dicee.models.clifford

Classes

Keci

Base class for all neural network modules.

KeciBase

Without learning dimension scaling

DeCaL

Base class for all neural network modules.

Module Contents

class dicee.models.clifford.Keci(args)[source]

Bases: dicee.models.base_model.BaseKGE

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

name = 'Keci'
p
q
r
requires_grad_for_interactions = True
compute_sigma_pp(hp, rp)[source]

Compute sigma_{pp} = sum_{i=1}^{p-1} sum_{k=i+1}^p (h_i r_k - h_k r_i) e_i e_k

sigma_{pp} captures the interactions between along p bases For instance, let p e_1, e_2, e_3, we compute interactions between e_1 e_2, e_1 e_3 , and e_2 e_3 This can be implemented with a nested two for loops

results = [] for i in range(p - 1):

for k in range(i + 1, p):

results.append(hp[:, :, i] * rp[:, :, k] - hp[:, :, k] * rp[:, :, i])

sigma_pp = torch.stack(results, dim=2) assert sigma_pp.shape == (b, r, int((p * (p - 1)) / 2))

Yet, this computation would be quite inefficient. Instead, we compute interactions along all p, e.g., e1e1, e1e2, e1e3,

e2e1, e2e2, e2e3, e3e1, e3e2, e3e3

Then select the triangular matrix without diagonals: e1e2, e1e3, e2e3.

compute_sigma_qq(hq, rq)[source]

Compute sigma_{qq} = sum_{j=1}^{p+q-1} sum_{k=j+1}^{p+q} (h_j r_k - h_k r_j) e_j e_k sigma_{q} captures the interactions between along q bases For instance, let q e_1, e_2, e_3, we compute interactions between e_1 e_2, e_1 e_3 , and e_2 e_3 This can be implemented with a nested two for loops

results = [] for j in range(q - 1):

for k in range(j + 1, q):

results.append(hq[:, :, j] * rq[:, :, k] - hq[:, :, k] * rq[:, :, j])

sigma_qq = torch.stack(results, dim=2) assert sigma_qq.shape == (b, r, int((q * (q - 1)) / 2))

Yet, this computation would be quite inefficient. Instead, we compute interactions along all p, e.g., e1e1, e1e2, e1e3,

e2e1, e2e2, e2e3, e3e1, e3e2, e3e3

Then select the triangular matrix without diagonals: e1e2, e1e3, e2e3.

compute_sigma_pq(*, hp, hq, rp, rq)[source]

sum_{i=1}^{p} sum_{j=p+1}^{p+q} (h_i r_j - h_j r_i) e_i e_j

results = [] sigma_pq = torch.zeros(b, r, p, q) for i in range(p):

for j in range(q):

sigma_pq[:, :, i, j] = hp[:, :, i] * rq[:, :, j] - hq[:, :, j] * rp[:, :, i]

print(sigma_pq.shape)

apply_coefficients(hp, hq, rp, rq)[source]

Multiplying a base vector with its scalar coefficient

clifford_multiplication(h0, hp, hq, r0, rp, rq)[source]

Compute our CL multiplication

h = h_0 + sum_{i=1}^p h_i e_i + sum_{j=p+1}^{p+q} h_j e_j r = r_0 + sum_{i=1}^p r_i e_i + sum_{j=p+1}^{p+q} r_j e_j

ei ^2 = +1 for i =< i =< p ej ^2 = -1 for p < j =< p+q ei ej = -eje1 for i

eq j

h r = sigma_0 + sigma_p + sigma_q + sigma_{pp} + sigma_{q}+ sigma_{pq} where

  1. sigma_0 = h_0 r_0 + sum_{i=1}^p (h_0 r_i) e_i - sum_{j=p+1}^{p+q} (h_j r_j) e_j

  2. sigma_p = sum_{i=1}^p (h_0 r_i + h_i r_0) e_i

  3. sigma_q = sum_{j=p+1}^{p+q} (h_0 r_j + h_j r_0) e_j

  4. sigma_{pp} = sum_{i=1}^{p-1} sum_{k=i+1}^p (h_i r_k - h_k r_i) e_i e_k

  5. sigma_{qq} = sum_{j=1}^{p+q-1} sum_{k=j+1}^{p+q} (h_j r_k - h_k r_j) e_j e_k

  6. sigma_{pq} = sum_{i=1}^{p} sum_{j=p+1}^{p+q} (h_i r_j - h_j r_i) e_i e_j

construct_cl_multivector(x: torch.FloatTensor, r: int, p: int, q: int) → tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor][source]

Construct a batch of multivectors Cl_{p,q}(mathbb{R}^d)

Parameter

x: torch.FloatTensor with (n,d) shape

returns:

  • a0 (torch.FloatTensor with (n,r) shape)

  • ap (torch.FloatTensor with (n,r,p) shape)

  • aq (torch.FloatTensor with (n,r,q) shape)

forward_k_vs_with_explicit(x: torch.Tensor)[source]
k_vs_all_score(bpe_head_ent_emb, bpe_rel_ent_emb, E)[source]
forward_k_vs_all(x: torch.Tensor) → torch.FloatTensor[source]

Kvsall training

  1. Retrieve real-valued embedding vectors for heads and relations mathbb{R}^d .

  2. Construct head entity and relation embeddings according to Cl_{p,q}(mathbb{R}^d) .

  3. Perform Cl multiplication

  4. Inner product of (3) and all entity embeddings

forward_k_vs_with_explicit and this funcitons are identical Parameter ——— x: torch.LongTensor with (n,2) shape :rtype: torch.FloatTensor with (n, |E|) shape

construct_batch_selected_cl_multivector(x: torch.FloatTensor, r: int, p: int, q: int) → tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor][source]

Construct a batch of batchs multivectors Cl_{p,q}(mathbb{R}^d)

Parameter

x: torch.FloatTensor with (n,k, d) shape

returns:

  • a0 (torch.FloatTensor with (n,k, m) shape)

  • ap (torch.FloatTensor with (n,k, m, p) shape)

  • aq (torch.FloatTensor with (n,k, m, q) shape)

forward_k_vs_sample(x: torch.LongTensor, target_entity_idx: torch.LongTensor) → torch.FloatTensor[source]

Parameter

x: torch.LongTensor with (n,2) shape

target_entity_idx: torch.LongTensor with (n, k ) shape k denotes the selected number of examples.

rtype:

torch.FloatTensor with (n, k) shape

score(h, r, t)[source]
forward_triples(x: torch.Tensor) → torch.FloatTensor[source]

Parameter

x: torch.LongTensor with (n,3) shape

rtype:

torch.FloatTensor with (n) shape

class dicee.models.clifford.KeciBase(args)[source]

Bases: Keci

Without learning dimension scaling

name = 'KeciBase'
requires_grad_for_interactions = False
class dicee.models.clifford.DeCaL(args)[source]

Bases: dicee.models.base_model.BaseKGE

Base class for all neural network modules.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import torch.nn as nn
import torch.nn.functional as F

class Model(nn.Module):
    def __init__(self) -> None:
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables:

training (bool) – Boolean represents whether this module is in training or evaluation mode.

name = 'DeCaL'
entity_embeddings
relation_embeddings
p
q
r
re
forward_triples(x: torch.Tensor) → torch.FloatTensor[source]

Parameter

x: torch.LongTensor with (n, ) shape

rtype:

torch.FloatTensor with (n) shape

cl_pqr(a: torch.tensor) → torch.tensor[source]

Input: tensor(batch_size, emb_dim) —> output: tensor with 1+p+q+r components with size (batch_size, emb_dim/(1+p+q+r)) each.

