prda 1.2.0

Prda contains packages for data processing, analysis and visualization. The ultimate goal is to fill the “last mile” between analysts and packages.

prda

Prda contains packages for data processing, analysis and visualization.

Prda ultimate goal is to fill the “last mile” between analysts and packages. During my research practice, I have felt how “learning a package before utilizing” can be time-consuming and exhausting. The resulted inefficiency leads to the creation of prda.

Usage

pip install prda

See details in: https://pypi.org/project/prda/

You are welcome to clone prda for personal use and pull request of your modification is super!! encouraged.

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To utilize prda, you only need to be familiar with pandas as most inputs is pd.DataFrame.

Currently with the help of ChatGPT, you can just tailor the input of demonstration code below to your data. And you don't need to be familiar with pandas or even python.

Examples of Useage

1. For Visulization

import prda
import pandas as pd
import numpy as np
df = pd.DataFrame(data=np.array([np.arange(100) for i in range(5)]).T,columns=['a', 'b', 'c', 'd', 'e'])
prda.graphic.scatter_3d_html(df, x='a', y='b', z='c', color_hue='d', size_hue='e', title='demo_3d_scatter', filepath='demo_3d_scatter.html')

the above code will provide an interactive html figure that look like this:

demo_3d_scatter.html

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import prda
import pandas as pd
import numpy as np
datalen = 500
indices = np.arange(datalen)
col_a = np.arange(0, 10, 10/datalen)
col_b = np.random.randint(3, 8, datalen)
data = np.array([indices, col_a, col_b]).T
df = pd.DataFrame(data=data, columns=['idx', 'a', 'b'])

# draw
import random
point_markers = {
    'a': [(indices[i], col_a[i]) for i in random.sample(list(indices), 20)]
}
prda.graphic.lineplot_html(df, x='idx', y=['a', 'b'], markpoints=point_markers, filepath='demo_lineplot.html')

	idx	a	b
0	0.0	0.00	6.0
1	1.0	0.02	3.0
2	2.0	0.04	4.0
...	...	...	...
498	498.0	9.96	6.0
499	499.0	9.98	5.0

And code with the above DataFrame will draw anther plot look like this:

demo_lineplot.html

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2. For Data Preparation

Code for filtering continuous variables in data with unique-value threshold of 5:

from prda import prep
prep.select_continuous_variables(data, unique_threshold=5)

3. For Machine Learning

Code for evaluating hyperparameters combinations for a given algorithm using user-specified cross-validation method:

from prda.ml import evaluations
param_grid = {'k': [4,5,6,7]}
evaluations.evaluate_param_combinations(X, y, knn_algorithm, param_grid=param_grid, cv=10, visualize_results=True)

4. For IO

A common usage during my research practice is to make well structured folders to save experimential results. With the following function, you only need to think about how you want your files to be structured. All related folders will be created automatically:

from prda import iostream
iostream.create_dirs([
    'results/experiment1/f1_score.csv',
    'results/experiment1/accuracy.csv',
    'results/experiment2/',
    'results/experiment10/accuracy/',
    'results/experiment10/f1_score/r1.txt',
    ])

The above one-line code will create all the folders for you which will have the corresponding structure below, after which you can then store your results without worrying about file structures whatsoever.

results/
├── experiment1/
│   ├── f1_score.csv
│   └── accuracy.csv
├── experiment2/
└── experiment10/
    ├── accuracy/
    └── f1_score/
        └── r1.txt

The prda's methods are quite self-explanatory, as a result, we think providing the above demonstration is suffice at the moment. Although the current prda is far from completion, let along perfection. It is under improvement regularly.

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Updates

2023.5.3 Major Updates

Add several easy-to-use functions, including prep::pca, select_continuous_variables, handle_missing_data, apply_linear_func(row-wisely), and ml::match_clusters, evaluate_param_combinations(optimal parameters searching, with base class::sklearn.base.BaseEstimator), etc.

2023.11.10 Major Updates

1. Including a variant of kNN which allows you to allocate customized k (K sequence) for each sample in ml::neighbors::VariableKNN. The algorithm behaves as a sklearn.classifier which means you can employ it directly via fit(·) and predict(·).
2. Add functions, e.g. iostream::create_dirs.