RTest

class RTest(self, models: Literal['all'] | list[sam_ml.models.main_regressor.Regressor] = 'all', vectorizer: str | sam_ml.data.preprocessing.embeddings.Embeddings_builder | None | list[str | sam_ml.data.preprocessing.embeddings.Embeddings_builder | None] = None, scaler: str | sam_ml.data.preprocessing.scaler.Scaler | None | list[str | sam_ml.data.preprocessing.scaler.Scaler | None] = None, selector: str | tuple[str, int] | sam_ml.data.preprocessing.feature_selection.Selector | None | list[str | tuple[str, int] | sam_ml.data.preprocessing.feature_selection.Selector | None] = None, sampler: str | sam_ml.data.preprocessing.sampling.Sampler | sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline | None | list[str | sam_ml.data.preprocessing.sampling.Sampler | sam_ml.data.preprocessing.sampling_pipeline.SamplerPipeline | None] = None)

AutoML class for regressor - parent class AutoML

Parameters

models{“all”} or list, default=”all” ‘all’: use all Wrapperclass models list of Wrapperclass models from sam_ml library vectorizerstr, Embeddings_builder, or None object or algorithm of Embeddings_builder class which will be used for automatic string column vectorizing (None for no vectorizing) scalerstr, Scaler, or None object or algorithm of Scaler class for scaling the data (None for no scaling) selectorstr, Selector, or None object, tuple of algorithm and feature number, or algorithm of Selector class for feature selection (None for no selecting) samplerstr, Sampler, SamplerPipeline, or None object or algorithm of Sampler / SamplerPipeline class for sampling the train data (None for no sampling)

Attributes

modelsdict dictionary with model names as keys and model instances as values scoresdict[str, float] dictionary with scores for every model as dictionary

Note

If a list is provided for one or multiple of the preprocessing steps, all model with preprocessing steps combination will be added as pipelines.

Example

>>> from sam_ml.models.automl import RTest
>>>
>>> model = RTest()
>>> print(model)
RTest(models='all', vectorizer=[None], scaler=[None], selector=[None], sampler=[None])

Methods

Method	Description
add_model	Function for adding model in self.models
eval_models	Function to train and evaluate every model
eval_models_cv	Function to run a cross validation on every model
find_best_model_mass_search	Function to run a successive halving hyperparameter search for every model
find_best_model_randomCV	Function to run a random cross validation hyperparameter search for every model
find_best_model_smac	Function to run a Hyperparametertuning with SMAC library HyperparameterOptimizationFacade for every model [can only be used in the sam_ml version with swig]
model_combs	Function for mapping string to set of models
output_scores_as_pd	Function to output self.scores as pd.DataFrame
remove_model	Function for deleting model in self.models

RTest.add_model(model)

Function for adding model in self.models

Parameters

modelestimator instance

add model instance to self.models

RTest.eval_models(x_train: DataFrame, y_train: Series, x_test: DataFrame, y_test: Series, scoring: Literal['r2', 'rmse', 'd2_tweedie'] | Callable[[list[float], list[float]], float] = 'r2') → dict[str, dict]

Function to train and evaluate every model

Parameters

x_train, y_trainpd.DataFrame, pd.Series

Data to train the models

x_test, y_testpd.DataFrame, pd.Series

Data to evaluate the models

scoring{“r2”, “rmse”, “d2_tweedie”} or callable (custom score), default=”r2”

metrics to evaluate the models

custom score function (or loss function) with signature score_func(y, y_pred, **kwargs)

Returns

scoresdict[str, dict]

dictionary with scores for every model as dictionary

also saves metrics in self.scores

Notes

if you interrupt the keyboard during the run of eval_models, the interim result will be returned

