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utils.py
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utils.py
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import itertools
import random
from turtle import color
import numpy as np
import os
from math import ceil, floor
import matplotlib.pyplot as plt
from sklearn.manifold import TSNE
import matplotlib.patches as mp
from datetime import datetime
from sklearn.preprocessing import LabelEncoder
from sklearn.model_selection import train_test_split
random.seed(datetime.now())
def create_directory(directory_path):
if not os.path.isdir(directory_path):
os.mkdir(directory_path)
def load_data(file_name):
folder_path = "/home/hadi/datasets/UCRArchive_2018/"
# folder_path = "/mnt/nfs/ceres/bla/archives/new/UCRArchive_2018/UCRArchive_2018/"
folder_path += (file_name + "/")
train_path = folder_path + file_name + "_TRAIN.tsv"
test_path = folder_path + file_name + "_TEST.tsv"
if (os.path.exists(test_path) <= 0):
print("File not found")
return None, None, None, None
train = np.loadtxt(train_path, dtype=np.float64)
test = np.loadtxt(test_path, dtype=np.float64)
ytrain = train[:, 0]
ytest = test[:, 0]
xtrain = np.delete(train, 0, axis=1)
xtest = np.delete(test, 0, axis=1)
return xtrain, ytrain, xtest, ytest
def znormalisation(x):
stds = np.std(x,axis=1,keepdims=True)
if len(stds[stds == 0.0]) > 0:
stds[stds == 0.0] = 1.0
return (x - x.mean(axis=1, keepdims=True)) / stds
return (x - x.mean(axis=1, keepdims=True)) / (x.std(axis=1, keepdims=True))
def semi_supervised_indices(xtrain,ytrain,perc=0.3):
n = int(xtrain.shape[0])
l = int(xtrain.shape[1])
classes, class_counts = np.unique(ytrain,return_counts=True)
n_classes = len(classes)
if (class_counts < ceil(ceil(n*perc)/n_classes)).sum() == 0:
semi_indices = []
for i in range(len(classes)):
semi_indices.append(np.random.choice(a=np.where(ytrain==classes[i])[0],size=ceil(n*perc/n_classes)))
indices_semi = []
for semi_indices_class in semi_indices:
for i in semi_indices_class:
indices_semi.append(i)
return indices_semi
classes_uniform = np.where(class_counts >= ceil(n*perc/n_classes))[0]
classes_non_uniform = np.where(class_counts < ceil(n*perc/n_classes))[0]
if len(classes_non_uniform) == 0:
semi_indices = []
for i in range(len(classes)):
semi_indices.append(np.random.choice(a=np.where(ytrain==classes[i])[0],size=class_counts[i]))
indices_semi = []
for semi_indices_class in semi_indices:
for i in semi_indices_class:
indices_semi.append(i)
return indices_semi
else:
semi_indices = []
for c in classes_uniform:
semi_indices.append(np.random.choice(a=np.where(ytrain==classes[c])[0],size=floor(n*perc/n_classes)))
for c in classes_non_uniform:
semi_indices.append(np.random.choice(a=np.where(ytrain==classes[c])[0],size=class_counts[c]))
indices_semi = []
for semi_indices_class in semi_indices:
for i in semi_indices_class:
indices_semi.append(i)
indices_all = np.arange(len(ytrain))
indices_all = np.delete(arr=indices_all,obj=indices_semi)
rest_of_semi = np.random.choice(a=indices_all,size=ceil(n*perc) - len(indices_semi))
for i in rest_of_semi:
indices_semi.append(i)
return indices_semi
def split_ypred(ypred_train, ypred_test):
size_of_new_vector = int(ypred_train.shape[1])
xtrain = ypred_train.copy()
xtest = ypred_test.copy()
xtrain = np.delete(xtrain, obj=[1, 2], axis=2)
xtest = np.delete(xtest, obj=[1, 2], axis=2)
xtrain.shape = (-1, size_of_new_vector)
xtest.shape = (-1, size_of_new_vector)
return xtrain, xtest
def generate_array_of_colors(n):
colors_list = []
r = int(np.random.choice(np.arange(start=32,stop=255),size=1))
g = int(np.random.choice(np.arange(start=128,stop=255),size=1))
b = int(np.random.choice(np.arange(start=64,stop=255),size=1))
alpha = 1.0
step = 256 / n
for _ in range(n):
r += step
g += step
b += step
r = int(r) % 256
g = int(g) % 256
b = int(b) % 256
colors_list.append((r / 255, g / 255, b / 255, alpha))
# return colors_list
colors = ["#"+''.join([random.choice('0123456789ABCDEF') for i in range(6)])
for j in range(n)]
return colors
def draw(ypred_test,labels_test,output_directory,colors=None):
classes=np.unique(labels_test)
classes=np.sort(classes)
if colors is None:
colors=generate_array_of_colors(classes.shape[0])
colors=np.sort(colors)
fig,sub=plt.subplots()
num_units = int(ypred_test.shape[1])
temp = ypred_test.copy()
if len(temp.shape) > 2:
temp = np.delete(temp, obj=[1, 2], axis=2)
temp.shape = (labels_test.shape[0], num_units)
embd=TSNE(n_components=2,random_state=12)
temp=embd.fit_transform(temp)
for i in range(labels_test.shape[0]):
index = int(np.where(classes == labels_test[i])[0])
sub.scatter(temp[i, 0], temp[i, 1],s=80, color=colors[index], marker="o")
legends=[]
for i in range(classes.shape[0]):
temp_str="Class -"+str(classes[i])+"-"
legend=mp.Patch(color=colors[i],hatch='o',linewidth=3,label=temp_str)
legends.append(legend)
plt.legend(handles=legends,prop={'size': 25})
plt.title("On latent space.",fontsize=30)
plt.savefig(output_directory+'2D.pdf')
def draw_before(xtest,ytest,output_directory,colors=None):
classes = np.unique(ytest)
classes = np.sort(classes)
if colors is None:
colors = generate_array_of_colors(classes.shape[0])
colors = np.sort(colors)
fig,sub = plt.subplots()
embd = TSNE(n_components=2,random_state=12)
xtest = embd.fit_transform(xtest)
for i in range(ytest.shape[0]):
index = int(np.where(classes == ytest[i])[0])
sub.scatter(xtest[i,0], xtest[i,1],s=80, color=colors[index], marker="o")
legends=[]
for i in range(classes.shape[0]):
temp_str="Class -"+str(classes[i])+"-"
legend=mp.Patch(color=colors[i],hatch='o',linewidth=3,label=temp_str)
legends.append(legend)
plt.legend(handles=legends,prop={'size': 25})
plt.title("On raw data.",fontsize=30)
plt.savefig(output_directory+'2D_before.pdf')
def encode_labels(y):
y = np.expand_dims(y,axis=1)
labenc = LabelEncoder()
return labenc.fit_transform(y)
if __name__ == "__main__":
n = 4
xtrain, ytrain, xtest, ytest = load_data(file_name="WordSynonyms")
semi_indices = semi_supervised_indices(xtrain=xtrain,ytrain=ytrain)
print(len(ytrain),len(semi_indices))