def SVM():
train_y = list()
train_X = loadData('datatraining.txt', train_y, 8000)
print(len(train_y))
print(len(train_X))
test_y = list()
test_X = loadData('datatest.txt', test_y)
#print(train_X, '/n', test_X)
sc = StandardScaler()
sc.fit(train_X)
train_X_std = sc.transform(train_X)
test_X_std = sc.transform(test_X)
model = svm.SVC(gamma=0.01)
model.fit(train_X_std, train_y)
y_pred = model.predict(test_X_std)
print("Std accuracy: {0: .2f}%".format(
accuracy_score(test_y, y_pred) * 100))
print("Std accuracy: {0: .4f}".format(accuracy_score(test_y, y_pred)))
model2 = svm.SVC(gamma=0.01)
model2.fit(train_X, train_y)
y_pred_2 = model.predict(test_X)
print("NonStd accuracy: {0: .2f}%".format(
accuracy_score(test_y, y_pred_2) * 100))
print("NonStd accuracy: {0: .4f}".format(accuracy_score(test_y, y_pred_2)))
def SVM():
train_y = list()
train_X = loadData('train.csv', train_y, 1000)
print(len(train_y))
print(len(train_X))
test_y = list()
test_X = loadData('test.csv', test_y)
# print(train_X, '/n', test_X)
sc = StandardScaler()
sc.fit(train_X)
train_X_std = sc.transform(train_X)
test_X_std = sc.transform(test_X)
model = svm.SVC(gamma=0.01)
model.fit(train_X_std, train_y)
y_pred = model.predict(test_X_std)
print("accuracy: {0: .2f}%".format(accuracy_score(test_y, y_pred) * 100))
print(train_y)
print(test_y)
def mcsvmTrain(X,Y,C,kernelFunction,tol=1e-3):
uniqueLabels = np.unique(Y)
labelCounts = uniqueLabels.size
#print 'multiclass SVM training'
#print 'number of classes:',labelCounts
model = {}
classifiers = np.array([])
model['uniqueLabels'] = uniqueLabels
model['labelCounts'] = labelCounts
classifiers = np.array([])
#print 'Multiclass SVM classification using one-to-one strategy'
nclassifiers = np.round(labelCounts*(labelCounts+1)/2)
#print nclassifiers, 'SVM classifiers will be trained'
count = 0
for i in np.arange(labelCounts):
for j in np.arange(i+1,labelCounts):
label1 = uniqueLabels[i]
label2 = uniqueLabels[j]
count+=1
print '{0}. SVM classification: {1:d} and {2:d}'.format(count,label1,label2)
idx = (Y==label1) + (Y==label2)
subX1 = X[Y==label1,:]
subX2 = X[Y==label2,:]
subX = np.append(subX1,subX2,axis=0)
subY = np.append(np.ones(subX1.shape[0]),np.zeros(subX2.shape[0]))
m = svm.SVC(C,kernelFunction)
m.fit(subX,subY)
classifier={}
classifier['m'] = m
classifier['idx1'] = i
classifier['idx2'] = j
classifiers=np.append(classifiers,classifier)
model['classifiers'] = classifiers
return model
def svm_baseline():
training_data, validation_data, test_data = mnist_loader.load_data()
# train
clf = svm.SVC()
clf.fit(training_data[0], training_data[1])
# test
predictions = [int(a) for a in clf.predict(test_data[0])]
num_correct = sum(int(a == y) for a, y in zip(predictions, test_data[1]))
print("\nBaseline Classifier using a svm\n")
print("%s of %s values correct." % (num_correct, len(test_data[1])))
def svm_classifier(train, query, train_cols): ###特征数据，带查询的特征数据，去除字符串的特征信息
clf = svm.SVC() ##分类器
train[train_cols] = preprocessing.scale(train[train_cols])
query[train_cols] = preprocessing.scale(query[train_cols])
print clf.fit(train[train_cols], train['firmware_bool'])
scores = cv.cross_val_score(clf,
train[train_cols],
train['firmware_bool'],
cv=30)
print('Estimated score SVM: %0.5f (+/- %0.5f)' %
(scores.mean(), scores.std() / 2))
query['result'] = clf.predict(query[train_cols])
print query[['url', 'result']]
def main():
import os
if not os.path.exists('data'):
os.chdir('..')
