# Source code for isotree

```
import numpy as np, pandas as pd
from scipy.sparse import csc_matrix, csr_matrix, issparse, isspmatrix_csc, isspmatrix_csr, vstack as sp_vstack
import warnings
import multiprocessing
import ctypes
import json
import os
from copy import deepcopy
from ._cpp_interface import isoforest_cpp_obj, _sort_csc_indices, _reconstruct_csr_sliced, _reconstruct_csr_with_categ, _get_has_openmp
__all__ = ["IsolationForest"]
### Helpers
def _get_num_dtype(X_num=None, sample_weights=None, column_weights=None):
if X_num is not None:
return np.empty(0, dtype=X_num.dtype)
elif sample_weights is not None:
return np.empty(0, dtype=column_weights.dtype)
elif column_weights is not None:
return np.empty(0, dtype=sample_weights.dtype)
else:
return np.empty(0, dtype=ctypes.c_double)
def _get_int_dtype(X_num):
if (X_num is not None) and (issparse(X_num)):
return np.empty(0, dtype=X_num.indices.dtype)
else:
return np.empty(0, dtype=ctypes.c_size_t)
def _is_row_major(X_num):
if (X_num is None) or (issparse(X_num)):
return False
else:
return X_num.strides[1] == X_num.dtype.itemsize
def _is_col_major(X_num):
if (X_num is None) or (issparse(X_num)):
return False
else:
return X_num.strides[0] == X_num.dtype.itemsize
def _copy_if_subview(X_num, prefer_row_major=False):
### TODO: the C++ functions should accept a 'leading dimension'
### parameter so as to avoid copying the data here
if (X_num is not None) and (not issparse(X_num)):
col_major = _is_col_major(X_num)
leading_dimension = int(X_num.strides[1 if col_major else 0] / X_num.dtype.itemsize)
if (
(leading_dimension != X_num.shape[0 if col_major else 1]) or
(len(X_num.strides) != 2) or
(not X_num.flags.aligned) or
(not _is_row_major(X_num) and not _is_col_major(X_num))
):
X_num = X_num.copy()
if _is_col_major(X_num) != col_major:
if prefer_row_major:
X_num = np.ascontiguousarray(X_num)
else:
X_num = np.asfortranarray(X_num)
return X_num
def _all_equal(x, y):
if x.shape[0] != y.shape[0]:
return False
return np.all(x == y)
def _encode_categorical(cl, categories):
if (cl.shape[0] >= 100) and (cl.dtype.name == "category"):
if _all_equal(cl.cat.categories, categories):
return cl.cat.codes
return pd.Categorical(cl, categories).codes
[docs]class IsolationForest:
"""
Isolation Forest model
Isolation Forest is an algorithm originally developed for outlier detection that consists in splitting
sub-samples of the data according to some attribute/feature/column at random. The idea is that, the rarer
the observation, the more likely it is that a random uniform split on some feature would put outliers alone
in one branch, and the fewer splits it will take to isolate an outlier observation like this. The concept
is extended to splitting hyperplanes in the extended model (i.e. splitting by more than one column at a time), and to
guided (not entirely random) splits in the SCiForest and FCF models that aim at isolating outliers faster and/or
finding clustered outliers.
This version adds heuristics to handle missing data and categorical variables. Can be used to aproximate pairwise
distances by checking the depth after which two observations become separated, and to approximate densities by fitting
trees beyond balanced-tree limit. Offers options to vary between randomized and deterministic splits too.
Note
----
The default parameters in this software do not correspond to the suggested parameters in
any of the references.
In particular, the following default values are likely to cause huge differences when compared to the
defaults in other software: ``ndim``, ``sample_size``, ``ntrees``. The defaults here are
nevertheless more likely to result in better models. In order to mimic scikit-learn for example, one
would need to pass ``ndim=1``, ``sample_size=256``, ``ntrees=100``, ``missing_action="fail"``, ``nthreads=1``.
Note
----
Shorthands for parameter combinations that match some of the references:
'iForest' (reference [1]_):
``ndim=1``, ``sample_size=256``, ``max_depth=8``, ``ntrees=100``, ``missing_action="fail"``.
'EIF' (reference [3]_):
``ndim=2``, ``sample_size=256``, ``max_depth=8``, ``ntrees=100``, ``missing_action="fail"``,
``coefs="uniform"``, ``standardize_data=False`` (plus standardizing the data **before** passing it).
'SCiForest' (reference [4]_):
``ndim=2``, ``sample_size=256``, ``max_depth=8``, ``ntrees=100``, ``missing_action="fail"``,
``coefs="normal"``, ``ntry=10``, ``prob_pick_avg_gain=1``, ``penalize_range=True``.
Might provide much better results with ``max_depth=None`` despite the reference's recommendation.
'FCF' (reference [11]_):
``ndim=2``, ``sample_size=256``, ``max_depth=None``, ``ntrees=200``, ``missing_action="fail"``,
``coefs="normal"``, ``ntry=1``, ``prob_pick_pooled_gain=1``.
Might provide similar or better results with ``ndim=1``.
For the FCF model aimed at imputing missing values,
might give better results with ``ntry=10`` or higher and much larger sample sizes.
Note
----
The model offers many tunable parameters (see reference [11]_ for a comparison).
The most likely candidate to tune is
``prob_pick_pooled_gain``, for which higher values tend to
result in a better ability to flag outliers in multimodal datasets, at the expense of poorer
generalizability to inputs with values outside the variables' ranges to which the model was fit
(see plots generated from the examples in GitHub notebook for a better idea of the difference). The next candidate to tune is
``sample_size`` - the default is to use all rows, but in some datasets introducing sub-sampling can help,
especially for the single-variable model. In smaller datasets, one might also want to experiment
with ``weigh_by_kurtosis`` and perhaps lower ``ndim``. If using ``prob_pick_pooled_gain``, models
are likely to benefit from deeper trees (controlled by ``max_depth``), but using large samples
and/or deeper trees can result in significantly slower model fitting and predictions - in such cases,
using ``min_gain`` (with a value like 0.25) with ``max_depth=None`` can offer a better speed/performance
trade-off than changing ``max_depth``.
Note
----
The default parameters will not scale to large datasets. In particular,
if the amount of data is large, it's suggested to set a smaller sample size for each tree (parameter ``sample_size``)
and to fit fewer of them (parameter ``ntrees``).
As well, the default option for 'missing_action' might slow things down significantly.
See the documentation of the parameters for more details.
These defaults can also result in very big model sizes in memory and as serialized
files (e.g. models that weight over 10GB) when the number of rows in the data is large.
Using fewer trees, smaller sample sizes, and shallower trees can help to reduce model
sizes if that becomes a problem.
Note
----
See the documentation of ``predict`` for some considerations when serving models generated through
this library.
Parameters
----------
sample_size : str "auto", int, float(0,1), or None
Sample size of the data sub-samples with which each binary tree will be built. If passing 'None', each
tree will be built using the full data. Recommended value in [1]_, [2]_, [3]_ is 256, while
the default value in the author's code in [5]_ is 'None' here.
If passing "auto", will use the full number of rows in the data, up to 10,000 (i.e.
will take 'sample_size=min(nrows(X), 10000)') **when calling fit**, and the full amount
of rows in the data **when calling the variants** ``fit_predict`` or ``fit_transform``.
If passing ``None``, will take the full number of rows in the data (no sub-sampling).
If passing a number between zero and one, will assume it means taking a sample size that represents
that proportion of the rows in the data.
Hint: seeing a distribution of scores which is on average too far below 0.5 could mean that the
model needs more trees and/or bigger samples to reach convergence (unless using non-random
splits, in which case the distribution is likely to be centered around a much lower number),
or that the distributions in the data are too skewed for random uniform splits.
ntrees : int
Number of binary trees to build for the model. Recommended value in [1]_ is 100, while the default value in the
author's code in [5]_ is 10. In general, the number of trees required for good results
is higher when (a) there are many columns, (b) there are categorical variables, (c) categorical variables have many
categories, (d) `ndim` is high, (e) ``prob_pick_pooled_gain`` is used.
Hint: seeing a distribution of scores which is on average too far below 0.5 could mean that the
model needs more trees and/or bigger samples to reach convergence (unless using non-random
splits, in which case the distribution is likely to be centered around a much lower number),
or that the distributions in the data are too skewed for random uniform splits.
ndim : int
Number of columns to combine to produce a split. If passing 1, will produce the single-variable model described
in [1]_ and [2]_, while if passing values greater than 1, will produce the extended model described in [3]_ and [4]_.
Recommended value in [4]_ is 2, while [3]_ recommends a low value such as 2 or 3. Models with values higher than 1
are referred hereafter as the extended model (as in [3]_).
Note that, when using ``ndim>1`` plus ``standardize_data=True``, the variables are standardized at
each step as suggested in [4]_, which makes the models slightly different than in [3]_.
ntry : int
When using ``prob_pick_pooled_gain`` and/or ``prob_pick_avg_gain``, how many variables (with ``ndim=1``)
or linear combinations (with ``ndim>1``) to try for determining the best one according to gain.
Recommended value in reference [4]_ is 10 (with ``prob_pick_avg_gain``, for outlier detection), while the
recommended value in reference [11]_ is 1 (with ``prob_pick_pooled_gain``, for outlier detection), and the
recommended value in reference [9]_ is 10 to 20 (with ``prob_pick_pooled_gain``, for missing value imputations).
categ_cols : None or array-like
Columns that hold categorical features, when the data is passed as an array or matrix.
Categorical columns should contain only integer values with a continuous numeration starting at zero,
with negative values and NaN taken as missing,
and the array or list passed here should correspond to the column numbers, with numeration starting
at zero. The maximum categorical value should not exceed 'INT_MAX' (typically :math:`2^{31}-1`).
This might be passed either at construction time or when calling ``fit`` or variations of ``fit``.
This is ignored when the input is passed as a ``DataFrame`` as then it will consider columns as
categorical depending on their dtype (see the documentation for ``fit`` for details).
max_depth : int, None, or str "auto"
Maximum depth of the binary trees to grow. If passing None, will build trees until each observation ends alone
in a terminal node or until no further split is possible. If using "auto", will limit it to the corresponding
depth of a balanced binary tree with number of terminal nodes corresponding to the sub-sample size (the reason
being that, if trying to detect outliers, an outlier will only be so if it turns out to be isolated with shorter average
depth than usual, which corresponds to a balanced tree depth). When a terminal node has more than 1 observation, the
remaining isolation depth for them is estimated assuming the data and splits are both uniformly random (separation depth
follows a similar process with expected value calculated as in [6]_). Default setting for [1]_, [2]_, [3]_, [4]_ is "auto",
but it's recommended to pass higher values if using the model for purposes other than outlier detection.
Note that models that use ``prob_pick_pooled_gain`` or ``prob_pick_avg_gain`` are likely to benefit from
deeper trees (larger ``max_depth``), but deeper trees can result in much slower model fitting and
predictions.
If using pooled gain, one might want to substitute ``max_depth`` with ``min_gain``.
ncols_per_tree : None, int, or float(0,1]
Number of columns to use (have as potential candidates for splitting at each iteration) in each tree,
somewhat similar to the 'mtry' parameter of random forests.
In general, this is only relevant when using non-random splits and/or weighting by kurtosis.
If passing a number between zero and one, will assume it means taking a sample size that represents
that proportion of the columns in the data. If passing exactly 1, will assume it means taking
100% of the columns rather than taking 1 column.
If passing ``None`` (the default) or zero, will use the full number of available columns.
prob_pick_pooled_gain : float(0, 1)
This parameter indicates the probability of choosing the threshold on which to split a variable
(with ``ndim=1``) or a linear combination of variables (when using ``ndim>1``) as the threshold
that maximizes a pooled standard deviation gain criterion (see references [9]_ and [11]_) on the
same variable or linear combination, similarly to regression trees such as CART.
If using ``ntry>1``, will try several variables or linear combinations thereof and choose the one
in which the largest standardized gain can be achieved.
For categorical variables with ``ndim=1``, will use shannon entropy instead (like in [7]_).
Compared to a simple averaged gain, this tends to result in more evenly-divided splits and more clustered
groups when they are smaller. Recommended to pass higher values when used for imputation of missing values.
When used for outlier detection, datasets with multimodal distributions usually see better performance
under this type of splits.
Note that, since this makes the trees more even and thus it takes more steps to produce isolated nodes,
the resulting object will be heavier. When splits are not made according to any of ``prob_pick_avg_gain``
or ``prob_pick_pooled_gain``, both the column and the split point are decided at random. Note that, if
passing value 1 (100%) with no sub-sampling and using the single-variable model,
every single tree will have the exact same splits.
Be aware that ``penalize_range`` can also have a large impact when using ``prob_pick_pooled_gain``.
Be aware also that, if passing a value of 1 (100%) with no sub-sampling and using the single-variable
model, every single tree will have the exact same splits.
Under this option, models are likely to produce better results when increasing ``max_depth``.
Alternatively, one can also control the depth through ``min_gain`` (for which one might want to
set ``max_depth=None``).
Important detail: if using either ``prob_pick_avg_gain`` or ``prob_pick_pooled_gain``, the distribution of
outlier scores is unlikely to be centered around 0.5.
prob_pick_avg_gain : float(0, 1)
This parameter indicates the probability of choosing the threshold on which to split a variable
(with ``ndim=1``) or a linear combination of variables (when using ``ndim>1``) as the threshold
that maximizes an averaged standard deviation gain criterion (see references [4]_ and [11]_) on the
same variable or linear combination.
If using ``ntry>1``, will try several variables or linear combinations thereof and choose the one
in which the largest standardized gain can be achieved.
For categorical variables with ``ndim=1``, will take the expected standard deviation that would be
gotten if the column were converted to numerical by assigning to each category a random
number ~ Unif(0, 1) and calculate gain with those assumed standard deviations.
Compared to a pooled gain, this tends to result in more cases in which a single observation or very
few of them are put into one branch. Typically, datasets with outliers defined by extreme values in
some column more or less independently of the rest, usually see better performance under this type
of split. Recommended to use sub-samples (parameter ``sample_size``) when
passing this parameter. Note that, since this will create isolated nodes faster, the resulting object
will be lighter (use less memory).
When splits are
not made according to any of ``prob_pick_avg_gain`` or ``prob_pick_pooled_gain``,
both the column and the split point are decided at random. Default setting for [1]_, [2]_, [3]_ is
zero, and default for [4]_ is 1. This is the randomization parameter that can be passed to the author's original code in [5]_,
but note that the code in [5]_ suffers from a mathematical error in the calculation of running standard deviations,
so the results from it might not match with this library's.
Be aware that, if passing a value of 1 (100%) with no sub-sampling and using the single-variable model, every single tree will have
the exact same splits.
Under this option, models are likely to produce better results when increasing ``max_depth``.
Important detail: if using either ``prob_pick_avg_gain`` or ``prob_pick_pooled_gain``, the distribution of
outlier scores is unlikely to be centered around 0.5.
min_gain : float > 0
Minimum gain that a split threshold needs to produce in order to proceed with a split. Only used when the splits
are decided by a gain criterion (either pooled or averaged). If the highest possible gain in the evaluated
splits at a node is below this threshold, that node becomes a terminal node.
