1391 lines
47 KiB
Cython
1391 lines
47 KiB
Cython
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"""
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Template for each `dtype` helper function for hashtable
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WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in
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"""
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{{py:
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# name
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complex_types = ['complex64',
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'complex128']
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}}
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{{for name in complex_types}}
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cdef kh{{name}}_t to_kh{{name}}_t({{name}}_t val) nogil:
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cdef kh{{name}}_t res
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res.real = val.real
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res.imag = val.imag
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return res
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{{endfor}}
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{{py:
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# name
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c_types = ['khcomplex128_t',
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'khcomplex64_t',
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'float64_t',
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'float32_t',
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'int64_t',
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'int32_t',
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'int16_t',
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'int8_t',
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'uint64_t',
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'uint32_t',
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'uint16_t',
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'uint8_t']
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}}
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{{for c_type in c_types}}
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cdef bint is_nan_{{c_type}}({{c_type}} val) nogil:
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{{if c_type in {'khcomplex128_t', 'khcomplex64_t'} }}
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return val.real != val.real or val.imag != val.imag
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{{elif c_type in {'float64_t', 'float32_t'} }}
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return val != val
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{{else}}
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return False
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{{endif}}
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{{if c_type in {'khcomplex128_t', 'khcomplex64_t', 'float64_t', 'float32_t'} }}
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# are_equivalent_{{c_type}} is cimported via khash.pxd
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{{else}}
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cdef bint are_equivalent_{{c_type}}({{c_type}} val1, {{c_type}} val2) nogil:
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return val1 == val2
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{{endif}}
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{{endfor}}
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{{py:
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# name
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cimported_types = ['complex64',
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'complex128',
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'float32',
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'float64',
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'int8',
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'int16',
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'int32',
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'int64',
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'pymap',
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'str',
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'strbox',
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'uint8',
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'uint16',
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'uint32',
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'uint64']
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}}
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{{for name in cimported_types}}
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from pandas._libs.khash cimport (
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kh_destroy_{{name}},
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kh_exist_{{name}},
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kh_get_{{name}},
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kh_init_{{name}},
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kh_put_{{name}},
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kh_resize_{{name}},
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)
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{{endfor}}
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# ----------------------------------------------------------------------
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# VectorData
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# ----------------------------------------------------------------------
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from pandas._libs.tslibs.util cimport get_c_string
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from pandas._libs.missing cimport C_NA
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{{py:
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# name, dtype, c_type
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# the generated StringVector is not actually used
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# but is included for completeness (rather ObjectVector is used
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# for uniques in hashtables)
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dtypes = [('Complex128', 'complex128', 'khcomplex128_t'),
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('Complex64', 'complex64', 'khcomplex64_t'),
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('Float64', 'float64', 'float64_t'),
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('Float32', 'float32', 'float32_t'),
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('Int64', 'int64', 'int64_t'),
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('Int32', 'int32', 'int32_t'),
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('Int16', 'int16', 'int16_t'),
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('Int8', 'int8', 'int8_t'),
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('String', 'string', 'char *'),
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('UInt64', 'uint64', 'uint64_t'),
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('UInt32', 'uint32', 'uint32_t'),
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('UInt16', 'uint16', 'uint16_t'),
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('UInt8', 'uint8', 'uint8_t')]
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}}
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{{for name, dtype, c_type in dtypes}}
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{{if dtype != 'int64'}}
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# Int64VectorData is defined in the .pxd file because it is needed (indirectly)
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# by IntervalTree
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ctypedef struct {{name}}VectorData:
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{{c_type}} *data
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Py_ssize_t n, m
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{{endif}}
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@cython.wraparound(False)
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@cython.boundscheck(False)
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cdef inline void append_data_{{dtype}}({{name}}VectorData *data,
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{{c_type}} x) nogil:
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data.data[data.n] = x
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data.n += 1
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{{endfor}}
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ctypedef fused vector_data:
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Int64VectorData
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Int32VectorData
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Int16VectorData
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Int8VectorData
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UInt64VectorData
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UInt32VectorData
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UInt16VectorData
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UInt8VectorData
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Float64VectorData
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Float32VectorData
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Complex128VectorData
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Complex64VectorData
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StringVectorData
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cdef inline bint needs_resize(vector_data *data) nogil:
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return data.n == data.m
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# ----------------------------------------------------------------------
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# Vector
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# ----------------------------------------------------------------------
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cdef class Vector:
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# cdef readonly:
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# bint external_view_exists
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def __cinit__(self):
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self.external_view_exists = False
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{{py:
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# name, dtype, c_type
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dtypes = [('Complex128', 'complex128', 'khcomplex128_t'),
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('Complex64', 'complex64', 'khcomplex64_t'),
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('Float64', 'float64', 'float64_t'),
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('UInt64', 'uint64', 'uint64_t'),
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('Int64', 'int64', 'int64_t'),
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('Float32', 'float32', 'float32_t'),
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('UInt32', 'uint32', 'uint32_t'),
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('Int32', 'int32', 'int32_t'),
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('UInt16', 'uint16', 'uint16_t'),
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('Int16', 'int16', 'int16_t'),
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('UInt8', 'uint8', 'uint8_t'),
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('Int8', 'int8', 'int8_t')]
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}}
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{{for name, dtype, c_type in dtypes}}
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cdef class {{name}}Vector(Vector):
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# For int64 we have to put this declaration in the .pxd file;
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# Int64Vector is the only one we need exposed for other cython files.
