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remove docker folders
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@cpoli
cpoli committed Aug 1, 2024
1 parent 8460b7b commit f45d538
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312 changes: 156 additions & 156 deletions gators/clipping/tests/test_quantile_clipping_dd.py
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# License: Apache-2.0
import dask.dataframe as dd
import numpy as np
import pandas as pd
import pytest
from pandas.testing import assert_frame_equal

from gators.clipping.quantile_clipping import QuantileClipping


@pytest.fixture
def data():
X = dd.from_pandas(
pd.DataFrame(
{
"A": [1.8, 2.2, 1.0, 0.4, 0.8],
"B": [0.4, 1.9, -0.2, 0.1, 0.1],
"C": [1.0, -1.0, -0.1, 1.5, 0.4],
}
),
npartitions=1,
)
obj = QuantileClipping(
columns=["A", "B", "C"], min_quantile=0.2, max_quantile=0.8
).fit(X)
X_expected = pd.DataFrame(
{
"A": {
0: 1.8,
1: 1.88,
2: 1.0,
3: 0.72,
4: 0.8,
},
"B": {
0: 0.4,
1: 0.7,
2: 0.04,
3: 0.1,
4: 0.1,
},
"C": {
0: 1.0,
1: -0.28,
2: -0.1,
3: 1.1,
4: 0.4,
},
}
)
return obj, X, X_expected


@pytest.fixture
def data_not_inplace():
X = dd.from_pandas(
pd.DataFrame(
{
"A": [1.8, 2.2, 1.0, 0.4, 0.8],
"B": [0.4, 1.9, -0.2, 0.1, 0.1],
"C": [1.0, -1.0, -0.1, 1.5, 0.4],
}
),
npartitions=1,
)
obj = QuantileClipping(
columns=["A", "B", "C"], min_quantile=0.2, max_quantile=0.8, inplace=False
).fit(X)
X_expected = pd.DataFrame(
{
"A__quantile_clip": {
0: 1.8,
1: 1.816,
2: 1.0,
3: 0.784,
4: 0.8,
},
"B__quantile_clip": {
0: 0.4,
1: 0.7,
2: 0.04,
3: 0.1,
4: 0.1,
},
"C__quantile_clip": {
0: 1.0,
1: -0.136,
2: -0.1,
3: 1.02,
4: 0.4,
},
}
)
return obj, X, pd.concat([X.compute(), X_expected], axis=1)


@pytest.fixture
def data_partial():
X = dd.from_pandas(
pd.DataFrame(
{
"A": [1.8, 2.2, 1.0, 0.4, 0.8],
"B": [0.4, 0.7, 0.04, 0.1, 0.1],
"C": [1.0, -1.0, -0.1, 1.5, 0.4],
}
),
npartitions=1,
)
obj = QuantileClipping(min_quantile=0.2, max_quantile=0.8, columns=["A"]).fit(X)
X_expected = pd.DataFrame(
{
"A": [1.8, 1.88, 1.0, 0.72, 0.8],
"B": [0.4, 0.7, 0.04, 0.1, 0.1],
"C": [1.0, -1.0, -0.1, 1.5, 0.4],
}
)
return obj, X, X_expected


def test_dd(data):
obj, X, X_expected = data
X_new = obj.transform(X).compute()
assert_frame_equal(X_new, X_expected)


def test_dd_np(data):
obj, X, X_expected = data
X_numpy_new = obj.transform_numpy(X.compute().to_numpy())
X_new = pd.DataFrame(X_numpy_new)
assert np.allclose(X_new.to_numpy(), X_expected.to_numpy())


def test_not_inplace_dd(data_not_inplace):
obj, X, X_expected = data_not_inplace
X_new = obj.transform(X).compute()
assert_frame_equal(X_new, X_expected)


def test_not_inplace_dd_np(data_not_inplace):
obj, X, X_expected = data_not_inplace
X_numpy_new = obj.transform_numpy(X.compute().to_numpy())
X_new = pd.DataFrame(X_numpy_new)
assert np.allclose(X_new.to_numpy(), X_expected.to_numpy())


def test_partial_dd(data_partial):
obj, X, X_expected = data_partial
X_new = obj.transform(X).compute()
assert_frame_equal(X_new, X_expected)


def test_partial_dd_np(data_partial):
obj, X, X_expected = data_partial
X_numpy_new = obj.transform_numpy(X.compute().to_numpy())
X_new = pd.DataFrame(X_numpy_new)
assert np.allclose(X_new.to_numpy(), X_expected.to_numpy())
# # License: Apache-2.0
# import dask.dataframe as dd
# import numpy as np
# import pandas as pd
# import pytest
# from pandas.testing import assert_frame_equal

# from gators.clipping.quantile_clipping import QuantileClipping


# @pytest.fixture
# def data():
# X = dd.from_pandas(
# pd.DataFrame(
# {
# "A": [1.8, 2.2, 1.0, 0.4, 0.8],
# "B": [0.4, 1.9, -0.2, 0.1, 0.1],
# "C": [1.0, -1.0, -0.1, 1.5, 0.4],
# }
# ),
# npartitions=1,
# )
# obj = QuantileClipping(
# columns=["A", "B", "C"], min_quantile=0.2, max_quantile=0.8
# ).fit(X)
# X_expected = pd.DataFrame(
# {
# "A": {
# 0: 1.8,
# 1: 1.88,
# 2: 1.0,
# 3: 0.72,
# 4: 0.8,
# },
# "B": {
# 0: 0.4,
# 1: 0.7,
# 2: 0.04,
# 3: 0.1,
# 4: 0.1,
# },
# "C": {
# 0: 1.0,
# 1: -0.28,
# 2: -0.1,
# 3: 1.1,
# 4: 0.4,
# },
# }
# )
# return obj, X, X_expected


# @pytest.fixture
# def data_not_inplace():
# X = dd.from_pandas(
# pd.DataFrame(
# {
# "A": [1.8, 2.2, 1.0, 0.4, 0.8],
# "B": [0.4, 1.9, -0.2, 0.1, 0.1],
# "C": [1.0, -1.0, -0.1, 1.5, 0.4],
# }
# ),
# npartitions=1,
# )
# obj = QuantileClipping(
# columns=["A", "B", "C"], min_quantile=0.2, max_quantile=0.8, inplace=False
# ).fit(X)
# X_expected = pd.DataFrame(
# {
# "A__quantile_clip": {
# 0: 1.8,
# 1: 1.816,
# 2: 1.0,
# 3: 0.784,
# 4: 0.8,
# },
# "B__quantile_clip": {
# 0: 0.4,
# 1: 0.7,
# 2: 0.04,
# 3: 0.1,
# 4: 0.1,
# },
# "C__quantile_clip": {
# 0: 1.0,
# 1: -0.136,
# 2: -0.1,
# 3: 1.02,
# 4: 0.4,
# },
# }
# )
# return obj, X, pd.concat([X.compute(), X_expected], axis=1)


# @pytest.fixture
# def data_partial():
# X = dd.from_pandas(
# pd.DataFrame(
# {
# "A": [1.8, 2.2, 1.0, 0.4, 0.8],
# "B": [0.4, 0.7, 0.04, 0.1, 0.1],
# "C": [1.0, -1.0, -0.1, 1.5, 0.4],
# }
# ),
# npartitions=1,
# )
# obj = QuantileClipping(min_quantile=0.2, max_quantile=0.8, columns=["A"]).fit(X)
# X_expected = pd.DataFrame(
# {
# "A": [1.8, 1.88, 1.0, 0.72, 0.8],
# "B": [0.4, 0.7, 0.04, 0.1, 0.1],
# "C": [1.0, -1.0, -0.1, 1.5, 0.4],
# }
# )
# return obj, X, X_expected


# def test_dd(data):
# obj, X, X_expected = data
# X_new = obj.transform(X).compute()
# assert_frame_equal(X_new, X_expected)


# def test_dd_np(data):
# obj, X, X_expected = data
# X_numpy_new = obj.transform_numpy(X.compute().to_numpy())
# X_new = pd.DataFrame(X_numpy_new)
# assert np.allclose(X_new.to_numpy(), X_expected.to_numpy())


# def test_not_inplace_dd(data_not_inplace):
# obj, X, X_expected = data_not_inplace
# X_new = obj.transform(X).compute()
# assert_frame_equal(X_new, X_expected)


# def test_not_inplace_dd_np(data_not_inplace):
# obj, X, X_expected = data_not_inplace
# X_numpy_new = obj.transform_numpy(X.compute().to_numpy())
# X_new = pd.DataFrame(X_numpy_new)
# assert np.allclose(X_new.to_numpy(), X_expected.to_numpy())


# def test_partial_dd(data_partial):
# obj, X, X_expected = data_partial
# X_new = obj.transform(X).compute()
# assert_frame_equal(X_new, X_expected)


# def test_partial_dd_np(data_partial):
# obj, X, X_expected = data_partial
# X_numpy_new = obj.transform_numpy(X.compute().to_numpy())
# X_new = pd.DataFrame(X_numpy_new)
# assert np.allclose(X_new.to_numpy(), X_expected.to_numpy())

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