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hw3.cs4641_kMeans_Restaurants.py
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#Sheena Ganju, CS 4641 HW 1
#Decision Trees
from __future__ import print_function
#info from http://scikit-learn.org/stable/modules/
#generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans
#import sklearn statements
import sklearn as sklearn
from sklearn.metrics import accuracy_score
from sklearn import preprocessing
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import learning_curve
from sklearn.model_selection import train_test_split
#for graph from http://scikit-learn.org/stable/modules/generated/sklearn.cluster.KMeans.html#sklearn.cluster.KMeans
from sklearn.metrics import silhouette_samples, silhouette_score
import matplotlib.pyplot as plt
import matplotlib.cm as cm
print(__doc__)
#k-means specific imports
from sklearn.cluster import KMeans
from sklearn.metrics import accuracy_score
#other imports
import scikitplot as skplt
import matplotlib.pyplot as plt
import csv
import numpy as np
import pandas as pd
import time
from sklearn.model_selection import validation_curve
from datetime import date
#Read data in using pandas
trainDataSet = pd.read_csv("geoplaces2.csv", sep = ',', header = None, low_memory = False)
#encode text data to integers using getDummies
traindata = pd.get_dummies(trainDataSet)
# Create decision Tree using major_category, month, year, to predict violent or not
# train split uses default gini node, split using train_test_split
X = traindata.values[1:, 1:]
Y = traindata.values[1:,0]
#start timer
t0= time.clock()
#cv = train_test_split(X, Y, test_size=.30, random_state= 20)
##X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.33, random_state= 20)
##
k = 2
clf = sklearn.cluster.KMeans(n_clusters= k, init='k-means++', n_init = 5, max_iter = 500)
clf = clf.fit(traindata)
##print("Labels" + clf.labels_)
##print("Cluster Centers" + clf.cluster_centers_)
##train_prediction = clf.predict(traindata)
###trainaccuracy = accuracy_score(train_prediction, Y_train)*100
##print("The training accuracy for this is " +str(trainaccuracy))
##
###precision outcomes
##from sklearn.metrics import precision_score
##from sklearn.metrics import log_loss
##precision = precision_score(Y_test, Y_prediction, average = "weighted")*100
##loss = log_loss(Y_test, Y_prediction)*100
##print("Precision: " + str(precision))
##print("Loss: " + str(loss))
##
###Visualizations for model accuracy
###Learning Curve Estimator, Cross Validation
t1= time.clock()
time = str(t1-t0)
print("Computation Time" + time)
skplt.estimators.plot_learning_curve(clf, X, Y, title = "Learning Curve: k-Means")
plt.show()
##
###Visualization for kmeans
range_n_clusters = [2, 8]
y=Y
for n_clusters in range_n_clusters:
# Create a subplot with 1 row and 2 columns
fig, (ax1, ax2) = plt.subplots(1, 2)
fig.set_size_inches(18, 7)
# The 1st subplot is the silhouette plot
# The silhouette coefficient can range from -1, 1 but in this example all
# lie within [-0.1, 1]
ax1.set_xlim([-0.1, 1])
# The (n_clusters+1)*10 is for inserting blank space between silhouette
# plots of individual clusters, to demarcate them clearly.
ax1.set_ylim([0, len(X) + (n_clusters + 1) * 10])
# Initialize the clusterer with n_clusters value and a random generator
# seed of 10 for reproducibility.
clusterer = KMeans(n_clusters=n_clusters, random_state=10)
cluster_labels = clusterer.fit_predict(X)
# The silhouette_score gives the average value for all the samples.
# This gives a perspective into the density and separation of the formed
# clusters
silhouette_avg = silhouette_score(X, cluster_labels)
print("For n_clusters =", n_clusters,
"The average silhouette_score is :", silhouette_avg)
# Compute the silhouette scores for each sample
sample_silhouette_values = silhouette_samples(X, cluster_labels)
y_lower = 10
for i in range(n_clusters):
# Aggregate the silhouette scores for samples belonging to
# cluster i, and sort them
ith_cluster_silhouette_values = \
sample_silhouette_values[cluster_labels == i]
ith_cluster_silhouette_values.sort()
size_cluster_i = ith_cluster_silhouette_values.shape[0]
y_upper = y_lower + size_cluster_i
color = cm.spectral(float(i) / n_clusters)
ax1.fill_betweenx(np.arange(y_lower, y_upper),
0, ith_cluster_silhouette_values,
facecolor=color, edgecolor=color, alpha=0.7)
# Label the silhouette plots with their cluster numbers at the middle
ax1.text(-0.05, y_lower + 0.5 * size_cluster_i, str(i))
# Compute the new y_lower for next plot
y_lower = y_upper + 10 # 10 for the 0 samples
ax1.set_title("The silhouette plot for the various clusters.")
ax1.set_xlabel("The silhouette coefficient values")
ax1.set_ylabel("Cluster label")
# The vertical line for average silhouette score of all the values
ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
ax1.set_yticks([]) # Clear the yaxis labels / ticks
ax1.set_xticks([-0.1, 0, 0.2, 0.4, 0.6, 0.8, 1])
# 2nd Plot showing the actual clusters formed
colors = cm.spectral(cluster_labels.astype(float) / n_clusters)
ax2.scatter(X[:, 0], X[:, 1], marker='.', s=30, lw=0, alpha=0.7,
c=colors, edgecolor='k')
# Labeling the clusters
centers = clusterer.cluster_centers_
# Draw white circles at cluster centers
ax2.scatter(centers[:, 0], centers[:, 1], marker='o',
c="white", alpha=1, s=200, edgecolor='k')
for i, c in enumerate(centers):
ax2.scatter(c[0], c[1], marker='$%d$' % i, alpha=1,
s=50, edgecolor='k')
ax2.set_title("The visualization of the clustered data.")
ax2.set_xlabel("Feature space for the 1st feature")
ax2.set_ylabel("Feature space for the 2nd feature")
plt.suptitle(("Silhouette analysis for KMeans clustering on sample data "
"with n_clusters = %d" % n_clusters),
fontsize=14, fontweight='bold')
plt.show()