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{{if dtype != 'int64'}}
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cdef:
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{{name}}VectorData *data
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ndarray ao
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{{endif}}
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def __cinit__(self):
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self.data = <{{name}}VectorData *>PyMem_Malloc(
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sizeof({{name}}VectorData))
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if not self.data:
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raise MemoryError()
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self.data.n = 0
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self.data.m = _INIT_VEC_CAP
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self.ao = np.empty(self.data.m, dtype=np.{{dtype}})
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self.data.data = <{{c_type}}*>self.ao.data
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cdef resize(self):
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self.data.m = max(self.data.m * 4, _INIT_VEC_CAP)
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self.ao.resize(self.data.m, refcheck=False)
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self.data.data = <{{c_type}}*>self.ao.data
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def __dealloc__(self):
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if self.data is not NULL:
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PyMem_Free(self.data)
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self.data = NULL
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def __len__(self) -> int:
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return self.data.n
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cpdef ndarray to_array(self):
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if self.data.m != self.data.n:
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if self.external_view_exists:
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# should never happen
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raise ValueError("should have raised on append()")
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self.ao.resize(self.data.n, refcheck=False)
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self.data.m = self.data.n
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self.external_view_exists = True
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return self.ao
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cdef inline void append(self, {{c_type}} x):
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if needs_resize(self.data):
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if self.external_view_exists:
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raise ValueError("external reference but "
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"Vector.resize() needed")
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self.resize()
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append_data_{{dtype}}(self.data, x)
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cdef extend(self, const {{c_type}}[:] x):
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for i in range(len(x)):
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self.append(x[i])
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{{endfor}}
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cdef class StringVector(Vector):
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cdef:
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StringVectorData *data
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def __cinit__(self):
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self.data = <StringVectorData *>PyMem_Malloc(sizeof(StringVectorData))
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if not self.data:
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raise MemoryError()
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self.data.n = 0
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self.data.m = _INIT_VEC_CAP
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self.data.data = <char **>malloc(self.data.m * sizeof(char *))
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if not self.data.data:
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raise MemoryError()
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cdef resize(self):
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cdef:
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char **orig_data
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Py_ssize_t i, m
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m = self.data.m
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self.data.m = max(self.data.m * 4, _INIT_VEC_CAP)
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orig_data = self.data.data
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self.data.data = <char **>malloc(self.data.m * sizeof(char *))
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if not self.data.data:
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raise MemoryError()
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for i in range(m):
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self.data.data[i] = orig_data[i]
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def __dealloc__(self):
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if self.data is not NULL:
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if self.data.data is not NULL:
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free(self.data.data)
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PyMem_Free(self.data)
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self.data = NULL
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def __len__(self) -> int:
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return self.data.n
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cpdef ndarray[object, ndim=1] to_array(self):
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cdef:
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ndarray ao
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Py_ssize_t n
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object val
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ao = np.empty(self.data.n, dtype=object)
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for i in range(self.data.n):
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val = self.data.data[i]
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ao[i] = val
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self.external_view_exists = True
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self.data.m = self.data.n
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return ao
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cdef inline void append(self, char *x):
|
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if needs_resize(self.data):
|
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self.resize()
|
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append_data_string(self.data, x)
|
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cdef extend(self, ndarray[object] x):
|
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for i in range(len(x)):
|
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self.append(x[i])
|
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cdef class ObjectVector(Vector):
|
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|
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cdef:
|
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PyObject **data
|
||
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Py_ssize_t n, m
|
||
|
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ndarray ao
|
||
|
|
|
||
|
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def __cinit__(self):
|
||
|
|
self.n = 0
|
||
|
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self.m = _INIT_VEC_CAP
|
||
|
|
self.ao = np.empty(_INIT_VEC_CAP, dtype=object)
|
||
|
|
self.data = <PyObject**>self.ao.data
|
||
|
|
|
||
|
|
def __len__(self) -> int:
|
||
|
|
return self.n
|
||
|
|
|
||
|
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cdef inline append(self, object obj):
|
||
|
|
if self.n == self.m:
|
||
|
|
if self.external_view_exists:
|
||
|
|
raise ValueError("external reference but "
|
||
|
|
"Vector.resize() needed")
|
||
|
|
self.m = max(self.m * 2, _INIT_VEC_CAP)
|
||
|
|
self.ao.resize(self.m, refcheck=False)
|
||
|
|
self.data = <PyObject**>self.ao.data
|
||
|
|
|
||
|
|
Py_INCREF(obj)
|
||
|
|
self.data[self.n] = <PyObject*>obj
|
||
|
|
self.n += 1
