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Fix typing issue, avoid using .get() when unnecessary, convert to fstrings

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This commit is contained in:
robcaulk
2022-07-29 08:12:50 +02:00
parent efbd83c56d
commit c84d54b35e
5 changed files with 57 additions and 57 deletions
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28
freqtrade/freqai/data_kitchen.py
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@@ -116,7 +116,7 @@ class FreqaiDataKitchen:
:filtered_dataframe: cleaned dataframe ready to be split.
:labels: cleaned labels ready to be split.
"""
feat_dict = self.freqai_config.get("feature_parameters", {})
feat_dict = self.freqai_config["feature_parameters"]
weights: npt.ArrayLike
if feat_dict.get("weight_factor", 0) > 0:
@@ -515,7 +515,9 @@ class FreqaiDataKitchen:
return
if predict:
assert self.svm_model, "No svm model available for outlier removal"
if not self.svm_model:
logger.warning("No svm model available for outlier removal")
return
y_pred = self.svm_model.predict(self.data_dictionary["prediction_features"])
do_predict = np.where(y_pred == -1, 0, y_pred)
@@ -528,7 +530,7 @@ class FreqaiDataKitchen:
else:
# use SGDOneClassSVM to increase speed?
nu = self.freqai_config.get("feature_parameters", {}).get("svm_nu", 0.2)
nu = self.freqai_config["feature_parameters"].get("svm_nu", 0.2)
self.svm_model = linear_model.SGDOneClassSVM(nu=nu).fit(
self.data_dictionary["train_features"]
)
@@ -551,7 +553,7 @@ class FreqaiDataKitchen:
)
# same for test data
if self.freqai_config.get('data_split_parameters', {}).get('test_size', 0.1) != 0:
if self.freqai_config['data_split_parameters'].get('test_size', 0.1) != 0:
y_pred = self.svm_model.predict(self.data_dictionary["test_features"])
dropped_points = np.where(y_pred == -1, 0, y_pred)
self.data_dictionary["test_features"] = self.data_dictionary["test_features"][
@@ -605,7 +607,7 @@ class FreqaiDataKitchen:
self.DI_values = distance.min(axis=0) / self.data["avg_mean_dist"]
do_predict = np.where(
self.DI_values < self.freqai_config.get("feature_parameters", {}).get("DI_threshold"),
self.DI_values < self.freqai_config["feature_parameters"]["DI_threshold"],
1,
0,
)
@@ -640,7 +642,7 @@ class FreqaiDataKitchen:
self.append_df[f"{label}_std"] = self.data["labels_std"][label]
self.append_df["do_predict"] = do_predict
if self.freqai_config.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
if self.freqai_config["feature_parameters"].get("DI_threshold", 0) > 0:
self.append_df["DI_values"] = self.DI_values
if self.full_df.empty:
@@ -701,7 +703,7 @@ class FreqaiDataKitchen:
full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
self.full_path = Path(
self.config["user_data_dir"] / "models" / str(self.freqai_config.get("identifier"))
self.config["user_data_dir"] / "models" / f"{self.freqai_config['identifier']}"
)
config_path = Path(self.config["config_files"][0])
@@ -741,10 +743,10 @@ class FreqaiDataKitchen:
data_load_timerange = TimeRange()
# find the max indicator length required
max_timeframe_chars = self.freqai_config.get("feature_parameters", {}).get(
max_timeframe_chars = self.freqai_config["feature_parameters"].get(
"include_timeframes"
)[-1]
max_period = self.freqai_config.get("feature_parameters", {}).get(
max_period = self.freqai_config["feature_parameters"].get(
"indicator_max_period_candles", 50
)
additional_seconds = 0
@@ -832,7 +834,7 @@ class FreqaiDataKitchen:
refresh_backtest_ohlcv_data(
exchange,
pairs=self.all_pairs,
timeframes=self.freqai_config.get("feature_parameters", {}).get("include_timeframes"),
timeframes=self.freqai_config["feature_parameters"].get("include_timeframes"),
datadir=self.config["datadir"],
timerange=timerange,
new_pairs_days=new_pairs_days,
@@ -845,7 +847,7 @@ class FreqaiDataKitchen:
def set_all_pairs(self) -> None:
self.all_pairs = copy.deepcopy(
self.freqai_config.get("feature_parameters", {}).get("include_corr_pairlist", [])
self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
)
for pair in self.config.get("exchange", "").get("pair_whitelist"):
if pair not in self.all_pairs:
@@ -876,8 +878,8 @@ class FreqaiDataKitchen:
# for prediction dataframe creation, we let dataprovider handle everything in the strategy
# so we create empty dictionaries, which allows us to pass None to
# `populate_any_indicators()`. Signaling we want the dp to give us the live dataframe.
tfs = self.freqai_config.get("feature_parameters", {}).get("include_timeframes")
pairs = self.freqai_config.get("feature_parameters", {}).get("include_corr_pairlist", [])
tfs = self.freqai_config["feature_parameters"].get("include_timeframes")
pairs = self.freqai_config["feature_parameters"].get("include_corr_pairlist", [])
if not prediction_dataframe.empty:
dataframe = prediction_dataframe.copy()
for tf in tfs: