def merge_synonym():
synsets = load_csv('./data/synsets/ANEW_synsets.csv')
syn_clusters = []
for i, synset_i in enumerate(synsets):
for synset_j in synsets[i + 1:]:
if synset_i[0] in synset_j or synset_j[0] in synset_i:
syn_cluster = (synset_i[0], synset_j[0])
syn_clusters.append(syn_cluster)
# zz = set()
# for ll in syn_clusters:
# zz=zz.union(set(ll))
# print(len(zz))
# exit()
outs = []
for a, b in syn_clusters:
# 如果a, b 都没有出现过
if all(len(set([a, b]) & set(l)) == 0 for l in outs):
# 创建新的
out = [a, b]
outs.append(out)
# 否则
else:
# 合并进去
for i, k in enumerate(outs):
if set([a, b]) & set(k) != set():
outs[i] = list(set(outs[i] + [a, b]))
break
# leng = 0
# for i, j in enumerate(outs):
# leng += len(j)
# print('| cluster_%s | %s |' % (str(i), str(j)))
# print(leng)
return outs
def replacer(word=None):
syn_map = dict()
synsets = load_csv('./data/synsets/ANEW_synsets.csv')
for synset in synsets:
if len(synset)>1:
for w in synset[1:]:
syn_map[w]=synset[0]
# if word in syn_map.keys():
# return syn_map[word]
return syn_map
def replacer(word=None):
syn_map = dict()
synsets = load_csv('./data/synsets/ANEW_synsets.csv')
for synset in synsets:
if len(synset) > 1:
for w in synset[1:]:
syn_map[w] = synset[0]
# if word in syn_map.keys():
# return syn_map[word]
return syn_map
def dialog_load_network():
dir_name = 'network_data'
if path.isdir(dir_name) == False:
dir_name = getcwd()
f_name = QFileDialog.getOpenFileName(None,
'Load Electic Network',
directory=dir_name,
filter="Network files *.csv")
try:
assert (os.path.exists(f_name))
except AssertionError:
sys.exit(' *** No file selected *** ')
return load_csv(str(f_name))
def sonar_run():
seed(2)
filePath = '../data/sonar.csv'
dataset = load_data.load_csv(filePath, True)
# convert string attributes to integers
for i in range(0, len(dataset[0]) - 1):
load_data.str_column_to_float(dataset, i)
# convert class column to integers
load_data.str_column_to_int(dataset, len(dataset[0]) - 1)
# evaluate algorithm
n_folds = 5
max_depth = 10
min_size = 1
sample_size = 1.0
n_features = int(sqrt(len(dataset[0]) - 1))
for n_trees in [1, 5, 10]:
scores = evaluate_split.evaluate_algorithm(dataset,
randomforest.random_forest,
n_folds, max_depth,
min_size, sample_size,
n_trees, n_features)
print('Trees: %d' % n_trees)
print('Scores: %s' % scores)
print('Mean Accuracy: %.3f%%' % (sum(scores) / float(len(scores))))
Created on Thu May 3 11:51:18 2018
decission tree with banknote
@author: shifuddin
"""
from load_data import load_csv
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error
from math import sqrt
'''
Load feature values as X and target as Y
here we read day dataset
'''
uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/forest-fires/forestfires.csv'
X, y = load_csv(uri, ',', 4, 12, 12, 13, True)
'''
Split into training and test set
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1)
'''
Feature scaling
'''
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
sc_y = StandardScaler()
Created on Sun Apr 15 22:01:23 2018
knn with Concrete Slump dataset from uci
@author: shifuddin
"""
from sklearn.model_selection import train_test_split
from sklearn import neighbors
from sklearn.metrics import mean_squared_error
from math import sqrt
from load_data import load_csv
'''
Load feature values as X and target as Y
here we read day dataset
'''
uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/concrete/slump/slump_test.data'
X, y = load_csv(uri, ',', 1, 8, 8, 11, True)
'''
Split into training and test set
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1)
knn_regressor = neighbors.KNeighborsRegressor(algorithm='auto',
n_neighbors=30,
weights='uniform')
knn_regressor.fit(X_train, y_train)
y_pred = knn_regressor.predict(X_test)
Created on Thu May 3 11:51:18 2018
decission tree with banknote
@author: shifuddin
"""
from load_data import load_csv
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import mean_squared_error
from math import sqrt
'''
Load feature values as X and target as Y
here we read day dataset
'''
uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00374/energydata_complete.csv'
X, y = load_csv(uri, ',', 1, 27, 27, 28, True)
'''
Split into training and test set
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1)
'''
Fit DecisionTreeRegressor with Bike Day data
'''
regressor = RandomForestRegressor(n_estimators=10, random_state=0)
regressor.fit(X_train, y_train)
'''
Predicting result
'''
from tensorflow.keras.callbacks import (TensorBoard, ModelCheckpoint,
EarlyStopping)
from load_data import load_csv, train_valid_test_datasets, show_batch
from features import (PackNumericFeatures, categorical2onehot,
categorical2embedding, normalization)
from utils import get_unique
from train_model import get_dense_two_layer_net
# load data and create Dataset obj
train_fileName = '../inputs/train.csv'
test_fileName = '../inputs/test.csv'
batch_size = 128 # 32
train_data, test_data = load_csv(train_fileName, test_fileName)
train_data.pop("id")
test_data_id = test_data.pop("id")
train_dataset, valid_dataset, test_dataset = train_valid_test_datasets(
train_data,
test_data,
valid_size=0.2,
batch_size=batch_size,
test_shuffle=False)
train_size = int(train_data.shape[0] * 0.8)
valid_size = int(train_data.shape[0] * 0.2)
print(train_data.shape, test_data.shape)
print(train_dataset.element_spec)
numeric_features = ['month', 'day']
train_dataset = train_dataset.map(PackNumericFeatures(numeric_features))
num_classes = 30
seq_len = 4500 if is_dna_data else 1500
model_name = 'blstm_openset'
data_dir = '/mnt/data/computervision/train80_val10_test10'
if is_dna_data:
model_name = 'blstm_dna_conv3_4500'
data_dir = '/mnt/data/computervision/dna_train80_val10_test10'
model_file = '../models/' + model_name + '.h5'
model = load_model(model_file)
av_model = Model(inputs=model.input, outputs=model.get_layer("AV").output)
print av_model.summary()
train_data = load_csv(data_dir + '/train.csv')
batch_size = 10000
avs = []
actual = []
lower = 0
while lower < len(train_data):
print lower
upper = min(lower + batch_size, len(train_data))
x, y = get_onehot(train_data[lower:upper],
None,
is_dna_data=is_dna_data,
seq_len=seq_len)
pred = av_model.predict(x, batch_size=500)
avs.append(pred)
actual.append(y)
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 21 23:19:47 2018
@author: shifuddin
"""
from load_data import load_csv
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
'''
Load X, y from uri
'''
uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data'
X, y = load_csv(uri, ',', 1, 5, 9, 10)
'''
Split into training and test set
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
'''
Perform logistic regression
'''
""" Created on Thu May 3 11:51:18 2018 decission tree with banknote @author: shifuddin """ from load_data import load_csv from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB from sklearn.metrics import confusion_matrix ''' Load X, y from uri ''' uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/spect/SPECTF.test' X, y = load_csv(uri,',', 1, 45, 0, 1, True) ''' Split into training and test set ''' X_train, X_test, y_train, y_test =train_test_split(X, y,test_size=0.2, random_state=1) ''' Feature scaling ''' from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) '''
import numpy as np
import normalizer as norm
from load_data import load_csv
base_path = '/home/daniel/Documentos/Projetos/TCC/Normalizador/tests' # Trocar por caminho relativo do projeto.
