checkpoint

  • Available in: GBM, DRF, Deep Learning
  • Hyperparameter: no

Description

In real-world scenarios, data can change. For example, you may have a model currently in production that was built using 1 million records. At a later date, you may receive several hundred thousand more records. Rather than building a new model from scratch, you can use checkpointing to create a new model based on the existing model.

The checkpoint option allows you to specify a model key associated with a previously trained model. This will build a new model as a continuation of a previously generated model. If this is not specified, then the algorithm will start training a new model instead of continuing building a previous model.

When setting parameters that continue to build on a previous model, such as ntrees or epoch, the new parameter value must be greater than the original value. For example, if the first model builds 1 tree, the continuation model (using checkpointing) must build ntrees equal to 2 (meaning build one additional tree) or greater.

Note: The following options cannot be modified when rebuilding a model using checkpoint:

GBM/DRF Options

  • build_tree_one_node
  • max_depth
  • min_rows
  • nbins
  • nbins_cats
  • nbins_top_level
  • sample_rate

Deep Learning Options

  • activation
  • autoencoder
  • backend
  • channels
  • distribution
  • drop_na20_cols
  • ignore_const_cols
  • max_categorical_features
  • mean_image_file
  • missing_values_handling
  • momentum_ramp
  • momentum_stable
  • momentum_start
  • network
  • network_definition_file
  • nfolds
  • problem_type
  • standardize
  • use_all_factor_levels
  • y (response column)

Example

library(h2o)
h2o.init()

# import the cars dataset:
# this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made
cars <- h2o.importFile("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# convert response column to a factor
cars["economy_20mpg"] <- as.factor(cars["economy_20mpg"])

# set the predictor names and the response column name
predictors <- c("displacement","power","weight","acceleration","year")
response <- "economy_20mpg"

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

# build a GBM with 1 tree (ntrees = 1) for the first model:
cars_gbm <- h2o.gbm(x = predictors, y = response, training_frame = train,
                    validation_frame = valid, ntrees = 1, seed = 1234)

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

# re-start the training process on a saved GBM model using the ‘checkpoint‘ argument:
# the checkpoint argument requires the model id of the model on which you wish to continue building
# get the model's id from "cars_gbm" model using `cars_gbm@model_id`
# the first model has 1 tree, let's continue building the GBM with an additional 49 more trees, so set ntrees = 50

# to see how many trees the original model built you can look at the `ntrees` attribute
print(paste("Number of trees built for cars_gbm model:", cars_gbm@allparameters$ntrees))

# build and train model with 49 additional trees for a total of 50 trees:
cars_gbm_continued <- h2o.gbm(x = predictors, y = response, training_frame = train,
                    validation_frame = valid, checkpoint = cars_gbm@model_id, ntrees = 50, seed = 1234)

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

# you can also use checkpointing to pass in a new dataset (see options above for parameters you cannot change)
# simply change out the training and validation frames with your new dataset

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

# import the cars dataset:
# this dataset is used to classify whether or not a car is economical based on
# the car's displacement, power, weight, and acceleration, and the year it was made
cars = h2o.import_file("https://s3.amazonaws.com/h2o-public-test-data/smalldata/junit/cars_20mpg.csv")

# convert response column to a factor
cars["economy_20mpg"] = cars["economy_20mpg"].asfactor()

# set the predictor names and the response column name
predictors = ["displacement","power","weight","acceleration","year"]
response = "economy_20mpg"

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

# build a GBM with 1 tree (ntrees = 1) for the first model:
cars_gbm = H2OGradientBoostingEstimator(ntrees = 1, seed = 1234)
cars_gbm.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

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

# re-start the training process on a saved GBM model using the ‘checkpoint‘ argument:
# the checkpoint argument requires the model id of the model on which you wish to continue building
# get the model's id from "cars_gbm" model using `cars_gbm.model_id`
# the first model has 1 tree, let's continue building the GBM with an additional 49 more trees, so set ntrees = 50

# to see how many trees the original model built you can look at the `ntrees` attribute
print("Number of trees built for cars_gbm model:", cars_gbm.ntrees)

# build and train model with 49 additional trees for a total of 50 trees:
cars_gbm_continued = H2OGradientBoostingEstimator(checkpoint= cars_gbm.model_id, ntrees = 50, seed = 1234)
cars_gbm_continued.train(x = predictors, y = response, training_frame = train, validation_frame = valid)

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

# you can also use checkpointing to pass in a new dataset in addition to increasing/ (see options above for parameters you cannot change)
# simply change out the training and validation frames with your new dataset