def validate_k(X, y, k_list):
"""
Perform 5-fold validation on dataset X, y with
varying K values from k_list. Used to derive some
intuition about what range of k to validate over.
Returns a 5 x len(k_list) array of test accuracies
"""
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=1)
accuracy = np.zeros((5, len(k_list)))
i = 0
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]
for j, k in enumerate(k_list):
classifier = kNN()
classifier.train(X_train, y_train)
y_pred = classifier.predict(X_test, k)
acc = (y_pred == y_test).mean()
accuracy[i, j] = acc
print("Q1 -- TIME: {} Fold {}, k: {}, Accuracy: {}".format(
datetime.now().strftime('%Y-%m-%d %H:%M:%S'), i + 1, k, acc))
i += 1
statistics = np.zeros((2, len(k_list)))
statistics[0, :] = np.mean(accuracy, 0)
statistics[1, :] = np.std(accuracy, 0)
return accuracy
def q1_regularised(X, y, k_list):
"""
For 20 different test/train splits, calculate
the classification accuracy on the test set using
the values in k_list.
Returns:
test_acc_ar: np.array: test errors for all 20 runs,
across all hyperparameters in k_list.
"""
test_acc_ar = np.zeros((20, len(k_list)))
for i in range(20):
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=i,
stratify=y)
print("Run {}".format(i))
for j, k in enumerate(k_list):
classifier = kNN()
classifier.train(X_train, y_train)
y_pred = classifier.predict(X_test, k)
test_acc = (y_pred == y_test).mean()
test_acc_ar[i, j] = test_acc
print("k Parameter: {}, Accuracy: {}".format(k, test_acc))
return test_acc_ar
def result(xtrain,xtest,ytrain,ytest,k): print 'Results for Knn with k =',k clf = knn.kNN(k=k,distance_m = distance) clf.fit(xtrain,ytrain) prd = clf.predict(xtest) print "Accuracy:",accuracy_score(ytest,prd) print 'Confusion Matrix' print confusion_matrix(ytest,prd) return accuracy_score(ytest,prd)
def main():
"""Do a test if called from the command line"""
data = pd.read_csv(DATAFILE, header=None, names=HEADER)
X = data[FEATURES]
y = data.species
model = kNN(k=10)
model.fit(X, y)
print "Accuracy on training set:", model.score(X, y)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=.80)
model.fit(X_train, y_train)
print "Accuracy on test set: ", model.score(X_test, y_test)
def main():
"""Do a test if called from the command line"""
data = pd.read_csv(DATAFILE, header=None, names=HEADER)
X = data[FEATURES]
y = data.species
model = kNN(k=10)
model.fit(X, y)
print "Accuracy on training set:", model.score(X, y)
X_train, X_test, y_train, y_test = train_test_split(X, y, train_size=.80)
model.fit(X_train, y_train)
print "Accuracy on test set: ", model.score(X_test, y_test)
def main():
# List of patient objects
patient_list = parse_csv()
# create the ten folds
ten_folds_strat_list = stratify_data(patient_list)
# create the classifer objects
knn = kNN()
naive_bayes = naiveBayes()
# call the 10-fold cross validation
ten_fold_strat_cross_validation(knn, ten_folds_strat_list, 10)
ten_fold_strat_cross_validation(naive_bayes, ten_folds_strat_list)
def runTests(trainning, tr_classes, test, t_classes, k):
errors = 0
for i in range(len(test)):
c = knn.kNN(k, test[i], trainning, tr_classes)
if (c != t_classes[i]):
#print("Classification: " + str(c) + " Correct answer: " + str(t_classes[i]))
errors = errors + 1
print("######## K = " + str(k) + " ########")
print("Dataset size: " + str(len(trainning) + len(test)))
print("Trainning set size: " + str(len(trainning)))
print("Test set size: " + str(len(test)))
print("Errors: " + str(errors))
print("Accuracy: " + str(1 - float(errors) / len(test)))
print("\n")
return (1 - errors / len(test))
def nca_mnist_experiment(trial, train_percentage=0.1, test_percentage=0.1):
encoding_train_imgs_path = './data/MNIST_encoding/tf_train.encoding'
encoding_test_imgs_path = './data/MNIST_encoding/tf_test.encoding'
