# 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

if (warn_if_no_omp) and (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 "
warnings.warn(msg_omp)

[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 and/or sample size as low as 32.
For the FCF model aimed at imputing missing values,
might give better results with ntry=10 or higher and much larger sample sizes.
'RRCF' (reference [12]_):
ndim=1, prob_pick_col_by_range=1, sample_size=256 or more, max_depth=None,
ntrees=100 or more, missing_action="fail". Note however that reference [12]_ proposed a
different method for calculation of anomaly scores, while this library uses isolation depth just
like for 'iForest', so results might differ significantly from those of other libraries.
Nevertheless, experiments in reference [11]_ suggest that isolation depth might be a better
scoring metric for this model.

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.

If the data has categorical variables and these are more important important for determining
outlierness compared to numerical columns, one might want to experiment with ndim=1,
categ_split_type="single_categ", and scoring_metric="density".

For small datasets, one might also want to experiment with ndim=1, scoring_metric="adj_depth"
and penalize_range=True.

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, (f) scoring_metric="density"
or scoring_metric="boxed_density" are 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]_.

In general, when the data has categorical variables, models with ndim=1 plus
categ_split_type="single_categ" tend to produce better results, while models ndim>1
tend to produce better results for numerical-only data, especially in the presence of missing values.
ntry : int
When using any of prob_pick_pooled_gain, prob_pick_avg_gain, prob_pick_full_gain, prob_pick_dens, 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 weighted column choices.

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,
prob_pick_pooled_gain, prob_pick_full_gain, prob_pick_dens, 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.

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 any of prob_pick_avg_gain, prob_pick_pooled_gain,
prob_pick_full_gain, prob_pick_dens, 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 :math:\\sim \\text{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, prob_pick_pooled_gain, prob_pick_full_gain, prob_pick_dens,
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 any of prob_pick_avg_gain, prob_pick_pooled_gain,
prob_pick_full_gain, prob_pick_dens, the distribution of
outlier scores is unlikely to be centered around 0.5.
prob_pick_full_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 minimizes the pooled sums of variances of all columns (or a subset of them if using
ncols_per_tree).

In general, this is much slower to evaluate than the other gain types, and does not tend to
lead to better results. When using this option, one might want to use a different scoring
metric (particulatly "density", "boxed_density2" or "boxed_ratio"). Note that
the calculations are all done through the (exact) sorted-indices approach, while is much
slower than the (approximate) histogram approach used by other decision tree software.

Be aware that the data is not standardized in any way for the variance calculations, thus the scales
of features will make a large difference under this option, which might not make it suitable for
all types of data.

This option is not compatible with categorical data, and min_gain does not apply to it.

When splits are
not made according to any of prob_pick_avg_gain, prob_pick_pooled_gain, prob_pick_full_gain, prob_pick_dens,
both the column and the split point are decided at random. Default setting for [1]_, [2]_, [3]_, [4]_ is
zero.
prob_pick_dens : 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 the pooled densities of the branch distributions.

The min_gain option does not apply to this type of splits.

When splits are
not made according to any of prob_pick_avg_gain, prob_pick_pooled_gain, prob_pick_full_gain, prob_pick_dens,
both the column and the split point are decided at random. Default setting for [1]_, [2]_, [3]_, [4]_ is
zero.
prob_pick_col_by_range : float[0, 1]
When using ndim=1, this denotes the probability of choosing the column to split with a probability
proportional to the range spanned by each column within a node as proposed in reference [12]_.

When using ndim>1, this denotes the probability of choosing columns to create a hyperplane with a
probability proportional to the range spanned by each column within a node.

This option is not compatible with categorical data. If passing column weights, the
effect will be multiplicative.

Be aware that the data is not standardized in any way for the range calculations, thus the scales
of features will make a large difference under this option, which might not make it suitable for
all types of data.

If there are infinite values, all columns having infinite values will be treated as having the
same weight, and will be chosen before every other column with non-infinite values.

Note that the proposed RRCF model from [12]_ uses a different scoring metric for producing anomaly
scores, while this library uses isolation depth regardless of how columns are chosen, thus results
are likely to be different from those of other software implementations. Nevertheless, as explored
in [11]_, isolation depth as a scoring metric typically provides better results than the
"co-displacement" metric from [12]_ under these split types.
prob_pick_col_by_var : float[0, 1]
When using ndim=1, this denotes the probability of choosing the column to split with a probability
proportional to the variance of each column within a node.

When using ndim>1, this denotes the probability of choosing columns to create a hyperplane with a
probability proportional to the variance of each column within a node.

For categorical data, it will calculate the expected variance if the column were converted to
numerical by assigning to each category a random number :math:\\sim \\text{Unif}(0, 1), which depending on the number of
categories and their distribution, produces numbers typically a bit smaller than standardized numerical
variables.

Note that when using sparse matrices, the calculation of variance will rely on a procedure that
uses sums of squares, which has less numerical precision than the
calculation used for dense inputs, and as such, the results might differ slightly.

Be aware that this calculated variance is not standardized in any way, so the scales of
features will make a large difference under this option.

If passing column weights, the effect will be multiplicative.

If passing a missing_action different than "fail", infinite values will be ignored for the
variance calculation. Otherwise, all columns with infinite values will have the same probability
and will be chosen before columns with non-infinite values.
prob_pick_col_by_kurt : float[0, 1]
When using ndim=1, this denotes the probability of choosing the column to split with a probability
proportional to the kurtosis of each column **within a node** (unlike the option weigh_by_kurtosis
which calculates this metric only at the root).

When using ndim>1, this denotes the probability of choosing columns to create a hyperplane with a
probability proportional to the kurtosis of each column within a node.

For categorical data, it will calculate the expected kurtosis if the column were converted to
numerical by assigning to each category a random number :math:\\sim \\text{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.

If passing column weights, the effect will be multiplicative. This option is not compatible
with weigh_by_kurtosis.

