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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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45
freqtrade/freqai/data_drawer.py
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@@ -11,7 +11,7 @@ import numpy as np
import pandas as pd
from joblib import dump, load
from joblib.externals import cloudpickle
from numpy.typing import ArrayLike
from numpy.typing import ArrayLike, NDArray
from pandas import DataFrame
from freqtrade.configuration import TimeRange
@@ -233,12 +233,13 @@ class FreqaiDataDrawer:
mrv_df[f"{label}_mean"] = dk.data["labels_mean"][label]
mrv_df[f"{label}_std"] = dk.data["labels_std"][label]
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
mrv_df["DI_values"] = dk.DI_values
mrv_df["do_predict"] = do_preds
def append_model_predictions(self, pair: str, predictions: DataFrame, do_preds: ArrayLike,
def append_model_predictions(self, pair: str, predictions: DataFrame,
do_preds: NDArray[np.int_],
dk: FreqaiDataKitchen, len_df: int) -> None:
# strat seems to feed us variable sized dataframes - and since we are trying to build our
@@ -266,10 +267,10 @@ class FreqaiDataDrawer:
df[label].iloc[-1] = predictions[label].iloc[-1]
df[f"{label}_mean"].iloc[-1] = dk.data["labels_mean"][label]
df[f"{label}_std"].iloc[-1] = dk.data["labels_std"][label]
# df['prediction'].iloc[-1] = predictions[-1]
df["do_predict"].iloc[-1] = do_preds[-1]
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
df["DI_values"].iloc[-1] = dk.DI_values[-1]
# append the new predictions to persistent storage
@@ -309,7 +310,7 @@ class FreqaiDataDrawer:
# dataframe['prediction'] = 0
dataframe["do_predict"] = 0
if self.freqai_info.get("feature_parameters", {}).get("DI_threshold", 0) > 0:
if self.freqai_info["feature_parameters"].get("DI_threshold", 0) > 0:
dataframe["DI_value"] = 0
dk.return_dataframe = dataframe
@@ -379,24 +380,24 @@ class FreqaiDataDrawer:
model.save(save_path / f"{dk.model_filename}_model.h5")
if dk.svm_model is not None:
dump(dk.svm_model, save_path / str(dk.model_filename + "_svm_model.joblib"))
dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib")
dk.data["data_path"] = str(dk.data_path)
dk.data["model_filename"] = str(dk.model_filename)
dk.data["training_features_list"] = list(dk.data_dictionary["train_features"].columns)
dk.data["label_list"] = dk.label_list
# store the metadata
with open(save_path / str(dk.model_filename + "_metadata.json"), "w") as fp:
with open(save_path / f"{dk.model_filename}_metadata.json", "w") as fp:
json.dump(dk.data, fp, default=dk.np_encoder)
# save the train data to file so we can check preds for area of applicability later
dk.data_dictionary["train_features"].to_pickle(
save_path / str(dk.model_filename + "_trained_df.pkl")
save_path / f"{dk.model_filename}_trained_df.pkl"
)
if self.freqai_info.get("feature_parameters", {}).get("principal_component_analysis"):
if self.freqai_info["feature_parameters"].get("principal_component_analysis"):
cloudpickle.dump(
dk.pca, open(dk.data_path / str(dk.model_filename + "_pca_object.pkl"), "wb")
dk.pca, open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "wb")
)
# if self.live:
@@ -429,27 +430,27 @@ class FreqaiDataDrawer:
/ dk.data_path.parts[-1]
)
with open(dk.data_path / str(dk.model_filename + "_metadata.json"), "r") as fp:
with open(dk.data_path / f"{dk.model_filename}_metadata.json", "r") as fp:
dk.data = json.load(fp)
dk.training_features_list = dk.data["training_features_list"]
dk.label_list = dk.data["label_list"]
dk.data_dictionary["train_features"] = pd.read_pickle(
dk.data_path / str(dk.model_filename + "_trained_df.pkl")
dk.data_path / f"{dk.model_filename}_trained_df.pkl"
)
# try to access model in memory instead of loading object from disk to save time
if dk.live and dk.model_filename in self.model_dictionary:
model = self.model_dictionary[dk.model_filename]
elif not dk.keras:
model = load(dk.data_path / str(dk.model_filename + "_model.joblib"))
model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
else:
from tensorflow import keras
model = keras.models.load_model(dk.data_path / str(dk.model_filename + "_model.h5"))
model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
if Path(dk.data_path / str(dk.model_filename + "_svm_model.joblib")).resolve().exists():
dk.svm_model = load(dk.data_path / str(dk.model_filename + "_svm_model.joblib"))
if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")
if not model:
raise OperationalException(
@@ -458,7 +459,7 @@ class FreqaiDataDrawer:
if self.config["freqai"]["feature_parameters"]["principal_component_analysis"]:
dk.pca = cloudpickle.load(
open(dk.data_path / str(dk.model_filename + "_pca_object.pkl"), "rb")
open(dk.data_path / f"{dk.model_filename}_pca_object.pkl", "rb")
)
return model
@@ -471,7 +472,7 @@ class FreqaiDataDrawer:
:params:
dataframe: DataFrame = strategy provided dataframe
"""
feat_params = self.freqai_info.get("feature_parameters", {})
feat_params = self.freqai_info["feature_parameters"]
with self.history_lock:
history_data = self.historic_data
@@ -524,7 +525,7 @@ class FreqaiDataDrawer:
for pair in dk.all_pairs:
if pair not in history_data:
history_data[pair] = {}
for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"):
for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
history_data[pair][tf] = load_pair_history(
datadir=self.config["datadir"],
timeframe=tf,
@@ -550,11 +551,11 @@ class FreqaiDataDrawer:
corr_dataframes: Dict[Any, Any] = {}
base_dataframes: Dict[Any, Any] = {}
historic_data = self.historic_data
pairs = self.freqai_info.get("feature_parameters", {}).get(
pairs = self.freqai_info["feature_parameters"].get(
"include_corr_pairlist", []
)
for tf in self.freqai_info.get("feature_parameters", {}).get("include_timeframes"):
for tf in self.freqai_info["feature_parameters"].get("include_timeframes"):
base_dataframes[tf] = dk.slice_dataframe(timerange, historic_data[pair][tf])
if pairs:
for p in pairs: