DistributedGradientBoostedTreesLearner
DistributedGradientBoostedTreesLearner
DistributedGradientBoostedTreesLearner(label: str, task: Task = generic_learner.Task.CLASSIFICATION, weights: Optional[str] = None, ranking_group: Optional[str] = None, uplift_treatment: Optional[str] = None, features: ColumnDefs = None, include_all_columns: bool = False, max_vocab_count: int = 2000, min_vocab_frequency: int = 5, discretize_numerical_columns: bool = False, num_discretized_numerical_bins: int = 255, max_num_scanned_rows_to_infer_semantic: int = 10000, max_num_scanned_rows_to_compute_statistics: int = 10000, data_spec: Optional[DataSpecification] = None, apply_link_function: Optional[bool] = True, force_numerical_discretization: Optional[bool] = False, max_depth: Optional[int] = 6, max_unique_values_for_discretized_numerical: Optional[int] = 16000, maximum_model_size_in_memory_in_bytes: Optional[float] = 1.0, maximum_training_duration_seconds: Optional[float] = 1.0, min_examples: Optional[int] = 5, num_candidate_attributes: Optional[int] = 1, num_candidate_attributes_ratio: Optional[float] = 1.0, num_trees: Optional[int] = 300, pure_serving_model: Optional[bool] = False, random_seed: Optional[int] = 123456, shrinkage: Optional[float] = 0.1, use_hessian_gain: Optional[bool] = False, worker_logs: Optional[bool] = True, num_threads: Optional[int] = None, working_dir: Optional[str] = None, resume_training: bool = False, resume_training_snapshot_interval_seconds: int = 1800, tuner: Optional[AbstractTuner] = None, workers: Optional[Sequence[str]] = None)
Bases: GenericLearner
Distributed Gradient Boosted Trees learning algorithm.
Exact distributed version of the Gradient Boosted Tree learning algorithm. See the documentation of the nondistributed Gradient Boosted Tree learning algorithm for an introduction to GBTs.
Usage example:
import ydf
import pandas as pd
dataset = pd.read_csv("project/dataset.csv")
model = ydf.DistributedGradientBoostedTreesLearner().train(dataset)
print(model.summary())
Hyperparameters are configured to give reasonable results for typical
datasets. Hyperparameters can also be modified manually (see descriptions)
below or by applying the hyperparameter templates available with
DistributedGradientBoostedTreesLearner.hyperparameter_templates()
(see this function's documentation for
details).
Attributes:
Name  Type  Description 

label 
Label of the dataset. The label column
should not be identified as a feature in the 

task 
Task to solve (e.g. Task.CLASSIFICATION, Task.REGRESSION, Task.RANKING, Task.CATEGORICAL_UPLIFT, Task.NUMERICAL_UPLIFT). 

weights 
Name of a feature that identifies the weight of each example. If
weights are not specified, unit weights are assumed. The weight column
should not be identified as a feature in the 

ranking_group 
Only for 

uplift_treatment 
Only for 

features 
If None, all columns are used as features. The semantic of the
features is determined automatically. Otherwise, if
include_all_columns=False (default) only the column listed in 

include_all_columns 
See 

max_vocab_count 
Maximum size of the vocabulary of CATEGORICAL and CATEGORICAL_SET columns stored as strings. If more unique values exist, only the most frequent values are kept, and the remaining values are considered as outofvocabulary. 

min_vocab_frequency 
Minimum number of occurrence of a value for CATEGORICAL
and CATEGORICAL_SET columns. Value observed less than


discretize_numerical_columns 
If true, discretize all the numerical columns
before training. Discretized numerical columns are faster to train with,
but they can have a negative impact on the model quality. Using


num_discretized_numerical_bins 
Number of bins used when disretizing numerical columns. 

max_num_scanned_rows_to_infer_semantic 
Number of rows to scan when inferring the column's semantic if it is not explicitly specified. Only used when reading from file, inmemory datasets are always read in full. Setting this to a lower number will speed up dataset reading, but might result in incorrect column semantics. Set to 1 to scan the entire dataset. 

max_num_scanned_rows_to_compute_statistics 
Number of rows to scan when computing a column's statistics. Only used when reading from file, inmemory datasets are always read in full. A column's statistics include the dictionary for categorical features and the mean / min / max for numerical features. Setting this to a lower number will speed up dataset reading, but skew statistics in the dataspec, which can hurt model quality (e.g. if an important category of a categorical feature is considered OOV). Set to 1 to scan the entire dataset. 

data_spec 
Dataspec to be used (advanced). If a data spec is given,


apply_link_function 
If true, applies the link function (a.k.a. activation function), if any, before returning the model prediction. If false, returns the prelink function model output. For example, in the case of binary classification, the prelink function output is a logic while the postlink function is a probability. Default: True. 

force_numerical_discretization 
If false, only the numerical column safisfying "max_unique_values_for_discretized_numerical" will be discretized. If true, all the numerical columns will be discretized. Columns with more than "max_unique_values_for_discretized_numerical" unique values will be approximated with "max_unique_values_for_discretized_numerical" bins. This parameter will impact the model training. Default: False. 

max_depth 
Maximum depth of the tree. 

