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743 lines (575 loc) · 27.5 KB
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# *****************************************************************************
# Copyright (c) 2020, Intel Corporation All rights reserved.
#
# Redistribution and use in source and binary forms, with or without
# modification, are permitted provided that the following conditions are met:
#
# Redistributions of source code must retain the above copyright notice,
# this list of conditions and the following disclaimer.
#
# Redistributions in binary form must reproduce the above copyright notice,
# this list of conditions and the following disclaimer in the documentation
# and/or other materials provided with the distribution.
#
# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO,
# THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
# PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR
# CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS;
# OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY,
# WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR
# OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE,
# EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
# *****************************************************************************
"""
| This file contains SDC overloads for common algorithms used internally
"""
import numpy
import pandas
from pandas.core.indexing import IndexingError
import numba
from numba.targets import quicksort
from numba import types
from numba.errors import TypingError
from numba.extending import register_jitable
from numba import numpy_support
from numba.typed import Dict
import sdc
from sdc.hiframes.api import isna
from sdc.hiframes.pd_series_type import SeriesType
from sdc.str_arr_type import string_array_type
from sdc.str_arr_ext import (num_total_chars, append_string_array_to,
str_arr_is_na, pre_alloc_string_array, str_arr_set_na, string_array_type,
cp_str_list_to_array, create_str_arr_from_list, get_utf8_size,
str_arr_set_na_by_mask)
from sdc.utilities.prange_utils import parallel_chunks
from sdc.utilities.utils import sdc_overload, sdc_register_jitable
from sdc.utilities.sdc_typing_utils import (find_common_dtype_from_numpy_dtypes,
TypeChecker)
class SDCLimitation(Exception):
"""Exception to be raised in case of SDC limitation"""
pass
def hpat_arrays_append(A, B):
pass
@sdc_overload(hpat_arrays_append, jit_options={'parallel': False})
def hpat_arrays_append_overload(A, B):
"""Function for appending underlying arrays (A and B) or list/tuple of arrays B to an array A"""
if isinstance(A, types.Array):
if isinstance(B, types.Array):
def _append_single_numeric_impl(A, B):
return numpy.concatenate((A, B,))
return _append_single_numeric_impl
elif isinstance(B, (types.UniTuple, types.List)):
# TODO: this heavily relies on B being a homogeneous tuple/list - find a better way
# to resolve common dtype of heterogeneous sequence of arrays
numba_common_dtype = find_common_dtype_from_numpy_dtypes([A.dtype, B.dtype.dtype], [])
# TODO: refactor to use numpy.concatenate when Numba supports building a tuple at runtime
def _append_list_numeric_impl(A, B):
total_length = len(A) + numpy.array([len(arr) for arr in B]).sum()
new_data = numpy.empty(total_length, numba_common_dtype)
stop = len(A)
new_data[:stop] = A
for arr in B:
start = stop
stop = start + len(arr)
new_data[start:stop] = arr
return new_data
return _append_list_numeric_impl
elif A == string_array_type:
if B == string_array_type:
def _append_single_string_array_impl(A, B):
total_size = len(A) + len(B)
total_chars = num_total_chars(A) + num_total_chars(B)
new_data = sdc.str_arr_ext.pre_alloc_string_array(total_size, total_chars)
pos = 0
pos += append_string_array_to(new_data, pos, A)
pos += append_string_array_to(new_data, pos, B)
return new_data
return _append_single_string_array_impl
elif (isinstance(B, (types.UniTuple, types.List)) and B.dtype == string_array_type):
def _append_list_string_array_impl(A, B):
array_list = [A] + list(B)
total_size = numpy.array([len(arr) for arr in array_list]).sum()
total_chars = numpy.array([num_total_chars(arr) for arr in array_list]).sum()
new_data = sdc.str_arr_ext.pre_alloc_string_array(total_size, total_chars)
pos = 0
pos += append_string_array_to(new_data, pos, A)
for arr in B:
pos += append_string_array_to(new_data, pos, arr)
return new_data
return _append_list_string_array_impl
@sdc_register_jitable
def fill_array(data, size, fill_value=numpy.nan, push_back=True):
"""
Fill array with given values to reach the size
"""
if push_back:
return numpy.append(data, numpy.repeat(fill_value, size - data.size))
return numpy.append(numpy.repeat(fill_value, size - data.size), data)
@sdc_register_jitable
def fill_str_array(data, size, push_back=True):
"""
Fill StringArrayType array with given values to reach the size
"""
string_array_size = len(data)
nan_array_size = size - string_array_size
num_chars = sdc.str_arr_ext.num_total_chars(data)
result_data = sdc.str_arr_ext.pre_alloc_string_array(size, num_chars)
# Keep NaN values of initial array
arr_is_na_mask = numpy.array([sdc.hiframes.api.isna(data, i) for i in range(string_array_size)])
data_str_list = sdc.str_arr_ext.to_string_list(data)
nan_list = [''] * nan_array_size
result_list = data_str_list + nan_list if push_back else nan_list + data_str_list
cp_str_list_to_array(result_data, result_list)
# Batch=64 iteration to avoid threads competition
batch_size = 64
if push_back:
for i in numba.prange(size//batch_size + 1):
for j in range(i*batch_size, min((i+1)*batch_size, size)):
if j < string_array_size:
if arr_is_na_mask[j]:
str_arr_set_na(result_data, j)
else:
str_arr_set_na(result_data, j)
else:
for i in numba.prange(size//batch_size + 1):
for j in range(i*batch_size, min((i+1)*batch_size, size)):
if j < nan_array_size:
str_arr_set_na(result_data, j)
else:
str_arr_j = j - nan_array_size
if arr_is_na_mask[str_arr_j]:
str_arr_set_na(result_data, j)
return result_data
@numba.njit
def _hpat_ensure_array_capacity(new_size, arr):
""" Function ensuring that the size of numpy array is at least as specified
Returns newly allocated array of bigger size with copied elements if existing size is less than requested
"""
k = len(arr)
if k >= new_size:
return arr
n = k
while n < new_size:
n = 2 * n
res = numpy.empty(n, arr.dtype)
res[:k] = arr[:k]
return res
def sdc_join_series_indexes(left, right):
pass
@sdc_overload(sdc_join_series_indexes, jit_options={'parallel': False})
def sdc_join_series_indexes_overload(left, right):
"""Function for joining arrays left and right in a way similar to pandas.join 'outer' algorithm"""
# TODO: eliminate code duplication by merging implementations for numeric and StringArray
# requires equivalents of numpy.arsort and _hpat_ensure_array_capacity for StringArrays
if (isinstance(left, types.Array) and isinstance(right, types.Array)):
numba_common_dtype = find_common_dtype_from_numpy_dtypes([left.dtype, right.dtype], [])
if isinstance(numba_common_dtype, types.Number):
def sdc_join_series_indexes_impl(left, right):
# allocate result arrays
lsize = len(left)
rsize = len(right)
est_total_size = int(1.1 * (lsize + rsize))
lidx = numpy.empty(est_total_size, numpy.int64)
ridx = numpy.empty(est_total_size, numpy.int64)
joined = numpy.empty(est_total_size, numba_common_dtype)
left_nan = []
right_nan = []
for i in range(lsize):
if numpy.isnan(left[i]):
left_nan.append(i)
for i in range(rsize):
if numpy.isnan(right[i]):
right_nan.append(i)
# sort arrays saving the old positions
sorted_left = numpy.argsort(left, kind='mergesort')
sorted_right = numpy.argsort(right, kind='mergesort')
# put the position of the nans in an increasing sequence
sorted_left[lsize-len(left_nan):] = left_nan
sorted_right[rsize-len(right_nan):] = right_nan
i, j, k = 0, 0, 0
while (i < lsize and j < rsize):
joined = _hpat_ensure_array_capacity(k + 1, joined)
lidx = _hpat_ensure_array_capacity(k + 1, lidx)
ridx = _hpat_ensure_array_capacity(k + 1, ridx)
left_index = left[sorted_left[i]]
right_index = right[sorted_right[j]]
if (left_index < right_index) or numpy.isnan(right_index):
joined[k] = left_index
lidx[k] = sorted_left[i]
ridx[k] = -1
i += 1
k += 1
elif (left_index > right_index) or numpy.isnan(left_index):
joined[k] = right_index
lidx[k] = -1
ridx[k] = sorted_right[j]
j += 1
k += 1
else:
# find ends of sequences of equal index values in left and right
ni, nj = i, j
while (ni < lsize and left[sorted_left[ni]] == left_index):
ni += 1
while (nj < rsize and right[sorted_right[nj]] == right_index):
nj += 1
# join the blocks found into results
for s in numpy.arange(i, ni, 1):
block_size = nj - j
to_joined = numpy.repeat(left_index, block_size)
to_lidx = numpy.repeat(sorted_left[s], block_size)
to_ridx = numpy.array([sorted_right[k] for k in numpy.arange(j, nj, 1)], numpy.int64)
joined = _hpat_ensure_array_capacity(k + block_size, joined)
lidx = _hpat_ensure_array_capacity(k + block_size, lidx)
ridx = _hpat_ensure_array_capacity(k + block_size, ridx)
joined[k:k + block_size] = to_joined
lidx[k:k + block_size] = to_lidx
ridx[k:k + block_size] = to_ridx
k += block_size
i = ni
j = nj
# fill the end of joined with remaining part of left or right
if i < lsize:
block_size = lsize - i
joined = _hpat_ensure_array_capacity(k + block_size, joined)
lidx = _hpat_ensure_array_capacity(k + block_size, lidx)
ridx = _hpat_ensure_array_capacity(k + block_size, ridx)
ridx[k: k + block_size] = numpy.repeat(-1, block_size)
while i < lsize:
joined[k] = left[sorted_left[i]]
lidx[k] = sorted_left[i]
i += 1
k += 1
elif j < rsize:
block_size = rsize - j
joined = _hpat_ensure_array_capacity(k + block_size, joined)
lidx = _hpat_ensure_array_capacity(k + block_size, lidx)
ridx = _hpat_ensure_array_capacity(k + block_size, ridx)
lidx[k: k + block_size] = numpy.repeat(-1, block_size)
while j < rsize:
joined[k] = right[sorted_right[j]]
ridx[k] = sorted_right[j]
j += 1
k += 1
return joined[:k], lidx[:k], ridx[:k]
return sdc_join_series_indexes_impl
else:
# TODO: support joining indexes with common dtype=object - requires Numba
# support of such numpy arrays in nopython mode, for now just return None
return None
elif (left == string_array_type and right == string_array_type):
def sdc_join_series_indexes_impl(left, right):
# allocate result arrays
lsize = len(left)
rsize = len(right)
est_total_size = int(1.1 * (lsize + rsize))
lidx = numpy.empty(est_total_size, numpy.int64)
ridx = numpy.empty(est_total_size, numpy.int64)
# use Series.sort_values since argsort for StringArrays not implemented
original_left_series = pandas.Series(left)
