no model save backtest, plot features backtest, ensure inlier plays nice, doc
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robcaulk
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f4fac53a13
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873d2a5069
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@ -110,7 +110,7 @@ Mandatory parameters are marked as **Required**, which means that they are requi
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| `indicator_periods_candles` | Calculate indicators for `indicator_periods_candles` time periods and add them to the feature set. <br> **Datatype:** List of positive integers.
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| `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.
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| `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)
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| `plot_feature_importance` | Create an interactive feature importance plot for each model.<br> **Datatype:** Boolean.<br> **Datatype:** Boolean, defaults to `False`
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| `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`
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| `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).
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| `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.
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| `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.
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@ -313,6 +313,7 @@ class FreqaiDataDrawer:
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"""
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dk.find_features(dataframe)
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dk.find_labels(dataframe)
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full_labels = dk.label_list + dk.unique_class_list
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@ -376,7 +377,27 @@ class FreqaiDataDrawer:
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if self.config.get("freqai", {}).get("purge_old_models", False):
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self.purge_old_models()
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# Functions pulled back from FreqaiDataKitchen because they relied on DataDrawer
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def save_metaddata(self, dk: FreqaiDataKitchen) -> None:
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"""
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Saves only metadata for backtesting studies if user prefers
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not to save model data. This saves tremendous amounts of space
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for users generating huge studies.
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This is only active when `save_backtest_models`: false (not default)
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"""
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if not dk.data_path.is_dir():
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dk.data_path.mkdir(parents=True, exist_ok=True)
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save_path = Path(dk.data_path)
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dk.data["data_path"] = str(dk.data_path)
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dk.data["model_filename"] = str(dk.model_filename)
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dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
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dk.data["label_list"] = dk.label_list
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with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
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rapidjson.dump(dk.data, fp, default=self.np_encoder, number_mode=rapidjson.NM_NATIVE)
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return
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def save_data(self, model: Any, coin: str, dk: FreqaiDataKitchen) -> None:
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"""
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@ -831,7 +831,7 @@ class FreqaiDataKitchen:
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inlier_metric = pd.DataFrame(
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data=inliers.sum(axis=1) / no_prev_pts,
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columns=['inlier_metric'],
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columns=['%-inlier_metric'],
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index=compute_df.index
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)
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@ -881,11 +881,14 @@ class FreqaiDataKitchen:
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"""
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column_names = dataframe.columns
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features = [c for c in column_names if "%" in c]
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labels = [c for c in column_names if "&" in c]
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if not features:
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raise OperationalException("Could not find any features!")
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self.training_features_list = features
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def find_labels(self, dataframe: DataFrame) -> None:
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column_names = dataframe.columns
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labels = [c for c in column_names if "&" in c]
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self.label_list = labels
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def check_if_pred_in_training_spaces(self) -> None:
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@ -1206,7 +1209,8 @@ class FreqaiDataKitchen:
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def get_unique_classes_from_labels(self, dataframe: DataFrame) -> None:
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self.find_features(dataframe)
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# self.find_features(dataframe)
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self.find_labels(dataframe)
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for key in self.label_list:
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if dataframe[key].dtype == object:
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@ -92,6 +92,7 @@ class IFreqaiModel(ABC):
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self.begin_time_train: float = 0
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self.base_tf_seconds = timeframe_to_seconds(self.config['timeframe'])
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self.continual_learning = self.freqai_info.get('continual_learning', False)
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self.plot_features = self.ft_params.get("plot_feature_importances", 0)
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self._threads: List[threading.Thread] = []
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self._stop_event = threading.Event()
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@ -278,22 +279,24 @@ class IFreqaiModel(ABC):
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append_df = dk.get_backtesting_prediction()
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dk.append_predictions(append_df)
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else:
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if not self.model_exists(
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pair, dk, trained_timestamp=trained_timestamp_int
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):
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if not self.model_exists(dk):
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dk.find_features(dataframe_train)
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dk.find_labels(dataframe_train)
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self.model = self.train(dataframe_train, pair, dk)
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self.dd.pair_dict[pair]["trained_timestamp"] = int(
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trained_timestamp.stopts)
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if self.plot_features:
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plot_feature_importance(self.model, pair, dk, self.plot_features)
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if self.save_backtest_models:
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logger.info('Saving backtest model to disk.')
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self.dd.save_data(self.model, pair, dk)
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else:
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logger.info('Saving metadata to disk.')
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self.dd.save_metaddata(dk)
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else:
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self.model = self.dd.load_data(pair, dk)
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self.check_if_feature_list_matches_strategy(dataframe_train, dk)
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# self.check_if_feature_list_matches_strategy(dataframe_train, dk)
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pred_df, do_preds = self.predict(dataframe_backtest, dk)
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append_df = dk.get_predictions_to_append(pred_df, do_preds)
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dk.append_predictions(append_df)
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@ -372,8 +375,7 @@ class IFreqaiModel(ABC):
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self.dd.return_null_values_to_strategy(dataframe, dk)
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return dk
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# ensure user is feeding the correct indicators to the model
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self.check_if_feature_list_matches_strategy(dataframe, dk)
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dk.find_labels(dataframe)
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self.build_strategy_return_arrays(dataframe, dk, metadata["pair"], trained_timestamp)
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@ -492,7 +494,7 @@ class IFreqaiModel(ABC):
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if ft_params.get(
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"principal_component_analysis", False
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):
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dk.pca_transform(self.dk.data_dictionary['prediction_features'])
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dk.pca_transform(dk.data_dictionary['prediction_features'])
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if ft_params.get("use_SVM_to_remove_outliers", False):
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dk.use_SVM_to_remove_outliers(predict=True)
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@ -503,14 +505,10 @@ class IFreqaiModel(ABC):
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if ft_params.get("use_DBSCAN_to_remove_outliers", False):
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dk.use_DBSCAN_to_remove_outliers(predict=True)
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def model_exists(
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self,
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pair: str,
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dk: FreqaiDataKitchen,
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trained_timestamp: int = None,
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model_filename: str = "",
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scanning: bool = False,
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) -> bool:
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# ensure user is feeding the correct indicators to the model
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self.check_if_feature_list_matches_strategy(dk.data_dictionary['prediction_features'], dk)
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def model_exists(self, dk: FreqaiDataKitchen) -> bool:
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"""
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Given a pair and path, check if a model already exists
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:param pair: pair e.g. BTC/USD
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@ -518,11 +516,11 @@ class IFreqaiModel(ABC):
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:return:
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:boolean: whether the model file exists or not.
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"""
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path_to_modelfile = Path(dk.data_path / f"{model_filename}_model.joblib")
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path_to_modelfile = Path(dk.data_path / f"{dk.model_filename}_model.joblib")
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file_exists = path_to_modelfile.is_file()
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if file_exists and not scanning:
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if file_exists:
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logger.info("Found model at %s", dk.data_path / dk.model_filename)
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elif not scanning:
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else:
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logger.info("Could not find model at %s", dk.data_path / dk.model_filename)
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return file_exists
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@ -569,6 +567,7 @@ class IFreqaiModel(ABC):
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# find the features indicated by strategy and store in datakitchen
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dk.find_features(unfiltered_dataframe)
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dk.find_labels(unfiltered_dataframe)
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model = self.train(unfiltered_dataframe, pair, dk)
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@ -576,8 +575,8 @@ class IFreqaiModel(ABC):
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dk.set_new_model_names(pair, new_trained_timerange)
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self.dd.save_data(model, pair, dk)
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if self.freqai_info["feature_parameters"].get("plot_feature_importance", False):
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plot_feature_importance(model, pair, dk)
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if self.plot_features:
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plot_feature_importance(model, pair, dk, self.plot_features)
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if self.freqai_info.get("purge_old_models", False):
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self.dd.purge_old_models()
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@ -170,7 +170,7 @@ def plot_feature_importance(model: Any, pair: str, dk: FreqaiDataKitchen,
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# Data preparation
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fi_df = pd.DataFrame({
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"feature_names": np.array(dk.training_features_list),
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"feature_names": np.array(dk.data_dictionary['train_features'].columns),
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"feature_importance": np.array(feature_importance)
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})
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fi_df_top = fi_df.nlargest(count_max, "feature_importance")[::-1]
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