mirror of
https://github.com/freqtrade/freqtrade.git
synced 2024-11-10 10:21:59 +00:00
increase test coverage for dk, improve function naming, extra cleaning
This commit is contained in:
robcaulk
parent
7e8e29e42d
commit
c9be66b5b6
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@ -1,7 +1,7 @@
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import copy
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import datetime
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import logging
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import shutil
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from datetime import datetime, timezone
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from pathlib import Path
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from typing import Any, Dict, List, Tuple
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@ -345,7 +345,7 @@ class FreqaiDataKitchen:
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def denormalize_labels_from_metadata(self, df: DataFrame) -> DataFrame:
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"""
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Normalize a set of data using the mean and standard deviation from
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Denormalize a set of data using the mean and standard deviation from
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the associated training data.
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:param df: Dataframe of predictions to be denormalized
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"""
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@ -384,7 +384,7 @@ class FreqaiDataKitchen:
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config_timerange = TimeRange.parse_timerange(self.config["timerange"])
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if config_timerange.stopts == 0:
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config_timerange.stopts = int(
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datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
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datetime.now(tz=timezone.utc).timestamp()
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)
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timerange_train = copy.deepcopy(full_timerange)
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timerange_backtest = copy.deepcopy(full_timerange)
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@ -401,8 +401,8 @@ class FreqaiDataKitchen:
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timerange_train.stopts = timerange_train.startts + train_period_days
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first = False
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start = datetime.datetime.utcfromtimestamp(timerange_train.startts)
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stop = datetime.datetime.utcfromtimestamp(timerange_train.stopts)
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start = datetime.utcfromtimestamp(timerange_train.startts)
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stop = datetime.utcfromtimestamp(timerange_train.stopts)
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tr_training_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
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tr_training_list_timerange.append(copy.deepcopy(timerange_train))
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@ -415,8 +415,8 @@ class FreqaiDataKitchen:
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if timerange_backtest.stopts > config_timerange.stopts:
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timerange_backtest.stopts = config_timerange.stopts
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start = datetime.datetime.utcfromtimestamp(timerange_backtest.startts)
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stop = datetime.datetime.utcfromtimestamp(timerange_backtest.stopts)
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start = datetime.utcfromtimestamp(timerange_backtest.startts)
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stop = datetime.utcfromtimestamp(timerange_backtest.stopts)
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tr_backtesting_list.append(start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d"))
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tr_backtesting_list_timerange.append(copy.deepcopy(timerange_backtest))
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@ -436,8 +436,8 @@ class FreqaiDataKitchen:
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it is sliced down to just the present training period.
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"""
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start = datetime.datetime.fromtimestamp(timerange.startts, tz=datetime.timezone.utc)
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stop = datetime.datetime.fromtimestamp(timerange.stopts, tz=datetime.timezone.utc)
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start = datetime.fromtimestamp(timerange.startts, tz=timezone.utc)
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stop = datetime.fromtimestamp(timerange.stopts, tz=timezone.utc)
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df = df.loc[df["date"] >= start, :]
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df = df.loc[df["date"] <= stop, :]
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@ -808,6 +808,8 @@ class FreqaiDataKitchen:
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[compute_df, inlier_metric], axis=1)
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self.data_dictionary['prediction_features'].fillna(0, inplace=True)
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logger.info('Inlier metric computed and added to features.')
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return None
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def remove_beginning_points_from_data_dict(self, set_='train', no_prev_pts: int = 10):
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@ -948,14 +950,14 @@ class FreqaiDataKitchen:
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"Please indicate the end date of your desired backtesting. "
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"timerange.")
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# backtest_timerange.stopts = int(
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# datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
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# datetime.now(tz=timezone.utc).timestamp()
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# )
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backtest_timerange.startts = (
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backtest_timerange.startts - backtest_period_days * SECONDS_IN_DAY
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)
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start = datetime.datetime.utcfromtimestamp(backtest_timerange.startts)
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stop = datetime.datetime.utcfromtimestamp(backtest_timerange.stopts)
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start = datetime.utcfromtimestamp(backtest_timerange.startts)
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stop = datetime.utcfromtimestamp(backtest_timerange.stopts)
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full_timerange = start.strftime("%Y%m%d") + "-" + stop.strftime("%Y%m%d")
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self.full_path = Path(
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@ -981,7 +983,7 @@ class FreqaiDataKitchen:
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:return:
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bool = If the model is expired or not.
