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kmeanspca.py
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kmeanspca.py
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import pandas as pd
import matplotlib.pyplot as plt
from kneed import KneeLocator
from sklearn.cluster import KMeans
from sklearn.metrics import silhouette_score
from sklearn.preprocessing import StandardScaler
from matplotlib import rcParams
rcParams.update({'figure.autolayout': True})
# Forwards
df = pd.read_csv("f_transformed_metrics.csv")
df_normal = pd.read_csv("f_clustering_metrics.csv")
# normalizing variables
scaler = StandardScaler()
df['Var1'] = scaler.fit_transform(df[["Var1"]])
df['Var2'] = scaler.fit_transform(df[["Var2"]])
# K Means Clustering
kmeans = KMeans(init="k-means++", n_clusters=5, n_init=10, max_iter=300, random_state=26)
kmeans = kmeans.fit(df[['Var1', 'Var2']])
df.loc[:, 'labels'] = kmeans.labels_
df.loc[df['Player'] == "Valeria Pavlova", 'labels'] = 1
plt.style.use("fivethirtyeight")
plt.scatter("Var1", "Var2", data=df, c="labels",cmap="tab10")
plt.xlabel("PCA Variable 1", fontsize=12)
plt.ylabel("PCA Variable 2", fontsize=12)
plt.title("Forwards", fontsize=16)
plt.savefig('f_pca.png')
plt.show()
df.loc[(df.labels == 0), 'labels'] = 'Dependent'
df.loc[(df.labels == 1), 'labels'] = 'Shooter'
df.loc[(df.labels == 2), 'labels'] = 'Balanced'
df.loc[(df.labels == 4), 'labels'] = 'Playmaker'
df_normal = df_normal.merge(df, on='Player', how='left')
df_normal.to_csv('kmeanspca_results_f.csv', index=False)
index_table_f = df_normal.groupby('labels').agg(['mean'])
index_table_f.columns = index_table_f.columns.droplevel(1)
index_table_f = index_table_f.round(decimals=3)
index_table_f['Count'] = [82, 26, 44, 9]
index_table_f.rename(index={0: 'Dependent', 1: 'Shooter', 2: 'Balanced', 4: 'Playmaker'}, inplace=True)
fig, ax = plt.subplots()
fig.patch.set_visible(False)
ax.axis('off')
ax.axis('tight')
table = ax.table(cellText=index_table_f.values, colLabels=index_table_f.columns, rowLabels=index_table_f.index, loc='center')
table.auto_set_font_size(False)
table.set_fontsize(8)
plt.show()
# Defenseman
df2 = pd.read_csv("d_transformed_metrics.csv")
df_normal2 = pd.read_csv("d_clustering_metrics.csv")
# normalizing variables
df2['Var1'] = scaler.fit_transform(df2[["Var1"]])
df2['Var2'] = scaler.fit_transform(df2[["Var2"]])
# K Means Clustering
kmeans = KMeans(init="k-means++", n_clusters=3, n_init=10, max_iter=300, random_state=26)
kmeans = kmeans.fit(df2[['Var1', 'Var2']])
df2.loc[:, 'labels'] = kmeans.labels_
plt.style.use("fivethirtyeight")
plt.scatter("Var1", "Var2", data=df2, c="labels",cmap="tab10")
plt.xlabel("PCA Variable 3", fontsize=12)
plt.ylabel("PCA Variable 4", fontsize=12)
plt.title("Defenders", fontsize=16)
plt.savefig('d_pca.png')
plt.show()
df2.loc[(df2.labels == 0), 'labels'] = 'Disruptor'
df2.loc[(df2.labels == 1), 'labels'] = 'Two-Way'
df2.loc[(df2.labels == 2), 'labels'] = 'Defensive'
df_normal2 = df_normal2.merge(df2, on='Player', how='left')
df_normal2.to_csv('kmeanspca_results_d.csv', index=False)
index_table_d = df_normal2.groupby('labels').agg(['mean'])
index_table_d.columns = index_table_d.columns.droplevel(1)
index_table_d = index_table_d.round(decimals=3)
index_table_d['Count'] = [28, 17, 43]
index_table_d.rename(index={0: 'Disruptor', 1: 'Two-Way', 2: 'Defensive'}, inplace=True)
fig, ax = plt.subplots()
fig.patch.set_visible(False)
ax.axis('off')
ax.axis('tight')
table = ax.table(cellText=index_table_d.values, colLabels=index_table_d.columns, rowLabels=index_table_d.index, loc='center')
table.auto_set_font_size(False)
table.set_fontsize(8)
plt.show()
# kmeans_kwargs = {"init": "k-means++", "n_init": 10, "max_iter": 300, "random_state": 26, }
# sse = []
# for k in range(1, 11):
# kmeans = KMeans(n_clusters = k, **kmeans_kwargs)
# kmeans.fit(df2[['Var1', 'Var2']])
# sse.append(kmeans.inertia_)
#
# plt.style.use("fivethirtyeight")
# plt.plot(range(1, 11), sse)
# plt.xticks(range(1, 11))
# plt.xlabel("Number of Clusters", fontsize=12)
# plt.ylabel("SSE", fontsize=12)
# plt.title("Defense Elbow Graph", fontsize=16)
# plt.savefig('d_elbow.png')
# plt.show()
#
# kl = KneeLocator(range(1, 11), sse, curve="convex", direction="decreasing")
# print(kl.elbow)
#
# silhouette_coefficients = []
# for k in range(2, 11):
# kmeans = KMeans(n_clusters=k, **kmeans_kwargs)
# kmeans.fit(df2[['Var1', 'Var2']])
# score = silhouette_score(df2[['Var1', 'Var2']], kmeans.labels_)
# silhouette_coefficients.append(score)
#
# plt.style.use("fivethirtyeight")
# plt.plot(range(2, 11), silhouette_coefficients)
# plt.xticks(range(2, 11))
# plt.xlabel("Number of Clusters", fontsize=12)
# plt.ylabel("Silhouette Coefficient", fontsize=12)
# plt.title("Defense Silhouette Scores", fontsize=16)
# plt.savefig('d_silhouette.png')
# plt.show()