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fix: pandas.
This commit is contained in:
robcaulk
parent
6a8abd5d45
commit
08d0e76187
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@ -4,6 +4,7 @@ import logging
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import re
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import shutil
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import threading
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import warnings
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from datetime import datetime, timedelta, timezone
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from pathlib import Path
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from typing import Any, Dict, Tuple, TypedDict
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@ -262,10 +263,11 @@ class FreqaiDataDrawer:
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self.pair_dict[metadata["pair"]] = self.empty_pair_dict.copy()
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return
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def set_initial_return_values(self, pair: str,
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pred_df: DataFrame,
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dataframe: DataFrame
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) -> None:
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def set_initial_return_values(
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self, pair: str,
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pred_df: DataFrame,
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dataframe: DataFrame
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) -> None:
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"""
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Set the initial return values to the historical predictions dataframe. This avoids needing
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to repredict on historical candles, and also stores historical predictions despite
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@ -280,8 +282,13 @@ class FreqaiDataDrawer:
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# set new_pred values to nans (we want to signal to user that there was nothing
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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[:, :] = new_pred.astype(float).values * np.nan
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new_pred["date_pred"] = dataframe["date"]
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# set everything to nan except date_pred
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columns_to_nan = new_pred.columns.difference(['date_pred'])
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new_pred[columns_to_nan] = new_pred[columns_to_nan].astype(
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float).values * np.nan
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hist_preds = self.historic_predictions[pair].copy()
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# ensure both dataframes have the same date format so they can be merged
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@ -290,7 +297,8 @@ class FreqaiDataDrawer:
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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_pred", how="inner")
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common_dates = pd.merge(new_pred, hist_preds,
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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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@ -298,15 +306,27 @@ class FreqaiDataDrawer:
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"predictions. You likely left your FreqAI instance offline "
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f"for more than {len(dataframe.index)} candles.")
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# reindex new_pred columns to match the historic predictions dataframe
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new_pred_reindexed = new_pred.reindex(columns=hist_preds.columns)
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df_concat = pd.concat([hist_preds, new_pred_reindexed], ignore_index=True)
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if len(new_pred.index) == 0:
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df_concat = hist_preds.tail(
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len(dataframe.index)).reset_index(drop=True)
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else:
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# Pandas warns that its keeping dtypes of non NaN columns...
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# yea we know and we already want that behavior. Ignoring.
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with warnings.catch_warnings():
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warnings.filterwarnings("ignore", category=FutureWarning)
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# reindex new_pred columns to match the historic predictions dataframe
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new_pred_reindexed = new_pred.reindex(columns=hist_preds.columns)
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df_concat = pd.concat(
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[hist_preds, new_pred_reindexed],
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ignore_index=True
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)
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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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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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self.model_return_values[pair] = df_concat.tail(
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len(dataframe.index)).reset_index(drop=True)
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def append_model_predictions(self, pair: str, predictions: DataFrame,
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do_preds: NDArray[np.int_],
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@ -323,38 +343,56 @@ class FreqaiDataDrawer:
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index = self.historic_predictions[pair].index[-1:]
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columns = self.historic_predictions[pair].columns
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zeros_df = pd.DataFrame(np.zeros((1, len(columns))), index=index, columns=columns)
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zeros_df = pd.DataFrame(
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np.zeros((1, len(columns))),
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index=index,
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columns=columns
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)
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self.historic_predictions[pair] = pd.concat(
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[self.historic_predictions[pair], zeros_df], ignore_index=True, axis=0)
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[self.historic_predictions[pair], zeros_df],
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ignore_index=True,
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axis=0
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)
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df = self.historic_predictions[pair]
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# model outputs and associated statistics
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for label in predictions.columns:
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df[label].iloc[-1] = predictions[label].iloc[-1]
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label_loc = df.columns.get_loc(label)
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pred_label_loc = predictions.columns.get_loc(label)
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df.iloc[-1, label_loc] = predictions.iloc[-1, pred_label_loc]
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if df[label].dtype == object:
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continue
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df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
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df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
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label_mean_loc = df.columns.get_loc(f"{label}_mean")
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label_std_loc = df.columns.get_loc(f"{label}_std")
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df.iloc[-1, label_mean_loc] = dk.data["labels_mean"][label]
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df.iloc[-1, label_std_loc] = dk.data["labels_std"][label]
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# outlier indicators
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df["do_predict"].iloc[-1] = do_preds[-1]
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do_predict_loc = df.columns.get_loc("do_predict")
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df.iloc[-1, do_predict_loc] = do_preds[-1]
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if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
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df["DI_values"].iloc[-1] = dk.DI_values[-1]
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DI_values_loc = df.columns.get_loc("DI_values")
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df.iloc[-1, DI_values_loc] = dk.DI_values[-1]
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# extra values the user added within custom prediction model
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if dk.data['extra_returns_per_train']:
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rets = dk.data['extra_returns_per_train']
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for return_str in rets:
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df[return_str].iloc[-1] = rets[return_str]
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return_loc = df.columns.get_loc(return_str)
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df.iloc[-1, return_loc] = rets[return_str]
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# this logic carries users between version without needing to
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# change their identifier
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if 'close_price' not in df.columns:
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df['close_price'] = np.nan
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df['date_pred'] = np.nan
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df['close_price'].iloc[-1] = strat_df['close'].iloc[-1]
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df['date_pred'].iloc[-1] = strat_df['date'].iloc[-1]
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high_price_loc = df.columns.get_loc("high_price")
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high_loc = strat_df.columns.get_loc("high")
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df.iloc[-1, high_price_loc] = strat_df.iloc[-1, high_loc]
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low_price_loc = df.columns.get_loc("low_price")
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low_loc = strat_df.columns.get_loc("low")
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df.iloc[-1, low_price_loc] = strat_df.iloc[-1, low_loc]
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close_price_loc = df.columns.get_loc("close_price")
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close_loc = strat_df.columns.get_loc("close")
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df.iloc[-1, close_price_loc] = strat_df.iloc[-1, close_loc]
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date_pred_loc = df.columns.get_loc("date_pred")
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date_loc = strat_df.columns.get_loc("date")
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df.iloc[-1, date_pred_loc] = strat_df.iloc[-1, date_loc]
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self.model_return_values[pair] = df.tail(len_df).reset_index(drop=True)
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