Ecosystem Compatibility

skeval implements the full scikit-learn estimator interface (fit, predict, score, get_params, set_params), which means it works out-of-the-box with every library that consumes sklearn-compatible estimators.

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scikit-learn

SentenceClassifier passes sklearn.utils.estimator_checks.check_estimator and works directly with all sklearn model-selection utilities.

GridSearchCV

from sklearn.model_selection import GridSearchCV
from skeval.classifier import SentenceClassifier

param_grid = {"embed_dim": [32, 64], "epochs": [20, 40], "lr": [0.005, 0.01]}
search = GridSearchCV(SentenceClassifier(random_state=42), param_grid, cv=2)
search.fit(sentences, labels)
print(search.best_params_)

cross_val_score

from sklearn.model_selection import cross_val_score
from skeval.classifier import SentenceClassifier

scores = cross_val_score(
    SentenceClassifier(embed_dim=64, epochs=30, random_state=42),
    sentences, labels, cv=3, scoring="accuracy",
)
print(scores.mean())

See also Usage Guide for a full training example.

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skore

skore is an open-source ML experiment tracker that integrates with any sklearn-compatible estimator.

pip install skore

import skore
from sklearn.model_selection import cross_val_score
from skeval.classifier import SentenceClassifier

project = skore.open("my_project", overwrite=True)

clf = SentenceClassifier(embed_dim=64, epochs=40, random_state=42)
scores = cross_val_score(clf, sentences, labels, cv=2)

project.put("cv_accuracy", scores.mean())
project.put("params", clf.get_params())

Launch the interactive UI to compare runs:

skore launch my_project

A complete working script is in examples/07_skore.py.

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Optuna

Optuna is a hyperparameter optimisation framework that works with any Python callable.

pip install optuna

import optuna
from sklearn.model_selection import cross_val_score
from skeval.classifier import SentenceClassifier

def objective(trial):
    clf = SentenceClassifier(
        embed_dim=trial.suggest_categorical("embed_dim", [32, 64, 128]),
        epochs=trial.suggest_int("epochs", 10, 60, step=10),
        lr=trial.suggest_float("lr", 1e-3, 1e-1, log=True),
        random_state=42,
    )
    return cross_val_score(clf, sentences, labels, cv=2).mean()

study = optuna.create_study(direction="maximize")
study.optimize(objective, n_trials=20)
print(study.best_params)

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Ray Tune

Ray Tune runs distributed hyperparameter searches and integrates with sklearn via its sklearn wrappers.

pip install "ray[tune]"

from ray import tune
from sklearn.model_selection import cross_val_score
from skeval.classifier import SentenceClassifier

def train_fn(config):
    clf = SentenceClassifier(**config, random_state=42)
    score = cross_val_score(clf, sentences, labels, cv=2).mean()
    tune.report({"accuracy": score})

tuner = tune.Tuner(
    train_fn,
    param_space={
        "embed_dim": tune.choice([32, 64, 128]),
        "epochs": tune.choice([20, 40]),
        "lr": tune.loguniform(1e-3, 1e-1),
    },
)
results = tuner.fit()
print(results.get_best_result().config)

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MLflow

MLflow tracks experiments, parameters, and metrics.

pip install mlflow

import mlflow
from sklearn.model_selection import cross_val_score
from skeval.classifier import SentenceClassifier

with mlflow.start_run():
    params = {"embed_dim": 64, "epochs": 40, "lr": 0.01}
    mlflow.log_params(params)

    clf = SentenceClassifier(**params, random_state=42)
    scores = cross_val_score(clf, sentences, labels, cv=2)

    mlflow.log_metric("cv_accuracy_mean", scores.mean())
    mlflow.log_metric("cv_accuracy_std", scores.std())

    clf.fit(sentences, labels)
    mlflow.sklearn.log_model(clf, "skeval_model")

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Weights & Biases

W&B provides experiment tracking, visualisation, and model registry.

pip install wandb

import wandb
from sklearn.model_selection import cross_val_score
from skeval.classifier import SentenceClassifier

wandb.init(project="skeval-runs")

config = wandb.config
config.embed_dim = 64
config.epochs = 40
config.lr = 0.01

clf = SentenceClassifier(
    embed_dim=config.embed_dim,
    epochs=config.epochs,
    lr=config.lr,
    random_state=42,
)
scores = cross_val_score(clf, sentences, labels, cv=2)
wandb.log({"cv_accuracy": scores.mean()})

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BentoML

BentoML packages trained models into production-ready APIs.

pip install bentoml

import bentoml
from skeval.classifier import SentenceClassifier

clf = SentenceClassifier(embed_dim=64, epochs=40, random_state=42)
clf.fit(sentences, labels)

# Save the model to the BentoML model store
bento_model = bentoml.picklable_model.save_model("skeval_classifier", clf)
print(f"Saved: {bento_model}")

# Load it back for serving
loaded = bentoml.picklable_model.load_model("skeval_classifier:latest")
print(loaded.predict(["Water boils at 100 degrees Celsius"]))

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LIME

LIME explains individual predictions by perturbing the input. It requires predict_proba(), which SentenceClassifier provides as of v0.2.0.

pip install lime

from lime.lime_text import LimeTextExplainer
from skeval.classifier import SentenceClassifier

clf = SentenceClassifier(embed_dim=64, epochs=40, random_state=42)
clf.fit(sentences, labels)

explainer = LimeTextExplainer(class_names=clf.label_encoder.idx2label.values())
explanation = explainer.explain_instance(
    "Water boils at 100 degrees Celsius",
    clf.predict_proba,
    num_features=5,
)
explanation.show_in_notebook()

Note

predict_proba() is available from v0.2.0 onwards (issue #41).

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SHAP

SHAP computes Shapley values to explain model output globally and locally.

pip install shap

import shap
import numpy as np
from skeval.classifier import SentenceClassifier

clf = SentenceClassifier(embed_dim=64, epochs=40, random_state=42)
clf.fit(sentences, labels)

# KernelExplainer works with any predict_proba function
background = shap.sample(np.array(sentences), 10)
explainer = shap.KernelExplainer(clf.predict_proba, background)
shap_values = explainer.shap_values(np.array(test_sentences))
shap.summary_plot(shap_values, np.array(test_sentences))

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Summary table

Library	Use case	Works today	Requires
scikit-learn	GridSearchCV, cross_val_score, Pipeline	Yes	pip install skeval
skore	Experiment tracking & UI	Yes	pip install skore
Optuna	Bayesian hyperparameter search	Yes	pip install optuna
Ray Tune	Distributed hyperparameter search	Yes	pip install ray[tune]
MLflow	Experiment tracking & model registry	Yes	pip install mlflow
Weights & Biases	Experiment tracking & visualisation	Yes	pip install wandb
BentoML	Model serving & packaging	Yes	pip install bentoml
LIME	Local prediction explanations	Yes (v0.2.0+)	pip install lime
SHAP	Shapley value explanations	Yes (v0.2.0+)	pip install shap