Remark: I have no affiliation with these projects, but I love using
quarto and Visual Studio Code’s extension Jupyter To
Markdown
for these posts. Just wanted to say that.

Today:

• I’ll show you how to use Python’s nnetsauce (version 0.17.0) to
  download data from the R universe
  API.
• I’ll use nnetsauce’s automated Machine Learning (AutoML) to adjust a model to the
  Affairs
  data set also studied in this post from 2023-12-25.
• Understand the model’s decisions and obtain prediction intervals on the test set.

0 – Install packages

pip install nnetsauce the-teller imbalanced-learn matplotlib IPython

1- Import data from R universe API

import nnetsauce as ns
import teller as tr 
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt

from IPython.display import display
from sklearn.preprocessing import OneHotEncoder
from sklearn.model_selection import train_test_split
from imblearn.over_sampling import RandomOverSampler
from time import time

downloader = ns.Downloader()

df = downloader.download(pkgname="AER", dataset="Affairs", source = "https://zeileis.r-universe.dev/")

print(f"===== df: \n {df} \n")
print(f"===== df.dtypes: \n {df.dtypes}")

===== df: 
      affairs  gender   age  yearsmarried children  religiousness  education  \
0          0    male 37.00         10.00       no              3         18   
1          0  female 27.00          4.00       no              4         14   
2          0  female 32.00         15.00      yes              1         12   
3          0    male 57.00         15.00      yes              5         18   
4          0    male 22.00          0.75       no              2         17   
..       ...     ...   ...           ...      ...            ...        ...   
596        1    male 22.00          1.50      yes              1         12   
597        7  female 32.00         10.00      yes              2         18   
598        2    male 32.00         10.00      yes              2         17   
599        2    male 22.00          7.00      yes              3         18   
600        1  female 32.00         15.00      yes              3         14   

     occupation  rating  
0             7       4  
1             6       4  
2             1       4  
3             6       5  
4             6       3  
..          ...     ...  
596           2       5  
597           5       4  
598           6       5  
599           6       2  
600           1       5  

[601 rows x 9 columns] 

===== df.dtypes: 
 affairs            int64
gender            object
age              float64
yearsmarried     float64
children          object
religiousness      int64
education          int64
occupation         int64
rating             int64
dtype: object

The data set is extensively in studied in this
post.

2 – Data preprocessing

encoder = OneHotEncoder(sparse_output=False).set_output(transform="pandas")
df_char_encoded = encoder.fit_transform(df[['gender', 'children']])
df_char_encoded.drop(columns = ['gender_female', 'children_no'], axis=1, inplace=True)
df_minus_char = df.drop(columns=['affairs', 'gender', 'children'])

X = pd.concat((df_minus_char, df_char_encoded), axis=1)
y = np.float64(df['affairs'].values)

Upsampling

ros = RandomOverSampler(random_state=42)
X_res, y_res = ros.fit_resample(X, y)

Split data into training test and test set

X_train, X_test, y_train, y_test = train_test_split(X_res, y_res,
                                                    test_size=0.2, 
                                                    random_state=13)

3 – Automated Machine Learning with nnetsauce

# build the AutoML regressor 
clf = ns.LazyRegressor(verbose=0, ignore_warnings=True, custom_metric=None, 
n_hidden_features=10)

# adjust on training and evaluate on test data
models, predictions = clf.fit(X_train, X_test, y_train, y_test)

  0%|          | 0/32 [00:00<?, ?it/s]  3%|▎         | 1/32 [00:00<00:03,  7.76it/s]  9%|▉         | 3/32 [00:00<00:02, 13.29it/s] 22%|██▏       | 7/32 [00:00<00:01, 16.00it/s] 28%|██▊       | 9/32 [00:00<00:02,  9.88it/s] 38%|███▊      | 12/32 [00:00<00:01, 13.48it/s] 50%|█████     | 16/32 [00:01<00:01, 14.66it/s] 62%|██████▎   | 20/32 [00:01<00:00, 18.59it/s] 72%|███████▏  | 23/32 [00:02<00:01,  6.32it/s] 78%|███████▊  | 25/32 [00:02<00:01,  6.57it/s] 84%|████████▍ | 27/32 [00:03<00:01,  4.74it/s] 97%|█████████▋| 31/32 [00:03<00:00,  7.31it/s]100%|██████████| 32/32 [00:03<00:00,  8.57it/s]

Models leaderboard

display(models)

4 – Explaining best model’s “decisions”

Notice the best here.

model_dictionary = clf.provide_models(X_train, X_test, y_train, y_test)
regr = model_dictionary['CustomRegressor(ExtraTreesRegressor)']
regr

CustomRegressor(n_hidden_features=10, obj=ExtraTreesRegressor(random_state=42))

In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.

CustomRegressor(n_hidden_features=10, obj=ExtraTreesRegressor(random_state=42))

ExtraTreesRegressor(random_state=42)

ExtraTreesRegressor(random_state=42)

# creating the explainer
expr0 = tr.Explainer(obj=regr)
expr0

Explainer(obj=CustomRegressor(n_hidden_features=10,
                              obj=ExtraTreesRegressor(random_state=42)))