ludwig-ai--ludwig
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248 行
8.0 KiB
Python
248 行
8.0 KiB
Python
import copy
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import pytest
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from ludwig.constants import (
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CATEGORY,
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COMBINER,
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DECODER,
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DEFAULTS,
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DEPENDENCIES,
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DROP_ROW,
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EARLY_STOP,
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ENCODER,
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EXECUTOR,
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FILL_WITH_MODE,
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HYPEROPT,
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INPUT_FEATURES,
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LOSS,
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MISSING_VALUE_STRATEGY,
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MODEL_ECD,
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MODEL_TYPE,
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OUTPUT_FEATURES,
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PREPROCESSING,
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REDUCE_DEPENDENCIES,
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REDUCE_INPUT,
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SCHEDULER,
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SUM,
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TIED,
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TOP_K,
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TRAINER,
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TYPE,
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)
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from ludwig.schema.model_config import ModelConfig
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from ludwig.schema.trainer import ECDTrainerConfig
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from ludwig.utils.backward_compatibility import upgrade_config_dict_to_latest_version
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from ludwig.utils.misc_utils import merge_dict, set_default_values
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from tests.integration_tests.utils import (
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binary_feature,
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category_feature,
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number_feature,
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sequence_feature,
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text_feature,
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vector_feature,
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)
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HYPEROPT_CONFIG = {
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"parameters": {
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"trainer.learning_rate": {
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"space": "loguniform",
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"lower": 0.001,
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"upper": 0.1,
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},
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"combiner.num_fc_layers": {"space": "randint", "lower": 2, "upper": 6},
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"utterance.encoder.norm": {"space": "grid_search", "values": ["layer", "batch"]},
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"utterance.encoder.dropout": {"space": "choice", "categories": [0.0001, 0.001, 0.01]},
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"utterance.encoder.fc_layers": {
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"space": "choice",
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"categories": [
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[{"output_size": 512}, {"output_size": 256}],
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[{"output_size": 512}],
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[{"output_size": 256}],
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],
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},
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},
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"search_alg": {"type": "variant_generator"},
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"executor": {"type": "ray"},
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"goal": "minimize",
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}
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SCHEDULER_DICT = {"type": "async_hyperband", "time_attr": "time_total_s"}
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@pytest.mark.parametrize(
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"use_train,use_hyperopt_scheduler",
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[
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(True, True),
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(False, True),
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(True, False),
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(False, False),
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],
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)
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def test_merge_with_defaults_early_stop(use_train, use_hyperopt_scheduler):
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all_input_features = [
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binary_feature(),
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category_feature(),
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number_feature(),
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text_feature(name="utterance"),
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]
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all_output_features = [
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category_feature(output_feature=True),
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sequence_feature(output_feature=True),
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vector_feature(),
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]
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# validate config with all features
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config = {
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INPUT_FEATURES: all_input_features,
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OUTPUT_FEATURES: all_output_features,
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HYPEROPT: HYPEROPT_CONFIG,
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}
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config = copy.deepcopy(config)
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if use_train:
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config[TRAINER] = {"batch_size": 42}
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if use_hyperopt_scheduler:
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# hyperopt scheduler cannot be used with early stopping
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config[HYPEROPT][EXECUTOR][SCHEDULER] = SCHEDULER_DICT
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merged_config = ModelConfig.from_dict(config).to_dict()
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# When a scheulder is provided, early stopping in the rendered config needs to be disabled to allow the
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# hyperopt scheduler to manage trial lifecycle.
