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:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 | 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | 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:
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 | 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 | mean0 & mean1: 1.2294126451015472 1.2294126 even_index_a0: [[[0.13723138 0.9006097 0.3493789 0.44825 0.13616809 0.4024927 0.6450527 0.17922235] [0.48185617 0.282816 0.75944275 0.24627198 0.8182284 0.18384837 0.08547486 0.9604025 ]] [[0.8219079 0.04606199 0.0119232 0.78770083 0.02226636 0.88412106 0.96176594 0.04573173] [0.9745139 0.28592265 0.441946 0.6375823 0.14268544 0.22947483 0.94044775 0.2916046 ]] [[0.8783443 0.78902316 0.9104793 0.42777672 0.93995047 0.8039973 0.13855527 0.62445456] [0.17089196 0.8980362 0.36965516 0.8972051 0.7073777 0.6190486 0.41212767 0.0873727 ]] [[0.10034288 0.5474378 0.4005498 0.09674212 0.8707602 0.19540341 0.20896177 0.83527726] [0.4612895 0.5774035 0.20000546 0.54493123 0.821435 0.9663748 0.7256626 0.9078696 ]]] even_index_a1: [[[0.13723138 0.9006097 0.3493789 0.44825 0.13616809 0.4024927 0.6450527 0.17922235] [0.48185617 0.282816 0.75944275 0.24627198 0.8182284 0.18384837 0.08547486 0.9604025 ]] [[0.8219079 0.04606199 0.0119232 0.78770083 0.02226636 0.88412106 0.96176594 0.04573173] [0.9745139 0.28592265 0.441946 0.6375823 0.14268544 0.22947483 0.94044775 0.2916046 ]] [[0.8783443 0.78902316 0.9104793 0.42777672 0.93995047 0.8039973 0.13855527 0.62445456] [0.17089196 0.8980362 0.36965516 0.8972051 0.7073777 0.6190486 0.41212767 0.0873727 ]] [[0.10034288 0.5474378 0.4005498 0.09674212 0.8707602 0.19540341 0.20896177 0.83527726] [0.4612895 0.5774035 0.20000546 0.54493123 0.821435 0.9663748 0.7256626 0.9078696 ]]] |
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.