机器学习算法加强——贝叶斯网络实践
马航案例
若
则所有的可行位置都收敛了，碎片稳定在某一地方
文本分类实验
import numpy as np
from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import RidgeClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import GridSearchCV
from sklearn import metrics
from time import time
from pprint import pprint
import matplotlib.pyplot as plt
import matplotlib as mpl
def test_clf(clf):
print(u'分类器：', clf)
alpha_can = np.logspace(-3, 2, 10)
model = GridSearchCV(clf, param_grid={'alpha': alpha_can}, cv=5)
m = alpha_can.size
if hasattr(clf, 'alpha'):
model.set_params(param_grid={'alpha': alpha_can})
m = alpha_can.size
if hasattr(clf, 'n_neighbors'):
neighbors_can = np.arange(1, 15)
model.set_params(param_grid={'n_neighbors': neighbors_can})
m = neighbors_can.size
if hasattr(clf, 'C'):
C_can = np.logspace(1, 3, 3)
gamma_can = np.logspace(-3, 0, 3)
model.set_params(param_grid={'C':C_can, 'gamma':gamma_can})
m = C_can.size * gamma_can.size
if hasattr(clf, 'max_depth'):
max_depth_can = np.arange(4, 10)
model.set_params(param_grid={'max_depth': max_depth_can})
m = max_depth_can.size
t_start = time()
model.fit(x_train, y_train)
t_end = time()
t_train = (t_end - t_start) / (5*m)
print(u'5折交叉验证的训练时间为：%.3f秒/(5*%d)=%.3f秒' % ((t_end - t_start), m, t_train))
print(u'最优超参数为：', model.best_params_)
t_start = time()
y_hat = model.predict(x_test)
t_end = time()
t_test = t_end - t_start
print(u'测试时间：%.3f秒' % t_test)
acc = metrics.accuracy_score(y_test, y_hat)
print(u'测试集准确率：%.2f%%' % (100 * acc))
name = str(clf).split('(')[0]
index = name.find('Classifier')
if index != -1:
name = name[:index] # 去掉末尾的Classifier
if name == 'SVC':
name = 'SVM'
return t_train, t_test, 1-acc, name
if __name__ == "__main__":
print(u'开始下载/加载数据...')
t_start = time()
# remove = ('headers', 'footers', 'quotes')
remove = ()
categories = 'alt.atheism', 'talk.religion.misc', 'comp.graphics', 'sci.space'
# categories = None # 若分类所有类别，请注意内存是否够用
data_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=0, remove=remove)
data_test = fetch_20newsgroups(subset='test', categories=categories, shuffle=True, random_state=0, remove=remove)
t_end = time()
print(u'下载/加载数据完成，耗时%.3f秒' % (t_end - t_start))
print(u'数据类型：', type(data_train))
print(u'训练集包含的文本数目：', len(data_train.data))
print(u'测试集包含的文本数目：', len(data_test.data))
print(u'训练集和测试集使用的%d个类别的名称：' % len(categories))
categories = data_train.target_names
pprint(categories)
y_train = data_train.target
y_test = data_test.target
print(u' -- 前10个文本 -- ')
for i in np.arange(10):
print(u'文本%d(属于类别 - %s)：' % (i+1, categories[y_train[i]]))
print(data_train.data[i])
print('\n\n')
vectorizer = TfidfVectorizer(input='content', stop_words='english', max_df=0.5, sublinear_tf=True)
x_train = vectorizer.fit_transform(data_train.data) # x_train是稀疏的，scipy.sparse.csr.csr_matrix
x_test = vectorizer.transform(data_test.data)
print(u'训练集样本个数：%d，特征个数：%d' % x_train.shape)
print(u'停止词:\n')
pprint(vectorizer.get_stop_words())
feature_names = np.asarray(vectorizer.get_feature_names())
print(u'\n\n===================\n分类器的比较：\n')
clfs = (MultinomialNB(), # 0.87(0.017), 0.002, 90.39%
BernoulliNB(), # 1.592(0.032), 0.010, 88.54%
KNeighborsClassifier(), # 19.737(0.282), 0.208, 86.03%
RidgeClassifier(), # 25.6(0.512), 0.003, 89.73%
RandomForestClassifier(n_estimators=200), # 59.319(1.977), 0.248, 77.01%
SVC() # 236.59(5.258), 1.574, 90.10%
)
result = []
for clf in clfs:
a = test_clf(clf)
result.append(a)
print('\n')
result = np.array(result)
time_train, time_test, err, names = result.T
time_train = time_train.astype(np.float)
time_test = time_test.astype(np.float)
err = err.astype(np.float)
x = np.arange(len(time_train))
mpl.rcParams['font.sans-serif'] = [u'simHei']
mpl.rcParams['axes.unicode_minus'] = False
plt.figure(figsize=(10, 7), facecolor='w')
ax = plt.axes()
b1 = ax.bar(x, err, width=0.25, color='#77E0A0')
ax_t = ax.twinx()
b2 = ax_t.bar(x+0.25, time_train, width=0.25, color='#FFA0A0')
b3 = ax_t.bar(x+0.5, time_test, width=0.25, color='#FF8080')
plt.xticks(x+0.5, names)
plt.legend([b1[0], b2[0], b3[0]], (u'错误率', u'训练时间', u'测试时间'), loc='upper left', shadow=True)
plt.title(u'新闻组文本数据不同分类器间的比较', fontsize=18)
plt.xlabel(u'分类器名称')
plt.grid(True)
plt.tight_layout(2)
plt.show()
word2vec
from time import time
from gensim.models import Word2Vec
import sys
import os
reload(sys)
sys.setdefaultencoding('utf-8')
class LoadCorpora(object):
def __init__(self, s):
self.path = s
def __iter__(self):
f = open(self.path,'r')
for line in f:
yield line.split(' ')
def print_list(a):
for i, s in enumerate(a):
if i != 0:
print('+')
print(s)
if __name__ == '__main__':
if not os.path.exists('news.model'):
sentences = LoadCorpora('news.dat')
t_start = time()
model = Word2Vec(sentences, size=200, min_count=5, workers=8) # 词向量维度为200，丢弃出现次数少于5次的词
model.save('news.model')
print('OK:', time() - t_start)
model = Word2Vec.load('news.model')
print('词典中词的个数：', len(model.vocab))
for i, word in enumerate(model.vocab):
print(word)
if i % 25 == 24:
print
print
intrested_words = ('中国', '手机', '学习', '人民', '名义')
print('特征向量：')
for word in intrested_words:
print(word, len(model[word]), model[word])
for word in intrested_words:
result = model.most_similar(word)
print('与', word, '最相近的词：')
for w, s in result:
print('\t', w, s)
words = ('中国', '祖国', '毛泽东', '人民')
for i in range(len(words)):
w1 = words[i]
for j in range(i+1, len(words)):
w2 = words[j]
print('%s 和 %s 的相似度为：%.6f' % (w1, w2, model.similarity(w1, w2)))
print('========================')
opposites = ((['中国', '城市'], ['学生']),
(['男', '工作'], ['女']),
(['俄罗斯', '美国', '英国'], ['日本']))
for positive, negative in opposites:
result = model.most_similar(positive=positive, negative=negative)
print_list(positive)
print('-',)
print_list(negative)
print('：')
for word, similar in result:
print('\t', word, similar)
print('========================')
words_list = ('苹果 三星 美的 海尔', '中国 日本 韩国 美国 北京',
'医院 手术 护士 医生 感染 福利', '爸爸 妈妈 舅舅 爷爷 叔叔 阿姨 老婆')
for words in words_list:
print(words, '离群词：', model.doesnt_match(words.split(' ')))燕尾花高斯朴素贝叶斯
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
from sklearn.preprocessing import StandardScaler, MinMaxScaler, PolynomialFeatures
from sklearn.naive_bayes import GaussianNB, MultinomialNB
from sklearn.pipeline import Pipeline
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
def iris_type(s):
it = {'Iris-setosa': 0, 'Iris-versicolor': 1, 'Iris-virginica': 2}
return it[s]
if __name__ == "__main__":
data = pd.read_csv('/Users/zhucan/Desktop/iris.csv', header=None)
x, y = data[np.arange(4)], data[4]
y = pd.Categorical(values=y).codes
feature_names = u'花萼长度', u'花萼宽度', u'花瓣长度', u'花瓣宽度'
features = [0,1]
x = x[features]
x, x_test, y, y_test = train_test_split(x, y, train_size=0.7, random_state=0)
priors = np.array((1,2,4), dtype=float)
priors /= priors.sum()
gnb = Pipeline([
('sc', StandardScaler()),
('poly', PolynomialFeatures(degree=1)),
('clf', GaussianNB(priors=priors))]) # 由于鸢尾花数据是样本均衡的，其实不需要设置先验值
# gnb = KNeighborsClassifier(n_neighbors=3).fit(x, y.ravel())
gnb.fit(x, y.ravel())
y_hat = gnb.predict(x)
print('训练集准确度: %.2f%%' % (100 * accuracy_score(y, y_hat)))
y_test_hat = gnb.predict(x_test)
print('测试集准确度：%.2f%%' % (100 * accuracy_score(y_test, y_test_hat))