integrate inlier metric function
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@ -655,7 +655,7 @@ class FreqaiDataKitchen:
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return
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def compute_inlier_metric(self) -> None:
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def compute_inlier_metric(self, set_='train') -> None:
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"""
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Compute inlier metric from backwards distance distributions.
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@ -665,70 +665,93 @@ class FreqaiDataKitchen:
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import scipy.stats as ss
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nmb_previous_points = self.data['InlierMetric_nmb_points']
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weibull_percentile = self.data['InlierMetric_weib_perc']
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no_prev_pts = self.freqai_config["feature_parameters"]["inlier_metric_window"]
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weib_pct = self.freqai_config["feature_parameters"]["inlier_metric_weibull_cutoff"]
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train_ft_df = self.data_dictionary['train_features']
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train_ft_df_reindexed = train_ft_df.reindex(
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index=np.flip(train_ft_df.index)
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if set_ == 'train':
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compute_df = copy.deepcopy(self.data_dictionary['train_features'])
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elif set_ == 'test':
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compute_df = copy.deepcopy(self.data_dictionary['test_features'])
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else:
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compute_df = copy.deepcopy(self.data_dictionary['prediction_features'])
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compute_df_reindexed = compute_df.reindex(
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index=np.flip(compute_df.index)
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)
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pairwise = pd.DataFrame(
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np.triu(
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pairwise_distances(train_ft_df_reindexed, n_jobs=self.thread_count)
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pairwise_distances(compute_df_reindexed, n_jobs=self.thread_count)
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),
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columns=train_ft_df_reindexed.index,
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index=train_ft_df_reindexed.index
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columns=compute_df_reindexed.index,
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index=compute_df_reindexed.index
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)
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pairwise = pairwise.round(5)
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column_labels = [
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'{}{}'.format('d', i) for i in range(1, nmb_previous_points+1)
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'{}{}'.format('d', i) for i in range(1, no_prev_pts + 1)
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]
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distances = pd.DataFrame(
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columns=column_labels, index=train_ft_df.index
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columns=column_labels, index=compute_df.index
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)
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for index in train_ft_df.index[nmb_previous_points]:
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for index in compute_df.index[no_prev_pts:]:
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current_row = pairwise.loc[[index]]
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current_row_no_zeros = current_row.loc[
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:, (current_row!=0).any(axis=0)
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:, (current_row != 0).any(axis=0)
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]
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distances.loc[[index]] = current_row_no_zeros.iloc[
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:, :nmb_previous_points
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:, :no_prev_pts
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]
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distances = distances.replace([np.inf, -np.inf], np.nan)
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drop_index = pd.isnull(distances).any(1)
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distances = distances[drop_index==0]
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distances = distances[drop_index == 0]
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inliers = pd.DataFrame(index=distances.index)
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for key in distances.keys():
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current_distances = distances[key].dropna()
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fit_params = ss.weibull_min.fit(current_distances)
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cutoff = ss.weibull_min.ppf(weibull_percentile, *fit_params)
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cutoff = ss.weibull_min.ppf(weib_pct, *fit_params)
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is_inlier = np.where(
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current_distances<=cutoff, 1, 0
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current_distances <= cutoff, 1, 0
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)
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df_inlier = pd.DataFrame(
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{key+'_IsInlier':is_inlier}, index=distances.index
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{key + '_IsInlier': is_inlier}, index=distances.index
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)
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inliers = pd.concat(
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[inliers, df_inlier], axis=1
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)
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self.data_dictionary['train_features'] = pd.DataFrame(
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data=inliers.sum(axis=1)/nmb_previous_points,
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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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index = train_ft_df.index
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index=compute_df.index
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)
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percent_outliers = np.round(
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100*(1-self.data_dictionary['iniler_metric'].sum()/
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len(train_ft_df.index)), 2
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)
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logger.info('{percent_outliers}%% of data points were identified as outliers')
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inlier_metric = 2 * (inlier_metric - inlier_metric.min()) / \
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(inlier_metric.max() - inlier_metric.min()) - 1
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if set_ in ('train', 'test'):
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inlier_metric = inlier_metric.iloc[no_prev_pts:]
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compute_df = compute_df.iloc[no_prev_pts:]
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self.remove_beginning_points_from_data_dict(set_, no_prev_pts)