max_unique_values_for_discretized_numerical 
Maximum number of unique value of a numerical feature to allow its prediscretization. In case of large datasets, discretized numerical features with a small number of unique values are more efficient to learn than classical / nondiscretized numerical features. This parameter does not impact the final model. However, it can speedup or slown the training. Default: 16000. 

maximum_model_size_in_memory_in_bytes 
Limit the size of the model when stored in ram. Different algorithms can enforce this limit differently. Note that when models are compiled into an inference, the size of the inference engine is generally much smaller than the original model. Default: 1.0. 

maximum_training_duration_seconds 
Maximum training duration of the model expressed in seconds. Each learning algorithm is free to use this parameter at it sees fit. Enabling maximum training duration makes the model training nondeterministic. Default: 1.0. 

min_examples 
Minimum number of examples in a node. Default: 5. 

num_candidate_attributes 
Number of unique valid attributes tested for each
node. An attribute is valid if it has at least a valid split. If


num_candidate_attributes_ratio 
Ratio of attributes tested at each node. If
set, it is equivalent to 

num_trees 
Maximum number of decision trees. The effective number of trained tree can be smaller if early stopping is enabled. Default: 300. 

pure_serving_model 
Clear the model from any information that is not required for model serving. This includes debugging, model interpretation and other metadata. The size of the serialized model can be reduced significatively (50% model size reduction is common). This parameter has no impact on the quality, serving speed or RAM usage of model serving. Default: False. 

random_seed 
Random seed for the training of the model. Learners are expected to be deterministic by the random seed. Default: 123456. 

shrinkage 
Coefficient applied to each tree prediction. A small value (0.02) tends to give more accurate results (assuming enough trees are trained), but results in larger models. Analogous to neural network learning rate. Default: 0.1. 

use_hessian_gain 
Use true, uses a formulation of split gain with a hessian term i.e. optimizes the splits to minimize the variance of "gradient / hessian. Available for all losses except regression. Default: False. 

worker_logs 
If true, workers will print training logs. Default: True. 

num_threads 
Number of threads used to train the model. Different learning
algorithms use multithreading differently and with different degree of
efficiency. If 

resume_training 
If true, the model training resumes from the checkpoint
stored in the 

working_dir 
Path to a directory available for the learning algorithm to store intermediate computation results. Depending on the learning algorithm and parameters, the working_dir might be optional, required, or ignored. For instance, distributed training algorithm always need a "working_dir", and the gradient boosted tree and hyperparameter tuners will export artefacts to the "working_dir" if provided. 

resume_training_snapshot_interval_seconds 
Indicative number of seconds in
between snapshots when 

tuner 
If set, automatically select the best hyperparameters using the provided tuner. When using distributed training, the tuning is distributed. 

workers 
If set, enable distributed training. "workers" is the list of IP
addresses of the workers. A worker is a process running

cross_validation
cross_validation(ds: InputDataset, folds: int = 10, bootstrapping: Union[bool, int] = False, parallel_evaluations: int = 1) > Evaluation
Crossvalidates the learner and return the evaluation.
Usage example:
import pandas as pd
import ydf
dataset = pd.read_csv("my_dataset.csv")
learner = ydf.RandomForestLearner(label="label")
evaluation = learner.cross_validation(dataset)
# In a notebook, display an interractive evaluation
evaluation
# Print the evaluation
print(evaluation)
# Look at specific metrics
print(evaluation.accuracy)
Parameters:
Name  Type  Description  Default 

ds 
InputDataset

Dataset for the crossvalidation. 
required 
folds 
int

Number of crossvalidation folds. 
10

bootstrapping 
Union[bool, int]

Controls whether bootstrapping is used to evaluate the confidence intervals and statistical tests (i.e., all the metrics ending with "[B]"). If set to false, bootstrapping is disabled. If set to true, bootstrapping is enabled and 2000 bootstrapping samples are used. If set to an integer, it specifies the number of bootstrapping samples to use. In this case, if the number is less than 100, an error is raised as bootstrapping will not yield useful results. 
False

parallel_evaluations 
int

Number of model to train and evaluate in parallel
using multithreading. Note that each model is potentially already
trained with multithreading (see 
1

Returns:
Type  Description 

Evaluation

The crossvalidation evaluation. 
hyperparameter_templates
classmethod
train
train(ds: InputDataset, valid: Optional[InputDataset] = None) > GradientBoostedTreesModel
Trains a model on the given dataset.
Options for dataset reading are given on the learner. Consult the documentation of the learner or ydf.create_vertical_dataset() for additional information on dataset reading in YDF.
Usage example:
import ydf
import pandas as pd
train_ds = pd.read_csv(...)
learner = ydf.DistributedGradientBoostedTreesLearner(label="label")
model = learner.train(train_ds)
print(model.summary())
If training is interrupted (for example, by interrupting the cell execution in Colab), the model will be returned to the state it was in at the moment of interruption.
Parameters:
Name  Type  Description  Default 

ds 
InputDataset

Training dataset. 
required 
valid 
Optional[InputDataset]

Optional validation dataset. Some learners, such as Random Forest, do not need validation dataset. Some learners, such as GradientBoostedTrees, automatically extract a validation dataset from the training dataset if the validation dataset is not provided. 
None

Returns:
Type  Description 

GradientBoostedTreesModel

A trained model. 