Algorithms¶

Buffalo provides the following algorithm implementations:

Alternating Least Squares

Bayesian Personalized Ranking Matrix Factorization

Weighted Approximate-Rank Pairwise

Word2Vec

CoFactors

All algorithms inherit common parent classes such as Algo, Serializable, TensorboardExtension, Optimizable, Evaluable.

Algo¶

Serializable¶

TensorboardExtension¶

Optimizable¶

class buffalo.algo.optimize.Optimizable(*args, **kwargs)¶

Bases: object

get_optimization_data()¶

optimize()¶

Evaluable¶

class buffalo.evaluate.base.Evaluable(*args, **kargs)¶

Bases: object

get_topk(scores, k, sorted=True, num_threads=4)¶

get_validation_results()¶

prepare_evaluation()¶

show_validation_results()¶

Alternating Least Squares¶

class buffalo.algo.options.ALSOption(*args, **kwargs)¶

Bases: buffalo.algo.options.AlgoOption

get_default_optimize_option()¶

Optimization Options for ALS.

Variables:

loss (str) – Target loss to optimize.
max_trials (int) – The maximum experiments for optimization. If not given, run forever.
min_trials (int) – The minimum experiments before deploying model. (Since the best parameter may not be found after min_trials, the first best parameter is always deployed)
deployment (bool) – Set True to train model with the best parameter. During the optimization, it try to dump the model which beated the previous best loss.
start_with_default_parameters (bool) – If set to True, the loss value of the default parameter is used as the starting loss to beat.
space (dict) – The parameter space definition. For more information, please check reference hyperopt’s express. Note) Due to hyperopt’s randint does not provide lower value, we had to implement it a bait tricky. Please see optimize.py to check how we deal with randint.

get_default_option()¶

Options for Alternating Least Squares.

Variables:

adaptive_reg (bool) – Set True, for adaptive regularization. (default: False)
save_factors (bool) – Set True, to save models. (default: False)
accelerator (bool) – Set True, to accelerate training using GPU. (default: False)
d (int) – The number of latent feature dimension. (default: 20)
num_iters (int) – The number of iterations for training. (default: 10)
num_workers (int) – The number of threads. (default: 1)
hyper_threads (int) – The number of hyper threads when using cuda cores. (default: 256)
reg_u (float) – The L2 regularization coefficient for user embedding matrix. (default: 0.1)
reg_i (float) – The L2 regularization coefficient for item embedding matrix. (default: 0.1)
alpha (float) – The coefficient of giving more weights to losses on positive samples. (default: 8)
eps (float) – epsilon for numerical stability (default: 1e-10)
cg_tolerance (float) – tolerance of conjugate gradient for early stopping iterations (default: 1e-10)
optimizer (str) – The name of optimizer, should be in [llt, ldlt, manual_cg, eigen_cg, eigen_bicg, eigen_gmres, eigen_dgmres, eigen_minres]. (default: manual_cg)
num_cg_max_iters (int) – The number of maximum iterations for conjugate gradient optimizer. (default: 3)
model_path (str) – Where to save model.
data_opt (dict) – This option will be used to load data if given.

Bayesian Personalized Ranking Matrix Factorization¶

class buffalo.algo.options.BPRMFOption(*args, **kwargs)¶

Bases: buffalo.algo.options.AlgoOption

get_default_optimize_option()¶: Optimization options for BPRMF.

get_default_option()¶

Options for Bayesian Personalized Ranking Matrix Factorization.

Variables:

accelerator (bool) – Set True, to accelerate training using GPU. (default: False)
use_bias (bool) – Set True, to use bias term for the model.
evaluation_period (int) – (default: 100)
num_workers (int) – The number of threads. (default: 1)
hyper_threads (int) – The number of hyper threads when using cuda cores. (default: 256)
num_iters (int) – The number of iterations for training. (default: 100)
d (int) – The number of latent feature dimension. (default: 20)
update_i (bool) – Set True, to update positive item feature. (default: True)
update_j (bool) – Set True, to update negative item feature. (default: True)
reg_u (float) – The L2 regularization coefficient for user embedding matrix. (default: 0.025)
reg_i (float) – The L2 regularization coefficient for positive item embedding matrix. (default: 0.025)
reg_j (float) – The L2 regularization coefficient for negative item embedding matrix. (default: 0.025)
reg_b (float) – The L2 regularization coefficient for bias term. (default: 0.025)
optimizer (str) – The name of optimizer, should be one of [sgd, adagrad, adam]. (default: sgd)
lr (float) – The learning rate.
min_lr (float) – The minimum of learning rate, to prevent going to zero by learning rate decaying. (default: 0.0001)
beta1 (float) – The parameter of Adam optimizer. (default: 0.9)
beta2 (float) – The parameter of Adam optimizer. (default: 0.999)
per_coordinate_normalize (bool) – This is a bit tricky option for Adam optimizer. Before update factors with gradients, do normalize gradients per class by its number of contributed samples. (default: False)
sampling_power (float) – This parameter control sampling distribution. When it set to 0, it draw negative items from uniform distribution, while to set 1, it draw from the given data popularation. (default: 0.0)
random_positive (bool) – Set True, to draw positive sample uniformly instead of using straight forward positive sample, only implemented in cuda mode, according to the original paper, set True, but we found out False usually produces better results) (default: False)
verify_neg (bool) – Set True, to ensure negative sample does not belong to positive samples. (default True)
model_path (str) – Where to save model.
data_opt (dict) – This option will be used to load data if given.

Weighted Approximate-Rank Pairwise¶

class buffalo.algo.options.WARPOption(*args, **kwargs)¶

Bases: buffalo.algo.options.AlgoOption

get_default_optimize_option()¶: Optimization options for WARP.

get_default_option()¶

Options for WARP Matrix Factorization.

Variables:

accelerator (bool) – Set True, to accelerate training using GPU. (default: False)
use_bias (bool) – Set True, to use bias term for the model.
evaluation_period (int) – (default: 15)
num_workers (int) – The number of threads. (default: 1)
hyper_threads (int) – The number of hyper threads when using cuda cores. (default: 256)
num_iters (int) – The number of iterations for training. (default: 15)
d (int) – The number of latent feature dimension. (default: 30)
max_trials (int) – The maximum number of attempts to find a violating negative sample during training.
update_i (bool) – Set True, to update positive item feature. (default: True)
update_j (bool) – Set True, to update negative item feature. (default: True)
reg_u (float) – The L2 regularization coefficient for user embedding matrix. (default: 0.0)
reg_i (float) – The L2 regularization coefficient for positive item embedding matrix. (default: 0.0)
reg_j (float) – The L2 regularization coefficient for negative item embedding matrix. (default: 0.0)
reg_b (float) – The L2 regularization coefficient for bias term. (default: 0.0)
optimizer (str) – The name of optimizer, should be one of [adagrad, adam]. (default: adagrad)
lr (float) – The learning rate. (default: 0.1)
min_lr (float) – The minimum of learning rate, to prevent going to zero by learning rate decaying. (default: 0.0001)
beta1 (float) – The parameter of Adam optimizer. (default: 0.9)
beta2 (float) – The parameter of Adam optimizer. (default: 0.999)
per_coordinate_normalize (bool) – This is a bit tricky option for Adam optimizer. Before update factors with gradients, do normalize gradients per class by its number of contributed samples. (default: False)
random_positive (bool) – Set True, to draw positive sample uniformly instead of using straight forward positive sample, only implemented in cuda mode, according to the original paper, set True, but we found out False usually produces better results) (default: False)
model_path (str) – Where to save model.
data_opt (dict) – This option will be used to load data if given.

CoFactors¶

class buffalo.algo.options.CFROption(*args, **kwargs)¶

Bases: buffalo.algo.options.AlgoOption

get_default_optimize_option()¶

Optimization options for CoFactor.

Variables:

loss (str) – Target loss to optimize.
max_trials (int) – Maximum experiments for optimization. If not given, run forever.
min_trials (int) – Minimum experiments before deploying model. (Since the best parameter may not be found after min_trials, the first best parameter is always deployed)
deployment( (bool) – Set True to train model with the best parameter. During the optimization, it try to dump the model which beated the previous best loss.
start_with_default_parameters (bool) – If set to True, the loss value of the default parameter is used as the starting loss to beat.
space (dict) – Parameter space definition. For more information, please check reference hyperopt’s express. Note) Due to hyperopt’s randint does not provide lower value, we had to implement it a bait tricky. Please see optimize.py to check how we deal with randint.

get_default_option()¶

Basic Options for CoFactor.

