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train | WideResnet | WideResnet from https://arxiv.org/pdf/1605.07146.pdf.
Args:
num_blocks: int, number of blocks in a group.
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: int, number of classes to distinguish.
mode: is it training or eval.
Returns:
The WideResnet model w... | tensor2tensor/trax/models/resnet.py | def WideResnet(num_blocks=3, hidden_size=64, num_output_classes=10,
mode='train'):
"""WideResnet from https://arxiv.org/pdf/1605.07146.pdf.
Args:
num_blocks: int, number of blocks in a group.
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: int, num... | def WideResnet(num_blocks=3, hidden_size=64, num_output_classes=10,
mode='train'):
"""WideResnet from https://arxiv.org/pdf/1605.07146.pdf.
Args:
num_blocks: int, number of blocks in a group.
hidden_size: the size of the first hidden layer (multiplied later).
num_output_classes: int, num... | [
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train | GRUCell | Builds a traditional GRU cell with dense internal transformations.
Gated Recurrent Unit paper: https://arxiv.org/abs/1412.3555
Args:
units: Number of hidden units.
Returns:
A Stax model representing a traditional GRU RNN cell. | tensor2tensor/trax/layers/rnn.py | def GRUCell(units):
"""Builds a traditional GRU cell with dense internal transformations.
Gated Recurrent Unit paper: https://arxiv.org/abs/1412.3555
Args:
units: Number of hidden units.
Returns:
A Stax model representing a traditional GRU RNN cell.
"""
return GeneralGRUCell(
candidate_tra... | def GRUCell(units):
"""Builds a traditional GRU cell with dense internal transformations.
Gated Recurrent Unit paper: https://arxiv.org/abs/1412.3555
Args:
units: Number of hidden units.
Returns:
A Stax model representing a traditional GRU RNN cell.
"""
return GeneralGRUCell(
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train | ConvGRUCell | Builds a convolutional GRU.
Paper: https://arxiv.org/abs/1511.06432.
Args:
units: Number of hidden units
kernel_size: Kernel size for convolution
Returns:
A Stax model representing a GRU cell with convolution transforms. | tensor2tensor/trax/layers/rnn.py | def ConvGRUCell(units, kernel_size=(3, 3)):
"""Builds a convolutional GRU.
Paper: https://arxiv.org/abs/1511.06432.
Args:
units: Number of hidden units
kernel_size: Kernel size for convolution
Returns:
A Stax model representing a GRU cell with convolution transforms.
"""
def BuildConv():
... | def ConvGRUCell(units, kernel_size=(3, 3)):
"""Builds a convolutional GRU.
Paper: https://arxiv.org/abs/1511.06432.
Args:
units: Number of hidden units
kernel_size: Kernel size for convolution
Returns:
A Stax model representing a GRU cell with convolution transforms.
"""
def BuildConv():
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train | GeneralGRUCell | r"""Parametrized Gated Recurrent Unit (GRU) cell construction.
GRU update equations:
$$ Update gate: u_t = \sigmoid(U' * s_{t-1} + B') $$
$$ Reset gate: r_t = \sigmoid(U'' * s_{t-1} + B'') $$
$$ Candidate memory: c_t = \tanh(U * (r_t \odot s_{t-1}) + B) $$
$$ New State: s_t = u_t \odot s_{t-1} + (1 - u_t) \o... | tensor2tensor/trax/layers/rnn.py | def GeneralGRUCell(candidate_transform,
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gate_nonlinearity=core.Sigmoid,
candidate_nonlinearity=core.Tanh,
dropout_rate_c=0.1,
sigmoid_bias=0.5):
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memory_transform=combinators.Identity,
gate_nonlinearity=core.Sigmoid,
candidate_nonlinearity=core.Tanh,
dropout_rate_c=0.1,
sigmoid_bias=0.5):
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train | MakeTargetMask | Create an attention mask to hide padding and future words. | tensor2tensor/trax/layers/attention.py | def MakeTargetMask(target, pad=0):
"""Create an attention mask to hide padding and future words."""
target_mask = (target != pad)[ :, np.newaxis, :]
target_dtype = target_mask.dtype
causal_mask = onp.tril(onp.ones((1, target.shape[-1], target.shape[-1]),
dtype=target_dtype), k=... | def MakeTargetMask(target, pad=0):
"""Create an attention mask to hide padding and future words."""
target_mask = (target != pad)[ :, np.newaxis, :]
target_dtype = target_mask.dtype
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train | PreparePairedSequenceBatch | Build masks for this batch.
Args:
source: (batch, source_len) array of integer-coded symbols for inputs
target_in: (batch, batch_len) array of integer-coded symbols for targets
pad: int: the padding symbol used to pad the above
Returns:
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Args:
source: (batch, source_len) array of integer-coded symbols for inputs
target_in: (batch, batch_len) array of integer-coded symbols for targets
pad: int: the padding symbol used to pad the above
Returns:
... | def PreparePairedSequenceBatch(source, target_in, pad=0):
"""Build masks for this batch.
