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add tensorflow interface

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This commit is contained in:
robcaulk
2022-09-26 21:55:23 +02:00
parent 853a4d1014
commit f5870a7540
6 changed files with 249 additions and 15 deletions
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84
freqtrade/freqai/base_models/BaseTensorFlowModel.py
View File

@@ -3,10 +3,10 @@ from time import time
from typing import Any
from pandas import DataFrame
import numpy as np
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
from freqtrade.freqai.freqai_interface import IFreqaiModel
import tensorflow as tf
logger = logging.getLogger(__name__)
@@ -17,6 +17,13 @@ class BaseTensorFlowModel(IFreqaiModel):
User *must* inherit from this class and set fit() and predict().
"""
def __init__(self, **kwargs):
super().__init__(config=kwargs['config'])
self.keras = True
if self.ft_params.get("DI_threshold", 0):
self.ft_params["DI_threshold"] = 0
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
def train(
self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
) -> Any:
@@ -68,3 +75,76 @@ class BaseTensorFlowModel(IFreqaiModel):
f"({end_time - start_time:.2f} secs) --------------------")
return model
class WindowGenerator:
def __init__(
self,
input_width,
label_width,
shift,
train_df=None,
val_df=None,
test_df=None,
train_labels=None,
val_labels=None,
test_labels=None,
batch_size=None,
):
# Store the raw data.
self.train_df = train_df
self.val_df = val_df
self.test_df = test_df
self.train_labels = train_labels
self.val_labels = val_labels
self.test_labels = test_labels
self.batch_size = batch_size
self.input_width = input_width
self.label_width = label_width
self.shift = shift
self.total_window_size = input_width + shift
self.input_slice = slice(0, input_width)
self.input_indices = np.arange(self.total_window_size)[self.input_slice]
def make_dataset(self, data, labels=None):
data = np.array(data, dtype=np.float32)
if labels is not None:
labels = np.array(labels, dtype=np.float32)
ds = tf.keras.preprocessing.timeseries_dataset_from_array(
data=data,
targets=labels,
sequence_length=self.total_window_size,
sequence_stride=1,
sampling_rate=1,
shuffle=False,
batch_size=self.batch_size,
)
return ds
@property
def train(self):
return self.make_dataset(self.train_df, self.train_labels)
@property
def val(self):
return self.make_dataset(self.val_df, self.val_labels)
@property
def test(self):
return self.make_dataset(self.test_df, self.test_labels)
@property
def inference(self):
return self.make_dataset(self.test_df)
@property
def example(self):
"""Get and cache an example batch of `inputs, labels` for plotting."""
result = getattr(self, "_example", None)
if result is None:
# No example batch was found, so get one from the `.train` dataset
result = next(iter(self.train))
# And cache it for next time
self._example = result
return result

18
freqtrade/freqai/data_drawer.py
View File

@@ -413,10 +413,13 @@ class FreqaiDataDrawer:
save_path = Path(dk.data_path)
# Save the trained model
if not dk.keras:
model_type = self.freqai_info.get('model_save_type', 'joblib')
if model_type == 'joblib':
dump(model, save_path / f"{dk.model_filename}_model.joblib")
else:
elif model_type == 'keras':
model.save(save_path / f"{dk.model_filename}_model.h5")
elif 'stable_baselines' in model_type:
model.save(save_path / f"{dk.model_filename}_model.zip")
if dk.svm_model is not None:
dump(dk.svm_model, save_path / f"{dk.model_filename}_svm_model.joblib")
@@ -492,15 +495,20 @@ class FreqaiDataDrawer:
dk.data_path / f"{dk.model_filename}_trained_df.pkl"
)
model_type = self.freqai_info.get('model_save_type', 'joblib')
# try to access model in memory instead of loading object from disk to save time
if dk.live and coin in self.model_dictionary:
model = self.model_dictionary[coin]
elif not dk.keras:
elif model_type == 'joblib':
model = load(dk.data_path / f"{dk.model_filename}_model.joblib")
else:
elif model_type == 'keras':
from tensorflow import keras
model = keras.models.load_model(dk.data_path / f"{dk.model_filename}_model.h5")
elif model_type == 'stable_baselines':
mod = __import__('stable_baselines3', fromlist=[
self.freqai_info['rl_config']['model_type']])
MODELCLASS = getattr(mod, self.freqai_info['rl_config']['model_type'])
model = MODELCLASS.load(dk.data_path / f"{dk.model_filename}_model")
if Path(dk.data_path / f"{dk.model_filename}_svm_model.joblib").is_file():
dk.svm_model = load(dk.data_path / f"{dk.model_filename}_svm_model.joblib")

