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Description:

plotmetric 0.0.6

plot_metric
|PyPI-Versions| |doc_badge|
Librairie to simplify plotting of metric like ROC curve, confusion matrix etc..
Installation
Using pip :
.. code:: sh
pip install plot-metric

Example BinaryClassification
Simple binary classification

Let's load a simple dataset and make a train & test set :

.. code:: python

from sklearn.datasets import make_classification
from sklearn.model_selection import train_test_split

X, y = make_classification(n_samples=1000, n_classes=2, weights=[1,1], random_state=1)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=2)

Train our classifier and predict our test set :

.. code:: python

from sklearn.ensemble import RandomForestClassifier

clf = RandomForestClassifier(n_estimators=50, random_state=23)
model = clf.fit(X_train, y_train)

# Use predict_proba to predict probability of the class
y_pred = clf.predict_proba(X_test)[:,1]

We can now use ``plot_metric`` to plot ROC Curve, distribution class and classification matrix :

.. code:: python

# Visualisation with plot_metric
bc = BinaryClassification(y_test, y_pred, labels=["Class 1", "Class 2"])

# Figures
plt.figure(figsize=(15,10))
plt.subplot2grid(shape=(2,6), loc=(0,0), colspan=2)
bc.plot_roc_curve()
plt.subplot2grid((2,6), (0,2), colspan=2)
bc.plot_precision_recall_curve()
plt.subplot2grid((2,6), (0,4), colspan=2)
bc.plot_class_distribution()
plt.subplot2grid((2,6), (1,1), colspan=2)
bc.plot_confusion_matrix()
plt.subplot2grid((2,6), (1,3), colspan=2)
bc.plot_confusion_matrix(normalize=True)
plt.show()
bc.print_report()

>>> ________________________
>>> | Classification Report |
>>> ‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾‾
>>> precision recall f1-score support
>>> 0 1.00 0.93 0.96 43
>>> 1 0.96 1.00 0.98 71
>>> micro avg 0.97 0.97 0.97 114
>>> macro avg 0.98 0.97 0.97 114
>>> weighted avg 0.97 0.97 0.97 114

.. image:: example/images/example_binary_classification.png

Custom parameters
~~~~~~~~~~~~~~~~~

It is possible to customize a lot of figures parameters. You can find all parameters with documentation on the official package documentation : https://plot-metric.readthedocs.io/en/latest/
Or you can retrieve a python dictionnary with all available parameters with the following :

.. code:: python

# Use the function get_function_parameters(function) to get parameters
bc.get_function_parameters(bc.plot_roc_curve)

>>> {'threshold': None,
'plot_threshold': True,
'beta': 1,
'linewidth': 2,
'fscore_iso': [0.2, 0.4, 0.6, 0.8],
'iso_alpha': 0.7,
'y_text_margin': 0.03,
'x_text_margin': 0.2,
'c_pr_curve': 'black',
'c_mean_prec': 'red',
'c_thresh': 'black',
'c_f1_iso': 'grey',
'c_thresh_point': 'red',
'ls_pr_curve': '-',
'ls_mean_prec': '--',
'ls_thresh': ':',
'ls_fscore_iso': ':',
'marker_pr_curve': None}

From a custom dictionnary you can set all parameters you want and plot a figures :

.. code:: python

# Example custom param using dictionnary
param_pr_plot = {
'c_pr_curve':'blue',
'c_mean_prec':'cyan',
'c_thresh_lines':'red',
'c_f1_iso':'green',
'beta': 2,
}

plt.figure(figsize=(6,6))
bc.plot_precision_recall_curve(**param_pr_plot)
plt.show()

.. image:: example/images/example_binary_class_PRCurve_custom.png

.. |PyPI-Versions| image:: https://img.shields.io/badge/plot__metric-v0.0.4-blue.svg
:target: https://pypi.org/project/plot-metric/

.. |doc_badge| image:: https://readthedocs.org/projects/plot-metric/badge/?version=latest
:target: https://plot-metric.readthedocs.io/en/latest/?badge=latest
:alt: Documentation Status