def create_feature_importance_chart(regressor, X_train, y_train):
"""Create feature importance chart.
Tip:
Check Sklearn-Neptune integration
`documentation <https://docs-beta.neptune.ai/essentials/integrations/machine-learning-frameworks/sklearn>`_
for the full example.
Args:
regressor (:obj:`regressor`):
| Fitted sklearn regressor object
X_train (:obj:`ndarray`):
| Training data matrix
y_train (:obj:`ndarray`):
| The regression target for training
Returns:
``neptune.types.File`` object that you can assign to run's ``base_namespace``.
Examples:
.. code:: python3
import neptune.new.integrations.sklearn as npt_utils
rfr = RandomForestRegressor()
rfr.fit(X_train, y_train)
run = neptune.init(project='my_workspace/my_project')
run['visuals/feature_importance'] = npt_utils.create_feature_importance_chart(rfr, X_train, y_train)
"""
assert is_regressor(regressor), 'regressor should be sklearn regressor.'
chart = None
try:
fig, ax = plt.subplots()
visualizer = FeatureImportances(regressor, is_fitted=True, ax=ax)
visualizer.fit(X_train, y_train)
visualizer.finalize()
chart = neptune.types.File.as_image(fig)
plt.close(fig)
except Exception as e:
print('Did not log feature importance chart. Error: {}'.format(e))
return chart
def log_feature_importance_chart(regressor, X_train, y_train, experiment=None):
"""Log feature importance chart.
Make sure you created an experiment by using ``neptune.create_experiment()`` before you use this method.
Tip:
Check `Neptune documentation <https://docs.neptune.ai/integrations/scikit_learn.html>`_ for the full example.
Args:
regressor (:obj:`regressor`):
| Fitted sklearn regressor object
X_train (:obj:`ndarray`):
| Training data matrix
y_train (:obj:`ndarray`):
| The regression target for training
experiment (:obj:`neptune.experiments.Experiment`, optional, default is ``None``):
| Neptune ``Experiment`` object to control to which experiment you log the data.
| If ``None``, log to currently active, and most recent experiment.
Returns:
``None``
Examples:
.. code:: python3
rfr = RandomForestRegressor()
rfr.fit(X_train, y_train)
neptune.init('my_workspace/my_project')
neptune.create_experiment()
log_feature_importance_chart(rfr, X_train, y_train)
"""
assert is_regressor(regressor), 'regressor should be sklearn regressor.'
exp = _validate_experiment(experiment)
try:
fig, ax = plt.subplots()
visualizer = FeatureImportances(regressor, is_fitted=True, ax=ax)
visualizer.fit(X_train, y_train)
visualizer.finalize()
exp.log_image('charts_sklearn', fig, image_name='Feature Importance')
plt.close(fig)
except Exception as e:
print('Did not log feature importance chart. Error: {}'.format(e))