ad multiclass target names encoder to ints
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@ -79,7 +79,8 @@
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"test_size": 0.33,
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"random_state": 1
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},
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"model_training_parameters": {}
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"model_training_parameters": {},
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"multiclass_target_names": ["down", "neither", "up"]
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},
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"bot_name": "",
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"force_entry_enable": true,
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@ -1,6 +1,6 @@
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import logging
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from typing import Any, Dict, Tuple
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from typing import Any, Dict, Tuple, List
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import numpy.typing as npt
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import numpy as np
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@ -9,6 +9,7 @@ import torch
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from pandas import DataFrame
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from torch.nn import functional as F
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from freqtrade.exceptions import OperationalException
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from freqtrade.freqai.data_kitchen import FreqaiDataKitchen
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from freqtrade.freqai.base_models.BasePyTorchModel import BasePyTorchModel
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@ -23,13 +24,23 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
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def __init__(self, **kwargs):
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super().__init__(**kwargs)
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# todo move to config
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self.labels = ['0.0', '1.0', '2.0']
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self.n_hidden = 1024
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self.max_iters = 100
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self.batch_size = 64
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self.learning_rate = 3e-4
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self.eval_iters = 10
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self.multiclass_names = self.freqai_info["multiclass_target_names"]
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if not self.multiclass_names:
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raise OperationalException(
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"Missing 'multiclass_names' in freqai_info,"
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" multi class pytorch model requires predefined list of"
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" class names matching the strategy being used"
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)
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self.class_name_to_index = {s: i for i, s in enumerate(self.multiclass_names)}
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self.index_to_class_name = {i: s for i, s in enumerate(self.multiclass_names)}
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model_training_parameters = self.freqai_info["model_training_parameters"]
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self.n_hidden = model_training_parameters.get("n_hidden", 1024)
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self.max_iters = model_training_parameters.get("max_iters", 100)
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self.batch_size = model_training_parameters.get("batch_size", 64)
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self.learning_rate = model_training_parameters.get("learning_rate", 3e-4)
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self.eval_iters = model_training_parameters.get("eval_iters", 10)
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def fit(self, data_dictionary: Dict, dk: FreqaiDataKitchen, **kwargs) -> Any:
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"""
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@ -37,12 +48,13 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
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:param tensor_dictionary: the dictionary constructed by DataHandler to hold
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all the training and test data/labels.
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"""
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n_features = data_dictionary['train_features'].shape[-1]
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self.encode_classes_name(data_dictionary, dk)
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n_features = data_dictionary['train_features'].shape[-1]
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model = PyTorchMLPModel(
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input_dim=n_features,
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hidden_dim=self.n_hidden,
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output_dim=len(self.labels)
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output_dim=len(self.multiclass_names)
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)
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model.to(self.device)
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optimizer = torch.optim.AdamW(model.parameters(), lr=self.learning_rate)
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@ -87,9 +99,34 @@ class PyTorchClassifierMultiTarget(BasePyTorchModel):
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logits = self.model.model(dk.data_dictionary["prediction_features"])
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probs = F.softmax(logits, dim=-1)
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label_ints = torch.argmax(probs, dim=-1)
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predicted_classes = torch.argmax(probs, dim=-1)
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predicted_classes_str = self.decode_classes_name(predicted_classes)
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pred_df_prob = DataFrame(probs.detach().numpy(), columns=self.labels)
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pred_df = DataFrame(label_ints, columns=dk.label_list).astype(float).astype(str)
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pred_df_prob = DataFrame(probs.detach().numpy(), columns=self.multiclass_names)
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pred_df = DataFrame(predicted_classes_str, columns=[dk.label_list[0]])
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pred_df = pd.concat([pred_df, pred_df_prob], axis=1)
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return (pred_df, dk.do_predict)
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def encode_classes_name(self, data_dictionary: Dict[str, pd.DataFrame], dk: FreqaiDataKitchen):
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"""
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encode class name str -> int
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assuming first column of *_labels data frame to contain class names
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"""
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target_column_name = dk.label_list[0]
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for split in ["train", "test"]:
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label_df = data_dictionary[f"{split}_labels"]
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self.assert_valid_class_names(label_df[target_column_name])
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label_df[target_column_name] = list(
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map(lambda x: self.class_name_to_index[x], label_df[target_column_name])
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)
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def assert_valid_class_names(self, labels: pd.Series):
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non_defined_labels = set(labels) - set(self.multiclass_names)
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if len(non_defined_labels) != 0:
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raise OperationalException(
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f"Found non defined labels {non_defined_labels} ",
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f"expecting labels {self.multiclass_names}"
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)
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def decode_classes_name(self, classes: List[int]) -> List[str]:
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return list(map(lambda x: self.index_to_class_name[x], classes))
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