If passing a missing_action different than "fail", infinite values will be ignored for the
kurtosis calculation. Otherwise, all columns with infinite values will have the same probability
and will be chosen before columns with non-infinite values.

If using missing_action="impute", the calculation of kurtosis will not use imputed values
in order not to favor columns with missing values (which would increase kurtosis by all having
the same central value).

Be aware that kurtosis can be a rather slow metric to calculate.
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 variance gain criterion (prob_pick_pooled_gain
or prob_pick_avg_gain, but not prob_pick_full_gain nor prob_pick_dens).
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) (but note that the calculation of medians does not take
into account sample weights when using weights_as_sample_prob=False).
When using ndim=1, gain calculations will use median-imputed values for missing data under this option.
"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.

Typically, models with 'ndim>1' are less affected by missing data that models with 'ndim=1'.
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 (but note that this median calculation does not use sample weights when
using weights_as_sample_prob=False).
"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 "auto", "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.

If passing "auto", will select "subset" for the extended model and "single_categ" for
the single-variable model.
all_perm : bool
When doing categorical variable splits by pooled gain with ndim=1 (single-variable 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 single-variable 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, but does not give it a
higher chance of being sampled if the data uses sub-sampling).
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.

Note that it is not possible to call fit_predict or fit_transform when using this option.
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.

This option is not supported when using density-based outlier scoring metrics.

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).
scoring_metric : str
Metric to use for determining outlier scores (see reference [13]_). Options are:

"depth"
Will use isolation depth as proposed in reference [1]_. This is typically the safest choice
and plays well with all model types offered by this library.
"density"
Will set scores for each terminal node as the ratio between the fraction of points in the sub-sample
that end up in that node and the fraction of the volume in the feature space which defines
the node according to the splits that lead to it.
If using ndim=1, for categorical variables, this is defined in terms
of number of categories that go towards each side of the split divided by number of categories
in the observations that reached that node.

The standardized outlier score from density for a given observation is calculated as the
negative of the logarithm of the geometric mean from the per-tree densities, which unlike
the standardized score produced from depth, is unbounded, but just like the standardized
score from depth, has a natural threshold for definining outlierness, which in this case
is zero is instead of 0.5. The non-standardized outlier score is calculated as the
geometric mean, while the per-tree scores are calculated as the density values.

This might lead to better predictions when using ndim=1, particularly in the presence
of categorical variables. Note however that using density requires more trees for convergence
of scores (i.e. good results) compared to isolation-based metrics.

This option is incompatible with penalize_range.
"adj_depth"
Will use an adjusted isolation depth that takes into account the number of points that
go to each side of a given split vs. the fraction of the range of that feature that each
side of the split occupies, by a metric as follows:
:math:d = \\frac{2}{ 1 + \\frac{1}{2 p} }

Where :math:p is defined as:
:math:p = \\frac{n_s}{n_t} / \\frac{r_s}{r_t}

With :math:n_t being the number of points that reach a given node, :math:n_s the
number of points that are sent to a given side of the split/branch at that node,
:math:r_t being the range (maximum minus minimum) of the splitting feature or
linear combination among the points that reached the node, and :math:r_s being the
range of the same feature or linear combination among the points that are sent to this
same side of the split/branch. This makes each split add a number between zero and two
to the isolation depth, with this number's probabilistic distribution being centered
around 1 and thus the expected isolation depth remaing the same as in the original
"depth" metric, but having more variability around the extremes.

Scores (standardized, non-standardized, per-tree) are aggregated in the same way
as for "depth".

This might lead to better predictions when using ndim=1, particularly in the prescence
of categorical variables and for smaller datasets, and for smaller datasets, might make
sense to combine it with penalize_range=True.
"adj_density"
Will use the same metric from "adj_depth", but applied multiplicatively instead
of additively. The expected value for this adjusted density is not strictly the same
as for isolation, but using the expected isolation depth as standardizing criterion
tends to produce similar standardized score distributions (centered around 0.5).

Scores (standardized, non-standardized, per-tree) are aggregated in the same way
as for "depth".

This option is incompatible with penalize_range.
"boxed_ratio"
Will set the scores for each terminal node as the ratio between the volume of the boxed
feature space for the node as defined by the smallest and largest values from the split
conditions for each column (bounded by the variable ranges in the sample) and the
variable ranges in the tree sample.
If using ndim=1, for categorical variables this is defined in terms of number of
categories.
If using ndim=>1, this is defined in terms of the maximum achievable value for the
splitting linear combination determined from the minimum and maximum values for each
variable among the points in the sample, and as such, it has a rather different meaning
compared to the score obtained with ndim=1 - boxed ratio scores with ndim>1
typically provide very poor quality results and this metric is thus not recommended to
use in the extended model. With 'ndim>1', it also has a tendency of producing too small
values which round to zero.

The standardized outlier score from boxed ratio for a given observation is calculated
simply as the the average from the per-tree boxed ratios. This metric
has a lower bound of zero and a theorical upper bound of one, but in practice the scores
tend to be very small numbers close to zero, and its distribution across
different datasets is rather unpredictable. In order to keep rankings comparable with
the rest of the metrics, the non-standardized outlier scores are calculated as the
negative of the average instead. The per-tree scores are calculated as the ratios.

This metric can be calculated in a fast-but-not-so-precise way, and in a low-but-precise
way, which is controlled by parameter fast_bratio. Usually, both should give the
same results, but in some fatasets, the fast way can lead to numerical inaccuracies
due to roundoffs very close to zero.

This metric might lead to better predictions in datasets with many rows when using ndim=1
and a relatively small sample_size. Note that more trees are required for convergence
of scores when using this metric. In some datasets, this metric might result in very bad
predictions, to the point that taking its inverse produces a much better ranking of outliers.

This option is incompatible with penalize_range.
"boxed_density2"
Will set the score as the ratio between the fraction of points within the sample that
end up in a given terminal node and the boxed ratio metric.

