_list': ['roc_auc_score', 'accuracy_score'],
'probability': True, ### output probability for classifier
},
'data_pars': {
'n_sample': n_sample,
### columns from raw file, based on data type, #############
'cols_input_type': cols_input_type_1,
### family of columns for MODEL ########################################################
# "colnum", "colnum_bin", "colnum_onehot", "colnum_binmap", #### Colnum columns
# "colcat", "colcat_bin", "colcat_onehot", "colcat_bin_map", #### colcat columns
# 'colcross_single_onehot_select', "colcross_pair_onehot", 'colcross_pair', #### colcross columns
# 'coldate',
# 'coltext',
'cols_model_group': ['colnum_bin', 'colcat_bin'],
'cols_model_type': {}
### Filter data rows #####################################
,
'filter_pars': {
'ymax': 5,
'ymin': -1
}
}
}
##### Filling Global parameters #############################################################
model_dict = global_pars_update(model_dict,
data_name,
config_name=os_get_function_name())
return model_dict
def config1() :
""" ONE SINGLE DICT Contains all needed informations for
"""
data_name = "titanic" ### in data/input/
model_class = 'AutoML' ### ACTUAL Class name for model_sklearn.py
n_sample = 1000
def post_process_fun(y): ### After prediction is done
return int(y)
def pre_process_fun(y): ### Before the prediction is done
return int(y)
model_dict = {
'model_pars': {
'model_class': model_class
,'model_pars' : {
'total_time_limit' : 20,
'algorithms' : 'auto',
'results_path' : root_repo + f'/data/output/{data_name}/{os_get_function_name()}/automl_1',
'eval_metric' : 'auto'
# mode='Explain',
# ml_task='auto', model_time_limit=None, algorithms='auto', train_ensemble=True,
# stack_models='auto', eval_metric='auto', validation_strategy='auto', explain_level='auto',
# golden_features='auto', features_selection='auto', start_random_models='auto',
# hill_climbing_steps='auto', top_models_to_improve='auto', verbose=1, random_state=1234)
}
, 'post_process_fun' : post_process_fun ### After prediction ##########################################
, 'pre_process_pars' : {'y_norm_fun' : pre_process_fun , ### Before training ##########################
### Pipeline for data processing ##############################
'pipe_list': [
#### coly target prorcessing
{'uri': 'source/prepro.py::pd_coly', 'pars': {}, 'cols_family': 'coly', 'cols_out': 'coly', 'type': 'coly' },
{'uri': 'source/prepro.py::pd_colnum_bin', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'colnum_bin', 'type': '' },
{'uri': 'source/prepro.py::pd_colnum_binto_onehot', 'pars': {}, 'cols_family': 'colnum_bin', 'cols_out': 'colnum_onehot', 'type': '' },
#### catcol INTO integer, colcat into OneHot
{'uri': 'source/prepro.py::pd_colcat_bin', 'pars': {}, 'cols_family': 'colcat', 'cols_out': 'colcat_bin', 'type': '' },
# {'uri': 'source/prepro.py::pd_colcat_to_onehot', 'pars': {}, 'cols_family': 'colcat_bin', 'cols_out': 'colcat_onehot', 'type': '' },
### Cross_feat = feat1 X feat2
# {'uri': 'source/prepro.py::pd_colcross', 'pars': {}, 'cols_family': 'colcross', 'cols_out': 'colcross_pair', 'type': 'cross'},
],
}
},
'compute_pars': { 'metric_list': ['accuracy_score','average_precision_score']
,'mlflow_pars' : None # {} ### Not empty --> use mlflow
},
'data_pars': { 'n_sample' : n_sample,
'download_pars' : None,
'cols_input_type' : cols_input_type_1,
### family of columns for MODEL #########################################################
# "colnum", "colnum_bin", "colnum_onehot", "colnum_binmap", #### Colnum columns
# "colcat", "colcat_bin", "colcat_onehot", "colcat_bin_map", #### colcat columns
# 'colcross_single_onehot_select', "colcross_pair_onehot", 'colcross_pair', #### colcross columns 'coldate', 'coltext',
'cols_model_group': [ 'colnum_bin',
'colcat_bin',
# 'coltext',
# 'coldate',
#'colcross_pair',
],
'cols_model_type' : {
'cols_cross_input': [ "colcat", ],
'cols_deep_input': ['colnum', ],
}
### Filter data rows ##################################################################
,'filter_pars': { 'ymax' : 2 ,'ymin' : -1 }
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict, data_name, config_name=os_get_function_name() )
return model_dict
def titanic1(path_model_out=""):
""" One big dict
"""
config_name = os_get_function_name()
data_name = "titanic" ### in data/input/
model_class = 'LGBMClassifier' ### ACTUAL Class name for model_sklearn.py
n_sample = 500
model_dict = {
'model_pars': {
'model_class': model_class,
'model_pars': {
'objective': 'binary',
'n_estimators': 10,
},
'post_process_fun': post_process_fun,
'pre_process_pars': {
'y_norm_fun':
pre_process_fun,
### Pipeline for data processing ##############################
'pipe_list': [
{
'uri': 'source/prepro.py::pd_coly',
'pars': {},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
},
{
'uri': 'source/prepro.py::pd_colnum_bin',
'pars': {},
'cols_family': 'colnum',
'cols_out': 'colnum_bin',
'type': ''
},
