path
stringlengths 13
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sequencelengths 1
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stringlengths 0
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stringclasses 1
value |
|---|---|---|---|
32062669/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
test_df.head(5) | code |
32062669/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
train_df_copy.head(5) | code |
32062669/cell_15 | [
"text_html_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
gpu_status = torch.cuda.is_available()
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
class LeafDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.transform = transform
def __len__(self):
return self.df.shape[0]
def __getitem__(self, idx):
img_src = '../input/plant-pathology-2020-fgvc7/images/' + self.df.loc[idx, 'image_id'] + '.jpg'
image = PIL.Image.open(img_src).convert('RGB')
if self.transform:
image = self.transform(image)
if self.df.shape[1] == 5:
labels = self.df.loc[idx, ['healthy', 'multiple_diseases', 'rust', 'scab']].values
labels = torch.from_numpy(labels.astype(np.uint8))
labels = labels.unsqueeze(-1).long()
labels = labels.numpy().tolist().index([1])
labels = torch.from_numpy(np.asarray(labels))
return (image, labels)
else:
return image
leaf_sample_dataset = LeafDataset(df=train_df, transform=None)
fig, ax = plt.subplots(1,3)
for i in range(3):
img, label = leaf_sample_dataset[i]
ax[i].imshow(img)
print(type(img), img.size,label)
leaf_transform = transforms.Compose([transforms.Resize((512, 512)), transforms.CenterCrop((384, 384)), transforms.RandomAffine(degrees=15), transforms.RandomHorizontalFlip(p=0.4), transforms.RandomVerticalFlip(p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
leaf_train_dataset = LeafDataset(df=train_df, transform=leaf_transform)
leaf_train_loader = DataLoader(leaf_train_dataset, shuffle=True, batch_size=16)
images, labels = next(iter(leaf_train_loader))
print(len(leaf_train_dataset)) | code |
32062669/cell_16 | [
"text_plain_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
gpu_status = torch.cuda.is_available()
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
class LeafDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.transform = transform
def __len__(self):
return self.df.shape[0]
def __getitem__(self, idx):
img_src = '../input/plant-pathology-2020-fgvc7/images/' + self.df.loc[idx, 'image_id'] + '.jpg'
image = PIL.Image.open(img_src).convert('RGB')
if self.transform:
image = self.transform(image)
if self.df.shape[1] == 5:
labels = self.df.loc[idx, ['healthy', 'multiple_diseases', 'rust', 'scab']].values
labels = torch.from_numpy(labels.astype(np.uint8))
labels = labels.unsqueeze(-1).long()
labels = labels.numpy().tolist().index([1])
labels = torch.from_numpy(np.asarray(labels))
return (image, labels)
else:
return image
leaf_sample_dataset = LeafDataset(df=train_df, transform=None)
fig, ax = plt.subplots(1,3)
for i in range(3):
img, label = leaf_sample_dataset[i]
ax[i].imshow(img)
print(type(img), img.size,label)
leaf_transform = transforms.Compose([transforms.Resize((512, 512)), transforms.CenterCrop((384, 384)), transforms.RandomAffine(degrees=15), transforms.RandomHorizontalFlip(p=0.4), transforms.RandomVerticalFlip(p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
leaf_train_dataset = LeafDataset(df=train_df, transform=leaf_transform)
leaf_train_loader = DataLoader(leaf_train_dataset, shuffle=True, batch_size=16)
images, labels = next(iter(leaf_train_loader))
dataset_size = len(leaf_train_dataset)
indices = list(range(dataset_size))
np.random.shuffle(indices)
split = int(np.floor(0.2 * dataset_size))
train_idx, val_idx = (indices[split:], indices[:split])
print(split)
print(len(train_idx), len(val_idx))
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(val_idx) | code |
32062669/cell_31 | [
"text_plain_output_1.png"
] | from IPython.display import FileLink
from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import datetime
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
gpu_status = torch.cuda.is_available()
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
class LeafDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.transform = transform
