#!/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[3]:


from sklearn.datasets import make_classification
from sklearn.linear_model import LogisticRegression
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split

X, y = make_classification(n_samples=10000, n_informative=5, random_state=0)
X_train, X_test, y_train, y_test = train_test_split(X, y)

lr = LogisticRegression(solver='lbfgs', max_iter=1000)
gbc = GradientBoostingClassifier()
rfc = RandomForestClassifier(n_estimators=100)

lr.fit(X_train, y_train)
gbc.fit(X_train, y_train)
rfc.fit(X_train, y_train)


# ## First plot

# In[9]:


from sklearn_plot_api import plot_roc_curve
viz_lr = plot_roc_curve(lr, X, y)


# ### Change line color

# In[10]:


viz_lr.line_.set_color('red')
viz_lr.figure_


# ## Plot multiple - Function Call

# In[11]:


fig, ax = plt.subplots()
viz_lr = plot_roc_curve(lr, X, y, ax=ax)
viz_gbc = plot_roc_curve(gbc, X, y, ax=ax)
viz_rfc = plot_roc_curve(rfc, X, y, ax=ax)
ax.legend()


# ## Replot using ax

# In[12]:


fig, ax = plt.subplots()
viz_lr.plot(ax=ax)
viz_gbc.plot(ax=ax)
viz_rfc.plot(ax=ax)
ax.legend()


# ## View Figure

# In[13]:


viz_lr.figure_