def __init__(self):
self.img_path = './data/images'
self.anno_path = './data/annotations'
self.ft_path = './feature_maps/'
self.model_path = './checkpoint/'
self.model_name = 'segmentation.ckpt-285'
self.model = os.path.join(self.model_path, self.model_name)
# Parameters
self.depth = 7
self.classes = 1
self.img_size = 32
# Placeholders
self.x = tf.placeholder(tf.float32,
shape=[None, None, None, self.depth],
name='input')
self.y_true = tf.placeholder(tf.float32,
shape=[None, None, None, self.classes],
name='y_true')
self.rate = tf.placeholder(tf.float32, name='dropout_rate')
self.is_training = tf.placeholder(tf.bool, shape=())
# Build network
self.y01 = cvmodel.build_model(input=self.x,
drop_rate=0,
is_training=False)
# Calculate loss + f1
self.cost_reg, self.f1_vec, self.recall, \
self.precision, self.specificity, self.accuracy = utils.loss(logits=[self.y01],
labels=self.y_true,
classes_weights=[2.])
# Open session and restore model
self.sess = tf.Session()
self.sess.run(tf.global_variables_initializer())
self.saver = tf.train.Saver()
self.saver.restore(self.sess, self.model)
# Load data
self.img_names = utils.load_train(path=self.img_path)
self.anno_names = utils.load_train(path=self.anno_path)
self.imgs_ = utils.get_image_array(self.img_names, self.img_size)
self.annos_ = utils.get_annotation_array(self.anno_names,
self.img_size)
n = self.imgs_.shape[0]
print('\nNumber of images:', n)
# Get number of trainable variables
v_nb = np.sum([
np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()
])
print('Number of trainable variables:', v_nb)
def knn():
train_data, train_labels = load_train()
#for validation
valid_data, valid_labels = load_valid()
#for test
#valid_data, valid_labels = load_test()
values = [1, 3, 5, 7, 9]
ratio = []
for k in values:
c = 0
prediction_labels = run_knn(k, train_data, train_labels, valid_data)
for i in range(len(valid_labels)):
if valid_labels[i] == prediction_labels[i]:
c += 1
ratio.append(float(c) / len(prediction_labels))
plt.plot(values, ratio)
#for validation
plt.axis([1, 9, 0.81, 0.87])
#for test
#plt.axis([1, 9, 0.87, 0.95])
plt.show()
def knn():
train_data, train_labels = load_train()
#for validation
valid_data, valid_labels = load_valid()
#for test
#valid_data, valid_labels = load_test()
values = [1, 3, 5, 7, 9]
ratio = []
for k in values:
c = 0
prediction_labels = run_knn(k, train_data, train_labels, valid_data)
for i in range(len(valid_labels)):
if valid_labels[i] == prediction_labels[i]:
c += 1
ratio.append(float(c) / len(prediction_labels))
plt.plot(values, ratio)
#for validation
plt.axis([1, 9, 0.81, 0.87])
#for test
#plt.axis([1, 9, 0.87, 0.95])
plt.show()
def train():
tr_X, tr_y = load_train(size='_t')
tr_X = norm4d_per_sample(tr_X)
te_X, te_y = load_test(size='_t')
te_X = norm4d_per_sample(te_X)
model = PlainCNN(istrained=False, args=(0.01, 0.1, 0.9))
model.train(tr_X, tr_y, te_X, te_y)
def load_train(is_gabor):
tr_identity, tr_labels, tr_images = utils.load_train()
pc_tr_identity = reshape_labels(tr_identity)
pc_tr_labels = reshape_labels(tr_labels)
pc_tr_images = reshape_images(tr_images, is_gabor)
return pc_tr_identity, pc_tr_labels, pc_tr_images
def run_logistic_regression():
train_inputs, train_targets = load_train()
valid_inputs, valid_targets = load_valid()
# TODO: initialize parameters
parameters = {
'learning_rate': 0.01 ,
'weight_regularization': 0 ,
'num_iterations': 10
}
# logistic regression weights
dimension = 28*28
z = np.ones([dimension+1, 1], int)
z = z/100.0
#weight = np.matrix('1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1')
for i in xrange(0,28*28):
if i%2 == 1:
z[i] = 0
weights = z
#weights = 1,1,2,1
# Verify that your logistic function produces the right gradient.
# diff should be very close to 0.
#run_check_grad(parameters)
# Begin learning with gradient descent
for t in xrange(parameters['num_iterations']):
# TODO: you will need to modify this loop to create plots, etc.
# find the negative log likelihood and derivatives w.r.t. weights
f, df, frac_correct_train = logistic(weights,
train_inputs,
train_targets,
parameters)
_, _, frac_correct_valid = logistic(weights,
valid_inputs,
valid_targets,
parameters)
if np.isnan(f) or np.isinf(f):
raise ValueError("nan/inf error")
# update parameters
for i in range(weights.shape[0]):
weights[i] = weights[i] + parameters['learning_rate'] * (df[i] - 0.001*(weights[i]))
# print some stats
print ("ITERATION:{:4d} LOGL:{:4.2f} "
"TRAIN FRAC:{:2.2f} VALID FRAC:{:2.2f}").format(t+1,
f,
frac_correct_train*100,
frac_correct_valid*100)
def train():
tr_X, tr_y = load_train(size='_t')
tr_X = norm4d_per_sample(tr_X)
tr_y = one_hot(tr_y, 2)
te_X, te_y = load_test(size='_t')
te_y = one_hot(te_y, 2)
te_X = norm4d_per_sample(te_X)
model = DeepCNN('vgg')
model.train(tr_X, tr_y, te_X, te_y)
def train():
tr_X, tr_y = load_train(size='_t')
tr_X = norm4d_per_sample(tr_X)
tr_y = one_hot(tr_y, 2)
te_X, te_y = load_test(size='_t')
te_y = one_hot(te_y, 2)
te_X = norm4d_per_sample(te_X)
model = RL(istrained=False, name='rl_noun')
model.train(tr_X, tr_y, te_X, te_y)
def main(args):
srcnn = SRCNN(
image_size=args.image_size,
c_dim=args.c_dim,
is_training=True,
learning_rate=args.learning_rate,
batch_size=args.batch_size,
epochs=args.epochs)
X_train, Y_train = load_train(image_size=args.image_size, stride=args.stride, scale=args.scale)
srcnn.train(X_train, Y_train)
def run_logistic_regression(hyperparameters):
# TODO specify training data
train_inputs, train_targets = load_train()
valid_inputs, valid_targets = load_valid()
