adelorenzo/stable
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

Add training elapsed time

Browse Source
This commit is contained in:
Emre 2022-09-23 01:18:34 -07:00
parent 7b4af85425
commit a4eaff4da6
No known key found for this signature in database
GPG Key ID: 0EAD2EE11B666ABA
4 changed files with 36 additions and 19 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

16
freqtrade/freqai/base_models/BaseClassifierModel.py
View File

@ -1,4 +1,5 @@
import logging
from time import time
from typing import Any, Tuple
import numpy as np
@ -32,7 +33,9 @@ class BaseClassifierModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("-------------------- Starting training " f"{pair} --------------------")
logger.info(f"-------------------- Starting training {pair} --------------------")
start_time = time()
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
@ -48,7 +51,7 @@ class BaseClassifierModel(IFreqaiModel):
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
@ -57,13 +60,16 @@ class BaseClassifierModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
end_time = time()
logger.info(f"-------------------- Done training {pair} "
f"({end_time - start_time:.2f} secs) --------------------")
return model

16
freqtrade/freqai/base_models/BaseRegressionModel.py
View File

@ -1,4 +1,5 @@
import logging
from time import time
from typing import Any, Tuple
import numpy as np
@ -31,7 +32,9 @@ class BaseRegressionModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("-------------------- Starting training " f"{pair} --------------------")
logger.info(f"-------------------- Starting training {pair} --------------------")
start_time = time()
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
@ -47,7 +50,7 @@ class BaseRegressionModel(IFreqaiModel):
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
@ -56,13 +59,16 @@ class BaseRegressionModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
end_time = time()
logger.info(f"-------------------- Done training {pair} "
f"({end_time - start_time:.2f} secs) --------------------")
return model

16
freqtrade/freqai/base_models/BaseTensorFlowModel.py
View File

@ -1,4 +1,5 @@
import logging
from time import time
from typing import Any
from pandas import DataFrame
@ -28,7 +29,9 @@ class BaseTensorFlowModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict)
"""
logger.info("-------------------- Starting training " f"{pair} --------------------")
logger.info(f"-------------------- Starting training {pair} --------------------")
start_time = time()
# filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features(
@ -44,7 +47,7 @@ class BaseTensorFlowModel(IFreqaiModel):
f"{end_date} --------------------")
# split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
if not self.freqai_info.get('fit_live_predictions', 0) or not self.live:
if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
dk.fit_labels()
# normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary)
@ -53,12 +56,15 @@ class BaseTensorFlowModel(IFreqaiModel):
self.data_cleaning_train(dk)
logger.info(
f'Training model on {len(dk.data_dictionary["train_features"].columns)}' " features"
f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
)
logger.info(f'Training model on {len(data_dictionary["train_features"])} data points')
logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
model = self.fit(data_dictionary, dk)
logger.info(f"--------------------done training {pair}--------------------")
end_time = time()
logger.info(f"-------------------- Done training {pair} "
f"({end_time - start_time:.2f} secs) --------------------")
return model

1
freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py
View File

@ -1,4 +1,3 @@
from joblib import Parallel
from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
from sklearn.utils.fixes import delayed