import evaluation
import optimize
import svm
for dataset in [0, 1, 2]:
print('DATASET={}'.format(dataset))
X = data.load(k=dataset)
spec_k = data.precomputed_kernels(None, 'cum_spectrum_31')[0][dataset]
def levenshtein_kernel_diff(params, I):
factors = ag.exp(params)
dists = levenshtein_distance_v2(X[I],
X[I],
weights=factors[:10],
tqdm=False)
scale = factors[10]
return ag.exp(
-dists / (dists.mean() + 1e-3) *
scale) + factors[11] * spec_k[I][:, I].astype(np.float32)
n = 512
num_folds = 2
θ = ag.zeros(12)
λ = ag.zeros(1)
θ, λ, stats = optimize.optimize(
kernel=levenshtein_kernel_diff,
clf=optimize.KernelRidge,
Y=data.train_Ys[dataset],
indices=lambda: np.random.permutation(len(X))[:n],
folds=lambda p: data.k_folds_indices(p, num_folds),
θ=θ,
λ=λ,
β=1e2,
iters=50,
verbose=False,
)
print(θ, λ)
K = levenshtein_kernel_diff(θ, np.arange(len(X))).data
for _ in range(3):
print(
evaluation.evaluate(svm.SVC(C=10),
K,
data.train_Ys[dataset],
folds=20))
def __init__(self, string):
self.string1 = string
df = pd.read_csv(
"E:\Hackathon\HackathonProject\Music\FirstPythonProject\static\TrainDataSet.csv",
sep="\t")
senti, twt = list(df["Sentiment"]), list(df["Tweet"])
path = "E:\Hackathon\HackathonProject\Music\FirstPythonProject\static\\" + self.string1
df1 = pd.read_csv(path, header=None, names=["sentiment"])
tweets, self.stringTweets = list(df1["sentiment"]), str(
df1["sentiment"])
tf = TfidfVectorizer(min_df=0,
max_df=1.0,
stop_words='english',
ngram_range=(1, 1))
features_train, features_test, labels_train, labels_test = cross_validation.train_test_split(
twt, senti, train_size=0.90, random_state=42)
features_train_transform = tf.fit_transform(features_train,
labels_train)
features_test_transform = tf.transform(tweets)
# selector = SelectPercentile(f_classif, percentile=50)
# selector.fit(features_train_transform, labels_train)
# selector.transform(features_test_transform)
# features_train_transform = selector.transform(features_train_transform).toarray()
# features_test_transform = selector.transform(features_test_transform).toarray()
#
# mnb = MultinomialNB()
# mnb.fit(features_train_transform, labels_train)
# prediction = mnb.predict(features_test_transform)
# print("Accuracy Using sklearn metrics : {}".format(accuracy_score(prediction, labels_test)))
svm_ = svm.SVC(gamma=1, C=100)
svm_.fit(features_train_transform, labels_train)
predictionsvm = svm_.predict(features_test_transform)
# print(prediction)2
print(predictionsvm)
# print("Accuracy Using sklearn metrics for SVM : {}".format(accuracy_score(predictionsvm, labels_test)))
resultList = list(predictionsvm)
self.pos, self.neg, self.neu, self.worcloud = resultList.count(
'4'), resultList.count('0'), resultList.count('2'), str(
df1["sentiment"])
def svm_classifier(train, query, train_cols):
clf = svm.SVC()
train[train_cols] = preprocessing.scale(train[train_cols])
query[train_cols] = preprocessing.scale(query[train_cols])
print clf.fit(train[train_cols], train['malicious'])
scores = cv.cross_val_score(clf,
train[train_cols],
train['malicious'],
cv=30)
print('Estimated score SVM: %0.5f (+/- %0.5f)' %
(scores.mean(), scores.std() / 2))