|
||
|
|
|
||
|
|
cpdef ndarray[object, ndim=1] to_array(self):
|
||
|
|
if self.m != self.n:
|
||
|
|
if self.external_view_exists:
|
||
|
|
raise ValueError("should have raised on append()")
|
||
|
|
self.ao.resize(self.n, refcheck=False)
|
||
|
|
self.m = self.n
|
||
|
|
self.external_view_exists = True
|
||
|
|
return self.ao
|
||
|
|
|
||
|
|
cdef extend(self, ndarray[object] x):
|
||
|
|
for i in range(len(x)):
|
||
|
|
self.append(x[i])
|
||
|
|
|
||
|
|
# ----------------------------------------------------------------------
|
||
|
|
# HashTable
|
||
|
|
# ----------------------------------------------------------------------
|
||
|
|
|
||
|
|
|
||
|
|
cdef class HashTable:
|
||
|
|
|
||
|
|
pass
|
||
|
|
|
||
|
|
{{py:
|
||
|
|
|
||
|
|
# name, dtype, c_type, to_c_type
|
||
|
|
dtypes = [('Complex128', 'complex128', 'khcomplex128_t', 'to_khcomplex128_t'),
|
||
|
|
('Float64', 'float64', 'float64_t', ''),
|
||
|
|
('UInt64', 'uint64', 'uint64_t', ''),
|
||
|
|
('Int64', 'int64', 'int64_t', ''),
|
||
|
|
('Complex64', 'complex64', 'khcomplex64_t', 'to_khcomplex64_t'),
|
||
|
|
('Float32', 'float32', 'float32_t', ''),
|
||
|
|
('UInt32', 'uint32', 'uint32_t', ''),
|
||
|
|
('Int32', 'int32', 'int32_t', ''),
|
||
|
|
('UInt16', 'uint16', 'uint16_t', ''),
|
||
|
|
('Int16', 'int16', 'int16_t', ''),
|
||
|
|
('UInt8', 'uint8', 'uint8_t', ''),
|
||
|
|
('Int8', 'int8', 'int8_t', '')]
|
||
|
|
|
||
|
|
}}
|
||
|
|
|
||
|
|
|
||
|
|
{{for name, dtype, c_type, to_c_type in dtypes}}
|
||
|
|
|
||
|
|
cdef class {{name}}HashTable(HashTable):
|
||
|
|
|
||
|
|
def __cinit__(self, int64_t size_hint=1):
|
||
|
|
self.table = kh_init_{{dtype}}()
|
||
|
|
size_hint = min(kh_needed_n_buckets(size_hint), SIZE_HINT_LIMIT)
|
||
|
|
kh_resize_{{dtype}}(self.table, size_hint)
|
||
|
|
|
||
|
|
def __len__(self) -> int:
|
||
|
|
return self.table.size
|
||
|
|
|
||
|
|
def __dealloc__(self):
|
||
|
|
if self.table is not NULL:
|
||
|
|
kh_destroy_{{dtype}}(self.table)
|
||
|
|
self.table = NULL
|
||
|
|
|
||
|
|
def __contains__(self, object key) -> bool:
|
||
|
|
cdef:
|
||
|
|
khiter_t k
|
||
|
|
{{c_type}} ckey
|
||
|
|
ckey = {{to_c_type}}(key)
|
||
|
|
k = kh_get_{{dtype}}(self.table, ckey)
|
||
|
|
return k != self.table.n_buckets
|
||
|
|
|
||
|
|
def sizeof(self, deep: bool = False) -> int:
|
||
|
|
""" return the size of my table in bytes """
|
||
|
|
overhead = 4 * sizeof(uint32_t) + 3 * sizeof(uint32_t*)
|
||
|
|
for_flags = max(1, self.table.n_buckets >> 5) * sizeof(uint32_t)
|
||
|
|
for_pairs = self.table.n_buckets * (sizeof({{dtype}}_t) + # keys
|
||
|
|
sizeof(Py_ssize_t)) # vals
|
||
|
|
return overhead + for_flags + for_pairs
|
||
|
|
|
||
|
|
def get_state(self) -> dict[str, int]:
|
||
|
|
""" returns infos about the state of the hashtable"""
|
||
|
|
return {
|
||
|
|
'n_buckets' : self.table.n_buckets,
|
||
|
|
'size' : self.table.size,
|
||
|
|
'n_occupied' : self.table.n_occupied,
|
||
|
|
'upper_bound' : self.table.upper_bound,
|
||
|
|
}
|
||
|
|
|
||
|
|
cpdef get_item(self, {{dtype}}_t val):
|
||
|
|
# Used in core.sorting, IndexEngine.get_loc
|
||
|
|
cdef:
|
||
|
|
khiter_t k
|
||
|
|
{{c_type}} cval
|
||
|
|
cval = {{to_c_type}}(val)
|
||
|
|
k = kh_get_{{dtype}}(self.table, cval)
|
||
|
|
if k != self.table.n_buckets:
|
||
|
|
return self.table.vals[k]
|
||
|
|
else:
|
||
|
|
raise KeyError(val)
|
||
|
|
|
||
|
|
cpdef set_item(self, {{dtype}}_t key, Py_ssize_t val):
|
||
|
|
# Used in libjoin
|
||
|
|
cdef:
|
||
|
|
khiter_t k
|
||
|
|
int ret = 0
|
||
|
|
{{c_type}} ckey
|
||
|
|
ckey = {{to_c_type}}(key)
|
||
|
|
k = kh_put_{{dtype}}(self.table, ckey, &ret)
|
||
|
|
if kh_exist_{{dtype}}(self.table, k):
|
||
|
|
self.table.vals[k] = val
|
||
|
|
else:
|
||
|
|
raise KeyError(key)
|
||
|
|
|
||
|
|
{{if dtype == "int64" }}
|
||
|
|
# We only use this for int64, can reduce build size and make .pyi
|
||
|
|
# more accurate by only implementing it for int64
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
def map_keys_to_values(
|
||
|
|
self, const {{dtype}}_t[:] keys, const int64_t[:] values
|
||
|
|
) -> None:
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, n = len(values)
|
||
|
|
int ret = 0
|
||
|
|
{{c_type}} key
|
||
|
|
khiter_t k
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
for i in range(n):
|
||
|
|
key = {{to_c_type}}(keys[i])
|
||
|
|
k = kh_put_{{dtype}}(self.table, key, &ret)
|
||
|
|
self.table.vals[k] = <Py_ssize_t>values[i]
|
||
|
|
{{endif}}
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
def map_locations(self, const {{dtype}}_t[:] values) -> None:
|
||
|
|
# Used in libindex, safe_sort
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, n = len(values)
|
||
|
|
int ret = 0
|
||
|
|
{{c_type}} val
|
||
|
|
khiter_t k
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
for i in range(n):
|
||
|
|
val= {{to_c_type}}(values[i])
|
||
|
|
k = kh_put_{{dtype}}(self.table, val, &ret)
|
||
|
|
self.table.vals[k] = i
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
def lookup(self, const {{dtype}}_t[:] values) -> ndarray:
|
||
|
|
# -> np.ndarray[np.intp]
|
||
|
|
# Used in safe_sort, IndexEngine.get_indexer
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, n = len(values)
|
||
|
|
int ret = 0
|
||
|
|
{{c_type}} val
|
||
|
|
khiter_t k
|
||
|
|
intp_t[::1] locs = np.empty(n, dtype=np.intp)
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
for i in range(n):
|
||
|
|
val = {{to_c_type}}(values[i])
|
||
|
|
k = kh_get_{{dtype}}(self.table, val)
|
||
|
|
if k != self.table.n_buckets:
|
||
|
|
locs[i] = self.table.vals[k]
|
||
|
|
else:
|
||
|
|
locs[i] = -1
|
||
|
|
|
||
|
|
return np.asarray(locs)
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def _unique(self, const {{dtype}}_t[:] values, {{name}}Vector uniques,
|
||
|
|
Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
|
||
|
|
object na_value=None, bint ignore_na=False,
|
||
|
|
object mask=None, bint return_inverse=False, bint use_result_mask=False):
|
||
|
|
"""
|
||
|
|
Calculate unique values and labels (no sorting!)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : ndarray[{{dtype}}]
|
||
|
|
Array of values of which unique will be calculated
|
||
|
|
uniques : {{name}}Vector
|
||
|
|
Vector into which uniques will be written
|
||
|
|
count_prior : Py_ssize_t, default 0
|
||
|
|
Number of existing entries in uniques
|
||
|
|
na_sentinel : Py_ssize_t, default -1
|
||
|
|
Sentinel value used for all NA-values in inverse
|
||
|
|
na_value : object, default None
|
||
|
|
Value to identify as missing. If na_value is None, then
|
||
|
|
any value "val" satisfying val != val is considered missing.
|
||
|
|
If na_value is not None, then _additionally_, any value "val"
|
||
|
|
satisfying val == na_value is considered missing.
|
||
|
|
ignore_na : bool, default False
|
||
|
|
Whether NA-values should be ignored for calculating the uniques. If
|
||
|
|
True, the labels corresponding to missing values will be set to
|
||
|
|
na_sentinel.
|
||
|
|
mask : ndarray[bool], optional
|
||
|
|
If not None, the mask is used as indicator for missing values
|
||
|
|
(True = missing, False = valid) instead of `na_value` or
|
||
|
|
condition "val != val".
|
||
|
|
return_inverse : bool, default False
|
||
|
|
Whether the mapping of the original array values to their location
|
||
|
|
in the vector of uniques should be returned.
|
||
|
|
use_result_mask: bool, default False
|
||
|
|
Whether to create a result mask for the unique values. Not supported
|
||
|
|
with return_inverse=True.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
uniques : ndarray[{{dtype}}]
|
||
|
|
Unique values of input, not sorted
|
||
|
|
labels : ndarray[intp_t] (if return_inverse=True)
|
||
|
|
The labels from values to uniques
|
||
|
|
result_mask: ndarray[bool], if use_result_mask is true
|
||
|
|
The mask for the result values.
|
||
|
|
"""
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, idx, count = count_prior, n = len(values)
|
||
|
|
intp_t[::1] labels
|
||
|
|
int ret = 0
|
||
|
|
{{c_type}} val, na_value2
|
||
|
|
khiter_t k
|
||
|
|
{{name}}VectorData *ud
|
||
|
|
UInt8Vector result_mask
|
||
|
|
UInt8VectorData *rmd
|
||
|
|
bint use_na_value, use_mask, seen_na = False
|
||
|
|
uint8_t[:] mask_values
|
||
|
|
|
||
|
|
if return_inverse:
|
||
|
|
labels = np.empty(n, dtype=np.intp)
|
||
|
|
ud = uniques.data
|
||
|
|
use_na_value = na_value is not None
|
||
|
|
use_mask = mask is not None
|
||
|
|
if not use_mask and use_result_mask:
|
||
|
|
raise NotImplementedError # pragma: no cover
|
||
|
|
|
||
|
|
if use_result_mask and return_inverse:
|
||
|
|
raise NotImplementedError # pragma: no cover
|
||
|
|
|
||
|
|
result_mask = UInt8Vector()
|
||
|
|
rmd = result_mask.data
|
||
|
|
|
||
|
|
if use_mask:
|
||
|
|
mask_values = mask.view("uint8")
|
||
|
|
|
||
|
|
if use_na_value:
