Variable selection: an introduction

If you want to try the examples in this tutorial:

Ce chapitre utilise toujours le même jeu de données, présenté dans l’introduction de cette partie : les données de vote aux élections présidentielles américaines croisées à des variables sociodémographiques. Le code est disponible sur Github.

!pip install --upgrade xlrd #colab bug verson xlrd
!pip install geopandas

import requests

url = 'https://raw.githubusercontent.com/linogaliana/python-datascientist/main/content/modelisation/get_data.py'
r = requests.get(url, allow_redirects=True)
open('getdata.py', 'wb').write(r.content)

import getdata
votes = getdata.create_votes_dataframes()

So far, we have assumed that the variables useful for predicting the Republican vote were known to the modeler. Thus, we have only used a limited portion of the available variables in our data. However, beyond the computational burden of building a model with a large number of variables, choosing a limited number of variables (a parsimonious model) reduces the risk of overfitting.

How, then, can we choose the right number of variables and the best combination of these variables? There are multiple methods, including:

• Relying on statistical performance criteria that penalize non-parsimonious models. For example, BIC.
• Backward elimination techniques.
• Building models where the statistic of interest penalizes the lack of parsimony (which is what we aim to do here).

In this chapter, we will present the main challenges of variable selection through LASSO.

We will subsequently use the following functions or packages:

import numpy as np
from sklearn.svm import LinearSVC
from sklearn.feature_selection import SelectFromModel
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.impute import SimpleImputer
from sklearn.linear_model import Lasso
import sklearn.metrics
from sklearn.linear_model import LinearRegression
import matplotlib.cm as cm
import matplotlib.pyplot as plt
from sklearn.linear_model import lasso_path
import seaborn as sns

1 The Principle of LASSO

1.1 General Principle

The class of feature selection models is very broad and includes a diverse range of models. We will focus on LASSO (Least Absolute Shrinkage and Selection Operator), which is an extension of linear regression aimed at selecting sparse models. This type of model is central to the field of Compressed sensing (where the term L1-regularization is more commonly used than LASSO). LASSO is a special case of elastic-net regressions, with another famous case being ridge regression. Unlike classical linear regression, these methods also work in a framework where \(p>N\), i.e., where the number of predictors is much larger than the number of observations.

1.2 Regularization

By adopting the principle of a penalized objective function, LASSO allows certain coefficients to be set to 0. Variables with non-zero norms thus pass the selection test.

Tip

LASSO is a constrained optimization problem. It seeks to find the estimator \(\beta\) that minimizes the quadratic error (linear regression) under an additional constraint regularizing the parameters: \[ \min_{\beta} \frac{1}{2}\mathbb{E}\bigg( \big( X\beta - y \big)^2 \bigg) \\ \text{s.t. } \sum_{j=1}^p |\beta_j| \leq t \]

This program is reformulated using the Lagrangian, allowing for a more tractable minimization program:

\[ \beta^{\text{LASSO}} = \arg \min_{\beta} \frac{1}{2}\mathbb{E}\bigg( \big( X\beta - y \big)^2 \bigg) + \alpha \sum_{j=1}^p |\beta_j| = \arg \min_{\beta} ||y-X\beta||_{2}^{2} + \lambda ||\beta||_1 \]

where \(\lambda\) is a reformulation of the previous regularization term, depending on \(\alpha\). The strength of the penalty applied to non-parsimonious models depends on this parameter.

1.3 First LASSO Regression

As we aim to find the best predictors of the Republican vote, we will remove variables that can be directly derived from these: the competitors’ scores!

import pandas as pd

df2 = pd.DataFrame(votes.drop(columns='geometry'))
df2 = df2.loc[
  :,
  ~df2.columns.str.endswith(
    ('_democrat','_green','_other', 'winner', 'per_point_diff', 'per_dem')
    )
  ]


df2 = df2.loc[:,~df2.columns.duplicated()]

In this exercise, we will also use a function to extract the variables selected by LASSO, here it is:

from sklearn.linear_model import Lasso
from sklearn.pipeline import Pipeline

def extract_features_selected(lasso: Pipeline, preprocessing_step_name: str = 'preprocess') -> pd.Series:
    """
    Extracts selected features based on the coefficients obtained from Lasso regression.

