Kaggle competition: Google QUEST Q&A Labeling
histories = []
steps = 0
if isfile('.. /input/humpback-whale-identification-model-files/mpiotte-standard.model'):
print('pretrained exist.')
 tmp = keras.models.load_model('.. /input/humpback-whale-identification-model-files/mpiotte-standard.model')
 model.set_weights(tmp.get_weights())
 else:
make_steps(10, 1000)
 ampl = 100.0
for _ in range(2):
print('noise ampl.= ', ampl)
 make_steps(5, ampl)
 ampl = max(1.0, 100 ** -0.1 * ampl)
 model.summary()<load_pretrained>
kf = np.load(".. /input/split-dataset/GroupMultilabelStratifiedKfold.npy", allow_pickle=True )
def prepare_submission(threshold, filename):

 vtop = 0
vhigh = 0
pos = [0, 0, 0, 0, 0, 0]
with open(filename, 'wt', newline='
')as f:
f.write('Image,Id
')
 for i, p in enumerate(tqdm(submit)) :
t = []
s = set()
a = score[i, :]
for j in list(reversed(np.argsort(a))):
h = known[j]
if a[j] < threshold and new_whale not in s:
pos[len(t)] += 1
s.add(new_whale)
 t.append(new_whale)
 if len(t)== 5: break;
for w in h2ws[h]:
assert w != new_whale
if w not in s:
if a[j] > 1.0:
vtop += 1
elif a[j] >= threshold:
vhigh += 1
s.add(w)
 t.append(w)
 if len(t)== 5: break;
if len(t)== 5: break;
if new_whale not in s: pos[5] += 1
assert len(t)== 5 and len(s)== 5
f.write(p + ',' + ' '.join(t[:5])+ '
')
 return vtop, vhigh, pos<feature_engineering>
train_df = pd.read_csv('.. /input/google-quest-challenge/train.csv')
 sub_df = pd.read_csv('.. /input/google-quest-challenge/sample_submission.csv' )
tic = time.time()
h2ws = {}
for p, w in tagged.items() :
if w != new_whale:
h = p2h[p]
if h not in h2ws: h2ws[h] = []
if w not in h2ws[h]: h2ws[h].append(w)
 known = sorted(list(h2ws.keys()))
h2i = {}
for i, h in enumerate(known): h2i[h] = i
fknown = branch_model.predict_generator(FeatureGen(known), max_queue_size=20, workers=10, verbose=0)
 fsubmit = branch_model.predict_generator(FeatureGen(submit), max_queue_size=20, workers=10, verbose=0)
 score = head_model.predict_generator(ScoreGen(fknown, fsubmit), max_queue_size=20, workers=10, verbose=0)
 score = score_reshape(score, fknown, fsubmit)
 prepare_submission(0.99, 'submission_for_em.csv')
 toc = time.time()
print("Submission time: ",(toc - tic)/ 60.) 
<define_variables>
model_names = ["feat_multi", "feat_nn", "tf_bert_large",
"tf_bert_base_epoch1", "tf_bert_base_epoch2", "tf_bert_base_epoch3"]
sub_files = ['.. /input/em-whale/submission_0.csv',
'.. /input/em-whale/submission_7.csv',
'.. /input/em-whale/submission_8.csv',
'.. /input/em-whale/submission_3.csv']
sub_weight = [0.8500**2,
0.8500**2,
0.8500**2,
0.8500**2]
Hlabel = 'Image'
Htarget = 'Id'
npt = 6
place_weights = {}
for i in range(npt):
place_weights[i] =(1 /(i + 1))
print(place_weights)
 lg = len(sub_files)
 sub = [None]*lg
for i, file in enumerate(sub_files):
print("Reading {}: w={} - {}".format(i, sub_weight[i], file))
reader = csv.DictReader(open(file,"r"))
sub[i] = sorted(reader, key=lambda d: str(d[Hlabel]))
out = open("submission.csv", "w", newline='')
 writer = csv.writer(out)
 writer.writerow([Hlabel,Htarget])
 for p, row in enumerate(sub[0]):
target_weight = {}
for s in range(lg):
row1 = sub[s][p]
for ind, trgt in enumerate(row1[Htarget].split(' ')) :
target_weight[trgt] = target_weight.get(trgt,0)+(place_weights[ind]*sub_weight[s])
 tops_trgt = sorted(target_weight, key=target_weight.get, reverse=True)[:npt]
writer.writerow([row1[Hlabel], " ".join(tops_trgt)])
 out.close()<import_modules>
def compute_spearmanr(trues, preds):
rhos = []
for col_true, col_pred in zip(trues.T, preds.T):
rhos.append(spearmanr(col_true, col_pred + np.random.normal(0, 1e-7, col_pred.shape[0])).correlation)
 return np.mean(rhos)
import pandas as pd
import csv<define_variables>
target_columns = list(sub_df.columns)
 results = []
for fold_i in range(1, 6):
result_df = pd.DataFrame(index=model_names, columns=target_columns[-30:] + ["ave"])
 for model_name in model_names:
for target in target_columns[-30:]:
true = train_df[target_columns].iloc[kf[fold_i-1][1]].reset_index(drop=True)
 oof = pd.read_csv(model_name + f"/oof{fold_i}.csv")
 r = spearmanr(true[target].values, oof[target].values + np.random.normal(0, 1e-7, oof.shape[0])).correlation
result_df.loc[model_name, target] = r
result_df.loc[model_name, "ave"] = np.mean(result_df.loc[model_name, target_columns[-30:]].values)
 results.append(result_df)
sub_files = [
".. /input/ensemble-whale/whale1.csv",
".. /input/ensemble-whale/whale2.csv",
".. /input/ensemble-whale/whale10.csv",
".. /input/whale9/whale9.csv"
]
sub_weight = [
0.842**2,
0.855**2,
0.868**2,
0.867**2
]<save_to_csv>
def norm_with_rankdata(df):
for col_name in target_columns[-30:]:
df[col_name] = rankdata(df[col_name].values)/ len(df)
 return df
kfold = 5
model_number = len(model_names)
 true_dfs = []
for i in range(5):
oof = train_df.iloc[kf[i][1]]
true_dfs.append(oof[target_columns])
 model_df = defaultdict(lambda: [])
 for model_name in model_names:
print(model_name)
 scores = []
pred_dfs = []
for i in range(5):
pred = pd.read_csv(f"{model_name}/oof{i+1}.csv")
 pred = norm_with_rankdata(pred)
 model_df[model_name].append(pred)
 true = true_dfs[i]
rho = compute_spearmanr(true.values, pred.values)
 scores.append(rho)
 pred_dfs.append(pred.copy())
 print([f"fold{i+1}: {scores[i]:.4}" for i in range(5)])
 scores = [{} for _ in range(kfold)]
all_coeffs = []
for col_num, col_name in enumerate(target_columns[-30:]):
coefficients = np.zeros(( kfold, model_number))
print(col_name, "
")
 for fold_num,(_, vld_idx)in enumerate(kf):
print("
"+"-"*50+'
[Fold {}/{}]'.format(fold_num + 1, kfold))
def function_spearmanr(x):
true = true_dfs[fold_num].loc[:, col_name].values
pred = np.zeros(len(true))
for k,(model_name, df)in enumerate(model_df.items()):
pred += x[k] * df[fold_num].loc[:, col_name].values
return -spearmanr(true, pred ).correlation
x0 = [1 for _ in range(model_number)]
res = minimize(function_spearmanr, x0, method='nelder-mead', options={'xtol': 1e-4, 'disp': True})
 coeffs = [max(val, 0)for val in res.x]
coefficients[fold_num,:] = coeffs / np.sum(coeffs)
 print('
final vector: {}'.format(coefficients[fold_num,:]))
y_true = true_dfs[fold_num].loc[:, col_name].values
y_pred = np.zeros(len(y_true))
for k,(model_name, df)in enumerate(model_df.items()):
y_pred += coefficients[fold_num, k] * df[fold_num].loc[:, col_name].values
score = spearmanr(y_true, y_pred ).correlation
scores[fold_num][col_name] = score
print(f"valid's spearmanr: {score:<7.4f}
")
 all_coeffs.append(coefficients)
Hlabel = 'Image'
Htarget = 'Id'
npt = 6
place_weights = {}
for i in range(npt):
place_weights[i] =(1 /(i + 1))
print(place_weights)
 lg = len(sub_files)
 sub = [None]*lg
for i, file in enumerate(sub_files):
print("Reading {}: w={} - {}".format(i, sub_weight[i], file))
reader = csv.DictReader(open(file,"r"))
sub[i] = sorted(reader, key=lambda d: str(d[Hlabel]))
out = open("whale_ensemble_3.csv", "w", newline='')
 writer = csv.writer(out)
 writer.writerow([Hlabel,Htarget])
 for p, row in enumerate(sub[0]):
target_weight = {}
for s in range(lg):
row1 = sub[s][p]
for ind, trgt in enumerate(row1[Htarget].split(' ')) :
target_weight[trgt] = target_weight.get(trgt,0)+(place_weights[ind]*sub_weight[s])
 tops_trgt = sorted(target_weight, key=target_weight.get, reverse=True)[:npt]
writer.writerow([row1[Hlabel], " ".join(tops_trgt)])
 out.close()<install_modules>
sub_df = pd.read_csv(".. /input/google-quest-challenge/sample_submission.csv")
 submission = np.zeros(( len(sub_df), 30)).T
FOLD_NUM = 5
inf_sub = []
for fold_num in range(FOLD_NUM):
l = []
for model_num, model_name in enumerate(model_names):
sub_ = pd.read_csv(f"{model_name}/submission{fold_num+1}.csv")
 sub_ = norm_with_rankdata(sub_)
 l.append(sub_.copy())
 inf_sub.append(l.copy() )
!pip -q install aiohttp faiss-prebuilt pyxtools pymltools
!apt -qq install -y libopenblas-base libomp-dev
tensorflow.__version__<import_modules>
for col_num, col_name in enumerate(target_columns[-30:]):
for fold_num in range(FOLD_NUM):
for model_num in range(len(model_names)) :
coef = all_coeffs[col_num][fold_num][model_num]
submission[col_num] += coef / FOLD_NUM * inf_sub[fold_num][model_num][col_name].values
sub_df.iloc[:, -30:] = submission.T
sub_df
show_embedding, AbstractEstimator, estimator_iter_process, colab_save_file_func, OptimizerType, get_wsl_path, \
map_per_set, LossStepHookForTrain, ProcessMode, load_data_from_h5file, tf_model_fn, balance_class_dict, \
store_data_in_h5file, get_triplet_pair_np, map_per_image, show_distance_dense_plot, init_logger, pre_process_utils
FileCache, random_choice, NormType, get_pretty_float, expend_bounding_box, image_utils
try:
except ImportError:
resnet_arg_scope = resnet_utils.resnet_arg_scope
resnet_v2_50 = resnet_v2.resnet_v2_50
def combine_csv(file_weight: dict, out_file: str):
sub_files = []
sub_weight = []
for csv_file, weight in file_weight.items() :
sub_files.append(csv_file)
 sub_weight.append(weight)
 place_weights = {}
for i in range(5):
place_weights[i] = 10 - i * 2
h_label = 'Image'
h_target = 'Id'
sub = [None] * len(sub_files)
 for i, file in enumerate(sub_files):
print("Reading {}: w={} - {}".format(i, sub_weight[i], file))
reader = csv.DictReader(open(file, "r"))
sub[i] = sorted(reader, key=lambda d: d[h_label])
 out = open(out_file, "w", newline='')
 writer = csv.writer(out)
 writer.writerow([h_label, h_target])
 p = 0
for row in sub[0]:
target_weight = {}
for s in range(len(sub_files)) :
row1 = sub[s][p]
for ind, trgt in enumerate(row1[h_target].split(' ')) :
target_weight[trgt] = target_weight.get(trgt, 0)+(place_weights[ind] * sub_weight[s])
 tops_trgt = sorted(target_weight, key=target_weight.get, reverse=True)[:5]
writer.writerow([row1[h_label], " ".join(tops_trgt)])
 p += 1
out.close()
class PathManager(object):
def __init__(self, mode: str = "default"):
self._mode = mode
self.init_all_path()
self.init_bounding_boxes()
self._submission_csv = None
@property
def data_set_root_path(self)-> str:
if self._mode == "default":
return get_wsl_path("E:/frkhit/Download/AI/data-set/kaggle/whale")
 elif self._mode == "fake":
return get_wsl_path("E:/frkhit/Download/AI/data-set/kaggle/whale/fake")
 elif self._mode == "colab":
return "/content"
elif self._mode == "kaggle":
return ".. /input/humpback-whale-identification"
return "./"
@property
def working_path(self):
if self._mode == "kaggle":
return "/kaggle/working/"
return self.data_set_root_path
@property
def output_path(self,):
if self._mode == "default" or self._mode == "fake":
return "/mnt/e/s"
return self.working_path
@property
def data_set_train_path(self):
return os.path.join(self.data_set_root_path, "train")
 @property
def data_set_test_path(self,):
return os.path.join(self.data_set_root_path, "test")
 @property
def train_csv(self,):
return os.path.join(self.data_set_root_path, "train.csv")
 @property
def submission_csv(self,):
if self._submission_csv:
return self._submission_csv
return os.path.join(self.output_path, "whale.submission.csv")
 @submission_csv.setter
def submission_csv(self, submission_csv):
self._submission_csv = submission_csv
@property
def bounding_boxes_csv(self,):
return os.path.join(self.working_path, "bounding_boxes.csv")
 @property
def ckpt_pretrained_rv250(self,):
if self._mode == "default" or self._mode == "fake":
return get_wsl_path(
 "E:/frkhit/Download/AI/pre-trained-model/resnet_v2_50.ckpt" 
)
 return os.path.join(self.working_path, "resnet_v2_50.ckpt")
 def init_bounding_boxes(self):
if not os.path.exists(self.bounding_boxes_csv):
download_big_file("https://raw.githubusercontent.com/frkhit/file_servers/master/bounding_boxes.csv",
self.bounding_boxes_csv)
 if not os.path.exists(self.bounding_boxes_csv):
raise ValueError("fail to download bounding_boxes_csv!")
