def tryLinearDiscriminantAnalysis(goFast):
from sklearn.datasets import dump_svmlight_file, load_svmlight_file
if goFast:
training_data, training_labels = load_svmlight_file("dt1_1500.trn.svm", n_features=253659, zero_based=True)
validation_data, validation_labels = load_svmlight_file("dt1_1500.vld.svm", n_features=253659, zero_based=True)
testing_data, testing_labels = load_svmlight_file("dt1_1500.tst.svm", n_features=253659, zero_based=True)
else:
training_data, training_labels = load_svmlight_file("dt1.trn.svm", n_features=253659, zero_based=True)
validation_data, validation_labels = load_svmlight_file("dt1.vld.svm", n_features=253659, zero_based=True)
testing_data, testing_labels = load_svmlight_file("dt1.tst.svm", n_features=253659, zero_based=True)
from sklearn.lda import LDA
from sklearn.metrics import accuracy_score
from sklearn.grid_search import ParameterGrid
from sklearn.decomposition import RandomizedPCA
rpcaDataGrid = [{"n_components": [10,45,70,100],
"iterated_power": [2, 3, 4],
"whiten": [True]}]
for rpca_parameter_set in ParameterGrid(rpcaDataGrid):
rpcaOperator = RandomizedPCA(**rpca_parameter_set)
rpcaOperator.fit(training_data,training_labels)
new_training_data = rpcaOperator.transform(training_data,training_labels)
new_validation_data = rpcaOperator.transform(validation_data,validation_labels)
ldaOperator = LDA()
ldaOperator.fit(new_training_data,training_labels)
print "Score = " + str(accuracy_score(validation_labels,ldaOperator.predict(new_validation_data)))
def getPrincipleComponents(xtr, xte, n_components=50):
train = np.array(xtr)
test = np.array(xte)
pca = RandomizedPCA(n_components=n_components).fit(train)
xtrain = pca.transform(train)
xtest = pca.transform(test)
return xtrain, xtest
def SVM(X, y):
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=TRAIN_TEST_SPLIT_RATIO)
print(len(X_train))
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 150
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
X_train_pca = equalize_hist(X_train_pca)
preprocessing.scale(X_train_pca * 1.0, axis=1)
X_test_pca = equalize_hist(X_test_pca)
preprocessing.scale(X_test_pca * 1.0, axis=1)
# classifier = svm.SVC(kernel='poly', degree = 3)
# classifier.fit(X_train, y_train)
# # print("======",3,"========")
# print('TRAIN SCORE', classifier.score(X_train, y_train))
# print('TEST SCORE', classifier.score(X_test, y_test))
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
classifier2 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
classifier2.fit(X_train_pca, y_train)
# print("======",3,"========")
print('TRAIN SCORE', classifier2.score(X_train_pca, y_train))
print('TEST SCORE', classifier2.score(X_test_pca, y_test))
def SVM(X_data, y_data):
X_data = equalize_hist(X_data)
preprocessing.normalize(X_data, 'max')
preprocessing.scale(X_data, axis=1)
# preprocessing.normalize(X_data, 'max')
# X_data = equalize_hist(X_data)
# divide our data set into a training set and a test set
X_train, X_test, y_train, y_test = cross_validation.train_test_split(X_data, y_data, test_size=TRAIN_TEST_SPLIT_RATIO)
n_components = 120
print("Extracting the top %d eigenfaces from %d faces"
% (n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
classifier = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
classifier.fit(X_train_pca, y_train)
print("====== PCA 150 ========")
print('TRAIN SCORE', classifier.score(X_train_pca, y_train))
print('TEST SCORE', classifier.score(X_test_pca, y_test))
def SVM(X_train, y_train, X_test):
print("SVM with PCA of rbf, writening all on, no normalize")
preprocessing.normalize(X_train, 'max')
preprocessing.normalize(X_test, 'max')
#preprocessing.robust_scale(X, axis=1, with_centering = True) #bad
X_train = equalize_hist(X_train)
X_test = equalize_hist(X_test)
'''X_train, X_test, y_train, y_test = cross_validation.train_test_split(X, y, test_size=TRAIN_TEST_SPLIT_RATIO)'''
n_components = 147
print("Extracting the top %d eigenfaces from %d faces"
% (n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=False).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
classifier13.fit(X_train_pca, y_train)
return list(classifier13.predict(X_test_pca))
def do_nbnn(train_folder, test_folder):
train = load_patches(args.train_folder)
test = load_patches(args.test_folder)
if options.relu:
get_logger().info("Applying RELU")
for class_data in train:
class_data.patches = class_data.patches.clip(min=0)
for class_data in test:
class_data.patches = class_data.patches.clip(min=0)
if options.scale:
get_logger().info("Applying standardization")
scaler = StandardScaler(copy=False)
scaler.fit(np.vstack([t.patches for t in train]))
for class_data in train:
class_data.patches = scaler.transform(class_data.patches)
for class_data in test:
class_data.patches = scaler.transform(class_data.patches)
if options.pca:
get_logger().info("Calculating PCA")
pca = RandomizedPCA(n_components=options.pca)
pca.fit(np.vstack([t.patches for t in train]))
#for class_data in train:
#get_logger().info("Fitting class " + class_data.name)
#pca.partial_fit(class_data.patches)
get_logger().info("Keeping " + str(pca.explained_variance_ratio_.sum()) + " variance (" + str(options.pca) +
") components\nApplying PCA")
for class_data in train:
class_data.patches = pca.transform(class_data.patches)
for class_data in test:
class_data.patches = pca.transform(class_data.patches)
nbnn(train, test, NN_Engine())
def main():
#create the training & test sets, skipping the header row with [1:]
dataset = genfromtxt(open('data/train.csv','r'), delimiter=',', dtype='u1')[1:]
target = [x[0] for x in dataset]
train = [x[1:] for x in dataset]
test = genfromtxt(open('data/test.csv','r'), delimiter=',', dtype='u1')[1:]
#build crossvalidation training set
train_train, train_test, target_train, target_test = cross_validation.train_test_split(train, target, test_size=0.2, random_state=0)
print train_train.shape
print train_test.shape
#PCA
pca = RandomizedPCA(n_components=40)
pca.fit(train_train)
#create and train the random forest
rf = RandomForestClassifier(n_estimators=1000, n_jobs=4)
rf.fit(hstack((train_train, pca.transform(train_train))), target_train)
print "crossval score is: ", rf.score(hstack((train_test, pca.transform(train_test))), target_test)
labelid = np.array(range(1,28001))
output = rf.predict(hstack((test, pca.transform(test))))
savetxt('data/submission.csv', np.column_stack((labelid, output)), delimiter=',', header="ImageId,Label", fmt='%u', comments='')
def pca_data(test_x, train_x, params):
print 'pcaing data ...'
components = int(params['components'])
pca = RandomizedPCA(components, whiten=True).fit(train_x)
pca_train_x = pca.transform(train_x)
pca_test_x = pca.transform(test_x)
return pca_test_x, pca_train_x
def test_sparse_randomized_pca_inverse():
"""Test that RandomizedPCA is inversible on sparse data"""
rng = np.random.RandomState(0)
n, p = 50, 3
X = rng.randn(n, p) # spherical data
X[:, 1] *= 0.00001 # make middle component relatively small
# no large means because the sparse version of randomized pca does not do
# centering to avoid breaking the sparsity
X = csr_matrix(X)
# same check that we can find the original data from the transformed signal
# (since the data is almost of rank n_components)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", DeprecationWarning)
pca = RandomizedPCA(n_components=2, random_state=0).fit(X)
assert_equal(len(w), 1)
assert_equal(w[0].category, DeprecationWarning)
Y = pca.transform(X)
Y_inverse = pca.inverse_transform(Y)
assert_almost_equal(X.todense(), Y_inverse, decimal=2)
# same as above with whitening (approximate reconstruction)
with warnings.catch_warnings(record=True) as w:
warnings.simplefilter("always", DeprecationWarning)
pca = RandomizedPCA(n_components=2, whiten=True, random_state=0).fit(X)
assert_equal(len(w), 1)
assert_equal(w[0].category, DeprecationWarning)
Y = pca.transform(X)
Y_inverse = pca.inverse_transform(Y)
relative_max_delta = (np.abs(X.todense() - Y_inverse) / np.abs(X).mean()).max()
# XXX: this does not seam to work as expected:
assert_almost_equal(relative_max_delta, 0.91, decimal=2)
def LogisticRegressionPCA(X, y):
# divide our data set into a training set and a test set
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X, y, test_size=TRAIN_TEST_SPLIT_RATIO)
# get randomized PCA model
num_components = 147
print("Extracting the top %d eigenfaces from %d faces"
% (num_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=num_components, whiten=True).fit(X_train)
# use the PCA model on our training set and test set.
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
h = .02 # step size in the mesh
logistic_regression = linear_model.LogisticRegression(C=1e5)
# we create an instance of Neighbours Classifier and fit the data.
logistic_regression.fit(X, y)
# print the performance of logistic regression
print("====== Logistic Regression with PCA ========")
print('TRAIN SCORE', logistic_regression.score(X_train, y_train))
print('TEST SCORE', logistic_regression.score(X_test, y_test))
def pca_estimator(data, targets, estimator, components_number=DEFAULT_COMPONENTS_NUMBER,
folds_number=DEFAULT_FOLDS_NUMBER):
kf = KFold(len(targets), n_folds=folds_number)
# 'scores' is numpy array. An index is a number of a fold. A value is a percent of right
# predicted samples from a test.
scores = np.zeros(folds_number)
start = time()
index = 0
for train, test in kf:
x_train, x_test, y_train, y_test = data[train], data[test], targets[train], targets[test]
pca = RandomizedPCA(n_components=components_number, whiten=True).fit(x_train)
x_train_pca = pca.transform(x_train)
x_test_pca = pca.transform(x_test)
clf = estimator.fit(x_train_pca, y_train)
scores[index] = clf.score(x_test_pca, y_test)
index += 1
# print("Iteration %d from %d has done! Score: %f" % (index, folds_number,
# scores[index - 1]))
finish = time()
return scores.mean(), scores.std() * 2, (finish - start)
def rpca(train_X, test_X, n):
start_time = time.time()
pca = RandomizedPCA(n_components=n)
pca.fit(train_X.toarray())
train_X_pca = pca.transform(train_X.toarray())
test_X_pca = pca.transform(test_X.toarray())
print("--- %s seconds ---" % (time.time() - start_time))
return pca, train_X_pca, test_X_pca
class Cluster(object):
def __init__(self, name):
self.name = name
self.raw_dataset = []
self.dataset = []
self.dataset_red = []
def get_featurevec(self, data):
'''Takes in data in the form of an array of EmoPackets, and outputs
a list of feature vectors.'''
# CHECKED, all good :)
num_bins = (len(data)/int(dsp.SAMPLE_RATE*dsp.STAGGER) -
int(dsp.BIN_SIZE / dsp.STAGGER) + 1)
size = int(dsp.BIN_SIZE*dsp.SAMPLE_RATE)
starts = int(dsp.SAMPLE_RATE*dsp.STAGGER)
points = []
for i in range(num_bins):
points.append(dsp.get_features(data[i*starts:i*starts+size]))
return points
def add_data(self, raw):
'''Allows the addition of new data. Will retrain upon addition.
Expects a list of EmoPackets.'''
self.dataset.extend(self.get_featurevec(raw))
def extract_features(self):
'''Does feature extraction for all of the datasets.'''
self.dataset = []
for sess in self.raw_dataset:
self.dataset.extend(self.get_featurevec(sess))
def reduce_dim(self, NDIM=5):
'''Reduces the dimension of the extracted feature vectors.'''
X = np.array(self.dataset)
self.pca = RandomizedPCA(n_components=NDIM).fit(X)
self.dataset_red = self.pca.transform(X)
def train(self):
'''Trains the classifier.'''
self.svm = OneClassSVM()
self.svm.fit(self.dataset_red)
def is_novel(self, pt):
'''Says whether or not the bin is novel. Expects an array of EmoPackets'''
X = self.pca.transform(np.array(self.get_featurevec(data)[0]))
ans = self.svm.predict(X)
self.dataset_red.append(X)
self.train()
return ans
def save(self):
'''Saves this classifier to a data directory.'''
this_dir, this_filename = os.path.split(__file__)
DATA_PATH = os.path.join(this_dir, "data", self.name+'.pkl')
dumpfile = open(DATA_PATH, "wb")
pickle.dump(self, dumpfile, pickle.HIGHEST_PROTOCOL)
dumpfile.close()
def reduce_dim(self, NDIM=5):
'''Reduces the dimension of the extracted feature vectors.'''
X = np.array(self.neutral)
pca = RandomizedPCA(n_components=NDIM).fit(X)
print pca.explained_variance_ratio_
self.pca = pca
self.neutral_red = pca.transform(X)
for label in self.labelled:
X = np.array(self.labelled[label])
self.labelled_red[label] = pca.transform(X)
def compute_pca(self):
# print 'We have ', self.x.shape[1], 'features. Reducing dimensionality.'
pca_count = 200
pca = RandomizedPCA(pca_count, copy = False, whiten=True)
pca.fit(self.x_train)
self.x_train = pca.transform(self.x_train)
if self.do_submission:
self.x_test = pca.transform(self.x_test)
if self.do_validation():
self.x_validate = pca.transform(self.x_validate)
def face_rec():
IMG_RES = 100 * 100 # img resolution
NUM_EIGENFACES = 10 # images per train person
NUM_TRAINIMAGES = 110 # total images in training set
#loading training set from folder train_faces
folders = glob.glob('/home/pi/New/SelfieLibrary/cropped/gallery')
# Create an array with flattened images X
# and an array with ID of the people on each image y
X = np.zeros([NUM_TRAINIMAGES, IMG_RES], dtype='int8')
y = []
# Populate training array with flattened imags from subfolders of train_faces and names
c = 0
for x, folder in enumerate(folders):
train_faces = glob.glob(folder + '/*')
for i, face in enumerate(train_faces):
X[c,:] = prepare_image(face)
y.append(face)
c = c + 1
# perform principal component analysis on the images
pca = RandomizedPCA(n_components=NUM_EIGENFACES, whiten=True).fit(X)
X_pca = pca.transform(X)
# load test faces (usually one), located in folder test_faces
test_faces = glob.glob('/home/pi/New/SelfieLibrary/cropped/probe/*')
# Create an array with flattened images X
X = np.zeros([len(test_faces), IMG_RES], dtype='int8')
# Populate test array with flattened imags from subfolders of train_faces
for i, face in enumerate(test_faces):
X[i,:] = prepare_image(face)
# run through test images (usually one)
for j, ref_pca in enumerate(pca.transform(X)):
distances = []
# Calculate euclidian distance from test image to each of the known images and save distances
for i, test_pca in enumerate(X_pca):
if i<c:
dist = math.sqrt(sum([diff**2 for diff in (ref_pca - test_pca)]))
distances.append((dist, y[i]))
found_ID = min(distances)[1]
print float(min(distances)[0])
print "Identified (result: "+ str(found_ID) +" - dist - " + str(min(distances)[0]) + ")"
return distances
def tryDenseOperator(goFast, operatorClass, parameterGrid):
bestScore = 0
bestRpcaParams = None
bestOperatorParams = None
from sklearn.datasets import dump_svmlight_file, load_svmlight_file
if goFast:
training_data, training_labels = load_svmlight_file("dt1_1500.trn.svm", n_features=253659, zero_based=True)
validation_data, validation_labels = load_svmlight_file("dt1_1500.vld.svm", n_features=253659, zero_based=True)
testing_data, testing_labels = load_svmlight_file("dt1_1500.tst.svm", n_features=253659, zero_based=True)
else:
training_data, training_labels = load_svmlight_file("dt1.trn.svm", n_features=253659, zero_based=True)
validation_data, validation_labels = load_svmlight_file("dt1.vld.svm", n_features=253659, zero_based=True)
testing_data, testing_labels = load_svmlight_file("dt1.tst.svm", n_features=253659, zero_based=True)
from sklearn.metrics import accuracy_score
from sklearn.grid_search import ParameterGrid
from sklearn.decomposition import RandomizedPCA
rpcaDataGrid = [{"n_components": [10,45,70,100],
"iterated_power": [1, 2, 3, 4],
"whiten": [False, True]}]
for rpca_parameter_set in ParameterGrid(rpcaDataGrid):
try:
rpcaOperator = RandomizedPCA(**rpca_parameter_set)
rpcaOperator.fit(training_data,training_labels)
new_training_data = rpcaOperator.transform(training_data,training_labels)
new_validation_data = rpcaOperator.transform(validation_data,validation_labels)
for dense_operator_parameter_set in ParameterGrid(parameterGrid):
try:
denseOperator = operatorClass(**dense_operator_parameter_set)
denseOperator.fit(new_training_data,training_labels)
score = accuracy_score(validation_labels,denseOperator.predict(new_validation_data))
print "Score = " + str(score)
if score > bestScore:
bestScore = score
bestRpcaParams = rpca_parameter_set
bestOperatorParams = dense_operator_parameter_set
print "***New best score: " + str(bestScore)
print "***RPCA params: " + str(bestRpcaParams)
print "***Operator params: " + str(bestOperatorParams)
except:
print "Illegal combination skipped"
print sys.exc_info()[:2]
except:
print "Illegal combination skipped."
print sys.exc_info()[:2]
print "***New best score: " + str(bestScore)
print "***RPCA params: " + str(bestRpcaParams)
print "***Operator params: " + str(bestOperatorParams)
def test(n_components):
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)
clf = clf.fit(X_train_pca, y_train)
y_pred = clf.predict(X_test_pca)
print classification_report(y_test, y_pred, target_names=target_names)
print confusion_matrix(y_test, y_pred, labels=range(n_classes))
def run_stuff(n_components):
###############################################################################
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
#n_components = 10
print "Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0])
t0 = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print "done in %0.3fs" % (time() - t0)
print "Explaining variance:"
print np.round(pca.explained_variance_ratio_[:10], decimals=3)
eigenfaces = pca.components_.reshape((n_components, h, w))
print "Projecting the input data on the eigenfaces orthonormal basis"
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print "done in %0.3fs" % (time() - t0)
###############################################################################
# Train a SVM classification model
print "Fitting the classifier to the training set"
t0 = time()
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
# for sklearn version 0.16 or prior, the class_weight parameter value is 'auto'
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
clf = clf.fit(X_train_pca, y_train)
print "done in %0.3fs" % (time() - t0)
print "Best estimator found by grid search:"
print clf.best_estimator_
###############################################################################
# Quantitative evaluation of the model quality on the test set
print "Predicting the people names on the testing set"
t0 = time()
y_pred = clf.predict(X_test_pca)
print "done in %0.3fs" % (time() - t0)
print "Results for " + str(n_components) + " components:"
print classification_report(y_test, y_pred, target_names=target_names)
print confusion_matrix(y_test, y_pred, labels=range(n_classes))
return y_pred, y_test, target_names, X_test, h, w, eigenfaces
def perform_rec():
num_files = sum([len(files) for r, d, files in os.walk('train_faces/')])
IMG_RES = 92 * 112 # img resolution
NUM_EIGENFACES = 10 # images per train person
NUM_TRAINIMAGES = num_files - 1 # total images in training set
#loading training set from folder train_faces
folders = glob.glob('train_faces/*')
# Create an array with flattened images X
# and an array with ID of the people on each image y
X = np.zeros([NUM_TRAINIMAGES, IMG_RES], dtype='int8')
y = []
# Populate training array with flattened imags from subfolders of train_faces and names
c = 0
for x, folder in enumerate(folders):
train_faces = glob.glob(folder + '/*')
for i, face in enumerate(train_faces):
X[c,:] = prepare_image(face)
y.append(ID_from_filename(face))
c = c + 1
# perform principal component analysis on the images
pca = RandomizedPCA(n_components=NUM_EIGENFACES, whiten=True).fit(X)
X_pca = pca.transform(X)
# load test faces (usually one), located in folder test_faces
test_faces = glob.glob('test_faces/*')
# Create an array with flattened images X
X = np.zeros([len(test_faces), IMG_RES], dtype='int8')
# Populate test array with flattened imags from subfolders of train_faces
for i, face in enumerate(test_faces):
X[i,:] = prepare_image(face)
for j, ref_pca in enumerate(pca.transform(X)):
distances = []
# Calculate euclidian distance from test image to each of the known images and save distances
for i, test_pca in enumerate(X_pca):
dist = math.sqrt(sum([diff**2 for diff in (ref_pca - test_pca)]))
distances.append((dist, y[i]))
found_ID = min(distances)[1]
print "Identified (result: "+ str(found_ID) +" - dist - " + str(min(distances)[0]) + ")"
return {'ident':found_ID, 'dist':min(distances)[0] }
def return_pca_transformed_data(train_features, test_features):
# Principal Component Analysis: we are going to find the optimum principal
# components and transform the training and testing data accordingly for other classifier
# that I will using while pipelining them.
pca = RandomizedPCA(n_components=11, whiten=True).fit(train_features)
# Array containing means of individual features for the entire dataset.
means = pca.mean_
total = sum(pca.explained_variance_)
print "PCA trained"
print "The first component expains: ", (float(pca.explained_variance_[0]) / total) * 100, "% of the variance"
# Training and Testing data transformed by PCA
features_train_pca = pca.transform(train_features)
features_test_pca = pca.transform(test_features)
return features_train_pca, features_test_pca
def pcaFaces(X,y,n_components,h,w):
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.25)
print "Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0])
# insert code here
t0 = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print "done in %0.3fs" % (time() - t0)
print "Projecting the input data on the eigenfaces orthonormal basis"
# insert code here
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
eigenfaces = pca.components_.reshape((n_components, h, w))
print "done in %0.3fs" % (time() - t0)
return X_train_pca,X_test_pca,X_test,y_train,y_test,eigenfaces
def pca(X_train, X_test, n):
"""Use PCA to perform unsupervised feature extraction."""
print "Extracting %d principle components from %d features" % (n, X_train.shape[1])
t0 = time()
pca = RandomizedPCA(n_components=n, whiten=True, random_state=47).fit(X_train)
print "done in %0.3fs" % (time() - t0)
print "Transforming the input data"
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print "done in %0.3fs" % (time() - t0)
return X_train_pca, X_test_pca
def preprocess(cross_validation_tuple, preprocess_correlation=False, preprocess_scaling=False):
X = cross_validation_tuple.X_training
X_test = cross_validation_tuple.X_testing
if preprocess_scaling:
scaler = preprocessing.StandardScaler().fit(cross_validation_tuple.X_training)
X = scaler.transform(X)
X_test = scaler.transform(X_test)
if preprocess_correlation:
from sklearn.decomposition import RandomizedPCA
pca = RandomizedPCA(n_components=0.99, whiten=True) # n between 0 and 1 to select number of componnents to explain 99 percents of the data
pca.fit(X)
print("PCA component keeping {}".format(pca.n_components))
X = pca.transform(X)
X_test = pca.transform(X_test)
return Cross_validation_split(X, X_test, cross_validation_tuple.Y_training, cross_validation_tuple.Y_testing)
def test_explained_variance():
# Check that PCA output has unit-variance
rng = np.random.RandomState(0)
n_samples = 100
n_features = 80
X = rng.randn(n_samples, n_features)
pca = PCA(n_components=2).fit(X)
rpca = RandomizedPCA(n_components=2, random_state=rng).fit(X)
assert_array_almost_equal(pca.explained_variance_ratio_,
rpca.explained_variance_ratio_, 1)
# compare to empirical variances
X_pca = pca.transform(X)
assert_array_almost_equal(pca.explained_variance_,
np.var(X_pca, axis=0))
X_rpca = rpca.transform(X)
assert_array_almost_equal(rpca.explained_variance_, np.var(X_rpca, axis=0),
decimal=1)
# Same with correlated data
X = datasets.make_classification(n_samples, n_features,
n_informative=n_features-2,
random_state=rng)[0]
pca = PCA(n_components=2).fit(X)
rpca = RandomizedPCA(n_components=2, random_state=rng).fit(X)
assert_array_almost_equal(pca.explained_variance_ratio_,
rpca.explained_variance_ratio_, 5)
def dimentionality_reduction(train_x , test_x): print "Dimentionality reduction to 10D on training and test data...." pca = RandomizedPCA(n_components=10) train_x = pca.fit_transform(train_x) test_x = pca.transform(test_x) print "Done." return train_x , test_x
def fit(self):
wordids_map = NameToIndex()
labs_map = NameToIndex()
wordscount = self._word_cluster.get_words_count()
print "start compute_tfidf ..."
