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continued integration of NCES2 and ROCES
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@Jean-KOUAGOU
Jean-KOUAGOU committed Dec 10, 2024
1 parent 4c9a9d2 commit 7c81717
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Showing 2 changed files with 107 additions and 87 deletions.
15 changes: 8 additions & 7 deletions ontolearn/concept_learner.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1271,15 +1271,16 @@ def fit_one(self, pos: Union[Set[OWLNamedIndividual], Set[str]], neg: Union[Set[
neg_str = neg
else:
raise ValueError(f"Invalid input type, was expecting OWLNamedIndividual or str but found {type(pos[0])}")
Pos = np.random.choice(pos_str, size=(self.num_predictions, len(pos_str)), replace=True)
Neg = np.random.choice(neg_str, size=(self.num_predictions, len(neg_str)), replace=True)

assert self.load_pretrained and self.m, \
"No pretrained model found. Please first train NCES2"

dataset = NCES2DatasetInference([("", Pos_str, Neg_str) for (Pos_str, Neg_str) in zip(Pos, Neg)],
self.instance_embeddings,
self.vocab, self.inv_vocab, False, self.sorted_examples)

#data, triples_data, k, vocab, inv_vocab, num_examples, sampling_strategy='p', num_pred_per_lp=1, random_sample=False
dataset = ROCESDatasetInference([("", pos_str, neg_str)],
self.triples_data,
self.vocab, self.inv_vocab,
self.num_examples,
sampling_strategy="nces2",
num_pred_per_lp=self.num_predictions)

dataloader = DataLoader(dataset, batch_size=self.batch_size,
num_workers=self.num_workers,
Expand Down
179 changes: 99 additions & 80 deletions ontolearn/data_struct.py
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Original file line number Diff line number Diff line change
Expand Up @@ -147,6 +147,81 @@ def clear(self):
self.current_states.clear()
self.next_states.clear()
self.rewards.clear()


class CLIPDataset(torch.utils.data.Dataset): # pragma: no cover

def __init__(self, data: list, embeddings, shuffle_examples, example_sizes: list=None,
k=5, sorted_examples=True):
super().__init__()
self.data = data
self.embeddings = embeddings
self.shuffle_examples = shuffle_examples
self.example_sizes = example_sizes
self.k = k
self.sorted_examples = sorted_examples

def __len__(self):
return len(self.data)

def __getitem__(self, idx):
key, value = self.data[idx]
pos = value['positive examples']
neg = value['negative examples']
length = value['length']
if self.example_sizes is not None:
k_pos, k_neg = random.choice(self.example_sizes)
k_pos = min(k_pos, len(pos))
k_neg = min(k_neg, len(neg))
selected_pos = random.sample(pos, k_pos)
selected_neg = random.sample(neg, k_neg)
elif self.k is not None:
prob_pos_set = 1.0/(1+np.array(range(min(self.k, len(pos)), len(pos)+1, self.k)))
prob_pos_set = prob_pos_set/prob_pos_set.sum()
prob_neg_set = 1.0/(1+np.array(range(min(self.k, len(neg)), len(neg)+1, self.k)))
prob_neg_set = prob_neg_set/prob_neg_set.sum()
k_pos = np.random.choice(range(min(self.k, len(pos)), len(pos)+1, self.k), replace=False, p=prob_pos_set)
k_neg = np.random.choice(range(min(self.k, len(neg)), len(neg)+1, self.k), replace=False, p=prob_neg_set)
selected_pos = random.sample(pos, k_pos)
selected_neg = random.sample(neg, k_neg)
else:
selected_pos = pos
selected_neg = neg
if self.shuffle_examples:
random.shuffle(selected_pos)
random.shuffle(selected_neg)

datapoint_pos = torch.FloatTensor(self.embeddings.loc[selected_pos].values.squeeze())
datapoint_neg = torch.FloatTensor(self.embeddings.loc[selected_neg].values.squeeze())

return datapoint_pos, datapoint_neg, torch.LongTensor([length])


