#!/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[5]:


from sklearn import datasets
from sklearn.decomposition import PCA
from sklearn.linear_model import SGDClassifier
from sklearn.pipeline import Pipeline
from sklearn.model_selection import GridSearchCV

logistic = SGDClassifier(loss='log', penalty='l2', early_stopping=True,
                         max_iter=10000, tol=1e-5, random_state=0)
pca = PCA()
pipe = Pipeline(steps=[('pca', pca), ('logistic', logistic)])

digits = datasets.load_digits()
X_digits = digits.data
y_digits = digits.target

# Parameters of pipelines can be set using ‘__’ separated parameter names:
param_grid = {
    'pca__n_components': [5, 20, 30, 40, 50, 64],
    'logistic__alpha': np.logspace(-4, 4, 5),
    'logistic__max_iter': [10000, 5000]
}
search = GridSearchCV(pipe, param_grid, cv=5, iid=False)


# In[6]:


search.fit(X_digits, y_digits)


# ## First plot

# In[8]:


from sklearn_plot_api import plot_grid_search
viz = plot_grid_search(search, ['pca__n_components', 'logistic__alpha'], include_values=True)


# ## Adjust cmap (with plot)

# In[9]:


viz.plot(cmap='plasma', include_values=True)


# ## Adjust cmap directly 

# In[10]:


viz.im_.set_cmap('magma')
viz.figure_