|
||
|
|
# We need this na_value2 because we want to allow users
|
||
|
|
# to *optionally* specify an NA sentinel *of the correct* type.
|
||
|
|
# We use None, to make it optional, which requires `object` type
|
||
|
|
# for the parameter. To please the compiler, we use na_value2,
|
||
|
|
# which is only used if it's *specified*.
|
||
|
|
na_value2 = {{to_c_type}}(na_value)
|
||
|
|
else:
|
||
|
|
na_value2 = {{to_c_type}}(0)
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
for i in range(n):
|
||
|
|
val = {{to_c_type}}(values[i])
|
||
|
|
|
||
|
|
if ignore_na and use_mask:
|
||
|
|
if mask_values[i]:
|
||
|
|
labels[i] = na_sentinel
|
||
|
|
continue
|
||
|
|
elif ignore_na and (
|
||
|
|
is_nan_{{c_type}}(val) or
|
||
|
|
(use_na_value and are_equivalent_{{c_type}}(val, na_value2))
|
||
|
|
):
|
||
|
|
# if missing values do not count as unique values (i.e. if
|
||
|
|
# ignore_na is True), skip the hashtable entry for them,
|
||
|
|
# and replace the corresponding label with na_sentinel
|
||
|
|
labels[i] = na_sentinel
|
||
|
|
continue
|
||
|
|
elif not ignore_na and use_result_mask:
|
||
|
|
if mask_values[i]:
|
||
|
|
if seen_na:
|
||
|
|
continue
|
||
|
|
|
||
|
|
seen_na = True
|
||
|
|
if needs_resize(ud):
|
||
|
|
with gil:
|
||
|
|
if uniques.external_view_exists:
|
||
|
|
raise ValueError("external reference to "
|
||
|
|
"uniques held, but "
|
||
|
|
"Vector.resize() needed")
|
||
|
|
uniques.resize()
|
||
|
|
if result_mask.external_view_exists:
|
||
|
|
raise ValueError("external reference to "
|
||
|
|
"result_mask held, but "
|
||
|
|
"Vector.resize() needed")
|
||
|
|
result_mask.resize()
|
||
|
|
append_data_{{dtype}}(ud, val)
|
||
|
|
append_data_uint8(rmd, 1)
|
||
|
|
continue
|
||
|
|
|
||
|
|
k = kh_get_{{dtype}}(self.table, val)
|
||
|
|
|
||
|
|
if k == self.table.n_buckets:
|
||
|
|
# k hasn't been seen yet
|
||
|
|
k = kh_put_{{dtype}}(self.table, val, &ret)
|
||
|
|
|
||
|
|
if needs_resize(ud):
|
||
|
|
with gil:
|
||
|
|
if uniques.external_view_exists:
|
||
|
|
raise ValueError("external reference to "
|
||
|
|
"uniques held, but "
|
||
|
|
"Vector.resize() needed")
|
||
|
|
uniques.resize()
|
||
|
|
if use_result_mask:
|
||
|
|
if result_mask.external_view_exists:
|
||
|
|
raise ValueError("external reference to "
|
||
|
|
"result_mask held, but "
|
||
|
|
"Vector.resize() needed")
|
||
|
|
result_mask.resize()
|
||
|
|
append_data_{{dtype}}(ud, val)
|
||
|
|
if use_result_mask:
|
||
|
|
append_data_uint8(rmd, 0)
|
||
|
|
|
||
|
|
if return_inverse:
|
||
|
|
self.table.vals[k] = count
|
||
|
|
labels[i] = count
|
||
|
|
count += 1
|
||
|
|
elif return_inverse:
|
||
|
|
# k falls into a previous bucket
|
||
|
|
# only relevant in case we need to construct the inverse
|
||
|
|
idx = self.table.vals[k]
|
||
|
|
labels[i] = idx
|
||
|
|
|
||
|
|
if return_inverse:
|
||
|
|
return uniques.to_array(), labels.base # .base -> underlying ndarray
|
||
|
|
if use_result_mask:
|
||
|
|
return uniques.to_array(), result_mask.to_array()
|
||
|
|
return uniques.to_array()
|
||
|
|
|
||
|
|
def unique(self, const {{dtype}}_t[:] values, bint return_inverse=False, object mask=None):
|
||
|
|
"""
|
||
|
|
Calculate unique values and labels (no sorting!)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : ndarray[{{dtype}}]
|
||
|
|
Array of values of which unique will be calculated
|
||
|
|
return_inverse : bool, default False
|
||
|
|
Whether the mapping of the original array values to their location
|
||
|
|
in the vector of uniques should be returned.
|
||
|
|
mask : ndarray[bool], optional
|
||
|
|
If not None, the mask is used as indicator for missing values
|
||
|
|
(True = missing, False = valid) instead of `na_value` or
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
uniques : ndarray[{{dtype}}]
|
||
|
|
Unique values of input, not sorted
|
||
|
|
labels : ndarray[intp_t] (if return_inverse)
|
||
|
|
The labels from values to uniques
|
||
|
|
result_mask: ndarray[bool], if mask is given as input
|
||
|
|
The mask for the result values.
|
||
|
|
"""
|
||
|
|
uniques = {{name}}Vector()
|
||
|
|
use_result_mask = True if mask is not None else False
|
||
|
|
return self._unique(values, uniques, ignore_na=False,
|
||
|
|
return_inverse=return_inverse, mask=mask, use_result_mask=use_result_mask)
|
||
|
|
|
||
|
|
def factorize(self, const {{dtype}}_t[:] values, Py_ssize_t na_sentinel=-1,
|
||
|
|
object na_value=None, object mask=None, ignore_na=True):
|
||
|
|
"""
|
||
|
|
Calculate unique values and labels (no sorting!)
|
||
|
|
|
||
|
|
Missing values are not included in the "uniques" for this method.
|
||
|
|
The labels for any missing values will be set to "na_sentinel"
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : ndarray[{{dtype}}]
|
||
|
|
Array of values of which unique will be calculated
|
||
|
|
na_sentinel : Py_ssize_t, default -1
|
||
|
|
Sentinel value used for all NA-values in inverse
|
||
|
|
na_value : object, default None
|
||
|
|
Value to identify as missing. If na_value is None, then
|
||
|
|
any value "val" satisfying val != val is considered missing.
|
||
|
|
If na_value is not None, then _additionally_, any value "val"
|
||
|
|
satisfying val == na_value is considered missing.
|
||
|
|
mask : ndarray[bool], optional
|
||
|
|
If not None, the mask is used as indicator for missing values
|
||
|
|
(True = missing, False = valid) instead of `na_value` or
|
||
|
|
condition "val != val".
