stopping_metric

• Available in: GBM, DRF, Deep Learning, GLM, GAM, AutoML, XGBoost, Isolation Forest, UpliftDRF

• Hyperparameter: yes

Description

This option specifies the metric to consider when early stopping is specified (i.e., when stopping_rounds > 0). For example, given the following options:

• stopping_rounds=3

• stopping_metric=misclassification

• stopping_tolerance=1e-3

then the model will stop training after reaching three scoring events in a row in which a model’s missclassication value does not improve by 1e-3. These stopping options are used to increase performance by restricting the number of models that get built.

Available options for stopping_metric include the following:

• AUTO: This defaults to logloss for classification, deviance (mean residual deviance) for regression, and anomaly_score for Isolation Forest.

• anomaly_score (for Isolation Forest only)

• deviance

• logloss

• MSE

• RMSE

• MAE

• RMSLE

• AUC (area under the ROC curve)

• AUCPR (area under the Precision-Recall curve)

• lift_top_group

• misclassification

• mean_per_class_error

• AUUC (area under the uplift curve, for UpliftDRF only)

• qini (difference between the Qini AUUC and area under the random uplift curve, for UpliftDRF only)

• ATE (average treatment effect, for UpliftDRF only)

• ATT (average treatment effect on the Treated, for UpliftDRF only)

• ATC (average treatment effect on the Control, for UpliftDRF only)

• custom (for custom metric functions where “less is better”. It is expected that the lower bound is 0.) Note that this is currently only supported in the Python client for GBM and DRF. More information available in Python example below and here.

• custom_increasing (for custom metric functions where “more is better”.) Note that this is currently only supported in the Python client for GBM and DRF. More information available in Python example below and here.

Note: stopping_rounds must be enabled for stopping_metric or stopping_tolerance to work.

Related Parameters

• stopping_rounds

• stopping_tolerance

Example

R

Python

library(h2o)
h2o.init()
# 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.importFile("http://s3.amazonaws.com/h2o-public-test-data/smalldata/airlines/allyears2k_headers.zip")

# convert columns to factors
airlines["Year"] <- as.factor(airlines["Year"])
airlines["Month"] <- as.factor(airlines["Month"])
airlines["DayOfWeek"] <- as.factor(airlines["DayOfWeek"])
airlines["Cancelled"] <- as.factor(airlines["Cancelled"])
airlines['FlightNum'] <- as.factor(airlines['FlightNum'])

# set the predictor names and the response column name
predictors <- c("Origin", "Dest", "Year", "UniqueCarrier", "DayOfWeek", "Month", "Distance", "FlightNum")
response <- "IsDepDelayed"

# split into train and validation
airlines_splits <- h2o.splitFrame(data = airlines, ratios = 0.8, seed = 1234)
train <- airlines_splits[[1]]
valid <- airlines_splits[[2]]

# 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
airlines_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train, validation_frame = valid,
                        stopping_metric = "AUC", stopping_rounds = 3,
                        stopping_tolerance = 1e-2, seed = 1234)

# print the auc for the validation data
print(h2o.auc(airlines_gbm, valid = TRUE))

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)

# Example using 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)