no model save backtest, plot features backtest, ensure inlier plays nice, doc

docs/freqai.md

@@ -110,7 +110,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
 | `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set. <br> **Datatype:** List of positive integers.
 | `stratify_training_data` | This value is used to indicate the grouping of the data. For example, 2 would set every 2nd data point into a separate dataset to be pulled from during training/testing. See details about how it works [here](#stratifying-the-data-for-training-and-testing-the-model) <br> **Datatype:** Positive integer.
 | `principal_component_analysis` | Automatically reduce the dimensionality of the data set using Principal Component Analysis. See details about how it works [here](#reducing-data-dimensionality-with-principal-component-analysis)
-| `plot_feature_importance` | Create an interactive feature importance plot for each model.<br> **Datatype:** Boolean.<br> **Datatype:** Boolean, defaults to `False`
+| `plot_feature_importances` | Create a feature importance plot for each model for the top/bottom `plot_feature_importances` number of features.<br> **Datatype:** Boolean.<br> **Datatype:** Boolean, defaults to `0`
 | `DI_threshold` | Activates the Dissimilarity Index for outlier detection when > 0. See details about how it works [here](#removing-outliers-with-the-dissimilarity-index). <br> **Datatype:** Positive float (typically < 1).
 | `use_SVM_to_remove_outliers` | Train a support vector machine to detect and remove outliers from the training data set, as well as from incoming data points. See details about how it works [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Boolean.
 | `svm_params` | All parameters available in Sklearn's `SGDOneClassSVM()`. See details about some select parameters [here](#removing-outliers-using-a-support-vector-machine-svm). <br> **Datatype:** Dictionary.

freqtrade/freqai/data_drawer.py

@@ -313,6 +313,7 @@ class FreqaiDataDrawer:
         """
 
         dk.find_features(dataframe)
+        dk.find_labels(dataframe)
 
         full_labels = dk.label_list + dk.unique_class_list
 
@@ -376,7 +377,27 @@ class FreqaiDataDrawer:
         if self.config.get("freqai", {}).get("purge_old_models", False):
             self.purge_old_models()
 
-    # Functions pulled back from FreqaiDataKitchen because they relied on DataDrawer
+    def save_metaddata(self, dk: FreqaiDataKitchen) -> None:
+        """
+        Saves only metadata for backtesting studies if user prefers
+        not to save model data. This saves tremendous amounts of space
+        for users generating huge studies.
+        This is only active when `save_backtest_models`: false (not default)
+        """
+        if not dk.data_path.is_dir():
+            dk.data_path.mkdir(parents=True, exist_ok=True)
+
+        save_path = Path(dk.data_path)
+
+        dk.data["data_path"] = str(dk.data_path)
+        dk.data["model_filename"] = str(dk.model_filename)
+        dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
+        dk.data["label_list"] = dk.label_list
+
+        with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
+            rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
+
+        return
 
     def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
         """

freqtrade/freqai/data_kitchen.py

@@ -831,7 +831,7 @@ class FreqaiDataKitchen:
 
         inlier_metric = pd.DataFrame(
             data=inliers.sum(axis=1) / no_prev_pts,
-            columns=['inlier_metric'],
+            columns=['%-inlier_metric'],
             index=compute_df.index
         )
 
@@ -881,11 +881,14 @@ class FreqaiDataKitchen:
         """
         column_names = dataframe.columns
         features = [c for c in column_names if "%" in c]
-        labels = [c for c in column_names if "&" in c]
         if not features:
             raise OperationalException("Could not find any features!")
 
         self.training_features_list = features
+
+    def find_labels(self, dataframe: DataFrame) -> None:
+        column_names = dataframe.columns
+        labels = [c for c in column_names if "&" in c]
         self.label_list = labels
 
     def check_if_pred_in_training_spaces(self) -> None:
@@ -1206,7 +1209,8 @@ class FreqaiDataKitchen:
 
     def get_unique_classes_from_labels(self, dataframe: DataFrame) -> None:
 
