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TabPFN: Add ignore_pretraining_limits to classifiers and regressors #1847

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Merged
bgruening merged 4 commits into from
Apr 22, 2026
Merged

TabPFN: Add ignore_pretraining_limits to classifiers and regressors #1847

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5b0023e
Add ignore_pretraining_limits to classifiers and regressors
anuprulez Apr 21, 2026
4be1bb4
Merge branch 'bgruening:master' into patch-12
anuprulez Apr 22, 2026
56bb815
toggle pretraining limits based on number of samples
Apr 22, 2026
bab7c7e
Merge branch 'bgruening:master' into patch-12
anuprulez Apr 22, 2026
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12 changes: 10 additions & 2 deletions tools/tabpfn/main.py
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Original file line number Diff line number Diff line change
Expand Up @@ -84,6 +84,8 @@ def train_evaluate(args):
"""
Train TabPFN and predict
"""
MAX_IGNORE_PRETRAINING_LIMITS_SAMPLES = 1000
SEED = 42
# prepare train data
tr_features, tr_labels = separate_features_labels(args["train_data"], args["train_header"])
# prepare test data
Expand All @@ -94,7 +96,10 @@ def train_evaluate(args):
te_labels = []
s_time = time.time()
if args["selected_task"] == "Classification":
classifier = TabPFNClassifier(random_state=42, model_path=args["model_path"])
if tr_features.shape[0] <= MAX_IGNORE_PRETRAINING_LIMITS_SAMPLES:
classifier = TabPFNClassifier(random_state=SEED, model_path=args["model_path"])
else:
classifier = TabPFNClassifier(random_state=SEED, model_path=args["model_path"], ignore_pretraining_limits=True)
classifier.fit(tr_features, tr_labels)
y_eval = classifier.predict(te_features)
pred_probas_test = classifier.predict_proba(te_features)
Expand All @@ -105,7 +110,10 @@ def train_evaluate(args):
"output_predicted_data", sep="\t", index=None
)
else:
regressor = TabPFNRegressor(random_state=42, model_path=args["model_path"])
if tr_features.shape[0] <= MAX_IGNORE_PRETRAINING_LIMITS_SAMPLES:
regressor = TabPFNRegressor(random_state=SEED, model_path=args["model_path"])
else:
regressor = TabPFNRegressor(random_state=SEED, model_path=args["model_path"], ignore_pretraining_limits=True)
regressor.fit(tr_features, tr_labels)
y_eval = regressor.predict(te_features)
if len(te_labels) > 0:
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