def __len__(self):
return self.df.shape[0]
def __getitem__(self, idx):
img_src = '../input/plant-pathology-2020-fgvc7/images/' + self.df.loc[idx, 'image_id'] + '.jpg'
image = PIL.Image.open(img_src).convert('RGB')
if self.transform:
image = self.transform(image)
if self.df.shape[1] == 5:
labels = self.df.loc[idx, ['healthy', 'multiple_diseases', 'rust', 'scab']].values
labels = torch.from_numpy(labels.astype(np.uint8))
labels = labels.unsqueeze(-1).long()
labels = labels.numpy().tolist().index([1])
labels = torch.from_numpy(np.asarray(labels))
return (image, labels)
else:
return image
leaf_sample_dataset = LeafDataset(df=train_df, transform=None)
fig, ax = plt.subplots(1,3)
for i in range(3):
img, label = leaf_sample_dataset[i]
ax[i].imshow(img)
print(type(img), img.size,label)
leaf_transform = transforms.Compose([transforms.Resize((512, 512)), transforms.CenterCrop((384, 384)), transforms.RandomAffine(degrees=15), transforms.RandomHorizontalFlip(p=0.4), transforms.RandomVerticalFlip(p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
leaf_train_dataset = LeafDataset(df=train_df, transform=leaf_transform)
leaf_train_loader = DataLoader(leaf_train_dataset, shuffle=True, batch_size=16)
images, labels = next(iter(leaf_train_loader))
dataset_size = len(leaf_train_dataset)
indices = list(range(dataset_size))
np.random.shuffle(indices)
split = int(np.floor(0.2 * dataset_size))
train_idx, val_idx = (indices[split:], indices[:split])
train_sampler = SubsetRandomSampler(train_idx)
valid_sampler = SubsetRandomSampler(val_idx)
leaf_train_loader = DataLoader(leaf_train_dataset, sampler=train_sampler, batch_size=64)
leaf_valid_loader = DataLoader(leaf_train_dataset, sampler=valid_sampler, batch_size=64)
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
leaf_test_dataset = LeafDataset(df=test_df, transform=leaf_transform)
leaf_test_loader = DataLoader(leaf_test_dataset, batch_size=64)
test_images = next(iter(leaf_test_loader))
diagnosis = ['healthy', 'multiple_diseases', 'rust', 'scab']
train_images, train_labels = next(iter(leaf_train_loader))
fig = plt.figure(figsize=(25,4))
for idx in np.arange(8):
ax = fig.add_subplot(2, 16/2, idx+1, xticks=[], yticks=[])
plt.imshow(train_images[idx].numpy().transpose(1,2,0))
ax.set_title(diagnosis[labels[idx]])
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 8, 3, padding=1)
self.conv2 = nn.Conv2d(8, 16, 3, padding=1)
self.conv3 = nn.Conv2d(16, 32, 3, padding=1)
self.conv4 = nn.Conv2d(32, 64, 3, padding=1)
self.conv5 = nn.Conv2d(64, 128, 3, padding=1)
self.conv6 = nn.Conv2d(128, 256, 2, padding=1)
self.conv7 = nn.Conv2d(256, 512, 2, padding=1)
self.pool2 = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(12 * 12 * 512, 2048)
self.fc2 = nn.Linear(2048, 4)
self.dropout = nn.Dropout(0.2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool2(F.relu(self.conv2(x)))
x = F.relu(self.conv3(x))
x = self.pool2(F.relu(self.conv4(x)))
x = self.pool2(F.relu(self.conv5(x)))
x = self.pool2(F.relu(self.conv6(x)))
x = self.pool2(F.relu(self.conv7(x)))
x = x.view(-1, 12 * 12 * 512)
x = self.dropout(x)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
model = Net()
if gpu_status:
model.cuda()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.0008)
no_epochs = 40
valid_loss_min = np.Inf
curr_time = datetime.datetime.now()
curr_timestamp = str(datetime.datetime.now())
for epoch in range(1, no_epochs + 1):
train_loss = 0.0
valid_loss = 0.0
model.train()
for data, target in leaf_train_loader:
if gpu_status:
data = data.cuda()
target = target.cuda()
optimizer.zero_grad()
output = model(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
train_loss += loss.item() * data.size(0)
model.eval()
for data, target in leaf_valid_loader:
if gpu_status:
data = data.cuda()
target = target.cuda()
output = model(data)
loss = criterion(output, target)
valid_loss += loss.item() * data.size(0)
train_loss = train_loss / len(leaf_train_loader.dataset)
valid_loss = valid_loss / len(leaf_valid_loader.dataset)
if valid_loss < valid_loss_min:
torch.save(model.state_dict(), 'Kaggle_kernel_model_apple_leaf' + curr_timestamp + '.pt')
valid_loss_min = valid_loss
