Kaggle competition: Northeastern SMILE Lab - Recognizing Faces in the Wild
coefficients_ = coefficients.copy()
coefficients_[0] = 1.66
coefficients_[1] = 2.13
coefficients_[3] = 2.85
train_predictions = optR.predict(oof_train, coefficients_ ).astype(np.int8)
 print(f'train pred distribution: {Counter(train_predictions)}')
 test_predictions = optR.predict(oof_test.mean(axis=1), coefficients_ ).astype(np.int8)
 print(f'test pred distribution: {Counter(test_predictions)}' )<count_values>
from collections import defaultdict
from glob import glob
from random import choice, sample
from keras.preprocessing import image
import cv2
import numpy as np
import pandas as pd
from keras.callbacks import ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Input, Dense, GlobalMaxPool2D, GlobalAvgPool2D, Concatenate, Multiply, Dropout, Subtract
from keras.models import Model
from keras.optimizers import Adam
Counter(train_predictions )<count_values>
!pip install git+https://github.com/rcmalli/keras-vggface.git
Counter(test_predictions )<create_dataframe>
from keras_vggface.utils import preprocess_input
from keras_vggface.vggface import VGGFace
submission1 = pd.DataFrame({'PetID': test['PetID'].values, 'AdoptionSpeed': test_predictions} )<set_options>
train_file_path = ".. /input/train_relationships.csv"
train_folders_path = ".. /input/train/"
val_famillies = "F09"
all_images = glob(train_folders_path + "*/*/*.jpg")
 train_images = [x for x in all_images if val_famillies not in x]
val_images = [x for x in all_images if val_famillies in x]
train_person_to_images_map = defaultdict(list)
 ppl = [x.split("/")[-3] + "/" + x.split("/")[-2] for x in all_images]
for x in train_images:
train_person_to_images_map[x.split("/")[-3] + "/" + x.split("/")[-2]].append(x)
 val_person_to_images_map = defaultdict(list)
 for x in val_images:
val_person_to_images_map[x.split("/")[-3] + "/" + x.split("/")[-2]].append(x )
%matplotlib inline
np.random.seed(seed=1337)
 warnings.filterwarnings('ignore')
 split_char = '/'
os.listdir('.. /input' )<load_from_csv>
relationships = pd.read_csv(train_file_path)
 relationships = list(zip(relationships.p1.values, relationships.p2.values))
relationships = [x for x in relationships if x[0] in ppl and x[1] in ppl]
train = [x for x in relationships if val_famillies not in x[0]]
val = [x for x in relationships if val_famillies in x[0]]
train = pd.read_csv('.. /input/petfinder-adoption-prediction/train/train.csv')
 test = pd.read_csv('.. /input/petfinder-adoption-prediction/test/test.csv')
 sample_submission = pd.read_csv('.. /input/petfinder-adoption-prediction/test/sample_submission.csv' )<load_from_csv>
def read_img(path):
img = image.load_img(path, target_size=(197, 197))
img = np.array(img ).astype(np.float)
 return preprocess_input(img, version=2)
 def gen(list_tuples, person_to_images_map, batch_size=16):
ppl = list(person_to_images_map.keys())
 while True:
batch_tuples = sample(list_tuples, batch_size // 2)
 labels = [1] * len(batch_tuples)
 while len(batch_tuples)< batch_size:
p1 = choice(ppl)
 p2 = choice(ppl)
 if p1 != p2 and(p1, p2)not in list_tuples and(p2, p1)not in list_tuples:
batch_tuples.append(( p1, p2))
labels.append(0)
 for x in batch_tuples:
if not len(person_to_images_map[x[0]]):
print(x[0])
 X1 = [choice(person_to_images_map[x[0]])for x in batch_tuples]
X1 = np.array([read_img(x)for x in X1])
 X2 = [choice(person_to_images_map[x[1]])for x in batch_tuples]
X2 = np.array([read_img(x)for x in X2])
 yield [X1, X2], labels
def baseline_model() :
input_1 = Input(shape=(197, 197, 3))
input_2 = Input(shape=(197, 197, 3))
base_model = VGGFace(model='resnet50', include_top=False)
 for x in base_model.layers[:-3]:
x.trainable = True
x1 = base_model(input_1)
 x2 = base_model(input_2)
 x1 = Concatenate(axis=-1 )([GlobalMaxPool2D()(x1), GlobalAvgPool2D()(x1)])
 x2 = Concatenate(axis=-1 )([GlobalMaxPool2D()(x2), GlobalAvgPool2D()(x2)])
 x3 = Subtract()([x1, x2])
 x3 = Multiply()([x3, x3])
 x1_ = Multiply()([x1, x1])
 x2_ = Multiply()([x2, x2])
 x4 = Subtract()([x1_, x2_])
 x = Concatenate(axis=-1 )([x4, x3])
 x = Dense(100, activation="relu" )(x)
 x = Dropout(0.01 )(x)
 out = Dense(1, activation="sigmoid" )(x)
 model = Model([input_1, input_2], out)
 model.compile(loss="binary_crossentropy", metrics=['acc'], optimizer=Adam(0.00001))
model.summary()
return model
labels_breed = pd.read_csv('.. /input/petfinder-adoption-prediction/breed_labels.csv')
 labels_state = pd.read_csv('.. /input/petfinder-adoption-prediction/color_labels.csv')
 labels_color = pd.read_csv('.. /input/petfinder-adoption-prediction/state_labels.csv')
 train_image_files = sorted(glob.glob('.. /input/petfinder-adoption-prediction/train_images/*.jpg'))
train_metadata_files = sorted(glob.glob('.. /input/petfinder-adoption-prediction/train_metadata/*.json'))
train_sentiment_files = sorted(glob.glob('.. /input/petfinder-adoption-prediction/train_sentiment/*.json'))
print(f'num of train images files: {len(train_image_files)}')
 print(f'num of train metadata files: {len(train_metadata_files)}')
 print(f'num of train sentiment files: {len(train_sentiment_files)}')
 test_image_files = sorted(glob.glob('.. /input/petfinder-adoption-prediction/test_images/*.jpg'))
test_metadata_files = sorted(glob.glob('.. /input/petfinder-adoption-prediction/test_metadata/*.json'))
test_sentiment_files = sorted(glob.glob('.. /input/petfinder-adoption-prediction/test_sentiment/*.json'))
print(f'num of test images files: {len(test_image_files)}')
 print(f'num of test metadata files: {len(test_metadata_files)}')
 print(f'num of test sentiment files: {len(test_sentiment_files)}')
<load_pretrained>
file_path = "vgg_face.h5"
checkpoint = ModelCheckpoint(file_path, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
 reduce_on_plateau = ReduceLROnPlateau(monitor="val_acc", mode="max", factor=0.1, patience=20, verbose=1)
 callbacks_list = [checkpoint, reduce_on_plateau]
model = baseline_model()
model.fit_generator(gen(train, train_person_to_images_map, batch_size=16), use_multiprocessing=True,
validation_data=gen(val, val_person_to_images_map, batch_size=16), epochs=100, verbose=2,
workers=4, callbacks=callbacks_list, steps_per_epoch=200, validation_steps=100)
class PetFinderParser(object):
def __init__(self, debug=False):
self.debug = debug
self.sentence_sep = ' '
self.extract_sentiment_text = False
def open_json_file(self, filename):
with open(filename, 'r', encoding='utf-8')as f:
json_file = json.load(f)
 return json_file
def parse_sentiment_file(self, file):

 file_sentiment = file['documentSentiment']
file_entities = [x['name'] for x in file['entities']]
file_entities = self.sentence_sep.join(file_entities)
 file_sentences_sentiment = [x['sentiment'] for x in file['sentences']]
file_sentences_sentiment = pd.DataFrame.from_dict(
 file_sentences_sentiment, orient='columns')
 file_sentences_sentiment_df = pd.DataFrame(
 {
'magnitude_sum': file_sentences_sentiment['magnitude'].sum(axis=0),
'score_sum': file_sentences_sentiment['score'].sum(axis=0),
}, index=[0] 
)
 df_sentiment = pd.DataFrame.from_dict(file_sentiment, orient='index' ).T
df_sentiment = pd.concat([df_sentiment, file_sentences_sentiment_df], axis=1)
 df_sentiment['entities'] = file_entities
df_sentiment = df_sentiment.add_prefix('sentiment_')
 return df_sentiment
def parse_metadata_file(self, file):

 file_keys = list(file.keys())
 if 'labelAnnotations' in file_keys:
file_annots = file['labelAnnotations']
file_top_score = np.asarray([x['score'] for x in file_annots] ).mean()
file_top_desc = [x['description'] for x in file_annots]
else:
file_top_score = np.nan
file_top_desc = ['']
file_colors = file['imagePropertiesAnnotation']['dominantColors']['colors']
file_crops = file['cropHintsAnnotation']['cropHints']
file_color_score = np.asarray([x['score'] for x in file_colors] ).mean()
file_color_pixelfrac = np.asarray([x['pixelFraction'] for x in file_colors] ).mean()
file_crop_conf = np.asarray([x['confidence'] for x in file_crops] ).mean()
if 'importanceFraction' in file_crops[0].keys() :
file_crop_importance = np.asarray([x['importanceFraction'] for x in file_crops] ).mean()
else:
file_crop_importance = np.nan
df_metadata = {
'annots_score': file_top_score,
'crop_importance': file_crop_importance,
'annots_top_desc': self.sentence_sep.join(file_top_desc)
 }
df_metadata = pd.DataFrame.from_dict(df_metadata, orient='index' ).T
df_metadata = df_metadata.add_prefix('metadata_')
 return df_metadata
def extract_additional_features(pet_id, mode='train'):
sentiment_filename = f'.. /input/petfinder-adoption-prediction/{mode}_sentiment/{pet_id}.json'
try:
sentiment_file = pet_parser.open_json_file(sentiment_filename)
 df_sentiment = pet_parser.parse_sentiment_file(sentiment_file)
 df_sentiment['PetID'] = pet_id
except FileNotFoundError:
df_sentiment = []
dfs_metadata = []
metadata_filenames = sorted(glob.glob(f'.. /input/petfinder-adoption-prediction/{mode}_metadata/{pet_id}*.json'))
if len(metadata_filenames)> 0:
for f in metadata_filenames:
metadata_file = pet_parser.open_json_file(f)
 df_metadata = pet_parser.parse_metadata_file(metadata_file)
 df_metadata['PetID'] = pet_id
dfs_metadata.append(df_metadata)
 dfs_metadata = pd.concat(dfs_metadata, ignore_index=True, sort=False)
 dfs = [df_sentiment, dfs_metadata]
return dfs
pet_parser = PetFinderParser()<categorify>
test_path = ".. /input/test/"
def chunker(seq, size=32):
return(seq[pos:pos + size] for pos in range(0, len(seq), size))
submission = pd.read_csv('.. /input/sample_submission.csv')
 predictions = []
for batch in tqdm(chunker(submission.img_pair.values)) :
X1 = [x.split("-")[0] for x in batch]
X1 = np.array([read_img(test_path + x)for x in X1])
 X2 = [x.split("-")[1] for x in batch]
X2 = np.array([read_img(test_path + x)for x in X2])
 pred = model.predict([X1, X2] ).ravel().tolist()
predictions += pred
submission['is_related'] = predictions
submission.to_csv("vgg_face.csv", index=False )