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5985 lines (4879 loc) · 233 KB
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# *****************************************************************************
# Copyright (c) 2019, 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.
# *****************************************************************************
# -*- coding: utf-8 -*-
import string
import unittest
import platform
import pandas as pd
import numpy as np
import pyarrow.parquet as pq
import sdc
from itertools import islice, permutations, product, combinations, combinations_with_replacement
from sdc.tests.test_base import TestCase
from sdc.tests.test_utils import (
count_array_REPs, count_parfor_REPs, count_array_OneDs, get_start_end,
skip_numba_jit, skip_sdc_jit, sdc_limitation, skip_parallel, skip_inline)
from sdc.tests.gen_test_data import ParquetGenerator
from numba import types
from numba.config import IS_32BITS
from numba.errors import TypingError
_cov_corr_series = [(pd.Series(x), pd.Series(y)) for x, y in [
(
[np.nan, -2., 3., 9.1],
[np.nan, -2., 3., 5.0],
),
# TODO(quasilyte): more intricate data for complex-typed series.
# Some arguments make assert_almost_equal fail.
# Functions that yield mismaching results:
# _column_corr_impl and _column_cov_impl.
(
[complex(-2., 1.0), complex(3.0, 1.0)],
[complex(-3., 1.0), complex(2.0, 1.0)],
),
(
[complex(-2.0, 1.0), complex(3.0, 1.0)],
[1.0, -2.0],
),
(
[1.0, -4.5],
[complex(-4.5, 1.0), complex(3.0, 1.0)],
),
]]
min_float64 = np.finfo('float64').min
max_float64 = np.finfo('float64').max
test_global_input_data_float64 = [
[11., 35.2, -24., 0., np.NZERO, np.NINF, np.PZERO, min_float64],
[1., np.nan, -1., 0., min_float64, max_float64, max_float64, min_float64],
[np.nan, np.inf, np.inf, np.nan, np.nan, np.nan, np.NINF, np.NZERO]
]
min_int64 = np.iinfo('int64').min
max_int64 = np.iinfo('int64').max
max_uint64 = np.iinfo('uint64').max
test_global_input_data_signed_integer64 = [
[1, -1, 0],
[min_int64, max_int64, max_int64, min_int64],
]
test_global_input_data_integer64 = test_global_input_data_signed_integer64 + [[max_uint64, max_uint64]]
test_global_input_data_numeric = test_global_input_data_integer64 + test_global_input_data_float64
test_global_input_data_unicode_kind4 = [
'ascii',
'12345',
'1234567890',
'¡Y tú quién te crees?',
'🐍⚡',
'大处着眼，小处着手。',
]
test_global_input_data_unicode_kind1 = [
'ascii',
'12345',
'1234567890',
]
def gen_srand_array(size, nchars=8):
"""Generate array of strings of specified size based on [a-zA-Z] + [0-9]"""
accepted_chars = list(string.ascii_letters + string.digits)
rands_chars = np.array(accepted_chars, dtype=(np.str_, 1))
np.random.seed(100)
return np.random.choice(rands_chars, size=nchars * size).view((np.str_, nchars))
def gen_frand_array(size, min=-100, max=100):
"""Generate array of float of specified size based on [-100-100]"""
np.random.seed(100)
return (max - min) * np.random.sample(size) + min
def gen_strlist(size, nchars=8):
"""Generate list of strings of specified size based on [a-zA-Z] + [0-9]"""
accepted_chars = string.ascii_letters + string.digits
generated_chars = islice(permutations(accepted_chars, nchars), size)
return [''.join(chars) for chars in generated_chars]
# Restores a series and checks the correct arrangement of data,
# taking into account the same elements for unstable sortings
# Example: pd.Series([15, 3, 7, 3, 1],[2, 4, 6, 8, 10])
# Result can be pd.Series([4, 1, 3, 2, 0],[2, 4, 6, 8, 10]) or pd.Series([4, 2, 3, 1, 0],[2, 4, 6, 8, 10])
# return restore series
def restore_series_argsort(series, my_result):
nona = series.dropna()
data = np.copy(nona.data)
new_result = np.copy(series.data)