file_name = 'Kaggle_kernel_model_apple_leaf' + str(curr_timestamp)
model.load_state_dict(torch.load(file_name + '.pt'))
from IPython.display import FileLink
FileLink(file_name + '.pt') | code |
32062669/cell_24 | [
"text_html_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
import torch.nn as nn
import torch.nn.functional as F
gpu_status = torch.cuda.is_available()
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
class LeafDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.transform = transform
def __len__(self):
return self.df.shape[0]
def __getitem__(self, idx):
img_src = '../input/plant-pathology-2020-fgvc7/images/' + self.df.loc[idx, 'image_id'] + '.jpg'
image = PIL.Image.open(img_src).convert('RGB')
if self.transform:
image = self.transform(image)
if self.df.shape[1] == 5:
labels = self.df.loc[idx, ['healthy', 'multiple_diseases', 'rust', 'scab']].values
labels = torch.from_numpy(labels.astype(np.uint8))
labels = labels.unsqueeze(-1).long()
labels = labels.numpy().tolist().index([1])
labels = torch.from_numpy(np.asarray(labels))
return (image, labels)
else:
return image
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(3, 8, 3, padding=1)
self.conv2 = nn.Conv2d(8, 16, 3, padding=1)
self.conv3 = nn.Conv2d(16, 32, 3, padding=1)
self.conv4 = nn.Conv2d(32, 64, 3, padding=1)
self.conv5 = nn.Conv2d(64, 128, 3, padding=1)
self.conv6 = nn.Conv2d(128, 256, 2, padding=1)
self.conv7 = nn.Conv2d(256, 512, 2, padding=1)
self.pool2 = nn.MaxPool2d(2, 2)
self.fc1 = nn.Linear(12 * 12 * 512, 2048)
self.fc2 = nn.Linear(2048, 4)
self.dropout = nn.Dropout(0.2)
def forward(self, x):
x = F.relu(self.conv1(x))
x = self.pool2(F.relu(self.conv2(x)))
x = F.relu(self.conv3(x))
x = self.pool2(F.relu(self.conv4(x)))
x = self.pool2(F.relu(self.conv5(x)))
x = self.pool2(F.relu(self.conv6(x)))
x = self.pool2(F.relu(self.conv7(x)))
x = x.view(-1, 12 * 12 * 512)
x = self.dropout(x)
x = F.relu(self.fc1(x))
x = self.dropout(x)
x = self.fc2(x)
return x
model = Net()
print(model)
if gpu_status:
model.cuda() | code |
32062669/cell_14 | [
"text_html_output_1.png"
] | from torch.utils.data import Dataset, DataLoader, SubsetRandomSampler
from torchvision import transforms
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import torch
gpu_status = torch.cuda.is_available()
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
class LeafDataset(Dataset):
def __init__(self, df, transform=None):
self.df = df
self.transform = transform
def __len__(self):
return self.df.shape[0]
def __getitem__(self, idx):
img_src = '../input/plant-pathology-2020-fgvc7/images/' + self.df.loc[idx, 'image_id'] + '.jpg'
image = PIL.Image.open(img_src).convert('RGB')
if self.transform:
image = self.transform(image)
if self.df.shape[1] == 5:
labels = self.df.loc[idx, ['healthy', 'multiple_diseases', 'rust', 'scab']].values
labels = torch.from_numpy(labels.astype(np.uint8))
labels = labels.unsqueeze(-1).long()
labels = labels.numpy().tolist().index([1])
labels = torch.from_numpy(np.asarray(labels))
return (image, labels)
else:
return image
leaf_sample_dataset = LeafDataset(df=train_df, transform=None)
fig, ax = plt.subplots(1,3)
for i in range(3):
img, label = leaf_sample_dataset[i]
ax[i].imshow(img)
print(type(img), img.size,label)
leaf_transform = transforms.Compose([transforms.Resize((512, 512)), transforms.CenterCrop((384, 384)), transforms.RandomAffine(degrees=15), transforms.RandomHorizontalFlip(p=0.4), transforms.RandomVerticalFlip(p=0.3), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])
leaf_train_dataset = LeafDataset(df=train_df, transform=leaf_transform)
leaf_train_loader = DataLoader(leaf_train_dataset, shuffle=True, batch_size=16)
images, labels = next(iter(leaf_train_loader))
print(labels[0])
print(len(images))
plt.imshow(images[0].numpy().transpose((1, 2, 0))) | code |
32062669/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
def get_label(row):
for c in train_df.columns[1:]:
if row[c] == 1:
return c
train_df_copy = train_df.copy()
train_df_copy['label'] = train_df_copy.apply(get_label, axis=1)
sample_img = train_df.iloc[1, 0]
sample_labels = train_df.iloc[1, :]
sample_labels = np.asarray(sample_labels)
print('Image Name:{}'.format(sample_img))
print('Image Labels:{}'.format(sample_labels)) | code |
32062669/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
test_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/test.csv')
test_df.head(5) | code |
32062669/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/plant-pathology-2020-fgvc7/train.csv')
train_df.describe(include='all') | code |
122256053/cell_4 | [
"text_plain_output_1.png"
] | from io import open
import glob
import os
import string
import unicodedata
from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os
def findFiles(path):
return glob.glob(path)
print(findFiles('/kaggle/input/classifying-names-data-from-pytorch-tutorial/names/*.txt'))
import unicodedata
import string
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)
def unicodeToAscii(s):
return ''.join((c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn' and c in all_letters))
print(unicodeToAscii('Ślusàrski'))
category_lines = {}
all_categories = []
def readLines(filename):
lines = open(filename, encoding='utf-8').read().strip().split('\n')
return [unicodeToAscii(line) for line in lines]
for filename in findFiles('/kaggle/input/classifying-names-data-from-pytorch-tutorial/names/*.txt'):
category = os.path.splitext(os.path.basename(filename))[0]
all_categories.append(category)
lines = readLines(filename)
category_lines[category] = lines
n_categories = len(all_categories) | code |
122256053/cell_6 | [
"text_plain_output_1.png"
] | from io import open
import glob
import os
import string
import unicodedata
from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os
def findFiles(path):
return glob.glob(path)
import unicodedata
import string
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)
def unicodeToAscii(s):
return ''.join((c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn' and c in all_letters))
category_lines = {}
all_categories = []
def readLines(filename):
lines = open(filename, encoding='utf-8').read().strip().split('\n')
return [unicodeToAscii(line) for line in lines]
for filename in findFiles('/kaggle/input/classifying-names-data-from-pytorch-tutorial/names/*.txt'):
category = os.path.splitext(os.path.basename(filename))[0]
all_categories.append(category)
lines = readLines(filename)
category_lines[category] = lines
n_categories = len(all_categories)
print(category_lines['Italian'][:5]) | code |
122256053/cell_1 | [
"text_plain_output_1.png"
] | !pip install git+https://github.com/Ilykuleshov/pytorch-toolz.git | code |
122256053/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from io import open
import glob
import os
import string
import torch
import unicodedata
from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os
def findFiles(path):
return glob.glob(path)
import unicodedata
import string
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)
def unicodeToAscii(s):
return ''.join((c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn' and c in all_letters))
category_lines = {}
all_categories = []
def readLines(filename):
lines = open(filename, encoding='utf-8').read().strip().split('\n')
return [unicodeToAscii(line) for line in lines]
for filename in findFiles('/kaggle/input/classifying-names-data-from-pytorch-tutorial/names/*.txt'):
category = os.path.splitext(os.path.basename(filename))[0]
all_categories.append(category)
lines = readLines(filename)
category_lines[category] = lines
n_categories = len(all_categories)
import torch
import torch.nn as nn
def letterToIndex(letter):
return all_letters.find(letter)
def letterToTensor(letter):
tensor = torch.zeros(1, n_letters)
tensor[0][letterToIndex(letter)] = 1
return tensor
def lineToList(line):
lst = []
for li, letter in enumerate(line):
tensor = torch.zeros(1, n_letters)
tensor[0][letterToIndex(letter)] = 1
lst.append(tensor)
return lst
print(letterToTensor('J'))
print(len(lineToList('Jones'))) | code |
122256053/cell_10 | [
"text_plain_output_1.png"
] | from io import open
from pytorch_toolz.functools import Reduce
from pytorch_toolz.functools import Sequential
from pytorch_toolz.operator import Apply
import glob
import os
import string
import torch
import torch.nn as nn
import unicodedata
from __future__ import unicode_literals, print_function, division
from io import open
import glob
import os
def findFiles(path):
return glob.glob(path)
import unicodedata
import string
all_letters = string.ascii_letters + " .,;'"
n_letters = len(all_letters)
def unicodeToAscii(s):
return ''.join((c for c in unicodedata.normalize('NFD', s) if unicodedata.category(c) != 'Mn' and c in all_letters))
category_lines = {}
all_categories = []
def readLines(filename):
lines = open(filename, encoding='utf-8').read().strip().split('\n')
return [unicodeToAscii(line) for line in lines]
for filename in findFiles('/kaggle/input/classifying-names-data-from-pytorch-tutorial/names/*.txt'):
category = os.path.splitext(os.path.basename(filename))[0]
all_categories.append(category)
lines = readLines(filename)
category_lines[category] = lines
n_categories = len(all_categories)
import torch
import torch.nn as nn
def letterToIndex(letter):
return all_letters.find(letter)
def letterToTensor(letter):
tensor = torch.zeros(1, n_letters)
tensor[0][letterToIndex(letter)] = 1
return tensor
def lineToList(line):
lst = []
for li, letter in enumerate(line):
tensor = torch.zeros(1, n_letters)
tensor[0][letterToIndex(letter)] = 1
lst.append(tensor)
return lst
from pytorch_toolz.operator import Apply
from pytorch_toolz.functools import Reduce
from itertools import accumulate, repeat, chain
from typing import TypeVar, Tuple, Generic, Callable, overload
from torch.nn import Module, Sequential as Sequential_
from torch import Tensor
from functools import reduce
from pytorch_toolz.functools import Sequential
class RNN(nn.Module):
def __init__(self, input_size, hidden_size):
super(RNN, self).__init__()
self.hidden_size = hidden_size
self.i2h = nn.Linear(input_size + hidden_size, hidden_size)
def forward(self, input, hidden):
combined = torch.cat((input, hidden), 1)
hidden = self.i2h(combined)
return hidden
def initHidden(self):
return torch.zeros(1, self.hidden_size)
n_hidden = 128
rnn_cell = RNN(n_letters, n_hidden)
cell_swap_args = Sequential(Apply(lambda *args: reversed(args)), rnn_cell, unpack=True)
rnn = nn.Sequential(Reduce(cell_swap_args, rnn_cell.initHidden()), nn.Linear(n_hidden, n_categories), nn.LogSoftmax(dim=1)) | code |
18105807/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Survials_By_Age = train_data.groupby('Age')['Survived'].sum().reset_index()
Survials_By_Age_Segment = []
age_difference = 5
max_age = 70
for i in range(max_age // age_difference):
s = 0
for j in range(age_difference):
s = s + Survials_By_Age.loc[[i * age_difference + j, 'Age'], 'Survived'][0]
Survials_By_Age_Segment.append(s)
Survials_By_Age_Segment = pd.Series(Survials_By_Age_Segment, index=list(range(0, max_age, age_difference)))
sns.barplot(y=Survials_By_Age_Segment, x=Survials_By_Age_Segment.index)
print(Survials_By_Age_Segment) | code |
18105807/cell_1 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
import seaborn as sns
import os
print(os.listdir('../input'))
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.head() | code |
18105807/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Survials_By_Age = train_data.groupby('Age')['Survived'].sum().reset_index()
Survials_By_Age_Segment = []
age_difference = 5
max_age = 70
for i in range(max_age // age_difference):
s = 0
for j in range(age_difference):
s = s + Survials_By_Age.loc[[i * age_difference + j, 'Age'], 'Survived'][0]
Survials_By_Age_Segment.append(s)
Survials_By_Age_Segment = pd.Series(Survials_By_Age_Segment, index=list(range(0, max_age, age_difference)))
boolean_Survivals = train_data['Survived'] == 1
Survivals = train_data[boolean_Survivals]
sns.barplot(y='title', x='average_rating', data=ayu) | code |
18105807/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeRegressor
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import os
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
Survials_By_Age = train_data.groupby('Age')['Survived'].sum().reset_index()
Survials_By_Age_Segment = []
age_difference = 5
max_age = 70
for i in range(max_age // age_difference):
s = 0
for j in range(age_difference):
s = s + Survials_By_Age.loc[[i * age_difference + j, 'Age'], 'Survived'][0]
Survials_By_Age_Segment.append(s)
