Matrix上的Python PCA太大而无法融入内存

Question

我有一个100,000行×27,000列的csv,我试图在PCA上生成100,000行X 300列矩阵.csv大9GB.这是我正在做的事情:

from sklearn.decomposition import PCA as RandomizedPCA
import csv
import sys
import numpy as np
import pandas as pd

dataset = sys.argv[1]
X = pd.DataFrame.from_csv(dataset)
Y = X.pop("Y_Level")
X = (X - X.mean()) / (X.max() - X.min())
Y = list(Y)
dimensions = 300
sklearn_pca = RandomizedPCA(n_components=dimensions)
X_final = sklearn_pca.fit_transform(X)

当我运行上面的代码时,我的程序在执行.from_csv步骤时被杀死.我已经能够通过将csv分成10,000组来解决这个问题; 逐个读取它们,然后调用pd.concat.这允许我在被杀之前进入标准化步骤(X-X.mean()).... 我的数据对我的macbook空间来说太大了吗？或者有更好的方法来做到这一点.我真的很想将我拥有的所有数据用于我的机器学习应用程序.

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如果我想按照下面的答案建议使用增量PCA,我会这样做吗？:

from sklearn.decomposition import IncrementalPCA
import csv
import sys
import numpy as np
import pandas as pd

dataset = sys.argv[1]
chunksize_ = 10000
#total_size is 100000
dimensions = 300

reader = pd.read_csv(dataset, sep = ',', chunksize = chunksize_)
sklearn_pca = IncrementalPCA(n_components=dimensions)
Y = []
for chunk in reader:
    y = chunk.pop("virginica")
    Y = Y + list(y)
    sklearn_pca.partial_fit(chunk)
X = ???
#This is were i'm stuck, how do i take my final pca and output it to X,
#the normal transform method takes in an X, which I don't have because I
#couldn't fit it into memory.

我在网上找不到任何好的例子.

Answer 1

尝试划分您的数据或将其按批次加载到脚本中,并使用Incremetal PCA以及每个批次的partial_fit方法使您的PCA适合.

from sklearn.decomposition import IncrementalPCA
import csv
import sys
import numpy as np
import pandas as pd

dataset = sys.argv[1]
chunksize_ = 5 * 25000
dimensions = 300

reader = pd.read_csv(dataset, sep = ',', chunksize = chunksize_)
sklearn_pca = IncrementalPCA(n_components=dimensions)
for chunk in reader:
    y = chunk.pop("Y")
    sklearn_pca.partial_fit(chunk)

# Computed mean per feature
mean = sklearn_pca.mean_
# and stddev
stddev = np.sqrt(sklearn_pca.var_)

Xtransformed = None
for chunk in pd.read_csv(dataset, sep = ',', chunksize = chunksize_):
    y = chunk.pop("Y")
    Xchunk = sklearn_pca.transform(chunk)
    if Xtransformed == None:
        Xtransformed = Xchunk
    else:
        Xtransformed = np.vstack((Xtransformed, Xchunk))

有用的链接