quickshow 0.1.4

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Quick-Show

Quick-Show is a package that allows you to easily and quickly draw plots.
Quick Show is an abstraction using popular libraries such as sklearn and matplotlib, so it is very light and convenient.

Note: Quick-Show is sub-modules of other packages to manage quickshow more lightly and use more widly. This is a project under development as a submodule. With the end of the project, We plan to provide documents in major version 1 and sphinx. It is NOT recommended to use prior to major version 1.

Installation

$ pip install quickshow

Features

1 Related to dimensionality reduction

2D or 3D t-SNE and PCA plots using specific columns of a refined dataframe. Create a scatter plot very quickly and easily by inputting a clean dataframe and column names that do not have missing data.

1. vis_tsne2d: Simple visuallization of 2-dimensional t-distributed stochastic neighbor embedding
   
2. vis_tsne3d: Simple visuallization of 3-dimensional t-distributed stochastic neighbor embedding
   
3. vis_pca: Simple visuallization of Principal Component Analysis (PCA)

2 Related to classification model evaluation.

1. vis_cm: visuallization heatmap of confusion_matrix and return classification report dataframe.
   
2. get_total_cr_df
3. vis_multi_plot

3 Related to clustering.

1. vis_cluster_plot:
   

4 Utils

1. find_all_files:
   

Examples

Feature 1

See example dataframe...

import pandas as pd
df = pd.DataFrame([3,2,3,2,3,3,1,1])
df['val'] = [np.array([np.random.randint(0,10000),np.random.randint(0,10000),np.random.randint(0,10000)]) for x in df[0]]
df.columns = ['labels', 'values']
print(df)

	labels	values
0	3	[8231 3320 6894]
1	2	[3485 7 7374]
...	...	...
6	1	[5218 9846 2488]
7	1	[6661 5105 136]

from quickshow import vis_tsne2d, vis_tsne3d, vis_pca

return_df = vis_tsne2d(df, 'values', 'labels', True, './save/fig1.png')
return_df = vis_tsne3d(df, 'values', 'labels', True, './save/fig2.png')
return_df = vis_pca(df, 'values', 'labels', 2, True, './save/fig3.png')
return_df = vis_pca(df, 'values', 'labels', 3, True, './save/fig4.png')

See output figure...

• All function returns the dataframe which used to plot. Thus, use the returned dataframe object to customize your plot. Or use matplotlib's rcparam methods.
• If the label column does not exist, simply enter None as an argument.
• For more details, please check doc string.

Feature 2

See example dataframe...

import pandas as pd
label_list, num_rows = ['cat', 'dog', 'horse', 'dorphin'], 300
df = pd.DataFrame([label_list[np.random.randint(4)] for _ in range(num_rows)], columns=['real'])
df['predicted'] = [label_list[np.random.randint(4)] for _ in range(num_rows)]  
print(df)

	real	predicted
0	cat	cat
1	horse	cat
...	...	...
7	horse	dog
299	dorphin	horse

from quickshow import vis_cm

df_cr, cm = vis_cm(df, 'real', 'predicted', 'vis_cm.csv', 'vis_cm.png')

See output...

print(df_cr)

	cat	dog	dorphin	horse	accuracy	macro avg	weighted avg
precision	0.304878	0.344828	0.285714	0.276316	0.3	0.302934	0.304337
recall	0.328947	0.246914	0.328767	0.3	0.3	0.301157	0.3
f1-score	0.316456	0.28777	0.305732	0.287671	0.3	0.299407	0.299385
support	76	81	73	70	0.3	300	300

confusion matirx will be shown as below.

• This function return pandas.DataFrame obejct of classification report and confusion metix as shown below.

Use Case

[1] Korean-news-topic-classification-using-KO-BERT: all plots were created through Quick-Show.

References

[1] Scikit-Learn https://scikit-learn.org
[2] Matplotlib https://matplotlib.org/

Contacts

Project Owner(P.O): Daniel Park, South Korea e-mail [e-mail]
Maintainers: Daniel Park, South Korea e-mail [e-mail]