#271 Classification Evaluation

[WGierke]

I'm currently adding the possibility to evaluate a classification model based on the LIDC dataset as described in #271. Furthermore, I'm currently benchmarking the model that's implemented at the moment. Even if I still haven't finished, I wanted to show you what I'm currently working at.

CLA

• [X] I have signed the CLA; if other committers are in the commit history, they have signed the CLA as well

[WGierke]

The current implementation takes 8.5h to run, gives an average accuracy/precision of 10% and an average loss of 3.55 :/

[reubano]

It looks good, and no I don't see any major flaws. If someone with more experience in applying these metrics to CT scans has suggestions he/she can always improve this with future PRs. It also seems like the sys.path.insert... isn't making Travis happy. I'll see what can be done about that.

[reubano]

This is what I got...

src/algorithms/evaluation/evaluation.py

import os

import numpy as np
import pylidc as pl

try:
    from ....config import Config
except ValueError:
    from config import Config

...

src/tests/test_evaluate_classification.py

from ..algorithms.evaluation.evaluation import evaluate_classification

def test_evaluate_classification(model_path=None):
    assert evaluate_classification(model_path)

run with docker-compose -f local.yml run prediction pytest src/tests/test_evaluate_classification.py

I believe you'll also have to add tdmq to the requirements and fix whatever remaining style errors you may see via flake8 prediction

[WGierke]

@reubano Done. Thanks!

[lamby]

Awesome :)