ludwig-ai--ludwig
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96 行
4.1 KiB
Python
96 行
4.1 KiB
Python
"""This function will take in a Ludwig config, strip away all its parameters except input and output featuresand
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add some other parameters to run logistic regression hyperopt."""
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def process_config(ludwig_config: dict, experiment_dict: dict) -> dict:
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"""Modify a Ludwig config by programmatically adding elements to the config dictionary.
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The purpose is to apply changes for all datasets that are the same or are based on the
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attributes of `experiment_dict` (e.g. dataset_name) removing the need to manually apply
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small changes to configs on many datasets.
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Args:
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ludwig_config: A Ludwig config.
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experiment_dict: A benchmarking config experiment dictionary.
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Returns:
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A modified Ludwig config.
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"""
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# only keep input_features and output_features
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main_config_keys = list(ludwig_config.keys())
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for key in main_config_keys:
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if key not in ["input_features", "output_features"]:
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del ludwig_config[key]
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temp = {
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"preprocessing": {"split": {"type": "fixed"}},
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"trainer": {"epochs": 1024, "early_stop": 7, "eval_batch_size": 16384, "evaluate_training_set": False},
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"hyperopt": {
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"goal": "maximize",
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"output_feature": None,
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"metric": None,
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"split": "validation",
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"parameters": {
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"defaults.number.preprocessing.normalization": {"space": "choice", "categories": ["zscore", None]},
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"defaults.number.preprocessing.missing_value_strategy": {
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"space": "choice",
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"categories": ["fill_with_const", "fill_with_mean"],
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},
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"combiner.type": {"space": "choice", "categories": ["tabnet", "concat"]},
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"trainer.learning_rate_scheduler.decay": {"space": "choice", "categories": [True, False]},
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"trainer.learning_rate": {"space": "loguniform", "lower": 0.0001, "upper": 0.1},
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"trainer.learning_rate_scheduler.decay_rate": {"space": "uniform", "lower": 0.4, "upper": 0.96},
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"trainer.batch_size": {"space": "randint", "lower": 32, "upper": 2048},
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},
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"search_alg": {"type": "variant_generator"},
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"executor": {"type": "ray", "num_samples": 1000},
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"scheduler": {"type": "bohb", "reduction_factor": 2},
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},
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}
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# add config parameters from temp
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for key, value in temp.items():
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ludwig_config[key] = value
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dataset_name_to_metric = {
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"ames_housing": "r2",
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"mercedes_benz_greener": "r2",
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"mushroom_edibility": "accuracy",
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"amazon_employee_access_challenge": "roc_auc",
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"naval": "r2",
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"sarcos": "r2",
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"protein": "r2",
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"adult_census_income": "accuracy",
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"otto_group_product": "accuracy",
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"santander_customer_satisfaction": "accuracy",
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"amazon_employee_access": "roc_auc",
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"numerai28pt6": "accuracy",
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"bnp_claims_management": "accuracy",
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"allstate_claims_severity": "r2",
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"santander_customer_transaction": "accuracy",
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"connect4": "accuracy",
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"forest_cover": "accuracy",
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"ieee_fraud": "accuracy",
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"porto_seguro_safe_driver": "accuracy",
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"walmart_recruiting": "accuracy",
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"poker_hand": "accuracy",
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"higgs": "accuracy",
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}
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# add hyperopt output feature and metric.
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dataset_name = experiment_dict["dataset_name"]
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ludwig_config["hyperopt"]["metric"] = dataset_name_to_metric[dataset_name]
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ludwig_config["hyperopt"]["output_feature"] = ludwig_config["output_features"][0]["name"]
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# use sparse encoder for categorical features to mimic logistic regression.
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for i, feature in enumerate(ludwig_config["input_features"]):
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if feature["type"] == "category":
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ludwig_config["input_features"][i]["encoder"] = "sparse"
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for i, feature in enumerate(ludwig_config["output_features"]):
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if feature["type"] == "category":
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ludwig_config["output_features"][i]["encoder"] = "sparse"
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# make sure to return the ludwig_config
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return ludwig_config
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