def load_risk_data(name, base_dir=None):
from riskslim import load_data_from_csv
if base_dir is None:
base_dir = os.getcwd()
data_dir = os.path.join(base_dir, "rsep_explain/datasets/risk_dsets")
filepath = os.path.join(data_dir, f"{name}_data.csv")
data = load_data_from_csv(dataset_csv_file=filepath)
return data
def test_risk_slim(data_csv_file,
sample_weights_csv_file=None,
max_coefficient=5,
max_L0_value=5,
max_offset=50,
c0_value=1e-6,
w_pos=1.00,
settings=None):
# load dataset
data = riskslim.load_data_from_csv(
dataset_csv_file=data_csv_file,
sample_weights_csv_file=sample_weights_csv_file)
N, P = data['X'].shape
# offset value
coef_set = riskslim.CoefficientSet(variable_names=data['variable_names'],
lb=-max_coefficient,
ub=max_coefficient,
sign=0)
coef_set.update_intercept_bounds(X=data['X'],
y=data['Y'],
max_offset=max_offset,
max_L0_value=max_L0_value)
# create constraint dictionary
trivial_L0_max = P - np.sum(coef_set.C_0j == 0)
max_L0_value = min(max_L0_value, trivial_L0_max)
constraints = {
'L0_min': 0,
'L0_max': max_L0_value,
'coef_set': coef_set,
}
# Train model using lattice_cpa
model_info, mip_info, lcpa_info = riskslim.run_lattice_cpa(
data, constraints, settings)
#model info contains key results
pprint.pprint(model_info)
# lcpa_output contains detailed information about LCPA
pprint.pprint(lcpa_info)
return True
data_name = "breastcancer" # name of the data
data_dir = os.getcwd(
) + '/examples/data/' # directory where datasets are stored
data_csv_file = data_dir + data_name + '_data.csv' # csv file for the dataset
sample_weights_csv_file = None # csv file of sample weights for the dataset (optional)
# problem parameters
max_coefficient = 5 # value of largest/smallest coefficient
max_L0_value = 5 # maximum model size
max_offset = 50 # maximum value of offset parameter (optional)
c0_value = 1e-6 # L0-penalty parameter such that c0_value > 0; larger values -> sparser models; we set to a small value (1e-6) so that we get a model with max_L0_value terms
w_pos = 1.00 # relative weight on examples with y = +1; w_neg = 1.00 (optional)
# load data from disk
data = riskslim.load_data_from_csv(
dataset_csv_file=data_csv_file,
sample_weights_csv_file=sample_weights_csv_file)
N, P = data['X'].shape
# create coefficient set and set the value of the offset parameter
coef_set = riskslim.CoefficientSet(variable_names=data['variable_names'],
lb=-max_coefficient,
ub=max_coefficient,
sign=0)
coef_set.update_intercept_bounds(X=data['X'],
y=data['Y'],
max_offset=max_offset)
# create constraint
trivial_L0_max = P - np.sum(coef_set.C_0j == 0)
max_L0_value = min(max_L0_value, trivial_L0_max)
def run(file_path = None, returnSolution = False): # returnSolution为False时直接返回pred_y, pred_y_prob, test_y；为True时返回Solution
print("===========================RISKSLIM开始============================")
if file_path == None:
raise Exception('文件名不能为空')
# data
data_name = "breastcancer" # name of the data
data_dir = os.getcwd() + '/onehot/' # directory where datasets are stored
#data_csv_file = data_dir + data_name + '_data.csv' # csv file for the dataset
data_csv_file = file_path
sample_weights_csv_file = None # csv file of sample weights for the dataset (optional)
# problem parameters
max_coefficient = 10 # value of largest/smallest coefficient
max_L0_value = len(pd.read_csv(file_path).columns)-1 # 有几个特征就设置为几 maximum model size (set as float(inf))
max_offset = 50 # maximum value of offset parameter (optional)
c0_value = 1e-6 # L0-penalty parameter such that c0_value > 0; larger values -> sparser models; we set to a small value (1e-6) so that we get a model with max_L0_value terms
# load data from disk
data = riskslim.load_data_from_csv(dataset_csv_file = data_csv_file, sample_weights_csv_file = sample_weights_csv_file)
# 随机划分训练集与训练集 70%训练集 30%数据集
train_X,test_X,train_y,test_y = train_test_split(data['X'], data['Y'], test_size=0.3, random_state=666)
# create coefficient set and set the value of the offset parameter
# 设置变量的上下界，传入customSingleBoundary false代表使用max_coefficient统一上下界，为True则从配置文件中读取，此处传入的lb ub可忽略
coef_set = riskslim.CoefficientSet(variable_names = data['variable_names'], lb = -max_coefficient, ub = max_coefficient, sign = 0, customSingleBoundary = False, configPath = "config/ExternalRiskEstimate_lbub.csv")
# 设置intercept的上下界
coef_set.update_intercept_bounds(X = train_X, y = train_y, max_offset = max_offset)
constraints = {
'L0_min': 0,
'L0_max': max_L0_value,
'coef_set': coef_set,
}
# major settings (see riskslim_ex_02_complete for full set of options)
settings = {
# Problem Parameters
'c0_value': c0_value,
#
# LCPA Settings
'max_runtime': 30.0, # max runtime for LCPA
'max_tolerance': np.finfo('float').eps, # tolerance to stop LCPA (set to 0 to return provably optimal solution)
'display_cplex_progress': True, # print CPLEX progress on screen
'loss_computation': 'normal', # how to compute the loss function ('normal','fast','lookup')
#
# LCPA Improvements
'round_flag': True, # round continuous solutions with SeqRd
'polish_flag': True, # polish integer feasible solutions with DCD
'chained_updates_flag': True, # use chained updates
'add_cuts_at_heuristic_solutions': True, # add cuts at integer feasible solutions found using polishing/rounding
#
# Initialization
'initialization_flag': True, # use initialization procedure
'init_max_runtime': 120.0, # max time to run CPA in initialization procedure
'init_max_coefficient_gap': 0.49,
#
# 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 = riskslim.run_lattice_cpa(data, constraints, settings)
#print model contains model
riskslim.print_model(model_info['solution'], data, show_omitted_variables=True)
#model info contains key results
pprint.pprint(model_info)
if returnSolution:
return model_info['solution']
# 训练完毕，测试数据集
# print(test_X.shape, model_info['solution'].shape)
# print('test_X：', test_X)
# print('model_info["solution"]：', model_info['solution'])
pred_score = np.dot(test_X, model_info['solution'])
pred_y_prob = sigmoid(pred_score)
pred_y = np.zeros(pred_y_prob.shape, dtype = np.int)
pred_y[pred_y_prob < 0.5] = -1
pred_y[pred_y_prob >= 0.5] = 1
# test_y = np.array(test_y).reshape(test_y.shape[])
print("===========================RiskSlim结束============================")
if not returnSolution:
return pred_y, pred_y_prob, test_y