id int64 0 190k | prompt stringlengths 21 13.4M | docstring stringlengths 1 12k ⌀ |
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168,174 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `make_gaussian_encoder` function. Write a Python function `def m... | Gin wrapper to create and apply a Gaussian encoder configurable with gin. This is a separate function so that several different models (such as BetaVAE and FactorVAE) can call this function while the gin binding always stays 'encoder.(...)'. This makes it easier to configure models and parse the results files. Args: in... |
168,175 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `make_decoder` function. Write a Python function `def make_decod... | Gin wrapper to create and apply a decoder configurable with gin. This is a separate function so that several different models (such as BetaVAE and FactorVAE) can call this function while the gin binding always stays 'decoder.(...)'. This makes it easier to configure models and parse the results files. Args: latent_tens... |
168,176 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `make_discriminator` function. Write a Python function `def make... | Gin wrapper to create and apply a discriminator configurable with gin. This is a separate function so that several different models (such as FactorVAE) can potentially call this function while the gin binding always stays 'discriminator.(...)'. This makes it easier to configure models and parse the results files. Args:... |
168,177 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `fc_encoder` function. Write a Python function `def fc_encoder(i... | Fully connected encoder used in beta-VAE paper for the dSprites data. Based on row 1 of Table 1 on page 13 of "beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework" (https://openreview.net/forum?id=Sy2fzU9gl). Args: input_tensor: Input tensor of shape (batch_size, 64, 64, num_channels) to b... |
168,178 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `conv_encoder` function. Write a Python function `def conv_encod... | Convolutional encoder used in beta-VAE paper for the chairs data. Based on row 3 of Table 1 on page 13 of "beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework" (https://openreview.net/forum?id=Sy2fzU9gl) Args: input_tensor: Input tensor of shape (batch_size, 64, 64, num_channels) to build ... |
168,179 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `fc_decoder` function. Write a Python function `def fc_decoder(l... | Fully connected encoder used in beta-VAE paper for the dSprites data. Based on row 1 of Table 1 on page 13 of "beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework" (https://openreview.net/forum?id=Sy2fzU9gl) Args: latent_tensor: Input tensor to connect decoder to. output_shape: Shape of th... |
168,180 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `deconv_decoder` function. Write a Python function `def deconv_d... | Convolutional decoder used in beta-VAE paper for the chairs data. Based on row 3 of Table 1 on page 13 of "beta-VAE: Learning Basic Visual Concepts with a Constrained Variational Framework" (https://openreview.net/forum?id=Sy2fzU9gl) Args: latent_tensor: Input tensor of shape (batch_size,) to connect decoder to. output... |
168,181 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `fc_discriminator` function. Write a Python function `def fc_dis... | Fully connected discriminator used in FactorVAE paper for all datasets. Based on Appendix A page 11 "Disentangling by Factorizing" (https://arxiv.org/pdf/1802.05983.pdf) Args: input_tensor: Input tensor of shape (None, num_latents) to build discriminator on. is_training: Whether or not the graph is built for training (... |
168,182 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `test_encoder` function. Write a Python function `def test_encod... | Simple encoder for testing. Args: input_tensor: Input tensor of shape (batch_size, 64, 64, num_channels) to build encoder on. num_latent: Number of latent variables to output. is_training: Whether or not the graph is built for training (UNUSED). Returns: means: Output tensor of shape (batch_size, num_latent) with laten... |
168,183 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `test_decoder` function. Write a Python function `def test_decod... | Simple decoder for testing. Args: latent_tensor: Input tensor to connect decoder to. output_shape: Output shape. is_training: Whether or not the graph is built for training (UNUSED). Returns: Output tensor of shape (batch_size, 64, 64, num_channels) with the [0,1] pixel intensities. |
168,184 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import gin.tf
def make_optimizer(optimizer_fn, learning_rate):
"""Wrapper to create the optimizer with a given learning_rate."""
