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integrate inlier metric function

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This commit is contained in:
robcaulk 2022-08-18 19:15:29 +02:00
parent 52ee7fc981
commit 98c62dad91
2 changed files with 79 additions and 42 deletions
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85
freqtrade/freqai/data_kitchen.py
View File

@ -654,81 +654,104 @@ class FreqaiDataKitchen:
)
return
def compute_inlier_metric(self) -> None:
def compute_inlier_metric(self, set_='train') -> None:
"""
Compute inlier metric from backwards distance distributions.
This metric defines how well features from a timepoint fit
Compute inlier metric from backwards distance distributions.
This metric defines how well features from a timepoint fit
into previous timepoints.
"""
import scipy.stats as ss
nmb_previous_points = self.data['InlierMetric_nmb_points']
weibull_percentile = self.data['InlierMetric_weib_perc']
train_ft_df = self.data_dictionary['train_features']
train_ft_df_reindexed = train_ft_df.reindex(
index=np.flip(train_ft_df.index)
no_prev_pts = self.freqai_config["feature_parameters"]["inlier_metric_window"]
weib_pct = self.freqai_config["feature_parameters"]["inlier_metric_weibull_cutoff"]
if set_ == 'train':
compute_df = copy.deepcopy(self.data_dictionary['train_features'])
elif set_ == 'test':
compute_df = copy.deepcopy(self.data_dictionary['test_features'])
else:
compute_df = copy.deepcopy(self.data_dictionary['prediction_features'])
compute_df_reindexed = compute_df.reindex(
index=np.flip(compute_df.index)
)
pairwise = pd.DataFrame(
np.triu(
pairwise_distances(train_ft_df_reindexed, n_jobs=self.thread_count)
pairwise_distances(compute_df_reindexed, n_jobs=self.thread_count)
),
columns=train_ft_df_reindexed.index,
index=train_ft_df_reindexed.index
columns=compute_df_reindexed.index,
index=compute_df_reindexed.index
)
pairwise = pairwise.round(5)
column_labels = [
'{}{}'.format('d', i) for i in range(1, nmb_previous_points+1)
'{}{}'.format('d', i) for i in range(1, no_prev_pts + 1)
]
distances = pd.DataFrame(
columns=column_labels, index=train_ft_df.index
columns=column_labels, index=compute_df.index
)
for index in train_ft_df.index[nmb_previous_points]:
for index in compute_df.index[no_prev_pts:]:
current_row = pairwise.loc[[index]]
current_row_no_zeros = current_row.loc[
:, (current_row!=0).any(axis=0)
:, (current_row != 0).any(axis=0)
]
distances.loc[[index]] = current_row_no_zeros.iloc[
:, :nmb_previous_points
:, :no_prev_pts
]
distances = distances.replace([np.inf, -np.inf], np.nan)
drop_index = pd.isnull(distances).any(1)
distances = distances[drop_index==0]
distances = distances[drop_index == 0]
inliers = pd.DataFrame(index=distances.index)
for key in distances.keys():
current_distances = distances[key].dropna()
fit_params = ss.weibull_min.fit(current_distances)
cutoff = ss.weibull_min.ppf(weibull_percentile, *fit_params)
cutoff = ss.weibull_min.ppf(weib_pct, *fit_params)
is_inlier = np.where(
current_distances<=cutoff, 1, 0
current_distances <= cutoff, 1, 0
)
df_inlier = pd.DataFrame(
{key+'_IsInlier':is_inlier}, index=distances.index
{key + '_IsInlier': is_inlier}, index=distances.index
)
inliers = pd.concat(
[inliers, df_inlier], axis=1
)
self.data_dictionary['train_features'] = pd.DataFrame(
data=inliers.sum(axis=1)/nmb_previous_points,
inlier_metric = pd.DataFrame(
data=inliers.sum(axis=1) / no_prev_pts,
columns=['inlier_metric'],
index = train_ft_df.index
index=compute_df.index
)
percent_outliers = np.round(
100*(1-self.data_dictionary['iniler_metric'].sum()/
len(train_ft_df.index)), 2
)
logger.info('{percent_outliers}%% of data points were identified as outliers')
inlier_metric = 2 * (inlier_metric - inlier_metric.min()) / \
(inlier_metric.max() - inlier_metric.min()) - 1
if set_ in ('train', 'test'):
inlier_metric = inlier_metric.iloc[no_prev_pts:]
compute_df = compute_df.iloc[no_prev_pts:]
self.remove_beginning_points_from_data_dict(set_, no_prev_pts)
self.data_dictionary[f'{set_}_features'] = pd.concat(
[compute_df, inlier_metric], axis=1)
else:
self.data_dictionary['prediction_features'] = pd.concat(
[compute_df, inlier_metric], axis=1)
self.data_dictionary['prediction_features'].fillna(0, inplace=True)
return None
def remove_beginning_points_from_data_dict(self, set_='train', no_prev_pts: int = 10):
features = self.data_dictionary[f'{set_}_features']
weights = self.data_dictionary[f'{set_}_weights']
labels = self.data_dictionary[f'{set_}_labels']
self.data_dictionary[f'{set_}_weights'] = weights[no_prev_pts:]
self.data_dictionary[f'{set_}_features'] = features.iloc[no_prev_pts:]
self.data_dictionary[f'{set_}_labels'] = labels.iloc[no_prev_pts:]
def find_features(self, dataframe: DataFrame) -> None:
"""
Find features in the strategy provided dataframe

