New metrics

[anvelezec]

Salut,

Activities in this pull request:

• F1 score

• F1 score test cases

• MAPE as metric

• MAPE test cases

• MAPE functions Refactor due to name incompativility

[codecov-io]

Codecov Report

Merging #100 (27b7c93) into master (c7606f9) will increase coverage by 0.47%.
The diff coverage is 100.00%.

@@            Coverage Diff             @@
##           master     #100      +/-   ##
==========================================
+ Coverage   76.94%   77.41%   +0.47%     
==========================================
  Files         102      106       +4     
  Lines        4746     4836      +90     
==========================================
+ Hits         3652     3744      +92     
+ Misses       1094     1092       -2     

Impacted Files	Coverage Δ
elegy/losses/__init__.py	100.00% <ø> (ø)
...legy/losses/mean_squared_logarithmic_error_test.py	94.87% <ø> (ø)
elegy/losses/mean_absolute_percentage_error.py	100.00% <100.00%> (ø)
...legy/losses/mean_absolute_percentage_error_test.py	100.00% <100.00%> (ø)
elegy/losses/mean_squared_logarithmic_error.py	100.00% <100.00%> (ø)
elegy/metrics/__init__.py	100.00% <100.00%> (ø)
elegy/metrics/f1.py	100.00% <100.00%> (ø)
elegy/metrics/f1_test.py	100.00% <100.00%> (ø)
elegy/metrics/mean_absolute_error_test.py	100.00% <100.00%> (ø)
elegy/metrics/mean_absolute_percentage_error.py	100.00% <100.00%> (ø)
... and 7 more

---

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[cgarciae]

I think we should try to move to using random arrays and checking against the TF implementation to have stronger guarantees.

[cgarciae]

This is very good in general but tests should be generalized to random arrays and tested against the TF implementation. You can also test that sample_weights and reduction are well behaved.

[anvelezec]

@cgarciae I am having issues adding tensorflow_addons to the toml. I need the library to contrast elegy f1 against tfa f1

[cgarciae]

@anvelezec try updating poetry:

poetry self update --version 1.1.4