# N is number of examples; M is the number of features per example.
N, M = train_inputs.shape
# Logistic regression weights
# TODO:Initialize to random weights here.
#weights = np.random.normal(0, 0.2, (train_inputs.shape[1]+1,1))
weights = np.zeros(785).reshape((785, 1))
# Verify that your logistic function produces the right gradient.
# diff should be very close to 0.
run_check_grad(hyperparameters)
# Begin learning with gradient descent
logging = np.zeros((hyperparameters['num_iterations'], 5))
for t in xrange(hyperparameters['num_iterations']):
# Find the negative log likelihood and its derivatives w.r.t. the weights.
f, df, predictions = logistic(weights, train_inputs, train_targets,
hyperparameters)
# Evaluate the prediction.
cross_entropy_train, frac_correct_train = evaluate(
train_targets, predictions)
if np.isnan(f) or np.isinf(f):
raise ValueError("nan/inf error")
# update parameters
weights = weights - hyperparameters['learning_rate'] * df / N
# Make a prediction on the valid_inputs.
predictions_valid = logistic_predict(weights, valid_inputs)
# Evaluate the prediction.
cross_entropy_valid, frac_correct_valid = evaluate(
valid_targets, predictions_valid)
# print some stats
print(
"ITERATION:{:4d} TRAIN NLOGL:{:4.2f} TRAIN CE:{:.6f} "
"TRAIN FRAC:{:2.2f} VALID CE:{:.6f} VALID FRAC:{:2.2f}").format(
t + 1, f / N, cross_entropy_train, frac_correct_train * 100,
cross_entropy_valid, frac_correct_valid * 100)
logging[t] = [
f / N, cross_entropy_train, frac_correct_train * 100,
cross_entropy_valid, frac_correct_valid * 100
]
return logging
def kfold(classification_algorithm, k):
res = {"accuracy": 0, "precision": 0, "recall": 0, "f1": 0}
for i in range(1, k + 1):
validation = utils.load_train(i)
validation = validation["plus"] + validation["minus"]
train = {"plus": [], "minus": []}
for j in range(1, k + 1):
if j != i:
extension = utils.load_train(j)
train["plus"].extend(extension["plus"])
train["minus"].extend(extension["minus"])
classification = classification_algorithm(train, validation)
res["accuracy"] += utils.accuracy(classification)
res["precision"] += utils.precision(classification)
res["recall"] += utils.recall(classification)
res["f1"] += utils.F1_score(classification)
for k in res:
res[k] /= k
print res
return res
def main():
x_train, y_train = load_train('train.csv')
x_train = x_train[:20, :]
x_train = x_train.reshape(x_train.shape[0], 1, 48, 48, 1)
emotion_classifier = load_model('./model_cnn.h5')
input_img = emotion_classifier.input
for idx in tqdm(range(20)):
val_proba = emotion_classifier.predict(x_train[idx])
pred = val_proba.argmax(axis=-1)
target = K.mean(emotion_classifier.output[:, pred])
grads = K.gradients(target, input_img)[0]
fn = K.function([input_img, K.learning_phase()], [grads])
grads_value = fn([x_train[idx], 0])
heatmap = np.array(grads_value).reshape(48, 48)
s = np.sort(heatmap, axis = None)
clip_rate = 0.1
clip_size = int(len(s) * clip_rate)
heatmap = np.clip(heatmap, s[clip_size], s[len(s) - clip_size])
heatmap = abs(heatmap - np.mean(heatmap))
heatmap = (heatmap - np.mean(heatmap))/np.std(heatmap)
heatmap = (heatmap - heatmap.min())/ heatmap.ptp()
thres = 0.5
origin = x_train[idx].reshape(48, 48)*255
see = x_train[idx].reshape(48, 48)
see[np.where(heatmap <= thres)] = np.mean(see)
see *= 255
plt.figure()
plt.imshow(heatmap, cmap=plt.cm.jet)
plt.colorbar()
plt.tight_layout()
fig = plt.gcf()
plt.draw()
fig.savefig(os.path.join(cmap_dir, '{}.png'.format(idx)), dpi=100)
plt.figure()
plt.imshow(see, cmap='gray')
plt.colorbar()
plt.tight_layout()
fig = plt.gcf()
plt.draw()
fig.savefig(os.path.join(partial_see_dir, '{}.png'.format(idx)), dpi=100)
plt.figure()
plt.imshow(origin, cmap='gray')
plt.tight_layout()
fig = plt.gcf()
plt.draw()
fig.savefig(os.path.join(origin_dir, '{}.png'.format(idx)), dpi=100)
def main():
filter_dir = './img/'
if not os.path.isdir(filter_dir):
os.mkdir(filter_dir)
filter_dir = './img/filter/'
if not os.path.isdir(filter_dir):
os.mkdir(filter_dir)
emotion_classifier = load_model('./model_cnn.h5')
layer_dict = dict([layer.name, layer]
for layer in emotion_classifier.layers[1:])
input_img = emotion_classifier.input
name_ls = ['activation_1']
collect_layers = [
K.function([input_img, K.learning_phase()], [layer_dict[name].output])
for name in name_ls
]
x_train, y_train = load_train('train.csv')
x_train = x_train.reshape(x_train.shape[0], 1, 48, 48, 1)
choose_id = 2044
photo = x_train[choose_id]
for cnt, fn in enumerate(collect_layers):
im = fn([photo, 0]) #get the output of that layer
fig = plt.figure(figsize=(14, 8))
nb_filter = im[0].shape[3]
for i in range(nb_filter):
ax = fig.add_subplot(nb_filter / 16, 16, i + 1)
ax.imshow(im[0][0, :, :, i], cmap='YlGnBu')
plt.xticks(np.array([]))
plt.yticks(np.array([]))
plt.tight_layout()
fig.suptitle('Output of layer{} (Given image{})'.format(
cnt, choose_id))
img_path = filter_dir
if not os.path.isdir(img_path):
os.mkdir(img_path)
fig.savefig(os.path.join(img_path, 'layer{}'.format(cnt)))
def data_generator(dataset, shuffle=True, augment=True, batch_size=32):
b = 0
image_index = -1
image_ids = np.copy(dataset.image_ids())
#print(image_ids)
error_count = 0
while True:
try:
#print(b)
image_index = (image_index + 1) % len(image_ids)
if shuffle and image_index == 0:
np.random.shuffle(image_ids)
image_id = image_ids[image_index]
image_concat = utils.load_train(dataset, image_id, augment=augment)
label = dataset.load_label(image_id)
if b == 0:
batch_images = np.zeros((batch_size, 448, 224, 3),
dtype=np.float32)
