def test_pd_DataFrame_from_series_par(self):
def test_impl(n):
S1 = pd.Series(np.ones(n))
S2 = pd.Series(np.random.ranf(n))
df = pd.DataFrame({'A': S1, 'B': S2})
return df.A.sum()
hpat_func = hpat.jit(test_impl)
n = 11
self.assertEqual(hpat_func(n), test_impl(n))
self.assertEqual(count_array_REPs(), 0)
self.assertEqual(count_parfor_REPs(), 0)
self.assertEqual(count_parfor_OneDs(), 1)
def test_h5_group_keys(self):
def test_impl():
f = h5py.File("test_group_read.hdf5", "r")
g1 = f['G']
s = 0
for dname in g1.keys():
X = g1[dname][:]
s += X.sum()
f.close()
return s
hpat_func = hpat.jit(test_impl, h5_types={'X': hpat.int64[:]})
self.assertEqual(hpat_func(), test_impl())
def test_series_count1(self):
def test_impl(S):
return S.count()
hpat_func = hpat.jit(test_impl)
S = pd.Series([np.nan, 2., 3.])
self.assertEqual(hpat_func(S), test_impl(S))
S = pd.Series([np.nan, np.nan])
self.assertEqual(hpat_func(S), test_impl(S))
S = pd.Series(['aa', 'bb', np.nan])
self.assertEqual(hpat_func(S), test_impl(S))
def test_column_map_arg(self):
def test_impl(df):
df['B'] = df.A.map(lambda a: 2 * a)
return
n = 121
df1 = pd.DataFrame({'A': np.arange(n)})
df2 = pd.DataFrame({'A': np.arange(n)})
hpat_func = hpat.jit(test_impl)
hpat_func(df1)
self.assertTrue(hasattr(df1, 'B'))
test_impl(df2)
np.testing.assert_equal(df1.B.values, df2.B.values)
def test_decode_unicode2(self):
# test strings that start with ascii
def test_impl(S):
return S[0], S[1], S[2]
hpat_func = hpat.jit(test_impl)
S = pd.Series([
'abc¡Y tú quién te crees?',
'dd2🐍⚡',
'22 大处着眼，小处着手。',
])
self.assertEqual(hpat_func(S), test_impl(S))
def test_series_sort_values_index1(self):
def test_impl(A, B):
S = pd.Series(A, B)
return S.sort_values()
hpat_func = hpat.jit(test_impl)
n = 11
np.random.seed(0)
# TODO: support passing Series with Index
# S = pd.Series(np.random.ranf(n), np.random.randint(0, 100, n))
A = np.random.ranf(n)
B = np.random.ranf(n)
pd.testing.assert_series_equal(hpat_func(A, B), test_impl(A, B))
def test_merge_asof_seq1(self):
def test_impl(df1, df2):
return pd.merge_asof(df1, df2, on='time')
hpat_func = hpat.jit(test_impl)
df1 = pd.DataFrame(
{'time': pd.DatetimeIndex(
['2017-01-03', '2017-01-06', '2017-02-21']), 'B': [4, 5, 6]})
df2 = pd.DataFrame(
{'time': pd.DatetimeIndex(
['2017-01-01', '2017-01-02', '2017-01-04', '2017-02-23',
'2017-02-25']), 'A': [2,3,7,8,9]})
pd.testing.assert_frame_equal(hpat_func(df1, df2), test_impl(df1, df2))
def test_pass_return(self):
def test_impl(_str):
return _str
hpat_func = hpat.jit(test_impl)
# pass single string and return
arg = 'test_str'
self.assertEqual(hpat_func(arg), test_impl(arg))
# pass string list and return
arg = ['test_str1', 'test_str2']
self.assertEqual(hpat_func(arg), test_impl(arg))
def test_set_column1(self):
# set existing column
def test_impl(n):
df = pd.DataFrame({'A': np.ones(n, np.int64), 'B': np.random.ranf(n)})
df['A'] = np.arange(n)
return df.A.sum()
hpat_func = hpat.jit(test_impl)
n = 11
self.assertEqual(hpat_func(n), test_impl(n))
self.assertEqual(count_array_REPs(), 0)
self.assertEqual(count_parfor_REPs(), 0)
self.assertEqual(count_parfor_OneDs(), 1)
def test_set_column2(self):
# create new column
def test_impl(n):
df = pd.DataFrame({'A': np.ones(n), 'B': np.random.ranf(n)})
df['C'] = np.arange(n)
return df.C.sum()
hpat_func = hpat.jit(test_impl)
n = 11
self.assertEqual(hpat_func(n), test_impl(n))
self.assertEqual(count_array_REPs(), 0)
self.assertEqual(count_parfor_REPs(), 0)
self.assertEqual(count_parfor_OneDs(), 1)
def test_muti_hiframes_node_filter_agg(self):
def test_impl(df, cond):
