Automated way

  • Step 1. Specify problem type, create the FEDOT model and load the datasets.

import pandas as pd

# specify additional training parameters
timeout, with_tuning, n_jobs, logging_level = ...  # tested with 3, False, 1, logging.FATAL

Hint

See the API documentation for descriptions of these and others parameters.

# build model
auto_model = Fedot(
   problem='classification', timeout=timeout, preset='fast_train',
   with_tuning=with_tuning, n_jobs=n_jobs, loggging_level=logging_level,
   seed=42
)

# add all datasets paths and load datasets
train_file_path: Union[str, os.Pathlike] = ...
validation_file_path: Union[str, os.Pathlike] = ...
# tested with default scoring classification from FEDOT's datasets

dataset_to_train = pd.read_csv(train_file_path)
dataset_to_validate = pd.read_csv(validation_file_path)

# specify target column and validation data
target_col: str = ...  # 'target' by default
validation_target = dataset_to_validate[target_col]

  • Step 2. Fit the model’s pipeline.

pipeline = auto_model.fit(features=dataset_to_train, target=target_col)

  • Step 3. Obtain the prediction and calculate the metrics.

# get the prediction
prediction = auto_model.predict(features=dataset_to_validate)

# calculate the scores
auto_metrics = auto_model.get_metrics(validation_target)
print(f'metrics: {auto_metrics}')
>>> metrics: {'roc_auc': 0.833, 'f1': 0.936}

Congratulations! We’ve just automatically built a machine learning pipeline with FEDOT. This time our pipeline outperforms the assumptions from the previous example. To continue exploring the framework, there are pages with more detailed information on using FEDOT: