'init_sequential_rounding_max_runtime': 10.0, # max runtime for SeqRd in initialization procedure
'init_sequential_rounding_max_solutions': 5, # max solutions to round using SeqRd
#
'init_polishing_after': True, # polish after rounding
'init_polishing_max_runtime': 30.0, # max runtime for polishing
'init_polishing_max_solutions': 5, # max solutions to polish
#
# CPLEX Solver Parameters
'cplex_randomseed': 0, # random seed
'cplex_mipemphasis': 0, # cplex MIP strategy
}
# train model using lattice_cpa
model_info, mip_info, lcpa_info = run_lattice_cpa(data, constraints, settings)
#model info contains key results
pprint(model_info)
print_model(model_info['solution'], data)
# mip_output contains information to access the MIP
mip_info['risk_slim_mip'] #CPLEX mip
mip_info['risk_slim_idx'] #indices of the relevant constraints
# lcpa_output contains detailed information about LCPA
pprint(lcpa_info)
def risk_cv(KY_x, KY_y, FL_x, FL_y,
y_label,
max_coef,
max_coef_number,
max_offset,
max_runtime,
c,
seed):
FL_score = []
KY_score = []
KY_validation = []
## set up basic values
cols = KY_x.columns.tolist()
sample_weights = np.repeat(1, len(KY_y))
FL_x = FL_x.values
FL_y[FL_y == -1] = 0 ## change -1 to 0
## cross validation set up
outer_cv = KFold(n_splits=5,shuffle=True, random_state=seed)
inner_cv = KFold(n_splits=5,shuffle=True, random_state=seed)
for outer_train, test in outer_cv.split(KY_x, KY_y):
## split train & test
outer_train_x, outer_train_y = KY_x.iloc[outer_train], KY_y[outer_train]
outer_test_x, outer_test_y = KY_x.iloc[test], KY_y[test]
outer_train_sample_weights = sample_weights[outer_train]
## inner loop
for inner_train, validation in inner_cv.split(outer_train_x, outer_train_y):
## split inner train & validation
inner_train_x, inner_train_y = outer_train_x.iloc[inner_train].values, outer_train_y[inner_train]
validation_x, validation_y = outer_train_x.iloc[validation].values, outer_train_y[validation]
inner_train_sample_weights = outer_train_sample_weights[inner_train]
validation_sample_weights = outer_train_sample_weights[validation]
inner_train_y = inner_train_y.reshape(-1,1)
## new data
new_train_data = {
'X': inner_train_x,
'Y': inner_train_y,
'variable_names': cols,
'outcome_name': y_label,
'sample_weights': inner_train_sample_weights
}
## modeling
model_info, mip_info, lcpa_info = risk_slim(new_train_data,
max_coefficient=max_coef,
max_L0_value=max_coef_number,
c0_value=c,
max_runtime=max_runtime,
max_offset = max_offset)
## check validation auc
validation_x = validation_x[:,1:] ## remove the first column, which is "intercept"
validation_y[validation_y == -1] = 0 ## change -1 to 0
validation_prob = riskslim_prediction(validation_x, np.array(cols), model_info)
KY_validation.append(roc_auc_score(validation_y, validation_prob))
## outer loop
outer_train_x = outer_train_x.values
outer_train_y = outer_train_y.reshape(-1,1)
## new data
new_train_data = {
'X': outer_train_x,
'Y': outer_train_y,
'variable_names': cols,
'outcome_name': y_label,
'sample_weights': outer_train_sample_weights
}
## fit the model
model_info, mip_info, lcpa_info = risk_slim(new_train_data,
max_coefficient=max_coef,
max_L0_value=max_coef_number,
c0_value=c,
max_runtime=max_runtime,
max_offset = max_offset)
print_model(model_info['solution'], new_train_data)
## FL_score
FL_prob = riskslim_prediction(FL_x, np.array(cols), model_info).reshape(-1,1)
FL_score.append(roc_auc_score(FL_y, FL_prob))
## KY score
outer_test_x = outer_test_x.values[:, 1:]
outer_test_y[outer_test_y == -1] = 0 ## change -1 to 0