    Parameters:
    - lasso (Pipeline): The scikit-learn pipeline containing a trained Lasso regression model.
    - preprocessing_step_name (str): The name of the preprocessing step in the pipeline. Default is 'preprocess'.

    Returns:
    - pd.Series: A Pandas Series containing selected features with non-zero coefficients.
    """
    # Check if lasso object is provided
    if not isinstance(lasso, Pipeline):
        raise ValueError("The provided lasso object is not a scikit-learn pipeline.")

    # Extract the final transformer from the pipeline
    lasso_model = lasso[-1]

    # Check if lasso_model is a Lasso regression model
    if not isinstance(lasso_model, Lasso):
        raise ValueError("The final step of the pipeline is not a Lasso regression model.")

    # Check if lasso model has 'coef_' attribute
    if not hasattr(lasso_model, 'coef_'):
        raise ValueError("The provided Lasso regression model does not have 'coef_' attribute. "
                         "Make sure it is a trained Lasso regression model.")

    # Get feature names from the preprocessing step
    features_preprocessing = lasso[preprocessing_step_name].get_feature_names_out()

    # Extract selected features based on non-zero coefficients
    features_selec = pd.Series(features_preprocessing[np.abs(lasso_model.coef_) > 0])

    return features_selec

Exercise 1: First LASSO

We are still trying to predict the variable per_gop. Before making our estimation, we will create certain intermediate objects to define our pipeline:

1. In our DataFrame, replace infinite values with NaN.

2. Create a training sample and a test sample.

Now we can move on to defining our pipeline. This example might serve as inspiration, as well as this one.

3. First, create the preprocessing steps for our model. For this, it is common to separate the steps applied to continuous numerical variables from those applied to categorical variables.

• For numerical variables, impute with the mean and then standardize;
• For categorical variables, linear regression techniques require using one-hot encoding. Before performing one-hot encoding, impute with the most frequent value.

4. Finalize the pipeline by adding the estimation step and then estimate a LASSO model penalized with \(\alpha = 0.1\).

Assuming your pipeline is stored in an object named pipeline and the last step is named model, you can directly access this step using the object pipeline['model'].

5. Display the coefficient values. Which variables have a non-zero value?
6. Show that the selected variables are sometimes highly correlated.
7. Compare the performance of this parsimonious model with that of a model with more variables.

Help for Question 1

# Replace infinities with NaN
df2.replace([np.inf, -np.inf], np.nan, inplace=True)

Help for Question 3

The pipeline definition follows this structure:

numeric_pipeline = Pipeline(steps=[
    ('impute', # define the imputation method here
     ),
    ('scale', # define the standardization method here
    )
])

categorical_pipeline = # adapt the template

# Define numerical_features and categorical_features beforehand
preprocessor = ColumnTransformer(transformers=[
    ('number', numeric_pipeline, numerical_features),
    ('category', categorical_pipeline, categorical_features)
])

The preprocessing pipeline (question 3) takes the following form:

