Multi-classificação linear, exibição visual e precisão de teste do conjunto de dados da íris
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Familiarizado com a programação python no ambiente Jupyter, multiclassificação linear completa, exibição visual e experimentos de precisão de teste de um conjunto de dados de íris no Jupyter.
Suporte a máquina de vetores e exercícios de classificação linear SVM do conjunto de dados Iris .
1. Classificação do conjunto de dados da íris
1. Sépalas
Obtenha o conjunto de dados
import numpy as np
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import datasets
from sklearn import preprocessing
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=0)
x = df.values[:, :-1]
y = df.values[:, -1]
print('x = \n', x)
print('y = \n', y)
le = preprocessing.LabelEncoder()
le.fit(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'])
print(le.classes_)
y = le.transform(y)
print('Last Version, y = \n', y)
processamento de dados
x = x[:, :2]
print(x)
print(y)
x = StandardScaler().fit_transform(x)
lr = LogisticRegression() # Logistic回归模型
lr.fit(x, y.ravel()) # 根据数据[x,y]，计算回归参数
Desenhe um padrão de classificação
N, M = 500, 500 # 横纵各采样多少个值
x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第0列的范围
x2_min, x2_max = x[:, 1].min(), x[:, 1].max() # 第1列的范围
t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, M)
x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点
x_test = np.stack((x1.flat, x2.flat), axis=1) # 测试点
cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
y_hat = lr.predict(x_test) # 预测值
y_hat = y_hat.reshape(x1.shape) # 使之与输入的形状相同
plt.pcolormesh(x1, x2, y_hat, cmap=cm_light) # 预测值的显示
plt.scatter(x[:, 0], x[:, 1], c=y.ravel(), edgecolors='k', s=50, cmap=cm_dark)
plt.xlabel('petal length')
plt.ylabel('petal width')
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.grid()
plt.show()
Predição de modelo
y_hat = lr.predict(x)
y = y.reshape(-1)
result = y_hat == y
print(y_hat)
print(result)
acc = np.mean(result)
print('准确度: %.2f%%' % (100 * acc))
2. Pétalas
import numpy as np
from sklearn.linear_model import LogisticRegression
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn import datasets
from sklearn import preprocessing
import pandas as pd
from sklearn.preprocessing import StandardScaler
from sklearn.pipeline import Pipeline
df = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', header=0)
x = df.values[:, :-1]
y = df.values[:, -1]
print('x = \n', x)
print('y = \n', y)
le = preprocessing.LabelEncoder()
le.fit(['Iris-setosa', 'Iris-versicolor', 'Iris-virginica'])
print(le.classes_)
y = le.transform(y)
print('Last Version, y = \n', y)
x = x[:, 2:]
print(x)
print(y)
x = StandardScaler().fit_transform(x)
lr = LogisticRegression() # Logistic回归模型
lr.fit(x, y.ravel()) # 根据数据[x,y]，计算回归参数
N, M = 500, 500 # 横纵各采样多少个值
x1_min, x1_max = x[:, 0].min(), x[:, 0].max() # 第0列的范围
x2_min, x2_max = x[:, 1].min(), x[:, 1].max() # 第1列的范围
t1 = np.linspace(x1_min, x1_max, N)
t2 = np.linspace(x2_min, x2_max, M)
x1, x2 = np.meshgrid(t1, t2) # 生成网格采样点
x_test = np.stack((x1.flat, x2.flat), axis=1) # 测试点
cm_light = mpl.colors.ListedColormap(['#77E0A0', '#FF8080', '#A0A0FF'])
cm_dark = mpl.colors.ListedColormap(['g', 'r', 'b'])
y_hat = lr.predict(x_test) # 预测值
y_hat = y_hat.reshape(x1.shape) # 使之与输入的形状相同
plt.pcolormesh(x1, x2, y_hat, cmap=cm_light) # 预测值的显示
plt.scatter(x[:, 0], x[:, 1], c=y.ravel(), edgecolors='k', s=50, cmap=cm_dark)
plt.xlabel('petal length')
plt.ylabel('petal width')
plt.xlim(x1_min, x1_max)
plt.ylim(x2_min, x2_max)
plt.grid()
plt.show()
y_hat = lr.predict(x)
y = y.reshape(-1)
result = y_hat == y
print(y_hat)
print(result)
acc = np.mean(result)
print('准确度: %.2f%%' % (100 * acc))
2. Resumo e materiais de referência
1. Resumo
Python é muito conveniente para análise de dados.