5
freqtrade/freqai/data_kitchen.py
View File

@@ -76,9 +76,10 @@ class FreqaiDataKitchen:
self.backtest_predictions_folder: str = "backtesting_predictions"
self.live = live
self.pair = pair
self.model_save_type = self.freqai_config.get('model_save_type', 'joblib')
self.svm_model: linear_model.SGDOneClassSVM = None
self.keras: bool = self.freqai_config.get("keras", False)
# self.model_save_type: bool = self.freqai_config.get("keras", False)
self.set_all_pairs()
if not self.live:
if not self.config["timerange"]:
@@ -571,7 +572,7 @@ class FreqaiDataKitchen:
predict: bool = If true, inference an existing SVM model, else construct one
"""
if self.keras:
if self.model_save_type == 'keras':
logger.warning(
"SVM outlier removal not currently supported for Keras based models. "
"Skipping user requested function."

11
freqtrade/freqai/freqai_interface.py
View File

@@ -72,10 +72,10 @@ class IFreqaiModel(ABC):
self.identifier: str = self.freqai_info.get("identifier", "no_id_provided")
self.scanning = False
self.ft_params = self.freqai_info["feature_parameters"]
self.keras: bool = self.freqai_info.get("keras", False)
if self.keras and self.ft_params.get("DI_threshold", 0):
self.ft_params["DI_threshold"] = 0
logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
# self.keras: bool = self.freqai_info.get("keras", False)
# if self.keras and self.ft_params.get("DI_threshold", 0):
# self.ft_params["DI_threshold"] = 0
# logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
self.CONV_WIDTH = self.freqai_info.get("conv_width", 2)
if self.ft_params.get("inlier_metric_window", 0):
self.CONV_WIDTH = self.ft_params.get("inlier_metric_window", 0) * 2
@@ -627,7 +627,8 @@ class IFreqaiModel(ABC):
# # for keras type models, the conv_window needs to be prepended so
# # viewing is correct in frequi
if self.freqai_info.get('keras', False) or self.ft_params.get('inlier_metric_window', 0):
if (not self.freqai_info.get('model_save_type', 'joblib') or
self.ft_params.get('inlier_metric_window', 0)):
n_lost_points = self.freqai_info.get('conv_width', 2)
zeros_df = DataFrame(np.zeros((n_lost_points, len(hist_preds_df.columns))),
columns=hist_preds_df.columns)

144
freqtrade/freqai/prediction_models/CNNPredictionModel.py Normal file
View File