query['result'] = clf.predict(query[train_cols])
print query[['URL', 'result']]
- Use the scaler to transform `X`, with the `.transform()` method. from sklearn.preprocessing import StandardScaler scaler = StandardScaler() scaler.fit(X) Xscale = scaler.transform(X) #### 1\. Fit a SVC (i.e., linear SVM) Fitting an SVM can be done using `sklearn.svm`'s `SVC` module. from sklearn import svm Do the fitting here: my_svc = svm.SVC(C=1, kernel="linear") my_svc.fit(Xscale, y) What's the accuracy? Try changing the `C` parameter. sum(my_svc.predict(Xscale) == y) / len(y) #### 2\. Fit a radial-basis SVM Try again, this time with radial SVM. What's the accuracy? Try changing the parameters. my_svc = svm.SVC(C=1, kernel="rbf", gamma=100) my_svc.fit(Xscale, y) sum(my_svc.predict(Xscale) == y) / len(y) ## Cross validation
import pandas as pd
import svm
from random import randint
#read data
dataframe=pd.read_csv("tweets_all.csv")
X = dataframe[['tweet']]
y = dataframe[['sentiment']]
best_score = 0
best_params = {'C': None, 'gamma': None}
#for a preset number of iterations
for i in range(10):
#try random values for each hyperparameter
svc = svm.SVC(C=randint(0, 9), gamma=randint(0, 3))
svc.fit(X, y)
score = svc.score(Xval, yval)
if score > best_score:
best_score = score
best_params['C'] = C
best_params['gamma'] = gamma
best_score, best_params
import matplotlib.pyplot as plt
import numpy as np
import svm
plt.axes().set_aspect("equal")
np.random.seed(0)
X0 = np.random.randn(100, 2)
X1 = np.random.randn(100, 2) + np.array([2.5, 3])
y = np.array([1] * 100 + [-1] * 100)
X = np.r_[X0, X1]
model = svm.SVC()
model.fit(X, y)
xmin, xmax = X[:, 0].min(), X[:, 0].max()
ymin, ymax = X[:, 1].min(), X[:, 1].max()
plt.scatter(X0[:, 0], X0[:, 1], color="k", marker="*")
plt.scatter(X1[:, 0], X1[:, 1], color="k", marker="+")
xmesh, ymesh = np.meshgrid(np.linspace(xmin, xmax, 200),
np.linspace(ymin, ymax, 200))
Z = model.predict(np.c_[xmesh.ravel(), ymesh.ravel()]).reshape(xmesh.shape)
plt.contour(xmesh, ymesh, Z, levels=[0], colors="k")
print("正しく分類できた数:", (model.predict(X) == y).sum())
plt.show()
pd.set_option('display.width', desired_width)
roomOcc = pd.read_csv("datatraining.txt")
roomOcc = roomOcc.drop(["date"], axis=1)
#print(roomOcc.head())
#print(roomOcc.shape)
d = roomOcc.describe()
print(d)
#corr = roomOcc.corr()
#plt.figure(figsize=(10, 10))
#sns.heatmap(corr, vmax=.8, linewidths=0.01,
# square=True, annot=True, cmap='Purples', linecolor="white")
#plt.title('Correlation between features')
roomOccTest = pd.read_csv("datatest.txt")
roomOccTest = roomOccTest.drop(["date"], axis=1)
#print(roomOccTest.head())
y_train = roomOcc.pop('Occupancy').values
y_test = roomOccTest.pop('Occupancy').values
print(len(y_train))
print(len(y_test))
svmModel = svm.SVC(C=0.5)
#Set the number of training data
svmModel.fit(roomOcc[:17000], y_train[:17000])
predict = svmModel.predict(roomOccTest)
print(accuracy_score(y_test, predict))
# for gamma in np.logspace(-7,2,10):
# print("gamma : %f, C : %f"%(gamma, C))
def kernel(x,y):
term = np.dot(x, y.T)
for i in range(term.shape[0]):
for j in range(i+1):
k = np.exp(- gamma * np.linalg.norm(x[i]-y[j], ord=2)**2)
