Reorder data cleaning

freqtrade/freqai/data_drawer.py

@@ -496,6 +496,10 @@ class FreqaiDataDrawer:
             save_path / f"{dk.model_filename}_trained_df.pkl"
         )
 
+        dk.data_dictionary["train_features_no_transf"].to_pickle(
+            save_path / f"{dk.model_filename}_trained_df_no_transf.pkl"
+        )
+
         dk.data_dictionary["train_dates"].to_pickle(
             save_path / f"{dk.model_filename}_trained_dates_df.pkl"
         )
@@ -513,6 +517,8 @@ class FreqaiDataDrawer:
         if coin not in self.meta_data_dictionary:
             self.meta_data_dictionary[coin] = {}
         self.meta_data_dictionary[coin]["train_df"] = dk.data_dictionary["train_features"]
+        self.meta_data_dictionary[coin]["train_df_no_transf"] = \
+            dk.data_dictionary["train_features_no_transf"]
         self.meta_data_dictionary[coin]["meta_data"] = dk.data
         self.save_drawer_to_disk()
 
@@ -553,6 +559,8 @@ class FreqaiDataDrawer:
         if coin in self.meta_data_dictionary:
             dk.data = self.meta_data_dictionary[coin]["meta_data"]
             dk.data_dictionary["train_features"] = self.meta_data_dictionary[coin]["train_df"]
+            dk.data_dictionary["train_features_no_transf"] = \
+                self.meta_data_dictionary[coin]["train_df_no_transf"]
         else:
             with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
                 dk.data = rapidjson.load(fp, number_mode=rapidjson.NM_NATIVE)

freqtrade/freqai/data_kitchen.py

@@ -273,11 +273,15 @@ class FreqaiDataKitchen:
         test_labels: DataFrame,
         train_weights: Any,
         test_weights: Any,
+        train_df_no_transf: DataFrame = DataFrame(),
+        test_df_no_transf: DataFrame = DataFrame()
     ) -> Dict:
 
         self.data_dictionary = {
             "train_features": train_df,
+            "train_features_no_transf": train_df_no_transf,
             "test_features": test_df,
+            "test_features_no_transf": test_df_no_transf,
             "train_labels": train_labels,
             "test_labels": test_labels,
             "train_weights": train_weights,
@@ -289,7 +293,7 @@ class FreqaiDataKitchen:
 
     def normalize_data(self, data_dictionary: Dict) -> Dict[Any, Any]:
         """
-        Normalize all data in the data_dictionary according to the training dataset
+        Normalize all data in the data_dictionary according to the training dataset.
         :param data_dictionary: dictionary containing the cleaned and
                                 split training/test data/labels
         :returns:
@@ -495,6 +499,9 @@ class FreqaiDataKitchen:
         """
 
         from sklearn.decomposition import PCA  # avoid importing if we dont need it
+        self.data_dictionary["train_features_no_transf"] = self.data_dictionary["train_features"]
+        self.data["training_features_list_no_transf"] = \
+            self.data_dictionary["train_features"].columns
 
         pca = PCA(0.999)
         pca = pca.fit(self.data_dictionary["train_features"])
@@ -520,6 +527,7 @@ class FreqaiDataKitchen:
         self.training_features_list = self.data_dictionary["train_features"].columns
 
         if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
+            self.data_dictionary["test_features_no_transf"] = self.data_dictionary["test_features"]
             test_components = pca.transform(self.data_dictionary["test_features"])
             self.data_dictionary["test_features"] = pd.DataFrame(
                 data=test_components,
@@ -545,6 +553,8 @@ class FreqaiDataKitchen:
         Use an existing pca transform to transform data into components
         :param filtered_dataframe: DataFrame = the cleaned dataframe
         """
+        self.data_dictionary["prediction_features_no_transf"] = \
+            self.data_dictionary["prediction_features"]
         pca_components = self.pca.transform(filtered_dataframe)
         self.data_dictionary["prediction_features"] = pd.DataFrame(
             data=pca_components,
@@ -559,7 +569,8 @@ class FreqaiDataKitchen:
         """
         Compute distances between each training point and every other training
         point. This metric defines the neighborhood of trained data and is used
-        for prediction confidence in the Dissimilarity Index
+        for prediction confidence in the Dissimilarity Index.
+        Calculations are done on non-transformed (e.g., PCA) data.
         """
         # logger.info("computing average mean distance for all training points")
         pairwise = pairwise_distances(
@@ -586,7 +597,7 @@ class FreqaiDataKitchen:
     def use_SVM_to_remove_outliers(self, predict: bool) -> None:
         """
         Build/inference a Support Vector Machine to detect outliers
-        in training data and prediction
+        in training data and prediction, before any transformation (e.g., PCA).
         :param predict: bool = If true, inference an existing SVM model, else construct one
         """
 