# {'uri': 'source/prepro.py::pd_colnum_binto_onehot', 'pars': {}, 'cols_family': 'colnum_bin', 'cols_out': 'colnum_onehot', 'type': '' },
{
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
},
# {'uri': 'source/prepro.py::pd_colcat_to_onehot', 'pars': {}, 'cols_family': 'colcat_bin', 'cols_out': 'colcat_onehot', 'type': '' },
# {'uri': 'source/prepro.py::pd_colcross', 'pars': {}, 'cols_family': 'colcross', 'cols_out': 'colcross_pair_onehot', 'type': 'cross'},
],
}
},
'compute_pars': {
'metric_list': ['accuracy_score', 'average_precision_score']
},
'data_pars': {
'n_sample':
n_sample,
'cols_input_type':
cols_input_type_2,
'cols_model_group': [
'colnum', ### should be optional 'colcat'
'colcat_bin',
],
'cols_model_type': {
'continuous': [
'colnum',
],
'sparse': [
'colcat_bin',
'colnum_bin',
],
}
### Filter data rows ##################################################################
,
'filter_pars': {
'ymax': 2,
'ymin': -1
}
}
}
##### Filling Global parameters ########################################################
model_dict = global_pars_update(model_dict, data_name, config_name)
return model_dict
def adfraud_lightgbm(path_model_out=""):
"""
"""
config_name = os_get_function_name()
data_name = "adfraud" ### in data/input/
model_class = 'LGBMClassifier' ### ACTUAL Class name for model_sklearn.py
n_sample = 5000000
def post_process_fun(y): ### After prediction is done
return int(y)
def pre_process_fun(y): ### Before the prediction is done
return int(y)
model_dict = {
'model_pars': {
### LightGBM API model #######################################
'info': """
Use large max_bin (may be slower)
Use small learning_rate with large num_iterations
Use large num_leaves (may cause over-fitting)
Use bigger training data
Try dart
Deal with Over-fitting
Use small max_bin
Use small num_leaves
Use min_data_in_leaf and min_sum_hessian_in_leaf
Use bagging by set bagging_fraction and bagging_freq
Use feature sub-sampling by set feature_fraction
Use bigger training data
Try lambda_l1, lambda_l2 and min_gain_to_split for regularization
Try max_depth to avoid growing deep tree
Try extra_trees
Try increasing path_smooth
""",
'model_class': model_class,
'model_pars': {
'objective': 'binary',
'boosting_type':
'gbdt', # "seed": 1, 'boosting_type': 'dart',
'metric': 'auc,average_precision',
#'scale_pos_weight' : 99,
'is_unbalance': True,
'learning_rate': 0.001,
'num_leaves':
31, # we should let it be smaller than 2^(max_depth)
'max_depth': -1, # -1 means no limit
'min_child_samples':
20, # Minimum number of df need in a child(min_data_in_leaf)
'max_bin': 255, # Number of bucketed bin for feature values
'subsample': 0.6, # Subsample ratio of the training instance.
'subsample_freq':
0, # frequence of subsample, <=0 means no enable
'colsample_bytree':
0.3, # Subsample ratio of columns when constructing each tree.
'min_child_weight':
5, # Minimum sum of instance weight(hessian) needed in a child(leaf)
'subsample_for_bin':
2000, # Number of samples for constructing bin
'min_split_gain':
0, # lambda_l1, lambda_l2 and min_gain_to_split to regularization
'reg_alpha': 0, # L1 regularization term on weights
'reg_lambda': 0, # L2 regularization term on weights
# 'nthread' : -1,
'verbose': 0,
},
'post_process_fun':
post_process_fun ### After prediction ##########################################
,
'pre_process_pars': {
'y_norm_fun':
pre_process_fun, ### Before training ##########################
### Pipeline for data processing ##############################
'pipe_list': [
{
'uri': 'source/prepro.py::pd_coly',
'pars': {},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
},
# {'uri': 'source/prepro.py::pd_colnum_bin', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'colnum_bin', 'type': '' },
# {'uri': 'source/prepro.py::pd_colnum_binto_onehot', 'pars': {}, 'cols_family': 'colnum_bin', 'cols_out': 'colnum_onehot', 'type': '' },
{
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
},
# {'uri': 'source/prepro.py::pd_colcat_to_onehot', 'pars': {}, 'cols_family': 'colcat_bin', 'cols_out': 'colcat_onehot', 'type': '' },
# {'uri': 'source/prepro.py::pd_colcross', 'pars': {}, 'cols_family': 'colcross', 'cols_out': 'colcross_pair', 'type': 'cross'},
#### Example of Custom processor
# {'uri': 'titanic_classifier.py::pd_colnum_quantile_norm', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'colnum_quantile_norm', 'type': '' },
],
}
},
#### Sklearn
'compute_pars': {
'metric_list': [
'accuracy_score', 'average_precision_score', 'f1_score',
'recall_score'
]
},
'data_pars': {
'n_sample': n_sample,
'cols_input_type': cols_input_type_1,
### family of columns for MODEL ######################################################################
# "colnum", "colnum_bin", "colnum_onehot", "colnum_binmap", #### Colnum columns
# "colcat", "colcat_bin", "colcat_onehot", "colcat_bin_map", #### colcat columns
# 'colcross_single_onehot_select', "colcross_pair_onehot", 'colcross_pair', #### colcross columns
# 'coldate',
'cols_model_group': [ # 'colnum_bin',