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expected = -1 if use_hyperopt_scheduler else ECDTrainerConfig().early_stop
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assert merged_config[TRAINER]["early_stop"] == expected
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def test_missing_outputs_drop_rows():
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config = {
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INPUT_FEATURES: [category_feature()],
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OUTPUT_FEATURES: [category_feature(output_feature=True)],
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DEFAULTS: {CATEGORY: {PREPROCESSING: {MISSING_VALUE_STRATEGY: FILL_WITH_MODE}}},
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}
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merged_config = ModelConfig.from_dict(config).to_dict()
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global_preprocessing = merged_config[DEFAULTS]
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input_feature_config = merged_config[INPUT_FEATURES][0]
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output_feature_config = merged_config[OUTPUT_FEATURES][0]
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assert output_feature_config[PREPROCESSING][MISSING_VALUE_STRATEGY] == DROP_ROW
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assert global_preprocessing[input_feature_config[TYPE]][PREPROCESSING][MISSING_VALUE_STRATEGY] == FILL_WITH_MODE
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feature_preprocessing = merge_dict(
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global_preprocessing[output_feature_config[TYPE]][PREPROCESSING], output_feature_config[PREPROCESSING]
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)
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assert feature_preprocessing[MISSING_VALUE_STRATEGY] == DROP_ROW
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def test_default_model_type():
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config = {
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INPUT_FEATURES: [category_feature()],
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OUTPUT_FEATURES: [category_feature(output_feature=True)],
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}
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merged_config = ModelConfig.from_dict(config).to_dict()
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assert merged_config[MODEL_TYPE] == MODEL_ECD
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def test_set_default_values():
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config = {
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INPUT_FEATURES: [number_feature(encoder={"max_sequence_length": 10})],
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OUTPUT_FEATURES: [category_feature(decoder={})],
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}
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assert TIED not in config[INPUT_FEATURES][0]
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assert TOP_K not in config[OUTPUT_FEATURES][0]
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assert DEPENDENCIES not in config[OUTPUT_FEATURES][0]
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assert REDUCE_INPUT not in config[OUTPUT_FEATURES][0]
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assert REDUCE_DEPENDENCIES not in config[OUTPUT_FEATURES][0]
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set_default_values(config[INPUT_FEATURES][0], {ENCODER: {TYPE: "passthrough"}, TIED: None})
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set_default_values(
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config[OUTPUT_FEATURES][0],
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{
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DECODER: {
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TYPE: "classifier",
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},
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TOP_K: 3,
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DEPENDENCIES: [],
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REDUCE_INPUT: SUM,
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REDUCE_DEPENDENCIES: SUM,
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},
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)
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assert config[INPUT_FEATURES][0][ENCODER][TYPE] == "passthrough"
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assert config[INPUT_FEATURES][0][TIED] is None
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assert config[OUTPUT_FEATURES][0][DECODER][TYPE] == "classifier"
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assert config[OUTPUT_FEATURES][0][TOP_K] == 3
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assert config[OUTPUT_FEATURES][0][DEPENDENCIES] == []
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assert config[OUTPUT_FEATURES][0][REDUCE_INPUT] == SUM
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assert config[OUTPUT_FEATURES][0][REDUCE_DEPENDENCIES] == SUM
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def test_merge_with_defaults():
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# configuration with legacy parameters
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legacy_config_format = {
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"ludwig_version": "0.4",
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INPUT_FEATURES: [
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{"type": "numerical", "name": "number_input_feature", "encoder": {"type": "dense"}},
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{
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"type": "image",
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"name": "image_input_feature",
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"encoder": "stacked_cnn",
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"conv_bias": True,
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"conv_layers": [
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{"num_filters": 32, "pool_size": 2, "pool_stride": 2, "bias": False},
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{
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"num_filters": 64,
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"pool_size": 2,
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"pool_stride": 2,
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},
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],
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},
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],
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OUTPUT_FEATURES: [
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{
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"type": "numerical",
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"name": "number_output_feature",
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},
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],
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"training": {"eval_batch_size": 0, "optimizer": {"type": "adadelta"}},
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HYPEROPT: {
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"parameters": {
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"training.learning_rate": {"space": "choice", "categories": [0.0001, 0.001, 0.01]},
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"training.early_stop": {"space": "choice", "categories": [5, 10, 15]},
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"number_input_feature.encoder.num_layers": {"space": "choice", "categories": [2, 3, 4]},
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"number_output_feature.decoder.fc_output_size": {"space": "choice", "categories": [128, 256, 512]},
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"number_output_feature.decoder.fc_dropout": {"space": "uniform", "lower": 0, "upper": 1},
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},
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"executor": {
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"type": "serial",
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"search_alg": {TYPE: "variant_generator"},
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},
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"sampler": {
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"num_samples": 99,
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"scheduler": {},
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},
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},
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}
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updated_config = upgrade_config_dict_to_latest_version(legacy_config_format)
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merged_config = ModelConfig.from_dict(updated_config).to_dict()
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assert len(merged_config[DEFAULTS]) == 13
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assert ENCODER in merged_config[DEFAULTS][CATEGORY]
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assert PREPROCESSING in merged_config[DEFAULTS][CATEGORY]
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assert DECODER in merged_config[DEFAULTS][CATEGORY]
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assert LOSS in merged_config[DEFAULTS][CATEGORY]
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assert COMBINER in merged_config
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assert merged_config[TRAINER][EARLY_STOP] == 5
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assert SCHEDULER in merged_config[HYPEROPT][EXECUTOR]
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assert merged_config[HYPEROPT][EXECUTOR][SCHEDULER]["type"] == "fifo"
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assert TYPE in merged_config[INPUT_FEATURES][1][ENCODER]
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assert TYPE in merged_config[OUTPUT_FEATURES][0][DECODER]
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