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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def induce(self, mode, pos, neg, b, filestem='default', printOutput=False):
 """ Induce a theory or features in 'mode'. :param filestem: The base name of this experiment. :param mode: In which mode to induce rules/features. :param pos: String of positive examples. :param neg: String of negative examples. :param b: String of background knowledge. :return: The theory as a string or an arff dataset in induce_features mode. :rtype: str """

        # Write the inputs to appropriate files.
        self.__prepare(filestem, pos, neg, b)

        # Make a script to run aleph (with appropriate settings).
        self.__script(mode, filestem)

        logger.info("Running aleph...")

        dumpFile = None
        if not printOutput:
            dumpFile = tempfile.TemporaryFile()

        # Run the aleph script.
        p = SafePopen(['yap', '-s50000', '-h200000', '-L', Aleph.SCRIPT],
                      cwd=self.tmpdir,
                      stdout=dumpFile,
                      stderr=dumpFile
                      ).safe_run()
        stdout_str, stderr_str = p.communicate()

        logger.info("Done.")

        result = None
        if mode != 'induce_features':
            # Return the rules written in the output file.
            rules_fn = filestem + Aleph.RULES_SUFFIX
            result = open('%s/%s' % (self.tmpdir, rules_fn)).read()
            features = None
        else:
            features_fn = filestem + Aleph.FEATURES_SUFFIX
            features = open('%s/%s' % (self.tmpdir, features_fn)).read()
            dataset_fn = filestem + Aleph.PROP_DATASET_SUFFIX
            pl_dataset = open('%s/%s' % (self.tmpdir, dataset_fn)).read()
            result = self.__to_arff(features, pl_dataset, filestem)

        # Cleanup.
        self.__cleanup()
        return (result, features)