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
uniques : ndarray[{{dtype}}]
|
||
|
|
Unique values of input, not sorted
|
||
|
|
labels : ndarray[intp_t]
|
||
|
|
The labels from values to uniques
|
||
|
|
"""
|
||
|
|
uniques_vector = {{name}}Vector()
|
||
|
|
return self._unique(values, uniques_vector, na_sentinel=na_sentinel,
|
||
|
|
na_value=na_value, ignore_na=ignore_na, mask=mask,
|
||
|
|
return_inverse=True)
|
||
|
|
|
||
|
|
def get_labels(self, const {{dtype}}_t[:] values, {{name}}Vector uniques,
|
||
|
|
Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
|
||
|
|
object na_value=None):
|
||
|
|
# -> np.ndarray[np.intp]
|
||
|
|
_, labels = self._unique(values, uniques, count_prior=count_prior,
|
||
|
|
na_sentinel=na_sentinel, na_value=na_value,
|
||
|
|
ignore_na=True, return_inverse=True)
|
||
|
|
return labels
|
||
|
|
|
||
|
|
{{if dtype == 'int64'}}
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
def get_labels_groupby(
|
||
|
|
self, const {{dtype}}_t[:] values
|
||
|
|
) -> tuple[ndarray, ndarray]:
|
||
|
|
# tuple[np.ndarray[np.intp], np.ndarray[{{dtype}}]]
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, n = len(values)
|
||
|
|
intp_t[::1] labels
|
||
|
|
Py_ssize_t idx, count = 0
|
||
|
|
int ret = 0
|
||
|
|
{{c_type}} val
|
||
|
|
khiter_t k
|
||
|
|
{{name}}Vector uniques = {{name}}Vector()
|
||
|
|
{{name}}VectorData *ud
|
||
|
|
|
||
|
|
labels = np.empty(n, dtype=np.intp)
|
||
|
|
ud = uniques.data
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
for i in range(n):
|
||
|
|
val = {{to_c_type}}(values[i])
|
||
|
|
|
||
|
|
# specific for groupby
|
||
|
|
if val < 0:
|
||
|
|
labels[i] = -1
|
||
|
|
continue
|
||
|
|
|
||
|
|
k = kh_get_{{dtype}}(self.table, val)
|
||
|
|
if k != self.table.n_buckets:
|
||
|
|
idx = self.table.vals[k]
|
||
|
|
labels[i] = idx
|
||
|
|
else:
|
||
|
|
k = kh_put_{{dtype}}(self.table, val, &ret)
|
||
|
|
self.table.vals[k] = count
|
||
|
|
|
||
|
|
if needs_resize(ud):
|
||
|
|
with gil:
|
||
|
|
uniques.resize()
|
||
|
|
append_data_{{dtype}}(ud, val)
|
||
|
|
labels[i] = count
|
||
|
|
count += 1
|
||
|
|
|
||
|
|
arr_uniques = uniques.to_array()
|
||
|
|
|
||
|
|
return np.asarray(labels), arr_uniques
|
||
|
|
{{endif}}
|
||
|
|
|
||
|
|
{{endfor}}
|
||
|
|
|
||
|
|
|
||
|
|
cdef class StringHashTable(HashTable):
|
||
|
|
# these by-definition *must* be strings
|
||
|
|
# or a sentinel np.nan / None missing value
|
||
|
|
na_string_sentinel = '__nan__'
|
||
|
|
|
||
|
|
def __init__(self, int64_t size_hint=1):
|
||
|
|
self.table = kh_init_str()
|
||
|
|
size_hint = min(kh_needed_n_buckets(size_hint), SIZE_HINT_LIMIT)
|
||
|
|
kh_resize_str(self.table, size_hint)
|
||
|
|
|
||
|
|
def __dealloc__(self):
|
||
|
|
if self.table is not NULL:
|
||
|
|
kh_destroy_str(self.table)
|
||
|
|
self.table = NULL
|
||
|
|
|
||
|
|
def sizeof(self, deep: bool = False) -> int:
|
||
|
|
overhead = 4 * sizeof(uint32_t) + 3 * sizeof(uint32_t*)
|
||
|
|
for_flags = max(1, self.table.n_buckets >> 5) * sizeof(uint32_t)
|
||
|
|
for_pairs = self.table.n_buckets * (sizeof(char *) + # keys
|
||
|
|
sizeof(Py_ssize_t)) # vals
|
||
|
|
return overhead + for_flags + for_pairs
|
||
|
|
|
||
|
|
def get_state(self) -> dict[str, int]:
|
||
|
|
""" returns infos about the state of the hashtable"""
|
||
|
|
return {
|
||
|
|
'n_buckets' : self.table.n_buckets,
|
||
|
|
'size' : self.table.size,
|
||
|
|
'n_occupied' : self.table.n_occupied,
|
||
|
|
'upper_bound' : self.table.upper_bound,
|
||
|
|
}
|
||
|
|
|
||
|
|
cpdef get_item(self, str val):
|
||
|
|
cdef:
|
||
|
|
khiter_t k
|
||
|
|
const char *v
|
||
|
|
v = get_c_string(val)
|
||
|
|
|
||
|
|
k = kh_get_str(self.table, v)
|
||
|
|
if k != self.table.n_buckets:
|
||
|
|
return self.table.vals[k]
|
||
|
|
else:
|
||
|
|
raise KeyError(val)
|
||
|
|
|
||
|
|
cpdef set_item(self, str key, Py_ssize_t val):
|
||
|
|
cdef:
|
||
|
|
khiter_t k
|
||
|
|
int ret = 0
|
||
|
|
const char *v
|
||
|
|
|
||
|
|
v = get_c_string(key)
|
||
|
|
|
||
|
|
k = kh_put_str(self.table, v, &ret)
|
||
|
|
if kh_exist_str(self.table, k):
|
||
|
|
self.table.vals[k] = val
|
||
|
|
else:
|
||
|
|
raise KeyError(key)
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
def get_indexer(self, ndarray[object] values) -> ndarray:
|
||
|
|
# -> np.ndarray[np.intp]
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, n = len(values)
|
||
|
|
ndarray[intp_t] labels = np.empty(n, dtype=np.intp)
|
||
|
|
intp_t *resbuf = <intp_t*>labels.data
|
||
|
|
khiter_t k
|
||
|
|
kh_str_t *table = self.table
|
||
|
|
const char *v
|
||
|
|
const char **vecs
|
||
|
|
|
||
|
|
vecs = <const char **>malloc(n * sizeof(char *))
|
||
|
|
for i in range(n):
|
||
|
|
val = values[i]
|
||
|
|
v = get_c_string(val)
|
||
|
|
vecs[i] = v
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
for i in range(n):
|
||
|
|
k = kh_get_str(table, vecs[i])
|
||
|
|
if k != table.n_buckets:
|
||
|
|
resbuf[i] = table.vals[k]
|
||
|
|
else:
|
||
|
|
resbuf[i] = -1
|
||
|
|
|
||
|
|
free(vecs)
|
||
|
|
return labels
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
def lookup(self, ndarray[object] values) -> ndarray:
|
||
|
|
# -> np.ndarray[np.intp]
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, n = len(values)
|
||
|
|
int ret = 0
|
||
|
|
object val
|
||
|
|
const char *v
|
||
|
|
khiter_t k
|
||
|
|
intp_t[::1] locs = np.empty(n, dtype=np.intp)
|
||
|
|
|
||
|
|
# these by-definition *must* be strings
|
||
|
|
vecs = <const char **>malloc(n * sizeof(char *))
|
||
|
|
for i in range(n):
|
||
|
|
val = values[i]
|
||
|
|
|
||
|
|
if isinstance(val, str):