Variables:

d (int) – The number of latent feature dimension. (default: 20)
num_iters (int) – The number of iterations for training. (default: 10)
num_workers (int) – The number of threads. (default: 1)
reg_u (float) – The L2 regularization coefficient for user embedding matrix. (default: 0.1)
reg_i (float) – The L2 regularization coefficient for item embedding matrix. (default: 0.1)
reg_c (float) – The L2 regularization coefficient for context embedding matrix. (default: 0.1)
eps (float) – epsilon for numerical stability (default: 1e-10)
cg_tolerance (float) – The tolerance for early stopping conjugate gradient optimizer. (default: 1e-10)
alpha (float) – The coefficient of giving more weights to losses on positive samples. (default: 8.0)
l (float) – The relative weight of loss on user-item relation over item-context relation. (default: 1.0)
optimizer (str) – The name of optimizer, should be in [llt, ldlt, manual_cg, eigen_cg, eigen_bicg, eigen_gmres, eigen_dgmres, eigen_minres]. (default: manual_cg)
num_cg_max_iters (int) – The number of maximum iterations for conjugate gradient optimizer. (default: 3)
model_path (str) – Where to save model. (default: ‘’)
data_opt (dict) – This option will be used to load data if given. (default: {})

is_valid_option(opt)¶

Word2Vec¶

class buffalo.algo.options.W2VOption(*args, **kwargs)¶

Bases: buffalo.algo.options.AlgoOption

get_default_optimize_option()¶: Optimization options for W2V

get_default_option()¶

Options for Word2Vec.

Variables:

evaluation_on_learning (bool) – Set True to do run evaluation on training phrase. (default: False)
num_workers (int) – The number of threads. (default: 1)
num_iters (int) – The number of iterations for training. (default: 100)
d (int) – The number of latent feature dimension. (default: 20)
window (int) – The window size. (default: 5)
min_count (int) – The minimum required frequency of the words to use training vocabulary. (default: 5)
sample (float) – The sampling ratio to downsample the frequent words. (default: 0.001)
num_negative_samples (int) – The number of negative noise words. (default: 5)
lr (float) – The learning rate.
model_path (str) – Where to save model.
data_opt (dict) – This option will be used to load data if given.

pLSI¶

class buffalo.algo.options.PLSIOption(*args, **kwargs)¶

Bases: buffalo.algo.options.AlgoOption

get_default_optimize_option()¶

Optimization options for pLSI.

Variables:

loss (str) – Target loss to optimize.
max_trials (int) – Maximum experiments for optimization. If not given, run forever.
min_trials (int) – Minimum experiments before deploying model. (Since the best parameter may not be found after min_trials, the first best parameter is always deployed)
deployment (bool) – Set True to train model with the best parameter. During the optimization, it try to dump the model which beated the previous best loss.
start_with_default_parameters (bool) – If set to True, the loss value of the default parameter is used as the starting loss to beat.
space (dict) – Parameter space definition. For more information, please check reference hyperopt’s express. Note) Due to hyperopt’s randint does not provide lower value, we had to implement it a bait tricky. Please see optimize.py to check how we deal with randint.

get_default_option()¶

Basic Options for pLSI.

Variables:

d (int) – The number of latent feature dimension. (default: 20)
num_iters (int) – The number of iterations for training. (default: 10)
num_workers (int) – The number of threads. (default: 1)
alpha1 (float) – The coefficient of regularization term for clustering assignment. (default: 1.0)
alpha2 (float) – The coefficient of regularization term for item preference in each cluster. (default: 1.0)
eps (float) – epsilon for numerical stability (default: 1e-10)
model_path (str) – Where to save model. (default: ‘’)
save_factors (bool) – Set True, to save models. (default: False)
data_opt (dict) – This option will be used to load data if given. (default: {})