-        self.find_features(dataframe)
+        # self.find_features(dataframe)
+        self.find_labels(dataframe)
 
         for key in self.label_list:
             if dataframe[key].dtype == object:

freqtrade/freqai/freqai_interface.py

@@ -92,6 +92,7 @@ class IFreqaiModel(ABC):
         self.begin_time_train: float = 0
         self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
         self.continual_learning = self.freqai_info.get('continual_learning', False)
+        self.plot_features = self.ft_params.get("plot_feature_importances", 0)
 
         self._threads: List[threading.Thread] = []
         self._stop_event = threading.Event()
@@ -278,22 +279,24 @@ class IFreqaiModel(ABC):
                 append_df = dk.get_backtesting_prediction()
                 dk.append_predictions(append_df)
             else:
-                if not self.model_exists(
+                if not self.model_exists(dk):
-                    pair, dk, trained_timestamp=trained_timestamp_int
-                ):
                     dk.find_features(dataframe_train)
+                    dk.find_labels(dataframe_train)
                     self.model = self.train(dataframe_train, pair, dk)
                     self.dd.pair_dict[pair]["trained_timestamp"] = int(
                         trained_timestamp.stopts)
-
+                    if self.plot_features:
+                        plot_feature_importance(self.model, pair, dk, self.plot_features)
                     if self.save_backtest_models:
                         logger.info('Saving backtest model to disk.')
                         self.dd.save_data(self.model, pair, dk)
+                    else:
+                        logger.info('Saving metadata to disk.')
+                        self.dd.save_metaddata(dk)
                 else:
                     self.model = self.dd.load_data(pair, dk)
 
-                self.check_if_feature_list_matches_strategy(dataframe_train, dk)
+                # self.check_if_feature_list_matches_strategy(dataframe_train, dk)
-
                 pred_df, do_preds = self.predict(dataframe_backtest, dk)
                 append_df = dk.get_predictions_to_append(pred_df, do_preds)
                 dk.append_predictions(append_df)
@@ -372,8 +375,7 @@ class IFreqaiModel(ABC):
             self.dd.return_null_values_to_strategy(dataframe, dk)
             return dk
 
-        # ensure user is feeding the correct indicators to the model
+        dk.find_labels(dataframe)
-        self.check_if_feature_list_matches_strategy(dataframe, dk)
 
         self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
 
@@ -492,7 +494,7 @@ class IFreqaiModel(ABC):
         if ft_params.get(
             "principal_component_analysis", False
         ):
-            dk.pca_transform(self.dk.data_dictionary['prediction_features'])
+            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)
@@ -503,14 +505,10 @@ class IFreqaiModel(ABC):
         if ft_params.get("use_DBSCAN_to_remove_outliers", False):
             dk.use_DBSCAN_to_remove_outliers(predict=True)
 
-    def model_exists(
+        # ensure user is feeding the correct indicators to the model
-        self,
+        self.check_if_feature_list_matches_strategy(dk.data_dictionary['prediction_features'], dk)
-        pair: str,
+
-        dk: FreqaiDataKitchen,
+    def model_exists(self, dk: FreqaiDataKitchen) -> bool:
-        trained_timestamp: int = None,
-        model_filename: str = "",
-        scanning: bool = False,
-    ) -> bool:
         """
         Given a pair and path, check if a model already exists
         :param pair: pair e.g. BTC/USD
@@ -518,11 +516,11 @@ class IFreqaiModel(ABC):
         :return:
         :boolean: whether the model file exists or not.
         """
-        path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib")
+        path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model.joblib")
         file_exists = path_to_modelfile.is_file()
-        if file_exists and not scanning:
+        if file_exists:
             logger.info("Found model at %s", dk.data_path / dk.model_filename)
-        elif not scanning:
+        else:
             logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
         return file_exists
 
@@ -569,6 +567,7 @@ class IFreqaiModel(ABC):
 
         # find the features indicated by strategy and store in datakitchen
         dk.find_features(unfiltered_dataframe)
+        dk.find_labels(unfiltered_dataframe)
 
         model = self.train(unfiltered_dataframe, pair, dk)
 
@@ -576,8 +575,8 @@ class IFreqaiModel(ABC):
         dk.set_new_model_names(pair, new_trained_timerange)
         self.dd.save_data(model, pair, dk)
 
-        if self.freqai_info["feature_parameters"].get("plot_feature_importance", False):
+        if self.plot_features:
-            plot_feature_importance(model, pair, dk)
+            plot_feature_importance(model, pair, dk, self.plot_features)
 
         if self.freqai_info.get("purge_old_models", False):
             self.dd.purge_old_models()

freqtrade/freqai/utils.py

@@ -170,7 +170,7 @@ def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
 
         # Data preparation
         fi_df = pd.DataFrame({
-            "feature_names": np.array(dk.training_features_list),
+            "feature_names": np.array(dk.data_dictionary['train_features'].columns),
             "feature_importance": np.array(feature_importance)
         })
         fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]