if learning_rate is None:
# Learning rat... | Wrapper that uses gin to construct an optimizer for VAEs. |
168,185 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import gin.tf
def make_optimizer(optimizer_fn, learning_rate):
"""Wrapper to create the optimizer with a given learning_rate."""
if learning_rate is None:
# Learning rat... | Wrapper that uses gin to construct an optimizer for the discriminator. |
168,186 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from disentanglement_lib.data.ground_truth import named_data
from disentanglement_lib.methods.semi_supervised import semi_supervised_utils
from disentanglement_lib.methods.semi_supervised ... | Trains a model based on the provided gin configuration. This function will set the provided gin bindings, call the train() function and clear the gin config. Please see the train() for required gin bindings. Args: model_dir: String with path to directory where model output should be saved. overwrite: Boolean indicating... |
168,187 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import gin.tf.external_configurables
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `perfect_labeller` function. Write a Python func... | Returns the true factors of variations without artifacts. Args: labels: True observations of the factors of variations. Numpy array of shape (num_labelled_samples, num_factors) of Float32. dataset: Dataset class. random_state: Random state for the noise (unused). Returns: labels: True observations of the factors of var... |
168,188 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import gin.tf.external_configurables
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `bin_labeller` function. Write a Python function... | Returns simplified factors of variations. The factors of variations are binned to take at most num_bins different values to simulate the process of a human roughly labelling the factors of variations. Args: labels: True observations of the factors of variations. dataset: Dataset class. random_state: Random state for th... |
168,189 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import gin.tf.external_configurables
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `noisy_labeller` function. Write a Python functi... | Returns noisy factors of variations. With probability prob_random, the observation of the factor of variations is uniformly sampled from all possible factor values. Args: labels: True observations of the factors of variations. dataset: Dataset class. random_state: Random state for the noise. prob_random: Probability of... |
168,190 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import gin.tf.external_configurables
import gin.tf
def permute(factor, num_values, random_state):
"""Permutes the ordinal information of a given factor.
Args:
factor: Nump... | Returns factors of variations where the ordinal information is broken. Args: labels: True observations of the factors of variations. dataset: Dataset class. random_state: Random state for the noise (unused). Returns: labels: Noisy factors of variations. Numpy array of shape (num_labelled_samples, num_factors) of Float3... |
168,191 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
import numpy as np
import gin.tf.external_configurables
import gin.tf
def filter_factors(labels, num_observed_factors, random_state):
"""Filter observed factor keeping only a random subset of them... | Returns a few factors of variations without artifacts. Args: labels: True observations of the factors of variations. Numpy array of shape (num_labelled_samples, num_factors) of Float32. dataset: Dataset class. random_state: Random state for the noise (unused). num_observed_factors: How many factors are observed. Return... |
168,192 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Sample from the encoder distribution with reparametrization trick. |
168,193 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Compute KL divergence between input Gaussian and Standard Normal. |
168,194 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Utility function to report tf.metrics in model functions. |
168,195 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Wrapper that creates annealing function. |
168,196 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | No annealing. |
168,197 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Linear annealing. |
168,198 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Fine tuning. This annealer returns zero if step < iteration_threshold and gamma otherwise. Args: gamma: Weight of supervised loss. step: Current step of training. iteration_threshold: When to return gamma instead of zero. Returns: Either gamma or zero. |
168,199 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Wrapper that creates supervised loss. |
168,200 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Implements a supervised l2 regularizer. If the number of latent dimension is greater than the number of factor of variations it only uses the first dimensions of the latent code to regularize. The number of factors of variation must be smaller or equal to the number of latent codes. The representation can be scaled wit... |