ColumnTransformer(transformers=[('number',
                                 Pipeline(steps=[('impute', SimpleImputer()),
                                                 ('scale', StandardScaler())]),
                                 ['ALAND', 'AWATER', 'votes_gop', 'votes_dem',
                                  'total_votes', 'diff', 'FIPS_y',
                                  'Rural_Urban_Continuum_Code_2013',
                                  'Rural_Urban_Continuum_Code_2023',
                                  'Urban_Influence_2013',
                                  'Economic_typology_2015', 'CENSUS_2020_POP',
                                  'ESTIMATES_BASE_2020', 'POP_EST...
                                  'DEATHS_2020', 'DEATHS_2021', 'DEATHS_2022',
                                  'DEATHS_2023', 'NATURAL_CHG_2020', ...]),
                                ('category',
                                 Pipeline(steps=[('impute',
                                                  SimpleImputer(strategy='most_frequent')),
                                                 ('one-hot',
                                                  OneHotEncoder(handle_unknown='ignore',
                                                                sparse_output=False))]),
                                 ['STATEFP', 'COUNTYFP', 'COUNTYNS', 'AFFGEOID',
                                  'GEOID', 'NAME', 'LSAD', 'FIPS_x',
                                  'state_name', 'county_fips', 'county_name',
                                  'State', 'Area_Name', 'FIPS'])])

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.

/opt/conda/lib/python3.12/site-packages/sklearn/impute/_base.py:598: UserWarning:

Skipping features without any observed values: ['POV04_2021' 'CI90LB04_2021' 'CI90UB04_2021' 'PCTPOV04_2021'
 'CI90LB04P_2021' 'CI90UB04P_2021']. At least one non-missing value is needed for imputation with strategy='mean'.

Pipeline(steps=[('preprocess',
                 ColumnTransformer(transformers=[('number',
                                                  Pipeline(steps=[('impute',
                                                                   SimpleImputer()),
                                                                  ('scale',
                                                                   StandardScaler())]),
                                                  ['ALAND', 'AWATER',
                                                   'votes_gop', 'votes_dem',
                                                   'total_votes', 'diff',
                                                   'FIPS_y',
                                                   'Rural_Urban_Continuum_Code_2013',
                                                   'Rural_Urban_Continuum_Code_2023',
                                                   'Urban_Influence_2013',
                                                   'Economic_typology_2015',
                                                   'CENSUS_2020_POP',...
                                                   'NATURAL_CHG_2020', ...]),
                                                 ('category',
                                                  Pipeline(steps=[('impute',
                                                                   SimpleImputer(strategy='most_frequent')),
                                                                  ('one-hot',
                                                                   OneHotEncoder(handle_unknown='ignore',
                                                                                 sparse_output=False))]),
                                                  ['STATEFP', 'COUNTYFP',
                                                   'COUNTYNS', 'AFFGEOID',
                                                   'GEOID', 'NAME', 'LSAD',
                                                   'FIPS_x', 'state_name',
                                                   'county_fips', 'county_name',
                                                   'State', 'Area_Name',
                                                   'FIPS'])])),
                ('model', Lasso(alpha=0.1))])

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.

The pipeline takes the following form once finalized (question 4):

Pipeline(steps=[('preprocess',
                 ColumnTransformer(transformers=[('number',
                                                  Pipeline(steps=[('impute',
                                                                   SimpleImputer()),
                                                                  ('scale',
                                                                   StandardScaler())]),
                                                  ['ALAND', 'AWATER',
                                                   'votes_gop', 'votes_dem',
                                                   'total_votes', 'diff',
                                                   'FIPS_y',
                                                   'Rural_Urban_Continuum_Code_2013',
                                                   'Rural_Urban_Continuum_Code_2023',
                                                   'Urban_Influence_2013',
                                                   'Economic_typology_2015',
                                                   'CENSUS_2020_POP',...
                                                   'NATURAL_CHG_2020', ...]),
                                                 ('category',
                                                  Pipeline(steps=[('impute',
                                                                   SimpleImputer(strategy='most_frequent')),
                                                                  ('one-hot',
                                                                   OneHotEncoder(handle_unknown='ignore',
                                                                                 sparse_output=False))]),
                                                  ['STATEFP', 'COUNTYFP',
                                                   'COUNTYNS', 'AFFGEOID',
                                                   'GEOID', 'NAME', 'LSAD',
                                                   'FIPS_x', 'state_name',
                                                   'county_fips', 'county_name',
                                                   'State', 'Area_Name',
                                                   'FIPS'])])),
                ('model', Lasso(alpha=0.1))])

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At the end of question 5, the selected variables are:

The model is quite parsimonious as it uses a subset of our initial variables (especially since our categorical variables have been split into numerous variables through one hot encoding).