train_labels_path = './data/MNIST_encoding/tf_train.labels'
test_labels_path = './data/MNIST_encoding/tf_test.labels'
encoding_train = pickle.load(open(encoding_train_imgs_path, 'rb'))
encoding_test = pickle.load(open(encoding_test_imgs_path, 'rb'))
print(encoding_train.shape)
train_labels = pickle.load(open(train_labels_path, 'rb'))
test_labels = pickle.load(open(test_labels_path, 'rb'))
print(train_labels.shape)
m = len(encoding_train)
train_m = int(m * train_percentage)
sel = random.sample(range(m), train_m)
X = encoding_train.astype(np.float)[sel]
y = train_labels[sel]
print(X.shape)
print(y.shape)
m = len(encoding_test)
test_m = int(m * test_percentage)
sel = random.sample(range(m), test_m)
X_test = encoding_test.astype(np.float)[sel]
y_test = test_labels[sel]
print(X_test.shape)
print(y_test.shape)
knn = kNN()
k_valus = [1, 3, 5, 7]
for k in k_valus:
knn.k = k
acc_list = []
for _ in range(trial):
acc = knn.evaluate(X, y, X_test, y_test)
acc_list.append(acc)
print(np.mean(np.array(acc_list)))
nca = NCA(max_iter=100, learning_rate=0.01)
nca.fit(X, y)
x_train = nca.transform()
x_test = nca.transform(X_test)
for k in k_valus:
knn.k = k
acc_list = []
for _ in range(trial):
acc = knn.evaluate(x_train, y, x_test, y_test)
acc_list.append(acc)
print(np.mean(np.array(acc_list)))
import datetime
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
from load_data import *
from knn import kNN
from plot import plot
if __name__ == "__main__":
train_set = load_data(10)
plot(train_set)
new_data = ['ZL', 169, 2]
train_data = np.array(train_set[['打斗镜头', '接吻镜头']])
train_labels = np.array(train_set[['电影类别']])
time_s = datetime.datetime.now()
# ===========================手动实现======================
label = kNN(new_data[1:], train_data, train_labels, k=3)
time_e = datetime.datetime.now() - time_s
print('用时：', time_e)
print('新数据的类别：', label)
# ===========================sklearn实现======================
clf = KNeighborsClassifier(n_neighbors=3)
clf.fit(train_data, train_labels)
label = clf.predict([new_data[1:]]) # 输入是2D数据
time_e = datetime.datetime.now() - time_s
print('用时：', time_e)
print('新数据的类别：', label[0])
# -*- coding: utf-8 -*-
import f_test as ftest
import knn as knn
import centroid as cc
import lr as lr
import svm as svm
if __name__ == '__main__':
file_name = 'GenomeTrainXY.txt'
raw_data = ftest.get_data(file_name)
features, scores = ftest.f_test(raw_data)
ftest.print_scores(features, scores)
train = knn.pickTrainingData(file_name, features)
test = knn.pickTestData("GenomeTestX.txt", features)
print("\n\nPredictions for KNN (k=3) Classifier: ")
knn.kNN(3, train, test)
print("\nPredictions for Centroid Classifier: ")
cc.centroid_classifier(train, test)
print("\nPredictions for Linear Regression: ")
lr.linear_regression(train, test)
print("\nPredictions for SVM: ")
svm.svm_classifier(train, test)
def main_worker(gpu, ngpus_per_node, args):
args.gpu = gpu
# suppress printing if not master
if args.multiprocessing_distributed and args.gpu != 0:
def print_pass(*args):
pass
builtins.print = print_pass
if args.gpu is not None:
print("Use GPU: {} for training".format(args.gpu))
if args.distributed:
if args.dist_url == "env://" and args.rank == -1:
args.rank = int(os.environ["RANK"])
if args.multiprocessing_distributed:
# For multiprocessing distributed training, rank needs to be the
# global rank among all the processes
args.rank = args.rank * ngpus_per_node + gpu
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_url,
world_size=args.world_size,
rank=args.rank)
# create model
print("=> creating model '{}'".format(args.arch))
model = moco.builder.MoCo(
# models.__dict__[args.arch],
netalexnet.alexnet,
args.moco_dim,
args.moco_k,
args.moco_m,
args.moco_t,
args.mlp)
print(model)
if args.distributed:
