disarray

This package calculates metrics derived from a confusion matrix and makes them directly accessible from a pandas DataFrame. Simply install and import disarray.

import pandas as pd

df = pd.DataFrame([[18, 1], [0, 1]])

import disarray

df.da.sensitivity
0    0.947368
1    1.000000
dtype: float64

Why disarray?
Working with a confusion matrix is an everyday occurrence for most data science projects. Sometimes, a data scientist is responsible for generating a confusion matrix using machine learning libraries like scikit-learn. But it's not uncommon to work with confusion matrices directly as pandas DataFrames.

Since pandas version 0.23.0, users can easily register custom accessors, which is how disarray is implemented.

Table of contents

• Installation
• Usage
  • class counts
  • export metrics
  • multi-class classification
  • supported metrics
• Contributing

Installation

Install using pip

$ pip install disarray

Clone from GitHub

$ git clone https://github.com/arvkevi/disarray.git
$ python setup.py install

Usage

The disarray package is intended to be used similar to a pandas attribute or method. disarray is registered as a pandas extension under da. For a DataFrame named df, access the library using df.da..

To understand the input and usage for disarray, build an example confusion matrix for a binary classification problem from scratch with scikit-learn.
(You can install the packages you need to run the demo with: pip install -r requirements.demo.txt)

from sklearn import svm, datasets
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
# Generate a random binary classification dataset
X, y = datasets.make_classification(n_classes=2, random_state=42)
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42)
# fit and predict an SVM
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)

cm = confusion_matrix(y_test, y_pred)
print(cm)
[[13  2]
 [ 0 10]]

Using disarray is as easy as importing it and instantiating a DataFrame object from a square array of positive integers.

import disarray
import pandas as pd

df = pd.DataFrame(cm)
print(df.da.sensitivity)
0    0.866667
1    1.000000

Class Counts

disarray stores per-class counts of true positives, false positives, false negatives, and true negatives. Each of these are stored as capitalized abbreviations, TP, FP, FN, and TN.

df.da.TP

0    13
1    10
dtype: int64

Export Metrics

Use df.da.export_metrics() to store and/or visualize many common performance metrics in a new pandas DataFrame object. Use the metrics_to_include= argument to pass a list of metrics defined in disarray/metrics.py (default is to use __all_metrics__).

df.da.export_metrics(metrics_to_include=['precision', 'recall', 'f1'])

	0	1	micro-average
precision	1	0.833333	0.92
recall	0.866667	1	0.92
f1	0.928571	0.909091	0.92

Multi-Class Classification

disarray works with multi-class classification confusion matrices also. Try it out on the iris dataset. Notice, the DataFrame is instantiated with an index and columns here, but it is not required.

# load the iris dataset
iris = datasets.load_iris()
X = iris.data
y = iris.target
class_names = iris.target_names
# split the training and testing data
X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)
# train and fit a SVM
classifier = svm.SVC(kernel='linear', C=0.01)
y_pred = classifier.fit(X_train, y_train).predict(X_test)
cm = confusion_matrix(y_test, y_pred)

# Instantiate the confusion matrix DataFrame with index and columns
df = pd.DataFrame(cm, index=class_names, columns=class_names)
print(df)

	setosa	versicolor	virginica
setosa	13	0	0
versicolor	0	10	6
virginica	0	0	9

disarray can provide per-class metrics:

df.da.sensitivity

setosa        1.000
versicolor    0.625
virginica     1.000
dtype: float64

In a familiar fashion, one of the classes can be accessed with bracket indexing.

df.da.sensitivity['setosa']

1.0

Currently, a micro-average is supported for both binary and multi-class classification confusion matrices. (Although it only makes sense in the multi-class case).

df.da.micro_sensitivity

0.8421052631578947

Finally, a DataFrame can be exported with selected metrics.

df.da.export_metrics(metrics_to_include=['sensitivity', 'specificity', 'f1'])

	setosa	versicolor	virginica	micro-average
sensitivity	1	0.625	1	0.842105
specificity	1	1	0.793103	0.921053
f1	1	0.769231	0.75	0.842105

Supported Metrics

'accuracy',
'f1',
'false_discovery_rate',
'false_negative_rate',
'false_positive_rate',
'negative_predictive_value',
'positive_predictive_value',
'precision',
'recall',
'sensitivity',
'specificity',
'true_negative_rate',
'true_positive_rate',

As well as micro-averages for each of these, accessible via df.da.micro_recall, for example.

Contributing

Contributions are welcome, please refer to CONTRIBUTING to learn more about how to contribute.