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titanic_classifier.py
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titanic_classifier.py
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# pylint: disable=C0321,C0103,E1221,C0301,E1305,E1121,C0302,C0330
# -*- coding: utf-8 -*-
"""
python titanic_classifier.py train > zlog/log_titanic_train.txt 2>&1
python titanic_classifier.py predict > zlog/log_titanic_predict.txt 2>&1
"""
import warnings, copy, os, sys
warnings.filterwarnings('ignore')
####################################################################################
###### Path ########################################################################
root_repo = os.path.abspath(os.getcwd()).replace("\\", "/") + "/" ; print(root_repo)
THIS_FILEPATH = os.path.abspath(__file__)
sys.path.append(root_repo)
from source.util_feature import save,os_get_function_name
def global_pars_update(model_dict, data_name, config_name):
print("config_name", config_name)
dir_data = root_repo + "/data/" ; print("dir_data", dir_data)
m = {}
m['config_path'] = THIS_FILEPATH
m['config_name'] = config_name
#### peoprocess input path
m['path_data_preprocess'] = dir_data + f'/input/{data_name}/train/'
#### train input path
m['path_data_train'] = dir_data + f'/input/{data_name}/train/'
m['path_data_test'] = dir_data + f'/input/{data_name}/test/'
#m['path_data_val'] = dir_data + f'/input/{data_name}/test/'
#### train output path
m['path_train_output'] = dir_data + f'/output/{data_name}/{config_name}/'
m['path_train_model'] = dir_data + f'/output/{data_name}/{config_name}/model/'
m['path_features_store'] = dir_data + f'/output/{data_name}/{config_name}/features_store/'
m['path_pipeline'] = dir_data + f'/output/{data_name}/{config_name}/pipeline/'
#### predict input path
m['path_pred_data'] = dir_data + f'/input/{data_name}/test/'
m['path_pred_pipeline'] = dir_data + f'/output/{data_name}/{config_name}/pipeline/'
m['path_pred_model'] = dir_data + f'/output/{data_name}/{config_name}/model/'
#### predict output path
m['path_pred_output'] = dir_data + f'/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
####################################################################################
##### Params########################################################################
config_default = 'titanic_lightgbm' ### name of function which contains data configuration
# data_name = "titanic" ### in data/input/
cols_input_type_1 = {
"coly" : "Survived"
,"colid" : "PassengerId"
,"colcat" : ["Sex", "Embarked" ]
,"colnum" : ["Pclass", "Age","SibSp", "Parch","Fare"]
,"coltext" : []
,"coldate" : []
,"colcross" : [ "Name", "Sex", "Ticket","Embarked","Pclass", "Age", "SibSp", ]
}
cols_input_type_2 = {
"coly" : "Survived"
,"colid" : "PassengerId"
,"colcat" : ["Sex", "Embarked" ]
,"colnum" : ["Pclass", "Age","SibSp", "Parch","Fare"]
,"coltext" : ["Name", "Ticket"]
,"coldate" : []
,"colcross" : [ "Name", "Sex", "Ticket","Embarked","Pclass", "Age","SibSp", "Parch","Fare" ]
}
####################################################################################
def titanic_lightgbm() :
"""
Contains all needed informations for Light GBM Classifier model,
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']
},
'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 pd_col_myfun(df=None, col=None, pars={}):
"""
Example of custom Processor
"""
from source.util_feature import save, load
prefix = 'col_myfun`'
if 'path_pipeline' in pars : #### Inference time LOAD previous pars
prepro = load(pars['path_pipeline'] + f"/{prefix}_model.pkl" )
pars = load(pars['path_pipeline'] + f"/{prefix}_pars.pkl" )
pars = {} if pars is None else pars
#### Do something #################################################################
df_new = df[col] ### Do nithi
df_new.columns = [ col + "_myfun" for col in df.columns ]
cols_new = list(df_new.columns)
prepro = None
pars_new = None
###################################################################################
if 'path_features_store' in pars and 'path_pipeline_export' in pars:
save(prepro, pars['path_pipeline_export'] + f"/{prefix}_model.pkl" )
save(cols_new, pars['path_pipeline_export'] + f"/{prefix}.pkl" )
save(pars_new, pars['path_pipeline_export'] + f"/{prefix}_pars.pkl" )
col_pars = {'prefix' : prefix , 'path' : pars.get('path_pipeline_export', pars.get('path_pipeline', None)) }
col_pars['cols_new'] = {
'col_myfun' : cols_new ### list
}
return df_new, col_pars
#####################################################################################
########## Profile data #############################################################
from core_run import data_profile
# def data_profile(path_data="", path_output="", n_sample= 5000):
"""
def data_profile(path_data="", path_output="", n_sample= 5000):
from source.run_feature_profile import run_profile
run_profile(path_data = path_data,
path_output = path_output + "/profile/",
n_sample = n_sample,
)
"""
###################################################################################
########## 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']
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 #################################################################
from core_run import 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 ##################################################################
# predict(config='', nsample=10000)
from core_run import predict
"""
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
)
"""
###########################################################################################################
###########################################################################################################
"""
python titanic_classifier.py data_profile
python titanic_classifier.py preprocess --nsample 100
python titanic_classifier.py train --nsample 200
python titanic_classifier.py check
python titanic_classifier.py predict
"""
if __name__ == "__main__":
d = { 'data_profile': data_profile, 'train' : train, 'predict' : predict, 'config' : config_default }
import fire
fire.Fire(d)