Args:
source: (batch, source_len) array of integer-coded symbols for inputs
target_in: (batch, batch_len) array of integer-coded symbols for targets
pad: int: the padding symbol used to pad the above
Returns:
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train | _layer_norm_new_params | Helper: create layer norm parameters. | tensor2tensor/trax/layers/attention.py | def _layer_norm_new_params(input_shape, rng, epsilon=1e-6): # pylint: disable=invalid-name
"""Helper: create layer norm parameters."""
del rng, epsilon
features = input_shape[-1]
scale = np.ones(features)
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return (scale, bias) | def _layer_norm_new_params(input_shape, rng, epsilon=1e-6): # pylint: disable=invalid-name
"""Helper: create layer norm parameters."""
del rng, epsilon
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train | _positional_encoding_new_params | Helper: create positional encoding parameters. | tensor2tensor/trax/layers/attention.py | def _positional_encoding_new_params(input_shape, rng, max_len=2048): # pylint: disable=invalid-name
"""Helper: create positional encoding parameters."""
del rng
# Check if we are operating on chunked inputs by checking if the first
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is_... | def _positional_encoding_new_params(input_shape, rng, max_len=2048): # pylint: disable=invalid-name
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train | PositionalEncoding | Implements bare positional encoding. | tensor2tensor/trax/layers/attention.py | def PositionalEncoding(x, params, **unused_kwargs):
"""Implements bare positional encoding."""
if not isinstance(x, (list, tuple)): # non-chunked inputs
symbol_size = np.shape(x)[1]
return x + params[:, :symbol_size, :]
# Chunked case: apply to all chunks selecting as much as needed.
offset = 0
resul... | def PositionalEncoding(x, params, **unused_kwargs):
"""Implements bare positional encoding."""
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train | DotProductAttention | Core dot product self-attention.
Args:
query: array of representations
key: array of representations
value: array of representations
mask: attention-mask, gates attention
dropout: float: dropout rate
mode: 'eval' or 'train': whether to use dropout
rng: JAX PRNGKey: subkey for disposable u... | tensor2tensor/trax/layers/attention.py | def DotProductAttention(query, key, value, mask, dropout, mode, rng):
"""Core dot product self-attention.
Args:
query: array of representations
key: array of representations
value: array of representations
mask: attention-mask, gates attention
dropout: float: dropout rate
mode: 'eval' or 't... | def DotProductAttention(query, key, value, mask, dropout, mode, rng):
"""Core dot product self-attention.
Args:
query: array of representations
key: array of representations
value: array of representations
mask: attention-mask, gates attention
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train | PureDotProductAttention | Pure single-headed self-attention.
Args:
dropout: float: dropout rate
mode: str: 'train' or 'eval'
Returns:
Pure single-headed attention layer. (No Dense transforms on input.) | tensor2tensor/trax/layers/attention.py | def PureDotProductAttention(dropout=0.0, mode='train'):
"""Pure single-headed self-attention.
Args:
dropout: float: dropout rate
mode: str: 'train' or 'eval'
Returns:
Pure single-headed attention layer. (No Dense transforms on input.)
"""
def init_fun(_, input_shapes): # pylint: disable=invalid... | def PureDotProductAttention(dropout=0.0, mode='train'):
"""Pure single-headed self-attention.
Args:
dropout: float: dropout rate
mode: str: 'train' or 'eval'
Returns:
Pure single-headed attention layer. (No Dense transforms on input.)
"""
def init_fun(_, input_shapes): # pylint: disable=invalid... | [
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train | PureMultiHeadedAttention | Pure transformer-style multi-headed attention.
Args:
x: inputs ((q, k, v), mask)
params: parameters (none)
num_heads: int: number of attention heads
dropout: float: dropout rate
mode: str: 'train' or 'eval'
**kwargs: other arguments including the rng
Returns:
Pure Multi-headed attentio... | tensor2tensor/trax/layers/attention.py | def PureMultiHeadedAttention(x, params, num_heads=8, dropout=0.0,
mode='train', **kwargs):
"""Pure transformer-style multi-headed attention.
Args:
x: inputs ((q, k, v), mask)
params: parameters (none)
num_heads: int: number of attention heads
dropout: float: dropout rat... | def PureMultiHeadedAttention(x, params, num_heads=8, dropout=0.0,
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Args:
x: inputs ((q, k, v), mask)
params: parameters (none)
num_heads: int: number of attention heads
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train | MultiHeadedAttentionQKV | Transformer-style multi-headed attention.
Accepts inputs of the form (q, k, v), mask.
Args:
feature_depth: int: depth of embedding
num_heads: int: number of attention heads
dropout: float: dropout rate
mode: str: 'train' or 'eval'
Returns:
Multi-headed self-attention layer. | tensor2tensor/trax/layers/attention.py | def MultiHeadedAttentionQKV(
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"""Transformer-style multi-headed attention.
Accepts inputs of the form (q, k, v), mask.
Args:
feature_depth: int: depth of embedding
num_heads: int: number of attention heads
dropout: float: dropout rate
m... | def MultiHeadedAttentionQKV(
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Accepts inputs of the form (q, k, v), mask.
Args:
feature_depth: int: depth of embedding
num_heads: int: number of attention heads
dropout: float: dropout rate
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train | MultiHeadedAttention | Transformer-style multi-headed attention.
Accepts inputs of the form (x, mask) and constructs (q, k, v) from x.
