[MRG] Add LazyTensor for large scale OT

[rflamary]

Types of changes

This PR adds a very simple LazyTensor class that can implement large scale matrices and tensors that are computed only lazily when getting slices of the tensor.

This class will be very interesting when implementing large scale OT problems such as factored OT, low rank OT and lazy implementation of sinkhorn. The objective is to be very simple with a storage of all necessary data and a function that compute the values on sliced (without computing the full tensor).

Exemple of use

import numpy as np
from ot.utils import LazyTensor, 

n1=100
n2=200
x1 = np.random.randn(n1,2)
x2 = np.random.randn(n2,2)

# i,j can be integers or slices, x1,x2 have to be passed as keyword arguments
def getitem(i,j, x1, x2):
    return np.dot(x1[i], x2[j].T)

# create a lazy tensor (giv data as arguments)
T = LazyTensor((n1,n2),getitem, x1=x1, x2=x2)

print(T.shape)
# (100, 200)
print(T)
# LazyTensor(shape=(100, 200),attributes=(x1,x2))

# get the full tensor (not lazy)
full_T = T[:]

# get one component
T11 = T[1,1]

# get one row
T1 = T[1]

# get one column with slices
Tsliced = T[::10,5]

# get the data as attributes
x1_0 = T.x1
x2_0 = T.x2

# create a LazyTensor from another one : exp(T)
T2 = ot.utils.LazyTensor(T.shape,lambda i,j,T: np.exp(T[i,j]) , T=T)

# compute sum
s = reduce_lazytensor(T, np.sum) 

# compute marginals
c = reduce_lazytensor(T, np.sum, axis=1) 
r = reduce_lazytensor(T, np.sum, axis=0, batch_size=50) 

Motivation and context / Related issue

How has this been tested (if it applies)

PR checklist

• I have read the CONTRIBUTING document.
• The documentation is up-to-date with the changes I made (check build artifacts).
• All tests passed, and additional code has been covered with new tests.
• I have added the PR and Issue fix to the RELEASES.md file.

[codecov]

Codecov Report

Merging #544 (418a1de) into master (6a29551) will increase coverage by 0.02%.
The diff coverage is 98.86%.

Additional details and impacted files

@@            Coverage Diff             @@
##           master     #544      +/-   ##
==========================================
+ Coverage   96.46%   96.49%   +0.02%     
==========================================
  Files          67       67              
  Lines       14490    14663     +173     
==========================================
+ Hits        13978    14149     +171     
- Misses        512      514       +2