def worker(user_rating, train, test, Itrain, Itest, movies_data, epochs,
status):
if status == "GD":
error, users, movies = MMF(user_rating, train, Itrain, epochs)
ploting = plot_error(error, "Gradient_error.png")
elif status == "R_GD":
error, users, movies = MMF_r(user_rating, train, Itrain, beta, epochs)
ploting = plot_error(error, "Randomize_gradient_error.png")
if ploting == False:
print("Something is wrong with errors.")
print("Error you have : ", error)
print("Training error : ", accuracy(users, movies, train, Itrain))
print("Testing error : ", accuracy(users, movies, test, Itest))
print("\n**********************************************************\n")
print("It's time to recommend : \n")
print("Enter User ID : ")
user_id = 2
movie_index = recommend(users, movies, user_id, user_rating)
for i in movie_index:
temp = movies_data.iloc[i]
print("\n", temp["movieId"], "\t\t\t", temp["title"])
def model_selection(self, grid, plot=False, fpath='../images/'):
"""
Holdout model selection
Parameters
----------
grid : instance of HyperRandomGrid class
hyperparameter grid
plot : bool
if plot=True plots the learning curve for each grid parameter
fpath : str
path for images storing
Returns
-------
neural network object
"""
self.fpath = fpath
params = []
errors_va = []
for i, pars in enumerate(grid):
net = nn.NeuralNetwork(self.X_train, self.y_train, **pars)
net.train(self.X_train, self.y_train)
print('trained')
params.append(net.get_params())
# assess on validation set
errors_va.append(
net.predict(self.X_va, self.y_va) / (self.X_va.shape[0]))
if plot is True:
u.plot_error(net,
fname=fpath + 'learning_curve_{}.png'.format(i))
# choosing the best hyperparameters
self.best_index = np.argmin(errors_va)
best_hyperparams = params[self.best_index]
# retraining on design set
net_retrained = nn.NeuralNetwork(
hidden_sizes=best_hyperparams.pop('hidden_sizes'))
net_retrained.train(self.X_design, self.y_design, **best_hyperparams)
df_pars = pd.DataFrame(list(grid))
df_pars['error'] = errors_va
self.best_hyperparams = best_hyperparams
self.df_pars = df_pars
self.model = net_retrained
return self.model
def optimizer_function(user_rating, train, test, Itrain, Itest, movies_data):
print("\nInitiating sliding window optimizer : \n")
errors, users, movies = MMF_sliding_window(user_rating, train, Itrain,
10000, 5)
ploting = plot_error(errors, "Sliding_window_error.png")
if ploting == False:
print("Something is wrong with errors.")
print("Error you have : ", errors)
print("Training error : ", accuracy(users, movies, train, Itrain))
print("Testing error : ", accuracy(users, movies, test, Itest))
print("\n**********************************************************\n")
print("It's time to recommend : \n")
print("Enter User ID : ")
user_id = 2
movie_index = recommend(users, movies, user_id, user_rating)
for i in movie_index:
temp = movies_data.iloc[i]
print("\n", temp["movieId"], "\t\t\t", temp["title"])
print("\nInitiating line search optimizer : \n")
errors, users, movies = MMF_line_search(user_rating, train, Itrain, 10000)
ploting = plot_error(errors, "Line_search_error.png")
if ploting == False:
print("Something is wrong with errors.")
print("Error you have : ", errors)
print("Training error : ", accuracy(users, movies, train, Itrain))
print("Testing error : ", accuracy(users, movies, test, Itest))
print("\n**********************************************************\n")
print("It's time to recommend : \n")
print("Enter User ID : ")
user_id = 2
movie_index = recommend(users, movies, user_id, user_rating)
for i in movie_index:
temp = movies_data.iloc[i]
print("\n", temp["movieId"], "\t\t\t", temp["title"])
def plots_info(opt, name):
plt.close('all')
k, x, e, fxk = calc_trajectory(opt, error, error_to_optim, more_data=True)
#import ipdb; ipdb.set_trace()
plt.figure(figsize=(13,13))
aux()
plot_trayectory(x, title=f'Trayectoria {name}', k=k, with_lines=True,
step_numbers=1000000)
plt.xlim(-1.1,1.1)
plt.ylim(-1.1,1.1)
plt.figure()
plot_error(e, title=f'Error {name}')
print_info(k,x,e,error,f'{name}')
plot_f_evolution(fxk, title=f'Evolucion de f {name}')
max_parents = res[0].astype(int)
mean_ll, std_ll = res[1], res[2]
#from_size, to_size, n_sample, n_restart, max_condset, alpha = parameters
#fig_title = curve.capitalize() + " for " + method + " on " + distribution \
# + " data " + "generated from " + structure + " network\n" \
# + "Mode: " + mode + ", Restarts: " + n_restart \
# + ", Alpha: " + str(int(alpha)/100) \
# + ", MaxCondSet: " + max_condset
fig_title = "Wine Train"
fig, ax = plt.subplots()
ax.set_xlabel('Maximum number of parents')
ax.set_ylabel('10-fold train log-probability / instance')
ax.set_xlim([0, 4])
ax.set_ylim(0, 3)
ax.set_title(fig_title)
alpha_t = 0.4
ax.plot(max_parents, mean_ll)
ut.plot_error(max_parents, mean_ll, std_ll, alpha_t, ax=ax)
plt.savefig(path.join(fig_directory, res_file_name + ".pdf"), transparent=True)
print("Saving figure in ", path.join(fig_directory, res_file_name + ".pdf"))
plt.show()
#ax.yaxis.tick_right()
#nameOfPlot = 'GDP per hour (constant prices, indexed to 2007)'
#plt.ylabel(nameOfPlot,rotation=0)
#ax.legend(frameon=False, loc='upper left',ncol=2,handlelength=4)
alpha_t = 0.4
if method == "cpc":
ax.plot(sizes,
res[3],
linestyle="-.",
linewidth=1.25,
color="green",
label='cpc')
ut.plot_error(sizes,
mean_fscore,
std_fscore,
alpha_t,
ax=ax,
color="green")
elif method == "elidan":
ax.plot(sizes,
res[3],
linestyle="--",
linewidth=1.25,
color="orange",
label='elidan')
ut.plot_error(sizes,
mean_fscore,
std_fscore,
alpha_t,
ax=ax,
color="orange")
def dataset_test(classifier,
validation,
sample_estimate=False,
shuffle=True,
real_dataset=False):
"""
:param classifier: choose between bayes, kNN, MLP and Tree
:param validation: choose between resub, holdout and cross
:param sample_estimate: works only with bayes classifier
:param shuffle: if False dataset is composed by all class1 elements followed by all class2 elements, otherwise all
samples are mixed-up
:param real_dataset: if True use the bank loan dataset, else dataset is generated from two 1-d gaussian
distribution, does not work with bayes classifier
:return: print results in a excel file and save the plots in the plot folder
"""
wb = load_workbook("error-estimates.xlsx")
sheet1 = wb['Foglio1']
row = 29
column = 3
error1 = []
error2 = []
error = []
test_list = [100, 200, 500, 1000, 2000, 5000, 10000, 20000,
50000] # single element correspond to sample per class
e1, e2, b1, b2, tmp1, tmp2, tmp = 0, 0, 0, 0, 0, 0, 0
mu1 = 0
sigma1 = math.sqrt(1)
mu2 = 0
sigma2 = math.sqrt(0.25)
clf = ClassifierSelector(classifier)
for test in test_list:
for i in range(10):
if real_dataset:
x, y = dataset_loader(test)
if shuffle:
shuffle_idx = np.arange(len(y))
np.random.shuffle(shuffle_idx)
x = x[shuffle_idx, :]
y = y[shuffle_idx]
else:
x1 = np.random.normal(mu1, sigma1, test)
x2 = np.random.normal(mu2, sigma2, test)
x = np.concatenate((x1, x2), axis=0)
y1 = np.zeros(test)
y2 = np.full(test, 1)
y = np.concatenate((y1, y2), axis=0)
if shuffle:
shuffle_idx = np.arange(len(y))
np.random.shuffle(shuffle_idx)
x = x[shuffle_idx]
y = y[shuffle_idx]
if validation == 'resub':
if classifier == 'bayes':
if sample_estimate:
mu1 = np.mean(x1)
mu2 = np.mean(x2)
sigma1 = math.sqrt(np.var(x1))
sigma2 = math.sqrt(np.var(x2))
y_pred, e1, e2, b1, b2 = bayes_rule(
x, mu1, sigma1, mu2, sigma2, 0.5, 0.5)
else:
clf.fit(x, y)
y_pred = clf.predict(x)
