Kaggle competition: House Prices - Advanced Regression Techniques
<SOS> metric: rmsle Kaggle data source: house-prices-advanced-regression-techniques<define_variables>
%matplotlib inline
color = sns.color_palette()
sns.set_style('darkgrid')
 def ignore_warn(*args, **kwargs):
pass
warnings.warn = ignore_warn
BIRD_CODE = {
'aldfly': 0, 'ameavo': 1, 'amebit': 2, 'amecro': 3, 'amegfi': 4,
'amekes': 5, 'amepip': 6, 'amered': 7, 'amerob': 8, 'amewig': 9,
'amewoo': 10, 'amtspa': 11, 'annhum': 12, 'astfly': 13, 'baisan': 14,
'baleag': 15, 'balori': 16, 'banswa': 17, 'barswa': 18, 'bawwar': 19,
'belkin1': 20, 'belspa2': 21, 'bewwre': 22, 'bkbcuc': 23, 'bkbmag1': 24,
'bkbwar': 25, 'bkcchi': 26, 'bkchum': 27, 'bkhgro': 28, 'bkpwar': 29,
'bktspa': 30, 'blkpho': 31, 'blugrb1': 32, 'blujay': 33, 'bnhcow': 34,
'boboli': 35, 'bongul': 36, 'brdowl': 37, 'brebla': 38, 'brespa': 39,
'brncre': 40, 'brnthr': 41, 'brthum': 42, 'brwhaw': 43, 'btbwar': 44,
'btnwar': 45, 'btywar': 46, 'buffle': 47, 'buggna': 48, 'buhvir': 49,
'bulori': 50, 'bushti': 51, 'buwtea': 52, 'buwwar': 53, 'cacwre': 54,
'calgul': 55, 'calqua': 56, 'camwar': 57, 'cangoo': 58, 'canwar': 59,
'canwre': 60, 'carwre': 61, 'casfin': 62, 'caster1': 63, 'casvir': 64,
'cedwax': 65, 'chispa': 66, 'chiswi': 67, 'chswar': 68, 'chukar': 69,
'clanut': 70, 'cliswa': 71, 'comgol': 72, 'comgra': 73, 'comloo': 74,
'commer': 75, 'comnig': 76, 'comrav': 77, 'comred': 78, 'comter': 79,
'comyel': 80, 'coohaw': 81, 'coshum': 82, 'cowscj1': 83, 'daejun': 84,
'doccor': 85, 'dowwoo': 86, 'dusfly': 87, 'eargre': 88, 'easblu': 89,
'easkin': 90, 'easmea': 91, 'easpho': 92, 'eastow': 93, 'eawpew': 94,
'eucdov': 95, 'eursta': 96, 'evegro': 97, 'fiespa': 98, 'fiscro': 99,
'foxspa': 100, 'gadwal': 101, 'gcrfin': 102, 'gnttow': 103, 'gnwtea': 104,
'gockin': 105, 'gocspa': 106, 'goleag': 107, 'grbher3': 108, 'grcfly': 109,
'greegr': 110, 'greroa': 111, 'greyel': 112, 'grhowl': 113, 'grnher': 114,
'grtgra': 115, 'grycat': 116, 'gryfly': 117, 'haiwoo': 118, 'hamfly': 119,
'hergul': 120, 'herthr': 121, 'hoomer': 122, 'hoowar': 123, 'horgre': 124,
'horlar': 125, 'houfin': 126, 'houspa': 127, 'houwre': 128, 'indbun': 129,
'juntit1': 130, 'killde': 131, 'labwoo': 132, 'larspa': 133, 'lazbun': 134,
'leabit': 135, 'leafly': 136, 'leasan': 137, 'lecthr': 138, 'lesgol': 139,
'lesnig': 140, 'lesyel': 141, 'lewwoo': 142, 'linspa': 143, 'lobcur': 144,
'lobdow': 145, 'logshr': 146, 'lotduc': 147, 'louwat': 148, 'macwar': 149,
'magwar': 150, 'mallar3': 151, 'marwre': 152, 'merlin': 153, 'moublu': 154,
'mouchi': 155, 'moudov': 156, 'norcar': 157, 'norfli': 158, 'norhar2': 159,
'normoc': 160, 'norpar': 161, 'norpin': 162, 'norsho': 163, 'norwat': 164,
'nrwswa': 165, 'nutwoo': 166, 'olsfly': 167, 'orcwar': 168, 'osprey': 169,
'ovenbi1': 170, 'palwar': 171, 'pasfly': 172, 'pecsan': 173, 'perfal': 174,
'phaino': 175, 'pibgre': 176, 'pilwoo': 177, 'pingro': 178, 'pinjay': 179,
'pinsis': 180, 'pinwar': 181, 'plsvir': 182, 'prawar': 183, 'purfin': 184,
'pygnut': 185, 'rebmer': 186, 'rebnut': 187, 'rebsap': 188, 'rebwoo': 189,
'redcro': 190, 'redhea': 191, 'reevir1': 192, 'renpha': 193, 'reshaw': 194,
'rethaw': 195, 'rewbla': 196, 'ribgul': 197, 'rinduc': 198, 'robgro': 199,
'rocpig': 200, 'rocwre': 201, 'rthhum': 202, 'ruckin': 203, 'rudduc': 204,
'rufgro': 205, 'rufhum': 206, 'rusbla': 207, 'sagspa1': 208, 'sagthr': 209,
'savspa': 210, 'saypho': 211, 'scatan': 212, 'scoori': 213, 'semplo': 214,
'semsan': 215, 'sheowl': 216, 'shshaw': 217, 'snobun': 218, 'snogoo': 219,
'solsan': 220, 'sonspa': 221, 'sora': 222, 'sposan': 223, 'spotow': 224,
'stejay': 225, 'swahaw': 226, 'swaspa': 227, 'swathr': 228, 'treswa': 229,
'truswa': 230, 'tuftit': 231, 'tunswa': 232, 'veery': 233, 'vesspa': 234,
'vigswa': 235, 'warvir': 236, 'wesblu': 237, 'wesgre': 238, 'weskin': 239,
'wesmea': 240, 'wessan': 241, 'westan': 242, 'wewpew': 243, 'whbnut': 244,
'whcspa': 245, 'whfibi': 246, 'whtspa': 247, 'whtswi': 248, 'wilfly': 249,
'wilsni1': 250, 'wiltur': 251, 'winwre3': 252, 'wlswar': 253, 'wooduc': 254,
'wooscj2': 255, 'woothr': 256, 'y00475': 257, 'yebfly': 258, 'yebsap': 259,
'yehbla': 260, 'yelwar': 261, 'yerwar': 262, 'yetvir': 263
}
INV_BIRD_CODE = {v: k for k, v in BIRD_CODE.items() }<choose_model_class>
train = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/train.csv')
 test = pd.read_csv('.. /input/house-prices-advanced-regression-techniques/test.csv')
 train.describe()
def get_model(config: dict, weights_path: str):
model = ResNet(**config)
 checkpoint = torch.load(weights_path)
 model.load_state_dict(checkpoint["model_state_dict"])
 device = torch.device("cuda")
 model.to(device)
 model.eval()
return model<create_dataframe>
train_ID = train['Id']
test_ID = test['Id']
train.drop("Id", axis = 1, inplace = True)
 test.drop("Id", axis = 1, inplace = True )
def prediction_for_clip(test_df: pd.DataFrame,
clip: np.ndarray,
model: ResNet,
mel_params: dict,
threshold=0.60):
dataset = TestDataset(df=test_df,
clip=clip,
img_size=224,
melspectrogram_parameters=mel_params)
 loader = data.DataLoader(dataset, batch_size=1, shuffle=False)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.eval()
prediction_dict = {}
for image, row_id, site in progress_bar(loader):
site = site[0]
row_id = row_id[0]
if site in {"site_1", "site_2"}:
image = image.to(device)
 with torch.no_grad() :
prediction = model(image)
 proba = prediction["multilabel_proba"].detach().cpu().numpy().reshape(-1)
 events = proba >= threshold
labels = np.argwhere(events ).reshape(-1 ).tolist()
else:
image = image.squeeze(0)
 batch_size = 16
whole_size = image.size(0)
 if whole_size % batch_size == 0:
n_iter = whole_size // batch_size
else:
n_iter = whole_size // batch_size + 1
all_events = set()
for batch_i in range(n_iter):
batch = image[batch_i * batch_size:(batch_i + 1)* batch_size]
if batch.ndim == 3:
batch = batch.unsqueeze(0)
 batch = batch.to(device)
 with torch.no_grad() :
prediction = model(batch)
 proba = prediction["multilabel_proba"].detach().cpu().numpy()
events = proba >= threshold
for i in range(len(events)) :
event = events[i, :]
labels = np.argwhere(event ).reshape(-1 ).tolist()
for label in labels:
all_events.add(label)
 labels = list(all_events)
 if len(labels)== 0:
prediction_dict[row_id] = "nocall"
else:
labels_str_list = list(map(lambda x: INV_BIRD_CODE[x], labels))
label_string = " ".join(labels_str_list)
 prediction_dict[row_id] = label_string
return prediction_dict<load_pretrained>
train = train.drop(train[(train['GrLivArea']>4000)&(train['SalePrice']<300000)].index )
def prediction(test_df: pd.DataFrame,
test_audio: Path,
model_config: dict,
mel_params: dict,