# For multiprocessing distributed, DistributedDataParallel constructor
# should always set the single device scope, otherwise,
# DistributedDataParallel will use all available devices.
if args.gpu is not None:
torch.cuda.set_device(args.gpu)
model.cuda(args.gpu)
# When using a single GPU per process and per
# DistributedDataParallel, we need to divide the batch size
# ourselves based on the total number of GPUs we have
args.batch_size = int(args.batch_size / ngpus_per_node)
args.workers = int(
(args.workers + ngpus_per_node - 1) / ngpus_per_node)
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
else:
model.cuda()
# DistributedDataParallel will divide and allocate batch_size to all
# available GPUs if device_ids are not set
model = torch.nn.parallel.DistributedDataParallel(model)
elif args.gpu is not None:
torch.cuda.set_device(args.gpu)
model = model.cuda(args.gpu)
# comment out the following line for debugging
raise NotImplementedError("Only DistributedDataParallel is supported.")
else:
# AllGather implementation (batch shuffle, queue update, etc.) in
# this code only supports DistributedDataParallel.
raise NotImplementedError("Only DistributedDataParallel is supported.")
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda(args.gpu)
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
if args.gpu is None:
checkpoint = torch.load(args.resume)
else:
# Map model to be loaded to specified single gpu.
loc = 'cuda:{}'.format(args.gpu)
checkpoint = torch.load(args.resume, map_location=loc)
args.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})".format(
args.resume, checkpoint['epoch']))
else:
print("=> no checkpoint found at '{}'".format(args.resume))
cudnn.benchmark = True
# Data loading code
traindir = os.path.join(args.data, 'train')
testdir = os.path.join(args.data, 'val')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
transform_train = transforms.Compose([
transforms.Resize(size=256),
transforms.RandomResizedCrop(size=224, scale=(0.2, 1.)),
transforms.ColorJitter(0.4, 0.4, 0.4, 0.4),
transforms.RandomGrayscale(p=0.2),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
transform_test = transforms.Compose([
transforms.Resize(size=256),
transforms.CenterCrop(224),
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465),
(0.2023, 0.1994, 0.2010)),
])
train_dataset = ImageFolderInstance(
'/data2/zyf/ImageNet/ILSVRC2012-100/train',
transform=transform_train,
two_crop=True)
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset)
else:
train_sampler = None
# train_loader = torch.utils.data.DataLoader(
# train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
# num_workers=args.workers, pin_memory=True, sampler=train_sampler, drop_last=True)
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
num_workers=args.workers,
drop_last=True)
test_dataset = ImageFolderInstance(
'/data2/zyf/ImageNet/ILSVRC2012-100/val', transform=transform_test)
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=100,
shuffle=False,
num_workers=args.workers,
drop_last=True)
ndata = train_dataset.__len__()
print(ndata)
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
adjust_learning_rate(optimizer, epoch, args)
# print('*******************')
# acc = kNN(0, model, train_loader, test_loader, 200, 0.1, ndata, low_dim=128)
# print('+++++++++++++++++')
# train for one epoch
train(train_loader, model, criterion, optimizer, epoch, args)
print('----------Evaluation---------')
start = time.time()
acc = kNN(0,
model,
train_loader,
test_loader,
200,
0.1,
ndata,
low_dim=128)
print("Evaluation Time: '{}'s".format(time.time() - start))
writer.add_scalar('nn_acc', acc, epoch)
if not args.multiprocessing_distributed or (
args.multiprocessing_distributed
and args.rank % ngpus_per_node == 0):
save_checkpoint(
{
'epoch': epoch + 1,
'arch': args.arch,
'state_dict': model.state_dict(),
'optimizer': optimizer.state_dict(),
},
is_best=False,
filename='checkpoint_{:04d}.pth.tar'.format(epoch))
writer.close()
# -*- coding: utf-8 -*- import numpy as np import cv2 as cv import Dataset import knn path = "ordo_2.csv" DS = Dataset.Dataset(path) df = DS.getDF() print(df.head()) X, Y = DS.getXY() print(X) k = 7 #gnb = GNB.GNB(X, Y) knn = knn.kNN(k, X, Y) accuracy = knn.getAccuracy() print(accuracy)
plt.ylabel("m")
plt.title("{} Sample Complexity".format(title))
plt.show()
if __name__ == "__main__":
# run search for all algorithms
A = [
"Perceptron()",
"Winnow()",
"LinearRegression()",
]
Atitle = [
"Perceptron", "Winnow", "Least Squares", "One nearest-neighbours"
]
for j, i in enumerate(A):
alg = eval(i)
neg = -1
if i == "Winnow()":
neg = 0
mean, std = find_trend_m(alg, neg=neg)
plot_trend(mean, std, Atitle[j])
alg = kNN()
mean, std = find_trend_m(alg,
neg=neg,
num_runs=10,
test_size=6000,
max_n=18)
plot_trend(mean, std, 'OneNN')
import numpy as np
from sklearn import datasets
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap
cmap = ListedColormap(['#FF0000', '#00FF00', '#0000FF'])
iris = datasets.load_iris()
X, y = iris['data'], iris['target']
# print(iris['target_names']) ['setosa' 'versicolor' 'virginica']
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1234)
from knn import kNN
clf = kNN(k=5)
clf.fit(X_train, y_train)
prediction = clf.predict(X_test)
acc = np.sum(prediction == y_test) / len(y_test)
print(acc)
# plt.figure()
# plt.scatter(X[:, 2], X[:, 3], c=y, cmap=cmap, edgecolor='k', s=20)
# plt.show()
def q2_regularised(X, y, n, k_list):
"""
For n different 5-fold test/train splits, calculate
the classification accuracy on the test set using
the values in k_list
Returns
statistics: n by len(k_list) array of the mean and std
test errors for each x-validated run.
