# Training

Import two logs (low-level event log and related high-level eventlog) and train a new ECSEA model. Afterwards, this
model can be applied to another low-level log.
Furthermore, the model can exported and reused.

```python
import pm4py
from ecsea import train_model, ParameterKey, get_default_parameter, validate_parameter

# Load the event logs
low_level_log = pm4py.read_xes('resources/test_data/test-low-level-log.xes')
high_level_log = pm4py.read_xes('resources/test_data/test-high-level-log.xes')
# Train the model with the standard parameter
model = train_model(low_level_log, high_level_log)
# OR: Optional: Add a dict with the parameters as third argument
parameter = get_default_parameter()
parameter[ParameterKey.PARAM_TIME_SPAN] = 5000
# Optional: Validate the parameters
validate_parameter(parameter)
model = train_model(low_level_log, high_level_log, parameter=parameter)
# Export the model
model.export("model.pickle")
```

See [Training API](api.html#module-ecsea.training) for details.

## Parameters

Parameters that can be used in the training phase:

See [CLI](cli.html#parameters) for the description of the various parameters.

## Optimization
You can optimize the parameter by conducting a hyperparameter-optimization:

```python
import pm4py
from src.ecsea import optimize, ParameterKey, get_default_parameter

# Load the event logs
low_level_log = pm4py.read_xes('resources/test_data/test-low-level-log.xes')
high_level_log = pm4py.read_xes('resources/test_data/test-high-level-log.xes')

parameter = get_default_parameter()
parameter[ParameterKey.PARAM_GROUP_ATTRIBUTES] = []
optimize(low_level_log, high_level_log, parameter, n_trials=10)
```

See [optimize API](api.html#module-ecsea.optimize) for details.