def EpsDBSCAN(D, k):
nn = NearestNeighbors(n_neighbors=k + 1)
nn.fit(D)
distances, indices = nn.kneighbors(D)
distances = np.delete(distances, 0, 1)
Dist = distances.max(axis=1)
Array = sorted(Dist)
AvgDist = distances.sum(axis=1) / k
Avg_Array = sorted(AvgDist)
plt.plot(Avg_Array, 'b')
num = len(Avg_Array)
n_Array = [0 for i in range(num)]
minArray = min(Avg_Array)
maxArray = max(Avg_Array)
for i in range(num):
n_Array[i] = (Avg_Array[i] - minArray) / (maxArray - minArray) * (1.0 -
0.0)
bins = np.linspace(0, 1, 10)
bin_indice = np.digitize(n_Array, bins)
Eps = []
Avg_Array = np.array(Avg_Array)
count_max = 0
for i in range(10):
count = len(np.where(bin_indice == i)[0])
if count >= k:
#print count
e = np.sum(Avg_Array[bin_indice == i], axis=0) / count
plt.hlines(e, xmin=0, xmax=len(Array), colors='r')
Eps.append(e)
N = len(Eps)
Eps_index = []
for i in range(N):
for j in range(num):
if Avg_Array[j] > Eps[i]:
Eps_index.append(j)
break
ave_slope = (maxArray - minArray) / num
#print 'ave slope'
#print ave_slope
#print ''
for i in range(N - 1):
slope = (Eps[i + 1] - Eps[i]) / (Eps_index[i + 1] - Eps_index[i])
#print slope
if slope > ave_slope * 2:
out = Eps[i]
break
else:
out = Eps[i + 1]
return Eps
def EpsValue(D, k):
nn = NearestNeighbors(n_neighbors=k + 1)
nn.fit(D)
distances, indices = nn.kneighbors(D)
distances = np.delete(distances, 0, 1)
Dist = distances.max(axis=1)
AvgDist = distances.sum(axis=1) / k
out = (max(Dist) - min(AvgDist)) / 100
return min(AvgDist), out
def nn_latentspace(self, verbose=False):
data_train, _, labels_train = self.labelled_set.get_latent()
data_test, _, labels_test = self.unlabelled_set.get_latent()
nn = KNeighborsClassifier()
nn.fit(data_train, labels_train)
score = nn.score(data_test, labels_test)
if verbose:
print("NN classifier score:", score)
print("NN classifier tuple:",
compute_accuracy_tuple(labels_test, nn.predict(data_test)))
return score
def uniformly_random_subsample(pairs_file, n_samples, out_file):
pairs = pd.read_csv(pairs_file, sep='\t')
samples = np.random.uniform(size=(n_samples,pairs.shape[1]-2))
nn = NearestNeighbors(1, n_jobs=-1)
nn.fit(pairs[['vec_sim', 'jac_sim', 'len_sim', 'top_sim']])
index = pd.DataFrame(nn.kneighbors(samples, return_distance=False), columns=['index'])
df = pairs.reset_index().merge(index).drop_duplicates()
df.to_csv(out_file, sep='\t', index=None)
def plot(X_tr, y_tr, a_tr, X_val, y_val, a_val):
class_err = np.zeros((11, 1))
mae = np.zeros((11, 1))
count = 0
for i in [0.0, 0.01, 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09, 0.10]:
nn = NN(i, 20)
nn.fit(X_tr, y_tr, a_tr)
