--- /dev/null	Thu Jan 01 00:00:00 1970 +0000
+++ b/lexmapr/pipeline.py	Wed Aug 31 14:32:07 2022 -0400
@@ -0,0 +1,261 @@
+"""Pipeline script"""
+
+import csv, datetime, logging, re, sqlite3, sys
+import lexmapr.create_databases as cdkp
+import lexmapr.ontology_reasoner as ontr
+import lexmapr.pipeline_helpers as helpers
+import lexmapr.pipeline_resources as pipeline_resources
+import lexmapr.run_summary as summarize
+from itertools import permutations
+from collections import OrderedDict
+from nltk.tokenize import word_tokenize
+from lexmapr.definitions import not_provided, arg_bins, ontol_db, ontol_interest
+
+
+# TODO: deal with ambiguous terms: nut milk, dairy (building ENVO_00003862 v dairy food product)
+# TODO: combine ScorLex.csv and misspellings.csv and edit _add_predefined_resources function
+# TODO: decide what to do with same synonym, ex custom-customize vs custom-tradition
+# TODO: make web on database instead of pulling relationships from API?
+# TODO: remove synonyms over time from SynLex and resource_synonyms.csv; edit get_synonyms
+
+def run(run_args):
+    '''Main text processing and mapping pipeline'''
+
+    # Add information from EMBL and predefined_resources folder
+    t0 = datetime.datetime.now()
+    global ontol_interest
+    if run_args.embl_ontol:
+        ontol_interest = run_args.embl_ontol
+
+    print('\nBuilding databases...')
+    cdkp.get_synonyms(run_args.remake_cache, ontol_interest)
+    cdkp.get_resource_ids(run_args.remake_cache, ontol_interest)
+    lookup_table = pipeline_resources.get_predefined_resources()
+    t1 = datetime.datetime.now()
+    print(f'\tDone! {t1-t0} passed'.ljust(60) + '\nMapping terms...')
+    logging.info(f'Database build/confirm: {t1}')
+
+    # Apply other arguments and initiate mapping cache
+    term_cache = {'':'\t\t\t\t',}
+    output_fields = ['Sample_Id',
+                     'Sample_Desc',
+                     'Processed_Sample',
+                     'Annotated_Sample',
+                     'Matched_Components']
+    if run_args.full:
+        output_fields += ['Match_Status (Macro Level)',
+                          'Match_Status (Micro Level)',
+                          'Sample_Transformations']
+        term_cache[''] += '\t\t'
+    else:
+        output_fields += ['Match_Status (Macro Level)']
+
+    if run_args.bin:
+        global arg_bins
+        if run_args.user_bin is not None:
+            arg_bins = run_args.user_bin
+        for x in arg_bins:
+            arg_bins[x] = ontr.Ontology_package(x, arg_bins[x])
+        term_cache[''] += '\t'*len(arg_bins)
+        output_fields += list(arg_bins.keys())
+    else:
+        arg_bins = {}
+
+    OUT_file = open(run_args.output, 'w') if run_args.output else sys.stdout
+    if OUT_file is sys.stdout:
+        OUT_file.write('\n')
+    OUT_file.write('\t'.join(output_fields))
+
+    IN_file = open(run_args.input, 'r')
+    if run_args.input[-4:] == '.csv':
+        fr_reader = csv.reader(IN_file, delimiter=',')
+    elif run_args.input[-4:] == '.tsv':
+        fr_reader = csv.reader(IN_file, delimiter='\t')
+    next(fr_reader)
+    
+    # Connect to primary database
+    conn = sqlite3.connect(ontol_db)
+    c = conn.cursor()
+
+    # Iterate over samples in input file
+    for sample_row in fr_reader:
+        sample_id = '\n' + sample_row[0].strip() + '\t' + ','.join(sample_row[1:])
+        original_sample = ' '.join(sample_row[1:]).strip()
+        cleaned_sample = ''
+        cleaned_annotated = ''
+        macro_status = 'No Match'
+        matched_components = []
+        synonym_match = []
+        micro_status = []
+        bin_class = {x:[] for x in arg_bins}
+        ancestors = set()
+        sample_conversion_status = {}
+        synonym_map = {}
+        treated_sample = helpers.punctuation_treatment(original_sample)
+
+        # Determine if sample in predefined list of null values
+        if treated_sample in not_provided:
+            write_line = '\t' + treated_sample + '\tNot annotated\t\t' + macro_status