'colnum',
'colcat_bin',
# 'coltext',
# 'coldate',
# 'colcross_pair',
### example of custom
# 'colnum_quantile_norm'
]
### Filter data rows ##################################################################
,
'filter_pars': {
'ymax': 2,
'ymin': -1
}
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict, data_name, config_name)
return model_dict
def online_lightgbm():
"""
Contains all needed informations for Light GBM Classifier model,
used for titanic classification task
"""
data_name = "online_shopping" ### in data/input/
model_class = 'LGBMClassifier' ### ACTUAL Class name for model_sklearn.py
n_sample = 3816
def post_process_fun(y): ### After prediction is done
return int(y)
def pre_process_fun(y): ### Before the prediction is done
return int(y)
model_dict = {'model_pars': {
### LightGBM API model #######################################
'model_class': model_class
, 'model_pars': {'objective': 'binary',
'n_estimators': 10,
'learning_rate': 0.001,
'boosting_type': 'gbdt', ### Model hyperparameters
'early_stopping_rounds': 5
}
, 'post_process_fun': post_process_fun ### After prediction ##########################################
, 'pre_process_pars': {'y_norm_fun': pre_process_fun, ### Before training ##########################
### Pipeline for data processing ##############################
'pipe_list': [
#### coly target prorcessing
{'uri': 'source/prepro.py::pd_coly', 'pars': {}, 'cols_family': 'coly', 'cols_out': 'coly', 'type': 'coly'},
{'uri': 'source/prepro.py::pd_colnum_bin', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'colnum_bin', 'type': ''},
{'uri': 'source/prepro.py::pd_colnum_binto_onehot', 'pars': {}, 'cols_family': 'colnum_bin', 'cols_out': 'colnum_onehot', 'type': ''},
#### catcol INTO integer, colcat into OneHot
{'uri': 'source/prepro.py::pd_colcat_bin', 'pars': {}, 'cols_family': 'colcat', 'cols_out': 'colcat_bin', 'type': ''},
{'uri': 'source/prepro.py::pd_colcat_to_onehot', 'pars': {},'cols_family': 'colcat_bin', 'cols_out': 'colcat_onehot', 'type': ''},
### Cross_feat = feat1 X feat2
{'uri': 'source/prepro.py::pd_colcross', 'pars': {}, 'cols_family': 'colcross', 'cols_out': 'colcross_pair', 'type': 'cross'},
],
}
},
'compute_pars': {'metric_list': ['accuracy_score', 'average_precision_score']
},
'data_pars': {'n_sample': n_sample,
'cols_input_type': cols_input_type_1,
### family of columns for MODEL #########################################################
# "colnum", "colnum_bin", "colnum_onehot", "colnum_binmap", #### Colnum columns
# "colcat", "colcat_bin", "colcat_onehot", "colcat_bin_map", #### colcat columns
# 'colcross_single_onehot_select', "colcross_pair_onehot", 'colcross_pair', #### colcross columns
# 'coldate', 'coltext',
'cols_model_group': ['colnum_bin',
'colcat_bin',
# 'coltext',
# 'coldate',
'colcross_pair',
### example of custom
# 'col_myfun'
]
### Filter data rows ##################################################################
, 'filter_pars': {'ymax': 2, 'ymin': -1}
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict, data_name, config_name=os_get_function_name())
return model_dict
def config1():
"""
ONE SINGLE DICT Contains all needed informations for
used for tseries_demand classification task
"""
data_name = "tseries_demand" ### in data/input/
model_class = "LGBMRegressor" ### ACTUAL Class name for model_sklearn.py
n_sample = 100000
def post_process_fun(y): ### After prediction is done
# ynew = np.exp(y) - 1.0
ynew = float(y)
return ynew
def pre_process_fun(y): ### Before the prediction is done
# ynew = np.log(y+1)
ynew = float(y)
return ynew
model_dict = {
"model_pars": {
### LightGBM API model #######################################
"model_class": model_class,
"model_pars": {
"objective": "huber", ### Regression Type Loss
"n_estimators": 100,
"learning_rate": 0.001,
"boosting_type": "gbdt", ### Model hyperparameters
"early_stopping_rounds": 5
},
"post_process_fun": post_process_fun ### After prediction #######
,
"pre_process_pars": {
"y_norm_fun":
pre_process_fun, ### Before training ##########################
### Pipeline for data processing ##############################
"pipe_list": [
#### Example of Custom processor
{
"uri": THIS_FILEPATH + "::pd_dsa2_custom",
"pars": {
'coldate': 'date'
},
"cols_family": "col_tseries",
"cols_out": "tseries_feat",
"type": ""
},
],
}
},
"compute_pars": {
"metric_list": [
'root_mean_squared_error', 'mean_absolute_error',
'explained_variance_score', 'r2_score', 'median_absolute_error'
]
},
"data_pars": {
"n_sample": n_sample,
"download_pars": None,
### Raw data: column input ##############################################################
"cols_input_type": cols_input_type_1,
### Model Input : Merge family of columns #############################################
"cols_model_group": [
### cols_out of pd_dsa2_custom
"tseries_feat"
]