0                                                 ALAND
1                                                FIPS_y
2                       Rural_Urban_Continuum_Code_2023
3                                        N_POP_CHG_2020
4                                INTERNATIONAL_MIG_2023
5                                     DOMESTIC_MIG_2023
6                                         RESIDUAL_2020
7                                         RESIDUAL_2021
8                       2023 Rural-urban Continuum Code
9     Percent of adults with a bachelor's degree or ...
10    Percent of adults with a high school diploma o...
11    Percent of adults with a bachelor's degree or ...
12    Percent of adults with less than a high school...
13    Percent of adults with a bachelor's degree or ...
14                                           Metro_2013
15                               Unemployment_rate_2000
16                               Unemployment_rate_2002
17                               Unemployment_rate_2003
18                               Unemployment_rate_2012
19                               Unemployment_rate_2014
20                      Rural-urban_Continuum_Code_2003
21                      Rural-urban_Continuum_Code_2013
22                                      CI90LB017P_2021
23                                      CI90LB517P_2021
24                       candidatevotes_2016_republican
25                                share_2012_republican
26                                share_2016_republican
dtype: object

Some variables make sense, such as education-related variables. Notably, one of the best predictors for the Republican score in 2020 is… the Republican score (and mechanically the Democratic score) in 2016 and 2012.

Additionally, redundant variables are being selected. A more thorough data cleaning phase would actually be necessary.

The parsimonious model is (slightly) more performant:

	parcimonieux	non parcimonieux
RMSE	2.699583	2.491548
R2	0.972809	0.976838
Nombre de paramètres	27.000000	256.000000

Moreover, it can already be noted that regressing the 2020 score on the 2016 score results in very good explanatory performance, suggesting that voting behaves like an autoregressive process:

import statsmodels.api as sm
import statsmodels.formula.api as smf
smf.ols("per_gop ~ share_2016_republican", data = df2).fit().summary()

OLS Regression Results

Dep. Variable:	per_gop	R-squared:	0.968
Model:	OLS	Adj. R-squared:	0.968
Method:	Least Squares	F-statistic:	9.292e+04
Date:	Mon, 26 May 2025	Prob (F-statistic):	0.00
Time:	18:22:51	Log-Likelihood:	6603.5
No. Observations:	3107	AIC:	-1.320e+04
Df Residuals:	3105	BIC:	-1.319e+04
Df Model:	1
Covariance Type:	nonrobust

	coef	std err	t	P>|t|	[0.025	0.975]
Intercept	0.0109	0.002	5.056	0.000	0.007	0.015
share_2016_republican	1.0101	0.003	304.835	0.000	1.004	1.017

Omnibus:	2045.232	Durbin-Watson:	1.982
Prob(Omnibus):	0.000	Jarque-Bera (JB):	51553.266
Skew:	2.731	Prob(JB):	0.00
Kurtosis:	22.193	Cond. No.	9.00

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

2 Role of the Penalty \(\alpha\) in Variable Selection

So far, we have taken the hyperparameter \(\alpha\) as given. What role does it play in the conclusions of our modeling? To investigate this, we can explore the effect of its value on the number of variables passing the selection step.

