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flake8 passing, use pathlib in lieu of os.path to accommodate windows/mac OS

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This commit is contained in:
robcaulk
2022-05-04 17:42:34 +02:00
parent 2600ba4e74
commit 99f7e44c30
7 changed files with 593 additions and 439 deletions
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121
freqtrade/freqai/freqai_interface.py
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@@ -1,20 +1,23 @@
import gc
import shutil
from abc import ABC
from pathlib import Path
from typing import Any, Dict, Tuple
import os
import numpy as np
import pandas as pd
from pandas import DataFrame
import shutil
import gc
from typing import Any, Dict, Optional, Tuple
from abc import ABC
from freqtrade.freqai.data_handler import DataHandler
pd.options.mode.chained_assignment = None
class IFreqaiModel(ABC):
"""
Class containing all tools for training and prediction in the strategy.
User models should inherit from this class as shown in
User models should inherit from this class as shown in
templates/ExamplePredictionModel.py where the user overrides
train(), predict(), fit(), and make_labels().
Author: Robert Caulk, rob.caulk@gmail.com
@@ -23,61 +26,71 @@ class IFreqaiModel(ABC):
def __init__(self, config: Dict[str, Any]) -> None:
self.config = config
self.freqai_info = config['freqai']
self.data_split_parameters = config['freqai']['data_split_parameters']
self.model_training_parameters = config['freqai']['model_training_parameters']
self.feature_parameters = config['freqai']['feature_parameters']
self.full_path = (str(config['user_data_dir'])+
"/models/"+self.freqai_info['full_timerange']+
'-'+self.freqai_info['identifier'])
self.metadata = {}
self.data = {}
self.freqai_info = config["freqai"]
self.data_split_parameters = config["freqai"]["data_split_parameters"]
self.model_training_parameters = config["freqai"]["model_training_parameters"]
self.feature_parameters = config["freqai"]["feature_parameters"]
self.full_path = Path(
config["user_data_dir"]
/ "models"
/ str(self.freqai_info["full_timerange"] + self.freqai_info["identifier"])
)
self.time_last_trained = None
self.current_time = None
self.model = None
self.predictions = None
if not os.path.exists(self.full_path):
os.mkdir(self.full_path)
shutil.copy(self.config['config_files'][0],self.full_path+"/"+self.config['config_files'][0])
if not self.full_path.is_dir():
self.full_path.mkdir(parents=True, exist_ok=True)
shutil.copy(
self.config["config_files"][0],
Path(self.full_path / self.config["config_files"][0]),
)
def start(self, dataframe: DataFrame, metadata: dict) -> DataFrame:
"""
Entry point to the FreqaiModel, it will train a new model if
Entry point to the FreqaiModel, it will train a new model if
necesssary before making the prediction.
The backtesting and training paradigm is a sliding training window
with a following backtest window. Both windows slide according to the
length of the backtest window. This function is not intended to be
overridden by children of IFreqaiModel, but technically, it can be
length of the backtest window. This function is not intended to be
overridden by children of IFreqaiModel, but technically, it can be
if the user wishes to make deeper changes to the sliding window
logic.
:params:
:dataframe: Full dataframe coming from strategy - it contains entire
backtesting timerange + additional historical data necessary to train
backtesting timerange + additional historical data necessary to train
the model.
:metadata: pair metadataa coming from strategy.
:metadata: pair metadataa coming from strategy.