#计算文档的词袋模型
docs = self._word_cluster.get_samples()
count =0
bow = []
labs = []
for k,v in docs.iteritems():
vec = numpy.zeros(wordscount).tolist()
for i in v:
vec[wordids_map.map(i)]+=1
bow.append(vec)
labs.append(labs_map.map(k[0]))
labs = numpy.array(labs)
tfidf = TfidfTransformer(smooth_idf=True, sublinear_tf=True,use_idf=True)
datas = numpy.array(tfidf.fit_transform(bow).toarray())
print "compute_tfidf done"
pca = RandomizedPCA(n_components=20, whiten=True).fit(datas)
svc = train_svc(numpy.array(labs_map.names), labs, pca.transform(datas))
self._tfidf = tfidf
self._svc = svc
self._labs_map = labs_map
self._wordids_map = wordids_map
self._pca = pca
def test_explained_variance():
"""Check that PCA output has unit-variance"""
rng = np.random.RandomState(0)
n_samples = 100
n_features = 80
X = rng.randn(n_samples, n_features)
pca = PCA(n_components=2).fit(X)
rpca = RandomizedPCA(n_components=2, random_state=42).fit(X)
assert_array_almost_equal(pca.explained_variance_,
rpca.explained_variance_, 1)
assert_array_almost_equal(pca.explained_variance_ratio_,
rpca.explained_variance_ratio_, 3)
# compare to empirical variances
X_pca = pca.transform(X)
assert_array_almost_equal(pca.explained_variance_,
np.var(X_pca, axis=0))
X_rpca = rpca.transform(X)
assert_array_almost_equal(rpca.explained_variance_,
np.var(X_rpca, axis=0))
# Compare with RandomizedPCA using sparse data
X = csr_matrix(X)
rpca = assert_warns(DeprecationWarning, rpca.fit, X)
assert_array_almost_equal(pca.explained_variance_,
rpca.explained_variance_, 1)
assert_array_almost_equal(pca.explained_variance_ratio_,
rpca.explained_variance_ratio_, 3)
def _eliminate_features(X_test, X_train, attribute_count, y_train):
print "Eliminating features until %d has been reached" % attribute_count
pca = RandomizedPCA(n_components=attribute_count+10).fit(X_train)
X_train = pca.transform(to_float(X_train))
print "Finished pca"
clf = SVC(**SVC_parameters)
rfe = RFE(clf, n_features_to_select=attribute_count, step=0.1)
fit = rfe.fit(X_train, y_train)
print "Finished rfe"
# Reduce the feature matrices to contain just the selected features
X_train = [fit.transform(X) for X in X_train]
X_test = [fit.transform(X) for X in pca.transform(to_float(X_test))]
return X_test, X_train
def main():
img_dir = 'images/'
images = [img_dir + f for f in os.listdir(img_dir)]
labels = [f.split('/')[-1].split('_')[0] for f in images]
label2ids = {v: i for i, v in enumerate(sorted(set(labels),
key=labels.index))}
y = np.array([label2ids[l] for l in labels])
data = []
for image_file in images:
img = img_to_matrix(image_file)
img = flatten_image(img)
data.append(img)
data = np.array(data)
# training samples
is_train = np.random.uniform(0, 1, len(data)) <= 0.7
train_X, train_y = data[is_train], y[is_train]
# training a classifier
pca = RandomizedPCA(n_components=5)
train_X = pca.fit_transform(train_X)
multi_svm = OneVsRestClassifier(LinearSVC())
multi_svm.fit(train_X, train_y)
# evaluating the model
test_X, test_y = data[is_train == False], y[is_train == False]
test_X = pca.transform(test_X)
print pd.crosstab(test_y, multi_svm.predict(test_X),
rownames=['Actual'], colnames=['Predicted'])
montage.addResult(component)
mean = pca.mean_.reshape((62,47))
mean = exposure.rescale_intensity(mean, out_range=(0,255)).astype("uint8")
cv2.imshow("Mean", mean)
cv2.imshow("components", montage.montage)
cv2.WaitKey(0)
# train a classifier on the eigenfaces representation
print("[INFO] training classifier...")
model = SVC(kernel="rbf", C=10.0, gamma=0.001, random_state=84)
model.fit(trainData, training.target)
# evaluate the model
print("[INFO] evaluating model...")
predictions = model.predict(pca.transform(testing.data))
print(classification_report(testing.target, predictions))
# loop over the desired number of samples
for i in np.random.randint(0, high=len(testing.data), size=(args["sample_size"],)):
# grab the face and classify it
face = testing.data[i].reshape((62, 47)).astype("uint8")
prediction = model.predict(pca.transform(testing.data[i].reshape(1, -1)))
# resize the face to make it more visable, then display the face and the prediction
print("[INFO] Prediction: {}, Actual: {}".format(prediction[0], testing.target[i]))
face = imutils.resize(face, width=face.shape[1] * 2, inter=cv2.INTER_CUBIC)
cv2.imshow("Face", face)
cv2.waitKey(0)
__author__ = 'pglebow' from sklearn.decomposition import RandomizedPCA from sklearn.neighbors import KNeighborsClassifier pca = RandomizedPCA(n_components=5) train_x = pca.fit_transform(train_x) test_x = pca.transform(test_x) print train_x[:5] #array([[ 12614.55016475, -9156.62662224, -7649.37090539, -3230.94749506, # 2495.71170459], # [ 16111.39363837, -259.55063579, 699.60464599, 3058.59026495, # -1552.34714653], # [ 15019.71069584, -6403.86621428, 1968.44401114, 2896.76676466, # -2157.76499726], # [ 13410.53053415, -1658.3751377 , 261.26829049, 1991.33404567, # -486.60683822], # [ 12717.28773107, -1544.27233216, -1279.70167969, 503.33658729, # -38.00244617]]) knn = KNeighborsClassifier() knn.fit(train_x, train_y)
ax1.set_xlabel("Silhouette coefficient")
ax1.set_ylabel("Cluster label")
# The vertical line for average silhoutte score of all the values
ax1.axvline(x=silhouette_avg, color="red", linestyle="--")
ax1.set_yticks([]) # Clear the yaxis labels / ticks
ax1.set_xticks([0, 0.02, 0.04, 0.06, 0.08, .1])
plt.show()
# fig.savefig('silhouette50_%s'%n_clusters, dpi=700)
# # 2nd Plot showing the actual clusters formed
pca = RandomizedPCA(n_components=2)
reduced_data = pca.fit_transform(X.toarray())
print "PCA done"
centroids = pca.transform(clusterer.cluster_centers_)
for i in range(n_clusters):
col = cm.spectral(float(i) / n_clusters)
ax2.plot(reduced_data[np.argwhere(cluster_labels==i), 0], reduced_data[np.argwhere(cluster_labels==i), 1], \
'.', markersize=4, alpha=0.6,color=col)
ax2.scatter(centroids[:, 0],
centroids[:, 1],
marker='x',
s=169,
linewidths=3,
color='w',
zorder=10)
ax2.set_xticks([])
ax2.set_yticks([])
ax2.set_xticklabels([])
ax2.set_xlabel("1st PCA component")
def pca(n_components=150):
n_components = n_components
print "Extracting the top %d eigenfaces from %d faces" % (n_components,
X_train.shape[0])
t0 = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print "done in %0.3fs" % (time() - t0)
eigenfaces = pca.components_.reshape((n_components, h, w))
print "Projecting the input data on the eigenfaces orthonormal basis"
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print "done in %0.3fs" % (time() - t0)
###############################################################################
# Train a SVM classification model
print "Fitting the classifier to the training set"
t0 = time()
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
# for sklearn version 0.16 or prior, the class_weight parameter value is 'auto'
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
clf = clf.fit(X_train_pca, y_train)
print "done in %0.3fs" % (time() - t0)
print "Best estimator found by grid search:"
print clf.best_estimator_
###############################################################################
# Quantitative evaluation of the model quality on the test set
print "Predicting the people names on the testing set"
t0 = time()
y_pred = clf.predict(X_test_pca)
print "done in %0.3fs" % (time() - t0)
print classification_report(y_test, y_pred, target_names=target_names)
print confusion_matrix(y_test, y_pred, labels=range(n_classes))
###############################################################################
# Qualitative evaluation of the predictions using matplotlib
#def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
# """Helper function to plot a gallery of portraits"""
# pl.figure(figsize=(1.8 * n_col, 2.4 * n_row))
# pl.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
# for i in range(n_row * n_col):
# pl.subplot(n_row, n_col, i + 1)
# pl.imshow(images[i].reshape((h, w)), cmap=pl.cm.gray)
# pl.title(titles[i], size=12)
# pl.xticks(())
# pl.yticks(())
# plot the result of the prediction on a portion of the test set
def title(y_pred, y_test, target_names, i):
pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]
true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]
return 'predicted: %s\ntrue: %s' % (pred_name, true_name)
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 150
print("Extracting the top %d eigenfaces from %d faces"
% (n_components, X_train.shape[0]))
t0 = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("done in %0.3fs" % (time() - t0))
eigenfaces = pca.components_.reshape((n_components, h, w))
print("Projecting the input data on the eigenfaces orthonormal basis")
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done in %0.3fs" % (time() - t0))
# Train a SVM classification model
print("Fitting the classifier to the training set")
t0 = time()
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
clf = clf.fit(X_train_pca, y_train)
print("done in %0.3fs" % (time() - t0))
print("Best estimator found by grid search:")
print(clf.best_estimator_)
# Quantitative evaluation of the model quality on the test set
for cc in CHROMOSOMES:
curs.execute("select * from %s" % chrtable + condition)
for rr in ResultIter(curs):
# print(rr)
cs = CoverageSqlite(cc, rr, args)
# cs.print_range("snp_cov", subrange, h)
feature_array[ii, :] = cs.feature_vector(subrange, "snp_cov")
ii += 1
# X = feature_array - np.mean(feature_array, axis = 0)
from sklearn.decomposition import RandomizedPCA # import sklearn
pca = RandomizedPCA(n_components=3, iterated_power=7)
pca.fit(feature_array)
print(pca.explained_variance_ratio_)
Y = pca.transform(feature_array)
from sklearn.decomposition import FastICA # import sklearn
pca = FastICA(n_components=3)
pca.fit(feature_array)
# print(pca.explained_variance_ratio_)
Y = pca.transform(feature_array)
import matplotlib.pyplot as plt
fig = plt.figure(figsize=(5, 5))
plt.plot(Y[:, 0], Y[:, 1], 'b.')
plt.show()
fig = plt.figure(figsize=(5, 5))
plt.plot(Y[:, 0], Y[:, 2], 'r.')
for i, f in enumerate(files):
print i, "of", len(files)
data.append(get_image_data(f))
labels.append(int(f.split(".")[-2][-1]))
print "done."
pca = RandomizedPCA(n_components=10)
std_scaler = StandardScaler()
X_train, X_test, y_train, y_test = train_test_split(data,
labels,
test_size=0.1)
print "scaling data..."
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)
print "done."
print "transforming data..."
X_train = std_scaler.fit_transform(X_train)
X_test = std_scaler.transform(X_test)
print "done."
print "training model..."
clf = KNeighborsClassifier(n_neighbors=33)
clf.fit(X_train, y_train)
print "done"
print "=" * 20
print clf
print "Confusion Matrix"
def build_SVC(face_profile_data, face_profile_name_index, face_dim):
"""
Build the SVM classification modle using the face_profile_data matrix (numOfFace X numOfPixel) and face_profile_name_index array, face_dim is a tuple of the dimension of each image(h,w) Returns the SVM classification modle
Parameters
----------
face_profile_data : ndarray (number_of_images_in_face_profiles, width * height of the image)
The pca that contains the top eigenvectors extracted using approximated Singular Value Decomposition of the data
face_profile_name_index : ndarray
The name corresponding to the face profile is encoded in its index
face_dim : tuple (int, int)
The dimension of the face data is reshaped to
Returns
-------
clf : theano object
The trained SVM classification model
pca : theano ojbect
The pca that contains the top 150 eigenvectors extracted using approximated Singular Value Decomposition of the data
"""
X = face_profile_data
y = face_profile_name_index
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=42)
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 150 # maximum number of components to keep
print("\nExtracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
eigenfaces = pca.components_.reshape(
(n_components, face_dim[0], face_dim[1]))
# This portion of the code is used if the data is scarce, it uses the number
# of imputs as the number of features
# pca = RandomizedPCA(n_components=None, whiten=True).fit(X_train)
# eigenfaces = pca.components_.reshape((pca.components_.shape[0], face_dim[0], face_dim[1]))
print("\nProjecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
# Train a SVM classification model
print("\nFitting the classifier to the training set")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
# clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
# Best Estimator found using Radial Basis Function Kernal:
clf = SVC(C=1000.0,
cache_size=200,
class_weight='balanced',
coef0=0.0,
decision_function_shape=None,
degree=3,
gamma=0.0001,
kernel='rbf',
max_iter=-1,
probability=False,
random_state=None,
shrinking=True,
tol=0.001,
verbose=False)
# Train_pca with Alex Test Error Rate: 0.088424437299
# Train_pca with Alex Test Recognition Rate: 0.911575562701
clf = clf.fit(X_train_pca, y_train)
# print("\nBest estimator found by grid search:")
# print(clf.best_estimator_)
###############################################################################
# Quantitative evaluation of the model quality on the test set
print("\nPredicting people's names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)
print("\nPrediction took %s per sample on average" %
((time() - t0) / y_pred.shape[0] * 1.0))
# print "predicated names: ", y_pred
# print "actual names: ", y_test
error_rate = errorRate(y_pred, y_test)
print("\nTest Error Rate: %0.4f %%" % (error_rate * 100))
print("Test Recognition Rate: %0.4f %%" % ((1.0 - error_rate) * 100))
return clf, pca
# Compute a PCA(eigenfaces) on the face dataset (treated as unlabeled dataset)
# unsupervised feature extraction/dimensioanlity reduction
n_components = 150
print("Extracting the top %d eigenfaces from %d faces" % (n_components, X_train.shape[0])
t0 = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("done in %0.3fs" % (time() - t0)
eigenfaces = pcs.components_.reshape((n_components, h, w))
print("Projecting the input data on the eigenfaces orthonormal basis")
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pcs.transform(X_test)
print("Done in %0.3fs" %(time() - t0)
# Train a SVM classification model
print("Fitting the classifier to the training set")
t0 = time()
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5]
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)
clf = clf.fit(X_train_pca, y_train)
gridspec_kw=dict(hspace=0.1, wspace=0.1))
for i, ax in enumerate(axes.flat):
ax.imshow(pca.components_[i].reshape(62, 47), cmap='bone')
plt.show()
plt.plot(np.cumsum(pca.explained_variance_ratio_))
plt.xlabel('number of components')
plt.ylabel('cumulative explained variance')
plt.show()
# compute the components and projected faces
pca = RandomizedPCA(150).fit(faces.data)
components = pca.transform(faces.data)
projected = pca.inverse_transform(components)
# plot the results
fig,ax=plt.subplots(2,10,figsize=(10,2.5),subplot_kw={'xticks':[],'yticks':[]},\
gridspec_kw=dict(hspace=0.1,wspace=0.1))
for i in range(10):
ax[0, i].imshow(faces.data[i].reshape(62, 47), cmap='binary_r')
ax[1, i].imshow(projected[i].reshape(62, 47), cmap='binary_r')
ax[0, 0].set_ylabel('full-dim\ninput')
ax[1, 0].set_ylabel('150-dim\nreconstruction')
plt.show()
n_components = 150 # #component in PCA
cv2.destroyAllWindows()
pca = RandomizedPCA(n_components=n_components, whiten=True)
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
testing_data = []
for i in range(len(images)):
testing_data.append(images[i].flatten())
pca = pca.fit(testing_data)
transformed = pca.transform(testing_data)
# if lda is done than #component = 80
#lda = LinearDiscriminantAnalysis(n_components=80)
#transformed = lda.fit(transformed, labels).transform(transformed)
clf.fit(transformed, labels)
directory2 = 'yalefaces_5' # test directory name
image_paths = [
os.path.join(directory2, filename) for filename in os.listdir(directory2)
]
j = 0
for image_path in image_paths:
pred_image_pil = Image.open(image_path).convert('L')
pred_image = np.array(pred_image_pil, 'uint8')
faces = faceCascade.detectMultiScale(pred_image)
if (len(faces) == 0):
def SVM(X, y):
# divide our data set into a training set and a test set
X_train, X_test, y_train, y_test = cross_validation.train_test_split(
X, y, test_size=TRAIN_TEST_SPLIT_RATIO)
classifier_poly2 = svm.SVC(kernel='poly', degree=2)
classifier_poly2.fit(X_train, y_train)
print("======= poly degree=2 ========")
print('TRAIN SCORE', classifier_poly2.score(X_train, y_train))
print('TEST SCORE', classifier_poly2.score(X_test, y_test))
n_components = 10
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier11 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier11.fit(X_train_pca, y_train)
print("====== PCA 10 ========")
print('TRAIN SCORE', classifier11.score(X_train_pca, y_train))
print('TEST SCORE', classifier11.score(X_test_pca, y_test))
n_components = 50
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier12 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier12.fit(X_train_pca, y_train)
print("====== PCA 50 ========")
print('TRAIN SCORE', classifier12.score(X_train_pca, y_train))
print('TEST SCORE', classifier12.score(X_test_pca, y_test))
n_components = 100
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier13.fit(X_train_pca, y_train)
print("====== PCA 100 ========")
print('TRAIN SCORE', classifier13.score(X_train_pca, y_train))
print('TEST SCORE', classifier13.score(X_test_pca, y_test))
n_components = 120
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier13.fit(X_train_pca, y_train)
print("====== PCA 120 ========")
print('TRAIN SCORE', classifier13.score(X_train_pca, y_train))
print('TEST SCORE', classifier13.score(X_test_pca, y_test))
n_components = 135
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier13.fit(X_train_pca, y_train)
print("====== PCA 135 ========")
print('TRAIN SCORE', classifier13.score(X_train_pca, y_train))
print('TEST SCORE', classifier13.score(X_test_pca, y_test))
n_components = 150
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier13.fit(X_train_pca, y_train)
print("====== PCA 150 ========")
print('TRAIN SCORE', classifier13.score(X_train_pca, y_train))
print('TEST SCORE', classifier13.score(X_test_pca, y_test))
n_components = 165
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier13.fit(X_train_pca, y_train)
print("====== PCA 165 ========")
print('TRAIN SCORE', classifier13.score(X_train_pca, y_train))
print('TEST SCORE', classifier13.score(X_test_pca, y_test))
n_components = 180
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier13.fit(X_train_pca, y_train)
print("====== PCA 180 ========")
print('TRAIN SCORE', classifier13.score(X_train_pca, y_train))
print('TEST SCORE', classifier13.score(X_test_pca, y_test))
n_components = 200
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier13.fit(X_train_pca, y_train)
print("====== PCA 200 ========")
print('TRAIN SCORE', classifier13.score(X_train_pca, y_train))
print('TEST SCORE', classifier13.score(X_test_pca, y_test))
n_components = 400
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("Projecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done ")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
classifier13 = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
classifier13.fit(X_train_pca, y_train)
print("====== PCA 400 ========")
print('TRAIN SCORE', classifier13.score(X_train_pca, y_train))
print('TEST SCORE', classifier13.score(X_test_pca, y_test))
# In[6]:
scaler = StandardScaler(with_std=False)
scaled = scaler.fit_transform(df)
pca = RandomizedPCA(n_components=15, random_state=1)
pca.fit(scaled)
pca.explained_variance_ratio_.cumsum()[-1]
# In[7]:
df_fuzzy = pd.read_csv('features_fuzzy_train.csv', usecols=columns,dtype=np.float32)
scaled = scaler.transform(df_fuzzy)
X = pca.transform(scaled)
# In[8]:
df_fuzzy_test = pd.read_csv('features_fuzzy_test.csv', usecols=columns,dtype=np.float32)
scaled = scaler.transform(df_fuzzy_test)
X_test = pca.transform(scaled)
# In[9]:
for i in range(5):
df_res_train['pca_fuzzy_%d' % i] = X[:, i]
df_res_test['pca_fuzzy_%d' % i] = X_test[:, i]
def test_SVM(face_profile_data, face_profile_name_index, face_dim,
face_profile_names):
"""
Testing: Build the SVM classification modle using the face_profile_data matrix (numOfFace X numOfPixel) and face_profile_name_index array, face_dim is a tuple of the dimension of each image(h,w) Returns the SVM classification modle
Parameters
----------
face_profile_data : ndarray (number_of_images_in_face_profiles, width * height of the image)
The pca that contains the top eigenvectors extracted using approximated Singular Value Decomposition of the data
face_profile_name_index : ndarray
The name corresponding to the face profile is encoded in its index
face_dim : tuple (int, int)
The dimension of the face data is reshaped to
face_profile_names: ndarray
The names corresponding to the face profiles
Returns
-------
clf : theano object
The trained SVM classification model
pca : theano ojbect
The pca that contains the top 150 eigenvectors extracted using approximated Singular Value Decomposition of the data
"""
X = face_profile_data
y = face_profile_name_index
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=42)
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 150 # maximum number of components to keep
print("\nExtracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
eigenfaces = pca.components_.reshape(
(n_components, face_dim[0], face_dim[1]))
# This portion of the code is used if the data is scarce, it uses the number
# of imputs as the number of features
# pca = RandomizedPCA(n_components=None, whiten=True).fit(X_train)
# eigenfaces = pca.components_.reshape((pca.components_.shape[0], face_dim[0], face_dim[1]))
print("\nProjecting the input data on the eigenfaces orthonormal basis")
