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Remove methods and tests, add coverage for generic index in get_dummies
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@isVoid
isVoid committed Jan 6, 2022
1 parent eba4f03 commit fc0fa93
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72 changes: 0 additions & 72 deletions python/cudf/cudf/core/dataframe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3051,78 +3051,6 @@ def as_matrix(self, columns=None):
)
return self.as_gpu_matrix(columns=columns).copy_to_host()

def one_hot_encoding(
self, column, prefix, cats, prefix_sep="_", dtype="float64"
):
"""
Expand a column with one-hot-encoding.
Parameters
----------
column : str
the source column with binary encoding for the data.
prefix : str
the new column name prefix.
cats : sequence of ints
the sequence of categories as integers.
prefix_sep : str
the separator between the prefix and the category.
dtype :
the dtype for the outputs; defaults to float64.
Returns
-------
a new dataframe with new columns append for each category.
Examples
--------
>>> import pandas as pd
>>> import cudf
>>> pet_owner = [1, 2, 3, 4, 5]
>>> pet_type = ['fish', 'dog', 'fish', 'bird', 'fish']
>>> df = pd.DataFrame({'pet_owner': pet_owner, 'pet_type': pet_type})
>>> df.pet_type = df.pet_type.astype('category')
Create a column with numerically encoded category values
>>> df['pet_codes'] = df.pet_type.cat.codes
>>> gdf = cudf.from_pandas(df)
Create the list of category codes to use in the encoding
>>> codes = gdf.pet_codes.unique()
>>> gdf.one_hot_encoding('pet_codes', 'pet_dummy', codes).head()
pet_owner pet_type pet_codes pet_dummy_0 pet_dummy_1 pet_dummy_2
0 1 fish 2 0.0 0.0 1.0
1 2 dog 1 0.0 1.0 0.0
2 3 fish 2 0.0 0.0 1.0
3 4 bird 0 1.0 0.0 0.0
4 5 fish 2 0.0 0.0 1.0
"""

warnings.warn(
"DataFrame.one_hot_encoding is deprecated and will be removed in "
"future, use `get_dummies` instead.",
FutureWarning,
)

if hasattr(cats, "to_arrow"):
cats = cats.to_arrow().to_pylist()
else:
cats = pd.Series(cats, dtype="object")

newnames = [
prefix_sep.join([prefix, "null" if cat is None else str(cat)])
for cat in cats
]
newcols = self[column].one_hot_encoding(cats=cats, dtype=dtype)
outdf = self.copy()
for name, col in zip(newnames, newcols):
outdf.insert(len(outdf._data), name, col)
return outdf

def label_encoding(
self, column, prefix, cats, prefix_sep="_", dtype=None, na_sentinel=-1
):
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12 changes: 6 additions & 6 deletions python/cudf/cudf/core/reshape.py
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Expand Up @@ -600,18 +600,18 @@ def get_dummies(
df : array-like, Series, or DataFrame
Data of which to get dummy indicators.
prefix : str, dict, or sequence, optional
prefix to append. Either a str (to apply a constant prefix), dict
Prefix to append. Either a str (to apply a constant prefix), dict
mapping column names to prefixes, or sequence of prefixes to apply with
the same length as the number of columns. If not supplied, defaults
to the empty string
prefix_sep : str, dict, or sequence, optional, default '_'
separator to use when appending prefixes
Separator to use when appending prefixes
dummy_na : boolean, optional
Add a column to indicate Nones, if False Nones are ignored.
cats : dict, optional
dictionary mapping column names to sequences of integers representing
that column's category. See `cudf.DataFrame.one_hot_encoding` for more
information. if not supplied, it will be computed
Dictionary mapping column names to sequences of values representing
that column's category. If not supplied, it is computed as the unique
values of the column.
sparse : boolean, optional
Right now this is NON-FUNCTIONAL argument in rapids.
drop_first : boolean, optional
Expand All @@ -621,7 +621,7 @@ def get_dummies(
columns. Note this is different from pandas default behavior, which
encodes all columns with dtype object or categorical
dtype : str, optional
output dtype, default 'uint8'
Output dtype, default 'uint8'
Examples
--------
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77 changes: 0 additions & 77 deletions python/cudf/cudf/core/series.py
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Original file line number Diff line number Diff line change
Expand Up @@ -2264,83 +2264,6 @@ def reverse(self):
{self.name: self._column[rinds]}, self.index._values[rinds]
)

def one_hot_encoding(self, cats, dtype="float64"):
"""Perform one-hot-encoding
Parameters
----------
cats : sequence of values
values representing each category.
dtype : numpy.dtype
specifies the output dtype.
Returns
-------
Sequence
A sequence of new series for each category. Its length is
determined by the length of ``cats``.
Examples
--------
>>> import cudf
>>> s = cudf.Series(['a', 'b', 'c', 'a'])
>>> s
0 a
1 b
2 c
3 a
dtype: object
>>> s.one_hot_encoding(['a', 'c', 'b'])
[0 1.0
1 0.0
2 0.0
3 1.0
dtype: float64, 0 0.0
1 0.0
2 1.0
3 0.0
dtype: float64, 0 0.0
1 1.0
2 0.0
3 0.0
dtype: float64]
"""

warnings.warn(
"Series.one_hot_encoding is deprecated and will be removed in "
"future, use `get_dummies` instead.",
FutureWarning,
)

if hasattr(cats, "to_arrow"):
cats = cats.to_pandas()
else:
cats = pd.Series(cats, dtype="object")
dtype = cudf.dtype(dtype)

try:
cats_col = as_column(cats, nan_as_null=False, dtype=self.dtype)
except TypeError:
raise ValueError("Cannot convert `cats` as cudf column.")