Examples

>>> # load data (replace with own data)
>>> import pandas as pd
>>> from sklearn.datasets import make_regression
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_regression(n_samples=3000, n_features=4, noise=1, random_state=42)
>>> X, y = pd.DataFrame(X, columns=["col1", "col2", "col3", "col4"]), pd.Series(abs(y))
>>> x_train, x_test, y_train, y_test = train_test_split(X,y, train_size=0.80, random_state=42)
>>> 
>>> # start modelling
>>> from sam_ml.models.automl import RTest
>>> 
>>> # initialise auot-ml class
>>> rtest = RTest(models = "all", scaler = "standard")
>>> 
>>> # start eval_models to evaluate all modeltypes on train-test-data
>>> rtest.eval_models(x_train,y_train,x_test,y_test, scoring="r2")
>>> 
>>> # output and sort results
>>> score_df = rtest.output_scores_as_pd(sort_by=["r2", "train_time"])
                                                      r2          rmse         d2_tweedie   train_time  train_score
ExtraTreesRegressor (vec=None, scaler=standard,...    0.973874    8.065976     0.954158     0:00:00     1.000000
RandomForestRegressor (vec=None, scaler=standar...    0.947177    11.469144    0.921423     0:00:00     0.992563
XGBRegressor (vec=None, scaler=standard, select...    0.945507    11.648970    -1.000000    0:00:00     0.995029
DecisionTreeRegressor (vec=None, scaler=standar...    0.872008    17.852977    0.788164     0:00:00     1.000000
...

RTest.eval_models_cv(X: DataFrame, y: Series, cv_num: int = 5, small_data_eval: bool = False, custom_score: Callable[[list[float], list[float]], float] | None = None) → dict[str, dict]

Function to run a cross validation on every model

Parameters

X, ypd.DataFrame, pd.Series

Data to cross validate on

cv_numint, default=5

number of different random splits (only used when small_data_eval=False)

small_data_evalbool, default=False

if True, cross_validation_small_data will be used (one-vs-all evaluation). Otherwise, random split cross validation

custom_scorecallable or None, default=None

custom score function (or loss function) with signature score_func(y, y_pred, **kwargs)

If None, no custom score will be calculated and also the key “custom_score” does not exist in the returned dictionary.

Returns

scoresdict[str, dict]

dictionary with scores for every model as dictionary

also saves metrics in self.scores

Notes

if you interrupt the keyboard during the run of eval_models_cv, the interim result will be returned

Examples

>>> # load data (replace with own data)
>>> import pandas as pd
>>> from sklearn.datasets import make_regression
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_regression(n_samples=3000, n_features=4, noise=1, random_state=42)
>>> X, y = pd.DataFrame(X, columns=["col1", "col2", "col3", "col4"]), pd.Series(abs(y))
>>> 
>>> # start modelling
>>> from sam_ml.models.automl import RTest
>>> 
>>> # initialise auot-ml class
>>> rtest = RTest(models = "all", scaler = "standard")
>>> 
>>> # start eval_models_cv which will cross validate all model types
>>> rtest.eval_models_cv(X,y, cv_num=3)
>>> 
>>> # output and sort results
>>> score_df = rtest.output_scores_as_pd(sort_by=["r2", "train_time"])
                                                      r2          rmse         d2_tweedie  train_time  train_score
ExtraTreesRegressor (vec=None, scaler=standard,...    0.968637    8.620645     0.947802    0:00:00     1.000000
XGBRegressor (vec=None, scaler=standard, select...    0.940348    11.946437    0.905749    0:00:00     0.996965
RandomForestRegressor (vec=None, scaler=standar...    0.939039    12.045418    0.915000    0:00:00     0.992059
DecisionTreeRegressor (vec=None, scaler=standar...    0.834557    19.894214    0.752740    0:00:00     1.000000
...

RTest.find_best_model_mass_search(x_train: DataFrame, y_train: Series, x_test: DataFrame, y_test: Series, n_trails: int = 10, scoring: Literal['r2', 'rmse', 'd2_tweedie'] | Callable[[list[float], list[float]], float] = 'r2', leave_loadbar: bool = True, save_results_path: str | None = 'find_best_model_mass_search_results.csv') → tuple[str, dict[str, float]]

Function to run a successive halving hyperparameter search for every model

It uses the warm_start parameter of the model and is an own implementation. Recommended to use as a fast method to narrow down different preprocessing steps and model combinations, but find_best_model_sma or randomCVsearch return better results.

