mirror of
https://github.com/webclinic017/drift.git
synced 2026-07-27 18:57:55 +00:00
Mark Aron Szulyovszky
1d6eed3a94
* feat(Core): added the first classification model & the feature necessary * feat(Models): added basic transformers model
122 lines
3.5 KiB
Python
122 lines
3.5 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_pos_neg_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('data/', add_features=True, log_returns=False)
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data.reset_index(drop=True, inplace=True)
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data = data[[column for column in data.columns if not column.endswith('volume')]]
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# data = data[["BTC_returns", "BTC_mom_10", "BTC_mom_20", "BTC_mom_30", "BTC_mom_60", "BTC_vol_10", "BTC_vol_20", "BTC_vol_60", "day_month", "day_week", "month"]]
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target_col = 'target'
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data = create_target_pos_neg_classes(data, 'ETH_returns', 30)
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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 = 10
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future = 1
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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(10):
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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_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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model.compile(optimizer=optimizer, loss="categorical_crossentropy", 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") |