refactor: remove obsolete regressor

Signed-off-by: Jérôme Benoit <jerome.benoit@piment-noir.org>

diff --git a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV3.py b/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV3.py
deleted file mode 100644 (file)
index e2a0aa2..0000000
--- a/quickadapter/user_data/freqaimodels/XGBoostRegressorQuickAdapterV3.py
+++ /dev/null
@@ -1,153 +0,0 @@
-import logging
-from typing import Any, Dict
-
-from xgboost import XGBRegressor
-import time
-from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
-from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
-import pandas as pd
-import scipy as spy
-
-import warnings
-
-warnings.simplefilter(action="ignore", category=FutureWarning)
-
-logger = logging.getLogger(__name__)
-
-
-class XGBoostRegressorQuickAdapterV3(BaseRegressionModel):
-    """
-    The following freqaimodel is released to sponsors of the non-profit FreqAI open-source project.
-    If you find the FreqAI project useful, please consider supporting it by becoming a sponsor.
-    We use sponsor money to help stimulate new features and to pay for running these public
-    experiments, with a an objective of helping the community make smarter choices in their
-    ML journey.
-
-    This strategy is experimental (as with all strategies released to sponsors). Do *not* expect
-    returns. The goal is to demonstrate gratitude to people who support the project and to
-    help them find a good starting point for their own creativity.
-
-    If you have questions, please direct them to our discord: https://discord.gg/xE4RMg4QYw
-
-    https://github.com/sponsors/robcaulk
-    """
-
-    def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
-        """
-        User sets up the training and test data to fit their desired model here
-        :param data_dictionary: the dictionary constructed by DataHandler to hold
-                                all the training and test data/labels.
-        """
-
-        X = data_dictionary["train_features"]
-        y = data_dictionary["train_labels"]
-        train_weights = data_dictionary["train_weights"]
-
-        X_test = data_dictionary["test_features"]
-        y_test = data_dictionary["test_labels"]
-        test_weights = data_dictionary["test_weights"]
-
-        xgb_model = self.get_init_model(dk.pair)
-
-        model = XGBRegressor(**self.model_training_parameters)
-
-        eval_set, eval_weights = self.eval_set_and_weights(X_test, y_test, test_weights)
-
-        start = time.time()
-        model.fit(
-            X=X,
-            y=y,
-            sample_weight=train_weights,
-            eval_set=eval_set,
-            sample_weight_eval_set=eval_weights,
-            xgb_model=xgb_model,
-        )
-        time_spent = time.time() - start
-        self.dd.update_metric_tracker("fit_time", time_spent, dk.pair)
-
-        return model
-
-    def fit_live_predictions(self, dk: FreqaiDataKitchen, pair: str) -> None:
-        warmed_up = True
-
-        num_candles = self.freqai_info.get("fit_live_predictions_candles", 100)
-        if self.live:
-            if not hasattr(self, "exchange_candles"):
-                self.exchange_candles = len(self.dd.model_return_values[pair].index)
-            candle_diff = len(self.dd.historic_predictions[pair].index) - (
-                num_candles + self.exchange_candles
-            )
-            if candle_diff < 0:
-                logger.warning(
-                    f"Fit live predictions not warmed up yet. Still {abs(candle_diff)} candles to go"
-                )
-                warmed_up = False
-
-        pred_df_full = (
-            self.dd.historic_predictions[pair].tail(num_candles).reset_index(drop=True)
-        )
-        pred_df_sorted = pd.DataFrame()
-        for label in pred_df_full.keys():
-            if pred_df_full[label].dtype == object:
-                continue
-            pred_df_sorted[label] = pred_df_full[label]
-
-        # pred_df_sorted = pred_df_sorted
-        for col in pred_df_sorted:
-            pred_df_sorted[col] = pred_df_sorted[col].sort_values(
-                ascending=False, ignore_index=True
-            )
-        frequency = num_candles / (
-            self.freqai_info["feature_parameters"]["label_period_candles"] * 2
-        )
-        max_pred = pred_df_sorted.iloc[: int(frequency)].mean()
-        min_pred = pred_df_sorted.iloc[-int(frequency) :].mean()
-
-        if not warmed_up:
-            dk.data["extra_returns_per_train"]["&s-maxima_sort_threshold"] = 2
-            dk.data["extra_returns_per_train"]["&s-minima_sort_threshold"] = -2
-        else:
-            dk.data["extra_returns_per_train"]["&s-maxima_sort_threshold"] = max_pred[
-                "&s-extrema"
-            ]
-            dk.data["extra_returns_per_train"]["&s-minima_sort_threshold"] = min_pred[
-                "&s-extrema"
-            ]
-
-        dk.data["labels_mean"], dk.data["labels_std"] = {}, {}
-        for ft in dk.label_list:
-            # f = spy.stats.norm.fit(pred_df_full[ft])
-            dk.data["labels_std"][ft] = 0  # f[1]
-            dk.data["labels_mean"][ft] = 0  # f[0]
-
-        # fit the DI_threshold
-        if not warmed_up:
-            f = [0, 0, 0]
-            cutoff = 2
-        else:
-            di_values = pd.to_numeric(pred_df_full["DI_values"], errors="coerce")
-            di_values = di_values.dropna()
-            f = spy.stats.weibull_min.fit(di_values)
-            cutoff = spy.stats.weibull_min.ppf(
-                self.freqai_info.get("outlier_threshold", 0.999), *f
-            )
-
-        dk.data["DI_value_mean"] = pred_df_full["DI_values"].mean()
-        dk.data["DI_value_std"] = pred_df_full["DI_values"].std()
-        dk.data["extra_returns_per_train"]["DI_value_param1"] = f[0]
-        dk.data["extra_returns_per_train"]["DI_value_param2"] = f[1]
-        dk.data["extra_returns_per_train"]["DI_value_param3"] = f[2]
-        dk.data["extra_returns_per_train"]["DI_cutoff"] = cutoff
-
-    def eval_set_and_weights(self, X_test, y_test, test_weights):
-        if (
-            self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.25)
-            == 0
-        ):
-            eval_set = None
-            eval_weights = None
-        else:
-            eval_set = [(X_test, y_test)]
-            eval_weights = [test_weights]
-
-        return eval_set, eval_weights