def __init__(
self,
best_model,
cv_results,
cv_data,
model_reprs,
partition,
X_train,
y_train,
frequency,
horizon,
country_code_column,
):
self.best_model = best_model
self.cv_results = cv_results
self.cv_data = cv_data
self.model_reprs = model_reprs
self.partition = partition
self.X_train = X_train
self.y_train = y_train
self.frequency = frequency
self.horizon = horizon
self.country_code_column = country_code_column
self.best_model_hash = generate_estimator_hash(best_model)
self.best_model_cv_data = self.cv_data.rename({self.best_model_hash: "best_model"}, axis=1)[
["split", "y_true", "best_model"]
]
self.best_model_name = get_estimator_name(best_model).replace("model__", "")
self.best_model_cv_results = self.cv_results[self.cv_results["rank_test_score"] == 1].iloc[0]
self.best_model_repr = self.model_reprs[self.best_model_hash]
self.partition_hash = generate_partition_hash(self.partition)
self._persist_attrs = sorted(set(self.__dict__.keys()).difference(["self"]))
self._df_plot = None
def _score(self, method_caller, estimator, X, y_true, sample_weight=None):
"""Evaluate predicted target values for X relative to y_true.
Parameters
----------
method_caller : callable
Returns predictions given an estimator, method name, and other
arguments, potentially caching results.
estimator : object
Trained estimator to use for scoring. Must have a predict_proba
method; the output of that is used to compute the score.
X : array-like or sparse matrix
Test data that will be fed to estimator.predict.
y_true : array-like
Gold standard target values for X.
sample_weight : array-like
Sample weights.
Returns
-------
score : float
Score function applied to prediction of estimator on X.
"""
y_pred = estimator.predict(X)
estimator_repr = get_estimator_repr(estimator)
estimator_hash = generate_estimator_hash(estimator)
self._upsert_estimator_hash(estimator_repr, estimator_hash)
self._save_prediction(y_pred=y_pred,
estimator_label=estimator_hash,
y_true=y_true)
if y_pred.isna().any().any() or np.isinf(y_pred).any().any():
return np.nan
else:
if sample_weight is not None:
return self._sign * self._score_func(
y_true,
y_pred,
sample_weight=sample_weight,
**self._kwargs)
else:
return self._sign * self._score_func(y_true, y_pred, **
self._kwargs)
