In [1]:

    

%load_ext autoreload
%autoreload 2
%matplotlib inline
import seaborn as sns
import matplotlib.pyplot as plt

sns.set_context('poster')
sns.set_color_codes()
sns.set_style('white')


    

In [24]:

    

from sklearn.datasets import load_iris
data = load_iris()
data = pd.DataFrame(data.data, columns=data.feature_names)


    

In [35]:

    

from clumpy.preprocessing import process_data
X, num_cols, cat_cols = process_data(data, impute='mean', num_preprocessing='minmax')


    

In [36]:

    

from clumpy import auto_kmeans
kmeans = auto_kmeans(X, n_clusters=[3, 4, 5])


    

In [69]:

    

from clumpy import importance
X, num_cols, cat_cols = process_data(data, categorical_columns=cat_cols)
feature_names = num_cols + cat_cols

importances = importance.anova_importance(X, kmeans.labels_, feature_names=feature_names, n_features='all')
importances


    

    
Out[69]:





{0: ['sepal length (cm)',
  'sepal width (cm)',
  'petal length (cm)',
  'petal width (cm)'],
 1: ['sepal length (cm)',
  'sepal width (cm)',
  'petal length (cm)',
  'petal width (cm)'],
 2: ['sepal length (cm)',
  'sepal width (cm)',
  'petal length (cm)',
  'petal width (cm)']}

In [70]:

    

from clumpy.rules import tree_descriptions
rules = tree_descriptions(
    data, 
    kmeans.labels_, 
    categorical_columns=cat_cols,
    feature_names=importances,
    max_depth=5)

pd.DataFrame({'cluster_id': range(len(rules)), 'description': rules})
for cluster_id, rule in enumerate(rules):
    print('cluster_id: {}'.format(cluster_id))
    print
    print(rule)
    print
    
from pandas.tools.plotting import parallel_coordinates
full_data = load_iris()
full_data = pd.DataFrame(
    np.hstack((full_data.data, full_data.target[:, np.newaxis])), columns=full_data.feature_names + ['type'])
parallel_coordinates(full_data, 'type')


    

    




cluster_id: 0

petal length (cm) <= 2.45 

cluster_id: 1

petal length (cm) <= 5.2 AND
petal width (cm) <= 2.2 AND
sepal length (cm) > 4.7 AND
sepal width (cm) <= 2.95 

cluster_id: 2

petal length (cm) > 5.15 AND
sepal length (cm) > 6.15 







    
Out[70]:





<matplotlib.axes._subplots.AxesSubplot at 0x2e60d4d0>






    

In [72]:

    

from clumpy.plots import plot_cluster_statistics

cluster_id = 0
cluster_importances = importances[cluster_id]
cat_vars = [var for var in cluster_importances if var in cat_cols]
num_vars = [var for var in cluster_importances if var in num_cols]

plot_cluster_statistics(
    cluster_labels=kmeans.labels_, 
    cluster_id=cluster_id, 
    data=data, scale=True,
    quant_var=num_vars,
    qual_var=cat_vars,
    figsize=(15,15))


    

    




   sepal length (cm)  sepal width (cm)  petal length (cm)  petal width (cm)  \
0           5.843333             3.054           3.758667          1.198667   
0           5.006000             3.418           1.464000          0.244000   

         id  
0  marginal  
0   cluster  






    

In [73]:

    

full_data = load_iris()
full_data = pd.DataFrame(
    np.hstack((full_data.data, kmeans.labels_[:, np.newaxis])), columns=full_data.feature_names + ['type'])
parallel_coordinates(full_data, 'type')


    

    
Out[73]:





<matplotlib.axes._subplots.AxesSubplot at 0x15e8aa90>






    

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