batch_actions = np.zeros((batch_size, 1), dtype=np.int32)
batch_images[b] = image_concat
batch_actions[b] = label
b += 1
if b >= batch_size:
#inputs=[batch_images, batch_actions]
inputs = batch_images
outputs = batch_actions
yield inputs, outputs
b = 0
except (GeneratorExit, KeyboardInterrupt):
raise
except:
# Log it and skip the image
logging.exception("Error processing image {}".format(
dataset.image_info[image_id]))
error_count += 1
if error_count > 5:
raise
import numpy as np
import pandas as pd
import sys
import os
from sklearn.externals import joblib
scriptpath = os.path.dirname(os.path.realpath(sys.argv[0])) + '/../'
sys.path.append(os.path.abspath(scriptpath))
import utils
train = utils.load_train('group_by')
train_2013 = train[train.date_time < '2014-01-01 00:00:00']
train_2014 = train[train.date_time >= '2014-01-01 00:00:00']
def top_k_relevence(group, topk = utils.k):
"""
Order and get the topk hotel cluters by the relevance score in desc order
:param group: the aggregate group with hotel cluster relevance scores
:param topk: the top k value
:return: the topk hotel clusters for the aggregate group
"""
idx = group.relevance.nlargest(topk).index
top_k_relevence = group.hotel_cluster[idx].values
return np.array_str(top_k_relevence)[1:-1]
def gen_top_k_group_by_model(group_by_field, click_weight = utils.click_weight, year = 'all'):
def run_logistic_regression(hyperparameters):
# specify training data
xIn = False
while xIn == False:
x = raw_input('Training Set LARGE or SMALL? ')
print(x)
if x == 'LARGE':
print("HELLO")
train_inputs, train_targets = load_train()
xIn = True
elif x == 'SMALL':
print("hello")
train_inputs, train_targets = load_train_small()
xIn = True
else:
print("Please input LARGE or SMALL")
valid_inputs, valid_targets = load_valid()
test_inputs, test_targets = load_test()
# N is number of examples; M is the number of features per example.
N, M = train_inputs.shape
print("N:", N, " M:", M)
# Logistic regression weights
# Initialize to random weights here.
weights = np.random.normal(0, 0.001, (M+1, 1))
# Verify that your logistic function produces the right gradient.
# diff should be very close to 0.
run_check_grad(hyperparameters)
# Begin learning with gradient descent
logging = np.zeros((hyperparameters['num_iterations'], 5))
for t in xrange(hyperparameters['num_iterations']):
# Find the negative log likelihood and its derivatives w.r.t. the weights.
f, df, predictions = logistic(weights, train_inputs, train_targets, hyperparameters)
# Evaluate the prediction.
cross_entropy_train, frac_correct_train = evaluate(train_targets, predictions)
if np.isnan(f) or np.isinf(f):
raise ValueError("nan/inf error")
# update parameters
weights = weights - hyperparameters['learning_rate'] * df / N
# Make a prediction on the valid_inputs.
predictions_valid = logistic_predict(weights, valid_inputs)
# Evaluate the prediction.
cross_entropy_valid, frac_correct_valid = evaluate(valid_targets, predictions_valid)
# print some stats
print ("ITERATION:{:4d} TRAIN NLOGL:{:4.2f} TRAIN CE:{:.6f} "
"TRAIN FRAC:{:2.2f} VALID CE:{:.6f} VALID FRAC:{:2.2f}").format(
t+1, f / N, cross_entropy_train, frac_correct_train*100,
cross_entropy_valid, frac_correct_valid*100)
logging[t] = [f / N, cross_entropy_train, frac_correct_train*100, cross_entropy_valid, frac_correct_valid*100]
return logging
import pandas as pd
import numpy as np
import utils
path_to_data = ''
data, meta = utils.load_train(path_to_data)
objects = meta['object_id'].values
for obj in objects:
df = data.loc[data['object_id'] == obj]
arr = utils.conv_preprocess_data(df, 355feature)
print pd.DataFrame(arr[0][0])
break
# -*- coding: utf-8 -*-
from utils import load_train, load_valid
from run_knn import run_knn
(train_inputs, train_targets) = load_train()
(valid_inputs, valid_targets) = load_valid()
for k in [1, 3, 5, 7, 9]:
print run_knn(k, train_inputs, train_targets, valid_inputs)
'NAME_HOUSING_TYPE',
'OCCUPATION_TYPE',
'WEEKDAY_APPR_PROCESS_START',
'ORGANIZATION_TYPE',
'FONDKAPREMONT_MODE',
'HOUSETYPE_MODE',
'WALLSMATERIAL_MODE',
]
# =============================================================================
#
# =============================================================================
skf = StratifiedKFold(n_splits=FOLD, shuffle=True, random_state=SEED)
train = utils.load_train(categorical_features+['TARGET']).fillna('na dayo')
test = utils.load_test(categorical_features).fillna('na dayo')
col = []
cat_comb = list(combinations(categorical_features, 2))
for c1,c2 in cat_comb:
train[f'{c1}-{c2}'] = train[c1] + train[c2]
test[f'{c1}-{c2}'] = test[c1] + test[c2]
col.append( f'{c1}-{c2}' )
# =============================================================================
# cardinality check
# =============================================================================
train['fold'] = 0
for i,(train_index, test_index) in enumerate(skf.split(train, train.TARGET)):
#
# =============================================================================
prev = utils.read_pickles('../data/previous_application')
base = prev[[KEY]].drop_duplicates().set_index(KEY)
gr = prev.groupby(KEY)
gr_app = prev[prev['NAME_CONTRACT_STATUS'] == 'Approved'].groupby(KEY)
gr_ref = prev[prev['NAME_CONTRACT_STATUS'] == 'Refused'].groupby(KEY)
gr_act = prev[prev['active'] == 1].groupby(KEY)
gr_cmp = prev[prev['completed'] == 1].groupby(KEY)
col = [
'AMT_INCOME_TOTAL', 'AMT_CREDIT', 'AMT_ANNUITY',
'AMT_CREDIT-d-AMT_ANNUITY', 'DAYS_BIRTH'
]
train = utils.load_train([KEY] + col)