Parameters

x_train, y_trainpd.DataFrame, pd.Series

Data to train and optimise the models

x_test, y_testpd.DataFrame, pd.Series

Data to evaluate the models

n_trailsint, default=10

max number of parameter sets to test for each model

scoring{“r2”, “rmse”, “d2_tweedie”} or callable (custom score), default=”r2”

metrics to evaluate the models

custom score function (or loss function) with signature score_func(y, y_pred, **kwargs)

leave_loadbarbool, default=True

shall the loading bar of the model training during the different splits be visible after training (True - load bar will still be visible)

save_result_pathstr or None, default=”find_best_model_mass_search_results.csv”

path to use for saving the results after each step. If None no results will be saved

Returns

best_model_namestr

name of the best model in search

scoredict[str, float]

scores of the best model

Examples

>>> # load data (replace with own data)
>>> import pandas as pd
>>> from sklearn.datasets import make_regression
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_regression(n_samples=3000, n_features=4, noise=1, random_state=42)
>>> X, y = pd.DataFrame(X, columns=["col1", "col2", "col3", "col4"]), pd.Series(abs(y))
>>> x_train, x_test, y_train, y_test = train_test_split(X,y, train_size=0.80, random_state=42)
>>> 
>>> # start modelling
>>> from sam_ml.models.automl import RTest
>>> 
>>> # initialise auot-ml class
>>> rtest = RTest(models = "all", scaler = "standard")
>>> 
>>> # start find_best_model_mass_search with 10 configurations per model type and find the best combination
>>> best_model_name, score = rtest.find_best_model_mass_search(x_train,y_train,x_test,y_test, scoring="r2", n_trails=5)
2023-12-18 11:39:12,599 - sam_ml.automl.main_auto_ml - WARNING - modeltype in 'DecisionTreeRegressor (vec=None, scaler=standard, selector=None, sampler=None)' is not supported for this search -> will be skipped
2023-12-18 11:39:12,618 - sam_ml.automl.main_auto_ml - WARNING - modeltype in 'LassoLarsCV (vec=None, scaler=standard, selector=None, sampler=None)' is not supported for this search -> will be skipped
...
2023-12-18 11:40:04,678 - sam_ml.automl.main_auto_ml - INFO - total number of models: 50
2023-12-18 11:40:04,678 - sam_ml.automl.main_auto_ml - INFO - split number: 5, split_size (x_train): 400
2023-12-18 11:40:04,685 - sam_ml.automl.main_auto_ml - INFO - split 1: length x_train/y_train 400/400, length x_test/y_test 2000/2000
2023-12-18 11:40:05,032 - sam_ml.automl.main_auto_ml - INFO - new best r2: -1 -> 0.5096384300695788 (RandomForestRegressor (vec=None, scaler=standard, selector=None, sampler=None) {'bootstrap': True, 'criterion': 'squared_error', 'max_depth': 5, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100})
2023-12-18 11:40:08,302 - sam_ml.automl.main_auto_ml - INFO - new best r2: 0.5096384300695788 -> 0.8496957972325866 (XGBRegressor (vec=None, scaler=standard, selector=None, sampler=None) {'colsample_bytree': 1.0, 'gamma': 0.0, 'learning_rate': 0.1, 'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 1.0})
...
2023-12-18 11:40:09,366 - sam_ml.automl.main_auto_ml - INFO - Split scores (top 5):
                                                      r2         rmse         d2_tweedie   train_time  train_score
XGBRegressor (vec=None, scaler=standard, select...    0.849696   18.83303     0.793563     0:00:00     0.994897
XGBRegressor (vec=None, scaler=standard, select...    0.80944    21.205614    -1.0         0:00:00     0.977253
...
2023-12-18 11:40:09,368 - sam_ml.automl.main_auto_ml - INFO - removed 25 models
2023-12-18 11:40:09,369 - sam_ml.automl.main_auto_ml - INFO - split 2: length x_train/y_train 400/400, length x_test/y_test 1600/1600
2023-12-18 11:40:09,429 - sam_ml.automl.main_auto_ml - INFO - new best r2: -1 -> 0.5117121227247716 (RandomForestRegressor (vec=None, scaler=standard, selector=None, sampler=None) {'bootstrap': True, 'criterion': 'squared_error', 'max_depth': 5, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100})
2023-12-18 11:40:10,413 - sam_ml.automl.main_auto_ml - INFO - new best r2: 0.5117121227247716 -> 0.8989539000553437 (XGBRegressor (vec=None, scaler=standard, selector=None, sampler=None) {'colsample_bytree': 1.0, 'gamma': 0.0, 'learning_rate': 0.1, 'max_depth': 6, 'min_child_weight': 1, 'n_estimators': 100, 'reg_alpha': 0, 'reg_lambda': 1.0})
...
2023-12-18 11:40:11,134 - sam_ml.automl.main_auto_ml - INFO - removed 13 models
2023-12-18 11:40:11,135 - sam_ml.automl.main_auto_ml - INFO - split 3: length x_train/y_train 400/400, length x_test/y_test 1200/1200
...
2023-12-18 11:40:12,401 - sam_ml.automl.main_auto_ml - INFO - removed 6 models
2023-12-18 11:40:12,402 - sam_ml.automl.main_auto_ml - INFO - split 4: length x_train/y_train 400/400, length x_test/y_test 800/800
...
2023-12-18 11:40:13,327 - sam_ml.automl.main_auto_ml - INFO - removed 3 models
2023-12-18 11:40:13,328 - sam_ml.automl.main_auto_ml - INFO - split 5: length x_train/y_train 400/400, length x_test/y_test 400/400
...
2023-12-18 11:40:13,706 - sam_ml.automl.main_auto_ml - INFO - removed 2 models
2023-12-18 11:40:13,707 - sam_ml.automl.main_auto_ml - INFO - Evaluating best model:

XGBRegressor (vec=None, scaler=standard, selector=None, sampler=None) {'colsample_bytree': 0.9330880728874675, 'gamma': 2.7381801866358395, 'learning_rate': 0.08810003129071789, 'max_depth': 10, 'min_child_weight': 10, 'n_estimators': 314, 'reg_alpha': 49, 'reg_lambda': 0.7722447692966574}

r2: 0.8767084599472158
rmse: 17.52205436361105
d2_tweedie: 0.8295259477160681

RTest.find_best_model_randomCV(x_train: DataFrame, y_train: Series, x_test: DataFrame, y_test: Series, n_trails: int = 5, cv_num: int = 3, scoring: Literal['r2', 'rmse', 'd2_tweedie'] | Callable[[list[float], list[float]], float] = 'r2', small_data_eval: bool = False, leave_loadbar: bool = True) → dict[str, dict]

Function to run a random cross validation hyperparameter search for every model

Parameters

x_train, y_trainpd.DataFrame, pd.Series

Data to train and optimise the models

x_test, y_testpd.DataFrame, pd.Series

Data to evaluate the models

n_trailsint, default=5

max number of parameter sets to test

cv_numint, default=3

number of different random splits (only used when small_data_eval=False)

scoring{“r2”, “rmse”, “d2_tweedie”} or callable (custom score), default=”r2”

metrics to evaluate the models

custom score function (or loss function) with signature score_func(y, y_pred, **kwargs)

small_data_evalbool, default=False

if True: trains model on all datapoints except one and does this for all datapoints (recommended for datasets with less than 150 datapoints)

leave_loadbarbool, default=True

shall the loading bar of the randomCVsearch of each individual model be visible after training (True - load bar will still be visible)

Returns

scoresdict[str, dict]

dictionary with scores for every model as dictionary

also saves metrics in self.scores

Notes

If you interrupt the keyboard during the run of randomCVsearch of a model, the interim result for this model will be used and the next model starts.

Examples

>>> # load data (replace with own data)
>>> import pandas as pd
>>> from sklearn.datasets import make_regression
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_regression(n_samples=3000, n_features=4, noise=1, random_state=42)
>>> X, y = pd.DataFrame(X, columns=["col1", "col2", "col3", "col4"]), pd.Series(abs(y))
>>> x_train, x_test, y_train, y_test = train_test_split(X,y, train_size=0.80, random_state=42)
>>> 
>>> # start modelling
>>> from sam_ml.models.automl import RTest
>>> 
>>> # initialise auot-ml class
>>> rtest = RTest(models = "all", scaler = "standard")
>>> 
>>> # start randomCVsearch with 5 configurations per model type and evaluate the best parameters
>>> rtest.find_best_model_randomCV(x_train,y_train,x_test,y_test, scoring="r2", n_trails=5, cv_num=3)
>>> 
>>> # output and sort results
>>> score_df = rtest.output_scores_as_pd(sort_by=["r2", "train_time"])
randomCVsearch (RandomForestRegressor (vec=None, scaler=standard, selector=None, sampler=None)): 100%|██████████| 5/5 [00:01<00:00,  2.89it/s]
2023-12-18 11:20:25,284 - sam_ml.automl.main_auto_ml - INFO - RandomForestRegressor (vec=None, scaler=standard, selector=None, sampler=None) - score: 0.5568605014361813 (r2) - parameters: {'bootstrap': True, 'criterion': 'squared_error', 'max_depth': 5, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'n_estimators': 100}