#### Model Input : Separate Category Sparse from Continuous : Aribitrary name is OK (!)
,
'cols_model_type': {
'My123_continuous': [
'tseries_feat',
],
'my_sparse': [
'colcat',
],
}
### Filter data rows ##################################################################
,
"filter_pars": {
"ymax": 999999999,
"ymin": -1
}
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict,
data_name,
config_name=os_get_function_name())
return model_dict
def income_status_lightgbm(path_model_out=""):
"""
"""
data_name = "income_status" ### in data/input/
model_class = 'LGBMClassifier' # 'LGBMClassifier_optuna' ACTUAL Class name for model_sklearn.py
n_sample = 32500 # 32560
def post_process_fun(y): ### After prediction is done
return int(y)
def pre_process_fun(y): ### Before the prediction is done
return int(y)
model_dict = {
'model_pars': {
### LightGBM API model #######################################
'model_class': model_class,
'model_pars': {
'boosting_type': 'gbdt',
'class_weight': None,
'colsample_bytree': 1.0,
'importance_type': 'split',
'learning_rate': 0.001,
'max_depth': -1,
'min_child_samples': 20,
'min_child_weight': 0.001,
'min_split_gain': 0,
'n_estimators': 5000,
'n_jobs': -1,
'num_leaves': 31,
'objective': None,
'random_state': None,
'reg_alpha': 0,
'reg_lambda': 0.0,
'silent': True,
'subsample': 1.0,
'subsample_for_bin': 200000,
'subsample_freq': 0
},
'post_process_fun': post_process_fun,
'pre_process_pars': {
'y_norm_fun':
pre_process_fun,
### Pipeline for data processing ##############################
'pipe_list': [
#{'uri': 'data/input/income/manual_preprocessing.py::pd_income_processor', 'pars': {}, 'cols_family': 'colall', 'cols_out': 'colall',
# 'type': 'filter' },
{
'uri': 'source/prepro.py::pd_coly',
'pars': {},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
},
{
'uri': 'source/prepro.py::pd_colnum_bin',
'pars': {},
'cols_family': 'colnum',
'cols_out': 'colnum_bin',
'type': ''
},
{
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
},
### Cross Features
{
'uri': 'source/prepro.py::pd_colcat_to_onehot',
'pars': {},
'cols_family': 'colcat_bin',
'cols_out': 'colcat_onehot',
'type': ''
},
{
'uri': 'source/prepro.py::pd_colnum_binto_onehot',
'pars': {},
'cols_family': 'colnum_bin',
'cols_out': 'colnum_onehot',
'type': ''
},
{
'uri': 'source/prepro.py::pd_colcross',
'pars': {},
'cols_family': 'colcross',
'cols_out': 'colcross_pair',
'type': 'cross'
},
### Quantile normalization
{
'uri': 'source/prepro.py::pd_colnum_quantile_norm',
'pars': {
'colsparse': []
},
'cols_family': 'colnum',
'cols_out': 'colnum_quantile_norm',
'type': ''
},
],
}
},
'compute_pars': {
'metric_list': ['accuracy_score', 'average_precision_score'],
'optuna_params': {
"early_stopping_rounds": 5,
'verbose_eval': 100,
# folds=KFold(n_splits=3)
},
'optuna_engine':
'LightGBMTuner' ### LightGBMTuner', LightGBMTunerCV
},
'data_pars': {
'n_sample': n_sample,
'cols_input_type': cols_input_type_1,
### family of columns for MODEL ########################################################
# "colnum", "colnum_bin", "colnum_onehot", "colnum_binmap", #### Colnum columns
# "colcat", "colcat_bin", "colcat_onehot", "colcat_bin_map", #### colcat columns
# 'colcross_single_onehot_select', "colcross_pair_onehot", 'colcross_pair', #### colcross columns
# 'coldate', 'coltext',
'cols_model_group': [ # 'colnum_bin',
'colcat_bin',
'colnum_quantile_norm',
# 'coltext',
# 'coldate',
'colcross_pair',
]
### Filter data rows ##################################################################
,
'filter_pars': {
'ymax': 2,
'ymin': -1
}
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict,
data_name,
config_name=os_get_function_name())
return model_dict
def titanic_lightoptuna():
"""
Contains all needed informations for Light GBM Classifier model,
used for titanic classification task
"""
config_name = os_get_function_name()
data_name = "titanic" ### in data/input/
model_class = 'LGBMModel_optuna' ### ACTUAL Class name for model_sklearn.py
n_sample = 1000
def post_process_fun(y):
### After prediction is done
return int(y)
def pre_process_fun(y):
### Before the prediction is done
return int(y)
model_dict = {
'model_pars': {
### LightGBM API model #######################################
'model_file': 'optuna_lightgbm.py', ###Optional one
'model_class': model_class,
'model_pars': {
'objective': 'binary',
'n_estimators': 50,
'learning_rate': 0.001,
'boosting_type': 'gbdt', ### Model hyperparameters
'early_stopping_rounds': 5
}