weights_path: str,
threshold = 0.75):
model = get_model(model_config, weights_path)
 unique_audio_id = test_df.audio_id.unique()
warnings.filterwarnings("ignore")
 prediction_dfs = []
for audio_id in unique_audio_id:
with timer(f"Loading {audio_id}", logger):
clip, _ = librosa.load(test_audio /(audio_id + ".mp3"),
sr=TARGET_SR,
mono=True,
res_type="kaiser_fast")
 test_df_for_audio_id = test_df.query(
 f"audio_id == '{audio_id}'" ).reset_index(drop=True)
 with timer(f"Prediction on {audio_id}", logger):
prediction_dict = prediction_for_clip(test_df_for_audio_id,
clip=clip,
model=model,
mel_params=mel_params,
threshold=threshold)
 row_id = list(prediction_dict.keys())
 birds = list(prediction_dict.values())
 prediction_df = pd.DataFrame({
"row_id": row_id,
"birds": birds
})
 prediction_dfs.append(prediction_df)
 prediction_df = pd.concat(prediction_dfs, axis=0, sort=False ).reset_index(drop=True)
 return prediction_df<save_to_csv>
train["SalePrice"] = np.log1p(train["SalePrice"])
 check_skewness('SalePrice' )
submission = prediction(test_df=test,
test_audio=test_audio,
model_config=model_config,
mel_params=melspectrogram_parameters,
weights_path=weights_path,
threshold=0.99)
 submission.to_csv("submission.csv", index=False )<train_model>
ntrain = train.shape[0]
ntest = test.shape[0]
y_train = train.SalePrice.values
all_data = pd.concat(( train, test)).reset_index(drop=True)
 all_data.drop(['SalePrice'], axis=1, inplace=True)
 print("all_data size is : {}".format(all_data.shape))
print("submission successful" )<import_modules>
all_data_na =(all_data.isnull().sum() / len(all_data)) * 100
all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index ).sort_values(ascending=False)[:30]
missing_data = pd.DataFrame({'Missing Ratio' :all_data_na} )
import cv2
import audioread
import logging
import os
import random
import time
import warnings
import librosa
import numpy as np
import pandas as pd
import soundfile as sf
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
from contextlib import contextmanager
from pathlib import Path
from typing import Optional
from fastprogress import progress_bar
from sklearn.metrics import f1_score
from torchvision import models<set_options>
all_data.PoolQC.loc[all_data.PoolQC.notnull() ]
def set_seed(seed: int = 42):
random.seed(seed)
 np.random.seed(seed)
 os.environ["PYTHONHASHSEED"] = str(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
def get_logger(out_file=None):
logger = logging.getLogger()
formatter = logging.Formatter("%(asctime)s - %(levelname)s - %(message)s")
 logger.handlers = []
logger.setLevel(logging.INFO)
 handler = logging.StreamHandler()
handler.setFormatter(formatter)
 handler.setLevel(logging.INFO)
 logger.addHandler(handler)
 if out_file is not None:
fh = logging.FileHandler(out_file)
 fh.setFormatter(formatter)
 fh.setLevel(logging.INFO)
 logger.addHandler(fh)
 logger.info("logger set up")
 return logger
@contextmanager
def timer(name: str, logger: Optional[logging.Logger] = None):
t0 = time.time()
msg = f"[{name}] start"
if logger is None:
print(msg)
 else:
logger.info(msg)
 yield
msg = f"[{name}] done in {time.time() - t0:.2f} s"
if logger is None:
print(msg)
 else:
logger.info(msg )<set_options>
all_data["PoolQC"] = all_data["PoolQC"].fillna("None" )
logger = get_logger("main.log")
 set_seed(1213 )<define_variables>
all_data["MiscFeature"] = all_data["MiscFeature"].fillna("None" )
TARGET_SR = 32000
TEST = Path(".. /input/birdsong-recognition/test_audio" ).exists()<load_from_csv>
all_data["Alley"] = all_data["Alley"].fillna("None" )
if TEST:
DATA_DIR = Path(".. /input/birdsong-recognition/")
 else:
DATA_DIR = Path(".. /input/birdcall-check/")
 test = pd.read_csv(DATA_DIR / "test.csv")
 test_audio = DATA_DIR / "test_audio"
test.head()<save_to_csv>
all_data["Fence"] = all_data["Fence"].fillna("None" )
sub = pd.read_csv(".. /input/birdsong-recognition/sample_submission.csv")
 sub.to_csv("submission.csv", index=False )<choose_model_class>
all_data["FireplaceQu"] = all_data["FireplaceQu"].fillna("None" )
class ResNet(nn.Module):
def __init__(self, base_model_name: str, pretrained=False,
num_classes=264):
super().__init__()
base_model = models.__getattribute__(base_model_name )(
 pretrained=pretrained)
 layers = list(base_model.children())[:-2]
layers.append(nn.AdaptiveMaxPool2d(1))
self.encoder = nn.Sequential(*layers)
 in_features = base_model.fc.in_features
self.classifier = nn.Sequential(
 nn.Linear(in_features, 1024), nn.ReLU() , nn.Dropout(p=0.2),
nn.Linear(1024, 1024), nn.ReLU() , nn.Dropout(p=0.2),
nn.Linear(1024, num_classes))
def forward(self, x):
batch_size = x.size(0)
 x = self.encoder(x ).view(batch_size, -1)
 x = self.classifier(x)
 multiclass_proba = F.softmax(x, dim=1)
 multilabel_proba = F.sigmoid(x)
 return {
"logits": x,
"multiclass_proba": multiclass_proba,
"multilabel_proba": multilabel_proba
}<define_variables>
grouped_df = all_data.groupby('Neighborhood')['LotFrontage']
for key, item in grouped_df:
print(key,"
")
 print(grouped_df.get_group(key))
break
model_config = {
"base_model_name": "resnet50",
"pretrained": False,
"num_classes": 264
}
melspectrogram_parameters = {
"n_mels": 128,
"fmin": 20,
"fmax": 16000
}
weights_path = ".. /input/birdcall-resnet50-init-weights/best.pth"<define_variables>
all_data["LotFrontage"] = all_data.groupby("Neighborhood")["LotFrontage"].transform(
 lambda x: x.fillna(x.median()))
BIRD_CODE = {
'aldfly': 0, 'ameavo': 1, 'amebit': 2, 'amecro': 3, 'amegfi': 4,
'amekes': 5, 'amepip': 6, 'amered': 7, 'amerob': 8, 'amewig': 9,
'amewoo': 10, 'amtspa': 11, 'annhum': 12, 'astfly': 13, 'baisan': 14,
'baleag': 15, 'balori': 16, 'banswa': 17, 'barswa': 18, 'bawwar': 19,
'belkin1': 20, 'belspa2': 21, 'bewwre': 22, 'bkbcuc': 23, 'bkbmag1': 24,
'bkbwar': 25, 'bkcchi': 26, 'bkchum': 27, 'bkhgro': 28, 'bkpwar': 29,
'bktspa': 30, 'blkpho': 31, 'blugrb1': 32, 'blujay': 33, 'bnhcow': 34,
'boboli': 35, 'bongul': 36, 'brdowl': 37, 'brebla': 38, 'brespa': 39,
'brncre': 40, 'brnthr': 41, 'brthum': 42, 'brwhaw': 43, 'btbwar': 44,
'btnwar': 45, 'btywar': 46, 'buffle': 47, 'buggna': 48, 'buhvir': 49,
'bulori': 50, 'bushti': 51, 'buwtea': 52, 'buwwar': 53, 'cacwre': 54,
'calgul': 55, 'calqua': 56, 'camwar': 57, 'cangoo': 58, 'canwar': 59,
'canwre': 60, 'carwre': 61, 'casfin': 62, 'caster1': 63, 'casvir': 64,
'cedwax': 65, 'chispa': 66, 'chiswi': 67, 'chswar': 68, 'chukar': 69,
'clanut': 70, 'cliswa': 71, 'comgol': 72, 'comgra': 73, 'comloo': 74,
'commer': 75, 'comnig': 76, 'comrav': 77, 'comred': 78, 'comter': 79,
'comyel': 80, 'coohaw': 81, 'coshum': 82, 'cowscj1': 83, 'daejun': 84,
'doccor': 85, 'dowwoo': 86, 'dusfly': 87, 'eargre': 88, 'easblu': 89,