nn.predict(X_tr)
[class_err_1, class_err_2, mae1,
mae2] = nn.calc_error(X_tr, y_tr, a_tr, X_val, y_val, a_val)
class_err[count] = class_err_2
mae[count] = mae2
count = count + 1
fig1 = plt.gcf()
plt.plot(
np.arange(0.0, 1.1, 0.1).tolist(),
class_err,
marker='o',
linestyle='dashed',
label=
r'Observed Classification Error Rate (Y-axis) at Trade-off parameter $\alpha$ (X-axis)'
)
plt.legend(prop={'size': 22})
plt.ylabel('Classification Error on Validation Data', fontsize=22)
plt.yticks(np.arange(0.0, 1.1, 0.1).tolist())
plt.xlabel(r'Trade-off parameter $\alpha$', fontsize=22)
plt.xticks(np.arange(0.0, 1.1, 0.1).tolist())
plt.title('Classification Error vs Trade-off Parameter', fontsize=22)
mng = plt.get_current_fig_manager()
mng.window.showMaximized()
plt.show()
fig1.savefig('C.png')
fig2 = plt.gcf()
plt.plot(
np.arange(0.0, 1.1, 0.1).tolist(),
mae,
marker='o',
linestyle='dashed',
label=
r'Observed Mean Absolute Error (Y-axis) at Trade-off parameter $\alpha$ (X-axis)'
)
plt.legend(prop={'size': 22})
plt.ylabel('Mean Absolute Error on Validation Data', fontsize=22)
plt.xlabel(r'Trade-off parameter $\alpha$', fontsize=22)
plt.xticks(np.arange(0.0, 1.1, 0.1).tolist())
plt.title('Mean Absolute Error vs Trade-off Parameter', fontsize=22)
mng = plt.get_current_fig_manager()
mng.window.showMaximized()
plt.show()
fig2.savefig('M.png')
def main():
DATA_DIR = '../data'
data = np.load(os.path.join(DATA_DIR, "mnist_rot_train.npz"))
X_tr, y_tr, a_tr = data["X"], data["labels"], data["angles"]
data = np.load(os.path.join(DATA_DIR, "mnist_rot_validation.npz"))
X_val, y_val, a_val = data["X"], data["labels"], data["angles"]
#Note: test class labels and angles are not provided
#in the data set
data = np.load(os.path.join(DATA_DIR, "mnist_rot_test.npz"))
X_te, y_te, a_te = data["X"], data["labels"], data["angles"]
nn = BestNN(0.2, 20)
nn.fit(X_tr, y_tr, a_tr)
def experiment_scikitlearn_baselines(train_X, train_Y, test_X, test_Y):
train_X = train_X.numpy().reshape(316, 28*50)
train_Y.numpy()
sv = svm.SVC()
sv.fit(train_X, train_Y)
nn = NearestCentroid()
nn.fit(train_X, train_Y)
ga = GaussianNB()
ga.fit(train_X, train_Y)
dt = tree.DecisionTreeClassifier()
dt.fit(train_X, train_Y)
test_X = test_X.numpy().reshape(100, 28*50)
test_Y.numpy()
print("SVM " + str(accuracy_score(test_Y, sv.predict(test_X))))
print("NN " + str(accuracy_score(test_Y, nn.predict(test_X))))
print("Gausian " + str(accuracy_score(test_Y, ga.predict(test_X))))
print("DT " + str(accuracy_score(test_Y, dt.predict(test_X))))
print("Warning: The following is taking approximately 1.5 hours in an average laptop.")
def main():
DATA_DIR = 'data'
data = np.load(os.path.join(DATA_DIR, "mnist_rot_train.npz"))
X_tr, y_tr, a_tr = data["X"], data["labels"], data["angles"]
data = np.load(os.path.join(DATA_DIR, "mnist_rot_validation.npz"))
X_val, y_val, a_val = data["X"], data["labels"], data["angles"]
#Note: test class labels and angles are not provided
#in the data set
data = np.load(os.path.join(DATA_DIR, "mnist_rot_test.npz"))
X_te, y_te, a_te = data["X"], data["labels"], data["angles"]
#plt.imshow(X_tr[0].reshape((28,28)))
#plt.show()
nn = BestNN(1.0, 30)
#[X_tr, X_val, X_te] = nn.preprocess(X_tr, X_val, X_te)
nn.fit(X_tr, y_tr, a_tr)
nn.predict(X_tr)
nn.calc_error(X_tr, y_tr, a_tr, X_val, y_val, a_val)
nn.savetestpred(X_te)
def update_unlabelled_knn(self):
with torch.set_grad_enabled(False):
self.model.eval()
latent_train, _, labels_train = self.labelled_set.get_latent()
latent_test, _, labels_test = self.unlabelled_set.sequential(
).get_latent() # need original counts as input
counts_test, _ = self.unlabelled_set.sequential().raw_data()
nn = KNeighborsClassifier()
# on a subset of the train data restriction
# latent_train = latent_train
# labels_train = labels_train
nn.fit(latent_train, labels_train)
print("SCORE nn :", nn.score(latent_test, labels_test))
proba_test = nn.predict_proba(latent_test)
classification_ratio = np.zeros(
(len(latent_test), self.gene_dataset.n_labels))
classification_ratio[:, nn.classes_] = proba_test
to_keep = proba_test.max(axis=1) >= 0.8 # Threshold of confidence
# # 1 - Maxime's initial formulation
# classification_ratio = np.log(classification_ratio + 1e-8) * self.knn_classification_ratio
# # 2 - Alternative here classification_ratio > 0 implies the opposite sign somewher
# classification_ratio = -np.log(1 - classification_ratio)
counts_test, labels_test, classification_ratio = \
counts_test[to_keep], labels_test[to_keep], classification_ratio[to_keep]
labelled_test_set = TensorDataset(
torch.from_numpy(counts_test.astype(np.float32)),
torch.from_numpy(labels_test.astype(np.int64)),
torch.from_numpy(classification_ratio.astype(np.float32)))
self.unlabelled_knn_set = self.create_posterior(
gene_dataset=labelled_test_set, shuffle=True)
self.model.train()
def neuralNetworkSK(xTrain, yTrain):
xTrainFlat = flattenComponents(xTrain)
nn = MLPClassifier(activation='logistic', solver='sag')
nn.fit(xTrainFlat, yTrain)
return nn
X_tr = X_tr.reshape(X_tr.shape[0], -1)
X_t = X_t.reshape(X_t.shape[0], -1)
one_hot = OneHotEncoder()
y_tr = train_dataset.targets.numpy().reshape(-1, 1)
y_t = test_dataset.targets.numpy().reshape(-1, 1)
y_tr1 = one_hot.fit_transform(y_tr).toarray()
y_t1 = one_hot.fit_transform(y_t).toarray()
nn.fit(X_tr,
y_tr1,
epochs=1200,
batch_size=64,
loss=BinaryCrossEntropy(),
optimizer=Adam(lr=0.001),
show_progress=TQDM_TERMINAL)
preds = np.round(nn.predict(X_t))
total = len(preds)
correct = 0
for pred, y in zip(preds, y_t1):
if pred.argmax() == y.argmax():
correct += 1
print(f"Accuracy: {float(correct) * 100 / total}%")
def main():
# if GPU is availale, use GPU
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print("Use " + str(device))
# create dataset
file_list = None
for path, dirs, files in os.walk(test_path, topdown=False):
file_list = list(files)
# preprocessing steps
transform = transforms.Compose([
transforms.Resize((512, 512)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
test_dataset = Leaf_test_Dataset(file_list, test_path, transform)
test_loader = DataLoader(dataset=test_dataset, batch_size=batchSize)
print("Start testing:")
# net model
eff_models = []
