fix the import logic, fix tests, put all tensorboard in a single folder

freqtrade/freqai/RL/BaseReinforcementLearningModel.py

@@ -23,7 +23,7 @@ from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.freqai_interface import IFreqaiModel
 from freqtrade.freqai.RL.Base5ActionRLEnv import Actions, Base5ActionRLEnv
 from freqtrade.freqai.RL.BaseEnvironment import BaseActions, BaseEnvironment, Positions
-from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
+from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
 from freqtrade.persistence import Trade
 
 

freqtrade/freqai/__init__.py

@@ -1,12 +1,13 @@
 # ensure users can still use a non-torch freqai version
 try:
-    from freqtrade.freqai.tensorboard import TensorBoardCallback, TensorboardLogger
+    from freqtrade.freqai.tensorboard.tensorboard import TensorBoardCallback, TensorboardLogger
     TBLogger = TensorboardLogger
     TBCallback = TensorBoardCallback
 except ModuleNotFoundError:
-    from freqtrade.freqai.tensorboard import BaseTensorBoardCallback, BaseTensorboardLogger
+    from freqtrade.freqai.tensorboard.base_tensorboard import (BaseTensorBoardCallback,
-    TBLogger = BaseTensorboardLogger  # type: ignore
+                                                               BaseTensorboardLogger)
-    TBCallback = BaseTensorBoardCallback  # type: ignore
+    TBLogger = BaseTensorboardLogger
+    TBCallback = BaseTensorBoardCallback
 
 __all__ = (
     "TBLogger",

freqtrade/freqai/prediction_models/ReinforcementLearner_multiproc.py

@@ -8,7 +8,7 @@ from stable_baselines3.common.vec_env import SubprocVecEnv, VecMonitor
 from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
 from freqtrade.freqai.prediction_models.ReinforcementLearner import ReinforcementLearner
 from freqtrade.freqai.RL.BaseReinforcementLearningModel import make_env
-from freqtrade.freqai.RL.TensorboardCallback import TensorboardCallback
+from freqtrade.freqai.tensorboard.TensorboardCallback import TensorboardCallback
 
 
 logger = logging.getLogger(__name__)

freqtrade/freqai/tensorboard.py

@@ -1,77 +0,0 @@
-import logging
-from pathlib import Path
-from typing import Any
-
-import xgboost as xgb
-
-
-logger = logging.getLogger(__name__)
-
-
-class BaseTensorboardLogger:
-    def __init__(self, logdir: str = "tensorboard", id: str = "unique-id"):
-        logger.warning("Tensorboard is not installed, no logs will be written."
-                       "Use ensure torch is installed, or use the torch/RL docker images")
-
-    def log_scaler(self, tag: str, scalar_value: Any, step: int):
-        return
-
-    def close(self):
-        return
-
-
-class BaseTensorBoardCallback(xgb.callback.TrainingCallback):
-
-    def __init__(self, logdir: str = "tensorboard", id: str = "uniqu-id", test_size=1):
-        logger.warning("Tensorboard is not installed, no logs will be written."
-                       "Use ensure torch is installed, or use the torch/RL docker images")
-
-    def after_iteration(
-        self, model, epoch: int, evals_log: xgb.callback.TrainingCallback.EvalsLog
-    ) -> bool:
-        return False
-
-    def after_training(self, model):
-        return model
-
-
-class TensorboardLogger(BaseTensorboardLogger):
-    def __init__(self, logdir: Path = Path("tensorboard")):
-        from torch.utils.tensorboard import SummaryWriter
-        self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")
-
-    def log_scalar(self, tag: str, scalar_value: Any, step: int):
-        self.writer.add_scalar(tag, scalar_value, step)
-
-    def close(self):
-        self.writer.flush()
-        self.writer.close()
-
-
-class TensorBoardCallback(BaseTensorBoardCallback):
-
-    def __init__(self, logdir: Path = Path("tensorboard")):
-        from torch.utils.tensorboard import SummaryWriter
-        self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")
-
-    def after_iteration(
-        self, model, epoch: int, evals_log: xgb.callback.TrainingCallback.EvalsLog
-    ) -> bool:
-        if not evals_log:
-            return False
-
-        for data, metric in evals_log.items():
-            for metric_name, log in metric.items():
-                score = log[-1][0] if isinstance(log[-1], tuple) else log[-1]
-                if data == "train":
-                    self.writer.add_scalar("train_loss", score**2, epoch)
-                else:
-                    self.writer.add_scalar("valid_loss", score**2, epoch)
-
-        return False
-
-    def after_training(self, model):
-        self.writer.flush()
-        self.writer.close()
-
-        return model