### After prediction ##########################################
,
'post_process_fun': post_process_fun
### Before training ##########################################
,
'pre_process_pars': {
'y_norm_fun':
pre_process_fun,
### Pipeline for data processing ##############################
'pipe_list': [
{
'uri': 'source/prepro.py::pd_coly',
'pars': {},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
},
{
'uri': 'source/prepro.py::pd_colnum_bin',
'pars': {},
'cols_family': 'colnum',
'cols_out': 'colnum_bin',
'type': ''
},
# {'uri': 'source/prepro.py::pd_colnum_binto_onehot', 'pars': {}, 'cols_family': 'colnum_bin', 'cols_out': 'colnum_onehot', 'type': ''},
{
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
},
# {'uri': 'source/prepro.py::pd_colcat_to_onehot', 'pars': {}, 'cols_family': 'colcat_bin', 'cols_out': 'colcat_onehot', 'type': ''},
#{'uri': 'source/prepro.py::pd_colcross', 'pars': {},'cols_family': 'colcross', 'cols_out': 'colcross_pair_onehot', 'type': 'cross'}
],
}
},
#classoptuna.integration.lightgbm.LightGBMTuner(params: Dict[str, Any], train_set: lgb.Dataset,
# num_boost_round: int = 1000, valid_sets: Optional[VALID_SET_TYPE] = None,
# valid_names: Optional[Any] = None, fobj: Optional[Callable[[…], Any]] = None,
# feval: Optional[Callable[[…], Any]] = None, feature_name: str = 'auto', categorical_feature: str = 'auto', early_stopping_rounds: Optional[int] = None, evals_result: Optional[Dict[Any, Any]] = None, verbose_eval: Union[bool, int, None] = True, learning_rates: Optional[List[float]] = None, keep_training_booster: bool = False, callbacks: Optional[List[Callable[[…], Any]]] = None, time_budget: Optional[int] = None, sample_size: Optional[int] = None, study: Optional[optuna.study.Study] = None, optuna_callbacks: Optional[List[Callable[[optuna.study.Study, optuna.trial._frozen.FrozenTrial], None]]] = None, model_dir: Optional[str] = None, verbosity: Optional[int] = None, show_progress_bar: bool = True)[source]
'compute_pars': {
'metric_list': ['accuracy_score', 'average_precision_score'],
'optuna_params': {
"early_stopping_rounds": 5,
'verbose_eval': 100,
# folds=KFold(n_splits=3)
},
'optuna_engine':
'LightGBMTuner' ### LightGBMTuner', LightGBMTunerCV
},
'data_pars': {
'n_sample': n_sample,
'cols_input_type': cols_input_type_1,
### family of columns for MODEL #########################################################
# "colnum", "colnum_bin", "colnum_onehot", "colnum_binmap", #### Colnum columns
# "colcat", "colcat_bin", "colcat_onehot", "colcat_bin_map", #### colcat columns
# 'colcross_single_onehot_select', "colcross_pair_onehot", 'colcross_pair', #### colcross columns
# 'coldate',
# 'coltext',
'cols_model_group': [
'colnum_bin',
'colcat_bin',
# 'coltext',
# 'coldate',
# 'colcross_pair'
]
### Filter data rows ##################################################################
,
'filter_pars': {
'ymax': 2,
'ymin': -1
}
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict, data_name, config_name)
return model_dict
def config2(path_model_out=""):
"""
Contains all needed informations
"""
config_name = os_get_function_name()
data_name = "titanic" ### in data/input/
model_class = 'LGBMClassifier' ### ACTUAL Class name for model_sklearn.py
n_sample = 1000
def post_process_fun(y):
return int(y)
def pre_process_fun(y):
return int(y)
model_dict = {
'model_pars': {
### LightGBM API model #######################################
'model_class': model_class,
'model_pars': {
'objective': 'binary',
'n_estimators': 5,
},
'post_process_fun': post_process_fun,
'pre_process_pars': {
'y_norm_fun':
pre_process_fun,
### Pipeline for data processing ##############################
'pipe_list': [
### coly encoding
{
'uri': 'source/prepro.py::pd_coly',
'pars': {
'ymin': -9999999999.0,
'ymax': 999999999.0,
'y_norm_fun': None
},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
},
{
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {
'path_pipeline': False
},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
}
#### Text
,
{
"uri": "source/prepro_text.py::pd_coltext",
"pars": {
'dimpca': 1,
"word_minfreq": 2
},
"cols_family": "coltext",
"cols_out": "col_text",
"type": ""
},
{
"uri":
"source/prepro_text.py::pd_coltext_universal_google",
"pars": {
'model_uri':
"https://tfhub.dev/google/universal-sentence-encoder-multilingual/3"
},
"cols_family": "coltext",
"cols_out": "col_text",
"type": ""
}
],
}
},
'compute_pars': {
'metric_list': ['accuracy_score', 'average_precision_score']
},
'data_pars': {
'n_sample': n_sample,
#### columns as raw data input
'cols_input_type': cols_input_type_2,
### columns for model input #########################################################
'cols_model_group': [ # 'colnum',
'colcat_bin',
],
#### Separate Category Sparse from Continuous (DLearning input)
'cols_model_type': {
'continuous': [
'colnum',
],
'discreate': [
'colcat_bin',
]
}
### Filter data rows ###################################################################
,
'filter_pars': {
'ymax': 2,
'ymin': -1
}
}
}
##### Filling Global parameters #########################################################
model_dict = global_pars_update(model_dict, data_name, config_name)
return model_dict
def adfraud_lightgbm(path_model_out=""):
"""
"""
config_name = os_get_function_name()
data_name = "adfraud" ### in data/input/
model_class = 'LGBMClassifier' ### ACTUAL Class name for model_sklearn.py
n_sample = 1000
def post_process_fun(y): ### After prediction is done
return int(y)
def pre_process_fun(y): ### Before the prediction is done
return int(y)
model_dict = {
'model_pars': {
### LightGBM API model #######################################
'model_class': model_class,
'model_pars': {
'objective': 'binary',
'n_estimators': 10,
'learning_rate': 0.001,
'boosting_type': 'gbdt', ### Model hyperparameters
'early_stopping_rounds': 5
},
'post_process_fun':
post_process_fun ### After prediction ##########################################
,
'pre_process_pars': {
'y_norm_fun':
pre_process_fun, ### Before training ##########################
### Pipeline for data processing ##############################
'pipe_list': [
{
'uri': 'source/prepro.py::pd_coly',
'pars': {},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
},
{
'uri': 'source/prepro.py::pd_colnum_bin',
'pars': {},
'cols_family': 'colnum',
'cols_out': 'colnum_bin',
'type': ''
},
{
'uri': 'source/prepro.py::pd_colnum_binto_onehot',
'pars': {},
'cols_family': 'colnum_bin',
'cols_out': 'colnum_onehot',
'type': ''
},
{
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
},
{
'uri': 'source/prepro.py::pd_colcat_to_onehot',
'pars': {},
'cols_family': 'colcat_bin',
'cols_out': 'colcat_onehot',
'type': ''
},
# {'uri': 'source/prepro.py::pd_colcross', 'pars': {}, 'cols_family': 'colcross', 'cols_out': 'colcross_pair', 'type': 'cross'},
#### Example of Custom processor
# {'uri': 'titanic_classifier.py::pd_colnum_quantile_norm', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'colnum_quantile_norm', 'type': '' },
],
}
},
'compute_pars': {
'metric_list': ['accuracy_score', 'average_precision_score']
},
'data_pars': {
'n_sample': n_sample,
'cols_input_type': cols_input_type_1,
### family of columns for MODEL #########################################################
# "colnum", "colnum_bin", "colnum_onehot", "colnum_binmap", #### Colnum columns
# "colcat", "colcat_bin", "colcat_onehot", "colcat_bin_map", #### colcat columns
# 'colcross_single_onehot_select', "colcross_pair_onehot", 'colcross_pair', #### colcross columns
# 'coldate',
'cols_model_group': [
'colnum_bin',
'colcat_bin',
# 'coltext',
# 'coldate',
# 'colcross_pair',
### example of custom
# 'colnum_quantile_norm'
]
### Filter data rows ##################################################################
,
'filter_pars': {
'ymax': 2,
'ymin': -1
}
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict, data_name, config_name)
return model_dict
def config9(path_model_out=""):
"""
python example/test_features.py train --nsample 500 --config config1
"""
config_name = os_get_function_name()
data_name = "titanic" ### in data/input/
model_class = 'LGBMClassifier' ### ACTUAL Class name for model_sklearn.py
n_sample = 1000
def post_process_fun(y):
return int(y)
def pre_process_fun(y):
return int(y)
model_dict = {
'model_pars': {
### LightGBM API model #######################################
'model_class': model_class,
'model_pars': {
'objective': 'binary',
'n_estimators': 3,
},
'post_process_fun': post_process_fun,
'pre_process_pars': {
'y_norm_fun':
pre_process_fun,
### Pipeline for data processing ##############################
'pipe_list': [
### coly processing
{
'uri': 'source/prepro.py::pd_coly',
'pars': {
'y_norm_fun': None
},
'cols_family': 'coly',
'cols_out': 'coly',
'type': 'coly'
},
{
'uri': 'source/prepro.py::pd_colcat_bin',
'pars': {
'path_pipeline': False
},
'cols_family': 'colcat',
'cols_out': 'colcat_bin',
'type': ''
}
#### Bug in NA values
,
{
'uri': 'source/prepro.py::pd_colcat_encoder_generic',
