Selective: Feature Selection Library

Selective is a white-box feature selection library that supports supervised and unsupervised selection methods for classification and regression tasks.

The library provides:

• Simple to complex selection methods: Variance, Correlation, Statistical, Linear, Tree-based, or Customized.
• Text-based selection to maximize diversity in text embeddings and metadata coverage.
• Interoperable with data frames as the input.
• Automated task detection. No need to know what feature selection method works with what machine learning task.
• Benchmarking multiple selectors using cross-validation with built-in parallelization.
• Inspection of the results and feature importance.

Selective also provides optimized item selection based on diversity of text embeddings via TextWiser and coverage of binary labels via multi-objective optimization (AMAI'24, CPAIOR'21, DSO@IJCAI'22). This approach speeds-up online experimentation and boosts recommender systems significantly as presented at NVIDIA GTC'22.

Selective is developed by the Artificial Intelligence Center of Excellence at Fidelity Investments.

Quick Start

# Import Selective and SelectionMethod
from sklearn.datasets import fetch_california_housing
from feature.utils import get_data_label
from feature.selector import Selective, SelectionMethod

# Data
data, label = get_data_label(fetch_california_housing())

# Feature selectors from simple to more complex
selector = Selective(SelectionMethod.Variance(threshold=0.0))
selector = Selective(SelectionMethod.Correlation(threshold=0.5, method="pearson"))
selector = Selective(SelectionMethod.Statistical(num_features=3, method="anova"))
selector = Selective(SelectionMethod.Linear(num_features=3, regularization="none"))
selector = Selective(SelectionMethod.TreeBased(num_features=3))

# Feature reduction
subset = selector.fit_transform(data, label)
print("Reduction:", list(subset.columns))
print("Scores:", list(selector.get_absolute_scores()))

Available Methods

Method	Options
Variance per Feature	threshold
Correlation pairwise Features	Pearson Correlation Coefficient Kendall Rank Correlation Coefficient Spearman's Rank Correlation Coefficient
Statistical Analysis	ANOVA F-test Classification F-value Regression Chi-Square Mutual Information Classification Variance Inflation Factor
Linear Methods	Linear Regression Logistic Regression Lasso Regularization Ridge Regularization
Tree-based Methods	Decision Tree Random Forest Extra Trees Classifier XGBoost LightGBM AdaBoost CatBoost Gradient Boosting Tree
Text-based Methods	featurization_method = TextWiser optimization_method = ["exact", "greedy", "kmeans", "random"] cost_metric = ["unicost", "diverse"]

Benchmarking

# Imports
from sklearn.datasets import fetch_california_housing
from feature.utils import get_data_label
from xgboost import XGBClassifier, XGBRegressor
from feature.selector import SelectionMethod, benchmark, calculate_statistics

# Data
data, label = get_data_label(fetch_california_housing())

# Selectors
corr_threshold = 0.5
num_features = 3
tree_params = {"n_estimators": 50, "max_depth": 5, "random_state": 111, "n_jobs": 4}
selectors = {

  # Correlation methods
  "corr_pearson": SelectionMethod.Correlation(corr_threshold, method="pearson"),
  "corr_kendall": SelectionMethod.Correlation(corr_threshold, method="kendall"),
  "corr_spearman": SelectionMethod.Correlation(corr_threshold, method="spearman"),
  
  # Statistical methods
  "stat_anova": SelectionMethod.Statistical(num_features, method="anova"),
  "stat_chi_square": SelectionMethod.Statistical(num_features, method="chi_square"),
  "stat_mutual_info": SelectionMethod.Statistical(num_features, method="mutual_info"),
  
  # Linear methods
  "linear": SelectionMethod.Linear(num_features, regularization="none"),
  "lasso": SelectionMethod.Linear(num_features, regularization="lasso", alpha=1000),
  "ridge": SelectionMethod.Linear(num_features, regularization="ridge", alpha=1000),
  