Aggregation of scores (standardized, non-standardized, per-tree) is done in the same
way as for density, and it also has a natural threshold at zero for determining
outliers and inliers.

This metric is typically usable with 'ndim>1', but tends to produce much bigger values
compared to 'ndim=1'.

Albeit unintuitively, in many datasets, one can usually get better results with metric
"boxed_density" instead.

The calculation of this metric is also controlled by fast_bratio.

This option is incompatible with penalize_range.
"boxed_density"
Will set the score as the ratio between the fraction of points within the sample that
end up in a  given terminal node and the ratio between the boxed volume of the feature
space in the sample and the boxed volume of a node given by the split conditions (inverse
as in "boxed_density2"). This metric does not have any theoretical or intuitive
justification behind its existence, and it is perhaps ilogical to use it as a
scoring metric, but tends to produce good results in some datasets.

The standardized outlier scores are defined as the negative of the geometric mean
of this metric, while the non-standardized scores are the geometric mean, and the
per-tree scores are simply the 'density' values.

The calculation of this metric is also controlled by fast_bratio.

This option is incompatible with penalize_range.
fast_bratio : bool
When using "boxed" metrics for scoring, whether to calculate them in a fast way through
cumulative sum of logarithms of ratios after each split, or in a slower way as sum of
logarithms of a single ratio per column for each terminal node.

Usually, both methods should give the same results, but in some datasets, particularly
when variables have too small or too large ranges, the first method can be prone to
numerical inaccuracies due to roundoff close to zero.

Note that this does not affect calculations for models with 'ndim>1', since given the
split types, the calculation for them is different.
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. For categorical columns, will calculate expected kurtosis if the column were converted to
numerical by assigning to each category a random number :math:\\sim \\text{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.

This is intended as a cheap feature selector, while the parameter prob_pick_col_by_kurt
provides the option to do this at each node in the tree for a different overall type of model.

If passing column weights or using weighted column choices proportional to some other metric
(prob_pick_col_by_range, prob_pick_col_by_var), the effect will be multiplicative.

If passing missing_action="fail" and the data has infinite values, columns with rows
having infinite values will get a weight of zero. If passing a different value for missing
action, infinite values will be ignored in the kurtosis calculation.

If using missing_action="impute", the calculation of kurtosis will not use imputed values
in order not to favor columns with missing values (which would increase kurtosis by all having
the same central value).
coefs : str, one of "normal" or "uniform"
For the extended model, whether to sample random coefficients according to a normal distribution :math:\\sim \\text{Normal}(0, 1)
(as proposed in [4]_) or according to a uniform distribution :math:\\sim \\text{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').

This was added for experimentation purposes only and it's not recommended to pass False.
Note that when calculating distances using a tree indexer (after calling build_index), there
might be slight discrepancies between the numbers produced with or without the indexer due to what
are considered "additional" observations in this calculation.
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.
use_long_double : bool
Whether to use 'long double' (extended precision) type for more precise calculations about
standard deviations, means, ratios, weights, gain, and other potential aggregates. This makes
such calculations accurate to a larger number of decimals (provided that the compiler used has
wider long doubles than doubles) and it is highly recommended to use when the input data has
a number of rows or columns exceeding :math:2^{53} (an unlikely scenario), and also highly recommended
to use when the input data has problematic scales (e.g. numbers that differ from each other by
something like :math:10^{-100} or columns that include values like :math:10^{100}, :math:10^{-10}, and :math:10^{-100} and still need to
be sensitive to a difference of :math:10^{-10}), but will make the calculations slower, the more so in
platforms in which 'long double' is a software-emulated type (e.g. Power8 platforms).
Note that some platforms (most notably windows with the msvc compiler) do not make any difference
between 'double' and 'long double'.

If 'long double' is not going to be used, the library can be compiled without support for it
(making the library size smaller) by defining an environment variable NO_LONG_DOUBLE before
installing this package (e.g. through export NO_LONG_DOUBLE=1 before running the pip command).

This option is not available on Windows, due to lack of support in some compilers (e.g. msvc)
and lack of thread-safety in the calculations in others (e.g. mingw).
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).
.. [12] Guha, Sudipto, et al. "Robust random cut forest based anomaly detection on streams."
International conference on machine learning. PMLR, 2016.
.. [13] Cortes, David. "Isolation forests: looking beyond tree depth."
arXiv preprint arXiv:2111.11639 (2021).
.. [14] Ting, Kai Ming, Yue Zhu, and Zhi-Hua Zhou. "Isolation kernel and its effect on SVM."
Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018.
"""
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,
prob_pick_full_gain = 0.0, prob_pick_dens = 0.0,
prob_pick_col_by_range = 0.0, prob_pick_col_by_var = 0.0,
prob_pick_col_by_kurt = 0.0,
min_gain = 0., missing_action = "auto", new_categ_action = "auto",
categ_split_type = "auto", 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,
scoring_metric = "depth", fast_bratio = True, weigh_by_kurtosis = False,
coefs = "uniform", assume_full_distr = True,
build_imputer = False, min_imp_obs = 3,
depth_imp = "higher", weigh_imp_rows = "inverse",
random_seed = 1, use_long_double = False, 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.prob_pick_full_gain = prob_pick_full_gain
self.prob_pick_dens = prob_pick_dens
self.prob_pick_col_by_range = prob_pick_col_by_range
self.prob_pick_col_by_var = prob_pick_col_by_var
self.prob_pick_col_by_kurt = prob_pick_col_by_kurt
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.scoring_metric = scoring_metric
self.fast_bratio = fast_bratio
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.use_long_double = use_long_double
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,
prob_pick_full_gain = self.prob_pick_full_gain, prob_pick_dens = self.prob_pick_dens,
prob_pick_col_by_range = self.prob_pick_col_by_range,
prob_pick_col_by_var = self.prob_pick_col_by_var,
prob_pick_col_by_kurt = self.prob_pick_col_by_kurt,
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,
scoring_metric = self.scoring_metric, fast_bratio = self.fast_bratio,
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,
use_long_double = self.use_long_double,

