带有用于绘制度量的工具的包
plot-metric的Python项目详细描述
绘图度量
| PYPI版本文档徽章
简化roc曲线、混淆矩阵等度量的绘制。
安装
使用pip:
。代码：：sh
pip install plot-metric
二进制分类示例
简单的二进制分类
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
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: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
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