@@ -0,0 +1,144 @@
import logging
from typing import Any, Dict, Tuple
from pandas import DataFrame
from freqtrade.exceptions import OperationalException
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
import tensorflow as tf
from freqtrade.freqai.base_models.BaseTensorFlowModel import BaseTensorFlowModel, WindowGenerator
from tensorflow.keras.layers import Input, Conv1D, Dense
from tensorflow.keras.models import Model
import numpy as np
logger = logging.getLogger(__name__)
# tf.config.run_functions_eagerly(True)
# tf.data.experimental.enable_debug_mode()
MAX_EPOCHS = 10
class CNNPredictionModel(BaseTensorFlowModel):
"""
User created prediction model. The class needs to override three necessary
functions, predict(), fit().
"""
def fit(self, data_dictionary: Dict[str, Any], dk: FreqaiDataKitchen) -> 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.
"""
train_df = data_dictionary["train_features"]
train_labels = data_dictionary["train_labels"]
test_df = data_dictionary["test_features"]
test_labels = data_dictionary["test_labels"]
n_labels = len(train_labels.columns)
if n_labels > 1:
raise OperationalException(
"Neural Net not yet configured for multi-targets. Please "
" reduce number of targets to 1 in strategy."
)
n_features = len(data_dictionary["train_features"].columns)
BATCH_SIZE = self.freqai_info.get("batch_size", 64)
input_dims = [BATCH_SIZE, self.CONV_WIDTH, n_features]
w1 = WindowGenerator(
input_width=self.CONV_WIDTH,
label_width=1,
shift=1,
train_df=train_df,
val_df=test_df,
train_labels=train_labels,
val_labels=test_labels,
batch_size=BATCH_SIZE,
)
model = self.create_model(input_dims, n_labels)
steps_per_epoch = np.ceil(len(test_df) / BATCH_SIZE)
lr_schedule = tf.keras.optimizers.schedules.InverseTimeDecay(
0.001, decay_steps=steps_per_epoch * 1000, decay_rate=1, staircase=False
)
early_stopping = tf.keras.callbacks.EarlyStopping(
monitor="loss", patience=3, mode="min", min_delta=0.0001
)
model.compile(
loss=tf.losses.MeanSquaredError(),
optimizer=tf.optimizers.Adam(lr_schedule),
metrics=[tf.metrics.MeanAbsoluteError()],
)
model.fit(
w1.train,
epochs=MAX_EPOCHS,
shuffle=False,
validation_data=w1.val,
callbacks=[early_stopping],
verbose=1,
)
return model
def predict(
self, unfiltered_dataframe: DataFrame, dk: FreqaiDataKitchen, first=True
) -> Tuple[DataFrame, DataFrame]:
"""
Filter the prediction features data and predict with it.
:param: unfiltered_dataframe: Full dataframe for the current backtest period.
: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)
"""
dk.find_features(unfiltered_dataframe)
filtered_dataframe, _ = dk.filter_features(
unfiltered_dataframe, dk.training_features_list, training_filter=False
)
filtered_dataframe = dk.normalize_data_from_metadata(filtered_dataframe)
dk.data_dictionary["prediction_features"] = filtered_dataframe
# optional additional data cleaning/analysis
self.data_cleaning_predict(dk, filtered_dataframe)
if first:
full_df = dk.data_dictionary["prediction_features"]
w1 = WindowGenerator(
input_width=self.CONV_WIDTH,
label_width=1,
shift=1,
test_df=full_df,
batch_size=len(full_df),
)
predictions = self.model.predict(w1.inference)
len_diff = len(dk.do_predict) - len(predictions)
if len_diff > 0:
dk.do_predict = dk.do_predict[len_diff:]
else:
data = dk.data_dictionary["prediction_features"]
data = tf.expand_dims(data, axis=0)
predictions = self.model(data, training=False)
predictions = predictions[:, 0, 0]
pred_df = DataFrame(predictions, columns=dk.label_list)
pred_df = dk.denormalize_labels_from_metadata(pred_df)
return (pred_df, np.ones(len(pred_df)))
def create_model(self, input_dims, n_labels) -> Any:
input_layer = Input(shape=(input_dims[1], input_dims[2]))
Layer_1 = Conv1D(filters=32, kernel_size=(self.CONV_WIDTH,), activation="relu")(input_layer)
Layer_3 = Dense(units=32, activation="relu")(Layer_1)
output_layer = Dense(units=n_labels)(Layer_3)
return Model(inputs=input_layer, outputs=output_layer)