From 9119f208a03352a840d1ca8ae03ffb02bb5636fc Mon Sep 17 00:00:00 2001
From: Qrh <17882988+RuihongQiu@users.noreply.github.com>
Date: Thu, 8 Jul 2021 17:19:01 +1000
Subject: [PATCH] Add CausalRec Model (#415)
---
README.md | 1 +
cornac/models/__init__.py | 1 +
cornac/models/causalrec/__init__.py | 16 +
cornac/models/causalrec/recom_causalrec.py | 408 +++++++++++++++++++++
docs/source/models.rst | 5 +
examples/README.md | 2 +
examples/causalrec_clothing.py | 64 ++++
7 files changed, 497 insertions(+)
create mode 100644 cornac/models/causalrec/__init__.py
create mode 100644 cornac/models/causalrec/recom_causalrec.py
create mode 100644 examples/causalrec_clothing.py
diff --git a/README.md b/README.md
index 3f9976c8..fab641c3 100644
--- a/README.md
+++ b/README.md
@@ -107,6 +107,7 @@ The recommender models supported by Cornac are listed below. Why don't you join
| Year | Model and paper | Additional dependencies | Examples |
| :---: | --- | :---: | :---: |
| 2021 | [Bilateral Variational Autoencoder for Collaborative Filtering (BiVAECF)](cornac/models/bivaecf), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441759) | [requirements.txt](cornac/models/bivaecf/requirements.txt) | [PreferredAI/bi-vae](https://github.com/PreferredAI/bi-vae)
+| | [CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation](cornac/models/causalrec), [paper](https://arxiv.org/abs/2107.02390) | N/A | [causalrec_clothing.py](examples/causalrec_clothing.py)
| | [Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER)](cornac/models/comparer), [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441754) | N/A | [PreferredAI/ComparER](https://github.com/PreferredAI/ComparER)
| 2018 | [Collaborative Context Poisson Factorization (C2PF)](cornac/models/c2pf), [paper](https://www.ijcai.org/proceedings/2018/0370.pdf) | N/A | [c2pf_exp.py](examples/c2pf_example.py)
| | [Multi-Task Explainable Recommendation (MTER)](cornac/models/mter), [paper](https://arxiv.org/pdf/1806.03568.pdf) | N/A | [mter_exp.py](examples/mter_example.py)
diff --git a/cornac/models/__init__.py b/cornac/models/__init__.py
index 379c2508..d032090a 100644
--- a/cornac/models/__init__.py
+++ b/cornac/models/__init__.py
@@ -19,6 +19,7 @@ from .baseline_only import BaselineOnly
from .bivaecf import BiVAECF
from .bpr import BPR
from .bpr import WBPR
+from .causalrec import CausalRec
from .c2pf import C2PF
from .cdl import CDL
from .cdr import CDR
diff --git a/cornac/models/causalrec/__init__.py b/cornac/models/causalrec/__init__.py
new file mode 100644
index 00000000..701c7137
--- /dev/null
+++ b/cornac/models/causalrec/__init__.py
@@ -0,0 +1,16 @@
+# Copyright 2018 The Cornac Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+from .recom_causalrec import CausalRec
\ No newline at end of file
diff --git a/cornac/models/causalrec/recom_causalrec.py b/cornac/models/causalrec/recom_causalrec.py
new file mode 100644
index 00000000..965c7b40
--- /dev/null
+++ b/cornac/models/causalrec/recom_causalrec.py
@@ -0,0 +1,408 @@
+# Copyright 2018 The Cornac Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+
+import numpy as np
+from tqdm.auto import tqdm
+from scipy.special import expit
+
+from ..recommender import Recommender
+from ...exception import CornacException
+from ...exception import ScoreException
+from ...utils import fast_dot
+from ...utils.common import intersects
+from ...utils import get_rng
+from ...utils.init_utils import zeros, xavier_uniform
+
+
+class CausalRec(Recommender):
+ """CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
+
+ Parameters
+ ----------
+ k: int, optional, default: 10
+ The dimension of the gamma latent factors.
+
+ k2: int, optional, default: 10
+ The dimension of the theta latent factors.
+
+ n_epochs: int, optional, default: 20
+ Maximum number of epochs for SGD.
+
+ batch_size: int, optional, default: 100
+ The batch size for SGD.
+
+ learning_rate: float, optional, default: 0.001
+ The learning rate for SGD.
+
+ lambda_w: float, optional, default: 0.01
+ The regularization hyper-parameter for latent factor weights.
+
+ lambda_b: float, optional, default: 0.01
+ The regularization hyper-parameter for biases.
