From f0805ef2c43f69919b65369b24835c8900e0031a Mon Sep 17 00:00:00 2001 From: Richard Wu Date: Sat, 22 Jun 2019 01:10:50 -0400 Subject: [PATCH] Fixed some things in domain. --- domain/domain.py | 6 ++++-- domain/estimators/tuple_embedding.py | 16 ---------------- tests/start_test.sh | 2 +- 3 files changed, 5 insertions(+), 19 deletions(-) diff --git a/domain/domain.py b/domain/domain.py index 5f1fc34e5..51f926c7f 100644 --- a/domain/domain.py +++ b/domain/domain.py @@ -226,8 +226,10 @@ def generate_domain(self): # point to run inference on it since we cannot even generate # a random domain. Therefore, we just ignore it from the # final tensor. - # if len(rand_dom_values) == 0: - # continue + # We do not drop NULL cells since we stil have to repair them + # with their 1 domain value. + if init_value != NULL_REPR and len(rand_dom_values) == 0: + continue # Otherwise, just add the random domain values to the domain # and set the cell status accordingly. diff --git a/domain/estimators/tuple_embedding.py b/domain/estimators/tuple_embedding.py index 747704e0d..bb32972d3 100644 --- a/domain/estimators/tuple_embedding.py +++ b/domain/estimators/tuple_embedding.py @@ -642,18 +642,6 @@ def __init__(self, env, dataset, domain_df, max(list(map(len, numerical_attr_groups)) or [0]), self._embed_size) raise Exception() - # Convert non numerical init values in numerical attributes with _nan_. - # if self._numerical_attrs is not None: - # fil_attr = self.domain_df['attribute'].isin(self._numerical_attrs) - # fil_notnull = self.domain_df['weak_label'] != NULL_REPR - # fil_notnumeric = self.domain_df['weak_label'].str.contains(NONNUMERICS) - # bad_numerics = fil_attr & fil_notnull & fil_notnumeric - # if bad_numerics.sum(): - # self.domain_df.loc[bad_numerics, 'weak_label'] = NULL_REPR - # logging.warning('%s: replaced %d non-numerical values in DOMAIN as "%s" (NULL)', - # type(self).__name__, - # bad_numerics.sum(), - # NULL_REPR) # Remove domain for numerical attributes. fil_numattr = self.domain_df['attribute'].isin(self._numerical_attrs) @@ -739,9 +727,6 @@ def __init__(self, env, dataset, domain_df, self.in_num_w1 = torch.nn.Parameter(torch.zeros(self._n_num_attr_groups, self._embed_size, self._embed_size)) self.in_num_bias1 = torch.nn.Parameter(torch.zeros(self._n_num_attr_groups, self._embed_size)) - # out_num_zeros_vecs may not be necessary - self.out_num_zero_vecs = torch.nn.Parameter(torch.zeros(self._n_train_num_attrs, self._embed_size)) - self.out_num_bases = torch.nn.Parameter(torch.zeros(self._n_train_num_attrs, self._embed_size, self._max_num_dim)) # Non-linearity for combined_init for each numerical attr self.out_num_w1 = torch.nn.Parameter(torch.zeros(self._n_train_num_attrs, self._embed_size, self._embed_size)) @@ -783,7 +768,6 @@ def __init__(self, env, dataset, domain_df, torch.nn.init.xavier_uniform_(self.in_num_bias1) if self._n_train_num_attrs > 0: - torch.nn.init.xavier_uniform_(self.out_num_zero_vecs) torch.nn.init.xavier_uniform_(self.out_num_bases) torch.nn.init.xavier_uniform_(self.out_num_w1) torch.nn.init.xavier_uniform_(self.out_num_bias1) diff --git a/tests/start_test.sh b/tests/start_test.sh index c69227ea7..3399530cb 100755 --- a/tests/start_test.sh +++ b/tests/start_test.sh @@ -5,4 +5,4 @@ source ../set_env.sh # Launch tests. echo "Launching tests..." -pytest -n 1 +pytest