 def init_image_net_model(self):
working_dir = os.path.dirname(self.ckpt_pretrained_mv2)
 if not os.path.exists(working_dir):
os.mkdir(working_dir)
 if not os.path.exists(self.ckpt_pretrained_mv2 + ".index"):
tar_file = "mobilenet_v2_1.0_224.tgz"
download_big_file("https://storage.googleapis.com/mobilenet_v2/checkpoints/mobilenet_v2_1.0_224.tgz",
os.path.join(working_dir, tar_file))
if not os.path.exists(tar_file):
raise ValueError("fail to download pretrained model!")
 raw_dir = os.getcwd()
try:
os.chdir(working_dir)
 cmd = subprocess.Popen(["tar", "-xvf", tar_file])
 cmd.wait()
if not os.path.exists(self.ckpt_pretrained_mv2 + ".index"):
raise ValueError("fail to download pretrained model!")
 finally:
os.chdir(raw_dir)
 def download_resnet_v2_50(self):
working_dir = os.path.dirname(self.ckpt_pretrained_rv250)
 if not os.path.exists(working_dir):
os.mkdir(working_dir)
 if not os.path.exists(self.ckpt_pretrained_rv250):
tar_file = "resnet_v2_50_2017_04_14.tar.gz"
download_big_file("http://download.tensorflow.org/models/resnet_v2_50_2017_04_14.tar.gz",
os.path.join(working_dir, tar_file))
if not os.path.exists(tar_file):
raise ValueError("fail to download pretrained model!")
 raw_dir = os.getcwd()
try:
os.chdir(working_dir)
 cmd = subprocess.Popen(["tar", "-xzvf", tar_file])
 cmd.wait()
if not os.path.exists(self.ckpt_pretrained_rv250):
raise ValueError("fail to download pretrained model!")
 finally:
os.chdir(raw_dir)
 def init_all_path(self):
members = [attr for attr in dir(self)if not attr.startswith("__")]
for member in members:
if(member.endswith("path")or member.endswith("dir")) and not member.endswith("init_all_path"):
tmp_dir = getattr(self, member)
 if callable(tmp_dir):
tmp_dir = tmp_dir()
if not os.path.exists(tmp_dir):
try:
os.mkdir(tmp_dir)
 except Exception as e:
logging.error(e)
 @property
def is_kaggle(self):
return bool(self._mode == "kaggle")
 def create_fake_data(self, x: int = 1):
raw_path_manager = PathManager()
whales = pd.read_csv(raw_path_manager.train_csv)
 train_info = {}
for index, image_name in enumerate(whales.Image):
train_info[image_name] = whales.Id[index]
shutil.rmtree(self.data_set_root_path)
 os.mkdir(self.data_set_root_path)
 os.mkdir(self.data_set_test_path)
 test_image_list = list_files(raw_path_manager.data_set_test_path)
 for image_file in random.choices(test_image_list, k=10 * x):
shutil.copy(image_file, os.path.join(self.data_set_test_path, os.path.basename(image_file)))
 os.mkdir(self.data_set_train_path)
 raw_image_list = list_files(raw_path_manager.data_set_train_path)
 image_id_vs_image_file = {
os.path.basename(image_file): image_file for image_file in raw_image_list}
class_id_vs_image_list = {}
for image_id, class_id in train_info.items() :
class_id_vs_image_list.setdefault(class_id, [] ).append(image_id_vs_image_file[image_id])
 anchor_class_id_list = [class_id for class_id, _image_list in class_id_vs_image_list.items() if
len(_image_list)> 1]
all_anchor_class_id_list = list(train_info.values())
 try:
anchor_class_id_list.remove(WhaleDataUtils.blank_class_id)
 except Exception:
pass
to_move_list = []
for class_id in random.choices(anchor_class_id_list, k=2 * x):
to_move_list.extend(class_id_vs_image_list[class_id][:3 * x])
 for class_id in random.choices(all_anchor_class_id_list, k=3 * x):
to_move_list.extend(class_id_vs_image_list[class_id][:3 * x])
 to_move_list.extend(class_id_vs_image_list[WhaleDataUtils.blank_class_id][:4 * x])
 for image_file in to_move_list:
shutil.copy(image_file, os.path.join(self.data_set_train_path, os.path.basename(image_file)))
 with open(self.train_csv, "w")as f:
f.write("Image,Id
")
 for image_file in to_move_list:
image_id = os.path.basename(image_file)
 class_id = train_info[image_id]
f.write("{},{}
".format(image_id, class_id))
self.init_bounding_boxes()
class WhaleDataUtils(object):
blank_class_id = "new_whale"
not_gray_str = ".ng"
def __init__(self, path_manager: PathManager, loss_hook: LossStepHookForTrain = None,
gen_data_setting: dict = None):
self.logger = logging.getLogger(self.__class__.__name__)
 self._file = "./{}.pkl".format(self.__class__.__name__)
 self.path_manager = path_manager
self.loss_hook = loss_hook
self.file_cache = FileCache(pickle_file=self._file)
 self.dimension = 512
self.margin = 1.0
self.gen_data_setting = {
"x_train_num": 1,
"ignore_blank_prob": 0.95,
"ignore_single_prob": 0.2,
"stop_calc_feature": False,
"gen_data_by_random_prob": 0,
"use_norm_when_calc_apn": True,
}
if gen_data_setting:
self.gen_data_setting.update(gen_data_setting)
 _cache = None
if os.path.exists(self._file):
_cache = self.file_cache.get("cache")
 if not _cache:
self._cache =([], [], {}, {}, {}, [])
 else:
self._cache = _cache
self._baseline = None
self._to_use_random = False
def _update_cache(self):
self.file_cache.set("cache", self._cache)
 def clear_cache(self):
self.file_cache.cache.clear()
self.file_cache.set("__key__", "value", ttl=10)
 _pkl_file = self._file + ".feature.h5"
if os.path.exists(_pkl_file):
os.remove(_pkl_file)
 def load_train_info(self)-> dict:
if self._cache and self._cache[3]:
return self._cache[3]
whales = pd.read_csv(self.path_manager.train_csv)
 info = self._cache[3]
for index, image_name in enumerate(whales.Image):
info[image_name] = whales.Id[index]
self.logger.info("there are {} record in train csv!".format(len(info)))
 self._update_cache()
return info
def load_train_label_info(self)-> dict:
if self._cache and self._cache[4]:
return self._cache[4]
class_id_list = [class_id for class_id in self.load_train_info().values() ]
class_id_list = list(set(class_id_list))
class_id_list.sort()
info = self._cache[4]
for index, class_id in enumerate(class_id_list):
info[class_id] = index
self.logger.info("there are {} classes in train set!".format(len(info)))
 self._update_cache()
return info
def load_boxes_info(self):
if self._cache and self._cache[2]:
return self._cache[2]
boxes = pd.read_csv(self.path_manager.bounding_boxes_csv)
 info = self._cache[2]
for index, image_id in enumerate(boxes.Image):
info[image_id] =(boxes.x0[index], boxes.y0[index], boxes.x1[index], boxes.y1[index])
 self.logger.info("there are {} record in boxes csv!".format(len(info)))
 file_list = self.list_train_image_file().copy()
file_list.extend(self.list_test_image_file())
 for image_file in file_list:
base_name = os.path.basename(image_file)
 if base_name not in info:
raise ValueError("{} not in boxes info!".format(image_file))
self._update_cache()
return self._cache[2]
def get_boxes(self, image_file)->(int, int, int, int):
base_name = os.path.basename(image_file)
 return self.load_boxes_info() [base_name]
def list_train_image_file(self, ignore_blank: bool = False)-> list:
if not self._cache[0]:
image_list = list_files(self.path_manager.data_set_train_path)
 result_list = [file_name for file_name in image_list if file_name.endswith(".jpg")]
assert len(result_list)== len(image_list)
 self.logger.info("there are {} jpg in train path!".format(len(result_list)))
 self._cache[0].clear()
self._cache[0].extend(result_list)
 self._update_cache()
if not self._cache[5]:
for _image_file in self._cache[0]:
if self.get_class_id(_image_file)!= self.blank_class_id:
self._cache[5].append(_image_file)
 self._update_cache()
if ignore_blank is False:
return self._cache[0]
return self._cache[5]
def list_test_image_file(self)-> list:
if self._cache and self._cache[1]:
return self._cache[1]
image_list = list_files(self.path_manager.data_set_test_path)
 result_list = [file_name for file_name in image_list if file_name.endswith(".jpg")]
assert len(result_list)== len(image_list)
 self.logger.info("there are {} jpg in test path!".format(len(result_list)))
 self._cache[1].clear()
self._cache[1].extend(result_list)
 self._update_cache()
return self._cache[1]
def get_class_id(self, image_file: str)-> str:
base_name = os.path.basename(image_file)
 if base_name.endswith(self.not_gray_str):
base_name = base_name[:-len(self.not_gray_str)]
return self.load_train_info() [base_name]
def list_class_id_with_multi_instance(self)-> dict:
info = {}
for class_id in self.load_train_info().values() :
info.setdefault(class_id, 0)
 info[class_id] += 1
key_to_remove_list = []
for key, count in info.items() :
if count <= 1:
key_to_remove_list.append(key)
 for key in key_to_remove_list:
info.pop(key)
 return info
def get_label(self, image_file: str)-> int:
class_id = self.get_class_id(image_file)
 return self.load_train_label_info() [class_id]
def submit_test_result(self, test_image_list: list, result_list: list, feature_list: list = None):
if len(test_image_list)!= len(result_list):
self.logger.error("len(test_image_list)!= len(result_list)!")
 if feature_list and len(feature_list)!= len(test_image_list):
self.logger.error("len(test_image_list)!= len(feature_list)!")
 with open(self.path_manager.submission_csv, "w")as f:
f.write("Image,Id
")
 for index, image_file in enumerate(test_image_list):
f.write("{},{}
".format(os.path.basename(image_file),
" ".join([class_id for class_id in result_list[index]])))
 self.logger.info("success to save result in {}".format(self.path_manager.submission_csv))
if feature_list and feature_list[0]:
self.logger.info("saving feature list...")
