Loading deepchem/datasets/__init__.py +11 −1 Original line number Diff line number Diff line Loading @@ -324,6 +324,10 @@ class Dataset(object): shard_perm = np.arange(num_shards) for i in range(num_shards): X, y, w, ids = self.get_shard(shard_perm[i]) ############################################################ DEBUG print("X.shape, y.shape, w.shape, ids.shape") print(X.shape, y.shape, w.shape, ids.shape) ############################################################ DEBUG n_samples = X.shape[0] if not deterministic: sample_perm = np.random.permutation(n_samples) Loading @@ -338,7 +342,13 @@ class Dataset(object): for j in range(len(interval_points)-1): indices = range(interval_points[j], interval_points[j+1]) perm_indices = sample_perm[indices] X_batch = X[perm_indices, :] ############################################################# DEBUG #print("len(indices)") #print(len(indices)) #print("perm_indices") #print(perm_indices) ############################################################# DEBUG X_batch = X[perm_indices] y_batch = y[perm_indices] w_batch = w[perm_indices] ids_batch = ids[perm_indices] Loading deepchem/splits/__init__.py +3 −2 Original line number Diff line number Diff line Loading @@ -99,7 +99,7 @@ class Splitter(object): return train_dataset, valid_dataset, test_dataset def train_test_split(self, samples, train_dir, test_dir, seed=None, frac_train=.8): frac_train=.8, compute_feature_statistics=True): """ Splits self into train/test sets. Returns Dataset objects. Loading @@ -107,7 +107,8 @@ class Splitter(object): valid_dir = tempfile.mkdtemp() train_samples, _, test_samples = self.train_valid_test_split( samples, train_dir, valid_dir, test_dir, frac_train=frac_train, frac_test=1-frac_train, frac_valid=0.) frac_train=frac_train, frac_test=1-frac_train, frac_valid=0., compute_feature_statistics=compute_feature_statistics) return train_samples, test_samples def split(self, dataset, frac_train=None, frac_valid=None, frac_test=None, Loading Loading
deepchem/datasets/__init__.py +11 −1 Original line number Diff line number Diff line Loading @@ -324,6 +324,10 @@ class Dataset(object): shard_perm = np.arange(num_shards) for i in range(num_shards): X, y, w, ids = self.get_shard(shard_perm[i]) ############################################################ DEBUG print("X.shape, y.shape, w.shape, ids.shape") print(X.shape, y.shape, w.shape, ids.shape) ############################################################ DEBUG n_samples = X.shape[0] if not deterministic: sample_perm = np.random.permutation(n_samples) Loading @@ -338,7 +342,13 @@ class Dataset(object): for j in range(len(interval_points)-1): indices = range(interval_points[j], interval_points[j+1]) perm_indices = sample_perm[indices] X_batch = X[perm_indices, :] ############################################################# DEBUG #print("len(indices)") #print(len(indices)) #print("perm_indices") #print(perm_indices) ############################################################# DEBUG X_batch = X[perm_indices] y_batch = y[perm_indices] w_batch = w[perm_indices] ids_batch = ids[perm_indices] Loading
deepchem/splits/__init__.py +3 −2 Original line number Diff line number Diff line Loading @@ -99,7 +99,7 @@ class Splitter(object): return train_dataset, valid_dataset, test_dataset def train_test_split(self, samples, train_dir, test_dir, seed=None, frac_train=.8): frac_train=.8, compute_feature_statistics=True): """ Splits self into train/test sets. Returns Dataset objects. Loading @@ -107,7 +107,8 @@ class Splitter(object): valid_dir = tempfile.mkdtemp() train_samples, _, test_samples = self.train_valid_test_split( samples, train_dir, valid_dir, test_dir, frac_train=frac_train, frac_test=1-frac_train, frac_valid=0.) frac_train=frac_train, frac_test=1-frac_train, frac_valid=0., compute_feature_statistics=compute_feature_statistics) return train_samples, test_samples def split(self, dataset, frac_train=None, frac_valid=None, frac_test=None, Loading
