def calc_measures()

in data_measurements/npmi/npmi.py [0:0]

• 22 lines of code
• 3 McCabe index (conditional complexity)

    def calc_measures(self):
        id_results = {}
        for subgroup in self.given_id_terms:
            logs.info("Calculating for %s " % subgroup)
            # Index of the identity term in the vocabulary
            subgroup_idx = self.vocabulary.index(subgroup)
            print("idx is %s" % subgroup_idx)
            logs.debug("Calculating co-occurrences...")
            vocab_cooc_df = self.calc_cooccurrences(subgroup, subgroup_idx)
            logs.debug("Calculating PMI...")
            pmi_df = self.calc_PMI(vocab_cooc_df, subgroup)
            logs.debug("PMI dataframe is:")
            logs.debug(pmi_df)
            logs.debug("Calculating nPMI...")
            npmi_df = self.calc_nPMI(pmi_df, vocab_cooc_df, subgroup)
            logs.debug("npmi df is")
            logs.debug(npmi_df)
            # Create a data structure for the identity term associations
            id_results[subgroup] = {"count": vocab_cooc_df,
                                    "pmi": pmi_df,
                                    "npmi": npmi_df}
            logs.debug("results_dict is:")
            print(id_results)
        return id_results