k_max_index: list[int]: indexes of the most performant
hyperparameters k for each one (use to derive optimal
hyperparams from k_list)
"""
accuracy = np.zeros((n, len(k_list), 5))
for i in range(n):
skf = StratifiedKFold(n_splits=5, shuffle=True, random_state=i)
l = 0
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]
for j, k in enumerate(k_list):
classifier = kNN()
classifier.train(X_train, y_train)
y_pred = classifier.predict(X_test, k)
acc = (y_pred == y_test).mean()
accuracy[i, j, l] = acc
print("Q2 -- Run {}, Fold {}, k: {}/{}, Accuracy: {}".format(
i + 1, l + 1, j + 1, len(k_list), acc))
l += 1
statistics = np.zeros((2, len(k_list)))
statistics[0, :] = np.mean(accuracy, axis=(0, 2))
statistics[1, :] = np.std(accuracy, axis=(0, 2))
k_stats = np.mean(accuracy, axis=2)
# Selecting most performant K parameter index
k_max_index = np.argmax(k_stats, axis=1)
print(k_max_index)
k_mean = np.mean(accuracy, axis=2)
k_max_index = np.argmax(k_mean, axis=1)
k_std = np.std(accuracy, axis=2)
# Selecting most performant K parameter
optimal_params = [k_list[i] for i in k_max_index]
print(optimal_params)
optimal_errors = []
# Rerun classification using most optimal parameter to find smallest error
for i, k in enumerate(optimal_params):
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=i,
stratify=y)
classifier = kNN()
classifier.train(X_train, y_train)
y_pred = classifier.predict(X_test, k)
error = (y_pred == y_test).mean()
print("K : {}, Error : {}".format(k, error))
optimal_errors.append(error)
optimal_mean = np.mean(optimal_errors)
optimal_std = np.std(optimal_errors)
print(optimal_errors)
print(k_max_index)
print("Optimal Mean Error: {} Optimal Std Error: {}".format(
optimal_mean, optimal_std))
return statistics, k_max_index
# projected training matrix
productMatrix = knnTrainingMatrix.dot(projMatrix)
# get test matrix
knnTestData = open('data/knntest.txt', 'r').readlines()
knnTestMatrix = []
for line in knnTestData:
knnTestMatrix.append(np.fromstring(line, dtype=int, sep=' '))
knnTestMatrix = np.array(knnTestMatrix)
# normal kNN test error
numErrors = 0
for row in knnTestMatrix:
vector = row[:-1]
predictedLabel = kNN(vector, knnTrainingMatrix, labels, 15)
if predictedLabel != row[-1]:
numErrors = numErrors + 1
print(float(numErrors) / len(knnTestMatrix))
# projected kNN test error
numErrors = 0
for row in knnTestMatrix:
vector = row[:-1]
projVector = vector.dot(projMatrix)
predictedLabel = kNN(projVector, productMatrix, labels, 15)
if predictedLabel != row[-1]:
numErrors = numErrors + 1
print(float(numErrors) / len(knnTestMatrix))
# ID3 Example Use
def modelKNN(self, instanceFeature, k):
"""Performs a kNN on the model data and returns a dictionary of the vote
proportions for the k nearest instances."""
return knn.kNN(self.data, instanceFeature, k)