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,
prob_pick_full_gain = 0.0, prob_pick_dens = 0.0,
prob_pick_col_by_range = 0.0, prob_pick_col_by_var = 0.0,
prob_pick_col_by_kurt = 0.0,
min_gain = 0., missing_action = "auto", new_categ_action = "auto",
categ_split_type = "auto", 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,
scoring_metric = "depth", fast_bratio = 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, use_long_double = False, 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)
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"):
if not (max_depth < sample_size):
warnings.warn("Passed 'max_depth' greater than 'sample_size'. Will be ignored.")
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",    "auto"]
assert coefs             in ["normal",        "uniform"]
assert depth_imp         in ["lower",         "higher",    "same"]
assert weigh_imp_rows    in ["inverse",       "prop",      "flat"]
"boxed_density", "boxed_density2",       "boxed_ratio"]

assert prob_pick_avg_gain     >= 0
assert prob_pick_pooled_gain  >= 0
assert prob_pick_full_gain    >= 0
assert prob_pick_dens         >= 0
assert prob_pick_col_by_range >= 0
assert prob_pick_col_by_var   >= 0
assert prob_pick_col_by_kurt  >= 0
assert min_gain               >= 0
s = prob_pick_avg_gain + prob_pick_pooled_gain + prob_pick_full_gain + prob_pick_dens
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
prob_pick_full_gain    /= s
prob_pick_dens         /= s

s = prob_pick_col_by_range + prob_pick_col_by_var + prob_pick_col_by_kurt
if s > 1:
warnings.warn("Column choice probabilities sum to more than 1, will standardize them")
prob_pick_col_by_range  /= s
prob_pick_col_by_var    /= s
prob_pick_col_by_kurt   /= s

if weigh_by_kurtosis and prob_pick_col_by_kurt:
raise ValueError("'weigh_by_kurtosis' is incompatible with 'prob_pick_col_by_kurt'.")

if (
(ndim == 1) and
((sample_size is None) or (sample_size == "auto")) and
(max([prob_pick_avg_gain, prob_pick_pooled_gain, prob_pick_full_gain, prob_pick_dens]) >= 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 non-random split probabilities to less than 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 categ_split_type == "auto":
if ndim == 1:
categ_split_type = "single_categ"
else:
categ_split_type = "subset"
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 penalize_range and scoring_metric in ["density", "adj_density", "boxed_density", "boxed_density2", "boxed_ratio"]:
raise ValueError("'penalize_range' is incompatible with density scoring.")

if categ_cols is not None:
categ_cols = np.array(categ_cols).reshape(-1).astype(int)
if categ_cols.shape[0]:
if prob_pick_col_by_range:
raise ValueError("'prob_pick_col_by_range' is incompatible with categorical data.")
if prob_pick_full_gain:
raise ValueError("'prob_pick_full_gain' is incompatible with categorical data.")
categ_cols.sort()
else:
categ_cols = None

## TODO: for better sklearn compatibility, should have versions of
## these with underscores at the end
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_     =  float(prob_pick_avg_gain)
self.prob_pick_pooled_gain_  =  float(prob_pick_pooled_gain)
self.prob_pick_full_gain_    =  float(prob_pick_full_gain)
self.prob_pick_dens_         =  float(prob_pick_dens)
self.prob_pick_col_by_range_ =  float(prob_pick_col_by_range)
self.prob_pick_col_by_var_   =  float(prob_pick_col_by_var)
self.prob_pick_col_by_kurt_  =  float(prob_pick_col_by_kurt)
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.scoring_metric          =  scoring_metric
self.min_imp_obs             =  min_imp_obs
self.random_seed             =  random_seed

self.fast_bratio             =  bool(fast_bratio)
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.use_long_double         =  bool(use_long_double)

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
"""
return deepcopy(self)

[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
object to a blank state - 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 hasattr(self, "prob_pick_avg_gain_"):
has_guided_splits = (self.prob_pick_avg_gain_ + self.prob_pick_pooled_gain_ + self.prob_pick_full_gain_ + self.prob_pick_dens_) > 0
else:
has_guided_splits = (self.prob_pick_avg_gain + self.prob_pick_pooled_gain + self.prob_pick_full_gain + self.prob_pick_dens) > 0
if has_guided_splits:
msg += " (using guided splits)"
msg += "\n"
ndim = self.ndim_ if hasattr(self, "ndim_") else self.ndim
if ndim > 1:
msg += "Splitting by %d variables at a time\n" % 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
if self.has_indexer_:
has_distances = self._cpp_obj.has_indexer_with_distances()
has_references = self._cpp_obj.has_reference_points()
msg += "(Has node indexer%s%s%s built-in)\n" & (
"  with distances" if has_distances else "",
" and" if (has_distances and has_references) else "",
" with reference points" if has_references else ""
)
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):
categ_split_type = self.categ_split_type
ndim = self.ndim_ if hasattr(self, "ndim_") else self.ndim
if categ_split_type == "auto":
if ndim == 1:
categ_split_type = "single_categ"
else:
categ_split_type = "subset"
if (self.build_imputer) and (ndim == 1) and (X_cat is not None) and (X_cat.shape[1]):
if (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.

If passing a DataFrame with categorical columns, then column names must be unique.
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. If used along with kurtosis weights, the
effect is multiplicative.
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.")
self._reset_obj()
X_num, X_cat, ncat, sample_weights, column_weights, nrows = self._process_data(X, sample_weights, column_weights)

if X_cat is not None:
if self.prob_pick_col_by_range_:
raise ValueError("'prob_pick_col_by_range' is incompatible with categorical data.")
if self.prob_pick_full_gain:
raise ValueError("'prob_pick_full_gain' is incompatible with categorical data.")

