caloinn loader v1

params/calo_inn.yaml

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+run_name: calo_inn
+
+#Dataset
+loader_module: calo_inn
+loader_params: 
+  geant_file: path_to_file.hdf5
+  generated_file: path_to_file.hdf5
+  add_log_energy: True
+  add_log_layer_ens: True
+  add_logit_step: False
+
+# Model
+activation: leaky_relu
+negative_slope: 0.1
+dropout: 0.1
+layers: 5
+hidden_size: 256
+
+# Training
+bayesian: False
+lr: 1.e-3
+betas: [0.9, 0.99]
+weight_decay: 0.0
+epochs: 50
+batch_size: 1024
+lr_scheduler: reduce_on_plateau
+lr_decay_factor: 0.1
+lr_patience: 5
+checkpoint_interval: 5
+
+# Evaluation
+#bayesian_samples: 2
+#lower_cluster_thresholds: [0.01, 0.1]
+#upper_cluster_thresholds: [0.9, 0.99]

src/loaders/calo_inn.py

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+import pandas as pd
+import numpy as np
+from types import SimpleNamespace
+
+from ..dataset import DiscriminatorData
+from ..observable import Observable
+
+def load(params: dict) -> list[DiscriminatorData]:
+    """
+    dataloader for calo data
+    parameters:
+
+    args:
+
+    return:
+
+    """
+    datasets = []
+    preproc_kwargs = {
+            "add_log_energy": params.get("add_log_energy", False),
+            "add_log_layer_ens": params.get("add_log_layer_ens", False),
+            "add_logit_step": params.get("add_logit_step", False),
+            }
+    datasets_list = [
+            {'level': 'low', 'normalize': True, 'label': 'Norm.', 'suffix': 'norm'},
+            {'level': 'low', 'normalize': False, 'label': 'Unnorm.', 'suffix': 'unnorm'},
+            {'level': 'high', 'normalize': False, 'label': 'High', 'suffix': 'high'},
+            ] 
+
+    for dataset in datasets_list:
+        if dataset['level'] == 'low':
+            geant_sample = create_data(params['geant_file'], dataset)
+            gen_sample = create_data(params['generated_file'], dataset)
+        elif dataset['level'] == 'high':
+            geant_sample = create_data_high(params['geant_file'], dataset)
+            gen_sample = create_data_high(params['generated_file'], dataset)
+        else:
+            raise ValueError('Classifier preprocessing running at unknown level.')
+
+        train_true, test_true, val_true = split_data(
+            geant_sample,
+            params["train_split"],
+            params["test_split"]
+        )
+        train_fake, test_fake, val_fake = split_data(
+            generated_sample,
+            params["train_split"],
+            params["test_split"]
+        )
+
+        datasets.append(DiscriminatorData(
+                label = dataset['label'],
+                suffix = dataset['suffix'],
+                dim = geant_sample.shape[-1],
+                train_true = train_true,
+                train_fake = train_fake,
+                test_true = test_true,
+                test_fake = test_fake,
+                val_true = val_true,
+                val_fake = val_fake,
+                observables = [],
+            )
+        )
+    return datasets
+
+#def create_data(data_path):
+
+def split_data(
+    data: np.ndarray,
+    train_split: float,
+    test_split: float
+) -> tuple[np.ndarray, ...]:
+    n_train = int(train_split * len(data))
+    n_test = int(test_split * len(data))
+    train_data = data[:n_train]
+    test_data = data[-n_test:]
+    val_data = data[n_train:-n_test]
+    return train_data, test_data, val_data
+
+