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fix: avoid duplicate date columns interfering with fit_live_predictions()
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@@ -282,15 +282,12 @@ class FreqaiDataDrawer:
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# historically made during downtime. The newest pred will get appeneded later in
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# append_model_predictions)
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new_pred.iloc[:, :] = np.nan
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new_pred["date"] = dataframe["date"]
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new_pred["date_pred"] = dataframe["date"]
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hist_preds = self.historic_predictions[pair].copy()
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# rename date_pred column to date so that we can merge on date
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hist_preds = hist_preds.rename(columns={"date_pred": "date"})
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# find the closest common date between new_pred and historic predictions
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# and cut off the new_pred dataframe at that date
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common_dates = pd.merge(new_pred, hist_preds, on="date", how="inner")
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common_dates = pd.merge(new_pred, hist_preds, on="date_pred", how="inner")
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if len(common_dates.index) > 0:
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new_pred = new_pred.iloc[len(common_dates):]
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else:
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@@ -299,18 +296,12 @@ class FreqaiDataDrawer:
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f"for more than {len(dataframe.index)} candles.")
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df_concat = pd.concat([hist_preds, new_pred], ignore_index=True, keys=hist_preds.keys())
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# remove last row because we will append that later in append_model_predictions()
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df_concat = df_concat.iloc[:-1]
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# any missing values will get zeroed out so users can see the exact
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# downtime in FreqUI
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df_concat = df_concat.fillna(0)
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# rename date column back to date_pred
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df_concat = df_concat.rename(columns={"date": "date_pred"})
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self.historic_predictions[pair] = df_concat
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self.model_return_values[pair] = df_concat.tail(len(dataframe.index)).reset_index(drop=True)
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def append_model_predictions(self, pair: str, predictions: DataFrame,
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