'easkin': 90, 'easmea': 91, 'easpho': 92, 'eastow': 93, 'eawpew': 94,
'eucdov': 95, 'eursta': 96, 'evegro': 97, 'fiespa': 98, 'fiscro': 99,
'foxspa': 100, 'gadwal': 101, 'gcrfin': 102, 'gnttow': 103, 'gnwtea': 104,
'gockin': 105, 'gocspa': 106, 'goleag': 107, 'grbher3': 108, 'grcfly': 109,
'greegr': 110, 'greroa': 111, 'greyel': 112, 'grhowl': 113, 'grnher': 114,
'grtgra': 115, 'grycat': 116, 'gryfly': 117, 'haiwoo': 118, 'hamfly': 119,
'hergul': 120, 'herthr': 121, 'hoomer': 122, 'hoowar': 123, 'horgre': 124,
'horlar': 125, 'houfin': 126, 'houspa': 127, 'houwre': 128, 'indbun': 129,
'juntit1': 130, 'killde': 131, 'labwoo': 132, 'larspa': 133, 'lazbun': 134,
'leabit': 135, 'leafly': 136, 'leasan': 137, 'lecthr': 138, 'lesgol': 139,
'lesnig': 140, 'lesyel': 141, 'lewwoo': 142, 'linspa': 143, 'lobcur': 144,
'lobdow': 145, 'logshr': 146, 'lotduc': 147, 'louwat': 148, 'macwar': 149,
'magwar': 150, 'mallar3': 151, 'marwre': 152, 'merlin': 153, 'moublu': 154,
'mouchi': 155, 'moudov': 156, 'norcar': 157, 'norfli': 158, 'norhar2': 159,
'normoc': 160, 'norpar': 161, 'norpin': 162, 'norsho': 163, 'norwat': 164,
'nrwswa': 165, 'nutwoo': 166, 'olsfly': 167, 'orcwar': 168, 'osprey': 169,
'ovenbi1': 170, 'palwar': 171, 'pasfly': 172, 'pecsan': 173, 'perfal': 174,
'phaino': 175, 'pibgre': 176, 'pilwoo': 177, 'pingro': 178, 'pinjay': 179,
'pinsis': 180, 'pinwar': 181, 'plsvir': 182, 'prawar': 183, 'purfin': 184,
'pygnut': 185, 'rebmer': 186, 'rebnut': 187, 'rebsap': 188, 'rebwoo': 189,
'redcro': 190, 'redhea': 191, 'reevir1': 192, 'renpha': 193, 'reshaw': 194,
'rethaw': 195, 'rewbla': 196, 'ribgul': 197, 'rinduc': 198, 'robgro': 199,
'rocpig': 200, 'rocwre': 201, 'rthhum': 202, 'ruckin': 203, 'rudduc': 204,
'rufgro': 205, 'rufhum': 206, 'rusbla': 207, 'sagspa1': 208, 'sagthr': 209,
'savspa': 210, 'saypho': 211, 'scatan': 212, 'scoori': 213, 'semplo': 214,
'semsan': 215, 'sheowl': 216, 'shshaw': 217, 'snobun': 218, 'snogoo': 219,
'solsan': 220, 'sonspa': 221, 'sora': 222, 'sposan': 223, 'spotow': 224,
'stejay': 225, 'swahaw': 226, 'swaspa': 227, 'swathr': 228, 'treswa': 229,
'truswa': 230, 'tuftit': 231, 'tunswa': 232, 'veery': 233, 'vesspa': 234,
'vigswa': 235, 'warvir': 236, 'wesblu': 237, 'wesgre': 238, 'weskin': 239,
'wesmea': 240, 'wessan': 241, 'westan': 242, 'wewpew': 243, 'whbnut': 244,
'whcspa': 245, 'whfibi': 246, 'whtspa': 247, 'whtswi': 248, 'wilfly': 249,
'wilsni1': 250, 'wiltur': 251, 'winwre3': 252, 'wlswar': 253, 'wooduc': 254,
'wooscj2': 255, 'woothr': 256, 'y00475': 257, 'yebfly': 258, 'yebsap': 259,
'yehbla': 260, 'yelwar': 261, 'yerwar': 262, 'yetvir': 263
}
INV_BIRD_CODE = {v: k for k, v in BIRD_CODE.items() }<choose_model_class>
for col in ['GarageType', 'GarageFinish', 'GarageQual', 'GarageCond']:
all_data[col] = all_data[col].fillna('None' )
def get_model(config: dict, weights_path: str):
model = ResNet(**config)
 checkpoint = torch.load(weights_path)
 model.load_state_dict(checkpoint["model_state_dict"])
 device = torch.device("cuda")
 model.to(device)
 model.eval()
return model<create_dataframe>
abc = ['GarageType', 'GarageFinish', 'GarageQual', 'GarageCond','GarageYrBlt', 'GarageArea', 'GarageCars']
all_data.groupby('GarageType')[abc].count()
def prediction_for_clip(test_df: pd.DataFrame,
clip: np.ndarray,
model: ResNet,
mel_params: dict,
threshold=0.5):
dataset = TestDataset(df=test_df,
clip=clip,
img_size=224,
melspectrogram_parameters=mel_params)
 loader = data.DataLoader(dataset, batch_size=1, shuffle=False)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.eval()
prediction_dict = {}
for image, row_id, site in progress_bar(loader):
site = site[0]
row_id = row_id[0]
if site in {"site_1", "site_2"}:
image = image.to(device)
 with torch.no_grad() :
prediction = model(image)
 proba = prediction["multilabel_proba"].detach().cpu().numpy().reshape(-1)
 events = proba >= threshold
labels = np.argwhere(events ).reshape(-1 ).tolist()
else:
image = image.squeeze(0)
 batch_size = 16
whole_size = image.size(0)
 if whole_size % batch_size == 0:
n_iter = whole_size // batch_size
else:
n_iter = whole_size // batch_size + 1
all_events = set()
for batch_i in range(n_iter):
batch = image[batch_i * batch_size:(batch_i + 1)* batch_size]
if batch.ndim == 3:
batch = batch.unsqueeze(0)
 batch = batch.to(device)
 with torch.no_grad() :
prediction = model(batch)
 proba = prediction["multilabel_proba"].detach().cpu().numpy()
events = proba >= threshold
for i in range(len(events)) :
event = events[i, :]
labels = np.argwhere(event ).reshape(-1 ).tolist()
for label in labels:
all_events.add(label)
 labels = list(all_events)
 if len(labels)== 0:
prediction_dict[row_id] = "nocall"
else:
labels_str_list = list(map(lambda x: INV_BIRD_CODE[x], labels))
label_string = " ".join(labels_str_list)
 prediction_dict[row_id] = label_string
return prediction_dict<load_pretrained>
for col in('GarageYrBlt', 'GarageArea', 'GarageCars'):
all_data[col] = all_data[col].fillna(0 )
def prediction(test_df: pd.DataFrame,
test_audio: Path,
model_config: dict,
mel_params: dict,
weights_path: str,
threshold=0.5):
model = get_model(model_config, weights_path)
 unique_audio_id = test_df.audio_id.unique()
warnings.filterwarnings("ignore")
 prediction_dfs = []
for audio_id in unique_audio_id:
with timer(f"Loading {audio_id}", logger):
clip, _ = librosa.load(test_audio /(audio_id + ".mp3"),
sr=TARGET_SR,
mono=True,
res_type="kaiser_fast")
 test_df_for_audio_id = test_df.query(
 f"audio_id == '{audio_id}'" ).reset_index(drop=True)
 with timer(f"Prediction on {audio_id}", logger):
prediction_dict = prediction_for_clip(test_df_for_audio_id,
clip=clip,
model=model,
mel_params=mel_params,
threshold=threshold)
 row_id = list(prediction_dict.keys())
 birds = list(prediction_dict.values())
 prediction_df = pd.DataFrame({
"row_id": row_id,
"birds": birds
})
 prediction_dfs.append(prediction_df)
 prediction_df = pd.concat(prediction_dfs, axis=0, sort=False ).reset_index(drop=True)
 return prediction_df<save_to_csv>
for col in('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath'):
all_data[col] = all_data[col].fillna(0 )
submission = prediction(test_df=test,
test_audio=test_audio,
model_config=model_config,
mel_params=melspectrogram_parameters,
weights_path=weights_path,
threshold=0.8)
 submission.to_csv("submission.csv", index=False )<set_options>
for col in('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):
all_data[col] = all_data[col].fillna('None' )
pd.options.display.max_rows = 500
pd.options.display.max_columns = 500<feature_engineering>
all_data["MasVnrType"] = all_data["MasVnrType"].fillna("None")
 all_data["MasVnrArea"] = all_data["MasVnrArea"].fillna(0 )
