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use logger in favor of print

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This commit is contained in:
robcaulk
2022-05-04 17:53:40 +02:00
parent 99f7e44c30
commit 29c2d1d189
3 changed files with 34 additions and 22 deletions
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18
freqtrade/templates/ExamplePredictionModel.py
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@@ -1,3 +1,4 @@
import logging
from typing import Any, Dict, Tuple
import pandas as pd
@@ -7,6 +8,9 @@ from pandas import DataFrame
from freqtrade.freqai.freqai_interface import IFreqaiModel
logger = logging.getLogger(__name__)
class ExamplePredictionModel(IFreqaiModel):
"""
User created prediction model. The class needs to override three necessary
@@ -32,7 +36,7 @@ class ExamplePredictionModel(IFreqaiModel):
self.dh.data["s_mean"] = dataframe["s"].mean()
self.dh.data["s_std"] = dataframe["s"].std()
print("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"])
logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"])
return dataframe["s"]
@@ -46,7 +50,7 @@ class ExamplePredictionModel(IFreqaiModel):
:returns:
:model: Trained model which can be used to inference (self.predict)
"""
print("--------------------Starting training--------------------")
logger.info("--------------------Starting training--------------------")
# create the full feature list based on user config info
self.dh.training_features_list = self.dh.build_feature_list(self.config)
@@ -73,12 +77,12 @@ class ExamplePredictionModel(IFreqaiModel):
if self.feature_parameters["DI_threshold"]:
self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
print("length of train data", len(data_dictionary["train_features"]))
logger.info("length of train data", len(data_dictionary["train_features"]))
model = self.fit(data_dictionary)
print("Finished training")
print(f'--------------------done training {metadata["pair"]}--------------------')
logger.info("Finished training")
logger.info(f'--------------------done training {metadata["pair"]}--------------------')
return model
@@ -121,7 +125,7 @@ class ExamplePredictionModel(IFreqaiModel):
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
print("--------------------Starting prediction--------------------")
logger.info("--------------------Starting prediction--------------------")
original_feature_list = self.dh.build_feature_list(self.config)
filtered_dataframe, _ = self.dh.filter_features(
@@ -150,6 +154,6 @@ class ExamplePredictionModel(IFreqaiModel):
# compute the non-standardized predictions
predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"]
print("--------------------Finished prediction--------------------")
logger.info("--------------------Finished prediction--------------------")
return (predictions, self.dh.do_predict)