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_eif.pyx
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_eif.pyx
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# Cython wrapper for Extended Isolation Forest
# distutils: language = C++
# distutils: sources = eif.cxx
# cython: language_level = 3
import cython
import numpy as np
cimport numpy as np
from version import __version__
cimport __eif
np.import_array()
cdef class iForest:
cdef int size_X
cdef int dim
cdef int _ntrees
cdef int _limit
cdef int sample
cdef int tree_index
cdef int exlevel
cdef __eif.iForest* thisptr
@cython.boundscheck(False)
@cython.wraparound(False)
def __cinit__ (self, np.ndarray[double, ndim=2] X not None, int ntrees, int sample_size, int limit=0, int ExtensionLevel=0, int seed=-1):
if ExtensionLevel < 0:
raise Exception("Wrong Extension")
self.thisptr = new __eif.iForest (ntrees, sample_size, limit, ExtensionLevel, seed)
if not X.flags['C_CONTIGUOUS']:
X = X.copy(order='C')
self.size_X = X.shape[0]
self.dim = X.shape[1]
self.sample = sample_size
self._ntrees = ntrees
self._limit = self.thisptr.limit
self.exlevel = ExtensionLevel
self.thisptr.fit (<double*> np.PyArray_DATA(X), self.size_X, self.dim)
@property
def ntrees(self):
return self._ntrees
@property
def limit(self):
return self._limit
def __dealloc__ (self):
del self.thisptr
@cython.boundscheck(False)
@cython.wraparound(False)
def compute_paths (self, np.ndarray[double, ndim=2] X_in=None):
cdef np.ndarray[double, ndim=1, mode="c"] S
if X_in is None:
S = np.empty(self.size_X, dtype=np.float64, order='C')
self.thisptr.predict (<double*> np.PyArray_DATA(S), NULL, 0)
else:
if not X_in.flags['C_CONTIGUOUS']:
X_in = X_in.copy(order='C')
S = np.empty(X_in.shape[0], dtype=np.float64, order='C')
self.thisptr.predict (<double*> np.PyArray_DATA(S), <double*> np.PyArray_DATA(X_in), X_in.shape[0])
return S
@cython.boundscheck(False)
@cython.wraparound(False)
def compute_paths_single_tree (self, np.ndarray[double, ndim=2] X_in=None, tree_index=0):
cdef np.ndarray[double, ndim=1, mode="c"] S
if X_in is None:
S = np.empty(self.size_X, dtype=np.float64, order='C')
self.thisptr.predictSingleTree (<double*> np.PyArray_DATA(S), NULL, 0, tree_index)
else:
if not X_in.flags['C_CONTIGUOUS']:
X_in = X_in.copy(order='C')
S = np.empty(X_in.shape[0], dtype=np.float64, order='C')
self.thisptr.predictSingleTree (<double*> np.PyArray_DATA(S), <double*> np.PyArray_DATA(X_in), X_in.shape[0], tree_index)
return S
def output_tree_nodes (self, int tree_index):
self.thisptr.OutputTreeNodes (tree_index)