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.
# 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()))| 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"] |
# 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)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))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)Selective requires Python 3.7+ and can be installed from PyPI using pip install selective.
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.whlcd selective
python -m unittest discover tests
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},
}
}Please submit bug reports and feature requests as Issues.
Selective is licensed under the GNU GPL 3.0.