KY_prob = riskslim_prediction(outer_test_x, np.array(cols), model_info).reshape(-1,1)
KY_score.append(roc_auc_score(outer_test_y, KY_prob))
return {'FL_score': FL_score,
'KY_score': KY_score,
'KY_validation': KY_validation}
def risk_nested_cv_constrain(X, Y, indicator, y_label, max_coef,
max_coef_number, max_runtime, max_offset, c,
seed):
## set up data
sample_weights = np.repeat(1, len(Y))
## set up cross validation
outer_cv = KFold(n_splits=5, random_state=seed, shuffle=True)
inner_cv = KFold(n_splits=5, random_state=seed, shuffle=True)
train_auc = []
validation_auc = []
test_auc = []
holdout_with_attrs_test = []
holdout_probability = []
holdout_prediction = []
holdout_y = []
confusion_matrix_rets = []
calibrations = []
race_auc = []
condition_pn = []
no_condition_pn = []
i = 0
for outer_train, outer_test in outer_cv.split(X, Y):
outer_train_x, outer_train_y = X.iloc[outer_train], Y[outer_train]
outer_test_x, outer_test_y = X.iloc[outer_test], Y[outer_test]
outer_train_sample_weight, outer_test_sample_weight = sample_weights[
outer_train], sample_weights[outer_test]
## holdout test
holdout_with_attrs = outer_test_x.copy().drop(['(Intercept)'], axis=1)
holdout_with_attrs = holdout_with_attrs.rename(columns={'sex1': 'sex'})
## remove unused feature in modeling
if indicator == 1:
outer_train_x = outer_train_x.drop([
'person_id', 'screening_date', 'race', 'age_at_current_charge',
'p_charges'
],
axis=1)
outer_test_x = outer_test_x.drop([
'person_id', 'screening_date', 'race', 'age_at_current_charge',
'p_charges'
],
axis=1)
else:
outer_train_x = outer_train_x.drop([
'person_id', 'screening_date', 'race', 'sex1',
'age_at_current_charge', 'p_charges'
],
axis=1)
outer_test_x = outer_test_x.drop([
'person_id', 'screening_date', 'race', 'sex1',
'age_at_current_charge', 'p_charges'
],
axis=1)
cols = outer_train_x.columns.tolist()
## inner cross validation
for inner_train, validation in inner_cv.split(outer_train_x,
outer_train_y):
## subset train data & store test data
inner_train_x, inner_train_y = outer_train_x.iloc[
inner_train].values, outer_train_y[inner_train]
validation_x, validation_y = outer_train_x.iloc[
validation].values, outer_train_y[validation]
inner_train_sample_weight = outer_train_sample_weight[inner_train]
validation_sample_weight = outer_train_sample_weight[validation]
inner_train_y = inner_train_y.reshape(-1, 1)
## create new data dictionary
new_train_data = {
'X': inner_train_x,
'Y': inner_train_y,
'variable_names': cols,
'outcome_name': y_label,
'sample_weights': inner_train_sample_weight
}
## fit the model
model_info, mip_info, lcpa_info = risk_slim_constrain(
new_train_data,
max_coefficient=max_coef,
max_L0_value=max_coef_number,
c0_value=c,
max_runtime=max_runtime,
max_offset=max_offset)
## check validation auc
validation_x = validation_x[:,
1:] ## remove the first column, which is "intercept"
validation_y[validation_y == -1] = 0 ## change -1 to 0
validation_prob = riskslim_prediction(validation_x, np.array(cols),
model_info)
validation_auc.append(roc_auc_score(validation_y, validation_prob))
## outer loop
outer_train_x = outer_train_x.values
outer_test_x = outer_test_x.values
outer_train_y = outer_train_y.reshape(-1, 1)
new_train_data = {
'X': outer_train_x,
'Y': outer_train_y,
'variable_names': cols,
'outcome_name': y_label,
'sample_weights': outer_train_sample_weight
}
## fit the model
model_info, mip_info, lcpa_info = risk_slim_constrain(
new_train_data,
max_coefficient=max_coef,
max_L0_value=max_coef_number,
c0_value=c,
max_runtime=max_runtime,
max_offset=max_offset)
print_model(model_info['solution'], new_train_data)