test = utils.load_test([KEY] + col)
train.AMT_ANNUITY.fillna(0, inplace=True)
test.AMT_ANNUITY.fillna(0, inplace=True)
train.columns = [KEY] + ['app_' + c for c in train.columns[1:]]
test.columns = [KEY] + ['app_' + c for c in test.columns[1:]]
col_init = train.columns.tolist()
# =============================================================================
# feature
# =============================================================================
# size
"""
Created on Feb 26 2017
Author: Weiping Song
"""
import os, sys
import tensorflow as tf
import numpy as np
import argparse, random
from model import GRU4Rec
from utils import load_train, load_valid
unfold_max = 20
error_during_training = False
train_x, train_y, n_items = load_train(unfold_max)
valid_x, valid_y, _ = load_valid(unfold_max)
class Args():
is_training = True
layers = 1
rnn_size = 100
n_epochs = 10
batch_size = 50
keep_prob = 1
learning_rate = 0.001
decay = 0.98
decay_steps = 2 * 1e3
sigma = 0.0001
init_as_normal = False
import theano
import lasagne
import utils
from lasagne.layers import *
from nolearn.lasagne import NeuralNet
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn import cross_validation
from nolearn.lasagne import TrainSplit
from nolearn.lasagne import objective
from lasagne.nonlinearities import softmax
from lasagne.updates import momentum
X, y=utils.load_train()
X_test=utils.load_test()
X=X.reshape([X.shape[0],3,32,32])
y=np.array(y,dtype="int32")
X_test=X_test.reshape([X_test.shape[0],3,32,32])
layers = [
# layer dealing with the input data
(InputLayer, {'shape': (None, 3, 32, 32)}),
# first stage of our convolutional layers
# second stage of our convolutional layers
(Conv2DLayer, {'pad':2,'num_filters': 32, 'filter_size': 5,'W':lasagne.init.Normal(std=0.01)}),
(ParametricRectifierLayer, {'alpha':lasagne.init.Constant(0)}),
(Pool2DLayer, {'pool_size': 2,'stride':2,'mode':'max'}),
np.reshape(Theta[:hidden_layer_size * (input_layer_size + 1)], (hidden_layer_size, input_layer_size + 1), order='F'))
Theta2 = np.matrix(
np.reshape(Theta[hidden_layer_size * (input_layer_size + 1):], (num_labels, hidden_layer_size + 1), order='F'))
p = fnx.predict(Theta1, Theta2, X)
precision = 0
for i in range(len(y)):
if y[i] == p[i]:
precision += 1
print('Training Set Accuracy:', (1.0 * precision) / len(y))
return Theta1, Theta2
if __name__ == '__main__':
cuisine_list, ingredients_list, X, y = utl.load_train('number')
ingredients_count = len(ingredients_list)
cuisines_count = len(cuisine_list)
Theta1, Theta2 = train_nn(ingredients_count, ingredients_count//16, cuisines_count, X, y)
T, ids = utl.load_test(ingredients_list)
p = fnx.predict(Theta1, Theta2, T)
utl.save_result('nn', cuisine_list, p, ids)
merged_df.reset_index(inplace = True)
if type == 'test':
result = merged_df[['index', 'hotel_cluster']]
result.columns = ['id', 'hotel_cluster']
elif type == 'train':
result = merged_df[['index', 'hotel_cluster_y']]
result.columns = ['id', 'hotel_cluster']
return result
#############################################################
#################### train dataset ####################
#############################################################
train = utils.load_train('group_by')
train_is_booking = train[train.is_booking == 1]
train_is_booking.reset_index(inplace = True)
del train
print 'generate top k hotel clusters with orig_destination_distance model...'
result = gen_top_k_hotel_cluster(train_is_booking, 'orig_destination_distance', 'train')
print 'generate top k hotel clusters with srch_destination_id model...'
result = utils.fill_all_top_5(train_is_booking, result, 'srch_destination_id', 'train')
print 'generate top k hotel clusters with user_id model...'
result = utils.fill_all_top_5(train_is_booking, result, 'user_id', 'train')
print 'generate top k hotel clusters with hotel_market model...'
result = utils.fill_all_top_5(train_is_booking, result, 'hotel_market', 'train')
print 'hotel clusters to ranking features...'
new_result = result.apply(lambda row: hotel_clusters_to_ranking_features(row), axis=1)
new_result.columns = ['_'.join(['hotel_cluster', str(hotel_cluster_id), 'rank']) for hotel_cluster_id in range(100)]
def main():
# ================
# time managment #
# ================
program_st = time.time()
# =====================================
# bert classification logging handler #
# =====================================
logging_filename = f"../logs/bertclf_{args.corpus_name}.log"
logging.basicConfig(level=logging.INFO,
filename=logging_filename,
filemode="w")
console = logging.StreamHandler()
console.setLevel(logging.INFO)
formatter = logging.Formatter("%(levelname)s: %(message)s")
console.setFormatter(formatter)
logging.getLogger('').addHandler(console)
# =======================
# predefined parameters #
# =======================
cv = args.cross_validation
num_labels = 3
batch_size = args.batch_size
epochs = args.epochs
learning_rate = args.learning_rate
max_length = args.max_length
if args.domain_adaption:
if args.model == "german":
if args.domain_adaption_alternative_path:
model_name = '../corpora/domain-adaption/german-alternative/'
else:
model_name = '../corpora/domain-adaption/german/'
elif args.model == "rede":
if args.domain_adaption_alternative_path:
model_name = '../corpora/domain-adaption/redewiedergabe-alternative/'
else:
model_name = '../corpora/domain-adaption/redewiedergabe/'
elif args.model == "test":
model_name = '../corpora/domain-adaption/test/'
else:
logging.warning(
f"Couldn't find a model with the name '{args.model}'.")
else:
if args.model == "german":
model_name = 'bert-base-german-dbmdz-cased'
elif args.model == "rede":
model_name = 'redewiedergabe/bert-base-historical-german-rw-cased'
else:
logging.warning(
f"Couldn't find a model with the name '{args.model}'.")
cv_acc_dict = defaultdict(list)
year_cv_dict = {}
poet_cv_dict = {}
class_name1 = "epoch_year"
class_name2 = "epoch_poet"
text_name = "poem"
false_clf_dict = {class_name1: {}, class_name2: {}}
# ================
# classification #
# ================
# =======================
# use GPU, if available #
# =======================
if torch.cuda.is_available():
device = torch.device("cuda")
logging.info(
f'There are {torch.cuda.device_count()} GPU(s) available.')
logging.info(f'Used GPU: {torch.cuda.get_device_name(0)}')
else:
logging.info('No GPU available, using the CPU instead.')
device = torch.device("cpu")
for i in range(1, cv + 1):
if args.corpus_name == "poet":
train_data = utils.load_train("../corpora/train_epochpoet", cv, i,
"epochpoet")
test_data = pd.read_csv(
f"../corpora/train_epochpoet/epochpoet{i}.csv")
elif args.corpus_name == "year":
train_data = utils.load_train("../corpora/train_epochyear", cv, i,
"epochyear")
test_data = pd.read_csv(
f"../corpora/train_epochyear/epochyear{i}.csv")
elif args.corpus_name == "poeta":
train_data = utils.load_train(
"../corpora/train_epochpoetalternative", cv, i,
"epochpoetalternative")
test_data = pd.read_csv(
f"../corpora/train_epochpoetalternative/epochpoetalternative{i}.csv"
)
else:
logging.warning(
f"Couldn't find a corpus with the name '{args.corpus_name}'.")
for class_name in [class_name1, class_name2]:
# tmp lists and result dicts #
input_ids = []
attention_masks = []
texts = train_data[text_name].values
encoder = LabelEncoder()
labels = encoder.fit_transform(train_data[class_name].values)
encoder_mapping = dict(
zip(encoder.transform(encoder.classes_), encoder.classes_))
# ==============
# tokenization #
# ==============
tokenizer = BertTokenizer.from_pretrained(model_name,
do_lower_case=False)
for sent in texts:
encoded_dict = tokenizer.encode_plus(
sent,
add_special_tokens=True,
max_length=args.max_length,
pad_to_max_length=True,
return_attention_mask=True,
return_tensors='pt')
input_ids.append(encoded_dict['input_ids'])
attention_masks.append(encoded_dict['attention_mask'])
input_ids = torch.cat(input_ids, dim=0)
attention_masks = torch.cat(attention_masks, dim=0)
labels = torch.tensor(labels)
# =================
# train val split #
# =================
dataset = TensorDataset(input_ids, attention_masks, labels)
train_size = int(0.9 * len(dataset))
val_size = len(dataset) - train_size
train_dataset, val_dataset = random_split(dataset,
[train_size, val_size])
# ============
# DataLoader #
# ============
train_dataloader = DataLoader(train_dataset,
sampler=RandomSampler(train_dataset),
batch_size=batch_size)
val_dataloader = DataLoader(val_dataset,
sampler=SequentialSampler(val_dataset),
batch_size=batch_size)
# ======== #
# Training #
# ======== #
model = BertForSequenceClassification.from_pretrained(
model_name,
num_labels=num_labels,
output_attentions=False,
output_hidden_states=False).cuda()
optimizer = AdamW(model.parameters(), lr=learning_rate, eps=1e-8)
total_steps = len(train_dataloader) * epochs
scheduler = get_linear_schedule_with_warmup(
optimizer, num_warmup_steps=0, num_training_steps=total_steps)
training_stats = []
total_t0 = time.time()
validation_losses = {}
for epoch_i in range(0, epochs):
print("")
print('======== Epoch {:} / {:} ========'.format(
epoch_i + 1, epochs))
print('Now Training.')
t0 = time.time()
total_train_loss = 0
model.train()
for step, batch in enumerate(train_dataloader):
if step % 50 == 0 and not step == 0:
elapsed = utils.format_time(time.time() - t0)
print('Batch {:>5,} of {:>5,}. Elapsed: {:}.'.
format(step, len(train_dataloader), elapsed))
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
model.zero_grad()
loss, logits = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
total_train_loss += loss.item()
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
optimizer.step()
scheduler.step()
# average loss (all batches)
avg_train_loss = total_train_loss / len(train_dataloader)
training_time = utils.format_time(time.time() - t0)
print("")
print(
" Average training loss: {0:.2f}".format(avg_train_loss))
print(" Training epoch took: {:}".format(training_time))
# ========== #
# Validation #
# ========== #
print("")
print("Now Validating.")
t0 = time.time()
model.eval()
total_eval_accuracy = 0
total_eval_loss = 0
nb_eval_steps = 0
for batch in val_dataloader:
b_input_ids = batch[0].to(device)
b_input_mask = batch[1].to(device)
b_labels = batch[2].to(device)
with torch.no_grad():
(loss, logits) = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask,
labels=b_labels)
# validation loss.
total_eval_loss += loss.item()
# Move logits and labels to CPU
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
total_eval_accuracy += utils.flat_f1(label_ids, logits)
# final validation accuracy / loss
avg_val_accuracy = total_eval_accuracy / len(val_dataloader)
print(
" Validation Accuracy: {0:.2f}".format(avg_val_accuracy))
avg_val_loss = total_eval_loss / len(val_dataloader)
validation_time = utils.format_time(time.time() - t0)
print(" Validation Loss: {0:.2f}".format(avg_val_loss))
print(" Validation took: {:}".format(validation_time))
training_stats.append({
'epoch': epoch_i + 1,
'train_loss': avg_train_loss,
'val_loss': avg_val_loss,
'val_acc': avg_val_accuracy,
'train_time': training_time,
'val_time': validation_time
})
current_epoch = f"epoch{epoch_i + 1}"
validation_losses[current_epoch] = avg_val_loss
# ================
# Early Stopping #
# ================
if utils.early_stopping(validation_losses, patience=2):
logging.info(
f"Stopping epoch run early (Epoch {epoch_i}).")
break
logging.info(f"Training for {class_name} done.")
logging.info("Training took {:} (h:mm:ss) \n".format(
utils.format_time(time.time() - total_t0)))
print("--------------------------------\n")
# =========
# Testing #
# =========
test_input_ids = []
test_attention_masks = []
X_test = test_data[text_name].values
y_test = LabelEncoder().fit_transform(test_data[class_name].values)
for sent in X_test:
encoded_dict = tokenizer.encode_plus(
sent,
add_special_tokens=True,
max_length=args.max_length,
pad_to_max_length=True,
return_attention_mask=True,
return_tensors='pt')
test_input_ids.append(encoded_dict['input_ids'])
test_attention_masks.append(encoded_dict['attention_mask'])
test_input_ids = torch.cat(test_input_ids, dim=0)
test_attention_masks = torch.cat(test_attention_masks, dim=0)
labels = torch.tensor(y_test)
prediction_data = TensorDataset(test_input_ids,
test_attention_masks, labels)
prediction_sampler = SequentialSampler(prediction_data)
prediction_dataloader = DataLoader(prediction_data,
sampler=prediction_sampler,
batch_size=batch_size)
model.eval()
predictions, true_labels = [], []
for batch in prediction_dataloader:
# Add batch to GPU
batch = tuple(t.to(device) for t in batch)
# Unpack the inputs from our dataloader
b_input_ids, b_input_mask, b_labels = batch
with torch.no_grad():
outputs = model(b_input_ids,
token_type_ids=None,
attention_mask=b_input_mask)
logits = outputs[0]
# Move logits and labels to CPU
logits = logits.detach().cpu().numpy()
label_ids = b_labels.to('cpu').numpy()
# Store predictions and true labels
predictions.append(logits)
true_labels.append(label_ids)
flat_predictions = np.concatenate(predictions, axis=0)
flat_predictions = np.argmax(flat_predictions, axis=1).flatten()
flat_true_labels = np.concatenate(true_labels, axis=0)
if args.save_misclassification:
logging.info("Saving misclassifications.")
test_pid = test_data["pid"].values
false_classifications = {
"Jahrhundertwende": {
"Naturalismus": [],
"Expressionismus": []
},
"Naturalismus": {
"Jahrhundertwende": [],
"Expressionismus": []
},
"Expressionismus": {
"Naturalismus": [],
"Jahrhundertwende": []
}
}
for idx, (t, p) in enumerate(
zip(flat_true_labels, flat_predictions)):
if t != p:
false_classifications[encoder_mapping[t]][
encoder_mapping[p]].append(int(test_pid[idx]))
false_clf_dict[class_name][i] = false_classifications
test_score = f1_score(flat_true_labels,
flat_predictions,
average="macro")
classes = test_data[class_name].drop_duplicates().tolist()
if args.save_confusion_matrices:
logging.info("Saving confusion matrices.")
cm = confusion_matrix(flat_true_labels, flat_predictions)
cm_df = pd.DataFrame(cm, index=classes, columns=classes)
if args.domain_adaption:
cm_name = f"{args.corpus_name}c_{class_name}_da_{args.model}"
else:
cm_name = f"{args.corpus_name}c_{class_name}_{args.model}"
if args.save_date:
cm_name += f"({datetime.now():%d.%m.%y}_{datetime.now():%H:%M})"
cm_df.to_csv(
f"../results/bert/confusion_matrices/cm{i}_{cm_name}.csv")
stats = pd.DataFrame(data=training_stats)
cv_acc_dict[class_name].append(test_score)
if class_name == "epoch_year":
year_cv_dict[f"cv{i}"] = training_stats
elif class_name == "epoch_poet":
poet_cv_dict[f"cv{i}"] = training_stats
else:
logging.info(f"The class {class_name} does not exist.")
logging.info(f"Testing for {class_name} done.")
logging.info(f"CV Test F1-Score: {test_score} (run: {i}/{cv}).")
logging.info("Testing took {:} (h:mm:ss) \n".format(
utils.format_time(time.time() - total_t0)))
print("--------------------------------\n")
logging.info(f"Training for run {i}/{cv} completed.")
logging.info("Training run took {:} (h:mm:ss)".format(
utils.format_time(time.time() - total_t0)))
print("________________________________")
print("________________________________\n")
# ================
# saving results #
# ================
result_path = "../results/bert/"
logging.info(f"Writing results to '{result_path}'.")
if args.domain_adaption:
output_name = f"{args.corpus_name}c_da_{args.model}"
else:
output_name = f"{args.corpus_name}c_{args.model}"
if args.save_date:
output_name += f"({datetime.now():%d.%m.%y}_{datetime.now():%H:%M})"
with open(f'{result_path}cv_{output_name}.json', 'w') as f:
json.dump(cv_acc_dict, f)
with open(f'{result_path}eyear_{output_name}.json', 'w') as f:
json.dump(year_cv_dict, f)
with open(f'{result_path}epoet_{output_name}.json', 'w') as f:
json.dump(poet_cv_dict, f)
if args.save_misclassification:
mis_output_path = f'{result_path}/misclassifications/pid_{output_name}'
with open(f'{mis_output_path}.json', 'w') as f:
json.dump(false_clf_dict, f)
program_duration = float(time.time() - program_st)
logging.info(f"Total duration: {int(program_duration)/60} minute(s).")
