"""
Run FairTest Discovery Investigations on Movie Recommender Dataset
Usage: python discovery.py
"""
import fairtest.utils.prepare_data as prepare
from fairtest import Discovery, train, test, report, DataSource
import ast
import pandas as pd
from sklearn import preprocessing
import sys
def main(argv=sys.argv):
if len(argv) != 1:
usage(argv)
FILENAME = "../../../data/recommender/recommendations.txt"
OUTPUT_DIR = "."
data = prepare.data_from_csv(FILENAME, sep='\\t',
to_drop=['RMSE', 'Avg Movie Age',
'Avg Recommended Rating',
'Avg Seen Rating', 'Occupation'])
TARGET = 'Types'
SENS = ['Gender']
EXPL = []
labeled_data = [ast.literal_eval(s) for s in data[TARGET]]
for labels in labeled_data:
assert len(labels) == 5
label_encoder = preprocessing.MultiLabelBinarizer()
labeled_data = label_encoder.fit_transform(labeled_data)
labels = label_encoder.classes_
df_labels = pd.DataFrame(labeled_data, columns=labels)
data = pd.concat([data.drop(TARGET, axis=1), df_labels], axis=1)
TARGET = labels.tolist()
data_source = DataSource(data)
# Instantiate the experiment
inv = Discovery(data_source, SENS, TARGET, EXPL, topk=10, random_state=0)
# Train the classifier
train([inv])
# Evaluate on the testing set
test([inv])
# Create the report
report([inv], "discovery", OUTPUT_DIR)
def usage(argv):
print "Usage:%s" % argv[0]
exit(-1)
if __name__ == '__main__':
sys.exit(main())
