hashtable_class_helper.pxi.in

"""
Template for each `dtype` helper function for hashtable

WARNING: DO NOT edit .pxi FILE directly, .pxi is generated from .pxi.in
"""


#----------------------------------------------------------------------
# VectorData
#----------------------------------------------------------------------

{{py:

# name, dtype, arg
# the generated StringVector is not actually used
# but is included for completeness (rather ObjectVector is used
# for uniques in hashtables)

dtypes = [('Float64', 'float64', 'float64_t'),
          ('Int64', 'int64', 'int64_t'),
          ('String', 'string', 'char *'),
          ('UInt64', 'uint64', 'uint64_t')]
}}

{{for name, dtype, arg in dtypes}}


{{if dtype != 'int64'}}

ctypedef struct {{name}}VectorData:
    {{arg}} *data
    Py_ssize_t n, m

{{endif}}


@cython.wraparound(False)
@cython.boundscheck(False)
cdef inline void append_data_{{dtype}}({{name}}VectorData *data,
                                       {{arg}} x) nogil:

    data.data[data.n] = x
    data.n += 1

{{endfor}}

ctypedef fused vector_data:
    Int64VectorData
    UInt64VectorData
    Float64VectorData
    StringVectorData

cdef inline bint needs_resize(vector_data *data) nogil:
    return data.n == data.m

#----------------------------------------------------------------------
# Vector
#----------------------------------------------------------------------

{{py:

# name, dtype, arg, idtype
dtypes = [('Float64', 'float64', 'float64_t', 'np.float64'),
          ('UInt64', 'uint64', 'uint64_t', 'np.uint64'),
          ('Int64', 'int64', 'int64_t', 'np.int64')]

}}

{{for name, dtype, arg, idtype in dtypes}}

cdef class {{name}}Vector:

    {{if dtype != 'int64'}}
    cdef:
        bint external_view_exists
        {{name}}VectorData *data
        ndarray ao
    {{endif}}

    def __cinit__(self):
        self.data = <{{name}}VectorData *>PyMem_Malloc(
            sizeof({{name}}VectorData))
        if not self.data:
            raise MemoryError()
        self.external_view_exists = False
        self.data.n = 0
        self.data.m = _INIT_VEC_CAP
        self.ao = np.empty(self.data.m, dtype={{idtype}})
        self.data.data = <{{arg}}*> self.ao.data

    cdef resize(self):
        self.data.m = max(self.data.m * 4, _INIT_VEC_CAP)
        self.ao.resize(self.data.m, refcheck=False)
        self.data.data = <{{arg}}*> self.ao.data

    def __dealloc__(self):
        if self.data is not NULL:
            PyMem_Free(self.data)
            self.data = NULL

    def __len__(self):
        return self.data.n

    cpdef to_array(self):
        if self.data.m != self.data.n:
            if self.external_view_exists:
                # should never happen
                raise ValueError("should have raised on append()")
            self.ao.resize(self.data.n, refcheck=False)
            self.data.m = self.data.n
        self.external_view_exists = True
        return self.ao

    cdef inline void append(self, {{arg}} x):

        if needs_resize(self.data):
            if self.external_view_exists:
                raise ValueError("external reference but "
                                 "Vector.resize() needed")
            self.resize()

        append_data_{{dtype}}(self.data, x)

    cdef extend(self, const {{arg}}[:] x):
        for i in range(len(x)):
            self.append(x[i])

{{endfor}}

cdef class StringVector:

    cdef:
        StringVectorData *data
        bint external_view_exists

    def __cinit__(self):
        self.data = <StringVectorData *>PyMem_Malloc(
            sizeof(StringVectorData))
        if not self.data:
            raise MemoryError()
        self.external_view_exists = False
        self.data.n = 0
        self.data.m = _INIT_VEC_CAP
        self.data.data = <char **> malloc(self.data.m * sizeof(char *))
        if not self.data.data:
            raise MemoryError()

    cdef resize(self):
        cdef:
            char **orig_data
            Py_ssize_t i, m

        m = self.data.m
        self.data.m = max(self.data.m * 4, _INIT_VEC_CAP)

