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basketball2.py
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basketball2.py
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from sklearn.cluster import Birch
from sklearn.cluster import KMeans
from sklearn import metrics
from numpy import mat
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
from sklearn.decomposition import PCA
import Canopy as ca
def read_points():
dataset = []
with open('D:\debt1.csv', 'r', encoding='utf8') as file:
for line in file:
if (line == '\n'):
continue
result = line.strip().split(',')
#移除 公司代码 日期 会计规则和专家规则的结果列
result.remove(result[0])
result.remove(result[0])
result1=result.remove(result[0])
fltline = [float(i) for i in result]
dataset.append(fltline)
file.close()
return dataset
def saveresult(temp):
with open('D:\Result.csv', 'a') as file:
file.write(temp)
file.close()
def Dimensionality_reduction(matrix):
X_tsne = TSNE(learning_rate=100).fit_transform(matrix)
X_pca = PCA(n_components=2).fit_transform(matrix)
plt.figure(figsize=(10, 5))
plt.subplot(121)
plt.title("T-SNE")
plt.scatter(X_tsne[:, 0], X_tsne[:, 1],marker='.')
plt.subplot(122)
plt.title("PCA")
plt.scatter(X_pca[:, 0], X_pca[:, 1],marker='.')
print("max value: " ,np.amax(X_pca,axis=0))
print("min value: " ,np.min(X_pca))
print("max position: ",np.where(X_pca == np.amax(X_pca,axis=0)))
print("min position: ",np.where(X_pca == np.min(X_pca)))
plt.show()
def showCanopy(canopies, dataset, t1, t2):
fig = plt.figure()
sc = fig.add_subplot(111)
colors = ['brown', 'green', 'blue', 'y', 'r', 'tan', 'dodgerblue', 'deeppink', 'orangered', 'peru', 'blue', 'y', 'r',
'gold', 'dimgray', 'darkorange', 'peru', 'blue', 'y', 'r', 'cyan', 'tan', 'orchid', 'peru', 'blue', 'y', 'r', 'sienna']
markers = ['*', 'h', 'H', '+', 'o', '1', '2', '3', ',', 'v', 'H', '+', '1', '2', '^',
'<', '>', '.', '4', 'H', '+', '1', '2', 's', 'p', 'x', 'D', 'd', '|', '_']
for i in range(len(canopies)):
canopy = canopies[i]
center = canopy[0]
components = canopy[1]
sc.plot(center[0], center[1], marker=markers[i],
color=colors[i], markersize=10)
t1_circle = plt.Circle(
xy=(center[0], center[1]), radius=t1, color='dodgerblue', fill=False)
t2_circle = plt.Circle(
xy=(center[0], center[1]), radius=t2, color='skyblue', alpha=0.2)
sc.add_artist(t1_circle)
sc.add_artist(t2_circle)
for component in components:
sc.plot(component[0], component[1],
marker=markers[i], color=colors[i], markersize=1.5)
maxvalue = np.amax(dataset)
minvalue = np.amin(dataset)
plt.xlim(minvalue - t1, maxvalue + t1)
plt.ylim(minvalue - t1, maxvalue + t1)
plt.show()
def main():
csvdata = read_points()
X = len(csvdata[0])
Y = len(csvdata)
New_matrix = np.zeros([Y,X])
for y in range(Y):
for x in range(X):
New_matrix[y, x] = csvdata[y][x]
#最后三列统计结果矩阵
add_matrix = np.zeros([Y,3])
for y in range(Y):
add_matrix[y, 0] = New_matrix[y, 0] - New_matrix[y, 1]
add_matrix[y, 1] = New_matrix[y, 2] - New_matrix[y, 3]
add_matrix[y, 2] = New_matrix[y, 4] - New_matrix[y, 5]
temp_mean = add_matrix[:, 0].mean()
temp_mean1 = add_matrix[:, 1].mean()
temp_mean2 = add_matrix[:, 2].mean()
col_std = np.std(add_matrix, axis=0)
col_mean = np.mean(add_matrix, axis=0)
# 需要对csvdata进行中心化和标准化处理
for y in range(Y):
for x in range(0,3):
add_matrix[y, x] -= col_mean[x]
add_matrix[y, x] /= col_std[x]
# for y in range(Y):
# temp = str(add_matrix[y,0]) + "," + str(add_matrix[y,1]) + "," + str(add_matrix[y,2]) + "\n"
# saveresult(temp)
predict_matrix = np.array([(7898765467.24,3235676823.00,3957177004.54,3444000321.55,5432112345.77,2900000089.12),
(133241575988.56, 39872238928.11, 14551119352.78, 3164290276.21, 3444305407.86,1015886389.47),
(93805217949.67,34975605193.08,2326015727.05,1922978314.70,2273603448.91,4777927001.24)])
predict_subtract = np.zeros([3,3])
for i in range(3):
predict_subtract[i, 0] = predict_matrix[i, 0] - predict_matrix[i, 1]
predict_subtract[i, 1] = predict_matrix[i, 2] - predict_matrix[i, 3]
predict_subtract[i, 2] = predict_matrix[i, 4] - predict_matrix[i, 5]
for i in range(3):
for x in range(0,3):
predict_subtract[i, x] -= col_mean[x]