result = np.copy(my_result)
for i in range(len(result)):
if result[i] != -1:
new_result[i] = data[result[i]]
else:
new_result[i] = np.nan
return new_result
# Restores a series and checks the correct arrangement of indices,
# taking into account the same elements for unstable sortings
# Example: pd.Series([15, 3, 7, 3, 1],[2, 4, 6, 8, 10])
# Result can be pd.Series([1, 3, 3, 7, 15],[10, 4, 8, 6, 2]) or pd.Series([1, 3, 3, 7, 15],[10, 8, 4, 6, 2])
# if indices correct - return 0; wrong - return 1
def restore_series_sort_values(series, my_result_index, ascending):
value_dict = {}
nan_list = []
data = np.copy(series.data)
index = np.copy(series.index)
for value in range(len(data)):
# if np.isnan(data[value]):
if series.isna()[index[value]]:
nan_list.append(index[value])
if data[value] in value_dict:
value_dict[data[value]].append(index[value])
else:
value_dict[data[value]] = [index[value]]
na = series.isna().sum()
sort = np.argsort(data)
result = np.copy(my_result_index)
if not ascending:
sort[:len(result)-na] = sort[:len(result)-na][::-1]
for i in range(len(result)-na):
check = 0
for j in value_dict[data[sort[i]]]:
if j == result[i]:
check = 1
if check == 0:
return 1
for i in range(len(result)-na, len(result)):
check = 0
for j in nan_list:
if result[i] == j:
check = 1
if check == 0:
return 1
return 0
def _make_func_from_text(func_text, func_name='test_impl'):
loc_vars = {}
exec(func_text, {}, loc_vars)
test_impl = loc_vars[func_name]
return test_impl
def _make_func_use_binop1(operator):
func_text = "def test_impl(A, B):\n"
func_text += " return A {} B\n".format(operator)
return _make_func_from_text(func_text)
def _make_func_use_binop2(operator):
func_text = "def test_impl(A, B):\n"
func_text += " A {} B\n".format(operator)
func_text += " return A\n"
return _make_func_from_text(func_text)
def _make_func_use_method_arg1(method):
func_text = "def test_impl(A, B):\n"
func_text += " return A.{}(B)\n".format(method)
return _make_func_from_text(func_text)
def ljust_usecase(series, width):
return series.str.ljust(width)
def ljust_with_fillchar_usecase(series, width, fillchar):
return series.str.ljust(width, fillchar)
def rjust_usecase(series, width):
return series.str.rjust(width)
def rjust_with_fillchar_usecase(series, width, fillchar):
return series.str.rjust(width, fillchar)
def istitle_usecase(series):
return series.str.istitle()
def isspace_usecase(series):
return series.str.isspace()
def isalpha_usecase(series):
return series.str.isalpha()
def islower_usecase(series):
return series.str.islower()
def isalnum_usecase(series):
return series.str.isalnum()
GLOBAL_VAL = 2
class TestSeries(TestCase):
@skip_numba_jit
def test_create1(self):
def test_impl():
df = pd.DataFrame({'A': [1, 2, 3]})
return (df.A == 1).sum()
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(), test_impl())
@unittest.skip('Feature request: implement Series::ctor with list(list(type))')
def test_create_list_list_unicode(self):
def test_impl():
S = pd.Series([
['abc', 'defg', 'ijk'],
['lmn', 'opq', 'rstuvwxyz']
])
return S
hpat_func = self.jit(test_impl)
result_ref = test_impl()
result = hpat_func()
pd.testing.assert_series_equal(result, result_ref)
@unittest.skip('Feature request: implement Series::ctor with list(list(type))')
def test_create_list_list_integer(self):
def test_impl():
S = pd.Series([
[123, 456, -789],
[-112233, 445566, 778899]
])
return S
hpat_func = self.jit(test_impl)
result_ref = test_impl()
result = hpat_func()
pd.testing.assert_series_equal(result, result_ref)
@unittest.skip('Feature request: implement Series::ctor with list(list(type))')
def test_create_list_list_float(self):
def test_impl():
S = pd.Series([
[1.23, -4.56, 7.89],
[11.2233, 44.5566, -778.899]
