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README_index_doc.py
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mlmodels\benchmark.py
----------------methods----------------
---------------functions---------------
benchmark_run( bench_pars=None, args=None, config_mode="test", )
cli_load_arguments( config_file=None, )
config_model_list( folder=None, )
get_all_json_path(json_path, )
main( )
metric_eval( actual=None, pred=None, metric_name="mean_absolute_error", )
mlmodels\data.py
----------------methods----------------
---------------functions---------------
download_data(data_pars, )
get_dataset(data_pars, )
import_data( )
import_data_dask( **kw)
import_data_fromfile( **kw)
import_data_tch( name="", mode="train", node_id=0, data_folder_root="", )
tf_dataset(dataset_pars, )
mlmodels\dataloader.py
----------------methods----------------
DataLoader.__init__(self, data_pars, )
DataLoader.compute(self, )
DataLoader.get_data(self, intermediate=False, )
---------------functions---------------
_check_output_shape(self, inter_output, shape, max_len, )
_interpret_input_pars(self, input_pars, )
batch_generator(iterable, n=1, )
image_dir_load(path, )
pickle_dump(t, path, )
pickle_load(file, )
split_xy_from_dict(out, data_pars, )
mlmodels\distri_torch.py
----------------methods----------------
---------------functions---------------
load_arguments( )
metric_average(val, name, )
test( )
train(epoch, )
mlmodels\metrics.py
----------------methods----------------
---------------functions---------------
log( n=0, m=1, *s)
metrics_eval( metric_list=["mean_squared_error"], ytrue=None, ypred=None, ypred_proba=None, return_dict=0, )
test( )
mlmodels\models.py
----------------methods----------------
---------------functions---------------
cli_load_arguments( config_file=None, )
config_generate_json(modelname, to_path="ztest/new_model/", )
config_get_pars(config_file, config_mode="test", )
config_init( to_path=".", )
config_model_list( folder=None, )
fit(module, model, sess=None, data_pars=None, compute_pars=None, out_pars=None, **kwarg)
fit_metrics(module, model, sess=None, data_pars=None, compute_pars=None, out_pars=None, **kwarg)
get_params(module, params_pars, **kwarg)
load(module, load_pars, **kwarg)
main( )
metrics(module, model, sess=None, data_pars=None, compute_pars=None, out_pars=None, **kwarg)
model_create(module, model_pars=None, data_pars=None, compute_pars=None, **kwarg)
module_env_build( model_uri="", verbose=0, do_env_build=0, )
module_load( model_uri="", verbose=0, env_build=0, )
module_load_full( model_uri="", model_pars=None, data_pars=None, compute_pars=None, choice=None, **kwarg)
os_folder_copy(src, dst, )
predict(module, model, sess=None, data_pars=None, compute_pars=None, out_pars=None, **kwarg)
save(module, model, session, save_pars, **kwarg)
test(modelname, )
test_all( folder=None, )
test_api( model_uri="model_xxxx/yyyy.py", param_pars=None, )
test_global(modelname, )
test_module( model_uri="model_xxxx/yyyy.py", param_pars=None, )
mlmodels\optim.py
----------------methods----------------
---------------functions---------------
cli_load_arguments( config_file=None, )
main( )
optim( model_uri="model_tf.1_lstm.py", hypermodel_pars={}, model_pars={}, data_pars={}, compute_pars={}, out_pars={}, )
optim_optuna( model_uri="model_tf.1_lstm.py", hypermodel_pars={"engine_pars": {}, )
post_process_best(model, module, model_uri, model_pars_update, data_pars, compute_pars, out_pars, )
test_all( )
test_fast( ntrials=2, )
test_json( path_json="", config_mode="test", )
mlmodels\parse.py
----------------methods----------------
---------------functions---------------
cli_load_arguments( config_file=None, )
extract_args(txt, outfile, )
mlmodels\pipeline.py
----------------methods----------------
Pipe.__init__(self, pipe_list, in_pars, out_pars, compute_pars=None, **kw)
Pipe.get_checkpoint(self, )
Pipe.get_fitted_pipe_list(self, key="", )
Pipe.get_model_path(self, )
Pipe.run(self, )
---------------functions---------------
drop_cols(df, cols=None, **kw)
generate_data(df, num_data=0, means=[], cov=[[1, 0], )
get_params( choice="", data_path="dataset/", config_mode="test", **kw)
load_model(path, )
log( n=0, m=1, *s)
os_package_root_path(filepath, sublevel=0, path_add="", )
pd_concat(df1, df2, colid1, )
pd_na_values(df, cols=None, default=0.0, **kw)
pipe_checkpoint(df, **kw)
pipe_load(df, **in_pars)
pipe_merge(in_pars, out_pars, compute_pars=None, **kw)
pipe_run_inference(pipe_list, in_pars, out_pars, compute_pars=None, checkpoint=True, **kw)
pipe_split(in_pars, out_pars, compute_pars, **kw)
save_model(model, path, )
test( data_path="/dataset/", pars_choice="colnum", )
mlmodels\preprocessor.py
----------------methods----------------
MissingDataPreprocessorError.__init__(self, )
Preprocessor.__init__(self, preprocessor_dict, )
Preprocessor._interpret_preprocessor_dict(self, pars, )
Preprocessor._name_outputs(self, names, outputs, )
Preprocessor.fit_transform(self, data, )
Preprocessor.transform(self, data, )
PreprocessorNotFittedError.__init__(self, )
---------------functions---------------
mlmodels\test_dataloader.py
