<jupyter_start><jupyter_text>Unsupervised Analysis of Days of WeekTresting crossings each dat as features to learn about the relationships bwteen days of the week<jupyter_code>%matplotlib inline
import matplotlib.pyplot as plt
plt.style.use('seaborn')

import numpy as np
import pandas as pd

from sklearn.decomposition import PCA
from sklearn.mixture import GaussianMixture<jupyter_output><empty_output><jupyter_text>Get Data<jupyter_code>from jupyterworkflow.data import get_fremont_data
data = get_fremont_data()
pivoted = data.pivot_table('Total', index=data.index.time, columns=data.index.date)
pivoted.plot(legend=False, alpha=0.01);<jupyter_output><empty_output><jupyter_text>Principal Component Analysis<jupyter_code>X = pivoted.fillna(0).T.values
X.shape

X2 = PCA(2, svd_solver='full').fit_transform(X)
X2.shape
plt.scatter(X2[:, 0], X2[:, 1]);<jupyter_output><empty_output><jupyter_text>Unsupervised Clustering<jupyter_code>gmm = GaussianMixture(2)
gmm.fit(X)
labels = gmm.predict(X)
plt.scatter(X2[:, 0], X2[:, 1], c=labels, cmap='rainbow');
plt.colorbar();
fig, ax = plt.subplots(1, 2, figsize=(14, 6))

pivoted.T[labels == 0].T.plot(legend=False, alpha=0.01, ax=ax[0]);
pivoted.T[labels == 1].T.plot(legend=False, alpha=0.01, ax=ax[1]);

ax[0].set_title('Purple Cluster');
ax[1].set_title('Red Cluster');<jupyter_output><empty_output><jupyter_text>Comparing with Day of the week<jupyter_code>dayofweek = pd.DatetimeIndex(pivoted.columns).dayofweek
plt.scatter(X2[:, 0], X2[:, 1], c=dayofweek, cmap='rainbow');
plt.colorbar();<jupyter_output><empty_output><jupyter_text>Analyzing OutliersThe following points are weekdays with a holiday-like pattern<jupyter_code>weekday_label = 0
weekend_label = 1
if pivoted.T[labels == 1].max().max() > pivoted.T[labels == 0].max().max() :
    weekday_label = 1
    weekend_label = 0
dates = pd.DatetimeIndex(pivoted.columns)
dates [(labels == weekend_label) & (dayofweek < 5)]<jupyter_output><empty_output>
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