'pars': {
'model_name': 'HashingEncoder',
'model_pars': {
'verbose': 1,
'return_df': True
}
},
'cols_family': 'colcat',
'cols_out': 'colcat_encoder2',
'type': ''
}
#### Example of Custom processor
,
{
"uri": THIS_FILEPATH + "::pd_col_amyfun",
"pars": {},
"cols_family": "colnum",
"cols_out": "col_myfun",
"type": ""
},
],
}
},
'compute_pars': {
'metric_list': ['accuracy_score', 'average_precision_score']
},
'data_pars': {
'n_sample': n_sample,
'cols_input_type': cols_input_type_2,
'cols_model_group': ['colnum', 'colcat_bin', 'col_myfun'],
#### Separate Category Sparse from Continuous (DLearning input)
'cols_model_type': {
'continuous': [
'colnum',
],
'discreate': [
'colcat_bin',
]
}
### Filter data rows ###################################################################
,
'filter_pars': {
'ymax': 2,
'ymin': -1
}
}
}
##### Filling Global parameters #########################################################
model_dict = global_pars_update(model_dict, data_name, config_name)
return model_dict
def config1():
"""
ONE SINGLE DICT Contains all needed informations for
used for titanic classification task
"""
data_name = "titanic" ### in data/input/
model_class = "LGBMClassifier" ### ACTUAL Class name for model_sklearn.py
n_sample = 1000
def post_process_fun(y): ### After prediction is done
return int(y)
def pre_process_fun(y): ### Before the prediction is done
return int(y)
model_dict = {
"model_pars": {
### LightGBM API model #######################################
"model_class": model_class,
"model_pars": {
"objective": "binary",
"n_estimators": 10,
"learning_rate": 0.001,
"boosting_type": "gbdt", ### Model hyperparameters
"early_stopping_rounds": 5
},
"post_process_fun":
post_process_fun ### After prediction ##########################################
,
"pre_process_pars": {
"y_norm_fun":
pre_process_fun, ### Before training ##########################
### Pipeline for data processing ##############################
"pipe_list": [
#### coly target prorcessing
{
"uri": "source/prepro.py::pd_coly",
"pars": {},
"cols_family": "coly",
"cols_out": "coly",
"type": "coly"
},
{
"uri": "source/prepro.py::pd_colnum_bin",
"pars": {},
"cols_family": "colnum",
"cols_out": "colnum_bin",
"type": ""
},
{
"uri": "source/prepro.py::pd_colnum_binto_onehot",
"pars": {},
"cols_family": "colnum_bin",
"cols_out": "colnum_onehot",
"type": ""
},
#### catcol INTO integer, colcat into OneHot
{
"uri": "source/prepro.py::pd_colcat_bin",
"pars": {},
"cols_family": "colcat",
"cols_out": "colcat_bin",
"type": ""
},
{
"uri": "source/prepro.py::pd_colcat_to_onehot",
"pars": {},
"cols_family": "colcat_bin",
"cols_out": "colcat_onehot",
"type": ""
},
### Cross_feat = feat1 X feat2
{
"uri": "source/prepro.py::pd_colcross",
"pars": {},
"cols_family": "colcross",
"cols_out": "colcross_pair",
"type": "cross"
},
#### Example of Custom processor
{
"uri": THIS_FILEPATH + "::pd_col_myfun",
"pars": {},
"cols_family": "colnum",
"cols_out": "col_myfun",
"type": ""
},
],
}
},
"compute_pars": {
"metric_list": ["accuracy_score", "average_precision_score"]
# ,"mlflow_pars" : {} ### Not empty --> use mlflow
},
"data_pars": {
"n_sample": n_sample,
"download_pars": None,
"cols_input_type": cols_input_type_1,
### family of columns for MODEL #########################################################
# "colnum", "colnum_bin", "colnum_onehot", "colnum_binmap", #### Colnum columns
# "colcat", "colcat_bin", "colcat_onehot", "colcat_bin_map", #### colcat columns
# "colcross_single_onehot_select", "colcross_pair_onehot", "colcross_pair", #### colcross columns "coldate", "coltext",
"cols_model_group": [
"colnum_bin",
"colcat_bin",
# "coltext",
# "coldate",
"colcross_pair",
### example of custom
"col_myfun"
]
### Filter data rows ##################################################################
,
"filter_pars": {
"ymax": 2,
"ymin": -1
}
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict,
data_name,
config_name=os_get_function_name())
return model_dict
def config_sampler() :
"""
ONE SINGLE DICT Contains all needed informations for
used for titanic classification task
"""
data_name = "titanic" ### in data/input/
model_class = "CTGAN" ### ACTUAL Class name for model_sklearn.py
n_sample = 1000
def post_process_fun(y): ### After prediction is done
return int(y)
def pre_process_fun(y): ### Before the prediction is done
return int(y)
model_dict = {
"model_pars": {
"model_class": model_class
,"model_pars" : { }