----------------methods----------------
SingleFunctionPreprocessor.__init__(self, func_dict, )
SingleFunctionPreprocessor.compute(self, data, )
SingleFunctionPreprocessor.get_data(self, )
---------------functions---------------
gluon_append_target_string(out, data_pars, )
identical_test_set_split(test_size, *args, **kwargs)
load_npz(path, )
pandas_load_train_test(path, test_path, **args)
pandas_split_xy(out, data_pars, )
read_csvs_from_directory(path, files=None, **args)
rename_target_to_y(out, data_pars, )
split_timeseries_df(out, data_pars, length, shift, )
split_xy_from_dict(out, data_pars, )
timeseries_split( test_size=0.2, *args)
tokenize_x(data, no_classes, max_words=None, )
mlmodels\util.py
----------------methods----------------
Model_empty.__init__(self, model_pars=None, data_pars=None, compute_pars=None, )
to_namespace.__init__(self, adict, )
to_namespace.get(self, key, )
---------------functions---------------
config_load_root( )
config_path_dataset( )
config_path_pretrained( )
config_set(ddict2, )
env_build(model_uri, env_pars, )
env_conda_build( env_pars=None, )
env_pip_check( env_pars=None, )
env_pip_requirement( env_pars=None, )
get_device_torch( )
get_model_uri(file, )
get_recursive_files(folderPath, ext='/*model*/*.py', )
get_recursive_files2(folderPath, ext, )
get_recursive_files3(folderPath, ext, )
json_norm(ddict, )
load(load_pars, )
load_callable_from_dict(function_dict, return_other_keys=False, )
load_callable_from_uri(uri, )
load_config(args, config_file, config_mode, verbose=0, )
load_function( package="mlmodels.util", name="path_norm", )
load_gluonts( load_pars=None, )
load_keras(load_pars, custom_pars=None, )
load_pkl(load_pars, )
load_tch(load_pars, )
load_tch_checkpoint(model, optimiser, load_pars, )
load_tf( load_pars="", )
log( n=0, m=1, *s)
model_get_list( folder=None, block_list=[], )
os_file_current_path( )
os_folder_copy(src, dst, )
os_get_file( folder=None, block_list=[], pattern=r'*.py', )
os_package_root_path( filepath="", sublevel=0, path_add="", )
os_path_split(path, )
params_json_load(path, config_mode="test", tlist=[ "model_pars", "data_pars", "compute_pars", "out_pars"], )
path_norm( path="", )
path_norm_dict(ddict, )
save( model=None, session=None, save_pars=None, )
save_gluonts( model=None, session=None, save_pars=None, )
save_keras( model=None, session=None, save_pars=None, )
save_pkl( model=None, session=None, save_pars=None, )
save_tch( model=None, optimizer=None, save_pars=None, )
save_tch_checkpoint(model, optimiser, save_pars, )
save_tf( model=None, sess=None, save_pars=None, )
test_module( model_uri="model_tf/1_lstm.py", data_path="dataset/", pars_choice="json", reset=True, )
tf_deprecation( )
val(x, xdefault, )
mlmodels\util_log.py
----------------methods----------------
to_name.__init__(self, adict, )
---------------functions---------------
create_appid(filename, )
create_logfilename(filename, )
create_uniqueid( )
load_arguments( config_file=None, arg_list=None, )
logger_handler_console( formatter=None, )
logger_handler_file( isrotate=False, rotate_time="midnight", formatter=None, log_file_used=None, )
logger_setup( logger_name=None, log_file=None, formatter=FORMATTER_1, isrotate=False, isconsole_output=True, logging_level=logging.DEBUG, )
logger_setup2( name=__name__, level=None, )
printlog( s="", s1="", s2="", s3="", s4="", s5="", s6="", s7="", s8="", s9="", s10="", app_id="", logfile=None, iswritelog=True, )
writelog( m="", f=None, )
mlmodels\ztest.py
----------------methods----------------
---------------functions---------------
cli_load_arguments( config_file=None, )
main( )
os_file_current_path( )
test_all( arg=None, )
test_benchmark( arg=None, )
test_custom( )
test_import(arg, )
test_json(arg, )
test_jupyter( arg=None, config_mode="test_all", )
test_list(mlist, )
test_model_structure( )
mlmodels\ztest_structure.py
----------------methods----------------
---------------functions---------------
code_check( sign_list=None, model_list=None, )
find_in_list(x, llist, )
get_recursive_files(folderPath, ext='/*model*/*.py', )
log( n=0, m=1, *s)
main( )
model_get_list( folder=None, block_list=[], )
os_file_current_path( )
os_package_root_path(filepath, sublevel=0, path_add="", )
mlmodels\__init__.py
----------------methods----------------
---------------functions---------------
mlmodels\config\json\model_tch\raw\vae_pretraining_encoder\text_beta.py
----------------methods----------------
---------------functions---------------
mlmodels\example\arun_hyper.py
----------------methods----------------
---------------functions---------------
mlmodels\example\arun_model.py
----------------methods----------------
---------------functions---------------
mlmodels\example\benchmark_timeseries_m4.py
----------------methods----------------
---------------functions---------------
benchmark_m4( )
mlmodels\example\benchmark_timeseries_m5.py
----------------methods----------------
M5Evaluator.get_aggregate_metrics(self, metric_per_ts, )
M5Evaluator.get_metrics_per_ts(self, time_series, forecast, )
---------------functions---------------
plot_prob_forecasts(ts_entry, forecast_entry, path, sample_id, inline=True, )
