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remove metadata redundancy, fix pca bug

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This commit is contained in:
robcaulk 2022-09-03 16:54:30 +02:00
parent 11b2bc269e
commit c21808ff98
1 changed files with 26 additions and 29 deletions
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55
freqtrade/freqai/data_kitchen.py
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@ -288,25 +288,20 @@ class FreqaiDataKitchen:
:data_dictionary: updated dictionary with standardized values. :data_dictionary: updated dictionary with standardized values.
""" """
df_train_features = data_dictionary["train_features"] df = data_dictionary["train_features"]
# standardize the data by training stats # standardize the data by training stats
train_max = df_train_features.max() train_max = df.max()
train_min = df_train_features.min() train_min = df.min()
df_train_features = ( df = (
2 * (df_train_features - train_min) / (train_max - train_min) - 1 2 * (df - train_min) / (train_max - train_min) - 1
) )
data_dictionary["test_features"] = ( data_dictionary["test_features"] = (
2 * (data_dictionary["test_features"] - train_min) / (train_max - train_min) - 1 2 * (data_dictionary["test_features"] - train_min) / (train_max - train_min) - 1
) )
for item in train_max.keys(): for item in train_max.keys():
if not [col for col in df_train_features.columns if col.startswith('PC')]: self.data[item + "_max"] = train_max[item]
self.data[item + "_max"] = train_max[item] self.data[item + "_min"] = train_min[item]
self.data[item + "_min"] = train_min[item]
else:
# if PCA is enabled and has transformed the training features
self.data[item + "_pca_max"] = train_max[item]
self.data[item + "_pca_min"] = train_min[item]
for item in data_dictionary["train_labels"].keys(): for item in data_dictionary["train_labels"].keys():
if data_dictionary["train_labels"][item].dtype == object: if data_dictionary["train_labels"][item].dtype == object:
@ -327,16 +322,24 @@ class FreqaiDataKitchen:
- 1 - 1
) )
if not [col for col in df_train_features.columns if col.startswith('PC')]: self.data[f"{item}_max"] = train_labels_max
self.data[f"{item}_max"] = train_labels_max # .to_dict() self.data[f"{item}_min"] = train_labels_min
self.data[f"{item}_min"] = train_labels_min # .to_dict()
else:
# if PCA is enabled and has transformed the training features
self.data[f"{item}_pca_max"] = train_labels_max # .to_dict()
self.data[f"{item}_pca_min"] = train_labels_min # .to_dict()
return data_dictionary return data_dictionary
def normalize_single_dataframe(self, df: DataFrame) -> DataFrame:
train_max = df.max()
train_min = df.min()
df = (
2 * (df - train_min) / (train_max - train_min) - 1
)
for item in train_max.keys():
self.data[item + "_max"] = train_max[item]
self.data[item + "_min"] = train_min[item]
return df
def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame: def normalize_data_from_metadata(self, df: DataFrame) -> DataFrame:
""" """
Normalize a set of data using the mean and standard deviation from Normalize a set of data using the mean and standard deviation from
@ -344,17 +347,11 @@ class FreqaiDataKitchen:
:param df: Dataframe to be standardized :param df: Dataframe to be standardized
""" """
if not [col for col in df.columns if col.startswith('PC')]:
id_str = ''
else:
# if PCA is enabled
id_str = '_pca'
for item in df.keys(): for item in df.keys():
df[item] = ( df[item] = (
2 2
* (df[item] - self.data[f"{item}{id_str}_min"]) * (df[item] - self.data[f"{item}_min"])
/ (self.data[f"{item}{id_str}_max"] - self.data[f"{item}{id_str}_min"]) / (self.data[f"{item}_max"] - self.data[f"{item}_min"])
- 1 - 1
) )
@ -484,7 +481,7 @@ class FreqaiDataKitchen:
index=self.data_dictionary["train_features"].index, index=self.data_dictionary["train_features"].index,
) )
# normalsing transformed training features # normalsing transformed training features
self.data_dictionary["train_features"] = self.normalize_data( self.data_dictionary["train_features"] = self.normalize_single_dataframe(
self.data_dictionary["train_features"]) self.data_dictionary["train_features"])
# keeping a copy of the non-transformed features so we can check for errors during # keeping a copy of the non-transformed features so we can check for errors during