use logger in favor of print
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robcaulk
parent
99f7e44c30
commit
29c2d1d189
@ -1,6 +1,7 @@
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import copy
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import datetime
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import json
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import logging
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import pickle as pk
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from pathlib import Path
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from typing import Any, Dict, List, Tuple
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@ -17,6 +18,8 @@ from freqtrade.configuration import TimeRange
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SECONDS_IN_DAY = 86400
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logger = logging.getLogger(__name__)
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class DataHandler:
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"""
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@ -175,7 +178,7 @@ class DataHandler:
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labels = labels[
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(drop_index == 0) & (drop_index_labels == 0)
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] # assuming the labels depend entirely on the dataframe here.
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print(
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logger.info(
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"dropped",
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len(unfiltered_dataframe) - len(filtered_dataframe),
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"training data points due to NaNs, ensure you have downloaded",
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@ -193,7 +196,7 @@ class DataHandler:
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# that was based on a single NaN is ultimately protected from buys with do_predict
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drop_index = ~drop_index
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self.do_predict = np.array(drop_index.replace(True, 1).replace(False, 0))
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print(
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logger.info(
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"dropped",
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len(self.do_predict) - self.do_predict.sum(),
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"of",
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@ -350,8 +353,8 @@ class DataHandler:
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pca2 = PCA(n_components=n_keep_components)
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self.data["n_kept_components"] = n_keep_components
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pca2 = pca2.fit(self.data_dictionary["train_features"])
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print("reduced feature dimension by", n_components - n_keep_components)
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print("explained variance", np.sum(pca2.explained_variance_ratio_))
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logger.info("reduced feature dimension by", n_components - n_keep_components)
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logger.info("explained variance", np.sum(pca2.explained_variance_ratio_))
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train_components = pca2.transform(self.data_dictionary["train_features"])
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test_components = pca2.transform(self.data_dictionary["test_features"])
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@ -377,10 +380,10 @@ class DataHandler:
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return None
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def compute_distances(self) -> float:
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print("computing average mean distance for all training points")
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logger.info("computing average mean distance for all training points")
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pairwise = pairwise_distances(self.data_dictionary["train_features"], n_jobs=-1)
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avg_mean_dist = pairwise.mean(axis=1).mean()
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print("avg_mean_dist", avg_mean_dist)
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logger.info("avg_mean_dist", avg_mean_dist)
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return avg_mean_dist
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@ -407,7 +410,7 @@ class DataHandler:
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drop_index = ~drop_index
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do_predict = np.array(drop_index.replace(True, 1).replace(False, 0))
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print(
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logger.info(
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"remove_outliers() tossed",
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len(do_predict) - do_predict.sum(),
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"predictions because they were beyond 3 std deviations from training data.",
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@ -472,7 +475,7 @@ class DataHandler:
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for p in config["freqai"]["corr_pairlist"]:
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features.append(p.split("/")[0] + "-" + ft + shift + "_" + tf)
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print("number of features", len(features))
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logger.info("number of features", len(features))
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return features
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def check_if_pred_in_training_spaces(self) -> None:
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@ -483,7 +486,7 @@ class DataHandler:
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from the training data set.