p = (10**i)
for c in tqdm(df.columns):
s = (df[c] * p * 2 + 1) // 2 / p # round
di = s.value_counts().to_dict()
feature[f'{PREF}_{c}_r{i}'] = s.map(di)
tr_ = feature.iloc[:200000]
output(tr_, 'train')
te_ = feature.iloc[200000:].reset_index(drop=True)
output(te_, 'test')
return
# =============================================================================
# main
# =============================================================================
if __name__ == "__main__":
utils.start(__file__)
tr = utils.load_train().drop(['ID_code', 'target'], axis=1)
te = utils.load_test().drop(['ID_code'], axis=1)
te = te.drop(np.load('../data/fake_index.npy'))
trte = pd.concat([tr, te], ignore_index=True)[tr.columns]
fe(trte)
utils.end(__file__)
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 16 19:55:54 2018
@author: kazuki.onodera
"""
import numpy as np
import pandas as pd
import utils, os
os.system('rm -rf ../sample')
os.system('mkdir ../sample')
tr = utils.load_train()
log = pd.read_feather('../data/train_log.f')
oids = tr.sample(999).object_id.tolist()
tr_ = tr[tr.object_id.isin(oids)]
log_ = log[log.object_id.isin(oids)]
tr_.to_csv('../sample/tr.csv', index=False)
log_.to_csv('../sample/tr_log.csv', index=False)
os.system(f'rm ../data/t*_{PREF}*')
os.system(f'rm ../feature/t*_{PREF}*')
#def mk_feats(df):
# df['hostgal_specz-m-hostgal_photoz'] = df['hostgal_specz'] - df['hostgal_photoz']
# df['hostgal_specz-d-hostgal_photoz'] = df['hostgal_specz'] / df['hostgal_photoz']
# df['hostgal_photoz-d-hostgal_photoz_err'] = df['hostgal_photoz'] / df['hostgal_photoz_err']
# df['hostgal_specz-d-hostgal_photoz_err'] = df['hostgal_specz'] / df['hostgal_photoz_err']
# return
# =============================================================================
# main
# =============================================================================
if __name__ == "__main__":
utils.start(__file__)
train = utils.load_train().drop(['object_id', 'target'], axis=1)
train.add_prefix(PREF+'_').to_pickle(f'../data/train_{PREF}.pkl')
train_aug = pd.read_pickle('../data/train_aug.pkl').drop(['object_id', 'object_id_bk', 'target'], axis=1)
train_aug.add_prefix(PREF+'_').to_pickle(f'../data/train_aug_{PREF}.pkl')
test = utils.load_test().drop(['object_id'], axis=1)
test.loc[test.hostgal_photoz==0, 'hostgal_specz'] = 0
test = test.add_prefix(PREF+'_')
test.to_pickle(f'../data/test_{PREF}.pkl')
utils.save_test_features(test)
utils.end(__file__)
import utils as utl
import tensorflow as tf
import numpy as np
cuisine_list, ingredients_list, xs, ys = utl.load_train('vector')
ts, ids = utl.load_test(ingredients_list)
cuisine_count = len(cuisine_list)
ingredients_count = len(ingredients_list)
x = tf.placeholder(tf.float32, [None, ingredients_count])
W = tf.Variable(tf.zeros([ingredients_count, cuisine_count]))
b = tf.Variable(tf.zeros([cuisine_count]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, cuisine_count])
t = tf.placeholder(tf.float32, [None, ingredients_count])
p = tf.nn.softmax(tf.matmul(t, W) + b)
cross_entropy = -tf.reduce_sum(y_*tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)
# train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(init)
from functools import partial
from sklearn.cross_validation import train_test_split
from sklearn.grid_search import ParameterGrid
from sklearn.externals.joblib import Parallel, delayed
from utils import load_train, load_test
from utils import find_threshold
from utils import rescale, rebalance
from utils import make_submission
def load_predictions(pattern):
return np.column_stack([np.load(f) for f in sorted(glob.glob(pattern))])
# Load training data
X, y, w, _ = load_train()
# Tune stacker
print "Optimize parameters in 5-CV..."
from sklearn.ensemble import GradientBoostingClassifier
from sklearn.ensemble import BaggingClassifier
#Classifier = partial(BaggingClassifier, base_estimator=GradientBoostingClassifier(n_estimators=500, learning_rate=0.025, max_depth=4, max_features=None, min_samples_leaf=250))
#grid = ParameterGrid({"n_estimators": [24], "max_features": [1.0, 0.9, 0.8], "n_jobs": [24]})
Classifier = GradientBoostingClassifier
grid = ParameterGrid({"n_estimators": [500], "max_features": [None, 0.95, 0.9], "learning_rate": [0.0225, 0.025, 0.0275], "max_depth": [4], "min_samples_leaf": [250]})
n_jobs = 24
def _parallel_eval(Classifier, params, X, y, w, n_repeat=5, verbose=1):
for suf in suffix_list:
col = [c for c in col_init if c.endswith(suf)]
df[f'{suf}_min'] = df[col].min(1)
df[f'{suf}_mean'] = df[col].mean(1)
df[f'{suf}_max'] = df[col].max(1)
df[f'{suf}_std'] = df[col].std(1)
return
# =============================================================================
# main
# =============================================================================
if __name__ == "__main__":
utils.start(__file__)
# train
tr = utils.load_train(['object_id'])
df = pd.read_pickle('../FROM_MYTEAM/LCfit_feature_allSN_r_train_v3_20181215.pkl.gz')
df = pd.merge(tr, df, on='object_id', how='left')
df.reset_index(drop=True, inplace=True)
get_feature(df)
del df['object_id']
df.add_prefix(PREF+'_').to_pickle(f'../data/train_{PREF}.pkl')
# test
te = utils.load_test(['object_id'])
df = pd.read_pickle('../FROM_MYTEAM/LCfit_feature_allSN_r_test_v3_20181215.pkl.gz')
df = pd.merge(te, df, on='object_id', how='left')
df.reset_index(drop=True, inplace=True)
get_feature(df)
import utils
#path_to_data = '/courses/cs342/Assignment2/'
path_to_data = ''
train, train_meta = utils.load_train(path_to_data)
g_train, eg_train, g_meta, eg_meta, g_target, eg_target = utils.gal_split_data(
train, train_meta, True)
g_features = utils.feature_engineering(g_train, g_meta)
g_wtable, g_labels, g_classes, g_target_map = utils.preprocess_target(g_target)
g_features = utils.standardize_data(g_features)
utils.train_mlp(g_features, g_wtable, g_labels, g_classes, g_target_map, True)
eg_features = utils.feature_engineering(eg_train, eg_meta)
eg_wtable, eg_labels, eg_classes, eg_target_map = utils.preprocess_target(
eg_target)
eg_features = utils.standardize_data(eg_features)
utils.train_mlp(eg_features, eg_wtable, eg_labels, eg_classes, eg_target_map,
False)
def classify(train, examples):
cv_res = {
"PP": 0,
"PN": 0,
"NP": 0,
"NN": 0,
"contradictory": 0,
}
plus = train["plus"]
minus = train["minus"]
l = len(examples)
i = 0
for elem in examples:
i += 1
print "%i/%i" % (i, l)
result = check_hypothesis(plus, minus, elem)
cv_res[result] += 1
return cv_res
if __name__ == "__main__":
index = int(sys.argv[1])
train = utils.load_train(index)
test = utils.load_test(index)
res = classify(train, test)
print res
print utils.summary(res)
return site_name_encoding, posa_continent_encoding, user_location_country_encoding, user_location_region_encoding, \
channel_encoding, srch_destination_type_id_encoding, hotel_continent_encoding, hotel_country_encoding
def fill_na_features(dataset):
"""
Fill the remaining missing values
:param dataset: train/test dataset
"""
dataset.fillna(-1, inplace=True)
#############################################################
#################### train dataset ####################
#############################################################
train = utils.load_train('baseline')
train_is_booking = train[train.is_booking == 1]
train_is_booking.reset_index(inplace = True)
train_is_booking.is_copy = False
del train
print 'generate train time features...'