def set_seed(seed: int = 42):
random.seed(seed)
 np.random.seed(seed)
 os.environ["PYTHONHASHSEED"] = str(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 @contextmanager
def timer(name: str)-> None:

 t0 = time.time()
print("[{}] start".format(name))
yield
print("[{}] done in {:.0f} s".format(name, time.time() - t0))<define_variables>
all_data['MSZoning'].value_counts()
set_seed(1213 )<define_variables>
all_data['MSZoning'] = all_data['MSZoning'].fillna(all_data['MSZoning'].mode() [0] )
ROOT = Path.cwd().parent
INPUT_ROOT = ROOT / "input"
RAW_DATA = INPUT_ROOT / "birdsong-recognition"
TRAIN_AUDIO_DIR = RAW_DATA / "train_audio"
TEST_AUDIO_DIR = RAW_DATA / "test_audio"<load_from_csv>
all_data['Utilities'].value_counts()
train = pd.read_csv(RAW_DATA / "train.csv" )<load_from_csv>
all_data = all_data.drop(['Utilities'], axis=1 )
if not TEST_AUDIO_DIR.exists() :
TEST_AUDIO_DIR = INPUT_ROOT / "birdcall-check" / "test_audio"
test = pd.read_csv(INPUT_ROOT / "birdcall-check" / "test.csv")
 else:
test = pd.read_csv(RAW_DATA / "test.csv" )<save_to_csv>
all_data["Functional"] = all_data["Functional"].fillna("Typ" )
sub = pd.read_csv(".. /input/birdsong-recognition/sample_submission.csv")
 sub.to_csv("submission.csv", index=False )<init_hyperparams>
mode_col = ['Electrical','KitchenQual', 'Exterior1st', 'Exterior2nd', 'SaleType']
for col in mode_col:
all_data[col] = all_data[col].fillna(all_data[col].mode() [0] )
TARGET_SR = 32000
model_config = {
"base_model_name": "resnest50_fast_1s1x64d",
"pretrained": False,
"num_classes": 264,
"trained_weights": ".. /input/training-birdsong-baseline-resnest50-fast/best_model.pth"
}
melspectrogram_parameters = {
"n_mels": 128,
"fmin": 20,
"fmax": 16000
}<define_variables>
all_data['MSSubClass'] = all_data['MSSubClass'].fillna("None" )
BIRD_CODE = {
'aldfly': 0, 'ameavo': 1, 'amebit': 2, 'amecro': 3, 'amegfi': 4,
'amekes': 5, 'amepip': 6, 'amered': 7, 'amerob': 8, 'amewig': 9,
'amewoo': 10, 'amtspa': 11, 'annhum': 12, 'astfly': 13, 'baisan': 14,
'baleag': 15, 'balori': 16, 'banswa': 17, 'barswa': 18, 'bawwar': 19,
'belkin1': 20, 'belspa2': 21, 'bewwre': 22, 'bkbcuc': 23, 'bkbmag1': 24,
'bkbwar': 25, 'bkcchi': 26, 'bkchum': 27, 'bkhgro': 28, 'bkpwar': 29,
'bktspa': 30, 'blkpho': 31, 'blugrb1': 32, 'blujay': 33, 'bnhcow': 34,
'boboli': 35, 'bongul': 36, 'brdowl': 37, 'brebla': 38, 'brespa': 39,
'brncre': 40, 'brnthr': 41, 'brthum': 42, 'brwhaw': 43, 'btbwar': 44,
'btnwar': 45, 'btywar': 46, 'buffle': 47, 'buggna': 48, 'buhvir': 49,
'bulori': 50, 'bushti': 51, 'buwtea': 52, 'buwwar': 53, 'cacwre': 54,
'calgul': 55, 'calqua': 56, 'camwar': 57, 'cangoo': 58, 'canwar': 59,
'canwre': 60, 'carwre': 61, 'casfin': 62, 'caster1': 63, 'casvir': 64,
'cedwax': 65, 'chispa': 66, 'chiswi': 67, 'chswar': 68, 'chukar': 69,
'clanut': 70, 'cliswa': 71, 'comgol': 72, 'comgra': 73, 'comloo': 74,
'commer': 75, 'comnig': 76, 'comrav': 77, 'comred': 78, 'comter': 79,
'comyel': 80, 'coohaw': 81, 'coshum': 82, 'cowscj1': 83, 'daejun': 84,
'doccor': 85, 'dowwoo': 86, 'dusfly': 87, 'eargre': 88, 'easblu': 89,
'easkin': 90, 'easmea': 91, 'easpho': 92, 'eastow': 93, 'eawpew': 94,
'eucdov': 95, 'eursta': 96, 'evegro': 97, 'fiespa': 98, 'fiscro': 99,
'foxspa': 100, 'gadwal': 101, 'gcrfin': 102, 'gnttow': 103, 'gnwtea': 104,
'gockin': 105, 'gocspa': 106, 'goleag': 107, 'grbher3': 108, 'grcfly': 109,
'greegr': 110, 'greroa': 111, 'greyel': 112, 'grhowl': 113, 'grnher': 114,
'grtgra': 115, 'grycat': 116, 'gryfly': 117, 'haiwoo': 118, 'hamfly': 119,
'hergul': 120, 'herthr': 121, 'hoomer': 122, 'hoowar': 123, 'horgre': 124,
'horlar': 125, 'houfin': 126, 'houspa': 127, 'houwre': 128, 'indbun': 129,
'juntit1': 130, 'killde': 131, 'labwoo': 132, 'larspa': 133, 'lazbun': 134,
'leabit': 135, 'leafly': 136, 'leasan': 137, 'lecthr': 138, 'lesgol': 139,
'lesnig': 140, 'lesyel': 141, 'lewwoo': 142, 'linspa': 143, 'lobcur': 144,
'lobdow': 145, 'logshr': 146, 'lotduc': 147, 'louwat': 148, 'macwar': 149,
'magwar': 150, 'mallar3': 151, 'marwre': 152, 'merlin': 153, 'moublu': 154,
'mouchi': 155, 'moudov': 156, 'norcar': 157, 'norfli': 158, 'norhar2': 159,
'normoc': 160, 'norpar': 161, 'norpin': 162, 'norsho': 163, 'norwat': 164,
'nrwswa': 165, 'nutwoo': 166, 'olsfly': 167, 'orcwar': 168, 'osprey': 169,
'ovenbi1': 170, 'palwar': 171, 'pasfly': 172, 'pecsan': 173, 'perfal': 174,
'phaino': 175, 'pibgre': 176, 'pilwoo': 177, 'pingro': 178, 'pinjay': 179,
'pinsis': 180, 'pinwar': 181, 'plsvir': 182, 'prawar': 183, 'purfin': 184,
'pygnut': 185, 'rebmer': 186, 'rebnut': 187, 'rebsap': 188, 'rebwoo': 189,
'redcro': 190, 'redhea': 191, 'reevir1': 192, 'renpha': 193, 'reshaw': 194,
'rethaw': 195, 'rewbla': 196, 'ribgul': 197, 'rinduc': 198, 'robgro': 199,
'rocpig': 200, 'rocwre': 201, 'rthhum': 202, 'ruckin': 203, 'rudduc': 204,
'rufgro': 205, 'rufhum': 206, 'rusbla': 207, 'sagspa1': 208, 'sagthr': 209,
'savspa': 210, 'saypho': 211, 'scatan': 212, 'scoori': 213, 'semplo': 214,
'semsan': 215, 'sheowl': 216, 'shshaw': 217, 'snobun': 218, 'snogoo': 219,
'solsan': 220, 'sonspa': 221, 'sora': 222, 'sposan': 223, 'spotow': 224,
'stejay': 225, 'swahaw': 226, 'swaspa': 227, 'swathr': 228, 'treswa': 229,
'truswa': 230, 'tuftit': 231, 'tunswa': 232, 'veery': 233, 'vesspa': 234,
'vigswa': 235, 'warvir': 236, 'wesblu': 237, 'wesgre': 238, 'weskin': 239,
'wesmea': 240, 'wessan': 241, 'westan': 242, 'wewpew': 243, 'whbnut': 244,
'whcspa': 245, 'whfibi': 246, 'whtspa': 247, 'whtswi': 248, 'wilfly': 249,
'wilsni1': 250, 'wiltur': 251, 'winwre3': 252, 'wlswar': 253, 'wooduc': 254,
'wooscj2': 255, 'woothr': 256, 'y00475': 257, 'yebfly': 258, 'yebsap': 259,
'yehbla': 260, 'yelwar': 261, 'yerwar': 262, 'yetvir': 263
}
INV_BIRD_CODE = {v: k for k, v in BIRD_CODE.items() }<define_search_model>
all_data_na =(all_data.isnull().sum() / len(all_data)) * 100
all_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index ).sort_values(ascending=False)
 missing_data = pd.DataFrame({'Missing Ratio' :all_data_na})
 missing_data.head()
class SplAtConv2d(Module):

 def __init__(self, in_channels, channels, kernel_size, stride=(1, 1), padding=(0, 0),
dilation=(1, 1), groups=1, bias=True,
radix=2, reduction_factor=4,
rectify=False, rectify_avg=False, norm_layer=None,
dropblock_prob=0.0, **kwargs):
super(SplAtConv2d, self ).__init__()
padding = _pair(padding)
 self.rectify = rectify and(padding[0] > 0 or padding[1] > 0)
 self.rectify_avg = rectify_avg
inter_channels = max(in_channels*radix//reduction_factor, 32)
 self.radix = radix
self.cardinality = groups
self.channels = channels
self.dropblock_prob = dropblock_prob
if self.rectify:
self.conv = RFConv2d(in_channels, channels*radix, kernel_size, stride, padding, dilation,
