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champion_cluster.py
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champion_cluster.py
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import sqlite3
import pandas as pd
from sklearn.cluster import KMeans
from sklearn.cluster import AgglomerativeClustering
from scipy import spatial
import matplotlib.pyplot as plt
# TODO: py2 print to py3
# All fields' name
fields = ['kills', 'deaths', 'assists', 'gold_earned', 'magic_damage', 'physical_damage', 'true_damage', 'damage_taken',
'crowd_control_dealt', 'ward_kills', 'wards_placed']
fields_total = ['total_gold_earned','total_magic_damage','total_physical_damage', 'total_true_damage', 'total_damage_taken',
'total_crowd_control_dealt','total_ward_kills','total_wards_placed']
fields_percent = ['gold_percent', 'magic_percent','physical_percent', 'true_percent', 'taken_percent', 'control_percent', 'wardK_percent',
'wardP_percent']
def count_avg_dict(label):
tmp_dict = {'kills': 0, 'deaths': 0, 'assists': 0, 'gold_earned': 0, 'magic_damage': 0, 'physical_damage': 0, 'true_damage': 0,
'damage_taken': 0, 'crowd_control_dealt': 0, 'ward_kills': 0, 'wards_placed': 0}
for name in label_dict[label]:
for key in tmp_dict.keys():
tmp_dict[key] += all_stats[name][key]
for key in tmp_dict.keys():
tmp_dict[key] /= len(label_dict[label])
return tmp_dict
def count_avg_arr(label):
tmp_arr = [0,0,0,0,0,0,0,0,0,0,0]
for name in new_label_dict[label]:
for i in range(0, len(fields)):
tmp_arr[i] += all_stats[name][fields[i]]
for i in range(0,len(tmp_arr)):
tmp_arr[i] /= len(new_label_dict[label])
return tmp_arr
# iterate all champions' name, retrieve every column by fields' name, calculate the average stats per game of each champions
conn = sqlite3.connect('lola.db')
cursor = conn.cursor()
df = pd.read_sql('SELECT * FROM ChampionMatchStats', conn, index_col=['champion'])
all_stats = {}
all_stats_arr = []
names = []
for champion in df.index:
names.append(champion)
kills = df.ix[champion]['kills']/df.ix[champion]['picks']
deaths = df.ix[champion]['deaths']/df.ix[champion]['picks']
assists = df.ix[champion]['assists']/df.ix[champion]['picks']
gold_earned = df.ix[champion]['gold_earned']/df.ix[champion]['picks']
magic_damage = df.ix[champion]['magic_damage']/df.ix[champion]['picks']
physical_damage = df.ix[champion]['physical_damage']/df.ix[champion]['picks']
true_damage = df.ix[champion]['true_damage']/df.ix[champion]['picks']
damage_taken = df.ix[champion]['damage_taken']/df.ix[champion]['picks']
crowd_control_dealt = df.ix[champion]['crowd_control_dealt']/df.ix[champion]['picks']
ward_kills = df.ix[champion]['ward_kills']/df.ix[champion]['picks']
wards_placed = df.ix[champion]['wards_placed']/df.ix[champion]['picks']
tmp_dict = {'kills': kills, 'assists': assists, 'deaths': deaths, 'gold_earned': gold_earned, 'magic_damage': magic_damage,
'physical_damage': physical_damage, 'true_damage': true_damage,'damage_taken': damage_taken, 'crowd_control_dealt': crowd_control_dealt,
'ward_kills': ward_kills,'wards_placed': wards_placed}
tmp_arr = [kills, assists, deaths, gold_earned, magic_damage, physical_damage, true_damage, damage_taken, crowd_control_dealt, ward_kills,
wards_placed]
all_stats[champion] = tmp_dict
all_stats_arr.append(tmp_arr)
#for (k, v) in all_stats.items():
# print k, '\n', v
#------------------k-means------------------#
meandistortions = []
num_clusters = 6
km = KMeans(n_clusters=num_clusters, max_iter=100, n_init=100)
km.fit(all_stats_arr)
label_dict = []
for i in range(0, num_clusters):
label_dict.append([])
i = 0
for label in km.labels_:
label_dict[label].append(names[i])
#print names[i], int(label)
cursor.execute('UPDATE ChampionMatchStats SET label=? WHERE champion=?', (int(label), names[i],))
i += 1
#Show clustering results and average statistics
for i in range(0, len(label_dict)):
print label_dict[i]
print count_avg_dict(i), '\n'
centers = km.cluster_centers_.tolist()
sum_dist = 0
for label in range(0, num_clusters):
for name in label_dict[label]:
sum_dist += spatial.distance.euclidean(all_stats_arr[names.index(name)], centers[label])
#print num_clusters, sum_dist/128
#Calculate distance between clusters
dist_cluster = 0
num_normalize = 0
for i in range(0, num_clusters):
for j in range(i+1, num_clusters):
dist_cluster += spatial.distance.euclidean(centers[i], centers[j])
num_normalize += 1
print 'k-means\nnumber of clusters:', num_clusters
print 'distortion:', sum_dist/128
print 'distance betweeen clusters/distortion: %.5f\n' % (dist_cluster/(num_normalize*sum_dist/128))
'''
meandistortions.append(dist_cluster/(num_normalize*sum_dist/128))
plt.plot(num_clusters, meandistortions, 'bx-')
plt.xlabel('k')
plt.ylabel('Average distance of clusters/distortion')
plt.title('Selecting k with the Elbow Method')
plt.show()
'''
#------------------agglomerative------------------#
meandistortions = []
num_clusters = 6
ward = AgglomerativeClustering(n_clusters=num_clusters, linkage='complete')
ward.fit(all_stats_arr)
new_label_dict = []
for i in range(0, num_clusters):
new_label_dict.append([])
i = 0
for label in ward.labels_:
new_label_dict[label].append(names[i])
i += 1
#Show clustering results and average statistics
'''for i in range(0, len(new_label_dict)):
#print new_label_dict[i]
#print count_avg_arr(i)
print label_dict[i]
print count_avg_dict(i), '\n'
'''
#Find centroids
centroids = []
for i in range(0, num_clusters):
centroids.append(count_avg_arr(i))
sum_dist = 0
#Calcultate distortions
for i in range(0, num_clusters):
for name in new_label_dict[i]:
sum_dist += spatial.distance.euclidean(all_stats_arr[names.index(name)], centroids[i])
#print num, sum_dist/128
#meandistortions.append(sum_dist/128)
#Calculate distance between clusters
dist_cluster = 0
num_normalize = 0
for i in range(0, num_clusters):
for j in range(i+1, num_clusters):
dist_cluster += spatial.distance.euclidean(centroids[i], centroids[j])
num_normalize += 1
print 'agglomerative clustering\nnumber of clusters:', num_clusters
print 'distortion:', sum_dist/128
print 'distance betweeen clusters/distortion: %.5f' % (dist_cluster/(num_normalize*sum_dist/128))
'''
print all_stats['Rumble']
print all_stats['Orianna']
print all_stats['Anivia']
meandistortions.append(dist_cluster/(num_normalize*sum_dist/128))
plt.plot(num_clusters, meandistortions, 'bx-')
plt.xlabel('n')
plt.ylabel('Average distance of clusters/distortion')
plt.title('Selecting n with the Elbow Method\nin Agglomerative Clustering')
plt.show()
'''
cursor.close()
conn.commit()
conn.close()