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
# Train a SVM classification model
print("\nFitting the classifier to the training set")
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
# clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'), param_grid)
# Train_pca Test Error Rate: 0.0670016750419
# Train_pca Test Recognition Rate: 0.932998324958
# clf = SVC(kernel='linear', C=1)
# 2452 samples from 38 people are loaded
# Extracting the top 150 eigenfaces from 1839 faces
# Extracting the top 150 eigenfaces from 1790 faces
# Train_pca Test Error Rate: 0.0904522613065
# Train_pca Test Recognition Rate: 0.909547738693
# clf = SVC(kernel='poly')
# Train_pca Test Error Rate: 0.201005025126
# Train_pca Test Recognition Rate: 0.798994974874
# clf = SVC(kernel='sigmoid')
# Train_pca Test Error Rate: 0.985318107667
# Train_pca Test Recognition Rate: 0.0146818923328
# clf = SVC(kernel='rbf').fit(X_train, y_train)
# Train_pca Test Error Rate: 0.0619765494137
# Train_pca Test Recognition Rate: 0.938023450586
# Best Estimator found using Radial Basis Function Kernal:
clf = SVC(C=1000.0,
cache_size=200,
class_weight='balanced',
coef0=0.0,
decision_function_shape=None,
degree=3,
gamma=0.0001,
kernel='rbf',
max_iter=-1,
probability=False,
random_state=None,
shrinking=True,
tol=0.001,
verbose=False)
# Train_pca with Alex Test Error Rate: 0.088424437299
# Train_pca with Alex Test Recognition Rate: 0.911575562701
clf = clf.fit(X_train_pca, y_train)
# print("\nBest estimator found by grid search:")
# print(clf.best_estimator_)
###############################################################################
# Quantitative evaluation of the model quality on the test set
print("\nPredicting people's names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)
print("\nPrediction took %0.8f second per sample on average" %
((time() - t0) / y_pred.shape[0] * 1.0))
# print "predicated names: ", y_pred
# print "actual names: ", y_test
error_rate = errorRate(y_pred, y_test)
print("\nTest Error Rate: %0.4f %%" % (error_rate * 100))
print("Test Recognition Rate: %0.4f %%" % ((1.0 - error_rate) * 100))
###############################################################################
# Testing
# X_test_pic1 = X_test[0]
# X_test_pic1_for_display = np.reshape(X_test_pic1, face_dim)
# t0 = time()
# pic1_pred_name = predict(clf, pca, X_test_pic1, face_profile_names)
# print("\nPrediction took %0.3fs" % (time() - t0))
# print "\nPredicated result for picture_1 name: ", pic1_pred_name
# for i in range(1,3): print ("\n")
# Display the picture
# plt.figure(1)
# plt.title(pic1_pred_name)
# plt.subplot(111)
# plt.imshow(X_test_pic1_for_display)
# plt.show()
###############################################################################
# Qualitative evaluation of the predictions using matplotlib
# import matplotlib.pyplot as plt
# def plot_gallery(images, titles, face_dim, n_row=3, n_col=4):
# """Helper function to plot a gallery of portraits"""
# plt.figure(figsize=(1.8 * n_col, 2.4 * n_row))
# plt.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
# for i in range(n_row * n_col):
# plt.subplot(n_row, n_col, i + 1)
# plt.imshow(images[i].reshape(face_dim), cmap=plt.cm.gray)
# plt.title(titles[i], size=12)
# plt.xticks(())
# plt.yticks(())
# # plot the result of the prediction on a portion of the test set
# def title(y_pred, y_test, face_profile_names, i):
# pred_name = face_profile_names[y_pred[i]].rsplit(' ', 1)[-1]
# true_name = face_profile_names[y_test[i]].rsplit(' ', 1)[-1]
# return 'predicted: %s\ntrue: %s' % (pred_name, true_name)
# prediction_titles = [title(y_pred, y_test, face_profile_names, i)
# for i in range(y_pred.shape[0])]
# plot_gallery(X_test, prediction_titles, face_dim)
# # plot the gallery of the most significative eigenfaces
# eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
# plot_gallery(eigenfaces, eigenface_titles, face_dim)
# plt.show()
return clf, pca
import numpy as np from scipy.cluster.vq import kmeans from scipy.spatial.distance import cdist, pdist from sklearn import datasets from sklearn.decomposition import RandomizedPCA from matplotlib import pyplot as plt from matplotlib import cm ##### data ##### # load digits dataset data = datasets.load_digits() t = data['target'] # perform PCA dimensionality reduction pca = RandomizedPCA(n_components=2).fit(data['data']) X = pca.transform(data['data']) console = [] ##### cluster data into K=1..20 clusters ##### K_MAX = 20 KK = range(1, K_MAX + 1) KM = [kmeans(X, k) for k in KK] centroids = [cent for (cent, var) in KM] D_k = [cdist(X, cent, 'euclidean') for cent in centroids] cIdx = [np.argmin(D, axis=1) for D in D_k] dist = [np.min(D, axis=1) for D in D_k] tot_withinss = [sum(d**2) for d in dist] # Total within-cluster sum of squares totss = sum(pdist(X)**2) / X.shape[0] # The total sum of squares betweenss = totss - tot_withinss # The between-cluster sum of squares
def dimention_reduction(financial_data):
pca = RandomizedPCA(n_components=2)
pca = pca.fit(financial_data)
trans_financial_data = pca.transform(financial_data)
return trans_financial_data
class RemoveRealtorTestModel(object):
def __init__(self):
self.features = None
self.pca_model = None
self.scorespriced = None
self.scores = None
self.features = None
self.components = None
self.log = None
self.model = None
self.divided_position = None
def get_data(self, fquery, lookback, dataget=' select * from final_table;'):
'''
'''
f = open(fquery)
q = f.readlines()
q = ' '.join(q)
q = q.replace('\n',' ')
q = q.replace('xxxxx',str(lookback))
engine = create_engine('postgresql://user@localhost:5432/mydb')
q += dataget
df = pd.read_sql_query(dataget,con=engine)
hold_out_cutoff = pd.datetime(2014,10,1)
dftest = df[df.listdate < hold_out_cutoff].reset_index(drop=True)
dfhold = df[df.listdate > hold_out_cutoff].reset_index(drop=True)
dftest.reset_index(inplace=True, drop=True)
dfhold.reset_index(inplace=True, drop=True)
return dftest, dftest_y, dfhold, dfhold_y
def init_final(self, features, components, log, divided_position, model):
'''
'''
self.features = features
self.components = components
self.log = log
self.divided_position = divided_position
self.model = model
return None
def fit(self, dfX, dfy):
'''
'''
start = time.time()
dfX.reset_index(inplace=True,drop=True)
boolvars = [col for col in dfX.columns.values if 'dvar' in col]
if self.components > 0:
self.pca_model = RandomizedPCA(n_components=self.components)
self.pca_model.fit(dfX[boolvars].values)
dftrain_bool = pd.DataFrame(self.pca_model.transform(dfX[boolvars].values))
dftrain = pd.concat([dfX[self.features], dftrain_bool],axis=1)
else:
self.pca_model=None
dftrain = dfX[self.features]
if self.log:
dftrain = self._log_feature(dftrain)
dfy = np.log(dfy.values)
self.log = True
else:
self.log = False
if self.divided_position is not None:
for i, j in self.divided_position:
test_X = self._divide_two_features(test_X, i, j)
train_X = self._divide_two_features(train_X, i, j)
self.model.fit(dftrain, dfy)
print self.model.get_params
print 'model fit {} homes in {}'.format(dfX.shape[0] ,time.time()-start)
return None
def predict(self, dfX, gettree):
'''
'''
start = time.time()
dfX.reset_index(inplace=True,drop=True)
boolvars = [col for col in dfX.columns.values if 'dvar' in col]
if self.pca_model is not None:
df_bool = pd.DataFrame(self.pca_model.transform(dfX[boolvars].values))
dftest = pd.concat([dfX[self.features], df_bool], axis=1)
else:
dftest = dfX[self.features]
if self.log:
dftest = self._log_feature(dftest)
point_estimates = self.model.predict(dftest)
tree_estimates = np.array([])
if gettree:
for est in self.model.estimators_:
tree_estimates = np.concatenate([tree_estimates, est.predict(dftest)], axis=1)
else:
tree_estimates = 0
print 'model predict (2x) {} in {}'.format(dfX.shape[0] ,time.time()-start)
if self.log:
return np.exp(point_estimates), np.exp(tree_estimates)
else:
return point_estimates, tree_estimates
def df_time_iterator(self, df, cv, splittype):
'''
'''
minlistdate = df.listdate.min()
maxlistdate = df.statusupdate.max()
dt = ((maxlistdate-minlistdate).days/cv)
c = 1
dayrandom = np.random.randint(0,16)-8
while c < cv+1:
#chunk does equal train/test splits
if splittype == 'chunk':
train = df[((df.listdate >= (minlistdate+timedelta(days=dt*(c-1)+dayrandom))) &
(df.statuschangedate <= (minlistdate+timedelta(days=dt*(c)))))].index
test = df[((df.listdate > (minlistdate+timedelta(days=dt*(c)-dayrandom))) &
(df.statuschangedate <= (minlistdate+timedelta(days=dt*(c+1)))))].index
#forward does train/test splits which grow in time
elif splittype == 'forward':
train = df[df.statuschangedate <= minlistdate+timedelta(days=dt*(c)-dayrandom)].index
test = df[df.listdate > minlistdate+timedelta(days=dt*(c))].index
c += 1
yield train, test
def cross_validate_model(self, dfX, dfy, features, components, log, divided_position, model, cv):
'''
'''
kf = KFold(dfX.shape[0], n_folds=cv, shuffle=True)
dfX.reset_index(inplace=True, drop=True)
dfy.reset_index(inplace=True, drop=True)
mets = [metrics.median_absolute_error, metrics.r2_score, self.percent_difference]
scores = np.zeros(len(mets))
scorespriced = np.zeros(len(mets))
boolvars = [col for col in dfX.columns.values if 'dvar' in col]
if components>0:
features = features+boolvars
dfX = dfX[features]
for train_index, test_index in kf:
train_X, train_y = dfX.loc[train_index,:].copy(), dfy.loc[train_index].copy()
test_X, test_y = dfX.loc[test_index,:].copy(), dfy.loc[test_index].copy()
if components>0:
train_X, test_X = self._pca_dummies(components, boolvars, train_X, test_X)
if divided_position is not None:
for i, j in divided_position:
test_X = self._divide_two_features(test_X, i, j)
train_X = self._divide_two_features(train_X, i, j)
self.features = train_X.columns.values
if log:
train_X = self._log_feature(train_X)
train_y = np.log(train_y.values)
test_X = self._log_feature(test_X)
test_y = np.log(test_y.values)
model.fit(train_X, train_y)
ypred = model.predict(test_X)
if log:
test_y = np.exp(test_y)
ypred = np.exp(ypred)
mask = (ypred>100000) & (ypred<230000)
for i, m in enumerate(mets):
scores[i] += m(test_y, ypred)
scorespriced[i] += m(test_y[mask], ypred[mask])
scores = scores/float(cv)
scorespriced = scorespriced/float(cv)
self.a_cved_model = model
print ''.join(['-']*40)
print ''.join(['-']*40)
print model.get_params
print ''.join(['-']*40)
self._display_scoring_metrics(zip(mets,scores), 'full')
print ''.join(['-']*40)
self._display_scoring_metrics(zip(mets,scorespriced), 'priced')
return None
def scorer(self, ytrue, ypred):
'''
'''
mask = (ypred>100000) & (ypred<230000)
mets = [metrics.median_absolute_error, metrics.r2_score, self._percent_difference]
scores = np.zeros(len(mets))
scorespriced = np.zeros(len(mets))
for i, m in enumerate(mets):
scores[i] += m(ytrue, ypred)
scorespriced[i] += m(ytrue[mask], ypred[mask])
print 'full_size {}'.format(len(ytrue))
self._display_scoring_metrics(zip(mets,scores), 'full')
print ''.join(['-']*40)
print 'full_size {}'.format(np.sum(mask))
self._display_scoring_metrics(zip(mets,scorespriced), 'priced')
return None
def percent_difference(self, ytest, ypred):
'''
'''
return np.mean(abs((ytest-ypred)/ytest)*100.)
