PCA with Python | Principal Component Analysis Machine Learning | KGP Talkie
Principal Component Analysis(PCA)
According to Wikipedia, PCA is a statistical procedure that uses an
orthogonal transformation to convert a set of observations of possibly
correlated variables (entities each of which takes on various numerical values) into a set of values of linearly uncorrelated variables called
These are the new axes that descibe the
variation in the data.
- Principal component 1: The axis which spans the
most variationof the data.
- Principal component 2: The axis which spans the
second most variationof the data.
- Principal component 3: The axis which spans the
third most variationof the data and so on.
When to use PCA
We can use PCA in the following cases:
- Data Visualization.
- It is used to find
inter-relationbetween variables in the data.
SpeedingMachine Learning (ML) Algorithm.
- It’s often used to visualize
- As number of variables are
decreasingit makes further analysis simpler.
Objectives of PCA
The main objectives of the PCA are:
- It is basically a
non-dependentprocedure in which it reduces attribute space from a large number of variables to a smaller number of factors.
PCAis basically a
dimension reductionprocess but there is no guarantee that the
- Main task in this
PCAis to select a subset of variables from a larger set, based on which original variables have the highest
correlationwith the principal amount.
How to do PCA
As there are as many
principal components as there are variables in the data, principal components are constructed in such a manner that the first principal component accounts for the largest possible
variance in the data set .
The second principal component is calculated in the same way, with the condition that it is
uncorrelated with (i.e., perpendicular to) the first principal component and that it accounts for the next
Once fit, the
eigenvalues and principal components can be accessed on the PCA class via the
Principal Axis Method
PCA will search a
linear combination of variables so that we can extract
maximum variance from the variables. Once this process completes it will remove it and search for another
linear combination which will give an explanation about the maximum proportion of remaining
variance which basically leads to
orthogonal factors. In this method, we analyze total
From the following figure, I will make you understand how
PCA works in a nutshell.
There are few steps, Let's see one after other:
In the first step we have a
correlated high dimenasion data. And then we calculate the
center of the points and calculate
variance of the data by using
covariance matrix of the data and with this matrix we calculate
eigen vectors and
After calculating these, we pick the value of
m such that less than original dimension.
Then after we will project
` data points into thoseeigen vectors
and we do the inverse transform so we will getuncorrelated low dimensional` data.
Though mathematically it looks little bit complex but fortunately in python we have
sklearn library there we have
PCA package just we call
PCA() and then call
pca.fit() as usual we do in ML algorithms.
Importing required libraries
import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt
Loading the training data set
from sklearn import datasets, metrics from sklearn.model_selection import train_test_split
cancer = datasets.load_breast_cancer()
Let's go ahead and get the description the
breast cancer data set.
.. _breast_cancer_dataset: Breast cancer wisconsin (diagnostic) dataset -------------------------------------------- **Data Set Characteristics:** :Number of Instances: 569 :Number of Attributes: 30 numeric, predictive attributes and the class :Attribute Information: - radius (mean of distances from center to points on the perimeter) - texture (standard deviation of gray-scale values) - perimeter - area - smoothness (local variation in radius lengths) - compactness (perimeter^2 / area - 1.0) - concavity (severity of concave portions of the contour) - concave points (number of concave portions of the contour) - symmetry - fractal dimension ("coastline approximation" - 1) The mean, standard error, and "worst" or largest (mean of the three worst/largest values) of these features were computed for each image, resulting in 30 features. For instance, field 0 is Mean Radius, field 10 is Radius SE, field 20 is Worst Radius. - class: - WDBC-Malignant - WDBC-Benign :Summary Statistics: ===================================== ====== ====== Min Max ===================================== ====== ====== radius (mean): 6.981 28.11 texture (mean): 9.71 39.28 perimeter (mean): 43.79 188.5 area (mean): 143.5 2501.0 smoothness (mean): 0.053 0.163 compactness (mean): 0.019 0.345 concavity (mean): 0.0 0.427 concave points (mean): 0.0 0.201 symmetry (mean): 0.106 0.304 fractal dimension (mean): 0.05 0.097 radius (standard error): 0.112 2.873 texture (standard error): 0.36 4.885 perimeter (standard error): 0.757 21.98 area (standard error): 6.802 542.2 smoothness (standard error): 0.002 0.031 compactness (standard error): 0.002 0.135 concavity (standard error): 0.0 0.396 concave points (standard error): 0.0 0.053 symmetry (standard error): 0.008 0.079 fractal dimension (standard error): 0.001 0.03 radius (worst): 7.93 36.04 texture (worst): 12.02 49.54 perimeter (worst): 50.41 251.2 area (worst): 185.2 4254.0 smoothness (worst): 0.071 0.223 compactness (worst): 0.027 1.058 concavity (worst): 0.0 1.252 concave points (worst): 0.0 0.291 symmetry (worst): 0.156 0.664 fractal dimension (worst): 0.055 0.208 ===================================== ====== ====== :Missing Attribute Values: None :Class Distribution: 212 - Malignant, 357 - Benign :Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian :Donor: Nick Street :Date: November, 1995 This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets. https://goo.gl/U2Uwz2 Features are computed from a digitized image of a fine needle aspirate (FNA) of a breast mass. They describe characteristics of the cell nuclei present in the image. Separating plane described above was obtained using Multisurface Method-Tree (MSM-T) [K. P. Bennett, "Decision Tree Construction Via Linear Programming." Proceedings of the 4th Midwest Artificial Intelligence and Cognitive Science Society, pp. 97-101, 1992], a classification method which uses linear programming to construct a decision tree. Relevant features were selected using an exhaustive search in the space of 1-4 features and 1-3 separating planes. The actual linear program used to obtain the separating plane in the 3-dimensional space is that described in: [K. P. Bennett and O. L. Mangasarian: "Robust Linear Programming Discrimination of Two Linearly Inseparable Sets", Optimization Methods and Software 1, 1992, 23-34]. This database is also available through the UW CS ftp server: ftp ftp.cs.wisc.edu cd math-prog/cpo-dataset/machine-learn/WDBC/
This data has
30-dimensions that is
30 features. Let's visualize the data set with
df = pd.DataFrame(cancer.data, columns=cancer.feature_names) df.head()
|mean radius||mean texture||mean perimeter||mean area||mean smoothness||mean compactness||mean concavity||mean concave points||mean symmetry||mean fractal dimension||...||worst radius||worst texture||worst perimeter||worst area||worst smoothness||worst compactness||worst concavity||worst concave points||worst symmetry||worst fractal dimension|
5 rows × 30 columns
If we see here scale of the each feature is different that is dome features are in the range
10s some are in
100s. It is better to
standardize our data for better visualization.
