ibm_aigov_facts_client.utils.support_scope_meta module

class Prop(name=None, framework_name=None, autolog=None, manual_log=None, version=None, trigger_methods=None, training_metrics=None, post_training_metrics=None, parameters=None, tags=None, artifacts=None, search_estimator=None)

Bases: object

class SupportBase(props_definitions)

Bases: object

get()

show()

class FrameworkSupportOptions

Bases: SupportBase

To view supported frameworks , use:

>>> client.FrameworkSupportNames.show()

To see only supported framework names , use:

>>> client.FrameworkSupportNames.get()

Set of Supported Frameworks for Auto logging. Manual log is not version dependent.

Available Options:

Name	Framework Name	Autolog	Manual Log	Version
scikit	sklearn	Y	Y	0.22.1 <= scikit-learn <= 1.1.2
Tensorflow	tensorflow	Y	Y	2.3.0 <= tensorflow <= 2.9.1
Keras	keras	Y	Y	2.3.0 <= keras <= 2.9.0
PySpark	pyspark	Y	Y	3.0.0 <= pyspark <= 3.4.0
Xgboost	xgboost	Y	Y	1.1.1 <= xgboost <= 1.6.1
LightGBM	lightgbm	Y	Y	2.3.1 <= lightgbm <= 3.3.2
PyTorch	pytorch	Y	Y	1.0.5 <= pytorch-lightning <= 1.7.1
Pycaret	pycaret	Y	N

class FrameworkSupportSklearn

Bases: SupportBase

Current autolog support scope for Scikit learn.

Available Options:

Framework Name	Trigger Methods	Training Metrics	Parameters	Tags	Post Training Metrics	Search Estimators
sklearn	estimator.fit() estimator.fit_predict() estimator.fit_transform()	Classifier: precision score recall score f1 score accuracy score If the classifier has method predict_proba log loss roc auc score Regression: mean squared error root mean squared error mean absolute error r2 score	estimator.get_params(deep=True)	estimator class name(e.g. “LinearRegression”) fully qualified estimator class name(e.g. “sklearn.linear_model._base.LinearRegression”)	Scikit-learn metric APIs: model.score metric APIs defined in the sklearn.metrics module Note: metric key format is: {metric_name}[-{call_index}]_{dataset_name} if sklearn.metrics: metric_name is the metric function name if model.score, then metric_name is {model_class_name}_score If multiple calls are made to the same scikit-learn metric API each subsequent call adds a “call_index” (starting from 2) to the metric key	Meta estimator: Pipeline, GridSearchCV , RandomizedSearchCV It logs child runs with metrics for each set of explored parameters, as well as parameters for the best model and the best parameters (if available)

limit = ''

class FrameworkSupportSpark

Bases: SupportBase

Current autolog support scope for Spark.

Available Options:

Framework Name	Trigger Methods	Training Metrics	Parameters	Tags	Post Training Metrics	Search Estimators
pyspark	estimator.fit(), except for estimators (featurizers) under pyspark.ml.feature	Not Supported	estimator.params If a param value is also an Estimator then params in the the wrapped estimator will also be logged, the nested param key will be {estimator_uid}.{param_name}	estimator class name(e.g. “LinearRegression”) fully qualified estimator class name(e.g. “pyspark.ml.regression.LinearRegression”)	pyspark ML evaluators used under Evaluator.evaluate metric key format is: {metric_name}[-{call_index}]_{dataset_name} Metric name: Evaluator.getMetricName() If multiple calls are made to the same pyspark ML evaluator metric API each subsequent call adds a “call_index” (starting from 2) to the metric key	Meta estimator: Pipeline, CrossValidator, TrainValidationSplit, OneVsRest It logs child runs with metrics for each set of explored parameters, as well as parameters for the best model and the best parameters (if available)

limit = ''

class FrameworkSupportKeras

Bases: SupportBase

Current autolog support scope for Keras.

Available Options:

Framework Name	Trigger Methods	Training Metrics	Parameters
keras	estimator.fit()	Training loss, Validation loss, User specified metrics, Metricss related EarlyStopping callbacks: stopped_epoch, restored_epoch, restore_best_weight, last_epoch etc.	fit() or fit_generator() params, Optimizer name, Learning rate, Epsilon, Params related to EarlyStopping: min-delta, patience, baseline, restore_best_weights etc.

limit = ''

class FrameworkSupportTensorflow

Bases: SupportBase

Current autolog support scope for Tensorflow.

Available Options:

Framework Name	Trigger Methods	Training Metrics	Parameters
tensorflow	estimator.fit()	Training loss, Validation loss, User specified metrics, Metricss related EarlyStopping callbacks: stopped_epoch, restored_epoch, restore_best_weight, last_epoch etc. TensorBoard metrics: average_loss, loss Tensorflow Core: tf.summary.scalar calls	fit() or fit_generator() params, Optimizer name, Learning rate, Epsilon, Params related to EarlyStopping: min-delta, patience, baseline, restore_best_weights etc. Tensorboard params: steps, max_steps

limit = ''

class FrameworkSupportXGB

Bases: SupportBase

Current autolog support scope for XGBoost.

Available Options:

Framework Name	Trigger Methods	Training Metrics	Parameters
xgboost	xgboost.train(), scikit-learn APIs.`fit()`	Metrics at each iteration (if evals specified), Metrics at best iteration (if early_stopping_rounds specified)	params specified in xgboost.train or fit()

limit = ''

class FrameworkSupportLGBM

Bases: SupportBase

Current autolog support scope for LightGBM.

Available Options:

Framework Name	Trigger Methods	Training Metrics	Parameters
lightgbm	lightgbm.train()	Metrics at each iteration (if evals specified), Metrics at best iteration (if early_stopping_rounds specified)	params specified in lightgbm.train

limit = ''

class FrameworkSupportPyTorch

Bases: SupportBase

Current autolog support scope for PyTorch.

Available Options:

Framework Name	Trigger Methods	Training Metrics	Parameters
pytorch	pytorch_lightning.Trainer() i.e., models that subclass pytorch_lightning.LightningModule	Training loss, Validation loss, average_test_accuracy, user defined metrics, Metricss related EarlyStopping callbacks: stopped_epoch, restored_epoch, restore_best_weight, last_epoch etc.	fit() parameters, optimizer name, learning rate, epsilon, Params related to EarlyStopping: min-delta, patience, baseline, restore_best_weights etc.

limit = ''