if len(args) < 1:
print parser.usage
print 'options:', options
print 'args', args
exit()
inname = args[0]
subset = None
if len(options.subset_keys):
subset = [x.strip() for x in options.subset_keys.split(',') if len(x.strip()) > 0]
if subset:
outname = os.path.splitext(inname)[0] + '.subset[%s]' % ','.join(subset)
else:
outname = os.path.splitext(inname)[0] + '.many'
data_dict = cleanDictKeysAndVals(csv.readCsvAsDict(inname))
data_dict_many = filterDict(data_dict, lambda k,v: getNumElements(v) >= len(data_dict['Person.ID'])/2)
data_dict_many = filterDict(data_dict_many, lambda k,v: getNumElementsWithFreq(v,3) >= 2)
if subset:
data_dict_many = filterDict(data_dict, lambda k,v: k in subset + ['Grant.Status'])
data_dict_many['Grant.Status'] = data_dict['Grant.Status']
#showStats(data_dict)
showStats(data_dict_many)
date_strings = data_dict['Start.date']
dates = ['%.2f' % stringToDate(x) for x in date_strings]
print 'dates', sorted(dates)[::100]
# Convert dates to numbers that Weka can understand
data_dict_many['Start.date'] = dates
test_knn()
if False:
test_knn_probability0()
if len(sys.argv) < 3:
print 'usage:', sys.argv[0], '<training file name> <test file name>'
exit()
training_data_csv = sys.argv[1]
test_data_csv = sys.argv[2]
k = 4
print 'training_data_csv:', training_data_csv
print 'test_data_csv:', test_data_csv
print 'k:', k
training_data_dict_str, _ = csv.readCsvAsDict(training_data_csv)
training_data_dict = {}
for k in training_data_dict_str.keys():
training_data_dict[k] = [float(x) for x in training_data_dict_str[k]]
print 'training keys:', training_data_dict.keys()
training_data_class = training_data_dict['Grant.Status']
training_data_keys = [k for k in sorted(training_data_dict.keys()) if k != 'Grant.Status']
training_data = misc.transpose([training_data_dict[k] for k in training_data_keys])
print 'training data:', len(training_data), len(training_data[0])
test_data_dict_str, _ = csv.readCsvAsDict(test_data_csv)
test_data_dict = {}
for k in test_data_dict_str.keys():
test_data_dict[k] = [float(x) for x in test_data_dict_str[k]]
# Use training data column headers to ensure data matches
test_data = misc.transpose([test_data_dict[k] for k in training_data_keys])
print 'options:', options
print 'args:', args
print 'has_class:', options.has_class
print 'num_rules:', num_rules
print 'weka_results_filename:', weka_results_filename
print 'data_file_csv:', data_file_csv
print 'output_dir:', options.output_dir
print 'knn_file_csv:', knn_file_csv
all_rules, compound_rules = get_rules_from_weka_results(weka_results_filename)
sorted_keys = get_sorted_rules_keys(all_rules)
attrs = sorted(list(set([attr for attr,_,_ in sorted_keys])))
print ' attrs:', len(attrs), attrs
data_dict, num_instances = csv.readCsvAsDict(data_file_csv)
header = [k for k in sorted(data_dict.keys()) if k != 'Grant.Status']
print 'header:', len(header), header
for a in attrs:
assert(a in header)
evals_dict = {}
if DO_COMPOUND_RULES:
evals_header = [compound_rule_to_string(compound) for compound in compound_rules[:num_rules]]
if False:
for i,e in enumerate(evals_header):
print i,e
else:
evals_header = [rule_to_string(rule) for rule in sorted_keys[:num_rules]]