def tree_importance(self, model, threshold):
'''
'''
fimport = pd.DataFrame(zip(self.features, model.feature_importances_), columns=['feature','importance']).sort('importance', ascending=False)
fvar=[]
for est in model.estimators_:
fvar.append(est.feature_importances_)
fimport['std'] = np.array(fvar).std(axis=0)
fimport['cumimport'] = fimport['importance'].cumsum().values
return fimport, fimport[fimport['cumimport'] < threshold]['feature'].tolist()
def _pca_dummies(self, components, boolvars, dftrain, dftest):
'''
'''
dftrain.reset_index(inplace=True, drop=True)
dftest.reset_index(inplace=True, drop=True)
self.pca_model = RandomizedPCA(n_components=components)
self.pca_model.fit(dftrain[boolvars].values)
dftrain_bool = pd.DataFrame(self.pca_model.transform(dftrain[boolvars].values))
dftest_bool = pd.DataFrame(self.pca_model.transform(dftest[boolvars].values))
dftrain.drop(boolvars, axis=1,inplace=True)
dftest.drop(boolvars, axis=1,inplace=True)
features = dftrain.columns.tolist()+['pca'+str(i) for i in range(components)]
dftrain = pd.concat([dftrain, dftrain_bool], axis=1, ignore_index=True)
dftest = pd.concat([dftest, dftest_bool], axis=1, ignore_index=True)
dftrain.columns = features
dftest.columns = features
return dftrain, dftest
def _display_scoring_metrics(self, met_scores, label):
'''
'''
for met, score in met_scores:
print label+' {}: {}'.format(met.func_name, np.round(score,3))
def _log_feature(self, df):
'''
'''
for feature in [col for col in df.columns.values if (('price' in str(col)) | ('taxes' in str(col)))]:
df[feature] = np.log(df[feature].values)
return df
def _divide_two_features(self, df, f1, f2):
'''
'''
df[f1+'_'+f2] = df[f1]/df[f2]
df.drop([f1,f2], axis=1, inplace=True)
return df
def main(opt_list, arg_list, runall=False):
""" Pass either only_ml, ml_svm, or only_svm"""
accuracies = {
'soft_unw': [],
'soft_wei': [],
'hard_wei': [],
'hard_unw': [],
'svm': 0,
'lda': 0
}
#print(__doc__)
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
###############################################################################
# Download the data, if not already on disk and load it as numpy arrays
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
# introspect the images arrays to find the shapes (for plotting)
n_samples, h, w = lfw_people.images.shape
# for machine learning we use the 2 data directly (as relative pixel
# positions info is ignored by this model)
X = lfw_people.data
n_features = X.shape[1]
# the label to predict is the id of the person
y = lfw_people.target
target_names = lfw_people.target_names
n_classes = target_names.shape[0]
print("Total dataset size:")
print("n_samples: %d" % n_samples)
print("n_features: %d" % n_features)
print("n_classes: %d" % n_classes)
###############################################################################
# Split into a training set and a test set using a stratified k fold
# split into a training and testing set
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.25, random_state=42)
###############################################################################
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
n_components = 150
print("Extracting the top %d eigenfaces from %d faces"
% (n_components, X_train.shape[0]))
t0 = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train)
print("done in %0.3fs" % (time() - t0))
eigenfaces = pca.components_.reshape((n_components, h, w))
print("Projecting the input data on the eigenfaces orthonormal basis")
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
print("done in %0.3fs" % (time() - t0))
if opt_list is "serial":
if not runall:
a = time()
acc, y_pred = assemble_series(X_train_pca, y_train, X_test_pca, y_test, ['lmnn', 'lsml', 'rca', 'ldml', 'lfda'], 'soft', "weighted")
print("accuracy = %s",acc)
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
b = time()
else:
if 'soft_unw' in arg_list:
mls = list_mls(['lmnn', 'lsml', 'rca', 'lfda', 'ldml'])
ml_strs = []
y_preds = []
for ml in mls:
if len(ml) == 0:
continue
print(ml)
acc, y_pred = assemble_series(X_train_pca, y_train, X_test_pca, y_test, ml, 'soft', 'unweighted')
y_preds.append(y_pred)
accuracies['soft_unw'].append(acc)
ml_strs.append(getStr(ml))
print("accuracy = %s",acc)
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
y_preds = np.array(y_preds)
num_samples = y_preds.shape[1]
majority_pred = np.zeros(num_samples)
for sample in xrange(y_preds.shape[1]):
majority_pred[sample] = np.bincount(y_preds[:,sample]).argmax()
majority_pred= np.array(majority_pred, dtype=np.int32)
c = np.sum(majority_pred == y_test)
accuracy = c * 100.0 / num_samples
accuracies['soft_unw'].append(accuracy)
ml_strs.append('all')
cleanCachedMls()
if 'soft_wei' in arg_list:
mls = list_mls(['lmnn', 'lsml', 'rca', 'lfda', 'ldml'])
ml_strs = []
y_preds = []
for ml in mls:
if len(ml) == 0:
continue
print(ml)
acc, y_pred = assemble_series(X_train_pca, y_train, X_test_pca, y_test, ml, 'soft', 'weighted')
y_preds.append(y_pred)
accuracies['soft_wei'].append(acc)
ml_strs.append(getStr(ml))
print("accuracy = %s",acc)
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
y_preds = np.array(y_preds)
num_samples = y_preds.shape[1]
majority_pred = np.zeros(num_samples)
for sample in xrange(y_preds.shape[1]):
majority_pred[sample] = np.bincount(y_preds[:,sample]).argmax()
majority_pred= np.array(majority_pred, dtype=np.int32)
c = np.sum(majority_pred == y_test)
accuracy = c * 100.0 / num_samples
accuracies['soft_wei'].append(accuracy)
ml_strs.append('all')
cleanCachedMls()
if 'hard_wei' in arg_list:
mls = list_mls(['lmnn', 'lsml', 'rca', 'lfda', 'ldml'])
ml_strs = []
y_preds = []
for ml in mls:
if len(ml) == 0:
continue
print(ml)
acc, y_pred = assemble_series(X_train_pca, y_train, X_test_pca, y_test, ml, 'hard', 'weighted')
y_preds.append(y_pred)
accuracies['hard_wei'].append(acc)
ml_strs.append(getStr(ml))
print("accuracy = %s",acc)
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
y_preds = np.array(y_preds, dtype=np.int32)
num_samples = y_preds.shape[1]
majority_pred = np.zeros(num_samples)
for sample in xrange(y_preds.shape[1]):
majority_pred[sample] = np.bincount(y_preds[:,sample]).argmax()
majority_pred= np.array(majority_pred, dtype=np.int32)
c = np.sum(majority_pred == y_test)
accuracy = c * 100.0 / num_samples
accuracies['hard_wei'].append(accuracy)
ml_strs.append('all')
cleanCachedMls()
if 'hard_unw' in arg_list:
mls = list_mls(['lmnn', 'lsml', 'rca', 'lfda', 'ldml'])
ml_strs = []
y_preds = []
for ml in mls:
if len(ml) == 0:
continue
print(ml)
acc, y_pred = assemble_series(X_train_pca, y_train, X_test_pca, y_test, ml, 'hard', 'unweighted')
y_preds.append(y_pred)
accuracies['hard_unw'].append(acc)
ml_strs.append(getStr(ml))
print("accuracy = %s",acc)
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
y_preds = np.array(y_preds, dtype=np.int32)
num_samples = y_preds.shape[1]
majority_pred = np.zeros(num_samples)
for sample in xrange(y_preds.shape[1]):
majority_pred[sample] = np.bincount(y_preds[:,sample]).argmax()
majority_pred= np.array(majority_pred, dtype=np.int32)
c = np.sum(majority_pred == y_test)
accuracy = c * 100.0 / num_samples
accuracies['hard_unw'].append(accuracy)
ml_strs.append('all')
cleanCachedMls()
if opt_list is "parallel":
""" TODO: Opt for the parallel thread implementation. """
if not runall:
a = time()
acc, y_pred = assemble_parallel(X_train_pca, y_train, X_test_pca, y_test, 'hard')
print("accuracy = %s",acc)
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
b = time()
print("Total time taken for all this: {0}".format(b-a))
else:
mls = list_mls(['lmnn', 'lsml', 'rca', 'lfda', 'ldml'])
ml_strs = []
y_preds = []
for ml in mls:
if len(ml) == 0:
continue
print(ml)
acc, y_pred = assemble_parallel(X_train_pca, y_train, X_test_pca, y_test, 'hard')
accuracies['hard'].append(acc)
y_preds.append(y_pred)
ml_strs.append(getStr(ml))
print("accuracy = %s", acc)
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
y_preds = np.array(y_preds)
num_samples = y_preds.shape[1]
majority_pred = np.zeros(num_samples)
for sample in xrange(y_preds.shape[1]):
majority_pred[sample] = np.bincount(y_preds[:,sample]).argmax()
majority_pred= np.array(majority_pred, dtype=np.int32)
c = np.sum(majority_pred == y_test)
accuracy = c * 100.0 / num_samples
accuracies['hard'].append(accuracy)
ml_strs.append('all')
###############################################################################
print("Without the LMNN structure")
# Train a SVM classification model
print("Fitting the classifier to the training set")
t0 = time()
param_grid = {'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1], }
clf = GridSearchCV(SVC(kernel='rbf'), param_grid)
clf.fit(X_train_pca, y_train)
print("done in %0.3fs" % (time() - t0))
print("Best estimator found by grid search:")
print(clf.best_estimator_)
###############################################################################
# Quantitative evaluation of the model quality on the test set
print("Predicting people's names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)
acc = 100.0*sum(y_pred == y_test) / len(y_test)
print("accuracy = %s",acc)
print("done in %0.3fs" % (time() - t0))
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
print("Fitting the classifier to the training set")
t0 = time()
clf = LDA()
clf.fit(X_train_pca, y_train)
print("done in %0.3fs" % (time() - t0))
print("Best estimator found by grid search:")
###############################################################################
# Quantitative evaluation of the model quality on the test set
print("Predicting people's names on the test set")
t0 = time()
y_pred = clf.predict(X_test_pca)
acc1 = 100.0*sum(y_pred == y_test) / len(y_test)
print("accuracy = %s",acc1)
print("done in %0.3fs" % (time() - t0))
print(classification_report(y_test, y_pred, target_names=target_names))
print(confusion_matrix(y_test, y_pred, labels=range(n_classes)))
if runall:
accuracies['svm'] = acc
accuracies['lda'] = acc1
ml_strs.append('svm')
ml_strs.append('lda')
ml_strs = ", ".join(ml_strs)
return ml_strs, accuracies
n_components = 150 #組成元素的數量
print("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
t0 = time()
#print(t0)
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(X_train) #隨機隆維
print("done in %0.3fs" % (time() - t0))
eigenfaces = pca.components_.reshape(
(n_components, h, w)) #從人臉提取一些特徵點，叫做eigenface
print("Projecting te input data on the eigenfaces orthonormal basis")
t0 = time()
#print(t0)
X_train_pca = pca.transform(X_train) #針對X_train執行降維動作
X_test_pca = pca.transform(X_test) #針對X_test執行降維動作
print("done in %0.3fs" % (time() - t0))
#===========================================================================================
# Train a SCM classification model
print("fitting the classifier to the training set")
t0 = time()
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
} #嚐試不同參數。共30種組合
# C:float,optional(default=1.0), Penalty parameter C of the error term 對錯誤進行懲罰，權重
# gamma:float, optional(default=0.0) Kernel coefficient for 'rbf", "poly" and "sigmoid"
# If gamma is 0.0 then 1/n_features will be used instead 多少特徵點會被使用將有一個比例
class MKSHomogenizationModel(BaseEstimator):
"""
The `MKSHomogenizationModel` takes in microstructures and a their
associated macroscopic property, and created a low dimensional structure
property linkage. The `MKSHomogenizationModel` model is designed to
integrate with dimensionality reduction techniques and predictive models.
Attributes:
degree: Degree of the polynomial used by
`property_linker`.
n_components: Number of components used by `dimension_reducer`.
dimension_reducer: Instance of a dimensionality reduction class.
property_linker: Instance of class that maps materials property to the
microstuctures.
correlations: spatial correlations to be computed
basis: instance of a basis class
reduced_fit_data: Low dimensionality representation of spatial
correlations used to fit the model.
reduced_predict_data: Low dimensionality representation of spatial
correlations predicted by the model.
Below is an examlpe of using MKSHomogenizationModel to predict (or
classify) the type of microstructure using PCA and Logistic Regression.
>>> n_states = 3
>>> domain = [-1, 1]
>>> from pymks.bases import LegendreBasis
>>> leg_basis = LegendreBasis(n_states=n_states, domain=domain)
>>> from sklearn.decomposition import PCA
>>> from sklearn.linear_model import LogisticRegression
>>> reducer = PCA(n_components=3)
>>> linker = LogisticRegression()
>>> model = MKSHomogenizationModel(
... basis=leg_basis, dimension_reducer=reducer, property_linker=linker)
>>> from pymks.datasets import make_cahn_hilliard
>>> X0, X1 = make_cahn_hilliard(n_samples=50)
>>> y0 = np.zeros(X0.shape[0])
>>> y1 = np.ones(X1.shape[0])
>>> X = np.concatenate((X0, X1))
>>> y = np.concatenate((y0, y1))
>>> model.fit(X, y)
>>> X0_test, X1_test = make_cahn_hilliard(n_samples=3)
>>> y0_test = model.predict(X0_test)
>>> y1_test = model.predict(X1_test)
>>> assert np.allclose(y0_test, [0, 0, 0])
>>> assert np.allclose(y1_test, [1, 1, 1])
"""
def __init__(self,
basis=None,
dimension_reducer=None,
n_components=None,
property_linker=None,
degree=1,
correlations=None,
compute_correlations=True):
"""
Create an instance of a `MKSHomogenizationModel`.
Args:
basis (class, optional): an instance of a bases class.
dimension_reducer (class, optional): an instance of a
dimensionality reduction class with a fit_transform method.
property_linker (class, optional): an instance for a machine
learning class with fit and predict methods.
n_components (int, optional): number of components kept by the
dimension_reducer
degree (int, optional): degree of the polynomial used by
property_linker.
correlations (list, optional): list of spatial correlations to
compute, default is the autocorrelation with the first local
state and all of its cross correlations. For example if basis
has n_states=3, correlation would be [(0, 0), (0, 1), (0, 2)]
compute_correlations (boolean, optional): If false spatial
correlations will not be calculated as part of the fit and
predict methods. The spatial correlations can be passed as `X`
to both methods, default is True.
"""
self.basis = basis
self.dimension_reducer = dimension_reducer
if self.dimension_reducer is None:
self.dimension_reducer = RandomizedPCA()
if n_components is None:
n_components = self.dimension_reducer.n_components
if n_components is None:
n_components = 2
if property_linker is None:
property_linker = LinearRegression()
if correlations is None and basis is not None:
if compute_correlations is True:
correlations = [(0, l) for l in range(basis.n_states)]
self._linker = Pipeline([('poly', PolynomialFeatures(degree=degree)),
('connector', property_linker)])
self._check_methods
self.degree = degree
self.n_components = n_components
self.property_linker = property_linker
self.correlations = correlations
self._fit = False
self.compute_correlations = compute_correlations
self.reduced_fit_data = None
self.reduced_predict_data = None
@property
def n_components(self):
return self._n_components
@n_components.setter
def n_components(self, value):
"""Setter for the number of components using by the dimension_reducer
"""
self._n_components = value
self.dimension_reducer.n_components = value
@property
def degree(self):
return self._degree
@degree.setter
def degree(self, value):
"""Setter for the polynomial degree for property_linker.
"""
self._degree = value
self._linker.set_params(poly__degree=value)
@property
def property_linker(self):
return self._property_linker
@property_linker.setter
def property_linker(self, prop_linker):
"""Setter for the property_linker class.