conf_matrix = metrics.confusion_matrix(y, y_pred)
tmp1 = conf_matrix[0, 1] / list(y).count(0)
tmp2 = conf_matrix[1, 0] / list(y).count(1)
tmp = (conf_matrix[0, 1] + conf_matrix[1, 0]) / len(y)
if validation == 'holdout':
x_train, x_test, y_train, y_test = train_test_split(
x, y, test_size=0.4, random_state=0, stratify=y)
if classifier == 'bayes':
if sample_estimate:
x1 = x_train[y_train == 0]
x2 = x_train[y_train == 1]
mu1 = np.mean(x1)
mu2 = np.mean(x2)
sigma1 = math.sqrt(np.var(x1))
sigma2 = math.sqrt(np.var(x2))
y_pred, e1, e2, b1, b2 = bayes_rule(
x_test, mu1, sigma1, mu2, sigma2, 0.5, 0.5)
else:
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
conf_matrix = metrics.confusion_matrix(y_test, y_pred)
tmp1 = conf_matrix[0, 1] / list(y_test).count(0)
tmp2 = conf_matrix[1, 0] / list(y_test).count(1)
tmp = (conf_matrix[0, 1] + conf_matrix[1, 0]) / len(y_test)
if validation == 'cross':
cross1 = []
cross2 = []
cross = []
if len(x.shape) == 1:
x = np.reshape(x, [len(x), 1])
skf = StratifiedKFold(n_splits=10)
skf.get_n_splits(x, y)
for train_index, test_index in skf.split(x, y):
x_train, x_test = x[train_index], x[test_index]
y_train, y_test = y[train_index], y[test_index]
if classifier == 'bayes':
if sample_estimate:
x1 = x_train[y_train == 0]
x2 = x_train[y_train == 1]
mu1 = np.mean(x1)
mu2 = np.mean(x2)
sigma1 = math.sqrt(np.var(x1))
sigma2 = math.sqrt(np.var(x2))
y_pred, e1, e2, b1, b2 = bayes_rule(
x_test, mu1, sigma1, mu2, sigma2, 0.5, 0.5)
else:
clf.fit(x_train, y_train)
y_pred = clf.predict(x_test)
conf_matrix = metrics.confusion_matrix(y_test, y_pred)
c1 = conf_matrix[0, 1] / list(y_test).count(0)
c2 = conf_matrix[1, 0] / list(y_test).count(1)
c = (conf_matrix[0, 1] + conf_matrix[1, 0]) / len(y_test)
cross1.append(c1)
cross2.append(c2)
cross.append(c)
tmp1 = np.average(cross1)
tmp2 = np.average(cross2)
tmp = np.average(cross)
error1.append(tmp1)
error2.append(tmp2)
error.append(tmp)
sheet1.cell(row=row, column=column).value = np.average(error1)
sheet1.cell(row=row + 1, column=column).value = np.var(error1)
sheet1.cell(row=row + 2, column=column).value = np.average(error2)
sheet1.cell(row=row + 3, column=column).value = np.var(error2)
sheet1.cell(row=row + 4, column=column).value = np.average(error)
sheet1.cell(row=row + 5, column=column).value = np.var(error)
if test == 100:
row1 = 14
col1 = 2
for er in error1:
sheet1.cell(row=row1, column=col1).value = er
row1 += 1
row1 = 14
col1 = 3
for er in error2:
sheet1.cell(row=row1, column=col1).value = er
row1 += 1
row1 = 14
col1 = 4
for er in error:
sheet1.cell(row=row1, column=col1).value = er
row1 += 1
wb.save("error-estimates.xlsx")
column += 1
if classifier == 'bayes' and validation == 'resub' and not sample_estimate:
info = [classifier, 'generic', test]
else:
info = [classifier, validation, test]
# plot_hist(x1, x2, info)
plot_error(error, e1 + e2, info)
# plot_distr(mu1, sigma1, mu2, sigma2, b1, b2, info)
error = []
print("Bayes error1", e1)
print("Bayes error2", e2)
print("Bayes error", e1 + e2)
print("Bayes border: {}, {}".format(b1, b2))
f = MinSquareRoot(A, b)
error_to_optim = ErrorToOptim(f.min())
error = Error()
x0 = np.full((2, 1), 0)
#%%
plt.close('all')
step = 1 / (2 * np.linalg.norm(A, 2)**2)
opt = Optimizer(f, 'constante', step=step, x0=x0)
k, x, e = calc_trajectory(opt, error, error_to_optim)
plt.figure()
plot_trayectory(x, title='Trayectoria Paso constante', k=k)
plt.figure()
plot_error(e, title='Paso constante')
print_info(k, x, e, error, 'Paso constante')
#%%
opt = Optimizer(f, 'decreciente', constant=0.001, x0=x0)
k, x, e = calc_trajectory(opt, error, error_to_optim)
plt.figure()
plot_trayectory(x, title='Trayectoria Paso decreciente', k=k)
plt.figure()
plot_error(e, title='Paso decreciente')
print_info(k, x, e, error, 'Paso constante')
#%%
opt = Optimizer(f, 'line_search', n_points=100, long=0.001, x0=x0)
k, x, e = calc_trajectory(opt, error, error_to_optim)
plt.figure()
print("Training Completed using L1 Regularization")
print("Plot Accuracy")
model_l1.plot_accuracy("L1 Regularization")
print("Plot Error")
model_l1.plot_error("L1 Regularization")
#Read test csv file
data.read(config.TEST_PATH)
print("Test Data Read Successfully")
model_l1.test(data)
print("Predicted test values using L1 Regularization!!!!")