 with open(self.path_manager.submission_csv + ".feature", "w")as f:
for index, image_file in enumerate(test_image_list):
f.write("{},{}
".format(os.path.basename(image_file), feature_list[index]))
self.logger.info("success to save feature result in {}".format(
 self.path_manager.submission_csv + ".feature"))
def get_random_baseline(self)->(float, float):

 if self._baseline is None:
class_count_dict = self.list_class_id_with_multi_instance()
class_list = sorted(class_count_dict.items() , key=lambda x: x[1], reverse=True)
 class_id_list = [_class_id for(_class_id, _count)in class_list]
random_class_id_list = [self.blank_class_id]
for _class_id in class_id_list:
if _class_id not in random_class_id_list:
random_class_id_list.append(_class_id)
 if len(random_class_id_list)>= 5:
break
labels = [self.get_class_id(image_file)for image_file in self.list_train_image_file() ]
class_id_result_list = [random_class_id_list] * len(labels)
 total_map_5 = map_per_set(labels=labels, predictions=class_id_result_list, k=5)
 total_map_1 = map_per_set(labels=labels, predictions=class_id_result_list, k=1)
 self._baseline =(total_map_5, total_map_1)
 return self._baseline
def list_debug_image_list(self, count: int = 128)-> list:
all_image_list, class_id_vs_image_list, all_class_id_list, anchor_class_id_list, single_class_id_list = \
self.get_basic_info(self.list_train_image_file())
 if count >= len(all_image_list):
return all_image_list
single_blank_class_files = []
anchor_class_id_set = set(anchor_class_id_list)
 for class_id, file_list in class_id_vs_image_list.items() :
if class_id not in anchor_class_id_set:
single_blank_class_files.extend(file_list)
 image_list = []
anchor_file_count = int(count *(1 - len(single_blank_class_files)/ len(all_image_list)))
 if(anchor_file_count + len(single_blank_class_files)) > len(all_image_list):
anchor_file_count = len(all_image_list)- len(single_blank_class_files)
 for class_id in anchor_class_id_list:
image_list.extend(class_id_vs_image_list[class_id])
 if len(image_list)>= anchor_file_count:
break
while len(image_list)> anchor_file_count:
image_list.pop(-1)
 image_list.extend(random_choice(single_blank_class_files, k=count - anchor_file_count, unique=True))
assert len(image_list)== count
return image_list
def get_basic_info(self, all_image_list: list = None)->(list, dict, list, list, list):

 if all_image_list is None:
all_image_list = list(self.list_train_image_file(ignore_blank=False))
image_id_vs_image_file = {os.path.basename(image_file): image_file for image_file in all_image_list}
class_id_vs_image_list = {}
for image_id, class_id in self.load_train_info().items() :
if image_id in image_id_vs_image_file:
class_id_vs_image_list.setdefault(class_id, [] ).append(image_id_vs_image_file[image_id])
 anchor_class_id_list = [class_id for class_id, _image_list in class_id_vs_image_list.items() if
len(_image_list)> 1]
all_class_id_list = list(class_id_vs_image_list.keys())
 single_class_id_list = list(set(all_class_id_list)- set(anchor_class_id_list))
try:
anchor_class_id_list.remove(self.blank_class_id)
 except Exception:
pass
try:
single_class_id_list.remove(self.blank_class_id)
 except Exception:
pass
return all_image_list, class_id_vs_image_list, all_class_id_list, anchor_class_id_list, single_class_id_list
def get_file_list(self, is_training: bool = False, shuffle: bool = False, num_epochs: int = 1,
batch_size: int = None, online_batch_count: int = 0)-> list:
def get_random_batch_list(batch_count: int, add_blank_class: bool, class_id_vs_image_list: dict,
anchor_class_id_list: list, all_image_list: list, single_class_id_list: list,
anchor_unique: bool, single_unique: bool,)-> list:
batch_list = []
if add_blank_class:
batch_list.append(random.choice(class_id_vs_image_list[self.blank_class_id]))
chosen_class_id_list = random_choice(anchor_class_id_list, k=batch_count, unique=anchor_unique)
 for class_id in chosen_class_id_list:
if len(class_id_vs_image_list[class_id])>= 2 * online_batch_count:
batch_list.extend(
 random_choice(class_id_vs_image_list[class_id], k=2 * online_batch_count, unique=True))
else:
batch_list.extend(class_id_vs_image_list[class_id])
 if(batch_count - len(batch_list)) < online_batch_count:
break
if len(batch_list)< batch_count:
if single_class_id_list:
batch_list.extend([
class_id_vs_image_list[class_id][0] for class_id in
random_choice(single_class_id_list, k=batch_count - len(batch_list),
unique=single_unique)])
 else:
batch_list.extend(
 random_choice(all_image_list, k=batch_count - len(batch_list), unique=anchor_unique))
else:
while len(batch_list)> batch_count:
batch_list.pop(-1)
 return batch_list
def create_file_list_by_random() -> list:
all_image_list, class_id_vs_image_list, all_class_id_list, anchor_class_id_list, single_class_id_list = \
self.get_basic_info()
total_count = int(num_epochs * len(all_image_list)* self.gen_data_setting["x_train_num"])
 batch_count = total_count // batch_size
multi_batch_list = []
_unique = True if len(anchor_class_id_list)>= batch_size else False
_unique_single = True if len(single_class_id_list)>= batch_size else False
_has_blank_class = True if class_id_vs_image_list.get(self.blank_class_id)else False
for i in range(batch_count):
batch_list = get_random_batch_list(
 batch_count=batch_size, add_blank_class=_has_blank_class,
class_id_vs_image_list=class_id_vs_image_list, anchor_class_id_list=anchor_class_id_list,
all_image_list=all_image_list, single_class_id_list=single_class_id_list,
anchor_unique=_unique, single_unique=_unique_single, 
)
 random.shuffle(batch_list)
 multi_batch_list.append(batch_list)
 return multi_batch_list
def create_file_list_by_distance() -> list:
feature_arr, file_arr = self._load_feature(create_if_not_exist=False)
 if feature_arr is None:
self.logger.warning(
 "no feature in h5, use create_file_list_by_random instead of create_file_list_by_distance!")
 return create_file_list_by_random()
if self.gen_data_setting.get("use_norm_when_calc_apn", False):
feature_arr, norm_float = NormType.all.normalize_and_return_norm(feature_arr)
 self.logger.info("norm of feature is {}<type: {}>".format(norm_float, type(norm_float)))
 assert isinstance(norm_float, float)
 margin = self.margin / norm_float
else:
margin = self.margin
_file_list = []
for i in range(file_arr.shape[0]):
_file_list.append(file_arr[i].decode("utf-8"))
all_image_list, class_id_vs_image_list, all_class_id_list, anchor_class_id_list, single_class_id_list = \
self.get_basic_info(_file_list)
 total_count = int(num_epochs * len(all_image_list)* self.gen_data_setting["x_train_num"])
 batch_count = total_count // batch_size
multi_batch_list = []
single_class_id_set = set(single_class_id_list)
 anchor_index_end = -1
_anchor_search_end = False
labels = np.zeros(shape=file_arr.shape, dtype=np.int)
 for i in range(file_arr.shape[0]):
_file_name = file_arr[i].decode("utf-8")
 labels[i] = self.get_label(_file_name)
 if not _anchor_search_end:
class_id = self.get_class_id(_file_name)
 if class_id in single_class_id_set:
_anchor_search_end = True
else:
anchor_index_end = i
anchor_feature = feature_arr[:anchor_index_end + 1]
_apn_np_start = time.time()
apn_list = get_triplet_pair_np(anchor_feature, all_feature=feature_arr, all_label=labels,
margin=margin, logger=self.logger)
 _apn_set = set()
for(a, p, n)in apn_list:
_apn_set.add(a)
 _apn_set.add(p)
 _apn_set.add(n)
 self.logger.info(
 "get {} hardest-apn-pairs in {} files, time cost {}s.Unique file in apn pairs is {}.".format(
 len(apn_list), feature_arr.shape[0], time.time() - _apn_np_start, len(_apn_set)))
 _unique = True if len(anchor_class_id_list)>= batch_size else False
_unique_single = True if len(single_class_id_list)>= batch_size else False
_has_blank_class = True if class_id_vs_image_list.get(self.blank_class_id)else False
_blank_file_id_set = set()
if _has_blank_class:
_blank_file_label = self.get_label(class_id_vs_image_list[self.blank_class_id][0])
 for i in range(labels.shape[0]):
if labels[i] == _blank_file_label:
_blank_file_id_set.add(i)
 assert len(_blank_file_id_set)== len(class_id_vs_image_list.get(self.blank_class_id, []))
if len(apn_list)<= batch_size or(len(_apn_set - _blank_file_id_set)+ 1)<= batch_size:
self.logger.warning("apn list is too small, use create_file_list_by_random instead!")
 return create_file_list_by_random()
tmp_apn_list = []
for i in range(batch_count):
batch_file_id_id = set()
_blank_exists = False
while True:
if len(tmp_apn_list)== 0:
tmp_apn_list.extend(list(apn_list))
random.shuffle(tmp_apn_list)
(a, p, n)= tmp_apn_list.pop(-1)
 for _file_id in [a, p, n]:
if _file_id in _blank_file_id_set:
if _blank_exists is False:
batch_file_id_id.add(_file_id)
 _blank_exists = True
else:
continue
else:
batch_file_id_id.add(_file_id)
 if len(batch_file_id_id)>= batch_size:
break
batch_list = [file_arr[file_id].decode("utf-8")for file_id in batch_file_id_id]
assert len(batch_list)>= batch_size
batch_list = batch_list[:batch_size]
random.shuffle(batch_list)
 multi_batch_list.append(batch_list)
 return multi_batch_list
if is_training:
if online_batch_count > 0:
self.logger.info("using create_file_list_by_distance...")
 if self._to_use_random:
_multi_batch_list = create_file_list_by_random()
self._to_use_random = False
else:
_multi_batch_list = create_file_list_by_distance()
if shuffle is True:
random.shuffle(_multi_batch_list)
 image_file_list = []
for _batch_list in _multi_batch_list:
image_file_list.extend(_batch_list)
 return image_file_list
else:
return self.list_train_image_file()
return self.list_test_image_file()
def _list_file_to_calc_feature(self)-> list:
if random.random() < self.gen_data_setting["ignore_blank_prob"]:
all_image_list = list(self.list_train_image_file(ignore_blank=True))
if random.random() < self.gen_data_setting["ignore_single_prob"]:
_, class_id_vs_image_list, _, anchor_class_id_list, _ = \
self.get_basic_info(all_image_list)
 all_file_list = []
anchor_class_id_set = set(anchor_class_id_list)
 for class_id, _file_list in class_id_vs_image_list.items() :
if class_id in anchor_class_id_set:
all_file_list.extend(_file_list)
 self.logger.info("list train image file [ignore_single, ignore_blank] to calc feature!")
 return all_file_list
else:
self.logger.info("list train image file [ignore_blank] to calc feature!")
 return all_image_list
self.logger.info("list all train image file to calc feature!")
 return list(self.list_train_image_file())
 def calc_feature(self, callback, epoch_num: int = None):

 if self.gen_data_setting.get("stop_calc_feature", False):
self.logger.info("no need to calc feature: stop_calc_feature is True!")
 return
if epoch_num is not None and epoch_num == 0 and os.path.exists(self._file + ".feature.h5"):
self.logger.info("no need to calc feature: feature file exist and epoch_num == 0!")
 return
if random.random() < self.gen_data_setting.get("gen_data_by_random_prob", 0):
self.logger.info("no need to calc feature: going to gen data by random!")
 self._to_use_random = True
return
file_list = self._list_file_to_calc_feature()
if not file_list:
self.logger.info("no need to calc feature!")
 return
self.logger.info("calculating feature of {} file...".format(len(file_list)))
 feature_list, feature_file_list = callback(file_list, mode=ProcessMode.train)
 self.logger.info("success to calculate feature of {} file!".format(len(feature_file_list)))
 self.update_feature(feature_list, feature_file_list, is_training=True)
 def update_feature(self, feature_list, file_list, is_training: bool):

 if not is_training:
self.logger.info("give up update-feature for no train mode!")