df2 = df[cond]
c = df2.groupby('A')['B'].count()
return df2.C, c
hpat_func = hpat.jit(test_impl)
df = pd.DataFrame({'A': [2,1,1,1,2,2,1], 'B': [-8,2,3,1,5,6,7], 'C': [2,3,-1,1,2,3,-1]})
cond = df.A > 1
res = test_impl(df, cond)
h_res = hpat_func(df, cond)
self.assertEqual(set(res[1]), set(h_res[1]))
np.testing.assert_array_equal(res[0], h_res[0])
def test_df_astype_str1(self):
'''Verifies DataFrame.astype implementation converting various types to string'''
def test_impl(df):
return df.astype(str)
hpat_func = hpat.jit(test_impl)
# TODO: add column with float values when test_series_astype_float_to_str1 is fixed
df = pd.DataFrame({
'A': [-1, 2, 11, 5, 0, -7],
'B': ['aa', 'bb', 'cc', 'dd', '', 'fff']
})
pd.testing.assert_frame_equal(hpat_func(df), test_impl(df))
def test_shape1(self):
def test_impl(n):
df = pd.DataFrame({
'A': np.ones(n, np.int64),
'B': np.random.ranf(n)
})
return df.shape
hpat_func = hpat.jit(test_impl)
n = 11
self.assertEqual(hpat_func(n), test_impl(n))
self.assertEqual(count_array_REPs(), 0)
self.assertEqual(count_parfor_REPs(), 0)
def test_replace_noregex_std(self):
def test_impl(_str, pat, val):
s = unicode_to_std_str(_str)
e = unicode_to_std_str(pat)
val = unicode_to_std_str(val)
out = hpat.str_ext.str_replace_noregex(s, e, val)
return std_str_to_unicode(out)
_str = 'What does the fox say'
pat = 'does the fox'
val = 'does the cat'
hpat_func = hpat.jit(test_impl)
self.assertEqual(hpat_func(_str, pat, val), _str.replace(pat, val))
def test_array_reduce(self):
def test_impl(N):
A = np.ones(3);
B = np.ones(3);
for i in numba.prange(N):
A += B
return A
hpat_func = hpat.jit(test_impl)
n = 128
np.testing.assert_allclose(hpat_func(n), test_impl(n))
self.assertEqual(count_array_OneDs(), 0)
self.assertEqual(count_parfor_OneDs(), 1)
def test_df_describe(self):
def test_impl(n):
df = pd.DataFrame({'A': np.arange(0, n, 1, np.float32),
'B': np.arange(n)})
#df.A[0:1] = np.nan
return df.describe()
hpat_func = hpat.jit(test_impl)
n = 1001
hpat_func(n)
# XXX: test actual output
self.assertEqual(count_array_REPs(), 0)
self.assertEqual(count_parfor_REPs(), 0)
def test_str2str(self):
str2str_methods = ['capitalize', 'casefold', 'lower', 'lstrip',
'rstrip', 'strip', 'swapcase', 'title', 'upper']
for method in str2str_methods:
func_text = "def test_impl(_str):\n"
func_text += " return _str.{}()\n".format(method)
loc_vars = {}
exec(func_text, {}, loc_vars)
test_impl = loc_vars['test_impl']
hpat_func = hpat.jit(test_impl)
arg = ' \tbbCD\t '
self.assertEqual(hpat_func(arg), test_impl(arg))
def test_string_NA_box(self):
# create `example.parquet` file
ParquetGenerator.gen_pq_test()
def test_impl():
df = pq.read_table('example.parquet').to_pandas()
return df.five
hpat_func = hpat.jit(test_impl)
# XXX just checking isna() since Pandas uses None in this case
# instead of nan for some reason
np.testing.assert_array_equal(hpat_func().isna(), test_impl().isna())
def test_set_column1_issue(self):
# set existing column
def test_impl(n):
df = pd.DataFrame({
'A': np.ones(n, np.int64),
'B': np.arange(n) + 3.0
})
df['A'] = np.arange(n)
return df
hpat_func = hpat.jit(test_impl)
n = 11
pd.testing.assert_frame_equal(hpat_func(n), test_impl(n))
def test_join1_seq_str_na(self):
# test setting NA in string data column
def test_impl():
df1 = pd.DataFrame({'key1': ['foo', 'bar', 'baz']})
df2 = pd.DataFrame({
'key2': ['baz', 'bar', 'baz'],
'B': ['b', 'zzz', 'ss']
})
df3 = df1.merge(df2, left_on='key1', right_on='key2', how='left')
return df3.B
hpat_func = hpat.jit(test_impl)
self.assertEqual(set(hpat_func()), set(test_impl()))
