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multi_classifier.py
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# pylint: disable=C0321,C0103,E1221,C0301,E1305,E1121,C0302,C0330
# -*- coding: utf-8 -*-
"""
https://www.kaggle.com/tapioca/multiclass-lightgbm
https://medium.com/@nitin9809/lightgbm-binary-classification-multi-class-classification-regression-using-python-4f22032b36a2
python multi_classifier.py train
python multi_classifier.py check
python multi_classifier.py predict
"""
import warnings, copy, os, sys
warnings.filterwarnings('ignore')
####################################################################################
###### Path ########################################################################
from source import run_train
config_file = os.path.basename(__file__)
## config_file = "multi_classifier.py"
print( os.getcwd())
root = os.path.abspath(os.getcwd()).replace("\\", "/") + "/"
print(root)
dir_data = os.path.abspath( root + "/data/" ) + "/"
dir_data = dir_data.replace("\\", "/")
print(dir_data)
def os_get_function_name():
import sys
return sys._getframe(1).f_code.co_name
def global_pars_update(model_dict, data_name, config_name):
m = {}
m['config_path'] = root + f"/{config_file}"
m['config_name'] = config_name
##### run_Preoprocess ONLY
m['path_data_preprocess'] = root + f'/data/input/{data_name}/train/'
##### run_Train ONLY
m['path_data_train'] = root + f'/data/input/{data_name}/train/'
m['path_data_test'] = root + f'/data/input/{data_name}/test/'
#m['path_data_val'] = root + f'/data/input/{data_name}/test/'
m['path_train_output'] = root + f'/data/output/{data_name}/{config_name}/'
m['path_train_model'] = root + f'/data/output/{data_name}/{config_name}/model/'
m['path_features_store'] = root + f'/data/output/{data_name}/{config_name}/features_store/'
m['path_pipeline'] = root + f'/data/output/{data_name}/{config_name}/pipeline/'
##### Prediction
m['path_pred_data'] = root + f'/data/input/{data_name}/test/'
m['path_pred_pipeline']= root + f'/data/output/{data_name}/{config_name}/pipeline/'
m['path_pred_model'] = root + f'/data/output/{data_name}/{config_name}/model/'
m['path_pred_output'] = root + f'/data/output/{data_name}/pred_{config_name}/'
##### Generic
m['n_sample'] = model_dict['data_pars'].get('n_sample', 5000)
model_dict[ 'global_pars'] = m
return model_dict
####################################################################################
config_default = 'multi_lightgbm'
colid = 'pet_id'
coly = 'pet_category'
coldate = ['issue_date','listing_date']
colcat = ['color_type','condition']
colnum = ['length(m)','height(cm)','X1','X2'] # ,'breed_category'
colcross= ['condition', 'color_type','length(m)', 'height(cm)', 'X1', 'X2'] # , 'breed_category'
cols_input_type_1 = { "coly" : coly
,"colid" : colid
,"colcat" : colcat
,"colnum" : colnum
,"coltext" : []
,"coldate" : []
,"colcross" : colcross
}
#####################################################################################
##### Params ########################################################################
def multi_lightgbm(path_model_out="") :
"""
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'
}
### 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/preprocessors.py::pd_coly', 'pars': {}, 'cols_family': 'coly', 'cols_out': 'coly', 'type': 'coly' },
{'uri': 'source/preprocessors.py::pd_colnum_bin', 'pars': {}, 'cols_family': 'colnum', 'cols_out': 'colnum_bin', 'type': '' },
{'uri': 'source/preprocessors.py::pd_colnum_binto_onehot', 'pars': {}, 'cols_family': 'colnum_bin', 'cols_out': 'colnum_onehot', 'type': '' },
{'uri': 'source/preprocessors.py::pd_colcat_bin', 'pars': {}, 'cols_family': 'colcat', 'cols_out': 'colcat_bin', 'type': '' },
{'uri': 'source/preprocessors.py::pd_colcat_to_onehot', 'pars': {}, 'cols_family': 'colcat_bin', 'cols_out': 'colcat_onehot', 'type': '' },
{'uri': 'source/preprocessors.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']
### 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
#####################################################################################
########## Profile data #############################################################
def data_profile(path_data_train="", path_model="", n_sample= 5000):
from source.run_feature_profile import run_profile
run_profile(path_data = path_data_train,
path_output = path_model + "/profile/",
n_sample = n_sample,
)
###################################################################################
########## 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']
print(mdict)
from source import run_preprocess
run_preprocess.run_preprocess(config_name = config_name,
config_path = m['config_path'],
n_sample = nsample if nsample is not None else m['n_sample'],
### Optonal
mode = 'run_preprocess')
##################################################################################
########## Train #################################################################
def train(config=None, nsample=None):
config_name = config if config is not None else config_default
mdict = globals()[config_name]()
m = mdict['global_pars']
print(mdict)
from source import run_train
run_train.run_train(config_name = config_name,
config_path = m['config_path'],
n_sample = nsample if nsample is not None else m['n_sample'],
)
###################################################################################
######### Check data ##############################################################
def check():
pass
####################################################################################
####### Inference ##################################################################
def predict(config=None, nsample=None):
config_name = config if config is not None else config_default
mdict = globals()[config_name]()
m = mdict['global_pars']
from source import run_inference
run_inference.run_predict(config_name = config_name,
config_path = m['config_path'],
n_sample = nsample if nsample is not None else m['n_sample'],
#### Optional
path_data = m['path_pred_data'],
path_output = m['path_pred_output'],
model_dict = None
)
def run_all():
data_profile()
preprocess()
train()
check()
predict()
###########################################################################################################
###########################################################################################################
"""
python multi_classifier.py data_profile
python multi_classifier.py preprocess
python multi_classifier.py train
python multi_classifier.py check
python multi_classifier.py predict
python multi_classifier.py run_all
"""
if __name__ == "__main__":
import fire
fire.Fire()