"""
self._property_linker = prop_linker
self._linker.set_params(connector=prop_linker)
def _check_methods(self):
"""
Helper function to make check that the dimensionality reduction and
property linking methods have the appropriate methods.
"""
if not callable(getattr(self.dimension_reducer, "fit_transform",
None)):
raise RuntimeError(
"dimension_reducer does not have fit_transform() method.")
if not callable(getattr(self.dimension_reducer, "transform", None)):
raise RuntimeError(
"dimension_reducer does not have transform() method.")
if not callable(getattr(self.linker, "fit", None)):
raise RuntimeError("property_linker does not have fit() method.")
if not callable(getattr(self.linker, "predict", None)):
raise RuntimeError(
"property_linker does not have predict() method.")
def fit(self,
X,
y,
reduce_labels=None,
periodic_axes=None,
confidence_index=None,
size=None):
"""
Fits data by calculating 2-point statistics from X, preforming
dimension reduction using dimension_reducer, and fitting the reduced
data with the property_linker.
Args:
X (ND array): The microstructures or spatial correlations, a
`(n_samples, n_x, ...)` shaped array where `n_samples` is the
number of samples and `n_x` is the spatial discretization.
y (1D array): The material property associated with `X`.
reducer_labels (1D array, optional): label for X used during the
fit_transform method for the `dimension_reducer`.
periodic_axes (list, optional): axes that are periodic. (0, 2)
would indicate that axes x and z are periodic in a 3D
microstrucure.
confidence_index (ND array, optional): array with same shape as X
used to assign a confidence value for each data point.
Example
>>> from sklearn.decomposition import PCA
>>> from sklearn.linear_model import LinearRegression
>>> from pymks.bases import PrimitiveBasis
>>> from pymks.stats import correlate
>>> reducer = PCA(n_components=2)
>>> linker = LinearRegression()
>>> prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])
>>> correlations = [(0, 0), (1, 1), (0, 1)]
>>> model = MKSHomogenizationModel(prim_basis,
... dimension_reducer=reducer,
... property_linker=linker,
... correlations=correlations)
>>> np.random.seed(99)
>>> X = np.random.randint(2, size=(3, 15))
>>> y = np.array([1, 2, 3])
>>> model.fit(X, y)
>>> X_ = prim_basis.discretize(X)
>>> X_stats = correlate(X_)
>>> X_reshaped = X_stats.reshape((X_stats.shape[0], X_stats[0].size))
>>> X_pca = reducer.fit_transform(X_reshaped - np.mean(X_reshaped,
... axis=1)[:, None])
>>> assert np.allclose(model.reduced_fit_data, X_pca)
Now let's use the same method with spatial correlations instead of
microtructures.
>>> from sklearn.decomposition import PCA
>>> from sklearn.linear_model import LinearRegression
>>> from pymks.bases import PrimitiveBasis
>>> from pymks.stats import correlate
>>> reducer = PCA(n_components=2)
>>> linker = LinearRegression()
>>> prim_basis = PrimitiveBasis(n_states=2, domain=[0, 1])
>>> correlations = [(0, 0), (1, 1), (0, 1)]
>>> model = MKSHomogenizationModel(dimension_reducer=reducer,
... property_linker=linker,
... compute_correlations=False)
>>> np.random.seed(99)
>>> X = np.random.randint(2, size=(3, 15))
>>> y = np.array([1, 2, 3])
>>> X_ = prim_basis.discretize(X)
>>> X_stats = correlate(X_, correlations=correlations)
>>> model.fit(X_stats, y)
>>> X_reshaped = X_stats.reshape((X_stats.shape[0], X_stats[0].size))
>>> X_pca = reducer.fit_transform(X_reshaped - np.mean(X_reshaped,
... axis=1)[:, None])
>>> assert np.allclose(model.reduced_fit_data, X_pca)
"""
if self.compute_correlations is True:
if periodic_axes is None:
periodic_axes = []
if size is not None:
new_shape = (X.shape[0], ) + size
X = X.reshape(new_shape)
X = self._correlate(X, periodic_axes, confidence_index)
X_reshape = self._reduce_shape(X)
X_reduced = self.dimension_reducer.fit_transform(
X_reshape, reduce_labels)
self._linker.fit(X_reduced, y)
self.reduced_fit_data = X_reduced
self._fit = True
def predict(self, X, periodic_axes=None, confidence_index=None):
"""Predicts macroscopic property for the microstructures `X`.
Args:
X (ND array): The microstructure, an `(n_samples, n_x, ...)`
shaped array where `n_samples` is the number of samples and
`n_x` is the spatial discretization.
periodic_axes (list, optional): axes that are periodic. (0, 2)
would indicate that axes x and z are periodic in a 3D
microstrucure.
confidence_index (ND array, optional): array with same shape as X
used to assign a confidence value for each data point.
Returns:
The predicted macroscopic property for `X`.
Example
>>> from sklearn.manifold import LocallyLinearEmbedding
>>> from sklearn.linear_model import BayesianRidge
>>> from pymks.bases import PrimitiveBasis
>>> np.random.seed(99)
>>> X = np.random.randint(2, size=(50, 100))
>>> y = np.random.random(50)
>>> reducer = LocallyLinearEmbedding()
>>> linker = BayesianRidge()
>>> prim_basis = PrimitiveBasis(2, domain=[0, 1])
>>> model = MKSHomogenizationModel(prim_basis, n_components=2,
... dimension_reducer=reducer,
... property_linker=linker)
>>> model.fit(X, y)
>>> X_test = np.random.randint(2, size=(1, 100))
Predict with microstructures
>>> y_pred = model.predict(X_test)
Predict with spatial correlations
>>> from pymks.stats import correlate
>>> model.compute_correlations = False
>>> X_ = prim_basis.discretize(X_test)
>>> X_corr = correlate(X_, correlations=[(0, 0), (0, 1)])
>>> y_pred_stats = model.predict(X_corr)
>>> assert y_pred_stats == y_pred
"""
if not self._fit:
raise RuntimeError('fit() method must be run before predict().')
if self.compute_correlations is True:
if periodic_axes is None:
periodic_axes = []
X = self._correlate(X, periodic_axes, confidence_index)
X_reshape = self._reduce_shape(X)
X_reduced = self.dimension_reducer.transform(X_reshape)
self.reduced_predict_data = X_reduced
return self._linker.predict(X_reduced)
def _correlate(self, X, periodic_axes, confidence_index):
"""
Helper function used to calculated 2-point statistics from `X` and
reshape them appropriately for fit and predict methods.
Args:
X (ND array): The microstructure, an `(n_samples, n_x, ...)`
shaped array where `n_samples` is the number of samples and
`n_x` is the spatial discretization..
periodic_axes (list, optional): axes that are periodic. (0, 2)
would indicate that axes x and z are periodic in a 3D
microstrucure.
confidence_index (ND array, optional): array with same shape as X
used to assign a confidence value for each data point.
Returns:
Spatial correlations for each sample formated with dimensions
(n_samples, n_features).
Example
>>> from sklearn.manifold import Isomap
>>> from sklearn.linear_model import ARDRegression
>>> from pymks.bases import PrimitiveBasis
>>> reducer = Isomap()
>>> linker = ARDRegression()
>>> prim_basis = PrimitiveBasis(2, [0, 1])
>>> model = MKSHomogenizationModel(prim_basis, reducer, linker)
>>> X = np.array([[0, 1],
... [1, 0]])
>>> X_stats = model._correlate(X, [], None)
>>> X_test = np.array([[[ 0, 0],
... [0.5, 0]],
... [[0, 1,],
... [0.5, 0]]])
>>> assert np.allclose(X_test, X_stats)
"""
if self.basis is None:
raise AttributeError('basis must be specified')
X_ = self.basis.discretize(X)
X_stats = correlate(X_,
periodic_axes=periodic_axes,
confidence_index=confidence_index,
correlations=self.correlations)
return X_stats
def _reduce_shape(self, X_stats):
"""
Helper function used to reshape 2-point statistics appropriately for
fit and predict methods.
Args:
`X_stats`: The discretized microstructure function, an
`(n_samples, n_x, ..., n_states)` shaped array
Where `n_samples` is the number of samples, `n_x` is thes
patial discretization, and n_states is the number of local
states.
Returns:
Spatial correlations for each sample formated with dimensions
(n_samples, n_features).
Example
>>> X_stats = np.zeros((2, 2, 2, 2))
>>> X_stats[1] = 3.
>>> X_stats[..., 1] = 1.
>>> X_results = np.array([[-.5, .5, -.5, .5, -.5, .5, -.5, 0.5],
... [1., -1., 1., -1., 1., -1., 1., -1.]])
>>> from pymks import PrimitiveBasis
>>> prim_basis = PrimitiveBasis(2)
>>> model = MKSHomogenizationModel(prim_basis)
>>> assert np.allclose(X_results, model._reduce_shape(X_stats))
"""
X_reshaped = X_stats.reshape((X_stats.shape[0], X_stats[0].size))
return X_reshaped - np.mean(X_reshaped, axis=1)[:, None]
def score(self, X, y, periodic_axes=None, confidence_index=None):
"""
The score function for the MKSHomogenizationModel. It formats the
data and uses the score method from the property_linker.
Args:
X (ND array): The microstructure, an `(n_samples, n_x, ...)`
shaped array where `n_samples` is the number of samples and
`n_x` is the spatial discretization.
y (1D array): The material property associated with `X`.
periodic_axes (list, optional): axes that are periodic. (0, 2)
would indicate that axes x and z are periodic in a 3D
microstrucure.
confidence_index (ND array, optional): array with same shape as X
used to assign a confidence value for each data point.
Returns:
Score for MKSHomogenizationModel from the selected
property_linker.
"""
if periodic_axes is None:
periodic_axes = []
if not callable(getattr(self._linker, "score", None)):
raise RuntimeError("property_linker does not have score() method.")
X_corr = self._correlate(X, periodic_axes, confidence_index)
X_reshaped = self._reduce_shape(X_corr)
X_reduced = self.dimension_reducer.transform(X_reshaped)
return self._linker.score(X_reduced, y)
except IndexError:
print "Please specify trainingfile.csv testingfile.csv NumComponents"
sys.exit(1)
traindf = pandas.read_csv(trainfile)
testdf = pandas.read_csv(testfile)
columns = ["tfidfpca_%s" % x for x in xrange(ncomponents)]
trainCleanEssay = traindf.essay.str.decode('mac-roman')
testCleanEssay = testdf.essay.str.decode('mac-roman')
vectorizer = TfidfVectorizer(ngram_range=(1, 2), stop_words="english")
trainvec = vectorizer.fit_transform(trainCleanEssay)
testvec = vectorizer.transform(testCleanEssay)
pca = RandomizedPCA(n_components=ncomponents)
pca.fit(trainvec)
trainpca = pca.transform(trainvec)
trainpcadf = pandas.DataFrame(trainpca, columns=columns)
testpca = pca.transform(testvec)
testpcadf = pandas.DataFrame(testpca, columns=columns)
traindf = traindf.combine_first(trainpcadf)
testdf = testdf.combine_first(testpcadf)
nf = lambda x: os.path.splitext(os.path.basename(x))[0] + "_tfidf.csv"
traindf.to_csv(nf(trainfile))
testdf.to_csv(nf(testfile))
print "+".join(columns)
### using our testing script. Check the tester.py script in the final project
### folder for details on the evaluation method, especially the test_classifier
### function. Because of the small size of the dataset, the script uses
### stratified shuffle split cross validation. For more info:
### http://scikit-learn.org/stable/modules/generated/sklearn.cross_validation.StratifiedShuffleSplit.html
# Example starting point. Try investigating other evaluation techniques!
from sklearn.cross_validation import train_test_split
features_train, features_test, labels_train, labels_test = \
train_test_split(features, labels, test_size=0.3, random_state=42)
#use pca
from sklearn.decomposition import RandomizedPCA
n_components = 1
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(features_train)
pca_train = pca.transform(features_train)
pca_test = pca.transform(features_test)
#use pca train data
# clf.fit(pca_train,labels_train)
# print clf.score(pca_test,labels_test)
clf.fit(features_train, labels_train)
print clf.score(features_test, labels_test)
### Task 6: Dump your classifier, dataset, and features_list so anyone can
### check your results. You do not need to change anything below, but make sure
### that the version of poi_id.py that you submit can be run on its own and
### generates the necessary .pkl files for validating your results.
dump_classifier_and_data(clf, my_dataset, features_list)
pre_dispatch=1))])
print '\nGridSearchCV finished\n', clf
pca.fit(finance_features)
print '\npca.explained_variance_ratio_', pca.explained_variance_ratio_, '\n'
print 'CLF', clf
#print '\nBest estimator:', clf.best_estimator_
# extraction of components to plot them
print '\npca.explained_variance_ratio_', pca.explained_variance_ratio_, '\n'
financial_pc1 = pca.components_[0]
financial_pc2 = pca.components_[1]
transformed_data = pca.transform(features)
for ii, jj in zip(transformed_data, features):
plt.scatter(financial_pc1[0] * ii[0], financial_pc1[1] * ii[0], color='r')
plt.scatter(financial_pc2[0] * ii[0], financial_pc2[1] * ii[0], color='c')
plt.scatter(jj[0], jj[1], color="b")
plt.xlabel("bonus")
plt.ylabel("long-term incentive")
plt.show()
clf.fit(features, labels)
########################################################################
### Task 5: Tune your classifier to achieve better than .3 precision and recall
### using our testing script.
### Because of the small size of the dataset, the script uses stratified
# and an array with ID of the people on each image y
X = np.zeros([NUM_TRAINIMAGES, IMG_RES], dtype='int8')
y = []
# Populate training array with flattened imags from subfolders of train_faces and names
c = 0
for x, folder in enumerate(folders):
train_faces = glob.glob(folder + '/*')
for i, face in enumerate(train_faces):
X[c, :] = prepare_image(face)
y.append(ID_from_filename(face))
c = c + 1
# perform principal component analysis on the images
pca = RandomizedPCA(n_components=NUM_EIGENFACES, whiten=True).fit(X)
X_pca = pca.transform(X)
while 1:
r = ""
# load test faces (usually one), located in folder test_faces
test_faces = glob.glob('test_faces/*')
# Create an array with flattened images X
X = np.zeros([len(test_faces), IMG_RES], dtype='int8')
# Populate test array with flattened imags from subfolders of train_faces
for i, face in enumerate(test_faces):
img = cv2.imread(face)
if img is not None:
X[i, :] = prepare_image(face)
def feature_reduction_pca(full_features):
pca = RandomizedPCA(n_components=8, whiten=True).fit(full_features)
return pca.transform(full_features)
x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.25)
n_components = 150
print("extracting the top %d eigenfaces from the %d faces" %
(n_components, x_train.shape[0]))
start_time = time()
pca = RandomizedPCA(n_components=n_components, whiten=True).fit(x_train)
print("运行 %0.3fs" % (time() - start_time))
eigenfaces = pca.components_.reshape((n_components, h, w))
print("将输入数据降维")
start_time = time()
x_train_pca = pca.transform(x_train)
x_test_pca = pca.transform(x_test)
print("运行 %0.3fs" % (time() - start_time))
print("分类数据集的拟合")
start_time = time()
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
clf = GridSearchCV(SVC(kernel='rbf', class_weight="balanced"), param_grid)
clf = clf.fit(x_train_pca, y_train)
print("运行 %0.3fs" % (time() - start_time))
print("grid search 最佳估计:")
print(clf.best_estimator_)
n_components = 10
cv2.destroyAllWindows()
pca = RandomizedPCA(n_components=n_components, whiten=True)
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
clf = GridSearchCV(SVC(kernel='rbf', class_weight='auto'), param_grid)
testing_data = []
for i in range(len(images)):
testing_data.append(images[i].flatten())
pca = pca.fit(testing_data)
transformed = pca.transform(testing_data)
clf.fit(transformed, labels)
image_paths = [
os.path.join(directory, filename) for filename in os.listdir(directory)
if filename.endswith('sad')
]
for image_path in image_paths:
pred_image_pil = Image.open(image_path).convert('L')
pred_image = np.array(pred_image_pil, 'uint8')
faces = faceCascade.detectMultiScale(pred_image)
for (x, y, w, h) in faces:
X_test = pca.transform(
np.array(pred_image[y:y + col, x:x + row]).flatten())
mynbr = clf.predict(X_test)
nbr_act = int(
def main():
"""
CLI Arguments allowed:
--display_graphs Displays graphs
--retrain Trains a new model
--cross-validate Runs cross validation to fine tune the model
--test=validation_set Tests the latest trained model against the validation set
--test=test_set Tests the latets trained model against the test set
"""
global trainer, classifier
inputs_train, targets_train, inputs_valid, targets_valid, inputs_test, targets_test = load_parsed_data()
if '--display_graphs' in sys.argv:
display_graphs = True
print('using {} percent of all data in corpus'.format(PERCENTAGE_DATA_SET_TO_USE*100))
print('using {} most common words as features'.format(NUM_FEATURES))
if not trained_model_exists() or '--retrain' in sys.argv:
train_features, valid_features, test_features = extract_features(
inputs_train[:len(inputs_train)*PERCENTAGE_DATA_SET_TO_USE],
targets_train[:len(targets_train)*PERCENTAGE_DATA_SET_TO_USE],
inputs_valid[:len(inputs_valid)*PERCENTAGE_DATA_SET_TO_USE],
targets_valid[:len(targets_valid)*PERCENTAGE_DATA_SET_TO_USE],
inputs_test[:len(inputs_test)*PERCENTAGE_DATA_SET_TO_USE],
targets_test[:len(targets_test)*PERCENTAGE_DATA_SET_TO_USE]
)
save_features(train_features, valid_features, test_features)
pca = RandomizedPCA(n_components=N_COMPONENTS, whiten=False).fit(train_features)
save_pca(pca)
print ("Saved PCA")
X_train = pca.transform(train_features)
X_valid = pca.transform(valid_features)
pca = None
print ("Created PCAd features")
valid_data = ClassificationDataSet(N_COMPONENTS, target=1, nb_classes=2)
for i in range(len(X_valid)):
valid_data.addSample(X_valid[i], targets_test[i])
valid_data._convertToOneOfMany()
X_valid = None
train_data = ClassificationDataSet(N_COMPONENTS, target=1, nb_classes=2)
for i in range(len(X_train)):
train_data.addSample( X_train[i], targets_train[i])
train_data._convertToOneOfMany()
X_train = None
classifier = buildNetwork( train_data.indim, N_HIDDEN, train_data.outdim, outclass=SoftmaxLayer)
trainer = BackpropTrainer( classifier, dataset=train_data, momentum=0.1, learningrate=0.01 , verbose=True)
train_model(train_data, valid_data)
save_model(classifier)
train_data = None
valid_data = None
else:
train_features, valid_features, test_features = load_features()
pca = load_pca()
X_train = pca.transform(train_features)
pca = None
print ("Created PCAd features")
train_data = ClassificationDataSet(N_COMPONENTS, target=1, nb_classes=2)
for i in range(len(X_train)):
train_data.addSample( X_train[i], targets_train[i])
train_data._convertToOneOfMany()
X_train = None
classifier = load_trained_model()
trainer = BackpropTrainer( classifier, dataset=train_data, momentum=0.1, learningrate=0.01 , verbose=True)
if '--test=validation_set' in sys.argv:
print ("Running against validation set")
pca = load_pca()
X_valid = pca.transform(valid_features)
pca = None
valid_data = ClassificationDataSet(N_COMPONENTS, target=1, nb_classes=2)
for i in range(len(X_valid)):
valid_data.addSample( X_valid[i], targets_test[i])
valid_data._convertToOneOfMany()
X_valid = None
make_prediction(valid_data)
if '--test=test_set' in sys.argv:
print ("Running against test set")
pca = load_pca()
X_test = pca.transform(test_features)
pca = None
test_data = ClassificationDataSet(N_COMPONENTS, target=1, nb_classes=2)
for i in range(len(X_test)):
test_data.addSample( X_test[i], targets_test[i])
test_data._convertToOneOfMany()
y_pred = trainer.testOnClassData(dataset=test_data)
plot_precision_and_recall(y_pred, targets_test[:len(targets_test) * PERCENTAGE_DATA_SET_TO_USE])
X_test = None
make_prediction(test_data)
def main():
# Display progress logs on stdout
logging.basicConfig(level=logging.INFO, format='%(asctime)s %(message)s')
# Download the data, if not already on disk and load it as numpy arrays
lfw_people = fetch_lfw_people(min_faces_per_person=70, resize=0.4)
# introspect the images arrays to find the shapes (for plotting)
n_samples, h, w = lfw_people.images.shape
np.random.seed(42)
# for machine learning we use the data directly (as relative pixel
# position info is ignored by this model)
X = lfw_people.data
n_features = X.shape[1]
# the label to predict is the id of the person
y = lfw_people.target
target_names = lfw_people.target_names
n_classes = target_names.shape[0]
print("Total dataset size:")
print("n_samples: %d" % n_samples)
print("n_features: %d" % n_features)
print("n_classes: %d" % n_classes)
# Split into a training and testing set
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.25,
random_state=42)
def xprint(*args, **kwargs):
pass
N_COMPONENTS = [10, 15, 25, 50, 100, 250]
for n_components in N_COMPONENTS:
# Compute a PCA (eigenfaces) on the face dataset (treated as unlabeled
# dataset): unsupervised feature extraction / dimensionality reduction
xprint("Extracting the top %d eigenfaces from %d faces" %
(n_components, X_train.shape[0]))
t0 = time()
pca = RandomizedPCA(n_components=n_components,
whiten=True).fit(X_train)
xprint("done in %0.3fs" % (time() - t0))
eigenfaces = pca.components_.reshape((n_components, h, w))
xprint("Projecting the input data on the eigenfaces orthonormal basis")
t0 = time()
X_train_pca = pca.transform(X_train)
X_test_pca = pca.transform(X_test)
xprint("done in %0.3fs" % (time() - t0))
# Train a SVM classification model
xprint("Fitting the classifier to the training set")
t0 = time()
param_grid = {
'C': [1e3, 5e3, 1e4, 5e4, 1e5],
'gamma': [0.0001, 0.0005, 0.001, 0.005, 0.01, 0.1],
}
# for sklearn version 0.16 or prior,
# the class_weight parameter value is 'auto'
clf = GridSearchCV(SVC(kernel='rbf', class_weight='balanced'),
param_grid)
clf = clf.fit(X_train_pca, y_train)
xprint("done in %0.3fs" % (time() - t0))
xprint("Best estimator found by grid search:")
xprint(clf.best_estimator_)
# Quantitative evaluation of the model quality on the test set
xprint("Predicting the people names on the testing set")
t0 = time()
y_pred = clf.predict(X_test_pca)
xprint("done in %0.3fs" % (time() - t0))
print(n_components,
classification_report(y_test, y_pred, target_names=target_names))
return
# Qualitative evaluation of the predictions using matplotlib
def plot_gallery(images, titles, h, w, n_row=3, n_col=4):
"""Helper function to plot a gallery of portraits"""
pl.figure(figsize=(1.8 * n_col, 2.4 * n_row))
pl.subplots_adjust(bottom=0, left=.01, right=.99, top=.90, hspace=.35)
for i in range(n_row * n_col):
pl.subplot(n_row, n_col, i + 1)
pl.imshow(images[i].reshape((h, w)), cmap=pl.cm.gray)
pl.title(titles[i], size=12)
pl.xticks(())
pl.yticks(())
# plot the result of the prediction on a portion of the test set
def title(y_pred, y_test, target_names, i):
pred_name = target_names[y_pred[i]].rsplit(' ', 1)[-1]
true_name = target_names[y_test[i]].rsplit(' ', 1)[-1]
return 'predicted: %s\ntrue: %s' % (pred_name, true_name)
prediction_titles = [
title(y_pred, y_test, target_names, i) for i in range(y_pred.shape[0])
]
plot_gallery(X_test, prediction_titles, h, w)
# plot the gallery of the most significative eigenfaces
eigenface_titles = ["eigenface %d" % i for i in range(eigenfaces.shape[0])]
plot_gallery(eigenfaces, eigenface_titles, h, w)
pl.show()
pca = RandomizedPCA(n_components=5)
train_x = pca.fit_transform(data)
ones = np.ones(23)
zeros = np.zeros(33)
train_y = np.concatenate((ones,zeros))
knn = KNeighborsClassifier()
knn.fit(train_x, train_y)
#Test images
img_dir_test = "image_classification/test/
images_test = [img_dir_test+ f for f in os.listdir(img_dir_test)]
data_test = []
for image in images_test:
img = img_to_matrix(image)
img = flatten_image(img)
data_test.append(img)
test_x = pca.transform(data_test)
knn.predict(test_x)
pd.crosstab(train_y,knn.predict(train_x),rownames=['Act'],colnames=['Predicted'])