plot_metric

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

Installation

Using pip :

pip install plot-metric

Example BinaryClassification

Simple binary classification

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

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 :

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 :

# 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

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 :

# 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 :

# 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()