机器学习库（Numpy, Scikit-learn）

Numpy

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创建数组

import numpy as npa = np.array([1,2,3])
b = np.array([(1.5,2,3), (4,5,6)], dtype = float)
c = np.array([[(1.5,2,3), (4,5,6)], [(3,2,1), (4,5,6)]],dtype = float)

创建占位符

z1=np.zeros((3,4))
z2=np.ones((2,3,4),dtype=np.int16)
z3=d= np.arange(10,25,5)
z4=np.linspace(0,2,9)
z5=e=np.full((2,2),7)
z6=f=np.eye(2)
z7=np.random.random((2,2))
z8=np.empty((3,2))

输入输出

1.保存到磁盘和从磁盘导入
2.保存到文件和从文件导入

np.save('my_array',a)
np.savez('array.npz',a,b)
np.load('my_array.npy')

查看数组信息

a.shape
len(a)
b.ndim
z1.size
b.dtype
b.dtype.name
b.astype(int)

数据类型

np.int64
np.float32
np.complex
np.bool
np.object
np.string_
np.unicode_

帮助

np.info(np.ndarray.dtype)

数组运算:1.运算 2.比较 3.聚合

g=a-b
np.subtract(a,b)
b+a
np.add(b,a)
a/b
np.divide(a,b)
a*b
np.multiply(a,b)print(np.exp(b))
print(np.sqrt(b))
print(np.sin(a))
print(np.cos(b))
print(np.log(a))
print(e.dot(f))

print(a)
print(b)
print(a.sum())
print(a.min())
print(b.max(axis=0))
print(b.cumsum(axis=1))
print(a.mean())
#print(b.median())

拷贝数组

print(a)
h = a.view()
print(h)
c =np.copy(a)
print(c)
a[1]=3
print(c)
h = a.copy()
print(h)
a[0] =2
print(h)
print(a)

数组切片，布尔索引，高级索引

print(a[0:2])   
print(b[0:2,1])
print(b[:1])
print(c[1,...])
print(a[::-1])

print(a[a<2])    #选取所有a<2的元素

b[[1,0,1,0],[0,1,2,0]] #选取(1,0),(0,1),(1,2) 和(0,0)

数组操作 1.转置 2.增加删除元素 3.切分数组 4.改变数组形状 5.合并数组

i=np.transpose(b)

print(h.resize(2,6))
print(np.append(h,g))
print(np.insert(a,1,5))
print(np.delete(a,[1]))

print(np.hsplit(a,3))
print(np.vsplit(c,2))

b.ravel() #数组扁平化
g.reshape(3,-2) #

np.concatnate((a,d),axis=0)
np.vstack((a,b))
np.r_[e,f]
np.hstack((e,f))
np.column_stack((a,d))
np.c_[a,d]

Scikit-learn

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基本的scikit-learn操作代码

from sklearn import neighbors, datasets, preprocessing
from sklearn.model_selection import train_test_split
from sklearn.metrics import accuracy_score
iris = datasets.load_iris()
X, y = iris.data[:, :2], iris.target
X_train, X_test, y_train, y_test = train_test_split (X, y, random_state=33)
scaler = preprocessing.StandardScaler().fit(X_train)
X_train = scaler.transform(X_train)
X_test = scaler.transform(X_test)
knn = neighbors.KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred = knn.predict(X_test)
accuracy_score(y_test, y_pred)

Normalization这个名词在很多地方都会出现，但是对于数据却有两种截然不同且容易混淆的处理过程。对于某个多特征的机器学习数据集来说，第一种Normalization是对于将数据进行预处理时进行的操作，是对于数据集的各个特征分别进行处理，主要包括min-max normalization、Z-score normalization、 log函数转换和atan函数转换等。第二种Normalization对于每个样本缩放到单位范数（每个样本的范数为1），主要有L1-normalization（L1范数）、L2-normalization（L2范数）等，可以用于SVM等应用

第一种 Normalization
数据的标准化（normalization）是将数据按比例缩放，使之落入一个小的特定区间。在某些比较和评价的指标处理中经常会用到，去除数据的单位限制，将其转化为无量纲的纯数值，便于不同单位或量级的指标能够进行比较和加权。其中最典型的就是数据的标准化处理，即将数据统一映射到[0,1]区间上。标准化在0-1之间是统计的概率分布，标准化在某个区间上是统计的坐标分布。目前数据标准化方法有多种。不同的标准化方法，对系统的评价结果会产生不同的影响，然而不幸的是，在数据标准化方法的选择上，还没有通用的法则可以遵循。

标准化（normalization）的目的：
在数据分析之前，我们通常需要先将数据标准化（normalization），利用标准化后的数据进行数据分析。数据标准化处理主要包括数据同趋化处理和无量纲化处理两个方面。数据同趋化处理主要解决不同性质数据问题，对不同性质指标直接加总不能正确反映不同作用力的综合结果，须先考虑改变逆指标数据性质，使所有指标对测评方案的作用力同趋化，再加总才能得出正确结果。数据无量纲化处理主要解决数据的可比性。经过上述标准化处理，原始数据均转换为无量纲化指标测评值，即各指标值都处于同一个数量级别上，可以进行综合测评分析。也就说标准化（normalization）的目的是：