])
return S
hpat_func = self.jit(test_impl)
result_ref = test_impl()
result = hpat_func()
pd.testing.assert_series_equal(result, result_ref)
@skip_numba_jit
def test_create2(self):
def test_impl(n):
df = pd.DataFrame({'A': np.arange(n)})
return (df.A == 2).sum()
hpat_func = self.jit(test_impl)
n = 11
self.assertEqual(hpat_func(n), test_impl(n))
def test_create_series1(self):
def test_impl():
A = pd.Series([1, 2, 3])
return A
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(hpat_func(), test_impl())
def test_create_series_index1(self):
# create and box an indexed Series
def test_impl():
A = pd.Series([1, 2, 3], ['A', 'C', 'B'])
return A
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(hpat_func(), test_impl())
def test_create_series_index2(self):
def test_impl():
A = pd.Series([1, 2, 3], index=[2, 1, 0])
return A
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(hpat_func(), test_impl())
def test_create_series_index3(self):
def test_impl():
A = pd.Series([1, 2, 3], index=['A', 'C', 'B'], name='A')
return A
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(hpat_func(), test_impl())
def test_create_series_index4(self):
def test_impl(name):
A = pd.Series([1, 2, 3], index=['A', 'C', 'B'], name=name)
return A
hpat_func = self.jit(test_impl)
pd.testing.assert_series_equal(hpat_func('A'), test_impl('A'))
@skip_numba_jit
def test_create_str(self):
def test_impl():
df = pd.DataFrame({'A': ['a', 'b', 'c']})
return (df.A == 'a').sum()
hpat_func = self.jit(test_impl)
self.assertEqual(hpat_func(), test_impl())
@skip_numba_jit
def test_pass_df1(self):
def test_impl(df):
return (df.A == 2).sum()
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
self.assertEqual(hpat_func(df), test_impl(df))
@skip_numba_jit
def test_pass_df_str(self):
def test_impl(df):
return (df.A == 'a').sum()
hpat_func = self.jit(test_impl)
df = pd.DataFrame({'A': ['a', 'b', 'c']})
self.assertEqual(hpat_func(df), test_impl(df))
@skip_numba_jit
def test_pass_series1(self):
# TODO: check to make sure it is series type
def test_impl(A):
return (A == 2).sum()
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
self.assertEqual(hpat_func(df.A), test_impl(df.A))
@skip_numba_jit
def test_pass_series2(self):
# test creating dataframe from passed series
def test_impl(A):
df = pd.DataFrame({'A': A})
return (df.A == 2).sum()
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
self.assertEqual(hpat_func(df.A), test_impl(df.A))
@skip_numba_jit
def test_pass_series_str(self):
def test_impl(A):
return (A == 'a').sum()
hpat_func = self.jit(test_impl)
df = pd.DataFrame({'A': ['a', 'b', 'c']})
self.assertEqual(hpat_func(df.A), test_impl(df.A))
def test_pass_series_index1(self):
def test_impl(A):
return A
hpat_func = self.jit(test_impl)
S = pd.Series([3, 5, 6], ['a', 'b', 'c'], name='A')
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
def test_series_size(self):
def test_impl(S):
return S.size
hpat_func = self.jit(test_impl)
n = 11
for S, expected in [
(pd.Series(), 0),
(pd.Series([]), 0),
(pd.Series(np.arange(n)), n),
(pd.Series([np.nan, 1, 2]), 3),
(pd.Series(['1', '2', '3']), 3),
]:
with self.subTest(S=S, expected=expected):
self.assertEqual(hpat_func(S), expected)
self.assertEqual(hpat_func(S), test_impl(S))
@skip_numba_jit
def test_series_attr2(self):
def test_impl(A):
return A.copy().values
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
np.testing.assert_array_equal(hpat_func(df.A), test_impl(df.A))
def test_series_attr3(self):
def test_impl(A):
return A.min()
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
self.assertEqual(hpat_func(df.A), test_impl(df.A))
@skip_numba_jit("This test passes in group and fails in single.")