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"""
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time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
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time = datetime.now(tz=timezone.utc).timestamp()
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elapsed_time = (time - trained_timestamp) / 3600 # hours
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max_time = self.freqai_config.get("expiration_hours", 0)
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if max_time > 0:
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@ -993,7 +995,7 @@ class FreqaiDataKitchen:
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self, trained_timestamp: int
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) -> Tuple[bool, TimeRange, TimeRange]:
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time = datetime.datetime.now(tz=datetime.timezone.utc).timestamp()
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time = datetime.now(tz=timezone.utc).timestamp()
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trained_timerange = TimeRange()
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data_load_timerange = TimeRange()
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@ -1,10 +1,10 @@
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# import contextlib
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import datetime
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import logging
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import shutil
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import threading
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import time
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from abc import ABC, abstractmethod
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from datetime import datetime
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from pathlib import Path
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from threading import Lock
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from typing import Any, Dict, Tuple
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@ -174,7 +174,7 @@ class IFreqaiModel(ABC):
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if retrain:
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self.train_timer('start')
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self.train_model_in_series(
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self.extract_data_and_train_model(
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new_trained_timerange, pair, strategy, dk, data_load_timerange
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)
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self.train_timer('stop')
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@ -214,10 +214,10 @@ class IFreqaiModel(ABC):
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dataframe_backtest = dk.slice_dataframe(tr_backtest, dataframe)
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trained_timestamp = tr_train
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tr_train_startts_str = datetime.datetime.utcfromtimestamp(tr_train.startts).strftime(
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tr_train_startts_str = datetime.utcfromtimestamp(tr_train.startts).strftime(
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"%Y-%m-%d %H:%M:%S"
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)
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tr_train_stopts_str = datetime.datetime.utcfromtimestamp(tr_train.stopts).strftime(
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tr_train_stopts_str = datetime.utcfromtimestamp(tr_train.stopts).strftime(
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"%Y-%m-%d %H:%M:%S"
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)
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logger.info(
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@ -495,7 +495,7 @@ class IFreqaiModel(ABC):
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Path(self.full_path, Path(self.config["config_files"][0]).name),
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)
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def train_model_in_series(
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def extract_data_and_train_model(
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self,
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new_trained_timerange: TimeRange,
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pair: str,
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@ -82,6 +82,38 @@ def get_patched_freqaimodel(mocker, freqaiconf):
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return freqaimodel
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def make_unfiltered_dataframe(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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exchange = get_patched_exchange(mocker, freqai_conf)
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strategy.dp = DataProvider(freqai_conf, exchange)
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strategy.freqai_info = freqai_conf.get("freqai", {})
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freqai = strategy.freqai
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freqai.live = True
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freqai.dk = FreqaiDataKitchen(freqai_conf)
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freqai.dk.pair = "ADA/BTC"
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timerange = TimeRange.parse_timerange("20180110-20180130")
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freqai.dd.load_all_pair_histories(timerange, freqai.dk)
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freqai.dd.pair_dict = MagicMock()
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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corr_dataframes, base_dataframes = freqai.dd.get_base_and_corr_dataframes(
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data_load_timerange, freqai.dk.pair, freqai.dk
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)
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unfiltered_dataframe = freqai.dk.use_strategy_to_populate_indicators(
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strategy, corr_dataframes, base_dataframes, freqai.dk.pair
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)
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unfiltered_dataframe = freqai.dk.slice_dataframe(new_timerange, unfiltered_dataframe)
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return freqai, unfiltered_dataframe
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def make_data_dictionary(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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@ -6,7 +6,8 @@ import pytest
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from freqtrade.exceptions import OperationalException
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from tests.conftest import log_has_re
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from tests.freqai.conftest import get_patched_data_kitchen, make_data_dictionary
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from tests.freqai.conftest import (get_patched_data_kitchen, make_data_dictionary,
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make_unfiltered_dataframe)
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@pytest.mark.parametrize(
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@ -94,3 +95,72 @@ def test_use_SVM_to_remove_outliers_and_outlier_protection(mocker, freqai_conf,
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"SVM detected 8.46%",
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caplog,
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)
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def test_compute_inlier_metric(mocker, freqai_conf, caplog):
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freqai = make_data_dictionary(mocker, freqai_conf)
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freqai_conf['freqai']['feature_parameters'].update({"inlier_metric_window": 10})
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freqai.dk.compute_inlier_metric(set_='train')
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assert log_has_re(
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"Inlier metric computed and added to features.",
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caplog,
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)
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def test_add_noise_to_training_features(mocker, freqai_conf):
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freqai = make_data_dictionary(mocker, freqai_conf)
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freqai_conf['freqai']['feature_parameters'].update({"noise_standard_deviation": 0.1})
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freqai.dk.add_noise_to_training_features()
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def test_remove_beginning_points_from_data_dict(mocker, freqai_conf):
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freqai = make_data_dictionary(mocker, freqai_conf)
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freqai.dk.remove_beginning_points_from_data_dict(set_='train')
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def test_principal_component_analysis(mocker, freqai_conf, caplog):
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freqai = make_data_dictionary(mocker, freqai_conf)
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freqai.dk.principal_component_analysis()
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assert log_has_re(
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"reduced feature dimension by",
