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Add training elapsed time

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Emre 2022-09-23 01:18:34 -07:00
parent 7b4af85425
commit a4eaff4da6
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GPG Key ID: 0EAD2EE11B666ABA
4 changed files with 36 additions and 19 deletions
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18
freqtrade/freqai/base_models/BaseClassifierModel.py
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@ -1,4 +1,5 @@
import logging import logging
from time import time
from typing import Any, Tuple from typing import Any, Tuple
import numpy as np import numpy as np
@ -32,7 +33,9 @@ class BaseClassifierModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict) :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 # filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features( features_filtered, labels_filtered = dk.filter_features(
@ -45,10 +48,10 @@ class BaseClassifierModel(IFreqaiModel):
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d") start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d") end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to " logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------") f"{end_date} --------------------")
# split data into train/test data. # split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) 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() dk.fit_labels()
# normalize all data based on train_dataset only # normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary) data_dictionary = dk.normalize_data(data_dictionary)
@ -57,13 +60,16 @@ class BaseClassifierModel(IFreqaiModel):
self.data_cleaning_train(dk) self.data_cleaning_train(dk)
logger.info( 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) 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 return model

18
freqtrade/freqai/base_models/BaseRegressionModel.py
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@ -1,4 +1,5 @@
import logging import logging
from time import time
from typing import Any, Tuple from typing import Any, Tuple
import numpy as np import numpy as np
@ -31,7 +32,9 @@ class BaseRegressionModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict) :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 # filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features( features_filtered, labels_filtered = dk.filter_features(
@ -44,10 +47,10 @@ class BaseRegressionModel(IFreqaiModel):
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d") start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d") end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to " logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------") f"{end_date} --------------------")
# split data into train/test data. # split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) 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() dk.fit_labels()
# normalize all data based on train_dataset only # normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary) data_dictionary = dk.normalize_data(data_dictionary)
@ -56,13 +59,16 @@ class BaseRegressionModel(IFreqaiModel):
self.data_cleaning_train(dk) self.data_cleaning_train(dk)
logger.info( 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) 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 return model

18
freqtrade/freqai/base_models/BaseTensorFlowModel.py
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@ -1,4 +1,5 @@
import logging import logging
from time import time
from typing import Any from typing import Any
from pandas import DataFrame from pandas import DataFrame
@ -28,7 +29,9 @@ class BaseTensorFlowModel(IFreqaiModel):
:model: Trained model which can be used to inference (self.predict) :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 # filter the features requested by user in the configuration file and elegantly handle NaNs
features_filtered, labels_filtered = dk.filter_features( features_filtered, labels_filtered = dk.filter_features(
@ -41,10 +44,10 @@ class BaseTensorFlowModel(IFreqaiModel):
start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d") start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d") end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
logger.info(f"-------------------- Training on data from {start_date} to " logger.info(f"-------------------- Training on data from {start_date} to "
f"{end_date}--------------------") f"{end_date} --------------------")
# split data into train/test data. # split data into train/test data.
data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered) 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() dk.fit_labels()
# normalize all data based on train_dataset only # normalize all data based on train_dataset only
data_dictionary = dk.normalize_data(data_dictionary) data_dictionary = dk.normalize_data(data_dictionary)
@ -53,12 +56,15 @@ class BaseTensorFlowModel(IFreqaiModel):
self.data_cleaning_train(dk) self.data_cleaning_train(dk)
logger.info( 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) 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 return model

1
freqtrade/freqai/base_models/FreqaiMultiOutputRegressor.py
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@ -1,4 +1,3 @@
from joblib import Parallel from joblib import Parallel
from sklearn.multioutput import MultiOutputRegressor, _fit_estimator from sklearn.multioutput import MultiOutputRegressor, _fit_estimator
from sklearn.utils.fixes import delayed from sklearn.utils.fixes import delayed