Commit c9e3b7f6 authored by lucas_miranda's avatar lucas_miranda
Browse files

Removed outdated non-variational autoencoder model

parent a6d9fa9d
......@@ -434,7 +434,6 @@ else:
encoding_size=encoding_size,
hypertun_trials=hypertun_trials,
hpt_type=tune,
hypermodel=hyp,
k=k,
kl_warmup_epochs=kl_wu,
loss=loss,
......
......@@ -18,26 +18,13 @@ import tensorflow as tf
tf.config.experimental_run_functions_eagerly(True)
@settings(deadline=None, max_examples=10)
@given(
input_shape=st.tuples(
st.integers(min_value=100, max_value=1000),
st.integers(min_value=5, max_value=15),
st.integers(min_value=5, max_value=15),
)
)
def test_SEQ_2_SEQ_AE_hypermodel_build(input_shape):
deepof.hypermodels.SEQ_2_SEQ_AE(input_shape=input_shape).build(hp=HyperParameters())
@settings(deadline=None, max_examples=10)
@given(
encoding_size=st.integers(min_value=2, max_value=16),
loss=st.one_of(st.just("ELBO"), st.just("MMD"), st.just("ELBO+MMD")),
number_of_components=st.integers(min_value=1, max_value=5),
)
def test_SEQ_2_SEQ_GMVAE_hypermodel_build(
def test_GMVAE_hypermodel_build(
encoding_size,
loss,
number_of_components,
......
......@@ -17,19 +17,6 @@ import tensorflow as tf
tf.config.experimental_run_functions_eagerly(True)
@settings(deadline=None, max_examples=10)
@given(
input_shape=st.tuples(
st.integers(min_value=100, max_value=1000),
st.integers(min_value=5, max_value=15),
st.integers(min_value=5, max_value=15),
)
)
def test_SEQ_2_SEQ_AE_build(input_shape):
deepof.models.SEQ_2_SEQ_AE().build(input_shape=input_shape)
@settings(deadline=None, max_examples=10)
@given(
loss=st.one_of(st.just("ELBO"), st.just("MMD"), st.just("ELBO+MMD")),
......@@ -38,7 +25,7 @@ def test_SEQ_2_SEQ_AE_build(input_shape):
montecarlo_kl=st.integers(min_value=1, max_value=10),
number_of_components=st.integers(min_value=1, max_value=5),
)
def test_SEQ_2_SEQ_GMVAE_build(
def test_GMVAE_build(
loss,
kl_warmup_epochs,
mmd_warmup_epochs,
......
......@@ -294,6 +294,5 @@ def test_neighbor_latent_entropy():
k=10,
encoding_dim=6,
validation_data=X,
variational=True,
),
)
......@@ -45,12 +45,10 @@ def test_load_treatments():
next_sequence_prediction=st.floats(min_value=0.0, max_value=1.0),
phenotype_prediction=st.floats(min_value=0.0, max_value=1.0),
rule_based_prediction=st.floats(min_value=0.0, max_value=1.0),
variational=st.booleans(),
)
def test_get_callbacks(
X_train,
batch_size,
variational,
next_sequence_prediction,
phenotype_prediction,
rule_based_prediction,
......@@ -59,7 +57,6 @@ def test_get_callbacks(
callbacks = deepof.train_utils.get_callbacks(
X_train=X_train,
batch_size=batch_size,
variational=variational,
phenotype_prediction=phenotype_prediction,
next_sequence_prediction=next_sequence_prediction,
rule_based_prediction=rule_based_prediction,
......@@ -89,14 +86,12 @@ def test_get_callbacks(
next_sequence_prediction=st.one_of(st.just(0.0), st.just(1.0)),
phenotype_prediction=st.one_of(st.just(0.0), st.just(1.0)),
rule_based_prediction=st.one_of(st.just(0.0), st.just(1.0)),
variational=st.one_of(st.just(True), st.just(False)),
)
def test_autoencoder_fitting(
loss,
next_sequence_prediction,
phenotype_prediction,
rule_based_prediction,
variational,
):
X_train = np.random.uniform(-1, 1, [20, 5, 6])
y_train = np.round(np.random.uniform(0, 1, [20, 1]))
......@@ -131,7 +126,6 @@ def test_autoencoder_fitting(
next_sequence_prediction=next_sequence_prediction,
phenotype_prediction=phenotype_prediction,
rule_based_prediction=rule_based_prediction,
variational=variational,
entropy_samples=10,
entropy_knn=5,
)
......@@ -179,7 +173,6 @@ def test_tune_search(
deepof.train_utils.get_callbacks(
X_train=X_train,
batch_size=batch_size,
variational=(hypermodel == "S2SGMVAE"),
phenotype_prediction=phenotype_prediction,
next_sequence_prediction=next_sequence_prediction,
rule_based_prediction=rule_based_prediction,
......@@ -202,7 +195,6 @@ def test_tune_search(
encoding_size=encoding_size,
hpt_type=hpt_type,
hypertun_trials=1,
hypermodel=hypermodel,
k=k,
kl_warmup_epochs=0,
loss=loss,
......
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