This can be used as a more sophisticated depth control when using pooled gain (note that ``max_depth``
still applies on top of this heuristic).
missing_action : str, one of "divide" (single-variable only), "impute", "fail", "auto"
How to handle missing data at both fitting and prediction time. Options are:
``"divide"``:
(For the single-variable model only, recommended) Will follow both branches and combine the result with the
weight given by the fraction of the data that went to each branch when fitting the model.
``"impute"``:
Will assign observations to the branch with the most observations in the single-variable model, or fill in
missing values with the median of each column of the sample from which the split was made in the extended
model (recommended for the extended model).
``"fail"``:
Will assume there are no missing values and will trigger undefined behavior if it encounters any.
``"auto"``:
Will use "divide" for the single-variable model and "impute" for the extended model.
In the extended model, infinite values will be treated as missing.
Passing "fail" will produce faster fitting and prediction times along with decreased
model object sizes.
Models from [1]_, [2]_, [3]_, [4]_ correspond to "fail" here.
new_categ_action : str, one of "weighted" (single-variable only), "impute" (extended only), "smallest", "random"
What to do after splitting a categorical feature when new data that reaches that split has categories that
the sub-sample from which the split was done did not have. Options are:
``"weighted"``:
(For the single-variable model only, recommended) Will follow both branches and combine the result with weight given
by the fraction of the data that went to each branch when fitting the model.
``"impute"``:
(For the extended model only, recommended) Will assign them the median value for that column that was added to the linear
combination of features.
``"smallest"``:
In the single-variable case will assign all observations with unseen categories in the split to the branch that had
fewer observations when fitting the model, and in the extended case will assign them the coefficient of the least
common category.
``"random"``:
Will assing a branch (coefficient in the extended model) at random for each category beforehand, even if no observations
had that category when fitting the model. Note that this can produce biased results when deciding
splits by a gain criterion.
Important: under this option, if the model is fitted to a ``DataFrame``, when calling ``predict``
on new data which contains new categories (unseen in the data to which the model was fitted),
they will be added to the model's state on-the-fly. This means that, if calling ``predict`` on data
which has new categories, there might be inconsistencies in the results if predictions are done in
parallel or if passing the same data in batches or with different row orders. It also means that
the ``predict`` function will not be thread-safe (e.g. cannot be used alongside ``joblib`` with a
backend that uses shared memory).
``"auto"``:
Will select "weighted" for the single-variable model and "impute" for the extended model.
Ignored when passing 'categ_split_type' = 'single_categ'.
categ_split_type : str, one of "subset" or "single_categ"
Whether to split categorical features by assigning sub-sets of them to each branch, or by assigning
a single category to a branch and the rest to the other branch. For the extended model, whether to
give each category a coefficient, or only one while the rest get zero.
all_perm : bool
When doing categorical variable splits by pooled gain with ``ndim=1`` (regular model),
whether to consider all possible permutations of variables to assign to each branch or not. If ``False``,
will sort the categories by their frequency and make a grouping in this sorted order. Note that the
number of combinations evaluated (if ``True``) is the factorial of the number of present categories in
a given column (minus 2). For averaged gain, the best split is always to put the second most-frequent
category in a separate branch, so not evaluating all permutations (passing ``False``) will make it
possible to select other splits that respect the sorted frequency order.
Ignored when not using categorical variables or not doing splits by pooled gain or using ``ndim > 1``.
coef_by_prop : bool
In the extended model, whether to sort the randomly-generated coefficients for categories
according to their relative frequency in the tree node. This might provide better results when using
categorical variables with too many categories, but is not recommended, and not reflective of
real "categorical-ness". Ignored for the regular model (``ndim=1``) and/or when not using categorical
variables.
recode_categ : bool
Whether to re-encode categorical variables even in case they are already passed
as ``pd.Categorical``. This is recommended as it will eliminate potentially redundant categorical levels if
they have no observations, but if the categorical variables are already of type ``pd.Categorical`` with only
the levels that are present, it can be skipped for slightly faster fitting times. You'll likely
want to pass ``False`` here if merging several models into one through ``append_trees``.
weights_as_sample_prob : bool
If passing sample (row) weights when fitting the model, whether to consider those weights as row
sampling weights (i.e. the higher the weights, the more likely the observation will end up included
in each tree sub-sample), or as distribution density weights (i.e. putting a weight of two is the same
as if the row appeared twice, thus higher weight makes it less of an outlier). Note that sampling weight
is only used when sub-sampling data for each tree, which is not the default in this implementation.
sample_with_replacement : bool
Whether to sample rows with replacement or not (not recommended). Note that distance calculations,
if desired, don't work well with duplicate rows.
penalize_range : bool
Whether to penalize (add -1 to the terminal depth) observations at prediction time that have a value
of the chosen split variable (linear combination in extended model) that falls outside of a pre-determined
reasonable range in the data being split (given by 2 * range in data and centered around the split point),
as proposed in [4]_ and implemented in the authors' original code in [5]_. Not used in single-variable model
when splitting by categorical variables.
It's recommended to turn this off for faster predictions on sparse CSC matrices.
Note that this can make a very large difference in the results when using ``prob_pick_pooled_gain``.
Be aware that this option can make the distribution of outlier scores a bit different
(i.e. not centered around 0.5)
standardize_data : bool
Whether to standardize the features at each node before creating alinear combination of them as suggested
in [4]_. This is ignored when using ``ndim=1``.
weigh_by_kurtosis : bool
Whether to weigh each column according to the kurtosis obtained in the sub-sample that is selected
for each tree as briefly proposed in [1]_. Note that this is only done at the beginning of each tree
sample, so if not using sub-samples, it's better to pass column weights calculated externally. For
categorical columns, will calculate expected kurtosis if the column was converted to numerical by
assigning to each category a random number ~ Unif(0, 1).
Note that when using sparse matrices, the calculation of kurtosis will rely on a procedure that
uses sums of squares and higher-power numbers, which has less numerical precision than the
calculation used for dense inputs, and as such, the results might differ slightly.
Using this option makes the model more likely to pick the columns that have anomalous values
when viewed as a 1-d distribution, and can bring a large improvement in some datasets.
coefs : str, one of "normal" or "uniform"
For the extended model, whether to sample random coefficients according to a normal distribution ~ N(0, 1)
(as proposed in [4]_) or according to a uniform distribution ~ Unif(-1, +1) as proposed in [3]_. Ignored for the
single-variable model. Note that, for categorical variables, the coefficients will be sampled ~ N (0,1)
regardless - in order for both types of variables to have transformations in similar ranges (which will tend
to boost the importance of categorical variables), pass ``"uniform"`` here.
assume_full_distr : bool
When calculating pairwise distances (see [8]_), whether to assume that the fitted model represents
a full population distribution (will use a standardizing criterion assuming infinite sample,
and the results of the similarity between two points at prediction time will not depend on the
prescence of any third point that is similar to them, but will differ more compared to the pairwise
distances between points from which the model was fit). If passing 'False', will calculate pairwise distances
as if the new observations at prediction time were added to the sample to which each tree was fit, which
will make the distances between two points potentially vary according to other newly introduced points.
This will not be assumed when the distances are calculated as the model is being fit (see documentation
for method 'fit_transform').
build_imputer : bool
Whether to construct missing-value imputers so that later this same model could be used to impute
missing values of new (or the same) observations. Be aware that this will significantly increase the memory
requirements and serialized object sizes. Note that this is not related to 'missing_action' as missing
values inside the model are treated differently and follow their own imputation or division strategy.
min_imp_obs : int
Minimum number of observations with which an imputation value can be produced. Ignored if passing
'build_imputer' = 'False'.
depth_imp : str, one of "higher", "lower", "same"
How to weight observations according to their depth when used for imputing missing values. Passing
"higher" will weigh observations higher the further down the tree (away from the root node) the
terminal node is, while "lower" will do the opposite, and "same" will not modify the weights according
to node depth in the tree. Implemented for testing purposes and not recommended to change
from the default. Ignored when passing 'build_imputer' = 'False'.
weigh_imp_rows : str, one of "inverse", "prop", "flat"
How to weight node sizes when used for imputing missing values. Passing "inverse" will weigh
a node inversely proportional to the number of observations that end up there, while "proportional"
will weight them heavier the more observations there are, and "flat" will weigh all nodes the same
in this regard regardless of how many observations end up there. Implemented for testing purposes
and not recommended to change from the default. Ignored when passing 'build_imputer' = 'False'.
random_seed : int
Seed that will be used for random number generation.
nthreads : int
Number of parallel threads to use. If passing a negative number, will use
the same formula as joblib does for calculating number of threads (which is
n_cpus + 1 + n_jobs - i.e. pass -1 to use all available threads). Note that, the more threads,
the more memory will be allocated, even if the thread does not end up being used.
Be aware that most of the operations are bound by memory bandwidth, which means that
adding more threads will not result in a linear speed-up. For some types of data
(e.g. large sparse matrices with small sample sizes), adding more threads might result
in only a very modest speed up (e.g. 1.5x faster with 4x more threads),
even if all threads look fully utilized.
n_estimators : None or int
Synonym for ``ntrees``, kept for better compatibility with scikit-learn.
max_samples : None or int
Synonym for ``sample_size``, kept for better compatibility with scikit-learn.
n_jobs : None or int
Synonym for ``nthreads``, kept for better compatibility with scikit-learn.
random_state : None, int, or RandomState
Synonym for ``random_seed``, kept for better compatibility with scikit-learn.
bootstrap : None or bool
Synonym for ``sample_with_replacement``, kept for better compatibility with scikit-learn.
Attributes
----------
cols_numeric_ : array(n_num_features,)
Array with the names of the columns that were taken as numerical
(Only when fitting the model to a DataFrame object).
cols_categ_ : array(n_categ_features,)
Array with the names of the columns that were taken as categorical
(Only when fitting the model to a DataFrame object).
is_fitted_ : bool
Indicator telling whether the model has been fit to data or not.
References
----------
.. [1] Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. "Isolation forest."
2008 Eighth IEEE International Conference on Data Mining. IEEE, 2008.
.. [2] Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. "Isolation-based anomaly detection."
ACM Transactions on Knowledge Discovery from Data (TKDD) 6.1 (2012): 3.
.. [3] Hariri, Sahand, Matias Carrasco Kind, and Robert J. Brunner. "Extended Isolation Forest."
arXiv preprint arXiv:1811.02141 (2018).
.. [4] Liu, Fei Tony, Kai Ming Ting, and Zhi-Hua Zhou. "On detecting clustered anomalies using SCiForest."
Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Berlin, Heidelberg, 2010.
.. [5] https://sourceforge.net/projects/iforest/
.. [6] https://math.stackexchange.com/questions/3388518/expected-number-of-paths-required-to-separate-elements-in-a-binary-tree
.. [7] Quinlan, J. Ross. C4. 5: programs for machine learning. Elsevier, 2014.
.. [8] Cortes, David. "Distance approximation using Isolation Forests."
arXiv preprint arXiv:1910.12362 (2019).
.. [9] Cortes, David. "Imputing missing values with unsupervised random trees."
arXiv preprint arXiv:1911.06646 (2019).
.. [10] https://math.stackexchange.com/questions/3333220/expected-average-depth-in-random-binary-tree-constructed-top-to-bottom
.. [11] Cortes, David. "Revisiting randomized choices in isolation forests."
arXiv preprint arXiv:2110.13402 (2021).
"""
def __init__(self, sample_size = "auto", ntrees = 500, ndim = 3, ntry = 1,
categ_cols = None, max_depth = "auto", ncols_per_tree = None,
prob_pick_pooled_gain = 0.0, prob_pick_avg_gain = 0.0,
min_gain = 0., missing_action = "auto", new_categ_action = "auto",
categ_split_type = "subset", all_perm = False,
coef_by_prop = False, recode_categ = False,
weights_as_sample_prob = True, sample_with_replacement = False,
penalize_range = False, standardize_data = True, weigh_by_kurtosis = False,
coefs = "normal", assume_full_distr = True,
build_imputer = False, min_imp_obs = 3,
depth_imp = "higher", weigh_imp_rows = "inverse",
random_seed = 1, nthreads = -1,
n_estimators = None, max_samples = None,
n_jobs = None, random_state = None, bootstrap = None):
self.sample_size = sample_size
self.ntrees = ntrees
self.ndim = ndim
self.ntry = ntry
self.categ_cols = categ_cols
self.max_depth = max_depth
self.ncols_per_tree = ncols_per_tree
self.prob_pick_avg_gain = prob_pick_avg_gain
self.prob_pick_pooled_gain = prob_pick_pooled_gain
self.min_gain = min_gain
self.missing_action = missing_action
self.new_categ_action = new_categ_action
self.categ_split_type = categ_split_type
self.all_perm = all_perm
self.coef_by_prop = coef_by_prop
self.recode_categ = recode_categ
self.weights_as_sample_prob = weights_as_sample_prob
self.sample_with_replacement = sample_with_replacement
self.penalize_range = penalize_range
self.standardize_data = standardize_data
self.weigh_by_kurtosis = weigh_by_kurtosis
self.coefs = coefs
self.assume_full_distr = assume_full_distr
self.build_imputer = build_imputer
self.min_imp_obs = min_imp_obs
self.depth_imp = depth_imp
self.weigh_imp_rows = weigh_imp_rows
self.random_seed = random_seed
self.nthreads = nthreads
self.n_estimators = n_estimators
self.max_samples = max_samples
self.n_jobs = n_jobs
self.random_state = random_state
self.bootstrap = bootstrap
self._reset_obj()
def _init(self, categ_cols = None):
if categ_cols is not None:
if self.categ_cols is not None:
warnings.warn("Passed 'categ_cols' in constructor and fit method. Will take the latter.")