#"""
#L2 Regularization
data.read(config.TRAIN_PATH)
print("train data read successfully")
model_l2 = model_L2.Model(data.size[1])
acc_list_L2, error_list_L2 = model_l2.train(data)
print("Training Completed using L2 Regularization")
print("Plot Accuracy")
model_l2.plot_accuracy("L2 Regularization")
print("Plot Error")
model_l2.plot_error("L2 Regularization")
#Read test csv file
data.read(config.TEST_PATH)
print("Test Data Read Successfully")
model_l2.test(data)
print("Predicted test values using L2 Regularization!!!!")
#"""
utils.plot_accuracy(acc_list, acc_list_L1, acc_list_L2)
utils.plot_error(error_list, error_list_L1, error_list_L2)
from_size, to_size, n_sample, n_restart, max_condset, alpha = parameters
fig_title = curve.capitalize() + " for " + method + " on " + distribution \
+ " data " + "generated from " + structure + " network\n" \
+ ", Restarts: " + n_restart \
+ ", Alpha: " + str(int(alpha)/100) \
+ ", MaxCondSet: " + max_condset
elif method == "elidan":
from_size, to_size, n_sample, n_restart, max_parents, hc_restart = parameters
fig_title = curve.capitalize() + " for " + method + " on " + distribution \
+ " data " + "generated from " + structure + " network\n" \
+ ", Restarts: " + n_restart \
+ ", HCRestarts: " + hc_restart \
+ ", MaxParents: " + max_parents
fig, ax = plt.subplots()
ax.set_xlabel('Size')
ax.set_ylabel('Log-probability / instance')
ax.set_title(fig_title)
alpha_t = 0.4
ax.set_xlim([int(from_size), int(to_size)])
#ax.set_ylim(0.2,0.7)
ax.plot(sizes, mean_ll)
ut.plot_error(sizes, mean_ll, std_ll, alpha_t, ax=ax)
plt.savefig(path.join(fig_directory, res_file_name + ".pdf"), transparent=True)
print("Saving figure in ", path.join(fig_directory, res_file_name + ".pdf"))
sizes_elidan = res_elidan[0].astype(int)
mean_shd_cpc, std_shd_cpc = res_cpc[1], res_cpc[2]
mean_shd_elidan, std_shd_elidan = res_elidan[1], res_elidan[2]
fig, ax = plt.subplots()
ax.set_xlabel('')
ax.set_ylabel('')
alpha_t = 0.4
if method == "cpc":
ax.plot(sizes, res[1], linestyle="-.", linewidth=1.25, color="green", label='cpc')
ut.plot_error(sizes, mean_shd, std_shd, alpha_t, ax=ax, color="green")
elif method == "elidan":
ax.plot(sizes, res[1], linestyle="--", linewidth=1.25, color="orange", label='elidan')
ut.plot_error(sizes, mean_shd, std_shd, alpha_t, ax=ax, color="orange")
elif method == "both":
ax.plot(sizes_cpc, res_cpc[1], linestyle="-.", linewidth=1.25, color="green", label='cpc')
ut.plot_error(sizes_cpc, mean_shd_cpc, std_shd_cpc, alpha_t, ax=ax, color="green")
ax.plot(sizes_elidan, res_elidan[1], linestyle="--", linewidth=1.25, color="orange", label='elidan')
ut.plot_error(sizes_elidan, mean_shd_elidan, std_shd_elidan, alpha_t, ax=ax, color="orange")
ax.set_ylim([0, ax.set_ylim()[1]])
ax.set_xlim([int(from_size), int(to_size)])
if (method == "cpc") or (method == "elidan"):
ax.legend()
plt.savefig(path.join(fig_directory, res_file_name + ".pdf"), transparent=True)