Datacamp 笔记代码 Unsupervised Learning in Python 第三章 Decorrelating your data and dimension reduction

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更多原始数据文档和JupyterNotebook
Github: https://github.com/JinnyR/Datacamp_DataScienceTrack_Python

Datacamp track: Data Scientist with Python - Course 23 (3)

Exercise

Correlated data in nature

You are given an array grains giving the width and length of samples of grain. You suspect that width and length will be correlated. To confirm this, make a scatter plot of width vs length and measure their Pearson correlation.

Instruction

• Import:
  • matplotlib.pyplot as plt.
  • pearsonr from scipy.stats.
• Assign column 0 of grains to width and column 1 of grains to length.
• Make a scatter plot with width on the x-axis and lengthon the y-axis.
• Use the pearsonr() function to calculate the Pearson correlation of width and length.

import pandas as pdgrains = pd.read_csv('https://s3.amazonaws.com/assets.datacamp.com/production/course_2141/datasets/seeds-width-vs-length.csv', header=None).values

# Perform the necessary imports
import matplotlib.pyplot as plt
from scipy.stats import pearsonr# Assign the 0th column of grains: width
width = grains[:,0]# Assign the 1st column of grains: length
length = grains[:,1]# Scatter plot width vs length
plt.scatter(width, length)
plt.axis('equal')
plt.show()# Calculate the Pearson correlation
correlation, pvalue = pearsonr(width, length)# Display the correlation
print(correlation)

0.8604149377143467

Exercise

Decorrelating the grain measurements with PCA

You observed in the previous exercise that the width and length measurements of the grain are correlated. Now, you’ll use PCA to decorrelate these measurements, then plot the decorrelated points and measure their Pearson correlation.

Instruction

• Import PCA from sklearn.decomposition.
• Create an instance of PCA called model.
• Use the .fit_transform() method of model to apply the PCA transformation to grains. Assign the result to pca_features.
• The subsequent code to extract, plot, and compute the Pearson correlation of the first two columns pca_features has been written for you, so hit ‘Submit Answer’ to see the result!

# Import PCA
from sklearn.decomposition import PCA# Create PCA instance: model
model = PCA()# Apply the fit_transform method of model to grains: pca_features
pca_features = model.fit_transform(grains)# Assign 0th column of pca_features: xs
xs = pca_features[:,0]# Assign 1st column of pca_features: ys
ys = pca_features[:,1]# Scatter plot xs vs ys
plt.scatter(xs, ys)
plt.axis('equal')
plt.show()# Calculate the Pearson correlation of xs and ys
correlation, pvalue = pearsonr(xs, ys)# Display the correlation
print(correlation

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