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darwin-mendel-0.2.4
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Genetic Algorithm: Optimize the output of machine learning models
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | darwin-mendel-0.2.4 |
| نام | darwin-mendel |
| نسخه کتابخانه | 0.2.4 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Manish Agrawal |
| ایمیل نویسنده | manishagrawal.datascience@gmail.com |
| آدرس صفحه اصلی | https://github.com/manishagrawal-datascience/Genetic-Algorithm.git |
| آدرس اینترنتی | https://pypi.org/project/darwin-mendel/ |
| مجوز | - |
Genetic Algorithm: A unique way for hyper-parameter tuning of ML models.
The process of evolution and natural selection (Survival of the fittest) used in this project
to select the best hyper-parameters for certain regression techniques like
Decision Tree Regression, Random Forest Regression, Light Gradient Boosting Regression
and Extreme Gradient Boosting Regression.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic
inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms.
Genetic algorithms are commonly used to generate high-quality solutions to optimization
and search problems by relying on biologically inspired operators such as
selection, crossover and mutation.
Install the library
pip install darwin-mendel
Regression:
Following is an example of regression run.
1: Example for Extreme Gradient Boost Regression
from darwin_mendel.optimize_dtr import optimize_dtr
from darwin_mendel.optimize_rfr import optimize_rfr
from darwin_mendel.optimize_lgbmr import optimize_lgbmr
from darwin_mendel.optimize_xgbr import optimize_xgbr
import sklearn.datasets as datasets
iris = datasets.load_boston()
df = pd.DataFrame(iris.data)
x_train, x_test, y_train, y_test = train_test_split(df[[2,4,5,6,7,8,9,10,11]],
df[12], test_size=0.2, random_state=2021)
params = {'n_estimators': [100,200,300,400,500,600,700,800,900,1000],
'max_depth': [8,9,10,11,12,13,14,15],
'learning_rate': [0.001, 0.01, 0.1, 0.2, 0.7, 0.8, 0.9, 1],
'booster': ['gbtree','gblinear'],
'reg_alpha': [0],
'reg_lambda': [1]}
model, hyp_param = optimize_xgbr(x_train=x_train, y_train=y_train, y_test=y_test, x_test=x_test,
params=params, number_of_generation=10, population_size=30,
error_metric='RMSE', mutation_rate=0.1)
print(hyp_param)
2: OutPut:
n_estimators 400
max_depth 9
learning_rate 0.2
booster gbtree
reg_alpha 0
reg_lambda 1
RMSE 16.76
Name: 0, dtype: object
3. Arguments:
a. User must provide the x_train, y_train, x_test and y_test in the arguments. They should not
contain any missing values and strings values.
b. User can select the error_metreic between 'MAPE' and 'RMSE', it is used to select the best model.
Default is 'MAPE' (Mean Absolute Percentage Error).
c. population_size defines initial number of combination of hyper-parameters from which off-springs
are produced. Thumb rule is, it should be: 5 * number of variables. Default is 50.
d. number_of_generation is the number of new batches produced from the initial population.
The more the number of generation, the better will be the resut but it could increase the
time consumption. Default is 10.
e. mutation_rate is the percentage impurity added in a new batch of off springs, it helps in
reaching the global minimum. Default is 0.05 i.e. 5%
f. random_seed has to be fixed to make the results repeatable. Default is 2021.
g. Default params:
{'n_estimators': [2,3,4,5,6,......1000],
'max_depth': [2,3,4,5,6....20],
'learning_rate': [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
'booster': ['gbtree'],
'reg_alpha': [0],
'reg_lambda': [1]}
Above mentioned are the default ranges for each hyperparameter of Random Forest Regression.
User can give the range according to their need.
4. Default params ranges for other regression algorithms.
a. RFR:
{'n_estimators': [2,3,4,.....1000],
'max_features': ['sqrt', 'auto', 'log2', None],
'min_samples_leaf': [2,3,4,5,6,.....16],
'max_depth': [2,3,4,5,6,.....20]}
b. LGBMR:
{'n_estimators': [2,3,4,5,6,......1000],
'max_depth': [2,3,4,5,6....20],
'learning_rate': [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
'boosting_type': ['gbdt'],
'num_leaves': [2,3,4,5,6,....15],
'reg_alpha': [0],
'reg_lambda': [0]}
c. DTR:
{'min_samples_leaf': [1,2,3,4,5,6....20],
'max_depth': [2,3,4,5,6....20],
'max_features': ['auto', 'sqrt', 'log2'],
'splitter': ['best', 'random'],
'criterion': ['mse', 'friedman_mse', 'mae']}
Classification:
from darwin_mendel.optimize_dtc import optimize_dtc
from darwin_mendel.optimize_rfc import optimize_rfc
from darwin_mendel.optimize_lgbmc import optimize_lgbmc
from darwin_mendel.optimize_xgbc import optimize_xgbc
I: Default error_metric is 'accuracy_score', other available options are 'f1-score'
and 'roc_auc_score'. Is is used to select the best models and score them accordingly.
NOTE: Please don't use 'roc_auc_score' for multi-class models.
II: population_size, default value is 50.
III: number_of_generation, default value is 10.
IV: mutation_rate, default value is 0.05.
V: random_seed, default value is 2021.
Vi: The range of default hyper-parameters are given below, the used can provide a different range
for each of the parapeter according to their need.
1. Default params ranges for all Classification algorithms.
a. RFC:
{'n_estimators': [2,3,4,.....1000],
'max_features': ['sqrt', 'auto', 'log2', None],
'min_samples_leaf': [2,3,4,5,6,.....16],
'max_depth': [2,3,4,5,6,.....20],
'criterion': ['gini', 'entropy'],
'oob_score': [True, False]}
b. LGBMC:
{'n_estimators': [2,3,4,5,6,......1000],
'max_depth': [2,3,4,5,6....20],
'learning_rate': [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
'boosting_type': ['gbdt'],
'num_leaves': [2,3,4,5,6,....15],
'reg_alpha': [0],
'reg_lambda': [0]}
c. DTC:
{'min_samples_leaf': [1,2,3,4,5,6....20],
'max_depth': [2,3,4,5,6....20],
'max_features': ['auto', 'sqrt', 'log2'],
'splitter': ['best', 'random'],
'criterion': ['gini', 'entropy']}
d. XGBC:
{'n_estimators': [2,3,4,5,6,......1000],
'max_depth': [2,3,4,5,6....20],
'learning_rate': [0.001, 0.01, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1],
'booster': ['gbtree'],
'reg_alpha': [0],
'reg_lambda': [1]}
نیازمندی
| مقدار | نام |
|---|---|
| >=1.17.3 | numpy |
| >=1.0.2 | pandas |
| >=0.24.1 | scikit-learn |
| >=1.4.1 | scipy |
| >=1.4.2 | xgboost |
| >=3.2.1 | lightgbm |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.6 | Python |
نحوه نصب
نصب پکیج whl darwin-mendel-0.2.4:
pip install darwin-mendel-0.2.4.whl
نصب پکیج tar.gz darwin-mendel-0.2.4:
pip install darwin-mendel-0.2.4.tar.gz