标签：pre plt 机器 学习 train 3.1 test import data
机器学习 - 机器学习项目案例
案例1：利用岭回归研究波士顿放假
读取数据
from sklearn.datasets import load_boston
boston = load_boston()
print('feature_names:', boston.feature_names)
print('data (shape) :', boston.data.shape)
线性回归模型
from sklearn.linear_model import LinearRegression
lin_reg = LinearRegression()
lin_reg.fit(boston.data, boston.target) # Fit
pre = lin_reg.predict(boston.data) # Predict
lin_reg.score(boston.data, boston.target) #Score
岭回归模型
from sklearn.linear_model import Ridge
ridge_reg = Ridge(alpha=0.5) # alpha值越大 正则化项所占比重越大
ridge_reg.fit(boston.data, boston.target) # Fit
ridge_reg.score(boston.data, boston.target) # Score
test_Ridge_alpha
探究alpha不同值，得到的回归结果
%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
def test_Ridge_alpha(*data):
X_train, X_test, y_train, y_test = data
alphas = [0.01, 0.02, 0.05, 0.1, 0.2, 0.5, 1, 5, 10, 20, 50, 100, 200, 500, 1000]
scores = []
for i, alpha in enumerate(alphas):
ridge_reg = Ridge(alpha=alpha)
ridge_reg.fit(X_train, y_train)
scores.append(ridge_reg.score(X_test, y_test))
plt.xlabel('Alphas')
plt.ylabel('Scores')
sns.lineplot(x=alphas, y=scores)
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(boston.data, boston.target, test_size=0.3, random_state=31)
test_Ridge_alpha(X_train, X_test, y_train, y_test)
invalid value encountered in true_divide # Remove the CWD from sys.path while we load stuff.
import numpy as np
np.seterr(divide=‘ignore’, invalid=‘ignore’)
案例2：利用决策树回归预测波士顿放假
import numpy as np
from sklearn.datasets import load_boston
from sklearn.model_selection import cross_val_score, cross_validate
from sklearn.tree import DecisionTreeRegressor
# Load DataSet
boston = load_boston()
X, y = boston.data, boston.target
features = boston.feature_names
# Fit
regression_tree = DecisionTreeRegressor(min_samples_split=30, min_samples_leaf=10, random_state=0) #决策树
regression_tree.fit(X, y)
# Score
score = np.mean(cross_val_score(regression_tree, X, y, cv=3)) # cv=3
print('Mean squared error: {0}'.format(round(abs(score),2)))
案例3：Logistic回归实现对鸢尾花数据分类
import matplotlib.pyplot as plt
from sklearn import datasets
iris = datasets.load_iris() # 加载鸢尾花数据
sepal_length_list = iris.data[:, 0] # 花萼长度
sepal_width_list = iris.data[:, 1] # 花萼宽度
# 构建 setosa、versicolor、virginica 索引数组
setosa_index_list = iris.target == 0 # setosa 索引数组
versicolor_index_list = iris.target == 1 # versicolor 索引数组
virginica_index_list = iris.target == 2 # virginica 索引数组
plt.scatter(sepal_length_list[setosa_index_list],
sepal_width_list[setosa_index_list], color="red", marker='o', label="setosa")
plt.scatter(sepal_length_list[versicolor_index_list],
sepal_width_list[versicolor_index_list], color="blue", marker="x", label="versicolor")
plt.scatter(sepal_length_list[virginica_index_list],
sepal_width_list[virginica_index_list],color="green", marker="+", label="virginica")
# 设置 legend
plt.legend(loc="best", title="iris type")
# 设定横坐标名称
plt.xlabel("sepal_length (cm)")
# 设定纵坐标名称
plt.ylabel("sepal_width (cm)")
逻辑回归
from sklearn.linear_model import LogisticRegression
from sklearn.model_selection import train_test_split
from sklearn import datasets
# 加载鸢尾花数据
iris = datasets.load_iris()
# 设置训练集和测试集
X_train, X_test , y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.5, random_state=1)
# 创建一个Logistic回归分类器
logr = LogisticRegression(penalty='l2', random_state=0)
# 训练分类器
logr.fit(X_train, y_train)