168,201 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Implements a supervised cross_entropy regularizer. If the number of latent dimension is greater than the number of factor of variations it only uses the first dimensions of the latent code to regularize. If the number of factors of variation is larger than the latent code dimension it raise an exception. Labels are in ... |
168,202 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Implements a supervised regularizer using a covariance. Penalize the deviation from the identity of the covariance between representation and factors of varations. If the number of latent dimension is greater than the number of factor of variations it only uses the first dimensions of the latent code to regularize. Lab... |
168,203 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Embed factors in 1d and compute softmax with the representation. Assume a factor of variation indexed by j can take k values. We embed each value into k real numbers e_1, ..., e_k. Call e_label(r_j) the embedding of an observed label for the factor j. Then, for a dimension r_j of the representation, the loss is compute... |
168,204 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_lib.methods.un... | Computes I(X, Z). Uses the algorithm in "Mutual Information Neural Estimation" (https://arxiv.org/pdf/1801.04062.pdf). Args: x: Samples from x [batch_size, size_x]. z: Samples from z [batch_size, size_z]. name_net: Scope for the variables forming the network. Returns: Estimate of the mutual information and the update o... |
168,205 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from disentanglement_lib.data.ground_truth import named_data
from disentanglement_lib.data.ground_truth import util
from disentanglement_lib.methods.unsupervised import gaussian_encoder_mod... | Trains a model based on the provided gin configuration. This function will set the provided gin bindings, call the train() function and clear the gin config. Please see train() for required gin bindings. Args: model_dir: String with path to directory where model output should be saved. overwrite: Boolean indicating whe... |
168,206 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_li... | Compute KL divergence between input Gaussian and Standard Normal. |
168,207 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_li... | Utility function to report tf.metrics in model functions. |
168,208 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_li... | Anneal function for anneal_vae (https://arxiv.org/abs/1804.03599). Args: c_max: Maximum capacity. step: Current step. iteration_threshold: How many iterations to reach c_max. Returns: Capacity annealed linearly until c_max. |
168,209 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_li... | Computes the covariance of z_mean. Uses cov(z_mean) = E[z_mean*z_mean^T] - E[z_mean]E[z_mean]^T. Args: z_mean: Encoder mean, tensor of size [batch_size, num_latent]. Returns: cov_z_mean: Covariance of encoder mean, tensor of size [num_latent, num_latent]. |
168,210 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_li... | Compute on and off diagonal regularizers for DIP-VAE models. Penalize deviations of covariance_matrix from the identity matrix. Uses different weights for the deviations of the diagonal and off diagonal entries. Args: covariance_matrix: Tensor of size [num_latent, num_latent] to regularize. lambda_od: Weight of penalty... |
168,211 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import math
from disentanglement_lib.methods.shared import architectures
from disentanglement_lib.methods.shared import losses
from disentanglement_lib.methods.shared import optimizers
from disentanglement_li... | Estimate of total correlation on a batch. We need to compute the expectation over a batch of: E_j [log(q(z(x_j))) - log(prod_l q(z(x_j)_l))]. We ignore the constants as they do not matter for the minimization. The constant should be equal to (num_latents - 1) * log(batch_size * dataset_size) Args: z: [batch_size, num_l... |
168,212 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
from six.moves import range
import gin.tf
def _prune_dims(variances, threshold=0.):
"""Mask for dimensions c... | Computes the FactorVAE disentanglement metric. Args: ground_truth_data: GroundTruthData to be sampled from. representation_function: Function that takes observations as input and outputs a dim_representation sized representation for each observation. random_state: Numpy random state used for randomness. artifact_dir: O... |
168,213 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
from six.moves import range
import gin.tf
def _compute_loss(x_train, y_train, x_test, y_test, predictor_fn):
"""Compute average accur... | Computes loss of downstream task. This task is about strong generalization under covariate shifts. We first perform an intervention fixing a value for a factor in the whole training set. Then, we train a GBT classifier, and at test time, we consider all other values for that factor. We repeat the experiment n_experimen... |
168,214 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import range
import sklearn
from sklearn import ensemble
from sklearn import linear_model
from sklearn import model_selection
import gin.tf
