Added notebook with empirically derived radius for latent neighborhood radius

deepof/train_utils.py

@@ -51,11 +51,11 @@ def load_treatments(train_path):
     to be loaded as metadata in the coordinates class"""
     try:
         with open(
-                os.path.join(
-                    train_path,
-                    [i for i in os.listdir(train_path) if i.endswith(".pkl")][0],
-                ),
-                "rb",
+            os.path.join(
+                train_path,
+                [i for i in os.listdir(train_path) if i.endswith(".pkl")][0],
+            ),
+            "rb",
         ) as handle:
             treatment_dict = pickle.load(handle)
     except IndexError:
@@ -65,20 +65,20 @@ def load_treatments(train_path):
 
 
 def get_callbacks(
-        X_train: np.array,
-        batch_size: int,
-        variational: bool,
-        phenotype_class: float,
-        predictor: float,
-        loss: str,
-        X_val: np.array = None,
-        cp: bool = False,
-        reg_cat_clusters: bool = False,
-        reg_cluster_variance: bool = False,
-        entropy_samples: int = 15000,
-        entropy_radius: float = None,
-        logparam: dict = None,
-        outpath: str = ".",
+    X_train: np.array,
+    batch_size: int,
+    variational: bool,
+    phenotype_class: float,
+    predictor: float,
+    loss: str,
+    X_val: np.array = None,
+    cp: bool = False,
+    reg_cat_clusters: bool = False,
+    reg_cluster_variance: bool = False,
+    entropy_samples: int = 15000,
+    entropy_radius: float = None,
+    logparam: dict = None,
+    outpath: str = ".",
 ) -> List[Union[Any]]:
     """Generates callbacks for model training, including:
     - run_ID: run name, with coarse parameter details;
@@ -117,7 +117,7 @@ def get_callbacks(
             entropy_radius
             if entropy_radius is not None
             else 0.15 * logparam["encoding"]
-                 - 0.18  # equation derived empirically to keep neighbor number constant.
+            - 0.18  # equation derived empirically to keep neighbor number constant.
             # See examples/set_default_entropy_radius.ipynb for details
         ),
         samples=entropy_samples,
@@ -200,14 +200,14 @@ def log_hyperparameters(phenotype_class: float, rec: str):
 
 # noinspection PyUnboundLocalVariable
 def tensorboard_metric_logging(
-        run_dir: str,
-        hpms: Any,
-        ae: Any,
-        X_val: np.ndarray,
-        y_val: np.ndarray,
-        phenotype_class: float,
-        predictor: float,
-        rec: str,
+    run_dir: str,
+    hpms: Any,
+    ae: Any,
+    X_val: np.ndarray,
+    y_val: np.ndarray,
+    phenotype_class: float,
+    predictor: float,
+    rec: str,
 ):
     """Autoencoder metric logging in tensorboard"""
 
@@ -249,29 +249,29 @@ def tensorboard_metric_logging(
 
 
 def autoencoder_fitting(
-        preprocessed_object: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray],
-        batch_size: int,
-        encoding_size: int,
-        epochs: int,
-        hparams: dict,
-        kl_warmup: int,
-        log_history: bool,
-        log_hparams: bool,
-        loss: str,
-        mmd_warmup: int,
-        montecarlo_kl: int,
-        n_components: int,
-        output_path: str,
-        phenotype_class: float,
-        predictor: float,
-        pretrained: str,
-        save_checkpoints: bool,
-        save_weights: bool,
-        variational: bool,
-        reg_cat_clusters: bool,
-        reg_cluster_variance: bool,
-        entropy_radius: float,
-        entropy_samples: int,
+    preprocessed_object: Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray],
+    batch_size: int,
+    encoding_size: int,
+    epochs: int,
+    hparams: dict,
+    kl_warmup: int,
+    log_history: bool,
+    log_hparams: bool,
+    loss: str,
+    mmd_warmup: int,
+    montecarlo_kl: int,
+    n_components: int,
+    output_path: str,
+    phenotype_class: float,
+    predictor: float,
+    pretrained: str,
+    save_checkpoints: bool,
+    save_weights: bool,
+    variational: bool,
+    reg_cat_clusters: bool,
+    reg_cluster_variance: bool,
+    entropy_radius: float,
+    entropy_samples: int,
 ):
     """Implementation function for deepof.data.coordinates.deep_unsupervised_embedding"""
 
@@ -316,7 +316,7 @@ def autoencoder_fitting(
         logparams, metrics = log_hyperparameters(phenotype_class, rec)
 
         with tf.summary.create_file_writer(
-                os.path.join(output_path, "hparams", run_ID)
+            os.path.join(output_path, "hparams", run_ID)
         ).as_default():
             hp.hparams_config(
                 hparams=logparams,
@@ -375,14 +375,14 @@ def autoencoder_fitting(
                 verbose=1,
                 validation_data=(X_val, X_val),
                 callbacks=cbacks
-                          + [
-                              CustomStopper(
-                                  monitor="val_loss",
-                                  patience=5,
-                                  restore_best_weights=True,
-                                  start_epoch=max(kl_warmup, mmd_warmup),
-                              ),
-                          ],
+                + [
+                    CustomStopper(
+                        monitor="val_loss",
+                        patience=5,
+                        restore_best_weights=True,
+                        start_epoch=max(kl_warmup, mmd_warmup),
+                    ),
+                ],
             )
 
             if save_weights:
@@ -459,23 +459,23 @@ def autoencoder_fitting(
 
 
 def tune_search(
-        data: List[np.array],
-        encoding_size: int,
-        hypertun_trials: int,
-        hpt_type: str,
-        hypermodel: str,
-        k: int,
-        kl_warmup_epochs: int,
-        loss: str,
-        mmd_warmup_epochs: int,
-        overlap_loss: float,
-        phenotype_class: float,
-        predictor: float,
-        project_name: str,
-        callbacks: List,
-        n_epochs: int = 30,
-        n_replicas: int = 1,
-        outpath: str = ".",
+    data: List[np.array],
+    encoding_size: int,
+    hypertun_trials: int,
+    hpt_type: str,
+    hypermodel: str,
+    k: int,
+    kl_warmup_epochs: int,
+    loss: str,
+    mmd_warmup_epochs: int,
+    overlap_loss: float,
+    phenotype_class: float,
+    predictor: float,
+    project_name: str,
+    callbacks: List,
+    n_epochs: int = 30,
+    n_replicas: int = 1,
+    outpath: str = ".",
 ) -> Union[bool, Tuple[Any, Any]]:
     """Define the search space using keras-tuner and bayesian optimization
 
@@ -514,7 +514,7 @@ def tune_search(
 
     if hypermodel == "S2SAE":  # pragma: no cover
         assert (
-                predictor == 0.0 and phenotype_class == 0.0
+            predictor == 0.0 and phenotype_class == 0.0
         ), "Prediction branches are only available for variational models. See documentation for more details"
         batch_size = 1
         hypermodel = deepof.hypermodels.SEQ_2_SEQ_AE(input_shape=X_train.shape)