RuleFit¶
Introduction¶
H2O’s Rulefit algorithm combines tree ensembles and linear models to take advantage of both methods: the accuracy of a tree ensemble and the interpretability of a linear model.
The general algorithm fits a tree ensemble to the data, builds a rule ensemble by traversing each tree, evaluates the rules on the data to build a rule feature set, and fits a sparse linear model (LASSO) to the rule feature set joined with the original feature set.
Defining a RuleFit Model (Beta API)¶
model_id: (Optional) Specify a custom name for the model to use as a reference. By default, H2O automatically generates a destination key.
training_frame: (Required) Specify the dataset used to build the model.
Note: In Flow, if you click the Build a model button from the Parse cell, the training frame is entered automatically.
validation_frame: (Optional) Specify the dataset used to evaluate the accuracy of the model.
seed: Specify the random number generator (RNG) seed for algorithm components dependent on randomization. The seed is consistent for each H2O instance so that you can create models with the same starting conditions in alternate configurations. This value defaults to -1 (time-based random number).
y: (Required) Specify the column to use as the dependent variable.
For a regression model, this column must be numeric (Real or Int).
For a classification model, this column must be categorical (Enum or String). If the family is Binomial, the dataset cannot contain more than two levels.
x: Specify a vector containing the names or indicies of the predictor variables to use when building the model. If
xis missing, then all columns exceptyare used.algorithm: The algorithm to use to fit a tree ensemble. Must be one of: “AUTO”, “DRF”, or “GBM”. Defaults to “DRF”.
min_rule_length: Specify the minimal depth of trees to be fit. Defaults to 3.
max_rule_length: Specify the maximal depth of trees to be fit. Defaults to 3.
max_num_rules: The maximum number of rules to return. Defaults to -1, which means the number of rules are selected by diminishing returns in model deviance.
model_type: Specify the type of base learners in the ensemble. Must be one of: “rules_and_linear”, “rules”, or “linear”. Defaults to “rules_and_linear”.
weights_column: Specify a column to use for the observation weights, which are used for bias correction. The specified
weights_columnmust be included in the specifiedtraining_frame.Python only: To use a weights column when passing an H2OFrame to
xinstead of a list of column names, the specifiedtraining_framemust contain the specifiedweights_column.Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more due to the larger loss function pre-factor.
distribution: Specify the distribution (i.e. the loss function). The options are AUTO, bernoulli, multinomial, gaussian, poisson, gamma, laplace, quantile, huber, or tweedie.
If the distribution is
bernoulli, the response column must be 2-class categorical.If the distribution is
quasibinomial, the response column must be numeric and binary.If the distribution is
multinomial, the response column must be categorical.If the distribution is
poisson, the response column must be numeric.If the distribution is
tweedie, the response column must be numeric.If the distribution is
gaussian, the response column must be numberic.If the distribution is
gamma, the response column must be numeric.If the distribution is
fractionalbinomial, the response column must be numeric between 0 and 1.If the distribution is
negativebinomial, the response column must be numeric and non-negative.If the distribution is
ordinal, the response column must be categorical with at least 3 levels.If the distribution is
AUTO,and the response is Enum with cardinality = 2, then the family is automatically determined as bernoulli.
and the response is Enum with cardinality > 2, then the family is automatically determined as multinomial.
and the response is numeric (Real or Int), then the family is automatically determined as gaussian.
Interpreting a RuleFit Model¶
The output for the RuleFit model includes:
model parameters
rule importances in tabular form
training and validation metrics of the underlying linear model
Examples¶
library(h2o)
h2o.init()
# Import the titanic dataset:
f <- "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv"
coltypes <- list(by.col.name = c("pclass", "survived"), types=c("Enum", "Enum"))
df <- h2o.importFile(f, col.types = coltypes)
# Split the dataset into train and test
splits <- h2o.splitFrame(data = df, ratios = 0.8, seed = 1)
train <- splits[[1]]
test <- splits[[2]]
# Set the predictors and response; set the factors:
response <- "survived"
predictors <- c("age", "sibsp", "parch", "fare", "sex", "pclass")
# Build and train the model:
rfit <- h2o.rulefit(y = response,
x = predictors,
training_frame = train,
max_rule_length = 10,
max_num_rules = 100,
seed = 1)
# Retrieve the rule importance:
print(rfit@model$rule_importance)
# Predict on the test data:
h2o.predict(rfit, newdata = test)
import h2o
h2o.init()
from h2o.estimators import H2ORuleFitEstimators
# Import the titanic dataset and set the column types:
f = "https://s3.amazonaws.com/h2o-public-test-data/smalldata/gbm_test/titanic.csv"
df = h2o.import_file(path=f, col_types={'pclass': "enum", 'survived': "enum"})
# Split the dataset into train and test
train, test = df.split_frame(ratios=[0.8], seed=1)
# Set the predictors and response:
x = ["age", "sibsp", "parch", "fare", "sex", "pclass"]
y = "survived"
# Build and train the model:
rfit = H2ORuleFitEstimator(max_rule_length=10,
max_num_rules=100,
seed=1)
rfit.train(training_frame=train, x=x, y=y)
# Retrieve the rule importance:
print(rfit._model_json['output']['rule_importance'])
# Predict on the test data:
rfit.predict()