|
||
|
|
# GH#31499 if we have a np.str_ get_c_string won't recognize
|
||
|
|
# it as a str, even though isinstance does.
|
||
|
|
v = get_c_string(<str>val)
|
||
|
|
else:
|
||
|
|
v = get_c_string(self.na_string_sentinel)
|
||
|
|
vecs[i] = v
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
for i in range(n):
|
||
|
|
v = vecs[i]
|
||
|
|
k = kh_get_str(self.table, v)
|
||
|
|
if k != self.table.n_buckets:
|
||
|
|
locs[i] = self.table.vals[k]
|
||
|
|
else:
|
||
|
|
locs[i] = -1
|
||
|
|
|
||
|
|
free(vecs)
|
||
|
|
return np.asarray(locs)
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
def map_locations(self, ndarray[object] values) -> None:
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, n = len(values)
|
||
|
|
int ret = 0
|
||
|
|
object val
|
||
|
|
const char *v
|
||
|
|
const char **vecs
|
||
|
|
khiter_t k
|
||
|
|
|
||
|
|
# these by-definition *must* be strings
|
||
|
|
vecs = <const char **>malloc(n * sizeof(char *))
|
||
|
|
for i in range(n):
|
||
|
|
val = values[i]
|
||
|
|
|
||
|
|
if isinstance(val, str):
|
||
|
|
# GH#31499 if we have a np.str_ get_c_string won't recognize
|
||
|
|
# it as a str, even though isinstance does.
|
||
|
|
v = get_c_string(<str>val)
|
||
|
|
else:
|
||
|
|
v = get_c_string(self.na_string_sentinel)
|
||
|
|
vecs[i] = v
|
||
|
|
|
||
|
|
with nogil:
|
||
|
|
for i in range(n):
|
||
|
|
v = vecs[i]
|
||
|
|
k = kh_put_str(self.table, v, &ret)
|
||
|
|
self.table.vals[k] = i
|
||
|
|
free(vecs)
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def _unique(self, ndarray[object] values, ObjectVector uniques,
|
||
|
|
Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
|
||
|
|
object na_value=None, bint ignore_na=False,
|
||
|
|
bint return_inverse=False):
|
||
|
|
"""
|
||
|
|
Calculate unique values and labels (no sorting!)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : ndarray[object]
|
||
|
|
Array of values of which unique will be calculated
|
||
|
|
uniques : ObjectVector
|
||
|
|
Vector into which uniques will be written
|
||
|
|
count_prior : Py_ssize_t, default 0
|
||
|
|
Number of existing entries in uniques
|
||
|
|
na_sentinel : Py_ssize_t, default -1
|
||
|
|
Sentinel value used for all NA-values in inverse
|
||
|
|
na_value : object, default None
|
||
|
|
Value to identify as missing. If na_value is None, then any value
|
||
|
|
that is not a string is considered missing. If na_value is
|
||
|
|
not None, then _additionally_ any value "val" satisfying
|
||
|
|
val == na_value is considered missing.
|
||
|
|
ignore_na : bool, default False
|
||
|
|
Whether NA-values should be ignored for calculating the uniques. If
|
||
|
|
True, the labels corresponding to missing values will be set to
|
||
|
|
na_sentinel.
|
||
|
|
return_inverse : bool, default False
|
||
|
|
Whether the mapping of the original array values to their location
|
||
|
|
in the vector of uniques should be returned.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
uniques : ndarray[object]
|
||
|
|
Unique values of input, not sorted
|
||
|
|
labels : ndarray[intp_t] (if return_inverse=True)
|
||
|
|
The labels from values to uniques
|
||
|
|
"""
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, idx, count = count_prior, n = len(values)
|
||
|
|
intp_t[::1] labels
|
||
|
|
int64_t[::1] uindexer
|
||
|
|
int ret = 0
|
||
|
|
object val
|
||
|
|
const char *v
|
||
|
|
const char **vecs
|
||
|
|
khiter_t k
|
||
|
|
bint use_na_value
|
||
|
|
|
||
|
|
if return_inverse:
|
||
|
|
labels = np.zeros(n, dtype=np.intp)
|
||
|
|
uindexer = np.empty(n, dtype=np.int64)
|
||
|
|
use_na_value = na_value is not None
|
||
|
|
|
||
|
|
# assign pointers and pre-filter out missing (if ignore_na)
|
||
|
|
vecs = <const char **>malloc(n * sizeof(char *))
|
||
|
|
for i in range(n):
|
||
|
|
val = values[i]
|
||
|
|
|
||
|
|
if (ignore_na
|
||
|
|
and (not isinstance(val, str)
|
||
|
|
or (use_na_value and val == na_value))):
|
||
|
|
# if missing values do not count as unique values (i.e. if
|
||
|
|
# ignore_na is True), we can skip the actual value, and
|
||
|
|
# replace the label with na_sentinel directly
|
||
|
|
labels[i] = na_sentinel
|
||
|
|
else:
|
||
|
|
# if ignore_na is False, we also stringify NaN/None/etc.
|
||
|
|
try:
|
||
|
|
v = get_c_string(<str>val)
|
||
|
|
except UnicodeEncodeError:
|
||
|
|
v = get_c_string(<str>repr(val))
|
||
|
|
vecs[i] = v
|
||
|
|
|
||
|
|
# compute
|
||
|
|
with nogil:
|
||
|
|
for i in range(n):
|
||
|
|
if ignore_na and labels[i] == na_sentinel:
|
||
|
|
# skip entries for ignored missing values (see above)
|
||
|
|
continue
|
||
|
|
|
||
|
|
v = vecs[i]
|
||
|
|
k = kh_get_str(self.table, v)
|
||
|
|
if k == self.table.n_buckets:
|
||
|
|
# k hasn't been seen yet
|
||
|
|
k = kh_put_str(self.table, v, &ret)
|
||
|
|
uindexer[count] = i
|
||
|
|
if return_inverse:
|
||
|
|
self.table.vals[k] = count
|
||
|
|
labels[i] = count
|
||
|
|
count += 1
|
||
|
|
elif return_inverse:
|
||
|
|
# k falls into a previous bucket
|
||
|
|
# only relevant in case we need to construct the inverse
|
||
|
|
idx = self.table.vals[k]
|
||
|
|
labels[i] = idx
|
||
|
|
|
||
|
|
free(vecs)
|
||
|
|
|
||
|
|
# uniques
|
||
|
|
for i in range(count):
|
||
|
|
uniques.append(values[uindexer[i]])
|
||
|
|
|
||
|
|
if return_inverse:
|
||
|
|
return uniques.to_array(), labels.base # .base -> underlying ndarray
|
||
|
|
return uniques.to_array()
|
||
|
|
|
||
|
|
def unique(self, ndarray[object] values, bint return_inverse=False, object mask=None):
|
||
|
|
"""
|
||
|
|
Calculate unique values and labels (no sorting!)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : ndarray[object]
|
||
|
|
Array of values of which unique will be calculated
|
||
|
|
return_inverse : bool, default False
|
||
|
|
Whether the mapping of the original array values to their location
|
||
|
|
in the vector of uniques should be returned.