The provided code snippet includes n... | Discretization based on histograms. |
168,215 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import range
import sklearn
from sklearn import ensemble
from sklearn import linear_model
from sklearn import model_selection
import gin.tf
The provided code snippet includes n... | Logistic regression with 5 folds cross validation. |
168,216 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from six.moves import range
import sklearn
from sklearn import ensemble
from sklearn import linear_model
from sklearn import model_selection
import gin.tf
The provided code snippet includes n... | Default gradient boosting classifier. |
168,217 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
import gin.tf
def _drop_constant_dims(ys):
"""Returns a view of the matrix `ys` with dropped constant rows."... | Computes the Interventional Robustness Score. Args: ground_truth_data: GroundTruthData to be sampled from. representation_function: Function that takes observations as input and outputs a dim_representation sized representation for each observation. random_state: Numpy random state used for randomness. artifact_dir: Op... |
168,218 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
from six.moves import range
from sklearn import linear_model
from sklearn import metrics
from sklearn import preprocessing
import gin.t... | Computes the modularity metric according to Sec 3. Args: ground_truth_data: GroundTruthData to be sampled from. representation_function: Function that takes observations as input and outputs a dim_representation sized representation for each observation. random_state: Numpy random state used for randomness. artifact_di... |
168,219 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.evaluation.metrics import dci
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
from six.moves import range
import gin.tf
def compute_reduced_representation(mus... | Computes loss of a reduced downstream task. Measure the information leakage in each latent component after removing the k ("factors_to_remove") most informative features for the prediction task. Args: ground_truth_data: GroundTruthData to be sampled from. representation_function: Function that takes observations as inp... |
168,220 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.evaluation.metrics import dci
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
from six.moves import range
import gin.tf
"dci",
The provided code snippet inc... | Computes the DCI importance matrix of the attributes. Args: mus_train: latent means of the training batch. ys_train: labels of the training batch. mus_test: latent means of the test batch. ys_test: labels of the test batch. Returns: Matrix with importance scores. |
168,221 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
import numpy as np
from six.moves import range
from sklearn import linear_model
import gin.tf
def _generate_training_batch(ground_truth_data, representation_function,
... | Computes the BetaVAE disentanglement metric using scikit-learn. Args: ground_truth_data: GroundTruthData to be sampled from. representation_function: Function that takes observations as input and outputs a dim_representation sized representation for each observation. random_state: Numpy random state used for randomness... |
168,222 | from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
import gin.tf
def _compute_mig(mus_train, ys_train):
"""Computes score based on both training and testing codes and factors."""
score_dict = {}
discretized_mus = utils.make_discretizer(mus_train)
m = utils.discr... | Computes the mutual information gap. Args: ground_truth_data: GroundTruthData to be sampled from. representation_function: Function that takes observations as input and outputs a dim_representation sized representation for each observation. random_state: Numpy random state used for randomness. artifact_dir: Optional pa... |
168,223 | from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
import gin.tf
def _compute_mig(mus_train, ys_train):
"""Computes score based on both training and testing codes and factors."""
score_dict = {}
discretized_mus = utils.make_discretizer(mus_train)
m = utils.discr... | Computes the MIG scores on the fixed set of observations and labels. Args: observations: Observations on which to compute the score. Observations have shape (num_observations, 64, 64, num_channels). labels: Observed factors of variations. representation_function: Function that takes observations as input and outputs a ... |
168,224 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
from six.moves import range
import gin.tf
def _compute_loss(x_train, y_train, x_test, y_test, predictor_fn):
"""Compute average accur... | Computes loss of downstream task. Args: ground_truth_data: GroundTruthData to be sampled from. representation_function: Function that takes observations as input and outputs a dim_representation sized representation for each observation. random_state: Numpy random state used for randomness. artifact_dir: Optional path ... |
168,225 | from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
import scipy
import gin.tf
def gaussian_total_correlation(cov):
"""Computes the total correlation of a Gaussian with covariance matrix cov.