Survials_By_Age_Segment = pd.Series(Survials_By_Age_Segment, index=list(range(0, max_age, age_difference)))
from sklearn.tree import DecisionTreeRegressor
titanic_model = DecisionTreeRegressor(random_state=1)
X = pd.DataFrame(train_data['Age'].fillna(0))
y = train_data['Survived']
titanic_model.fit(X, y)
X_test = pd.DataFrame(test_data['Age'].fillna(0))
prediction = titanic_model.predict(X_test)
L = []
for i in range(len(prediction)):
if prediction[i] > 0.5:
L.append(1)
else:
L.append(0)
L = pd.DataFrame({'PassengerId': test_data['PassengerId'], 'Survived': L})
L.to_csv('Test_1.csv')
print(L) | code |
106210695/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum()
df.describe().T
catcols = df.select_dtypes(include=['object']).columns.to_list()
numcols = [col for col in df.columns if col not in catcols]
g = sns.FacetGrid(data=df, col='y', height=3, aspect=1.2)
g.map(sns.histplot, 'age', bins=20)
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.2)
g.map(sns.distplot, 'balance')
plt.show() | code |
106210695/cell_30 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum()
df.describe().T
catcols = df.select_dtypes(include=['object']).columns.to_list()
numcols = [col for col in df.columns if col not in catcols]
g = sns.FacetGrid(data=df, col='y', height=3, aspect=1.2)
g.map(sns.histplot, 'age', bins=20)
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.2)
g.map(sns.distplot, 'balance')
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.3, sharey=True, xlim=(-50, 3000))
g.map(sns.distplot, 'duration') | code |
106210695/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum()
df.describe().T
catcols = df.select_dtypes(include=['object']).columns.to_list()
numcols = [col for col in df.columns if col not in catcols]
g = sns.FacetGrid(data=df, col='y', height=3, aspect=1.2)
g.map(sns.histplot, 'age', bins=20)
plt.figure(figsize=(8, 4))
sns.boxplot(x='age', y='y', data=df) | code |
106210695/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum()
df.describe().T
catcols = df.select_dtypes(include=['object']).columns.to_list()
numcols = [col for col in df.columns if col not in catcols]
g = sns.FacetGrid(data=df, col='y', height=3, aspect=1.2)
g.map(sns.histplot, 'age', bins=20)
plt.show() | code |
106210695/cell_32 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum()
df.describe().T
catcols = df.select_dtypes(include=['object']).columns.to_list()
numcols = [col for col in df.columns if col not in catcols]
g = sns.FacetGrid(data=df, col='y', height=3, aspect=1.2)
g.map(sns.histplot, 'age', bins=20)
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.2)
g.map(sns.distplot, 'balance')
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.3, sharey=True, xlim=(-50, 3000))
g.map(sns.distplot, 'duration')
plt.figure(figsize=(10, 4))
sns.boxplot(x='duration', y='y', data=df) | code |
106210695/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.info() | code |
106210695/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum()
df.describe().T
catcols = df.select_dtypes(include=['object']).columns.to_list()
numcols = [col for col in df.columns if col not in catcols]
print('Numeric columns: {}'.format(numcols))
print()
print('Categorical Columns: {}'.format(catcols)) | code |
106210695/cell_38 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum()
df.describe().T
catcols = df.select_dtypes(include=['object']).columns.to_list()
numcols = [col for col in df.columns if col not in catcols]
g = sns.FacetGrid(data=df, col='y', height=3, aspect=1.2)
g.map(sns.histplot, 'age', bins=20)
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.2)
g.map(sns.distplot, 'balance')
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.3, sharey=True, xlim=(-50, 3000))
g.map(sns.distplot, 'duration')
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.2, xlim=(-1, 30))
g.map(sns.distplot, 'campaign', color='red')
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.3)
g.map(sns.distplot, 'pdays') | code |
106210695/cell_35 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum()
df.describe().T
catcols = df.select_dtypes(include=['object']).columns.to_list()
numcols = [col for col in df.columns if col not in catcols]
g = sns.FacetGrid(data=df, col='y', height=3, aspect=1.2)