Args:
feature_depth: int: depth of embedding
num_heads: int: number of attention heads
dropout: float: dropout rate
mode: str: 'train' or 'eval'
Returns:
Multi-headed self-attent... | tensor2tensor/trax/layers/attention.py | def MultiHeadedAttention(
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"""Transformer-style multi-headed attention.
Accepts inputs of the form (x, mask) and constructs (q, k, v) from x.
Args:
feature_depth: int: depth of embedding
num_heads: int: number of attention heads
dropout: fl... | def MultiHeadedAttention(
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num_heads: int: number of attention heads
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train | _chunked_selector_output_shape | Helper: calculate output shape for chunked key selector (see below). | tensor2tensor/trax/layers/attention.py | def _chunked_selector_output_shape( # pylint: disable=invalid-name
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# Read the main function below first, the shape logic just follows the ops.
selector = selector or (lambda x: [] if x < ... | def _chunked_selector_output_shape( # pylint: disable=invalid-name
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train | ChunkedCausalMultiHeadedAttention | Transformer-style causal multi-headed attention operating on chunks.
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train | ShiftRight | Layer to shift the tensor to the right by padding on axis 1. | tensor2tensor/trax/layers/attention.py | def ShiftRight(x, **unused_kwargs):
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pad_widths = [(0, 0), (1, 0)]
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train | zipf_distribution | Helper function: Create a Zipf distribution.
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alpha: float, Zipf's Law Distribution parameter. Default = 1.5.
Usually for modelling natural text distribution is in
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distr_map: list of float, Zipf's... | tensor2tensor/data_generators/algorithmic.py | def zipf_distribution(nbr_symbols, alpha):
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Args:
nbr_symbols: number of symbols to use in the distribution.
alpha: float, Zipf's Law Distribution parameter. Default = 1.5.
Usually for modelling natural text distribution is in
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... | def zipf_distribution(nbr_symbols, alpha):
"""Helper function: Create a Zipf distribution.
Args:
nbr_symbols: number of symbols to use in the distribution.
alpha: float, Zipf's Law Distribution parameter. Default = 1.5.
Usually for modelling natural text distribution is in
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train | zipf_random_sample | Helper function: Generate a random Zipf sample of given length.
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distr_map: list of float, Zipf's distribution over nbr_symbols.
sample_len: integer, length of sequence to generate.
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distr_map: list of float, Zipf's distribution over nbr_symbols.
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distr_map: list of float, Zipf's distribution over nbr_symbols.
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train | reverse_generator_nlplike | Generator for the reversing nlp-like task on sequences of symbols.
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train | lower_endian_to_number | Helper function: convert a list of digits in the given base to a number. | tensor2tensor/data_generators/algorithmic.py | def lower_endian_to_number(l, base):
"""Helper function: convert a list of digits in the given base to a number."""
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train | number_to_lower_endian | Helper function: convert a number to a list of digits in the given base. | tensor2tensor/data_generators/algorithmic.py | def number_to_lower_endian(n, base):
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train | random_number_lower_endian | Helper function: generate a random number as a lower-endian digits list. | tensor2tensor/data_generators/algorithmic.py | def random_number_lower_endian(length, base):
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train | remote_run | Run command on GCS instance, optionally detached. | tensor2tensor/data_generators/wikisum/parallel_launch.py | def remote_run(cmd, instance_name, detach=False, retries=1):
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train | wait_for_ssh | Wait for SSH to be available at given IP address. | tensor2tensor/data_generators/wikisum/parallel_launch.py | def wait_for_ssh(ip):
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train | launch_instance | Launch a GCE instance. | tensor2tensor/data_generators/wikisum/parallel_launch.py | def launch_instance(instance_name,
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train | evolved_transformer_encoder | Evolved Transformer encoder. See arxiv.org/abs/1901.11117 for more details.
Note: Pad remover is not supported.
Args:
encoder_input: a Tensor.
encoder_self_attention_bias: bias Tensor for self-attention (see
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hparams: hyperparameters for model.
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train | evolved_transformer_decoder | Evolved Transformer decoder. See arxiv.org/abs/1901.11117 for more details.
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decoder_input: a Tensor.
encoder_output: a Tensor.
decoder_self_attention_bias: bias Tensor for self-attention (see
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train | _add_attend_to_encoder_cache | Add attend-to-encoder layers to cache. | tensor2tensor/models/evolved_transformer.py | def _add_attend_to_encoder_cache(cache, attention_name, hparams, num_layers,
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train | _init_evolved_transformer_cache | Create the initial cache for Evolved Transformer fast decoding. | tensor2tensor/models/evolved_transformer.py | def _init_evolved_transformer_cache(cache, hparams, batch_size,
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train | add_evolved_transformer_hparams | Add Evolved Transformer hparams.
Note: These are for the Adam optimizer, not the Adafactor optimizer used in
the paper.
Args:
hparams: Current hparams.
Returns:
hparams updated with Evolved Transformer values. | tensor2tensor/models/evolved_transformer.py | def add_evolved_transformer_hparams(hparams):
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Note: These are for the Adam optimizer, not the Adafactor optimizer used in
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Args:
hparams: Current hparams.