original_right_series = pandas.Series(right)
# sort arrays saving the old positions
left_series = original_left_series.sort_values(kind='mergesort')
right_series = original_right_series.sort_values(kind='mergesort')
sorted_left = left_series._index
sorted_right = right_series._index
i, j, k = 0, 0, 0
while (i < lsize and j < rsize):
lidx = _hpat_ensure_array_capacity(k + 1, lidx)
ridx = _hpat_ensure_array_capacity(k + 1, ridx)
left_index = left[sorted_left[i]]
right_index = right[sorted_right[j]]
if (left_index < right_index):
lidx[k] = sorted_left[i]
ridx[k] = -1
i += 1
k += 1
elif (left_index > right_index):
lidx[k] = -1
ridx[k] = sorted_right[j]
j += 1
k += 1
else:
# find ends of sequences of equal index values in left and right
ni, nj = i, j
while (ni < lsize and left[sorted_left[ni]] == left_index):
ni += 1
while (nj < rsize and right[sorted_right[nj]] == right_index):
nj += 1
# join the blocks found into results
for s in numpy.arange(i, ni, 1):
block_size = nj - j
to_lidx = numpy.repeat(sorted_left[s], block_size)
to_ridx = numpy.array([sorted_right[k] for k in numpy.arange(j, nj, 1)], numpy.int64)
lidx = _hpat_ensure_array_capacity(k + block_size, lidx)
ridx = _hpat_ensure_array_capacity(k + block_size, ridx)
lidx[k:k + block_size] = to_lidx
ridx[k:k + block_size] = to_ridx
k += block_size
i = ni
j = nj
# fill the end of joined with remaining part of left or right
if i < lsize:
block_size = lsize - i
lidx = _hpat_ensure_array_capacity(k + block_size, lidx)
ridx = _hpat_ensure_array_capacity(k + block_size, ridx)
ridx[k: k + block_size] = numpy.repeat(-1, block_size)
while i < lsize:
lidx[k] = sorted_left[i]
i += 1
k += 1
elif j < rsize:
block_size = rsize - j
lidx = _hpat_ensure_array_capacity(k + block_size, lidx)
ridx = _hpat_ensure_array_capacity(k + block_size, ridx)
lidx[k: k + block_size] = numpy.repeat(-1, block_size)
while j < rsize:
ridx[k] = sorted_right[j]
j += 1
k += 1
# count total number of characters and allocate joined array
total_joined_size = k
num_chars_in_joined = 0
for i in numpy.arange(total_joined_size):
if lidx[i] != -1:
num_chars_in_joined += len(left[lidx[i]])
elif ridx[i] != -1:
num_chars_in_joined += len(right[ridx[i]])
joined = pre_alloc_string_array(total_joined_size, num_chars_in_joined)
# iterate over joined and fill it with indexes using lidx and ridx indexers
for i in numpy.arange(total_joined_size):
if lidx[i] != -1:
joined[i] = left[lidx[i]]
if (str_arr_is_na(left, lidx[i])):
str_arr_set_na(joined, i)
elif ridx[i] != -1:
joined[i] = right[ridx[i]]
if (str_arr_is_na(right, ridx[i])):
str_arr_set_na(joined, i)
else:
str_arr_set_na(joined, i)
return joined, lidx, ridx
return sdc_join_series_indexes_impl
return None
def sdc_check_indexes_equal(left, right):
pass
@sdc_overload(sdc_check_indexes_equal, jit_options={'parallel': False})
def sdc_check_indexes_equal_overload(A, B):
"""Function for checking arrays A and B of the same type are equal"""
if isinstance(A, types.Array):
def sdc_check_indexes_equal_numeric_impl(A, B):
return numpy.array_equal(A, B)
return sdc_check_indexes_equal_numeric_impl
elif A == string_array_type:
def sdc_check_indexes_equal_string_impl(A, B):
# TODO: replace with StringArrays comparison
is_index_equal = (len(A) == len(B)
and num_total_chars(A) == num_total_chars(B))
for i in numpy.arange(len(A)):
if (A[i] != B[i]
or str_arr_is_na(A, i) is not str_arr_is_na(B, i)):
return False
return is_index_equal
return sdc_check_indexes_equal_string_impl
@numba.njit
