def main():
df = pd.read_csv("/home/saxobeat/PythonML/MLCodes/Spambase/Dataset/spamdata.csv")
features = df.iloc[:,0:57].values
labels = df.iloc[:,57].values
X_train, X_test, y_train, y_test = tts(features, labels, test_size=0.25, shuffle=True, random_state=8)
models = []
models.append(('LR', LR(solver='lbfgs', max_iter=2000, tol=0.0001)))
models.append(('LDA', LDA()))
models.append(('DTC', DTC()))
models.append(('KNC', KNC()))
models.append(('MNB', MNB()))
models.append(('RFC', RFC(n_estimators=100)))
models.append(('SVC', SVC(gamma='scale', kernel='rbf', probability=True)))
x0 = np.linspace(0,1,10)
plt.plot([0,1],[0,1],'k',linestyle='--')
for name,model in models:
model.fit(X_train, y_train)
y_pred = model.predict_proba(X_test)
y_score = y_pred[:,1]
fpr, tpr, thresholds = roc_curve(y_test, y_score)
label = "{}({})".format(name,auc(fpr, tpr))
plt.plot(fpr,tpr,label=label)
plt.legend()
# plt.legend(name)
plt.title("Reciever Operating Characteristics")
plt.grid()
plt.cool()
plt.xlabel("Fasle Positive Rate")
plt.ylabel("True Positive Rate")
plt.savefig("roc.pdf")
def __call__(self,u,v,w,iteration):
q = 4
plt.cool()
if self.x == None:
ny = v.shape[1]
nz = v.shape[0]
self.x,self.y = np.meshgrid(range(ny),range(nz))
x,y = self.x,self.y
if self.iterations == None:
self.iterations = self.sim.bulk_calc(getIteration())
all_itr = self.iterations
if self.xvar == None:
class temp(sim_operation):
def get_params(self):
return ["u"]
def __call__(self,u):
return np.max(self.sim.ddx(u))
self.xvar = self.sim.bulk_calc(temp())
xvar_series = self.xvar
min = np.min(xvar_series)
max = np.max(xvar_series)
if min <= 0:
min = 0.000001
if max <= min:
max = 0.00001
avgu = np.average(u,2)
avgv = np.average(v,2)
avgw = -np.average(w,2)
xd = self.sim.ddx(u)
xd2d = np.max(xd,2)
xd1d = np.max(xd2d,1)
plt.subplot(221)
plt.imshow(avgu)
plt.quiver(x[::q,::q],y[::q,::q],avgv[::q,::q],avgw[::q,::q])
plt.title('Avg u')
plt.axis("tight")
plt.subplot(222)
plt.imshow(xd2d)
plt.title('Max x Variation (y-z)')
plt.axis("tight")
plt.subplot(223)
plt.plot(xd1d)
plt.title('Max x Variation (z)')
plt.axis("tight")
plt.subplot(224)
plt.plot(all_itr,xvar_series, '--')
plt.plot([iteration,iteration],[min,max])
plt.semilogy()
plt.title('Max x Variation (t)')
plt.axis("tight")
def plot():
# set default color
#colors = ['brown', 'r', 'b', 'oldlace', 'yellowgreen', 'teal', 'tomato', 'palegreen', 'cornsilk', 'pink', 'crimson', 'darkgreen', 'hotpink', 'gray', 'green', 'gold', 'beige', 'bisque']
colors = [(0., 1, 1),
(0.05, 1, 1), (0.11, 0, 0), (0.66, 1, 1), (0.89, 1, 1),
(1, 0.5, 0.5), (0, 1, 1), (0.05, 1, 1), (0.11, 0, 0),
(0.375, 1, 1), (0.64, 1, 1), (0.91, 0, 0), (1, 0, 0), (0., 1, 1),
(0.05, 1, 1), (0.11, 1, 1), (0.34, 1, 1), (0.65, 0, 0),
(1, 0, 0)]
Raw_edge = []