self.categ_cols = categ_cols
self._initialize_full(
sample_size = self.sample_size if (self.max_samples is None) else self.max_samples,
ntrees = self.ntrees if (self.n_estimators is None) else self.n_estimators,
ndim = self.ndim, ntry = self.ntry,
categ_cols = self.categ_cols,
max_depth = self.max_depth, ncols_per_tree = self.ncols_per_tree,
prob_pick_avg_gain = self.prob_pick_avg_gain, prob_pick_pooled_gain = self.prob_pick_pooled_gain,
min_gain = self.min_gain, missing_action = self.missing_action, new_categ_action = self.new_categ_action,
categ_split_type = self.categ_split_type, all_perm = self.all_perm,
coef_by_prop = self.coef_by_prop, recode_categ = self.recode_categ,
weights_as_sample_prob = self.weights_as_sample_prob,
sample_with_replacement = self.sample_with_replacement if (self.bootstrap is None) else self.bootstrap,
penalize_range = self.penalize_range, standardize_data = self.standardize_data,
weigh_by_kurtosis = self.weigh_by_kurtosis,
coefs = self.coefs, assume_full_distr = self.assume_full_distr,
build_imputer = self.build_imputer, min_imp_obs = self.min_imp_obs,
depth_imp = self.depth_imp, weigh_imp_rows = self.weigh_imp_rows,
random_seed = self.random_seed if (self.random_state is None) else self.random_state,
nthreads = self.nthreads if (self.n_jobs is None) else self.n_jobs)
def _initialize_full(self, sample_size = None, ntrees = 500, ndim = 3, ntry = 1,
categ_cols = None, max_depth = "auto", ncols_per_tree = None,
prob_pick_avg_gain = 0.0, prob_pick_pooled_gain = 0.0,
min_gain = 0., missing_action = "auto", new_categ_action = "auto",
categ_split_type = "subset", all_perm = False,
coef_by_prop = False, recode_categ = True,
weights_as_sample_prob = True, sample_with_replacement = False,
penalize_range = True, standardize_data = True, weigh_by_kurtosis = False,
coefs = "normal", assume_full_distr = True,
build_imputer = False, min_imp_obs = 3,
depth_imp = "higher", weigh_imp_rows = "inverse",
random_seed = 1, nthreads = -1):
if (sample_size is not None) and (sample_size != "auto"):
assert sample_size > 0
if sample_size > 1:
assert isinstance(sample_size, int)
elif sample_size == 1:
sample_size = None
if ncols_per_tree is not None:
assert ncols_per_tree > 0
if ncols_per_tree > 1:
assert isinstance(ncols_per_tree, int)
elif ncols_per_tree == 1:
ncols_per_tree = None
assert ntrees > 0
assert isinstance(ntrees, int)
if (max_depth != "auto") and (max_depth is not None):
assert max_depth > 0
assert isinstance(max_depth, int)
if (sample_size is not None) and (sample_size != "auto"):
assert max_depth < sample_size
assert ndim >= 1
assert isinstance(ndim, int)
assert ntry >= 1
assert isinstance(ntry, int)
if isinstance(random_seed, np.random.RandomState):
random_seed = random_seed.randint(np.iinfo(np.int32).max)
if isinstance(random_seed, np.random.Generator):
random_seed = random_seed.integers(np.iinfo(np.int32).max)
random_seed = int(random_seed)
assert random_seed >= 0
assert isinstance(min_imp_obs, int)
assert min_imp_obs >= 1
assert missing_action in ["divide", "impute", "fail", "auto"]
assert new_categ_action in ["weighted", "smallest", "random", "impute", "auto"]
assert categ_split_type in ["single_categ", "subset"]
assert coefs in ["normal", "uniform"]
assert depth_imp in ["lower", "higher", "same"]
assert weigh_imp_rows in ["inverse", "prop", "flat"]
assert prob_pick_avg_gain >= 0
assert prob_pick_pooled_gain >= 0
assert min_gain >= 0
s = prob_pick_avg_gain + prob_pick_pooled_gain
if s > 1:
warnings.warn("Split type probabilities sum to more than 1, will standardize them")
prob_pick_avg_gain /= s
prob_pick_pooled_gain /= s
if (ndim == 1) and ((sample_size is None) or (sample_size == "auto")) and ((prob_pick_avg_gain >= 1) or (prob_pick_pooled_gain >= 1)) and (not sample_with_replacement):
msg = "Passed parameters for deterministic single-variable splits"
msg += " with no sub-sampling. "
msg += "Every tree fitted will end up doing exactly the same splits. "
msg += "It's recommended to set 'prob_pick_avg_gain' < 1, 'prob_pick_pooled_gain' < 1, "
msg += "or to use the extended model (ndim > 1)."
warnings.warn(msg)
if missing_action == "auto":
if ndim == 1:
missing_action = "divide"
else:
missing_action = "impute"
if new_categ_action == "auto":
if ndim == 1:
new_categ_action = "weighted"
else:
new_categ_action = "impute"
if (build_imputer) and (missing_action == "fail"):
raise ValueError("Cannot impute missing values when passing 'missing_action' = 'fail'.")
if ndim == 1:
if (categ_split_type != "single_categ") and (new_categ_action == "impute"):
raise ValueError("'new_categ_action' = 'impute' not supported in single-variable model.")
else:
if missing_action == "divide":
raise ValueError("'missing_action' = 'divide' not supported in extended model.")
if (categ_split_type != "single_categ") and (new_categ_action == "weighted"):
raise ValueError("'new_categ_action' = 'weighted' not supported in extended model.")
if nthreads is None:
nthreads = 1
elif nthreads < 0:
nthreads = multiprocessing.cpu_count() + 1 + nthreads
assert nthreads > 0
assert isinstance(nthreads, int)
if (nthreads > 1) and (not _get_has_openmp()):
msg_omp = "Attempting to use more than 1 thread, but "
msg_omp += "package was built without multi-threading "
msg_omp += "support - see the project's GitHub page for "
msg_omp += "more information."
warnings.warn(msg_omp)
if categ_cols is not None:
categ_cols = np.array(categ_cols).reshape(-1).astype(int)
categ_cols.sort()
self.sample_size = sample_size
self.ntrees = ntrees
self.ndim = ndim
self.ntry = ntry
self.categ_cols = categ_cols
self.max_depth = max_depth
self.ncols_per_tree = ncols_per_tree
self.prob_pick_avg_gain = prob_pick_avg_gain
self.prob_pick_pooled_gain = prob_pick_pooled_gain
self.min_gain = min_gain
self.missing_action = missing_action
self.new_categ_action = new_categ_action
self.categ_split_type = categ_split_type
self.coefs = coefs
self.depth_imp = depth_imp
self.weigh_imp_rows = weigh_imp_rows
self.min_imp_obs = min_imp_obs
self.random_seed = random_seed
self.nthreads = nthreads
self.all_perm = bool(all_perm)
self.recode_categ = bool(recode_categ)
self.coef_by_prop = bool(coef_by_prop)
self.weights_as_sample_prob = bool(weights_as_sample_prob)
self.sample_with_replacement = bool(sample_with_replacement)
self.penalize_range = bool(penalize_range)
self.standardize_data = bool(standardize_data)
self.weigh_by_kurtosis = bool(weigh_by_kurtosis)
self.assume_full_distr = bool(assume_full_distr)
self.build_imputer = bool(build_imputer)
self._reset_obj()
def _reset_obj(self):
self.cols_numeric_ = np.array([])
self.cols_categ_ = np.array([])
self._cat_mapping = list()
self._cat_max_lev = np.array([])
self._ncols_numeric = 0
self._ncols_categ = 0
self.is_fitted_ = False
self._ntrees = 0
self._cpp_obj = isoforest_cpp_obj()
self._is_extended_ = self.ndim > 1
[docs] def copy(self):
"""
Get a deep copy of this object
Returns
-------
copied : obj
A deep copy of this object
"""
if not self.is_fitted_:
self._cpp_obj = isoforest_cpp_obj()
return deepcopy(self)
else:
obj_restore = self._cpp_obj
obj_new = self._cpp_obj.deepcopy()
try:
self._cpp_obj = None
out = deepcopy(self)
finally:
self._cpp_obj = obj_restore
out._cpp_obj = obj_new
return out
[docs] def get_params(self, deep=True):
"""
Get parameters for this estimator.
Kept for compatibility with scikit-learn.
Parameters
----------
deep : bool
Ignored.
Returns
-------
params : dict
Parameter names mapped to their values.
"""
import inspect
return {param.name:getattr(self, param.name) for param in inspect.signature(self.__init__).parameters.values()}
[docs] def set_params(self, **params):
"""
Set the parameters of this estimator.
Kept for compatibility with scikit-learn.
Note
----
Setting any parameter other than the number of threads will reset the model
- that is, if it was fitted to some data, the fitted model will be lost,
and it will need to be refitted before being able to make predictions.
Parameters
----------
**params : dict
Estimator parameters.
Returns
-------
self : estimator instance
Estimator instance.
"""
if not (len(params) == 1 and ("nthreads" in params or "n_jobs" in params)):
self.is_fitted_ = False
valid_params = self.get_params(deep=False)
for k,v in params.items():
if k not in valid_params:
raise ValueError("Invalid parameter: ", k)
setattr(self, k, v)
return self
def __str__(self):
msg = ""
if self._is_extended_:
msg += "Extended "
msg += "Isolation Forest model"
if (self.prob_pick_avg_gain + self.prob_pick_pooled_gain) > 0:
msg += " (using guided splits)"
msg += "\n"
if self.ndim > 1:
msg += "Splitting by %d variables at a time\n" % self.ndim
if self.is_fitted_:
msg += "Consisting of %d trees\n" % self._ntrees
if self._ncols_numeric > 0:
msg += "Numeric columns: %d\n" % self._ncols_numeric
if self._ncols_categ:
msg += "Categorical columns: %d\n" % self._ncols_categ
return msg
def __repr__(self):
return self.__str__()
def _get_model_obj(self):
return self._cpp_obj.get_cpp_obj(self._is_extended_)
def _get_imputer_obj(self):
return self._cpp_obj.get_imputer()
def _check_can_use_imputer(self, X_cat):
if (self.build_imputer) and (self.ndim == 1) and (X_cat is not None) and (X_cat.shape[1]):
if (self.categ_split_type != "single_categ") and (self.new_categ_action == "weighted"):
raise ValueError("Cannot build imputer with 'ndim=1' + 'new_categ_action=weighted'.")
if self.missing_action == "divide":
raise ValueError("Cannot build imputer with 'ndim=1' + 'missing_action=divide'.")
[docs] def fit(self, X, y = None, sample_weights = None, column_weights = None, categ_cols = None):
"""
Fit isolation forest model to data
Parameters
----------
X : array or array-like (n_samples, n_features)
Data to which to fit the model. Can pass a NumPy array, Pandas DataFrame, or SciPy sparse CSC matrix.
If passing a DataFrame, will assume that columns are:
- Numeric, if their dtype is a subtype of NumPy's 'number' or 'datetime64'.
- Categorical, if their dtype is 'object', 'Categorical', or 'bool'. Note that,
if `Categorical` dtypes are ordered, the order will be ignored here.
Other dtypes are not supported.
Note that, if passing NumPy arrays, they are used in column-major order (a.k.a. "Fortran arrays"),
and if they are not already in column-major format, will need to create a copy of the data.
y : None
Not used. Kept as argument for compatibility with SciKit-learn pipelining.
sample_weights : None or array(n_samples,)
Sample observation weights for each row of 'X', with higher weights indicating either higher sampling
probability (i.e. the observation has a larger effect on the fitted model, if using sub-samples), or
distribution density (i.e. if the weight is two, it has the same effect of including the same data
point twice), according to parameter 'weights_as_sample_prob' in the model constructor method.
column_weights : None or array(n_features,)
Sampling weights for each column in 'X'. Ignored when picking columns by deterministic criterion.
If passing None, each column will have a uniform weight. Cannot be used when weighting by kurtosis.
categ_cols : None or array-like
Columns that hold categorical features, when the data is passed as an array or matrix.
Categorical columns should contain only integer values with a continuous numeration starting at zero,
with negative values and NaN taken as missing,
and the array or list passed here should correspond to the column numbers, with numeration starting
at zero. The maximum categorical value should not exceed 'INT_MAX' (typically :math:`2^{31}-1`).
This might be passed either at construction time or when calling ``fit`` or variations of ``fit``.
This is ignored when the input is passed as a ``DataFrame`` as then it will consider columns as
categorical depending on their dtype.
Returns
-------
self : obj
This object.
"""
self._init(categ_cols)
if (
self.sample_size is None
and (sample_weights is not None)
and (self.weights_as_sample_prob)
):
raise ValueError("Sampling weights are only supported when using sub-samples for each tree.")
if column_weights is not None and self.weigh_by_kurtosis:
raise ValueError("Cannot pass column weights when weighting columns by kurtosis.")
self._reset_obj()
X_num, X_cat, ncat, sample_weights, column_weights, nrows = self._process_data(X, sample_weights, column_weights)
self._check_can_use_imputer(X_cat)
if self.sample_size is None:
sample_size = nrows
elif self.sample_size == "auto":
sample_size = min(nrows, 10000)
if (sample_weights is not None) and (self.weights_as_sample_prob):
raise ValueError("Sampling weights are only supported when using sub-samples for each tree.")
elif self.sample_size <= 1:
sample_size = int(np.ceil(self.sample_size * nrows))
if sample_size < 2:
raise ValueError("Sampling proportion amounts to a single row or less.")
else:
sample_size = self.sample_size
if self.max_depth == "auto":
max_depth = 0
limit_depth = True
elif self.max_depth is None:
max_depth = nrows - 1
limit_depth = False
else:
max_depth = self.max_depth
limit_depth = False
if self.ncols_per_tree is None:
ncols_per_tree = 0
elif self.ncols_per_tree <= 1:
ncols_tot = 0
if X_num is not None:
ncols_tot += X_num.shape[1]
if X_cat is not None:
ncols_tot += X_cat.shape[1]
ncols_per_tree = int(np.ceil(self.ncols_per_tree * ncols_tot))
else:
ncols_per_tree = self.ncols_per_tree
if (self.prob_pick_pooled_gain or self.prob_pick_avg_gain) and self.ndim == 1:
ncols_tot = (X_num.shape[1] if X_num is not None else 0) + (X_cat.shape[1] if X_cat is not None else 0)
if self.ntry > ncols_tot:
warnings.warn("Passed 'ntry' larger than number of columns, will decrease it.")
if isinstance(self.random_state, np.random.RandomState):
seed = self.random_state.randint(np.iinfo(np.int32).max)
else:
seed = self.random_seed
self._cpp_obj.fit_model(_get_num_dtype(X_num, sample_weights, column_weights),
_get_int_dtype(X_num),
X_num, X_cat, ncat, sample_weights, column_weights,
ctypes.c_size_t(nrows).value,
ctypes.c_size_t(self._ncols_numeric).value,
ctypes.c_size_t(self._ncols_categ).value,
ctypes.c_size_t(self.ndim).value,
ctypes.c_size_t(self.ntry).value,
self.coefs,
ctypes.c_bool(self.coef_by_prop).value,
ctypes.c_bool(self.sample_with_replacement).value,
ctypes.c_bool(self.weights_as_sample_prob).value,
ctypes.c_size_t(sample_size).value,
ctypes.c_size_t(self.ntrees).value,
ctypes.c_size_t(max_depth).value,
ctypes.c_size_t(ncols_per_tree).value,
ctypes.c_bool(limit_depth).value,
ctypes.c_bool(self.penalize_range).value,
ctypes.c_bool(self.standardize_data).value,
ctypes.c_bool(False).value,
ctypes.c_bool(False).value,
ctypes.c_bool(False).value,
ctypes.c_bool(False).value,
ctypes.c_bool(False).value,
ctypes.c_bool(self.weigh_by_kurtosis).value,
ctypes.c_double(self.prob_pick_pooled_gain).value,
ctypes.c_double(self.prob_pick_avg_gain).value,
ctypes.c_double(self.min_gain).value,
self.missing_action,
self.categ_split_type,
self.new_categ_action,
ctypes.c_bool(self.build_imputer).value,
ctypes.c_size_t(self.min_imp_obs).value,
self.depth_imp,
self.weigh_imp_rows,
ctypes.c_bool(self.build_imputer).value,
ctypes.c_bool(False).value,
ctypes.c_uint64(seed).value,
ctypes.c_int(self.nthreads).value)
self.is_fitted_ = True
self._ntrees = self.ntrees
return self
[docs] def fit_predict(self, X, column_weights = None, output_outlierness = "score",
output_distance = None, square_mat = False, output_imputed = False,
categ_cols = None):
"""
Fit the model in-place and produce isolation or separation depths along the way
See the documentation of other methods ('init', 'fit', 'predict', 'predict_distance')
for details.
Note
----
The data must NOT contain any duplicate rows.
Note
----
This function will be faster at predicting average depths than calling 'fit' + 'predict'
separately when using full row samples.