groups=groups*radix, bias=bias, average_mode=rectify_avg, **kwargs)
 else:
self.conv = Conv2d(in_channels, channels*radix, kernel_size, stride, padding, dilation,
groups=groups*radix, bias=bias, **kwargs)
 self.use_bn = norm_layer is not None
if self.use_bn:
self.bn0 = norm_layer(channels*radix)
 self.relu = ReLU(inplace=True)
 self.fc1 = Conv2d(channels, inter_channels, 1, groups=self.cardinality)
 if self.use_bn:
self.bn1 = norm_layer(inter_channels)
 self.fc2 = Conv2d(inter_channels, channels*radix, 1, groups=self.cardinality)
 if dropblock_prob > 0.0:
self.dropblock = DropBlock2D(dropblock_prob, 3)
 self.rsoftmax = rSoftMax(radix, groups)
 def forward(self, x):
x = self.conv(x)
 if self.use_bn:
x = self.bn0(x)
 if self.dropblock_prob > 0.0:
x = self.dropblock(x)
 x = self.relu(x)
 batch, rchannel = x.shape[:2]
if self.radix > 1:
if torch.__version__ < '1.5':
splited = torch.split(x, int(rchannel//self.radix), dim=1)
 else:
splited = torch.split(x, rchannel//self.radix, dim=1)
 gap = sum(splited)
 else:
gap = x
gap = F.adaptive_avg_pool2d(gap, 1)
 gap = self.fc1(gap)
 if self.use_bn:
gap = self.bn1(gap)
 gap = self.relu(gap)
 atten = self.fc2(gap)
 atten = self.rsoftmax(atten ).view(batch, -1, 1, 1)
 if self.radix > 1:
if torch.__version__ < '1.5':
attens = torch.split(atten, int(rchannel//self.radix), dim=1)
 else:
attens = torch.split(atten, rchannel//self.radix, dim=1)
 out = sum([att*split for(att, split)in zip(attens, splited)])
 else:
out = atten * x
return out.contiguous()
class rSoftMax(nn.Module):
def __init__(self, radix, cardinality):
super().__init__()
self.radix = radix
self.cardinality = cardinality
def forward(self, x):
batch = x.size(0)
 if self.radix > 1:
x = x.view(batch, self.cardinality, self.radix, -1 ).transpose(1, 2)
 x = F.softmax(x, dim=1)
 x = x.reshape(batch, -1)
 else:
x = torch.sigmoid(x)
 return x<import_modules>
all_data['OverallCond'].value_counts()
class DropBlock2D(object):
def __init__(self, *args, **kwargs):
raise NotImplementedError
class GlobalAvgPool2d(nn.Module):
def __init__(self):

 super(GlobalAvgPool2d, self ).__init__()
def forward(self, inputs):
return nn.functional.adaptive_avg_pool2d(inputs, 1 ).view(inputs.size(0), -1)
 class Bottleneck(nn.Module):

 expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None,
radix=1, cardinality=1, bottleneck_width=64,
avd=False, avd_first=False, dilation=1, is_first=False,
rectified_conv=False, rectify_avg=False,
norm_layer=None, dropblock_prob=0.0, last_gamma=False):
super(Bottleneck, self ).__init__()
group_width = int(planes *(bottleneck_width / 64.))* cardinality
self.conv1 = nn.Conv2d(inplanes, group_width, kernel_size=1, bias=False)
 self.bn1 = norm_layer(group_width)
 self.dropblock_prob = dropblock_prob
self.radix = radix
self.avd = avd and(stride > 1 or is_first)
 self.avd_first = avd_first
if self.avd:
self.avd_layer = nn.AvgPool2d(3, stride, padding=1)
 stride = 1
if dropblock_prob > 0.0:
self.dropblock1 = DropBlock2D(dropblock_prob, 3)
 if radix == 1:
self.dropblock2 = DropBlock2D(dropblock_prob, 3)
 self.dropblock3 = DropBlock2D(dropblock_prob, 3)
 if radix >= 1:
self.conv2 = SplAtConv2d(
 group_width, group_width, kernel_size=3,
stride=stride, padding=dilation,
dilation=dilation, groups=cardinality, bias=False,
radix=radix, rectify=rectified_conv,
rectify_avg=rectify_avg,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 elif rectified_conv:
self.conv2 = RFConv2d(
 group_width, group_width, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation,
groups=cardinality, bias=False,
average_mode=rectify_avg)
 self.bn2 = norm_layer(group_width)
 else:
self.conv2 = nn.Conv2d(
 group_width, group_width, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation,
groups=cardinality, bias=False)
 self.bn2 = norm_layer(group_width)
 self.conv3 = nn.Conv2d(
 group_width, planes * 4, kernel_size=1, bias=False)
 self.bn3 = norm_layer(planes*4)
 if last_gamma:
zeros_(self.bn3.weight)
 self.relu = nn.ReLU(inplace=True)
 self.downsample = downsample
self.dilation = dilation
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
 out = self.bn1(out)
 if self.dropblock_prob > 0.0:
out = self.dropblock1(out)
 out = self.relu(out)
 if self.avd and self.avd_first:
out = self.avd_layer(out)
 out = self.conv2(out)
 if self.radix == 0:
out = self.bn2(out)
 if self.dropblock_prob > 0.0:
out = self.dropblock2(out)
 out = self.relu(out)
 if self.avd and not self.avd_first:
out = self.avd_layer(out)
 out = self.conv3(out)
 out = self.bn3(out)
 if self.dropblock_prob > 0.0:
out = self.dropblock3(out)
 if self.downsample is not None:
residual = self.downsample(x)
 out += residual
out = self.relu(out)
 return out
class ResNet(nn.Module):

 def __init__(self, block, layers, radix=1, groups=1, bottleneck_width=64,
num_classes=1000, dilated=False, dilation=1,
deep_stem=False, stem_width=64, avg_down=False,
rectified_conv=False, rectify_avg=False,
avd=False, avd_first=False,
final_drop=0.0, dropblock_prob=0,
last_gamma=False, norm_layer=nn.BatchNorm2d):
self.cardinality = groups
self.bottleneck_width = bottleneck_width
self.inplanes = stem_width*2 if deep_stem else 64
self.avg_down = avg_down
self.last_gamma = last_gamma
self.radix = radix
self.avd = avd
self.avd_first = avd_first
super(ResNet, self ).__init__()
self.rectified_conv = rectified_conv
self.rectify_avg = rectify_avg
if rectified_conv:
conv_layer = RFConv2d
else:
conv_layer = nn.Conv2d
conv_kwargs = {'average_mode': rectify_avg} if rectified_conv else {}
if deep_stem:
self.conv1 = nn.Sequential(
 conv_layer(3, stem_width, kernel_size=3, stride=2, padding=1, bias=False, **conv_kwargs),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(stem_width, stem_width, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs),
norm_layer(stem_width),
nn.ReLU(inplace=True),
conv_layer(stem_width, stem_width*2, kernel_size=3, stride=1, padding=1, bias=False, **conv_kwargs), 
)
 else:
self.conv1 = conv_layer(3, 64, kernel_size=7, stride=2, padding=3,
bias=False, **conv_kwargs)
 self.bn1 = norm_layer(self.inplanes)
 self.relu = nn.ReLU(inplace=True)
 self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
 self.layer1 = self._make_layer(block, 64, layers[0], norm_layer=norm_layer, is_first=False)
 self.layer2 = self._make_layer(block, 128, layers[1], stride=2, norm_layer=norm_layer)
 if dilated or dilation == 4:
self.layer3 = self._make_layer(block, 256, layers[2], stride=1,
dilation=2, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
dilation=4, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 elif dilation==2:
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