+
+ lambda_e: float, optional, default: 0.0
+ The regularization hyper-parameter for embedding matrix E and beta prime vector.
+
+ mean_feat: torch.tensor, required, default: None
+ The mean feature of all item embeddings serving as the no-treatment during causal inference.
+
+ tanh: int, optional, default: 0
+ The number of tanh layers on the visual feature transformation.
+
+ lambda_2: float, optional, default: 0.8
+ The coefficient controlling the elimination of the visual bias in Eq. (28).
+
+ use_gpu: boolean, optional, default: True
+ Whether or not to use GPU to speed up training.
+
+ trainable: boolean, optional, default: True
+ When False, the model is not trained and Cornac assumes that the model already \
+ pre-trained (U and V are not None).
+
+ verbose: boolean, optional, default: True
+ When True, running logs are displayed.
+
+ init_params: dictionary, optional, default: None
+ Initial parameters, e.g., init_params = {'Bi': beta_item, 'Gu': gamma_user,
+ 'Gi': gamma_item, 'Tu': theta_user, 'E': emb_matrix, 'Bp': beta_prime}
+
+ seed: int, optional, default: None
+ Random seed for weight initialization.
+
+ References
+ ----------
+ * Qiu R., Wang S., Chen Z., Yin H., Huang Z. (2021). CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation.
+ """
+
+ def __init__(
+ self,
+ name="CausalRec",
+ k=10,
+ k2=10,
+ n_epochs=50,
+ batch_size=100,
+ learning_rate=0.005,
+ lambda_w=0.01,
+ lambda_b=0.01,
+ lambda_e=0.0,
+ mean_feat=None,
+ tanh=0,
+ lambda_2=0.8,
+ use_gpu=False,
+ trainable=True,
+ verbose=True,
+ init_params=None,
+ seed=None,
+ ):
+ super().__init__(name=name, trainable=trainable, verbose=verbose)
+ self.k = k
+ self.k2 = k2
+ self.n_epochs = n_epochs
+ self.batch_size = batch_size
+ self.learning_rate = learning_rate
+ self.lambda_w = lambda_w
+ self.lambda_b = lambda_b
+ self.lambda_e = lambda_e
+ self.mean_feat = mean_feat
+ self.tanh = tanh
+ self.lambda_2 = lambda_2
+ self.use_gpu = use_gpu
+ self.seed = seed
+
+ # Init params if provided
+ self.init_params = {} if init_params is None else init_params
+ self.beta_item = self.init_params.get("Bi", None)
+ self.gamma_user = self.init_params.get("Gu", None)
+ self.gamma_item = self.init_params.get("Gi", None)
+ self.theta_user = self.init_params.get("Tu", None)
+ self.emb_matrix = self.init_params.get("E", None)
+ self.emb_ind_matrix = self.init_params.get("E_ind", None)
+ if self.tanh == 2:
+ self.emb_matrix2 = self.init_params.get("E2", None)
+ self.emb_ind_matrix2 = self.init_params.get("E_ind2", None)
+ self.beta_prime = self.init_params.get("Bp", None)
+
+ def _init(self, n_users, n_items, features):
+ rng = get_rng(self.seed)
+
+ self.beta_item = zeros(n_items) if self.beta_item is None else self.beta_item
+ if self.gamma_user is None:
+ self.gamma_user = xavier_uniform((n_users, self.k), rng)
+ if self.gamma_item is None:
+ self.gamma_item = xavier_uniform((n_items, self.k), rng)
+ if self.theta_user is None:
+ self.theta_user = xavier_uniform((n_users, self.k2), rng)
+ if self.emb_matrix is None:
+ self.emb_matrix = xavier_uniform((features.shape[1], self.k2), rng)
+ if self.emb_ind_matrix is None:
+ self.emb_ind_matrix = xavier_uniform((features.shape[1], self.k), rng)
+ if self.tanh == 2:
+ if self.emb_matrix2 is None:
+ self.emb_matrix2 = xavier_uniform((self.k2, self.k2), rng)
+ if self.emb_ind_matrix2 is None:
+ self.emb_ind_matrix2 = xavier_uniform((self.k, self.k), rng)
+ if self.beta_prime is None:
+ self.beta_prime = xavier_uniform((features.shape[1], 1), rng)
+
+ # pre-computed for faster evaluation
+ self.theta_item = np.matmul(features, self.emb_matrix)
+ self.visual_bias = np.matmul(features, self.beta_prime).ravel()
+ self.direct_theta_item = np.matmul(features, self.emb_ind_matrix)
+
+ def fit(self, train_set, val_set=None):
+ """Fit the model to observations.
