DEV Community: Xi

Cross-Validation Clearly Explained in 5 Graphs

Xi — Sun, 10 Jul 2022 05:05:20 +0000

Introduction

In this article, I will introduce holdout validation and several common cross-validation methods.

I have created some beautiful illustrations to help understand these concepts.

Holdout Validation

Leave-p-out Cross-Validation(LpOCV)

Leave-one-out Cross-Validation(LOOCV)

K-Fold Cross-Validation

Repeated Random Subsampling Validation

Make AWS S3 bucket public accessible

Xi — Wed, 06 Jul 2022 04:55:19 +0000

Introduction

When you use AWS S3 for the first time, you may encounter access deny issues.
This issue occurs when you open the object URL in the browser.

The two most likely causes of this problem are the following.

Amazon S3 Block Public Access is disabled
The bucket policy denies public visit

Here is how to solve this problem.

Solve problem

1. Unchecking "block all public access"

(a). Goto permissions tab in your bucket and click edit in the block public access section

(b). Make sure "block all public access" is off

2. Apply a new bucket policy to your bucket

(a). Click edit in the bucket policy section

(b). Add the following bucket policy to your bucket

{
    "Version": "2012-10-17",
    "Statement": [
        {
            "Sid": "PublicReadGetObject",
            "Effect": "Allow",
            "Principal": "*",
            "Action": "s3:GetObject",
            "Resource": "{Bucket-ARN}/*",
        }
    ]
}

(Remember to change {Bucket-ARN} to your own.)

Success

After these two steps, you may notice an additional “Publicly accessible” icon under your bucket.

When we open the object URL in the browser, we will see that it is now accessible properly.

Understand plt and axes in matplotlib

Xi — Wed, 06 Jul 2022 04:51:36 +0000

Introduction

Matplotlib is one of the most popular python libraries for data visualization, but new users may have difficulties getting started. When I first started using matplotlib, I found plt and axes to be the two most confusing concepts. So this article will discuss how to use plt and axes.

Plot Single Graph

# Import libraries
import matplotlib.pyplot as plt
import numpy as np

When you want to draw a single graph, you can use plt.plot() directly, but of course, using plt.subplots() is also OK.

The following two methods will produce the same results:

1.Using plt.plot() directly

plt.plot(np.random.rand(10))  # 10 random number between [0, 1)
plt.title("title")
plt.show()

2.Using plt.subplots()

fig, ax = plt.subplots()  # Create a figure and a set of subplots.

ax.plot(np.random.rand(10))
ax.set_title("title")
plt.show()

Plot Multiple Graphs

When you want to draw multiple graphs, you should use plt.subplots().

# 2x2, a total of 4 graphs
n_rows = 2
n_cols = 2

fig, axes = plt.subplots(nrows=n_rows, ncols=n_cols)  # Create a figure and subplots of size 2x2

for row_num in range(n_rows):
    for col_num in range(n_cols):
        ax = axes[row_num][col_num]
        ax.plot(np.random.rand(10))

fig.suptitle("main title")
fig.tight_layout()  # Smaller padding
plt.show()