We decided to archive this project and migrate the most important functionality to MLTB2.
This is the machine learning tool box. A collection of userful machine learning tools intended for reuse and extension. The toolbox contains the following modules:
- hyperopt - Hyperopt tool to save and restart evaluations
- keras - Keras (tf.keras) callback for various metrics and various other Keras tools
- lightgbm - metric tool functions for LightGBM
- metrics - several metric implementations
- plot - plot and visualisation tools
- tools - various (i.a. statistical) tools
This module contains a tool function to save and restart Hyperopt evaluations.
This is done by saving and loading the hyperopt.Trials objects.
The usage looks like this:
from mltb.hyperopt import fmin
from hyperopt import tpe, hp, STATUS_OK
def objective(x):
return {
'loss': x ** 2,
'status': STATUS_OK,
'other_stuff': {'type': None, 'value': [0, 1, 2]},
}
best, trials = fmin(objective,
space=hp.uniform('x', -10, 10),
algo=tpe.suggest,
max_evals=100,
filename='trials_file')
print('best:', best)
print('number of trials:', len(trials.trials))
Output of first run:
No trials file "trials_file" found. Created new trials object.
100%|██████████| 100/100 [00:00<00:00, 338.61it/s, best loss: 0.0007185087453453681]
best: {'x': 0.026805013436769026}
number of trials: 100
Output of second run:
100 evals loaded from trials file "trials_file".
100%|██████████| 100/100 [00:00<00:00, 219.65it/s, best loss: 0.00012259809712488858]
best: {'x': 0.011072402500130158}
number of trials: 200
This module implements metric functions that are not included in LightGBM. At the moment this is the F1- and accuracy-score for binary and multi class problems. The usage looks like this:
bst = lgb.train(param,
train_data,
valid_sets=[validation_data]
early_stopping_rounds=10,
evals_result=evals_result,
feval=mltb.lightgbm.multi_class_f1_score_factory(num_classes, 'macro'),
)
This module provides custom metrics in form of a callback.
Because the callback adds these values to the internal logs dictionary it is
possible to use the EarlyStopping callback to do early stopping on these metrics.
| Parameter | Description | Type | Default values |
|---|---|---|---|
| val_data | Validation input | list | |
| val_label | Validation output | list | |
| pos_label | Which index is the positive label | Optional[int] | 1 |
| metrics | List of supported metric names or custom metric functions | List[Union[str, Callable]] | ['val_roc_auc', 'val_average_precision', 'val_f1', 'val_acc'] |
- val_roc_auc : ROC-AUC
- val_f1 : F1-score
- val_acc: Accuracy
- val_average_precision: Average precision
- val_mcc: Matthews correlation coefficient
The usage looks like this:
bcm_callback = mltb.keras.BinaryClassifierMetricsCallback(val_data, val_labels)
es_callback = callbacks.EarlyStopping(monitor='val_roc_auc', patience=5, mode='max')
history = network.fit(train_data, train_labels,
epochs=1000,
batch_size=128,
#do not give validation_data here or validation will be done twice
#validation_data=(val_data, val_labels),
#always provide BinaryClassifierMetricsCallback before the EarlyStopping callback
callbacks=[bcm_callback, es_callback],
)You can also define your own custom metric:
def custom_average_recall_score(y_true, y_pred, pos_label):
rounded_pred = np.rint(y_pred)
return sklearn.metrics.recall_score(y_true, rounded_pred, pos_label)
bcm_callback = mltb.keras.BinaryClassifierMetricsCallback(val_data, val_labels,metrics=[custom_average_recall_score])
es_callback = callbacks.EarlyStopping(monitor='custom_average_recall_score', patience=5, mode='max')
history = network.fit(train_data, train_labels,
epochs=1000,
batch_size=128,
#do not give validation_data here or validation will be done twice
#validation_data=(val_data, val_labels),
#always provide BinaryClassifierMetricsCallback before the EarlyStopping callback
callbacks=[bcm_callback, es_callback],
)
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