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

# In[1]:


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


# # Plot Sequential Feature Selection

# A matplotlib utility function for visualizing results from [`feature_selection.SequentialFeatureSelector`](`../feature_selection/SequentialFeatureSelector.md`).

# > from mlxtend.plotting import plot_sequential_feature_selection

# # Overview

# for more information on sequential feature selection, please see [`feature_selection.SequentialFeatureSelector`](`../feature_selection/SequentialFeatureSelector.md`).

# ## Example 1 - Plotting the results from SequentialFeatureSelector

# In[2]:


from mlxtend.plotting import plot_sequential_feature_selection as plot_sfs
from mlxtend.feature_selection import SequentialFeatureSelector as SFS
import matplotlib.pyplot as plt
from sklearn.neighbors import KNeighborsClassifier
from sklearn.datasets import load_iris

iris = load_iris()
X = iris.data
y = iris.target
knn = KNeighborsClassifier(n_neighbors=4)

sfs = SFS(knn, 
          k_features=4, 
          forward=True, 
          floating=False, 
          scoring='accuracy',
          cv=5)

sfs = sfs.fit(X, y)

fig1 = plot_sfs(sfs.get_metric_dict(), kind='std_dev')

plt.ylim([0.8, 1])
plt.title('Sequential Forward Selection (w. StdDev)')
plt.grid()
plt.show()


# # API

# In[1]:


with open('../../api_modules/mlxtend.plotting/plot_sequential_feature_selection.md', 'r') as f:
    s = f.read()
print(s)