Kaggle sushida

関連タイピング
-
Pythonで初心者が使いそうなワードのタイピング
プレイ回数468英語30秒 -
python 基本
プレイ回数338英字120打 -
python
プレイ回数303英字60秒 -
numpy(関数)
プレイ回数793英語272打 -
Pythonプログラムを打ちまくる
プレイ回数8430英語長文60秒 -
Pyhonのプログラミングで使いそうな単語・記号を集めました
プレイ回数562英語短文30秒 -
プログラミングで良く使う英単語、構文のタイピングです!
プレイ回数3366短文英字429打 -
プレイ回数106360秒
問題文
(import pandas as pd)
import pandas as pd
(import numpy as np)
import numpy as np
(import pdb; pdb.set_trace())
import pdb; pdb.set_trace()
(sub.to_csv('submission.csv', index=False))
sub.to_csv('submission.csv', index=False)
(pd.DataFrame())
pd.DataFrame()
(seed_everything(42))
seed_everything(42)
(torch.cuda.is_available())
torch.cuda.is_available()
(class MyDataset(Dataset):)
class MyDataset(Dataset):
(%matplotlib inline)
%matplotlib inline
(from sklearn.model_selection import StratifiedKFold)
from sklearn.model_selection import StratifiedKFold
(fig, axes = plt.subplots(2, 2))
fig, axes = plt.subplots(2, 2)
(import xgboost as xgb)
import xgboost as xgb
(from lightgbm import LGBMClassifier)
from lightgbm import LGBMClassifier
(for _, test_index in kf.split(x, y):)
for _, test_index in kf.split(x, y):
(train.head())
train.head()
(model = timm.create_model("resnet50"))
model = timm.create_model("resnet50")
(torch.cuda.empty_cache())
torch.cuda.empty_cache()
(img = cv2.imread(image_path))
img = cv2.imread(image_path)
(img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
(from transformers import BertModel)
from transformers import BertModel
(import albumentations as A)
import albumentations as A
(os.environ["CUDA_VISIBLE_DEVICES"] = "")
os.environ["CUDA_VISIBLE_DEVICES"] = ""
(for i, j in enumerate(tqdm(range(100))):)
for i, j in enumerate(tqdm(range(100))):
(plt.rcParams['figure.figsize'] = 30, 30)
plt.rcParams['figure.figsize'] = 30, 30
(df['target'].value_counts())
df['target'].value_counts()
(for i, row in tqdm(df.iterrows(), total=len(df)):)
for i, row in tqdm(df.iterrows(), total=len(df)):
(!nvidia-smi)
!nvidia-smi
(import tensorflow as tf)
import tensorflow as tf
(model.compile(optimizer='adam'))
model.compile(optimizer='adam')
(gc.collect())
gc.collect()
(from sklearn.metrics import roc_auc_score)
from sklearn.metrics import roc_auc_score
(kaggle competitions download -c titanic)
kaggle competitions download -c titanic
(os.makedirs(save_dir, exist_ok=True))
os.makedirs(save_dir, exist_ok=True)
(df.reset_index(drop=True))
df.reset_index(drop=True)
(df = pd.read_csv(file_name))
df = pd.read_csv(file_name)
(vectorizer = TfidfVectorizer())
vectorizer = TfidfVectorizer()
(warnings.filterwarnings('ignore'))
warnings.filterwarnings('ignore')
(plt.figure(figsize=(14,6)))
plt.figure(figsize=(14,6))
(from sklearn.ensemble import RandomForestRegressor)
from sklearn.ensemble import RandomForestRegressor
(model.fit(train_X, train_y))
model.fit(train_X, train_y)
(print(1, 2, 3, sep=' < '))
print(1, 2, 3, sep=' < ')
(np.random.randint(low=1, high=6, size=10))
np.random.randint(low=1, high=6, size=10)
(np.zeros_like(img, dtype=np.float32))
np.zeros_like(img, dtype=np.float32)
(from xgboost import XGBClassifier)
from xgboost import XGBClassifier
(df.iloc[100:200])
df.iloc[100:200]
(print("{}: {}".format(key, value)))
print("{}: {}".format(key, value))