dilation=1, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 self.layer4 = self._make_layer(block, 512, layers[3], stride=1,
dilation=2, norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 else:
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
norm_layer=norm_layer,
dropblock_prob=dropblock_prob)
 self.avgpool = GlobalAvgPool2d()
self.drop = nn.Dropout(final_drop)if final_drop > 0.0 else None
self.fc = nn.Linear(512 * block.expansion, num_classes)
 for m in self.modules() :
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2./ n))
elif isinstance(m, norm_layer):
m.weight.data.fill_(1)
 m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, dilation=1, norm_layer=None,
dropblock_prob=0.0, is_first=True):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
down_layers = []
if self.avg_down:
if dilation == 1:
down_layers.append(nn.AvgPool2d(kernel_size=stride, stride=stride,
ceil_mode=True, count_include_pad=False))
else:
down_layers.append(nn.AvgPool2d(kernel_size=1, stride=1,
ceil_mode=True, count_include_pad=False))
down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=1, bias=False))
else:
down_layers.append(nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False))
down_layers.append(norm_layer(planes * block.expansion))
downsample = nn.Sequential(*down_layers)
 layers = []
if dilation == 1 or dilation == 2:
layers.append(block(self.inplanes, planes, stride, downsample=downsample,
radix=self.radix, cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, avd_first=self.avd_first,
dilation=1, is_first=is_first, rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer, dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
elif dilation == 4:
layers.append(block(self.inplanes, planes, stride, downsample=downsample,
radix=self.radix, cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, avd_first=self.avd_first,
dilation=2, is_first=is_first, rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer, dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
else:
raise RuntimeError("=> unknown dilation size: {}".format(dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes,
radix=self.radix, cardinality=self.cardinality,
bottleneck_width=self.bottleneck_width,
avd=self.avd, avd_first=self.avd_first,
dilation=dilation, rectified_conv=self.rectified_conv,
rectify_avg=self.rectify_avg,
norm_layer=norm_layer, dropblock_prob=dropblock_prob,
last_gamma=self.last_gamma))
return nn.Sequential(*layers)
 def forward(self, x):
x = self.conv1(x)
 x = self.bn1(x)
 x = self.relu(x)
 x = self.maxpool(x)
 x = self.layer1(x)
 x = self.layer2(x)
 x = self.layer3(x)
 x = self.layer4(x)
 x = self.avgpool(x)
 x = torch.flatten(x, 1)
 if self.drop:
x = self.drop(x)
 x = self.fc(x)
 return x<choose_model_class>
all_data['MSSubClass'] = all_data['MSSubClass'].apply(str)
 all_data['OverallCond'] = all_data['OverallCond'].astype(str)
 all_data['YrSold'] = all_data['YrSold'].astype(str)
 all_data['MoSold'] = all_data['MoSold'].astype(str )
def get_model(args: tp.Dict):
model = ResNet(
 Bottleneck, [3, 4, 6, 3],
radix=1, groups=1, bottleneck_width=64,
deep_stem=True, stem_width=32, avg_down=True,
avd=True, avd_first=True)
 del model.fc
model.fc = nn.Sequential(
 nn.Linear(2048, 1024), nn.ReLU() , nn.Dropout(p=0.2),
nn.Linear(1024, 1024), nn.ReLU() , nn.Dropout(p=0.2),
nn.Linear(1024, args["num_classes"]))
state_dict = torch.load(args["trained_weights"])
 model.load_state_dict(state_dict)
 device = torch.device("cuda")
 model.to(device)
 model.eval()
return model<create_dataframe>
cols =('FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond',
'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1',
'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',
'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond',
'YrSold', 'MoSold')
 for c in cols:
lbl = LabelEncoder()
lbl.fit(list(all_data[c].values))
all_data[c] = lbl.transform(list(all_data[c].values))
print('Shape all_data: {}'.format(all_data.shape))
def prediction_for_clip(test_df: pd.DataFrame,
clip: np.ndarray,
model: ResNet,
mel_params: dict,
threshold=0.5):
dataset = TestDataset(df=test_df,
clip=clip,
img_size=224,
melspectrogram_parameters=mel_params)
 loader = data.DataLoader(dataset, batch_size=1, shuffle=False)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.eval()
prediction_dict = {}
for image, row_id, site in progress_bar(loader):
site = site[0]
row_id = row_id[0]
if site in {"site_1", "site_2"}:
image = image.to(device)
 with torch.no_grad() :
prediction = F.sigmoid(model(image))
proba = prediction.detach().cpu().numpy().reshape(-1)
 events = proba >= threshold
labels = np.argwhere(events ).reshape(-1 ).tolist()
else:
image = image.squeeze(0)
 batch_size = 16
whole_size = image.size(0)
 if whole_size % batch_size == 0:
n_iter = whole_size // batch_size
else:
n_iter = whole_size // batch_size + 1
all_events = set()
for batch_i in range(n_iter):
batch = image[batch_i * batch_size:(batch_i + 1)* batch_size]
if batch.ndim == 3:
batch = batch.unsqueeze(0)
 batch = batch.to(device)
 with torch.no_grad() :
prediction = F.sigmoid(model(batch))
proba = prediction.detach().cpu().numpy()
events = proba >= threshold
for i in range(len(events)) :
event = events[i, :]
labels = np.argwhere(event ).reshape(-1 ).tolist()
for label in labels:
all_events.add(label)
 labels = list(all_events)
 if len(labels)== 0:
prediction_dict[row_id] = "nocall"
else:
labels_str_list = list(map(lambda x: INV_BIRD_CODE[x], labels))
label_string = " ".join(labels_str_list)
 prediction_dict[row_id] = label_string
return prediction_dict<categorify>
all_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data['1stFlrSF'] + all_data['2ndFlrSF']
def prediction(test_df: pd.DataFrame,
test_audio: Path,
model_config: dict,
mel_params: dict,
target_sr: int,
threshold=0.5):
model = get_model(model_config)
 unique_audio_id = test_df.audio_id.unique()
warnings.filterwarnings("ignore")
 prediction_dfs = []
for audio_id in unique_audio_id:
with timer(f"Loading {audio_id}"):
clip, _ = librosa.load(test_audio /(audio_id + ".mp3"),
sr=target_sr,
mono=True,
res_type="kaiser_fast")
 test_df_for_audio_id = test_df.query(
 f"audio_id == '{audio_id}'" ).reset_index(drop=True)
 with timer(f"Prediction on {audio_id}"):
prediction_dict = prediction_for_clip(test_df_for_audio_id,
clip=clip,
model=model,
mel_params=mel_params,
threshold=threshold)
 row_id = list(prediction_dict.keys())
 birds = list(prediction_dict.values())
 prediction_df = pd.DataFrame({
"row_id": row_id,
"birds": birds
})
 prediction_dfs.append(prediction_df)
 prediction_df = pd.concat(prediction_dfs, axis=0, sort=False ).reset_index(drop=True)
