def decode_segmap(label_mask, dataset, plot=False):
if dataset == 'pascal' or dataset == 'coco':
n_classes = 21
label_colours = get_pascal_labels()
elif dataset == 'cityscapes':
n_classes = 19
label_colours = get_cityscapes_labels()
else:
raise NotImplementedError
r = label_mask.copy()
g = label_mask.copy()
b = label_mask.copy()
for ll in range(0, n_classes):
r[label_mask == ll] = label_colours[ll, 0]
g[label_mask == ll] = label_colours[ll, 1]
b[label_mask == ll] = label_colours[ll, 2]
rgb = np.zeros((label_mask.shape[0], label_mask.shape[1], 3))
rgb[:, :, 0] = r / 255.0
rgb[:, :, 1] = g / 255.0
rgb[:, :, 2] = b / 255.0
if plot:
plt.imshow(rgb)
plt.show()
else:
return (rgb * 255).astype(np.uint8)
def plot_loss_acc(hist):
loss = hist.history['loss']
acc = hist.history['acc']
val_loss = hist.history['val_loss']
val_acc = hist.history['val_acc']
# make a figure
fig = plt.figure(figsize=(8, 4))
# subplot loss
ax1 = fig.add_subplot(121)
ax1.plot(loss, label='train_loss')
ax1.plot(val_loss, label='val_loss')
ax1.set_xlabel('Epochs')
ax1.set_ylabel('Loss')
ax1.set_title('Loss on Training and Validation Data')
ax1.legend()
# subplot acc
ax2 = fig.add_subplot(122)
ax2.plot(acc, label='train_acc')
ax2.plot(val_acc, label='val_acc')
ax2.set_xlabel('Epochs')
ax2.set_ylabel('Accuracy')
ax2.set_title('Accuracy on Training and Validation Data')
ax2.legend()
plt.tight_layout()
plt.show()
def plot_lines(data):
fig = plt.figure()
ax = plt.axes()
x = np.linspace(0, len(data) - 1, len(data[0]))
for ii in range(data.shape[0]):
ax.plot(x, data[ii])
plt.show()
def show_train_val_metric_curve(self, metric="loss", figsize=(10, 8)):
fig, ax = plt.subplots(figsize=figsize)
train_pd = self.get_records_dataframe()
val_pd = self.get_records_dataframe(training=False)
train_pd.plot(y=metric, ax=ax, label=f"train-{metric}")
val_pd.plot(y=metric, ax=ax, label=f"val-{metric}")
plt.show()
def metric_auc(self, soft, target):
print("AUC: {}".format(roc_auc_score(target, soft, average = 'micro')))
fpr, tpr, thres = roc_curve(target, soft)
plt.plot(fpr, tpr)
plt.xlim([0.0,1.0])
plt.ylim([0.0,1.0])
plt.title('ROC Curve')
plt.xlabel('False Positive Rate[1 - Specificity]')
plt.ylabel('True Positive Rate[Sensitivity]')
plt.grid(True)
plt.show()
def plot_images(images: list, labels: list,
prediction=None,
cols=4,
target_shape=None):
# we want to plot this in grid of shape (?, 4) by default
if prediction is not None:
assert len(images) == len(labels) == len(prediction)
else:
assert len(images) == len(labels)
assert not len(images) % cols
rows = len(images) // cols
# Create figure
fig, axes = plt.subplots(rows, cols)
fig.subplots_adjust(hspace=.3, wspace=.5)
for i, ax in enumerate(axes.flat):
if target_shape is not None:
ax.imshow(images[i].reshape(target_shape), cmap='binary')
else:
ax.imshow(images[i], cmap='binary')
# Show true and predicted classes
if prediction is None:
xlabel = "True: {0}".format(labels[i])
else:
xlabel = "True: {0}, Pred: {1}".format(labels[i], prediction[i])
# Show the classes as the label on x_axis
ax.set_xlabel(xlabel)
# Remove the ticks from the plot
ax.set_xticks([])
ax.set_yticks([])
plt.show()
if ("ms" in t):
return float(t.replace("ms", ""))
print t
return t
path = "/localdisk/dchenTmp/scheduling/tunableOCLbenchmarks/result"
benches = ["2mm"]
kernels = {"2mm": ["mm2_kernel1", "mm2_kernel2"]}
for name in benches:
for kernel in kernels[name]:
times = []
for file in os.listdir(path):
if (name in file):
f = open(path + "/" + file, "r")
tmp = 0
cnt = 0
for line in f:
if (kernel in line):
cnt += 1
tmp += timeToFloat(line.split()[1])
tmp = tmp / cnt
times.append(tmp)
plt.plot(times)
plt.show()
#print file.replace(name + ".exe_result", "").replace(".txt","").split("_")
import statsmodels.api as sm
import statsmodels.formula.api as smf
from matplotlib.pyplot import plot
from sklearn import linear_model as lm
df = pd.read_csv('/Users/bonythomas/patients.csv')
x = pd.read_csv('/Users/bonythomas/patients.csv',
usecols=[
"Age", "Gender", "Smoker", "Weight", "Height",
"SelfAssessedHealthStatus", "Location"
])
y = pd.read_csv('/Users/bonythomas/patients.csv', usecols=["Systolic"])
obj_df = df.select_dtypes(include=['object']).copy()
converted_data = pd.factorize(obj_df['Gender'])[0]
converted_data1 = pd.factorize(obj_df['Location'])[0]
converted_data2 = pd.factorize(obj_df['SelfAssessedHealthStatus'])[0]
x['Gender'] = converted_data
x['Location'] = converted_data1
x['SelfAssessedHealthStatus'] = converted_data2
print('Predictors', x)
print('Predicted Value', y)
regr = lm.LinearRegression()
regr.fit(x, y)
model1 = smf.OLS(y, x)
result = model1.fit()
predictions = y - result
print('Intercept', regr.intercept_)
print('Coefficients', regr.coef_)
plot(result, predictions)
plot.show()
print(result.summary())