 return
if not feature_list:
return
self._save_feature(feature_list, file_list)
 def _save_feature(self, feature_list: list, file_list: list):
_pkl_file = self._file + ".feature.h5"
if os.path.exists(_pkl_file):
os.remove(_pkl_file)
 all_image_list, class_id_vs_image_list, all_class_id_list, anchor_class_id_list, single_class_id_list = \
self.get_basic_info(all_image_list=file_list)
 all_file_list = []
for class_id in anchor_class_id_list:
all_file_list.extend(class_id_vs_image_list[class_id])
 for class_id in single_class_id_list:
all_file_list.extend(class_id_vs_image_list[class_id])
 all_file_list.extend(class_id_vs_image_list.get(self.blank_class_id, []))
assert len(all_image_list)== len(all_file_list)
 sort_index_list = []
_dict = {}
for i, file_name in enumerate(file_list):
_dict[file_name] = i
for file_name in all_file_list:
sort_index_list.append(_dict[file_name])
 file_arr = np.asarray([f.encode("utf-8")for f in all_file_list], dtype=np.string_)
 feature_arr = np.zeros(shape=(len(all_file_list), self.dimension), dtype=np.float32)
 for index, key_index in enumerate(sort_index_list):
feature_arr[index][:] = feature_list[key_index].reshape(( self.dimension,))
_pkl_file = self._file + ".feature.h5"
store_data_in_h5file(_pkl_file, [feature_arr, file_arr], key_list=["feature", "file"])
 def _load_feature(self, create_if_not_exist: bool = True)->(np.ndarray, np.ndarray):

 _pkl_file = self._file + ".feature.h5"
if os.path.exists(_pkl_file):
feature, file_arr = None, None
try:
feature, file_arr = load_data_from_h5file(_pkl_file, key_list=["feature", "file"])
 except Exception as e:
os.remove(_pkl_file)
 self.logger.error(e)
 if feature is not None:
self.logger.info("feature shape is {}, file_arr shape is {}".format(feature.shape, file_arr.shape))
assert feature.shape[1] == self.dimension
assert file_arr.shape[0] == feature.shape[0]
return feature, file_arr
if not create_if_not_exist:
return None, None
all_image_list, class_id_vs_image_list, all_class_id_list, anchor_class_id_list, single_class_id_list = \
self.get_basic_info()
all_file_list = []
for class_id in anchor_class_id_list:
all_file_list.extend(class_id_vs_image_list[class_id])
 for class_id in single_class_id_list:
all_file_list.extend(class_id_vs_image_list[class_id])
 all_file_list.extend(class_id_vs_image_list[self.blank_class_id])
 assert len(all_image_list)== len(all_file_list)
 file_arr = np.asarray([f.encode("utf-8")for f in all_file_list], dtype=np.string_)
 feature = np.zeros(shape=(len(all_file_list), self.dimension))
return feature, file_arr 
<import_modules>
def norm_sub(df):
for col_name in df.columns[-30:]:
tmp_df = df[col_name].values
v_max = np.max(tmp_df)+ 0.01
v_min = np.min(tmp_df)- 0.01
df[col_name] = df[col_name].apply(lambda x:(x - v_min)/(v_max - v_min))
df[col_name] = df[col_name].values + np.random.normal(0, 1e-7, len(df))
return df
sub_df = norm_sub(sub_df )
class WhaleRankingUtils(object):
def __init__(self, data_utils: WhaleDataUtils, top_k: int = 5):
self.logger = logging.getLogger(self.__class__.__name__)
 self.top_k = top_k
self.data_utils = data_utils
def simple_rank(self, result_list: list, distance_cutoff: float = None, only_distance_fit: bool = False)-> list:

 _class_id_list = []
_class_id_set = set()
if distance_cutoff is None:
for result in result_list:
class_id = result[1]
if class_id not in _class_id_set:
_class_id_list.append(class_id)
 _class_id_set.add(class_id)
 if len(_class_id_list)< self.top_k:
if self.data_utils.blank_class_id not in _class_id_set:
_class_id_list.append(self.data_utils.blank_class_id)
 _class_id_set.add(self.data_utils.blank_class_id)
 else:
if result_list[0][0] > distance_cutoff:
_class_id_list.append(self.data_utils.blank_class_id)
 if only_distance_fit:
return _class_id_list
else:
_class_id_list.append(result_list[0][1])
 _class_id_set.add(_class_id_list[0])
 for result in result_list[1:]:
if result[0] <= distance_cutoff:
class_id = result[1]
if class_id not in _class_id_set:
_class_id_list.append(class_id)
 _class_id_set.add(class_id)
 else:
if only_distance_fit:
return _class_id_list
else:
break
if len(_class_id_list)< self.top_k:
if self.data_utils.blank_class_id not in _class_id_set:
_class_id_list.append(self.data_utils.blank_class_id)
 _class_id_set.add(self.data_utils.blank_class_id)
 if len(_class_id_list)< self.top_k:
for result in result_list[1:]:
if result[0] > distance_cutoff:
class_id = result[1]
if class_id not in _class_id_set:
_class_id_list.append(class_id)
 _class_id_set.add(class_id)
 return _class_id_list
def recreate_class_id_list(self, cutoff: float, class_id_list: list, feature_list: list)-> list:
new_class_id_list = []
count = 0
for i, class_list in enumerate(class_id_list):
_new_class_list = list(class_list)
 if feature_list[i][0][0] <= cutoff or _new_class_list[0] == self.data_utils.blank_class_id:
new_class_id_list.append(_new_class_list)
 continue
try:
found_index = _new_class_list.index(self.data_utils.blank_class_id)
 if found_index > -1:
_new_class_list.pop(found_index)
 except ValueError:
pass
_new_class_list.insert(0, self.data_utils.blank_class_id)
 new_class_id_list.append(_new_class_list)
 count += 1
self.logger.info("recreate_class_id_list change {}/{} record".format(count, len(class_id_list)))
 return new_class_id_list
def get_similar_cutoff(self, validating_labels: list, validating_feature_list: list)-> float:
_distance_list = []
_, class_id_vs_image_list, _, anchor_class_id_list, _ = \
self.data_utils.get_basic_info(self.data_utils.list_train_image_file())
 anchor_class_set = set(anchor_class_id_list)
 for i, class_id in enumerate(validating_labels):
if class_id not in anchor_class_set:
continue
for(_distance, _class_id, _file_name)in validating_feature_list[i]:
if class_id == _class_id:
_distance_list.append(_distance)
 self.logger.info("distance list: len is {}, first 5 is {}".format(len(_distance_list), _distance_list[:5]))
distance_mean, distance_std = show_distance_dense_plot(
 np.asarray(_distance_list), self.data_utils.path_manager.submission_csv + ".distance.jpg")
 distance_similar_cutoff = distance_mean + 3 * distance_std
self.logger.info("distance_similar_cutoff is {}".format(distance_similar_cutoff))
return distance_similar_cutoff
@staticmethod
def get_feature_list(feature_file: str)->(list, list):
feature_list = []
file_id_list = []
with open(feature_file, "r", encoding="utf-8")as f:
for line in f:
if len(line)< 2:
continue
line = line.rstrip()
file_id_list.append(line.split(",")[0])
 feature_list.append(eval(line[line.find(",")+ 1:]))
return file_id_list, feature_list
def analyze_validating_result(self, feature_file: str):
file_id_list, feature_list = self.get_feature_list(feature_file)
 class_id_result_list = [self.simple_rank(feature)[:self.top_k] for feature in feature_list]
labels = self.list_class_id(file_id_list)
 total_map_5 = map_per_set(labels=labels, predictions=class_id_result_list, k=5)
 total_map_1 = map_per_set(labels=labels, predictions=class_id_result_list, k=1)
 self.logger.info("validating data result is: map@5 is {}, map@1 is {}!".format(total_map_5, total_map_1))
distance_similar_cutoff = self.get_similar_cutoff(labels, feature_list)
 for i, image_id in enumerate(file_id_list):
map_5 = map_per_image(label=labels[i], predictions=class_id_result_list[i], k=5)
 if map_5 < 1.0:
self.logger.info("map@5 is {}
True: {}, Predict with cutoff is {}
{}
".format(
 map_5, labels[i],
self.data_utils.blank_class_id if feature_list[i][0][0] > distance_similar_cutoff else
feature_list[i][0][1],
feature_list[i])
)
 new_class_id_result = self.recreate_class_id_list(
 cutoff=distance_similar_cutoff, class_id_list=class_id_result_list, feature_list=feature_list)
 total_map_5 = map_per_set(labels=labels, predictions=new_class_id_result, k=5)
 total_map_1 = map_per_set(labels=labels, predictions=new_class_id_result, k=1)
 self.logger.info(
 "validating data result with cutoff is: map@5 is {}, map@1 is {}!".format(total_map_5, total_map_1))
def compare_submission_file(self, file_1, file_2):
def _compare_same_count(_result_1, _result_2, info):
assert set(_result_1.keys())== set(_result_2.keys())
 all_same_count = 0
for image_id, result_list in _result_1.items() :
if ",".join(result_list[:self.top_k])== ",".join(_result_2[image_id][:self.top_k]):
all_same_count += 1
self.logger.info("[{}] same count is {}/{}".format(info, all_same_count, len(_result_1)))
 def _compare_top1_count(_result_1, _result_2, info):
assert set(_result_1.keys())== set(_result_2.keys())
 all_same_count = 0
for image_id, result_list in _result_1.items() :
if result_list[0] == _result_2[image_id][0]:
all_same_count += 1
self.logger.info("[{}] same top1 count is {}/{}".format(info, all_same_count, len(_result_1)))
 def _compare_map(_result_1, _result_2, info):
assert set(_result_1.keys())== set(_result_2.keys())
 _file_id_list = list(_result_1.keys())
 _file_id_list.sort()
labels = self.list_class_id(_file_id_list)
 _class_id_result_list_1 = [_result_1[_image_id] for _image_id in _file_id_list]
map5_1 = map_per_set(labels=labels, predictions=_class_id_result_list_1, k=5)
 map1_1 = map_per_set(labels=labels, predictions=_class_id_result_list_1, k=1)
 _class_id_result_list_2 = [_result_2[_image_id] for _image_id in _file_id_list]
map5_2 = map_per_set(labels=labels, predictions=_class_id_result_list_2, k=5)
 map1_2 = map_per_set(labels=labels, predictions=_class_id_result_list_2, k=1)
 self.logger.info("[{}] map@5 is {}/{}, map@1 is {}/{}".format(
 info,
get_pretty_float(map5_1, count=3),
get_pretty_float(map5_2, count=3),
get_pretty_float(map1_1, count=3),
get_pretty_float(map1_2, count=3), 
))
result_1, result_2 = self.read_submission_file(file_1), self.read_submission_file(file_2)
 result_1_x, result_2_x = self._get_result_from_feature_file(file_1), self._get_result_from_feature_file(file_2)
 result_1_true, result_2_true = self._get_result_by_cutoff(file_1), self._get_result_by_cutoff(file_2)
 self.logger.info("Start to compare:
")
 self.logger.info("compare map5, map1")
 _compare_map(result_1, result_2, "map, raw, resenet vs siamese")
 _compare_map(result_1, result_1_x, "map, raw, resenet, csv vs feature")
 _compare_map(result_2, result_2_x, "map, raw, siamese, csv vs feature")
 _compare_map(result_1_true, result_2_true, "map, true, resenet vs siamese")
 _compare_map(result_1, result_1_true, "map, resent, raw vs true")
 _compare_map(result_2, result_2_true, "map, siamese, raw vs true")
 self.logger.info("End
")
 self.logger.info("compare top5")
 _compare_same_count(result_1, result_2, "top5, raw, resenet vs siamese")
 _compare_same_count(result_1_true, result_2_true, "top5, true, resenet vs siamese")
 _compare_same_count(result_1, result_1_true, "top5, resent, raw vs true")
 _compare_same_count(result_2, result_2_true, "top5, siamese, raw vs true")
 self.logger.info("End
")
 self.logger.info("compare top1")
 _compare_top1_count(result_1, result_2, "top1, raw, resenet vs siamese")
 _compare_top1_count(result_1_true, result_2_true, "top1, true, resenet vs siamese")
 _compare_top1_count(result_1, result_1_true, "top1, resent, raw vs true")
 _compare_top1_count(result_2, result_2_true, "top1, siamese, raw vs true")
 self.logger.info("End
")
 @staticmethod
def read_submission_file(csv_file: str)-> dict:
submission = pd.read_csv(csv_file)
 _result = {}
for index, _image_id in enumerate(submission.Image):
_result[_image_id] = submission.Id[index].rstrip().split(" ")
 return _result
def _get_result_from_feature_file(self, csv_file: str)-> dict:
feature_file = csv_file + ".feature"
assert os.path.exists(feature_file)
 cutoff, _, file_id_list, feature_list = self._read_feature_file(feature_file)
 _new_result = {}
for _index, file_id in enumerate(file_id_list):
_new_result[file_id] = self.simple_rank(
 feature_list[_index], distance_cutoff=cutoff, only_distance_fit=False)
 return _new_result
def _get_result_by_cutoff(self, csv_file: str)-> dict:
feature_file = csv_file + ".feature"
assert os.path.exists(feature_file)
 cutoff, labels, file_id_list, feature_list = self._read_feature_file(feature_file)
 self.logger.info("cutoff is {}".format(get_pretty_float(cutoff, count=3)))
 _new_result = {}
for _index, file_id in enumerate(file_id_list):
_new_result[file_id] = self.simple_rank(
 feature_list[_index], distance_cutoff=cutoff, only_distance_fit=True)
 return _new_result
def _read_feature_file(self, feature_file):
assert os.path.exists(feature_file)
 file_id_list, feature_list = self.get_feature_list(feature_file)
 labels = self.list_class_id(file_id_list)
 cutoff = self.get_similar_cutoff(labels, feature_list)
 return cutoff, labels, file_id_list, feature_list
@staticmethod
def _calc_weight_score(feature_list, file_id_list, cutoff)-> dict:
keep_count = 10
cutoff_score = 5.0
_new_result = {}
for _index, file_id in enumerate(file_id_list):
_new_result[file_id] = {}
_sort_num = -1
