metric learn ( python)

度量学习的应用基本可以涵盖各种领域，最近需要当作工具使用，用的语言是python,网上直接有工具包metric-learn，可以直接使用pip install metric-learn的方法来安装，或者根据前面github上的网址下载源码之后python setup.py install.
安装好之后就可以使用包中带的一些度量学习的方法了，以下的例子均摘自此处

Covariance metric(baseline method)

from metric_learn import Covariance
from sklearn.datasets import load_iris

iris = load_iris()['data']

cov = Covariance().fit(iris)
x = cov.transform(iris)
    
    
     
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Large Margin Nearest Neighbor (LMNN)

import numpy as np
from metric_learn import LMNN
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

lmnn = LMNN(k=5, learn_rate=1e-6)
lmnn.fit(X, Y, verbose=False)
    
    
     
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Information Theoretic Metric Learning (ITML)

from metric_learn import ITML_Supervised
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

itml = ITML_Supervised(num_constraints=200)
itml.fit(X, Y)
    
    
     
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Sparse Determinant Metric Learning (SDML)

from metric_learn import SDML_Supervised
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

sdml = SDML_Supervised(num_constraints=200)
sdml.fit(X, Y)
    
    
     
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Least Squares Metric Learning (LSML)

from metric_learn import LSML_Supervised
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

lsml = LSML_Supervised(num_constraints=200)
lsml.fit(X, Y)
    
    
     
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Neighborhood Components Analysis (NCA)

import numpy as np
from metric_learn import NCA
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

nca = NCA(max_iter=1000, learning_rate=0.01)
nca.fit(X, Y)
    
    
     
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Local Fisher Discriminant Analysis (LFDA)

import numpy as np
from metric_learn import LFDA
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

lfda = LFDA(k=2, dim=2)
lfda.fit(X, Y)
    
    
     
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Relative Components Analysis (RCA)

from metric_learn import RCA_Supervised
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

rca = RCA_Supervised(num_chunks=30, chunk_size=2)
rca.fit(X, Y)
    
    
     
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        <link rel="stylesheet" href="https://csdnimg.cn/release/phoenix/template/css/markdown_views-ea0013b516.css">
            </div>

Covariance metric(baseline method)

from metric_learn import Covariance
from sklearn.datasets import load_iris

iris = load_iris()['data']

cov = Covariance().fit(iris)
x = cov.transform(iris)
  
  
   
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Large Margin Nearest Neighbor (LMNN)

import numpy as np
from metric_learn import LMNN
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

lmnn = LMNN(k=5, learn_rate=1e-6)
lmnn.fit(X, Y, verbose=False)
  
  
   
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Information Theoretic Metric Learning (ITML)

from metric_learn import ITML_Supervised
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

itml = ITML_Supervised(num_constraints=200)
itml.fit(X, Y)
  
  
   
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Sparse Determinant Metric Learning (SDML)

from metric_learn import SDML_Supervised
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

sdml = SDML_Supervised(num_constraints=200)
sdml.fit(X, Y)
  
  
   
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Least Squares Metric Learning (LSML)

from metric_learn import LSML_Supervised
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

lsml = LSML_Supervised(num_constraints=200)
lsml.fit(X, Y)
  
  
   
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Neighborhood Components Analysis (NCA)

import numpy as np
from metric_learn import NCA
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

nca = NCA(max_iter=1000, learning_rate=0.01)
nca.fit(X, Y)
  
  
   
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Local Fisher Discriminant Analysis (LFDA)

import numpy as np
from metric_learn import LFDA
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

lfda = LFDA(k=2, dim=2)
lfda.fit(X, Y)
  
  
   
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Relative Components Analysis (RCA)

from metric_learn import RCA_Supervised
from sklearn.datasets import load_iris

iris_data = load_iris()
X = iris_data['data']
Y = iris_data['target']

rca = RCA_Supervised(num_chunks=30, chunk_size=2)
rca.fit(X, Y)
  
  
   
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        <link rel="stylesheet" href="https://csdnimg.cn/release/phoenix/template/css/markdown_views-ea0013b516.css">
            </div>

Covariance metric(baseline method)

Large Margin Nearest Neighbor (LMNN)

Information Theoretic Metric Learning (ITML)

Sparse Determinant Metric Learning (SDML)

Least Squares Metric Learning (LSML)

Neighborhood Components Analysis (NCA)

Local Fisher Discriminant Analysis (LFDA)

Relative Components Analysis (RCA)

Covariance metric(baseline method)

Large Margin Nearest Neighbor (LMNN)

Information Theoretic Metric Learning (ITML)

Sparse Determinant Metric Learning (SDML)

Least Squares Metric Learning (LSML)

Neighborhood Components Analysis (NCA)

Local Fisher Discriminant Analysis (LFDA)

Relative Components Analysis (RCA)

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