把特征的各个维度标准化到特定的区间

把有量纲表达式变为无量纲表达式

归一化后有两个好处：

1. 加快基于梯度下降法或随机梯度下降法模型的收敛速度
2. 提升模型的精度

数据预处理：标准化 $$x=x-u/\delta$$

from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(X_train)
standardized_X = scaler.transform(X_train)
standardized_X_test = scaler.transform(X_test)

from sklearn.preprocessing import Normalizer
scaler = Normalizer().fit(X_train)
normalized_X = scaler.transform(X_train)
normalized_X_test = scaler.transform(X_test)

二值化

from sklearn.preprocessing import Binarizer
X = [[ 1., -1.,  2.],[ 2.,  0.,  0.],[ 0.,  1., -1.]]
binarizer = preprocessing.Binarizer().fit(X)  # fit does nothing
binarizer.transform(X)

模型初始化

from sklearn.linear_model import LinearRegression
lr = LinearRegression(normalize=True)
from sklearn.svm import SVC
svc = SVC(kernel='linear')
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
from sklearn import neighbors
knn = neighbors.KNeighborsClassifier(n_neighbors=5)

模型训练

lr.fit(X, y)
knn.fit(X_train, y_train)
svc.fit(X_train, y_train)
k_means.fit(X_train)
#pca_model = pca.fit_transform(X_train)

预测

y_pred = svc.predict(np.random.radom((2,5)))
y_pred = lr.predict(X_test)
y_pred = knn.predict_proba(X_test)

数据预处理

#Standardization
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler().fit(X_train)
standardized_X = scaler.transform(X_train)
standardized_X_test = scaler.transform(X_test)#Normalization
from sklearn.preprocessing import Normalizer
scaler = Normalizer().fit(X_train)
normalized_X = scaler.transform(X_train)
normalized_X_test = scaler.transform(X_test)#Binarization
from sklearn.preprocessing import Binarizer
binarizer = Binarizer(threshold=0.0).fit(X)
binary_X = binarizer.transform(X)#Encoding Categorical Features
from sklearn.preprocessing import Imputer
imp = Imputer(missing_values=0, strategy='mean', axis=0)
imp.fit_transform(X_train)#Imputing Missing Values
from sklearn.preprocessing import Imputer
imp = Imputer(missing_values=0, strategy='mean', axis=0)
imp.fit_transform(X_train)#Generating Polynomial Features
from sklearn.preprocessing import PolynomialFeatures
poly = PolynomialFeatures(5)
poly.fit_transform(X)

评价模型：分类算法的指标

#Accuracy Score
knn.score(X_test, y_test)
from sklearn.metrics import accuracy_score
accuracy_score(y_test, y_pred)#Classification Report
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))#Confusion Matrix
from sklearn.metrics import confusion_matrix
print(confusion_matrix(y_test, y_pred))

评价模型：回归算法的指标

#Mean Absolute Error
from sklearn.metrics import mean_absolute_error
y_true = [3, -0.5, 2]
mean_absolute_error(y_true, y_pred)
#Mean Squared Error
from sklearn.metrics import mean_squared_error
mean_squared_error(y_test, y_pred)
#R² Score
from sklearn.metrics import r2_score
r2_score(y_true, y_pred)

评价模型：聚类算法的指标

#Adjusted Rand Index
from sklearn.metrics import adjusted_rand_score
adjusted_rand_score(y_true, y_pred)
#Homogeneity
from sklearn.metrics import homogeneity_score
homogeneity_score(y_true, y_pred)
#V-measure
from sklearn.metrics import v_measure_score
metrics.v_measure_score(y_true, y_pred)

评价模型：交叉验证

#Cross-Validation
from sklearn.model_selection import cross_val_score
print(cross_val_score(knn, X_train, y_train, cv=4))
print(cross_val_score(lr, X, y, cv=2))

训练模型

#监督学习
lr.fit(X, y)
knn.fit(X_train, y_train)
svc.fit(X_train, y_train)
#无监督学习
k_means.fit(X_train)
pca_model = pca.fit_transform(X_train)

训练和测试

from sklearn.model_selection  import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X,y)

模型调优

#Grid Search
from sklearn.model_selection import GridSearchCV
params = {"n_neighbors": np.arange(1,3),"metric": ["euclidean","cityblock"]}
grid = GridSearchCV(estimator=knn,param_grid=params)
grid.fit(X_train, y_train)
print(grid.best_score_)
print(grid.best_estimator_.n_neighbors)#Randomized Parameter Optimization
from sklearn.model_selection import RandomizedSearchCV
params = {"n_neighbors": range(1,5),"weights": ["uniform", "distance"]}
rsearch = RandomizedSearchCV(estimator=knn,param_distributions=params,cv=4,n_iter=8,random_state=5)
rsearch.fit(X_train, y_train)
print(rsearch.best_score_)