raw_data = load_csv(f'{base_path}/raw_data.csv')
normalized = norm.normalizer(raw_data)
np.savetxt(f'{base_path}/normalized.csv', normalized, fmt='%.8f')
print(normalized)
save_stats = True num_classes = 100 mask = True mask_len = 113 model_template = dna_mask_blstm num_letters = 4 if is_dna_data else 26 model = model_template(num_classes, num_letters, sequence_length, embed_size=256, mask_length=mask_len if mask else None) model.load_weights(model_file) model.summary() test_data = load_csv(data_dir + '/test.csv', divide=2 if is_dna_data else 1) print len(test_data) crop_count = 0.0 for seq, y in test_data: if len(seq) > sequence_length: crop_count += 1 print "percent cropped: ", crop_count / len(test_data) test_x, test_y, test_m = get_onehot(test_data, None, is_dna_data=is_dna_data, seq_len=sequence_length, num_classes=num_classes, rand_start=random_crop, mask_len=mask_len if mask else None) if print_acc: print "test accuracy: ", model.evaluate([test_x, test_m] if mask else test_x, test_y, batch_size=100) if save_stats: pred = model.predict([test_x, test_m] if mask else test_x, batch_size=100).argmax(axis=-1) log = Logger(model_name, num_classes, sequence_length)
import numpy as np
import paraconsistent
from load_data import load_csv
base_path = 'C:/Users/guermandi/Desktop/TCC/AnaliseParaconsistente/tests'
pathological = load_csv(f'{base_path}/patologicos-normalizados.csv')
healthy = load_csv(f'{base_path}/saudaveis-normalizados.csv')
pathological = np.delete(pathological, (0, 1, 2, 4, 5, 6), 1)
healthy = np.delete(healthy, (0, 1, 2, 4, 5, 6), 1)
classes = np.array([np.array(pathological), np.array(healthy)])
alpha = paraconsistent.alpha(classes)
beta = paraconsistent.beta(classes)
assurance = paraconsistent.assurance(alpha, beta)
contradiction = paraconsistent.contradiction(alpha, beta)
truth = paraconsistent.truth(assurance, contradiction)
# Classes de dados
classes = np.array([np.array(pathological), np.array(healthy)])
# Alfa e Beta
alpha = paraconsistent.alpha(classes)
beta = paraconsistent.beta(classes)
# Ponto G1
assurance = paraconsistent.assurance(alpha, beta)
return ''.join(l) model = load_model(model_file) model.summary() results = [] for percent in range(2,22,2): #mode 0: substitute, mode 1: 3-aligned cut, mode 2: unaligned cut row = [percent] for mode in range(3): test_data = load_csv(data_dir + '/test.csv', divide=2) print len(test_data) for i in range(len(test_data)): (x, y) = test_data[i] if mode == 0: test_data[i] = (substitute(x, percent), y) else: test_data[i] = (delete_segment(x, percent, mode == 1), y) #if i % 100000 == 99999: # print i+1 test_x, test_y, test_m = get_onehot(test_data, None, is_dna_data=True, seq_len=sequence_length, num_classes=num_classes, mask_len=mask_len) acc = model.evaluate([test_x, test_m], test_y, batch_size=100, verbose=1)[1] print percent, mode, acc
from ml_logging import Logger
num_classes = 30
num_amino_acids = 26
model = Sequential()
model.add(Masking(mask_value=0, input_shape=(1500, num_amino_acids)))
model.add(LSTM(50, activation='tanh'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(optimizer=Adam(lr=0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
model.summary()
data_dir = '/mnt/data/computervision/train80_val10_test10'
train_data = load_csv(data_dir + '/train.csv')
print len(train_data)
val_data = load_csv(data_dir + '/validation.csv')
val_x, val_y = get_onehot(val_data, None)
print len(val_data)
logger = Logger('lstm50')
save_path = '../models/lstm50.h5'
num_episodes = 20000
for i in range(num_episodes):
x, y = get_onehot(train_data, 1000)
print i
print model.train_on_batch(x, y)
if (i % 1000 == 0) or i == num_episodes - 1:
[5.332441248,2.088626775,1],
[6.922596716,1.77106367,1],
[8.675418651,-0.242068655,1],
[7.673756466,3.508563011,1]]
n_inputs = len(dataset[0]) - 1
n_outputs = len(set([row[-1] for row in dataset]))
network = initialize_network(n_inputs, 2, n_outputs)
train_network(network, dataset, 0.5, 20, n_outputs)
for layer in network:
print(layer)
for row in dataset:
prediction = predict(network, row)
print('Expected=%d, Got=%d' % (row[-1], prediction))
filename = 'seeds_dataset.csv'
dataset = load_csv(filename)
for i in range(len(dataset[0])-1):
print(dataset[i])
str_column_to_float(dataset, i)
str_column_to_int(dataset, len(dataset[0])-1)
minmax = dataset_minmax(dataset)
normalize_dataset(dataset, minmax)
n_folds = 5
l_rate = 0.3