        orig_data = self.data.data
        self.data.data = <char **> malloc(self.data.m * sizeof(char *))
        if not self.data.data:
            raise MemoryError()
        for i in range(m):
            self.data.data[i] = orig_data[i]

    def __dealloc__(self):
        if self.data is not NULL:
            if self.data.data is not NULL:
                free(self.data.data)
            PyMem_Free(self.data)
            self.data = NULL

    def __len__(self):
        return self.data.n

    def to_array(self):
        cdef:
            ndarray ao
            Py_ssize_t n
            object val

        ao = np.empty(self.data.n, dtype=np.object)
        for i in range(self.data.n):
            val = self.data.data[i]
            ao[i] = val
        self.external_view_exists = True
        self.data.m = self.data.n
        return ao

    cdef inline void append(self, char * x):

        if needs_resize(self.data):
            self.resize()

        append_data_string(self.data, x)

    cdef extend(self, ndarray[:] x):
        for i in range(len(x)):
            self.append(x[i])


cdef class ObjectVector:

    cdef:
        PyObject **data
        Py_ssize_t n, m
        ndarray ao
        bint external_view_exists

    def __cinit__(self):
        self.external_view_exists = False
        self.n = 0
        self.m = _INIT_VEC_CAP
        self.ao = np.empty(_INIT_VEC_CAP, dtype=object)
        self.data = <PyObject**> self.ao.data

    def __len__(self):
        return self.n

    cdef inline append(self, object o):
        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(o)
        self.data[self.n] = <PyObject*> o
        self.n += 1

    def 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[:] x):
        for i in range(len(x)):
            self.append(x[i])

#----------------------------------------------------------------------
# HashTable
#----------------------------------------------------------------------


cdef class HashTable:

    pass

{{py:

# name, dtype, float_group, default_na_value
dtypes = [('Float64', 'float64', True, 'nan'),
          ('UInt64', 'uint64', False, 0),
          ('Int64', 'int64', False, 'iNaT')]

}}


{{for name, dtype, float_group, default_na_value in dtypes}}

cdef class {{name}}HashTable(HashTable):

    def __cinit__(self, size_hint=1):
        self.table = kh_init_{{dtype}}()
        if size_hint is not None:
            kh_resize_{{dtype}}(self.table, size_hint)

    def __len__(self):
        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):
        cdef khiter_t k
        k = kh_get_{{dtype}}(self.table, key)
        return k != self.table.n_buckets

    def sizeof(self, deep=False):
        """ return the size of my table in bytes """
        return self.table.n_buckets * (sizeof({{dtype}}_t) + # keys
                                       sizeof(Py_ssize_t) + # vals
                                       sizeof(uint32_t)) # flags

    cpdef get_item(self, {{dtype}}_t val):
        cdef khiter_t k
        k = kh_get_{{dtype}}(self.table, val)
        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):
        cdef:
            khiter_t k
            int ret = 0

        k = kh_put_{{dtype}}(self.table, key, &ret)
        self.table.keys[k] = key
        if kh_exist_{{dtype}}(self.table, k):
            self.table.vals[k] = val
        else:
            raise KeyError(key)

    @cython.boundscheck(False)
    def map(self, const {{dtype}}_t[:] keys, const int64_t[:] values):
        cdef:
            Py_ssize_t i, n = len(values)
            int ret = 0
            {{dtype}}_t key
            khiter_t k

        with nogil:
            for i in range(n):
                key = keys[i]
                k = kh_put_{{dtype}}(self.table, key, &ret)
                self.table.vals[k] = <Py_ssize_t> values[i]

    @cython.boundscheck(False)
    def map_locations(self, ndarray[{{dtype}}_t, ndim=1] values):
        cdef:
            Py_ssize_t i, n = len(values)
            int ret = 0
            {{dtype}}_t val
            khiter_t k

        with nogil:
            for i in range(n):
                val = 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):
        cdef:
            Py_ssize_t i, n = len(values)
            int ret = 0
            {{dtype}}_t val
            khiter_t k
            int64_t[:] locs = np.empty(n, dtype=np.int64)

        with nogil:
            for i in range(n):
                val = 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 _factorize(self, const {{dtype}}_t[:] values, {{name}}Vector uniques,
                   Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
                   object na_value=None):
        """
        Calculate unique values and labels (no sorting); ignores all NA-values

        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 satisfying val!=val are considered missing.

