From 76fbec0c175714148cead01a6c85f36d91de3244 Mon Sep 17 00:00:00 2001 From: Yinon Polak Date: Wed, 8 Mar 2023 14:29:38 +0200 Subject: [PATCH] ad multiclass target names encoder to ints --- config_examples/config_freqai.example.json | 3 +- .../PyTorchClassifierMultiTarget.py | 63 +++++++++++++++---- 2 files changed, 52 insertions(+), 14 deletions(-) diff --git a/config_examples/config_freqai.example.json b/config_examples/config_freqai.example.json index 65a93379e..479e94aa3 100644 --- a/config_examples/config_freqai.example.json +++ b/config_examples/config_freqai.example.json @@ -79,7 +79,8 @@ "test_size": 0.33, "random_state": 1 }, - "model_training_parameters": {} + "model_training_parameters": {}, + "multiclass_target_names": ["down", "neither", "up"] }, "bot_name": "", "force_entry_enable": true, diff --git a/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py b/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py index 9504fffb8..aead0e46c 100644 --- a/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py +++ b/freqtrade/freqai/prediction_models/PyTorchClassifierMultiTarget.py @@ -1,6 +1,6 @@ import logging -from typing import Any, Dict, Tuple +from typing import Any, Dict, Tuple, List import numpy.typing as npt import numpy as np @@ -9,6 +9,7 @@ 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 @@ -23,13 +24,23 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel): def __init__(self, **kwargs): super().__init__(**kwargs) - # todo move to config - self.labels = ['0.0', '1.0', '2.0'] - self.n_hidden = 1024 - self.max_iters = 100 - self.batch_size = 64 - self.learning_rate = 3e-4 - self.eval_iters = 10 + self.multiclass_names = self.freqai_info["multiclass_target_names"] + if not self.multiclass_names: + raise OperationalException( + "Missing 'multiclass_names' in freqai_info," + " multi class pytorch 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)} + + 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: """ @@ -37,12 +48,13 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel): :param tensor_dictionary: the dictionary constructed by DataHandler to hold all the training and test data/labels. """ - n_features = data_dictionary['train_features'].shape[-1] + 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.labels) + output_dim=len(self.multiclass_names) ) model.to(self.device) optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate) @@ -87,9 +99,34 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel): logits = self.model.model(dk.data_dictionary["prediction_features"]) probs = F.softmax(logits, dim=-1) - label_ints = torch.argmax(probs, 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.labels) - pred_df = DataFrame(label_ints, columns=dk.label_list).astype(float).astype(str) + 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: List[int]) -> List[str]: + return list(map(lambda x: self.index_to_class_name[x], classes)) \ No newline at end of file