n_epoch = 50
n_hidden = 5
decission tree with banknote
@author: shifuddin
"""
from load_data import load_csv
from sklearn.model_selection import train_test_split
from sklearn.svm import SVR
from sklearn.metrics import mean_squared_error
from math import sqrt
import pandas as pd
'''
Load feature values as X and target as Y
here we read day dataset
'''
uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00244/fertility_Diagnosis.txt'
X, y = load_csv(uri, ',', 0, 9, 9, 10, True)
y = pd.get_dummies(y.ravel(), drop_first=True)
'''
Split into training and test set
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1)
'''
Feature scaling
'''
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
data = data.asfreq(step, method='bfill')
data = data.reset_index()
return data
def train_test_split(data, train_ratio, method=0):
train = []
test = []
if method == 0:
n_rows = int(train_ratio * len(data))
train.append(data.iloc[:n_rows, :])
test.append(data.iloc[n_rows:, :])
return train, test
if __name__ == "__main__":
names_dict = {"Date": "Local time"}
data = load_csv(csv_name="EURCAD_Ticks_05.12.2017-05.12.2017.csv",
names_dict=names_dict)
print(data["Date"].head())
sys.exit(0)
print(data.shape)
data = select_data(dataframe=data, start="2017/05/13", stop="2017/05/20")
print(data.shape)
print(data["Date"].head())
# -*- coding: utf-8 -*-
"""
Created on Thu Apr 12 13:23:12 2018
@author: shifuddin
"""
from sklearn.neural_network import MLPClassifier
from load_data import load_csv
from sklearn.model_selection import train_test_split
from sklearn.metrics import confusion_matrix
'''
Load X, y from uri
'''
uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data'
X, y = load_csv(uri, ',', 1, 5, 9, 10, True)
'''
Split into training and test set
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1)
'''
Feature scaling
'''
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
'''
input_file = '/mnt/data/computervision/dna_100class_train80_val10_test10/test.csv'
display_classes = 10
n = 100
is_dna_data = True
seq_len = 4500
mask_len = 113
model_file = '../models/' + model_name + '.h5'
model = load_model(model_file)
embed_model = Model(inputs=model.input,
outputs=model.get_layer("lstm_2").output)
embed_model.summary()
counts = np.zeros(display_classes, dtype=np.int8)
data = load_csv(input_file, divide=1)
chosen_data = []
for (x, y) in data:
if y < display_classes and counts[y] < n:
counts[y] = counts[y] + 1
chosen_data.append((x, y))
x, y, m = get_onehot(chosen_data,
None,
is_dna_data=is_dna_data,
seq_len=seq_len,
mask_len=mask_len)
embed = embed_model.predict([x, m], batch_size=100, verbose=1)
tsne = TSNE(n_components=2, random_state=0)
""" Created on Thu May 3 11:51:18 2018 decission tree with banknote @author: shifuddin """ from load_data import load_csv from sklearn.model_selection import train_test_split from sklearn.naive_bayes import GaussianNB from sklearn.metrics import confusion_matrix ''' Load X, y from uri ''' uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00267/data_banknote_authentication.txt' X, y = load_csv(uri,',', 0,4, 4,5, True) ''' Split into training and test set ''' X_train, X_test, y_train, y_test =train_test_split(X, y,test_size=0.2, random_state=1) ''' Feature scaling ''' from sklearn.preprocessing import StandardScaler sc = StandardScaler() X_train = sc.fit_transform(X_train) X_test = sc.transform(X_test) '''
Created on Thu May 3 11:51:18 2018
decission tree with banknote
@author: shifuddin
"""
from load_data import load_csv
from sklearn.model_selection import train_test_split
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_squared_error
from math import sqrt
'''
Load feature values as X and target as Y
here we read day dataset
'''
uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/wine-quality/winequality-white.csv'
X, y = load_csv(uri, ';', 0, 11, 11, 12, True)
'''
Split into training and test set
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1)
'''
Fit DecisionTreeRegressor with Bike Day data
'''
regressor = DecisionTreeRegressor(random_state=0)
regressor.fit(X_train, y_train)
'''
Predicting result
'''
def replacer():
outs = merge_synonym()
syn_map = load_csv('./data/synsets/ANEW_synsets.csv')
replace_map = build_syn_map(syn_map, outs)
print(replace_map['gusto'])
return replace_map
seq_len = 4500
#data_file = '/mnt/data/computervision/dna_train80_val10_test10/test.csv'
