Unsupervised Analysis of Days of Week



In [1]:

    
%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn')

import pandas as pd
import numpy as np

from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture

Get data



In [2]:

    
from jupyterworkflow.data import get_data
data = get_data()



In [3]:

    
pivoted = data.pivot_table('Total', index= data.index.time, columns=data.index.date)
pivoted.plot(legend=False, alpha=0.01)









    Out[3]:





<matplotlib.axes._subplots.AxesSubplot at 0x10fd261d0>

Pinciple Component Analysis



In [4]:

    
X = pivoted.fillna(0).T.values
X.shape









    Out[4]:





(1855, 24)



In [10]:

    
X2 = PCA(2, svd_solver="full").fit_transform(X)
X2.shape









    Out[10]:





(1855, 2)



In [12]:

    
plt.scatter(X2[:, 0], X2[:, 1]);

Unsuprvised Clustering



In [13]:

    
gmm = GaussianMixture(2).fit(X)
labels = gmm.predict(X)
labels









    Out[13]:





array([1, 1, 1, ..., 0, 1, 1])



In [16]:

    
plt.scatter(X2[:,0], X2[:,1], c=labels, cmap='rainbow')
plt.colorbar()









    Out[16]:





<matplotlib.colorbar.Colorbar at 0x114090828>



In [15]:

    
fig, ax = plt.subplots(1, 2, figsize=(14, 6))

pivoted.T[labels == 0].T.plot(legend=False, alpha=0.1, ax=ax[0]);
pivoted.T[labels == 1].T.plot(legend=False, alpha=0.1, ax=ax[1]);

ax[0].set_title('Pupple Cluster')
ax[1].set_title('Red Cluster');

Comparing with Days of Week



In [17]:

    
dayofweek = pd.DatetimeIndex(pivoted.columns).dayofweek



In [18]:

    
plt.scatter(X2[:,0], X2[:,1], c=dayofweek, cmap='rainbow')
plt.colorbar()









    Out[18]:





<matplotlib.colorbar.Colorbar at 0x1140cafd0>

Analyzing Outliners

The following points are weekdays with a holiday-like pattern



In [19]:

    
dates = pd.DatetimeIndex(pivoted.columns)
dates[(labels==1) & (dayofweek < 5)]









    Out[19]:





DatetimeIndex(['2012-10-03', '2012-10-04', '2012-10-05', '2012-10-08',
               '2012-10-09', '2012-10-10', '2012-10-11', '2012-10-12',
               '2012-10-15', '2012-10-16',
               ...
               '2017-10-18', '2017-10-19', '2017-10-20', '2017-10-23',
               '2017-10-24', '2017-10-25', '2017-10-26', '2017-10-27',
               '2017-10-30', '2017-10-31'],
              dtype='datetime64[ns]', length=1281, freq=None)

What's up with Feb 6, 2017? Snow Storm



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