Bcc_matrix = []
cut_arr = []
n = readRawFile(
"/Users/luodian/Desktop/DSA/Graph Plus/Graph Plus/DATA/in.txt",
Raw_edge)
readBccFile(
"/Users/luodian/Desktop/DSA/Graph Plus/Graph Plus/DATA/Bcc.txt",
Bcc_matrix, cut_arr)
all_node = range(1, n + 1)
G = nx.Graph()
G.add_edges_from(Raw_edge)
G.add_nodes_from(all_node)
pos = nx.spring_layout(G)
# color the bcc nodes
for i in xrange(0, len(Bcc_matrix)):
nx.draw_networkx_nodes(G,
pos,
nodelist=Bcc_matrix[i],
alpha=1,
node_color=colors[i])
# color the cut nodes
nx.draw_networkx_nodes(G, pos, nodelist=cut_arr, node_color='salmon')
nx.draw_networkx_edges(G, pos, edge_color='black', alpha=1, width=0.8)
nx.draw_networkx_labels(G, pos)
# print type(node_blue)
# nx.draw_networkx_edges(G, pos = nx.spring_layout(G), edgelist=[(1,2)], edge_color = 'blue')
# nx.draw_networkx_nodes(G, pos, nodelist = Bcc_matrix[1], node_color = 'b')
font = {'color': 'k', 'fontweight': 'bold', 'fontsize': 14}
plt.title("The cut and bcc info", font)
plt.annotate('The cut vertexs', (1, 1.15))
plt.plot([0.95], [1.1666], color='salmon', marker='o', markersize=15)
plt.axis('off')
plt.cool()
plt.savefig('Bcc.png', dpi=233)
plt.show()
def plot(x, y, z, c, experimentNumber):
x = np.asarray(x)
y = np.asarray(y)
z = np.asarray(z)
c = np.asarray(c)
fig = plt.figure()
ax = fig.gca(projection='3d')
img = ax.scatter(x, y, z, c=c, cmap=plt.cool())
ax.set_xlabel('Inputs')
ax.set_ylabel('Outputs')
ax.set_zlabel('$P_{expected}$')
cbrar = fig.colorbar(img)
cbrar.set_label('$P_{real}$')
# ax.legend()
plt.title("Relation of inputs, outputs and non-determinism")
outF = open("results/experiment2.txt", "w")
sys.stdout = outF
print("inputs,outputs,prevalence,nondeterminism")
for i, _ in enumerate(x):
print("{},{},{:04.2f},{:06.4f}".format(x[i], y[i], z[i], c[i]))
plt.savefig('results/experiment4.png'.format(experimentNumber))
plt.show()
def plot_convergence(
order_of_probed_points: List[List[int]],
loss_function: Callable,
truth_value: List[int],
step_sizes: List[int],
) -> None:
fig = plt.figure()
ax = fig.add_subplot(projection='3d')
# probed points
x = np.array([point[0]
for point in order_of_probed_points] + [truth_value[0]])
y = np.array([point[1]
for point in order_of_probed_points] + [truth_value[1]])
z = np.array([point[2]
for point in order_of_probed_points] + [truth_value[2]])
c = np.array([
loss_function(point, step_sizes, truth_value)
for point in order_of_probed_points
] + [0])
# ax.plot3D(x, y, z, 'grey')
image = ax.scatter3D(x, y, z, c=c, cmap=plt.cool())
ax.set_xlabel('print temperature')
ax.set_ylabel('retraction distance')
ax.set_zlabel('flow rate')
fig.colorbar(image, pad=0.05, label="loss", location='left')
plt.show()
def write_snrmap(outputfile, intsnr, ds):
"""
Update the CTF parameters file with new CTF file lines.
Parameters:
outputfile Filename of output image file
intsnr Integrated SNR
ds Frequency sampling
"""
# Import plotting libaries
import matplotlib
import numpy as np
import matplotlib.cm as cm
import matplotlib.mlab as mlab
import matplotlib.pyplot as plt
# Setup plotting parameters
matplotlib.rcParams['xtick.direction'] = 'out'
matplotlib.rcParams['ytick.direction'] = 'out'
# Create plotting variables
x = np.arange(0., ds * len(intsnr), ds)
y = np.arange(0., 0.5, 0.005)
X, Y = np.meshgrid(x, y)
Z = np.transpose(np.array(intsnr))
# Plot result
plt.figure()
CS = plt.contourf(X,
Y,
Z,
np.arange(0.,
np.max(intsnr) + old_div(np.max(intsnr), 20.),
old_div(np.max(intsnr), 20.)),
antialiased=True)
CB = plt.colorbar(CS, shrink=0.8, format='%i')
plt.cool()
plt.title('Image Coverage of Frequency Space')
plt.xlabel('Frequency (1/A)')
plt.ylabel('Signal-to-Noise Ratio')
plt.savefig(outputfile)
def plot(label):
# set default color
# colors = ['brown', 'r', 'b', 'oldlace', 'yellowgreen', 'teal', 'tomato', 'palegreen', 'cornsilk', 'pink', 'crimson', 'darkgreen', 'hotpink', 'gray', 'green', 'gold', 'beige', 'bisque']
colors = [(0., 1, 1),
(0.05, 1, 1), (0.11, 0, 0), (0.66, 1, 1), (0.89, 1, 1),
(1, 0.5, 0.5), (0, 1, 1), (0.05, 1, 1), (0.11, 0, 0),
(0.375, 1, 1), (0.64, 1, 1), (0.91, 0, 0), (1, 0, 0), (0., 1, 1),
(0.05, 1, 1), (0.11, 1, 1), (0.34, 1, 1), (0.65, 0, 0),
(1, 0, 0)]
Raw_edge = []
n = readRawFile(
"/Users/luodian/Desktop/network_alignment/graphs/{}.txt".format(label),
Raw_edge)
n = int(n)
all_node = range(0, n)
G = nx.DiGraph()
G.add_edges_from(Raw_edge)
G.add_nodes_from(all_node)
pos = nx.spring_layout(G)
nx.draw_networkx_edges(G,
pos,
edge_color='blue',
arrows=True,
arrowstyle='->',
arrowsize=20,
alpha=1,
width=0.8)
nx.draw_networkx_labels(G, pos)
# print type(node_blue)
# nx.draw_networkx_edges(G, pos = nx.spring_layout(G), edgelist=[(1,2)], edge_color = 'blue')
nx.draw_networkx_nodes(G, pos, nodelist=all_node, node_color='orange')
font = {'color': 'k', 'fontweight': 'bold', 'fontsize': 14}
plt.title("Graph of {}".format(label), font)
# plt.annotate('The cut vertexs', (1, 1.15))
# plt.plot([0.95], [1.1666], color='salmon', marker='o', markersize=15)
plt.axis('off')
plt.cool()
plt.savefig('{}_digraph.png'.format(label), dpi=200)
plt.show()
def write_snrmap( outputfile, intsnr, ds ) : """ Update the CTF parameters file with new CTF file lines. Parameters: outputfile Filename of output image file intsnr Integrated SNR ds Frequency sampling """ # Import plotting libaries import matplotlib import numpy as np import matplotlib.cm as cm import matplotlib.mlab as mlab import matplotlib.pyplot as plt # Setup plotting parameters matplotlib.rcParams['xtick.direction'] = 'out' matplotlib.rcParams['ytick.direction'] = 'out' # Create plotting variables x = np.arange( 0., ds * len( intsnr ), ds ) y = np.arange( 0., 0.5, 0.005 ) X,Y = np.meshgrid( x, y ) Z = np.transpose( np.array( intsnr ) ) # Plot result plt.figure( ) CS = plt.contourf( X, Y, Z, np.arange( 0., np.max( intsnr ) + np.max( intsnr ) / 20., np.max( intsnr ) / 20. ), antialiased = True ) CB = plt.colorbar( CS, shrink = 0.8, format = '%i' ) plt.cool( ) plt.title( 'Image Coverage of Frequency Space' ) plt.xlabel( 'Frequency (1/A)' ) plt.ylabel( 'Signal-to-Noise Ratio' ) plt.savefig( outputfile )
