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merge develop into feat/freqai-rl-dev

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robcaulk
2022-10-30 10:13:03 +01:00
parent 8d7adfabe9 5c14aeddc6
commit d1a0874683
129 changed files with 2648 additions and 1004 deletions
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21
freqtrade/freqai/prediction_models/CatboostClassifier.py
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@@ -1,4 +1,6 @@
import logging
import sys
from pathlib import Path
from typing import Any, Dict
from catboost import CatBoostClassifier, Pool
@@ -20,9 +22,8 @@ class CatboostClassifier(BaseClassifierModel):
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
train_data = Pool(
@@ -30,15 +31,25 @@ class CatboostClassifier(BaseClassifierModel):
label=data_dictionary["train_labels"],
weight=data_dictionary["train_weights"],
)
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
test_data = None
else:
test_data = Pool(
data=data_dictionary["test_features"],
label=data_dictionary["test_labels"],
weight=data_dictionary["test_weights"],
)
cbr = CatBoostClassifier(
allow_writing_files=False,
allow_writing_files=True,
loss_function='MultiClass',
train_dir=Path(dk.data_path),
**self.model_training_parameters,
)
init_model = self.get_init_model(dk.pair)
cbr.fit(train_data, init_model=init_model)
cbr.fit(X=train_data, eval_set=test_data, init_model=init_model,
log_cout=sys.stdout, log_cerr=sys.stderr)
return cbr

8
freqtrade/freqai/prediction_models/CatboostRegressor.py
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@@ -1,4 +1,6 @@
import logging
import sys
from pathlib import Path
from typing import Any, Dict
from catboost import CatBoostRegressor, Pool
@@ -41,10 +43,12 @@ class CatboostRegressor(BaseRegressionModel):
init_model = self.get_init_model(dk.pair)
model = CatBoostRegressor(
allow_writing_files=False,
allow_writing_files=True,
train_dir=Path(dk.data_path),
**self.model_training_parameters,
)
model.fit(X=train_data, eval_set=test_data, init_model=init_model)
model.fit(X=train_data, eval_set=test_data, init_model=init_model,
log_cout=sys.stdout, log_cerr=sys.stderr)
return model

11
freqtrade/freqai/prediction_models/CatboostRegressorMultiTarget.py
View File

@@ -1,4 +1,6 @@
import logging
import sys
from pathlib import Path
from typing import Any, Dict
from catboost import CatBoostRegressor, Pool
@@ -26,7 +28,8 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
"""
cbr = CatBoostRegressor(
allow_writing_files=False,
allow_writing_files=True,
train_dir=Path(dk.data_path),
**self.model_training_parameters,
)
@@ -56,8 +59,10 @@ class CatboostRegressorMultiTarget(BaseRegressionModel):
fit_params = []
for i in range(len(eval_sets)):
fit_params.append(
{'eval_set': eval_sets[i], 'init_model': init_models[i]})
fit_params.append({
'eval_set': eval_sets[i], 'init_model': init_models[i],
'log_cout': sys.stdout, 'log_cerr': sys.stderr,
})
model = FreqaiMultiOutputRegressor(estimator=cbr)
thread_training = self.freqai_info.get('multitarget_parallel_training', False)

5
freqtrade/freqai/prediction_models/LightGBMClassifier.py
View File

@@ -20,9 +20,8 @@ class LightGBMClassifier(BaseClassifierModel):
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:

7
freqtrade/freqai/prediction_models/XGBoostClassifier.py
View File

@@ -26,9 +26,8 @@ class XGBoostClassifier(BaseClassifierModel):
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:params:
:data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"].to_numpy()
@@ -65,7 +64,7 @@ class XGBoostClassifier(BaseClassifierModel):
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_df: Full dataframe for the current backtest period.
:param unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the predictions
:do_predict: np.array of 1s and 0s to indicate places where freqai needed to remove