mlmodels\example\lightgbm_glass.py
----------------methods----------------
---------------functions---------------
mlmodels\example\vision_mnist.py
----------------methods----------------
---------------functions---------------
mlmodels\model_dev\__init__.py
----------------methods----------------
---------------functions---------------
mlmodels\model_dev\dev\ml_mosaic.py
----------------methods----------------
---------------functions---------------
mlmodels\model_dev\dev\mytest.py
----------------methods----------------
---------------functions---------------
mlmodels\model_dev\raw\abstractive-summarization\pointer_generator_helper.py
----------------methods----------------
PointerGeneratorAttentionWrapper.__init__(self, cell, attention_mechanism, attention_layer_size=None, alignment_history=False, cell_input_fn=None, output_attention=True, initial_cell_state=None, name=None, coverage=False, )
PointerGeneratorAttentionWrapper.call(self, inputs, state, )
PointerGeneratorAttentionWrapper.zero_state(self, batch_size, dtype, )
PointerGeneratorBahdanauAttention.__call__(self, query, state, )
PointerGeneratorBahdanauAttention.__init__(self, num_units, memory, memory_sequence_length=None, normalize=False, probability_fn=None, score_mask_value=float('-inf'), name='PointerGeneratorBahdanauAttention', coverage=False, )
PointerGeneratorDecoder.__init__(self, source_extend_tokens, source_oov_words, coverage, cell, helper, initial_state, output_layer=None, )
PointerGeneratorDecoder.output_dtype(self, )
PointerGeneratorDecoder.output_size(self, )
PointerGeneratorDecoder.step(self, time, inputs, state, name=None, )
PointerGeneratorGreedyEmbeddingHelper.__init__(self, embedding, start_tokens, end_token, )
PointerGeneratorGreedyEmbeddingHelper.next_inputs(self, time, outputs, state, sample_ids, name=None, )
PointerGeneratorGreedyEmbeddingHelper.sample(self, time, outputs, state, name=None, )
---------------functions---------------
_pg_bahdanau_score(processed_query, keys, coverage, coverage_vector, )
mlmodels\model_dev\raw\chatbot\35.byte-net.py
----------------methods----------------
ByteNet.__init__(self, from_vocab_size, to_vocab_size, channels, encoder_dilations, decoder_dilations, encoder_filter_width, decoder_filter_width, learning_rate=0.001, beta1=0.5, )
---------------functions---------------
build_dataset(words, n_words, atleast=1, )
bytenet_residual_block(input_, dilation, layer_no, residual_channels, filter_width, causal=True, )
clean_text(text, )
conv1d(input_, output_channels, dilation=1, filter_width=1, causal=False, )
layer_normalization(x, epsilon=1e-8, )
pad_sentence_batch(sentence_batch, pad_int, maxlen, )
str_idx(corpus, dic, )
mlmodels\model_dev\raw\chatbot\37.capsule-lstm-seq2seq-greedy.py
----------------methods----------------
Chatbot.__init__(self, size_layer, num_layers, embedded_size, seq_len, maxlen, from_dict_size, to_dict_size, learning_rate, batch_size, kernels=[2, 4, 4], strides=[3,2,1], epsilon=1e-8, )
---------------functions---------------
build_dataset(words, n_words, atleast=1, )
clean_text(text, )
conv_layer(X, num_output, num_vector, kernel=None, stride=None, )
fully_conn_layer(X, num_output, dimension_out, )
pad_sentence_batch(sentence_batch, pad_int, )
pad_sentence_batch_static(sentence_batch, pad_int, )
routing(X, b_IJ, seq_len, dimension_out, routing_times=2, )
squash(X, epsilon=1e-9, )
str_idx(corpus, dic, )
mlmodels\model_dev\raw\chatbot\38.capsule-lstm-seq2seq-luong-beam.py
----------------methods----------------
Chatbot.__init__(self, size_layer, num_layers, embedded_size, seq_len, maxlen, from_dict_size, to_dict_size, learning_rate, batch_size, kernels=[2, 4, 4], strides=[3,2,1], epsilon=1e-8, force_teaching_ratio=0.5, beam_width=5, )
---------------functions---------------
build_dataset(words, n_words, atleast=1, )
clean_text(text, )
conv_layer(X, num_output, num_vector, kernel=None, stride=None, )
fully_conn_layer(X, num_output, dimension_out, )
pad_sentence_batch(sentence_batch, pad_int, )
pad_sentence_batch_static(sentence_batch, pad_int, )
routing(X, b_IJ, seq_len, dimension_out, routing_times=2, )
squash(X, epsilon=1e-9, )
str_idx(corpus, dic, )
mlmodels\model_dev\raw\chatbot\access.py
----------------methods----------------
MemoryAccess._linear(first_dim, second_dim, name, activation=None, )
---------------functions---------------
_erase_and_write(memory, address, reset_weights, values, )
mlmodels\model_dev\raw\chatbot\addressing.py
----------------methods----------------
---------------functions---------------
_vector_norms(m, )
weighted_softmax(activations, strengths, strengths_op, )
mlmodels\model_dev\raw\chatbot\dnc.py
----------------methods----------------
---------------functions---------------
mlmodels\model_dev\raw\chatbot\gpt_2.py
----------------methods----------------
---------------functions---------------
attention_mask(nd, ns, )
attn(x, scope, n_state, )
block(x, scope, )
conv1d(x, scope, nf, )
expand_tile(value, size, )
gelu(x, )
merge_states(x, )
mlp(x, scope, n_state, )
model(hparams, X, past=None, scope='model', reuse=False, )