+
+ Parameters
+ ----------
+ train_set: :obj:`cornac.data.Dataset`, required
+ User-Item preference data as well as additional modalities.
+
+ val_set: :obj:`cornac.data.Dataset`, optional, default: None
+ User-Item preference data for model selection purposes (e.g., early stopping).
+
+ Returns
+ -------
+ self : object
+ """
+ Recommender.fit(self, train_set, val_set)
+
+ if train_set.item_image is None:
+ raise CornacException("item_image modality is required but None.")
+
+ # Item visual feature from CNN
+ train_features = train_set.item_image.features[: self.train_set.total_items]
+ train_features = train_features.astype(np.float32)
+ self._init(
+ n_users=train_set.total_users,
+ n_items=train_set.total_items,
+ features=train_features,
+ )
+
+ if self.trainable:
+ self._fit_torch(train_features)
+
+ return self
+
+ def _fit_torch(self, train_features):
+ import torch
+
+ def _l2_loss(*tensors):
+ l2_loss = 0
+ for tensor in tensors:
+ l2_loss += tensor.pow(2).sum()
+ return l2_loss / 2
+
+ def _inner(a, b):
+ return (a * b).sum(dim=1)
+
+ dtype = torch.float
+ device = (
+ torch.device("cuda:0")
+ if (self.use_gpu and torch.cuda.is_available())
+ else torch.device("cpu")
+ )
+
+ F = torch.tensor(train_features, device=device, dtype=dtype)
+ # Learned parameters
+ Bi = torch.tensor(
+ self.beta_item, device=device, dtype=dtype, requires_grad=True
+ )
+ Gu = torch.tensor(
+ self.gamma_user, device=device, dtype=dtype, requires_grad=True
+ )
+ Gi = torch.tensor(
+ self.gamma_item, device=device, dtype=dtype, requires_grad=True
+ )
+ Tu = torch.tensor(
+ self.theta_user, device=device, dtype=dtype, requires_grad=True
+ )
+ E = torch.tensor(
+ self.emb_matrix, device=device, dtype=dtype, requires_grad=True
+ )
+ Bp = torch.tensor(
+ self.beta_prime, device=device, dtype=dtype, requires_grad=True
+ )
+ E_ind = torch.tensor(
+ self.emb_ind_matrix, device=device, dtype=dtype, requires_grad=True
+ )
+ mean_feat = torch.tensor(
+ [self.mean_feat], device=device, dtype=dtype, requires_grad=False
+ )
+ param = [Bi, Gu, Gi, Tu, E, Bp, E_ind]
+
+ if self.tanh == 2:
+ E2 = torch.tensor(
+ self.emb_matrix2, device=device, dtype=dtype, requires_grad=True
+ )
+ E_ind2 = torch.tensor(
+ self.emb_ind_matrix2, device=device, dtype=dtype, requires_grad=True
+ )
+ param.append(E2)
+ param.append(E_ind2)
+
+ optimizer = torch.optim.Adam(param, lr=self.learning_rate)
+
+ for epoch in range(1, self.n_epochs + 1):
+ sum_loss = 0.0
+ count = 0
+ progress_bar = tqdm(
+ total=self.train_set.num_batches(self.batch_size),
+ desc="Epoch {}/{}".format(epoch, self.n_epochs),
+ disable=not self.verbose,
+ )
+ for batch_u, batch_i, batch_j in self.train_set.uij_iter(
+ self.batch_size, shuffle=True
+ ):
+ gamma_u = Gu[batch_u]
+ theta_u = Tu[batch_u]
+
+ beta_i = Bi[batch_i]
+ beta_j = Bi[batch_j]
+ gamma_i = Gi[batch_i]
+ gamma_j = Gi[batch_j]
+ feat_i = F[batch_i]
+ feat_j = F[batch_j]
+
+ if self.tanh == 0:
+ direct_feat_i = feat_i.mm(E)
+ ind_feat_i = feat_i.mm(E_ind)
+ elif self.tanh == 1:
+ direct_feat_i = torch.tanh(feat_i.mm(E))
+ ind_feat_i = torch.tanh(feat_i.mm(E_ind))
+ elif self.tanh == 2:
+ direct_feat_i = torch.tanh(torch.tanh(feat_i.mm(E)).mm(E2))
+ ind_feat_i = torch.tanh(torch.tanh(feat_i.mm(E_ind)).mm(E_ind2))
+