We use that the total correlation is the KL divergence between the Gaussian... | Computes unsupervised scores based on covariance and mutual information. Args: ground_truth_data: GroundTruthData to be sampled from. representation_function: Function that takes observations as input and outputs a dim_representation sized representation for each observation. random_state: Numpy random state used for r... |
168,226 | from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
import scipy
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `kl_gaussians_numerically_unstable` function. Write a Python function `def kl_gaussians_numerically_unstable(mea... | Unstable version used for testing gaussian_total_correlation. |
168,227 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import logging
from disentanglement_lib.evaluation.metrics import dci
from disentanglement_lib.evaluation.metrics import modularity_explicitness
from disentanglement_lib.evaluation.metrics im... | Computes the unified disentanglement scores. Args: ground_truth_data: GroundTruthData to be sampled from. representation_function: Function that takes observations as input and outputs a dim_representation sized representation for each observation. random_state: Numpy random state used for randomness. artifact_dir: Opt... |
168,228 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import logging
from disentanglement_lib.evaluation.metrics import dci
from disentanglement_lib.evaluation.metrics import modularity_explicitness
from disentanglement_lib.evaluation.metrics im... | Computes the unified scores on the fixed set of observations and labels. Args: observations: Observations on which to compute the score. Observations have shape (num_observations, 64, 64, num_channels). labels: Observed factors of variations. representation_function: Function that takes observations as input and output... |
168,229 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import logging
from disentanglement_lib.evaluation.metrics import dci
from disentanglement_lib.evaluation.metrics import modularity_explicitness
from disentanglement_lib.evaluation.metrics im... | Computes the importance matrix of the DCI Disentanglement score. The importance matrix is based on the importance of each code to predict a factor of variation with GBT. Args: mus_train: Batch of learned representations to be used for training. ys_train: Observed factors of variation corresponding to the representation... |
168,230 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import logging
from disentanglement_lib.evaluation.metrics import dci
from disentanglement_lib.evaluation.metrics import modularity_explicitness
from disentanglement_lib.evaluation.metrics im... | Computes the mutual information matrix between codes and factors. The mutual information matrix is used to compute the MIG and Modularity scores. Args: mus_train: Batch of learned representations to be used for training. ys_train: Observed factors of variation corresponding to the representations in mus_train. mus_test... |
168,231 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import logging
from disentanglement_lib.evaluation.metrics import dci
from disentanglement_lib.evaluation.metrics import modularity_explicitness
from disentanglement_lib.evaluation.metrics im... | Prediction accuracy of a SVM predicting a factor from a single code. The matrix of accuracies is used to compute the SAP score. Args: mus_train: Batch of learned representations to be used for training. ys_train: Observed factors of variation corresponding to the representations in mus_train. mus_test: Batch of learned... |
168,232 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import logging
from disentanglement_lib.evaluation.metrics import dci
from disentanglement_lib.evaluation.metrics import modularity_explicitness
from disentanglement_lib.evaluation.metrics im... | Aggregation function of the DCI Disentanglement. |
168,233 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import logging
from disentanglement_lib.evaluation.metrics import dci
from disentanglement_lib.evaluation.metrics import modularity_explicitness
from disentanglement_lib.evaluation.metrics im... | Aggregation function of the MIG. |
168,234 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import logging
from disentanglement_lib.evaluation.metrics import dci
from disentanglement_lib.evaluation.metrics import modularity_explicitness
from disentanglement_lib.evaluation.metrics im... | Aggregation function of the SAP score. |
168,235 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from absl import logging
from disentanglement_lib.evaluation.metrics import dci
from disentanglement_lib.evaluation.metrics import modularity_explicitness
from disentanglement_lib.evaluation.metrics im... | Aggregation function of the modularity score. |
168,236 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
from six.moves import range
import gin.tf
def compute_scores_dict(metric, prefix):
"""Computes scores for combinations of predictive ... | Computes unfairness scores. We first compute either the mean or maximum total variation for a given sensitive and target variable. Then, we either average or take the maximum with respect to target and sensitive variable. For convenience, we compute and save all combinations. The score used in Section 4 of the paper is... |
168,237 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
import scipy
from six.moves import range
from sklearn import ensemble
import gin.tf
def _compute_dci(mus_train... | Computes the DCI scores according to Sec 2. Args: ground_truth_data: GroundTruthData to be sampled from. representation_function: Function that takes observations as input and outputs a dim_representation sized representation for each observation. random_state: Numpy random state used for randomness. artifact_dir: Opti... |
168,238 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
import scipy
from six.moves import range
from sklearn import ensemble
import gin.tf
def _compute_dci(mus_train... | Computes the DCI scores on the fixed set of observations and labels. Args: observations: Observations on which to compute the score. Observations have shape (num_observations, 64, 64, num_channels). labels: Observed factors of variations. representation_function: Function that takes observations as input and outputs a ... |
168,239 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
from six.moves import range
from sklearn import svm
import gin.tf
def _compute_sap(mus, ys, mus_test, ys_test,... | Computes the SAP score. Args: ground_truth_data: GroundTruthData to be sampled from. representation_function: Function that takes observations as input and outputs a dim_representation sized representation for each observation. random_state: Numpy random state used for randomness. artifact_dir: Optional path to directo... |
168,240 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
from disentanglement_lib.evaluation.metrics import utils
import numpy as np
from six.moves import range
from sklearn import svm
import gin.tf
def _compute_sap(mus, ys, mus_test, ys_test,... | Computes the SAP score on the fixed set of observations and labels. Args: observations: Observations on which to compute the score. Observations have shape (num_observations, 64, 64, num_channels). labels: Observed factors of variations. representation_function: Function that takes observations as input and outputs a d... |
168,241 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl import logging
import numpy as np
import scipy
from sklearn import linear_model
from sklearn import preprocessing
import gin.tf
def relative_strength_disentanglement(corr_matrix):
"""Computes disenta... | Computes the UDR score using scikit-learn. Args: ground_truth_data: GroundTruthData to be sampled from. representation_functions: functions that takes observations as input and outputs a dim_representation sized representation for each observation. random_state: numpy random state used for randomness. batch_size: Numbe... |
168,242 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import contextlib
import os
import time
from absl import flags
from disentanglement_lib.data.ground_truth import named_data
from disentanglement_lib.evaluation.udr.metrics import udr
from disentanglement_lib.ut... | Loads a trained estimator and evaluates it according to beta-VAE metric. |
168,243 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `sample_easy_alternative` function. Write a Python function `def sample_easy_alternative(design, matrix, already... | Samples easy alternative based on sampling a new PGM. |
168,244 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
The provided code snippet includes necessary dependencies for implementing the `sample_hard_alternative` function. Write a Python function `def sample_hard_alternative(design, matrix, already... | Samples hard alternative based on sampling a new PGM. |
168,245 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def is_constant_row(row):
return len(np.unique(row)) == 1 | null |
168,246 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
def is_distinct_row(row):
return len(np.unique(row)) == len(row) | null |
168,247 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from disentanglement_lib.evaluation.abstract_reasoning import models
from disentanglement_lib.evaluation.abstract_reasoning import pgm_data
from disentanglement_lib.utils import results
im... | Trains a model based on the provided gin configuration. This function will set the provided gin bindings, call the reason() function and clear the gin config. Please see reason() for required gin bindings. Args: input_dir: String with path to directory where the representation is saved. output_dir: String with the path... |
168,248 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.data.ground_truth import dsprites
from disentanglement_lib.data.ground_truth import dummy_data
from disentanglement_lib.data.ground_truth import ground_truth_data as gtd
from disentangle... | Returns an image of the question mark. |
