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generate.py
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import argparse
import os
import time
import traceback
import dice_ml
import numpy as np
import pandas as pd
import shap
from baselines.landmark import Landmark
from baselines.lime_c import LimeCounterfactual
from baselines.mojito import Mojito
from baselines.shap_c import ShapCounterfactual
from certa.explain import CertaExplainer
from certa.local_explain import get_original_prediction, get_row
from certa.utils import merge_sources
from certa.models.utils import get_model
experiments_dir = 'experiments/'
base_datadir = 'datasets/'
def generate(mtype: str, samples: int = -1, filtered_datasets: list = [], exp_dir: str = experiments_dir,
compare=False, da=None):
if not exp_dir.endswith('/'):
exp_dir = exp_dir + '/'
exp_dir = exp_dir + 'generated/'
os.makedirs(exp_dir, exist_ok=True)
for dataset in filtered_datasets:
os.makedirs(exp_dir + dataset, exist_ok=True)
modeldir = 'models/saved/' + mtype + '/' + dataset
model_name = mtype
datadir = base_datadir + dataset
model = get_model(mtype, modeldir, datadir, dataset)
def predict_fn(x, **kwargs):
return model.predict(x, **kwargs)
def predict_fn_mojito(x):
return model.predict(x, mojito=True)
test = pd.read_csv(datadir + '/test.csv')
lsource = pd.read_csv(datadir + '/tableA.csv')
rsource = pd.read_csv(datadir + '/tableB.csv')
gt = pd.read_csv(datadir + '/train.csv')
test_df = merge_sources(test, 'ltable_', 'rtable_', lsource, rsource, ['label'], [])[:samples]
train_df = merge_sources(gt, 'ltable_', 'rtable_', lsource, rsource, ['label'], ['id'])
generate_all(compare, dataset, exp_dir, lsource, model, model_name, predict_fn, predict_fn_mojito,
rsource, test_df, train_df, da)
def generate_all(compare, dataset, exp_dir, lsource, model, model_name, predict_fn, predict_fn_mojito, rsource,
test_df, train_df, da):
train_noids = train_df.copy().astype(str)
if 'ltable_id' in train_noids.columns and 'rtable_id' in train_noids.columns:
train_noids = train_df.drop(['ltable_id', 'rtable_id'], axis=1)
certa_explainer = CertaExplainer(lsource, rsource, data_augmentation=da)
if compare:
mojito = Mojito(test_df.columns,
attr_to_copy='left',
split_expression=" ",
class_names=['no_match', 'match'],
lprefix='', rprefix='',
feature_selection='lasso_path')
landmark_explainer = Landmark(lambda x: predict_fn(x)['match_score'].values, test_df, lprefix='',
exclude_attrs=['id', 'ltable_id', 'rtable_id', 'label'], rprefix='',
split_expression=r' ')
shap_explainer = shap.KernelExplainer(lambda x: predict_fn(x)['match_score'].values,
train_df.drop(['label'], axis=1).astype(str)[:100], link='identity')
landmarks = pd.DataFrame()
shaps = pd.DataFrame()
mojitos = pd.DataFrame()
examples = pd.DataFrame()
certas = pd.DataFrame()
for idx in range(len(test_df)):
rand_row = test_df.iloc[idx]
l_id = int(rand_row['ltable_id'])
l_tuple = lsource.iloc[l_id]
r_id = int(rand_row['rtable_id'])
r_tuple = rsource.iloc[r_id]
prediction = get_original_prediction(l_tuple, r_tuple, predict_fn)
class_to_explain = np.argmax(prediction)
label = rand_row["label"]
row_id = str(l_id) + '-' + str(r_id)
item = get_row(l_tuple, r_tuple)
try:
cf_dir = exp_dir + dataset + '/' + model_name + '/' + str(idx)
os.makedirs(cf_dir, exist_ok=True)
dest_file = cf_dir + '/certa.csv'
if not os.path.exists(dest_file):
# CERTA
print('certa')
t0 = time.perf_counter()
certa_saliency = None
num_triangles = 10
while certa_saliency is None:
try:
saliency_df, cf_summary, cf_ex, triangles, lattices = certa_explainer.explain(l_tuple, r_tuple, predict_fn,
debug=True, num_triangles=num_triangles)
latency_c = time.perf_counter() - t0
certa_saliency = saliency_df.transpose().to_dict()[0]
except:
pass
num_triangles += 50
if num_triangles > 200:
break
certa_row = {'explanation': certa_saliency, 'summary' : cf_summary.to_dict(), 'type': 'certa', 'latency': latency_c,
'match': class_to_explain,
'label': label, 'row': row_id, 'prediction': prediction}
cf_ex.to_csv(dest_file)
lidx = 0
for lattice in lattices:
lattice.triangle.to_csv(cf_dir + '/triangle_' + str(lidx) + '.csv')
dot_lattice = lattice.hasse()
with open(cf_dir + '/lattice_' + str(lidx) + '.dot', 'w') as f:
f.write(dot_lattice)
lidx += 1
certas = certas.append(certa_row, ignore_index=True)
if compare:
instance = pd.DataFrame(rand_row).transpose().astype(str)
for c in ['label', 'ltable_id', 'rtable_id']:
if c in instance.columns:
instance = instance.drop([c], axis=1)
if not os.path.exists(cf_dir + '/limec.csv'):
print('lime-c')
try:
limec_explainer = LimeCounterfactual(model, predict_fn_mojito, None, 0.5,
train_noids.drop(['label'], axis=1).columns, max_features=6,
time_maximum=300)
limec_exp = limec_explainer.explanation(instance)
print(limec_exp)
if limec_exp is not None:
limec_exp['cf_example'].to_csv(cf_dir + '/limec.csv')
except:
print(traceback.format_exc())
print(f'skipped item {str(idx)}')
pass
if not os.path.exists(cf_dir + '/shapc.csv'):
print('shap-c')
try:
shapc_explainer = ShapCounterfactual(lambda x: predict_fn(x)[['nomatch_score','match_score']].values, 0.5,
train_noids.drop(['label'], axis=1).columns, time_maximum=300, max_features=6)
sc_exp = shapc_explainer.explanation(instance, train_noids.drop(['label'], axis=1)[:50])
print(f'{idx}- shap-c:{sc_exp}')
if sc_exp is not None:
sc_exp['cf_example'].to_csv(cf_dir + '/shapc.csv')
except:
print(traceback.format_exc())
print(f'skipped item {str(idx)}')
pass
if not os.path.exists(cf_dir + '/dice_random.csv'):
print('dice_r')
try:
d = dice_ml.Data(dataframe=test_df.drop(['ltable_id', 'rtable_id'], axis=1),
continuous_features=[],
outcome_name='label')
# random
m = dice_ml.Model(model=model, backend='sklearn')
exp = dice_ml.Dice(d, m, method='random')
dice_exp = exp.generate_counterfactuals(instance,
total_CFs=10, desired_class="opposite")
dice_exp_df = dice_exp.cf_examples_list[0].final_cfs_df
print(f'random:{idx}:{dice_exp_df}')
if dice_exp_df is not None:
dice_exp_df.to_csv(cf_dir + '/dice_random.csv')
except:
print(traceback.format_exc())
print(f'skipped item {str(idx)}')
pass
# Mojito
print('mojito')
if class_to_explain == 1:
t0 = time.perf_counter()
mojito_exp_drop = mojito.drop(predict_fn_mojito, item,
num_features=15,
num_perturbation=100)
latency_m = time.perf_counter() - t0
mojito_exp = mojito_exp_drop.groupby('attribute')['weight'].mean().to_dict()
else:
t0 = time.perf_counter()
mojito_exp_copy = mojito.copy(predict_fn_mojito, item,
num_features=15,
num_perturbation=100)
latency_m = time.perf_counter() - t0
mojito_exp = mojito_exp_copy.groupby('attribute')['weight'].mean().to_dict()
if 'id' in mojito_exp:
mojito_exp.pop('id', None)
mojito_row = {'explanation': mojito_exp, 'type': 'mojito', 'latency': latency_m,
'match': class_to_explain,
'label': label, 'row': row_id, 'prediction': prediction}
mojitos = mojitos.append(mojito_row, ignore_index=True)
# landmark
print('landmark')
labelled_item = item.copy()
labelled_item['label'] = int(label)
labelled_item['id'] = idx
t0 = time.perf_counter()
land_explanation = landmark_explainer.explain(labelled_item)
latency_l = time.perf_counter() - t0
land_exp = land_explanation.groupby('column')['impact'].sum().to_dict()
land_row = {'explanation': str(land_exp), 'type': 'landmark', 'latency': latency_l,
'match': class_to_explain,
'label': label, 'row': row_id, 'prediction': prediction}
landmarks = landmarks.append(land_row, ignore_index=True)
# SHAP
print('shap')
shap_instance = test_df.iloc[idx, 1:].drop(['ltable_id', 'rtable_id']).astype(str)
t0 = time.perf_counter()
shap_values = shap_explainer.shap_values(shap_instance, nsamples=10)
latency_s = time.perf_counter() - t0
match_shap_values = shap_values
shap_saliency = dict()
for sv in range(len(match_shap_values)):
shap_saliency[train_df.columns[1 + sv]] = match_shap_values[sv]
shap_row = {'explanation': str(shap_saliency), 'type': 'shap', 'latency': latency_s,
'match': class_to_explain,
'label': label, 'row': row_id, 'prediction': prediction}
shaps = shaps.append(shap_row, ignore_index=True)
item['match'] = prediction[1]
item['label'] = label
examples = examples.append(item, ignore_index=True)
print(item)
print(idx)
except:
print(traceback.format_exc())
print(f'skipped item {str(idx)}')
item.head()
os.makedirs(exp_dir + dataset + '/' + model_name, exist_ok=True)
if compare:
mojitos.to_csv(exp_dir + dataset + '/' + model_name + '/mojito.csv')
landmarks.to_csv(exp_dir + dataset + '/' + model_name + '/landmark.csv')
shaps.to_csv(exp_dir + dataset + '/' + model_name + '/shap.csv')
examples.to_csv(exp_dir + dataset + '/' + model_name + '/examples.csv')
certas.to_csv(exp_dir + dataset + '/' + model_name + '/certa.csv')
import warnings
warnings.filterwarnings("ignore")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Run saliency experiments.')
parser.add_argument('--base_dir', metavar='b', type=str, help='the datasets base directory',
required=True)
parser.add_argument('--model_type', metavar='m', type=str, help='the ER model type to evaluate',
choices=['dm', 'deeper', 'ditto'], required=True)
parser.add_argument('--datasets', metavar='d', type=str, nargs='+', required=True,
help='the datasets to be used for the evaluation')
parser.add_argument('--samples', metavar='s', type=int, default=-1,
help='no. of samples from the test set used for the evaluation')
parser.add_argument('--compare', metavar='c', type=bool, default=False,
help='whether comparing CERTA with baselines')
parser.add_argument('--da', metavar='da', type=str, default='on_demand',
help='whether enabling CERTA data-augmentation feature')
args = parser.parse_args()
base_datadir = args.base_dir
if not base_datadir.endswith('/'):
base_datadir = base_datadir + '/'
filtered_datasets = args.datasets
mtype = args.model_type
samples = args.samples
compare = args.compare
da = args.da
generate(mtype, filtered_datasets=filtered_datasets, samples=samples, compare=compare, da=da)