        Returns
        -------
        uniques : ndarray[{{dtype}}]
            Unique values of input, not sorted
        labels : ndarray[int64]
            The labels from values to uniques
        """
        cdef:
            Py_ssize_t i, idx, count = count_prior, n = len(values)
            int64_t[:] labels
            int ret = 0
            {{dtype}}_t val, na_value2
            khiter_t k
            {{name}}VectorData *ud
            bint use_na_value

        labels = np.empty(n, dtype=np.int64)
        ud = uniques.data
        use_na_value = na_value is not None

        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 = <{{dtype}}_t>na_value
        else:
            na_value2 = {{default_na_value}}

        with nogil:
            for i in range(n):
                val = values[i]

                if val != val or (use_na_value and val == na_value2):
                    labels[i] = na_sentinel
                    continue

                k = kh_get_{{dtype}}(self.table, val)

                if k != self.table.n_buckets:
                    # k falls into a previous bucket
                    idx = self.table.vals[k]
                    labels[i] = idx
                else:
                    # k hasn't been seen yet
                    k = kh_put_{{dtype}}(self.table, val, &ret)
                    self.table.vals[k] = count

                    if needs_resize(ud):
                        with gil:
                            if uniques.external_view_exists:
                                raise ValueError("external reference to "
                                                 "uniques held, but "
                                                 "Vector.resize() needed")
                            uniques.resize()
                    append_data_{{dtype}}(ud, val)
                    labels[i] = count
                    count += 1

        return np.asarray(labels)

    def factorize(self, const {{dtype}}_t[:] values, Py_ssize_t na_sentinel=-1,
                  object na_value=None):
        uniques = {{name}}Vector()
        labels = self._factorize(values, uniques=uniques,
                                 na_sentinel=na_sentinel, na_value=na_value)
        return labels, uniques.to_array()

    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):
        return self._factorize(values, uniques, count_prior=count_prior,
                               na_sentinel=na_sentinel, na_value=na_value)

    @cython.boundscheck(False)
    def get_labels_groupby(self, const {{dtype}}_t[:] values):
        cdef:
            Py_ssize_t i, n = len(values)
            int64_t[:] labels
            Py_ssize_t idx, count = 0
            int ret = 0
            {{dtype}}_t val
            khiter_t k
            {{name}}Vector uniques = {{name}}Vector()
            {{name}}VectorData *ud

        labels = np.empty(n, dtype=np.int64)
        ud = uniques.data

        with nogil:
            for i in range(n):
                val = values[i]

                # specific for groupby
                {{if dtype != 'uint64'}}
                if val < 0:
                    labels[i] = -1
                    continue
                {{endif}}

                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

    @cython.boundscheck(False)
    @cython.wraparound(False)
    def unique(self, const {{dtype}}_t[:] values):
        """
        Calculate unique values without sorting

        Parameters
        ----------
        values : ndarray[{{dtype}}]
            Array of values of which unique will be calculated

        Returns
        -------
        uniques : ndarray[{{dtype}}]
            Unique values of input, not sorted
        """
        cdef:
            Py_ssize_t i, n = len(values)
            int ret = 0
            {{dtype}}_t val
            khiter_t k
            {{name}}Vector uniques = {{name}}Vector()
            {{name}}VectorData *ud

        ud = uniques.data

        with nogil:
            for i in range(n):
                val = values[i]
                k = kh_get_{{dtype}}(self.table, val)
                if k == self.table.n_buckets:
                    kh_put_{{dtype}}(self.table, val, &ret)
                    if needs_resize(ud):
                        with gil:
                            uniques.resize()
                    append_data_{{dtype}}(ud, val)
        return uniques.to_array()