(docker run --rm -it kaggle/python-build /bin/bash)
docker run --rm -it kaggle/python-build /bin/bash
(docker-compose up)
docker-compose up
(git push origin master)
git push origin master
(s = pd.Series([1, 3, 5, np.nan, 6, 8]))
s = pd.Series([1, 3, 5, np.nan, 6, 8])
(df.sort_index(axis=1, ascending=False))
df.sort_index(axis=1, ascending=False)
(df.iloc[[1, 2, 4], [0, 2]])
df.iloc[[1, 2, 4], [0, 2]]
(df[df["A"] > 0])
df[df["A"] > 0]
(df.dropna(how="any"))
df.dropna(how="any")
(df.fillna(value=5))
df.fillna(value=5)
(df.apply(np.cumsum))
df.apply(np.cumsum)
(df.groupby("target").sum())
df.groupby("target").sum()
(np.arange(15).reshape(3, 5))
np.arange(15).reshape(3, 5)
(np.array([1, 2, 3, 4]))
np.array([1, 2, 3, 4])
(np.transpose(x, (1, 0, 2)).shape)
np.transpose(x, (1, 0, 2)).shape
(nn.Linear(512, 512))
nn.Linear(512, 512)
(nn.CrossEntropyLoss())
nn.CrossEntropyLoss()
(torch.optim.SGD(model.parameters(), lr=1e-3))
torch.optim.SGD(model.parameters(), lr=1e-3)
(optimizer.zero_grad())
optimizer.zero_grad()
(loss.backward())
loss.backward()
(optimizer.step())
optimizer.step()
(model.eval())
model.eval()
(torch.save(model.state_dict(), "model.pth"))
torch.save(model.state_dict(), "model.pth")
(with torch.no_grad():)
with torch.no_grad():
(nn.Parameter(torch.zeros(10)))
nn.Parameter(torch.zeros(10))
(nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1))
nn.Conv2d(3, 32, kernel_size=3, stride=1, padding=1)
(lambda x: x.view(x.size(0), -1))
lambda x: x.view(x.size(0), -1)
(x = np.linspace(-math.pi, math.pi, 2000))
x = np.linspace(-math.pi, math.pi, 2000)
(y = np.sin(x))
y = np.sin(x)
(torch.nn.MSELoss(reduction='sum'))
torch.nn.MSELoss(reduction='sum')
(boxes.append([xmin, ymin, xmax, ymax]))
boxes.append([xmin, ymin, xmax, ymax])
(img, target = self.transforms(img, target))
img, target = self.transforms(img, target)
(if __name__ == "__main__":)
if __name__ == "__main__":
(import os)
import os
(import collections)
import collections
(import json)
import json
(import numpy as np)
import numpy as np
(import tensorflow as tf)
import tensorflow as tf
(from tensorflow import keras)
from tensorflow import keras
(from tensorflow.keras import layers)
from tensorflow.keras import layers
(import tensorflow_hub as hub)
import tensorflow_hub as hub
(import tensorflow_text as text)
import tensorflow_text as text
(import tensorflow_addons as tfa)
import tensorflow_addons as tfa
(import matplotlib.pyplot as plt)
import matplotlib.pyplot as plt
(import matplotlib.image as mpimg)
import matplotlib.image as mpimg
(from tqdm import tqdm)
from tqdm import tqdm
(metric = "sparse_categorical_accuracy")
metric = "sparse_categorical_accuracy"
(plt.figure())
plt.figure()
(plt.plot(history.history[metric]))
plt.plot(history.history[metric])
(plt.plot(history.history["val_" + metric]))
plt.plot(history.history["val_" + metric])
(plt.title("model " + metric))
plt.title("model " + metric)
(plt.ylabel(metric, fontsize="large"))
plt.ylabel(metric, fontsize="large")
(plt.xlabel("epoch", fontsize="large"))
plt.xlabel("epoch", fontsize="large")
(plt.legend(["train", "val"], loc="best"))
plt.legend(["train", "val"], loc="best")
(plt.show())
plt.show()