151 lines
5.0 KiB
Python
151 lines
5.0 KiB
Python
import logging
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from time import time
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from typing import Any
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from pandas import DataFrame
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import numpy as np
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.freqai_interface import IFreqaiModel
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import tensorflow as tf
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logger = logging.getLogger(__name__)
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class BaseTensorFlowModel(IFreqaiModel):
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"""
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Base class for TensorFlow type models.
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User *must* inherit from this class and set fit() and predict().
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"""
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def __init__(self, **kwargs):
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super().__init__(config=kwargs['config'])
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self.keras = True
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if self.ft_params.get("DI_threshold", 0):
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self.ft_params["DI_threshold"] = 0
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logger.warning("DI threshold is not configured for Keras models yet. Deactivating.")
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def train(
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self, unfiltered_df: DataFrame, pair: str, dk: FreqaiDataKitchen, **kwargs
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) -> Any:
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"""
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Filter the training data and train a model to it. Train makes heavy use of the datakitchen
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for storing, saving, loading, and analyzing the data.
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:param unfiltered_df: Full dataframe for the current training period
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:param metadata: pair metadata from strategy.
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:return:
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:model: Trained model which can be used to inference (self.predict)
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"""
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logger.info(f"-------------------- Starting training {pair} --------------------")
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start_time = time()
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# filter the features requested by user in the configuration file and elegantly handle NaNs
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features_filtered, labels_filtered = dk.filter_features(
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unfiltered_df,
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dk.training_features_list,
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dk.label_list,
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training_filter=True,
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)
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start_date = unfiltered_df["date"].iloc[0].strftime("%Y-%m-%d")
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end_date = unfiltered_df["date"].iloc[-1].strftime("%Y-%m-%d")
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logger.info(f"-------------------- Training on data from {start_date} to "
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f"{end_date} --------------------")
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# split data into train/test data.
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data_dictionary = dk.make_train_test_datasets(features_filtered, labels_filtered)
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if not self.freqai_info.get("fit_live_predictions", 0) or not self.live:
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dk.fit_labels()
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# normalize all data based on train_dataset only
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data_dictionary = dk.normalize_data(data_dictionary)
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# optional additional data cleaning/analysis
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self.data_cleaning_train(dk)
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logger.info(
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f"Training model on {len(dk.data_dictionary['train_features'].columns)} features"
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)
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logger.info(f"Training model on {len(data_dictionary['train_features'])} data points")
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model = self.fit(data_dictionary, dk)
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end_time = time()
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logger.info(f"-------------------- Done training {pair} "
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f"({end_time - start_time:.2f} secs) --------------------")
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return model
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class WindowGenerator:
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def __init__(
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self,
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input_width,
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label_width,
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shift,
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train_df=None,
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val_df=None,
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test_df=None,
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train_labels=None,
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val_labels=None,
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test_labels=None,
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batch_size=None,
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):
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# Store the raw data.
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self.train_df = train_df
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self.val_df = val_df
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self.test_df = test_df
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self.train_labels = train_labels
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self.val_labels = val_labels
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self.test_labels = test_labels
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self.batch_size = batch_size
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self.input_width = input_width
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self.label_width = label_width
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self.shift = shift
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self.total_window_size = input_width + shift
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self.input_slice = slice(0, input_width)
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self.input_indices = np.arange(self.total_window_size)[self.input_slice]
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def make_dataset(self, data, labels=None):
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data = np.array(data, dtype=np.float32)
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if labels is not None:
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labels = np.array(labels, dtype=np.float32)
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ds = tf.keras.preprocessing.timeseries_dataset_from_array(
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data=data,
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targets=labels,
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sequence_length=self.total_window_size,
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sequence_stride=1,
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sampling_rate=1,
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shuffle=False,
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batch_size=self.batch_size,
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)
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return ds
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@property
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def train(self):
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return self.make_dataset(self.train_df, self.train_labels)
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@property
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def val(self):
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return self.make_dataset(self.val_df, self.val_labels)
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@property
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def test(self):
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return self.make_dataset(self.test_df, self.test_labels)
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@property
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def inference(self):
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return self.make_dataset(self.test_df)
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@property
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def example(self):
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"""Get and cache an example batch of `inputs, labels` for plotting."""
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result = getattr(self, "_example", None)
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if result is None:
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# No example batch was found, so get one from the `.train` dataset
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result = next(iter(self.train))
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# And cache it for next time
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self._example = result
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return result
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