def _sdc_pandas_format_percentiles(arr):
""" Function converting float array of percentiles to a list of strings formatted
the same as in pandas.io.formats.format.format_percentiles
"""
percentiles_strs = []
for percentile in arr:
p_as_string = str(percentile * 100)
trim_index = len(p_as_string) - 1
while trim_index >= 0:
if p_as_string[trim_index] == '0':
trim_index -= 1
continue
elif p_as_string[trim_index] == '.':
break
trim_index += 1
break
if trim_index < 0:
p_as_string_trimmed = '0'
else:
p_as_string_trimmed = p_as_string[:trim_index]
percentiles_strs.append(p_as_string_trimmed + '%')
return percentiles_strs
def sdc_arrays_argsort(A, kind='quicksort'):
pass
@sdc_overload(sdc_arrays_argsort, jit_options={'parallel': False})
def sdc_arrays_argsort_overload(A, kind='quicksort'):
"""Function providing pandas argsort implementation for different 1D array types"""
# kind is not known at compile time, so get this function here and use in impl if needed
quicksort_func = quicksort.make_jit_quicksort().run_quicksort
kind_is_default = isinstance(kind, str)
if isinstance(A, types.Array):
def _sdc_arrays_argsort_array_impl(A, kind='quicksort'):
_kind = 'quicksort' if kind_is_default == True else kind # noqa
return numpy.argsort(A, kind=_kind)
return _sdc_arrays_argsort_array_impl
elif A == string_array_type:
def _sdc_arrays_argsort_str_arr_impl(A, kind='quicksort'):
nan_mask = sdc.hiframes.api.get_nan_mask(A)
idx = numpy.arange(len(A))
old_nan_positions = idx[nan_mask]
data = A[~nan_mask]
keys = idx[~nan_mask]
if kind == 'quicksort':
zipped = list(zip(list(data), list(keys)))
zipped = quicksort_func(zipped)
argsorted = [zipped[i][1] for i in numpy.arange(len(data))]
elif kind == 'mergesort':
sdc.hiframes.sort.local_sort((data, ), (keys, ))
argsorted = list(keys)
else:
raise ValueError("Unrecognized kind of sort in sdc_arrays_argsort")
argsorted.extend(old_nan_positions)
return numpy.asarray(argsorted, dtype=numpy.int32)
return _sdc_arrays_argsort_str_arr_impl
elif isinstance(A, types.List):
return None
return None
def _sdc_pandas_series_check_axis(axis):
pass
@sdc_overload(_sdc_pandas_series_check_axis, jit_options={'parallel': False})
def _sdc_pandas_series_check_axis_overload(axis):
if isinstance(axis, types.UnicodeType):
def _sdc_pandas_series_check_axis_impl(axis):
if axis != 'index':
raise ValueError("Method sort_values(). Unsupported parameter. Given axis != 'index'")
return _sdc_pandas_series_check_axis_impl
elif isinstance(axis, types.Integer):
def _sdc_pandas_series_check_axis_impl(axis):
if axis != 0:
raise ValueError("Method sort_values(). Unsupported parameter. Given axis != 0")
return _sdc_pandas_series_check_axis_impl
return None
def _sdc_asarray(data):
pass
@sdc_overload(_sdc_asarray)
def _sdc_asarray_overload(data):
# TODO: extend with other types
if not isinstance(data, types.List):
return None
if isinstance(data.dtype, types.UnicodeType):
def _sdc_asarray_impl(data):
return create_str_arr_from_list(data)
return _sdc_asarray_impl
else:
result_dtype = data.dtype
def _sdc_asarray_impl(data):
# TODO: check if elementwise copy is needed at all
res_size = len(data)
res_arr = numpy.empty(res_size, dtype=result_dtype)
for i in numba.prange(res_size):
res_arr[i] = data[i]
return res_arr
return _sdc_asarray_impl
return None
def _sdc_take(data, indexes):
pass
@sdc_overload(_sdc_take, jit_options={'parallel': True})
def _sdc_take_overload(data, indexes):
if isinstance(data, types.Array):
arr_dtype = data.dtype
def _sdc_take_array_impl(data, indexes):
