def pyt_model_object():
backend = 'PYT'
ML_modelpath = helpers.get_adult_income_modelpath(backend=backend)
m = dice_ml.Model(model_path=ML_modelpath,
backend=backend,
func='ohe-min-max')
return m
def _get_exp(self, backend, method="random"):
dataset = helpers.load_adult_income_dataset()
d = dice_ml.Data(dataframe=dataset, continuous_features=['age', 'hours_per_week'], outcome_name='income')
ML_modelpath = helpers.get_adult_income_modelpath(backend=backend)
m = dice_ml.Model(model_path=ML_modelpath, backend=backend)
exp = dice_ml.Dice(d, m, method=method)
return exp
def tf_model_object():
backend = 'TF' + tf.__version__[0]
ML_modelpath = helpers.get_adult_income_modelpath(backend=backend)
m = dice_ml.Model(model_path=ML_modelpath,
backend=backend,
func='ohe-min-max')
return m
def _get_exp(self,
backend,
method="random",
is_public_data_interface=True):
if is_public_data_interface:
dataset = helpers.load_adult_income_dataset()
d = dice_ml.Data(dataframe=dataset,
continuous_features=['age', 'hours_per_week'],
outcome_name='income')
else:
d = dice_ml.Data(features={
'age': [17, 90],
'workclass':
['Government', 'Other/Unknown', 'Private', 'Self-Employed'],
'education': [
'Assoc', 'Bachelors', 'Doctorate', 'HS-grad', 'Masters',
'Prof-school', 'School', 'Some-college'
],
'marital_status':
['Divorced', 'Married', 'Separated', 'Single', 'Widowed'],
'occupation': [
'Blue-Collar', 'Other/Unknown', 'Professional', 'Sales',
'Service', 'White-Collar'
],
'race': ['Other', 'White'],
'gender': ['Female', 'Male'],
'hours_per_week': [1, 99]
},
outcome_name='income')
ML_modelpath = helpers.get_adult_income_modelpath(backend=backend)
m = dice_ml.Model(model_path=ML_modelpath, backend=backend)
exp = dice_ml.Dice(d, m, method=method)
return exp
def pyt_exp_object():
backend = 'PYT'
dataset = helpers.load_adult_income_dataset()
d = dice_ml.Data(dataframe=dataset, continuous_features=['age', 'hours_per_week'], outcome_name='income')
ML_modelpath = helpers.get_adult_income_modelpath(backend=backend)
m = dice_ml.Model(model_path= ML_modelpath, backend=backend)
exp = dice_ml.Dice(d, m)
return exp
def test_model_initiation_fullpath():
"""
Tests if model is initiated when full path to a model and explainer class is given to backend parameter.
"""
pyt = pytest.importorskip("torch")
backend = {'model': 'pytorch_model.PyTorchModel',
'explainer': 'dice_pytorch.DicePyTorch'}
ML_modelpath = helpers.get_adult_income_modelpath(backend=backend)
m = dice_ml.Model(model_path= ML_modelpath, backend=backend)
assert isinstance(m, dice_ml.model_interfaces.pytorch_model.PyTorchModel)
def test_model_initiation_fullpath():
"""
Tests if model is initiated when full path to a model and explainer class is given to backend parameter.
"""
tf_version = tf.__version__[0]
backend = {
'model':
'keras_tensorflow_model.KerasTensorFlowModel',
'explainer':
'dice_tensorflow' + tf_version + '.DiceTensorFlow' + tf_version
}
ML_modelpath = helpers.get_adult_income_modelpath(backend=backend)
m = dice_ml.Model(model_path=ML_modelpath, backend=backend)
assert isinstance(
m,
dice_ml.model_interfaces.keras_tensorflow_model.KerasTensorFlowModel)
def _get_model(self, backend):
ML_modelpath = helpers.get_adult_income_modelpath(backend=backend)
m = dice_ml.Model(model_path=ML_modelpath, backend=backend)
return m
from dice_ml.utils import helpers # helper functions
from dice_ml.data_interfaces import public_data_interface
import numpy as np
import tensorflow as tf
from dice_ml.model_interfaces import keras_tensorflow_model
from dice_ml.dice_interfaces import dice_tensorflow2
import sys
if __name__ == "__main__":
data = helpers.load_adult_income_dataset()
params = {}
params['dataframe'] = data
params['continuous_features'] = ['age', 'hours_per_week']
params['outcome_name'] = 'income'
data_inter = public_data_interface.PublicData(params)
model_path = helpers.get_adult_income_modelpath()
print("Loading model ")
model = keras_tensorflow_model.KerasTensorFlowModel(model_path=model_path)
print("Loading CE")
dice = dice_tensorflow2.DiceTensorFlow2(data_inter, model)
#
query_instance = {'age':22,
'workclass':'Private',
'education':'HS-grad',
'marital_status':'Single',
'occupation':'Service',
'race': 'White',
'gender':'Female',
'hours_per_week': 45}
counterfactual_samples = dice.generate_counterfactuals(query_instance, total_CFs=4, desired_class="opposite")
