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@@ -179,6 +179,9 @@ class PipelineSelector(ABC):
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self.start_tuning_time = datetime.datetime.today()
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self.total_tuning_time = None
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self.finished_tuning = False
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+
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+ self._additional_metrics = additional_metrics
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+ self._additional_averaging_funcs = additional_averaging_funcs
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except Exception as e:
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err = ("Failed to initialize the class. "
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@@ -341,10 +344,6 @@ class PipelineSelector(ABC):
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# with one validation split.
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cv = CVComposer.dummy_cv()
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-
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-
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-
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-
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train_inds = list(range(len(X_train)))
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val_inds = list(range(len(X_train),
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len(X_train) + len(X_val)))
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@@ -398,12 +397,17 @@ class PipelineSelector(ABC):
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try:
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assert(os.path.isfile(data_hdf5_store_path)),\
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"Parameter hdf5_store_path is not a file"
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+
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+ # close all opened files, because hdf5 will
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+ # fail to reopen an opened (for some reason) file
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+ import tables
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+ tables.file._open_files.close_all()
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store = pd.HDFStore(data_hdf5_store_path)
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self._data_path = data_hdf5_store_path
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- data_input = {key: store["key"] if key in store else None
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+ data_input = {key: store[key] if key in store else None
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for key in ["X_train", "y_train", "X_val", "y_val"]}
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if cv_pickle_path is not None:
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@@ -564,7 +568,7 @@ class PipelineSelector(ABC):
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over the folds.
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"""
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try:
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- scoring = {"score": make_scorer(self.cost_func)}
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+ scoring = {"score": make_scorer(self._cost_func)}
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scoring.update({metric_name: make_scorer(metric)
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for metric_name, metric
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@@ -575,7 +579,7 @@ class PipelineSelector(ABC):
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X=self._X,
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y=self._y,
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cv=self._cv,
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- scoring=self._scoring,
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+ scoring=scoring,
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error_score=np.nan)
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averaging_funcs = {
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