Basic Usage

This guide demonstrates basic usage of TabNet for classification, regression, and multi-task problems. Each example is standalone and can be run independently.

Classification Example

import numpy as np
from pytorch_tabnet.tab_model import TabNetClassifier

# Generate dummy data
X_train = np.random.rand(100, 10)
y_train = np.random.randint(0, 2, 100)
X_valid = np.random.rand(20, 10)
y_valid = np.random.randint(0, 2, 20)
X_test = np.random.rand(10, 10)

clf = TabNetClassifier()
clf.fit(X_train, y_train, eval_set=[(X_valid, y_valid)])
preds = clf.predict(X_test)
print('Predictions:', preds)

Regression Example

import numpy as np
from pytorch_tabnet.tab_model import TabNetRegressor

# Generate dummy data
X_train = np.random.rand(100, 10)
y_train = np.random.rand(100).reshape(-1, 1)
X_valid = np.random.rand(20, 10)
y_valid = np.random.rand(20).reshape(-1, 1)
X_test = np.random.rand(10, 10)

reg = TabNetRegressor()
reg.fit(X_train, y_train, eval_set=[(X_valid, y_valid)])
preds = reg.predict(X_test)
print('Predictions:', preds)

Multi-task Classification Example

import numpy as np
from pytorch_tabnet.multitask import TabNetMultiTaskClassifier

# Generate dummy data
X_train = np.random.rand(100, 10)
y_train = np.random.randint(0, 2, (100, 3))  # 3 tasks
X_valid = np.random.rand(20, 10)
y_valid = np.random.randint(0, 2, (20, 3))
X_test = np.random.rand(10, 10)

clf = TabNetMultiTaskClassifier()
clf.fit(X_train, y_train, eval_set=[(X_valid, y_valid)])
preds = clf.predict(X_test)
print('Predictions:', preds)