norm(x, scope, )
past_shape( )
positions_for(tokens, past_length, )
shape_list(x, )
softmax(x, axis=-1, )
split_states(x, n, )
mlmodels\model_dev\raw\chatbot\util.py
----------------methods----------------
---------------functions---------------
batch_gather(values, indices, )
batch_invert_permutation(permutations, )
one_hot(length, index, )
mlmodels\model_dev\raw\extractive-summarization\modeling.py
----------------methods----------------
---------------functions---------------
assert_rank(tensor, expected_rank, name=None, )
attention_layer(from_tensor, to_tensor, attention_mask=None, num_attention_heads=1, size_per_head=512, query_act=None, key_act=None, value_act=None, attention_probs_dropout_prob=0.0, initializer_range=0.02, do_return_2d_tensor=False, batch_size=None, from_seq_length=None, to_seq_length=None, )
create_attention_mask_from_input_mask(from_tensor, to_mask, )
create_initializer( initializer_range=0.02, )
dropout(input_tensor, dropout_prob, )
embedding_lookup(input_ids, vocab_size, embedding_size=128, initializer_range=0.02, word_embedding_name="word_embeddings", use_one_hot_embeddings=False, )
embedding_postprocessor(input_tensor, use_token_type=False, token_type_ids=None, token_type_vocab_size=16, token_type_embedding_name="token_type_embeddings", use_position_embeddings=True, position_embedding_name="position_embeddings", initializer_range=0.02, max_position_embeddings=512, dropout_prob=0.1, )
gelu(x, )
get_activation(activation_string, )
get_assignment_map_from_checkpoint(tvars, init_checkpoint, )
get_shape_list(tensor, expected_rank=None, name=None, )
layer_norm(input_tensor, name=None, )
layer_norm_and_dropout(input_tensor, dropout_prob, name=None, )
reshape_from_matrix(output_tensor, orig_shape_list, )
reshape_to_matrix(input_tensor, )
transformer_model(input_tensor, attention_mask=None, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, intermediate_act_fn=gelu, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, do_return_all_layers=False, )
mlmodels\model_dev\raw\neural-machine-translation\access.py
----------------methods----------------
MemoryAccess._linear(first_dim, second_dim, name, activation=None, )
---------------functions---------------
_erase_and_write(memory, address, reset_weights, values, )
mlmodels\model_dev\raw\neural-machine-translation\addressing.py
----------------methods----------------
---------------functions---------------
_vector_norms(m, )
weighted_softmax(activations, strengths, strengths_op, )
mlmodels\model_dev\raw\neural-machine-translation\dnc.py
----------------methods----------------
---------------functions---------------
mlmodels\model_dev\raw\neural-machine-translation\gpt_2.py
----------------methods----------------
---------------functions---------------
attention_mask(nd, ns, )
attn(x, scope, n_state, )
block(x, scope, )
conv1d(x, scope, nf, )
expand_tile(value, size, )
gelu(x, )
merge_states(x, )
mlp(x, scope, n_state, )
model(hparams, X, past=None, scope='model', reuse=False, )
norm(x, scope, )
past_shape( )
positions_for(tokens, past_length, )
shape_list(x, )
softmax(x, axis=-1, )
split_states(x, n, )
mlmodels\model_dev\raw\neural-machine-translation\util.py
----------------methods----------------
---------------functions---------------
batch_gather(values, indices, )
batch_invert_permutation(permutations, )
one_hot(length, index, )
mlmodels\model_dev\raw\question-answer\attention_gru.py
----------------methods----------------
---------------functions---------------
mlmodels\model_dev\raw\stemming\access.py
----------------methods----------------
MemoryAccess._linear(first_dim, second_dim, name, activation=None, )
---------------functions---------------
_erase_and_write(memory, address, reset_weights, values, )
mlmodels\model_dev\raw\stemming\addressing.py
----------------methods----------------
---------------functions---------------
_vector_norms(m, )
weighted_softmax(activations, strengths, strengths_op, )
mlmodels\model_dev\raw\stemming\dnc.py
----------------methods----------------
---------------functions---------------
mlmodels\model_dev\raw\stemming\util.py
----------------methods----------------
---------------functions---------------
batch_gather(values, indices, )
batch_invert_permutation(permutations, )
one_hot(length, index, )
mlmodels\model_dev\raw\text-augmentation\6.vae-varitional-bahdanau\attention_wrapper.py
----------------methods----------------
AttentionMechanism._maybe_mask(m, seq_len_mask, )
AttentionWrapper.__init__(self, cell, attention_mechanism, temperature=1.0, use_hmean=True, attention_layer_size=None, alignment_history=False, cell_input_fn=None, output_attention=True, initial_cell_state=None, name=None, )
AttentionWrapper._batch_size_checks(self, batch_size, error_message, )
AttentionWrapper._item_or_tuple(self, seq, )
AttentionWrapper.call(self, inputs, state, )
AttentionWrapper.output_size(self, )
AttentionWrapper.state_size(self, )
AttentionWrapper.zero_state(self, batch_size, dtype, )
AttentionWrapperState.clone(self, **kwargs)
BahdanauAttention.__call__(self, query, previous_alignments, )
