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delete automl
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@ -736,7 +736,6 @@ class AutoML(BaseEstimator):
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settings["use_ray"] = settings.get("use_ray", False)
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settings["metric_constraints"] = settings.get("metric_constraints", [])
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settings["cv_score_agg_func"] = settings.get("cv_score_agg_func", None)
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settings["lexico_objectives"] = settings.get("lexico_objectives", None)
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settings["fit_kwargs_by_estimator"] = settings.get(
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"fit_kwargs_by_estimator", {}
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)
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@ -2150,7 +2149,6 @@ class AutoML(BaseEstimator):
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cv_score_agg_func=None,
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skip_transform=None,
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fit_kwargs_by_estimator=None,
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lexico_objectives=None,
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**fit_kwargs,
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):
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"""Find a model for a given task.
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@ -2404,24 +2402,6 @@ class AutoML(BaseEstimator):
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[TrainingArgumentsForAuto](nlp/huggingface/training_args).
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e.g.,
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skip_transform: boolean, default=False | Whether to pre-process data prior to modeling.
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lexico_objectives: dict, default=None | It specifics information needed to perform multi-objective
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optimization with lexicographic preferences. When lexico_objectives it not None, the argument metric will be invaild,
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and flaml's AutoML uses "cfo" as the `hpo_method`, which makes the input (if provided) `hpo_method' invalid.
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This dictionary shall contain the following fields of key-value pairs:
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- "metrics": a list of optimization objectives with the orders reflecting the priorities/preferences of the
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objectives.
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- "modes" (optional): a list of optimization modes (each mode either "min" or "max") corresponding to the
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objectives in the metric list. If not provided, we use "min" as the default mode for all the objectives
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- "targets" (optional): a dictionary to specify the optimization targets on the objectives. The keys are the
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metric names (provided in "metric"), and the values are the numerical target values.
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- "tolerances"(optional): a dictionary to specify the optimality tolerances on objectives. The keys are the
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metric names (provided in "metrics"), and the values are the numerical tolerances values.
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E.g.,
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```python
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lexico_objectives = {"metrics":["error_rate","pred_time"], "modes":["min","min"],
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"tolerances":{"error_rate":0.01,"pred_time":0.0}, "targets":{"error_rate":0.0}}
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```
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fit_kwargs_by_estimator: dict, default=None | The user specified keywords arguments, grouped by estimator name.
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For TransformersEstimator, available fit_kwargs can be found from
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[TrainingArgumentsForAuto](nlp/huggingface/training_args).
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@ -2522,15 +2502,7 @@ class AutoML(BaseEstimator):
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self._settings.get("retrain_full") if retrain_full is None else retrain_full
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)
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split_type = split_type or self._settings.get("split_type")
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if lexico_objectives is None:
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hpo_method = hpo_method or self._settings.get("hpo_method")
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else:
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if hpo_method != "cfo":
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logger.warning(
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"If lexico_objectives is not None, hpo_method is forced to be cfo"
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)
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hpo_method = "cfo"
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hpo_method = hpo_method or self._settings.get("hpo_method")
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learner_selector = learner_selector or self._settings.get("learner_selector")
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no_starting_points = starting_points is None
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if no_starting_points:
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@ -2634,9 +2606,7 @@ class AutoML(BaseEstimator):
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self._state.cv_score_agg_func = cv_score_agg_func or self._settings.get(
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"cv_score_agg_func"
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)
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self._state.lexico_objectives = lexico_objectives or self._settings.get(
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"lexico_objectives"
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)
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self._retrain_in_budget = retrain_full == "budget" and (
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eval_method == "holdout" and self._state.X_val is None
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)
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@ -3026,7 +2996,6 @@ class AutoML(BaseEstimator):
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metric_constraints=self.metric_constraints,
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seed=self._seed,
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time_budget_s=time_left,
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lexico_objectives=self._state.lexico_objectives,
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)
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else:
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# if self._hpo_method is bo, sometimes the search space and the initial config dimension do not match
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@ -3051,7 +3020,7 @@ class AutoML(BaseEstimator):
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search_alg = ConcurrencyLimiter(search_alg, self._n_concurrent_trials)
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resources_per_trial = self._state.resources_per_trial
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analysis = ray.tune.run(
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analysis = ray.tune.run(
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self.trainable,
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search_alg=search_alg,
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config=space,
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@ -3263,7 +3232,6 @@ class AutoML(BaseEstimator):
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],
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metric_constraints=self.metric_constraints,
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seed=self._seed,
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lexico_objectives=self._state.lexico_objectives,
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)
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else:
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# if self._hpo_method is bo, sometimes the search space and the initial config dimension do not match
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@ -3721,4 +3689,4 @@ class AutoML(BaseEstimator):
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if inv[i]:
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q += inv[i] / s
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if p < q:
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return estimator_list[i]
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return estimator_list[i]
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@ -1,33 +0,0 @@
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from flaml import AutoML
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from flaml.data import load_openml_dataset
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def test_lexiflow():
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X_train, X_test, y_train, y_test = load_openml_dataset(
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dataset_id=179, data_dir="test/data"
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)
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lexico_objectives = {}
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lexico_objectives["metrics"] = ["val_loss", "pred_time"]
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lexico_objectives["tolerances"] = {"val_loss": 0.01, "pred_time": 0.0}
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lexico_objectives["targets"] = {"val_loss": 0.0, "pred_time": 0.0}
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lexico_objectives["modes"] = ["min", "min"]
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automl = AutoML()
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settings = {
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"time_budget": 100,
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"lexico_objectives": lexico_objectives,
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"use_ray": False,
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"task": "classification",
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"max_iter": -1,
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"mem_thres": 128 * (1024**3),
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}
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automl.fit(X_train=X_train, y_train=y_train, X_val=X_test, y_val=y_test, **settings)
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print(automl.predict(X_train))
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print(automl.model)
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print(automl.config_history)
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print(automl.best_iteration)
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print(automl.best_estimator)
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if __name__ == "__main__":
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test_lexiflow()
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