@@ -669,7 +680,7 @@ class FreqaiDataKitchen:
 
     def use_DBSCAN_to_remove_outliers(self, predict: bool, eps=None) -> None:
         """
-        Use DBSCAN to cluster training data and remove "noisy" data (read outliers).
+        Use DBSCAN to cluster training data and remove outliers before transformation (e.g., PCA).
         User controls this via the config param `DBSCAN_outlier_pct` which indicates the
         pct of training data that they want to be considered outliers.
         :param predict: bool = If False (training), iterate to find the best hyper parameters
@@ -682,7 +693,10 @@ class FreqaiDataKitchen:
         if predict:
             if not self.data['DBSCAN_eps']:
                 return
-            train_ft_df = self.data_dictionary['train_features']
+            if self.data_dictionary["train_features_no_transf"].empty:
+                train_ft_df = self.data_dictionary['train_features']
+            else:
+                train_ft_df = self.data_dictionary["train_features_no_transf"]
             pred_ft_df = self.data_dictionary['prediction_features']
             num_preds = len(pred_ft_df)
             df = pd.concat([train_ft_df, pred_ft_df], axis=0, ignore_index=True)
@@ -773,7 +787,8 @@ class FreqaiDataKitchen:
 
     def compute_inlier_metric(self, set_='train') -> None:
         """
-        Compute inlier metric from backwards distance distributions.
+        Compute inlier metric from backwards distance distributions before any transformation
+        (e.g., PCA).
         This metric defines how well features from a timepoint fit
         into previous timepoints.
         """
@@ -918,9 +933,13 @@ class FreqaiDataKitchen:
         and avoid making predictions on any points that are too far away
         from the training data set.
         """
+        if self.data_dictionary["train_features_no_transf"].empty:
+            train_features = self.data_dictionary["train_features"]
+        else:
+            train_features = self.data_dictionary["train_features_no_transf"]
 
         distance = pairwise_distances(
-            self.data_dictionary["train_features"],
+            train_features,
             self.data_dictionary["prediction_features"],
             n_jobs=self.thread_count,
         )

freqtrade/freqai/freqai_interface.py

@@ -465,17 +465,9 @@ class IFreqaiModel(ABC):
             if self.freqai_info["data_split_parameters"]["test_size"] > 0:
                 dk.compute_inlier_metric(set_='test')
 
-        if ft_params.get(
-            "principal_component_analysis", False
-        ):
-            dk.principal_component_analysis()
-
         if ft_params.get("use_SVM_to_remove_outliers", False):
             dk.use_SVM_to_remove_outliers(predict=False)
 
-        if ft_params.get("DI_threshold", 0):
-            dk.data["avg_mean_dist"] = dk.compute_distances()
-
         if ft_params.get("use_DBSCAN_to_remove_outliers", False):
             if dk.pair in self.dd.old_DBSCAN_eps:
                 eps = self.dd.old_DBSCAN_eps[dk.pair]
@@ -484,6 +476,14 @@ class IFreqaiModel(ABC):
             dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps)
             self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps']
 
+        if ft_params.get("DI_threshold", 0):
+            dk.data["avg_mean_dist"] = dk.compute_distances()
+
+        if ft_params.get(
+            "principal_component_analysis", False
+        ):
+            dk.principal_component_analysis()
+
         if self.freqai_info["feature_parameters"].get('noise_standard_deviation', 0):
             dk.add_noise_to_training_features()
 
@@ -500,19 +500,19 @@ class IFreqaiModel(ABC):
         if ft_params.get('inlier_metric_window', 0):
             dk.compute_inlier_metric(set_='predict')
 
-        if ft_params.get(
-            "principal_component_analysis", False
-        ):
-            dk.pca_transform(dk.data_dictionary['prediction_features'])
-
         if ft_params.get("use_SVM_to_remove_outliers", False):
             dk.use_SVM_to_remove_outliers(predict=True)
 
+        if ft_params.get("use_DBSCAN_to_remove_outliers", False):
+            dk.use_DBSCAN_to_remove_outliers(predict=True)
+
         if ft_params.get("DI_threshold", 0):
             dk.check_if_pred_in_training_spaces()
 
-        if ft_params.get("use_DBSCAN_to_remove_outliers", False):
+        if ft_params.get(
-            dk.use_DBSCAN_to_remove_outliers(predict=True)
+            "principal_component_analysis", False
+        ):
+            dk.pca_transform(dk.data_dictionary['prediction_features'])
 
     def model_exists(self, dk: FreqaiDataKitchen) -> bool:
         """

tests/freqai/test_freqai_datakitchen.py

@@ -157,5 +157,5 @@ def test_make_train_test_datasets(mocker, freqai_conf):
     data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered)
 
     assert data_dictionary
-    assert len(data_dictionary) == 7
+    assert len(data_dictionary) == 9
     assert len(data_dictionary['train_features'].index) == 1916

tests/freqai/test_freqai_interface.py

@@ -338,7 +338,11 @@ def test_follow_mode(mocker, freqai_conf):
     freqai.dd.load_all_pair_histories(timerange, freqai.dk)
 
     df = strategy.dp.get_pair_dataframe('ADA/BTC', '5m')
-
+    # import pytest
+    # pytest.set_trace()
+    freqai.dk.build_data_dictionary(
+                [], [], [], [], [], []
+                )
     freqai.start_live(df, metadata, strategy, freqai.dk)
 
     assert len(freqai.dk.return_dataframe.index) == 5702