for(distance, class_id, file_name)in feature_list[_index]:
if class_id not in _new_result[file_id]:
_sort_num += 1
_new_result[file_id][class_id] = keep_count - _sort_num
if distance < cutoff:
_new_result[file_id][class_id] += cutoff_score
if _sort_num >= keep_count:
break
return _new_result
@staticmethod
def _get_result_by_weight_score(score_dict: dict, top_k: int = 5)-> dict:
def _sort_by_value(info_dict: dict)-> list:
_tuple = sorted(info_dict.items() , key=lambda x: x[1], reverse=True)
 return [_class_id for(_class_id, _weight)in _tuple]
result_dict = {}
for image_id, score in score_dict.items() :
result_dict[image_id] = _sort_by_value(score)[:top_k]
return result_dict
def combine_submission_file(self, validating_file_list: list, submission_file_list: list, output_file: str):
def _create_final_result(_result_dict: dict):
_file_list = []
_result_list = []
for _image_id, _class_list in _result_dict.items() :
_file_list.append(_image_id)
 _result_list.append(_class_list)
 return _file_list, _result_list
def _update_weight_score(all_dict: dict, one_dict: dict, model_weight: float):
for _image_id, _score_dict in one_dict.items() :
if _image_id not in all_dict:
all_dict[_image_id] = {}
for _class_id, _score in _score_dict.items() :
all_dict[_image_id].setdefault(_class_id, 0.0)
 all_dict[_image_id][_class_id] += _score * model_weight
return all_dict
def eval_result(_result_dict: dict):
_file_list, _result_list = _create_final_result(_result_dict)
 _labels = self.list_class_id(_file_list)
 _map5 = map_per_set(labels=_labels, predictions=_result_list, k=5)
 _map1 = map_per_set(labels=_labels, predictions=_result_list, k=1)
 return _map5, _map1
assert len(validating_file_list)== len(submission_file_list)
 for i, validating_submission_csv in enumerate(validating_file_list):
predict_submission_csv = submission_file_list[i]
assert ".".join(validating_submission_csv.split(".")[:3])== ".".join(predict_submission_csv.split(".")[:3])
 assert os.path.exists(validating_submission_csv + ".feature")
 assert os.path.exists(predict_submission_csv + ".feature")
 total_predict_score_dict = {}
total_validate_score_dict = {}
total_eval_result_list = []
for i, validating_submission_csv in enumerate(validating_file_list):
predict_submission_csv = submission_file_list[i]
cutoff, _, v_file_id_list, v_feature_list = self._read_feature_file(validating_submission_csv + ".feature")
 v_model_weight = float("0.{}".format(validating_submission_csv.split(".")[-3]))
validating_score_dict = self._calc_weight_score(
 feature_list=v_feature_list, file_id_list=v_file_id_list, cutoff=cutoff)
 total_validate_score_dict = _update_weight_score(
 total_validate_score_dict, validating_score_dict, model_weight=v_model_weight)
 p_file_id_list, p_feature_list = self.get_feature_list(predict_submission_csv + ".feature")
 predict_score_dict = self._calc_weight_score(
 feature_list=p_feature_list, file_id_list=p_file_id_list, cutoff=cutoff)
 total_predict_score_dict = _update_weight_score(
 total_predict_score_dict, predict_score_dict, model_weight=v_model_weight)
 _validate_result_dict = self.read_submission_file(validating_submission_csv)
 total_eval_result_list.append(eval_result(_validate_result_dict))
_validate_result_dict = self._get_result_by_weight_score(total_validate_score_dict, top_k=self.top_k)
 total_eval_result_list.append(eval_result(_validate_result_dict))
self.logger.info("after combine {} models:
validate map@5 is {}/{},
validate map@1 is {}/{}
".format(
 len(validating_file_list),
total_eval_result_list[-1][0], [x[0] for x in total_eval_result_list[:-1]],
total_eval_result_list[-1][1], [x[1] for x in total_eval_result_list[:-1]], 
))
raw_submission = self.data_utils.path_manager.submission_csv
try:
predict_result_dict = self._get_result_by_weight_score(total_predict_score_dict, top_k=self.top_k)
 predict_file_list, predict_result_list = _create_final_result(predict_result_dict)
 self.data_utils.path_manager.submission_csv = output_file
self.data_utils.submit_test_result(test_image_list=predict_file_list, result_list=predict_result_list)
 self.logger.info("save final submission result in {}".format(self.data_utils.path_manager.submission_csv))
finally:
self.data_utils.path_manager.submission_csv = raw_submission
def debug_map_csv(self, csv_file):
self.logger.info("debug map set from submission file...
 ")
 result_dict = self.read_submission_file(csv_file)
 class_id_result_list = []
file_id_list = []
for _image_id, _result_list in result_dict.items() :
file_id_list.append(_image_id)
 class_id_result_list.append(_result_list)
 labels = self.list_class_id(file_id_list)
 self.logger.info("map5 is {}".format(map_per_set(labels=labels, predictions=class_id_result_list, k=5)))
 self.logger.info("map1 is {}".format(map_per_set(labels=labels, predictions=class_id_result_list, k=1)))
 def list_class_id(self, file_id_list)-> list:
_, class_id_vs_image_list, _, _, _ = self.data_utils.get_basic_info(self.data_utils.list_train_image_file())
 class_id_vs_image_count = {
class_id: len(_image_list)for class_id, _image_list in class_id_vs_image_list.items() }
return [self.get_class_id(class_id_vs_image_count, _image_id)for _image_id in file_id_list]
def get_class_id(self, class_id_vs_image_count, image_file_or_id)-> str:
_class_id = self.data_utils.get_class_id(image_file_or_id)
 if class_id_vs_image_count[_class_id] > 1:
return _class_id
else:
return self.data_utils.blank_class_id
class SimpleSearch(object):
model = None
def __init__(self, data_utils: WhaleDataUtils, shape: tuple, dimension: int, top_k: int = 5,
norm_type: NormType = NormType.none, more_search_top_k: int = None):
self.logger = logging.getLogger(self.__class__.__name__)
 self.data_utils = data_utils
self.faiss_index_path = os.path.join(self.data_utils.path_manager.working_path, self.__class__.__name__)
 self.shape = shape
self.dimension = dimension
self.top_k = top_k
self._distance_cutoff = None
self.manager = ImageIndexUtils(
 index_dir=self.faiss_index_path,
dimension=self.dimension)
 self._norm_type = norm_type
if more_search_top_k is None or more_search_top_k < 0:
more_search_top_k = max(20, self.top_k)
 self._search_top_k = more_search_top_k + self.top_k
def show_embedding(self, mode: ProcessMode):
key_name = "train" if mode == ProcessMode.train else "test"
list_image_file = self.data_utils.list_train_image_file if mode == ProcessMode.train \
else self.data_utils.list_test_image_file
log_dir = os.path.join(self.faiss_index_path, "logs_{}".format(key_name))
self.logger.info("trying to show {} embedding...".format(key_name))
feature_list, image_list = self.calc_or_load_feature(mode)
 self.logger.info("got {}-image feature: {}/{}".format(
 key_name, len(image_list), len(list_image_file())) 
)
 labels = []
if mode == ProcessMode.train:
for image_file in image_list:
labels.append(self.data_utils.get_class_id(image_file))
else:
for image_file in image_list:
labels.append(os.path.basename(image_file))
show_embedding(feature_list=feature_list, labels=labels, log_dir=log_dir)
 self.logger.info("success to show {} embedding!".format(key_name))
def list_features(self, mode: ProcessMode)->(list, list):

 raise NotImplementedError
def calc_or_load_feature(self, mode: ProcessMode)->(list, list):
key_name = "train" if mode == ProcessMode.train else "test"
feature_npy = os.path.join(self.faiss_index_path, "{}.npy.pkl".format(key_name))
self.logger.info("{}ing image...".format(key_name))
if not os.path.exists(feature_npy):
self.logger.info("calculating feature of {} image...".format(key_name))
feature_list, image_list = self.list_features(mode)
 self.logger.info("success to calculate feature of {} image!".format(key_name))
with open(feature_npy, "wb")as f:
pickle.dump(( feature_list, image_list), f)
 else:
self.logger.info("loading feature of {} image...".format(key_name))
with open(feature_npy, "rb")as f:
feature_list, image_list = pickle.load(f)
 self.logger.info("success to load feature of {} image!".format(key_name))
return feature_list, image_list
def train(self):
if os.path.exists(self.manager.manager.faiss_index_file):
self.logger.info("train before, no need to train again!")
 return
feature_list, image_list = self.calc_or_load_feature(ProcessMode.train)
 self.logger.info("got train-image feature: {}/{}".format(
 len(image_list), len(self.data_utils.list_train_image_file())))
 image_info_list = []
for index, image_file in enumerate(image_list):
image_info_list.append({
"index": index,
"file": image_file,
"class_id": self.data_utils.get_class_id(image_file)} 
)
 self.manager.add_images(feature_list, image_info_list=image_info_list)
 self.logger.info("success to train image!")
 def test(self):
feature_list, image_list = self.calc_or_load_feature(ProcessMode.test)
 if len(image_list)!= len(self.data_utils.list_test_image_file()):
self.logger.warning("got test-image file: {}/{}".format(
 len(image_list), len(self.data_utils.list_test_image_file())))
 assert len(image_list)% len(self.data_utils.list_test_image_file())== 0
self.logger.info("got test-image feature: {}/{}".format(
 len(image_list), len(self.data_utils.list_test_image_file())))
 num_feature = len(image_list)// len(self.data_utils.list_test_image_file())
 self.logger.info("testing image...")
 test_image_list = [image_list[index * num_feature] for index in
range(len(self.data_utils.list_test_image_file())) ]
class_id_result_list, test_feature_list = self.search(
 image_list=test_image_list, feature_list=feature_list, return_feature_list=True)
 self.logger.info("success to test image!")
 assert len(class_id_result_list)== len(self.data_utils.list_test_image_file())
 self.data_utils.submit_test_result(test_image_list=test_image_list,
result_list=class_id_result_list,
feature_list=test_feature_list)
 if self._distance_cutoff is None:
self.logger.info("submit predict result[without distance cutoff] in {}".format(
 self.data_utils.path_manager.submission_csv))
else:
self.logger.info("submit predict result[with distance cutoff {}] in {}".format(
 get_pretty_float(self._distance_cutoff, count=3), self.data_utils.path_manager.submission_csv))
def search(self, image_list: list, feature_list: list, cache_file: str = None, ignore_same: bool = False,
return_feature_list: bool = True)->(list, list):
if cache_file is None:
cache_file = os.path.join(self.faiss_index_path, "cache.pkl")
 cache = FileCache(cache_file)
 assert len(feature_list)% len(image_list)== 0
num_feature = len(feature_list)// len(image_list)
 if num_feature > 1:
self.logger.info("1 image vs {} feature when searching!".format(num_feature))
src_list = []
for index in range(len(image_list)) :
image_file = image_list[index]
if not cache.get(image_file):
src_list.append(( image_file, feature_list[index * num_feature:(index + 1)* num_feature]))
whale_rank_utils = WhaleRankingUtils(data_utils=self.data_utils, top_k=self.top_k)
 extend = False
_show_log_per_steps = 100 if len(image_list)< 1000 else 1000
for part_src_list in iter_list_with_size(src_list, size=10240 // num_feature):
feature_list = []
for(_, _feature_list)in part_src_list:
feature_list.extend(_feature_list)
 raw_result_iterator = self.manager.image_search_iterator(
 feature_list=feature_list,
top_k=self._search_top_k,
extend=extend 
)
 index = -1
cache_list = []
for raw_image_result_list in raw_result_iterator:
index += 1
if index % num_feature == 0:
_image_file = part_src_list[index // num_feature][0] if ignore_same else None
image_result_list = []
for(faiss_image_index, faiss_image_extend_list, faiss_distance)in raw_image_result_list:
image_result = self.manager.get_image_info(faiss_image_index)
 if _image_file and image_result.get("file")and image_result.get("file" ).find(_image_file)== 0:
continue
image_result_list.append(( faiss_distance, image_result["class_id"], image_result["file"]))
if(index + 1)% num_feature == 0:
image_result_list = sorted(image_result_list, key=lambda x: x[0], reverse=False)
 _class_id_list = whale_rank_utils.simple_rank(image_result_list,
distance_cutoff=self._distance_cutoff)
 if len(_class_id_list)< self.top_k:
self.logger.debug("length of class_id_list < {}!".format(self.top_k))
if return_feature_list:
cache_list.append(( _class_id_list[:self.top_k], list(image_result_list)))
 else:
cache_list.append(( _class_id_list[:self.top_k], None))
if index % _show_log_per_steps == 0:
self.logger.info("got class_id_list of {} images".format(index))
for j, _class_id_list_feature_list in enumerate(cache_list):
cache.unsafe_set(part_src_list[j][0], _class_id_list_feature_list)
 cache.set(part_src_list[0][0], cache_list[0])
 class_id_result_list = []
search_feature_list = []
for image_file in image_list:
_class_id_list, _search_feature_list = cache.get(image_file)
 class_id_result_list.append(_class_id_list)
 search_feature_list.append(_search_feature_list)
 self.logger.info("success to parse search result for test images!")