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 sample_size > nrows:
sample_size = nrows
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 max_depth >= sample_size:
max_depth = 0
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_ or
self.prob_pick_full_gain_ or
self.prob_pick_dens_
) 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,
self.scoring_metric,
ctypes.c_bool(self.fast_bratio).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.prob_pick_full_gain_).value,
ctypes.c_double(self.prob_pick_dens_).value,
ctypes.c_double(self.prob_pick_col_by_range_).value,
ctypes.c_double(self.prob_pick_col_by_var_).value,
ctypes.c_double(self.prob_pick_col_by_kurt_).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_bool(self.use_long_double).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.

If passing a DataFrame with categorical columns, then column names must be unique.
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. If used along with kurtosis weights, the
effect is multiplicative.
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.

Note that it might be much faster to calculate distances through a fitted object with
build_indexer instead or calling this method.
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") and
(self.sample_size != 1) and
(self.sample_size != nrows)
):
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 (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)

if (X_cat is not None) and (self.prob_pick_col_by_range_):
raise ValueError("'prob_pick_col_by_range' is incompatible with categorical data.")

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 max_depth >= nrows:
max_depth = 0
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_ or
self.prob_pick_full_gain_ or
self.prob_pick_dens_
) 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,
self.scoring_metric,
ctypes.c_bool(self.fast_bratio).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.prob_pick_full_gain_).value,
ctypes.c_double(self.prob_pick_dens_).value,
ctypes.c_double(self.prob_pick_col_by_range_).value,
ctypes.c_double(self.prob_pick_col_by_var_).value,
ctypes.c_double(self.prob_pick_col_by_kurt_).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_bool(self.use_long_double).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'
self.ndim_ = self.ndim

if X.__class__.__name__ == "DataFrame":

### TODO: this should also have a version with underscores
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.n_features_in_ = X.shape[1]
self.feature_names_in_ = np.array(X.columns.values)

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])]

### TODO: this could also be done by calling pandas' DataFrame.assign (see commented out code).
### However, at the time of writing, pandas' DataFrame.assign was resorting to calling method
### .copy() behind the scenes, which made it inefficient compared to copying the data right here.
### If .assign() at some point in the future solves this inefficiency, should change the code
### here to use 'assign' instead of making a full deep copy.
X_cat = X_cat.copy()

# # https://stackoverflow.com/questions/72535853/how-to-assign-to-column-with-non-string-name-or-index
# random_name = "a367410fed934a3e81ce03492df9af85"
# while random_name in X_cat.columns:
#     random_name += "7f2df7ee866942fe9b2187011bb2550c"

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.columns[cl]] = X_cat[X_cat.columns[cl]].cat.codes
# X_cat = \
#     X_cat\
#     .rename(columns = {X_cat.columns[cl] : random_name})\
#     .assign(**{random_name : X_cat[X_cat.columns[cl]].cat.codes})\
#     .rename(columns = {random_name : X_cat.columns[cl]})
else:
cl_values, self._cat_mapping[cl] = pd.factorize(X_cat[X_cat.columns[cl]])
X_cat[X_cat.columns[cl]] = cl_values
# X_cat = \
#     X_cat\
#     .rename(columns = {X_cat.columns[cl] : random_name})\
#     .assign(**{random_name : cl_values})\
#     .rename(columns = {random_name : X_cat.columns[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.")

self.n_features_in_ = X.shape[1]

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.")

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
if self.missing_action == "auto":
self.missing_action_ = "divide"
if self.new_categ_action == "auto":
self.new_categ_action_ = "weighted"

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_]

### See 'TODO' in '_process_data'.
X_cat = X_cat.copy()

# # https://stackoverflow.com/questions/72535853/how-to-assign-to-column-with-non-string-name-or-index
# random_name = "a367410fed934a3e81ce03492df9af85"
# while random_name in X_cat.columns:
#     random_name += "7f2df7ee866942fe9b2187011bb2550c"

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[self.cols_categ_[cl]] = _encode_categorical(X_cat[self.cols_categ_[cl]], self._cat_mapping[cl])
# X_cat = \
#     X_cat\
#     .rename(columns = {self.cols_categ_[cl] : random_name})\
#     .assign(**{
#         random_name : _encode_categorical(X_cat[self.cols_categ_[cl]],
#                                           self._cat_mapping[cl])
#     })\
#     .rename(columns = {random_name : self.cols_categ_[cl]})
else:
for cl in range(self._ncols_categ):
X_cat[self.cols_categ_[cl]] = pd.Categorical(X_cat[self.cols_categ_[cl]])
# X_cat = \
#     X_cat\
#     .rename(columns = {self.cols_categ_[cl] : random_name})\
#     .assign(**{
#         random_name : pd.Categorical(X_cat[self.cols_categ_[cl]])
#     })\
#     .rename(columns = {random_name : 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[self.cols_categ_[cl]] = _encode_categorical(X_cat[self.cols_categ_[cl]], self._cat_mapping[cl])
# X_cat = \
#     X_cat\
#     .rename(columns = {self.cols_categ_[cl] : random_name})\
#     .assign(**{
#         random_name : _encode_categorical(X_cat[self.cols_categ_[cl]],
#                                           self._cat_mapping[cl])
#     })\
#     .rename(columns = {random_name : self.cols_categ_[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 or density

Calculates the approximate depth that it takes to isolate an observation according to the
fitted model splits, or the average density of the branches in which observations fall.
Can output either the average depth/density, or a standardized outlier score
based on whether it takes more or fewer splits than average to isolate observations. In the
standardized outlier score for density-based metrics, 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).
When using scoring_metric="density", the standardized outlier scores are instead unbounded,
with larger values indicating more outlierness and a natural threshold of zero for determining
inliers and outliers.

