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Refactoring code for better performance and code quality #280

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Refactoring code for better performance and code quality #280

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335a16c
use np.sum to improve performance and make the code succinct,
maldil Jun 29, 2022
2ed9a22
add import
maldil Jun 29, 2022
d97c906
Add space for consistency
maldil Jun 29, 2022
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12 changes: 5 additions & 7 deletions tensorflow_transform/info_theory.py
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Original file line number Diff line number Diff line change
Expand Up @@ -14,7 +14,7 @@
"""Utilities for information-theoretic preprocessing algorithms."""

import math

import numpy as np
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Note that this is a library based on tensorflow, so all of the math operations done below are deferred by constructing the tensorflow graph, and should not be using numpy.
There are tensorflow alternatives for these operations though.

Were you able to run the tests in this repo to make sure that they don't break with the change?

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@maldil maldil Jun 29, 2022 •
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Thank you for your response. I'm new to this repository. Before adding the new import, I checked to see if this repo makes use of numpy APIs (https://github.com/tensorflow/transform/search?q=numpy). After confirming that , I automatically analyzed the code to identify any missed chances to use the APIs. Please close the PR if this script is not intended to use numpy APIs. Regarding test. No I did not. Now I'm trying to run the test. However, I am having difficulty locating the test scripts. Any assistance is much appreciated.

# math.log2 was added in Python 3.3
log2 = getattr(math, 'log2', lambda x: math.log(x, 2))

Expand Down Expand Up @@ -52,12 +52,10 @@ def calculate_partial_expected_mutual_information(n, x_i, y_j):
if x_i == 0 or y_j == 0:
return 0
coefficient = (-log2(x_i) - log2(y_j) + log2(n))
sum_probability = 0.0
partial_result = 0.0
for n_j, p_j in _hypergeometric_pmf(n, x_i, y_j):
if n_j != 0:
partial_result += n_j * (coefficient + log2(n_j)) * p_j
sum_probability += p_j
hyp_geo_pmf = _hypergeometric_pmf(n, x_i, y_j)
sum_probability = np.sum([p_j for n_j, p_j in hyp_geo_pmf])
partial_result = np.sum([n_j * (coefficient + log2(n_j)) * p_j for n_j, p_j in hyp_geo_pmf if n_j != 0])

# The values of p_j should sum to 1, but given approximate calculations for
# log2(x) and exp2(x) with large x, the full pmf might not sum to exactly 1.
# We correct for this by dividing by the sum of the probabilities.
Expand Down