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Args:
hparams: Current hparams.
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hparams updated with Evolved Transformer values.
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train | evolved_transformer_base_tpu | Base parameters for Evolved Transformer model on TPU. | tensor2tensor/models/evolved_transformer.py | def evolved_transformer_base_tpu():
"""Base parameters for Evolved Transformer model on TPU."""
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hparams.learning_rate_constant = 1 / hparams.learning_rate_warmup_steps ** 0.5
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train | evolved_transformer_big_tpu | Big parameters for Evolved Transformer model on TPU. | tensor2tensor/models/evolved_transformer.py | def evolved_transformer_big_tpu():
"""Big parameters for Evolved Transformer model on TPU."""
hparams = add_evolved_transformer_hparams(transformer.transformer_big_tpu())
hparams.learning_rate_constant = 1 / hparams.learning_rate_warmup_steps ** 0.5
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"""Big parameters for Evolved Transformer model on TPU."""
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train | transformer_moe_layer_v1 | Local mixture of experts that works well on TPU.
Adapted from the paper https://arxiv.org/abs/1701.06538
Note: until the algorithm and inferface solidify, we pass in a hyperparameters
dictionary in order not to complicate the interface in mtf_transformer.py .
Once this code moves out of "research", we should ... | tensor2tensor/models/research/moe.py | def transformer_moe_layer_v1(inputs, output_dim, hparams, train,
master_dtype=tf.bfloat16,
slice_dtype=tf.float32):
"""Local mixture of experts that works well on TPU.
Adapted from the paper https://arxiv.org/abs/1701.06538
Note: until the algorithm and ... | def transformer_moe_layer_v1(inputs, output_dim, hparams, train,
master_dtype=tf.bfloat16,
slice_dtype=tf.float32):
"""Local mixture of experts that works well on TPU.
Adapted from the paper https://arxiv.org/abs/1701.06538
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train | transformer_moe_layer_v2 | 2-level mixture of experts.
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Note: until the algorithm and inferface solidify, we pass in a hyperparameters
dictionary in order not to complicate the interface in mtf_transformer.py .
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"""2-level mixture of experts.
Adapted from the paper https://arxiv.org/abs/1701.06538
Note: until the algorithm and inferface solidify, we pass in a hyperparameters
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train | _top_2_gating | Compute gating for mixture-of-experts in TensorFlow.
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Once this code moves out of "research", we should pass the hyperparameters
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"""Compute gating for mixture-of-experts in TensorFlow.
Note: until the algorithm and inferface solidify, we pass in a hyperparameters
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train | set_default_moe_hparams | Add necessary hyperparameters for mixture-of-experts. | tensor2tensor/models/research/moe.py | def set_default_moe_hparams(hparams):
"""Add necessary hyperparameters for mixture-of-experts."""
hparams.moe_num_experts = 16
hparams.moe_loss_coef = 1e-2
hparams.add_hparam("moe_gating", "top_2")
# Experts have fixed capacity per batch. We need some extra capacity
# in case gating is not perfectly balanc... | def set_default_moe_hparams(hparams):
"""Add necessary hyperparameters for mixture-of-experts."""
hparams.moe_num_experts = 16
hparams.moe_loss_coef = 1e-2
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train | _split_into_groups | Helper function for figuring out how to split a dimensino into groups.
We have a dimension with size n and we want to split it into
two dimensions: n = num_groups * group_size
group_size should be the largest possible value meeting the constraints:
group_size <= max_group_size
(num_groups = n/group_size... | tensor2tensor/models/research/moe.py | def _split_into_groups(n, max_group_size, mesh_dim_size):
"""Helper function for figuring out how to split a dimensino into groups.
We have a dimension with size n and we want to split it into
two dimensions: n = num_groups * group_size
group_size should be the largest possible value meeting the constraints:
... | def _split_into_groups(n, max_group_size, mesh_dim_size):
"""Helper function for figuring out how to split a dimensino into groups.
We have a dimension with size n and we want to split it into
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train | InGraphBatchEnv.reset | Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset.
Returns:
Batch tensor of the new observations. | tensor2tensor/rl/envs/in_graph_batch_env.py | def reset(self, indices=None):
"""Reset the batch of environments.
Args:
indices: The batch indices of the environments to reset.
Returns:
Batch tensor of the new observations.
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train | adafactor_decay_rate_adam | Second-moment decay rate like Adam, subsuming the correction factor.
Args:
beta2: a float between 0 and 1
Returns:
a scalar | tensor2tensor/utils/adafactor.py | def adafactor_decay_rate_adam(beta2):
"""Second-moment decay rate like Adam, subsuming the correction factor.
Args:
beta2: a float between 0 and 1
Returns:
a scalar
"""
t = tf.to_float(tf.train.get_or_create_global_step()) + 1.0
decay = beta2 * (1.0 - tf.pow(beta2, t - 1.0)) / (1.0 - tf.pow(beta2, ... | def adafactor_decay_rate_adam(beta2):
"""Second-moment decay rate like Adam, subsuming the correction factor.
Args:
beta2: a float between 0 and 1
Returns:
a scalar
"""
t = tf.to_float(tf.train.get_or_create_global_step()) + 1.0
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train | adafactor_optimizer_from_hparams | Create an Adafactor optimizer based on model hparams.