, "post_process_fun" : post_process_fun ### After prediction ##########################################
, "pre_process_pars" : {
"y_norm_fun" : pre_process_fun , ### Before training ##########################
### Pipeline for data processing ##############################
"pipe_list": [
#### coly target prorcessing
{"uri": "source/prepro.py::pd_coly", "pars": {}, "cols_family": "coly", "cols_out": "coly", "type": "coly" },
{"uri": "source/prepro.py::pd_colnum_bin", "pars": {}, "cols_family": "colnum", "cols_out": "colnum_bin", "type": "" },
{"uri": "source/prepro.py::pd_colnum_binto_onehot", "pars": {}, "cols_family": "colnum_bin", "cols_out": "colnum_onehot", "type": "" },
#### catcol INTO integer, colcat into OneHot
{"uri": "source/prepro.py::pd_colcat_bin", "pars": {}, "cols_family": "colcat", "cols_out": "colcat_bin", "type": "" },
{"uri": "source/prepro.py::pd_colcat_to_onehot", "pars": {}, "cols_family": "colcat_bin", "cols_out": "colcat_onehot", "type": "" },
],
}
},
"compute_pars": { "metric_list": ["accuracy_score","average_precision_score"]
# ,"mlflow_pars" : {} ### Not empty --> use mlflow
},
"data_pars": {
"n_sample" : n_sample,
"download_pars" : None,
### Filter data rows ##################################################################
"filter_pars": { "ymax" : 2 ,"ymin" : -1 },
### Raw data: column input ##############################################################
"cols_input_type" : cols_input_type_1,
### Model Input : Merge family of columns #############################################
"cols_model_group": [ "colnum_bin", "colcat_bin",]
#### Model Input : Separate Category Sparse from Continuous : Aribitrary name is OK (!)
,'cols_model_type': {
'continuous' : [ 'colnum', ],
'sparse' : [ 'colcat_bin', 'colnum_bin', ],
'my_split_23' : [ 'colnum_bin', ],
}
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict, data_name, config_name=os_get_function_name() )
return model_dict
def config1() :
"""
ONE SINGLE DICT Contains all needed informations for used for titanic classification task
"""
data_name = "titanic" ### in data/input/
model_class = "source/models/keras_widedeep_dense.py" ### ACTUAL Class name for
n_sample = 1000
def post_process_fun(y): ### After prediction is done
return int(y)
def pre_process_fun(y): ### Before the prediction is done
return int(y)
model_dict = {"model_pars": {
### LightGBM API model #######################################
"model_class": model_class
,"model_pars" : {
}
, "post_process_fun" : post_process_fun ### After prediction ##########################################
, "pre_process_pars" : {"y_norm_fun" : pre_process_fun , ### Before training ##########################
### Pipeline for data processing ##############################
"pipe_list": [
{"uri": "source/prepro.py::pd_coly", "pars": {}, "cols_family": "coly", "cols_out": "coly", "type": "coly" },
{"uri": "source/prepro.py::pd_colnum_bin", "pars": {}, "cols_family": "colnum", "cols_out": "colnum_bin", "type": "" },
{"uri": "source/prepro.py::pd_colnum_binto_onehot", "pars": {}, "cols_family": "colnum_bin", "cols_out": "colnum_onehot", "type": "" },
{"uri": "source/prepro.py::pd_colcat_bin", "pars": {}, "cols_family": "colcat", "cols_out": "colcat_bin", "type": "" },
{"uri": "source/prepro.py::pd_colcat_to_onehot", "pars": {}, "cols_family": "colcat_bin", "cols_out": "colcat_onehot", "type": "" },
#### neeed to 0-1 Normalize the input
# {"uri": "source/prepro.py::pd_colcat_bin", "pars": {}, "cols_family": "colcat", "cols_out": "colcat_bin", "type": "" },
],
}},
"compute_pars": { "metric_list": ["accuracy_score","average_precision_score"],
'compute_pars': {'epochs': 1 },
'path_checkpoint' : "ztmp_checkpoint/"
},
"data_pars": { "n_sample" : n_sample,
"download_pars" : None,
### family of columns for raw input data #########################################################
"cols_input_type" : cols_input_type_1,
### family of columns used for model input #########################################################
"cols_model_group": [ "colnum_onehot", "colcat_onehot",
]
,'cols_model_type' : {
'cols_cross_input': [ "colcat_onehot", ],
'cols_deep_input': ['colnum_onehot', ],
}
### Filter data rows ##################################################################
,"filter_pars": { "ymax" : 2 ,"ymin" : -1 }
}
}
##### Filling Global parameters ############################################################
model_dict = global_pars_update(model_dict, data_name, config_name=os_get_function_name() )
return model_dict
