mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-28 03:08:01 +00:00
Mark Aron Szulyovszky
7aedb91069
* feat(Eval): added format_data_for_backtest() * feat(Data): added many configurable parameters to load_files to reduce boilerplate and prepare for HPO * feat(Core): added walk forward method of training/testing * fix(Model): remove the unnecessary softmax activation from the keras models * feat(Core): added walk_forward_train_test()
132 lines
3.7 KiB
Python
132 lines
3.7 KiB
Python
#%% Import all the stuff, load data, define constants
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from sklearn.utils import shuffle
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from load_data import load_files, create_target_classes
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import pandas as pd
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from tensorflow import keras
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from utils.normalize import normalize
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import tensorflow as tf
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from utils.visualize import visualize_loss
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from sklearn.preprocessing import MinMaxScaler
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from utils.evaluate import print_classification_metrics
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import numpy as np
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from utils.rolling import rolling_window
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from keras_models.classification import create_basic_cnn_model, create_basic_lstm_model, create_resnet_cnn_model
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from keras_models.classification_transformer import create_basic_transformer_model
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data = load_files(path='data/',
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own_asset='BTC_ETH',
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load_other_assets=True,
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log_returns=False,
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add_date_features=True,
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own_technical_features='level2',
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other_technical_features='level2',
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exogenous_features='none',
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index_column='int'
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)
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target_col = 'target'
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data = create_target_classes(data, 'BTC_ETH_returns', 1, 'two')
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num_classes = 2
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learning_rate = 0.002
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batch_size = 64
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epochs = 100
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split_fraction = 0.8
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train_split = int(split_fraction * int(data.shape[0]))
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past = 60
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future = 10
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start = past + future
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end = start + train_split
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#%% split data into training - validation sets
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train_data = data.loc[0 : train_split - 1]
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val_data = data.loc[train_split:]
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#%% create features and target for training set & keras dataset
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feature_scaler = MinMaxScaler(feature_range= (-1, 1))
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x_train = feature_scaler.fit_transform(train_data.drop(target_col, axis=1).values) # you get the mean and std
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y_train = keras.utils.to_categorical(data.iloc[start:end][target_col].values)
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dataset_train = keras.preprocessing.timeseries_dataset_from_array(
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x_train,
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y_train,
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sequence_length=past,
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batch_size=batch_size,
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)
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#%% create features and target for validation set & keras dataset
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x_end = len(val_data) - past - future
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label_start = train_split + past + future
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x_val = feature_scaler.transform(val_data.drop(target_col, axis=1).iloc[:x_end].values) # you use the training data's mean and std
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y_val = keras.utils.to_categorical(data.iloc[label_start:][target_col].values)
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dataset_val = keras.utils.timeseries_dataset_from_array(
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x_val,
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y_val,
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sequence_length=past,
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batch_size=batch_size,
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shuffle=False,
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)
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#%%
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for batch in dataset_train.take(1):
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batch_inputs, batch_targets = batch
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print("Input shape:", batch_inputs.shape)
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print("Target shape:", batch_targets.shape)
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n_timestamps = batch_inputs.shape[1]
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n_features = batch_inputs.shape[2]
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# print(batch_inputs)
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# print(batch_targets)
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# %%
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# model = create_basic_lstm_model(input_shape=(n_timestamps, n_features), num_classes=num_classes)
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# model = create_basic_cnn_model(input_shape=(n_timestamps, n_features), num_classes=num_classes)
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model = create_resnet_cnn_model(input_shape=(n_timestamps, n_features), num_classes=num_classes)
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# model = create_basic_transformer_model(
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# input_shape=(n_timestamps, n_features),
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# n_classes=num_classes,
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# head_size=64,
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# num_heads=4,
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# ff_dim=4,
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# num_transformer_blocks=4,
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# mlp_units=[64],
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# mlp_dropout=0.4,
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# dropout=0.25,
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# )
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optimizer = keras.optimizers.Adam(learning_rate=learning_rate)
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loss = keras.losses.CategoricalCrossentropy(from_logits=True)
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model.compile(optimizer=optimizer, loss=loss, metrics=['accuracy'])
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model.summary()
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# %%
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path_checkpoint = "model_checkpoint.h5"
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history = model.fit(
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dataset_train,
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epochs=epochs,
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validation_data=dataset_val,
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)
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#%%
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# pred = model.predict(rolling_window(x_val, 11))
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# pred = pred.reshape(pred.shape[0], 1)
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#%%
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# print_classification_metrics(y_val, pred)
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visualize_loss(history, "Training and Validation Loss") |