137 lines
5.7 KiB
Python
137 lines
5.7 KiB
Python
import logging
|
|
|
|
from typing import Any, Dict, Tuple, List
|
|
import numpy.typing as npt
|
|
|
|
import numpy as np
|
|
import pandas as pd
|
|
import torch
|
|
from pandas import DataFrame
|
|
from torch.nn import functional as F
|
|
|
|
from freqtrade.exceptions import OperationalException
|
|
from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
|
|
|
|
from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
|
|
from freqtrade.freqai.base_models.PyTorchModelTrainer import PyTorchModelTrainer
|
|
from freqtrade.freqai.prediction_models.PyTorchMLPModel import PyTorchMLPModel
|
|
|
|
|
|
logger = logging.getLogger(__name__)
|
|
|
|
|
|
class PyTorchClassifierMultiTarget(BasePyTorchModel):
|
|
|
|
def __init__(self, **kwargs):
|
|
super().__init__(**kwargs)
|
|
self.multiclass_names = self.freqai_info.get("multiclass_target_names", None)
|
|
logger.info(f"setting multiclass_names: {self.multiclass_names}")
|
|
if not self.multiclass_names:
|
|
raise OperationalException(
|
|
"Missing 'multiclass_names' in freqai_info, "
|
|
"multi class pytorch classifier model requires predefined list of "
|
|
"class names matching the strategy being used."
|
|
)
|
|
|
|
self.class_name_to_index = {s: i for i, s in enumerate(self.multiclass_names)}
|
|
self.index_to_class_name = {i: s for i, s in enumerate(self.multiclass_names)}
|
|
logger.info(f"class_name_to_index: {self.class_name_to_index}")
|
|
|
|
model_training_parameters = self.freqai_info["model_training_parameters"]
|
|
self.n_hidden = model_training_parameters.get("n_hidden", 1024)
|
|
self.max_iters = model_training_parameters.get("max_iters", 100)
|
|
self.batch_size = model_training_parameters.get("batch_size", 64)
|
|
self.learning_rate = model_training_parameters.get("learning_rate", 3e-4)
|
|
self.eval_iters = model_training_parameters.get("eval_iters", 10)
|
|
|
|
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 tensor_dictionary: the dictionary constructed by DataHandler to hold
|
|
all the training and test data/labels.
|
|
"""
|
|
self.encode_classes_name(data_dictionary, dk)
|
|
n_features = data_dictionary['train_features'].shape[-1]
|
|
model = PyTorchMLPModel(
|
|
input_dim=n_features,
|
|
hidden_dim=self.n_hidden,
|
|
output_dim=len(self.multiclass_names)
|
|
)
|
|
model.to(self.device)
|
|
optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
|
|
criterion = torch.nn.CrossEntropyLoss()
|
|
init_model = self.get_init_model(dk.pair)
|
|
trainer = PyTorchModelTrainer(
|
|
model=model,
|
|
optimizer=optimizer,
|
|
criterion=criterion,
|
|
device=self.device,
|
|
batch_size=self.batch_size,
|
|
max_iters=self.max_iters,
|
|
eval_iters=self.eval_iters,
|
|
init_model=init_model
|
|
)
|
|
trainer.fit(data_dictionary)
|
|
return trainer
|
|
|
|
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)
|
|
"""
|
|
|
|
dk.find_features(unfiltered_df)
|
|
filtered_df, _ = dk.filter_features(
|
|
unfiltered_df, dk.training_features_list, training_filter=False
|
|
)
|
|
filtered_df = dk.normalize_data_from_metadata(filtered_df)
|
|
dk.data_dictionary["prediction_features"] = filtered_df
|
|
|
|
self.data_cleaning_predict(dk)
|
|
dk.data_dictionary["prediction_features"] = torch.tensor(
|
|
dk.data_dictionary["prediction_features"].values
|
|
).float().to(self.device)
|
|
|
|
logits = self.model.model(dk.data_dictionary["prediction_features"])
|
|
probs = F.softmax(logits, dim=-1)
|
|
predicted_classes = torch.argmax(probs, dim=-1)
|
|
predicted_classes_str = self.decode_classes_name(predicted_classes)
|
|
|
|
pred_df_prob = DataFrame(probs.detach().numpy(), columns=self.multiclass_names)
|
|
pred_df = DataFrame(predicted_classes_str, columns=[dk.label_list[0]])
|
|
pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
|
|
return (pred_df, dk.do_predict)
|
|
|
|
def encode_classes_name(self, data_dictionary: Dict[str, pd.DataFrame], dk: FreqaiDataKitchen):
|
|
"""
|
|
encode class name str -> int
|
|
assuming first column of *_labels data frame to contain class names
|
|
"""
|
|
target_column_name = dk.label_list[0]
|
|
for split in ["train", "test"]:
|
|
label_df = data_dictionary[f"{split}_labels"]
|
|
self.assert_valid_class_names(label_df[target_column_name])
|
|
label_df[target_column_name] = list(
|
|
map(lambda x: self.class_name_to_index[x], label_df[target_column_name])
|
|
)
|
|
|
|
def assert_valid_class_names(self, labels: pd.Series):
|
|
non_defined_labels = set(labels) - set(self.multiclass_names)
|
|
if len(non_defined_labels) != 0:
|
|
raise OperationalException(
|
|
f"Found non defined labels: {non_defined_labels}, ",
|
|
f"expecting labels: {self.multiclass_names}"
|
|
)
|
|
|
|
def decode_classes_name(self, classes: torch.Tensor[int]) -> List[str]:
|
|
"""
|
|
decode class name int -> str
|
|
"""
|
|
return list(map(lambda x: self.index_to_class_name[x.item()], classes))
|