randomCVsearch (DecisionTreeRegressor (vec=None, scaler=standard, selector=None, sampler=None)): 100%|██████████| 5/5 [00:06<00:00,  1.30s/it]
2023-12-18 11:20:31,959 - sam_ml.automl.main_auto_ml - INFO - DecisionTreeRegressor (vec=None, scaler=standard, selector=None, sampler=None) - score: 0.38132412930754 (r2) - parameters: {'criterion': 'squared_error', 'max_depth': 5, 'max_features': 1.0, 'max_leaf_nodes': 90, 'min_samples_leaf': 1, 'min_samples_split': 2, 'min_weight_fraction_leaf': 0.0, 'splitter': 'best'}

...

                                                      r2          rmse         d2_tweedie  train_time  train_score   best_score (rCVs)  best_hyperparameters (rCVs)
XGBRegressor (vec=None, scaler=standard, select...    0.949305    11.235687    0.903326    0:00:00     0.983904      0.930262           {'colsample_bytree': 1.0, 'gamma': 0.0, 'learn...
RandomForestRegressor (vec=None, scaler=standar...    0.558927    33.141604    0.466038    0:00:00     0.634854      0.556861           {'bootstrap': True, 'criterion': 'squared_erro...
ExtraTreesRegressor (vec=None, scaler=standard,...    0.473561    36.207000    0.412317    0:00:00     0.513346      0.486602           {'bootstrap': False, 'criterion': 'squared_err...
DecisionTreeRegressor (vec=None, scaler=standar...    0.431751    37.617306    0.345249    0:00:00     0.522676      0.381324           {'criterion': 'squared_error', 'max_depth': 5,...
...

RTest.find_best_model_smac(x_train: DataFrame, y_train: Series, x_test: DataFrame, y_test: Series, n_trails: int = 5, cv_num: int = 3, scoring: Literal['r2', 'rmse', 'd2_tweedie'] | Callable[[list[float], list[float]], float] = 'r2', small_data_eval: bool = False, walltime_limit_per_modeltype: int = 600, smac_log_level: int = 30) → dict[str, dict]

Function to run a Hyperparametertuning with SMAC library HyperparameterOptimizationFacade for every model [can only be used in the sam_ml version with swig]

The smac_search-method will more “intelligent” search your hyperparameter space than the randomCVsearch and returns the best hyperparameter set. Additionally to the n_trails parameter, it also takes a walltime_limit parameter that defines the maximum time in seconds that the search will take.

Parameters

x_train, y_trainpd.DataFrame, pd.Series

Data to train and optimise the models

x_test, y_testpd.DataFrame, pd.Series

Data to evaluate the models

n_trailsint, default=5

max number of parameter sets to test for each model

cv_numint, default=3

number of different random splits (only used when small_data_eval=False)

scoring{“r2”, “rmse”, “d2_tweedie”} or callable (custom score), default=”r2”

metrics to evaluate the models

custom score function (or loss function) with signature score_func(y, y_pred, **kwargs)

small_data_evalbool, default=False

if True: trains model on all datapoints except one and does this for all datapoints (recommended for datasets with less than 150 datapoints)

walltime_limit_per_modeltypeint, default=600

the maximum time in seconds that SMAC is allowed to run for each model

smac_log_levelint, default=30

10 - DEBUG, 20 - INFO, 30 - WARNING, 40 - ERROR, 50 - CRITICAL (SMAC3 library log levels)

