IsolationForestLearner
IsolationForestLearner
IsolationForestLearner(label: Optional[str] = None, task: Task = ANOMALY_DETECTION, *, weights: Optional[str] = None, ranking_group: Optional[str] = None, uplift_treatment: Optional[str] = None, features: Optional[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 = 100000, max_num_scanned_rows_to_compute_statistics: int = 100000, data_spec: Optional[DataSpecification] = None, max_depth: int = -2, min_examples: int = 5, num_trees: int = 300, pure_serving_model: bool = False, random_seed: int = 123456, sparse_oblique_normalization: Optional[str] = None, sparse_oblique_projection_density_factor: Optional[float] = None, sparse_oblique_weights: Optional[str] = None, split_axis: str = 'AXIS_ALIGNED', subsample_count: Optional[int] = 256, subsample_ratio: Optional[float] = None, working_dir: Optional[str] = None, num_threads: Optional[int] = None, tuner: Optional[AbstractTuner] = None, explicit_args: Optional[Set[str]] = None)
Bases: GenericLearner
Isolation Forest learning algorithm.
An Isolation Forest is a collection of decision trees trained without labels and independently to partition the feature space. The Isolation Forest prediction is an anomaly score that indicates whether an example originates from a same distribution to the training examples. We refer to Isolation Forest as both the original algorithm by Liu et al. and its extensions.
Usage example:
import ydf
import pandas as pd
dataset = pd.read_csv("project/dataset.csv")
model = ydf.IsolationForestLearner().train(dataset)
print(model.describe())
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
IsolationForestLearner.hyperparameter_templates() (see this function's documentation for
details).
Attributes:
label: Label of the dataset. The label column
should not be identified as a feature in the features parameter.
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 features parameter.
ranking_group: Only for task=Task.RANKING. Name of a feature
that identifies queries in a query/document ranking task. The ranking
group should not be identified as a feature in the features parameter.
uplift_treatment: Only for task=Task.CATEGORICAL_UPLIFT and task=Task.
NUMERICAL_UPLIFT. Name of a numerical feature that identifies the
treatment in an uplift problem. The value 0 is reserved for the control
treatment. Currently, only 0/1 binary treatments are supported.
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 features
are imported. If include_all_columns=True, all the columns are imported as
features and only the semantic of the columns NOT in columns is
determined automatically. If specified, defines the order of the features
- any non-listed features are appended in-order after the specified
features (if include_all_columns=True).
The label, weights, uplift treatment and ranking_group columns should not
be specified as features.
include_all_columns: See features.
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 out-of-vocabulary.
min_vocab_frequency: Minimum number of occurrence of a value for CATEGORICAL
and CATEGORICAL_SET columns. Value observed less than
min_vocab_frequency are considered as out-of-vocabulary.
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
discretize_numerical_columns=True is equivalent as setting the column
semantic DISCRETIZED_NUMERICAL in the column argument. See the
definition of DISCRETIZED_NUMERICAL for more details.
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, in-memory 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,
in-memory 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,
columns, include_all_columns, max_vocab_count,
min_vocab_frequency, discretize_numerical_columns and
num_discretized_numerical_bins will be ignored.
max_depth: Maximum depth of the tree. max_depth=1 means that all trees
will be roots. max_depth=-1 means that tree depth unconstrained by this
parameter. max_depth=-2 means that the maximum depth is log2(number of
sampled examples per tree) (default). Default: -2.
min_examples: Minimum number of examples in a node. Default: 5.
num_trees: Number of individual decision trees. Increasing the number of
trees can increase the quality of the model at the expense of size,
training speed, and inference latency. 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 meta-data. 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.
sparse_oblique_normalization: For sparse oblique splits i.e.
split_axis=SPARSE_OBLIQUE. Normalization applied on the features,
before applying the sparse oblique projections.
- NONE: No normalization.
- STANDARD_DEVIATION: Normalize the feature by the estimated standard
deviation on the entire train dataset. Also known as Z-Score
normalization.