def test_series_attr4(self):
def test_impl(A):
return A.cumsum().values
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
np.testing.assert_array_equal(hpat_func(df.A), test_impl(df.A))
@skip_numba_jit
def test_series_argsort1(self):
def test_impl(A):
return A.argsort()
hpat_func = self.jit(test_impl)
n = 11
A = pd.Series(np.random.ranf(n))
pd.testing.assert_series_equal(hpat_func(A), test_impl(A))
@skip_numba_jit
def test_series_argsort2(self):
def test_impl(S):
return S.argsort()
hpat_func = self.jit(test_impl)
S = pd.Series([1, -1, 0, 1, np.nan], [1, 2, 3, 4, 5])
pd.testing.assert_series_equal(test_impl(S), hpat_func(S))
@skip_numba_jit
def test_series_argsort_full(self):
def test_impl(series, kind):
return series.argsort(axis=0, kind=kind, order=None)
hpat_func = self.jit(test_impl)
all_data = test_global_input_data_numeric
for data in all_data:
for kind in ['quicksort', 'mergesort']:
series = pd.Series(data * 3)
ref_result = test_impl(series, kind=kind)
jit_result = hpat_func(series, kind=kind)
ref = restore_series_argsort(series, ref_result.data)
jit = restore_series_argsort(series, jit_result.data)
if kind == 'mergesort':
pd.testing.assert_series_equal(ref_result, jit_result)
else:
np.testing.assert_array_equal(ref, jit)
@skip_numba_jit
def test_series_argsort_full_idx(self):
def test_impl(series, kind):
return series.argsort(axis=0, kind=kind, order=None)
hpat_func = self.jit(test_impl)
all_data = test_global_input_data_numeric
for data in all_data:
data = data * 3
for index in [gen_srand_array(len(data)), gen_frand_array(len(data)), range(len(data))]:
for kind in ['quicksort', 'mergesort']:
series = pd.Series(data, index)
ref_result = test_impl(series, kind=kind)
jit_result = hpat_func(series, kind=kind)
ref = restore_series_argsort(series, ref_result.data)
jit = restore_series_argsort(series, jit_result.data)
if kind == 'mergesort':
pd.testing.assert_series_equal(ref_result, jit_result)
else:
np.testing.assert_array_equal(ref, jit)
@skip_numba_jit
def test_series_attr6(self):
def test_impl(A):
return A.take([2, 3]).values
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
np.testing.assert_array_equal(hpat_func(df.A), test_impl(df.A))
def test_series_attr7(self):
def test_impl(A):
return A.astype(np.float64)
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
np.testing.assert_array_equal(hpat_func(df.A), test_impl(df.A))
def test_series_getattr_ndim(self):
'''Verifies getting Series attribute ndim is supported'''
def test_impl(S):
return S.ndim
hpat_func = self.jit(test_impl)
n = 11
S = pd.Series(np.arange(n))
self.assertEqual(hpat_func(S), test_impl(S))
def test_series_getattr_T(self):
'''Verifies getting Series attribute T is supported'''
def test_impl(S):
return S.T
hpat_func = self.jit(test_impl)
n = 11
S = pd.Series(np.arange(n))
np.testing.assert_array_equal(hpat_func(S), test_impl(S))
def test_series_copy_str1(self):
def test_impl(A):
return A.copy()
hpat_func = self.jit(test_impl)
S = pd.Series(['aa', 'bb', 'cc'])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
def test_series_copy_int1(self):
def test_impl(A):
return A.copy()
hpat_func = self.jit(test_impl)
S = pd.Series([1, 2, 3])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
def test_series_copy_deep(self):
def test_impl(A, deep):
return A.copy(deep=deep)
hpat_func = self.jit(test_impl)
for S in [
pd.Series([1, 2]),
pd.Series([1, 2], index=["a", "b"]),
pd.Series([1, 2], name='A'),
pd.Series([1, 2], index=["a", "b"], name='A'),
]:
with self.subTest(S=S):
for deep in (True, False):
with self.subTest(deep=deep):
actual = hpat_func(S, deep)
expected = test_impl(S, deep)
