def instantiate_networkdata_class(dataset_domain, dataset_path, impl_type, max_core_nodes, cutoff_rating, store_dataset, interact_type_val, min_interacts_beforeaftersplit_per_user): data = None #h = hpy() #h.setref() if dataset_domain == "twitter": data = HashtagDataPreparser(dataset_path, impl_type) elif dataset_domain== "lastfm": data = LastfmDataPreparserCSV(dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, use_artists=False) elif dataset_domain== "lastfm_simple": data = LastfmDataPreparserSimple(dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, use_artists=False, interact_type_val=interact_type_val, min_interactions_per_user=min_interacts_beforeaftersplit_per_user*2) elif dataset_domain== "lastfm_lovelisten": data = LastfmDataPreparserLovelisten(dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, use_artists=False, interact_type_val=interact_type_val, min_interactions_per_user=min_interacts_beforeaftersplit_per_user*2) elif dataset_domain=="goodreads": data = GoodreadsDataPreparser(dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, min_interactions_per_user = min_interacts_beforeaftersplit_per_user*2) elif dataset_domain=="flixster": data = FlixsterDataPreparser(dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, min_interactions_per_user=min_interacts_beforeaftersplit_per_user*2) elif dataset_domain=="flickr": data = FlickrDataPreparser(dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, min_interactions_per_user=min_interacts_beforeaftersplit_per_user*2) try: data.get_all_data() BasicNetworkAnalyzer(data).show_basic_stats() except: raise return data
node = self.netdata.nodes[node_id] node.get_details(self.netdata.interaction_types[0]) return """ def getItemPopularityInDataset(data): likes = {} for k, v in data.allusers.iteritems(): for itemid, created in v.likes: if itemid not in likes: likes[itemid] = 0 likes[itemid] += 1 likes_hist={} in_sum = 0 for val in likes.values(): if val<100: in_sum += 1 if val not in likes_hist: likes_hist[val] = 0 likes_hist[val] += 1 items_covered_ratio = in_sum /float(len(likes)) return list(likes_hist.iteritems()), items_covered_ratio, likes.values() """ if __name__ == "__main__": data = HashtagDataPreparser("/home/asharma/datasets/ttest/") data.get_all_data() net_analyzer = BasicNetworkAnalyzer(data) net_analyzer.show_basic_stats()
def instantiate_networkdata_class(dataset_domain, dataset_path, impl_type, max_core_nodes, cutoff_rating, store_dataset, interact_type_val, min_interacts_beforeaftersplit_per_user): data = None #h = hpy() #h.setref() if dataset_domain == "twitter": data = HashtagDataPreparser(dataset_path, impl_type) elif dataset_domain == "lastfm": data = LastfmDataPreparserCSV(dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, use_artists=False) elif dataset_domain == "lastfm_simple": data = LastfmDataPreparserSimple( dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, use_artists=False, interact_type_val=interact_type_val, min_interactions_per_user=min_interacts_beforeaftersplit_per_user * 2) elif dataset_domain == "lastfm_lovelisten": data = LastfmDataPreparserLovelisten( dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, use_artists=False, interact_type_val=interact_type_val, min_interactions_per_user=min_interacts_beforeaftersplit_per_user * 2) elif dataset_domain == "goodreads": data = GoodreadsDataPreparser( dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, min_interactions_per_user=min_interacts_beforeaftersplit_per_user * 2) elif dataset_domain == "flixster": data = FlixsterDataPreparser( dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, min_interactions_per_user=min_interacts_beforeaftersplit_per_user * 2) elif dataset_domain == "flickr": data = FlickrDataPreparser( dataset_path, impl_type, cutoff_rating, max_core_nodes, store_dataset, min_interactions_per_user=min_interacts_beforeaftersplit_per_user * 2) try: data.get_all_data() BasicNetworkAnalyzer(data).show_basic_stats() except: raise return data