#sklearn-evaluation
scikit-learn model evaluation made easy: plots, tables and markdown reports.
Works with Python 2 and 3.
pip install sklearn-evaluationIf you want to use the reports module you need to install mistune and tabulate for the tables module
# for generating reports
pip install mistune
# for the table module
pip install tabulate#Usage
##plot module
Generate evaluation plots with a single function call.
from sklearn_evaluation import plot
#code for data loading and model training
plot.confusion_matrix(y_true, y_pred, target_names=target_names)
##table module
Generate good looking tables from your model results.
from sklearn_evaluation import table
#code for data loading and training
table.feature_importances(model)+-----------+--------------+-----------+
| name | importance | std |
+===========+==============+===========+
| Feature 0 | 0.250398 | 0.0530907 |
+-----------+--------------+-----------+
| Feature 1 | 0.232397 | 0.0523836 |
+-----------+--------------+-----------+
| Feature 2 | 0.148898 | 0.0331814 |
+-----------+--------------+-----------+
| Feature 3 | 0.0553634 | 0.0128296 |
+-----------+--------------+-----------+
| Feature 8 | 0.05401 | 0.0122248 |
+-----------+--------------+-----------+
| Feature 5 | 0.053878 | 0.01289 |
+-----------+--------------+-----------+
| Feature 6 | 0.0525828 | 0.0130225 |
+-----------+--------------+-----------+
| Feature 9 | 0.0510197 | 0.0129436 |
+-----------+--------------+-----------+
| Feature 7 | 0.0509633 | 0.0117197 |
+-----------+--------------+-----------+
| Feature 4 | 0.0504887 | 0.012844 |
+-----------+--------------+-----------+
Also, running this in Jupyter will generate a pandas-like output.
##Using the OOP interface
A simplified API is available by packing the results of your estimator in the ClassifierEvaluator class.
from sklearn_evaluation import ClassifierEvaluator
# code for data loading and model training
ce = ClassifierEvaluator(classifier, y_test, y_pred, y_score,
feature_list, target_names)
# this plots the confusion matrix
ce.confusion_matrixGenerate reports using Markdown templates.
template = '''
# Report
{estimator_type}
{date}
{confusion_matrix}
{roc}
{precision_recall}
'''
ce.generate_report(template, path='report.html')The code above will generate a report like this one.
Reports are self-contained, all images are included in the html file using base64.