data_file = '../results/dna_unknown_100class_pairs.csv' #keep this 1000class so every model uses the same data
mask = False
mask_len = 113
model_file = '../models/' + model_name + '.h5'
model = load_model(model_file)
embed_model = Model(inputs=model.input,
outputs=model.get_layer("lstm_2").output)
print embed_model.summary()
single_dict = dict()
pair_dict = dict()
data = load_csv(data_file)
for (x, y) in data:
if y in pair_dict:
continue
if y in single_dict:
assert x != single_dict[y]
pair_dict[y] = [single_dict[y], x]
else:
single_dict[y] = x
if len(pair_dict) == num_classes:
break
chosen_data = []
for i in range(2):
for y in pair_dict:
x = pair_dict[y][i]
num_classes = 30
model_name = 'blstm_dna_conv3_4500'
data_file = '/mnt/data/computervision/dna_train80_val10_test10/test.csv'
#data_file = '/mnt/data/computervision/dna_train80_val10_test10/unknowns.csv'
data_divide = 4
dist_min = 0
dist_max = 20
model_file = '../models/' + model_name + '.h5'
model = load_model(model_file)
av_model = Model(inputs=model.input, outputs=model.get_layer("AV").output)
print av_model.summary()
data = load_csv(data_file, divide=data_divide)
print len(data)
x, y = get_onehot(data,
None,
is_dna_data=is_dna_data,
seq_len=4500 if is_dna_data else 1500)
avs = av_model.predict(x, batch_size=500)
print 'done getting avs'
del data, x, y
means = []
with open('../results/' + model_name + '_mean_activations.csv', 'r') as infile:
r = csv.reader(infile)
for row in r:
means.append(np.array(row, dtype=np.float32))
Created on Thu May 3 11:51:18 2018 decission tree with banknote @author: shifuddin """ from load_data import load_csv from sklearn.model_selection import train_test_split from sklearn.tree import DecisionTreeRegressor from sklearn.metrics import mean_squared_error from math import sqrt ''' Load feature values as X and target as Y here we read day dataset ''' uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00265/CASP.csv' X, y = load_csv(uri,',', 1,10,0,1, True) ''' Split into training and test set ''' X_train, X_test, y_train, y_test =train_test_split(X, y,test_size=0.2, random_state=1) ''' Fit DecisionTreeRegressor with Bike Day data ''' regressor = DecisionTreeRegressor(random_state = 0) regressor.fit(X_train, y_train) ''' Predicting result
# %%
from load_data import load_csv
from analyze_data import show_beta, show_gamma
from sir_model import long_time_later
from graph_plot import compare_graph
# %% load data
directory = "C:\\Users\\HasunSong\\PycharmProjects\\virus\\covid19_korea.csv"
data = load_csv(directory=directory)
header = data[0] #['날짜', '치료중', '누적확진', '누적격리해제', '누적사망', '확진', '격리해제', '사망']
# %% guess beta and gamma
show_beta(data)
show_gamma(data)
# 03.26. 기준
# %% 모델 돌려보기
BETA = 3e-10
GAMMA = 0.05
TOT_POP = 50000000
DAYS = 3000
rec = long_time_later([TOT_POP, 10000, 0], DAYS, beta=BETA, gamma=GAMMA)
# %% 실제 상황과 비교하기
compare_graph(data, rec, BETA, GAMMA)
# %% 여러 값에 대해
beta_list = [3e-10, 5e-10, 1e-9, 2.5e-9]
gamma_list = [0.01, 0.02, 0.03, 0.05]
for bt in beta_list:
for gm in gamma_list:
rec = long_time_later([TOT_POP, 10000, 0], DAYS, beta=bt, gamma=gm)
# -*- coding: utf-8 -*-
"""
Created on Sun Apr 22 21:26:57 2018
Kmean with bc wisconsin
@author: shifuddin
"""
from load_data import load_csv
from sklearn.cluster import KMeans
from sklearn.metrics import homogeneity_score
import pandas as pd
'''
Load X, y from uri
'''
uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/spect/SPECT.test'
X, y = load_csv(uri, ',', 1, 24, 0, 1, True)
'''
Fitting K-Means to the dataset
'''
kmeans = KMeans(n_clusters=10,
init='k-means++',
random_state=42,
max_iter=1000)
y_kmeans = kmeans.fit_predict(X)
cluster_centers = kmeans.cluster_centers_
labels = kmeans.labels_
homo_score = homogeneity_score(y.ravel(), y_kmeans)
# -*- coding: utf-8 -*-
"""
Created on Sat Apr 21 23:06:32 2018
uci bancknote authetication dataset
@author: shifuddin
"""
from load_data import load_csv
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
'''
Load X, y from uri
'''
uri = 'https://archive.ics.uci.edu/ml/machine-learning-databases/00267/data_banknote_authentication.txt'
X, y = load_csv(uri, ',', 0, 4, 4, 5)
'''
Split into training and test set
'''
X_train, X_test, y_train, y_test = train_test_split(X,
y,
test_size=0.2,
random_state=1)
# Feature Scaling
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
'''
Perform logistic regression
'''