for model_path in eff_model_paths:
eff_net = EfficientNet.from_name('efficientnet-b4')
eff_net._fc = nn.Linear(eff_net._fc.in_features, 5)
eff_net.load_state_dict(torch.load(model_path))
eff_net = eff_net.to(device)
eff_net.eval()
eff_models.append(eff_net)
preds = []
result = None
with torch.no_grad():
batch_num = len(test_loader)
for index, image in enumerate(test_loader):
image = image.to(device)
eff_result = []
for eff_net in eff_models:
output = eff_net(image)
output = output.to('cpu')
pred = output.argmax(dim=1, keepdim=True).flatten()
eff_result.append(pred)
if len(preds) == 0:
preds = np.dstack(eff_result)[0]
else:
preds = np.vstack([preds, np.dstack(eff_result)[0]])
# start train combine model
df = pd.read_csv(pred_train_csv)
# 移除全错选项
# get the pred acc for this line
def get_acc(pred_csv, index):
label = pred_csv.loc[index, 'label']
acc = 0
if pred_csv.loc[index, 'pred_0'] == label:
acc += 0.2
if pred_csv.loc[index, 'pred_1'] == label:
acc += 0.2
if pred_csv.loc[index, 'pred_2'] == label:
acc += 0.2
if pred_csv.loc[index, 'pred_3'] == label:
acc += 0.2
if pred_csv.loc[index, 'pred_4'] == label:
acc += 0.2
return round(acc, 1)
delete_index = []
for index in range(len(df)):
acc = get_acc(df, index)
# remove noise data
if acc <= 0:
delete_index.append(index)
df = df.drop(delete_index)
df = df.reset_index(drop=True)
X = np.array(df[["pred_0", "pred_1", "pred_2", "pred_3", "pred_4"]])
y = np.array(df[["label"]]).flatten()
from sklearn.neural_network import MLPClassifier
# Neural Network
nn = MLPClassifier(max_iter=2000)
nn.fit(X, y)
result = nn.predict(preds)
pred_result = pd.concat([
pd.DataFrame(file_list, columns=['image_id']),
pd.DataFrame(result, columns=['label'])
],
axis=1)
pred_result.to_csv(output_path + "submission.csv", index=False, sep=',')
print("Done.")
def EpsDBSCAN(D, k):
nn = NearestNeighbors(n_neighbors=k+1)
nn.fit(D)
distances, indices = nn.kneighbors(D)
distances = np.delete(distances, 0, 1)
Dist = distances.max(axis=1)
Array = sorted(Dist)
AvgDist = distances.sum(axis=1)/k
Avg_Array = sorted(AvgDist)
##plt.plot(Avg_Array, 'b')
num = len(Avg_Array)
n_Array = [0 for i in range(num)]
minArray = min(Avg_Array)
maxArray = max(Avg_Array)
for i in range(num):
n_Array[i] = (Avg_Array[i]-minArray)/(maxArray-minArray)*(1.0-0.0)
bins = np.linspace(0, 1, 10)
bin_indice = np.digitize(n_Array, bins)
Eps = []
Avg_Array = np.array(Avg_Array)
count_max = 0
for i in range(10):
count = len(np.where(bin_indice == i)[0])
if count >= k:
e = np.sum(Avg_Array[bin_indice == i], axis=0)/count
##plt.hlines(e, xmin=0, xmax=len(Array), colors='r')
Eps.append(e)
N = len(Eps)
Eps_index = []
for i in range(N):
for j in range(num):
if Avg_Array[j] > Eps[i]:
Eps_index.append(j)
break
ave_slope = (maxArray - minArray)/num
Slopes = []
old_slope = 0.0
for i in range(N-1):
slope = (Eps[i+1] - Eps[i]) / (Eps_index[i+1] - Eps_index[i])
Slopes.append(slope)
##if slope > old_slope and slope < old_slope * 1.1:
## out = Eps[i]
## break
#if i > 0 and slope > ave_slope:
# out = Eps[i]
# break
#else:
# out = Eps[i+1]
# old_slope = slope
ave_slope = sum(Slopes)/len(Slopes)
for i in range(N-1):
if i > 0 and Slopes[i] > ave_slope:
out = Eps[i]
break
else:
out = Eps[i+1]
#if N % 2 == 0:
# median1 = N/2
# median2 = N/2 + 1
# median1 = int(median1) - 1
# median2 = int(median2) - 1
# median = (Eps[median1] + Eps[median2]) / 2
#else:
# median = (N + 1) / 2
# median = int(median) - 1
# median = Eps[median]
#out = median
#out = Avg_Array[int(num*0.9)]
#out = Array[int(num*0.8)]
#out = float(sum(Eps)/len(Eps))
out = Eps[1]
##plt.show()
return out
