FlexTab How to use SAP/FlexTab with FlexTab:
pip install git+https://github.com/SAP-samples/flextab
# Run a classification task
from sklearn.datasets import load_breast_cancer
from sklearn.metrics import accuracy_score
from sklearn.model_selection import train_test_split
from flextab import FlexTabClassifier
# Load sample data
X, y = load_breast_cancer(return_X_y=True)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
# Initialize a classifier, 8k context and 8-fold bagging gives best performance, reduce if running out of memory
clf = FlexTabClassifier(max_context_size=8192, bagging=8)
clf.fit(X_train, y_train)
# Predict probabilities
prediction_probabilities = clf.predict_proba(X_test)
# Predict labels
predictions = clf.predict(X_test)
print("Accuracy", accuracy_score(y_test, predictions)) # Run a regression task
from sklearn.datasets import fetch_openml
from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split
from flextab import FlexTabRegressor
# Load sample data
df = fetch_openml(data_id=531, as_frame=True)
X = df.data
y = df.target.astype(float)
# Train-test split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.5, random_state=42)
# Initialize the regressor, 8k context and 8-fold bagging gives best performance, reduce if running out of memory
regressor = FlexTabRegressor(max_context_size=8192, bagging=8)
regressor.fit(X_train, y_train)
# Predict on the test set
predictions = regressor.predict(X_test)
r2 = r2_score(y_test, predictions)
print("R² Score:", r2) # Run a matching task
from sklearn.metrics import accuracy_score, roc_auc_score
from flextab import FlexTabMatcher
from flextab.utils.test_utils import load_febrl4
left_train, left_test, right_train, right_test, y_train, y_test = load_febrl4()
matcher = FlexTabMatcher(max_context_size=8192, bagging=1)
matcher.fit(left_train, right_train, y_train)
predictions = matcher.predict(left_test, right_test)
print(f'Accuracy {accuracy_score(y_test, predictions):.2%}')
# Probabilities (e.g. for thresholding or AUROC):
# probas = matcher.predict_proba_matching(left_test, right_test)
# print(f'AUROC {roc_auc_score(y_test, probas[:, 1]):.2%}') 