"""
self.pair = metadata['pair']
self.dh = DataHandler(self.config, dataframe, self.data)
self.pair = metadata["pair"]
self.dh = DataHandler(self.config, dataframe)
print('going to train',len(self.dh.training_timeranges),
'timeranges:',self.dh.training_timeranges)
print(
"going to train",
len(self.dh.training_timeranges),
"timeranges:",
self.dh.training_timeranges,
)
# Loop enforcing the sliding window training/backtesting paragigm
# tr_train is the training time range e.g. 1 historical month
# tr_backtest is the backtesting time range e.g. the week directly
# following tr_train. Both of these windows slide through the
# tr_backtest is the backtesting time range e.g. the week directly
# following tr_train. Both of these windows slide through the
# entire backtest
for tr_train, tr_backtest in zip(self.dh.training_timeranges,
self.dh.backtesting_timeranges):
for tr_train, tr_backtest in zip(
self.dh.training_timeranges, self.dh.backtesting_timeranges
):
gc.collect()
#self.config['timerange'] = tr_train
self.dh.data = {} # clean the pair specific data between models
self.freqai_info['training_timerange'] = tr_train
# self.config['timerange'] = tr_train
self.dh.data = {} # clean the pair specific data between models
self.freqai_info["training_timerange"] = tr_train
dataframe_train = self.dh.slice_dataframe(tr_train, dataframe)
dataframe_backtest = self.dh.slice_dataframe(tr_backtest, dataframe)
print("training",self.pair,"for",tr_train)
self.dh.model_path = self.full_path+"/"+ 'sub-train'+'-'+str(tr_train)+'/'
print("training", self.pair, "for", tr_train)
# self.dh.model_path = self.full_path + "/" + "sub-train" + "-" + str(tr_train) + "/"
self.dh.model_path = Path(self.full_path / str("sub-train" + "-" + str(tr_train)))
if not self.model_exists(self.pair, training_timerange=tr_train):
self.model = self.train(dataframe_train, metadata)
self.dh.save_data(self.model)
@@ -86,8 +99,8 @@ class IFreqaiModel(ABC):
preds, do_preds = self.predict(dataframe_backtest)
self.dh.append_predictions(preds,do_preds,len(dataframe_backtest))
self.dh.append_predictions(preds, do_preds, len(dataframe_backtest))
self.dh.fill_predictions(len(dataframe))
return self.dh.predictions, self.dh.do_predict, self.dh.target_mean, self.dh.target_std
@@ -107,7 +120,7 @@ class IFreqaiModel(ABC):
for storing, saving, loading, and analyzing the data.
:params:
:unfiltered_dataframe: Full dataframe for the current training period
:metadata: pair metadata from strategy.
:metadata: pair metadata from strategy.
:returns:
:model: Trained model which can be used to inference (self.predict)
"""
@@ -116,40 +129,40 @@ class IFreqaiModel(ABC):
def fit(self) -> Any:
"""
Most regressors use the same function names and arguments e.g. user
Most regressors use the same function names and arguments e.g. user
can drop in LGBMRegressor in place of CatBoostRegressor and all data
management will be properly handled by Freqai.
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
return None
def predict(self) -> Optional[Tuple[DataFrame, DataFrame]]:
return Any
def predict(self, dataframe: DataFrame) -> Tuple[np.array, np.array]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
:return:
:return:
:predictions: np.array of predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove
data (NaNs) or felt uncertain about data (PCA and DI index)
"""
return None
return np.array([]), np.array([])
def model_exists(self, pair: str, training_timerange: str = None) -> bool:
def model_exists(self, pair: str, training_timerange: str) -> bool:
"""
Given a pair and path, check if a model already exists
:param pair: pair e.g. BTC/USD
:param path: path to model
"""
coin,_ = pair.split('/')
self.dh.model_filename = f"cb_"+coin.lower()+"_"+training_timerange
file_exists = os.path.isfile(self.dh.model_path+
self.dh.model_filename+"_model.joblib")
coin, _ = pair.split("/")
self.dh.model_filename = "cb_" + coin.lower() + "_" + training_timerange
path_to_modelfile = Path(self.dh.model_path / str(self.dh.model_filename + "_model.joblib"))
file_exists = path_to_modelfile.is_file()
if file_exists:
print("Found model at", self.dh.model_path+self.dh.model_filename)
else: print("Could not find model at",
self.dh.model_path+self.dh.model_filename)
print("Found model at", self.dh.model_path / self.dh.model_filename)
else:
print("Could not find model at", self.dh.model_path / self.dh.model_filename)
return file_exists