def progressive(system_val):
global system
system = system_val
if configs.strategy == 'progressive':
configs.show_actual_lc = False
if configs.plot is True or configs.plot_real_cost is True:
plot.curr_system = system_val
data = load_data()
perf_values = load_perf_values()
data[data == 'Y'] = 1
data[data == 'N'] = 0
data = data.astype(bool)
repeat = configs.repeat
if configs.th == 2 or configs.th == 3:
total_range = range((details_map[system][1]//10)//configs.th)
else:
total_range = range(int((details_map[system][1]//10)/configs.th))
results = np.empty((len(total_range),repeat))
data_list = []
for j in range(repeat):
for i in total_range:
np.random.seed(j)
if configs.fix_test_set is True:
test_set_indices = np.random.choice(data.shape[0],details_map[system][1] // configs.fix_test_ratio,replace=False)
curr_size = 10*(i+1)
if configs.fix_test_set is True:
train_opt_indices = set(range(data.shape[0])) - set(test_set_indices)
training_set_indices = np.random.choice(np.array(list(train_opt_indices)),curr_size,replace=False)
else:
training_set_indices = np.random.choice(data.shape[0],curr_size,replace=False)
diff_indices = set(range(data.shape[0])) - set(training_set_indices)
training_set = data[training_set_indices]
if configs.fix_test_set is True:
test_set_indices = test_set_indices
else:
test_set_indices = np.random.choice(np.array(list(diff_indices)),curr_size,replace=False)
test_set = data[test_set_indices]
X = training_set
y = perf_values[training_set_indices]
if configs.model is 'cart':
built_tree = cart(X, y)
out = predict(built_tree, test_set, perf_values[test_set_indices])
else:
clf = SVR(C=1.0, epsilon=0.2)
clf.fit(X, y)
out = predict(clf, test_set, perf_values[test_set_indices])
results[i][j] = calc_accuracy(out,perf_values[test_set_indices])
if print_detail is True:
print('['+system+']' + " iteration :"+str(j+1))
print()
out_file = open(os.path.join(base_dir_out,system)+"_out_"+strategy+"_"+str(configs.model),'w')
out_file.truncate()
cost_prev = sys.maxsize
size_prev = 0
acc_prev = 0
opt_cost = 0
cost_list = []
if configs.show_actual_lc is True:
local_xdata = []
local_ydata = []
opt_found = False
for i in range(results.shape[0]):
size = (i+1)*10
error = mean(results[i])
out_file.write(str(size)+","+ str(error))
out_file.write('\n')
if configs.plot is True or configs.plot_real_cost is True:
plot.x_data_prog.append(size)
if configs.show_actual_lc is True:
local_xdata.append(size)
if configs.plot_real_cost is False:
if error > 100:
plot.y_data_prog.append(100-100)
if configs.show_actual_lc is True:
local_ydata.append(100-100)
else:
plot.y_data_prog.append(100-error)
if configs.show_actual_lc is True:
local_ydata.append(100-error)
if configs.calc_prog_opt is True:
R = configs.r
S = configs.details_map[system][1]//3
cost_curr = cost_eqn(configs.th,size,error,S,R)
cost_list.append(cost_curr)
if configs.plot_real_cost is True:
plot.y_data_prog.append(cost_curr)
if cost_curr > cost_prev and opt_found is False:
plot.opt_size = size_prev
plot.opt_accu = acc_prev
plot.opt_cost = cost_prev
opt_cost = cost_prev
opt_found = True
else:
cost_prev = cost_curr
size_prev = size
acc_prev = 100-error
if configs.show_actual_lc is True:
data_list.append((local_xdata,local_ydata))
real_cost = min(cost_list)
plot.real_min_cost = real_cost
if configs.print_detail is True:
print('Accuracy at optimal:',acc_prev)
if configs.plot is True and configs.strategy == 'progressive':
plot.plot_now()
return data_list,opt_cost,real_cost
def sample(system):
configs.extend_lambda = False
data_train = load_data(True)
perf_values_train = load_perf_values(True)
data_test = load_data(False)
perf_values_test = load_perf_values(False)
data_train[data_train == 'Y'] = 1
data_train[data_train == 'N'] = 0
data_train = data_train.astype(bool)
data_test[data_test == 'Y'] = 1
data_test[data_test == 'N'] = 0
data_test = data_test.astype(bool)
repeat = configs.repeat
if print_detail is True:
print('Size of '+str(system)+' '+str(configs.tway)+'-way sample is: '+str(data_train.shape[0]))
corr_list = []
for s in range(repeat):
if print_detail is True:
print('Iteration',s)
results = dict()
j = random.randint(1,30*100100)
if configs.fix_test_set is True:
test_set_indices = np.random.choice(data_test.shape[0],configs.details_map[system][1] // configs.fix_test_ratio,replace=False)
i=0
while True:
if i==data_train.shape[0]:
break
else:
i=i+1
curr_size = i
np.random.seed(j)
training_set_indices = np.random.choice(data_train.shape[0],curr_size,replace=False)
training_set = data_train[training_set_indices]
if configs.fix_test_set is True:
test_set_indices = test_set_indices
else:
test_set_indices = np.random.choice(data_test.shape[0],curr_size,replace=False)
test_set = data_test[test_set_indices]
X = training_set
y = perf_values_train[training_set_indices]
built_tree = base.cart(X, y)
out = base.predict(built_tree, test_set, perf_values_test[test_set_indices])
if curr_size in results:
print('%%%%%%%%%%%%%%%%%%%% SHOCK!! &&&&&&&&&&&&&&&&&&&')
else:
accu = base.calc_accuracy(out,perf_values_test[test_set_indices])
if accu <= 100:
results[curr_size] = accu
result_in_cluster = base.check_result_cluster(results)
if configs.add_origin_to_lambda is True and result_in_cluster is True:
results[0] = 100
if configs.transform_lambda is True:
results = base.transform_lambda_set(results)
if print_detail is True:
print('Size of lambda set: '+ str(len(results)))
'''
Transform the axes and calculate pearson correlation with
each learning curve
'''
curve_data = base.transform_axes(base.smooth(base.dict_to_array(results)))
parameter_dict = dict()
correlation_data = dict()
''' keys here are individual curves for a given system. Values are 2-d array. x: transformed "no. of sample" values
and y: transformed accuracy at that sample value'''
for keys in curve_data:
slope, intercept, rvalue, pvalue, stderr = sp.stats.linregress(curve_data[keys][configs.ignore_initial:,0],curve_data[keys][configs.ignore_initial:,1])
if print_detail is True:
print(keys,intercept,slope)
value_a = base.get_intercept(intercept,keys)
value_b = base.get_slope(slope,keys)
parameter_dict[keys] = {'a' : value_a, 'b':value_b}
value_r = configs.r
value_s = configs.details_map[system][1]/3
optimal_size = base.get_optimal(value_a,value_b,value_r,value_s,keys)
estimated_error = 100
weiss_within_range = True
if keys == 'weiss' and (abs(value_a) + abs(value_b)) > 100:
weiss_within_range = False
if optimal_size <= (data_train.shape[0]+data_test.shape[0])//configs.th and optimal_size > 1 and weiss_within_range is True:
mean_accu,sd = get_projected_accuracy(optimal_size,data_train,perf_values_train,data_test,perf_values_test,test_set_indices)
r = configs.r
th = configs.th
total_cost = base.cost_eqn(th,optimal_size, 100-float(mean_accu), configs.details_map[system][1] // 3, r)
estimated_error = base.get_error_from_curve(value_a,value_b,optimal_size,keys)
estimated_cost = base.cost_eqn(th,optimal_size,estimated_error,configs.details_map[system][1] // 3, r)
else:
mean_accu,sd,total_cost,estimated_cost = (None,None,None,None)
correlation_data[keys] = {'correlation' : rvalue,
'p-value' : str(pvalue),
'optimal sample size' :optimal_size,
'accuracy' :mean_accu,
'estimated accuracy': 100 - estimated_error,
'standard deviation' :sd,
'total cost' :total_cost,
'estimated cost' : estimated_cost,
'a' : value_a,
'b' : value_b,
'lambda size' : len(results)}
selected_curve = base.select_curve(correlation_data)
if print_detail is True:
print()
print('Detailed learning projections:')
print('<curve-id> : {<details>}')
print()
for keys in correlation_data:
if keys in selected_curve:
correlation_data[keys]['selected'] = True
if print_detail is True:
print(str(keys) +"**:"+str(correlation_data[keys]))
else:
correlation_data[keys]['selected'] = False
if print_detail is True:
print(str(keys) +":"+str(correlation_data[keys]))
if print_detail is True:
print("-----------------------------------------------")
print()
corr_list.append(correlation_data)
if configs.plot is True and configs.sense_curve is True:
plot.curr_system = system
plot.prog_data.append((results,correlation_data))
if configs.plot is True and configs.sense_curve is True:
plot.plot_now()
return base.mean_corr_list(corr_list)
else:
return base.mean_corr_list(corr_list)
def projective(system_val):
if print_detail is True:
print('System-id : '+system_val)
print('R value : '+str(configs.r))
print('th value : '+str(configs.th))
global system
if configs.plot is not True:
configs.show_actual_lc = False
system = system_val
data = load_data()
perf_values = load_perf_values()
data[data == 'Y'] = 1
data[data == 'N'] = 0
data = data.astype(bool)
repeat = configs.repeat
corr_list = []
if configs.plot is True and configs.show_actual_lc is True:
real_curve_points = progressive(system_val)
plot.real_curve_pts = real_curve_points[0]
for s in range(repeat):
if print_detail is True:
print('Running iteration :' +str(s))
if configs.fix_test_set is True:
test_set_indices = np.random.choice(data.shape[0],details_map[system][1] // configs.fix_test_ratio,replace=False)
else:
test_set_indices = []
results = dict()
results = build_data_points(results,repeat, data, perf_values, True,None,test_set_indices)
if print_detail is True:
print('Size of lambda set: '+ str(len(results)))
'''
Transform the axes and calculate pearson correlation with
each learning curve
'''
if configs.smooth is True:
curve_data = transform_axes(smooth(dict_to_array(results)))
else:
curve_data = transform_axes(dict_to_array(results))
parameter_dict = dict()
correlation_data = dict()
''' keys here are individual curves for a given system. Values are 2-d array. x: transformed "no. of sample" values
and y: transformed accuracy at that sample value'''
for keys in curve_data:
slope, intercept, rvalue, pvalue, stderr = sp.stats.linregress(curve_data[keys][configs.ignore_initial:,0],curve_data[keys][configs.ignore_initial:,1])
value_a = get_intercept(intercept,keys)
value_b = get_slope(slope,keys)
parameter_dict[keys] = {'a' : value_a, 'b':value_b}
value_r = configs.r
value_s = details_map[system][1]/3
optimal_size = get_optimal(value_a,value_b,value_r,value_s,keys)
estimated_error = 100
weiss_within_range = True
if keys == 'weiss' and (abs(value_a) + abs(value_b)) > 100:
weiss_within_range = False
if optimal_size <= data.shape[0]//configs.th and optimal_size > 1 and weiss_within_range is True:
mean_accu,sd = get_projected_accuracy(optimal_size,repeat,data,perf_values,test_set_indices)
r = configs.r
th = configs.th
total_cost = cost_eqn(th,optimal_size, 100-float(mean_accu), details_map[system][1] // 3, r)
estimated_error = get_error_from_curve(value_a,value_b,optimal_size,keys)
estimated_cost = cost_eqn(th,optimal_size,estimated_error,details_map[system][1] // 3, r)
else:
mean_accu,sd,total_cost,estimated_cost,optimal_size = (None,None,None,None,None)
correlation_data[keys] = {'correlation' : rvalue,
'p-value' : str(pvalue),
'optimal sample size' :optimal_size,
'accuracy' :mean_accu,
'estimated accuracy': 100 - estimated_error,
'standard deviation' :sd,
'total cost' :total_cost,
'estimated cost' : estimated_cost,
'a' : value_a,
'b' : value_b,
'stderr' : stderr,
'lambda size' : len(results)}
if configs.curve_selection == 'dynamic':
selected_curve,results = select_curve_dynamic(correlation_data,data,perf_values,parameter_dict,results,test_set_indices)
elif configs.curve_selection == 'static':
selected_curve = select_curve(correlation_data)
else:
selected_curve = select_curve_composite(correlation_data)
if print_detail is True:
print()
print('Detailed learning projections:')
print('<curve-id> : {<details>}')
print()
for keys in correlation_data:
if keys in selected_curve:
correlation_data[keys]['selected'] = True
if print_detail is True:
print(str(keys) +"**:"+str(correlation_data[keys]))
else:
correlation_data[keys]['selected'] = False
if print_detail is True and float(correlation_data[keys]['correlation']) < configs.min_corr:
print(str(keys) +":"+str(correlation_data[keys]))
if print_detail is True:
print("-----------------------------------------------")
print()
corr_list.append(correlation_data)
if configs.plot is True and configs.sense_curve is True:
plot.prog_data.append((results,correlation_data))
plot.curr_system = system_val
if configs.plot is True and configs.sense_curve is True:
p_value = mean_corr_list(corr_list)
plot.plot_now()
else:
p_value = mean_corr_list(corr_list)
return p_value