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

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


def with_treevalue(batch_):
    batch_ = [FastTreeValue(b) for b in batch_]
    batch_ = stack(batch_)
    batch_ = batch_.astype(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: 1.323505848646164 1.3235059


even_index_a0: [[[0.90539867 0.38841063 0.34561673 0.99831134 0.45578665 0.592899
   0.87183154 0.6574434 ]
  [0.91159683 0.09298854 0.71090615 0.8612857  0.60535073 0.5963975
   0.56070673 0.5195303 ]]

 [[0.23590541 0.84415245 0.8365436  0.665977   0.1597204  0.2570416
   0.35903034 0.02168042]
  [0.74665266 0.47121313 0.88155264 0.29203656 0.22009672 0.82261723
   0.9600288  0.2045388 ]]

 [[0.95245695 0.8706708  0.10267961 0.3616081  0.6888209  0.25009882
   0.6990164  0.17128341]
  [0.510822   0.52112025 0.32132077 0.9448549  0.40553337 0.56547546
   0.5634468  0.893396  ]]

 [[0.23426725 0.3908564  0.3983672  0.06658428 0.2986731  0.7164186
   0.24367206 0.660595  ]
  [0.5391705  0.13134257 0.7809654  0.96425056 0.5202109  0.32863
   0.41251257 0.885573  ]]]
even_index_a1: [[[0.90539867 0.38841063 0.34561673 0.99831134 0.45578665 0.592899
   0.87183154 0.6574434 ]
  [0.91159683 0.09298854 0.71090615 0.8612857  0.60535073 0.5963975
   0.56070673 0.5195303 ]]

 [[0.23590541 0.84415245 0.8365436  0.665977   0.1597204  0.2570416
   0.35903034 0.02168042]
  [0.74665266 0.47121313 0.88155264 0.29203656 0.22009672 0.82261723
   0.9600288  0.2045388 ]]

 [[0.95245695 0.8706708  0.10267961 0.3616081  0.6888209  0.25009882
   0.6990164  0.17128341]
  [0.510822   0.52112025 0.32132077 0.9448549  0.40553337 0.56547546
   0.5634468  0.893396  ]]

 [[0.23426725 0.3908564  0.3983672  0.06658428 0.2986731  0.7164186
   0.24367206 0.660595  ]
  [0.5391705  0.13134257 0.7809654  0.96425056 0.5202109  0.32863
   0.41251257 0.885573  ]]]

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.