predict_subtract[i, x] /= col_std[x]
matrix2list = add_matrix.tolist()
print(matrix2list)
print(add_matrix)
# 聚类前降维显示
# Dimensionality_reduction(matrix2list)
X_pca = PCA(n_components=2).fit_transform(add_matrix)
t1 = 20
t2 = 15
gc = ca.Canopy(X_pca)
gc.setThreshold(t1, t2)
canopies = gc.clustering()
# showCanopy(canopies,X_pca,t1,t2)
all_vrc = []
all_silh = []
sub = []
for k in range(20):
# kmeans聚类
if k==0 or k==1:
continue
clf = KMeans(n_clusters=k,init='k-means++')
y_pred = clf.fit_predict(matrix2list)
add_pred = clf.predict(predict_subtract.tolist())
# print(clf)
# print(y_pred)
sub.append(k)
VRC = metrics.calinski_harabaz_score(add_matrix, y_pred)
all_vrc.append(VRC)
silh = metrics.silhouette_score(add_matrix, y_pred, metric='euclidean')
all_silh.append(silh)
print("k= ",k)
print('VRC方差率：',VRC)
# print('轮廓系数：%10.3f' % silh)
print('轮廓系数：', silh)
lines = len(y_pred)
static_result = np.zeros([1, k])
first = 0
second = 0
third = 0
for item in y_pred:
for i in range(0,k):
if item == i:
static_result[0,i] += 1
for i , j in enumerate(y_pred):
if j == 1:
print(i,j)
for i in range(0,k):
print("第"+str(i)+"类数据占比: "+str(static_result[0,i]*100/lines)+"%")
# 降维显示数据
X_tsne = TSNE(learning_rate=100).fit_transform(matrix2list)
X_pca = PCA().fit_transform(matrix2list)
#单点降维
signal_pca = PCA().fit_transform(predict_subtract.tolist())
plt.close()
fig = plt.figure()
plt.ion() # interactive mode on
plt.subplot(121)
plt.title("T-SNE")
plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y_pred)
plt.subplot(122)
plt.title("PCA")
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_pred)
plt.scatter(signal_pca[:,0],signal_pca[:,1],c='r')
plt.pause(1)
plt.figure(figsize=(10,5))
plt.subplot(121)
plt.title("VRC")
# plt.scatter(sub,all_vrc,marker='o')
plt.plot(all_vrc)
plt.subplot(122)
plt.title("silh")
# plt.scatter(sub,all_silh,marker='x')
plt.plot(all_silh)
plt.show()
print("第一类数据占比：" + str(((first * 100) / lines)) + "%")
print("第二类数据占比：" + str(((second * 100) / lines)) + "%")
print("第三类数据占比：" + str(((third * 100) / lines)) + "%")
temp = "第一类数据占总数的:" + str(((first * 100) / lines)) + "%\n" + "第二类数据占总数的:" + str(((second * 100) / lines)) + "%\n" + "第三类数据占总数的:" + str(((third * 100) / lines)) + "%\n"
saveresult(temp)
X_tsne = TSNE(learning_rate=100).fit_transform(matrix2list)
X_pca = PCA().fit_transform(matrix2list)
plt.figure(figsize=(10, 5))
plt.subplot(121)
plt.title("T-SNE")
plt.scatter(X_tsne[:, 0], X_tsne[:, 1], c=y_pred)
plt.subplot(122)
plt.title("PCA")
plt.scatter(X_pca[:, 0], X_pca[:, 1], c=y_pred)
plt.show()
if __name__ == "__main__":
main()
pass
"""
csvdata = read_points()
X = len(csvdata[0])
Y = len(csvdata)
New_matrix = np.zeros([Y,X])
for y in range(Y):
for x in range(X):
New_matrix[y,x] = csvdata[y][x]
temp_mean = New_matrix[:,0].mean()
col_std = np.std(New_matrix,axis=0)
col_mean = np.mean(New_matrix,axis=0)
#需要对csvdata进行中心化和标准化处理
for y in range(Y):
for x in range(X):
New_matrix[y,x]-=col_mean[x]
New_matrix[y,x]/=col_std[x]
matrix2list = New_matrix.tolist()
X = [[0.0888, 0.5885],
[0.1399, 0.8291],
[0.0747, 0.4974],
[0.0983, 0.5772],
[0.1276, 0.5703],
[0.1671, 0.5835],
[0.1906, 0.5276],
[0.1061, 0.5523],
[0.2446, 0.4007],
[0.1670, 0.4770],
[0.2485, 0.4313],
[0.1227, 0.4909],
[0.1240, 0.5668],
[0.1461, 0.5113],
[0.2315, 0.3788],
[0.0494, 0.5590],
[0.1107, 0.4799],
[0.2521, 0.5735],
[0.1007, 0.6318],
[0.1067, 0.4326],
[0.1956, 0.4280]
]
print(X)
#kmeans聚类
clf = KMeans(n_clusters=3)
y_pred = clf.fit_predict(matrix2list)
print(clf)
print(y_pred)
lines = len(y_pred)
first = 0
second = 0
third = 0
for item in y_pred:
if item == 0:
first += 1
if item == 1:
second += 1
if item == 2:
third += 1
print("第一类数据占比：" + str(((first*100)/lines)) + "%")
print("第二类数据占比：" + str(((second*100)/lines)) + "%")
print("第三类数据占比：" + str(((third*100)/lines)) + "%")
"""
"""
import numpy as np
import matplotlib.pyplot as plt
x = [n[0] for n in X]
print(x)
y = [n[1] for n in X]
print(y)
#可视化操作
plt.scatter(x,y,c=y_pred,marker='x')
plt.title("kmeans basketball data")
plt.xlabel("assists_per_minute")
plt.ylabel("points_per_minute")
plt.legend(["Rank"])
plt.show()
"""