1) takes a tensor of size (batch_size, emb_dim), split it into 1 + p + q +r components, hence 1+p+q+r must be a divisor of the emb_dim. 2) Return a list of the 1+p+q+r components vectors, each are tensors of size (batch_size, emb_dim/(1+p+q+r))

compute_sigmas_single(list_h_emb, list_r_emb, list_t_emb)[source]

here we compute all the sums with no others vectors interaction taken with the scalar product with t, that is,

\[s0 = h_0r_0t_0 s1 = \sum_{i=1}^{p}h_ir_it_0 s2 = \sum_{j=p+1}^{p+q}h_jr_jt_0 s3 = \sum_{i=1}^{q}(h_0r_it_i + h_ir_0t_i) s4 = \sum_{i=p+1}^{p+q}(h_0r_it_i + h_ir_0t_i) s5 = \sum_{i=p+q+1}^{p+q+r}(h_0r_it_i + h_ir_0t_i)\]

and return:

\[sigma_0t = \sigma_0 \cdot t_0 = s0 + s1 -s2 s3, s4 and s5\]
compute_sigmas_multivect(list_h_emb, list_r_emb)[source]

Here we compute and return all the sums with vectors interaction for the same and different bases.

For same bases vectors interaction we have

\[\sigma_pp = \sum_{i=1}^{p-1}\sum_{i'=i+1}^{p}(h_ir_{i'}-h_{i'}r_i) (models the interactions between e_i and e_i' for 1 <= i, i' <= p) \sigma_qq = \sum_{j=p+1}^{p+q-1}\sum_{j'=j+1}^{p+q}(h_jr_{j'}-h_{j'} (models the interactions between e_j and e_j' for p+1 <= j, j' <= p+q) \sigma_rr = \sum_{k=p+q+1}^{p+q+r-1}\sum_{k'=k+1}^{p}(h_kr_{k'}-h_{k'}r_k) (models the interactions between e_k and e_k' for p+q+1 <= k, k' <= p+q+r)\]

For different base vector interactions, we have

\[\sigma_pq = \sum_{i=1}^{p}\sum_{j=p+1}^{p+q}(h_ir_j - h_jr_i) (interactionsn between e_i and e_j for 1<=i <=p and p+1<= j <= p+q) \sigma_pr = \sum_{i=1}^{p}\sum_{k=p+q+1}^{p+q+r}(h_ir_k - h_kr_i) (interactionsn between e_i and e_k for 1<=i <=p and p+q+1<= k <= p+q+r) \sigma_qr = \sum_{j=p+1}^{p+q}\sum_{j=p+q+1}^{p+q+r}(h_jr_k - h_kr_j) (interactionsn between e_j and e_k for p+1 <= j <=p+q and p+q+1<= j <= p+q+r)\]
forward_k_vs_all(x: torch.Tensor) → torch.FloatTensor[source]

Kvsall training

  1. Retrieve real-valued embedding vectors for heads and relations

  2. Construct head entity and relation embeddings according to Cl_{p,q, r}(mathbb{R}^d) .

  3. Perform Cl multiplication

  4. Inner product of (3) and all entity embeddings

forward_k_vs_with_explicit and this funcitons are identical Parameter ——— x: torch.LongTensor with (n, ) shape :rtype: torch.FloatTensor with (n, |E|) shape

apply_coefficients(h0, hp, hq, hk, r0, rp, rq, rk)[source]

Multiplying a base vector with its scalar coefficient

construct_cl_multivector(x: torch.FloatTensor, re: int, p: int, q: int, r: int) → tuple[torch.FloatTensor, torch.FloatTensor, torch.FloatTensor][source]

Construct a batch of multivectors Cl_{p,q,r}(mathbb{R}^d)

Parameter

x: torch.FloatTensor with (n,d) shape

returns:

  • a0 (torch.FloatTensor)

  • ap (torch.FloatTensor)

  • aq (torch.FloatTensor)

