conformal-tights 0.2.2

A scikit-learn meta-estimator for computing tight conformal predictions

👖 Conformal Tights

A scikit-learn meta-estimator that adds conformal prediction of coherent quantiles and intervals to any scikit-learn regressor. Features:

1. 🍬 Meta-estimator: add prediction of quantiles and intervals to any scikit-learn regressor
2. 🌡️ Conformally calibrated: accurate quantiles, and intervals with reliable coverage
3. 🚦 Coherent quantiles: quantiles increase monotonically instead of crossing each other
4. 👖 Tight quantiles: selects the lowest dispersion that provides the desired coverage
5. 🎁 Data efficient: requires only a small number of calibration examples to fit
6. 🐼 Pandas support: optionally predict on DataFrames and receive DataFrame output

Using

Installing

First, install this package with:

pip install conformal-tights

Predicting quantiles

Conformal Tights exposes a meta-estimator called ConformalCoherentQuantileRegressor that you can use to wrap any scikit-learn regressor, after which you can use predict_quantiles to predict conformally calibrated quantiles. Example usage:

from conformal_tights import ConformalCoherentQuantileRegressor
from sklearn.datasets import fetch_openml
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor

# Fetch dataset and split in train and test
X, y = fetch_openml("ames_housing", version=1, return_X_y=True, as_frame=True, parser="auto")
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)

# Create a regressor, wrap it, and fit on the train set
my_regressor = XGBRegressor(objective="reg:absoluteerror")
conformal_predictor = ConformalCoherentQuantileRegressor(estimator=my_regressor)
conformal_predictor.fit(X_train, y_train)

# Predict with the wrapped regressor
ŷ_test = conformal_predictor.predict(X_test)

# Predict quantiles with the conformal wrapper
ŷ_test_quantiles = conformal_predictor.predict_quantiles(X_test, quantiles=(0.025, 0.05, 0.1, 0.9, 0.95, 0.975))

When the input data is a pandas DataFrame, the output is also a pandas DataFrame. For example, printing the head of ŷ_test_quantiles yields:

house_id	0.025	0.05	0.1	0.9	0.95	0.975
1357	114784	120894	131618	175761	188052	205449
2367	67417	80074	86754	117854	127583	142322
2822	119423	132048	138725	178526	197246	214206
2126	94031	99850	110891	150249	164703	182528
1544	68996	81516	88232	121774	132425	147110

Let's visualize the predicted quantiles on the test set:

Expand to see the code that generated the graph above

import matplotlib.pyplot as plt
import matplotlib.ticker as ticker

%config InlineBackend.figure_format = "retina"
plt.rcParams["font.size"] = 8
idx = (-ŷ_test.sample(50, random_state=42)).sort_values().index
y_ticks = list(range(1, len(idx) + 1))
plt.figure(figsize=(4, 5))
for j in range(3):
    end = ŷ_test_quantiles.shape[1] - 1 - j
    coverage = round(100 * (ŷ_test_quantiles.columns[end] - ŷ_test_quantiles.columns[j]))
    plt.barh(
        y_ticks,
        ŷ_test_quantiles.loc[idx].iloc[:, end] - ŷ_test_quantiles.loc[idx].iloc[:, j],
        left=ŷ_test_quantiles.loc[idx].iloc[:, j],
        label=f"{coverage}% Prediction interval",
        color=["#b3d9ff", "#86bfff", "#4da6ff"][j],
    )
plt.plot(y_test.loc[idx], y_ticks, "s", markersize=3, markerfacecolor="none", markeredgecolor="#e74c3c", label="Actual value")
plt.plot(ŷ_test.loc[idx], y_ticks, "s", color="blue", markersize=0.6, label="Predicted value")
plt.xlabel("House price")
plt.ylabel("Test house index")
plt.yticks(y_ticks, y_ticks)
plt.tick_params(axis="y", labelsize=6)
plt.grid(axis="x", color="lightsteelblue", linestyle=":", linewidth=0.5)
plt.gca().xaxis.set_major_formatter(ticker.StrMethodFormatter("${x:,.0f}"))
plt.gca().spines["top"].set_visible(False)
plt.gca().spines["right"].set_visible(False)
plt.legend()
plt.tight_layout()
plt.show()

Predicting intervals

In addition to quantile prediction, you can use predict_interval to predict conformally calibrated prediction intervals. Compared to quantiles, these focus on reliable coverage over quantile accuracy. Example usage:

# Predict an interval for each example with the conformal wrapper
ŷ_test_interval = conformal_predictor.predict_interval(X_test, coverage=0.95)

# Measure the coverage of the prediction intervals on the test set
coverage = ((ŷ_test_interval.iloc[:, 0] <= y_test) & (y_test <= ŷ_test_interval.iloc[:, 1])).mean()
print(coverage)  # 96.6%

When the input data is a pandas DataFrame, the output is also a pandas DataFrame. For example, printing the head of ŷ_test_interval yields:

house_id	0.025	0.975
1357	107203	206290
2367	66665	146005
2822	115592	220315
2126	85288	183038
1544	67890	150646

Contributing

Prerequisites

1. Set up Git to use SSH

1. Generate an SSH key and add the SSH key to your GitHub account.
2. Configure SSH to automatically load your SSH keys:

   cat << EOF >> ~/.ssh/config
   Host *
     AddKeysToAgent yes
     IgnoreUnknown UseKeychain
     UseKeychain yes
   EOF
   

2. Install Docker

1. Install Docker Desktop.
   • Enable Use Docker Compose V2 in Docker Desktop's preferences window.
   • Linux only:
     • Export your user's user id and group id so that files created in the Dev Container are owned by your user:

       cat << EOF >> ~/.bashrc
       export UID=$(id --user)
       export GID=$(id --group)
       EOF
       

3. Install VS Code or PyCharm

1. Install VS Code and VS Code's Dev Containers extension. Alternatively, install PyCharm.
2. Optional: install a Nerd Font such as FiraCode Nerd Font and configure VS Code or configure PyCharm to use it.

Development environments

The following development environments are supported:

1. ⭐️ GitHub Codespaces: click on Code and select Create codespace to start a Dev Container with GitHub Codespaces.
2. ⭐️ Dev Container (with container volume): click on Open in Dev Containers to clone this repository in a container volume and create a Dev Container with VS Code.
3. Dev Container: clone this repository, open it with VS Code, and run Ctrl/⌘ + ⇧ + P → Dev Containers: Reopen in Container.
4. PyCharm: clone this repository, open it with PyCharm, and configure Docker Compose as a remote interpreter with the dev service.
5. Terminal: clone this repository, open it with your terminal, and run docker compose up --detach dev to start a Dev Container in the background, and then run docker compose exec dev zsh to open a shell prompt in the Dev Container.

Developing

• This project follows the Conventional Commits standard to automate Semantic Versioning and Keep A Changelog with Commitizen.
• Run poe from within the development environment to print a list of Poe the Poet tasks available to run on this project.
• Run poetry add {package} from within the development environment to install a run time dependency and add it to pyproject.toml and poetry.lock. Add --group test or --group dev to install a CI or development dependency, respectively.
• Run poetry update from within the development environment to upgrade all dependencies to the latest versions allowed by pyproject.toml.
• Run cz bump to bump the package's version, update the CHANGELOG.md, and create a git tag.