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1# Copyright 2021 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14# ============================================================================
15
16from typing import List
17from random import sample
18import mindspore.context as context
19import mindspore.nn as nn
20from mindspore import Tensor
21from mindspore.ops import PrimitiveWithInfer, prim_attr_register
22from mindspore._checkparam import Validator as validator
23from mindspore.common import dtype as mstype
24import numpy as np
25import pandas as pd
26import pytest
27
28context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
29
30
31class Rank(PrimitiveWithInfer):
32    """
33        Shift op frontend implementation
34    """
35
36    # size_t axis_{0};
37    # rank::Method method_{rank::MethodNotDefined};
38    # rank::NaOption option_{rank::OptionNotDefined};
39    # bool ascending_{true};
40    # bool pct_{false};
41    @prim_attr_register
42    def __init__(self, axis: int, method: str, na_option: str, ascending: bool, pct: bool):
43        """Initialize Sort"""
44        self.axis = validator.check_value_type("axis", axis, [int], self.name)
45        self.method = validator.check_value_type("method", method, [str], self.name)
46        self.na_option = validator.check_value_type("na_option", na_option, [str], self.name)
47        self.ascending = validator.check_value_type("ascending", ascending, [bool], self.name)
48        self.pct = validator.check_value_type("pct", pct, [bool], self.name)
49
50        self.init_prim_io_names(inputs=['x'], outputs=['output'])
51
52    def __infer__(self, x):
53        out_shapes = x['shape']
54        return {
55            'shape': tuple(out_shapes),
56            'dtype': mstype.float32,
57            'value': None
58        }
59
60
61class RankNet(nn.Cell):
62    def __init__(self, axis: int, method: str, na_option: str, ascending: bool, pct: bool):
63        super(RankNet, self).__init__()
64        self.rank = Rank(axis, method, na_option, ascending, pct)
65
66    def construct(self, x):
67        return self.rank(x)
68
69
70def pandas_rank(arr, **kwargs):
71    ser = pd.DataFrame(arr)
72    result = ser.rank(**kwargs)
73    return result.to_numpy()
74
75
76@pytest.mark.parametrize('shape', [(10,)])
77@pytest.mark.parametrize('dtype', [np.float32, np.float64, np.int32, np.int64])
78@pytest.mark.parametrize('method', ['dense', 'first', 'max', 'min', 'average'])
79@pytest.mark.parametrize('na_option', ["keep", "top", "bottom"])
80@pytest.mark.parametrize('ascending', [True, False])
81@pytest.mark.parametrize('pct', [False, True])
82def test_rank_1d(shape: List[int], dtype, method: str, ascending: bool, pct: bool, na_option: str):
83    np.random.seed(0)
84
85    if dtype in (np.int32, np.int64):
86        arr = np.random.randint(0, 100, size=shape).astype(dtype)
87    else:
88        arr = np.random.random(size=shape).astype(dtype)
89        arr.flat[sample(range(arr.size), int(arr.size / 10))] = np.nan
90
91    pd_result = pandas_rank(arr, method=method, ascending=ascending, pct=pct, na_option=na_option).flatten()
92    rank = RankNet(0, method=method, ascending=ascending, pct=pct, na_option=na_option)
93    mind_result = rank(Tensor(arr)).asnumpy()
94
95    print('arr: \n', arr, arr.dtype, arr.shape)
96    print('pandas: \n', pd_result, pd_result.dtype, pd_result.shape)
97    print('mind: \n', mind_result, mind_result.dtype, mind_result.shape)
98    print(f'method: {method}, ascending: {ascending}, pct: {pct} na_option: {na_option}')
99    assert np.allclose(pd_result, mind_result, equal_nan=True)
100
101
102@pytest.mark.parametrize('shape', [(5, 6)])
103@pytest.mark.parametrize('dtype', [np.float32, np.float64, np.int32, np.int64])
104@pytest.mark.parametrize('method', ['dense', 'first', 'max', 'min', 'average'])
105@pytest.mark.parametrize('na_option', ["keep", "top", "bottom"])
106@pytest.mark.parametrize('axis', [0, 1])
107@pytest.mark.parametrize('ascending', [True, False])
108@pytest.mark.parametrize('pct', [False, True])
109def test_rank_2d(shape: List[int], dtype, method: str, ascending: bool, pct: bool, axis: int, na_option: str):
110    np.random.seed(0)
111
112    if dtype in (np.int32, np.int64):
113        arr = np.random.randint(0, 100, size=shape).astype(dtype)
114    else:
115        arr = np.random.random(size=shape).astype(dtype)
116        arr.flat[sample(range(arr.size), int(arr.size / 10))] = np.nan
117
118    pd_result = pandas_rank(arr, method=method, ascending=ascending, pct=pct, na_option=na_option, axis=axis)
119    rank = RankNet(axis=axis, method=method, ascending=ascending, pct=pct, na_option=na_option)
120    mind_result = rank(Tensor(arr)).asnumpy()
121
122    print('arr: \n', arr, arr.dtype, arr.shape)
123    print('pandas: \n', pd_result, pd_result.dtype, pd_result.shape)
124    print('mind: \n', mind_result, mind_result.dtype, mind_result.shape)
125    print(f'axis: {axis}, method: {method}, ascending: {ascending}, pct: {pct} na_option: {na_option}')
126    assert np.allclose(pd_result, mind_result, equal_nan=True)
127