For the next exercise, we will consider exclusively quantitative variables to speed up the computations. Indeed, with non-parsimonious models, the multiple categories of our categorical variables make the optimization problem difficult to handle.

from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler

df2.replace([np.inf, -np.inf], np.nan, inplace=True)
X_train, X_test, y_train, y_test = train_test_split(
    df2.drop(["per_gop"], axis = 1),
    100*df2[['per_gop']], test_size=0.2, random_state=0
)

numerical_features = X_train.select_dtypes(include='number').columns.tolist()
categorical_features = X_train.select_dtypes(exclude='number').columns.tolist()

numeric_pipeline = Pipeline(steps=[
    ('impute', SimpleImputer(strategy='mean')),
    ('scale', StandardScaler())
])
preprocessed_features = pd.DataFrame(
  numeric_pipeline.fit_transform(
    X_train.drop(columns = categorical_features)
  )
)

/opt/conda/lib/python3.12/site-packages/sklearn/impute/_base.py:598: UserWarning:

Skipping features without any observed values: ['POV04_2021' 'CI90LB04_2021' 'CI90UB04_2021' 'PCTPOV04_2021'
 'CI90LB04P_2021' 'CI90UB04P_2021']. At least one non-missing value is needed for imputation with strategy='mean'.

Exercise 2: Role of the Penalty Parameter

Use the lasso_path function to evaluate the number of parameters selected by LASSO as \(\alpha\) varies (explore \(\alpha \in [0.001,0.01,0.02,0.025,0.05,0.1,0.25,0.5,0.8,1.0]\)).

The relationship you should obtain between \(\alpha\) and the number of parameters is as follows:

We see that the higher \(\alpha\) is, the fewer variables the model selects.

3 Cross-Validation to Select the Model

Which \(\alpha\) should be preferred? For this, cross-validation should be performed to choose the model for which the variables passing the selection phase best predict the Republican outcome.

from sklearn.linear_model import LassoCV

my_alphas = np.array([0.001,0.01,0.02,0.025,0.05,0.1,0.25,0.5,0.8,1.0])

lcv = (
  LassoCV(
    alphas=my_alphas,
    fit_intercept=False,
    random_state=0,
    cv=5
    ).fit(
      preprocessed_features, y_train
    )
)

The “best” \(\alpha\) can be retrieved as follows:

print("alpha optimal :", lcv.alpha_)

alpha optimal : 1.0

This can be used to run a new pipeline:

from sklearn.compose import make_column_transformer, ColumnTransformer
from sklearn.preprocessing import OneHotEncoder, StandardScaler

numeric_pipeline = Pipeline(steps=[
    ('impute', SimpleImputer(strategy='mean')),
    ('scale', StandardScaler())
])

categorical_pipeline = Pipeline(steps=[
    ('impute', SimpleImputer(strategy='most_frequent')),
    ('one-hot', OneHotEncoder(handle_unknown='ignore', sparse_output=False))
])

preprocessor = ColumnTransformer(transformers=[
    ('number', numeric_pipeline, numerical_features),
    ('category', categorical_pipeline, categorical_features)
])

model = Lasso(
  fit_intercept=False, 
  alpha = lcv.alpha_
)  

lasso_pipeline = Pipeline(steps=[
    ('preprocess', preprocessor),
    ('model', model)
])

lasso_optimal = lasso_pipeline.fit(X_train,y_train)

features_selec2 = extract_features_selected(lasso_optimal)

Les variables sélectionnées sont :

0                                          R_BIRTH_2021
1                                          R_DEATH_2023
2     Percent of adults completing some college or a...
3     Percent of adults with a bachelor's degree or ...
4     Percent of adults with a bachelor's degree or ...
5     Percent of adults with a high school diploma o...
6                                        CI90LBINC_2021
7                        candidatevotes_2016_republican
8                                 share_2008_republican
9                                 share_2012_republican
10                                share_2016_republican
11                                           STATEFP_22
12                                              LSAD_06
dtype: object

Cela correspond à un modèle avec 13 variables sélectionnées.

Tip

If the model appears to be insufficiently parsimonious, it would be necessary to revisit the variable definition phase to determine whether different scales for some variables might be more appropriate (e.g., using the log).

Informations additionnelles

environment files have been tested on.