term[i,j] += k
term[j,i] += k
return term
# m = svm_train(labels , data,'-s 0 -t 1 -c 5 -g 0.05 -q')
# clf = svm.SVC(kernel='rbf', C=5, gamma=0.05)
# clf.fit(data, labels)
# print("accuracy : %f"%(np.mean(clf.predict(test['imgs'])==test['lbls'])))
clf = svm.SVC(C=C, kernel=kernel)
clf.fit(data, labels)
# print("accuracy : %f"%(np.mean(clf.predict(make_kmat(lambda x, y: np.dot(x, y.T) + np.exp(- 0.05 * np.linalg.norm(x-y, ord=2)**2), test['imgs']))==test['lbls'])))
# sv = clf.support_vectors_
acc = np.mean(clf.predict(test["imgs"])==test['lbls'])
print("accuracy : %f"%(acc))
if acc > best_acc:
best_acc = acc
best_params = (gamma, C)
print('new best : ' + str(best_params))
# sv = sparse_to_dense_array(m.get_SV(), 784)
# sv = np.asarray(sv)
mnist_2d = PCA(data, 2)
import pandas as pd
import svm
#read data
dataframe = pd.read_fwf('tweets.txt')
X = dataframe[['tweet']]
y = dataframe[['sentiment']]
C_values = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100]
gamma_values = [0.01, 0.03, 0.1, 0.3, 1, 3, 10, 30, 100]
best_score = 0
best_params = {'C': None, 'gamma': None}
for C in C_values:
for gamma in gamma_values:
svc = svm.SVC(C=C, gamma=gamma)
svc.fit(X, y)
score = svc.score(Xval, yval)
if score > best_score:
best_score = score
best_params['C'] = C
best_params['gamma'] = gamma
best_score, best_params
print(t_model_building_ending - t_model_building_start)
### SVM Model Statistics and Evaluation
t_SVM_start = time()
#0.5 is for 10 thousand
#1 for 800
X_train, X_test, y_train, y_test = train_test_split(Model_Feature,
Model_Predictor,
test_size=0.2,
random_state=1989)
sys.exit()
# svm classification
clf = svm.SVC(kernel='linear', C=1.0).fit(X_train, y_train)
y_predicted = clf.predict(X_test)
cv = cross_validation.cross_val_score(clf, X_train, y_train, cv=10)
# performance
print("cross validation result(10 fold)")
print(np.mean(cv))
print("Classification report for %s" % clf)
print(metrics.classification_report(y_test, y_predicted))
print("Confusion matrix")
print(metrics.confusion_matrix(y_test, y_predicted))
sys.exit()
"""
sys.exit()
import matplotlib.pyplot as plt
import svm
import numpy as np
from sklearn import datasets
"""
Plot using the iris dataset
"""
iris = datasets.load_iris()
X = iris.data[:, :2] # we only take the first two features.
y = np.array(list(map(lambda x: -1 if x == 0 else 1, iris.target)))
model_linear = svm.SVC(C=0.5, kernel=svm.Kernel.linear())
model_poly = svm.SVC(C=0.5, kernel=svm.Kernel.polynomial(3))
model_rbf = svm.SVC(C=0.5, kernel=svm.Kernel.rbf(2))
for model in [model_linear, model_poly, model_rbf]:
model.fit(X, y)
def plot_svm(model, X, y, figname, title, axes=[0, 10, 0, 10]):
x0_lin = np.linspace(axes[0], axes[1], 100)
x1_lin = np.linspace(axes[2], axes[3], 100)
x0, x1 = np.meshgrid(x0_lin, x1_lin)
X_mesh = np.c_[x0.ravel(),
x1.ravel()] #convert mesh points into 2d for pred
y_pred = model.predict(X_mesh).reshape(
x0.shape) #predict then convert back to meshgrid for contour plot
y_decision = model.decision_function(X_mesh).reshape(x0.shape) #
plt.figure(figsize=(10, 10))
plt.plot(X[:, 0][y == -1], X[:, 1][y == -1], 'bo', label='Class: -1')