{{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, int size_hint=1):
        self.table = kh_init_str()
        if size_hint is not None:
            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=False):
        """ return the size of my table in bytes """
        return self.table.n_buckets * (sizeof(char *) + # keys
                                       sizeof(Py_ssize_t) + # vals
                                       sizeof(uint32_t)) # flags

    cpdef get_item(self, object val):
        cdef:
            khiter_t k
            const char *v
        v = util.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, object key, Py_ssize_t val):
        cdef:
            khiter_t k
            int ret = 0
            const char *v

        v = util.get_c_string(val)

        k = kh_put_str(self.table, v, &ret)
        self.table.keys[k] = key
        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):
        cdef:
            Py_ssize_t i, n = len(values)
            ndarray[int64_t] labels = np.empty(n, dtype=np.int64)
            int64_t *resbuf = <int64_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 = util.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)
    @cython.wraparound(False)
    def unique(self, ndarray[object] values):
        """
        Calculate unique values without sorting

        Parameters
        ----------
        values : ndarray[object]
            Array of values of which unique will be calculated

        Returns
        -------
        uniques : ndarray[object]
            Unique values of input, not sorted
        """
        cdef:
            Py_ssize_t i, count, n = len(values)
            int64_t[:] uindexer
            int ret = 0
            object val
            ObjectVector uniques
            khiter_t k
            const char *v
            const char **vecs

        vecs = <const char **> malloc(n * sizeof(char *))
        uindexer = np.empty(n, dtype=np.int64)
        for i in range(n):
            val = values[i]
            v = util.get_c_string(val)
            vecs[i] = v

        count = 0
        with nogil:
            for i in range(n):
                v = vecs[i]
                k = kh_get_str(self.table, v)
                if k == self.table.n_buckets:
                    kh_put_str(self.table, v, &ret)
                    uindexer[count] = i
                    count += 1
        free(vecs)

        # uniques
        uniques = ObjectVector()
        for i in range(count):
            uniques.append(values[uindexer[i]])
        return uniques.to_array()

    @cython.boundscheck(False)
    def lookup(self, ndarray[object] values):
        cdef:
            Py_ssize_t i, n = len(values)
            int ret = 0
            object val
            const char *v
            khiter_t k
            int64_t[:] locs = np.empty(n, dtype=np.int64)

        # these by-definition *must* be strings
        vecs = <char **> malloc(n * sizeof(char *))
        for i in range(n):
            val = values[i]

            if PyUnicode_Check(val) or PyString_Check(val):
                v = util.get_c_string(val)
            else:
                v = util.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):
        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 PyUnicode_Check(val) or PyString_Check(val):
                v = util.get_c_string(val)
            else:
                v = util.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 _factorize(self, ndarray[object] values, ObjectVector uniques,
                   Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
                   object na_value=None):
        """
        Calculate unique values and labels (no sorting); ignores all NA-values

        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

        Returns
        -------
        uniques : ndarray[object]
            Unique values of input, not sorted
        labels : ndarray[int64]
            The labels from values to uniques
        """
        cdef:
            Py_ssize_t i, idx, count = count_prior, n = len(values)
            int64_t[:] labels
            int64_t[:] uindexer
            int ret = 0
            object val
            const char *v
            const char **vecs
            khiter_t k
            bint use_na_value

        labels = np.zeros(n, dtype=np.int64)
        uindexer = np.empty(n, dtype=np.int64)
        use_na_value = na_value is not None

        # assign pointers and pre-filter out missing
        vecs = <const char **> malloc(n * sizeof(char *))
        for i in range(n):
            val = values[i]

            if ((PyUnicode_Check(val) or PyString_Check(val))
                    and not (use_na_value and val == na_value)):
                v = util.get_c_string(val)
                vecs[i] = v
            else:
                labels[i] = na_sentinel

        # compute
        with nogil:
            for i in range(n):
                if labels[i] == na_sentinel:
                    continue

                v = vecs[i]
                k = kh_get_str(self.table, v)
                if k != self.table.n_buckets:
                    # k falls into a previous bucket
                    idx = self.table.vals[k]
                    labels[i] = <int64_t>idx
                else:
                    # k hasn't been seen yet
                    k = kh_put_str(self.table, v, &ret)
                    self.table.vals[k] = count
                    uindexer[count] = i
                    labels[i] = <int64_t>count
                    count += 1

        free(vecs)