- MIN_MAX: Normalize the feature by the range (i.e. max-min) estimated
on the entire train dataset. Default: None.
sparse_oblique_projection_density_factor: Density of the projections as an
exponent of the number of features. Independently for each projection,
each feature has a probability "projection_density_factor / num_features"
to be considered in the projection.
The paper "Sparse Projection Oblique Random Forests" (Tomita et al, 2020)
calls this parameter lambda and recommends values in [1, 5].
Increasing this value increases training and inference time (on average).
This value is best tuned for each dataset. Default: None.
sparse_oblique_weights: For sparse oblique splits i.e.
split_axis=SPARSE_OBLIQUE. Possible values:
- BINARY: The oblique weights are sampled in {-1,1} (default).
- CONTINUOUS: The oblique weights are be sampled in [-1,1]. Default:
None.
split_axis: What structure of split to consider for numerical features.
- AXIS_ALIGNED: Axis aligned splits (i.e. one condition at a time).
This is the "classical" way to train a tree. Default value.
- SPARSE_OBLIQUE: Sparse oblique splits (i.e. random splits on a small
number of features) from "Sparse Projection Oblique Random Forests",
Tomita et al., 2020. This includes the splits described in "Extended
Isolation Forests" (Sahand Hariri et al., 2018). Default:
"AXIS_ALIGNED".
subsample_count: Number of examples used to grow each tree. Only one of
"subsample_ratio" and "subsample_count" can be set. By default, sample
256 examples per tree. Note that this parameter also restricts the tree's
maximum depth to log2(examples used per tree) unless max_depth is set
explicitly. Default: 256.
subsample_ratio: Ratio of number of training examples used to grow each
tree. Only one of "subsample_ratio" and "subsample_count" can be set. By
default, sample 256 examples per tree. Note that this parameter also
restricts the tree's maximum depth to log2(examples used per tree) unless
max_depth is set explicitly. Default: None.
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 hyper-parameter tuners
will export artefacts to the "working_dir" if provided.
num_threads: Number of threads used to train the model. Different learning
algorithms use multi-threading differently and with different degree of
efficiency. If None, num_threads will be automatically set to the
number of processors (up to a maximum of 32; or set to 6 if the number of
processors is not available). Making num_threads significantly larger
than the number of processors can slow-down the training speed. The
default value logic might change in the future.
tuner: If set, automatically select the best hyperparameters using the
provided tuner. When using distributed training, the tuning is
distributed.
explicit_args: Helper argument for internal use. Throws if supplied
explicitly by the user.
hyperparameters
property
A (mutable) dictionary of this learner's hyperparameters.
This object can be used to inspect or modify hyperparameters after creating
the learner. Modifying hyperparameters after constructing the learner is
suitable for some advanced use cases. Since this approach bypasses some
feasibility checks for the given set of hyperparameters, it generally better
to re-create the learner for each model. The current set of hyperparameters
can be validated manually with validate_hyperparameters().
cross_validation
cross_validation(ds: InputDataset, folds: int = 10, bootstrapping: Union[bool, int] = False, parallel_evaluations: int = 1) -> Evaluation
Cross-validates 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 cross-validation. |
required |
folds
|
int
|
Number of cross-validation 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 multi-threading. Note that each model is potentially already
trained with multithreading (see |
1
|
Returns:
| Type | Description |
|---|---|
Evaluation
|
The cross-validation evaluation. |
hyperparameter_templates
classmethod
train
train(ds: InputDataset, valid: Optional[InputDataset] = None, verbose: Optional[Union[int, bool]] = None) -> IsolationForestModel
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.IsolationForestLearner(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
|
verbose
|
Optional[Union[int, bool]]
|
Verbose level during training. If None, uses the global verbose
level of |
None
|
Returns:
| Type | Description |
|---|---|
IsolationForestModel
|
A trained model. |
validate_hyperparameters
Returns None if the hyperparameters are valid, raises otherwise.
This method is called automatically before training, but users may call it to fail early. It makes sense to call this method when changing manually the hyper-paramters of the learner. This is a relatively advanced approach that is not recommende (it is better to re-create the learner in most cases).
Usage example: