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Module: tfdf.tuner

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Specification of the parameters of a tuner.

The "tuner" is a meta-learning algorithm that find the optimal hyperparameter values of a base learner. "Tuner" is the TF-DF name for the YDF automatic Hyperparameter optimizer V2. For example, a tuner can find the hyper-parameters that maximize the accuracy of a GradientBoostedTreesModel model.

Usage example:

# Imports
import tensorflow_decision_forests as tfdf

# Load a dataset into a Pandas Dataframe.
dataset_df = pd.read_csv("/tmp/penguins.csv")

# Convert the Pandas dataframe to a tf dataset
tf_dataset = tfdf.keras.pd_dataframe_to_tf_dataset(dataset_df,label="species")

# Configure the tuner.
tuner = tfdf.tuner.RandomSearch(num_trials=20)
tuner.choice("num_candidate_attributes_ratio", [1.0, 0.8, 0.6])
tuner.choice("use_hessian_gain", [True, False])

local_search_space = tuner.choice("growing_strategy", ["LOCAL"])
local_search_space.choice("max_depth", [4, 5, 6, 7])

global_search_space = tuner.choice(
    "growing_strategy", ["BEST_FIRST_GLOBAL"], merge=True)
global_search_space.choice("max_num_nodes", [16, 32, 64, 128])

# Configure and train the model.
model = tfdf.keras.GradientBoostedTreesModel(num_trees=50, tuner=tuner)
model.fit(tf_dataset)

Classes

class HPOptProto: A ProtocolMessage

class RandomSearch: Tuner using random hyperparameter values.

class SearchSpace: Set of hyperparameter and their respective possible values.

class TrainConfig: A ProtocolMessage

class Tuner: Abstract tuner class.

annotations Instance of __future__._Feature