|
||
|
|
mask : ndarray[bool], optional
|
||
|
|
Not yet implemented for StringHashTable
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
uniques : ndarray[object]
|
||
|
|
Unique values of input, not sorted
|
||
|
|
labels : ndarray[intp_t] (if return_inverse)
|
||
|
|
The labels from values to uniques
|
||
|
|
"""
|
||
|
|
uniques = ObjectVector()
|
||
|
|
return self._unique(values, uniques, ignore_na=False,
|
||
|
|
return_inverse=return_inverse)
|
||
|
|
|
||
|
|
def factorize(self, ndarray[object] values, Py_ssize_t na_sentinel=-1,
|
||
|
|
object na_value=None, object mask=None, ignore_na=True):
|
||
|
|
"""
|
||
|
|
Calculate unique values and labels (no sorting!)
|
||
|
|
|
||
|
|
Missing values are not included in the "uniques" for this method.
|
||
|
|
The labels for any missing values will be set to "na_sentinel"
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : ndarray[object]
|
||
|
|
Array of values of which unique will be calculated
|
||
|
|
na_sentinel : Py_ssize_t, default -1
|
||
|
|
Sentinel value used for all NA-values in inverse
|
||
|
|
na_value : object, default None
|
||
|
|
Value to identify as missing. If na_value is None, then any value
|
||
|
|
that is not a string is considered missing. If na_value is
|
||
|
|
not None, then _additionally_ any value "val" satisfying
|
||
|
|
val == na_value is considered missing.
|
||
|
|
mask : ndarray[bool], optional
|
||
|
|
Not yet implemented for StringHashTable.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
uniques : ndarray[object]
|
||
|
|
Unique values of input, not sorted
|
||
|
|
labels : ndarray[intp]
|
||
|
|
The labels from values to uniques
|
||
|
|
"""
|
||
|
|
uniques_vector = ObjectVector()
|
||
|
|
return self._unique(values, uniques_vector, na_sentinel=na_sentinel,
|
||
|
|
na_value=na_value, ignore_na=ignore_na,
|
||
|
|
return_inverse=True)
|
||
|
|
|
||
|
|
def get_labels(self, ndarray[object] values, ObjectVector uniques,
|
||
|
|
Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
|
||
|
|
object na_value=None):
|
||
|
|
# -> np.ndarray[np.intp]
|
||
|
|
_, labels = self._unique(values, uniques, count_prior=count_prior,
|
||
|
|
na_sentinel=na_sentinel, na_value=na_value,
|
||
|
|
ignore_na=True, return_inverse=True)
|
||
|
|
return labels
|
||
|
|
|
||
|
|
|
||
|
|
cdef class PyObjectHashTable(HashTable):
|
||
|
|
|
||
|
|
def __init__(self, int64_t size_hint=1):
|
||
|
|
self.table = kh_init_pymap()
|
||
|
|
size_hint = min(kh_needed_n_buckets(size_hint), SIZE_HINT_LIMIT)
|
||
|
|
kh_resize_pymap(self.table, size_hint)
|
||
|
|
|
||
|
|
def __dealloc__(self):
|
||
|
|
if self.table is not NULL:
|
||
|
|
kh_destroy_pymap(self.table)
|
||
|
|
self.table = NULL
|
||
|
|
|
||
|
|
def __len__(self) -> int:
|
||
|
|
return self.table.size
|
||
|
|
|
||
|
|
def __contains__(self, object key) -> bool:
|
||
|
|
cdef:
|
||
|
|
khiter_t k
|
||
|
|
hash(key)
|
||
|
|
|
||
|
|
k = kh_get_pymap(self.table, <PyObject*>key)
|
||
|
|
return k != self.table.n_buckets
|
||
|
|
|
||
|
|
def sizeof(self, deep: bool = False) -> int:
|
||
|
|
""" return the size of my table in bytes """
|
||
|
|
overhead = 4 * sizeof(uint32_t) + 3 * sizeof(uint32_t*)
|
||
|
|
for_flags = max(1, self.table.n_buckets >> 5) * sizeof(uint32_t)
|
||
|
|
for_pairs = self.table.n_buckets * (sizeof(PyObject *) + # keys
|
||
|
|
sizeof(Py_ssize_t)) # vals
|
||
|
|
return overhead + for_flags + for_pairs
|
||
|
|
|
||
|
|
def get_state(self) -> dict[str, int]:
|
||
|
|
"""
|
||
|
|
returns infos about the current state of the hashtable like size,
|
||
|
|
number of buckets and so on.
|
||
|
|
"""
|
||
|
|
return {
|
||
|
|
'n_buckets' : self.table.n_buckets,
|
||
|
|
'size' : self.table.size,
|
||
|
|
'n_occupied' : self.table.n_occupied,
|
||
|
|
'upper_bound' : self.table.upper_bound,
|
||
|
|
}
|
||
|
|
|
||
|
|
cpdef get_item(self, object val):
|
||
|
|
cdef:
|
||
|
|
khiter_t k
|
||
|
|
|
||
|
|
k = kh_get_pymap(self.table, <PyObject*>val)
|
||
|
|
if k != self.table.n_buckets:
|
||
|
|
return self.table.vals[k]
|
||
|
|
else:
|
||
|
|
raise KeyError(val)
|
||
|
|
|
||
|
|
cpdef set_item(self, object key, Py_ssize_t val):
|
||
|
|
cdef:
|
||
|
|
khiter_t k
|
||
|
|
int ret = 0
|
||
|
|
char* buf
|
||
|
|
|
||
|
|
hash(key)
|
||
|
|
|
||
|
|
k = kh_put_pymap(self.table, <PyObject*>key, &ret)
|
||
|
|
if kh_exist_pymap(self.table, k):
|
||
|
|
self.table.vals[k] = val
|
||
|
|
else:
|
||
|
|
raise KeyError(key)
|
||
|
|
|
||
|
|
def map_locations(self, ndarray[object] values) -> None:
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, n = len(values)
|
||
|
|
int ret = 0
|
||
|
|
object val
|
||
|
|
khiter_t k
|
||
|
|
|
||
|
|
for i in range(n):
|
||
|
|
val = values[i]
|
||
|
|
hash(val)
|
||
|
|
|
||
|
|
k = kh_put_pymap(self.table, <PyObject*>val, &ret)
|
||
|
|
self.table.vals[k] = i
|
||
|
|
|
||
|
|
def lookup(self, ndarray[object] values) -> ndarray:
|
||
|
|
# -> np.ndarray[np.intp]
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, n = len(values)
|
||
|
|
int ret = 0
|
||
|
|
object val
|
||
|
|
khiter_t k
|
||
|
|
intp_t[::1] locs = np.empty(n, dtype=np.intp)
|
||
|
|
|
||
|
|
for i in range(n):
|
||
|
|
val = values[i]
|
||
|
|
hash(val)
|
||
|
|
|
||
|
|
k = kh_get_pymap(self.table, <PyObject*>val)
|
||
|
|
if k != self.table.n_buckets:
|
||
|
|
locs[i] = self.table.vals[k]
|
||
|
|
else:
|
||
|
|
locs[i] = -1
|
||
|
|
|
||
|
|
return np.asarray(locs)
|
||
|
|
|
||
|
|
@cython.boundscheck(False)
|
||
|
|
@cython.wraparound(False)
|
||
|
|
def _unique(self, ndarray[object] values, ObjectVector uniques,
|
||
|
|
Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
|
||
|
|
object na_value=None, bint ignore_na=False,
|
||
|
|
bint return_inverse=False):
|
||
|
|
"""
|
||
|
|
Calculate unique values and labels (no sorting!)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : ndarray[object]
|
||
|
|
Array of values of which unique will be calculated
|
||
|
|
uniques : ObjectVector
|
||
|
|
Vector into which uniques will be written
|
||
|
|
count_prior : Py_ssize_t, default 0
|
||
|
|
Number of existing entries in uniques
|
||
|
|
na_sentinel : Py_ssize_t, default -1
|
||
|
|
Sentinel value used for all NA-values in inverse
|
||
|
|
na_value : object, default None
|
||
|
|
Value to identify as missing. If na_value is None, then None _plus_
|
||
|
|
any value "val" satisfying val != val is considered missing.
