#!/usr/bin/env python
# coding: utf-8

# In[1]:


get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')

get_ipython().run_line_magic('matplotlib', 'inline')

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt


# In[2]:


plt.rcParams['figure.figsize'] = [14, 9]


# In[4]:


from sklearn_plot_api import plot_learning_curve

from sklearn.datasets import load_digits
from sklearn.naive_bayes import GaussianNB
from sklearn.model_selection import ShuffleSplit

cv = ShuffleSplit(n_splits=100, test_size=0.2, random_state=0)
estimator = GaussianNB()
digits = load_digits()
X, y = digits.data, digits.target


# ## First plot

# In[5]:


viz = plot_learning_curve(estimator, X, y, n_jobs=4, cv=cv)


# ## Adjust plot directly

# In[6]:


viz.train_fill_between_.set_color('red')
viz.train_line_.set_color('red')
viz.ax_.legend()
viz.figure_


# ## Plot another

# In[7]:


viz.plot()