Commit 1a28329d authored by lucas_miranda's avatar lucas_miranda
Browse files

Added latent regularization control to deepof.data.coordinates.deep_unsupervised_embedding()

parent 27daa823
Pipeline #93783 passed with stage
in 42 minutes and 11 seconds
...@@ -299,7 +299,7 @@ class project: ...@@ -299,7 +299,7 @@ class project:
for k, value in tab_dict.items(): for k, value in tab_dict.items():
imputed = IterativeImputer( imputed = IterativeImputer(
max_iter=250, skip_complete=True max_iter=1000, skip_complete=True
).fit_transform(value) ).fit_transform(value)
tab_dict[k] = pd.DataFrame( tab_dict[k] = pd.DataFrame(
imputed, index=value.index, columns=value.columns imputed, index=value.index, columns=value.columns
......
...@@ -89,13 +89,13 @@ def load_treatments(train_path): ...@@ -89,13 +89,13 @@ def load_treatments(train_path):
def get_callbacks( def get_callbacks(
X_train: np.array, X_train: np.array,
batch_size: int, batch_size: int,
cp: bool,
variational: bool, variational: bool,
phenotype_class: float, phenotype_class: float,
predictor: float, predictor: float,
loss: str, loss: str,
reg_cat_clusters: bool, cp: bool = False,
reg_cluster_variance: bool, reg_cat_clusters: bool = False,
reg_cluster_variance: bool = False,
logparam: dict = None, logparam: dict = None,
outpath: str = ".", outpath: str = ".",
) -> List[Union[Any]]: ) -> List[Union[Any]]:
......
...@@ -77,13 +77,13 @@ def test_get_callbacks( ...@@ -77,13 +77,13 @@ def test_get_callbacks(
runID, tbc, cycle1c, cpc = deepof.train_utils.get_callbacks( runID, tbc, cycle1c, cpc = deepof.train_utils.get_callbacks(
X_train, X_train,
batch_size, batch_size,
True,
variational, variational,
pheno_class, pheno_class,
predictor, predictor,
loss, loss,
True, True,
True, True,
True,
None, None,
) )
assert type(runID) == str assert type(runID) == str
...@@ -177,11 +177,11 @@ def test_tune_search( ...@@ -177,11 +177,11 @@ def test_tune_search(
deepof.train_utils.get_callbacks( deepof.train_utils.get_callbacks(
X_train, X_train,
batch_size, batch_size,
False,
hypermodel == "S2SGMVAE", hypermodel == "S2SGMVAE",
0, 0,
predictor, predictor,
loss, loss,
False,
True, True,
True, True,
None, None,
......
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