Args:
hparams: model hyperparameters
lr: learning rate scalar.
Returns:
an AdafactorOptimizer
Raises:
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"""Create an Adafactor optimizer based on model hparams.
Args:
hparams: model hyperparameters
lr: learning rate scalar.
Returns:
an AdafactorOptimizer
Raises:
ValueError: on illegal values
"""
if hparams.optimizer_adafactor_decay_type == "a... | def adafactor_optimizer_from_hparams(hparams, lr):
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Args:
hparams: model hyperparameters
lr: learning rate scalar.
Returns:
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Raises:
ValueError: on illegal values
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train | _nargs_validator | Makes validator for function to ensure it takes nargs args. | tensor2tensor/utils/registry.py | def _nargs_validator(nargs, message):
"""Makes validator for function to ensure it takes nargs args."""
if message is None:
message = "Registered function must take exactly %d arguments" % nargs
def f(key, value):
del key
spec = inspect.getfullargspec(value)
if (len(spec.args) != nargs or spec.va... | def _nargs_validator(nargs, message):
"""Makes validator for function to ensure it takes nargs args."""
if message is None:
message = "Registered function must take exactly %d arguments" % nargs
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train | parse_problem_name | Determines if problem_name specifies a copy and/or reversal.
Args:
name: str, problem name, possibly with suffixes.
Returns:
ProblemSpec: namedtuple with ["base_name", "was_reversed", "was_copy"]
Raises:
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"""Determines if problem_name specifies a copy and/or reversal.
Args:
name: str, problem name, possibly with suffixes.
Returns:
ProblemSpec: namedtuple with ["base_name", "was_reversed", "was_copy"]
Raises:
ValueError if name contains multiple suffixes of the same ty... | def parse_problem_name(name):
"""Determines if problem_name specifies a copy and/or reversal.
Args:
name: str, problem name, possibly with suffixes.
Returns:
ProblemSpec: namedtuple with ["base_name", "was_reversed", "was_copy"]
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train | get_problem_name | Construct a problem name from base and reversed/copy options.
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base_name: base problem name. Should not end in "_rev" or "_copy"
was_reversed: if the problem is to be reversed
was_copy: if the problem is to be copied
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Args:
base_name: base problem name. Should not end in "_rev" or "_copy"
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train | optimizer | Get pre-registered optimizer keyed by name.
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train | problem | Get possibly copied/reversed problem in `base_registry` or `env_registry`.
Args:
problem_name: string problem name. See `parse_problem_name`.
**kwargs: forwarded to env problem's initialize method.
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problem_name: string problem name. See `parse_problem_name`.
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train | env_problem | Get and initialize the `EnvProblem` with the given name and batch size.
Args:
env_problem_name: string name of the registered env problem.
**kwargs: forwarded to env problem's initialize method.
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env_problem_name: string name of the registered env problem.
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train | display_list_by_prefix | Creates a help string for names_list grouped by prefix. | tensor2tensor/utils/registry.py | def display_list_by_prefix(names_list, starting_spaces=0):
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train | help_string | Generate help string with contents of registry. | tensor2tensor/utils/registry.py | def help_string():
"""Generate help string with contents of registry."""
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Registry contents:
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Models:
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HParams:
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RangedHParams:
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Problems:
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Optimizers:
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Attacks:
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RangedHParams:
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Optimizers:
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Attacks:
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train | Registry.validate | Validation function run before setting. Uses function from __init__. | tensor2tensor/utils/registry.py | def validate(self, key, value):
"""Validation function run before setting. Uses function from __init__."""
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self._validator(key, value) | def validate(self, key, value):
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train | Registry.on_set | Callback called on successful set. Uses function from __init__. | tensor2tensor/utils/registry.py | def on_set(self, key, value):
"""Callback called on successful set. Uses function from __init__."""
if self._on_set is not None:
self._on_set(key, value) | def on_set(self, key, value):
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train | Registry.register | Decorator to register a function, or registration itself.
This is primarily intended for use as a decorator, either with or without
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```python
@my_registry.register('key1')
def value_fn(x, y, z):
pass
@my_registry.register()
def another_fn(x, y):
pass
@my... | tensor2tensor/utils/registry.py | def register(self, key_or_value=None):
"""Decorator to register a function, or registration itself.
This is primarily intended for use as a decorator, either with or without
a key/parentheses.
```python
@my_registry.register('key1')
def value_fn(x, y, z):
pass
@my_registry.register()... | def register(self, key_or_value=None):
"""Decorator to register a function, or registration itself.
This is primarily intended for use as a decorator, either with or without
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```python
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def value_fn(x, y, z):
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train | check_dependicies | Check the dynamic symbol versions.
Parameters
----------
objdump_string : string
The dynamic symbol table entries of the file (result of `objdump -T` command). | helpers/check_dynamic_dependencies.py | def check_dependicies(objdump_string):
"""Check the dynamic symbol versions.
Parameters
----------
objdump_string : string
The dynamic symbol table entries of the file (result of `objdump -T` command).
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objdump_string : string
The dynamic symbol table entries of the file (result of `objdump -T` command).
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train | _objective_function_wrapper | Decorate an objective function.