BahdanauAttention.__init__(self, num_units, memory, memory_sequence_length=None, normalize=False, probability_fn=None, score_mask_value=None, dtype=None, name="BahdanauAttention", )
BahdanauMonotonicAttention.__call__(self, query, previous_alignments, )
BahdanauMonotonicAttention.__init__(self, num_units, memory, memory_sequence_length=None, normalize=False, score_mask_value=None, sigmoid_noise=0., sigmoid_noise_seed=None, score_bias_init=0., mode="parallel", dtype=None, name="BahdanauMonotonicAttention", )
LuongAttention.__call__(self, query, previous_alignments, )
LuongAttention.__init__(self, num_units, memory, memory_sequence_length=None, scale=False, probability_fn=None, score_mask_value=None, dtype=None, name="LuongAttention", )
LuongMonotonicAttention.__call__(self, query, previous_alignments, )
LuongMonotonicAttention.__init__(self, num_units, memory, memory_sequence_length=None, scale=False, score_mask_value=None, sigmoid_noise=0., sigmoid_noise_seed=None, score_bias_init=0., mode="parallel", dtype=None, name="LuongMonotonicAttention", )
_BaseAttentionMechanism.__init__(self, query_layer, memory, probability_fn, memory_sequence_length=None, memory_layer=None, check_inner_dims_defined=True, score_mask_value=None, name=None, )
_BaseAttentionMechanism.alignments_size(self, )
_BaseAttentionMechanism.batch_size(self, )
_BaseAttentionMechanism.initial_alignments(self, batch_size, dtype, )
_BaseAttentionMechanism.keys(self, )
_BaseAttentionMechanism.memory_layer(self, )
_BaseAttentionMechanism.query_layer(self, )
_BaseAttentionMechanism.values(self, )
_BaseMonotonicAttentionMechanism.initial_alignments(self, batch_size, dtype, )
---------------functions---------------
_bahdanau_score(processed_query, keys, normalize, )
_compute_attention(attention_mechanism, cell_output, previous_alignments, attention_layer, temperature, use_hmean, )
_luong_score(query, keys, scale, )
_maybe_mask_score(score, memory_sequence_length, score_mask_value, )
_monotonic_probability_fn(score, previous_alignments, sigmoid_noise, mode, seed=None, )
_prepare_memory(memory, memory_sequence_length, check_inner_dims_defined, )
hardmax(logits, name=None, )
monotonic_attention(p_choose_i, previous_attention, mode, )
safe_cumprod(x, *args, **kwargs)
mlmodels\model_dev\raw\text-augmentation\6.vae-varitional-bahdanau\basic_decoder.py
----------------methods----------------
BasicDecoder.__init__(self, cell, helper, initial_state, latent_vector, output_layer=None, )
BasicDecoder._rnn_output_size(self, )
BasicDecoder.batch_size(self, )
BasicDecoder.initialize(self, name=None, )
BasicDecoder.output_dtype(self, )
BasicDecoder.output_size(self, )
BasicDecoder.step(self, time, inputs, state, name=None, )
---------------functions---------------
mlmodels\model_dev\raw\text-augmentation\6.vae-varitional-bahdanau\decoder.py
----------------methods----------------
Decoder._create(s, d, )
Decoder._t(s, )
Decoder.batch_size(self, )
Decoder.finalize(self, outputs, final_state, sequence_lengths, )
Decoder.initialize(self, name=None, )
Decoder.output_dtype(self, )
Decoder.output_size(self, )
Decoder.step(self, time, inputs, state, name=None, )
Decoder.tracks_own_finished(self, )
---------------functions---------------
_create_zero_outputs(size, dtype, batch_size, )
dynamic_decode(decoder, output_time_major=False, impute_finished=False, maximum_iterations=None, parallel_iterations=32, swap_memory=False, scope=None, )
mlmodels\model_dev\raw\text-classification\63.deep-pyramid-cnn.py
----------------methods----------------
Model.__init__(self, maxlen, dimension_output, vocab_size, embedding_size, kernel_size, num_filters, learning_rate, )
---------------functions---------------
mlmodels\model_dev\raw\text-classification\bert_model.py
----------------methods----------------
BertConfig.__init__(self, vocab_size, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, hidden_act='gelu', hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, max_position_embeddings=512, type_vocab_size=16, initializer_range=0.02, )
BertConfig.from_dict(cls, json_object, )
BertConfig.from_json_file(cls, json_file, )
BertConfig.to_dict(self, )
BertConfig.to_json_string(self, )
BertModel.__init__(self, config, is_training, input_ids, input_mask=None, token_type_ids=None, use_one_hot_embeddings=True, scope=None, )
BertModel.get_all_encoder_layers(self, )
BertModel.get_embedding_output(self, )
BertModel.get_embedding_table(self, )
BertModel.get_pooled_output(self, )
BertModel.get_sequence_output(self, )
BertModel.transpose_for_scores(input_tensor, batch_size, num_attention_heads, seq_length, width, )
---------------functions---------------
assert_rank(tensor, expected_rank, name=None, )
attention_layer(from_tensor, to_tensor, attention_mask=None, num_attention_heads=1, size_per_head=512, query_act=None, key_act=None, value_act=None, attention_probs_dropout_prob=0.0, initializer_range=0.02, do_return_2d_tensor=False, batch_size=None, from_seq_length=None, to_seq_length=None, )
create_attention_mask_from_input_mask(from_tensor, to_mask, )
create_initializer( initializer_range=0.02, )
dropout(input_tensor, dropout_prob, )
