<|file_name|>scikitbase.py<|end_file_name|><｜fim▁begin｜>from abc import ABC
import configargparse
from sklearn.externals import joblib
from termcolor import colored


class ScikitBase(ABC):
    """
    Base class for AI strategies
    """
    arg_parser = configargparse.get_argument_parser()
    arg_parser.add('-p', '--pipeline', help='trained model/pipeline (*.pkl file)', required=True)
    arg_parser.add('-f', '--feature_names', help='List of features list pipeline (*.pkl file)')
    pipeline = None

    def __init__(self):
        args = self.arg_parser.parse_known_args()[0]
        super(ScikitBase, self).__init__()
        self.pipeline = self.load_pipeline(args.pipeline)
        if args.feature_names:
            self.feature_names = self.load_pipeline(args.feature_names)

    @staticmethod
    def load_pipeline(pipeline_file):
        """
        Loads scikit model/pipeline
        """
        print(colored('Loading pipeline: ' + pipeline_file, 'green'))
        return joblib.load(pipeline_file)

    def fetch_pipeline_from_server(self):
        """
        Method fetches pipeline from server/cloud
        """
        # TODO
        pass

    def predict(self, df):
        <|fim_middle|>


<｜fim▁end｜>
        """
        Returns predictions based on the model/pipeline
        """
        try:
            return self.pipeline.predict(df)
        except (ValueError, TypeError):
            print(colored('Got ValueError while using scikit model.. ', 'red'))
            return None