def show_relative_error_results(self, expected_results):
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
result = self.anfis_estimate_labels(self.premises, self.op, self.tsk)
error_result = np.abs(expected_results - result) / expected_results
img = ax.scatter(self.training_data[0],
self.training_data[1],
self.training_data[2],
c=error_result.flatten(),
cmap=plt.cool())
fig.colorbar(img)
fig.canvas.set_window_title('Relative error')
plt.show()
def graph(s):
"""
This code is horrific. Unfortunately started local NSGA2 without checking so will have to do for now
:param s: seed
:return: nothing
"""
data = []
input = []
datat = True
mixed = [[], [], [], []]
for x in open("results/" + str(s) + "_results.txt"):
x = x.replace("\n", "")
if x == "#":
datat = False
continue
x = x.replace("\n", "")
x = x.split(",")
if datat:
data.append([-float(x[0]), -float(x[1])])
mixed[0].append(-float(x[0]))
mixed[1].append(-float(x[1]))
else:
input.append([int(float(x[0])), float(x[1])])
mixed[2].append(int(float(x[0])))
mixed[3].append(float(x[1]))
#Scatter(tight_layout=True).add(np.array(mixed),s=10).show()
#Scatter().add(np.array(data)).show()
#Scatter().add(np.array(input)).show()
fig = plt.figure()
ax = fig.add_subplot(111, projection='3d')
x = np.array(mixed[0])
y = np.array(mixed[1])
z = np.array(mixed[2])
c = np.array(mixed[3])
img = ax.scatter(x, y, z, c=c, cmap=plt.cool())
fig.colorbar(img)
plt.show()
def __call__(self,u,iteration):
plt.cool()
if self.iterations == None:
self.iterations = self.sim.bulk_calc(getIteration())
all_itr = self.iterations
if self.xvar == None:
class temp(sim_operation):
def get_params(self):
return ["u"]
def __call__(self,u):
return np.max(self.sim.ddx(u))
self.xvar = self.sim.bulk_calc(temp())
xvar_series = self.xvar
min = np.min(xvar_series)
max = np.max(xvar_series)
if min <= 0:
min = 0.000001
if max <= min:
max = 0.00001
uavg = np.average(u,2)
xd = self.sim.ddx(u)
xd2d = np.max(xd,2)
plt.subplot(241)
plt.imshow(xd2d)
plt.title('Max x Variation (y-z)')
plt.axis("tight")
plt.subplot(242)
plt.plot(uavg[58,:])
plt.title('z = 58')
plt.axis("tight")
plt.subplot(243)
plt.plot(uavg[60,:])
plt.title('z = 60')
plt.axis("tight")
plt.subplot(244)
plt.plot(uavg[62,:])
plt.title('z = 62')
plt.axis("tight")
plt.subplot(245)
plt.plot(all_itr,xvar_series, '--')
plt.plot([iteration,iteration],[min,max])
plt.semilogy()
plt.title('Max x Variation (t)')
plt.axis("tight")
plt.subplot(246)
plt.plot(uavg[64,:])
plt.title('z = 64')
plt.axis("tight")
plt.subplot(247)
plt.plot(uavg[64,:])
plt.title('z = 64')
plt.axis("tight")
plt.subplot(248)
plt.plot(uavg[66,:])
plt.title('z = 66')
plt.axis("tight")
x_range = np.linspace(x_min - 1, x_max + 1, 200)
y_range = np.linspace(y_min - 1, y_max + 1, 200)
xx, yy = np.meshgrid(x_range, y_range)
for k, model in enumerate((LDA(), QDA())):
