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Model builder.
The model builder let the user create models by hand i.e. by defining the tree structure manually.
The available builders are:
- RandomForestBuilder
- CARTBuilder
- GradientBoostedTreeBuilder
About categorical and categorical-set features with string dictionary:
Categorical and categorical-set features are tied to a dictionary of possible
values. In addition, the special value "out-of-dictionary" (OOD) designate all
the which are not in the dictionary. For example, the condition
"a in ["x","
The feature dictionaries are automatically assembled as the union of all the observed values in the tree conditions. Alternatively, dictionaries can be get/set manually with "{get,set}_dictionary()" or imported from an existing dataspec with the "import_dataspec" constructor argument.
About "file prefix": Multiple Yggdrasil decision forests models can be stored in a single directory. This is a requirement of the TensorFlow SavedModel API. To implement this logic, the files of each individual model are prefixed with a unique identifier. When loading a model from a directory path, this prefix can be provided or detected automatically. Note that the automatic detection will fail if a directory contains more than one model.
Usage:
# Create a binary classification CART model.
builder = builder_lib.CARTBuilder(
path="/path/to/model",
objective=py_tree.objective.ClassificationObjective(
label="color", classes=["red", "blue"]))
# Create the tree
# f1>=1.5
# ├─(pos)─ [0.1, 0.9]
# └─(neg)─ [0.8, 0.2]
#
# The component of the trees (e.g. `NonLeafNode`, `Tree`) are defined in
# `tfdf.py_tree.`.
#
builder.add_tree(
Tree(
NonLeafNode(
condition=NumericalHigherThanCondition(
feature=SimpleColumnSpec(
name="f1", type=py_tree.dataspec.ColumnType.NUMERICAL),
threshold=1.5,
missing_evaluation=False),
pos_child=LeafNode(
value=ProbabilityValue(probability=[0.1, 0.9])),
neg_child=LeafNode(
value=ProbabilityValue(probability=[0.8, 0.2])))))
# Create a second tree
# f2 in ["x", "y"]
# ├─(pos)─ [0.1, 0.9]
# └─(neg)─ [0.8, 0.2]
#
builder.add_tree(
Tree(
NonLeafNode(
condition=CategoricalIsInCondition(
feature=SimpleColumnSpec(
name="f2",
type=py_tree.dataspec.ColumnType.CATEGORICAL),
mask=["x", "y"],
missing_evaluation=False),
pos_child=LeafNode(
value=ProbabilityValue(probability=[0.1, 0.9])),
neg_child=LeafNode(
value=ProbabilityValue(probability=[0.8, 0.2])))))
# Optionally set the dictionary of the categorical feature "f2".
# If not set, all the values not seens in the model ("z" in this case) will not
# be known by the model and will be treated as OOD (out of dictionary).
#
# Defining a dictionary only has an impact if a condition is testing for the
# `<OOD>` item directly i.e. the test `f2 in ["<OOD>"]` depends on the content
# of the dictionary.
builder.set_dictionary("f2",["<OOD>", "x", "y", "z"]
builder.close()
# Load and use the model
model = tf_keras.models.load_model("/path/to/model")
predictions = model.predict(...)
Classes
class AbstractBuilder: Generic model builder.
class AbstractDecisionForestBuilder: Generic decision forest model builder.
class AdvancedArguments: Advanced control of the model building.
class CARTBuilder: CART model builder.
class Enum: Generic enumeration.
class GradientBoostedTreeBuilder: Gradient Boosted Tree model builder.
class ModelFormat: Model formats on disk.
class RandomForestBuilder: Random Forest model builder.
Functions
dataclass(...): Returns the same class as was passed in, with dunder methods added based on the fields defined in the class.