Alle Dateien aus dem Pythonkurs
You can not select more than 25 topics Topics must start with a letter or number, can include dashes ('-') and can be up to 35 characters long.
 
 
 
 

284 lines
9.5 KiB

import numpy as np
import pytest
from pandas.errors import NumbaUtilError
from pandas import (
DataFrame,
Series,
option_context,
)
import pandas._testing as tm
pytestmark = pytest.mark.single_cpu
def test_correct_function_signature():
pytest.importorskip("numba")
def incorrect_function(x):
return x + 1
data = DataFrame(
{"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
columns=["key", "data"],
)
with pytest.raises(NumbaUtilError, match="The first 2"):
data.groupby("key").transform(incorrect_function, engine="numba")
with pytest.raises(NumbaUtilError, match="The first 2"):
data.groupby("key")["data"].transform(incorrect_function, engine="numba")
def test_check_nopython_kwargs():
pytest.importorskip("numba")
def incorrect_function(values, index):
return values + 1
data = DataFrame(
{"key": ["a", "a", "b", "b", "a"], "data": [1.0, 2.0, 3.0, 4.0, 5.0]},
columns=["key", "data"],
)
with pytest.raises(NumbaUtilError, match="numba does not support"):
data.groupby("key").transform(incorrect_function, engine="numba", a=1)
with pytest.raises(NumbaUtilError, match="numba does not support"):
data.groupby("key")["data"].transform(incorrect_function, engine="numba", a=1)
@pytest.mark.filterwarnings("ignore")
# Filter warnings when parallel=True and the function can't be parallelized by Numba
@pytest.mark.parametrize("jit", [True, False])
@pytest.mark.parametrize("pandas_obj", ["Series", "DataFrame"])
@pytest.mark.parametrize("as_index", [True, False])
def test_numba_vs_cython(jit, pandas_obj, nogil, parallel, nopython, as_index):
pytest.importorskip("numba")
def func(values, index):
return values + 1
if jit:
# Test accepted jitted functions
import numba
func = numba.jit(func)
data = DataFrame(
{0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
)
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
grouped = data.groupby(0, as_index=as_index)
if pandas_obj == "Series":
grouped = grouped[1]
result = grouped.transform(func, engine="numba", engine_kwargs=engine_kwargs)
expected = grouped.transform(lambda x: x + 1, engine="cython")
tm.assert_equal(result, expected)
@pytest.mark.filterwarnings("ignore")
# Filter warnings when parallel=True and the function can't be parallelized by Numba
@pytest.mark.parametrize("jit", [True, False])
@pytest.mark.parametrize("pandas_obj", ["Series", "DataFrame"])
def test_cache(jit, pandas_obj, nogil, parallel, nopython):
# Test that the functions are cached correctly if we switch functions
pytest.importorskip("numba")
def func_1(values, index):
return values + 1
def func_2(values, index):
return values * 5
if jit:
import numba
func_1 = numba.jit(func_1)
func_2 = numba.jit(func_2)
data = DataFrame(
{0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
)
engine_kwargs = {"nogil": nogil, "parallel": parallel, "nopython": nopython}
grouped = data.groupby(0)
if pandas_obj == "Series":
grouped = grouped[1]
result = grouped.transform(func_1, engine="numba", engine_kwargs=engine_kwargs)
expected = grouped.transform(lambda x: x + 1, engine="cython")
tm.assert_equal(result, expected)
result = grouped.transform(func_2, engine="numba", engine_kwargs=engine_kwargs)
expected = grouped.transform(lambda x: x * 5, engine="cython")
tm.assert_equal(result, expected)
# Retest func_1 which should use the cache
result = grouped.transform(func_1, engine="numba", engine_kwargs=engine_kwargs)
expected = grouped.transform(lambda x: x + 1, engine="cython")
tm.assert_equal(result, expected)
def test_use_global_config():
pytest.importorskip("numba")
def func_1(values, index):
return values + 1
data = DataFrame(
{0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
)
grouped = data.groupby(0)
expected = grouped.transform(func_1, engine="numba")
with option_context("compute.use_numba", True):
result = grouped.transform(func_1, engine=None)
tm.assert_frame_equal(expected, result)
# TODO: Test more than just reductions (e.g. actually test transformations once we have
@pytest.mark.parametrize(
"agg_func", [["min", "max"], "min", {"B": ["min", "max"], "C": "sum"}]
)
def test_string_cython_vs_numba(agg_func, numba_supported_reductions):