Note
----
If using 'penalize_range' = 'True', the resulting scores/depths from this function might differ a bit
from those of 'fit' + 'predict' ran separately.
Note
----
Sample weights are not supported for this method.
Note
----
When using multiple threads, there can be small differences in the predicted scores or
average depth or separation/distance between runs due to roundoff error.
Parameters
----------
X : array or array-like (n_samples, n_features)
Data to which to fit the model. Can pass a NumPy array, Pandas DataFrame, or SciPy sparse CSC matrix.
If passing a DataFrame, will assume that columns are:
- Numeric, if their dtype is a subtype of NumPy's 'number' or 'datetime64'.
- Categorical, if their dtype is 'object', 'Categorical', or 'bool'. Note that,
if `Categorical` dtypes are ordered, the order will be ignored here.
Other dtypes are not supported.
column_weights : None or array(n_features,)
Sampling weights for each column in 'X'. Ignored when picking columns by deterministic criterion.
If passing None, each column will have a uniform weight. Cannot be used when weighting by kurtosis.
Note that, if passing a DataFrame with both numeric and categorical columns, the column names must
not be repeated, otherwise the column weights passed here will not end up matching.
output_outlierness : None or str in ["score", "avg_depth"]
Desired type of outlierness output. If passing "score", will output standardized outlier score.
If passing "avg_depth" will output average isolation depth without standardizing.
If passing 'None', will skip outlierness calculations.
output_distance : None or str in ["dist", "avg_sep"]
Type of distance output to produce. If passing "dist", will standardize the average separation
depths. If passing "avg_sep", will output the average separation depth without standardizing it
(note that lower separation depth means furthest distance). If passing 'None', will skip distance calculations.
square_mat : bool
Whether to produce a full square matrix with the distances. If passing 'False', will output
only the upper triangular part as a 1-d array in which entry (i,j) with 0 <= i < j < n is located at
position p(i,j) = (i * (n - (i+1)/2) + j - i - 1).
Ignored when passing 'output_distance' = 'None'.
output_imputed : bool
Whether to output the data with imputed missing values. Model object must have been initialized
with 'build_imputer' = 'True'.
categ_cols : None or array-like
Columns that hold categorical features, when the data is passed as an array or matrix.
Categorical columns should contain only integer values with a continuous numeration starting at zero,
with negative values and NaN taken as missing,
and the array or list passed here should correspond to the column numbers, with numeration starting
at zero. The maximum categorical value should not exceed 'INT_MAX' (typically :math:`2^{31}-1`).
This might be passed either at construction time or when calling ``fit`` or variations of ``fit``.
This is ignored when the input is passed as a ``DataFrame`` as then it will consider columns as
categorical depending on their dtype.
Returns
-------
output : array(n_samples,), or dict
Requested outputs about isolation depth (outlierness), pairwise separation depth (distance), and/or
imputed missing values. If passing either 'output_distance' or 'output_imputed', will return a dictionary
with keys "pred" (array(n_samples,)), "dist" (array(n_samples * (n_samples - 1) / 2,) or array(n_samples, n_samples)),
"imputed" (array-like(n_samples, n_columns)), according to whether each output type is present.
"""
self._init(categ_cols)
if (self.sample_size is not None) and (self.sample_size != "auto"):
raise ValueError("Cannot use 'fit_predict' when the sample size is limited.")
if self.sample_with_replacement:
raise ValueError("Cannot use 'fit_predict' or 'fit_transform' when sampling with replacement.")
if column_weights is not None and self.weigh_by_kurtosis:
raise ValueError("Cannot pass column weights when weighting columns by kurtosis.")
if (output_outlierness is None) and (output_distance is None):
raise ValueError("Must pass at least one of 'output_outlierness' or 'output_distance'.")
if output_outlierness is not None:
assert output_outlierness in ["score", "avg_depth"]
if output_distance is not None:
assert output_distance in ["dist", "avg_sep"]
if output_imputed:
if self.missing_action == "fail":
raise ValueError("Cannot impute missing values when using 'missing_action' = 'fail'.")
if not self.build_imputer:
msg = "Trying to impute missing values from object "
msg += "that was initialized with 'build_imputer' = 'False' "
msg += "- will force 'build_imputer' to 'True'."
warnings.warn(msg)
self.build_imputer = True
self._reset_obj()
X_num, X_cat, ncat, sample_weights, column_weights, nrows = self._process_data(X, None, column_weights)
self._check_can_use_imputer(X_cat)
if (output_imputed) and (issparse(X_num)):
msg = "Imputing missing values from CSC matrix on-the-fly can be very slow, "
msg += "it's recommended if possible to fit the model first and then pass the "
msg += "same matrix as CSR to 'transform'."
warnings.warn(msg)
if self.max_depth == "auto":
max_depth = 0
limit_depth = True
elif self.max_depth is None:
max_depth = nrows - 1
else:
max_depth = self.max_depth
limit_depth = False
if self.ncols_per_tree is None:
ncols_per_tree = 0
elif self.ncols_per_tree <= 1:
ncols_tot = 0
if X_num is not None:
ncols_tot += X_num.shape[1]
if X_cat is not None:
ncols_tot += X_cat.shape[1]
ncols_per_tree = int(np.ceil(self.ncols_per_tree * ncols_tot))
else:
ncols_per_tree = self.ncols_per_tree
if (self.prob_pick_pooled_gain or self.prob_pick_avg_gain) and self.ndim == 1:
ncols_tot = (X_num.shape[1] if X_num is not None else 0) + (X_cat.shape[1] if X_cat is not None else 0)
if self.ntry > ncols_tot:
warnings.warn("Passed 'ntry' larger than number of columns, will decrease it.")
if isinstance(self.random_state, np.random.RandomState):
seed = self.random_state.randint(np.iinfo(np.int32).max)
else:
seed = self.random_seed
depths, tmat, dmat, X_num, X_cat = self._cpp_obj.fit_model(_get_num_dtype(X_num, None, column_weights),
_get_int_dtype(X_num),
X_num, X_cat, ncat, None, column_weights,
ctypes.c_size_t(nrows).value,
ctypes.c_size_t(self._ncols_numeric).value,
ctypes.c_size_t(self._ncols_categ).value,
ctypes.c_size_t(self.ndim).value,
ctypes.c_size_t(self.ntry).value,
self.coefs,
ctypes.c_bool(self.coef_by_prop).value,
ctypes.c_bool(self.sample_with_replacement).value,
ctypes.c_bool(self.weights_as_sample_prob).value,
ctypes.c_size_t(nrows).value,
ctypes.c_size_t(self.ntrees).value,
ctypes.c_size_t(max_depth).value,
ctypes.c_size_t(ncols_per_tree).value,
ctypes.c_bool(limit_depth).value,
ctypes.c_bool(self.penalize_range).value,
ctypes.c_bool(self.standardize_data).value,
ctypes.c_bool(output_distance is not None).value,
ctypes.c_bool(output_distance == "dist").value,
ctypes.c_bool(square_mat).value,
ctypes.c_bool(output_outlierness is not None).value,
ctypes.c_bool(output_outlierness == "score").value,
ctypes.c_bool(self.weigh_by_kurtosis).value,
ctypes.c_double(self.prob_pick_pooled_gain).value,
ctypes.c_double(self.prob_pick_avg_gain).value,
ctypes.c_double(self.min_gain).value,
self.missing_action,
self.categ_split_type,
self.new_categ_action,
ctypes.c_bool(self.build_imputer).value,
ctypes.c_size_t(self.min_imp_obs).value,
self.depth_imp,
self.weigh_imp_rows,
ctypes.c_bool(output_imputed).value,
ctypes.c_bool(self.all_perm).value,
ctypes.c_uint64(seed).value,
ctypes.c_int(self.nthreads).value)
self.is_fitted_ = True
self._ntrees = self.ntrees
if (not output_distance) and (not output_imputed):
return depths
else:
outp = {"pred" : depths}
if output_distance:
if square_mat:
outp["dist"] = dmat
else:
outp["dist"] = tmat
if output_imputed:
outp["imputed"] = self._rearrange_imputed(X, X_num, X_cat)
return outp
def _process_data(self, X, sample_weights, column_weights):
### TODO: this needs a refactoring after introducing 'categ_cols'
if X.__class__.__name__ == "DataFrame":
if self.categ_cols is not None:
warnings.warn("'categ_cols' is ignored when passing a DataFrame as input.")
self.categ_cols = None
### https://stackoverflow.com/questions/25039626/how-do-i-find-numeric-columns-in-pandas
X_num = X.select_dtypes(include = [np.number, np.datetime64]).to_numpy()
if X_num.dtype not in [ctypes.c_double, ctypes.c_float]:
X_num = X_num.astype(ctypes.c_double)
if not _is_col_major(X_num):
X_num = np.asfortranarray(X_num)
X_cat = X.select_dtypes(include = [pd.CategoricalDtype, "object", "bool"])
if (X_num.shape[1] + X_cat.shape[1]) == 0:
raise ValueError("Input data has no columns of numeric or categorical type.")
elif (X_num.shape[1] + X_cat.shape[1]) < X.shape[1]:
cols_num = np.array(X.select_dtypes(include = [np.number, np.datetime64]).columns.values)
cols_cat = np.array(X_cat.columns.values)
msg = "Only numeric and categorical columns are supported."
msg += " Got passed the following types: ["
msg += ", ".join([str(X[cl].dtype) for cl in X.columns.values if cl not in cols_num and cl not in cols_cat][:3])
msg += "]\n(Sample problem columns: ["
msg += ", ".join([str(cl) for cl in X.columns.values if cl not in cols_num and cl not in cols_cat][:3])
msg += "])"
raise ValueError(msg)
self._ncols_numeric = X_num.shape[1]
self._ncols_categ = X_cat.shape[1]
self.cols_numeric_ = np.array(X.select_dtypes(include = [np.number, np.datetime64]).columns.values)
self.cols_categ_ = np.array(X.select_dtypes(include = [pd.CategoricalDtype, "object", "bool"]).columns.values)
if not self._ncols_numeric:
X_num = None
else:
nrows = X_num.shape[0]
if not self._ncols_categ:
X_cat = None
else:
nrows = X_cat.shape[0]
has_ordered = False
if X_cat is not None:
self._cat_mapping = [None for cl in range(X_cat.shape[1])]
for cl in range(X_cat.shape[1]):
if (X_cat[X_cat.columns[cl]].dtype.name == "category") and (X_cat[X_cat.columns[cl]].dtype.ordered):
has_ordered = True
if (not self.recode_categ) and (X_cat[X_cat.columns[cl]].dtype.name == "category"):
self._cat_mapping[cl] = np.array(X_cat[X_cat.columns[cl]].cat.categories)
X_cat = X_cat.assign(**{X_cat.columns[cl] : X_cat[X_cat.columns[cl]].cat.codes})
else:
cl, self._cat_mapping[cl] = pd.factorize(X_cat[X_cat.columns[cl]])
X_cat = X_cat.assign(**{X_cat.columns[cl] : cl})
if (self.all_perm
and (self.ndim == 1)
and (self.prob_pick_pooled_gain)
):
if np.math.factorial(self._cat_mapping[cl].shape[0]) > np.iinfo(ctypes.c_size_t).max:
msg = "Number of permutations for categorical variables is larger than "
msg += "maximum representable integer. Try using 'all_perm=False'."
raise ValueError(msg)
# https://github.com/pandas-dev/pandas/issues/30618
if self._cat_mapping[cl].__class__.__name__ == "CategoricalIndex":
self._cat_mapping[cl] = self._cat_mapping[cl].to_numpy()
X_cat = X_cat.to_numpy()
if X_cat.dtype != ctypes.c_int:
X_cat = X_cat.astype(ctypes.c_int)
if not _is_col_major(X_cat):
X_cat = np.asfortranarray(X_cat)
if has_ordered:
warnings.warn("Data contains ordered categoricals. These are treated as unordered.")
else:
if len(X.shape) != 2:
raise ValueError("Input data must be two-dimensional.")
X_cat = None
if self.categ_cols is not None:
if np.max(self.categ_cols) >= X.shape[1]:
raise ValueError("'categ_cols' contains indices higher than the number of columns in 'X'.")
self.cols_numeric_ = np.setdiff1d(np.arange(X.shape[1]), self.categ_cols)
if issparse(X) and not isspmatrix_csc(X):
X = csc_matrix(X)
X_cat = X[:, self.categ_cols]
X = X[:, self.cols_numeric_]
if X.shape[1]:
if issparse(X):
avoid_sort = False
if not isspmatrix_csc(X):
warnings.warn("Sparse matrices are only supported in CSC format, will be converted.")
X = csc_matrix(X)
avoid_sort = True
if X.nnz == 0:
raise ValueError("'X' has no non-zero entries")
if ((X.indptr.dtype not in [ctypes.c_int, np.int64, ctypes.c_size_t]) or
(X.indices.dtype not in [ctypes.c_int, np.int64, ctypes.c_size_t]) or
(X.indptr.dtype != X.indices.dtype) or
(X.data.dtype not in [ctypes.c_double, ctypes.c_float])
):
X = X.copy()
if X.data.dtype not in [ctypes.c_double, ctypes.c_float]:
X.data = X.data.astype(ctypes.c_double)
if (X.indptr.dtype != X.indices.dtype) or (X.indices.dtype not in [ctypes.c_int, np.int64, ctypes.c_size_t]):
X.indices = X.indices.astype(ctypes.c_size_t)
if (X.indptr.dtype != X.indices.dtype) or (X.indptr.dtype not in [ctypes.c_int, np.int64, ctypes.c_size_t]):
X.indptr = X.indptr.astype(ctypes.c_size_t)
if not avoid_sort:
_sort_csc_indices(X)
else:
if (X.__class__.__name__ == "ndarray") and (X.dtype not in [ctypes.c_double, ctypes.c_float]):
X = X.astype(ctypes.c_double)
if (X.__class__.__name__ != "ndarray") or (not _is_col_major(X)):
X = np.asfortranarray(X)
if X.dtype not in [ctypes.c_double, ctypes.c_float]:
X = X.astype(ctypes.c_double)
self._ncols_numeric = X.shape[1]
self._ncols_categ = 0 if (X_cat is None) else X_cat.shape[1]
if self.categ_cols is None:
self.cols_numeric_ = np.array([])
self.cols_categ_ = np.array([])
self._cat_mapping = list()
if (self._ncols_numeric + self._ncols_categ) == 0:
raise ValueError("'X' has zero columns.")
if X.shape[1]:
X_num = X
nrows = X_num.shape[0]
else:
X_num = None
if X_cat is not None:
if issparse(X_cat):
X_cat = X_cat.toarray()
if np.any(np.isnan(X_cat)):
X_cat = X_cat.copy()
X_cat[np.isnan(X_cat)] = -1
if X_cat.dtype != ctypes.c_int:
X_cat = X_cat.astype(ctypes.c_int)
if not _is_col_major(X_cat):
X_cat = np.asfortranarray(X_cat)
self._cat_max_lev = np.max(X_cat, axis=0)
if np.any(self._cat_max_lev < 0):
warnings.warn("Some categorical columns contain only missing values.")
nrows = X_cat.shape[0]
if nrows == 0:
raise ValueError("Input data has zero rows.")
elif nrows < 3:
raise ValueError("Input data must have at least 3 rows.")
elif (self.sample_size is not None) and (self.sample_size != "auto"):
if self.sample_size > nrows:
warnings.warn("Input data has fewer rows than sample_size, will forego sub-sampling.")
self.sample_size = None
if X_cat is not None:
if self.categ_cols is None:
ncat = np.array([self._cat_mapping[cl].shape[0] for cl in range(X_cat.shape[1])], dtype = ctypes.c_int)
else:
if self._cat_max_lev is None:
self._cat_max_lev = []
if not isinstance(self._cat_max_lev, np.ndarray):
self._cat_max_lev = np.array(self._cat_max_lev)
ncat = (self._cat_max_lev + 1).clip(0)
if ncat.dtype != ctypes.c_int:
ncat = ncat.astype(ctypes.c_int)
else:
ncat = None
if sample_weights is not None:
sample_weights = np.array(sample_weights).reshape(-1)
if (X_num is not None) and (X_num.dtype != sample_weights.dtype):
sample_weights = sample_weights.astype(X_num.dtype)
if sample_weights.dtype not in [ctypes.c_double, ctypes.c_float]:
sample_weights = sample_weights.astype(ctypes.c_double)
if sample_weights.shape[0] != nrows:
raise ValueError("'sample_weights' has different number of rows than 'X'.")
ncols = 0
if X_num is not None:
ncols += X_num.shape[1]
if X_cat is not None:
ncols += X_cat.shape[1]
if column_weights is not None:
column_weights = np.array(column_weights).reshape(-1)
if (X_num is not None) and (X_num.dtype != column_weights.dtype):
column_weights = column_weights.astype(X_num.dtype)
if column_weights.dtype not in [ctypes.c_double, ctypes.c_float]:
column_weights = column_weights.astype(ctypes.c_double)
if ncols != column_weights.shape[0]:
raise ValueError("'column_weights' has %d entries, but data has %d columns." % (column_weights.shape[0], ncols))
if (X_num is not None) and (X_cat is not None):
column_weights = np.r_[column_weights[X.columns.values == self.cols_numeric_],
column_weights[X.columns.values == self.cols_categ_]]
if (sample_weights is not None) and (column_weights is not None) and (sample_weights.dtype != column_weights.dtype):
sample_weights = sample_weights.astype(ctypes.c_double)
column_weights = column_weights.astype(ctypes.c_double)
if self.ndim > 1:
if self.ndim > ncols:
msg = "Model was meant to take %d variables for each split, but data has %d columns."
msg += " Will decrease number of splitting variables to match number of columns."
msg = msg % (self.ndim, ncols)
warnings.warn(msg)
self.ndim = ncols
if self.ndim < 2:
self._is_extended_ = False
X_num = _copy_if_subview(X_num, False)
X_cat = _copy_if_subview(X_cat, False)
return X_num, X_cat, ncat, sample_weights, column_weights, nrows
def _process_data_new(self, X, allow_csr = True, allow_csc = True, prefer_row_major = False,
keep_new_cat_levels = False):
if X.__class__.__name__ == "DataFrame":
if ((self.cols_numeric_.shape[0] + self.cols_categ_.shape[0]) > 0) and (self.categ_cols is None):
if self.categ_cols is None:
missing_cols = np.setdiff1d(np.r_[self.cols_numeric_, self.cols_categ_], np.array(X.columns.values))
if missing_cols.shape[0] > 0:
raise ValueError("Input data is missing %d columns - example: [%s]" % (missing_cols.shape[0], ", ".join(missing_cols[:3])))
else:
if X.shape[1] < (self.cols_numeric_.shape[0] + self.cols_categ_.shape[0]):
raise ValueError("Error: expected input with %d columns - got: %d." %
((self.cols_numeric_.shape[0] + self.cols_categ_.shape[0]), X.shape[1]))
if self._ncols_numeric > 0:
if self.categ_cols is None:
X_num = X[self.cols_numeric_].to_numpy()
else:
X_num = X.iloc[:, self.cols_numeric_].to_numpy()
if X_num.dtype not in [ctypes.c_double, ctypes.c_float]:
X_num = X_num.astype(ctypes.c_double)
if (not prefer_row_major) and (not _is_col_major(X_num)):
X_num = np.asfortranarray(X_num)
nrows = X_num.shape[0]
else:
X_num = None
if self._ncols_categ > 0:
if self.categ_cols is None:
X_cat = X[self.cols_categ_]
if (not keep_new_cat_levels) and \
(
(self.new_categ_action == "impute" and self.missing_action == "impute")
or
(self.new_categ_action == "weighted" and
self.categ_split_type != "single_categ"
and self.missing_action == "divide")
):
for cl in range(self._ncols_categ):
X_cat = X_cat.assign(**{
self.cols_categ_[cl] : _encode_categorical(X_cat[self.cols_categ_[cl]],
self._cat_mapping[cl])
})
else:
for cl in range(self._ncols_categ):
X_cat = X_cat.assign(**{
self.cols_categ_[cl] : pd.Categorical(X_cat[self.cols_categ_[cl]])
})
new_levs = np.setdiff1d(X_cat[self.cols_categ_[cl]].cat.categories, self._cat_mapping[cl])
if new_levs.shape[0]:
self._cat_mapping[cl] = np.r_[self._cat_mapping[cl], new_levs]
X_cat = X_cat.assign(**{
self.cols_categ_[cl] : _encode_categorical(X_cat[self.cols_categ_[cl]],
self._cat_mapping[cl])
})
else:
X_cat = X.iloc[:, self.categ_cols]
X_cat = X_cat.to_numpy()
if X_cat.dtype != ctypes.c_int:
X_cat = X_cat.astype(ctypes.c_int)
if (not prefer_row_major) and (not _is_col_major(X_cat)):
X_cat = np.asfortranarray(X_cat)
nrows = X_cat.shape[0]
else:
X_cat = None
elif self._ncols_categ == 0:
if X.shape[1] < self._ncols_numeric:
raise ValueError("Input has different number of columns than data to which model was fit.")
X_num = X.to_numpy()
if X_num.dtype not in [ctypes.c_double, ctypes.c_float]:
X_num = X_num.astype(ctypes.c_double)
if (not prefer_row_major) and (not _is_col_major(X_num)):
X_num = np.asfortranarray(X_num)
X_cat = None
nrows = X_num.shape[0]
elif self._ncols_numeric == 0:
if X.shape[1] < self._ncols_categ:
raise ValueError("Input has different number of columns than data to which model was fit.")
X_cat = X.to_numpy()[:, :self._ncols_categ]
if X_cat.dtype != ctypes.c_int:
X_cat = X_cat.astype(ctypes.c_int)
if (not prefer_row_major) and (not _is_col_major(X_cat)):
X_cat = np.asfortranarray(X_cat)
X_num = None
nrows = X_cat.shape[0]
else:
nrows = X.shape[0]
X_num = X.iloc[:, self.cols_numeric_].to_numpy()
X_cat = X.iloc[:, self.categ_cols].to_numpy()
if X_num.dtype not in [ctypes.c_double, ctypes.c_float]:
X_num = X_num.astype(ctypes.c_double)
if (not prefer_row_major) and (not _is_col_major(X_num)):
X_num = np.asfortranarray(X_num)
if X_cat.dtype != ctypes.c_int:
X_cat = X_cat.astype(ctypes.c_int)
if (not prefer_row_major) and (not _is_col_major(X_cat)):
X_cat = np.asfortranarray(X_cat)
if (X_num is not None) and (X_cat is not None) and (_is_col_major(X_num) != _is_col_major(X_cat)):
if prefer_row_major:
X_num = np.ascontiguousarray(X_num)
X_cat = np.ascontiguousarray(X_cat)
else:
X_num = np.asfortranarray(X_num)
X_cat = np.asfortranarray(X_cat)
else:
if (self._ncols_categ > 0) and (self.categ_cols is None):
raise ValueError("Model was fit to DataFrame with categorical columns, but new input is a numeric array/matrix.")
if len(X.shape) != 2:
raise ValueError("Input data must be two-dimensional.")
if (self.categ_cols is None) and (X.shape[1] < self._ncols_numeric):
raise ValueError("Input has different number of columns than data to which model was fit.")
if self.categ_cols is None:
X_cat = None
else:
if issparse(X) and (not isspmatrix_csc(X)) and (not isspmatrix_csr(X)):
X = csc_matrix(X)
X_cat = X[:, self.categ_cols]
if issparse(X_cat):
X_cat = X_cat.toarray()
X = X[:, self.cols_numeric_]
X_num = None
if X.shape[1]:
if issparse(X):
avoid_sort = False
if isspmatrix_csr(X) and not allow_csr:
warnings.warn("Cannot predict from CSR sparse matrix, will convert to CSC.")
X = csc_matrix(X)
avoid_sort = True
elif isspmatrix_csc(X) and not allow_csc:
warnings.warn("Method supports sparse matrices only in CSR format, will convert sparse format.")
X = csr_matrix(X)
avoid_sort = True
elif (not isspmatrix_csc(X)) and (not isspmatrix_csr(X)):
msg = "Sparse matrix inputs only supported as "
if allow_csc:
msg += "CSC"
if allow_csr:
msg += " or CSR"
else:
msg += "CSR"
msg += " format, will convert to "
if allow_csc:
msg += "CSC."
warnings.warn(msg)
X = csc_matrix(X)
else:
msg += "CSR."
warnings.warn(msg)
X = csr_matrix(X)
avoid_sort = True
if ((X.indptr.dtype not in [ctypes.c_int, np.int64, ctypes.c_size_t]) or
(X.indices.dtype not in [ctypes.c_int, np.int64, ctypes.c_size_t]) or
(X.indptr.dtype != X.indices.dtype) or
(X.data.dtype not in [ctypes.c_double, ctypes.c_float])
):
X = X.copy()
if X.data.dtype not in [ctypes.c_double, ctypes.c_float]:
X.data = X.data.astype(ctypes.c_double)
if (X.indptr.dtype != X.indices.dtype) or (X.indices.dtype not in [ctypes.c_int, np.int64, ctypes.c_size_t]):
X.indices = X.indices.astype(ctypes.c_size_t)
if (X.indptr.dtype != X.indices.dtype) or (X.indptr.dtype not in [ctypes.c_int, np.int64, ctypes.c_size_t]):
X.indptr = X.indptr.astype(ctypes.c_size_t)
if not avoid_sort:
_sort_csc_indices(X)
X_num = X
else:
if not isinstance(X, np.ndarray):
if prefer_row_major:
X = np.array(X)
else:
X = np.asfortranarray(X)
if X.dtype not in [ctypes.c_double, ctypes.c_float]:
X = X.astype(ctypes.c_double)
if (not prefer_row_major) and (not _is_col_major(X)):
X = np.asfortranarray(X)
X_num = X
nrows = X_num.shape[0]
if X_cat is not None:
nrows = X_cat.shape[0]
if np.any(np.isnan(X_cat)):
X_cat = X_cat.copy()
X_cat[np.isnan(X_cat)] = -1
if (X_num is not None) and (isspmatrix_csc(X_num)):
prefer_row_major = False
if (self.categ_cols is not None) and np.any(X_cat > self._cat_max_lev.reshape((1,-1))):
X_cat[X_cat > self._cat_max_lev] = -1
if X_cat.dtype != ctypes.c_int:
X_cat = X_cat.astype(ctypes.c_int)
if (not prefer_row_major) and (not _is_col_major(X_cat)):
X_cat = np.asfortranarray(X_cat)
X_num = _copy_if_subview(X_num, prefer_row_major)
X_cat = _copy_if_subview(X_cat, prefer_row_major)
if (X_num is not None) and (isspmatrix_csc(X_num)) and (X_cat is not None) and (not _is_col_major(X_cat)):
X_cat = np.asfortranarray(X_cat)
if (nrows > 1) and (X_cat is not None) and (X_num is not None) and (not isspmatrix_csc(X_num)):
if prefer_row_major:
if _is_row_major(X_num) != _is_row_major(X_cat):
X_num = np.ascontiguousarray(X_num)
X_cat = np.ascontiguousarray(X_cat)
else:
if _is_col_major(X_num) != _is_col_major(X_cat):
X_num = np.asfortranarray(X_num)
X_cat = np.asfortranarray(X_cat)
return X_num, X_cat, nrows
def _rearrange_imputed(self, orig, X_num, X_cat):
if orig.__class__.__name__ == "DataFrame":
ncols_imputed = 0
if X_num is not None:
if (self.cols_numeric_ is not None) and (self.cols_numeric_.shape[0]):
df_num = pd.DataFrame(X_num, columns = self.cols_numeric_ if (self.categ_cols is None) else orig.columns.values[self.cols_numeric_])
else:
df_num = pd.DataFrame(X_num)
ncols_imputed += df_num.shape[1]
if X_cat is not None:
if self.categ_cols is None:
df_cat = pd.DataFrame(X_cat, columns = self.cols_categ_)
for cl in range(self.cols_categ_.shape[0]):
df_cat[self.cols_categ_[cl]] = pd.Categorical.from_codes(df_cat[self.cols_categ_[cl]], self._cat_mapping[cl])
else:
df_cat = pd.DataFrame(X_cat, columns = orig.columns.values[self.categ_cols])
ncols_imputed += df_cat.shape[1]
if orig.columns.values.shape[0] != ncols_imputed:
if self.categ_cols is None:
cols_new = np.setdiff1d(orig.columns.values, np.r_[self.cols_numeric_, self.cols_categ_])
else:
cols_new = orig.columns[(self._ncols_numeric + self._ncols_categ):]
if (X_num is not None) and (X_cat is None):
out = pd.concat([df_num, orig[cols_new]], axis = 1)
elif (X_num is None) and (X_cat is not None):
out = pd.concat([df_cat, orig[cols_new]], axis = 1)
else:
out = pd.concat([df_num, df_cat, orig[cols_new]], axis = 1)
out = out[orig.columns.values]
return out
if (X_num is not None) and (X_cat is None):
return df_num[orig.columns.values]
elif (X_num is None) and (X_cat is not None):
return df_cat[orig.columns.values]
else:
df = pd.concat([df_num, df_cat], axis = 1)
df = df[orig.columns.values]
return df
else: ### not DataFrame
if issparse(orig):
outp = orig.copy()
if (self.categ_cols is None) and (orig.shape[1] == self._ncols_numeric):
outp.data[:] = X_num.data
elif self.categ_cols is None:
if isspmatrix_csr(orig):
_reconstruct_csr_sliced(
outp.data,
outp.indptr,
X_num.data if (X_num is not None) else np.empty(0, dtype=outp.data.dtype),
X_num.indptr if (X_num is not None) else np.zeros(1, dtype=outp.indptr.dtype),
outp.shape[0]
)
else:
outp[:, :self._ncols_numeric] = X_num
else:
if isspmatrix_csr(orig):
_reconstruct_csr_with_categ(
outp.data,
outp.indices,
outp.indptr,
X_num.data if (X_num is not None) else np.empty(0, dtype=outp.data.dtype),
X_num.indices if (X_num is not None) else np.empty(0, dtype=outp.indices.dtype),
X_num.indptr if (X_num is not None) else np.zeros(1, dtype=outp.indptr.dtype),
X_cat,
self.cols_numeric_.astype(ctypes.c_size_t) if (self.cols_numeric_ is not None) else np.empty(0, dtype=ctypes.c_size_t),
self.categ_cols.astype(ctypes.c_size_t),
outp.shape[0], outp.shape[1],
_is_col_major(X_cat),
)
else:
if np.any(X_cat < 0):
X_cat = X_cat.astype("float")
X_cat[X_cat < 0] = np.nan
outp[:, self.categ_cols] = X_cat
if X_num is not None:
outp[:, self.cols_numeric_] = X_num
return outp
else:
if (self.categ_cols is None) and (orig.shape[1] == self._ncols_numeric):
return X_num
elif self.categ_cols is None:
outp = orig.copy()
outp[:, :self._ncols_numeric] = X_num[:, :self._ncols_numeric]
else:
outp = orig.copy()
if np.any(X_cat < 0):
X_cat = X_cat.astype("float")
X_cat[X_cat < 0] = np.nan
outp[:, self.categ_cols] = X_cat
if X_num is not None:
outp[:, self.cols_numeric_] = X_num[:, :self._ncols_numeric]
return outp
[docs] def predict(self, X, output = "score"):
"""
Predict outlierness based on average isolation depth
Calculates the approximate depth that it takes to isolate an observation according to the
fitted model splits. Can output either the average depth, or a standardized outlier score
based on whether it takes more or fewer splits than average to isolate observations. In the
standardized outlier score metric, values closer to 1 indicate more outlierness, while values
closer to 0.5 indicate average outlierness, and close to 0 more averageness (harder to isolate).
