iNeuron Model Factory helps us to generate model training and grid search code automatically based
Project description
neuro-ml is a open source library designed to avoid writing duplicate code.
You can use new model of scikit learn without writing any cod. Model training can be control by configuration file
How to generate configuration file
It is very simple.
We will export sample model config file in config directory
You can use below command to export sample configuration
from neuro_mf.config import get_sample_model_config_yaml_file
if __name__ == "__main__":
export_file_path=get_sample_model_config_yaml_file(export_dir="config")
Check your config folder You will find a file name as "model.yaml"
content of model.yaml
grid_search:
class: GridSearchCV
module: sklearn.model_selection
params:
cv: 3
verbose: 1
model_selection:
module_0:
class: ModelClassName
module: module_of_model
params:
param_name1: value1
param_name2: value2
search_param_grid:
param_name:
- param_value_1
- param_value_2
Now update the content of model.yaml file with below content for testing
grid_search:
class: GridSearchCV
module: sklearn.model_selection
params:
cv: 3
verbose: 1
model_selection:
module_0:
class: RandomForestRegressor
module: sklearn.ensemble
params:
n_estimators: 200
criterion: squared_error
search_param_grid:
n_estimators:
- 150
- 200
- 250
max_depth:
- 2
- 5
- 6
Now Let's try to train a RandomForestRegressor
import os
from neuro_mf.config import get_sample_model_config_yaml_file
from neuro_mf import ModelFactory
if __name__ == "__main__":
# export_dir=get_sample_model_config_yaml_file(export_dir="config")
export_file_path = os.path.join("config", "model.yaml")
model_factory = ModelFactory(model_config_path=export_file_path)
x = None # input feature
y = None # target feature
best_model = model_factory.get_best_model(x, y, base_accuracy=0.9)
print(best_model.best_model)
print(f"best score:{best_model.best_score}")
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