MLPRegressor 中的稀疏矩阵错误

Question

上下文

我在尝试使用稀疏矩阵作为 sklearn.neural_network.MLPRegressor 的输入时遇到了一个错误运行。名义上，此方法能够处理稀疏矩阵。我认为这可能是 scikit-learn 中的错误，但我想在提交问题之前检查一下。

问题

将 scipy.sparse 输入传递给 sklearn.neural_network.MLPRegressor 时，我得到：

ValueError: input must be a square array

错误是由 numpy.matrixlab.defmatrix 中的 matrix_power 函数引发的。这似乎是因为 matrix_power 将稀疏矩阵传递给 numpy.asanyarray (L137), which returns an array of size=1, ndim=0 containing the sparse matrix object. matrix_power then performs some dimension checks (L138-141) 以确保输入是方阵，但失败是因为 numpy.asanyarray 返回的数组是不是正方形，即使基础稀疏矩阵是正方形。

据我所知，问题源于 numpy.asanyarray 阻止确定稀疏矩阵的维数。稀疏矩阵本身有一个大小属性，允许它通过维度检查，但前提是它不是运行通过 asanyarray.

我认为这可能是一个错误，但在我确认我不仅仅是个白痴之前不想深入讨论归档问题！请看下面，检查一下。

如果它是一个错误，那么在哪里提出问题最合适？麻麻？ SciPy？还是 Scikit-Learn？

最小示例

环境

Arch Linux
kernel     4.15.7-1

Python     3.6.4
numpy      1.14.1
scipy      1.0.0
sklearn    0.19.1

代码

import numpy as np
from scipy import sparse
from sklearn import model_selection
from sklearn.preprocessing import StandardScaler, Imputer
from sklearn.neural_network import MLPRegressor

## Generate some synthetic data

def fW(A, B, C):
    return A * np.random.normal(.3, .1) + B * np.random.normal(.6, .1)

def fX(A, B, C):
    return B * np.random.normal(-1, .1) + A * np.random.normal(-.9, .1) / C

# independent variables
N = int(1e4)
A = np.random.uniform(2, 12, N)
B = np.random.uniform(2, 12, N)
C = np.random.uniform(2, 12, N)

# synthetic data
mW = fW(A, B, C)
mX = fX(A, B, C)

# combine datasets
real = np.vstack([A, B, C]).T
meas = np.vstack([mW, mX]).T

# add noise to meas
meas *= np.random.normal(1, 0.0001, meas.shape)

## Make data sparse
prob_null = 0.2
real[np.random.choice([True, False], real.shape, p=[prob_null, 1-prob_null])] = np.nan
meas[np.random.choice([True, False], meas.shape, p=[prob_null, 1-prob_null])] = np.nan

# NB: problem persists whichever sparse matrix method is used.
real = sparse.csr_matrix(real)
meas = sparse.csr_matrix(meas)

# replace missing values with mean
rmnan = Imputer()
real = rmnan.fit_transform(real)
meas = rmnan.fit_transform(meas)

# split into test/training sets
real_train, real_test, meas_train, meas_test = model_selection.train_test_split(real, meas, test_size=0.3)

# create scalers and apply to data
real_scaler = StandardScaler(with_mean=False)
meas_scaler = StandardScaler(with_mean=False)

real_scaler.fit(real_train)
meas_scaler.fit(meas_train)

treal_train = real_scaler.transform(real_train)
tmeas_train = meas_scaler.transform(meas_train)

treal_test = real_scaler.transform(real_test)
tmeas_test = meas_scaler.transform(meas_test)

nn = MLPRegressor((100,100,10), solver='lbfgs', early_stopping=True, activation='tanh')
nn.fit(tmeas_train, treal_train)

## ERROR RAISED HERE

## The problem:

# the sparse matrix has a shape attribute that would pass the square matrix validation
tmeas_train.shape

# but not after it's been through asanyarray
np.asanyarray(tmeas_train).shape

Answer 1

MLPRegressor.fit() 因为 given in documentation 支持 X 的稀疏矩阵，但不支持 y

Parameters:

X : array-like or sparse matrix, shape (n_samples, n_features)
The input data.
y : array-like, shape (n_samples,) or (n_samples, n_outputs)
The target values (class labels in classification, real numbers in regression).

我能够成功运行您的代码：

nn.fit(tmeas_train, treal_train.toarray())

MLPRegressor 中的稀疏矩阵错误

Sparse Matrix error in MLPRegressor

python

numpy

scipy

sparse-matrix

scikit-learn

上下文

问题

最小示例

环境

代码