embedding_lookup(input_ids, vocab_size, embedding_size=128, initializer_range=0.02, word_embedding_name='word_embeddings', use_one_hot_embeddings=False, )
embedding_postprocessor(input_tensor, use_token_type=False, token_type_ids=None, token_type_vocab_size=16, token_type_embedding_name='token_type_embeddings', use_position_embeddings=True, position_embedding_name='position_embeddings', initializer_range=0.02, max_position_embeddings=512, dropout_prob=0.1, )
gelu(input_tensor, )
get_activation(activation_string, )
get_assignment_map_from_checkpoint(tvars, init_checkpoint, )
get_shape_list(tensor, expected_rank=None, name=None, )
layer_norm(input_tensor, name=None, )
layer_norm_and_dropout(input_tensor, dropout_prob, name=None, )
reshape_from_matrix(output_tensor, orig_shape_list, )
reshape_to_matrix(input_tensor, )
transformer_model(input_tensor, attention_mask=None, hidden_size=768, num_hidden_layers=12, num_attention_heads=12, intermediate_size=3072, intermediate_act_fn=gelu, hidden_dropout_prob=0.1, attention_probs_dropout_prob=0.1, initializer_range=0.02, do_return_all_layers=False, )
mlmodels\model_dev\raw\text-classification\dynamic_memory_network.py
----------------methods----------------
DynamicMemoryNetwork.__init__(self, num_classes, learning_rate, decay_steps, decay_rate, sequence_length, story_length, vocab_size, embed_size, hidden_size, num_pass=2, use_gated_gru=True, decode_with_sequences=False, initializer=tf.random_normal_initializer(stddev = 0.1), clip_gradients=5.0, l2_lambda=0.0001, )
DynamicMemoryNetwork.answer_module(self, )
DynamicMemoryNetwork.attention_mechanism_parallel(self, c_full, m, q, i, )
DynamicMemoryNetwork.episodic_memory_module(self, )
DynamicMemoryNetwork.gated_gru(self, c_current, h_previous, g_current, )
DynamicMemoryNetwork.gru_cell(self, Xt, h_t_minus_1, variable_scope, )
DynamicMemoryNetwork.inference(self, )
DynamicMemoryNetwork.input_module(self, )
DynamicMemoryNetwork.instantiate_weights(self, )
DynamicMemoryNetwork.loss(self, l2_lambda=0.0001, )
DynamicMemoryNetwork.question_module(self, )
DynamicMemoryNetwork.train(self, )
DynamicMemoryNetwork.x1Wx2_parallel(self, x1, x2, scope, )
---------------functions---------------
mlmodels\model_dev\raw\text-classification\entity_network.py
----------------methods----------------
EntityNetwork.__init__(self, num_classes, learning_rate, decay_steps, decay_rate, sequence_length, story_length, vocab_size, embed_size, hidden_size, block_size=20, initializer=tf.random_normal_initializer(stddev = 0.1), clip_gradients=5.0, use_bi_lstm=False, )
EntityNetwork.activation(self, features, scope=None, )
EntityNetwork.cell(self, s_t, h_all, w_all, i, )
EntityNetwork.embedding_with_mask(self, )
EntityNetwork.inference(self, )
EntityNetwork.input_encoder_bi_lstm(self, )
EntityNetwork.input_encoder_bow(self, )
EntityNetwork.instantiate_weights(self, )
EntityNetwork.loss(self, l2_lambda=0.0001, )
EntityNetwork.output_module(self, )
EntityNetwork.rnn_story(self, )
EntityNetwork.train(self, )
---------------functions---------------
mlmodels\model_dev\raw\text-classification\gpt_2.py
----------------methods----------------
---------------functions---------------
attention_mask(nd, ns, )
attn(x, scope, n_state, )
block(x, scope, )
conv1d(x, scope, nf, )
expand_tile(value, size, )
gelu(x, )
merge_states(x, )
mlp(x, scope, n_state, )
model(hparams, X, past=None, scope='model', reuse=False, )
norm(x, scope, )
past_shape( )
positions_for(tokens, past_length, )
shape_list(x, )
softmax(x, axis=-1, )
split_states(x, n, )
mlmodels\model_dev\raw\text-classification\modules.py
----------------methods----------------
---------------functions---------------
attention_decoder(inputs, memory, units=None, scope='attention_decoder', reuse=None, )
conv1d(inputs, filters=None, size=1, rate=1, padding='SAME', use_bias=False, activation_fn=None, scope='conv1d', reuse=None, )
conv1d_banks(inputs, K=16, is_training=True, scope='conv1d_banks', reuse=None, )
embed(inputs, vocab_size, dimension, scope='embedding', reuse=None, )
gru(inputs, units=None, bidirection=False, scope='gru', reuse=None, )
highwaynet(inputs, units=None, scope='highwaynet', reuse=None, )
normalize_bn(inputs, decay=0.99, is_training=True, activation_fn=None, scope='normalize_bn', )
normalize_in(inputs, activation_fn=None, scope='normalize_in', )
normalize_layer_norm(inputs, activation_fn=None, scope='normalize_layer_norm', )
prenet(inputs, is_training=True, scope='prenet', reuse=None, )
shift_by_one(inputs, )
mlmodels\model_dev\raw\text-classification\utils.py
----------------methods----------------
---------------functions---------------
build_dataset(words, n_words, )
clearstring(string, )
separate_dataset(trainset, ratio=0.5, )
str_idx(corpus, dic, maxlen, UNK=3, )
mlmodels\model_dev\raw\text-classification\xl.py
----------------methods----------------
---------------functions---------------
_cache_mem(curr_out, prev_mem, mem_len=None, )
_create_mask(qlen, mlen, same_length=False, )
embedding_lookup(lookup_table, x, )
mask_adaptive_embedding_lookup(x, n_token, d_embed, d_proj, cutoffs, initializer, proj_initializer, div_val=1, proj_same_dim=True, scope='adaptive_embed', **kwargs)