 return class_id_result_list, search_feature_list
def _calc_map(self, info, data_percent, labels, class_id_result_list,
image_list, multi_index_list)->(float, float):
fake_map5 = map_per_set(labels=labels, predictions=class_id_result_list, k=5)
 fake_map1 = map_per_set(labels=labels, predictions=class_id_result_list, k=1)
 multi_map_5 = map_per_set(
 labels=[labels[index] for index in multi_index_list],
predictions=[class_id_result_list[index] for index in multi_index_list],
k=5)
 multi_map_1 = map_per_set(
 labels=[labels[index] for index in multi_index_list],
predictions=[class_id_result_list[index] for index in multi_index_list],
k=1)
 new_index_list = []
for _index, _image_file in enumerate(image_list):
if self.data_utils.get_class_id(_image_file)== self.data_utils.blank_class_id:
new_index_list.append(_index)
 new_map_5 = map_per_set(
 labels=[labels[index] for index in new_index_list],
predictions=[class_id_result_list[index] for index in new_index_list],
k=5)
 new_map_1 = map_per_set(
 labels=[labels[index] for index in new_index_list],
predictions=[class_id_result_list[index] for index in new_index_list],
k=1)
 self.logger.info(
 "map@5 for validating data[{} of train data, {}] is: standard {}, new-whale {}, all {}!".format(
 data_percent, info, multi_map_5, new_map_5, fake_map5)
)
 self.logger.info(
 "map@1 for validating data[{} of train data, {}] is: standard {}, new-whale {}, all {}!".format(
 data_percent, info, multi_map_1, new_map_1, fake_map1)
)
 return multi_map_5, multi_map_1
def validate(self, data_percent: float = 1.0)->(float, float):

 train_feature_list, train_image_list = self.calc_or_load_feature(ProcessMode.train)
 num_feature = len(train_image_list)// len(self.data_utils.list_train_image_file())
 if len(train_image_list)!= len(self.data_utils.list_train_image_file()):
self.logger.warning("got validating-image file: {}/{}".format(
 len(train_image_list), len(self.data_utils.list_train_image_file())))
 assert len(train_image_list)% len(self.data_utils.list_train_image_file())== 0
chosen_index_list = random_choice([index for index in range(len(self.data_utils.list_train_image_file())) ],
k=int(len(self.data_utils.list_train_image_file())* data_percent),
unique=True)
 if num_feature == 1:
image_list = [train_image_list[index] for index in chosen_index_list]
feature_list = [train_feature_list[index] for index in chosen_index_list]
else:
image_list = [train_image_list[index * num_feature] for index in chosen_index_list]
feature_list = []
for index in chosen_index_list:
feature_list.extend(train_feature_list[index * num_feature:(index + 1)* num_feature])
 self.logger.info("validating image...")
 _raw_class_id_result_list, validating_feature_list = self.search(
 image_list=image_list, feature_list=feature_list, ignore_same=True, return_feature_list=True)
 self.logger.info("success to validate image!")
 image_id_vs_image_file = {os.path.basename(image_file): image_file for image_file in image_list}
class_id_vs_image_list = {}
for image_id, class_id in self.data_utils.load_train_info().items() :
class_id_vs_image_list.setdefault(class_id, [] ).append(image_id_vs_image_file[image_id])
 class_id_vs_image_count = {
class_id: len(_image_list)for class_id, _image_list in class_id_vs_image_list.items() }
rank_utils = WhaleRankingUtils(data_utils=self.data_utils, top_k=self.top_k)
 labels = [rank_utils.get_class_id(class_id_vs_image_count, image_file)for image_file in image_list]
multi_index_list = []
for _index, _image_file in enumerate(image_list):
if class_id_vs_image_count[self.data_utils.get_class_id(_image_file)] > 1:
multi_index_list.append(_index)
 distance_cutoff = rank_utils.get_similar_cutoff(
 validating_labels=labels, validating_feature_list=validating_feature_list)
 self.logger.info("got distance_cutoff {} and use it!".format(get_pretty_float(distance_cutoff, count=3)))
 self._distance_cutoff = distance_cutoff
class_id_result_list = rank_utils.recreate_class_id_list(
 cutoff=self._distance_cutoff, class_id_list=_raw_class_id_result_list, feature_list=validating_feature_list)
 total_map_5, total_map_1 = self._calc_map(
 "with distance cutoff {}".format(get_pretty_float(self._distance_cutoff, count=3)) ,
data_percent, labels, class_id_result_list, image_list, multi_index_list 
)
 random_map5, random_map1 = self.data_utils.get_random_baseline()
self.logger.info("random baseline for validating data[{} of train data]: map@5 is {}, map@1 is {}!".format(
 data_percent, random_map5, random_map1))
raw_submission_csv = self.data_utils.path_manager.submission_csv
try:
self.data_utils.path_manager.submission_csv = \
raw_submission_csv + ".{}.validate.csv".format(str(( total_map_5 + total_map_1)/ 2)[2:])
 self.data_utils.submit_test_result(
 test_image_list=image_list, result_list=class_id_result_list, feature_list=validating_feature_list)
 self.logger.info("submit validating result[{} of train data, with distance cutoff {}] in {}".format(
 data_percent, get_pretty_float(distance_cutoff, count=3), self.data_utils.path_manager.submission_csv))
finally:
self.data_utils.path_manager.submission_csv = raw_submission_csv
return total_map_5, total_map_1
class TripletSearch(SimpleSearch):

 model = None
def __init__(self, instance, data_utils: WhaleDataUtils, clear_cache_after_exists: bool,
norm_type: NormType = NormType.l2):
self.instance = instance
super(TripletSearch, self ).__init__(
 data_utils, shape=(None,), dimension=instance.params.dimension, top_k=5)
 self.smart_iterator = None
self.feature_record = {}
self._clear_cache_after_exists = clear_cache_after_exists
self._norm_type = norm_type
def list_features(self, mode: ProcessMode)->(list, list):

 if "train" not in self.feature_record and "test" not in self.feature_record:
train_feature_list, train_feature_file_list = self.instance.list_features(mode=ProcessMode.train)
 test_feature_list, test_feature_file_list = self.instance.list_features(mode=ProcessMode.test)
 feature = np.vstack(train_feature_list + test_feature_list ).\
reshape(( len(train_feature_list)+ len(test_feature_list), self.dimension))
if self._norm_type is None:
normalized_feature = feature
else:
self.logger.info("using {} to normalize feature".format(self._norm_type))
normalized_feature = self._norm_type.normalize(feature)
 self.feature_record["train"] = train_feature_file_list
self.feature_record["test"] = test_feature_file_list
self.feature_record["feature"] = normalized_feature
if mode == ProcessMode.train:
feature_file_list = self.feature_record["train"]
feature_list = [f.reshape(1, self.dimension)for f in
self.feature_record["feature"][:len(feature_file_list)]]
else:
feature_file_list = self.feature_record["test"]
feature_list = [f.reshape(1, self.dimension)for f in
self.feature_record["feature"][len(self.feature_record["train"]):]]
assert len(feature_list)== len(feature_file_list)
 assert feature_list[0].shape ==(1, self.dimension)
 return feature_list, feature_file_list
def clear_cache(self,):
if os.path.exists(self.faiss_index_path):
try:
if not self.data_utils.path_manager.is_kaggle:
tmp_dir = os.path.join(os.path.dirname(self.faiss_index_path), self.instance.__class__.__name__)
 if os.path.exists(tmp_dir):
shutil.rmtree(tmp_dir)
 shutil.copytree(self.faiss_index_path, tmp_dir)
 self.logger.info("backup Search Cache in {}".format(os.path.abspath(tmp_dir)))
 shutil.rmtree(self.faiss_index_path)
 except Exception as e:
self.logger.error(e, exc_info=True)
 def __enter__(self):
return self
def __exit__(self, exc_type, exc_val, exc_tb):
if self._clear_cache_after_exists:
self.clear_cache() 
<choose_model_class>
class TripletLossModelCNN(AbstractEstimator):
def __init__(self, train_ckpt_dir, data_utils: WhaleDataUtils, timeout: int = int(3600 * 5),
pretrained_ckpt_file: str = None):
super(TripletLossModelCNN, self ).__init__(
 model_name="TripletLoss",
train_ckpt_dir=train_ckpt_dir,
pretrained_ckpt_file=pretrained_ckpt_file 
)
 self.data_utils = data_utils
self.timeout = timeout + time.time()
self.batch_size = 8
self._features_images_key = "images"
self._features_filename_key = "filenames"
self._features_embedding_key = "embeddings"
self.params = Params({
"num_channels": 1,
"margin": 1.0,
"dimension": 512,
"image_size": 384,
"online_batch_count": 4,
})
 self.learning_rate = 64e-5
self.image_tmp_size = 400
assert self.image_tmp_size >= self.params.image_size
self.train_loss_hook = LossStepHookForTrain(log_after_run=True, log_after_end=False,
show_log_per_steps=10, run_after_run_per_steps=5)
 self.data_utils.loss_hook = self.train_loss_hook
self.using_training_status_of_train_data = None
self.optimizer_type = OptimizerType.adam
def show_embedding(self, mode: ProcessMode, count: int = -1, remove_log_dir_if_exists: bool = False,
norm_type: NormType = NormType.all):
key_name = "train" if mode == ProcessMode.train else "test"
log_dir = os.path.join(self.TRAIN_CKPT_DIR, "logs_{}".format(key_name))
if os.path.exists(log_dir)and remove_log_dir_if_exists:
shutil.rmtree(log_dir)
 self.logger.info("trying to show {} embedding...".format(key_name))
if count > 1:
if mode == ProcessMode.train:
image_list = self.data_utils.list_debug_image_list(count=count)
 else:
image_list = self.data_utils.get_file_list(
 online_batch_count=0, is_training=bool(mode == ProcessMode.train))
image_list = image_list[:count]
if len(image_list)!= count:
self.logger.warning("only have {} files".format(len(image_list)))
 else:
image_list = self.data_utils.get_file_list(
 online_batch_count=0, is_training=bool(mode == ProcessMode.train))
if mode == ProcessMode.train:
label_list = [self.data_utils.get_class_id(image_file)for image_file in image_list]
else:
label_list = [os.path.basename(image_file)for image_file in image_list]
feature_list, _ = self.list_features(file_list=image_list)
 if norm_type is not None:
feature = np.vstack(feature_list ).reshape(( len(feature_list), self.params.dimension))
self.logger.info("using {} to normalize feature".format(norm_type))
normalized_feature = norm_type.normalize(feature)
 feature_list = [f.reshape(1, self.params.dimension)for f in normalized_feature]
self.logger.info("got {}-image feature: {}/{}".format(
 key_name, len(feature_list), len(image_list)) 
)
 show_embedding(feature_list=feature_list, labels=label_list, log_dir=log_dir)
 self.logger.info("success to show {} embedding!".format(key_name))
def show_predict_result(self, count: int = 10, top_k: int = 5):
tmp_feature_list = glob.glob(
 os.path.join(self.data_utils.path_manager.output_path, "whale.sub_.*.submission.csv.feature"))
tmp_feature_list = sorted(tmp_feature_list, key=lambda x: os.path.getmtime(x), reverse=True)
 file_id_list, feature_list = WhaleRankingUtils.get_feature_list(feature_file=tmp_feature_list[0])
 if count < len(file_id_list):
index_list = random_choice(src_list=[i for i in range(len(file_id_list)) ], k=count, unique=True)
 else:
index_list = [i for i in range(len(file_id_list)) ]
image_file_list = []
for index in index_list:
_image_file_list = [os.path.join(self.data_utils.path_manager.data_set_test_path, file_id_list[index])]
_count = 0
for(_distance, _class_id, _file_name)in feature_list[index]:
_image_file_list.append(
 os.path.join(self.data_utils.path_manager.data_set_train_path, os.path.basename(_file_name)) 
)
 _count += 1
if _count >= top_k:
break
image_file_list.append(_image_file_list)
 image_utils.show_images_file(image_list=image_file_list, image_save_file=None, image_size=(200, 200), dpi=100)
 def model_fun(self,):

 raise NotImplementedError
def list_features(self, file_list: list = None, mode: ProcessMode = None)->(list, list):
if file_list is None and mode is not None:
if mode == ProcessMode.train:
input_list = self.data_utils.get_file_list(is_training=True)
 else:
input_list = self.data_utils.get_file_list(is_training=False)
 else:
input_list = file_list
input_fn = self.get_dataset_func(
 split_name=DatasetUtils.SPLIT_PREDICT, input_list=input_list,
num_epochs=1, shuffle=False, batch_size=self.batch_size, num_parallel_calls=2, prefetch_size=2, 
)
 self.logger.info("trying to list feature for {} file...".format(len(input_list)))
 classifier = self.get_classifier()
result_list = classifier.predict(input_fn=input_fn)
 feature_list = []
feature_file_list = []
count = 0
_steps_ = 100 if len(input_list)<= 1000 else 1000
for score in result_list:
feature_file_list.append(byte_to_string(score[self._features_filename_key]))
feature_list.append(score[self._features_embedding_key].reshape(1, self.params.dimension))
count += 1
if count % _steps_ == 0:
self.logger.info("calculated {} image...".format(count))
if count % _steps_ != 0:
self.logger.info("calculated {} image...".format(count))
self.logger.info("success to list feature for {} file!".format(len(input_list)))
 return feature_list, feature_file_list
def get_dataset_func(self, split_name, num_epochs=1, shuffle=True, batch_size=64, num_parallel_calls=2,
prefetch_size=2, shuffle_size=4, input_list=None):
if self.train_loss_hook is None:
raise ValueError("self.train_loss_hook cannot be null in this class!")