 return prediction_df<save_to_csv>
skewness = skewness[abs(skewness)> 0.75]
print("There are {} skewed numerical features to Box Cox transform".format(skewness.shape[0]))
skewed_features = skewness.index
lam = 0.15
for feat in skewed_features:
all_data[feat] = boxcox1p(all_data[feat], lam )
submission = prediction(test_df=test,
test_audio=TEST_AUDIO_DIR,
model_config=model_config,
mel_params=melspectrogram_parameters,
target_sr=TARGET_SR,
threshold=0.6)
 submission.loc[submission['birds']=='nocall','birds'] = 'aldfly'
submission.to_csv("submission.csv", index=False )<install_modules>
all_data = pd.get_dummies(all_data)
 all_data.shape
!pip uninstall torch --y
!pip uninstall torchaudio --y<install_modules>
train = all_data[:ntrain]
test = all_data[ntrain:]
train.shape
!pip install.. /input/pytorch-160-with-torchvision-070/torch-1.6.0cu101-cp37-cp37m-linux_x86_64.whl
!pip install.. /input/torchaudio/torchaudio-0.6.0-cp37-cp37m-manylinux1_x86_64.whl
!pip install.. /input/bird-panns/torchlibrosa-master/torchlibrosa-master/ > /dev/null
pd.options.display.max_rows = 500
pd.options.display.max_columns = 500<feature_engineering>
from sklearn.linear_model import ElasticNet, Lasso, BayesianRidge, LassoLarsIC
from sklearn.ensemble import RandomForestRegressor, GradientBoostingRegressor
from sklearn.kernel_ridge import KernelRidge
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import RobustScaler
from sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone
from sklearn.model_selection import KFold, cross_val_score, train_test_split
from sklearn.metrics import mean_squared_error
import xgboost as xgb
import lightgbm as lgb
def set_seed(seed: int = 42):
random.seed(seed)
 np.random.seed(seed)
 os.environ["PYTHONHASHSEED"] = str(seed)
 torch.manual_seed(seed)
 torch.cuda.manual_seed(seed)
 @contextmanager
def timer(name: str)-> None:

 t0 = time.time()
print("[{}] start".format(name))
yield
print("[{}] done in {:.0f} s".format(name, time.time() - t0))<load_from_csv>
n_folds = 5
def rmsle_cv(model):
kf = KFold(n_folds, shuffle=True, random_state=42 ).get_n_splits(train.values)
 rmse= np.sqrt(-cross_val_score(model, train.values, y_train, scoring="neg_mean_squared_error", cv = kf))
return(rmse )
args = {
'n_mels': 192,
'hop_length': 784,
'std_sr': 32000,
'threshold':.6,
'model': '.. /input/birdweights/b11_18_15.pt'
}
model = torch.jit.load(args['model'], map_location='cpu' ).cuda()
model.eval() ;
root = os.path.join('.. /input', "birdsong-recognition")
 test_dir = os.path.join(root, 'test_audio')
 print(test_dir)
 if os.path.isdir(test_dir):
print('Running actual submission!')
 test_df = pd.read_csv(os.path.join(root, 'test.csv'))
sub = pd.read_csv(".. /input/birdsong-recognition/sample_submission.csv")
 else:
print('Running debug submission')
 test_dir = os.path.join('.. /input', 'birdcall-check', 'test_audio')
 test_df = pd.read_csv(os.path.join('.. /input', 'birdcall-check', 'test.csv'))
sub = pd.read_csv('.. /input/inference-pytorch-birdcall-resnet-baseline/submission.csv')
 print('Naturally no test dir found')
 sub.to_csv("submission.csv", index=False )<define_variables>
KRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)
 score = rmsle_cv(KRR)
 print("Kernel Ridge score: {:.4f}({:.4f})
 ".format(score.mean() , score.std()))
BIRD_CODE = {
'aldfly': 0, 'ameavo': 1, 'amebit': 2, 'amecro': 3, 'amegfi': 4,
'amekes': 5, 'amepip': 6, 'amered': 7, 'amerob': 8, 'amewig': 9,
'amewoo': 10, 'amtspa': 11, 'annhum': 12, 'astfly': 13, 'baisan': 14,
'baleag': 15, 'balori': 16, 'banswa': 17, 'barswa': 18, 'bawwar': 19,
'belkin1': 20, 'belspa2': 21, 'bewwre': 22, 'bkbcuc': 23, 'bkbmag1': 24,
'bkbwar': 25, 'bkcchi': 26, 'bkchum': 27, 'bkhgro': 28, 'bkpwar': 29,
'bktspa': 30, 'blkpho': 31, 'blugrb1': 32, 'blujay': 33, 'bnhcow': 34,
'boboli': 35, 'bongul': 36, 'brdowl': 37, 'brebla': 38, 'brespa': 39,
'brncre': 40, 'brnthr': 41, 'brthum': 42, 'brwhaw': 43, 'btbwar': 44,
'btnwar': 45, 'btywar': 46, 'buffle': 47, 'buggna': 48, 'buhvir': 49,
'bulori': 50, 'bushti': 51, 'buwtea': 52, 'buwwar': 53, 'cacwre': 54,
'calgul': 55, 'calqua': 56, 'camwar': 57, 'cangoo': 58, 'canwar': 59,
'canwre': 60, 'carwre': 61, 'casfin': 62, 'caster1': 63, 'casvir': 64,
'cedwax': 65, 'chispa': 66, 'chiswi': 67, 'chswar': 68, 'chukar': 69,
'clanut': 70, 'cliswa': 71, 'comgol': 72, 'comgra': 73, 'comloo': 74,
'commer': 75, 'comnig': 76, 'comrav': 77, 'comred': 78, 'comter': 79,
'comyel': 80, 'coohaw': 81, 'coshum': 82, 'cowscj1': 83, 'daejun': 84,
'doccor': 85, 'dowwoo': 86, 'dusfly': 87, 'eargre': 88, 'easblu': 89,
'easkin': 90, 'easmea': 91, 'easpho': 92, 'eastow': 93, 'eawpew': 94,
'eucdov': 95, 'eursta': 96, 'evegro': 97, 'fiespa': 98, 'fiscro': 99,
'foxspa': 100, 'gadwal': 101, 'gcrfin': 102, 'gnttow': 103, 'gnwtea': 104,
'gockin': 105, 'gocspa': 106, 'goleag': 107, 'grbher3': 108, 'grcfly': 109,
'greegr': 110, 'greroa': 111, 'greyel': 112, 'grhowl': 113, 'grnher': 114,
'grtgra': 115, 'grycat': 116, 'gryfly': 117, 'haiwoo': 118, 'hamfly': 119,
'hergul': 120, 'herthr': 121, 'hoomer': 122, 'hoowar': 123, 'horgre': 124,
'horlar': 125, 'houfin': 126, 'houspa': 127, 'houwre': 128, 'indbun': 129,
'juntit1': 130, 'killde': 131, 'labwoo': 132, 'larspa': 133, 'lazbun': 134,
'leabit': 135, 'leafly': 136, 'leasan': 137, 'lecthr': 138, 'lesgol': 139,
'lesnig': 140, 'lesyel': 141, 'lewwoo': 142, 'linspa': 143, 'lobcur': 144,
'lobdow': 145, 'logshr': 146, 'lotduc': 147, 'louwat': 148, 'macwar': 149,
'magwar': 150, 'mallar3': 151, 'marwre': 152, 'merlin': 153, 'moublu': 154,
'mouchi': 155, 'moudov': 156, 'norcar': 157, 'norfli': 158, 'norhar2': 159,
'normoc': 160, 'norpar': 161, 'norpin': 162, 'norsho': 163, 'norwat': 164,
'nrwswa': 165, 'nutwoo': 166, 'olsfly': 167, 'orcwar': 168, 'osprey': 169,
'ovenbi1': 170, 'palwar': 171, 'pasfly': 172, 'pecsan': 173, 'perfal': 174,
'phaino': 175, 'pibgre': 176, 'pilwoo': 177, 'pingro': 178, 'pinjay': 179,
'pinsis': 180, 'pinwar': 181, 'plsvir': 182, 'prawar': 183, 'purfin': 184,
'pygnut': 185, 'rebmer': 186, 'rebnut': 187, 'rebsap': 188, 'rebwoo': 189,
'redcro': 190, 'redhea': 191, 'reevir1': 192, 'renpha': 193, 'reshaw': 194,
'rethaw': 195, 'rewbla': 196, 'ribgul': 197, 'rinduc': 198, 'robgro': 199,
'rocpig': 200, 'rocwre': 201, 'rthhum': 202, 'ruckin': 203, 'rudduc': 204,
'rufgro': 205, 'rufhum': 206, 'rusbla': 207, 'sagspa1': 208, 'sagthr': 209,
'savspa': 210, 'saypho': 211, 'scatan': 212, 'scoori': 213, 'semplo': 214,
'semsan': 215, 'sheowl': 216, 'shshaw': 217, 'snobun': 218, 'snogoo': 219,
'solsan': 220, 'sonspa': 221, 'sora': 222, 'sposan': 223, 'spotow': 224,
'stejay': 225, 'swahaw': 226, 'swaspa': 227, 'swathr': 228, 'treswa': 229,