res_size = len(indexes)
res_arr = numpy.empty(res_size, dtype=arr_dtype)
for i in numba.prange(res_size):
res_arr[i] = data[indexes[i]]
return res_arr
return _sdc_take_array_impl
elif data == string_array_type:
def _sdc_take_str_arr_impl(data, indexes):
res_size = len(indexes)
nan_mask = numpy.zeros(res_size, dtype=numpy.bool_)
num_total_bytes = 0
for i in numba.prange(res_size):
num_total_bytes += get_utf8_size(data[indexes[i]])
if isna(data, indexes[i]):
nan_mask[i] = True
res_arr = pre_alloc_string_array(res_size, num_total_bytes)
for i in numpy.arange(res_size):
res_arr[i] = data[indexes[i]]
if nan_mask[i]:
str_arr_set_na(res_arr, i)
return res_arr
return _sdc_take_str_arr_impl
elif (isinstance(data, types.RangeType) and isinstance(data.dtype, types.Integer)):
arr_dtype = data.dtype
def _sdc_take_array_impl(data, indexes):
res_size = len(indexes)
index_errors = 0
res_arr = numpy.empty(res_size, dtype=arr_dtype)
for i in numba.prange(res_size):
value = data.start + data.step * indexes[i]
if value >= data.stop:
index_errors += 1
res_arr[i] = value
if index_errors:
raise IndexError("_sdc_take: index out-of-bounds")
return res_arr
return _sdc_take_array_impl
return None
def _almost_equal(x, y):
"""Check if floats are almost equal based on the float epsilon"""
pass
@sdc_overload(_almost_equal)
def _almost_equal_overload(x, y):
ty_checker = TypeChecker('Function sdc.common_functions._almost_equal_overload().')
ty_checker.check(x, types.Float)
ty_checker.check(x, types.Float)
common_dtype = numpy.find_common_type([], [x.name, y.name])
def _almost_equal_impl(x, y):
return abs(x - y) <= numpy.finfo(common_dtype).eps
return _almost_equal_impl
def sdc_reindex_series(arr, index, name, by_index):
pass
@sdc_overload(sdc_reindex_series)
def sdc_reindex_series_overload(arr, index, name, by_index):
""" Reindexes series data by new index following the logic of pandas.core.indexing.check_bool_indexer """
same_index_types = index is by_index
data_dtype, index_dtype = arr.dtype, index.dtype
data_is_str_arr = isinstance(arr.dtype, types.UnicodeType)
def sdc_reindex_series_impl(arr, index, name, by_index):
# if index types are the same, we may not reindex if indexes are the same
if same_index_types == True: # noqa
if index is by_index:
return pandas.Series(data=arr, index=index, name=name)
if data_is_str_arr == True: # noqa
_res_data = [''] * len(by_index)
res_data_nan_mask = numpy.zeros(len(by_index), dtype=types.bool_)
else:
_res_data = numpy.empty(len(by_index), dtype=data_dtype)
# build a dict of self.index values to their positions:
map_index_to_position = Dict.empty(
key_type=index_dtype,
value_type=types.int32
)
for i, value in enumerate(index):
if value in map_index_to_position:
raise ValueError("cannot reindex from a duplicate axis")
else:
map_index_to_position[value] = i
index_mismatch = 0
for i in numba.prange(len(by_index)):
if by_index[i] in map_index_to_position:
pos_in_self = map_index_to_position[by_index[i]]
_res_data[i] = arr[pos_in_self]
if data_is_str_arr == True: # noqa
res_data_nan_mask[i] = isna(arr, i)
else:
index_mismatch += 1
if index_mismatch:
msg = "Unalignable boolean Series provided as indexer " + \
"(index of the boolean Series and of the indexed object do not match)."
raise IndexingError(msg)
if data_is_str_arr == True: # noqa
res_data = create_str_arr_from_list(_res_data)
str_arr_set_na_by_mask(res_data, res_data_nan_mask)
else:
res_data = _res_data
return pandas.Series(data=res_data, index=by_index, name=name)
return sdc_reindex_series_impl