class CLIPDatasetInference(torch.utils.data.Dataset): # pragma: no cover

def __init__(self, data: list, embeddings, shuffle_examples,
sorted_examples=True):
super().__init__()
self.data = data
self.embeddings = embeddings
self.shuffle_examples = shuffle_examples
self.sorted_examples = sorted_examples

def __len__(self):
return len(self.data)

def __getitem__(self, idx):
_, pos, neg = self.data[idx]
if self.sorted_examples:
pos, neg = sorted(pos), sorted(neg)
elif self.shuffle_examples:
random.shuffle(pos)
random.shuffle(neg)

datapoint_pos = torch.FloatTensor(self.embeddings.loc[pos].values.squeeze())
datapoint_neg = torch.FloatTensor(self.embeddings.loc[pos].values.squeeze())

return datapoint_pos, datapoint_neg


class NCESBaseDataset: # pragma: no cover
Expand Down Expand Up @@ -256,85 +331,10 @@ def __getitem__(self, idx):
datapoint_neg = torch.FloatTensor(self.embeddings.loc[neg].values.squeeze())

return datapoint_pos, datapoint_neg


class CLIPDataset(torch.utils.data.Dataset): # pragma: no cover

def __init__(self, data: list, embeddings, shuffle_examples, example_sizes: list=None,
k=5, sorted_examples=True):
super().__init__()
self.data = data
self.embeddings = embeddings
self.shuffle_examples = shuffle_examples
self.example_sizes = example_sizes
self.k = k
self.sorted_examples = sorted_examples

def __len__(self):
return len(self.data)

def __getitem__(self, idx):
key, value = self.data[idx]
pos = value['positive examples']
neg = value['negative examples']
length = value['length']
if self.example_sizes is not None:
k_pos, k_neg = random.choice(self.example_sizes)
k_pos = min(k_pos, len(pos))
k_neg = min(k_neg, len(neg))
selected_pos = random.sample(pos, k_pos)
selected_neg = random.sample(neg, k_neg)
elif self.k is not None:
prob_pos_set = 1.0/(1+np.array(range(min(self.k, len(pos)), len(pos)+1, self.k)))
prob_pos_set = prob_pos_set/prob_pos_set.sum()
prob_neg_set = 1.0/(1+np.array(range(min(self.k, len(neg)), len(neg)+1, self.k)))
prob_neg_set = prob_neg_set/prob_neg_set.sum()
k_pos = np.random.choice(range(min(self.k, len(pos)), len(pos)+1, self.k), replace=False, p=prob_pos_set)
k_neg = np.random.choice(range(min(self.k, len(neg)), len(neg)+1, self.k), replace=False, p=prob_neg_set)
selected_pos = random.sample(pos, k_pos)
selected_neg = random.sample(neg, k_neg)
else:
selected_pos = pos
selected_neg = neg
if self.shuffle_examples:
random.shuffle(selected_pos)
random.shuffle(selected_neg)

datapoint_pos = torch.FloatTensor(self.embeddings.loc[selected_pos].values.squeeze())
datapoint_neg = torch.FloatTensor(self.embeddings.loc[selected_neg].values.squeeze())

return datapoint_pos, datapoint_neg, torch.LongTensor([length])


class CLIPDatasetInference(torch.utils.data.Dataset): # pragma: no cover

def __init__(self, data: list, embeddings, shuffle_examples,
sorted_examples=True):
super().__init__()
self.data = data
self.embeddings = embeddings
self.shuffle_examples = shuffle_examples
self.sorted_examples = sorted_examples

def __len__(self):
return len(self.data)

def __getitem__(self, idx):
_, pos, neg = self.data[idx]
if self.sorted_examples:
pos, neg = sorted(pos), sorted(neg)
elif self.shuffle_examples:
random.shuffle(pos)
random.shuffle(neg)

datapoint_pos = torch.FloatTensor(self.embeddings.loc[pos].values.squeeze())
datapoint_neg = torch.FloatTensor(self.embeddings.loc[pos].values.squeeze())

return datapoint_pos, datapoint_neg



class ROCESDataset(BaseDataset, torch.utils.data.Dataset):
class ROCESDataset(NCESBaseDataset, torch.utils.data.Dataset):