 def tf_decode_with_crop(file_name, label, offset_height, offset_width, target_height, target_width):
image_str = tf.read_file(file_name)
 image = tf.image.decode_jpeg(image_str, channels=3)
 image = tf_image_crop(image, offset_height, offset_width, target_height, target_width)
 image = tf.image.resize_images(tf.image.rgb_to_grayscale(image),
size=(self.image_tmp_size, self.image_tmp_size))
processed_images = pre_process_utils.whale_gray_preprocess_image(
 image, self.params.image_size, self.params.image_size,
is_training=bool(split_name == DatasetUtils.SPLIT_TRAIN),
mean_tf_func=pre_process_utils.whale_siamese_image_mean_tf)
 return {self._features_images_key: processed_images, self._features_filename_key: file_name}, label
def input_fn() :
if input_list:
file_list = input_list
else:
file_list = self.data_utils.get_file_list(
 is_training=bool(split_name == DatasetUtils.SPLIT_TRAIN),
shuffle=shuffle, num_epochs=num_epochs, batch_size=batch_size,
online_batch_count=self.params.online_batch_count)
 self.logger.info("info of file_list: len is {}.".format(len(file_list)))
 if split_name == DatasetUtils.SPLIT_TRAIN:
labels = [self.data_utils.get_label(image_file)for image_file in file_list]
else:
labels = [0] * len(file_list)
 bounding_boxes = [expend_bounding_box(self.data_utils.get_boxes(image_file)) for
image_file in file_list]
offset_height_list, offset_width_list, target_height_list, target_width_list = \
parse_bounding_boxes_list(bounding_boxes)
 dataset = tf.data.Dataset.from_tensor_slices(
(file_list, labels, offset_height_list, offset_width_list, target_height_list, target_width_list))
dataset = dataset.map(tf_decode_with_crop, num_parallel_calls=num_parallel_calls)
 dataset = dataset.prefetch(buffer_size=prefetch_size * batch_size)
 dataset = dataset.batch(batch_size)
 iterator = dataset.make_one_shot_iterator()
features, labels = iterator.get_next()
return features, labels
return input_fn
def map_search(self, only_validate: bool = False, clear_cache_after_exists: bool = True,
norm_type: NormType = NormType.all):
with TripletSearch(instance=self, data_utils=self.data_utils, norm_type=norm_type,
clear_cache_after_exists=clear_cache_after_exists)as search:
self.logger.info("trying to search with {}".format(self.__class__.__name__))
raw_submission_csv = self.data_utils.path_manager.submission_csv
try:
_time_start = time.time()
search.train()
base_name = "whale.sub_.{}".format(create_fake_random_string(length=8))
self.data_utils.path_manager.submission_csv = os.path.join(self.data_utils.path_manager.output_path,
base_name)
 map_5, map_1 = search.validate(data_percent=1.0)
 if only_validate:
self.logger.info("success to validate with {}, time cost {} seconds!".format(
 self.__class__.__name__, round(time.time() - _time_start, 2)))
 return
self.data_utils.path_manager.submission_csv = \
self.data_utils.path_manager.submission_csv + \
".{}.submission.csv".format(str(( map_5 + map_1)/ 2)[2:])
 search.test()
self.logger.info("success to search with {}, time cost {} seconds, submission save in {}".format(
 self.__class__.__name__, round(time.time() - _time_start, 2),
self.data_utils.path_manager.submission_csv))
finally:
self.data_utils.path_manager.submission_csv = raw_submission_csv
def combine_submission_csv(self):
tmp_submission_csv_list = glob.glob(
 os.path.join(self.data_utils.path_manager.output_path, "whale.sub_.*.submission.csv"))
if not tmp_submission_csv_list:
self.logger.warning("no submission file found!")
 return
self.logger.info("found tmp submission files: {}".format(tmp_submission_csv_list))
result_weight = {}
for csv_file in tmp_submission_csv_list:
try:
result_weight[csv_file] = float("0.{}".format(csv_file.split(".")[-3]))
except Exception as e:
self.logger.error(e)
 combine_csv(result_weight, out_file=os.path.join(self.data_utils.path_manager.output_path, "sub_ens.csv"))
self.logger.info("success to merge all submission file into sub_ens.csv")
 if self.data_utils.path_manager.is_kaggle:
for csv_file in tmp_submission_csv_list:
os.remove(csv_file)
 def train_with_predict(self, safe_max_batch_size=32, calc_every_epoch=1, shuffle_data_every_epoch=1,
predict_every_epoch=100, max_epoch=100, ignore_error_in_train: bool = True):
assert max_epoch >= predict_every_epoch >= shuffle_data_every_epoch
assert max_epoch >= calc_every_epoch >= shuffle_data_every_epoch
self.batch_size = safe_max_batch_size
def end_process_func() :
self.map_search(only_validate=False, clear_cache_after_exists=True)
 def loop_process(total_epoch, num_epoch):
if total_epoch % calc_every_epoch == 0:
if total_epoch == 0:
self.train(batch_size=safe_max_batch_size, num_epochs=1, shuffle=True, steps=1)
 self.data_utils.calc_feature(self.list_features, epoch_num=total_epoch)
 if total_epoch > 0 and not self.data_utils.path_manager.is_kaggle and \
os.path.abspath(self.TRAIN_CKPT_DIR ).find("/content/")> -1:
colab_save_file_func(train_dir=self.TRAIN_CKPT_DIR, logger=self.logger, daemon=False,
only_save_latest_checkpoint=True)
 self.train(batch_size=safe_max_batch_size, num_epochs=num_epoch, shuffle=True, steps=None)
 self.set_epoch_num(count=total_epoch + num_epoch)
 if total_epoch > 0 and total_epoch % predict_every_epoch == 0:
end_process_func()
estimator_iter_process(loop_process, iter_stop_time=self.timeout,
loop_process_min_epoch=shuffle_data_every_epoch,
loop_process_start_epoch=self.get_epoch_num() ,
end_process_func=end_process_func, loop_process_max_epoch=max_epoch,
ignore_error_in_loop_process=ignore_error_in_train, logger=self.logger)
 self.combine_submission_csv()
class TripletLossModelResNet50(TripletLossModelCNN):
def __init__(self, train_ckpt_dir, data_utils: WhaleDataUtils, timeout: int = int(3600 * 5), is_softmax=True):
if train_ckpt_dir.endswith("/"):
train_ckpt_dir = train_ckpt_dir[:-1]
softmax_dir = os.path.join(os.path.dirname(train_ckpt_dir), os.path.basename(train_ckpt_dir)+ "_softmax")
 triplet_dir = os.path.join(os.path.dirname(train_ckpt_dir), os.path.basename(train_ckpt_dir)+ "_triplet")
 if is_softmax is None:
if tf.train.latest_checkpoint(triplet_dir):
is_softmax = False
else:
is_softmax = True
_pretrained_ckpt_file = None
if is_softmax:
_train_ckpt_dir = softmax_dir
_pretrained_ckpt_file = data_utils.path_manager.ckpt_pretrained_rv250
if not tf.train.latest_checkpoint(softmax_dir):
data_utils.path_manager.download_resnet_v2_50()
else:
_train_ckpt_dir = triplet_dir
if tf.train.latest_checkpoint(softmax_dir):
_pretrained_ckpt_file = tf.train.latest_checkpoint(softmax_dir)
 super(TripletLossModelResNet50, self ).__init__(
 train_ckpt_dir=_train_ckpt_dir,
data_utils=data_utils,
timeout=timeout,
pretrained_ckpt_file=_pretrained_ckpt_file, 
)
 self.params = Params({
"num_channels": 3,
"margin": 1.0,
"dimension": 512,
"image_size": 224,
"online_batch_count": 4,
})
 self.image_tmp_size = 250
self.is_softmax = is_softmax
if is_softmax:
self.use_softmax_net()
else:
self.use_triplet_net()
_, class_id_vs_image_list, _, _, _ = \
self.data_utils.get_basic_info(self.data_utils.list_train_image_file(ignore_blank=True))
_class_id_vs_image_count = {
class_id: len(_image_list)for class_id, _image_list in class_id_vs_image_list.items() }
if self.data_utils.blank_class_id in _class_id_vs_image_count:
_class_id_vs_image_count.pop(self.data_utils.blank_class_id)
 _image_count_list = list(_class_id_vs_image_count.values())
 _image_count_list.sort()
_max_count = int(sum(_image_count_list)* 0.2)
 _base_count = 3
_middle_count = 0
_total_count = 0
for _index, _count in enumerate(_image_count_list[::-1]):
_total_count += _count
if _total_count >= _max_count:
_base_count = _count
_middle_count = _image_count_list[-int(_index / 2)]
break
anchor_class_id_list = [
class_id for class_id, _image_count in _class_id_vs_image_count.items() if _image_count >= _base_count]
self.softmax_num_class = len(anchor_class_id_list)
 anchor_class_id_list.sort()
self.softmax_class_id_vs_label = {}
for index, class_id in enumerate(anchor_class_id_list):
self.softmax_class_id_vs_label[class_id] = index
self.softmax_image_list = []
_anchor_class_id_set = set(anchor_class_id_list)
 _class_vs_image = {}
for class_id, file_list in class_id_vs_image_list.items() :
if class_id in _anchor_class_id_set:
_class_vs_image[class_id] = file_list
_new_class_image = balance_class_dict(_class_vs_image, target_count=min(100, _middle_count))
for file_list in _new_class_image.values() :
self.softmax_image_list.extend(file_list)
 random.shuffle(self.softmax_image_list)
 self.softmax_image_vs_label = {}
for image_file in self.softmax_image_list:
self.softmax_image_vs_label[image_file] = self.softmax_class_id_vs_label[
self.data_utils.get_class_id(image_file)]
self.logger.info("train softmax net with {} classes(ignore blank, image_count >= {}), {}/{} files,".format(
 self.softmax_num_class, _base_count, len(set(self.softmax_image_list)) , len(self.softmax_image_list)))
 self._net_embedding_key = "embeddings"
def model_fun(self,):

 def get_model_net(scope_name, features, params, is_training, labels=None):
inputs = tf.reshape(features[self._features_images_key],
[-1, params.image_size, params.image_size, params.num_channels])
 with slim.arg_scope(resnet_arg_scope()):
global_pool, end_points = resnet_v2_50(inputs, num_classes=None, is_training=is_training)
 with tf.variable_scope(scope_name, 'triplet_loss', [global_pool])as sc:
net = slim.conv2d(global_pool, params.dimension, [1, 1], scope=self._net_embedding_key)
 end_points[sc.name + '/logits'] = net
net = tf.squeeze(net, [1, 2], name='SpatialSqueeze')
 end_points[sc.name + '/spatial_squeeze'] = net
end_points[self._net_embedding_key] = net
assert net.shape[-1] == params.dimension
assert len(net.shape)== 2
return net, end_points
def _softmax_bone_net(scope_name, features, params, is_training, labels=None):
embeddings, end_points = get_model_net(
 scope_name=scope_name, features=features, params=params, is_training=is_training)
 with slim.arg_scope(resnet_arg_scope()):
with tf.variable_scope("softmax_net", 'softmax_net', [embeddings]):
net = slim.fully_connected(embeddings, self.softmax_num_class, activation_fn=None,
normalizer_fn=None, scope='logits')
 return net, end_points
if self.is_softmax:
return tf_model_fn.tf_softmax_model_fn(
 network=_softmax_bone_net,
scope_name=self.model_name,
features_filename_key=self._features_filename_key,
get_learning_rate_func=self.get_learning_rate,
optimizer_type=self.optimizer_type,
logger=self.logger 
)
 return tf_model_fn.tf_triplet_loss_model_fn(
 network=get_model_net,
scope_name=self.model_name,
features_embedding_key=self._features_embedding_key,
features_filename_key=self._features_filename_key,
get_learning_rate_func=self.get_learning_rate,
optimizer_type=self.optimizer_type,
logger=self.logger,
use_l2_normalize=False)
 def get_dataset_func(self, split_name, num_epochs=1, shuffle=True, batch_size=64, num_parallel_calls=2,
prefetch_size=2, shuffle_size=4, input_list=None):
if self.train_loss_hook is None:
raise ValueError("self.train_loss_hook cannot be null in this class!")