168,249 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.data.ground_truth import dsprites
from disentanglement_lib.data.ground_truth import dummy_data
from disentanglement_lib.data.ground_truth import ground_truth_data as gtd
from disentangle... | Embeds the indices as one hot vectors. |
168,250 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.evaluation.abstract_reasoning import relational_layers
import gin
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
from tensorflow.contrib import tpu as contrib_tpu
def ge... | null |
168,251 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.evaluation.abstract_reasoning import relational_layers
import gin
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
from tensorflow.contrib import tpu as contrib_tpu
def ge... | null |
168,252 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
The provided code snippet includes necessary dependencies for implementing the `repeat` function. Write a Python function `def repeat(tensor, num, axis)` to solve the following... | Repeats tensor num times along the specified axis. |
168,253 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
The provided code snippet includes necessary dependencies for implementing the `positional_encoding_like` function. Write a Python function `def positional_encoding_like(tensor... | Creates positional encoding matching the provided tensor. Let each slice along the last axis of the tensor be a row. This function computes the index of each row with respect to the specified positional_encoding_axis and returns this index using a one-hot embedding. The resulting tensor has the same shape as the provid... |
168,254 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import inspect
import os
import time
import warnings
from disentanglement_lib.data.ground_truth import named_data
from disentanglement_lib.evaluation.metrics import beta_vae
from disentanglement_lib.evaluation.... | Evaluate a representation based on the provided gin configuration. This function will set the provided gin bindings, call the evaluate() function and clear the gin config. Please see the evaluate() for required gin bindings. Args: model_dir: String with path to directory where the representation is saved. output_dir: S... |
168,255 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.config import study
from disentanglement_lib.utils import resources
import disentanglement_lib.utils.hyperparams as h
from six.moves import range
def get_num_latent(sweep):
return h.s... | null |
168,256 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.config import study
from disentanglement_lib.utils import resources
import disentanglement_lib.utils.hyperparams as h
from six.moves import range
def get_datasets():
"""Returns all the... | Returns the hyperparameter configs for different experiments. |
168,258 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from disentanglement_lib.config import study
from disentanglement_lib.utils import resources
import disentanglement_lib.utils.hyperparams as h
from six.moves import range
def get_datasets():
"""Returns all the... | Returns the hyperparameter configs for different experiments. |
168,261 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import time
from disentanglement_lib.data.ground_truth import named_data
from disentanglement_lib.postprocessing import methods
from disentanglement_lib.utils import convolute_hub
from disentanglement... | Postprocess a trained model based on the provided gin configuration. This function will set the provided gin bindings, call the postprocess() function and clear the gin config. Please see the postprocess() for required gin bindings. Args: model_dir: String with path to directory where the model is saved. output_dir: St... |
168,262 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `mean_representation` function. Write a Python function `def mean_representation( ... | Extracts the mean representation from a Gaussian encoder. Args: ground_truth_data: GroundTruthData to be sampled from. gaussian_encoder: Function that takes observations as input and outputs a dictionary with mean and log variances of the encodings in the keys "mean" and "logvar" respectively. random_state: Numpy rando... |
168,263 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import gin.tf
The provided code snippet includes necessary dependencies for implementing the `sampled_representation` function. Write a Python function `def sampled_representat... | Extracts the random representation from a Gaussian encoder. Args: ground_truth_data: GroundTruthData to be sampled from. gaussian_encoder: Function that takes observations as input and outputs a dictionary with mean and log variances of the encodings in the keys "mean" and "logvar" respectively. random_state: Numpy ran... |
168,264 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numbers
import os
from disentanglement_lib.data.ground_truth import named_data
from disentanglement_lib.utils import results
from disentanglement_lib.visualize import visualize_util