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If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i]
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Note
----
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If you want to get i-th row y_pred in j-th class, the access way is y_pred[j * num_data + i]
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X : array-like or sparse matrix of shape = [n_samples, n_features]
Input feature matrix.
y : array-like of shape = [n_samples]
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Input features matrix.
raw_score : bool, optional (default=False)
Whether to predict raw scores.
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train | LGBMRegressor.fit | Docstring is inherited from the LGBMModel. | python-package/lightgbm/sklearn.py | def fit(self, X, y,
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Input features matrix.
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train | get_parameter_infos | Parse config header file.
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config_hpp : string
Path to the config header file.
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Path to the config header file.
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train | get_names | Get names of all parameters.
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infos : list
Content of the config header file.
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train | get_alias | Get aliases of all parameters.
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infos : list
Content of the config header file.
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-------
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train | set_one_var_from_string | Construct code for auto config file for one param value.
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Name of the parameter.
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Type of the parameter.
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train | gen_parameter_description | Write descriptions of parameters to the documentation file.
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train | gen_parameter_code | Generate auto config file.
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config_hpp : string
Path to the config header file.
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Path to the auto config file.
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Path to the config header file.
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train | _load_lib | Load LightGBM library. | python-package/lightgbm/basic.py | def _load_lib():
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train | list_to_1d_numpy | Convert data to 1-D numpy array. | python-package/lightgbm/basic.py | def list_to_1d_numpy(data, dtype=np.float32, name='list'):
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train | cfloat32_array_to_numpy | Convert a ctypes float pointer array to a numpy array. | python-package/lightgbm/basic.py | def cfloat32_array_to_numpy(cptr, length):
"""Convert a ctypes float pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_float)):
return np.fromiter(cptr, dtype=np.float32, count=length)
else:
raise RuntimeError('Expected float pointer') | def cfloat32_array_to_numpy(cptr, length):
"""Convert a ctypes float pointer array to a numpy array."""
if isinstance(cptr, ctypes.POINTER(ctypes.c_float)):
return np.fromiter(cptr, dtype=np.float32, count=length)
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raise RuntimeError('Expected float pointer') | [
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train | cfloat64_array_to_numpy | Convert a ctypes double pointer array to a numpy array. | python-package/lightgbm/basic.py | def cfloat64_array_to_numpy(cptr, length):
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train | cint32_array_to_numpy | Convert a ctypes int pointer array to a numpy array. | python-package/lightgbm/basic.py | def cint32_array_to_numpy(cptr, length):
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train | cint8_array_to_numpy | Convert a ctypes int pointer array to a numpy array. | python-package/lightgbm/basic.py | def cint8_array_to_numpy(cptr, length):
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train | param_dict_to_str | Convert Python dictionary to string, which is passed to C API. | python-package/lightgbm/basic.py | def param_dict_to_str(data):
"""Convert Python dictionary to string, which is passed to C API."""
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train | convert_from_sliced_object | Fix the memory of multi-dimensional sliced object. | python-package/lightgbm/basic.py | def convert_from_sliced_object(data):
"""Fix the memory of multi-dimensional sliced object."""
if data.base is not None and isinstance(data, np.ndarray) and isinstance(data.base, np.ndarray):
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train | c_float_array | Get pointer of float numpy array / list. | python-package/lightgbm/basic.py | def c_float_array(data):
"""Get pointer of float numpy array / list."""
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ptr_data ... | def c_float_array(data):
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train | c_int_array | Get pointer of int numpy array / list. | python-package/lightgbm/basic.py | def c_int_array(data):
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train | _InnerPredictor.predict | Predict logic.
Parameters
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data : string, numpy array, pandas DataFrame, H2O DataTable's Frame or scipy.sparse
Data source for prediction.
When data type is string, it represents the path of txt file.
num_iteration : int, optional (default=-1)
... | python-package/lightgbm/basic.py | def predict(self, data, num_iteration=-1,
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"""Predict logic.
Parameters
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train | _InnerPredictor.__get_num_preds | Get size of prediction result. | python-package/lightgbm/basic.py | def __get_num_preds(self, num_iteration, nrow, predict_type):
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train | _InnerPredictor.__pred_for_np2d | Predict for a 2-D numpy matrix. | python-package/lightgbm/basic.py | def __pred_for_np2d(self, mat, num_iteration, predict_type):
"""Predict for a 2-D numpy matrix."""
if len(mat.shape) != 2:
raise ValueError('Input numpy.ndarray or list must be 2 dimensional')
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raise ValueError('Input numpy.ndarray or list must be 2 dimensional')
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train | _InnerPredictor.__pred_for_csr | Predict for a CSR data. | python-package/lightgbm/basic.py | def __pred_for_csr(self, csr, num_iteration, predict_type):
"""Predict for a CSR data."""
def inner_predict(csr, num_iteration, predict_type, preds=None):
nrow = len(csr.indptr) - 1
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... | def __pred_for_csr(self, csr, num_iteration, predict_type):
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def inner_predict(csr, num_iteration, predict_type, preds=None):
nrow = len(csr.indptr) - 1
n_preds = self.__get_num_preds(num_iteration, nrow, predict_type)
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train | _InnerPredictor.__pred_for_csc | Predict for a CSC data. | python-package/lightgbm/basic.py | def __pred_for_csc(self, csc, num_iteration, predict_type):
"""Predict for a CSC data."""