Returns

scoresdict[str, dict]

dictionary with scores for every model as dictionary

also saves metrics in self.scores

Examples

>>> # load data (replace with own data)
>>> import pandas as pd
>>> from sklearn.datasets import make_regression
>>> from sklearn.model_selection import train_test_split
>>> X, y = make_regression(n_samples=3000, n_features=4, noise=1, random_state=42)
>>> X, y = pd.DataFrame(X, columns=["col1", "col2", "col3", "col4"]), pd.Series(abs(y))
>>> x_train, x_test, y_train, y_test = train_test_split(X,y, train_size=0.80, random_state=42)
>>> 
>>> # start modelling
>>> from sam_ml.models.automl import RTest
>>> 
>>> # initialise auot-ml class
>>> rtest = RTest(models = "all", scaler = "standard")
>>> 
>>> # start find_best_model_smac with 5 configurations per model type and evaluate the best parameters
>>> rtest.find_best_model_smac(x_train,y_train,x_test,y_test, scoring="r2", n_trails=5, cv_num=3)
>>> 
>>> # output and sort results
>>> score_df = rtest.output_scores_as_pd(sort_by=["r2", "train_time"])
2023-12-18 11:32:01,189 - sam_ml.automl.main_auto_ml - INFO - RandomForestRegressor (vec=None, scaler=standard, selector=None, sampler=None) - parameters: {'bootstrap': False, 'criterion': 'friedman_mse', 'max_depth': 13, 'min_samples_leaf': 4, 'min_samples_split': 8, 'min_weight_fraction_leaf': 0.06446314882742665, 'n_estimators': 20}
2023-12-18 11:32:04,663 - sam_ml.automl.main_auto_ml - INFO - DecisionTreeRegressor (vec=None, scaler=standard, selector=None, sampler=None) - parameters: {'criterion': 'squared_error', 'max_depth': 12, 'max_features': 'sqrt', 'max_leaf_nodes': 43, 'min_samples_leaf': 4, 'min_samples_split': 8, 'min_weight_fraction_leaf': 0.051022405374014035, 'splitter': 'best'}

...

                                                      r2          rmse         d2_tweedie  train_time  train_score   best_hyperparameters
XGBRegressor (vec=None, scaler=standard, select...    0.946443    11.548574    0.904768    0:00:00     0.996710      {'colsample_bytree': 0.8229470565333281, 'gamm...
RandomForestRegressor (vec=None, scaler=standar...    0.295580    41.882669    0.302303    0:00:00     0.357833      {'bootstrap': False, 'criterion': 'friedman_ms...
DecisionTreeRegressor (vec=None, scaler=standar...    0.276265    42.452967    0.289315    0:00:00     0.314012      {'criterion': 'squared_error', 'max_depth': 12...
ExtraTreesRegressor (vec=None, scaler=standard,...    0.193540    44.813584    0.187723    0:00:00     0.222682      {'bootstrap': False, 'criterion': 'friedman_ms...
...

RTest.model_combs(kind: Literal['all'])

Function for mapping string to set of models

Parameters

kind{“all”}

which kind of model set to use:

• ‘all’:

  use all Wrapperclass models

Returns

modelslist

list of model instances

RTest.output_scores_as_pd(sort_by: Literal['index', 'r2', 'rmse', 'd2_tweedie', 'custom_score', 'train_score', 'train_time'] | list[str] = 'index', console_out: bool = True) → DataFrame

Function to output self.scores as pd.DataFrame

Parameters

sorted_by{“index”, “r2”, “rmse”, “d2_tweedie”, “custom_score”, “train_score”, “train_time”} or list[str], default=”index”

key(s) to sort the scores by. You can provide also keys that are not in self.scores and they will be filtered out.

• “index”:

  sort index (ascending=True)

• “index”, “r2”, “rmse”, “d2_tweedie”, “custom_score”, “train_score”, “train_time”:

  sort by these columns (ascending=False)

• list with multiple keys (ascending=False), e.g., [‘r2’, ‘d2_tweedie’]:

  sort first by ‘r2’ and then by ‘d2_tweedie’

console_outbool, default=True

shall the DataFrame be printed out

Returns

scorespd.DataFrame

sorted DataFrame of self.scores

RTest.remove_model(model_name: str)

Function for deleting model in self.models

Parameters

model_namestr

name of model in self.models