neuro-ml is a open source library designed to avoid writing duplicate code.
You can use new model of scikit learn without writing any code.
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 python to export sample configuration file.
commandline
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
commandline
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.
commandline
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
commandline
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}")
v.0.1.1
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
commandline
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
commandline
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
commandline
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
commandline
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}")
v.0.0.5
neuro-ml is a open source library designed to avoid writing duplicate code.
You can use new model of scikit learn without writing any code.
Model training can be control by configuration file.
> <center>neuro_mf.ModelFactory</center>
>class neuro_mf.ModelFactory(model_config_path:str) [source](https://github.com/avnyadav/neuro_mf/blob/main/neuro_mf/__init__.py#L33)
<hr>
Parameters:
model_config_path: location of model.yaml file
How to generate configuration file
It is very simple.
We will export sample model config file in config directory
You can use below python to export sample configuration file.
python
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
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
module_1:
class: SVR
module: sklearn.svm
params:
kernel: rbf
search_param_grid:
kernel:
- poly
- rbf
<hr>
Attributes:
``config: dict
model.yaml file will be available as dict in this attribute
``
``
grid_search_cv_module: str
Module of grid search cv from sklearn lib sklearn.model_selection
``
``
grid_search_class_name: str
``
``
grid_search_property_data:dict
``
``
models_initialization_config: dict
``
``
initialized_model_list:
All model instance will be available in this attribute once get_initialized_model_list has been invoked on model factory object
``
``
grid_searched_best_model_list
Once grid search has been done for all model then every model with their tuned paramter will be available in this attribute
``
Few of the function return type can be inferred using
below named tuple
InitializedModelDetail = namedtuple("InitializedModelDetail",
["model_serial_number", "model", "param_grid_search", "model_name"])
GridSearchedBestModel = namedtuple("GridSearchedBestModel", ["model_serial_number",
"model",
"best_model",
"best_parameters",
"best_score",
])
BestModel = namedtuple("BestModel", ["model_serial_number",
"model",
"best_model",
"best_parameters",
"best_score", ])
<hr>
Methods:
<table>
<tr>
<td>class_for_name(module_name, class_name)</td>
</tr>
<tr><td>execute_grid_search_operation(self, initialized_model: InitializedModelDetail, input_feature,
output_feature)</td>
</tr>
<tr><td>get_best_model(self, X, y, base_accuracy=0.6) -> BestModel</td>
</tr>
<tr><td>get_best_model_from_grid_searched_best_model_list(grid_searched_best_model_list: List[GridSearchedBestModel],
base_accuracy=0.6
) -> BestModel</td>
</tr>
<tr><td>get_initialized_model_list(self) -> List[InitializedModelDetail]</td>
</tr>
<tr><td>initiate_best_parameter_search_for_initialized_model(self, initialized_model: InitializedModelDetail,
input_feature,
output_feature) -> GridSearchedBestModel</td>
</tr>
<tr><td>initiate_best_parameter_search_for_initialized_models(self,
initialized_model_list: List[InitializedModelDetail],
input_feature,
output_feature) -> List[GridSearchedBestModel]</td>
</tr>
<tr><td>read_params</td>
</tr>
<tr><td>update_property_of_class</td>
</tr>
</table>
How to generate configuration file
It is very simple.
We will export sample model config file in config directory
You can use below python to export sample configuration file.
python
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
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.
yaml
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
module_1:
class: SVR
module: sklearn.svm
params:
kernel: rbf
search_param_grid:
kernel:
- poly
- rbf
Above configuration file contains information about two model Random Forest and Support Vector
get_best_model_function will return best model after comparison
Now Let's try to train a RandomForestRegressor
python
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}")
v.0.0.3
neuro-ml is a open source library designed to avoid writing duplicate code.
You can use new model of scikit learn without writing any code.
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 python to export sample configuration file.
commandline
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
commandline
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.
commandline
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
commandline
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}")
dev
iNeuron Model Factory helps us to generate model training and grid search code automatically based
on the configuration provided.
v.0.0.2
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
commandline
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
commandline
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
commandline
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
commandline
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}")