from disentanglement_l... | Takes trained model from model_dir and visualizes it in output_dir. Args: model_dir: Path to directory where the trained model is saved. output_dir: Path to output directory. overwrite: Boolean indicating whether to overwrite output directory. num_animations: Integer with number of distinct animations to create. num_fr... |
168,265 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
from disentanglement_lib.data.ground_truth import named_data
from disentanglement_lib.visualize import visualize_util
import numpy as np
from six.moves import range
from tensorflow.compat.v1 import gfi... | Visualizes the data set by saving images to output_path. For each latent factor, outputs 16 images where only that latent factor is varied while all others are kept constant. Args: dataset_name: String with name of dataset as defined in named_data.py. output_path: String with path in which to create the visualizations.... |
168,266 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import multiprocessing
from absl import logging
import pandas as pd
import simplejson as json
from tensorflow.compat.v1 import gfile
def _get(pattern):
files = gfile.Glob(pattern)
pool = multiprocessing.Pool... | Aggregates all the results files in the pattern into a single JSON file. Args: result_file_pattern: String with glob pattern to all the result files that should be aggregated (e.g. /tmp/*/results/aggregate/evaluation.json). output_path: String with path to output json file (e.g. /tmp/results.json). |
168,267 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import multiprocessing
from absl import logging
import pandas as pd
import simplejson as json
from tensorflow.compat.v1 import gfile
The provided code snippet includes necessary dependencies for implementing th... | Convenience function to load aggregated results from JSON file. Args: source_path: String with path to aggregated json file (e.g. /tmp/results.json). Returns: pd.DataFrame with aggregated results. |
168,268 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import six
from six.moves import range
from six.moves import zip
def _escape_value(value):
if isinstance(value, (str, six.text_type)) and not value.startswith("@"):
return "'{}'".format(value)
return str... | null |
168,269 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
def get_files_in_folder(path):
import pkg_resources # pylint: disable=g-bad-import-order, g-import-not-at-top
for name in pkg_resources.resource_listdir("disentanglement_lib", path):
new_path... | null |
168,270 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow.compat.v1 as tf
import tensorflow_hub as hub
from tensorflow.contrib import framework as contrib_framework
The provided code snippet includes necessary dependencies for implementing the `save_... | Saves several NumpyArrays to variables in a TF checkpoint. Args: checkpoint_path: String with the path to the checkpoint file. **dict_with_arrays: Dictionary with keys that signify variable names and values that are the corresponding Numpy arrays to be saved. |
168,271 | import argparse
import os
import torch
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, set_peft_model_state_dict
from torch.utils.data import IterableDataset
from tqdm import tqdm
from transformers import AutoConfig, Auto... | null |
168,272 | import argparse
import os
import torch
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, set_peft_model_state_dict
from torch.utils.data import IterableDataset
from tqdm import tqdm
from transformers import AutoConfig, Auto... | null |
168,273 | import argparse
import os
import torch
from accelerate import Accelerator
from datasets import load_dataset
from peft import LoraConfig, get_peft_model, prepare_model_for_int8_training, set_peft_model_state_dict
from torch.utils.data import IterableDataset
from tqdm import tqdm
from transformers import AutoConfig, Auto... | null |
168,274 | from transformers import AutoModelForCausalLM, AutoTokenizer
from peft import PeftModel
import torch
import os
import argparse
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument("--base_model_name_or_path", type=str, default="bigcode/large-model")
parser.add_argument("--peft_model_path"... | null |
168,275 | import dataclasses
import os
from dataclasses import dataclass
from typing import List, Optional
from huggingface_hub import login
from transformers import HfArgumentParser
The provided code snippet includes necessary dependencies for implementing the `hf_login` function. Write a Python function `def hf_login()` to so... | Login to HuggingFace Hub if HF_TOKEN is defined in the environment |
168,276 | import json
import os
from dataclasses import asdict, dataclass
from pathlib import Path
from typing import Any, Dict, List, Optional, Type, TypeVar, Union
from huggingface_hub import ModelHubMixin, hf_hub_download
class DialogueTemplate(ModelHubMixin):
def get_training_prompt(self) -> str:
def get_inference_... | null |
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