.. _upload_custom_metric:

``upload_custom_metric``
------------------------

- Available in: GBM, DRF, GLM
- Hyperparameter: no

Description
~~~~~~~~~~~

Use this option to upload a custom metric function into a running H2O cluster. A custom metric function can be used to produce adhoc scoring metrics if actuals are presented.

Three separate fields must be specified when using this function:

- ``klazz``: Represents a custom function.

- ``func_name``: Assigns a name with uploaded custom functions. This name corresponds to the name of the key in the distributed key-value store.

- ``func_file``: The name of the file to store the function in an uploaded jar file. The source code of the given class is saved into a file that is subsequently zipped, uploaded as a zip-archive, and saved into the distributed key-value store.

The parameters ``func_name`` and ``func_file`` must be unique for each uploaded custom distribution.

**Note**: This option is only supported in the Python client.

Related Parameters
~~~~~~~~~~~~~~~~~~

- `custom_metric_func <custom_metric_func.html>`__

Example
~~~~~~~

.. example-code::

   .. code-block:: python

	import h2o
	from h2o.estimators.gbm import H2OGradientBoostingEstimator
	h2o.init()
	h2o.cluster().show_status()

	# import the airlines dataset:
	# This dataset is used to classify whether a flight will be delayed 'YES' or not "NO"
	# original data can be found at http://www.transtats.bts.gov/
	airlines= h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")

	# convert columns to factors
	airlines["Year"] = airlines["Year"].asfactor()
	airlines["Month"] = airlines["Month"].asfactor()
	airlines["DayOfWeek"] = airlines["DayOfWeek"].asfactor()
	airlines["Cancelled"] = airlines["Cancelled"].asfactor()
	airlines['FlightNum'] = airlines['FlightNum'].asfactor()

	# set the predictor names and the response column name
	predictors = ["Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum"]
	response = "IsDepDelayed"

	# split into train and validation sets 
	train, valid = airlines.split_frame(ratios=[.8], seed=1234)

	# try using the `stopping_metric` parameter: 
	# since this is a classification problem we will look at the AUC
	# you could also choose logloss, or misclassification, among other options
	# train your model, where you specify the stopping_metric, stopping_rounds, 
	# and stopping_tolerance
	# initialize the estimator then train the model
	airlines_gbm = H2OGradientBoostingEstimator(stopping_metric="auc",
	                                            stopping_rounds=3,
	                                            stopping_tolerance=1e-2,
	                                            seed=1234)
	airlines_gbm.train(x=predictors, y=response, training_frame=train, validation_frame=valid)

	# print the auc for the validation data
	airlines_gbm.auc(valid=True)

	# Use a custom metric
	# Create a custom RMSE Model metric and save as mm_rmse.py
	# Note that this references a java class java.lang.Math
	class CustomRmseFunc:
	def map(self, pred, act, w, o, model):
	    idx = int(act[0])
	    err = 1 - pred[idx + 1] if idx + 1 < len(pred) else 1
	    return [err * err, 1]

	def reduce(self, l, r):
	    return [l[0] + r[0], l[1] + r[1]]

	def metric(self, l):
	    # Use Java API directly
	    import java.lang.Math as math
	    return math.sqrt(l[0] / l[1])

	# Upload the custom metric
	custom_mm_func = h2o.upload_custom_metric(CustomRmseFunc, 
	                                          func_name="rmse", 
	                                          func_file="mm_rmse.py")

	# Train the model
	model = H2OGradientBoostingEstimator(ntrees=3, 
	                                     max_depth=5,
	                                     score_each_iteration=True,
	                                     custom_metric_func=custom_mm_func,
	                                     stopping_metric="custom",
	                                     stopping_tolerance=0.1,
	                                     stopping_rounds=3)
	model.train(x=predictors, y=response, training_frame=train, validation_frame=valid)