#fit, predict
clf = model
clf.fit(points, labels)
z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
z = z.reshape(200, 200)
z_p = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])
#draw areas and boundries
pl.figure()
pl.pcolormesh(xx, yy, z)
pl.cool()
for j in range(len(u)):
pl.contour(xx,
yy,
z_p[:, j].reshape(200, 200), [0.5],
lw=3,
colors='k')
#draw points
for i, point in enumerate(x):
pl.plot(point[:, 0], point[:, 1], c[i] + m[i])
#draw contours
for i in range(len(u)):
prob = mvn2d(x_range, y_range, u[i], sigma[i])
cs = pl.contour(xx, yy, prob, colors=c[i])
import matplotlib.pyplot as plt
import numpy as np
dx, dy = 0.015, 0.05
x = np.arange(-4.0, 4.0, dx)
y = np.arange(-4.0, 4.0, dy)
X, Y = np.meshgrid(x, y)
extent = np.min(x), np.max(x), np.min(y), np.max(y)
Z1 = np.add.outer(range(8), range(8)) % 2
plt.imshow(Z1,
cmap="binary_r",
interpolation="nearest",
extent=extent,
alpha=1)
def copyassignment(x, y):
return (1 - x / 2 + x**5 + y**6) * np.exp(-(x**2 + y**2))
z2 = copyassignment(X, Y)
plt.imshow(z2, alpha=0.7, interpolation='bilinear', extent=extent)
plt.cool()
plt.title('matplotlib.pyplot.cool() function example', fontweight="bold")
plt.show()
x_range = np.linspace(x_min - 1, x_max + 1, 200)
y_range = np.linspace(y_min - 1, y_max + 1, 200)
xx, yy = np.meshgrid(x_range, y_range)
for k, model in enumerate((LDA(), QDA())):
#fit, predict
clf = model
clf.fit(points, labels)
z = clf.predict(np.c_[xx.ravel(), yy.ravel()])
z = z.reshape(200, 200)
z_p = clf.predict_proba(np.c_[xx.ravel(), yy.ravel()])
#draw areas and boundries
pl.figure()
pl.pcolormesh(xx, yy, z)
pl.cool()
for j in range(len(u)):
pl.contour(xx, yy, z_p[:, j].reshape(200, 200),
[0.5], lw=3, colors='k')
#draw points
for i, point in enumerate(x):
pl.plot(point[:, 0], point[:, 1], c[i] + m[i])
#draw contours
for i in range(len(u)):
prob = mvn2d(x_range, y_range, u[i], sigma[i])
cs = pl.contour(xx, yy, prob, colors=c[i])
pl.title('Seperate {0} classes using {1}'.
format(len(u), model_names[k]))
from mpl_toolkits.mplot3d import Axes3D
import matplotlib.pyplot as plt
fig = plt.figure()
ax2 = fig.add_subplot(111, projection='3d')
x1 = df.ix[0:, 'x1']
x2 = df.ix[0:, 'x2']
x3 = df.ix[0:, 'x3']
y = df.ix[0:, 'y']
if sys.argv[1:] == ['winter']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.winter())
elif sys.argv[1:] == ['cool']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.cool())
elif sys.argv[1:] == ['viridis']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.viridis())
elif sys.argv[1:] == ['plasma']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.plasma())
elif sys.argv[1:] == ['inferno']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.inferno())
elif sys.argv[1:] == ['jet']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.jet())
elif sys.argv[1:] == ['gist_ncar']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.gist_ncar())
elif sys.argv[1:] == ['rainbow']:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.nipy_spectral())
else:
p = ax2.scatter(x1, x2, x3, c=y, cmap=plt.nipy_spectral())