+ i_m = beta_i + _inner(gamma_u, gamma_i) + _inner(gamma_u, gamma_i * ind_feat_i)
+ i_n = _inner(theta_u, direct_feat_i) + feat_i.mm(Bp)
+
+ if self.tanh == 0:
+ direct_feat_j = feat_j.mm(E)
+ ind_feat_j = feat_j.mm(E_ind)
+ elif self.tanh == 1:
+ direct_feat_j = torch.tanh(feat_j.mm(E))
+ ind_feat_j = torch.tanh(feat_j.mm(E_ind))
+ elif self.tanh == 2:
+ direct_feat_j = torch.tanh(torch.tanh(feat_j.mm(E)).mm(E2))
+ ind_feat_j = torch.tanh(torch.tanh(feat_j.mm(E_ind)).mm(E_ind2))
+
+ j_m = beta_j + _inner(gamma_u, gamma_j) + _inner(gamma_u, gamma_j * ind_feat_j)
+ j_n = _inner(theta_u, direct_feat_j) + feat_j.mm(Bp)
+
+ i_score = torch.sigmoid(i_m + i_n) * torch.sigmoid(i_m) * torch.sigmoid(i_n)
+ j_score = torch.sigmoid(j_m + j_n) * torch.sigmoid(j_m) * torch.sigmoid(j_n)
+
+ log_likelihood = torch.nn.functional.logsigmoid(i_score - j_score).sum()
+ log_likelihood_m = torch.nn.functional.logsigmoid(i_m - j_m).sum()
+ log_likelihood_n = torch.nn.functional.logsigmoid(i_n - j_n).sum()
+
+ if self.tanh < 2:
+ l2_e = _l2_loss(E, Bp, E_ind)
+ else:
+ l2_e = _l2_loss(E, Bp, E_ind, E2, E_ind2)
+
+ reg = (
+ _l2_loss(gamma_u, gamma_i, gamma_j, theta_u) * self.lambda_w
+ + _l2_loss(beta_i) * self.lambda_b
+ + _l2_loss(beta_j) * self.lambda_b / 10
+ + l2_e * self.lambda_e
+ )
+
+ loss = -log_likelihood + reg - log_likelihood_m - log_likelihood_n
+
+ optimizer.zero_grad()
+ loss.backward()
+ optimizer.step()
+
+ sum_loss += loss.data.item()
+ count += len(batch_u)
+ if count % (self.batch_size * 10) == 0:
+ progress_bar.set_postfix(loss=(sum_loss / count))
+ progress_bar.update(1)
+ progress_bar.close()
+
+ print("Optimization finished!")
+
+ self.beta_item = Bi.data.cpu().numpy()
+ self.gamma_user = Gu.data.cpu().numpy()
+ self.gamma_item = Gi.data.cpu().numpy()
+ self.theta_user = Tu.data.cpu().numpy()
+ self.emb_matrix = E.data.cpu().numpy()
+ self.beta_prime = Bp.data.cpu().numpy()
+ self.emb_dir_matrix = E_ind.data.cpu().numpy()
+ # pre-computed for faster evaluation
+ self.theta_item = F.mm(E)
+ if self.tanh == 0:
+ self.theta_item = self.theta_item.data.cpu().numpy()
+ elif self.tanh == 1:
+ self.theta_item = torch.tanh(self.theta_item).data.cpu().numpy()
+ elif self.tanh == 2:
+ self.theta_item = torch.tanh(torch.tanh(self.theta_item).mm(E2)).data.cpu().numpy()
+
+ self.visual_bias = F.mm(Bp).squeeze().data.cpu().numpy()
+
+ self.ind_theta_item = F.mm(E_ind)
+ if self.tanh == 0:
+ self.ind_theta_item = self.ind_theta_item.data.cpu().numpy()
+ elif self.tanh == 1:
+ self.ind_theta_item = torch.tanh(self.ind_theta_item).data.cpu().numpy()
+ elif self.tanh == 2:
+ self.ind_theta_item = torch.tanh(torch.tanh(self.ind_theta_item).mm(E_ind2)).data.cpu().numpy()
+
+ self.beta_item_mean = Bi.mean().unsqueeze(dim=0).data.cpu().numpy()
+ self.gamma_item_mean = Gi.mean(dim=0).unsqueeze(dim=0).data.cpu().numpy()
+
+ self.mean_feat = mean_feat.mm(E_ind)
+ if self.tanh == 0:
+ self.mean_feat = self.mean_feat.data.cpu().numpy()
+ elif self.tanh == 1:
+ self.mean_feat = torch.tanh(self.mean_feat).data.cpu().numpy()
+ elif self.tanh == 2:
+ self.mean_feat = torch.tanh(torch.tanh(self.mean_feat).mm(E_ind2)).data.cpu().numpy()
+
+ def score(self, user_idx, item_idx=None):
+ """Predict the debiased scores/ratings of a user for an item.
