AutoML & Pipeline
auto_classify
Try several classifiers, return the leaderboard sorted by score.
import seraplot as sp
result = sp.auto_classify(X_train, y_train)
print(result["best_model"], result["best_score"])
for row in result["leaderboard"]:
print(row)
Default models (2.3.89+): knn, decision_tree, gradient_boosting. The previous defaults logistic and random_forest are still available via models=["logistic","random_forest"] but were dropped from defaults pending stability fixes. Customize freely with e.g. models=["knn","gradient_boosting"].
Failed/panicking models are caught: their leaderboard entry has score = NaN and is sorted last.
Pipeline
Chain transformers + estimator (sklearn-compatible API).
from seraplot import StandardScaler, RandomForestClassifier, Pipeline
pipe = Pipeline([
("scaler", StandardScaler()),
("model", RandomForestClassifier(n_estimators=200)),
])
pipe.fit(X_train, y_train)
preds = pipe.predict(X_test)
auto_classify
Essaie plusieurs classifieurs, retourne le leaderboard trié par score.
import seraplot as sp
result = sp.auto_classify(X_train, y_train)
print(result["best_model"], result["best_score"])
for row in result["leaderboard"]:
print(row)
Modèles par défaut (2.3.89+) : knn, decision_tree, gradient_boosting. Les anciens défauts logistic et random_forest restent disponibles via models=["logistic","random_forest"] mais ont été retirés en attendant un correctif de stabilité. Personnalisable librement, ex. models=["knn","gradient_boosting"].
Les modèles qui échouent/paniquent sont capturés : leur entrée du leaderboard a score = NaN et passe en dernier.
Pipeline
Enchaîne transformers + estimateur (API compatible sklearn).
from seraplot import StandardScaler, RandomForestClassifier, Pipeline
pipe = Pipeline([
("scaler", StandardScaler()),
("model", RandomForestClassifier(n_estimators=200)),
])
pipe.fit(X_train, y_train)
preds = pipe.predict(X_test)