mask_adaptive_logsoftmax(hidden, target, n_token, d_embed, d_proj, cutoffs, params, tie_projs, initializer=None, proj_initializer=None, div_val=1, scope='adaptive_softmax', proj_same_dim=True, return_mean=True, **kwargs)
mul_adaptive_embedding_lookup(x, n_token, d_embed, d_proj, cutoffs, initializer, proj_initializer, div_val=1, perms=None, proj_same_dim=True, scope='adaptive_embed', )
mul_adaptive_logsoftmax(hidden, target, n_token, d_embed, d_proj, cutoffs, params, tie_projs, initializer=None, proj_initializer=None, div_val=1, perms=None, proj_same_dim=True, scope='adaptive_softmax', **kwargs)
positional_embedding(pos_seq, inv_freq, bsz=None, )
positionwise_FF(inp, d_model, d_inner, kernel_initializer, scope='ff', )
rel_multihead_attn(w, r, r_w_bias, r_r_bias, attn_mask, mems, d_model, n_head, d_head, kernel_initializer, scope='rel_attn', )
rel_shift(x, )
transformer(dec_inp, mems, n_token, n_layer, d_model, d_embed, n_head, d_head, d_inner, initializer, proj_initializer=None, mem_len=None, cutoffs=[], div_val=1, tie_projs=[], same_length=False, clamp_len=-1, untie_r=False, proj_same_dim=True, scope='transformer', )
mlmodels\model_dev\raw\text-to-speech\caching.py
----------------methods----------------
---------------functions---------------
mlmodels\model_dev\raw\text-to-speech\utils.py
----------------methods----------------
---------------functions---------------
get_cached(path, )
get_spectrogram(audio_file, )
get_wav(spectrogram, )
griffin_lim(spectrogram, )
invert_spectrogram(spectrogram, )
load_file(path, )
plot_alignment(alignment, e, )
spectrogram2wav(mag, )
text_normalize(text, )
mlmodels\model_dev\raw\text-to-speech\1.tacotron\caching.py
----------------methods----------------
---------------functions---------------
mlmodels\model_dev\raw\text-to-speech\1.tacotron\tacotron.py
----------------methods----------------
Tacotron.__init__(self, reuse=None, )
---------------functions---------------
bn(inputs, is_training=True, activation_fn=None, scope='bn', reuse=None, )
conv1d_banks(inputs, K=16, is_training=True, scope='conv1d_banks', )
guided_attention( g=0.2, )
highwaynet(inputs, num_units=None, scope='highwaynet', )
learning_rate_decay(init_lr, global_step, warmup_steps=4000.0, )
prenet(inputs, num_units=None, is_training=True, scope='prenet', )
mlmodels\model_dev\raw\text-to-speech\1.tacotron\utils.py
----------------methods----------------
---------------functions---------------
get_cached(path, )
get_spectrogram(audio_file, )
get_wav(spectrogram, )
griffin_lim(spectrogram, )
invert_spectrogram(spectrogram, )
load_file(path, )
plot_alignment(alignment, )
spectrogram2wav(mag, )
text_normalize(text, )
mlmodels\model_dev\raw\topic-model\modeling.py
----------------methods----------------
---------------functions---------------
_cache_mem(curr_out, prev_mem, mem_len, reuse_len=None, )
_create_mask(qlen, mlen, dtype=tf.float32, same_length=False, )
abs_attn_core(q_head, k_head, v_head, attn_mask, dropatt, is_training, scale, )
classification_loss(hidden, labels, n_class, initializer, scope, reuse=None, return_logits=False, )
embedding_lookup(x, n_token, d_embed, initializer, use_tpu=True, scope='embedding', reuse=None, dtype=tf.float32, )
gelu(x, )
head_projection(h, d_model, n_head, d_head, kernel_initializer, name, )
lm_loss(hidden, target, n_token, d_model, initializer, lookup_table=None, tie_weight=False, bi_data=True, use_tpu=False, )
multihead_attn(q, k, v, attn_mask, d_model, n_head, d_head, dropout, dropatt, is_training, kernel_initializer, residual=True, scope='abs_attn', reuse=None, )
positional_embedding(pos_seq, inv_freq, bsz=None, )
positionwise_ffn(inp, d_model, d_inner, dropout, kernel_initializer, activation_type='relu', scope='ff', is_training=True, reuse=None, )
post_attention(h, attn_vec, d_model, n_head, d_head, dropout, is_training, kernel_initializer, residual=True, )
regression_loss(hidden, labels, initializer, scope, reuse=None, return_logits=False, )
rel_attn_core(q_head, k_head_h, v_head_h, k_head_r, seg_embed, seg_mat, r_w_bias, r_r_bias, r_s_bias, attn_mask, dropatt, is_training, scale, )
rel_multihead_attn(h, r, r_w_bias, r_r_bias, seg_mat, r_s_bias, seg_embed, attn_mask, mems, d_model, n_head, d_head, dropout, dropatt, is_training, kernel_initializer, scope='rel_attn', reuse=None, )
rel_shift(x, klen=-1, )
relative_positional_encoding(qlen, klen, d_model, clamp_len, attn_type, bi_data, bsz=None, dtype=None, )
summarize_sequence(summary_type, hidden, d_model, n_head, d_head, dropout, dropatt, input_mask, is_training, initializer, scope=None, reuse=None, use_proj=True, )
transformer_xl(inp_k, n_token, n_layer, d_model, n_head, d_head, d_inner, dropout, dropatt, attn_type, bi_data, initializer, is_training, mem_len=None, inp_q=None, mems=None, same_length=False, clamp_len=-1, untie_r=False, use_tpu=True, input_mask=None, perm_mask=None, seg_id=None, reuse_len=None, ff_activation='relu', target_mapping=None, use_bfloat16=False, scope='transformer', **kwargs)
