Metadata-Version: 2.1
Name: geot_cluster
Version: 0.9.29
Summary: Reasearch project on clustering clients from transaction history
Home-page: https://github.com/LarsWallenstein/Geotrans_cluster
Author: Korsakov Aleksandr
Author-email: corsakov.alex@yandex.ru
License: UNKNOWN
Description: *********************************************
        Geographical Transctions clustering algorithm
        *********************************************
        
        Name of the module stands for geographical transactions clustering. This module is an implementation of the method, developed for the third course project in HSE University. It takes dataframe with clients transactions history of the
        specified format and returns list of clusters.
        
        For the record, it was intended to be for public usage in this form, as it is a research project seeking to find a way to deal with the described problem
        
        Installation
        ############
        
        Run the following to install:
        
        '''python
        
            pip install geot_cluster'''
        
        
        Usage
        #####
        
        Before using make sure, that your dataset corresponds with requirements. Csv file must contain the following columns in order to work correctly
        
        * user_id   :   string type, example: "423156821"
        * event_dt  :   string type, example: "20190312"
        * event_time:   string type, example: "2019-03-12 06:24:00.279"
        * lattitude :   float  type, example: 49.862621
        * longtitude:   see lattitude
        
        Workflow::
        
        
            import geot_cluster
        
            import markov_clustering as mc
            import matplotlib.pyplot as plt
            import networkx as nx
        
          
            path = [path to file with data]
            data, names = geot_cluster.data_load(path)
        
        
            %matplotlib notebook
            base = [path to the folder, where to store libs with information about clients]
        
            archivate = True
            libs= True
            graph_f = True
            cluster_f = True
        
        
            if(archivate):
                geot_cluster.archivate_maps(data, names, levels=4)
        
            if(libs):
                lib = geot_cluster.graph_preparation(data, names, base) 
                prob_lib = geot_cluster.znakomstvo_by_lib(lib,data)
        
            lib, prob_lib = load_libs(base = base)
            
            if(graph_f):
                graph = geot_cluster.graph_forming(lib, prob_lib, treshold=0.9)
        
            if(cluster_f):
                result = mc.run_mcl(graph,pruning_threshold=0.7, inflation=2,expansion=2) 
                clusters = mc.get_clusters(result)
        
                clust_0 = clusters_to_ids(lib=lib, prob_lib=prob_lib, clusters = clusters, number = 0)
                maps = get_cluster_maps(data = data, clust = clust_0)
                print("Number of clusters", len(clusters))
        
                plt.figure(figsize=(10,10))
                mc.drawing.draw_graph(result, clusters, edge_color="red",node_size=15,width = 1, with_labels=True, font_size = 8)
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
