Apply into NumpyΒΆ

In following parts, we will show some demos about how to use TreeValue in practice.

For example, now we have a group of structed data in python-dict type, we want to do different operations on differnent key-value pairs inplace, get some statistics such as mean value and task some slices.

In normal cases, we need to unroll multiple for-loop and if-else to implement cooresponding operations on each values, and declare additional temporal variables to save result. All the mentioned contents are executed serially, like the next code examples:

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import numpy as np

T, B = 3, 4


def without_treevalue(batch_):
    mean_b_list = []
    even_index_a_list = []
    for i in range(len(batch_)):
        for k, v in batch_[i].items():
            if k == 'a':
                v = v.astype(np.float32)
                even_index_a_list.append(v[::2])
            elif k == 'b':
                v = v.astype(np.float32)
                transformed_v = np.power(v, 2) + 1.0
                mean_b_list.append(transformed_v.mean())
            elif k == 'c':
                for k1, v1 in v.items():
                    if k1 == 'd':
                        v1 = v1.astype(np.float32)
                    else:
                        print('ignore keys: {}'.format(k1))
            else:
                print('ignore keys: {}'.format(k))
    for i in range(len(batch_)):
        for k in batch_[i].keys():
            if k == 'd':
                batch_[i][k]['noise'] = np.random.random(size=(3, 4, 5))

    mean_b = sum(mean_b_list) / len(mean_b_list)
    even_index_a = np.stack(even_index_a_list, axis=0)
    return batch_, mean_b, even_index_a

However, with the help of TreeValue, all the contents mentioned above can be implemented gracefully and efficiently. Users only need to func_treelize the primitive numpy functions and pack data with FastTreeValue, then execute desired operations just like using standard numpy array.

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import numpy as np

from treevalue import FastTreeValue, func_treelize

T, B = 3, 4
power = func_treelize()(np.power)
stack = func_treelize(subside=True)(np.stack)
split = func_treelize(rise=True)(np.split)


@func_treelize()
def astype(item, *args):
    return item.astype(*args)


def with_treevalue(batch_):
    batch_ = [FastTreeValue(b) for b in batch_]
    batch_ = stack(batch_)
    batch_ = astype(batch_, np.float32)
    batch_.b = power(batch_.b, 2) + 1.0
    batch_.c.noise = np.random.random(size=(B, 3, 4, 5))
    mean_b = batch_.b.mean()
    even_index_a = batch_.a[:, ::2]
    batch_ = split(batch_, indices_or_sections=B, axis=0)
    return batch_, mean_b, even_index_a

And we can run these two demos for comparison:

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import copy

import numpy as np
from with_treevalue import with_treevalue
from without_treevalue import without_treevalue

T, B = 3, 4


def get_data():
    return {
        'a': np.random.random(size=(T, 8)),
        'b': np.random.random(size=(6,)),
        'c': {
            'd': np.random.randint(0, 10, size=(1,))
        }
    }


if __name__ == "__main__":
    batch = [get_data() for _ in range(B)]
    batch0, mean0, even_index_a0 = without_treevalue(copy.deepcopy(batch))
    batch1, mean1, even_index_a1 = with_treevalue(copy.deepcopy(batch))

    assert np.abs(mean0 - mean1) < 1e-6
    print('mean0 & mean1:', mean0, mean1)
    print('\n')

    assert np.abs((even_index_a0 - even_index_a1).max()) < 1e-6
    print('even_index_a0:', even_index_a0)
    print('even_index_a1:', even_index_a1)

    assert len(batch0) == B
    assert len(batch1) == B

The final output should be the text below, and all the assertions can be passed.

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mean0 & mean1: 1.2749894559383392 1.2749895


even_index_a0: [[[0.37834427 0.9646841  0.328046   0.28542736 0.7011457  0.69364935
   0.3188184  0.28755647]
  [0.40876356 0.81300753 0.67786586 0.58019197 0.10023773 0.82433456
   0.21075118 0.28306565]]

 [[0.2917919  0.9863357  0.8129859  0.8171696  0.7204178  0.54368806
   0.6015232  0.726259  ]
  [0.24668133 0.28101483 0.85941833 0.95364785 0.72586256 0.34735528
   0.34409216 0.50412333]]

 [[0.6598945  0.43746746 0.28320622 0.8711718  0.5520788  0.39416298
   0.28364646 0.27646422]
  [0.7363669  0.3196517  0.23747641 0.40921682 0.5581021  0.218158
   0.19287592 0.82338685]]

 [[0.15835576 0.613668   0.5216213  0.09299935 0.23642638 0.95885175
   0.28526455 0.78218937]
  [0.67499644 0.15776694 0.3881674  0.7970917  0.32132053 0.23641382
   0.4785277  0.6596466 ]]]
even_index_a1: [[[0.37834427 0.9646841  0.328046   0.28542736 0.7011457  0.69364935
   0.3188184  0.28755647]
  [0.40876356 0.81300753 0.67786586 0.58019197 0.10023773 0.82433456
   0.21075118 0.28306565]]

 [[0.2917919  0.9863357  0.8129859  0.8171696  0.7204178  0.54368806
   0.6015232  0.726259  ]
  [0.24668133 0.28101483 0.85941833 0.95364785 0.72586256 0.34735528
   0.34409216 0.50412333]]

 [[0.6598945  0.43746746 0.28320622 0.8711718  0.5520788  0.39416298
   0.28364646 0.27646422]
  [0.7363669  0.3196517  0.23747641 0.40921682 0.5581021  0.218158
   0.19287592 0.82338685]]

 [[0.15835576 0.613668   0.5216213  0.09299935 0.23642638 0.95885175
   0.28526455 0.78218937]
  [0.67499644 0.15776694 0.3881674  0.7970917  0.32132053 0.23641382
   0.4785277  0.6596466 ]]]

In this case, we can see that the TreeValue can be properly applied into the numpy library. The tree-structured matrix calculation can be easily built with TreeValue like using standard numpy array.

Both the simplicity of logic structure and execution efficiency can be improve a lot.

And Last but not least, the only thing you need to do is to wrap the functions in Numpy library, and then use it painlessly like the primitive numpy.