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- builtins.object
-
- IsolationForest
- IsolationTree
- IsolationUtils
class IsolationForest(builtins.object) |
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IsolationForest(n_trees=100, max_samples=None, max_depth=10, n_jobs=1, force_true_length=False)
IsolationForest is an implementation of the Isolation Forest algorithm for anomaly detection.
Attributes:
n_trees (int): The number of isolation trees to build. Default is 100.
max_samples (int or None): The maximum number of samples to draw for each tree. If None, defaults to the minimum of 256 or the number of samples in the dataset.
max_depth (int): The maximum depth of each isolation tree. Default is 10.
n_jobs (int): The number of parallel jobs to run. Set to -1 to use all available cores. Default is 1.
force_true_length (bool): Whether to force the true path length calculation. Default is False.
trees (list): A list to store the trained isolation trees.
classes_ (numpy.ndarray): An array representing the classes (0 for normal, 1 for anomaly).
Methods:
__init__(n_trees=100, max_samples=None, max_depth=10, n_jobs=1, force_true_length=False):
Initializes the IsolationForest with the specified parameters.
fit(X):
Fits the isolation forest to the data.
X (array-like): The input features.
_fit_tree(X):
Fits a single isolation tree to a subset of the data.
X (array-like): The input features.
IsolationTree: A trained isolation tree.
anomaly_score(X):
Computes the anomaly scores for given samples.
X (array-like): The input samples.
numpy.ndarray: An array of anomaly scores.
predict(X, threshold=0.5):
Predicts whether samples are anomalies.
X (array-like): The input samples.
threshold (float): The threshold for classifying anomalies (default: 0.5).
numpy.ndarray: An array of predictions (1 if the sample is an anomaly, 0 otherwise).
__sklearn_is_fitted__():
Checks if the model has been fitted.
bool: True if the model is fitted, False otherwise.
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Methods defined here:
- __init__(self, n_trees=100, max_samples=None, max_depth=10, n_jobs=1, force_true_length=False)
- Initializes the IsolationForest with the specified parameters.
Args:
n_trees: (int), optional - The number of isolation trees to build (default: 100).
max_samples: (int or None), optional - The maximum number of samples to draw for each tree.
If None, defaults to the minimum of 256 or the number of samples in the dataset (default: None).
max_depth: (int), optional - The maximum depth of each isolation tree (default: 10).
n_jobs: (int), optional - The number of parallel jobs to run.
Set to -1 to use all available cores (default: 1).
force_true_length: (bool), optional - Whether to force the true path length calculation (default: False).
Attributes:
n_trees: (int) - The number of isolation trees.
max_samples: (int or None) - The maximum number of samples for each tree.
max_depth: (int) - The maximum depth of the trees.
force_true_length: (bool) - Indicates whether to use the true path length for scoring.
trees: (list) - A list to store the trained isolation trees.
n_jobs: (int) - The number of parallel jobs to run.
classes_: (np.ndarray) - An array representing the classes (0 for normal, 1 for anomaly).
- __sklearn_is_fitted__(self)
- Checks if the model has been fitted.
- anomaly_score(self, X)
- Computes the anomaly scores for given samples.
Args:
X: (array-like) - The input samples.
Returns:
array: An array of anomaly scores.
- fit(self, X, y=None)
- Fits the isolation forest to the data.
Args:
X: (array-like) - The input features.
y: (array-like) - The target labels (not used in this implementation).
- predict(self, X, threshold=0.5)
- Predicts whether samples are anomalies.
Args:
X: (array-like) - The input samples.
threshold: (float) - The threshold for classifying anomalies (default: 0.5).
Returns:
array: An array of predictions (1 if the sample is an anomaly, 0 otherwise).
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class IsolationTree(builtins.object) |
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IsolationTree(max_depth=10, force_true_length=False)
IsolationTree is a class that implements an isolation tree, which is a fundamental building block of the Isolation Forest algorithm.
The Isolation Forest is an unsupervised learning method used for anomaly detection.
Attributes:
max_depth (int): The maximum depth of the tree. Default is 10.
tree (dict): The learned isolation tree structure.
force_true_length (bool): If True, the true path length is used for scoring
instead of the average path length.
Methods:
__init__(max_depth=10, force_true_length=False):
Initializes the IsolationTree with the specified maximum depth and
scoring method.
fit(X, depth=0):
Fits the isolation tree to the input data by recursively partitioning
the data based on randomly selected features and split values.
path_length(X, tree=None, depth=0):
Computes the path length for a given sample by traversing the tree
structure. The path length is used to determine how isolated a sample is.
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Methods defined here:
- __init__(self, max_depth=10, force_true_length=False)
- Initializes the Isolation Forest with specified parameters.
Args:
max_depth: (int), optional - Maximum depth of the tree (default is 10).
force_true_length: (bool), optional - If True, use the true path length for scoring (default is False).
Attributes:
max_depth: (int) - Maximum depth of the tree.
tree: (object or None) - The tree structure used in the Isolation Forest (default is None).
force_true_length: (bool) - Indicates whether to use the true path length for scoring.
- fit(self, X, depth=0)
- Fits the isolation tree to the data.
Args:
X: (array-like) - The input features.
depth: (int) - The current depth of the tree (default: 0).
Returns:
dict: The learned isolation tree.
- path_length(self, X, tree=None, depth=0)
- Computes the path length for a given sample.
Args:
X: (array-like) - The input sample.
tree: (dict) - The current node of the tree (default: None).
depth: (int) - The current depth of the tree (default: 0).
Returns:
int: The path length.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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class IsolationUtils(builtins.object) |
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Utility functions for the Isolation Forest algorithm.
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Static methods defined here:
- compute_avg_path_length(size)
- Computes the average path length of unsuccessful searches in a binary search tree.
Args:
size: (int) - The size of the tree.
Returns:
average_path_length: (float) - The average path length.
Data descriptors defined here:
- __dict__
- dictionary for instance variables
- __weakref__
- list of weak references to the object
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