Makes it easy to run cross-validations on small datasets

[tensorflow]

Makes it easy to run cross-validations on small datasets

[Kareem-negm]

Hi @achoum
I found this, I think it describes what you are saying
Hope it will be useful to you
Here is a verbose example of 10-cross validation of TF-DF using sklearn.

from sklearn.model_selection import KFold
import numpy as np

accuraties_per_fold = [] # Test accuracy on the individual folds.

# Run a 10-folds cross-validation.
for  fold_idx, (train_indices, test_indices) in enumerate(KFold(n_splits=10, shuffle=True).split(all_df)):

 print(f"Running fold {fold_idx+1}")

 # Extract the training and testing examples.
 sub_train_df = all_df.iloc[train_indices]
 sub_test_df = all_df.iloc[test_indices]

 # Convert the examples into tensorflow datasets.
 sub_train_ds = tfdf.keras.pd_dataframe_to_tf_dataset(sub_train_df, label="income")
 sub_test_df = tfdf.keras.pd_dataframe_to_tf_dataset(sub_test_df, label="income")

 # Train the model.
 model = tfdf.keras.GradientBoostedTreesModel()
 model.fit(sub_train_ds, verbose=False)

 # Evaluate the model.
 model.compile(metrics=["accuracy"])
 evaluation = model.evaluate(sub_test_df, return_dict=True, verbose=False)
 print(f"Evaluation {evaluation}")

 accuraties_per_fold.append(evaluation["accuracy"])

print(f"Cross-validated accuracy: {np.mean(accuraties_per_fold)}")

---

Output:

Evaluation {'loss': 0.0, 'accuracy': 0.8780701756477356}
Running fold 2
Evaluation {'loss': 0.0, 'accuracy': 0.8833333253860474}
Running fold 3
Evaluation {'loss': 0.0, 'accuracy': 0.8841597437858582}
Running fold 4
Evaluation {'loss': 0.0, 'accuracy': 0.8692408800125122}
Running fold 5
Evaluation {'loss': 0.0, 'accuracy': 0.8679245114326477}
Running fold 6
Evaluation {'loss': 0.0, 'accuracy': 0.8639754056930542}
Running fold 7
Evaluation {'loss': 0.0, 'accuracy': 0.8745063543319702}
Running fold 8
Evaluation {'loss': 0.0, 'accuracy': 0.8679245114326477}
Running fold 9
Evaluation {'loss': 0.0, 'accuracy': 0.8609039187431335}
Running fold 10
Evaluation {'loss': 0.0, 'accuracy': 0.8613426685333252}
Cross-validated accuracy: 0.8711381494998932  

Credit: @mathieu