        # uniques
        for i in range(count):
            uniques.append(values[uindexer[i]])

        return np.asarray(labels)

    def factorize(self, ndarray[object] values, Py_ssize_t na_sentinel=-1,
                  object na_value=None):
        uniques = ObjectVector()
        labels = self._factorize(values, uniques=uniques,
                                 na_sentinel=na_sentinel, na_value=na_value)
        return labels, uniques.to_array()

    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):
        return self._factorize(values, uniques, count_prior=count_prior,
                               na_sentinel=na_sentinel, na_value=na_value)


cdef class PyObjectHashTable(HashTable):

    def __init__(self, size_hint=1):
        self.table = kh_init_pymap()
        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):
        return self.table.size

    def __contains__(self, object key):
        cdef khiter_t k
        hash(key)

        k = kh_get_pymap(self.table, <PyObject*>key)
        return k != self.table.n_buckets

    def sizeof(self, deep=False):
        """ return the size of my table in bytes """
        return self.table.n_buckets * (sizeof(PyObject *) + # keys
                                       sizeof(Py_ssize_t) + # vals
                                       sizeof(uint32_t)) # flags

    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)
        # self.table.keys[k] = key
        if kh_exist_pymap(self.table, k):
            self.table.vals[k] = val
        else:
            raise KeyError(key)

    def map_locations(self, ndarray[object] values):
        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):
        cdef:
            Py_ssize_t i, n = len(values)
            int ret = 0
            object val
            khiter_t k
            int64_t[:] locs = np.empty(n, dtype=np.int64)

        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):
        """
        Calculate unique values without sorting

        Parameters
        ----------
        values : ndarray[object]
            Array of values of which unique will be calculated

        Returns
        -------
        uniques : ndarray[object]
            Unique values of input, not sorted
        """
        cdef:
            Py_ssize_t i, n = len(values)
            int ret = 0
            object val
            khiter_t k
            ObjectVector uniques = ObjectVector()

        for i in range(n):
            val = values[i]
            hash(val)
            k = kh_get_pymap(self.table, <PyObject*>val)
            if k == self.table.n_buckets:
                kh_put_pymap(self.table, <PyObject*>val, &ret)
                uniques.append(val)

        return uniques.to_array()

    @cython.boundscheck(False)
    @cython.wraparound(False)
    def _factorize(self, ndarray[object] values, ObjectVector uniques,
                   Py_ssize_t count_prior=0, Py_ssize_t na_sentinel=-1,
                   object na_value=None):
        """
        Calculate unique values and labels (no sorting); ignores all NA-values

        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 satisfying val!=val are considered missing.

        Returns
        -------
        uniques : ndarray[object]
            Unique values of input, not sorted
        labels : ndarray[int64]
            The labels from values to uniques
        """
        cdef:
            Py_ssize_t i, idx, count = count_prior, n = len(values)
            int64_t[:] labels
            int ret = 0
            object val
            khiter_t k
            bint use_na_value

        labels = np.empty(n, dtype=np.int64)
        use_na_value = na_value is not None

        for i in range(n):
            val = values[i]
            hash(val)

            if ((val != val or val is None)
                    or (use_na_value and val == na_value)):
                labels[i] = na_sentinel
                continue

            k = kh_get_pymap(self.table, <PyObject*>val)
            if k != self.table.n_buckets:
                # k falls into a previous bucket
                idx = self.table.vals[k]
                labels[i] = idx
            else:
                # k hasn't been seen yet
                k = kh_put_pymap(self.table, <PyObject*>val, &ret)
                self.table.vals[k] = count
                uniques.append(val)
                labels[i] = count
                count += 1

        return np.asarray(labels)

    def factorize(self, ndarray[object] values, Py_ssize_t na_sentinel=-1,
                  object na_value=None):
        uniques = ObjectVector()
        labels = self._factorize(values, uniques=uniques,
                                 na_sentinel=na_sentinel, na_value=na_value)
        return labels, uniques.to_array()

    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):
        return self._factorize(values, uniques, count_prior=count_prior,
                               na_sentinel=na_sentinel, na_value=na_value)