## change data format
outer_train_x, outer_test_x = outer_train_x[:,
1:], outer_test_x[:,
1:] ## remove the first column, which is "intercept"
outer_train_y[outer_train_y == -1] = 0 ## change -1 to 0
outer_test_y[outer_test_y == -1] = 0 ## change -1 to 0
## probability & accuracy
outer_train_prob = riskslim_prediction(outer_train_x, np.array(cols),
model_info).reshape(-1, 1)
outer_test_prob = riskslim_prediction(outer_test_x, np.array(cols),
model_info)
outer_test_pred = (outer_test_prob > 0.5)
########################
## AUC
train_auc.append(roc_auc_score(outer_train_y, outer_train_prob))
test_auc.append(roc_auc_score(outer_test_y, outer_test_prob))
########################
## confusion matrix
confusion_matrix_fairness = compute_confusion_matrix_stats(
df=holdout_with_attrs,
preds=outer_test_pred,
labels=outer_test_y,
protected_variables=["sex", "race"])
cf_final = confusion_matrix_fairness.assign(
fold_num=[i] * confusion_matrix_fairness['Attribute'].count())
confusion_matrix_rets.append(cf_final)
########################
## calibration matrix
calibration = compute_calibration_fairness(
df=holdout_with_attrs,
probs=outer_test_prob,
labels=outer_test_y,
protected_variables=["sex", "race"])
calibration_final = calibration.assign(
fold_num=[i] * calibration['Attribute'].count())
calibrations.append(calibration_final)
########################
## race auc
try:
race_auc_matrix = fairness_in_auc(df=holdout_with_attrs,
probs=outer_test_prob,
labels=outer_test_y)
race_auc_matrix_final = race_auc_matrix.assign(
fold_num=[i] * race_auc_matrix['Attribute'].count())
race_auc.append(race_auc_matrix_final)
except:
pass
########################
## ebm_pn
no_condition_pn_matrix = balance_positive_negative(
df=holdout_with_attrs, probs=outer_test_prob, labels=outer_test_y)
no_condition_pn_matrix_final = no_condition_pn_matrix.assign(
fold_num=[i] * no_condition_pn_matrix['Attribute'].count())
no_condition_pn.append(no_condition_pn_matrix_final)
########################
## ebm_condition_pn
condition_pn_matrix = conditional_balance_positive_negative(
df=holdout_with_attrs, probs=outer_test_prob, labels=outer_test_y)
condition_pn_matrix_final = condition_pn_matrix.assign(
fold_num=[i] * condition_pn_matrix['Attribute'].count())
condition_pn.append(condition_pn_matrix_final)
########################
## store results
holdout_with_attrs_test.append(holdout_with_attrs)
holdout_probability.append(outer_test_prob)
holdout_prediction.append(outer_test_pred)
holdout_y.append(outer_test_y)
i += 1
## confusion matrix
confusion_df = pd.concat(confusion_matrix_rets, ignore_index=True)
confusion_df.sort_values(["Attribute", "Attribute Value"], inplace=True)
confusion_df = confusion_df.reset_index(drop=True)
## calibration matrix
calibration_df = pd.concat(calibrations, ignore_index=True)
calibration_df.sort_values(
["Attribute", "Lower Limit Score", "Upper Limit Score"], inplace=True)
calibration_df = calibration_df.reset_index(drop=True)
## race_auc
race_auc_df = []
try:
race_auc_df = pd.concat(race_auc, ignore_index=True)
race_auc_df.sort_values(["fold_num", "Attribute"], inplace=True)
race_auc_df = race_auc_df.reset_index(drop=True)
except:
pass
## no_condition_pn
no_condition_pn_df = pd.concat(no_condition_pn, ignore_index=True)
no_condition_pn_df.sort_values(["fold_num", "Attribute"], inplace=True)
no_condition_pn_df = no_condition_pn_df.reset_index(drop=True)
## condition_pn
condition_pn_df = pd.concat(condition_pn, ignore_index=True)
condition_pn_df.sort_values(["fold_num", "Attribute"], inplace=True)
condition_pn_df = condition_pn_df.reset_index(drop=True)
return {
'train_auc': train_auc,
'validation_auc': validation_auc,
'test_auc': test_auc,
'holdout_with_attrs_test': holdout_with_attrs_test,
'holdout_proba': holdout_probability,
'holdout_pred': holdout_prediction,
'holdout_y': holdout_y,
'confusion_matrix_stats': confusion_df,
'calibration_stats': calibration_df,
'race_auc': race_auc_df,
'condition_pn': condition_pn_df,
'no_condition_pn': no_condition_pn_df
}
def risk_cv(X, Y, indicator, y_label, max_coef, max_coef_number, max_runtime,
max_offset, c, seed):
## set up data
Y = Y.reshape(-1, 1)
sample_weights = np.repeat(1, len(Y))
## set up cross validation
cv = KFold(n_splits=5, random_state=seed, shuffle=True)
train_auc = []
validation_auc = []
i = 0
for train, validation in cv.split(X, Y):
## subset train data & store test data
train_x, train_y = X.iloc[train], Y[train]
validation_x, validation_y = X.iloc[validation], Y[validation]
sample_weights_train, sample_weights_validation = sample_weights[
train], sample_weights[validation]
## holdout test with "race" for fairness
holdout_with_attrs = validation_x.copy().drop(['(Intercept)'], axis=1)
holdout_with_attrs = holdout_with_attrs.rename(columns={'sex1': 'sex'})
## remove unused feature in modeling
if indicator == 1:
train_x = train_x.drop([
'person_id', 'screening_date', 'race', 'age_at_current_charge',
'p_charges'
],
axis=1)
validation_x = validation_x.drop([
'person_id', 'screening_date', 'race', 'age_at_current_charge',
'p_charges'
],
axis=1).values
else:
train_x = train_x.drop([
'person_id', 'screening_date', 'race', 'sex1',
'age_at_current_charge', 'p_charges'
],
axis=1)
validation_x = validation_x.drop([
'person_id', 'screening_date', 'race', 'sex1',
'age_at_current_charge', 'p_charges'
],
axis=1).values
cols = train_x.columns.tolist()
train_x = train_x.values
## create new data dictionary
new_train_data = {
'X': train_x,
'Y': train_y,
'variable_names': cols,
'outcome_name': y_label,
'sample_weights': sample_weights_train
}
## fit the model
model_info, mip_info, lcpa_info = risk_slim(
new_train_data,
max_coefficient=max_coef,
max_L0_value=max_coef_number,
max_offset=max_offset,
c0_value=c,
max_runtime=max_runtime)
print_model(model_info['solution'], new_train_data)
## change data format
train_x, validation_x = train_x[:,
1:], validation_x[:,
1:] ## remove the first column, which is "intercept"
train_y[train_y == -1] = 0 ## change -1 to 0
validation_y[validation_y == -1] = 0 ## change -1 to 0
## probability & accuracy
train_prob = riskslim_prediction(train_x, np.array(cols),
model_info).reshape(-1, 1)
validation_prob = riskslim_prediction(validation_x, np.array(cols),
model_info).reshape(-1, 1)
validation_pred = (validation_prob > 0.5)
## AUC
train_auc.append(roc_auc_score(train_y, train_prob))
validation_auc.append(roc_auc_score(validation_y, validation_prob))
i += 1
return {'train_auc': train_auc, 'validation_auc': validation_auc}
def fit(self, X, y):
X, y = check_X_y(X, y, accept_sparse=True)
self.is_fitted_ = True
# transforming data
raw_data = np.insert(X, 0, y, axis=1)
N = raw_data.shape[0]
# setup Y vector and Y_name
Y_col_idx = [0]
Y = raw_data[:, Y_col_idx]
Y_name = self.data_headers[Y_col_idx[0]]
Y[Y == 0] = -1
# setup X and X_names
X_col_idx = [j for j in range(raw_data.shape[1]) if j not in Y_col_idx]
X = raw_data[:, X_col_idx]
variable_names = [self.data_headers[j] for j in X_col_idx]
# insert a column of ones to X for the intercept
X = np.insert(arr=X, obj=0, values=np.ones(N), axis=1)
variable_names.insert(0, '(Intercept)')
if self.sample_weights is None or len(self.sample_weights) != N:
self.sample_weights = np.ones(N)
self.data = {
'X': X,
'Y': Y,
'variable_names': variable_names,
'outcome_name': Y_name,
'sample_weights': self.sample_weights,
}
#load folds
if self.fold_csv_file is not None:
if not os.path.isfile(self.fold_csv_file):
raise IOError('could not find fold_csv_file: %s' %
self.fold_csv_file)
else:
fold_idx = pd.read_csv(self.fold_csv_file,
sep=',',
header=None)
fold_idx = fold_idx.values.flatten()
K = max(fold_idx)
all_fold_nums = np.sort(np.unique(fold_idx))
assert len(
fold_idx
) == N, "dimension mismatch: read %r fold indices (expected N = %r)" % (
len(fold_idx), N)
assert np.all(all_fold_nums == np.arange(
1, K +
1)), "folds should contain indices between 1 to %r" % K
assert fold_num in np.arange(
0, K + 1
), "fold_num should either be 0 or an integer between 1 to %r" % K
if fold_num >= 1:
test_idx = fold_num == fold_idx
train_idx = fold_num != fold_idx
data['X'] = data['X'][train_idx, ]
data['Y'] = data['Y'][train_idx]
data['sample_weights'] = data['sample_weights'][train_idx]
assert check_data(self.data)
# create coefficient set and set the value of the offset parameter
coef_set = CoefficientSet(variable_names=self.data['variable_names'],
lb=-self.max_coefficient,
ub=self.max_coefficient,
sign=0)
conservative_offset = get_conservative_offset(self.data, coef_set,
self.max_L0_value)
self.max_offset = min(self.max_offset, conservative_offset)
coef_set['(Intercept)'].ub = self.max_offset
coef_set['(Intercept)'].lb = -self.max_offset
# edit contraints here
constraints = {
'L0_min': 0,
'L0_max': self.max_L0_value,
'coef_set': coef_set,
}
# initialize MIP for lattice CPA
mip_objects = setup_lattice_cpa(self.data, constraints, self.settings)
# add operational constraints
mip = mip_objects['mip']
indices = mip_objects['indices']
get_alpha_name = lambda var_name: 'alpha_' + str(self.data[
'variable_names'].index(var_name))
get_alpha_ind = lambda var_names: [
get_alpha_name(v) for v in var_names
]
# applies mutual exclusivity feature contraints
if self.op_constraints is not None:
names = []
expressions = []
for key in self.op_constraints.keys():
names.append("mutually_exclusive_%s" % key)
expressions.append(
cplex.SparsePair(
ind=get_alpha_ind(self.op_constraints[key]),
val=[1.0] * len(self.op_constraints[key])))
mip.linear_constraints.add(
names=names,
lin_expr=expressions,
senses=["L"] * len(self.op_constraints.keys()),
rhs=[1.0] * len(self.op_constraints.keys()))
mip_objects['mip'] = mip
# fit using ltca
model_info, mip_info, lcpa_info = finish_lattice_cpa(
self.data, constraints, mip_objects, self.settings)
rho = model_info['solution']
self.model_info = model_info
if np.sum(rho[1:]) != 0:
print_model(model_info['solution'], self.data)
print("solver_time = %d" % model_info['solver_time'])
print("optimality_gap = %.3f" % model_info['optimality_gap'])
print(rho)
variable_names = self.data['variable_names']
rho_values = np.copy(rho)
rho_names = list(variable_names)
# removes intercept value or sets it to 0
if '(Intercept)' in rho_names:
intercept_ind = variable_names.index('(Intercept)')
self.intercept_val = int(rho[intercept_ind])
rho_values = np.delete(rho_values, intercept_ind)
rho_names.remove('(Intercept)')
else:
self.intercept_val = 0
self.filter_mask = np.array(rho_values) != 0
# removes zero values
if not self.show_omitted_variables:
selected_ind = np.flatnonzero(rho_values)
self.rho_values = rho_values[selected_ind]
self.rho_names = [rho_names[i] for i in selected_ind]
return self