if self._column.size * cats_col.size >= np.iinfo("int32").max:
raise ValueError(
"Size limitation exceeded: series.size * category.size < "
"np.iinfo('int32').max. Consider reducing size of category"
)

res = libcudf.transform.one_hot_encode(self._column, cats_col)
if dtype.type == np.bool_:
return [
Series._from_data({None: x}, index=self._index)
for x in list(res.values())
]
else:
return [
Series._from_data({None: x.astype(dtype)}, index=self._index)
for x in list(res.values())
]

def label_encoding(self, cats, dtype=None, na_sentinel=-1):
"""Perform label encoding.
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105 changes: 10 additions & 95 deletions python/cudf/cudf/tests/test_onehot.py
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Expand Up @@ -7,108 +7,23 @@
import pytest

import cudf
from cudf import DataFrame, Index, Series
from cudf import DataFrame
from cudf.testing import _utils as utils


def test_onehot_simple():
np.random.seed(0)
df = DataFrame()
# Populate with data [0, 10)
df["vals"] = np.arange(10, dtype=np.int32)
# One Hot (Series)
for i, col in enumerate(df["vals"].one_hot_encoding(list(range(10)))):
arr = col.to_numpy()
# Verify 1 in the right position
np.testing.assert_equal(arr[i], 1)
# Every other slots are 0s
np.testing.assert_equal(arr[:i], 0)
np.testing.assert_equal(arr[i + 1 :], 0)
# One Hot (DataFrame)
df2 = df.one_hot_encoding(
column="vals", prefix="vals", cats=list(range(10))
)
assert df2.columns[0] == "vals"
for i in range(1, len(df2.columns)):
assert df2.columns[i] == "vals_%s" % (i - 1)
got = df2[df2.columns[1:]].values_host
expect = np.identity(got.shape[0])
np.testing.assert_equal(got, expect)


def test_onehot_random():
df = DataFrame()
low = 10
high = 17
size = 10
df["src"] = src = np.random.randint(low=low, high=high, size=size)
df2 = df.one_hot_encoding(
column="src", prefix="out_", cats=tuple(range(10, 17))
)
mat = df2[df2.columns[1:]].values_host

for val in range(low, high):
colidx = val - low
arr = mat[:, colidx]
mask = src == val
np.testing.assert_equal(arr, mask)


def test_onehot_masked():
np.random.seed(0)
high = 5
size = 100
arr = np.random.randint(low=0, high=high, size=size)
bitmask = utils.random_bitmask(size)
bytemask = np.asarray(
utils.expand_bits_to_bytes(bitmask)[:size], dtype=np.bool_
)
arr[~bytemask] = -1

df = DataFrame()
df["a"] = Series(arr).set_mask(bitmask)

out = df.one_hot_encoding(
"a", cats=list(range(high)), prefix="a", dtype=np.int32
)

assert tuple(out.columns) == ("a", "a_0", "a_1", "a_2", "a_3", "a_4")
np.testing.assert_array_equal((out["a_0"] == 1).to_numpy(), arr == 0)
np.testing.assert_array_equal((out["a_1"] == 1).to_numpy(), arr == 1)
np.testing.assert_array_equal((out["a_2"] == 1).to_numpy(), arr == 2)
np.testing.assert_array_equal((out["a_3"] == 1).to_numpy(), arr == 3)
np.testing.assert_array_equal((out["a_4"] == 1).to_numpy(), arr == 4)


def test_onehot_generic_index():
np.random.seed(0)
size = 33
indices = np.random.randint(low=0, high=100, size=size)
df = DataFrame()
values = np.random.randint(low=0, high=4, size=size)
df["fo"] = Series(values, index=Index(indices))
out = df.one_hot_encoding(
"fo", cats=df.fo.unique(), prefix="fo", dtype=np.int32
)
assert set(out.columns) == {"fo", "fo_0", "fo_1", "fo_2", "fo_3"}
np.testing.assert_array_equal(values == 0, out.fo_0.to_numpy())
np.testing.assert_array_equal(values == 1, out.fo_1.to_numpy())
np.testing.assert_array_equal(values == 2, out.fo_2.to_numpy())
np.testing.assert_array_equal(values == 3, out.fo_3.to_numpy())


@pytest.mark.parametrize(
"data",
"data, index",
[
np.arange(10),
["abc", "zyx", "pppp"],
[],
pd.Series(["cudf", "hello", "pandas"] * 10, dtype="category"),
(np.arange(10), None),
(["abc", "zyx", "pppp"], None),
([], None),
(pd.Series(["cudf", "hello", "pandas"] * 10, dtype="category"), None),
(range(10), [1, 2, 3, 4, 5] * 2),
],
)
def test_get_dummies(data):
gdf = DataFrame({"x": data})
pdf = pd.DataFrame({"x": data})
def test_get_dummies(data, index):
gdf = DataFrame({"x": data}, index=index)
pdf = pd.DataFrame({"x": data}, index=index)

encoded_expected = pd.get_dummies(pdf, prefix="test")
encoded_actual = cudf.get_dummies(gdf, prefix="test")
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