def run_log_reg_hmm(data_file_base,
num_support,
num_pools,
num_iterations,
lead,
lag,
train=False,
do_parallel=True):
start_time = time.time()
num_weeks = 15
data_prefix = "data/"
data_suffix = ".csv"
models_prefix = "models/"
models_suffix = "_support_%s_logreg" % (num_support)
data_file_train_input = data_prefix + data_file_base + "_train" + data_suffix
data_file_train_hmm = data_prefix + data_file_base + "_train_logreg" + data_suffix
data_file_test = data_prefix + data_file_base + "_test" + data_suffix
models_dir = models_prefix + data_file_base + models_suffix
train_data = np.genfromtxt(data_file_train_input,
delimiter=';',
skip_header=0)
test_data = np.genfromtxt(data_file_test, delimiter=";", skip_header=0)
#split into train 1 and train 2
num_students = len(train_data) / num_weeks
num_students_train_hmm = num_students / 2
train_hmm_data = train_data[:num_students_train_hmm * num_weeks]
train_logreg_data = train_data[num_students_train_hmm * num_weeks:]
#train hmm on train_hmm_data
np.savetxt(data_file_train_hmm, train_hmm_data, fmt="%d", delimiter=";")
if not os.path.exists(models_dir) or train:
run_train_hmm.train_model(data_file_base,
num_support,
num_pools=num_pools,
num_iterations=num_iterations,
logreg=True,
do_parallel=do_parallel)
assert os.path.exists(
models_dir), "There is no trained model in directory %s" % (models_dir)
def get_log_reg_features(data):
dropout_value = 0 #bin value for a student dropped out
command_base = ["./HMM_EM", "PredictStateDistribution", models_dir]
logreg_X = None
logreg_Y = []
for student in range(len(data) / num_weeks):
stud_data = data[student * num_weeks:(student + 1) * num_weeks]
end_week = lag - 1
label_week = lead + end_week
X = stud_data[0:end_week + 1, :].flatten()
truth_val = stud_data[label_week, 0]
if stud_data[end_week, 0] == dropout_value:
continue #student has already dropped out
features = np.array([])
for prediction_week in range(end_week + 1):
# get hidden state distribution for each prediction_week
command = command_base + [
str(prediction_week)
] + X.astype(str).tolist(
) #need to pass lead+end_week in- API asks for week to predict
results = subprocess.check_output(command)
state_dist = np.fromstring(results, sep=";")[1:-1]
prediction_week_features = state_dist[:-1]
features = np.concatenate(
[features,
np.atleast_1d(prediction_week_features)])
logreg_X = utils.add_to_data(logreg_X, features)
logreg_Y += [truth_val]
return logreg_X, logreg_Y
# do inference on hmm to get features for logreg
X_train, Y_train = get_log_reg_features(train_logreg_data)
print "got train log_reg features for lead %s lag %s cohort %s support %s" % (
lead, lag, data_file_base,
num_support), time.time() - start_time, "seconds"
#do inference on test set to get logreg features
start_time = time.time()
X_test, Y_test = get_log_reg_features(test_data)
print "got test log_reg features for lead %s lag %s cohort %s support %s" % (
lead, lag, data_file_base,
num_support), time.time() - start_time, "seconds"
return logistic_regression.run_regression(X_train, Y_train, X_test, Y_test,
lead, lag)
#Split data into training and testing sets x_train, x_test, y_train, y_test = train_test_split(X, T, test_size=0.40) #Run SVM with a linear kernel #svm.svm_linear(x_train, y_train, x_test, y_test) #Run SVM with a polynomial kernel #svm.svm_poly(x_train, y_train, x_test, y_test) #Run SVM with a radial basis function kernel #svm.svm_rbf(x_train, y_train, x_test, y_test) #Run SVM with a sigmoid kernel #svm.svm_sigmoid(x_train, y_train, x_test, y_test) #Run Logistic Regression lr.run_regression(True, 30, "Logistic Regression", x_train, y_train, x_test, y_test) #Run Scaled Logistic Regression lr.run_regression(True, 30, "Scaled Logistic Regression", x_train, y_train, x_test, y_test) #Run Log-transform Logistic Regression lr.run_regression(True, 30, "Log-transform Logistic Regression", x_train, y_train, x_test, y_test) #Run Binary-transform Logistic Regression lr.run_regression(True, 30, "Binary-transform Logistic Regression", x_train, y_train, x_test, y_test)
def run_log_reg_hmm(data_file_base, num_support, num_pools, num_iterations, lead, lag, train=False, do_parallel=True): start_time = time.time() num_weeks = 15 data_prefix = "data/" data_suffix = ".csv" models_prefix = "models/" models_suffix = "_support_%s_logreg" % (num_support) data_file_train_input = data_prefix + data_file_base + "_train" + data_suffix data_file_train_hmm = data_prefix + data_file_base + "_train_logreg" + data_suffix data_file_test = data_prefix + data_file_base + "_test" + data_suffix models_dir = models_prefix + data_file_base + models_suffix train_data = np.genfromtxt(data_file_train_input, delimiter = ';', skip_header = 0) test_data = np.genfromtxt(data_file_test, delimiter = ";", skip_header = 0) #split into train 1 and train 2 num_students = len(train_data) / num_weeks num_students_train_hmm = num_students / 2 train_hmm_data = train_data[: num_students_train_hmm * num_weeks] train_logreg_data = train_data[num_students_train_hmm * num_weeks :] #train hmm on train_hmm_data np.savetxt(data_file_train_hmm, train_hmm_data, fmt="%d", delimiter=";") if not os.path.exists(models_dir) or train: run_train_hmm.train_model(data_file_base, num_support, num_pools=num_pools, num_iterations=num_iterations, logreg=True, do_parallel=do_parallel) assert os.path.exists(models_dir), "There is no trained model in directory %s" % (models_dir) def get_log_reg_features(data): dropout_value = 0 #bin value for a student dropped out command_base = ["./HMM_EM", "PredictStateDistribution", models_dir] logreg_X = None logreg_Y = [] for student in range(len(data) / num_weeks): stud_data = data[student * num_weeks: (student + 1) * num_weeks] end_week = lag -1 label_week = lead + end_week X = stud_data[0: end_week + 1, :].flatten() truth_val = stud_data[label_week, 0] if stud_data[end_week, 0] == dropout_value: continue #student has already dropped out features = np.array([]) for prediction_week in range(end_week + 1): # get hidden state distribution for each prediction_week command = command_base + [str(prediction_week)]+ X.astype(str).tolist() #need to pass lead+end_week in- API asks for week to predict results = subprocess.check_output(command) state_dist = np.fromstring(results, sep=";")[1:-1] prediction_week_features = state_dist[:-1] features = np.concatenate([features, np.atleast_1d(prediction_week_features)]) logreg_X = utils.add_to_data(logreg_X, features) logreg_Y += [truth_val] return logreg_X, logreg_Y # do inference on hmm to get features for logreg X_train, Y_train = get_log_reg_features(train_logreg_data) print "got train log_reg features for lead %s lag %s cohort %s support %s" % (lead, lag, data_file_base, num_support), time.time() - start_time, "seconds" #do inference on test set to get logreg features start_time = time.time() X_test, Y_test = get_log_reg_features(test_data) print "got test log_reg features for lead %s lag %s cohort %s support %s" % (lead, lag, data_file_base, num_support), time.time() - start_time, "seconds" return logistic_regression.run_regression(X_train, Y_train, X_test, Y_test, lead, lag)