update example code

Author: Your Name

microFIM_example_code_1.py

@@ -0,0 +1,79 @@
+import os
+import sys
+import pandas as pd
+import numpy as np
+import csv
+from csv import writer
+import readline
+import re
+import string
+
+import fim
+import functions.microdir as md
+import functions.microfim as mf
+import functions.microimport as mi
+import functions.microinterestmeasures as mim
+
+
+""" microFIM example code on test/test1.csv files
+of microFIM github repository
+Input files to run microFIM:
+- test2.csv
+- metadata_test2.csv
+- parameters_test2.csv
+
+"""
+
+# setting files
+## dir
+set_dir = 'test'
+
+## metadata
+metadata = 'metadata_test2.csv'
+meta_sep = ','
+
+## otu/esv/taxa table
+data_table_name = 'test2.csv'
+data_sep = ','
+
+# SETTINGS
+## set dir
+data_dir = md.set_inputs_dir_rev(set_dir)
+print(data_dir)
+
+## SET OUTPUT NAME
+file_name = mi.output_file_name(data_table_name)
+
+# IMPORT FILES
+## import metadata
+metadata = mi.import_metadata(metadata, data_dir, meta_sep)
+print(metadata)
+
+## import data table (otu, esv or taxa table)
+data_table = mi.import_data_table(data_table_name, data_dir, data_sep)
+print(data_table)
+
+
+# FILTER DATA TABLE VIA SAMPLE METADATA
+filter_table = mf.filter_data_table(metadata, data_table)
+print(filter_table)
+
+
+# CONVERT DATA TABLE IN TRANSACTIONAL data
+
+t_list = mf.convert_in_transaction_list(filter_table, data_table_name)
+print(t_list)
+
+# save file
+mf.save_transaction_list(data_dir, t_list, file_name)
+
+
+# TO BE PRINT WHEN RUNNING THIS SCRIPT
+# remove old output to clean folder
+output = 'transactions_' + file_name[0]
+
+print(f'\n\n> File converted and saved as ' + output + '.csv' + ' in ' + data_dir + '\n\n')
+
+print(f'\n\n> Now run from your command line in {data_dir}:\n\n \
+sed -i -e "s/,/ /g" {output}\n\n \
+rm {output}-e\n\n')

microFIM_example_code_2.py

@@ -0,0 +1,136 @@
+import os
+import sys
+import pandas as pd
+import numpy as np
+import csv
+from csv import writer
+import readline
+import re
+import string
+
+import fim
+import functions.microdir as md
+import functions.microfim as mf
+import functions.microimport as mi
+import functions.microinterestmeasures as mim
+
+
+""" microFIM example code on test/test2.csv files
+of microFIM github repository
+Input files to run microFIM:
+- test2.csv
+- transactional file 2 (can be obtained with microFIM_example_code_1.py)
+- metadata_test2.csv
+- parameters_test2.csv
+
+Default is itemsets patterns (i), but also closed (c) and maximal (m) can be calculated.
+
+Finally, two pattern tables can be generated:
+- complete pattern table (patterns + interest measures)
+- clean pattern table
+
+"""
+
+# set fim options
+to_calculate = 'i' # default (can be changed in c or m)
+
+# setting files
+## dir
+set_dir = 'test'
+
+## metadata
+metadata = 'metadata_test2.csv'
+meta_sep = ','
+
+## otu/esv/taxa table
+data_table_name = 'test2.csv'
+data_sep = ','
+
+## parameter file
+par_file = 'parameters_test2.csv'
+
+# transactional file
+trans_file = 'transactions_test2'
+
+# set output name
+output_file = 'patterns_test2'
+add_interest_file = 'addm_patterns_test2'
+output_pattern_table = 'pattern_table_test2'
+
+
+# SETTINGS
+## set dir
+data_dir = md.set_inputs_dir_rev(set_dir)
+
+## SET OUTPUT NAME
+file_name = mi.output_file_name(data_table_name)
+
+
+# import files
+
+# IMPORT FILES
+## import metadata
+metadata = mi.import_metadata(metadata, data_dir, meta_sep)
+
+## import data table (otu, esv or taxa table)
+data_table = mi.import_data_table(data_table_name, data_dir, data_sep)
+
+# import parameters file
+
+## import transactions
+t = mf.read_transaction(os.path.join(data_dir, trans_file))
+
+## import file with paramaters
+minsupp, zmin, zmax= mi.itemsets_parameters(data_dir, par_file)
+
+
+# FILTER DATA TABLE VIA SAMPLE METADATA
+filter_table = mf.filter_data_table(metadata, data_table)
+
+
+# import transactions and file with paramaters
+t = mf.read_transaction(os.path.join(data_dir, trans_file))
+print(t)
+
+minsupp, zmin, zmax= mi.itemsets_parameters(data_dir, par_file)
+
+
+# calculate patterns
+results = mf.fim_calculation(t, to_calculate, minsupp, zmin, zmax)
+
+
+# write patterns results
+file, out_file, new_out_file = mf.write_results(results, data_dir, output_file)
+
+
+# convert itemsets results into a dataframe
+df = mf.itemsets_dataframe(new_out_file)
+print(df)
+
+# export as a csv
+mf.export_dataframe(df, data_dir, output_file)
+
+print('Results saved as ' + new_out_file + ' in ' + data_dir + '\n\n')
+
+
+
+# CALCULATE ADDITIONAL METRICS
+## import patterns dataframe
+df = mi.import_pattern_dataframe(data_dir, output_file)
+
+# calculate and add all-confidence values
+data_allc_update = mim.add_interest_measures(data_table, df, trans_file, data_dir)
+
+# export dataframe
+mim.add_table_export(data_allc_update, data_dir, add_interest_file)
+print('Results saved as df_' + add_interest_file + '.csv in ' + data_dir + '\n\n')
+
+
+
+# GENERATE PATTERN TABLE
+## generate pattern table with patterns and interest measures
+pattern_table_complete = mf.generate_pattern_table(data_allc_update, df, data_dir, trans_file, metadata, meta_sep)
+print(pattern_table_complete)
+
+## export pattern table as cv
+mf.export_pattern_tables(pattern_table_complete, data_dir, output_pattern_table)