In [1]:

    

import pandas as pd
import numpy as np

%pylab inline
pylab.style.use('ggplot')


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

url = 'https://archive.ics.uci.edu/ml/machine-learning-databases/balance-scale/balance-scale.data'

balance_df = pd.read_csv(url, header=None)


    

In [3]:

    

balance_df.columns = ['class_name', 'left_weight', 'left_distance', 'right_weight', 'right_distance']


    

In [4]:

    

balance_df.head()


    

    
Out[4]:







  

    

      

      
class_name
      
left_weight
      
left_distance
      
right_weight
      
right_distance
    
  
  

    

      
0
      
B
      
1
      
1
      
1
      
1
    
    

      
1
      
R
      
1
      
1
      
1
      
2
    
    

      
2
      
R
      
1
      
1
      
1
      
3
    
    

      
3
      
R
      
1
      
1
      
1
      
4
    
    

      
4
      
R
      
1
      
1
      
1
      
5
    
  

In [5]:

    

counts = balance_df['class_name'].value_counts()
counts.plot(kind='bar')


    

    
Out[5]:





<matplotlib.axes._subplots.AxesSubplot at 0x1dd0b82f9e8>






    

In [6]:

    

from sklearn.feature_selection import f_classif


    

In [7]:

    

features = balance_df.drop('class_name', axis=1)
names = balance_df['class_name']


    

In [8]:

    

# check for negative feature values
features[features < 0].sum(axis=0)


    

    
Out[8]:





left_weight       0.0
left_distance     0.0
right_weight      0.0
right_distance    0.0
dtype: float64

In [9]:

    

t_stats, p_vals = f_classif(features, names)

feature_importances = pd.DataFrame(np.column_stack([t_stats, p_vals]), 
                                   index=features.columns.copy(), 
                                   columns=['t_stats', 'p_vals'])


    

In [10]:

    

feature_importances.plot(subplots=True, kind='bar')
plt.xticks(rotation=30)


    

    
Out[10]:





(array([0, 1, 2, 3]), <a list of 4 Text xticklabel objects>)






    

In [11]:

    

import seaborn as sns

for colname in balance_df.columns.drop('class_name'):
    fg = sns.FacetGrid(col='class_name', data=balance_df)
    fg = fg.map(pylab.hist, colname)


    

In [12]:

    

from sklearn.model_selection import cross_val_score, StratifiedKFold
from sklearn.naive_bayes import GaussianNB

estimator = GaussianNB()
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=12345)

f1 = cross_val_score(estimator, features, names, cv=cv, scoring='f1_micro')
pd.Series(f1).plot(title='F1 Score (Micro)', kind='bar')


    

    
Out[12]:





<matplotlib.axes._subplots.AxesSubplot at 0x1dd0eea8d30>






    

In [14]:

    

estimator = GaussianNB()
cv = StratifiedKFold(n_splits=5, shuffle=True, random_state=12345)

f1 = cross_val_score(estimator, features, names, cv=cv, scoring='accuracy')
pd.Series(f1).plot(title='Accuracy', kind='bar')


    

    
Out[14]:





<matplotlib.axes._subplots.AxesSubplot at 0x1dd0ef50128>