%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

"""
Explanation: Unsupervised Analysis of Days of Week
Tresting crossings each dat as features to learn about the relationships bwteen days of the week
End of explanation
"""


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);

"""
Explanation: Get Data
End of explanation
"""


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]);

"""
Explanation: Principal Component Analysis
End of explanation
"""


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');

"""
Explanation: Unsupervised Clustering
End of explanation
"""


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

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

"""
Explanation: Comparing with Day of the week
End of explanation
"""


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)]

"""
Explanation: Analyzing Outliers
The following points are weekdays with a holiday-like pattern
End of explanation
"""