Latest built version: 2025-05-26

Python version used:

'3.12.6 | packaged by conda-forge | (main, Sep 30 2024, 18:08:52) [GCC 13.3.0]'

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annotated-types	0.7.0
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patsy	0.5.6
Pebble	5.1.1
pexpect	4.9.0
pickleshare	0.7.5
pillow	10.4.0
pip	24.2
platformdirs	4.3.6
plotly	5.24.1
plotnine	0.13.6
pluggy	1.5.0
polars	1.8.2
preshed	3.0.10
prometheus_client	0.21.0
prometheus_flask_exporter	0.23.1
prompt_toolkit	3.0.48
protobuf	4.25.3
psutil	6.0.0
ptyprocess	0.7.0
pure_eval	0.2.3
py7zr	0.22.0
pyarrow	17.0.0
pyarrow-hotfix	0.6
pyasn1	0.6.1
pyasn1_modules	0.4.1
pybcj	1.0.6
pycosat	0.6.6
pycparser	2.22
pycryptodomex	3.23.0
pydantic	2.11.5
pydantic_core	2.33.2
pydantic-settings	2.9.1
Pygments	2.18.0
PyNaCl	1.5.0
pynsee	0.1.8
pyogrio	0.10.0
pyOpenSSL	24.2.1
pyparsing	3.1.4
pyppmd	1.1.1
pyproj	3.7.0
pyshp	2.3.1
PySocks	1.7.1
python-dateutil	2.9.0
python-dotenv	1.0.1
python-magic	0.4.27
pytz	2024.1
pyu2f	0.1.5
pywaffle	1.1.1
PyYAML	6.0.2
pyzmq	26.2.0
pyzstd	0.16.2
querystring_parser	1.2.4
rasterio	1.4.3
referencing	0.36.2
regex	2024.9.11
requests	2.32.3
requests-cache	1.2.1
requests-toolbelt	1.0.0
retrying	1.3.4
rich	14.0.0
rpds-py	0.25.1
rsa	4.9
rtree	1.4.0
ruamel.yaml	0.18.6
ruamel.yaml.clib	0.2.8
s3fs	2024.9.0
s3transfer	0.10.2
scikit-image	0.24.0
scikit-learn	1.5.2
scipy	1.13.0
seaborn	0.13.2
selenium	4.33.0
setuptools	74.1.2
shapely	2.0.6
shellingham	1.5.4
six	1.16.0
smart-open	7.1.0
smmap	5.0.0
sniffio	1.3.1
sortedcontainers	2.4.0
soupsieve	2.5
spacy	3.8.4
spacy-legacy	3.0.12
spacy-loggers	1.0.5
SQLAlchemy	2.0.35
sqlparse	0.5.1
srsly	2.5.1
stack-data	0.6.2
statsmodels	0.14.4
tabulate	0.9.0
tblib	3.0.0
tenacity	9.0.0
texttable	1.7.0
thinc	8.3.6
threadpoolctl	3.5.0
tifffile	2025.5.24
toolz	1.0.0
topojson	1.9
tornado	6.4.1
tqdm	4.67.1
traitlets	5.14.3
trio	0.30.0
trio-websocket	0.12.2
truststore	0.9.2
typer	0.16.0
typing_extensions	4.13.2
typing-inspect	0.9.0
typing-inspection	0.4.1
tzdata	2024.2
Unidecode	1.4.0
url-normalize	2.2.1
urllib3	2.4.0
uv	0.7.8
wasabi	1.1.3
wcwidth	0.2.13
weasel	0.4.1
webdriver-manager	4.0.2
websocket-client	1.8.0
Werkzeug	3.0.4
wheel	0.44.0
wordcloud	1.9.3
wrapt	1.16.0
wsproto	1.2.0
xgboost	2.1.1
xlrd	2.0.1
xyzservices	2024.9.0
yarl	1.13.1
yellowbrick	1.5
zict	3.0.0
zipp	3.20.2
zstandard	0.23.0