Note
----
For multi-threaded predictions on many rows, it is recommended to set the number of threads
to the number of physical cores of the CPU rather than the number of logical cores, as it
will typically have better performance that way. Assuming a typical x86-64 desktop CPU,
this typically involves dividing the number of threads by 2 - for example:

import multiprocessing;model.set_params(nthreads=multiprocessing.cpu_count()/2)

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
----
For model serving purposes, in order to have a smaller and leaner library, it is recommended to
compile this library without support for 'long double' type, which can be done by setting up an
environment variable "NO_LONG_DOUBLE" before installation of this package (see the GitHub page
of this library for more details).

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. If this type of prediction is desired, it can
be sped up by building an index of terminal nodes through build_indexer.

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, prob_pick_full_gain, prob_pick_dens)
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. For all scoring metrics, higher values
indicate more outlierness.

"avg_depth":
Will output unstandardized average isolation depths. For scoring_metric="density",
will output the geometric mean instead. See the documentation for scoring_metric,
for more details about the calculation for other metrics.
For all scoring metrics, higher values indicate less outlierness.

"tree_num":
Will output the index of the terminal node under each tree in the model.
If this calculation is going to be perform frequently, it's recommended to
build node indices through build_indexer.

"tree_depths":
Will output non-standardized per-tree isolation depths or densities or log-densities
(note that they will not include range penalties from penalize_range=True).
See the documentation for scoring_metric for details about the calculation
for each metrics.

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") and (not self.has_indexer_):
warnings.warn("Predicting tree number is slow, not recommended to do for 1 row at a time without indexer.")

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_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 = True, X_ref = None, use_reference_points = True):
"""
Predict approximate distances or isolation kernels/proximities 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, or isolation
kernels (a.k.a. proximity matrix, which for example can be used for a generalized least-squares
regressions as a rough estimate of residual correlations) from the model based on the number
of trees in which two observations end up in the same terminal node.
Can output either the average number
of paths/steps it takes to separate two observations,
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, or a kernel/proximity metric, either standardized (values between zero and one)
or raw (values ranging from zero to the number of trees in the model).

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 (with an exception being
calculation of distances to reference points, which do not do this).

Note
----
Separation depths are very slow to calculate. By default, it will do it through a procedure
that counts steps as observations are passed down the trees, which is especially slow and
not recommended for more than a few thousand observations. If this function is going to be
called repeatedly and/or it is going to be called for a large number of rows, it's highly
recommended to build node distance indexes beforehand through build_indexer with
option with_distances=True, as then the computation will be done based on terminal node
indices instead, which is a much faster procedure. If the calculations are all going to be performed
with respect to a fixed set of points, it's highly recommended to set those points as references
through set_reference_points.

Note
----
If using assume_full_distr=False (not recommended to use such option), predictions with
and without an indexer will differ slightly due to differences in what they count towards

Parameters
----------
X : array or array-like (n_samples, n_features)
Observations for which to calculate approximate pairwise distances or kernels,
or first group for distances/kernels between sets of points. Can pass
a NumPy array, Pandas DataFrame, or SciPy sparse CSC matrix.
output : str, one of "dist", "avg_sep", "kernel", "kernel_raw"
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).
If passing "kernel", will output the fraction of the trees in which two observations end up
in the same terminal node. If passing "kernel_raw", will output the number (not fraction) of
trees in which two observations end up in the same terminal node.

Note that for "kernel" and "kernel_raw", having an indexer without reference points will not
speed up calculations, and if such calculations are going to be done frequently, it is highly
recommended to set reference points in the model object.
square_mat : bool
Whether to produce a full square matrix with the pairwise distances or kernels.
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 or use_reference_points=True plus having reference points.
X_ref : array or array-like (n_ref, n_features)
Second group of observations. If passing it, will calculate distances/kernels between each point in
X and each point in X_ref. If passing None (the default), will calculate
pairwise distances/kernels between the points in X.
Must be of the same type as X (e.g. array, DataFrame, CSC).

Note that, if X_ref is passed and the model object has an indexer with reference points
added (through set_reference_points), those reference points will be ignored for the
calculation.
use_reference_points : bool
When the model object has an indexer with reference points (which can be added through
set_reference_points), whether to calculate the distances/kernels from X to those reference
points instead of the pairwise distances/kernels between points in X.

This is ignored when passing X_ref or when the model object does not contain an indexer
or the indexer does not contain reference points.

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 or kernels/proximities between points,
according to parameter 'output'. Shape and size depends on parameters square_mat,
use_reference_points, and whether X_ref is passed.
"""
assert self.is_fitted_
assert output in ["dist", "avg_sep", "kernel", "kernel_raw"]

if X_ref is not None:
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])
else:
nobs_group1 = 0
use_reference_points = bool(use_reference_points)
if use_reference_points and self._cpp_obj.has_reference_points():
if (not output in ["kernel", "kernel_raw"]) and (not self._cpp_obj.has_indexer_with_distances()):
raise ValueError("Model indexer was built without distances. Cannot calculate distances to reference points.")

can_take_row_major = self._cpp_obj.has_indexer() and self._cpp_obj.has_indexer_with_distances() and not issparse(X)
X_num, X_cat, nrows = self._process_data_new(X, allow_csr = False, prefer_row_major = can_take_row_major, keep_new_cat_levels = False)
if nrows == 1 and not (use_reference_points and self._cpp_obj.has_reference_points()):
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_bool(self.use_long_double).value,
ctypes.c_bool(self.assume_full_distr).value,
ctypes.c_bool(output in ["dist", "kernel"]).value,
ctypes.c_bool(square_mat).value,
ctypes.c_size_t(nobs_group1).value,
ctypes.c_bool(use_reference_points).value,
ctypes.c_bool(output in ["kernel", "kernel_raw"]).value)

if (X_ref is not None) or (use_reference_points and rmat.shape[1]):
return rmat
elif square_mat:
return dmat
else:
return tmat

[docs]    def predict_kernel(self, X, square_mat = True, X_ref = None, use_reference_points = True):
"""
Predict isolation kernel between points

This is a shorthand for predict_distance with output="kernel".

