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@@ -232,7 +232,7 @@ class TFTModel(ModelFT):
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p90_forecast = self.data_formatter.format_predictions(output_map["p90"])
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p90_forecast = self.data_formatter.format_predictions(output_map["p90"])
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tf.keras.backend.set_session(default_keras_session)
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tf.keras.backend.set_session(default_keras_session)
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predict = format_score(p90_forecast, "pred", 0) # self.label_shift
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predict = format_score(p90_forecast, "pred", 0) # self.label_shift
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label = format_score(targets, "label", 0)
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label = format_score(targets, "label", 0)
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# ===========================Predicting Process===========================
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# ===========================Predicting Process===========================
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return predict, label
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return predict, label
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@@ -21,18 +21,16 @@ class DataLoader(abc.ABC):
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@abc.abstractmethod
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@abc.abstractmethod
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def load(self, instruments, start_time=None, end_time=None) -> pd.DataFrame:
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def load(self, instruments, start_time=None, end_time=None) -> pd.DataFrame:
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"""
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"""
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load the data as pd.DataFrame
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load the data as pd.DataFrame.
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Parameters
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Parameters
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----------
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----------
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self : [TODO:type]
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instruments : str or dict
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[TODO:description]
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it can either be the market name or the config file of instruments generated by InstrumentProvider.
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instruments : [TODO:type]
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start_time : str
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[TODO:description]
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start of the time range.
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start_time : [TODO:type]
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end_time : str
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[TODO:description]
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end of the time range.
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end_time : [TODO:type]
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[TODO:description]
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Returns
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Returns
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-------
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-------
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