Unsupervised analysis of Days of Week

Treating crossings each day as features to learn about the realtionships between various days



In [1]:

    
import pandas as pd
from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture

from jupyterworkflow.data import get_fremont_data

%matplotlib inline



In [2]:

    
import matplotlib.pyplot as plt

plt.style.use('seaborn-darkgrid')



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data = get_fremont_data()
pivoted = data.pivot_table("Total", index = data.index.time, columns = data.index.date)



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pivoted.plot(legend=False, alpha=0.01);

Principal Component Analysis



In [5]:

    
X = pivoted.values.T



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



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

Unsupervised Cluster Model



In [8]:

    
gm = GaussianMixture(2)



In [10]:

    
gm.fit(X)









    Out[10]:





GaussianMixture(covariance_type='full', init_params='kmeans', max_iter=100,
        means_init=None, n_components=2, n_init=1, precisions_init=None,
        random_state=None, reg_covar=1e-06, tol=0.001, verbose=0,
        verbose_interval=10, warm_start=False, weights_init=None)



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labels = gm.predict(X)



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

Bike Usage patterns



In [13]:

    
fig, ax = plt.subplots(1,2, figsize =[14,6])
pivoted.T[labels==1].T.plot(legend=False, alpha=0.01, ax=ax[0])
pivoted.T[labels==0].T.plot(legend=False, alpha=0.01, ax=ax[1])
ax[0].set_title("Commuter pattern")
ax[1].set_title("Holiday pattern")
fig.suptitle("Patterns comparison");

Analyzing outliers



In [14]:

    
day_of_week = pd.to_datetime(pivoted.columns).dayofweek

following dates are weekdays with holiday like bike usage pattern



In [15]:

    
pivoted.columns[(labels==0) & (day_of_week<5)]









    Out[15]:





Index([2012-11-22, 2012-11-23, 2012-12-24, 2012-12-25, 2013-01-01, 2013-05-27,
       2013-07-04, 2013-07-05, 2013-09-02, 2013-11-28, 2013-11-29, 2013-12-20,
       2013-12-24, 2013-12-25, 2014-01-01, 2014-04-23, 2014-05-26, 2014-07-04,
       2014-09-01, 2014-11-27, 2014-11-28, 2014-12-24, 2014-12-25, 2014-12-26,
       2015-01-01, 2015-05-25, 2015-07-03, 2015-09-07, 2015-11-26, 2015-11-27,
       2015-12-24, 2015-12-25, 2016-01-01, 2016-05-30, 2016-07-04, 2016-09-05,
       2016-11-24, 2016-11-25, 2016-12-26, 2017-01-02, 2017-02-06],
      dtype='object')



In [16]:

    
plt.scatter(X2[:,0], X2[:,1], c=day_of_week, cmap='GnBu')
plt.colorbar();



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