# 预测所属类别
category = logr.predict(X_test)
category
模型可视化
import numpy as np
import matplotlib.pyplot as plt
# 只考虑前两个特征，即花萼长度（sepal length）、花萼宽度（sepal width）
X = iris.data[:, 0:2]
y = iris.target
logreg = LogisticRegression(C=1e5) #C：惩罚项系数的倒数，越小，正则化项越大
logreg.fit(X, y)
# 网格大小
h = 0.02
x_min, x_max = X[:, 0].min() - 0.5, X[:, 0].max() + 0.5 # 将 X 的第一列（花萼长度）作为 x 轴，并求出 x 轴的最大值与最小值
y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5 # 将 X 的第二列（花萼宽度）作为 y 轴，并求出 y 轴的最大值与最小值
xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))
# 调用 ravel() 函数将 xx 和 yy 平铺，然后使用 np.c_ 将平铺后的列表拼接
# 生成需要预测的特征矩阵，每一行的表示一个样本，每一列表示每个特征的取值
pre_data = np.c_[xx.ravel(), yy.ravel()]
Z = logreg.predict(pre_data)
Z = Z.reshape(xx.shape)
plt.figure(1, figsize=(8, 6))
#
plt.pcolormesh(xx, yy, Z, cmap=plt.cm.Paired)
plt.scatter(X[:, 0], X[:, 1], c=y, edgecolors='k', cmap=plt.cm.Paired)
# 设置坐标轴label
plt.xlabel("sepal length")
plt.ylabel("sepal width")
# 设置坐标轴范围
plt.xlim(xx.min(), xx.max())
plt.ylim(yy.min(), yy.max())
# 设置坐标轴刻度
plt.xticks(np.arange(x_min, x_max, h * 10))
plt.yticks(np.arange(y_min, y_max, h * 10))
plt.show()
案例4：利用贝叶斯分类实现手写数字识别
加载数据集
import matplotlib.pyplot as plt
from sklearn.datasets import load_digits
digits = load_digits()
fig = plt.figure()
for i in range(25):
ax = fig.add_subplot(5, 5, i+1)
ax.imshow(digits.images[i], cmap=plt.cm.gray_r, interpolation='nearest')
测试集的样本数
# 划分数据集
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(digits.data, digits.target, test_size=0.3, random_state=0)
# 测试集的样本数
print("y_test(shape):", y_test.shape)
import numpy as np
np.seterr(divide='ignore', invalid='ignore')
GaussianNB
高斯贝叶斯分类器，特征的条件概率符合高斯分布
from sklearn.naive_bayes import GaussianNB
gau_nb = GaussianNB()
gau_nb.fit(X_train, y_train)
gy_pre = gau_nb.predict(X_test)
# 评估模型得分
print("Score:", gau_nb.score(X_test, y_test))
# 检验预测正确的数字个数
print("Right:", y_pre[(y_test / gy_pre) == 1].size)
MultinomialNB
多项式贝叶斯分类器，特征的条件概率符合多项式分布
from sklearn.naive_bayes import MultinomialNB
mul_nb = MultinomialNB()
mul_nb.fit(X_train, y_train)
my_pre = mul_nb.predict(X_test)
print("Score:", mul_nb.score(X_test, y_test))
print('Right:', my_pre[(y_test / my_pre) == 1].size)
BernoulliNB
伯努利贝叶斯分类器，符合伯努利分布（二项式分布）
from sklearn.naive_bayes import BernoulliNB
ber_nb = BernoulliNB()
ber_nb.fit(X_train, y_train)
by_pre = ber_nb.predict(X_test)
print("Score:", ber_nb.score(X_test, y_test))
print('Right:', by_pre[(y_test / by_pre) == 1].size)
模型可视化#1
import pandas as pd
naive_bayes = pd.DataFrame(['GaussianNB', 'MultinomialNB', 'BernoulliNB'])
score = pd.DataFrame([gau_nb.score(X_test, y_test), mul_nb.score(X_test, y_test), ber_nb.score(X_test, y_test)])
right = pd.DataFrame([y_pre[(y_test / gy_pre) == 1].size, my_pre[(y_test / my_pre) == 1].size,
by_pre[(y_test / by_pre) == 1].size])
vs = pd.concat([naive_bayes, score, right], axis=1)
vs.columns = ['NaiveBayes', 'Score', 'Right']
vs
vs.plot.barh()
模型可视化#2
vs_naive_bayes = pd.DataFrame({'NaiveBayes': pd.Series(['GaussianNB', 'MultinomialNB', 'BernoulliNB']),
'Score': pd.Series([gau_nb.score(X_test, y_test), mul_nb.score(X_test, y_test), ber_nb.score(X_test, y_test)]),
'Right': pd.Series([y_pre[(y_test / gy_pre) == 1].size, my_pre[(y_test / my_pre) == 1].size, by_pre[(y_test / by_pre) == 1].size])})
vs_naive_bayes
import seaborn as sns
sns.barplot(vs_naive_bayes.NaiveBayes, vs_naive_bayes.Right)