+            OUT_file.write(sample_id + write_line)
+            if run_args.full:
+                OUT_file.write('\t' + str(micro_status) + '\t' + str(sample_conversion_status))
+            if run_args.bin:
+                OUT_file.write('\t'*len(bin_class))
+            continue
+
+        # Remove negated words and some compound words, apply corrections
+        proc_sample = helpers.further_cleanup(treated_sample)
+        proc_sample, micro_status = helpers.process_sample(proc_sample,lookup_table,micro_status)
+
+        # Try finding processed sample in cache
+        try:
+            OUT_file.write(sample_id+term_cache[proc_sample])
+            continue
+        except(KeyError):
+            pass
+
+        # Attempt full term matches with and without suffixes
+        if OUT_file is not sys.stdout:
+            print('\tMatching '+sample_row[0].strip()+' '+ \
+                                '{:<40}'.format(proc_sample[:40]).ljust(60), end='\r')
+
+        full_term_match = helpers.map_term(treated_sample, lookup_table, c)
+        if full_term_match == []:
+            full_term_match = helpers.map_term(proc_sample, lookup_table, c)
+            if full_term_match != []:
+                micro_status.insert(0, 'Used Processed Sample')
+        if full_term_match == [] and 'FOODON' in ontol_interest:
+            full_term_match = helpers.map_term(proc_sample, lookup_table, c, True)
+            if full_term_match != []:
+                micro_status.insert(0, 'Used Processed Sample')
+
+        # Attempt full term match with cleaned sample using suffixes
+        if full_term_match == []:
+            for sw_token in word_tokenize(proc_sample):
+                if helpers.is_date(sw_token) or helpers.is_number(sw_token):
+                    continue
+                lemma, micro_status = helpers.singularize_token(sw_token, lookup_table,
+                                                               micro_status, c)
+                if not sw_token == lemma:
+                    sample_conversion_status[sw_token] = lemma
+                cleaned_sample = helpers.get_cleaned_sample(cleaned_sample, lemma, lookup_table)
+                # Not de-duplicating tokens because can't account for all legitimate double names
+
+            full_term_match = helpers.map_term(cleaned_sample, lookup_table, c)
+            if full_term_match == [] and 'FOODON' in ontol_interest:
+                full_term_match = helpers.map_term(cleaned_sample, lookup_table, c, True)
+            if full_term_match != []:
+                micro_status.insert(0, 'Used Cleaned Sample')
+
+        # Combine the matched terms
+        if full_term_match != []:
+            for x in full_term_match:
+                matched_components.append(x['term'] + ':' + x['id'])
+                macro_status = 'Full Term Match'
+                micro_status += x['status']
+
+        # Try matching permutations if full term match fails
+        # Functions mostly retained from v 0.7
+        if macro_status == 'No Match':
+            covered_tokens = set()
+            for i in range(5, 0, -1):
+                for gram_chunk in helpers.get_gram_chunks(cleaned_sample, i):
+                    concat_gram_chunk = ' '.join(gram_chunk)
+                    gram_permutations =\
+                        list(OrderedDict.fromkeys(permutations(concat_gram_chunk.split())))
+                    if set(gram_chunk) <= covered_tokens:
+                        continue
+                    for gram_permutation in gram_permutations:
+                        gram_permutation_str = ' '.join(gram_permutation)
+                        component_match = helpers.map_term(gram_permutation_str, lookup_table, c)
+                        if not component_match and 'FOODON' in ontol_interest:
+                            component_match = helpers.map_term(gram_permutation_str,
+                                                         lookup_table, c, True)
+                        if component_match:
+                            for x in component_match:
+                                matched_components.append(x['term'] + ':' + x['id'])
+                                macro_status = 'Component Match'
+                                micro_status += x['status']
+                                covered_tokens.update(gram_chunk)
+
+        # Try matching annotated synonyms if component match fails
+        if macro_status == 'No Match':
+            for clean_token in cleaned_sample.split():
+                cleaned_annotated,s_m=helpers.get_annotated_sample(cleaned_annotated,clean_token)
+                synonym_map.update(s_m)
+        cleaned_annotated = helpers.annotation_reduce(cleaned_annotated, synonym_map)
+
+        for x in helpers.get_annotated_synonyms(cleaned_annotated):
+            synonym_match.extend(helpers.map_term(x, lookup_table, c))
+        if synonym_match == [] and 'FOODON' in ontol_interest:
+            for x in helpers.get_annotated_synonyms(cleaned_annotated):
+                synonym_match.extend(helpers.map_term(x, lookup_table, c, True))
+        if synonym_match != []:
+            macro_status = 'Synonym Match'
+        for x in synonym_match:
+            matched_components.append(x['term'] + ':' + x['id'])
+            micro_status += x['status']
+
+        # Remove matches that are ancestral to other matches
+        if run_args.no_ancestors:
+            for match_term in matched_components:
+                match_term = match_term.replace('NCBITAXON','NCBITaxon')
+                ontol_acc = ontr.Ontology_accession.make_instance(match_term)
+                ontol_anc = ontol_acc.get_family('ancestors')
+                try:
+                    ontol_anc.remove('none found')
+                except(ValueError):
+                    pass
+                ancestors |= set([x.id for x in ontol_anc])
+
+            final_matches = []
+            for match_term in matched_components:
+                if match_term.split(':')[-1].replace('NCBITAXON','NCBITaxon') not in ancestors:
+                    final_matches.append(match_term)
+
+        # Bin matches
+        for x in arg_bins:
+            for y in matched_components:
+                ontol_y = ontr.Ontology_accession.make_instance(y)
+                bin_class[x].extend(ontol_y.bin_term(arg_bins[x]))
+
+        # Write to output
+        if cleaned_annotated == '':
+            cleaned_annotated = 'Not annotated'
+        write_line = '\t' + cleaned_sample + '\t' + cleaned_annotated +\
+                     '\t' + '|'.join(sorted(set(matched_components))) + '\t' + macro_status
+        while re.search('  ', write_line):
+            write_line = write_line.replace('  ',' ')
+        term_cache[proc_sample] = write_line
+        OUT_file.write(sample_id + write_line)
+        if run_args.full:
+            OUT_file.write('\t' + str(micro_status) + '\t' + str(sample_conversion_status))
+            term_cache[proc_sample] += '\t'+str(micro_status)+'\t'+str(sample_conversion_status)
+        if run_args.bin:
+            for x in list(bin_class):
+                OUT_file.write('\t' + '|'.join(sorted(set(bin_class[x]))).replace('[]',''))
+                term_cache[proc_sample] += '\t' + '|'.join(sorted(set(bin_class[x])))
+
+
+    IN_file.close()
+    conn.close()
+    if OUT_file is not sys.stdout:
+        OUT_file.close()
+    else:
+        OUT_file.write('\n\n')
+
+    # Report results to log and generate graphs
+    t2 = datetime.datetime.now()
+    print(f'\tDone! {t2-t1} passed'.ljust(60) + '\nReporting results...')
+    if run_args.output:
+        summarize.report_results(run_args.output, list(arg_bins.keys()))
+        if run_args.graph == True:
+            summarize.figure_folder()
+            summarize.visualize_results(run_args.output, list(arg_bins.keys()))
+    else:
+        match_counts = summarize.report_cache(term_cache)
+        if run_args.graph == True:
+            summarize.figure_folder()
+            summarize.visualize_cache(match_counts)
+
+    print('\t'+f'Done! {datetime.datetime.now()-t2} passed'.ljust(60)+'\n')