Parameters
----------
X : array or array-like (n_samples, n_features)
Observations for which to calculate approximate pairwise kernels/proximities,
or first group for kernels between sets of points. Can pass
a NumPy array, Pandas DataFrame, or SciPy sparse CSC matrix.
square_mat : bool
Whether to produce a full square matrix with the pairwise kernels. 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 kernels between each point in
X and each point in X_ref. If passing None (the default), will calculate
pairwise kernels between the points in X.
Must be of the same type as X (e.g. array, DataFrame, CSC).

Note that, if X_ref is passed and the model object has an indexer with reference points
added (through set_reference_points), those reference points will be ignored for the
calculation.
use_reference_points : bool
When the model object has an indexer with reference points (which can be added through
set_reference_points), whether to calculate the kernels from X to those reference
points instead of the pairwise kernels between points in X.

This is ignored when passing X_ref or when the model object does not contain an indexer
or the indexer does not contain reference points.

Returns
-------
dist : array(n_samples * (n_samples - 1) / 2,) or array(n_samples, n_samples) or array(n_samples, n_ref)
Approximate kernels between points, according to
parameter 'output'. Shape and size depends on parameter square_mat,
and whether X_ref is passed.
"""
return self.predict_distance(X, output = "kernel", square_mat = square_mat, X_ref = X_ref, use_reference_points = use_reference_points)

[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_bool(self.use_long_double).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).
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. If used along with kurtosis weights, the
effect is multiplicative.
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, categ_cols = categ_cols, output_imputed = True)
return outp["imputed"]
else:
self.fit(X = X, column_weights = column_weights, categ_cols = categ_cols)
return self.transform(X)

[docs]    def partial_fit(self, X, sample_weights = None, column_weights = None, X_ref = None):
"""

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 with depth-based metrics
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).

If trees are going to be fit to samples of different sizes, it's strongly recommended to use

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.

If passing an array and the array is not in column-major format, will be forcibly converted
to column-major, which implies an extra data copy.

If passing a DataFrame with categorical columns, then column names must be unique.
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. If used along with kurtosis weights, the
effect is multiplicative.
X_ref : array or array-like (n_references, n_features)
Reference points for distance and/or kernel calculations, if these were previously added to
the model object through set_reference_points. Must correspond to the same points that
were passed to the call to set_reference_points.

Might be passed in either row-major (preferred) or column-major order. If sparse, only CSC
format is supported.

This is ignored if the model has no stored reference points.

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 not self.is_fitted_:
trees_restore = self.ntrees
try:
self.ntrees = 1
self.fit(X = X, sample_weights = sample_weights, column_weights = column_weights)
if X_ref is not None:
self.set_reference_points(X_ref)
finally:
self.ntrees = trees_restore
return self

if self.is_fitted_:
if (X_ref is None) and (self.has_indexer_) and (self._cpp_obj.has_reference_points()):
msg  = "Must pass either pass 'X_ref' in order to maintain reference points in indexer,"
msg += " or drop reference points through 'drop_reference_points'."
raise ValueError(msg)
if (X_ref is not None) and (not self.has_indexer_ or not self._cpp_obj.has_reference_points()):
warnings.warn("Passed 'X_ref', but model object has no reference points. Will be ignored.")
X_ref = None

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_ or
self.prob_pick_full_gain_ or
self.prob_pick_dens_
) 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

if X_ref is None:
ref_X_num = None
ref_X_cat = None
else:
ref_X_num, ref_X_cat, ref_nrows = self._process_data_new(X_ref, allow_csr = False, prefer_row_major = True, keep_new_cat_levels = True)
expected_ref_nrows = self._cpp_obj.get_n_reference_points()
if ref_nrows != expected_ref_nrows:
raise ValueError("'X_ref' as %d rows, but previous reference data had %d rows."
% (ref_nrows, expected_ref_nrows))
if ref_X_num is not None:
matching_num_dtype = _get_num_dtype(X_num, sample_weights, column_weights)
if ref_X_num.data.dtype != matching_num_dtype.dtype:
ref_X_num = ref_X_num.astype(matching_num_dtype.dtype)
if issparse(ref_X_num):
matching_int_dtype = _get_int_dtype(X_num)
if ref_X_num.indptr.dtype != matching_int_dtype.dtype:
ref_X_num = ref_X_num.copy()
ref_X_num.indices = ref_X_num.indices.astype(matching_int_dtype.dtype)
ref_X_num.indptr = ref_X_num.indptr.astype(matching_int_dtype.dtype)

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.fast_bratio).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(getattr(self, "prob_pick_full_gain_", 0.)).value,
ctypes.c_double(getattr(self, "prob_pick_gain_avg_", 0.)).value,
ctypes.c_double(getattr(self, "prob_pick_col_by_range_", 0.)).value,
ctypes.c_double(getattr(self, "prob_pick_col_by_var_", 0.)).value,
ctypes.c_double(getattr(self, "prob_pick_col_by_kurt_", 0.)).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,
ref_X_num,
ref_X_cat,
ctypes.c_int(seed).value,
ctypes.c_bool(self.use_long_double).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,
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

"""
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

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
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.
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)
with open(file + ".metadata", "w") as of:
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
"""
file = os.path.expanduser(file)
obj = IsolationForest()
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

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).

Note
----
If using scoring_metric="density" or scoring_metric="boxed_ratio" plus
output_tree_num=False, the outputs will correspond to the logarithm of the
density rather than the density.

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).encode() 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).encode() for cl in column_names]
column_names_categ = [(enclose_left + cl + enclose_right).encode() for cl in column_names_categ]

out = [s.decode()
for s in self._cpp_obj.generate_sql(self.ndim_ > 1,
column_names, column_names_categ, categ_levels,
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:

- 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.
- 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,
pred_transform = "exponential_standard_ratio",
ratio_c = self._cpp_obj.get_expected_isolation_depth()
)
else:
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 drop_indexer(self):
"""
Drops the indexer sub-object from this model object

Drops the indexer sub-object from this model object, if it was constructed.
The indexer, if constructed, is likely to be a very heavy object which might
not be needed for all purposes.