Let's see the below code:
from sklearn.preprocessing import StandardScaler
scaler = StandardScaler() X_scaled = scaler.fit_transform(df) X_scaled[: 2
array([[ 1.09706398e+00, -2.07333501e+00, 1.26993369e+00, 9.84374905e-01, 1.56846633e+00, 3.28351467e+00, 2.65287398e+00, 2.53247522e+00, 2.21751501e+00, 2.25574689e+00, 2.48973393e+00, -5.65265059e-01, 2.83303087e+00, 2.48757756e+00, -2.14001647e-01, 1.31686157e+00, 7.24026158e-01, 6.60819941e-01, 1.14875667e+00, 9.07083081e-01, 1.88668963e+00, -1.35929347e+00, 2.30360062e+00, 2.00123749e+00, 1.30768627e+00, 2.61666502e+00, 2.10952635e+00, 2.29607613e+00, 2.75062224e+00, 1.93701461e+00], [ 1.82982061e+00, -3.53632408e-01, 1.68595471e+00, 1.90870825e+00, -8.26962447e-01, -4.87071673e-01, -2.38458552e-02, 5.48144156e-01, 1.39236330e-03, -8.68652457e-01, 4.99254601e-01, -8.76243603e-01, 2.63326966e-01, 7.42401948e-01, -6.05350847e-01, -6.92926270e-01, -4.40780058e-01, 2.60162067e-01, -8.05450380e-01, -9.94437403e-02, 1.80592744e+00, -3.69203222e-01, 1.53512599e+00, 1.89048899e+00, -3.75611957e-01, -4.30444219e-01, -1.46748968e-01, 1.08708430e+00, -2.43889668e-01, 2.81189987e-01]])
Principal Component Analysis
Linear dimensionality reduction using
Singular Value Decomposition of the data to project it to a lower dimensional space. The input data is centered but not scaled for each feature before applying the
PCA() function into training and testing set for analysis.
Let's discuss some important paameters of the function
It is a int, float, None or str.
Number of componentsto keep.
It is to pass an
intfor reproducible results across multiple function calls.
It is provide the amount of
varianceexplained by each of the selected components.
It helps to fit the model with X and apply the
dimensionality reductionon X.
It transforms data
We need to have
2-dimensional data set so
n_component is equal to
2 and we can get same result
random_state if we use same
PCA function into training and testing set for analysis look at the following code:
Fit the model X_scaled by using
from sklearn.decomposition import PCA
pca = PCA(n_components=2, random_state=42) pca.fit(X_scaled) PCA(n_components=2, random_state=42)
Training has been done now let's go ahead to transform it with
X_pca = pca.transform(X_scaled)
So we transformed the data into
Let's check the
shape of both data sets:
((569, 30), (569, 2))
Here we can observe shape of default data is
30 and after transformation it reduced to
Now we will try to plot the
scattering points for the second principal component and first principal component by using
Let's look into the following script:
plt.figure(figsize=(12,8)) plt.scatter(X_pca[:, 0], X_pca[:, 1], c = cancer.target, cmap = 'viridis') plt.xlabel('First Principal Component') plt.ylabel('Second Principal Component') plt.title('Scatter plot for Second principal component and First principal component') plt.show()
From the above plot we can observe, first principal component has
high variance compared to second principal component.
Now we will observe the respective
variances for the components by using bar graph.
See the following plot:
pca = PCA(n_components=20, random_state=42) X_pca = pca.fit_transform(X_scaled) variance = pca.explained_variance_ratio_ plt.ylabel('Variance') plt.xlabel('Principal components') plt.title('Bar graph for variances of the componets ') plt.bar(x = range(1, len(variance)+1), height=variance, width=0.7) plt.show()
array([0.44272026, 0.18971182, 0.09393163, 0.06602135, 0.05495768,
0.04024522, 0.02250734, 0.01588724, 0.01389649, 0.01168978,
0.00979719, 0.00870538, 0.00804525, 0.00523366, 0.00313783,
0.00266209, 0.00197997, 0.00175396, 0.00164925, 0.00103865])