pytest.importorskip("numba")
agg_func, kwargs = numba_supported_reductions
data = DataFrame(
{0: ["a", "a", "b", "b", "a"], 1: [1.0, 2.0, 3.0, 4.0, 5.0]}, columns=[0, 1]
)
grouped = data.groupby(0)
result = grouped.transform(agg_func, engine="numba", **kwargs)
expected = grouped.transform(agg_func, engine="cython", **kwargs)
tm.assert_frame_equal(result, expected)
result = grouped[1].transform(agg_func, engine="numba", **kwargs)
expected = grouped[1].transform(agg_func, engine="cython", **kwargs)
tm.assert_series_equal(result, expected)
def test_args_not_cached():
# GH 41647
pytest.importorskip("numba")
def sum_last(values, index, n):
return values[-n:].sum()
df = DataFrame({"id": [0, 0, 1, 1], "x": [1, 1, 1, 1]})
grouped_x = df.groupby("id")["x"]
result = grouped_x.transform(sum_last, 1, engine="numba")
expected = Series([1.0] * 4, name="x")
tm.assert_series_equal(result, expected)
result = grouped_x.transform(sum_last, 2, engine="numba")
expected = Series([2.0] * 4, name="x")
tm.assert_series_equal(result, expected)
def test_index_data_correctly_passed():
# GH 43133
pytest.importorskip("numba")
def f(values, index):
return index - 1
df = DataFrame({"group": ["A", "A", "B"], "v": [4, 5, 6]}, index=[-1, -2, -3])
result = df.groupby("group").transform(f, engine="numba")
expected = DataFrame([-4.0, -3.0, -2.0], columns=["v"], index=[-1, -2, -3])
tm.assert_frame_equal(result, expected)
def test_engine_kwargs_not_cached():
# If the user passes a different set of engine_kwargs don't return the same
# jitted function
pytest.importorskip("numba")
nogil = True
parallel = False
nopython = True
def func_kwargs(values, index):
return nogil + parallel + nopython
engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
df = DataFrame({"value": [0, 0, 0]})
result = df.groupby(level=0).transform(
func_kwargs, engine="numba", engine_kwargs=engine_kwargs
)
expected = DataFrame({"value": [2.0, 2.0, 2.0]})
tm.assert_frame_equal(result, expected)
nogil = False
engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
result = df.groupby(level=0).transform(
func_kwargs, engine="numba", engine_kwargs=engine_kwargs
)
expected = DataFrame({"value": [1.0, 1.0, 1.0]})
tm.assert_frame_equal(result, expected)
@pytest.mark.filterwarnings("ignore")
def test_multiindex_one_key(nogil, parallel, nopython):
pytest.importorskip("numba")
def numba_func(values, index):
return 1
df = DataFrame([{"A": 1, "B": 2, "C": 3}]).set_index(["A", "B"])
engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
result = df.groupby("A").transform(
numba_func, engine="numba", engine_kwargs=engine_kwargs
)
expected = DataFrame([{"A": 1, "B": 2, "C": 1.0}]).set_index(["A", "B"])
tm.assert_frame_equal(result, expected)
def test_multiindex_multi_key_not_supported(nogil, parallel, nopython):
pytest.importorskip("numba")
def numba_func(values, index):
return 1
df = DataFrame([{"A": 1, "B": 2, "C": 3}]).set_index(["A", "B"])
engine_kwargs = {"nopython": nopython, "nogil": nogil, "parallel": parallel}
with pytest.raises(NotImplementedError, match="more than 1 grouping labels"):
df.groupby(["A", "B"]).transform(
numba_func, engine="numba", engine_kwargs=engine_kwargs
)
def test_multilabel_numba_vs_cython(numba_supported_reductions):
pytest.importorskip("numba")
reduction, kwargs = numba_supported_reductions
df = DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
"C": np.random.default_rng(2).standard_normal(8),
"D": np.random.default_rng(2).standard_normal(8),
}
)
gb = df.groupby(["A", "B"])
res_agg = gb.transform(reduction, engine="numba", **kwargs)
expected_agg = gb.transform(reduction, engine="cython", **kwargs)
tm.assert_frame_equal(res_agg, expected_agg)
def test_multilabel_udf_numba_vs_cython():
pytest.importorskip("numba")
df = DataFrame(
{
"A": ["foo", "bar", "foo", "bar", "foo", "bar", "foo", "foo"],
"B": ["one", "one", "two", "three", "two", "two", "one", "three"],
"C": np.random.default_rng(2).standard_normal(8),
"D": np.random.default_rng(2).standard_normal(8),
}
)
gb = df.groupby(["A", "B"])
result = gb.transform(
lambda values, index: (values - values.min()) / (values.max() - values.min()),
engine="numba",
)
expected = gb.transform(
lambda x: (x - x.min()) / (x.max() - x.min()), engine="cython"
)
tm.assert_frame_equal(result, expected)