84
freqtrade/freqai/prediction_models/XGBoostRFClassifier.py Normal file
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@@ -0,0 +1,84 @@
import logging
from typing import Any, Dict, Tuple
import numpy as np
import numpy.typing as npt
import pandas as pd
from pandas import DataFrame
from pandas.api.types import is_integer_dtype
from sklearn.preprocessing import LabelEncoder
from xgboost import XGBRFClassifier
from freqtrade.freqai.base_models.BaseClassifierModel import BaseClassifierModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class XGBoostRFClassifier(BaseClassifierModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"].to_numpy()
y = data_dictionary["train_labels"].to_numpy()[:, 0]
le = LabelEncoder()
if not is_integer_dtype(y):
y = pd.Series(le.fit_transform(y), dtype="int64")
if self.freqai_info.get('data_split_parameters', {}).get('test_size', 0.1) == 0:
eval_set = None
else:
test_features = data_dictionary["test_features"].to_numpy()
test_labels = data_dictionary["test_labels"].to_numpy()[:, 0]
if not is_integer_dtype(test_labels):
test_labels = pd.Series(le.transform(test_labels), dtype="int64")
eval_set = [(test_features, test_labels)]
train_weights = data_dictionary["train_weights"]
init_model = self.get_init_model(dk.pair)
model = XGBRFClassifier(**self.model_training_parameters)
model.fit(X=X, y=y, eval_set=eval_set, sample_weight=train_weights,
xgb_model=init_model)
return model
def predict(
self, unfiltered_df: DataFrame, dk: FreqaiDataKitchen, **kwargs
) -> Tuple[DataFrame, npt.NDArray[np.int_]]:
"""
Filter the prediction features data and predict with it.
:param unfiltered_df: Full dataframe for the current backtest period.
:return:
:pred_df: dataframe containing the 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)
"""
(pred_df, dk.do_predict) = super().predict(unfiltered_df, dk, **kwargs)
le = LabelEncoder()
label = dk.label_list[0]
labels_before = list(dk.data['labels_std'].keys())
labels_after = le.fit_transform(labels_before).tolist()
pred_df[label] = le.inverse_transform(pred_df[label])
pred_df = pred_df.rename(
columns={labels_after[i]: labels_before[i] for i in range(len(labels_before))})
return (pred_df, dk.do_predict)

46
freqtrade/freqai/prediction_models/XGBoostRFRegressor.py Normal file
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@@ -0,0 +1,46 @@
import logging
from typing import Any, Dict
from xgboost import XGBRFRegressor
from freqtrade.freqai.base_models.BaseRegressionModel import BaseRegressionModel
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
logger = logging.getLogger(__name__)
class XGBoostRFRegressor(BaseRegressionModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), train(), fit(). The class inherits ModelHandler which
has its own DataHandler where data is held, saved, loaded, and managed.
"""
def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
"""
User sets up the training and test data to fit their desired model here
:param data_dictionary: the dictionary constructed by DataHandler to hold
all the training and test data/labels.
"""
X = data_dictionary["train_features"]
y = data_dictionary["train_labels"]
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
eval_set = None
eval_weights = None
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
eval_weights = [data_dictionary['test_weights']]
sample_weight = data_dictionary["train_weights"]
xgb_model = self.get_init_model(dk.pair)
model = XGBRFRegressor(**self.model_training_parameters)
model.fit(X=X, y=y, sample_weight=sample_weight, eval_set=eval_set,
sample_weight_eval_set=eval_weights, xgb_model=xgb_model)
return model

1
freqtrade/freqai/prediction_models/XGBoostRegressor.py
View File

@@ -29,6 +29,7 @@ class XGBoostRegressor(BaseRegressionModel):
if self.freqai_info.get("data_split_parameters", {}).get("test_size", 0.1) == 0:
eval_set = None
eval_weights = None
else:
eval_set = [(data_dictionary["test_features"], data_dictionary["test_labels"])]
eval_weights = [data_dictionary['test_weights']]