freqtrade/freqai/tensorboard/base_tensorboard.py

@@ -0,0 +1,35 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+import xgboost as xgb
+
+
+logger = logging.getLogger(__name__)
+
+
+class BaseTensorboardLogger:
+    def __init__(self, logdir: Path):
+        logger.warning("Tensorboard is not installed, no logs will be written."
+                       "Ensure torch is installed, or use the torch/RL docker images")
+
+    def log_scaler(self, tag: str, scalar_value: Any, step: int):
+        return
+
+    def close(self):
+        return
+
+
+class BaseTensorBoardCallback(xgb.callback.TrainingCallback):
+
+    def __init__(self, logdir: Path):
+        logger.warning("Tensorboard is not installed, no logs will be written."
+                       "Ensure torch is installed, or use the torch/RL docker images")
+
+    def after_iteration(
+        self, model, epoch: int, evals_log: xgb.callback.TrainingCallback.EvalsLog
+    ) -> bool:
+        return False
+
+    def after_training(self, model):
+        return model

freqtrade/freqai/tensorboard/tensorboard.py

@@ -0,0 +1,52 @@
+import logging
+from pathlib import Path
+from typing import Any
+
+from torch.utils.tensorboard import SummaryWriter
+from xgboost import callback
+
+from freqtrade.freqai.tensorboard.base_tensorboard import (BaseTensorBoardCallback,
+                                                           BaseTensorboardLogger)
+
+
+logger = logging.getLogger(__name__)
+
+
+class TensorboardLogger(BaseTensorboardLogger):
+    def __init__(self, logdir: Path):
+        self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")
+
+    def log_scalar(self, tag: str, scalar_value: Any, step: int):
+        self.writer.add_scalar(tag, scalar_value, step)
+
+    def close(self):
+        self.writer.flush()
+        self.writer.close()
+
+
+class TensorBoardCallback(BaseTensorBoardCallback):
+
+    def __init__(self, logdir: Path):
+        self.writer: SummaryWriter = SummaryWriter(f"{str(logdir)}/tensorboard")
+
+    def after_iteration(
+        self, model, epoch: int, evals_log: callback.TrainingCallback.EvalsLog
+    ) -> bool:
+        if not evals_log:
+            return False
+
+        for data, metric in evals_log.items():
+            for metric_name, log in metric.items():
+                score = log[-1][0] if isinstance(log[-1], tuple) else log[-1]
+                if data == "train":
+                    self.writer.add_scalar("train_loss", score**2, epoch)
+                else:
+                    self.writer.add_scalar("valid_loss", score**2, epoch)
+
+        return False
+
+    def after_training(self, model):
+        self.writer.flush()
+        self.writer.close()
+
+        return model

tests/freqai/test_freqai_datakitchen.py

@@ -188,7 +188,7 @@ def test_get_full_model_path(mocker, freqai_conf, model):
 
     data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
     new_timerange = TimeRange.parse_timerange("20180120-20180130")
-
+    freqai.dk.set_paths('ADA/BTC', None)
     freqai.extract_data_and_train_model(
         new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
 

tests/freqai/test_freqai_interface.py

@@ -282,6 +282,7 @@ def test_start_backtesting(mocker, freqai_conf, model, num_files, strat, caplog)
         df[f'%-constant_{i}'] = i
 
     metadata = {"pair": "LTC/BTC"}
+    freqai.dk.set_paths('LTC/BTC', None)
     freqai.start_backtesting(df, metadata, freqai.dk, strategy)
     model_folders = [x for x in freqai.dd.full_path.iterdir() if x.is_dir()]
 
@@ -439,6 +440,7 @@ def test_principal_component_analysis(mocker, freqai_conf):
 
     data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
     new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths('ADA/BTC', None)
 
     freqai.extract_data_and_train_model(
         new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)
@@ -472,6 +474,7 @@ def test_plot_feature_importance(mocker, freqai_conf):
 
     data_load_timerange = TimeRange.parse_timerange("20180110-20180130")
     new_timerange = TimeRange.parse_timerange("20180120-20180130")
+    freqai.dk.set_paths('ADA/BTC', None)
 
     freqai.extract_data_and_train_model(
         new_timerange, "ADA/BTC", strategy, freqai.dk, data_load_timerange)