Closed MKaptein closed 4 years ago
Classification | Regression | |
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Linear |
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SVM |
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Tree |
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Random Forest |
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Boosting |
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In the midst of adding this, but needs an update of the toolchain to support lightgbm
(which is also the package name in the bundle).
Ran tests for:
package | model | New | Tested (01052020) | Note |
---|---|---|---|---|
lightgbm | LGBMClassifier(rf booster only) | yes | ok | |
lightgbm | LGBMRegressor(rf booster only) | yes | ok | |
lightning | AdaGradClassifier | yes | no | Skip for now |
lightning | AdaGradRegressor | yes | no | Skip for now |
lightning | CDClassifier | yes | no | Skip for now |
lightning | CDRegressor | yes | no | Skip for now |
lightning | FistaClassifier | yes | no | Skip for now |
lightning | FistaRegressor | yes | no | Skip for now |
lightning | KernelSVC | yes | no | Skip for now |
lightning | SAGAClassifier | yes | no | Skip for now |
lightning | SAGARegressor | yes | no | Skip for now |
lightning | SAGClassifier | yes | no | Skip for now |
lightning | SAGRegressor | yes | no | Skip for now |
lightning | SDCAClassifier | yes | no | Skip for now |
lightning | SDCARegressor | yes | no | Skip for now |
sklearn | ARDRegression | no | ok | |
sklearn | BayesianRidge | yes | ok | |
sklearn | DecisionTreeClassifier | no | ok | |
sklearn | DecisionTreeRegressor | no | ok | |
sklearn | ElasticNet | no | ok | |
sklearn | ElasticNetCV | no | ok | |
sklearn | ExtraTreeClassifier | no | ok | |
sklearn | ExtraTreeRegresso | no | ok | |
sklearn | ExtraTreesClassifier | no | ok | |
sklearn | ExtraTreesRegressor | no | ok | |
sklearn | HuberRegressor | no | ok | |
sklearn | Lars | no | ok | |
sklearn | LarsCV | no | ok | |
sklearn | Lasso | no | ok | |
sklearn | LassoCV | no | ok | |
sklearn | LassoLars | no | ok | |
sklearn | LassoLarsCV | no | ok | |
sklearn | LassoLarsIC | no | ok | |
sklearn | LinearRegression | no | ok | |
sklearn | LinearSVC | no | ok | |
sklearn | LinearSVR | no | ok | |
sklearn | LogisticRegression | yes | ok | |
sklearn | LogisticRegressionCV | yes | ok | |
sklearn | NuSVC | no | ok | |
sklearn | NuSVR | no | ok | |
sklearn | OrthogonalMatchingPursuit | no | ok | |
sklearn | OrthogonalMatchingPursuitCV | no | ok | |
sklearn | PassiveAggressiveClassifier | no | ok | |
sklearn | PassiveAggressiveRegressor | no | ok | |
sklearn | Perceptron | yes | ok | |
sklearn | RandomForestClassifier | no | ok | |
sklearn | RandomForestRegressor | no | ok | |
sklearn | RANSACRegressor | no | ok | |
sklearn | Ridge | no | ok | |
sklearn | RidgeClassifier | yes | ok | |
sklearn | RidgeClassifierCV | yes | ok | |
sklearn | RidgeCV | no | ok | |
sklearn | SGDClassifier | no | ok | |
sklearn | SGDRegressor | no | ok | |
sklearn | SVC | no | ok | |
sklearn | SVR | no | ok | |
sklearn | TheilSenRegressor | no | ok | |
statsmodels | [Gaussian] Process Regression Using Maximum Likelihood-based Estimation (ProcessMLE) | yes | not ok | Skip for now |
statsmodels | Generalized Least Squares (GLS) | ok | ||
statsmodels | Generalized Least Squares with AR Errors (GLSAR) | yes | ok | |
statsmodels | Ordinary Least Squares (OLS) | ok | ||
statsmodels | Quantile Regression (QuantReg) | yes | ok | |
statsmodels | Weighted Least Squares (WLS) | ok | ||
xgboost | XGBClassifier(gbtree/gblinear booster only) | no | ok | |
xgboost | XGBRegressor(gbtree/gblinear booster only) | no | ok | |
xgboost | XGBRFClassifier(binary only, multiclass is not supported yet) | no | ok | |
xgboost | XGBRFRegressor | no | ok |
So, added a few models (including lightgbm) and xgboost. Not added the lightning ones (although in test_all_models.py
these are specified already and they should be simple to add by:
I am closing this for now.
package | model | Tested (05052020) | Note |
---|---|---|---|
lightgbm | LGBMClassifier | ok | |
lightgbm | LGBMRegressor | ok | |
sklearn | ARDRegression | ok | |
sklearn | BayesianRidge | ok | |
sklearn | DecisionTreeClassifier | ok | |
sklearn | DecisionTreeRegressor | ok | |
sklearn | ElasticNet | ok | |
sklearn | ElasticNetCV | ok | |
sklearn | ExtraTreeClassifier | ok | |
sklearn | ExtraTreeRegresso | ok | |
sklearn | ExtraTreesClassifier | ok | |
sklearn | ExtraTreesRegressor | ok | |
sklearn | HuberRegressor | ok | |
sklearn | Lars | ok | |
sklearn | LarsCV | ok | |
sklearn | Lasso | ok | |
sklearn | LassoCV | ok | |
sklearn | LassoLars | ok | |
sklearn | LassoLarsCV | ok | |
sklearn | LassoLarsIC | ok | |
sklearn | LinearRegression | ok | |
sklearn | LinearSVC | ok | |
sklearn | LinearSVR | ok | |
sklearn | LogisticRegression | ok | |
sklearn | LogisticRegressionCV | ok | |
sklearn | NuSVC | ok | |
sklearn | NuSVR | ok | |
sklearn | OrthogonalMatchingPursuit | ok | |
sklearn | OrthogonalMatchingPursuitCV | ok | |
sklearn | PassiveAggressiveClassifier | ok | |
sklearn | PassiveAggressiveRegressor | ok | |
sklearn | Perceptron | ok | |
sklearn | RandomForestClassifier | ok | |
sklearn | RandomForestRegressor | ok | |
sklearn | RANSACRegressor | ok | |
sklearn | Ridge | ok | |
sklearn | RidgeClassifier | ok | |
sklearn | RidgeClassifierCV | ok | |
sklearn | RidgeCV | ok | |
sklearn | SGDClassifier | ok | |
sklearn | SGDRegressor | ok | |
sklearn | SVC | ok | |
sklearn | SVR | ok | |
sklearn | TheilSenRegressor | ok | |
statsmodels | Generalized Least Squares (GLS) | ok | |
statsmodels | Generalized Least Squares with AR Errors (GLSAR) | ok | |
statsmodels | Ordinary Least Squares (OLS) | ok | |
statsmodels | Quantile Regression (QuantReg) | ok | |
statsmodels | Weighted Least Squares (WLS) | ok | |
xgboost | XGBClassifier | ok | |
xgboost | XGBRegressor | ok | |
xgboost | XGBRFClassifier | ok | Binary only |
xgboost | XGBRFRegressor | ok |
The list of models we export is actually larger than in mentioned in the current version. We need to update the docs and "re-push" the package.