Note
----
Depending on the model parameters, it might be possible to convert the models to 'treelite' format
for faster predictions or for easier model serving. See method ``to_treelite`` for details.
Note
----
If the model was built with 'nthreads>1', this prediction function will
use OpenMP for parallelization. In a linux setup, one usually has GNU's "gomp" as OpenMP as backend, which
will hang when used in a forked process - for example, if one tries to call this prediction function from
'flask'+'gunicorn', which uses process forking for parallelization, it will cause the whole application to freeze;
and if using kubernetes on top of a different backend such as 'falcon', might cause it to run slower than
needed or to hang too. A potential fix in these cases is to set the number of threads to 1 in the object
(e.g. 'model.nthreads = 1'), or to use a different version of this library compiled without OpenMP
(requires manually altering the 'setup.py' file), or to use a non-GNU OpenMP backend. This should not
be an issue when using this library normally in e.g. a jupyter notebook.
Note
----
The more threads that are set for the model, the higher the memory requirements will be as each
thread will allocate an array with one entry per row.
Note
----
In order to save memory when fitting and serializing models, the functionality for outputting
terminal node number will generate index mappings on the fly for all tree nodes, even if passing only
1 row, so it's only recommended for batch predictions.
Note
----
The outlier scores/depth predict functionality is optimized for making predictions on one or a
few rows at a time - for making large batches of predictions, it might be faster to use the
'fit_predict' functionality.
Note
----
If using non-random splits (parameters ``prob_pick_avg_gain``, ``prob_pick_pooled_gain``)
and/or range penalizations (which are off by default), the distribution of scores might
not be centered around 0.5.
Note
----
When making predictions on CSC matrices with many rows using multiple threads, there
can be small differences between runs due to roundoff error.
Parameters
----------
X : array or array-like (n_samples, n_features)
Observations for which to predict outlierness or average isolation depth. Can pass
a NumPy array, Pandas DataFrame, or SciPy sparse CSC or CSR matrix.
If 'X' is sparse and one wants to obtain the outlier score or average depth or tree
numbers, it's highly recommended to pass it in CSC format as it will be much faster
when the number of trees or rows is large.
While the 'X' used by ``fit`` always needs to be in column-major order, predictions
can be done on data that is in either row-major or column-major orders, with row-major
being faster for dense data.
output : str, one of "score", "avg_depth", "tree_num", "tree_depths"
Desired type of output. Options are:
``"score"``:
Will output standardized outlier scores.
``"avg_depth"``:
Will output unstandardized average isolation depths.
``"tree_num"``:
Will output the index of the terminal node under each tree in the model.
``"tree_depths"``:
Will output non-standardized per-tree isolation depths (note that they will not
include range penalties from ``penalize_range=True``).
Returns
-------
score : array(n_samples,) or array(n_samples, n_trees)
Requested output type for each row accoring to parameter 'output' (outlier scores,
average isolation depth, terminal node indices, or per-tree isolation depths).
"""
assert self.is_fitted_
assert output in ["score", "avg_depth", "tree_num", "tree_depths"]
X_num, X_cat, nrows = self._process_data_new(X, prefer_row_major = True, keep_new_cat_levels = False)
if (output in ["tree_num", "tree_depths"]) and (self.ndim == 1):
if self.missing_action == "divide":
raise ValueError("Cannot output tree numbers/depths when using 'missing_action' = 'divide'.")
if (self._ncols_categ > 0) and (self.new_categ_action == "weighted") and (self.categ_split_type != "single_categ"):
raise ValueError("Cannot output tree numbers/depths when using 'new_categ_action' = 'weighted'.")
if (nrows == 1) and (output == "tree_num"):
warnings.warn("Predicting tree number is slow, not recommended to do for 1 row at a time.")
depths, tree_num, tree_depths = self._cpp_obj.predict(
_get_num_dtype(X_num, None, None), _get_int_dtype(X_num),
X_num, X_cat, self._is_extended_,
ctypes.c_size_t(nrows).value,
ctypes.c_int(self.nthreads).value,
ctypes.c_bool(output == "score").value,
ctypes.c_bool(output == "tree_num").value,
ctypes.c_bool(output == "tree_depths").value
)
if output in ["score", "avg_depth"]:
return depths
elif output == "tree_depths":
return tree_depths
else:
return tree_num
[docs] def decision_function(self, X):
"""
Wrapper for 'predict' with 'output=score'
This function is kept for compatibility with SciKit-Learn.
Parameters
----------
X : array or array-like (n_samples, n_features)
Observations for which to predict outlierness or average isolation depth. Can pass
a NumPy array, Pandas DataFrame, or SciPy sparse CSC or CSR matrix.
Returns
-------
score : array(n_samples,)
Outlier scores for the rows in 'X' (the higher, the most anomalous).
"""
return self.predict(X, output="score")
[docs] def predict_distance(self, X, output = "dist", square_mat = False, X_ref = None):
"""
Predict approximate distances between points
Predict approximate pairwise distances between points or individual distances between
two sets of points based on how many
splits it takes to separate them. Can output either the average number of paths,
or a standardized metric (in the same way as the outlier score) in which values closer
to zero indicate nearer points, closer to one further away points, and closer to 0.5
average distance.
Note
----
The more threads that are set for the model, the higher the memory requirement will be as each
thread will allocate an array with one entry per combination.
Parameters
----------
X : array or array-like (n_samples, n_features)
Observations for which to calculate approximate pairwise distances,
or first group for distances between sets of points. Can pass
a NumPy array, Pandas DataFrame, or SciPy sparse CSC matrix.
output : str, one of "dist", "avg_sep"
Type of output to produce. If passing "dist", will standardize the average separation
depths. If passing "avg_sep", will output the average separation depth without standardizing it
(note that lower separation depth means furthest distance).
square_mat : bool
Whether to produce a full square matrix with the pairwise distances. If passing 'False', will output
only the upper triangular part as a 1-d array in which entry (i,j) with 0 <= i < j < n is located at
position p(i,j) = (i * (n - (i+1)/2) + j - i - 1).
Ignored when passing ``X_ref``.
X_ref : array or array-like (n_ref, n_features)
Second group of observations. If passing it, will calculate distances between each point in
``X`` and each point in ``X_ref``. If passing ``None`` (the default), will calculate
pairwise distances between the points in ``X``.
Must be of the same type as ``X`` (e.g. array, DataFrame, CSC).
Returns
-------
dist : array(n_samples * (n_samples - 1) / 2,) or array(n_samples, n_samples) or array(n_samples, n_ref)
Approximate distances or average separation depth between points, according to
parameter 'output'. Shape and size depends on parameter ``square_mat``, or ``X_ref`` if passed.
"""
assert self.is_fitted_
assert output in ["dist", "avg_sep"]
if X_ref is None:
nobs_group1 = 0
else:
if X.__class__ != X_ref.__class__:
raise ValueError("'X' and 'X_ref' must be of the same class.")
nobs_group1 = X.shape[0]
if X.__class__.__name__ == "DataFrame":
X = X.append(X_ref, ignore_index = True)
elif issparse(X):
X = sp_vstack([X, X_ref])
else:
X = np.vstack([X, X_ref])
X_num, X_cat, nrows = self._process_data_new(X, allow_csr = False, prefer_row_major = False, keep_new_cat_levels = False)
if nrows == 1:
raise ValueError("Cannot calculate pairwise distances for only 1 row.")
tmat, dmat, rmat = self._cpp_obj.dist(_get_num_dtype(X_num, None, None), _get_int_dtype(X_num),
X_num, X_cat, self._is_extended_,
ctypes.c_size_t(nrows).value,
ctypes.c_int(self.nthreads).value,
ctypes.c_bool(self.assume_full_distr).value,
ctypes.c_bool(output == "dist").value,
ctypes.c_bool(square_mat).value,
ctypes.c_size_t(nobs_group1).value)
if X_ref is not None:
return rmat
elif square_mat:
return dmat
else:
return tmat
[docs] def transform(self, X):
"""
Impute missing values in the data using isolation forest model
Note
----
In order to use this functionality, the model must have been built with imputation capabilities ('build_imputer' = 'True').
Note
----
Categorical columns, if imputed with a model fit to a DataFrame, will always come out
with pandas categorical dtype.
Note
----
The input may contain new columns (i.e. not present when the model was fitted),
which will be output as-is.
Parameters
----------
X : array or array-like (n_samples, n_features)
Data for which missing values should be imputed. Can pass a NumPy array, Pandas DataFrame, or SciPy sparse CSR matrix.
If the model was fit to a DataFrame with categorical columns, must also be a DataFrame.
Returns
-------
X_imputed : array or array-like (n_samples, n_features)
Object of the same type and dimensions as 'X', but with missing values already imputed. Categorical
columns will be output as pandas's 'Categorical' regardless of their dtype in 'X'.
"""
assert self.is_fitted_
if not self.build_imputer:
raise ValueError("Cannot impute missing values with model that was built with 'build_imputer' = 'False'.")
if self.missing_action == "fail":
raise ValueError("Cannot impute missing values when using 'missing_action' = 'fail'.")
X_num, X_cat, nrows = self._process_data_new(X, allow_csr = True, allow_csc = False, prefer_row_major = True, keep_new_cat_levels = False)
if X.__class__.__name__ != "DataFrame":
if X_num is not None:
if X_num.shape[1] == self._ncols_numeric:
X_num = X_num.copy()
else:
X_num = X_num[:, :self._ncols_numeric].copy()
if X_cat is not None:
X_cat = X_cat.copy()
X_num, X_cat = self._cpp_obj.impute(_get_num_dtype(X_num, None, None), _get_int_dtype(X_num),
X_num, X_cat,
ctypes.c_bool(self._is_extended_).value,
ctypes.c_size_t(nrows).value,
ctypes.c_int(self.nthreads).value)
return self._rearrange_imputed(X, X_num, X_cat)
[docs] def fit_transform(self, X, y = None, column_weights = None, categ_cols = None):
"""
SciKit-Learn pipeline-compatible version of 'fit_predict'
Will fit the model and output imputed missing values. Intended to be used as part of SciKit-learn
pipelining. Note that this is just a wrapper over 'fit_predict' with parameter 'output_imputed' = 'True'.
See the documentation of 'fit_predict' for details.
Parameters
----------
X : array or array-like (n_samples, n_features)
Data to which to fit the model and whose missing values need to be imputed. Can pass a NumPy array, Pandas DataFrame, or SciPy sparse CSC matrix (see the documentation of ``fit`` for more details).
If the model was fit to a DataFrame with categorical columns, must also be a DataFrame.
y : None
Not used. Kept for compatibility with SciKit-Learn.
column_weights : None or array(n_features,)
Sampling weights for each column in 'X'. Ignored when picking columns by deterministic criterion.
If passing None, each column will have a uniform weight. Cannot be used when weighting by kurtosis.
Note that, if passing a DataFrame with both numeric and categorical columns, the column names must
not be repeated, otherwise the column weights passed here will not end up matching.
categ_cols : None or array-like
Columns that hold categorical features, when the data is passed as an array or matrix.
Categorical columns should contain only integer values with a continuous numeration starting at zero,
with negative values and NaN taken as missing,
and the array or list passed here should correspond to the column numbers, with numeration starting
at zero. The maximum categorical value should not exceed 'INT_MAX' (typically :math:`2^{31}-1`).
This might be passed either at construction time or when calling ``fit`` or variations of ``fit``.
This is ignored when the input is passed as a ``DataFrame`` as then it will consider columns as
categorical depending on their dtype.
Returns
-------
imputed : array-like(n_samples, n_columns)
Input data 'X' with missing values imputed according to the model.
"""
if (self.sample_size is None) or (self.sample_size == "auto"):
outp = self.fit_predict(X = X, column_weights = column_weights, output_imputed = True)
return outp["imputed"]
else:
self.fit(X = X, column_weights = column_weights)
return self.transform(X)
[docs] def partial_fit(self, X, sample_weights = None, column_weights = None):
"""
Add additional (single) tree to isolation forest model
Adds a single tree fit to the full (non-subsampled) data passed here. Must
have the same columns as previously-fitted data.
Note
----
If constructing trees with different sample sizes, the outlier scores will not be centered around
0.5 and might have a very skewed distribution. The standardizing constant for the scores will be
taken according to the sample size passed in the construction argument (if that is ``None`` or
``"auto"``, will then set it as the sample size of the first tree).
Note
----
This function is not thread-safe - that is, it will produce problems if one tries to call
this function on the same model object in parallel through e.g. ``joblib`` with a shared-memory
backend (which is not the default for joblib).
Parameters
----------
X : array or array-like (n_samples, n_features)
Data to which to fit the new tree. Can pass a NumPy array, Pandas DataFrame, or SciPy sparse CSC matrix.
If passing a DataFrame, will assume that columns are:
- Numeric, if their dtype is a subtype of NumPy's 'number' or 'datetime64'.
- Categorical, if their dtype is 'object', 'Categorical', or 'bool'. Note that,
if `Categorical` dtypes are ordered, the order will be ignored here.
Categorical columns, if any, may have new categories.
Other dtypes are not supported.
sample_weights : None or array(n_samples,)
Sample observation weights for each row of 'X', with higher weights indicating
distribution density (i.e. if the weight is two, it has the same effect of including the same data
point twice). If not 'None', model must have been built with 'weights_as_sample_prob' = 'False'.
column_weights : None or array(n_features,)
Sampling weights for each column in 'X'. Ignored when picking columns by deterministic criterion.
If passing None, each column will have a uniform weight. Cannot be used when weighting by kurtosis.
Returns
-------
self : obj
This object.
