-
Notifications
You must be signed in to change notification settings - Fork 0
/
main.py
63 lines (57 loc) · 2 KB
/
main.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
# imports
from text_summary.pipeline.stage_01_data_ingestion import DataIngestionTrainingPipeline
from text_summary.pipeline.stage_02_data_validation import DataValidationTrainingPipeline
from text_summary.pipeline.stage_03_data_transformation import DataTransformationTrainingPipeline
from text_summary.pipeline.stage_04_model_trainer import ModelTrainerPipeline
from text_summary.pipeline.stage_05_model_evaluation import ModelEvaluationTrainingPipeline
from text_summary.logging import logger
# Stage 1 : Data Ingestion
STAGE_NAME = "Data Ingestion stage"
try:
logger.info(f"~~~ {STAGE_NAME} is started ~~~")
data_ingestion = DataIngestionTrainingPipeline()
data_ingestion.main()
logger.info(f"~~~ {STAGE_NAME} is completed ~~~")
except Exception as e:
logger.exception(e)
raise e
# STAGE 2 : Data Validation
STAGE_NAME = "Data Validation stage"
try:
logger.info(f"~~~ {STAGE_NAME} started ~~~")
data_validation = DataValidationTrainingPipeline()
data_validation.main()
logger.info(f"~~~ {STAGE_NAME} completed ~~~")
except Exception as e:
logger.exception(e)
raise e
# STAGE 3 : Data Transformation
STAGE_NAME = "Data Transformation stage"
try:
logger.info(f"~~~ {STAGE_NAME} started ~~~")
data_transformation = DataTransformationTrainingPipeline()
data_transformation.main()
logger.info(f"~~~ {STAGE_NAME} completed ~~~")
except Exception as e:
logger.exception(e)
raise e
# STAGE 4 : Model Trainer
STAGE_NAME = "Model Trainer stage"
try:
logger.info(f"~~~ {STAGE_NAME} started ~~~")
model_trainer = ModelTrainerPipeline()
model_trainer.main()
logger.info(f"~~~ {STAGE_NAME} completed ~~~")
except Exception as e:
logger.exception(e)
raise e
# STAGE 5 : Model Trainer
STAGE_NAME = "Model Evaluation stage"
try:
logger.info(f"~~~ {STAGE_NAME} started ~~~")
model_eval = ModelEvaluationTrainingPipeline()
model_eval.main()
logger.info(f"~~~ {STAGE_NAME} completed ~~~")
except Exception as e:
logger.exception(e)
raise e