time_features_enricher(train_is_booking)
print 'generate train one hot encoding features...'
site_name_encoding, posa_continent_encoding, user_location_country_encoding, user_location_region_encoding, \
channel_encoding, srch_destination_type_id_encoding, hotel_continent_encoding, hotel_country_encoding = \
gen_all_top_one_hot_encoding_columns(train_is_booking)
print 'fill train na features...'
# =============================================================================
# NAME_CONTRACT_STATUS
# =============================================================================
ct1 = pd.crosstab(pos[KEY], pos['NAME_CONTRACT_STATUS']).add_suffix('_cnt')
ct2 = pd.crosstab(pos[KEY], pos['NAME_CONTRACT_STATUS'],
normalize='index').add_suffix('_nrm')
base = pd.concat([base, ct1, ct2], axis=1)
# TODO: DPD
# =============================================================================
# merge
# =============================================================================
base.reset_index(inplace=True)
train = utils.load_train([KEY])
test = utils.load_test([KEY])
train_ = pd.merge(train, base, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(train_.add_prefix(PREF), '../feature/train')
test_ = pd.merge(test, base, on=KEY, how='left').drop(KEY, axis=1)
utils.to_feature(test_.add_prefix(PREF), '../feature/test')
#==============================================================================
utils.end(__file__)
import utils as utl
import tensorflow as tf
import numpy as np
cuisine_list, ingredients_list, xs, ys = utl.load_train('vector')
ts, ids = utl.load_test(ingredients_list)
cuisine_count = len(cuisine_list)
ingredients_count = len(ingredients_list)
x = tf.placeholder(tf.float32, [None, ingredients_count])
W = tf.Variable(tf.zeros([ingredients_count, cuisine_count]))
b = tf.Variable(tf.zeros([cuisine_count]))
y = tf.nn.softmax(tf.matmul(x, W) + b)
y_ = tf.placeholder(tf.float32, [None, cuisine_count])
t = tf.placeholder(tf.float32, [None, ingredients_count])
p = tf.nn.softmax(tf.matmul(t, W) + b)
cross_entropy = -tf.reduce_sum(y_ * tf.log(y))
train_step = tf.train.GradientDescentOptimizer(0.001).minimize(cross_entropy)
# train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)
init = tf.initialize_all_variables()
sess = tf.Session(config=tf.ConfigProto(allow_soft_placement=True))
sess.run(init)
"""
dist = l2_distance(valid_data.T, train_data.T)
nearest = np.argsort(dist, axis=1)[:,:k]
train_labels = train_labels.reshape(-1)
valid_labels = train_labels[nearest]
# note this only works for binary labels
valid_labels = (np.mean(valid_labels, axis=1) >= 0.5).astype(np.int)
valid_labels = valid_labels.reshape(-1,1)
return valid_labels
if __name__ == '__main__':
train_inputs, train_targets = utils.load_train()
valid_inputs, valid_targets = utils.load_valid()
test_inputs, test_targets = utils.load_test()
set_k = [1,3,5,7,9]
accuracy_valid_output = {}
accuracy_test_output = {}
length_valid = len(valid_inputs)
length_test = len(test_inputs)
for k in set_k:
valid_outputs = run_knn(k, train_inputs, train_targets, valid_inputs)
test_outputs = run_knn(k, train_inputs, train_targets, test_inputs)
"""
Created on Sun Jun 3 05:56:27 2018
@author: Kazuki
"""
import numpy as np
import pandas as pd
#from sklearn.preprocessing import LabelEncoder
import utils
utils.start(__file__)
#==============================================================================
PREF = 'app_002_'
train = utils.load_train().drop(['SK_ID_CURR', 'TARGET'], axis=1)
test = utils.load_test().drop(['SK_ID_CURR'], axis=1)
col_init = train.columns
df = pd.concat([train, test], ignore_index=True)
# =============================================================================
# features
# =============================================================================
df['AMT_CREDIT-by-AMT_INCOME_TOTAL'] = df['AMT_CREDIT'] / df['AMT_INCOME_TOTAL']
df['AMT_INCOME_TOTAL-AMT_CREDIT'] = df['AMT_INCOME_TOTAL'] - df['AMT_CREDIT']
df['AMT_ANNUITY-by-AMT_INCOME_TOTAL'] = df['AMT_ANNUITY'] / df[
'AMT_INCOME_TOTAL']
df['AMT_INCOME_TOTAL-AMT_ANNUITY'] = df['AMT_INCOME_TOTAL'] - df['AMT_ANNUITY']
from sklearn.multiclass import OneVsRestClassifier
from sklearn.linear_model import LogisticRegression
import utils as utl
cuisine_list, ingredients_list, x, y = utl.load_train('number')
classifier = OneVsRestClassifier(LogisticRegression(C=1e6)).fit(x, y)
p = classifier.predict(x)
precision = 0
for i in range(len(y)):
if y[i] == p[i]:
precision += 1
accuracy = (1.0 * precision) / len(y)
print('Training Set Accuracy:', accuracy)
t, ids = utl.load_test(ingredients_list)
p = classifier.predict(t)
utl.save_result('sk_lr', cuisine_list, p, ids, 'number')
#hyper_parameter #
#######################################################################
import utils
from run_knn import run_knn
import plot_digits
import numpy as np
import matplotlib.pyplot as plt
if __name__ == "__main__":
#loading the dataset
train_data, train_labels = utils.load_train()
#loading the validation set
valid_data,valid_labels = utils.load_valid()
# vector of each k
K = np.array([1,3,5,7,9])
#dictionnay result
results={}
for k in K:
#prediction
prediction = run_knn(k,train_data,train_labels,valid_data)