  • ar (torch.FloatTensor)

compute_sigma_pp(hp, rp)[source]

Compute .. math:

\sigma_{p,p}^* = \sum_{i=1}^{p-1}\sum_{i'=i+1}^{p}(x_iy_{i'}-x_{i'}y_i)

sigma_{pp} captures the interactions between along p bases For instance, let p e_1, e_2, e_3, we compute interactions between e_1 e_2, e_1 e_3 , and e_2 e_3 This can be implemented with a nested two for loops

results = [] for i in range(p - 1):

for k in range(i + 1, p):

results.append(hp[:, :, i] * rp[:, :, k] - hp[:, :, k] * rp[:, :, i])

sigma_pp = torch.stack(results, dim=2) assert sigma_pp.shape == (b, r, int((p * (p - 1)) / 2))

Yet, this computation would be quite inefficient. Instead, we compute interactions along all p, e.g., e1e1, e1e2, e1e3,

e2e1, e2e2, e2e3, e3e1, e3e2, e3e3

Then select the triangular matrix without diagonals: e1e2, e1e3, e2e3.

compute_sigma_qq(hq, rq)[source]

Compute

\[\sigma_{q,q}^* = \sum_{j=p+1}^{p+q-1}\sum_{j'=j+1}^{p+q}(x_jy_{j'}-x_{j'}y_j) Eq. 16\]

sigma_{q} captures the interactions between along q bases For instance, let q e_1, e_2, e_3, we compute interactions between e_1 e_2, e_1 e_3 , and e_2 e_3 This can be implemented with a nested two for loops

results = [] for j in range(q - 1):

for k in range(j + 1, q):

results.append(hq[:, :, j] * rq[:, :, k] - hq[:, :, k] * rq[:, :, j])

sigma_qq = torch.stack(results, dim=2) assert sigma_qq.shape == (b, r, int((q * (q - 1)) / 2))

Yet, this computation would be quite inefficient. Instead, we compute interactions along all p, e.g., e1e1, e1e2, e1e3,

e2e1, e2e2, e2e3, e3e1, e3e2, e3e3

Then select the triangular matrix without diagonals: e1e2, e1e3, e2e3.

compute_sigma_rr(hk, rk)[source]
\[\sigma_{r,r}^* = \sum_{k=p+q+1}^{p+q+r-1}\sum_{k'=k+1}^{p}(x_ky_{k'}-x_{k'}y_k)\]
compute_sigma_pq(*, hp, hq, rp, rq)[source]

Compute

\[\sum_{i=1}^{p} \sum_{j=p+1}^{p+q} (h_i r_j - h_j r_i) e_i e_j\]

results = [] sigma_pq = torch.zeros(b, r, p, q) for i in range(p):

for j in range(q):

sigma_pq[:, :, i, j] = hp[:, :, i] * rq[:, :, j] - hq[:, :, j] * rp[:, :, i]

print(sigma_pq.shape)

compute_sigma_pr(*, hp, hk, rp, rk)[source]

Compute

\[\sum_{i=1}^{p} \sum_{j=p+1}^{p+q} (h_i r_j - h_j r_i) e_i e_j\]

results = [] sigma_pq = torch.zeros(b, r, p, q) for i in range(p):

for j in range(q):

sigma_pq[:, :, i, j] = hp[:, :, i] * rq[:, :, j] - hq[:, :, j] * rp[:, :, i]

print(sigma_pq.shape)

compute_sigma_qr(*, hq, hk, rq, rk)[source]
\[\sum_{i=1}^{p} \sum_{j=p+1}^{p+q} (h_i r_j - h_j r_i) e_i e_j\]

results = [] sigma_pq = torch.zeros(b, r, p, q) for i in range(p):

for j in range(q):

sigma_pq[:, :, i, j] = hp[:, :, i] * rq[:, :, j] - hq[:, :, j] * rp[:, :, i]

print(sigma_pq.shape)