pd.testing.assert_series_equal(actual, expected)
self.assertEqual(actual.values is S.values, expected.values is S.values)
self.assertEqual(actual.values is S.values, not deep)
# Shallow copy of index is not supported yet
if deep:
self.assertEqual(actual.index is S.index, expected.index is S.index)
self.assertEqual(actual.index is S.index, not deep)
@skip_parallel
@skip_sdc_jit('Series.corr() parameter "min_periods" unsupported')
def test_series_corr(self):
def test_series_corr_impl(S1, S2, min_periods=None):
return S1.corr(S2, min_periods=min_periods)
hpat_func = self.jit(test_series_corr_impl)
test_input_data1 = [[.2, .0, .6, .2],
[.2, .0, .6, .2, .5, .6, .7, .8],
[],
[2, 0, 6, 2],
[.2, .1, np.nan, .5, .3],
[-1, np.nan, 1, np.inf]]
test_input_data2 = [[.3, .6, .0, .1],
[.3, .6, .0, .1, .8],
[],
[3, 6, 0, 1],
[.3, .2, .9, .6, np.nan],
[np.nan, np.nan, np.inf, np.nan]]
for input_data1 in test_input_data1:
for input_data2 in test_input_data2:
S1 = pd.Series(input_data1)
S2 = pd.Series(input_data2)
for period in [None, 2, 1, 8, -4]:
result_ref = test_series_corr_impl(S1, S2, min_periods=period)
result = hpat_func(S1, S2, min_periods=period)
np.testing.assert_allclose(result, result_ref)
@skip_sdc_jit('Series.corr() parameter "min_periods" unsupported')
def test_series_corr_unsupported_dtype(self):
def test_series_corr_impl(S1, S2, min_periods=None):
return S1.corr(S2, min_periods=min_periods)
hpat_func = self.jit(test_series_corr_impl)
S1 = pd.Series([.2, .0, .6, .2])
S2 = pd.Series(['abcdefgh', 'a', 'abcdefg', 'ab', 'abcdef', 'abc'])
S3 = pd.Series(['aaaaa', 'bbbb', 'ccc', 'dd', 'e'])
S4 = pd.Series([.3, .6, .0, .1])
with self.assertRaises(TypingError) as raises:
hpat_func(S1, S2, min_periods=5)
msg = 'Method corr(). The object other.data'
self.assertIn(msg, str(raises.exception))
with self.assertRaises(TypingError) as raises:
hpat_func(S3, S4, min_periods=5)
msg = 'Method corr(). The object self.data'
self.assertIn(msg, str(raises.exception))
@skip_sdc_jit('Series.corr() parameter "min_periods" unsupported')
def test_series_corr_unsupported_period(self):
def test_series_corr_impl(S1, S2, min_periods=None):
return S1.corr(S2, min_periods=min_periods)
hpat_func = self.jit(test_series_corr_impl)
S1 = pd.Series([.2, .0, .6, .2])
S2 = pd.Series([.3, .6, .0, .1])
with self.assertRaises(TypingError) as raises:
hpat_func(S1, S2, min_periods='aaaa')
msg = 'Method corr(). The object min_periods'
self.assertIn(msg, str(raises.exception))
with self.assertRaises(TypingError) as raises:
hpat_func(S1, S2, min_periods=0.5)
msg = 'Method corr(). The object min_periods'
self.assertIn(msg, str(raises.exception))
@skip_parallel
@skip_inline
def test_series_astype_int_to_str1(self):
'''Verifies Series.astype implementation with function 'str' as argument
converts integer series to series of strings
'''
def test_impl(S):
return S.astype(str)
hpat_func = self.jit(test_impl)
n = 11
S = pd.Series(np.arange(n))
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@skip_parallel
@skip_inline
def test_series_astype_int_to_str2(self):
'''Verifies Series.astype implementation with a string literal dtype argument
converts integer series to series of strings
'''
def test_impl(S):
return S.astype('str')
hpat_func = self.jit(test_impl)
n = 11
S = pd.Series(np.arange(n))
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@skip_parallel
@skip_inline
def test_series_astype_str_to_str1(self):
'''Verifies Series.astype implementation with function 'str' as argument
handles string series not changing it
'''
def test_impl(S):
return S.astype(str)
hpat_func = self.jit(test_impl)