+
+ Parameters
+ ----------
+ user_idx: int, required
+ The index of the user for whom to perform score prediction.
+
+ item_idx: int, optional, default: None
+ The index of the item for which to perform score prediction.
+ If None, scores for all known items will be returned.
+
+ Returns
+ -------
+ res : A scalar or a Numpy array
+ Relative scores that the user gives to the item or to all known items
+
+ """
+ if item_idx is None:
+ m_score = self.beta_item
+ fast_dot(self.gamma_user[user_idx], self.gamma_item, m_score)
+ fast_dot(self.gamma_user[user_idx], self.gamma_item * self.ind_theta_item, m_score)
+
+ m_star = self.beta_item_mean
+ fast_dot(self.gamma_user[user_idx], self.gamma_item_mean, m_star)
+ fast_dot(self.gamma_user[user_idx], self.gamma_item_mean * self.mean_feat, m_star)
+
+ n_score = self.visual_bias
+ fast_dot(self.theta_user[user_idx], self.theta_item, n_score)
+
+ return expit(m_score + n_score) * expit(m_score) * expit(n_score)\
+ - self.lambda_2 * expit(m_star + n_score) * expit(m_star) * expit(n_score)
+ else:
+ raise NotImplementedError("The sampled evaluation is not implemented!")
diff --git a/docs/source/models.rst b/docs/source/models.rst
index 2b26b637..7ab7a74f 100644
--- a/docs/source/models.rst
+++ b/docs/source/models.rst
@@ -15,6 +15,11 @@ Bilateral VAE for Collaborative Filtering (BiVAECF)
.. automodule:: cornac.models.bivaecf.recom_bivaecf
:members:
+CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation (CausalRec)
+---------------------------------------------------------------------------------------------
+.. automodule:: cornac.models.causalrec.recom_causalrec
+ :members:
+
Explainable Recommendation with Comparative Constraints on Product Aspects (ComparER)
-------------------------------------------------------------------------------------
.. automodule:: cornac.models.comparer.recom_comparer_sub
diff --git a/examples/README.md b/examples/README.md
index 063ca0c7..7e84804b 100644
--- a/examples/README.md
+++ b/examples/README.md
@@ -56,6 +56,8 @@
### Image
+[causalrec_clothing.py](causalrec_clothing.py) - CausalRec with Clothing dataset.
+
[vbpr_tradesy.py](vbpr_tradesy.py) - Visual Bayesian Personalized Ranking (VBPR) with Tradesy dataset.
[vmf_clothing.py](vmf_clothing.py) - Visual Matrix Factorization (VMF) with Amazon Clothing dataset.
diff --git a/examples/causalrec_clothing.py b/examples/causalrec_clothing.py
new file mode 100644
index 00000000..b00cc943
--- /dev/null
+++ b/examples/causalrec_clothing.py
@@ -0,0 +1,64 @@
+# Copyright 2018 The Cornac Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+# ============================================================================
+"""
+Example for CausalRec: Causal Inference for Visual Debiasing in Visually-Aware Recommendation
+Link: https://arxiv.org/abs/2107.02390
+"""
+
+import cornac
+from cornac.datasets import amazon_clothing
+from cornac.data import ImageModality
+from cornac.eval_methods import RatioSplit
+
+
+# CausalRec utilises the causal inference to debias the visual bias
+# The necessary data can be loaded as follows
+feedback = amazon_clothing.load_feedback()
+features, item_ids = amazon_clothing.load_visual_feature() # BIG file
+
+# Instantiate a ImageModality, it makes it convenient to work with visual auxiliary information
+# For more details, please refer to the tutorial on how to work with auxiliary data
+item_image_modality = ImageModality(features=features, ids=item_ids, normalized=True)
+
+# Define an evaluation method to split feedback into train and test sets
+ratio_split = RatioSplit(
+ data=feedback,
+ test_size=0.1,
+ rating_threshold=0.5,
+ exclude_unknowns=True,
+ verbose=True,
+ item_image=item_image_modality,
+)
+
+# Instantiate CausalRec
+causalrec = cornac.models.CausalRec(
+ k=32,
+ k2=32,
+ n_epochs=1,
+ batch_size=100,
+ learning_rate=0.001,
+ lambda_w=1,
+ lambda_b=0.01,
+ lambda_e=0.0,
+ use_gpu=True,
+ mean_feat=features.mean(axis=0),
+ tanh=1
+)
+
+# Instantiate evaluation measures
+rec_50 = cornac.metrics.Recall(k=50)
+
+# Put everything together into an experiment and run it
+cornac.Experiment(eval_method=ratio_split, models=[causalrec], metrics=[rec_50]).run()
--
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