jarvis.ai.pkgs.sklearn package¶
Submodules¶
jarvis.ai.pkgs.sklearn.classification module¶
Simple ML models for classifcation and regression.
Designed for educational purposes only
-
jarvis.ai.pkgs.sklearn.classification.classification(X=[], Y=[], tol=100, plot=False, preprocess=True, models=[DecisionTreeClassifier(), MLPClassifier(), GradientBoostingClassifier(), KNeighborsClassifier(), GaussianProcessClassifier(), RandomForestClassifier(), AdaBoostClassifier(), SVC()], model_name='my_model', save_model=False)[source]¶ Quickly train some of the classifcation algorithms in scikit-learn.
-
jarvis.ai.pkgs.sklearn.classification.classify_roc_ml(X=[], y=[], classes=[0, 1, 2], names=['High val', 'Low val', ''], n_plot=1, method='', preprocess=True, plot=False, test_size=0.1)[source]¶ Classifcation module for ROC curve for upto three classes.
It can be expanded in more classes as well. Args:
X: input feature vectors
y: target data obtained from binary_class_dat
classes: dummy classes
names: name holders for the target data
method: ML method
preprocess: whether to apply standard preprocessing techniques
plot: whether to plot the ROC curve
jarvis.ai.pkgs.sklearn.hyper_params module¶
Set of ranges for hyperparameters.
# Modified from https://github.com/EpistasisLab/tpot
jarvis.ai.pkgs.sklearn.regression module¶
Simple ML models for regression.
Designed for educational purposes only.
-
jarvis.ai.pkgs.sklearn.regression.regression(X=[], Y=[], plot=False, models=[GaussianProcessRegressor(), RandomForestRegressor(), GradientBoostingRegressor(), AdaBoostRegressor(), SVR(), Lasso(), LinearRegression(), KernelRidge(), MLPRegressor(), DecisionTreeRegressor()], preprocess=True, test_size=0.1)[source]¶ Provide model as models to get accuracy.
- Args:
X: input features
Y: Target data
models : collection array of models
plot: whether to make a parity plot with ML models
preprocess: whether to apply standard preprocessing techniques
Module contents¶
Modules for scikit-learn appications.