S = pd.Series(['aa', 'bb', 'cc'])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@skip_parallel
@skip_inline
def test_series_astype_str_to_str2(self):
'''Verifies Series.astype implementation with a string literal dtype argument
handles string series not changing it
'''
def test_impl(S):
return S.astype('str')
hpat_func = self.jit(test_impl)
S = pd.Series(['aa', 'bb', 'cc'])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@skip_parallel
@skip_inline
def test_series_astype_str_to_str_index_str(self):
'''Verifies Series.astype implementation with function 'str' as argument
handles string series not changing it
'''
def test_impl(S):
return S.astype(str)
hpat_func = self.jit(test_impl)
S = pd.Series(['aa', 'bb', 'cc'], index=['d', 'e', 'f'])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@skip_parallel
@skip_inline
def test_series_astype_str_to_str_index_int(self):
'''Verifies Series.astype implementation with function 'str' as argument
handles string series not changing it
'''
def test_impl(S):
return S.astype(str)
hpat_func = self.jit(test_impl)
S = pd.Series(['aa', 'bb', 'cc'], index=[1, 2, 3])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@unittest.skip('TODO: requires str(datetime64) support in Numba')
def test_series_astype_dt_to_str1(self):
'''Verifies Series.astype implementation with function 'str' as argument
converts datetime series to series of strings
'''
def test_impl(A):
return A.astype(str)
hpat_func = self.jit(test_impl)
S = pd.Series([pd.Timestamp('20130101 09:00:00'),
pd.Timestamp('20130101 09:00:02'),
pd.Timestamp('20130101 09:00:03')
])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@unittest.skip('AssertionError: Series are different'
'[left]: [0.000000, 1.000000, 2.000000, 3.000000, ...'
'[right]: [0.0, 1.0, 2.0, 3.0, ...'
'TODO: needs alignment to NumPy on Numba side')
def test_series_astype_float_to_str1(self):
'''Verifies Series.astype implementation with function 'str' as argument
converts float series to series of strings
'''
def test_impl(A):
return A.astype(str)
hpat_func = self.jit(test_impl)
n = 11.0
S = pd.Series(np.arange(n))
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
def test_series_astype_int32_to_int64(self):
'''Verifies Series.astype implementation with NumPy dtype argument
converts series with dtype=int32 to series with dtype=int64
'''
def test_impl(A):
return A.astype(np.int64)
hpat_func = self.jit(test_impl)
n = 11
S = pd.Series(np.arange(n), dtype=np.int32)
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
def test_series_astype_int_to_float64(self):
'''Verifies Series.astype implementation with NumPy dtype argument
converts integer series to series of float
'''
def test_impl(A):
return A.astype(np.float64)
hpat_func = self.jit(test_impl)
n = 11
S = pd.Series(np.arange(n))
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
def test_series_astype_float_to_int32(self):
'''Verifies Series.astype implementation with NumPy dtype argument
converts float series to series of integers
'''
def test_impl(A):
return A.astype(np.int32)
hpat_func = self.jit(test_impl)
n = 11.0
S = pd.Series(np.arange(n))
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
def test_series_astype_literal_dtype1(self):
'''Verifies Series.astype implementation with a string literal dtype argument
converts float series to series of integers
'''
def test_impl(A):
return A.astype('int32')
hpat_func = self.jit(test_impl)
n = 11.0
S = pd.Series(np.arange(n))
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@unittest.skip('TODO: needs Numba astype impl support converting unicode_type to int')