two_stream_rel_attn(h, g, r, mems, r_w_bias, r_r_bias, seg_mat, r_s_bias, seg_embed, attn_mask_h, attn_mask_g, target_mapping, d_model, n_head, d_head, dropout, dropatt, is_training, kernel_initializer, scope='rel_attn', )
mlmodels\model_dev\raw\topic-model\utils.py
----------------methods----------------
---------------functions---------------
build_dataset(words, n_words, )
clearstring(string, )
separate_dataset(trainset, ratio=0.5, )
str_idx(corpus, dic, maxlen, UNK=3, )
mlmodels\model_dev\raw\topic-model\xlnet.py
----------------methods----------------
RunConfig.__init__(self, is_training, use_tpu, use_bfloat16, dropout, dropatt, init='normal', init_range=0.1, init_std=0.02, mem_len=None, reuse_len=None, bi_data=False, clamp_len=-1, same_length=False, )
XLNetConfig.__init__(self, FLAGS=None, json_path=None, )
XLNetConfig.init_from_flags(self, FLAGS, )
XLNetConfig.init_from_json(self, json_path, )
XLNetConfig.to_json(self, json_path, )
XLNetModel.__init__(self, xlnet_config, run_config, input_ids, seg_ids, input_mask, mems=None, perm_mask=None, target_mapping=None, inp_q=None, **kwargs)
XLNetModel.get_embedding_table(self, )
XLNetModel.get_initializer(self, )
XLNetModel.get_new_memory(self, )
XLNetModel.get_pooled_out(self, summary_type, use_summ_proj=True, )
XLNetModel.get_sequence_output(self, )
---------------functions---------------
_get_initializer(FLAGS, )
create_run_config(is_training, is_finetune, FLAGS, )
mlmodels\model_dev\raw\vectorizer\utils.py
----------------methods----------------
---------------functions---------------
build_dataset(words, n_words, )
clearstring(string, )
separate_dataset(trainset, ratio=0.5, )
str_idx(corpus, dic, maxlen, UNK=3, )
mlmodels\model_flow\__init__.py
----------------methods----------------
---------------functions---------------
mlmodels\model_flow\dev\mlflow_run.py
----------------methods----------------
---------------functions---------------
cli_load_arguments( )
log_scalar(name, value, step, )
mlflow_add(args, )
session_init(args, )
tfboard_add_weights(step, )
tfboard_writer_create( )
mlmodels\model_gluon\fb_prophet.py
----------------methods----------------
Model.__init__(self, model_pars=None, data_pars=None, compute_pars=None, )
---------------functions---------------
fit( model=None, data_pars={}, compute_pars={}, out_pars={}, **kw)
get_dataset(data_pars, )
get_params( param_pars={}, **kw)
load( load_pars={}, **kw)
metrics_plot(metrics_params, )
predict( model=None, model_pars=None, sess=None, data_pars=None, compute_pars=None, out_pars=None, **kwargs)
save( model=None, session=None, save_pars={}, )
test( data_path="dataset/", pars_choice="test0", config_mode="test", )
mlmodels\model_gluon\gluonts_model.py
----------------methods----------------
Model.__init__(self, model_pars=None, data_pars=None, compute_pars=None, **kwargs)
---------------functions---------------
fit(model, sess=None, data_pars=None, model_pars=None, compute_pars=None, out_pars=None, session=None, **kwargs)
fit_metrics(ypred, data_pars, compute_pars=None, out_pars=None, **kw)
get_dataset(data_pars, )
get_params( choice="", data_path="dataset/timeseries/", config_mode="test", **kw)
load(path, )
metrics(ypred, data_pars, compute_pars=None, out_pars=None, **kw)
plot_predict(item_metrics, out_pars=None, )
plot_prob_forecasts(ypred, out_pars=None, )
predict(model, sess=None, data_pars=None, compute_pars=None, out_pars=None, **kw)
save(model, path, )
test( )
test_single( data_path="dataset/", choice="", config_mode="test", )
mlmodels\model_gluon\gluon_automl.py
----------------methods----------------
Model.__init__(self, model_pars=None, compute_pars=None, )
---------------functions---------------
_config_process(config, )
get_params( choice="", data_path="dataset/", config_mode="test", **kw)
path_setup( out_folder="", sublevel=0, data_path="dataset/", )
test( data_path="dataset/", pars_choice="json", )
mlmodels\model_gluon\util.py
----------------methods----------------
Model_empty.__init__(self, model_pars=None, compute_pars=None, )
---------------functions---------------
_config_process(data_path, config_mode="test", )
fit(model, sess=None, data_pars=None, model_pars=None, compute_pars=None, out_pars=None, session=None, **kwargs)
get_dataset(data_pars, )
load(path, )
metrics(ypred, data_pars, compute_pars=None, out_pars=None, **kwargs)
plot_predict(item_metrics, out_pars=None, )
plot_prob_forecasts(ypred, out_pars=None, )
predict(model, sess=None, data_pars=None, compute_pars=None, out_pars=None, **kwargs)
save(model, path, )
mlmodels\model_gluon\util_autogluon.py
----------------methods----------------
Model_empty.__init__(self, model_pars=None, compute_pars=None, )
---------------functions---------------
_get_dataset_from_aws( **kw)
fit(model, data_pars=None, model_pars=None, compute_pars=None, out_pars=None, session=None, **kwargs)
get_dataset( **kw)
import_data_fromfile( **kw)
load(path, )
log( n=0, m=1, *s)
metrics(model, ypred, ytrue, data_pars, compute_pars=None, out_pars=None, **kwargs)
os_package_root_path(filepath, sublevel=0, path_add="", )
predict(model, data_pars, compute_pars=None, out_pars=None, **kwargs)
save(model, out_pars, )