View file history

md`Ce fichier a été modifié __${table_commit.length}__ fois depuis sa création le ${creation_string} (dernière modification le ${last_modification_string})`

creation = d3.min(
  table_commit.map(d => new Date(d.Date))
)

last_modification = d3.max(
  table_commit.map(d => new Date(d.Date))
)

creation_string = creation.toLocaleString("fr", {
  "day": "numeric",
  "month": "long",
  "year": "numeric"
})

last_modification_string = last_modification.toLocaleString("fr", {
  "day": "numeric",
  "month": "long",
  "year": "numeric"
})

html`<div>${git_history_table}</div>`

html`<div>${git_history_plot}</div>`

SHA	Date	Author	Description
48dccf14	2025-01-14 21:45:34	lgaliana	Fix bug in modeling section
8c8ca4c0	2024-12-20 10:45:00	lgaliana	Traduction du chapitre clustering
a5ecaedc	2024-12-20 09:36:42	Lino Galiana	Traduction du chapitre modélisation (#582)
5ff770b5	2024-12-04 10:07:34	lgaliana	Partie ML plus esthétique
d2422572	2024-08-22 18:51:51	Lino Galiana	At this point, notebooks should now all be functional ! (#547)
c641de05	2024-08-22 11:37:13	Lino Galiana	A series of fix for notebooks that were bugging (#545)
0908656f	2024-08-20 16:30:39	Lino Galiana	English sidebar (#542)
06d003a1	2024-04-23 10:09:22	Lino Galiana	Continue la restructuration des sous-parties (#492)
8c316d0a	2024-04-05 19:00:59	Lino Galiana	Fix cartiflette deprecated snippets (#487)
005d89b8	2023-12-20 17:23:04	Lino Galiana	Finalise l’affichage des statistiques Git (#478)
3437373a	2023-12-16 20:11:06	Lino Galiana	Améliore l’exercice sur le LASSO (#473)
7d12af8b	2023-12-05 10:30:08	linogaliana	Modularise la partie import pour l’avoir partout
417fb669	2023-12-04 18:49:21	Lino Galiana	Corrections partie ML (#468)
0b405bc2	2023-11-27 20:58:37	Lino Galiana	Update box lasso
a06a2689	2023-11-23 18:23:28	Antoine Palazzolo	2ème relectures chapitres ML (#457)
889a71ba	2023-11-10 11:40:51	Antoine Palazzolo	Modification TP 3 (#443)
9a4e2267	2023-08-28 17:11:52	Lino Galiana	Action to check URL still exist (#399)
a8f90c2f	2023-08-28 09:26:12	Lino Galiana	Update featured paths (#396)
3bdf3b06	2023-08-25 11:23:02	Lino Galiana	Simplification de la structure 🤓 (#393)
78ea2cbd	2023-07-20 20:27:31	Lino Galiana	Change titles levels (#381)
29ff3f58	2023-07-07 14:17:53	linogaliana	description everywhere
f21a24d3	2023-07-02 10:58:15	Lino Galiana	Pipeline Quarto & Pages 🚀 (#365)
e12187b2	2023-06-12 10:31:40	Lino Galiana	Feature selection deprecated functions (#363)
f5ad0210	2022-11-15 17:40:16	Lino Galiana	Relec clustering et lasso (#322)
f10815b5	2022-08-25 16:00:03	Lino Galiana	Notebooks should now look more beautiful (#260)
494a85ae	2022-08-05 14:49:56	Lino Galiana	Images featured ✨ (#252)
d201e3cd	2022-08-03 15:50:34	Lino Galiana	Pimp la homepage ✨ (#249)
12965bac	2022-05-25 15:53:27	Lino Galiana	:launch: Bascule vers quarto (#226)
9c71d6e7	2022-03-08 10:34:26	Lino Galiana	Plus d’éléments sur S3 (#218)
70587527	2022-03-04 15:35:17	Lino Galiana	Relecture Word2Vec (#216)
c3bf4d42	2021-12-06 19:43:26	Lino Galiana	Finalise debug partie ML (#190)
fb14d406	2021-12-06 17:00:52	Lino Galiana	Modifie l’import du script (#187)
37ecfa3c	2021-12-06 14:48:05	Lino Galiana	Essaye nom différent (#186)
2c8fd0dd	2021-12-06 13:06:36	Lino Galiana	Problème d’exécution du script import data ML (#185)
5d0a5e38	2021-12-04 07:41:43	Lino Galiana	MAJ URL script recup data (#184)
5c104904	2021-12-03 17:44:08	Lino Galiana	Relec @antuki partie modelisation (#183)
2a8809fb	2021-10-27 12:05:34	Lino Galiana	Simplification des hooks pour gagner en flexibilité et clarté (#166)
2e4d5862	2021-09-02 12:03:39	Lino Galiana	Simplify badges generation (#130)
4cdb759c	2021-05-12 10:37:23	Lino Galiana	:sparkles: :star2: Nouveau thème hugo :snake: :fire: (#105)
7f9f97bc	2021-04-30 21:44:04	Lino Galiana	🐳 + 🐍 New workflow (docker 🐳) and new dataset for modelization (2020 🇺🇸 elections) (#99)
8fea62ed	2020-11-13 11:58:17	Lino Galiana	Correction de quelques typos partie ML (#85)
347f50f3	2020-11-12 15:08:18	Lino Galiana	Suite de la partie machine learning (#78)