|
||
|
|
If na_value is not None, then _additionally_, any value "val"
|
||
|
|
satisfying val == na_value is considered missing.
|
||
|
|
ignore_na : bool, default False
|
||
|
|
Whether NA-values should be ignored for calculating the uniques. If
|
||
|
|
True, the labels corresponding to missing values will be set to
|
||
|
|
na_sentinel.
|
||
|
|
return_inverse : bool, default False
|
||
|
|
Whether the mapping of the original array values to their location
|
||
|
|
in the vector of uniques should be returned.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
uniques : ndarray[object]
|
||
|
|
Unique values of input, not sorted
|
||
|
|
labels : ndarray[intp_t] (if return_inverse=True)
|
||
|
|
The labels from values to uniques
|
||
|
|
"""
|
||
|
|
cdef:
|
||
|
|
Py_ssize_t i, idx, count = count_prior, n = len(values)
|
||
|
|
intp_t[::1] labels
|
||
|
|
int ret = 0
|
||
|
|
object val
|
||
|
|
khiter_t k
|
||
|
|
bint use_na_value
|
||
|
|
|
||
|
|
if return_inverse:
|
||
|
|
labels = np.empty(n, dtype=np.intp)
|
||
|
|
use_na_value = na_value is not None
|
||
|
|
|
||
|
|
for i in range(n):
|
||
|
|
val = values[i]
|
||
|
|
hash(val)
|
||
|
|
|
||
|
|
if ignore_na and (
|
||
|
|
checknull(val)
|
||
|
|
or (use_na_value and val == na_value)
|
||
|
|
):
|
||
|
|
# if missing values do not count as unique values (i.e. if
|
||
|
|
# ignore_na is True), skip the hashtable entry for them, and
|
||
|
|
# replace the corresponding label with na_sentinel
|
||
|
|
labels[i] = na_sentinel
|
||
|
|
continue
|
||
|
|
|
||
|
|
k = kh_get_pymap(self.table, <PyObject*>val)
|
||
|
|
if k == self.table.n_buckets:
|
||
|
|
# k hasn't been seen yet
|
||
|
|
k = kh_put_pymap(self.table, <PyObject*>val, &ret)
|
||
|
|
uniques.append(val)
|
||
|
|
if return_inverse:
|
||
|
|
self.table.vals[k] = count
|
||
|
|
labels[i] = count
|
||
|
|
count += 1
|
||
|
|
elif return_inverse:
|
||
|
|
# k falls into a previous bucket
|
||
|
|
# only relevant in case we need to construct the inverse
|
||
|
|
idx = self.table.vals[k]
|
||
|
|
labels[i] = idx
|
||
|
|
|
||
|
|
if return_inverse:
|
||
|
|
return uniques.to_array(), labels.base # .base -> underlying ndarray
|
||
|
|
return uniques.to_array()
|
||
|
|
|
||
|
|
def unique(self, ndarray[object] values, bint return_inverse=False, object mask=None):
|
||
|
|
"""
|
||
|
|
Calculate unique values and labels (no sorting!)
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : ndarray[object]
|
||
|
|
Array of values of which unique will be calculated
|
||
|
|
return_inverse : bool, default False
|
||
|
|
Whether the mapping of the original array values to their location
|
||
|
|
in the vector of uniques should be returned.
|
||
|
|
mask : ndarray[bool], optional
|
||
|
|
Not yet implemented for PyObjectHashTable
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
uniques : ndarray[object]
|
||
|
|
Unique values of input, not sorted
|
||
|
|
labels : ndarray[intp_t] (if return_inverse)
|
||
|
|
The labels from values to uniques
|
||
|
|
"""
|
||
|
|
uniques = ObjectVector()
|
||
|
|
return self._unique(values, uniques, ignore_na=False,
|
||
|
|
return_inverse=return_inverse)
|
||
|
|
|
||
|
|
def factorize(self, ndarray[object] values, Py_ssize_t na_sentinel=-1,
|
||
|
|
object na_value=None, object mask=None, ignore_na=True):
|
||
|
|
"""
|
||
|
|
Calculate unique values and labels (no sorting!)
|
||
|
|
|
||
|
|
Missing values are not included in the "uniques" for this method.
|
||
|
|
The labels for any missing values will be set to "na_sentinel"
|
||
|
|
|
||
|
|
Parameters
|
||
|
|
----------
|
||
|
|
values : ndarray[object]
|
||
|
|
Array of values of which unique will be calculated
|
||
|
|
na_sentinel : Py_ssize_t, default -1
|
||
|
|
Sentinel value used for all NA-values in inverse
|
||
|
|
na_value : object, default None
|
||
|
|
Value to identify as missing. If na_value is None, then None _plus_
|
||
|
|
any value "val" satisfying val != val is considered missing.
|
||
|
|
If na_value is not None, then _additionally_, any value "val"
|
||
|
|
satisfying val == na_value is considered missing.
|
||
|
|
mask : ndarray[bool], optional
|
||
|
|
Not yet implemented for PyObjectHashTable.
|
||
|
|
|
||
|
|
Returns
|
||
|
|
-------
|
||
|
|
uniques : ndarray[object]
|
||
|
|
Unique values of input, not sorted
|
||
|
|
labels : ndarray[intp_t]
|
||
|
|
The labels from values to uniques
|
||
|
|
"""
|
||
|
|
uniques_vector = ObjectVector()
|
||
|
|
return self._unique(values, uniques_vector, na_sentinel=na_sentinel,
|
||
|
|
na_value=na_value, ignore_na=ignore_na,
|
||
|
|
return_inverse=True)
|
||
|
|
|
||
|
|
def get_labels(self, ndarray[object] values, ObjectVector uniques,
|
||
|
|
Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
|
||
|
|
object na_value=None):
|
||
|
|
# -> np.ndarray[np.intp]
|
||
|
|
_, labels = self._unique(values, uniques, count_prior=count_prior,
|
||
|
|
na_sentinel=na_sentinel, na_value=na_value,
|
||
|
|
ignore_na=True, return_inverse=True)
|
||
|
|
return labels
|