forked from mindspore-Ecosystem/mindspore
127 lines
5.2 KiB
Python
127 lines
5.2 KiB
Python
# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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from typing import List
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from random import sample
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import PrimitiveWithInfer, prim_attr_register
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from mindspore._checkparam import Validator as validator
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from mindspore.common import dtype as mstype
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import numpy as np
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import pandas as pd
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import pytest
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context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
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class Rank(PrimitiveWithInfer):
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"""
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Shift op frontend implementation
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"""
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# size_t axis_{0};
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# rank::Method method_{rank::MethodNotDefined};
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# rank::NaOption option_{rank::OptionNotDefined};
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# bool ascending_{true};
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# bool pct_{false};
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@prim_attr_register
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def __init__(self, axis: int, method: str, na_option: str, ascending: bool, pct: bool):
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"""Initialize Sort"""
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self.axis = validator.check_value_type("axis", axis, [int], self.name)
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self.method = validator.check_value_type("method", method, [str], self.name)
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self.na_option = validator.check_value_type("na_option", na_option, [str], self.name)
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self.ascending = validator.check_value_type("ascending", ascending, [bool], self.name)
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self.pct = validator.check_value_type("pct", pct, [bool], self.name)
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self.init_prim_io_names(inputs=['x'], outputs=['output'])
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def __infer__(self, x):
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out_shapes = x['shape']
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return {
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'shape': tuple(out_shapes),
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'dtype': mstype.float32,
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'value': None
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}
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class RankNet(nn.Cell):
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def __init__(self, axis: int, method: str, na_option: str, ascending: bool, pct: bool):
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super(RankNet, self).__init__()
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self.rank = Rank(axis, method, na_option, ascending, pct)
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def construct(self, x):
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return self.rank(x)
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def pandas_rank(arr, **kwargs):
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ser = pd.DataFrame(arr)
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result = ser.rank(**kwargs)
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return result.to_numpy()
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@pytest.mark.parametrize('shape', [(10,)])
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@pytest.mark.parametrize('dtype', [np.float32, np.float64, np.int32, np.int64])
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@pytest.mark.parametrize('method', ['dense', 'first', 'max', 'min', 'average'])
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@pytest.mark.parametrize('na_option', ["keep", "top", "bottom"])
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@pytest.mark.parametrize('ascending', [True, False])
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@pytest.mark.parametrize('pct', [False, True])
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def test_rank_1d(shape: List[int], dtype, method: str, ascending: bool, pct: bool, na_option: str):
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np.random.seed(0)
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if dtype in (np.int32, np.int64):
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arr = np.random.randint(0, 100, size=shape).astype(dtype)
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else:
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arr = np.random.random(size=shape).astype(dtype)
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arr.flat[sample(range(arr.size), int(arr.size / 10))] = np.nan
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pd_result = pandas_rank(arr, method=method, ascending=ascending, pct=pct, na_option=na_option).flatten()
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rank = RankNet(0, method=method, ascending=ascending, pct=pct, na_option=na_option)
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mind_result = rank(Tensor(arr)).asnumpy()
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print('arr: \n', arr, arr.dtype, arr.shape)
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print('pandas: \n', pd_result, pd_result.dtype, pd_result.shape)
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print('mind: \n', mind_result, mind_result.dtype, mind_result.shape)
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print(f'method: {method}, ascending: {ascending}, pct: {pct} na_option: {na_option}')
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assert np.allclose(pd_result, mind_result, equal_nan=True)
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@pytest.mark.parametrize('shape', [(5, 6)])
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@pytest.mark.parametrize('dtype', [np.float32, np.float64, np.int32, np.int64])
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@pytest.mark.parametrize('method', ['dense', 'first', 'max', 'min', 'average'])
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@pytest.mark.parametrize('na_option', ["keep", "top", "bottom"])
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@pytest.mark.parametrize('axis', [0, 1])
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@pytest.mark.parametrize('ascending', [True, False])
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@pytest.mark.parametrize('pct', [False, True])
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def test_rank_2d(shape: List[int], dtype, method: str, ascending: bool, pct: bool, axis: int, na_option: str):
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np.random.seed(0)
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if dtype in (np.int32, np.int64):
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arr = np.random.randint(0, 100, size=shape).astype(dtype)
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else:
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arr = np.random.random(size=shape).astype(dtype)
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arr.flat[sample(range(arr.size), int(arr.size / 10))] = np.nan
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pd_result = pandas_rank(arr, method=method, ascending=ascending, pct=pct, na_option=na_option, axis=axis)
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rank = RankNet(axis=axis, method=method, ascending=ascending, pct=pct, na_option=na_option)
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mind_result = rank(Tensor(arr)).asnumpy()
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print('arr: \n', arr, arr.dtype, arr.shape)
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print('pandas: \n', pd_result, pd_result.dtype, pd_result.shape)
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print('mind: \n', mind_result, mind_result.dtype, mind_result.shape)
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print(f'axis: {axis}, method: {method}, ascending: {ascending}, pct: {pct} na_option: {na_option}')
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assert np.allclose(pd_result, mind_result, equal_nan=True)
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