Note that reference points as added through set_reference_points are
associated with the indexer object and will also be dropped if any were added.

Returns
-------
self : obj
This object
"""
self._cpp_obj.drop_indexer()
return self

[docs]    def drop_reference_points(self):
"""
Drops reference points from this model

Drops any reference points used for distance and/or kernel calculations
from the model object, if any were set through set_reference_points.

Returns
-------
self : obj
This object
"""
self._cpp_obj.drop_reference_points()
return self

[docs]    def build_indexer(self, with_distances = False):
"""
Build indexer for faster terminal node predictions and/or distance calculations

Builds an index of terminal nodes for faster prediction of terminal node numbers
(calling predict with output="tree_num").

Optionally, can also pre-calculate terminal node distances in order to speed up
distance calculations (calling predict_distance).

Note
----
This feature is not available for models that use missing_action="divide"
or new_categ_action="weighted" (which are the defaults when passing ndim=1).

Parameters
----------
with_distances : bool
Whether to also pre-calculate node distances in order to speed up predict_distance.
Note that this will consume a lot more memory and make the resulting object significantly
heavier.

Returns
-------
self : obj
This object
"""
assert self.is_fitted_
if self.missing_action_ == "divide":
raise ValueError("Cannot build tree indexer when using missing_action='divide'.")
if self.new_categ_action_ == "weighted" and self.categ_split_type_ != "single_categ":
if self._ncols_categ or self.cols_categ_.shape[0]:
raise ValueError("Cannot build tree indexer when using new_categ_action='weighted'.")
return self

@property
def has_indexer_(self):
return self._cpp_obj.has_indexer()

@property
def has_reference_points_(self):
return self._cpp_obj.has_reference_points()

[docs]    def set_reference_points(self, X, with_distances=False):
"""
Set reference points to calculate distances or kernels with

Sets some points as pre-defined landmarks with respect to which distances and/or
isolation kernel values will be calculated for arbitrary new points in calls to
predict_distance and/or predict_kernel. If any points have already been set
as references in the model object, they will be overwritten with the new points passed here.

Note that points are added in terms of their terminal node indices, but the raw data about
them is not kept - thus, calling partial_fit later on a model with reference points
requires passing those reference points again to add their node indices to the new tree.

Be aware that adding reference points requires building a tree indexer.

Parameters
----------
X : array or array-like (n_samples, n_features)
Observations to set as references for future distance and/or isolation kernel calculations.
Can pass a NumPy array, Pandas DataFrame, or SciPy sparse CSC matrix.
with_distances : bool
Whether to pre-calculate node distances (this is required to calculate distance
from arbitrary points to the reference points).

Note that reference points for distances can only be set when using assume_full_distr=False
(which is the default).

Returns
-------
self : obj
This object
"""
assert self.is_fitted_
with_distances = bool(with_distances)

if with_distances and (not self.assume_full_distr):
raise ValueError("Cannot set reference points for distance when using 'assume_full_distr=False'.")

if self.missing_action_ == "divide":
raise ValueError("Cannot set reference points when using missing_action='divide'.")
if self.new_categ_action_ == "weighted" and self.categ_split_type_ != "single_categ":
if self._ncols_categ or self.cols_categ_.shape[0]:
raise ValueError("Cannot set reference points when using new_categ_action='weighted'.")

X_num, X_cat, nrows = self._process_data_new(X, prefer_row_major = True, keep_new_cat_levels = True, allow_csr = False)
self._cpp_obj.set_reference_points(
_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_bool(with_distances).value
)
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._is_extended_, 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]

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"])

try:
except Exception:
model_info = {
"ndim" : int(self.ndim_),
"use_long_double" : bool(self.use_long_double),
"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_),
"prob_pick_full_gain" : float(self.prob_pick_full_gain_),
"prob_pick_dens" : float(self.prob_pick_dens_),
"prob_pick_col_by_range" : float(self.prob_pick_col_by_range_),
"prob_pick_col_by_var" : float(self.prob_pick_col_by_var_),
"prob_pick_col_by_kurt" : float(self.prob_pick_col_by_kurt_),
"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,
"scoring_metric" : self.scoring_metric,
"fast_bratio" : self.fast_bratio,
"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}

self._cat_mapping = [np.array(lst) for lst in metadata["data_info"]["cat_levels"]]
self.categ_cols_ = self.categ_cols
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_ = self.ndim
try:
except Exception:
self.use_long_double = False

self._ntrees = self.ntrees
try:
except Exception:
self.prob_pick_full_gain = 0.0
try:
except Exception:
self.prob_pick_dens = 0.0
try:
except Exception:
self.prob_pick_col_by_range = 0.0
try:
except Exception:
self.prob_pick_col_by_var = 0.0
try:
except Exception:
self.prob_pick_col_by_kurt = 0.0
self.prob_pick_avg_gain_ = self.prob_pick_avg_gain
self.prob_pick_pooled_gain_ = self.prob_pick_pooled_gain
self.prob_pick_full_gain_ = self.prob_pick_full_gain
self.prob_pick_dens_ = self.prob_pick_dens
self.prob_pick_col_by_range_ = self.prob_pick_col_by_range
self.prob_pick_col_by_var_ = self.prob_pick_col_by_var
self.prob_pick_col_by_kurt_ = self.prob_pick_col_by_kurt
self.missing_action_ = self.missing_action
self.new_categ_action_ = self.new_categ_action
self.categ_split_type_ = self.categ_split_type
try:
except Exception:
self.standardize_data = True
try:
except Exception:
self.scoring_metric = "depth"
try:
except Exception:
self.fast_bratio = True

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)