36
freqtrade/freqai/freqai_interface.py
View File

@ -66,7 +66,6 @@ class IFreqaiModel(ABC):
"data_split_parameters", {})
self.model_training_parameters: Dict[str, Any] = config.get("freqai", {}).get(
"model_training_parameters", {})
self.feature_parameters = config.get("freqai", {}).get("feature_parameters")
self.retrain = False
self.first = True
self.set_full_path()
@ -74,11 +73,14 @@ class IFreqaiModel(ABC):
self.dd = FreqaiDataDrawer(Path(self.full_path), self.config, self.follow_mode)
self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
self.scanning = False
self.ft_params = self.freqai_info["feature_parameters"]
self.keras: bool = self.freqai_info.get("keras", False)
if self.keras and self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0):
self.freqai_info["feature_parameters"]["DI_threshold"] = 0
if self.keras and self.ft_params.get("DI_threshold", 0):
self.ft_params["DI_threshold"] = 0
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
if self.ft_params.get("inlier_metric_window", 0):
self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
self.pair_it = 0
self.total_pairs = len(self.config.get("exchange", {}).get("pair_whitelist"))
self.last_trade_database_summary: DataFrame = {}
@ -389,18 +391,20 @@ class IFreqaiModel(ABC):
example of how outlier data points are dropped from the dataframe used for training.
"""
if self.freqai_info["feature_parameters"].get(
ft_params = self.freqai_info["feature_parameters"]
if ft_params.get(
"principal_component_analysis", False
):
dk.principal_component_analysis()
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
if ft_params.get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=False)
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
if ft_params.get("DI_threshold", 0):
dk.data["avg_mean_dist"] = dk.compute_distances()
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
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]
else:
@ -408,6 +412,11 @@ 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('inlier_metric_window', 0):
dk.compute_inlier_metric(set_='train')
if self.freqai_info["data_split_parameters"]["test_size"] > 0:
dk.compute_inlier_metric(set_='test')
def data_cleaning_predict(self, dk: FreqaiDataKitchen, dataframe: DataFrame) -> None:
"""
Base data cleaning method for predict.
@ -419,18 +428,23 @@ class IFreqaiModel(ABC):
of how the do_predict vector is modified. do_predict is ultimately passed back to strategy
for buy signals.
"""
if self.freqai_info["feature_parameters"].get(
ft_params = self.freqai_info["feature_parameters"]
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(dataframe)
if self.freqai_info["feature_parameters"].get("use_SVM_to_remove_outliers", False):
if ft_params.get("use_SVM_to_remove_outliers", False):
dk.use_SVM_to_remove_outliers(predict=True)
if self.freqai_info["feature_parameters"].get("DI_threshold", 0):
if ft_params.get("DI_threshold", 0):
dk.check_if_pred_in_training_spaces()
if self.freqai_info["feature_parameters"].get("use_DBSCAN_to_remove_outliers", False):
if ft_params.get("use_DBSCAN_to_remove_outliers", False):
dk.use_DBSCAN_to_remove_outliers(predict=True)
def model_exists(