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"""
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print("checking if prediction features are in AOA")
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logger.info("checking if prediction features are in AOA")
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distance = pairwise_distances(
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self.data_dictionary["train_features"],
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self.data_dictionary["prediction_features"],
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@ -497,7 +500,7 @@ class DataHandler:
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0,
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)
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print(
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logger.info(
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"Distance checker tossed",
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len(do_predict) - do_predict.sum(),
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"predictions for being too far from training data",
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@ -1,6 +1,7 @@
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import gc
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import logging
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import shutil
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from abc import ABC
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from abc import ABC, abstractmethod
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from pathlib import Path
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from typing import Any, Dict, Tuple
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@ -12,6 +13,7 @@ from freqtrade.freqai.data_handler import DataHandler
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pd.options.mode.chained_assignment = None
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logger = logging.getLogger(__name__)
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class IFreqaiModel(ABC):
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@ -67,7 +69,7 @@ class IFreqaiModel(ABC):
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self.pair = metadata["pair"]
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self.dh = DataHandler(self.config, dataframe)
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print(
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logger.info(
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"going to train",
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len(self.dh.training_timeranges),
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"timeranges:",
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@ -88,7 +90,7 @@ class IFreqaiModel(ABC):
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self.freqai_info["training_timerange"] = tr_train
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dataframe_train = self.dh.slice_dataframe(tr_train, dataframe)
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dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe)
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print("training", self.pair, "for", tr_train)
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logger.info("training", self.pair, "for", tr_train)
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# self.dh.model_path = self.full_path + "/" + "sub-train" + "-" + str(tr_train) + "/"
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self.dh.model_path = Path(self.full_path / str("sub-train" + "-" + str(tr_train)))
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if not self.model_exists(self.pair, training_timerange=tr_train):
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@ -114,6 +116,7 @@ class IFreqaiModel(ABC):
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return dataframe
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@abstractmethod
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def train(self, unfiltered_dataframe: DataFrame, metadata: dict) -> Any:
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datahandler
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@ -127,6 +130,7 @@ class IFreqaiModel(ABC):
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return Any
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@abstractmethod
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def fit(self) -> Any:
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"""
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Most regressors use the same function names and arguments e.g. user
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@ -139,6 +143,7 @@ class IFreqaiModel(ABC):
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return Any
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@abstractmethod
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def predict(self, dataframe: DataFrame) -> Tuple[np.array, np.array]:
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"""
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Filter the prediction features data and predict with it.
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@ -162,7 +167,7 @@ class IFreqaiModel(ABC):
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path_to_modelfile = Path(self.dh.model_path / str(self.dh.model_filename + "_model.joblib"))
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file_exists = path_to_modelfile.is_file()
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if file_exists:
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print("Found model at", self.dh.model_path / self.dh.model_filename)
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logger.info("Found model at", self.dh.model_path / self.dh.model_filename)
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else:
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print("Could not find model at", self.dh.model_path / self.dh.model_filename)
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logger.info("Could not find model at", self.dh.model_path / self.dh.model_filename)
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return file_exists
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@ -1,3 +1,4 @@
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import logging
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from typing import Any, Dict, Tuple
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import pandas as pd
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@ -7,6 +8,9 @@ from pandas import DataFrame
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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logger = logging.getLogger(__name__)
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class ExamplePredictionModel(IFreqaiModel):
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"""
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User created prediction model. The class needs to override three necessary
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@ -32,7 +36,7 @@ class ExamplePredictionModel(IFreqaiModel):
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self.dh.data["s_mean"] = dataframe["s"].mean()
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self.dh.data["s_std"] = dataframe["s"].std()
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print("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"])
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logger.info("label mean", self.dh.data["s_mean"], "label std", self.dh.data["s_std"])
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return dataframe["s"]
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@ -46,7 +50,7 @@ class ExamplePredictionModel(IFreqaiModel):
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:returns:
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:model: Trained model which can be used to inference (self.predict)
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"""
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print("--------------------Starting training--------------------")
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logger.info("--------------------Starting training--------------------")
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# create the full feature list based on user config info
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self.dh.training_features_list = self.dh.build_feature_list(self.config)
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@ -73,12 +77,12 @@ class ExamplePredictionModel(IFreqaiModel):
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if self.feature_parameters["DI_threshold"]:
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self.dh.data["avg_mean_dist"] = self.dh.compute_distances()
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print("length of train data", len(data_dictionary["train_features"]))
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logger.info("length of train data", len(data_dictionary["train_features"]))
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model = self.fit(data_dictionary)
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print("Finished training")
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print(f'--------------------done training {metadata["pair"]}--------------------')
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logger.info("Finished training")
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logger.info(f'--------------------done training {metadata["pair"]}--------------------')
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return model
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@ -121,7 +125,7 @@ class ExamplePredictionModel(IFreqaiModel):
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data (NaNs) or felt uncertain about data (PCA and DI index)
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"""
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print("--------------------Starting prediction--------------------")
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logger.info("--------------------Starting prediction--------------------")
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original_feature_list = self.dh.build_feature_list(self.config)
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filtered_dataframe, _ = self.dh.filter_features(
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@ -150,6 +154,6 @@ class ExamplePredictionModel(IFreqaiModel):
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# compute the non-standardized predictions
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predictions = predictions * self.dh.data["labels_std"] + self.dh.data["labels_mean"]
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print("--------------------Finished prediction--------------------")
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logger.info("--------------------Finished prediction--------------------")
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return (predictions, self.dh.do_predict)
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