Merge pull request #1275 from xuewanqi/dev-postgresql

Add the dataset for the diabetic readmission

examples/healthcare/data/diabetic.py

@@ -0,0 +1,78 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License.  You may obtain a copy of the License at
+#
+#   http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied.  See the License for the
+# specific language governing permissions and limitations
+# under the License.
+#
+
+from ucimlrepo import fetch_ucirepo
+from sklearn.model_selection import train_test_split
+import pandas as pd
+import numpy as np
+
+
+def load_dataset(columns_to_encode=None, flag=True):
+    """
+    Load the dataset and apply one-hot encoding to features (all columns or specific columns).
+    Targets will first be one-hot encoded and then converted to categorical integer labels.
+
+    Parameters:
+        columns_to_encode (list or None): List of column names to be one-hot encoded.
+                                          If None and `flag=True`, all columns are encoded.
+        flag (bool): Whether to apply one-hot encoding to all columns.
+                     If True, `columns_to_encode` will be ignored, and all columns will be processed.
+
+    Returns:
+        train_x, train_y, test_x, test_y (numpy.ndarray):
+            Train features, train labels, test features, and test labels in NumPy array format.
+    """
+    # Load the dataset
+    diabetes_data = fetch_ucirepo(id=296)
+
+    # Extract features and targets
+    features = diabetes_data.data.features
+    targets = diabetes_data.data.targets
+
+    # Apply one-hot encoding to features
+    if flag or columns_to_encode is None:
+        features_encoded = pd.get_dummies(features, drop_first=True)
+    else:
+        features_encoded = pd.get_dummies(features, columns=columns_to_encode, drop_first=True)
+
+    # One-hot encode targets and convert to a single categorical variable
+    targets_encoded = pd.get_dummies(targets, drop_first=False)
+    targets_categorical = targets_encoded.idxmax(axis=1)  # Get the column name with the max value (One-Hot index)
+    targets_categorical = targets_categorical.astype('category').cat.codes  # Convert to integer codes
+
+    # Convert to NumPy arrays
+    features_np = features_encoded.to_numpy(dtype=np.float32)
+    targets_np = targets_categorical.to_numpy(dtype=np.float32)
+
+    # Split the data
+    train_x, test_x, train_y, test_y = train_test_split(
+        features_np, targets_np, test_size=0.2, random_state=42
+    )
+
+    return train_x, train_y, test_x, test_y
+
+
+
+def load():
+    train_x, train_y, val_x, val_y = load_dataset()
+    train_x = train_x.astype(np.float32)
+    val_x = val_x.astype(np.float32)
+    train_y = train_y.astype(np.int32)
+    val_y = val_y.astype(np.int32)
+    return train_x, train_y, val_x, val_y