"""
if not self.is_fitted_:
self._init()
if (sample_weights is not None) and (self.weights_as_sample_prob):
raise ValueError("Cannot use sampling weights with 'partial_fit'.")
if (column_weights is not None) and (self.weigh_by_kurtosis):
raise ValueError("Cannot pass column weights when weighting columns by kurtosis.")
if not self.is_fitted_:
trees_restore = self.ntrees
try:
self.ntrees = 1
self.fit(X = X, sample_weights = sample_weights, column_weights = column_weights)
finally:
self.ntrees = trees_restore
return self
X_num, X_cat, nrows = self._process_data_new(X, allow_csr = False, prefer_row_major = False, keep_new_cat_levels = True)
if sample_weights is not None:
sample_weights = np.array(sample_weights).reshape(-1)
if (X_num is not None) and (X_num.dtype != sample_weights.dtype):
sample_weights = sample_weights.astype(X_num.dtype)
if sample_weights.dtype not in [ctypes.c_double, ctypes.c_float]:
sample_weights = sample_weights.astype(ctypes.c_double)
assert sample_weights.shape[0] == X.shape[0]
if column_weights is not None:
column_weights = np.array(column_weights).reshape(-1)
if (X_num is not None) and (X_num.dtype != column_weights.dtype):
column_weights = column_weights.astype(X_num.dtype)
if column_weights.dtype not in [ctypes.c_double, ctypes.c_float]:
column_weights = column_weights.astype(ctypes.c_double)
assert column_weights.shape[0] == X.shape[1]
if (sample_weights is not None) and (column_weights is not None) and (sample_weights.dtype != column_weights.dtype):
sample_weights = sample_weights.astype(ctypes.c_double)
column_weights = column_weights.astype(ctypes.c_double)
ncat = None
if self._ncols_categ > 0:
ncat = np.array([arr.shape[0] for arr in self._cat_mapping]).astype(ctypes.c_int)
if (ncat is None) and (X_cat is not None) and (X_cat.shape[1]):
ncat = X_cat.max(axis=0).clip(0)
if self.max_depth == "auto":
max_depth = 0
limit_depth = True
elif self.max_depth is None:
max_depth = nrows - 1
else:
max_depth = self.max_depth
limit_depth = False
if self.ncols_per_tree is None:
ncols_per_tree = 0
elif self.ncols_per_tree <= 1:
ncols_tot = 0
if X_num is not None:
ncols_tot += X_num.shape[1]
if X_cat is not None:
ncols_tot += X_cat.shape[1]
ncols_per_tree = int(np.ceil(self.ncols_per_tree * ncols_tot))
else:
ncols_per_tree = self.ncols_per_tree
if (self.prob_pick_pooled_gain or self.prob_pick_avg_gain) and self.ndim == 1:
ncols_tot = (X_num.shape[1] if X_num is not None else 0) + (X_cat.shape[1] if X_cat is not None else 0)
if self.ntry > ncols_tot:
warnings.warn("Passed 'ntry' larger than number of columns, will decrease it.")
if isinstance(self.random_state, np.random.RandomState):
seed = self.random_state.randint(np.iinfo(np.int32).max)
else:
seed = self.random_seed
seed += self._ntrees
self._cpp_obj.fit_tree(_get_num_dtype(X_num, sample_weights, column_weights),
_get_int_dtype(X_num),
X_num, X_cat, ncat, sample_weights, column_weights,
ctypes.c_size_t(nrows).value,
ctypes.c_size_t(self._ncols_numeric).value,
ctypes.c_size_t(self._ncols_categ).value,
ctypes.c_size_t(self.ndim).value,
ctypes.c_size_t(self.ntry).value,
self.coefs,
ctypes.c_bool(self.coef_by_prop).value,
ctypes.c_size_t(max_depth).value,
ctypes.c_size_t(ncols_per_tree).value,
ctypes.c_bool(limit_depth).value,
ctypes.c_bool(self.penalize_range).value,
ctypes.c_bool(self.standardize_data),
ctypes.c_bool(self.weigh_by_kurtosis).value,
ctypes.c_double(self.prob_pick_pooled_gain).value,
ctypes.c_double(self.prob_pick_avg_gain).value,
ctypes.c_double(self.min_gain).value,
self.missing_action,
self.categ_split_type,
self.new_categ_action,
ctypes.c_bool(self.build_imputer).value,
ctypes.c_size_t(self.min_imp_obs).value,
self.depth_imp,
self.weigh_imp_rows,
ctypes.c_bool(self.all_perm).value,
ctypes.c_int(seed).value)
self._ntrees += 1
return self
[docs] def get_num_nodes(self):
"""
Get number of nodes per tree
Gets the number of nodes per tree, along with the number of terminal nodes.
Returns
-------
nodes : tuple(array(n_trees,), array(n_trees,))
A tuple in which the first element denotes the total number of nodes
in each tree, and the second element denotes the number of terminal
nodes. Both are returned as arrays having one entry per tree.
"""
assert self.is_fitted_
n_nodes, n_terminal = self._cpp_obj.get_n_nodes(ctypes.c_bool(self._is_extended_).value,
ctypes.c_int(self.nthreads).value)
return n_nodes, n_terminal
[docs] def append_trees(self, other):
"""
Appends isolation trees from another Isolation Forest model into this one
This function is intended for merging models **that use the same hyperparameters** but
were fitted to different subsets of data.
In order for this to work, both models must have been fit to data in the same format -
that is, same number of columns, same order of the columns, and same column types, although
not necessarily same object classes (e.g. can mix ``np.array`` and ``scipy.sparse.csc_matrix``).
If the data has categorical variables, the models should have been built with parameter
``recode_categ=False`` in the class constructor,
and the categorical columns passed as type ``pd.Categorical`` with the same encoding -
otherwise different models might be using different encodings for each categorical column,
which will not be preserved as only the trees will be appended without any associated metadata.
Note
----
This function will not perform any checks on the inputs, and passing two incompatible
models (e.g. fit to different numbers of columns) will result in wrong results and
potentially crashing the Python process when using it.
Note
----
This function is not thread-safe - that is, it will produce problems if one tries to call
this function on the same model object in parallel through e.g. ``joblib`` with a shared-memory
backend (which is not the default for joblib).
Parameters
----------
other : IsolationForest
Another Isolation Forest model from which trees will be appended to this model.
It will not be modified during the call to this function.
Returns
-------
self : obj
This object.
"""
assert self.is_fitted_
assert other.is_fitted_
assert isinstance(other, IsolationForest)
if (self._is_extended_) != (other._is_extended_):
raise ValueError("Cannot mix extended and regular isolation forest models (ndim=1).")
if self.cols_categ_.shape[0]:
warnings.warn("Merging models with categorical features might give wrong results.")
self._cpp_obj.append_trees_from_other(other._cpp_obj, self._is_extended_)
self._ntrees += other._ntrees
return self
[docs] def export_model(self, file, add_metada_file = False):
"""
Export Isolation Forest model
Save Isolation Forest model to a serialized file along with its
metadata, in order to be re-used in Python or in the R or the C++ versions of this package.
This function is not suggested to be used for passing models to and from Python -
in such case, one can use ``pickle`` instead, although the function
still works correctly for serializing objects between Python processes.
Note that, if the model was fitted to a ``DataFrame``, the column names must be
something exportable as JSON, and must be something that R could
use as column names (for example, using integers as column names is valid in pandas
but not in R).
Can optionally generate a JSON file with metadata such as the column names and the
levels of categorical variables, which can be inspected visually in order to detect
potential issues (e.g. character encoding) or to make sure that the columns are of
the right types.
The metadata file, if produced, will contain, among other things, the encoding that was used for
categorical columns - this is under ``data_info.cat_levels``, as an array of arrays by column,
with the first entry for each column corresponding to category 0, second to category 1,
and so on (the C++ version takes them as integers). When passing ``categ_cols``, there
will be no encoding but it will save the maximum category integer and the column
numbers instead of names.
The serialized file can be used in the C++ version by reading it as a binary file
and de-serializing its contents using the C++ function 'deserialize_combined'
(recommended to use 'inspect_serialized_object' beforehand).
Be aware that this function will write raw bytes from memory as-is without compression,
so the file sizes can end up being much larger than when using ``pickle``.
The metadata is not used in the C++ version, but is necessary for the R and Python versions.
Note
----
While in earlier versions of this library this functionality used to be faster than
``pickle``, starting with version 0.3.0, this function and ``pickle`` should have
similar timings and it's recommended to use ``pickle`` for serializing objects
across Python processes.
Note
----
**Important:** The model treats boolean variables as categorical. Thus, if the model was fit
to a ``DataFrame`` with boolean columns, when importing this model into C++, they need to be
encoded in the same order - e.g. the model might encode ``True`` as zero and ``False``
as one - you need to look at the metadata for this. Also, if using some of Pandas' own
Boolean types, these might end up as non-boolean categorical, and if importing the model into R,
you might need to pass values as e.g. ``"True"`` instead of ``TRUE`` (look at the ``.metadata``
file to determine this).
Note
----
The files produced by this function will be compatible between:
* Different operating systems.
* Different compilers.
* Different Python/R versions.
* Systems with different 'size_t' width (e.g. 32-bit and 64-bit),
as long as the file was produced on a system that was either 32-bit or 64-bit,
and as long as each saved value fits within the range of the machine's 'size_t' type.
* Systems with different 'int' width,
as long as the file was produced on a system that was 16-bit, 32-bit, or 64-bit,
and as long as each saved value fits within the range of the machine's int type.
* Systems with different bit endianness (e.g. x86 and PPC64 in non-le mode).
* Versions of this package from 0.3.0 onwards, **but only forwards compatible**
(e.g. a model saved with versions 0.3.0 to 0.3.5 can be loaded under version
0.3.6, but not the other way around, and attempting to do so will cause crashes
and memory curruptions without an informative error message). **This last point applies
also to models saved through pickle**. Note that loading a
model produced by an earlier version of the library might be slightly slower.
But will not be compatible between:
* Systems with different floating point numeric representations
(e.g. standard IEEE754 vs. a base-10 system).
* Versions of this package earlier than 0.3.0.
This pretty much guarantees that a given file can be serialized and de-serialized
in the same machine in which it was built, regardless of how the library was compiled.
Reading a serialized model that was produced in a platform with different
characteristics (e.g. 32-bit vs. 64-bit) will be much slower.
Note
----
On Windows, if compiling this library with a compiler other than MSVC or MINGW,
there might be issues exporting models larger than 2GB.
Parameters
----------
file : str
The output file path into which to export the model. Must be a file name, not a
file handle.
add_metada_file : bool
Whether to generate a JSON file with metadata, which will have
the same name as the model but will end in '.metadata'. This file is not used by the
de-serialization function, it's only meant to be inspected manually, since such contents
will already be written in the produced model file.
Returns
-------
self : obj
This object.
"""
assert self.is_fitted_
file = os.path.expanduser(file)
metadata = self._export_metadata()
if add_metada_file:
with open(file + ".metadata", "w") as of:
json.dump(metadata, of, indent=4)
metadata = json.dumps(metadata)
metadata = metadata.encode('utf-8')
self._cpp_obj.serialize_obj(file, metadata, self.ndim > 1, has_imputer=self.build_imputer)
return self
[docs] @staticmethod
def import_model(file):
"""
Load an Isolation Forest model exported from R or Python
Loads a serialized Isolation Forest model as produced and exported
by the function ``export_model`` or by the R version of this package.
Note that the metadata must be something
importable in Python - e.g. column names must be valid for Pandas.
It's recommended to generate a '.metadata' file (passing ``add_metada_file=True``) and
to visually inspect said file in any case.
See the documentation for ``export_model`` for details about compatibility
of the generated files across different machines and versions.
Note
----
This is a static class method - that is, it should be called like this:
``iso = IsolationForest.import_model(...)``
(i.e. no parentheses after `IsolationForest`)
Note
----
While in earlier versions of this library this functionality used to be faster than
``pickle``, starting with version 0.3.0, this function and ``pickle`` should have
similar timings and it's recommended to use ``pickle`` for serializing objects
across Python processes.
Parameters
----------
file : str
The input file path containing an exported model along with its metadata file.
Must be a file name, not a file handle.
Returns
-------
iso : IsolationForest
An Isolation Forest model object reconstructed from the serialized file
and ready to use.
"""
file = os.path.expanduser(file)
obj = IsolationForest()
metadata = obj._cpp_obj.deserialize_obj(file)
metadata = json.loads(metadata)
obj._take_metadata(metadata)
return obj
[docs] def generate_sql(self, enclose="doublequotes", output_tree_num = False, tree = None,
table_from = None, select_as = "outlier_score",
column_names = None, column_names_categ = None):
"""
Generate SQL statements representing the model prediction function
Generate SQL statements - either separately per tree (the default),
for a single tree if needed (if passing ``tree``), or for all trees
concatenated together (if passing ``table_from``). Can also be made
to output terminal node numbers (numeration starting at zero).
Note
----
Making predictions through SQL is much less efficient than from the model
itself, as each terminal node will have to check all of the conditions
that lead to it instead of passing observations down a tree.
Note
----
If constructed with the default arguments, the model will not perform any
sub-sampling, which can lead to very big trees. If it was fit to a large
dataset, the generated SQL might consist of gigabytes of text, and might
lay well beyond the character limit of commands accepted by SQL vendors.
Note
----
The generated SQL statements will not include range penalizations, thus
predictions might differ from calls to ``predict`` when using
``penalize_range=True``.
Note
----
The generated SQL statements will only include handling of missing values
when using ``missing_action="impute"``. When using the single-variable
model with categorical variables + subset splits, the rule buckets might be
incomplete due to not including categories that were not present in a given
node - this last point can be avoided by using ``new_categ_action="smallest"``,
``new_categ_action="random"``, or ``missing_action="impute"`` (in the latter
case will treat them as missing, but the ``predict`` function might treat
them differently).
Note
----
The resulting statements will include all the tree conditions as-is,
with no simplification. Thus, there might be lots of redundant conditions
in a given terminal node (e.g. "X > 2" and "X > 1", the second of which is
redundant).
Parameters
----------
enclose : str
With which symbols to enclose the column names in the select statement
so as to make them SQL compatible in case they include characters like dots.
Options are:
``"doublequotes"``:
Will enclose them as ``"column_name"`` - this will work for e.g. PostgreSQL.
``"squarebraces"``:
Will enclose them as ``[column_name]`` - this will work for e.g. SQL Server.
``"none"``:
Will output the column names as-is (e.g. ``column_name``)
output_tree_num : bool
Whether to make the statements return the terminal node number
instead of the isolation depth. The numeration will start at zero.
tree : int or None
Tree for which to generate SQL statements. If passed, will generate
the statements only for that single tree. If passing 'None', will
generate statements for all trees in the model.
table_from : str or None
If passing this, will generate a single select statement for the
outlier score from all trees, selecting the data from the table
name passed here. In this case, will always output the outlier
score, regardless of what is passed under ``output_tree_num``.
select_as : str
Alias to give to the generated outlier score in the select statement.
Ignored when not passing ``table_from``.
column_names : None or list[str]
Column names to use for the **numeric** columns.
If not passed and the model was fit to a ``DataFrame``, will use the column
names from that ``DataFrame``, which can be found under ``self.cols_numeric_``.
If not passing it and the model was fit to data in a format other than
``DataFrame``, the columns will be named "column_N" in the resulting
SQL statement. Note that the names will be taken verbatim - this function will
not do any checks for whether they constitute valid SQL or not, and will not
escape characters such as double quotation marks.
column_names_categ : None or list[str]
Column names to use for the **categorical** columns.