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caplog,
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)
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def test_normalize_data(mocker, freqai_conf):
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freqai = make_data_dictionary(mocker, freqai_conf)
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data_dict = freqai.dk.data_dictionary
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freqai.dk.normalize_data(data_dict)
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assert len(freqai.dk.data) == 56
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def test_filter_features(mocker, freqai_conf):
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freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
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freqai.dk.find_features(unfiltered_dataframe)
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filtered_df, labels = freqai.dk.filter_features(
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unfiltered_dataframe,
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freqai.dk.training_features_list,
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freqai.dk.label_list,
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training_filter=True,
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)
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assert len(filtered_df.columns) == 26
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def test_make_train_test_datasets(mocker, freqai_conf):
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freqai, unfiltered_dataframe = make_unfiltered_dataframe(mocker, freqai_conf)
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freqai.dk.find_features(unfiltered_dataframe)
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features_filtered, labels_filtered = freqai.dk.filter_features(
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unfiltered_dataframe,
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freqai.dk.training_features_list,
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freqai.dk.label_list,
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training_filter=True,
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)
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data_dictionary = freqai.dk.make_train_test_datasets(features_filtered, labels_filtered)
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assert data_dictionary
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assert len(data_dictionary) == 7
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assert len(data_dictionary['train_features'].index) == 1916
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@ -17,7 +17,7 @@ def is_arm() -> bool:
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return "arm" in machine or "aarch64" in machine
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def test_train_model_in_series_LightGBM(mocker, freqai_conf):
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def test_extract_data_and_train_model_LightGBM(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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strategy = get_patched_freqai_strategy(mocker, freqai_conf)
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@ -35,7 +35,8 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf):
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
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freqai.extract_data_and_train_model(
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new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
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@ -45,7 +46,7 @@ def test_train_model_in_series_LightGBM(mocker, freqai_conf):
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
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def test_extract_data_and_train_model_LightGBMMultiModel(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"strategy": "freqai_test_multimodel_strat"})
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freqai_conf.update({"freqaimodel": "LightGBMRegressorMultiTarget"})
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@ -64,7 +65,8 @@ def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
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freqai.extract_data_and_train_model(
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new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
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assert len(freqai.dk.label_list) == 2
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
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@ -77,7 +79,7 @@ def test_train_model_in_series_LightGBMMultiModel(mocker, freqai_conf):
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@pytest.mark.skipif(is_arm(), reason="no ARM for Catboost ...")
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def test_train_model_in_series_Catboost(mocker, freqai_conf):
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def test_extract_data_and_train_model_Catboost(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"freqaimodel": "CatboostRegressor"})
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# freqai_conf.get('freqai', {}).update(
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@ -98,8 +100,8 @@ def test_train_model_in_series_Catboost(mocker, freqai_conf):
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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freqai.train_model_in_series(new_timerange, "ADA/BTC",
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strategy, freqai.dk, data_load_timerange)
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freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
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strategy, freqai.dk, data_load_timerange)
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
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@ -110,7 +112,7 @@ def test_train_model_in_series_Catboost(mocker, freqai_conf):
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@pytest.mark.skipif(is_arm(), reason="no ARM for Catboost ...")
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def test_train_model_in_series_CatboostClassifier(mocker, freqai_conf):
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def test_extract_data_and_train_model_CatboostClassifier(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"freqaimodel": "CatboostClassifier"})
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freqai_conf.update({"strategy": "freqai_test_classifier"})
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@ -130,8 +132,8 @@ def test_train_model_in_series_CatboostClassifier(mocker, freqai_conf):
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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freqai.train_model_in_series(new_timerange, "ADA/BTC",
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strategy, freqai.dk, data_load_timerange)
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freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
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strategy, freqai.dk, data_load_timerange)
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
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@ -141,7 +143,7 @@ def test_train_model_in_series_CatboostClassifier(mocker, freqai_conf):
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shutil.rmtree(Path(freqai.dk.full_path))
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def test_train_model_in_series_LightGBMClassifier(mocker, freqai_conf):
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def test_extract_data_and_train_model_LightGBMClassifier(mocker, freqai_conf):
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freqai_conf.update({"timerange": "20180110-20180130"})
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freqai_conf.update({"freqaimodel": "LightGBMClassifier"})
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freqai_conf.update({"strategy": "freqai_test_classifier"})
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@ -161,8 +163,8 @@ def test_train_model_in_series_LightGBMClassifier(mocker, freqai_conf):
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data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
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freqai.train_model_in_series(new_timerange, "ADA/BTC",
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strategy, freqai.dk, data_load_timerange)
|
||||
freqai.extract_data_and_train_model(new_timerange, "ADA/BTC",
|
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strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").exists()
|
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assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").exists()
|
||||
|
|
@ -289,7 +291,8 @@ def test_follow_mode(mocker, freqai_conf):
|
|||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
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new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
freqai.extract_data_and_train_model(
|
||||
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_model.joblib").is_file()
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_metadata.json").is_file()
|
||||
|
|
@ -338,7 +341,8 @@ def test_principal_component_analysis(mocker, freqai_conf):
|
|||
data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
|
||||
new_timerange = TimeRange.parse_timerange("20180120-20180130")
|
||||
|
||||
freqai.train_model_in_series(new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
freqai.extract_data_and_train_model(
|
||||
new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
|
||||
|
||||
assert Path(freqai.dk.data_path / f"{freqai.dk.model_filename}_pca_object.pkl")
|
||||
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user