我正在编写代码,以同时高效地从多个大型并行scipy sparse.csc
矩阵(意味着所有矩阵具有相同的暗淡,并且所有nnz元素位于相同的位置)中删除多列。我这样做是通过仅索引要保留给一个矩阵的列,然后为其他矩阵重用索引和indptr列表。但是,当我通过列表对csc矩阵进行索引时,它将对数据列表进行重新排序,因此无法重用索引。有没有一种方法可以强制scipy将数据列表保持原始顺序?为什么仅在按列表建立索引时才重新排序?
import scipy.sparse
import numpy as np
mat = scipy.sparse.csc_matrix(np.array([[1,0,0,0,2,5],
[1,0,1,0,0,0],
[0,0,0,4,0,1],
[0,3,0,1,0,4]]))
print mat[:,3].data
返回数组([4,1])
print mat[:,[3]].data
返回数组([1,4])
答案 0 :(得分:1)
Expression<Func<ProductDTO, bool>>
标量选择:
using System;
using System.Collections.Generic;
using System.Linq;
using System.Linq.Expressions;
using Microsoft.AspNet.OData.Query;
using AutoMapper.Extensions.ExpressionMapping;
using AutoMapper.QueryableExtensions;
namespace ProductApp
{
public class DomainLayer
{
public IEnumerable<ProductDTO> GetProductsByEntityOptions(ODataQueryOptions<ProductDTO> options)
{
var mapper = MyMapper.GetMapper();
// This is the trick to get the expression out of the FilterQueryOption...
IQueryable queryable = Enumerable.Empty<ProductDTO>().AsQueryable();
queryable = options.Filter.ApplyTo(queryable, new ODataQuerySettings());
var exp = (MethodCallExpression) queryable.Expression; // <-- This comes back as a MethodCallExpression...
// Map the expression to my intermediate Product object type
var mappedExp = mapper.Map<Expression<Func<Product, bool>>>(exp); // <-- But I want it as a Expression<Func<ProductDTO, bool>> so I can map it...
IEnumerable<Product> results = _dataAccessLayer.GetProducts(mappedExp);
return mapper.Map<IEnumerable<ProductDTO>>(results);
}
}
public class DataAccessLayer
{
public IEnumerable<Product> GetProducts(Expression<Func<Product, bool>> exp)
{
var mapper = MyMapper.GetMapper();
var mappedExp = mapper.Map<Expression<Func<ProductEntity, bool>>>(exp);
IEnumerable<ProductEntity> result = _dataContext.GetTable<ProductEntity>().Where(mappedExpression).ToList();
return mapper.Map<IEnumerable<Product>>(result);
}
}
}
列表索引:
In [43]: mat = sparse.csc_matrix(np.array([[1,0,0,0,2,5],[1,0,1,0,0,0],[0,0,0,4,
...: 0,1],[0,3,0,1,0,4]]))
...:
...:
In [44]: mat
Out[44]:
<4x6 sparse matrix of type '<class 'numpy.int64'>'
with 10 stored elements in Compressed Sparse Column format>
In [45]: mat.data
Out[45]: array([1, 1, 3, 1, 4, 1, 2, 5, 1, 4], dtype=int64)
In [46]: mat.indices
Out[46]: array([0, 1, 3, 1, 2, 3, 0, 0, 2, 3], dtype=int32)
In [47]: mat.indptr
Out[47]: array([ 0, 2, 3, 4, 6, 7, 10], dtype=int32)
排序:
In [48]: m1 = mat[:,3]
In [49]: m1
Out[49]:
<4x1 sparse matrix of type '<class 'numpy.int64'>'
with 2 stored elements in Compressed Sparse Column format>
In [50]: m1.data
Out[50]: array([4, 1])
In [51]: m1.indices
Out[51]: array([2, 3], dtype=int32)
In [52]: m1.indptr
Out[52]: array([0, 2], dtype=int32)
带有列表的csc索引使用矩阵乘法。它根据索引构造一个提取器矩阵,然后进行点乘法。因此,这是一个全新的稀疏矩阵。不仅仅是csc数据和索引属性的子集。
csc矩阵具有一种方法来确保索引值排序(在列中)。应用该方法可能有助于确保以相同的方式对数组进行排序。