clana 0.3.9

CLANA is a toolkit for classifier analysis.

clana

clana is a toolkit for classifier analysis. One key contribution of clana is Confusion Matrix Ordering (CMO) as explained in chapter 5 of Analysis and Optimization of Convolutional Neural Network Architectures. It is a technique that can be applied to any multi-class classifier and helps to understand which groups of classes are most similar.

Installation

The recommended way to install clana is:

$ pip install clana --user

If you want the latest version:

$ git clone https://github.com/MartinThoma/clana.git; cd clana
$ pip install -e . --user

Usage

$ clana --help
Usage: clana [OPTIONS] COMMAND [ARGS]...

Options:
  --version  Show the version and exit.
  --help     Show this message and exit.

Commands:
  distribution   Get the distribution of classes in a dataset.
  get-cm         Calculate the confusion matrix (CSV inputs).
  get-cm-simple  Calculate the confusion matrix (one label per...
  visualize      Optimize confusion matrix.

The visualize command gives you images like this:

MNIST example

$ cd docs/
$ python mnist_example.py  # creates `train-pred.csv` and `test-pred.csv`
$ clana get-cm --gt gt-train.csv  --predictions train-pred.csv --n 10
2019-09-14 09:47:30,655 - root - INFO - cm was written to 'cm.json'
$ clana visualize --cm cm.json --zero_diagonal
Score: 13475
2019-09-14 09:49:41,593 - root - INFO - n=10
2019-09-14 09:49:41,593 - root - INFO - ## Starting Score: 13475.00
2019-09-14 09:49:41,594 - root - INFO - Current: 13060.00 (best: 13060.00, hot_prob_thresh=100.0000%, step=0, swap=False)
[...]
2019-09-14 09:49:41,606 - root - INFO - Current: 9339.00 (best: 9339.00, hot_prob_thresh=100.0000%, step=238, swap=False)
Score: 9339
Perm: [0, 6, 5, 8, 3, 2, 1, 7, 9, 4]
2019-09-14 09:49:41,639 - root - INFO - Classes: [0, 6, 5, 8, 3, 2, 1, 7, 9, 4]
Accuracy: 93.99%
2019-09-14 09:49:41,725 - root - INFO - Save figure at '/home/moose/confusion_matrix.tmp.pdf'
2019-09-14 09:49:41,876 - root - INFO - Found threshold for local connection: 398
2019-09-14 09:49:41,876 - root - INFO - Found 9 clusters
2019-09-14 09:49:41,877 - root - INFO - silhouette_score=-0.012313948323292875
    1: [0]
    1: [6]
    1: [5]
    1: [8]
    1: [3]
    1: [2]
    1: [1]
    2: [7, 9]
    1: [4]

This gives

Label Manipulation

Prepare a labels.csv which has to have a header row:

$ clana visualize --cm cm.json --zero_diagonal --labels mnist/labels.csv

Data distribution

$ clana distribution --gt gt.csv --labels labels.csv [--out out/] [--long]

prints one line per label, e.g.

60% cat (56789 elements)
20% dog (12345 elements)
 5% mouse (1337 elements)
 1% tux (314 elements)

If --out is specified, it creates a horizontal bar chart. The first bar is the most common class, the second bar is the second most common class, ...

It uses the short labels, except --long is added to the command.

Metrics

$ clana metrics --gt gt.csv --preds preds.csv

gives the following metrics by

• Line 1: Accuracy
• Line 2: Precision
• Line 3: Recall
• Line 4: F1-Score
• Line 5: Mean accuracy

Visualizations

See visualizations

Development

Check tests with tox.