nrow = csc.shape[0]
if nrow > MAX_INT32:
return self.__pred_for_csr(csc.tocsr(), num_iteration, predict_type)
n_preds = self.__get_num_preds(num_iteration, nrow, predict_type)
pr... | def __pred_for_csc(self, csc, num_iteration, predict_type):
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nrow = csc.shape[0]
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train | Dataset.__init_from_np2d | Initialize data from a 2-D numpy matrix. | python-package/lightgbm/basic.py | def __init_from_np2d(self, mat, params_str, ref_dataset):
"""Initialize data from a 2-D numpy matrix."""
if len(mat.shape) != 2:
raise ValueError('Input numpy.ndarray must be 2 dimensional')
self.handle = ctypes.c_void_p()
if mat.dtype == np.float32 or mat.dtype == np.float6... | def __init_from_np2d(self, mat, params_str, ref_dataset):
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if len(mat.shape) != 2:
raise ValueError('Input numpy.ndarray must be 2 dimensional')
self.handle = ctypes.c_void_p()
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train | Dataset.__init_from_list_np2d | Initialize data from a list of 2-D numpy matrices. | python-package/lightgbm/basic.py | def __init_from_list_np2d(self, mats, params_str, ref_dataset):
"""Initialize data from a list of 2-D numpy matrices."""
ncol = mats[0].shape[1]
nrow = np.zeros((len(mats),), np.int32)
if mats[0].dtype == np.float64:
ptr_data = (ctypes.POINTER(ctypes.c_double) * len(mats))()
... | def __init_from_list_np2d(self, mats, params_str, ref_dataset):
"""Initialize data from a list of 2-D numpy matrices."""
ncol = mats[0].shape[1]
nrow = np.zeros((len(mats),), np.int32)
if mats[0].dtype == np.float64:
ptr_data = (ctypes.POINTER(ctypes.c_double) * len(mats))()
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train | Dataset.__init_from_csr | Initialize data from a CSR matrix. | python-package/lightgbm/basic.py | def __init_from_csr(self, csr, params_str, ref_dataset):
"""Initialize data from a CSR matrix."""
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ptr_indptr, type_ptr_... | def __init_from_csr(self, csr, params_str, ref_dataset):
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train | Dataset.__init_from_csc | Initialize data from a CSC matrix. | python-package/lightgbm/basic.py | def __init_from_csc(self, csc, params_str, ref_dataset):
"""Initialize data from a CSC matrix."""
if len(csc.indices) != len(csc.data):
raise ValueError('Length mismatch: {} vs {}'.format(len(csc.indices), len(csc.data)))
self.handle = ctypes.c_void_p()
ptr_indptr, type_ptr_... | def __init_from_csc(self, csc, params_str, ref_dataset):
"""Initialize data from a CSC matrix."""
if len(csc.indices) != len(csc.data):
raise ValueError('Length mismatch: {} vs {}'.format(len(csc.indices), len(csc.data)))
self.handle = ctypes.c_void_p()
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train | Dataset.construct | Lazy init.
Returns
-------
self : Dataset
Constructed Dataset object. | python-package/lightgbm/basic.py | def construct(self):
"""Lazy init.
Returns
-------
self : Dataset
Constructed Dataset object.
"""
if self.handle is None:
if self.reference is not None:
if self.used_indices is None:
# create valid
... | def construct(self):
"""Lazy init.
Returns
-------
self : Dataset
Constructed Dataset object.
"""
if self.handle is None:
if self.reference is not None:
if self.used_indices is None:
# create valid
... | [
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train | Dataset.create_valid | Create validation data align with current Dataset.
Parameters
----------
data : string, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.sparse or list of numpy arrays
Data source of Dataset.
If string, it represents the path to txt file.
label : list,... | python-package/lightgbm/basic.py | def create_valid(self, data, label=None, weight=None, group=None,
init_score=None, silent=False, params=None):
"""Create validation data align with current Dataset.
Parameters
----------
data : string, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.spar... | def create_valid(self, data, label=None, weight=None, group=None,
init_score=None, silent=False, params=None):
"""Create validation data align with current Dataset.
Parameters
----------
data : string, numpy array, pandas DataFrame, H2O DataTable's Frame, scipy.spar... | [
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train | Dataset.subset | Get subset of current Dataset.
Parameters
----------
used_indices : list of int
Indices used to create the subset.
params : dict or None, optional (default=None)
These parameters will be passed to Dataset constructor.
Returns
-------
subs... | python-package/lightgbm/basic.py | def subset(self, used_indices, params=None):
"""Get subset of current Dataset.
Parameters
----------
used_indices : list of int
Indices used to create the subset.
params : dict or None, optional (default=None)
These parameters will be passed to Dataset co... | def subset(self, used_indices, params=None):
"""Get subset of current Dataset.
Parameters
----------
used_indices : list of int
Indices used to create the subset.
params : dict or None, optional (default=None)
These parameters will be passed to Dataset co... | [
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train | Dataset.save_binary | Save Dataset to a binary file.
