# 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 `principal components`

.

### Principal components

These are the new axes that descibe the `variation`

in the data.

**Principal component 1:**The axis which spans the`most variation`

of the data.**Principal component 2:**The axis which spans the`second most variation`

of the data.**Principal component 3**: The axis which spans the`third most variation`

of 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-relation`

between variables in the data. `Speeding`

Machine Learning (ML) Algorithm.- It’s often used to visualize
`genetic distance`

and`relatedness`

between populations. - As number of variables are
`decreasing`

it makes further analysis simpler.

### Objectives of PCA

The main objectives of the PCA are:

- It is basically a
`non-dependent`

procedure in which it reduces attribute space from a large number of variables to a smaller number of factors. `PCA`

is basically a`dimension reduction`

process but there is no guarantee that the`dimension`

is interpretable.- Main task in this
`PCA`

is to select a subset of variables from a larger set, based on which original variables have the highest`correlation`

with 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 `highest variance`

.

Once fit, the `eigenvalues`

and principal components can be accessed on the PCA class via the `explained_variance_`

and `components_ attributes`

.

### 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 `variance`

.

## PCA Summary

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 `eigen values`

.

After calculating these, we pick the value of `m`

such that less than original dimension.

Then after we will project`` data points into those`

eigen vectors`and we do the inverse transform so we will get`

uncorrelated 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.

print(cancer.DESCR)

.. _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 `dataframe`

.

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 | |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

0 | 17.99 | 10.38 | 122.80 | 1001.0 | 0.11840 | 0.27760 | 0.3001 | 0.14710 | 0.2419 | 0.07871 | … | 25.38 | 17.33 | 184.60 | 2019.0 | 0.1622 | 0.6656 | 0.7119 | 0.2654 | 0.4601 | 0.11890 |

1 | 20.57 | 17.77 | 132.90 | 1326.0 | 0.08474 | 0.07864 | 0.0869 | 0.07017 | 0.1812 | 0.05667 | … | 24.99 | 23.41 | 158.80 | 1956.0 | 0.1238 | 0.1866 | 0.2416 | 0.1860 | 0.2750 | 0.08902 |

2 | 19.69 | 21.25 | 130.00 | 1203.0 | 0.10960 | 0.15990 | 0.1974 | 0.12790 | 0.2069 | 0.05999 | … | 23.57 | 25.53 | 152.50 | 1709.0 | 0.1444 | 0.4245 | 0.4504 | 0.2430 | 0.3613 | 0.08758 |

3 | 11.42 | 20.38 | 77.58 | 386.1 | 0.14250 | 0.28390 | 0.2414 | 0.10520 | 0.2597 | 0.09744 | … | 14.91 | 26.50 | 98.87 | 567.7 | 0.2098 | 0.8663 | 0.6869 | 0.2575 | 0.6638 | 0.17300 |

4 | 20.29 | 14.34 | 135.10 | 1297.0 | 0.10030 | 0.13280 | 0.1980 | 0.10430 | 0.1809 | 0.05883 | … | 22.54 | 16.67 | 152.20 | 1575.0 | 0.1374 | 0.2050 | 0.4000 | 0.1625 | 0.2364 | 0.07678 |

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]])

### PCA( )

`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 `SVD`

.

Applying the `PCA()`

function into training and testing set for analysis.

Let’s discuss some important paameters of the function `PCA()`

:

**n_components**

It is a int, float, None or str.`Number of components`

to keep.**random_state**

It is to pass an`int`

for reproducible results across multiple function calls.**explained_variance**

It is provide the amount of`variance`

explained by each of the selected components.**fit_transform**

It helps to fit the model with X and apply the`dimensionality reduction`

on X.**inverse_transform**

It transforms data`back`

to its`original`

space.

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 `random_state`

.

Applying the `PCA`

function into training and testing set for analysis look at the following code:

Fit the model X_scaled by using `pac.fit()`

:

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 `pca.transform()`

:

X_pca = pca.transform(X_scaled)

So we transformed the data into `2`

-dimensions.

Let’s check the `shape`

of both data sets:

X_scaled.shape, X_pca.shape

((569, 30), (569, 2))

Here we can observe shape of default data is `30`

and after transformation it reduced to `2`

.

Now we will try to plot the `scattering points`

for the second principal component and first principal component by using `plt.scatter()`

function.

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.

pca.explained_variance_ratio_

array([0.44272026, 0.18971182])

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()

variance

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])

## 1 Comment