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        "author": "Martin Thoma",
        "author_email": "info@martin-thoma.de",
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            "License :: OSI Approved :: MIT License",
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        "description": "[![DOI](https://zenodo.org/badge/102892750.svg)](https://zenodo.org/badge/latestdoi/102892750)\n[![PyPI version](https://badge.fury.io/py/clana.svg)](https://badge.fury.io/py/clana)\n[![Python Support](https://img.shields.io/pypi/pyversions/clana.svg)](https://pypi.org/project/clana/)\n[![Documentation Status](https://readthedocs.org/projects/clana/badge/?version=latest)](http://clana.readthedocs.io/en/latest/?badge=latest)\n[![Build Status](https://travis-ci.org/MartinThoma/clana.svg?branch=master)](https://travis-ci.org/MartinThoma/clana)\n[![Coverage Status](https://coveralls.io/repos/github/MartinThoma/clana/badge.svg?branch=master)](https://coveralls.io/github/MartinThoma/clana?branch=master)\n\n# clana\n\n`clana` is a toolkit for classifier analysis. One key contribution of clana is\nConfusion Matrix Ordering (CMO) as explained in chapter 5 of [Analysis and Optimization of Convolutional Neural Network Architectures](https://arxiv.org/abs/1707.09725). It is a technique\nthat can be applied to any multi-class classifier and helps to understand which\ngroups of classes are most similar.\n\n\n## Installation\n\nThe recommended way to install clana is:\n\n```\n$ pip install clana --user\n```\n\nIf you want the latest version:\n\n```\n$ git clone https://github.com/MartinThoma/clana.git; cd clana\n$ pip install -e . --user\n```\n\n## Usage\n\n```\n$ clana --help\nUsage: clana [OPTIONS] COMMAND [ARGS]...\n\nOptions:\n  --version  Show the version and exit.\n  --help     Show this message and exit.\n\nCommands:\n  distribution   Get the distribution of classes in a dataset.\n  get-cm         Calculate the confusion matrix (CSV inputs).\n  get-cm-simple  Calculate the confusion matrix (one label per...\n  visualize      Optimize confusion matrix.\n```\n\nThe visualize command gives you images like this:\n\n![Confusion Matrix after Confusion Matrix Ordering of the WiLI-2018 dataset](cm-wili-2018.png)\n\n### MNIST example\n\n```\n$ cd docs/\n$ python mnist_example.py  # creates `train-pred.csv` and `test-pred.csv`\n$ clana get-cm --gt gt-train.csv  --predictions train-pred.csv --n 10\n2019-09-14 09:47:30,655 - root - INFO - cm was written to 'cm.json'\n$ clana visualize --cm cm.json --zero_diagonal\nScore: 13475\n2019-09-14 09:49:41,593 - root - INFO - n=10\n2019-09-14 09:49:41,593 - root - INFO - ## Starting Score: 13475.00\n2019-09-14 09:49:41,594 - root - INFO - Current: 13060.00 (best: 13060.00, hot_prob_thresh=100.0000%, step=0, swap=False)\n[...]\n2019-09-14 09:49:41,606 - root - INFO - Current: 9339.00 (best: 9339.00, hot_prob_thresh=100.0000%, step=238, swap=False)\nScore: 9339\nPerm: [0, 6, 5, 8, 3, 2, 1, 7, 9, 4]\n2019-09-14 09:49:41,639 - root - INFO - Classes: [0, 6, 5, 8, 3, 2, 1, 7, 9, 4]\nAccuracy: 93.99%\n2019-09-14 09:49:41,725 - root - INFO - Save figure at '/home/moose/confusion_matrix.tmp.pdf'\n2019-09-14 09:49:41,876 - root - INFO - Found threshold for local connection: 398\n2019-09-14 09:49:41,876 - root - INFO - Found 9 clusters\n2019-09-14 09:49:41,877 - root - INFO - silhouette_score=-0.012313948323292875\n    1: [0]\n    1: [6]\n    1: [5]\n    1: [8]\n    1: [3]\n    1: [2]\n    1: [1]\n    2: [7, 9]\n    1: [4]\n```\n\nThis gives\n\n![](mnist_confusion_matrix.png)\n\n#### Label Manipulation\n\nPrepare a `labels.csv` which **has to have a header row**:\n\n```\n$ clana visualize --cm cm.json --zero_diagonal --labels mnist/labels.csv\n```\n\n![](mnist_confusion_matrix_labels.png)\n\n\n### Data distribution\n\n```\n$ clana distribution --gt gt.csv --labels labels.csv [--out out/] [--long]\n```\n\nprints one line per label, e.g.\n\n```\n60% cat (56789 elements)\n20% dog (12345 elements)\n 5% mouse (1337 elements)\n 1% tux (314 elements)\n```\n\nIf `--out` is specified, it creates a horizontal bar chart. The first bar is\nthe most common class, the second bar is the second most common class, ...\n\nIt uses the short labels, except `--long` is added to the command.\n\n\n### Metrics\n\n```\n$ clana metrics --gt gt.csv --preds preds.csv\n```\n\ngives the following metrics by\n\n* Line 1: Accuracy\n* Line 2: Precision\n* Line 3: Recall\n* Line 4: F1-Score\n* Line 5: Mean accuracy\n\n### Visualizations\n\nSee [visualizations](visualizations.md)\n\n\n## Development\n\nCheck tests with `tox`.\n\n\n",
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