integrate inlier metric function
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robcaulk
@@ -723,81 +723,104 @@ class FreqaiDataKitchen:
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)
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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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This metric defines how well features from a timepoint fit
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Compute inlier metric from backwards distance distributions.
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This metric defines how well features from a timepoint fit
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into previous timepoints.
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"""
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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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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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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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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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