'truswa': 230, 'tuftit': 231, 'tunswa': 232, 'veery': 233, 'vesspa': 234,
'vigswa': 235, 'warvir': 236, 'wesblu': 237, 'wesgre': 238, 'weskin': 239,
'wesmea': 240, 'wessan': 241, 'westan': 242, 'wewpew': 243, 'whbnut': 244,
'whcspa': 245, 'whfibi': 246, 'whtspa': 247, 'whtswi': 248, 'wilfly': 249,
'wilsni1': 250, 'wiltur': 251, 'winwre3': 252, 'wlswar': 253, 'wooduc': 254,
'wooscj2': 255, 'woothr': 256, 'y00475': 257, 'yebfly': 258, 'yebsap': 259,
'yehbla': 260, 'yelwar': 261, 'yerwar': 262, 'yetvir': 263
}
INV_BIRD_CODE = {v: k for k, v in BIRD_CODE.items() }<set_options>
lasso = make_pipeline(RobustScaler() , Lasso(alpha =0.0005, random_state=1))
score = rmsle_cv(lasso)
 print("Lasso score: {:.4f}({:.4f})
 ".format(score.mean() , score.std()))
fmin, fmax = 25, 11000
f1 = MelSpectrogram(sample_rate=args['std_sr'], n_fft=1024, hop_length=args['hop_length'], f_min=fmin, f_max=fmax, n_mels=args['n_mels'] ).cuda()
f2 = MelSpectrogram(sample_rate=args['std_sr'], n_fft=2048, hop_length=args['hop_length'], f_min=fmin, f_max=fmax, n_mels=args['n_mels'] ).cuda()
f3 = MelSpectrogram(sample_rate=args['std_sr'], n_fft=4096, hop_length=args['hop_length'], f_min=fmin, f_max=fmax, n_mels=args['n_mels'] ).cuda()<prepare_x_and_y>
ENet = make_pipeline(RobustScaler() , ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))
score = rmsle_cv(ENet)
 print("ElasticNet score: {:.4f}({:.4f})
 ".format(score.mean() , score.std()))
class TestDataset(data.Dataset):
def __init__(self, df, spectrogram):
self.df = df
self.spectrogram = spectrogram
def __len__(self):
return len(self.df)
 def __getitem__(self, idx):
sample = self.df.loc[idx, :]
site = sample.site
row_id = sample.row_id
if site == "site_3":
y = self.spectrogram.astype(np.float32)
 len_y = y.shape[1]
start = 0
end = args['std_sr'] * 5 // args['hop_length']
y_all = []
while len_y > start:
y_batch = y[:, start:end].astype(np.float32)
 if end >= len_y:
clip_length = args['std_sr'] * 5 // args['hop_length']
y_batch = y[:, -clip_length:, :]
y_all.append(y_batch)
 break
start = end
end = end + args['std_sr'] * 5 // args['hop_length']
y_all.append(y_batch)
 y_all = np.asarray(y_all)
 y_all = torch.from_numpy(y_all ).permute(0, 3, 1, 2 ).float() / 127.5 - 1
return y_all, row_id, site
else:
end_seconds = int(sample.seconds)
 start_seconds = int(end_seconds - 5)
 start_index = args['std_sr'] * start_seconds // args['hop_length']
end_index = args['std_sr'] * end_seconds // args['hop_length']
y = self.spectrogram[:, start_index:end_index].astype(np.float32)
 y = torch.from_numpy(y ).permute(2, 0, 1 ).float() / 127.5 - 1
return y, row_id, site
def prediction_for_clip(dataset, threshold=0.5):
loader = data.DataLoader(dataset, batch_size=1, shuffle=False)
 device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
 model.eval()
prediction_dict = {}
for image, row_id, site in loader:
site = site[0]
row_id = row_id[0]
image = image.to(device ).float()
if site in {"site_1", "site_2"}:
image = image.to(device ).float()
with torch.no_grad() :
prediction = model(image)
 proba = prediction.view(-1 ).sigmoid().cpu().numpy()
events = proba >= threshold
labels = np.argwhere(events ).reshape(-1 ).tolist()
else:
image = image.squeeze(0)
 batch_size = 16
whole_size = image.size(0)
 if whole_size % batch_size == 0:
n_iter = whole_size // batch_size
else:
n_iter = whole_size // batch_size + 1
all_events = set()
for batch_i in range(n_iter):
batch = image[batch_i * batch_size:(batch_i + 1)* batch_size]
if batch.ndim == 3:
batch = batch.unsqueeze(0)
 batch = batch.to(device)
 with torch.no_grad() :
prediction = model(batch)
 proba = prediction.view(batch.size(0), -1 ).sigmoid().cpu().numpy()
events = proba >= threshold
for i in range(len(events)) :
event = events[i, :]
labels = np.argwhere(event ).reshape(-1 ).tolist()
for label in labels:
all_events.add(label)
 labels = list(all_events)
 if len(labels)== 0:
prediction_dict[row_id] = "nocall"
else:
labels_str_list = list(map(lambda x: INV_BIRD_CODE[x], labels))
label_string = " ".join(labels_str_list)
 prediction_dict[row_id] = label_string
return prediction_dict
unique_audio_id = test_df.audio_id.unique()
warnings.filterwarnings("ignore")
 prediction_dfs = []
for audio_id in progress_bar(unique_audio_id):
signal, sample_rate = torchaudio.load(os.path.join(test_dir, audio_id+'.mp3'))
signal = signal.mean(0 ).cuda()
signal = torchaudio.transforms.Resample(orig_freq=sample_rate, new_freq=args['std_sr'] )(signal)
 spectrogram = AmplitudeToDB()(torch.stack([f1(signal), f2(signal), f3(signal)], axis=0)).cpu()
min_value, _ = spectrogram.view(3, -1 ).min(-1)
 max_value, _ = spectrogram.view(3, -1 ).max(-1)
 min_value, max_value = min_value.unsqueeze(-1 ).unsqueeze(-1), max_value.unsqueeze(-1 ).unsqueeze(-1)
 spectrogram =(( spectrogram - min_value)*255 /(max_value - min_value)).permute(1, 2, 0 ).numpy().astype(np.uint8)
 cv2.imwrite('cache.png', spectrogram)
 spectrogram = cv2.imread('cache.png')
 test_df_for_audio_id = test_df.query(
 f"audio_id == '{audio_id}'" ).reset_index(drop=True)
 dataset = TestDataset(test_df_for_audio_id, spectrogram)
 prediction_dict = prediction_for_clip(dataset=dataset, threshold=args['threshold'])
 row_id = list(prediction_dict.keys())
 birds = list(prediction_dict.values())
 prediction_df = pd.DataFrame({
"row_id": row_id,
"birds": birds
})
 prediction_dfs.append(prediction_df)
 prediction_df = pd.concat(prediction_dfs, axis=0, sort=False ).reset_index(drop=True)
 prediction_df['birds'].value_counts()<save_to_csv>
GBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,
max_depth=4, max_features='sqrt',
min_samples_leaf=15, min_samples_split=10,
loss='huber', random_state =5)
 score = rmsle_cv(GBoost)
 print("Gradient Boosting score: {:.4f}({:.4f})
 ".format(score.mean() , score.std()))
prediction_df.to_csv('submission.csv', index=False)
 prediction_df<import_modules>
LassoMd = lasso.fit(train.values,y_train)
 ENetMd = ENet.fit(train.values,y_train)
 KRRMd = KRR.fit(train.values,y_train)
 GBoostMd = GBoost.fit(train.values,y_train )
import numpy as np
import pandas as pd
from sklearn.ensemble import RandomForestRegressor
from sklearn.model_selection import cross_val_score
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_absolute_error
from sklearn.metrics import mean_squared_error
from sklearn.metrics import mean_squared_log_error<load_from_csv>
finalMd =(np.expm1(LassoMd.predict(test.values)) + np.expm1(ENetMd.predict(test.values)) + np.expm1(KRRMd.predict(test.values)) + np.expm1(GBoostMd.predict(test.values)))/ 4
finalMd
<load_from_csv><EOS> 
sub = pd.DataFrame()
sub['Id'] = test_ID
sub['SalePrice'] = finalMd
sub.to_csv('submission.csv',index=False )