  # Non-linear tree-based methods
  "random_forest": SelectionMethod.TreeBased(num_features),
  "xgboost_classif": SelectionMethod.TreeBased(num_features, estimator=XGBClassifier(**tree_params)),
  "xgboost_regress": SelectionMethod.TreeBased(num_features, estimator=XGBRegressor(**tree_params))
}

# Benchmark (sequential)
score_df, selected_df, runtime_df = benchmark(selectors, data, label, cv=5)
print(score_df, "\n\n", selected_df, "\n\n", runtime_df)

# Benchmark (in parallel)
score_df, selected_df, runtime_df = benchmark(selectors, data, label, cv=5, n_jobs=4)
print(score_df, "\n\n", selected_df, "\n\n", runtime_df)

# Get benchmark statistics by feature
stats_df = calculate_statistics(score_df, selected_df)
print(stats_df)

Text-based Selection

This example shows how to use text-based selection. In this scenario, we would like to select a subset of articles that is most diverse in the text embedding space and covers a range of topics.

# Import Selective and TextWiser
import pandas as pd
from feature.selector import Selective, SelectionMethod
from textwiser import TextWiser, Embedding, Transformation

# Data with the text content of each article
data = pd.DataFrame({"article_1": ["article text here"],
                     "article_2": ["article text here"],
                     "article_3": ["article text here"],
                     "article_4": ["article text here"],
                     "article_5": ["article text here"]})

# Labels to denote 0/1 coverage metadata for each article 
# across four labels, e.g., sports, international, entertainment, science    
labels = pd.DataFrame({"article_1": [1, 1, 0, 1],
                       "article_2": [0, 1, 0, 0],
                       "article_3": [0, 0, 1, 0],
                       "article_4": [0, 0, 1, 1],
                       "article_5": [1, 1, 1, 0]},
                      index=["label_1", "label_2", "label_3", "label_4"])

# TextWiser featurization method to create text embeddings
textwiser = TextWiser(Embedding.TfIdf(), Transformation.NMF(n_components=20))

# Text-based selection
# The goal is to select a subset of articles 
# that is most diverse in the text embedding space of articles
# and covers the most labels in each topic
selector = Selective(SelectionMethod.TextBased(num_features=2, featurization_method=textwiser))

# Feature reduction
subset = selector.fit_transform(data, labels)
print("Reduction:", list(subset.columns))

Visualization

import pandas as pd
from sklearn.datasets import fetch_california_housing
from feature.utils import get_data_label
from feature.selector import SelectionMethod, Selective, plot_importance

# Data
data, label = get_data_label(fetch_california_housing())

# Feature Selector
selector = Selective(SelectionMethod.Linear(num_features=8, regularization="none"))
subset = selector.fit_transform(data, label)

# Plot Feature Importance
df = pd.DataFrame(selector.get_absolute_scores(), index=data.columns)
plot_importance(df)

Installation

Selective requires Python 3.7+ and can be installed from PyPI using pip install selective.

Source

Alternatively, you can build a wheel package on your platform from scratch using the source code:

git clone https://github.com/fidelity/selective.git
cd selective
pip install setuptools wheel # if wheel is not installed
python setup.py sdist bdist_wheel
pip install dist/selective-X.X.X-py3-none-any.whl

Test your setup

cd selective
python -m unittest discover tests

Citation

If you use Selective in a publication, please cite it as:

    @article{DBLP:journals/amai/HaDVH98,
    author       = {Kad\i{}o\u{g}lu, Serdar and Kleynhans, Bernard and Wang, Xin},
    title        = {Integrating optimized item selection with active learning for continuous exploration in recommender systems},
    journal      = {Ann. Math. Artif. Intell.},
    year         = {2024},
    url          = {https://doi.org/10.1007/s10472-024-09941-x},
    doi          = {10.1007/s10472-024-09941-x},
    }
}

Support

Please submit bug reports and feature requests as Issues.

License

Selective is licensed under Apache 2.0