diff --git a/deepof/data.py b/deepof/data.py
index d942f710b322299e95c938de64b0c80b894591b0..27622f9ee4e08614eb46ab024e9c8a890532909f 100644
--- a/deepof/data.py
+++ b/deepof/data.py
@@ -903,6 +903,7 @@ class coordinates:
reg_cluster_variance: bool = False,
entropy_samples: int = 10000,
entropy_knn: int = 100,
+ input_type: str = False,
run: int = 0,
) -> Tuple:
"""
@@ -967,6 +968,7 @@ class coordinates:
reg_cluster_variance=reg_cluster_variance,
entropy_samples=entropy_samples,
entropy_knn=entropy_knn,
+ input_type=input_type,
run=run,
)
diff --git a/deepof/train_model.py b/deepof/train_model.py
index fab73c23b4a52796a95a0ca0e717379db1985081..ad6adbe664b78f214dc52b13929651cacffc9259 100644
--- a/deepof/train_model.py
+++ b/deepof/train_model.py
@@ -403,6 +403,7 @@ if not tune:
reg_cluster_variance=("variance" in latent_reg),
entropy_samples=entropy_samples,
entropy_knn=entropy_knn,
+ input_type=input_type,
run=run,
)
@@ -413,16 +414,17 @@ else:
run_ID, tensorboard_callback, entropy, onecycle = get_callbacks(
X_train=X_train,
- X_val=(X_val if X_val.shape != (0,) else None),
batch_size=batch_size,
- cp=False,
variational=variational,
- entropy_samples=entropy_samples,
- entropy_knn=entropy_knn,
- next_sequence_prediction=next_sequence_prediction,
phenotype_prediction=phenotype_prediction,
+ next_sequence_prediction=next_sequence_prediction,
rule_based_prediction=rule_base_prediction,
loss=loss,
+ X_val=(X_val if X_val.shape != (0,) else None),
+ input_type=input_type,
+ cp=False,
+ entropy_samples=entropy_samples,
+ entropy_knn=entropy_knn,
logparam=logparam,
outpath=output_path,
run=run,
diff --git a/deepof/train_utils.py b/deepof/train_utils.py
index c784233927582d5466977cd70c5ee14a87e22f42..4057fbf8acbdfd1ca54d389686958037031f6afc 100644
--- a/deepof/train_utils.py
+++ b/deepof/train_utils.py
@@ -74,6 +74,7 @@ def get_callbacks(
rule_based_prediction: float,
loss: str,
X_val: np.array = None,
+ input_type: str = False,
cp: bool = False,
reg_cat_clusters: bool = False,
reg_cluster_variance: bool = False,
@@ -86,8 +87,10 @@ def get_callbacks(
"""Generates callbacks for model training, including:
- run_ID: run name, with coarse parameter details;
- tensorboard_callback: for real-time visualization;
- - cp_callback: for checkpoint saving,
- - onecycle: for learning rate scheduling"""
+ - cp_callback: for checkpoint saving;
+ - onecycle: for learning rate scheduling;
+ - entropy: neighborhood entropy in the latent space;
+ """
latreg = "none"
if reg_cat_clusters and not reg_cluster_variance:
@@ -99,6 +102,7 @@ def get_callbacks(
run_ID = "{}{}{}{}{}{}{}_{}".format(
("GMVAE" if variational else "AE"),
+ ("_input_type={}".format(input_type) if input_type else "coords"),
("_NextSeqPred={}".format(next_sequence_prediction) if variational else ""),
("_PhenoPred={}".format(phenotype_prediction) if variational else ""),
("_RuleBasedPred={}".format(rule_based_prediction) if variational else ""),
@@ -293,6 +297,7 @@ def autoencoder_fitting(
reg_cluster_variance: bool,
entropy_samples: int,
entropy_knn: int,
+ input_type: str,
run: int = 0,
):
"""Implementation function for deepof.data.coordinates.deep_unsupervised_embedding"""
@@ -315,18 +320,19 @@ def autoencoder_fitting(
# Load callbacks
run_ID, *cbacks = get_callbacks(
X_train=X_train,
- X_val=(X_val if X_val.shape != (0,) else None),
batch_size=batch_size,
- cp=save_checkpoints,
variational=variational,
- next_sequence_prediction=next_sequence_prediction,
phenotype_prediction=phenotype_prediction,
+ next_sequence_prediction=next_sequence_prediction,
rule_based_prediction=rule_based_prediction,
loss=loss,
- entropy_samples=entropy_samples,
- entropy_knn=entropy_knn,
+ input_type=input_type,
+ X_val=(X_val if X_val.shape != (0,) else None),
+ cp=save_checkpoints,
reg_cat_clusters=reg_cat_clusters,
reg_cluster_variance=reg_cluster_variance,
+ entropy_samples=entropy_samples,
+ entropy_knn=entropy_knn,
logparam=logparam,
outpath=output_path,
run=run,
diff --git a/deepof_experiments.smk b/deepof_experiments.smk
index 0666e506124335db1e3840ac94edadbf7ce32189..d39d526087334461f76d02d52158f3194ac3460a 100644
--- a/deepof_experiments.smk
+++ b/deepof_experiments.smk
@@ -15,7 +15,7 @@ import os
outpath = "/psycl/g/mpsstatgen/lucas/DLC/DLC_autoencoders/DeepOF/deepof/logs/"
-losses = ["ELBO"] #, "MMD", "ELBO+MMD"]
+losses = ["ELBO"] # , "MMD", "ELBO+MMD"]
encodings = [6] # [2, 4, 6, 8, 10, 12, 14, 16]
cluster_numbers = [25] # [1, 5, 10, 15, 20, 25]
latent_reg = ["none"] # ["none", "categorical", "variance", "categorical+variance"]
diff --git a/tests/test_train_utils.py b/tests/test_train_utils.py
index 249195114cdecbefd58b153f16d012646d0864d7..abdfd7509e1ca3e98c912936ccfd54051dd613a4 100644
--- a/tests/test_train_utils.py
+++ b/tests/test_train_utils.py
@@ -60,11 +60,12 @@ def test_get_callbacks(
X_train=X_train,
batch_size=batch_size,
variational=variational,
- next_sequence_prediction=next_sequence_prediction,
phenotype_prediction=phenotype_prediction,
+ next_sequence_prediction=next_sequence_prediction,
rule_based_prediction=rule_based_prediction,
loss=loss,
X_val=X_train,
+ input_type=False,
cp=True,
reg_cat_clusters=False,
reg_cluster_variance=False,
@@ -179,11 +180,12 @@ def test_tune_search(
X_train=X_train,
batch_size=batch_size,
variational=(hypermodel == "S2SGMVAE"),
- next_sequence_prediction=next_sequence_prediction,
phenotype_prediction=phenotype_prediction,
+ next_sequence_prediction=next_sequence_prediction,
rule_based_prediction=rule_based_prediction,
loss=loss,
X_val=X_train,
+ input_type=False,
cp=False,
reg_cat_clusters=True,
reg_cluster_variance=True,