如何使用pyspark确定pca中的最佳特征数

lf3rwulv 于 2021-05-27 发布在 Spark

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使用sci kit learn，我们可以根据累积方差图决定要保留的特征数，如下所示

from sklearn.decomposition import PCA

pca = PCA() # init pca
pca.fit(dataset) # fit the dataset into pca model

pca.explained_variance_ratio # this attribute shows how much variance is explained by each of the seven individual component

we can plot the cumulative value as below
plt.figure(figsize= (10, 8)) # size of the chart(size of the vectors)
cumulativeValue = pca.explained_variance_ratio_.cumsum() # get the cumulative sum

plt.plot(range(1,8), cumulativeValue, marker = 'o', linestyle="--")

然后接近80%是我们可以为pca选择的最佳特征数。。

我的问题是如何确定pyspark的最佳特性数量

apache-spark pyspark pca

来源：https://stackoverflow.com/questions/63190529/how-to-determine-the-optimum-number-of-features-in-pca-with-pyspark

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按热度按时间

kmynzznz1#

我们可以借助 explainedVariance 我是怎么做到的。

from pyspark.ml.feature import VectorAssembler
from pyspark.ml.feature import PCA

# used vector assembler to create the input the vector

vectorAssembler = VectorAssembler(inputCols=['inputCol1', 'inputCol2', 'inputCol3', 'inputCol4'], outputCol='pcaInput')

df = vectorAssembler.transform(dataset) # fetch data into vector assembler
pca = PCA(k=8, inputCol="pcaInput", outputCol="features") # here I Have defined maximum number of features that I have
pcaModel = pca.fit(df) # fit the data to pca to make the model
print(pcaModel.explainedVariance) # here it will explain the variances
cumValues = pcaModel.explainedVariance.cumsum() # get the cumulative values

# plot the graph

plt.figure(figsize=(10,8))
plt.plot(range(1,9), cumValues, marker = 'o', linestyle='--')
plt.title('variance by components')
plt.xlabel('num of components')
plt.ylabel('cumulative explained variance')

选择接近80%的参数数

所以在这种情况下，参数的最佳数目是2

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如何使用pyspark确定pca中的最佳特征数

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