神经网络中的数据预处理方法 Data Preprocessing

0、Principal component analysis (PCA)

Principal component analysis (PCA) is a statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables called principal components (or sometimes, principal modes of variation). The number of principal components is less than or equal to the smaller of the number of original variables or the number of observations. This transformation is defined in such a way that the first principal component has the largest possible variance (that is, accounts for as much of the variability in the data as possible), and each succeeding component in turn has the highest variance possible under the constraint that it is orthogonal to the preceding components. The resulting vectors are an uncorrelated orthogonal basis set. PCA is sensitive to the relative scaling of the original variables.

 

 

1. Rescale Data

When your data is comprised of attributes with varying scales, many machine learning algorithms can benefit from rescaling the attributes to all have the same scale.

Often this is referred to as normalization and attributes are often rescaled into the range between 0 and 1. This is useful for optimization algorithms in used in the core of machine learning algorithms like gradient descent. It is also useful for algorithms that weight inputs like regression and neural networks and algorithms that use distance measures like K-Nearest Neighbors.

You can rescale your data using scikit-learn using the MinMaxScaler class.

# Rescale data (between 0 and 1)
import pandas
import scipy
import numpy
from sklearn.preprocessing import MinMaxScaler
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
# separate array into input and output components
X = array[:,0:8]
Y = array[:,8]
scaler = MinMaxScaler(feature_range=(0, 1))
rescaledX = scaler.fit_transform(X)
# summarize transformed data
numpy.set_printoptions(precision=3)
print(rescaledX[0:5,:])

After rescaling you can see that all of the values are in the range between 0 and 1.

[[ 0.353  0.744  0.59   0.354  0.     0.501  0.234  0.483]
 [ 0.059  0.427  0.541  0.293  0.     0.396  0.117  0.167]
 [ 0.471  0.92   0.525  0.     0.     0.347  0.254  0.183]
 [ 0.059  0.447  0.541  0.232  0.111  0.419  0.038  0.   ]
 [ 0.     0.688  0.328  0.354  0.199  0.642  0.944  0.2  ]]

2. Standardize Data

Standardization is a useful technique to transform attributes with a Gaussian distribution and differing means and standard deviations to a standard Gaussian distribution with a mean of 0 and a standard deviation of 1.

It is most suitable for techniques that assume a Gaussian distribution in the input variables and work better with rescaled data, such as linear regression, logistic regression and linear discriminate analysis.

You can standardize data using scikit-learn with the StandardScaler class.

# Standardize data (0 mean, 1 stdev)
from sklearn.preprocessing import StandardScaler
import pandas
import numpy
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
# separate array into input and output components
X = array[:,0:8]
Y = array[:,8]
scaler = StandardScaler().fit(X)
rescaledX = scaler.transform(X)
# summarize transformed data
numpy.set_printoptions(precision=3)
print(rescaledX[0:5,:])

The values for each attribute now have a mean value of 0 and a standard deviation of 1.

[[ 0.64   0.848  0.15   0.907 -0.693  0.204  0.468  1.426]
 [-0.845 -1.123 -0.161  0.531 -0.693 -0.684 -0.365 -0.191]
 [ 1.234  1.944 -0.264 -1.288 -0.693 -1.103  0.604 -0.106]
 [-0.845 -0.998 -0.161  0.155  0.123 -0.494 -0.921 -1.042]
 [-1.142  0.504 -1.505  0.907  0.766  1.41   5.485 -0.02 ]]

3. Normalize Data

Normalizing in scikit-learn refers to rescaling each observation (row) to have a length of 1 (called a unit norm in linear algebra).

This preprocessing can be useful for sparse datasets (lots of zeros) with attributes of varying scales when using algorithms that weight input values such as neural networks and algorithms that use distance measures such as K-Nearest Neighbors.

You can normalize data in Python with scikit-learn using the Normalizer class.

# Normalize data (length of 1)
from sklearn.preprocessing import Normalizer
import pandas
import numpy
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
# separate array into input and output components
X = array[:,0:8]
Y = array[:,8]
scaler = Normalizer().fit(X)
normalizedX = scaler.transform(X)
# summarize transformed data
numpy.set_printoptions(precision=3)
print(normalizedX[0:5,:])

The rows are normalized to length 1.

[[ 0.034  0.828  0.403  0.196  0.     0.188  0.004  0.28 ]
 [ 0.008  0.716  0.556  0.244  0.     0.224  0.003  0.261]
 [ 0.04   0.924  0.323  0.     0.     0.118  0.003  0.162]
 [ 0.007  0.588  0.436  0.152  0.622  0.186  0.001  0.139]
 [ 0.     0.596  0.174  0.152  0.731  0.188  0.01   0.144]]

4. Binarize Data (Make Binary)

You can transform your data using a binary threshold. All values above the threshold are marked 1 and all equal to or below are marked as 0.

This is called binarizing your data or threshold your data. It can be useful when you have probabilities that you want to make crisp values. It is also useful when feature engineering and you want to add new features that indicate something meaningful.

You can create new binary attributes in Python using scikit-learn with the Binarizer class.

# binarization
from sklearn.preprocessing import Binarizer
import pandas
import numpy
url = "https://archive.ics.uci.edu/ml/machine-learning-databases/pima-indians-diabetes/pima-indians-diabetes.data"
names = ['preg', 'plas', 'pres', 'skin', 'test', 'mass', 'pedi', 'age', 'class']
dataframe = pandas.read_csv(url, names=names)
array = dataframe.values
# separate array into input and output components
X = array[:,0:8]
Y = array[:,8]
binarizer = Binarizer(threshold=0.0).fit(X)
binaryX = binarizer.transform(X)
# summarize transformed data
numpy.set_printoptions(precision=3)
print(binaryX[0:5,:])

You can see that all values equal or less than 0 are marked 0 and all of those above 0 are marked 1.

[[ 1.  1.  1.  1.  0.  1.  1.  1.]
 [ 1.  1.  1.  1.  0.  1.  1.  1.]
 [ 1.  1.  1.  0.  0.  1.  1.  1.]
 [ 1.  1.  1.  1.  1.  1.  1.  1.]
 [ 0.  1.  1.  1.  1.  1.  1.  1.]]

Summary

In this post you discovered how you can prepare your data for machine learning in Python using scikit-learn.

You now have recipes to:

• Rescale data.
• Standardize data.
• Normalize data.
• Binarize data.

Your action step for this post is to type or copy-and-paste each recipe and get familiar with data preprocesing in scikit-learn.