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Created March 18, 2012 13:10
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Pairwise ranking using scikit-learn LinearSVC
"""
Implementation of pairwise ranking using scikit-learn LinearSVC
Reference: "Large Margin Rank Boundaries for Ordinal Regression", R. Herbrich,
T. Graepel, K. Obermayer.
Authors: Fabian Pedregosa <fabian@fseoane.net>
Alexandre Gramfort <alexandre.gramfort@inria.fr>
"""
import itertools
import numpy as np
from sklearn import svm, linear_model, cross_validation
def transform_pairwise(X, y):
"""Transforms data into pairs with balanced labels for ranking
Transforms a n-class ranking problem into a two-class classification
problem. Subclasses implementing particular strategies for choosing
pairs should override this method.
In this method, all pairs are choosen, except for those that have the
same target value. The output is an array of balanced classes, i.e.
there are the same number of -1 as +1
Parameters
----------
X : array, shape (n_samples, n_features)
The data
y : array, shape (n_samples,) or (n_samples, 2)
Target labels. If it's a 2D array, the second column represents
the grouping of samples, i.e., samples with different groups will
not be considered.
Returns
-------
X_trans : array, shape (k, n_feaures)
Data as pairs
y_trans : array, shape (k,)
Output class labels, where classes have values {-1, +1}
"""
X_new = []
y_new = []
y = np.asarray(y)
if y.ndim == 1:
y = np.c_[y, np.ones(y.shape[0])]
comb = itertools.combinations(range(X.shape[0]), 2)
for k, (i, j) in enumerate(comb):
if y[i, 0] == y[j, 0] or y[i, 1] != y[j, 1]:
# skip if same target or different group
continue
X_new.append(X[i] - X[j])
y_new.append(np.sign(y[i, 0] - y[j, 0]))
# output balanced classes
if y_new[-1] != (-1) ** k:
y_new[-1] = - y_new[-1]
X_new[-1] = - X_new[-1]
return np.asarray(X_new), np.asarray(y_new).ravel()
class RankSVM(svm.LinearSVC):
"""Performs pairwise ranking with an underlying LinearSVC model
Input should be a n-class ranking problem, this object will convert it
into a two-class classification problem, a setting known as
`pairwise ranking`.
See object :ref:`svm.LinearSVC` for a full description of parameters.
"""
def fit(self, X, y):
"""
Fit a pairwise ranking model.
Parameters
----------
X : array, shape (n_samples, n_features)
y : array, shape (n_samples,) or (n_samples, 2)
Returns
-------
self
"""
X_trans, y_trans = transform_pairwise(X, y)
super(RankSVM, self).fit(X_trans, y_trans)
return self
def predict(self, X):
"""
Predict an ordering on X. For a list of n samples, this method
returns a list from 0 to n-1 with the relative order of the rows of X.
Parameters
----------
X : array, shape (n_samples, n_features)
Returns
-------
ord : array, shape (n_samples,)
Returns a list of integers representing the relative order of
the rows in X.
"""
if hasattr(self, 'coef_'):
np.argsort(np.dot(X, self.coef_.T))
else:
raise ValueError("Must call fit() prior to predict()")
def score(self, X, y):
"""
Because we transformed into a pairwise problem, chance level is at 0.5
"""
X_trans, y_trans = transform_pairwise(X, y)
return np.mean(super(RankSVM, self).predict(X_trans) == y_trans)
if __name__ == '__main__':
# as showcase, we will create some non-linear data
# and print the performance of ranking vs linear regression
np.random.seed(1)
n_samples, n_features = 300, 5
true_coef = np.random.randn(n_features)
X = np.random.randn(n_samples, n_features)
noise = np.random.randn(n_samples) / np.linalg.norm(true_coef)
y = np.dot(X, true_coef)
y = np.arctan(y) # add non-linearities
y += .1 * noise # add noise
Y = np.c_[y, np.mod(np.arange(n_samples), 5)] # add query fake id
cv = cross_validation.KFold(n_samples, 5)
train, test = iter(cv).next()
# make a simple plot out of it
import pylab as pl
pl.scatter(np.dot(X, true_coef), y)
pl.title('Data to be learned')
pl.xlabel('<X, coef>')
pl.ylabel('y')
pl.show()
# print the performance of ranking
rank_svm = RankSVM().fit(X[train], Y[train])
print 'Performance of ranking ', rank_svm.score(X[test], Y[test])
# and that of linear regression
ridge = linear_model.RidgeCV(fit_intercept=True)
ridge.fit(X[train], y[train])
X_test_trans, y_test_trans = transform_pairwise(X[test], y[test])
score = np.mean(np.sign(np.dot(X_test_trans, ridge.coef_)) == y_test_trans)
print 'Performance of linear regression ', score
@valdezpa
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valdezpa commented May 4, 2021

I am very new to python code and ML, i see that this was tested with created data; how would I use this code with a real dataset that I already have partitioned into X and y?
Should I start on the cv KFold line and continue with the train, test=cv.split(X,y).next(), skipping all the lines before these? However, since this is a main not sure how I will add my data there. many thanks for your help with this

@Omaimah-AlHosni
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can we use this code to transfer multi-label to pairwise label?

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