sns.barplot(vs_naive_bayes.NaiveBayes, vs_naive_bayes.Score)
案例5：利用随机森林分类筛查乳腺癌
from sklearn.datasets import load_breast_cancer
dataset = load_breast_cancer()
dataset.target_names
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.metrics import confusion_matrix
NAMES = ["CodeNumber", "ClumpThickness", "UniformityCellSize", "UniformityCellShape", "MarginalAdhesion", "SingleEpithelialCellSize", "BareNuclei", "BlandChromatin", "NormalNucleoli", "Mitoses", "CancerType"]
breast_cancer_data =pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data',
header=None,
names=NAMES)
breast_cancer_data
breast_cancer_data.describe()
train_x, test_x, train_y, test_y = train_test_split(breast_cancer_data[NAMES[1:-1]], breast_cancer_data[NAMES[-1]], train_size=0.7)
print("Train_x Shape :: ", train_x.shape)
print("Train_y Shape :: ", train_y.shape)
print("Test_x Shape :: ", test_x.shape)
print("Test_y Shape :: ", test_y.shape)
RandomForestClassifier #1
# 利用随机森林分类进行筛选
clf = RandomForestClassifier()
clf.fit(train_x, train_y)
predictions = clf.predict(test_x)
for i in range(0, 5):
print("Actual outcome :: {} and Predicted outcome :: {}".format(list(test_y)[i], predictions[i]))
print("Train Accuracy :: ", accuracy_score(train_y, clf.predict(train_x)))
print("Test Accuracy :: ", accuracy_score(test_y, predictions))
print(" Confusion matrix ", confusion_matrix(test_y, predictions))
breast_cancer_data.info()
breast_cancer_data.iloc[np.where(breast_cancer_data['BareNuclei'] == '?')]
# 计算异常值列的平均值
mean_value = breast_cancer_data[breast_cancer_data["BareNuclei"] != "?"]["BareNuclei"].astype(np.int).mean()
mean_value
breast_cancer_data['BareNuclei'] = breast_cancer_data['BareNuclei'].replace('?', mean_value) # mean_value替换？
breast_cancer_data.iloc[np.where(breast_cancer_data['BareNuclei'] == '?')]
breast_cancer_data["BareNuclei"] = breast_cancer_data["BareNuclei"].astype(np.int64)
breast_cancer_data.info()
RandomForestClassifier #2
train_x, test_x, train_y, test_y = train_test_split(breast_cancer_data[NAMES[1:-1]], breast_cancer_data[NAMES[-1]], train_size=0.7)
# 利用随机森林分类进行筛选
clf = RandomForestClassifier()
clf.fit(train_x, train_y)
predictions = clf.predict(test_x)
for i in range(0, 5):
print("Actual outcome :: {} and Predicted outcome :: {}".format(list(test_y)[i], predictions[i]))
print("Train Accuracy :: ", accuracy_score(train_y, clf.predict(train_x)))
print("Test Accuracy :: ", accuracy_score(test_y, predictions))
print("Confusion matrix :: \n", confusion_matrix(test_y, predictions))
参考资料
Matplotlib
help(plt.pcolormesh)
Create a pseudocolor plot with a non-regular rectangular grid.
Numpy
help(np.meshgrid)
Return coordinate matrices from coordinate vectors.
help(np.ravel)
Return a contiguous flattened array.
help(np.c_)
Translates slice objects to concatenation along the second axis.
help(np.seterr)
Set how floating-point errors are handled.
喝醉酒的小白
发布了8 篇原创文章 · 获赞 7 · 访问量 163
私信
关注
标签：pre,plt,机器,学习,train,3.1,test,import,data 来源： https://blog.csdn.net/hezuijiudexiaobai/article/details/104393593
本站声明： 1. iCode9 技术分享网（下文简称本站）提供的所有内容，仅供技术学习、探讨和分享； 2. 关于本站的所有留言、评论、转载及引用，纯属内容发起人的个人观点，与本站观点和立场无关； 3. 关于本站的所有言论和文字，纯属内容发起人的个人观点，与本站观点和立场无关； 4. 本站文章均是网友提供，不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属；如您发现该文章侵犯了您的权益，可联系我们第一时间进行删除； 5. 本站为非盈利性的个人网站，所有内容不会用来进行牟利，也不会利用任何形式的广告来间接获益，纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。