 def tf_decode_with_crop(file_name, label, offset_height, offset_width, target_height, target_width):
image_str = tf.read_file(file_name)
 image = tf.image.decode_jpeg(image_str, channels=3)
 image = tf_image_crop(image, offset_height, offset_width, target_height, target_width)
 image = tf.image.resize_images(image, size=(self.image_tmp_size, self.image_tmp_size))
processed_images = pre_process_utils.whale_rgb_preprocess_image(
 image, self.params.image_size, self.params.image_size,
is_training=bool(split_name == DatasetUtils.SPLIT_TRAIN))
return {self._features_images_key: processed_images, self._features_filename_key: file_name}, label
def input_fn() :
if input_list:
file_list = input_list
else:
file_list = self.data_utils.get_file_list(
 is_training=bool(split_name == DatasetUtils.SPLIT_TRAIN),
shuffle=shuffle, num_epochs=num_epochs, batch_size=batch_size,
online_batch_count=self.params.online_batch_count)
 self.logger.info("info of file_list: len is {}.".format(len(file_list)))
 if split_name == DatasetUtils.SPLIT_TRAIN:
labels = [self.data_utils.get_label(image_file)for image_file in file_list]
else:
labels = [0] * len(file_list)
 bounding_boxes = [expend_bounding_box(self.data_utils.get_boxes(image_file)) for
image_file in file_list]
offset_height_list, offset_width_list, target_height_list, target_width_list = \
parse_bounding_boxes_list(bounding_boxes)
 dataset = tf.data.Dataset.from_tensor_slices(
(file_list, labels, offset_height_list, offset_width_list, target_height_list, target_width_list))
dataset = dataset.map(tf_decode_with_crop, num_parallel_calls=num_parallel_calls)
 dataset = dataset.prefetch(buffer_size=prefetch_size * batch_size)
 dataset = dataset.batch(batch_size)
 iterator = dataset.make_one_shot_iterator()
features, labels = iterator.get_next()
return features, labels
def softmax_input_fn() :
if input_list:
file_list = input_list
else:
file_list = list(self.softmax_image_list)* num_epochs
if shuffle:
random.shuffle(file_list)
 self.logger.info("info of file_list: len is {}.".format(len(file_list)))
 labels = [self.softmax_image_vs_label[image_file] for image_file in file_list]
bounding_boxes = [expend_bounding_box(self.data_utils.get_boxes(image_file)) for
image_file in file_list]
offset_height_list, offset_width_list, target_height_list, target_width_list = \
parse_bounding_boxes_list(bounding_boxes)
 dataset = tf.data.Dataset.from_tensor_slices(
(file_list, labels, offset_height_list, offset_width_list, target_height_list, target_width_list))
dataset = dataset.map(tf_decode_with_crop, num_parallel_calls=num_parallel_calls)
 dataset = dataset.prefetch(buffer_size=prefetch_size * batch_size)
 dataset = dataset.batch(batch_size)
 iterator = dataset.make_one_shot_iterator()
features, labels = iterator.get_next()
return features, labels
if self.is_softmax:
return softmax_input_fn
return input_fn
def use_softmax_net(self):
self.logger.info("using softmax net...")
 self.is_softmax = True
self.data_utils.gen_data_setting["stop_calc_feature"] = True
def use_triplet_net(self):
self.logger.info("using triplet net...")
 self.is_softmax = False
self.data_utils.gen_data_setting["stop_calc_feature"] = False 
<choose_model_class>
def compute_actual_spearmanr(trues, preds):
rhos = []
for col_true, col_pred in zip(trues.T, preds.T):
rhos.append(spearmanr(col_true, col_pred ).correlation)
 return np.mean(rhos)
 class OptimizedRounder(object):
def __init__(self, n):
self.coef_ = 0
self.n = n
def _kappa_loss(self, coef, X, y):
X_p = np.copy(X)
 for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
continue
for i in range(self.n-2):
if coef[i] <= pred and pred < coef[i+1]:
X_p[i] =(i + 1)/(self.n - 1)
 break
else:
X_p[i] = 1
ll = spearmanr(y, X_p ).correlation
return -ll
def fit(self, X, y, init_coef=None):
loss_partial = partial(self._kappa_loss, X=X, y=y)
 if init_coef == None:
initial_coef = [1/self.n *(x+1)for x in range(self.n-1)]
else:
initial_coef = init_coef
self.coef_ = sp.optimize.minimize(loss_partial, initial_coef, method='nelder-mead')
 def predict(self, X, coef):
X_p = np.copy(X)
 for i, pred in enumerate(X_p):
if pred < coef[0]:
X_p[i] = 0
continue
for i in range(self.n-2):
if coef[i] <= pred and pred < coef[i+1]:
X_p[i] =(i + 1)/(self.n - 1)
 else:
X_p[i] = 1
return X_p
def coefficients(self):
return self.coef_['x']
!cp.. /input/whale-triplet-pretrained-model/tripletresnet/tripletResNet./triplet_triplet -R
init_logger()
path_manager = PathManager("kaggle")
 data_utils = WhaleDataUtils(path_manager=path_manager, gen_data_setting={
"x_train_num": 4,
"ignore_blank_prob": 0.9,
"ignore_single_prob": 0.5,
"stop_calc_feature": False,
"gen_data_by_random_prob": 0,
"use_norm_when_calc_apn": True,
})
 estimator = TripletLossModelResNet50(
 train_ckpt_dir="./triplet",
data_utils=data_utils,
timeout=int(5 * 3600),
is_softmax=False, 
)
 estimator.map_search(only_validate=False, clear_cache_after_exists=True)
 estimator.combine_submission_csv() 
<predict_on_test>
class color:
PURPLE = '\033[95m'
CYAN = '\033[96m'
DARKCYAN = '\033[36m'
BLUE = '\033[94m'
GREEN = '\033[92m'
YELLOW = '\033[93m'
RED = '\033[91m'
BOLD = '\033[1m'
UNDERLINE = '\033[4m'
END = '\033[0m'
def compute_spearmanr(trues, preds, columns=None):
rhos = []
for i,(col_true, col_pred)in enumerate(zip(trues.T, preds.T)) :
if i == 0:
continue
r = spearmanr(col_true, col_pred ).correlation
if columns:
print("{:.3f}\t{}".format(r, columns[i-1]))
rhos.append(r)
 return np.mean(rhos)
 def compare_spearmanr(trues, preds, pp_preds, columns=None):
rhos = []
pp_rhos = []
for i,(col_true, col_pred, col_pp_pred)in enumerate(zip(trues.T, preds.T, pp_preds.T)) :
if i == 0:
continue
r = spearmanr(col_true, col_pred ).correlation
pp_r = spearmanr(col_true, col_pp_pred ).correlation
if columns:
if r < pp_r:
print("{:.3f} ->".format(r)+ color.BOLD + " {:.3f}".format(pp_r)+ color.END + " \t{}".format(columns[i-1]))
elif(r - pp_r)< 10**(-6):
print("{:.3f} ->".format(r)+ " {:.3f}".format(pp_r)+ " \t{}".format(columns[i-1]))
else:
print("{:.3f} ->".format(r)+ color.RED + " {:.3f}".format(pp_r)+ color.END + " \t{}".format(columns[i-1]))
rhos.append(r)
 pp_rhos.append(pp_r)
 whole_base = np.mean(rhos)
 whole_pp = np.mean(pp_rhos)
 print("Whole Score")
 if whole_base < whole_pp:
print("{:.3f} ->".format(whole_base)+ color.BOLD + " {:.3f}".format(whole_pp)+ color.END + " +{}".format(abs(whole_pp - whole_base)))
 elif(whole_base - whole_pp)< 10**(-6):
print("{:.3f} ->".format(whole_base)+ " {:.3f}".format(whole_pp))
else:
print("{:.3f} ->".format(whole_base)+ color.RED + " {:.3f}".format(whole_pp)+ color.END + " -{}".format(abs(whole_pp - whole_base)))
 return whole_base, whole_pp
def inf_numbert_of_annotators(v):
s = set(v)
 d = 10
for v in s:
for u in s:
if v != u:
d = min(abs(v - u), d)
 for i in range(100):
if d * i >= 1 - 10**(-3):
return i
def zero_one_scale(v):
max_x = np.max(v)
 min_x = np.min(v)
 return(v - min_x)/(max_x - min_x)
 oofs = []
for fold_num in range(FOLD_NUM):
oof_ = np.zeros(( len(model_df[model_names[0]][fold_num]), 30)).T
for col_num, col_name in enumerate(target_columns[-30:]):
for model_num, model_name in enumerate(model_names):
coef = all_coeffs[col_num][fold_num][model_num]
oof_[col_num] += coef * model_df[model_name][fold_num][col_name].values
oofs.append(oof_.T)
 gold_label_df = train_df[target_columns]
pred_label_df = gold_label_df.copy()
for fold_num in range(5):
pred_label_df.loc[kf[fold_num][1], target_columns[1:]] = oofs[fold_num]
pred_label_df = norm_with_rankdata(pred_label_df)
 def post_process(train_df, train, pred_df):
df = pred_df.copy()
df.loc[(train["host"] != "english.stackexchange.com")&(train["host"] != "ell.stackexchange.com"), "question_type_spelling"] = 0
df = norm_with_rankdata(df)
 for col_name in target_columns[1:]:
d = defaultdict(int)
 for v in gold_label_df[col_name].values:
d[v] += 1
d = sorted(d.items())
 data_length = len(gold_label_df)
 coefs = []
for i,(_, v)in enumerate(d[:-1]):
v_ = v / data_length
if i > 0:
v_ += coefs[i-1]
coefs.append(v_)
 n = inf_numbert_of_annotators(train_df[col_name].values)
 gold = gold_label_df[col_name].values
pred = pred_label_df[col_name].values
base_score = spearmanr(gold, pred ).correlation
optR = OptimizedRounder(len(d))
optR.fit(pred, gold, coefs)
 opt_v = optR.predict(pred, optR.coefficients())
 opt_score = spearmanr(gold, opt_v ).correlation
if base_score < opt_score and n < 10:
df[col_name] = optR.predict(df[col_name].values, coefs)
 if len(set(df[col_name].values)) == 1:
df[col_name] = pred_df[col_name].values
df.loc[(( train["host"] == "english.stackexchange.com")|(train["host"] == "ell.stackexchange.com")) , "question_type_spelling"] += 0.1
df["question_type_spelling"] = zero_one_scale(df["question_type_spelling"].values)
 return df
estimator.show_predict_result(count=10, top_k=5)
 estimator.show_predict_result(count=10, top_k=3)
<install_modules>
train_df = pd.read_csv('.. /input/google-quest-challenge/train.csv')
 test_df = pd.read_csv('.. /input/google-quest-challenge/test.csv')
 sub_df = post_process(train_df, test_df, sub_df )
!rm *.pkl
!pip uninstall aiohttp faiss-prebuilt pyxtools pymltools -y
!apt remove -y libopenblas-base libomp-dev 
<set_options>
cv_score = 0
for fold_num in range(FOLD_NUM):
fold_base = model_df[model_names[0]][fold_num].copy()
fold_base.iloc[:, -30:] = oofs[fold_num]
r = compute_spearmanr(true_dfs[fold_num].iloc[:, -30:].values, fold_base.iloc[:, -30:].values)
 cv_score += r / FOLD_NUM
print(cv_score )
train_on_gpu = True 
<define_variables>
cv_score = 0
for fold_num in range(FOLD_NUM):
fold_base = model_df[model_names[0]][fold_num].copy()
fold_base.iloc[:, -30:] = oofs[fold_num]
fold_base = post_process(train_df, train_df.iloc[kf[fold_num][1]].reset_index(drop=True), fold_base)
 r = compute_actual_spearmanr(true_dfs[fold_num].iloc[:, -30:].values, fold_base.iloc[:, -30:].values)
 cv_score += r / FOLD_NUM
print(cv_score )
model_name = "vgg"
num_classes = 5005
feature_extract = False<load_from_csv>
sub_df[sub_df["question_type_spelling"] > 0]
<categorify><EOS> 
sub_df.to_csv("submission.csv", index=False )