plot-metric 0.0.4

A package with tools for plotting metrics

plot_metric

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Librairie to simplify plotting of metric like ROC curve, confusion matrix etc..

Installation

Using pip :

.. code:: sh

pip install plot-metric

Example

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

.. code:: python

from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import train_test_split
import pandas as pd

X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(pd.DataFrame(X), y, test_size=0.2, random_state=42)

Train our classifier and predict our test set :

.. code:: python

from sklearn.naive_bayes import GaussianNB

gnb = GaussianNB()
model = gnb.fit(X_train, y_train)
# Use predict_proba to predict probability of the class
y_pred = gnb.predict_proba(X_test)[:,1]

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

.. code:: python

from plot_metric.functions import  BinaryClassification
import matplotlib.pyplot as plt
bc = BinaryClassification(y_test, y_pred, labels=[0, 1])

plt.figure(figsize=(10,9))
plt.subplot(141)
bc.plot_roc()
plt.subplot(142)
bc.plot_class_distribution()
plt.subplot(143)
bc.plot_confusion_matrix()
plt.subplot(144)
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

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