Parameters
----------
filename : string
Name of the output file.
Returns
-------
self : Dataset
Returns self. | python-package/lightgbm/basic.py | def save_binary(self, filename):
"""Save Dataset to a binary file.
Parameters
----------
filename : string
Name of the output file.
Returns
-------
self : Dataset
Returns self.
"""
_safe_call(_LIB.LGBM_DatasetSaveBinary(
... | def save_binary(self, filename):
"""Save Dataset to a binary file.
Parameters
----------
filename : string
Name of the output file.
Returns
-------
self : Dataset
Returns self.
"""
_safe_call(_LIB.LGBM_DatasetSaveBinary(
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train | Dataset.set_field | Set property into the Dataset.
Parameters
----------
field_name : string
The field name of the information.
data : list, numpy 1-D array, pandas Series or None
The array of data to be set.
Returns
-------
self : Dataset
Datase... | python-package/lightgbm/basic.py | def set_field(self, field_name, data):
"""Set property into the Dataset.
Parameters
----------
field_name : string
The field name of the information.
data : list, numpy 1-D array, pandas Series or None
The array of data to be set.
Returns
... | def set_field(self, field_name, data):
"""Set property into the Dataset.
Parameters
----------
field_name : string
The field name of the information.
data : list, numpy 1-D array, pandas Series or None
The array of data to be set.
Returns
... | [
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train | Dataset.get_field | Get property from the Dataset.
Parameters
----------
field_name : string
The field name of the information.
Returns
-------
info : numpy array
A numpy array with information from the Dataset. | python-package/lightgbm/basic.py | def get_field(self, field_name):
"""Get property from the Dataset.
Parameters
----------
field_name : string
The field name of the information.
Returns
-------
info : numpy array
A numpy array with information from the Dataset.
""... | def get_field(self, field_name):
"""Get property from the Dataset.
Parameters
----------
field_name : string
The field name of the information.
Returns
-------
info : numpy array
A numpy array with information from the Dataset.
""... | [
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train | Dataset.set_categorical_feature | Set categorical features.
Parameters
----------
categorical_feature : list of int or strings
Names or indices of categorical features.
Returns
-------
self : Dataset
Dataset with set categorical features. | python-package/lightgbm/basic.py | def set_categorical_feature(self, categorical_feature):
"""Set categorical features.
Parameters
----------
categorical_feature : list of int or strings
Names or indices of categorical features.
Returns
-------
self : Dataset
Dataset with ... | def set_categorical_feature(self, categorical_feature):
"""Set categorical features.
Parameters
----------
categorical_feature : list of int or strings
Names or indices of categorical features.
Returns
-------
self : Dataset
Dataset with ... | [
"Set",
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"."
] | Microsoft/LightGBM | python | https://github.com/Microsoft/LightGBM/blob/8d2ec69f4f685b0ab1c4624d59ee2d3287bb3147/python-package/lightgbm/basic.py#L1201-L1230 | [
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train | Dataset._set_predictor | Set predictor for continued training.
It is not recommended for user to call this function.
Please use init_model argument in engine.train() or engine.cv() instead. | python-package/lightgbm/basic.py | def _set_predictor(self, predictor):
"""Set predictor for continued training.
It is not recommended for user to call this function.
Please use init_model argument in engine.train() or engine.cv() instead.
"""
if predictor is self._predictor:
return self
if se... | def _set_predictor(self, predictor):
"""Set predictor for continued training.
It is not recommended for user to call this function.
Please use init_model argument in engine.train() or engine.cv() instead.
"""
if predictor is self._predictor:
return self
if se... | [
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train | Dataset.set_reference | Set reference Dataset.
Parameters
----------
reference : Dataset
Reference that is used as a template to construct the current Dataset.
Returns
-------
self : Dataset
Dataset with set reference. | python-package/lightgbm/basic.py | def set_reference(self, reference):
"""Set reference Dataset.
Parameters
----------
reference : Dataset
Reference that is used as a template to construct the current Dataset.
Returns
-------
self : Dataset
Dataset with set reference.
... | def set_reference(self, reference):
"""Set reference Dataset.
Parameters
----------
reference : Dataset
Reference that is used as a template to construct the current Dataset.
Returns
-------
self : Dataset
Dataset with set reference.
... | [
"Set",
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] | Microsoft/LightGBM | python | https://github.com/Microsoft/LightGBM/blob/8d2ec69f4f685b0ab1c4624d59ee2d3287bb3147/python-package/lightgbm/basic.py#L1247-L1271 | [
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train | Dataset.set_feature_name | Set feature name.
Parameters
----------
feature_name : list of strings
Feature names.
Returns
-------
self : Dataset
Dataset with set feature name. | python-package/lightgbm/basic.py | def set_feature_name(self, feature_name):
"""Set feature name.
Parameters
----------
feature_name : list of strings
Feature names.
Returns
-------
self : Dataset
Dataset with set feature name.
"""
if feature_name != 'auto'... | def set_feature_name(self, feature_name):
"""Set feature name.
Parameters
----------
feature_name : list of strings
Feature names.
Returns
-------
self : Dataset
Dataset with set feature name.
"""
if feature_name != 'auto'... | [
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