If not passed, will use the column names from the ``DataFrame`` to which the
model was fit. These can be found under ``self.cols_categ_``.
Returns
-------
sql : list[str] or str
A list of SQL statements for each tree as strings, or the SQL statement
for a single tree if passing 'tree', or a single select-from SQL statement
with all the trees concatenated if passing ``table_from``.
"""
assert self.is_fitted_
single_tree = False
if tree is not None:
if isinstance(tree, float):
tree = int(tree)
assert isinstance(tree, int)
assert tree >= 0
assert tree < self._ntrees
single_tree = True
else:
tree = 0
output_tree_num = bool(output_tree_num)
if self._ncols_numeric:
if column_names is not None:
if len(column_names) != self._ncols_numeric:
raise ValueError("'column_names' must have %d entries." % self._ncols_numeric)
else:
if self.cols_numeric_.shape[0]:
column_names = self.cols_numeric_
else:
column_names = ["column_" + str(cl) for cl in range(self._ncols_numeric)]
else:
column_names = []
if self.cols_categ_.shape[0]:
if column_names_categ is not None:
if len(column_names_categ) != self.cols_categ_.shape[0]:
raise ValueError("'column_names_categ' must have %d entries." % self.cols_categ_.shape[0])
else:
column_names_categ = self.cols_categ_
categ_levels = [[str(lev) for lev in mp] for mp in self._cat_mapping]
else:
column_names_categ = []
categ_levels = []
assert enclose in ["doublequotes", "squarebraces", "none"]
if enclose != "none":
enclose_left = '"' if (enclose == "doublequotes") else '['
enclose_right = '"' if (enclose == "doublequotes") else ']'
column_names = [enclose_left + cl + enclose_right for cl in column_names]
column_names_categ = [enclose_left + cl + enclose_right for cl in column_names_categ]
out = [s for s in self._cpp_obj.generate_sql(self.ndim > 1,
column_names, column_names_categ, categ_levels,
output_tree_num, single_tree, tree, self.nthreads)]
if single_tree:
return out[0]
return out
[docs] def to_treelite(self, use_float32 = False):
"""
Convert model to 'treelite' format
Converts an IsolationForest model to a 'treelite' object, which can be compiled into a small
standalone runtime library for smaller models and usually faster predictions:
https://treelite.readthedocs.io/en/latest/index.html
A couple notes about this conversion:
- It is only possible to convert to 'treelite' when using ``ndim=1`` (which is not the default).
- The 'treelite' and 'treelite_runtime' libraries must be installed for this to work.
- The options for handling missing values in 'treelite' are more limited.
This function will always produce models that force ``missing_action="impute"``, regardless
of how the IsolationForest model itself handles them.
- The options for handling unseen categories in categorical variables are also more
limited in 'treelite'. It's not possible to convert models that use ``new_categ_action="weighted"``,
and categories that were not present within the training data (which are not meant to be passed to
'treelite') will always be sent to the right side of the split, which might produce different
results from ``predict``.
- Some features such as range penalizations will not be kept in the 'treelite' model.
- While this library always uses C 'double' precision (typically 'float64') for model objects and
prediction outputs, 'treelite' (a) can use 'float32' precision, (b) converts floating point numbers
to a decimal representation and back to floating point; which combined can result in some precision
loss which leads to producing slightly different predictions from the ``predict`` function in this
package.
- The output returned from a compiled 'treelite' model when calling ``predict`` will be the
average isolation depth, as it does not (yet?) support the standardized outlier score from
isolation forests.
- If the model was fit to a DataFrame having a mixture of numerical and categorical columns, the
resulting 'treelite' object will be built assuming all the numerical columns come before the
categorical columns, regardless of which order they originally had in the data that was passed to
'fit'. In such cases, it is possible to check the order of the columns under attributes
``self.cols_numeric_`` and ``self.cols_categ_``.
- Categorical columns in 'treelite' are passed as integer values. if the model was fit to a DataFrame
with categorical columns, the encoding that is used can be found under ``self._cat_mapping``.
- The 'treelite' object returned by this function will not yet have been compiled. It's necessary to
call ``compile`` and ``export_lib`` afterwards in order to be able to use it.
Parameters
----------
use_float32 : bool
Whether to use 'float32' type for the model. This is typically faster but has less precision
than the typical 'float64' (outside of this conversion, models from this library always use
'float64').
Returns
-------
model : obj
A 'treelite' model object.
"""
assert self.ndim == 1
assert self.is_fitted_
if (self._ncols_categ and
self.categ_split_type != "single_categ" and
self.new_categ_action not in ["smallest", "random"]
):
raise ValueError("Cannot convert to 'treelite' with the current parameters for categorical columns.")
if self.missing_action != "impute":
warnings.warn("'treelite' conversion will switch 'missing_action' to 'impute'.")
if self.penalize_range:
warnings.warn("'penalize_range' is ignored (assumed 'False') for 'treelite' conversion.")
import treelite
float_dtype = 'float32' if bool(use_float32) else 'float64'
num_node_info = np.empty(6, dtype=ctypes.c_double)
n_nodes = self.get_num_nodes()[0]
if self.categ_cols is None:
mapping_num_cols = np.arange(self._ncols_numeric)
mapping_cat_cols = np.arange(self._ncols_numeric, self._ncols_numeric + self._ncols_categ)
else:
mapping_num_cols = np.setdiff1d(np.arange(self._ncols_numeric + self._ncols_categ),
self.categ_cols, assume_unique=True)
mapping_cat_cols = np.array(self.categ_cols).reshape(-1).astype(int)
builder = treelite.ModelBuilder(
num_feature = self._ncols_numeric + self._ncols_categ,
average_tree_output = True,
threshold_type = float_dtype,
leaf_output_type = float_dtype
)
for tree_ix in range(self._ntrees):
tree = treelite.ModelBuilder.Tree(threshold_type = float_dtype, leaf_output_type = float_dtype)
for node_ix in range(n_nodes[tree_ix]):
cat_left = self._cpp_obj.get_node(tree_ix, node_ix, num_node_info)
if num_node_info[0] == 1:
tree[node_ix].set_leaf_node(num_node_info[1], leaf_value_type = float_dtype)
elif num_node_info[0] == 0:
tree[node_ix].set_numerical_test_node(
feature_id = mapping_num_cols[int(num_node_info[1])],
opname = "<=",
threshold = num_node_info[2],
threshold_type = float_dtype,
default_left = bool(num_node_info[3]),
left_child_key = int(num_node_info[4]),
right_child_key = int(num_node_info[5])
)
else:
tree[node_ix].set_categorical_test_node(
feature_id = mapping_cat_cols[int(num_node_info[1])],
left_categories = cat_left,
default_left = bool(num_node_info[3]),
left_child_key = int(num_node_info[4]),
right_child_key = int(num_node_info[5])
)
tree[0].set_root()
builder.append(tree)
model = builder.commit()
return model
[docs] def drop_imputer(self):
"""
Drops the imputer sub-object from this model object
Drops the imputer sub-object from this model object, if it was fitted with data imputation
capabilities. The imputer, if constructed, is likely to be a very heavy object which might
not be needed for all purposes.
Returns
-------
self : obj
This object
"""
self._cpp_obj.drop_imputer()
return self
[docs] def subset_trees(self, trees_take):
"""
Subset trees of a given model
Creates a new model containing only selected trees of this
model object.
Parameters
----------
trees_take : array_like(n,)
Indices of the trees of this model to copy over to the new model.
Must be integers with numeration starting at zero.
Returns
-------
new_model : obj
A new IsolationForest model object, containing only the subset of trees
from this object that was specified under 'trees_take'.
"""
assert self.is_fitted_
trees_take = np.array(trees_take).reshape(-1).astype(ctypes.c_size_t)
if not trees_take.shape[0]:
raise ValueError("'trees_take' is empty.")
if trees_take.max() >= self.ntrees:
raise ValueError("Attempting to take tree indices that the model does not have.")
new_cpp_obj = self._cpp_obj.subset_model(trees_take, self.ndim>1, self.build_imputer)
old_cpp_obj = self._cpp_obj
try:
self._cpp_obj = None
new_obj = deepcopy(self)
new_obj._cpp_obj = new_cpp_obj
finally:
self._cpp_obj = old_cpp_obj
return new_obj
### https://github.com/numpy/numpy/issues/19069
def _is_np_int(self, el):
return (
np.issubdtype(el.__class__, int) or
np.issubdtype(el.__class__, np.integer) or
np.issubdtype(el.__class__, np.int8) or
np.issubdtype(el.__class__, np.int16) or
np.issubdtype(el.__class__, np.int16) or
np.issubdtype(el.__class__, np.int32) or
np.issubdtype(el.__class__, np.int64) or
np.issubdtype(el.__class__, np.uint8) or
np.issubdtype(el.__class__, np.uint16) or
np.issubdtype(el.__class__, np.uint16) or
np.issubdtype(el.__class__, np.uint32) or
np.issubdtype(el.__class__, np.uint64)
)
def _denumpify_list(self, lst):
return [int(el) if self._is_np_int(el) else el for el in lst]
def _export_metadata(self):
if (self.max_depth is not None) and (self.max_depth != "auto"):
self.max_depth = int(self.max_depth)
data_info = {
"ncols_numeric" : int(self._ncols_numeric), ## is in c++
"ncols_categ" : int(self._ncols_categ), ## is in c++
"cols_numeric" : list(self.cols_numeric_),
"cols_categ" : list(self.cols_categ_),
"cat_levels" : [list(m) for m in self._cat_mapping],
"categ_cols" : [] if self.categ_cols is None else list(self.categ_cols),
"categ_max" : [] if self._cat_max_lev is None else list(self._cat_max_lev)
}
### Beaware of np.int64, which looks like a Python integer but is not accepted by json
data_info["cols_numeric"] = self._denumpify_list(data_info["cols_numeric"])
data_info["cols_categ"] = self._denumpify_list(data_info["cols_categ"])
data_info["categ_cols"] = self._denumpify_list(data_info["categ_cols"])
data_info["categ_max"] = self._denumpify_list(data_info["categ_max"])
if len(data_info["cat_levels"]):
data_info["cat_levels"] = [self._denumpify_list(lst) for lst in data_info["cat_levels"]]
if len(data_info["categ_cols"]):
data_info["categ_cols"] = self._denumpify_list(data_info["categ_cols"])
model_info = {
"ndim" : int(self.ndim),
"nthreads" : int(self.nthreads),
"build_imputer" : bool(self.build_imputer)
}
params = {
"sample_size" : self.sample_size,
"ntrees" : int(self._ntrees), ## is in c++
"ntry" : int(self.ntry),
"max_depth" : self.max_depth,
"ncols_per_tree" : self.ncols_per_tree,
"prob_pick_avg_gain" : float(self.prob_pick_avg_gain),
"prob_pick_pooled_gain" : float(self.prob_pick_pooled_gain),
"min_gain" : float(self.min_gain),
"missing_action" : self.missing_action, ## is in c++
"new_categ_action" : self.new_categ_action, ## is in c++
"categ_split_type" : self.categ_split_type, ## is in c++
"coefs" : self.coefs,
"depth_imp" : self.depth_imp,
"weigh_imp_rows" : self.weigh_imp_rows,
"min_imp_obs" : int(self.min_imp_obs),
"random_seed" : self.random_seed,
"all_perm" : self.all_perm,
"coef_by_prop" : self.coef_by_prop,
"weights_as_sample_prob" : self.weights_as_sample_prob,
"sample_with_replacement" : self.sample_with_replacement,
"penalize_range" : self.penalize_range,
"standardize_data" : self.standardize_data,
"weigh_by_kurtosis" : self.weigh_by_kurtosis,
"assume_full_distr" : self.assume_full_distr,
}
if params["max_depth"] == "auto":
params["max_depth"] = 0
return {"data_info" : data_info, "model_info" : model_info, "params" : params}
def _take_metadata(self, metadata):
self._ncols_numeric = metadata["data_info"]["ncols_numeric"]
self._ncols_categ = metadata["data_info"]["ncols_categ"]
self.cols_numeric_ = np.array(metadata["data_info"]["cols_numeric"])
self.cols_categ_ = np.array(metadata["data_info"]["cols_categ"])
self._cat_mapping = [np.array(lst) for lst in metadata["data_info"]["cat_levels"]]
self.categ_cols = np.array(metadata["data_info"]["categ_cols"]).reshape(-1).astype(int) if len(metadata["data_info"]["categ_cols"]) else None
self._cat_max_lev = np.array(metadata["data_info"]["categ_max"]).reshape(-1).astype(int) if (self.categ_cols is not None) else []
self.ndim = metadata["model_info"]["ndim"]
self.nthreads = metadata["model_info"]["nthreads"]
self.build_imputer = metadata["model_info"]["build_imputer"]
self.sample_size = metadata["params"]["sample_size"]
self.ntrees = metadata["params"]["ntrees"]
self._ntrees = self.ntrees
self.ntry = metadata["params"]["ntry"]
self.max_depth = metadata["params"]["max_depth"]
self.ncols_per_tree = metadata["params"]["ncols_per_tree"]
self.prob_pick_avg_gain = metadata["params"]["prob_pick_avg_gain"]
self.prob_pick_pooled_gain = metadata["params"]["prob_pick_pooled_gain"]
self.min_gain = metadata["params"]["min_gain"]
self.missing_action = metadata["params"]["missing_action"]
self.new_categ_action = metadata["params"]["new_categ_action"]
self.categ_split_type = metadata["params"]["categ_split_type"]
self.coefs = metadata["params"]["coefs"]
self.depth_imp = metadata["params"]["depth_imp"]
self.weigh_imp_rows = metadata["params"]["weigh_imp_rows"]
self.min_imp_obs = metadata["params"]["min_imp_obs"]
self.random_seed = metadata["params"]["random_seed"]
self.all_perm = metadata["params"]["all_perm"]
self.coef_by_prop = metadata["params"]["coef_by_prop"]
self.weights_as_sample_prob = metadata["params"]["weights_as_sample_prob"]
self.sample_with_replacement = metadata["params"]["sample_with_replacement"]
self.penalize_range = metadata["params"]["penalize_range"]
try:
self.standardize_data = metadata["params"]["standardize_data"]
except:
self.standardize_data = True
self.weigh_by_kurtosis = metadata["params"]["weigh_by_kurtosis"]
self.assume_full_distr = metadata["params"]["assume_full_distr"]
if "prob_split_avg_gain" in metadata["params"].keys():
if metadata["params"]["prob_split_avg_gain"] > 0:
msg = "'prob_split_avg_gain' has been deprecated in favor of 'prob_pick_avg_gain' + 'ntry'."
if self.ndim > 1:
msg += " Be sure to change these parameters if refitting this model or adding trees."
warnings.warn(msg)
if "prob_split_pooled_gain" in metadata["params"].keys():
if metadata["params"]["prob_split_pooled_gain"] > 0:
msg = "'prob_split_pooled_gain' has been deprecated in favor of 'prob_pick_pooled_gain' + 'ntry'."
if self.ndim > 1:
msg += " Be sure to change these parameters if refitting this model or adding trees."
warnings.warn(msg)
self.is_fitted_ = True
self._is_extended_ = self.ndim > 1
return self
def __is_fitted__(self):
return self.is_fitted_
```