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self.data_dictionary[f'{set_}_features'] = pd.concat(
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[compute_df, inlier_metric], axis=1)
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else:
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self.data_dictionary['prediction_features'] = pd.concat(
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[compute_df, inlier_metric], axis=1)
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self.data_dictionary['prediction_features'].fillna(0, inplace=True)
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return None
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def remove_beginning_points_from_data_dict(self, set_='train', no_prev_pts: int = 10):
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features = self.data_dictionary[f'{set_}_features']
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weights = self.data_dictionary[f'{set_}_weights']
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labels = self.data_dictionary[f'{set_}_labels']
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self.data_dictionary[f'{set_}_weights'] = weights[no_prev_pts:]
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self.data_dictionary[f'{set_}_features'] = features.iloc[no_prev_pts:]
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self.data_dictionary[f'{set_}_labels'] = labels.iloc[no_prev_pts:]
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def find_features(self, dataframe: DataFrame) -> None:
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"""
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Find features in the strategy provided dataframe
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@ -66,7 +66,6 @@ class IFreqaiModel(ABC):
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"data_split_parameters", {})
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self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
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"model_training_parameters", {})
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self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
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self.retrain = False
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self.first = True
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self.set_full_path()
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@ -74,11 +73,14 @@ class IFreqaiModel(ABC):
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self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
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self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
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self.scanning = False
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self.ft_params = self.freqai_info["feature_parameters"]
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self.keras: bool = self.freqai_info.get("keras", False)
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if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
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self.freqai_info["feature_parameters"]["DI_threshold"] = 0
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if self.keras and self.ft_params.get("DI_threshold", 0):
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self.ft_params["DI_threshold"] = 0
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logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
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self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
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if self.ft_params.get("inlier_metric_window", 0):
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self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
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self.pair_it = 0
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self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
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self.last_trade_database_summary: DataFrame = {}
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@ -389,18 +391,20 @@ class IFreqaiModel(ABC):
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example of how outlier data points are dropped from the dataframe used for training.
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"""
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if self.freqai_info["feature_parameters"].get(
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ft_params = self.freqai_info["feature_parameters"]
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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.principal_component_analysis()
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if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
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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=False)
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if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
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if ft_params.get("DI_threshold", 0):
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dk.data["avg_mean_dist"] = dk.compute_distances()
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if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
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if ft_params.get("use_DBSCAN_to_remove_outliers", False):
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if dk.pair in self.dd.old_DBSCAN_eps:
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eps = self.dd.old_DBSCAN_eps[dk.pair]
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else:
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@ -408,6 +412,11 @@ class IFreqaiModel(ABC):
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dk.use_DBSCAN_to_remove_outliers(predict=False, eps=eps)
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self.dd.old_DBSCAN_eps[dk.pair] = dk.data['DBSCAN_eps']
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if ft_params.get('inlier_metric_window', 0):
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dk.compute_inlier_metric(set_='train')
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if self.freqai_info["data_split_parameters"]["test_size"] > 0:
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dk.compute_inlier_metric(set_='test')
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def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
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"""
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Base data cleaning method for predict.
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@ -419,18 +428,23 @@ class IFreqaiModel(ABC):
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of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
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for buy signals.
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"""
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if self.freqai_info["feature_parameters"].get(
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ft_params = self.freqai_info["feature_parameters"]
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if ft_params.get('inlier_metric_window', 0):
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dk.compute_inlier_metric(set_='predict')
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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(dataframe)
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if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
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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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if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
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if ft_params.get("DI_threshold", 0):
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dk.check_if_pred_in_training_spaces()
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if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
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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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