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%load_ext watermark
%watermark -a 'Sebastian Raschka' -u -d -v -p numpy,mlxtend









    



Sebastian Raschka 
last updated: 2016-06-11 

CPython 3.5.1
IPython 4.2.0

numpy 1.11.0
mlxtend 0.4.2.dev0

This Jupyter notebook contains the code to create the data visualizations for the article "Model evaluation, model selection, and algorithm selection in machine learning - Part I" at http://sebastianraschka.com/blog/2016/model-evaluation-selection-part1.html.



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%matplotlib inline

Iris Feature by Class Distribution in Random Subsampling



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import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from mlxtend.data import iris_data
from mlxtend.preprocessing import shuffle_arrays_unison

X, y = iris_data()

X, y = shuffle_arrays_unison([X, y], random_seed=123)
X_train, X_test = X[:100], X[100:150]
y_train, y_test = y[:100], y[100:150]



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np.bincount(y)









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array([50, 50, 50])



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np.bincount(y_train)









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array([38, 28, 34])



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np.bincount(y_test)









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array([12, 22, 16])



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df = pd.DataFrame(X)
df['class'] = y

df_train = pd.DataFrame(X_train)
df_train['class'] = y_train

df_test = pd.DataFrame(X_test)
df_test['class'] = y_test



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def stackhist(x, y, **kws):
    grouped = pd.groupby(x, y)
    data = [d for _, d in grouped]
    labels = [l for l, _ in grouped]
    plt.hist(data, histtype="barstacked", 
             label=labels, alpha=0.8, normed=True,
             bins=np.arange(4.0, 8.1, 0.25))
    plt.ylim([0, 0.6])
    plt.xlim([4, 8])
    plt.xlabel('Sepal Width [cm]')
    plt.ylabel('Frequency')

with plt.style.context('fivethirtyeight'):    
    
    stackhist(df[0], df['class'])
    #plt.legend(['Setosa', 'Virginica', 'Versicolor'], fontsize=12)
    plt.title('All')
    plt.tight_layout()
    plt.savefig('./all.svg')
    plt.show()    

    stackhist(df_train[0], df_train['class'])
    plt.title('Train')
    plt.tight_layout()
    #plt.savefig('./train.svg')
    plt.show()    

    stackhist(df_test[0], df_test['class'])
    plt.title('Test')
    plt.tight_layout()
    #plt.savefig('./test.svg')
    plt.show() 
    
    """
    fig = plt.figure()
    figlegend = plt.figure(figsize=(3,4))
    ax = fig.add_subplot(111)
    lines = ax.plot(range(10), np.random.randn(10), range(10), np.random.randn(10), np.random.randn(10), range(10),)
    figlegend.legend(lines, ['Setosa', 'Virginica', 'Versicolor'], 'center')
    fig.show()
    figlegend.show()
    figlegend.savefig('legend.svg')
    """



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