Python：計算類別分佈CalculateClassDistribution

import numpy as np
import pandas as pd
from sklearn import datasets
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import cohen_kappa_score
from sklearn.metrics import precision_score
from sklearn.datasets import fetch_covtype
from sklearn.datasets import fetch_mldata
from sklearn.decomposition import PCA

# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\1-Glass\glass.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\Spiral\spiral.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\1-Aggregation（788）\aggregation.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\Three blobs\ThreeBlobs.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\COIL-20\COIL20_PCA.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\Banknote\banknote.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# pca = PCA(0.9)
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\1-Semeion\semeion.csv', header=None))
# X = data[:, :-1]
# X = pca.fit_transform(X)
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\1-Robot Navigation（5456）\Robot_Navigation_24.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\Twonorm\twonorm.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\1-Electrical Grid Stability Simulated Data Data Set\ELectricalGrid.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\Pendigits\pendigits.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\HTRU2 Data Set\HTRU_2.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\Avila\avila.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\1-Dataset for Sensorless Drive Diagnosis(58509)\Sensorless_drive_diagnosis.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\Satlog(shuttle)\Satlog(shuttle).csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# mnist = fetch_mldata('MNIST original')
# X = mnist['data']
# y = mnist['target']
# --------------------------------------#
# X, y = fetch_covtype(return_X_y=True)
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\Page Blocks\page-blocks.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
# data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\Wine Quality\winequality-red.csv', header=None))
# X = data[:, :-1]
# y = data[:, -1]
# --------------------------------------#
data = np.array(pd.read_csv(r'E:\dataset\ExperimentalData\Wine Quality\winequality-white.csv', header=None))
# X = data[:, :-1]
y = data[:, -1]


#################上面是數據##########################
# #########################################
label,count = np.unique(y,return_counts=True)
print(label)
print(list(count))