def test_pivot(self):
def test_impl(df):
pt = df.pivot_table(index='A',
columns='C',
values='D',
aggfunc='sum')
return (pt.small.values, pt.large.values)
hpat_func = hpat.jit(pivots={'pt': ['small', 'large']})(test_impl)
self.assertEqual(set(hpat_func(_pivot_df1)[0]),
set(test_impl(_pivot_df1)[0]))
self.assertEqual(set(hpat_func(_pivot_df1)[1]),
set(test_impl(_pivot_df1)[1]))
def test_setitem_series3(self):
def test_impl(A, i):
S = pd.Series(A)
S[i] = 100
hpat_func = hpat.jit(test_impl)
n = 11
A = np.arange(n)
A1 = A.copy()
A2 = A
hpat_func(A1, 0)
test_impl(A2, 0)
np.testing.assert_array_equal(A1, A2)
def test_csv_str1(self):
def test_impl():
return pd.read_csv("csv_data_date1.csv",
names=['A', 'B', 'C', 'D'],
dtype={
'A': np.int,
'B': np.float,
'C': str,
'D': np.int
})
hpat_func = hpat.jit(test_impl)
pd.testing.assert_frame_equal(hpat_func(), test_impl())
def test_fixed_apply2(self):
# test sequentially with generated dfs
def test_impl(df, w, c):
return df.rolling(w, center=c).apply(lambda a: a.sum())
hpat_func = hpat.jit(test_impl)
sizes = (1, 2, 10, 11, 121, 1000)
wins = (2, 3, 5)
centers = (False, True)
for n, w, c in itertools.product(sizes, wins, centers):
df = pd.DataFrame({'B': np.arange(n)})
pd.testing.assert_frame_equal(hpat_func(df, w, c),
test_impl(df, w, c))
def test_df_astype_int1(self):
'''Verifies DataFrame.astype implementation converting various types to int'''
def test_impl(df):
return df.astype(np.int32)
hpat_func = hpat.jit(test_impl)
n = 6
# TODO: uncomment column with string values when test_series_astype_str_to_int32 is fixed
df = pd.DataFrame({'A': np.ones(n, dtype=np.int64),
'B': np.arange(n, dtype=np.int32),
# 'C': ['-1', '2', '3', '0', '-7', '99'],
'D': np.arange(float(n), dtype=np.float32)
})
pd.testing.assert_frame_equal(hpat_func(df), test_impl(df))
def test_unbox2(self):
def test_impl(df, cond):
n = len(df)
if cond:
df['A'] = np.arange(n) + 2.0
return df.A
hpat_func = hpat.jit(test_impl)
n = 11
df = pd.DataFrame({'A': np.ones(n), 'B': np.random.ranf(n)})
pd.testing.assert_series_equal(hpat_func(df.copy(), True),
test_impl(df.copy(), True))
pd.testing.assert_series_equal(hpat_func(df.copy(), False),
test_impl(df.copy(), False))
def test_sort_values_copy(self):
def test_impl(df):
df2 = df.sort_values('A')
return df2.B.values
n = 1211
np.random.seed(2)
df = pd.DataFrame({
'A': np.random.ranf(n),
'B': np.arange(n),
'C': np.random.ranf(n)
})
hpat_func = hpat.jit(test_impl)
np.testing.assert_almost_equal(hpat_func(df.copy()), test_impl(df))
def test_box_categorical(self):
def test_impl(df):
df['A'] = df['A'] + 1
return df
hpat_func = hpat.jit(test_impl)
df = pd.DataFrame({
'A': [1, 2, 3],
'B':
pd.Series(['N', 'Y', 'Y'],
dtype=pd.api.types.CategoricalDtype(['N', 'Y']))
})
pd.testing.assert_frame_equal(hpat_func(df.copy(deep=True)),
test_impl(df))
def test_var_rename(self):
# tests df variable replacement in hiframes_untyped where inlining
# can cause extra assignments and definition handling errors
# TODO: inline freevar
def test_impl():
df = pd.DataFrame({'A': [1, 2, 3], 'B': [2, 3, 4]})
# TODO: df['C'] = [5,6,7]
df['C'] = np.ones(3)
return inner_get_column(df)
hpat_func = hpat.jit(test_impl)
pd.testing.assert_series_equal(hpat_func(),
test_impl(),
check_names=False)
def test_concat_columns1(self):
def test_impl(S1, S2):
return pd.concat([S1, S2], axis=1)
hpat_func = hpat.jit(test_impl)
S1 = pd.Series([4, 5])
S2 = pd.Series([6., 7.])
# TODO: support int as column name
pd.testing.assert_frame_equal(
hpat_func(S1, S2),
test_impl(S1, S2).rename(columns={
0: '0',
1: '1'
}))