Clay

A simple plot utils for model validation

Based on sklearn examples for plotting model validation curves, making easy to analyse results for models and avoiding boilerplate, mainly on notebooks.

Installation

TBD

Usage

import numpy as np
from sklearn.datasets import load_breast_cancer
from sklearn.model_selection import validation_curve, learning_curve
from sklearn.linear_model import LogisticRegression
from clay import plot_validation_curve, plot_learning_curve
from matplotlib import pyplot as plt

# dateset
X, y = load_breast_cancer(True)

# base estimator
estimator = LogisticRegression()

# configure plot
plt.grid()
plt.title('{} Learning Curve'.format(type(estimator).__name__))
plt.xlabel("Training Examples")
plt.ylabel("Score")

train_sizes, train_score, test_score = learning_curve(estimator, X, y, cv=5)
plot_learning_curve(train_sizes, train_score, test_score, train_color='deepskyblue', test_color='rebeccapurple')
plt.legend(loc='best')

plt.show()

# configure plot
plt.grid()
plt.title('{} Validation Curve'.format(type(estimator).__name__))
plt.xlabel("C")
plt.ylabel("Score")

param_range = [1, 1e1, 1e2, 1e3, 1e4, 1e5]
train_scores, test_scores = validation_curve(estimator, X, y, param_name='max_iter', param_range=param_range)
plot_validation_curve(train_scores, test_scores, param_range, train_color='deepskyblue', test_color='rebeccapurple')
plt.legend(loc='best')

plt.show()

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Licensed under BSD 2-Clause