def test_series_astype_str_to_int32(self):
'''Verifies Series.astype implementation with NumPy dtype argument
converts series of strings to series of integers
'''
import numba
def test_impl(A):
return A.astype(np.int32)
hpat_func = self.jit(test_impl)
n = 11
S = pd.Series([str(x) for x in np.arange(n) - n // 2])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@unittest.skip('TODO: needs Numba astype impl support converting unicode_type to float')
def test_series_astype_str_to_float64(self):
'''Verifies Series.astype implementation with NumPy dtype argument
converts series of strings to series of float
'''
def test_impl(A):
return A.astype(np.float64)
hpat_func = self.jit(test_impl)
S = pd.Series(['3.24', '1E+05', '-1', '-1.3E-01', 'nan', 'inf'])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@skip_parallel
@skip_inline
def test_series_astype_str_index_str(self):
'''Verifies Series.astype implementation with function 'str' as argument
handles string series not changing it
'''
def test_impl(S):
return S.astype(str)
hpat_func = self.jit(test_impl)
S = pd.Series(['aa', 'bb', 'cc'], index=['a', 'b', 'c'])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@skip_parallel
@skip_inline
def test_series_astype_str_index_int(self):
'''Verifies Series.astype implementation with function 'str' as argument
handles string series not changing it
'''
def test_impl(S):
return S.astype(str)
hpat_func = self.jit(test_impl)
S = pd.Series(['aa', 'bb', 'cc'], index=[2, 3, 5])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
def test_series_astype_errors_ignore_return_self_str(self):
'''Verifies Series.astype implementation return self object on error
if errors='ignore' is passed in arguments
'''
def test_impl(S):
return S.astype(np.float64, errors='ignore')
hpat_func = self.jit(test_impl)
S = pd.Series(['aa', 'bb', 'cc'], index=[2, 3, 5])
pd.testing.assert_series_equal(hpat_func(S), test_impl(S))
@skip_numba_jit
def test_np_call_on_series1(self):
def test_impl(A):
return np.min(A)
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
np.testing.assert_array_equal(hpat_func(df.A), test_impl(df.A))
def test_series_values(self):
def test_impl(A):
return A.values
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
np.testing.assert_array_equal(hpat_func(df.A), test_impl(df.A))
@skip_numba_jit
def test_series_values1(self):
def test_impl(A):
return (A == 2).values
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
np.testing.assert_array_equal(hpat_func(df.A), test_impl(df.A))
def test_series_shape1(self):
def test_impl(A):
return A.shape
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
self.assertEqual(hpat_func(df.A), test_impl(df.A))
@skip_numba_jit
def test_static_setitem_series1(self):
def test_impl(A):
A[0] = 2
return (A == 2).sum()
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
self.assertEqual(hpat_func(df.A), test_impl(df.A))
@skip_numba_jit
def test_setitem_series1(self):
def test_impl(A, i):
A[i] = 2
return (A == 2).sum()
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
self.assertEqual(hpat_func(df.A.copy(), 0), test_impl(df.A.copy(), 0))
@skip_numba_jit
def test_setitem_series2(self):
def test_impl(A, i):
A[i] = 100
hpat_func = self.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.arange(n)})
A1 = df.A.copy()
A2 = df.A
hpat_func(A1, 0)
test_impl(A2, 0)
pd.testing.assert_series_equal(A1, A2)
@unittest.skip("enable after remove dead in hiframes is removed")
def test_setitem_series3(self):
def test_impl(A, i):
S = pd.Series(A)
S[i] = 100
hpat_func = self.jit(test_impl)
n = 11
A = np.arange(n)
A1 = A.copy()
A2 = A
hpat_func(A1, 0)