def __init__(self, data, triples_data, vocab, inv_vocab, sampling_strategy="p"):
super(ROCESDataset, self).__init__(vocab, inv_vocab)
Expand All @@ -360,16 +360,28 @@ def __getitem__(self, idx):
key, value = self.data[idx]
pos = value['positive examples']
neg = value['negative examples']
if self.sampling_strategy != 'uniform':
if self.sampling_strategy == 'p':
prob_pos_set = 1.0/(1+np.array(range(min(self.k, len(pos)), len(pos)+1, self.k)))
prob_pos_set = prob_pos_set/prob_pos_set.sum()
prob_neg_set = 1.0/(1+np.array(range(min(self.k, len(neg)), len(neg)+1, self.k)))
prob_neg_set = prob_neg_set/prob_neg_set.sum()
k_pos = np.random.choice(range(min(self.k, len(pos)), len(pos)+1, self.k), replace=False, p=prob_pos_set)
k_neg = np.random.choice(range(min(self.k, len(neg)), len(neg)+1, self.k), replace=False, p=prob_neg_set)
elif self.sampling_strategy == 'nces2':
if random.random() > 0.5:
prob_pos_set = 1.0/(1+np.array(range(min(self.k, len(pos)), len(pos)+1, self.k)))
prob_pos_set = prob_pos_set/prob_pos_set.sum()
prob_neg_set = 1.0/(1+np.array(range(min(self.k, len(neg)), len(neg)+1, self.k)))
prob_neg_set = prob_neg_set/prob_neg_set.sum()
k_pos = max(1, 2*len(pos)//3)
k_neg = max(1, 2*len(neg)//3)
else:
k_pos = len(pos)
k_neg = len(neg)
else:
k_pos = np.random.choice(range(min(self.k, len(pos)), len(pos)+1, self.k), replace=False)
k_neg = np.random.choice(range(min(self.k, len(neg)), len(neg)+1, self.k), replace=False)

selected_pos = random.sample(pos, k_pos)
selected_neg = random.sample(neg, k_neg)

Expand All @@ -380,9 +392,9 @@ def __getitem__(self, idx):
return datapoint_pos, datapoint_neg, torch.cat([torch.tensor(labels), self.vocab['PAD']*torch.ones(max(0,self.max_length-length))]).long()


class ROCESDatasetInference(BaseDataset, torch.utils.data.Dataset):
class ROCESDatasetInference(NCESBaseDataset, torch.utils.data.Dataset):

def __init__(self, data, triples_data, k, vocab, inv_vocab, sampling_strategy, num_examples, num_pred_per_lp=1, random_sample=False):
def __init__(self, data, triples_data, k, vocab, inv_vocab, num_examples, sampling_strategy='p', num_pred_per_lp=1):
super(ROCESDatasetInference, self).__init__(vocab, inv_vocab)
self.data = data
self.triples_data = triples_data
Expand All @@ -408,7 +420,7 @@ def __getitem__(self, idx):
pos = value['positive examples']
neg = value['negative examples']

if self.sampling_strategy != 'uniform':
if self.sampling_strategy == 'p':
prob_pos_set = 1.0/(1+np.array(range(min(self.k, len(pos)), len(pos)+1, self.k)))
prob_pos_set = prob_pos_set/prob_pos_set.sum()
prob_neg_set = 1.0/(1+np.array(range(min(self.k, len(neg)), len(neg)+1, self.k)))
Expand All @@ -419,6 +431,13 @@ def __getitem__(self, idx):
k_neg = np.random.choice(range(min(self.k, len(neg)), len(neg)+1, self.k),
size=(self.num_predictions,),
replace=True, p=prob_neg_set)
elif self.sampling_strategy == "nces2":
k_pos = np.random.choice([len(pos), 2*len(pos)//3],
size=(self.num_predictions,),
replace=True)
k_neg = np.random.choice([len(neg), 2*len(neg)//3],
size=(self.num_predictions,),
replace=True)
else:
k_pos = np.random.choice(range(min(self.k, len(pos)), len(pos)+1, self.k),
size=(self.num_predictions,), replace=True)
Expand Down

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