git_history_table = Inputs.table(
  table_commit,
  {
    format: {
      SHA: x => md`[${x}](${github_repo}/commit/${x})`,
      Description: x => md`${replacePullRequestPattern(x, github_repo)}`,
      /*Date: x => x.toLocaleString("fr", {
        "month": "numeric",
        "day": "numeric",
        "year": "numeric"
        })
      */
    }
  }
)

git_history_plot = Plot.plot({
  marks: [
    Plot.ruleY([0], {stroke: "royalblue"}),
    Plot.dot(
          table_commit,
          Plot.pointerX({x: (d) => new Date(d.date), y: 0, stroke: "red"})),
    Plot.dot(table_commit, {x: (d) => new Date(d.Date), y: 0, fill: "royalblue"})
  ]
})

function replacePullRequestPattern(inputString, githubRepo) {
    // Use a regular expression to match the pattern #digit
    var pattern = /#(\d+)/g;

    // Replace the pattern with ${github_repo}/pull/#digit
    var replacedString = inputString.replace(pattern, '[#$1](' + githubRepo + '/pull/$1)');

    return replacedString;
}

github_repo = "https://github.com/linogaliana/python-datascientist"

table_commit = {

// Get the HTML table by its class name
var table = document.querySelector('.commit-table');

// Check if the table exists
if (table) {
    // Initialize an array to store the table data
    var dataArray = [];

    // Extract headers from the first row
    var headers = [];
    for (var i = 0; i < table.rows[0].cells.length; i++) {
        headers.push(table.rows[0].cells[i].textContent.trim());
    }

    // Iterate through the rows, starting from the second row
    for (var i = 1; i < table.rows.length; i++) {
        var row = table.rows[i];
        var rowData = {};

        // Iterate through the cells in the row
        for (var j = 0; j < row.cells.length; j++) {
            // Use headers as keys and cell content as values
            rowData[headers[j]] = row.cells[j].textContent.trim();
        }

        // Push the rowData object to the dataArray
        dataArray.push(rowData);
    }
  }

  return dataArray

}

// Get the element with class 'git-details'
{
  var gitDetails = document.querySelector('.commit-table');

  // Check if the element exists
  if (gitDetails) {
      // Hide the element
      gitDetails.style.display = 'none';
  }
}

Plot = require('@observablehq/plot@0.6.12/dist/plot.umd.min.js')