g.map(sns.histplot, 'age', bins=20)
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.2)
g.map(sns.distplot, 'balance')
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.3, sharey=True, xlim=(-50, 3000))
g.map(sns.distplot, 'duration')
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.2, xlim=(-1, 30))
g.map(sns.distplot, 'campaign', color='red') | code |
106210695/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum()
df.describe().T
catcols = df.select_dtypes(include=['object']).columns.to_list()
numcols = [col for col in df.columns if col not in catcols]
g = sns.FacetGrid(data=df, col='y', height=3, aspect=1.2)
g.map(sns.histplot, 'age', bins=20)
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.2)
g.map(sns.distplot, 'balance')
plt.figure(figsize=(10, 4))
sns.boxplot(x='balance', y='y', data=df) | code |
106210695/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum() | code |
106210695/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum()
df.describe().T
catcols = df.select_dtypes(include=['object']).columns.to_list()
numcols = [col for col in df.columns if col not in catcols]
g = sns.FacetGrid(data=df, col='y', height=3, aspect=1.2)
g.map(sns.histplot, 'age', bins=20)
g = sns.FacetGrid(data=df, col='y', height=4, aspect=1.2)
g.map(sns.distplot, 'balance')
plt.figure(figsize=(10, 5))
sns.countplot(x='day', data=df) | code |
106210695/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
df = full_data
df.isnull().sum()
df.describe().T | code |
106210695/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
plt.style.use('ggplot')
pd.set_option('max_columns', 200)
full_data = pd.read_csv('../input/portuguese-bank-marketing-data-set/bank-full.csv', sep=';', na_values='None')
full_data.head(2) | code |
106196764/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from torchvision import datasets, transforms
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms
import time
transform_train = transforms.Compose([transforms.RandomHorizontalFlip(p=0.5), transforms.ToTensor(), transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))])
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize(std=(0.485, 0.456, 0.406), mean=(0.226, 0.224, 0.225))])
train_dataset = datasets.CIFAR10(root='../input/cifar10/cifar-10-batches-py', train=True, transform=transform_train, download=True)
test_dataset = datasets.CIFAR10(root='../input/cifar10/cifar-10-batches-py', train=False, transform=transform, download=True) | code |
128040664/cell_21 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train0.head() | code |
128040664/cell_13 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
print(classification_report(test2['label'], test2['prediction'])) | code |
128040664/cell_9 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test8.head() | code |
128040664/cell_25 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/train.csv')
train4.head() | code |
128040664/cell_4 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test3.head() | code |
128040664/cell_34 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/train.csv')
print(classification_report(train4['label'], train4['prediction'])) | code |
128040664/cell_23 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train2.head() | code |
128040664/cell_30 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/train.csv')
train5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/train.csv')
train6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/train.csv')
train7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/train.csv')
train8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/train.csv')
train9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/train.csv')
train9.head() | code |
128040664/cell_33 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
print(classification_report(train3['label'], train3['prediction'])) | code |
128040664/cell_20 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
print(classification_report(test9['label'], test9['prediction'])) | code |
128040664/cell_6 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test5.head() | code |
128040664/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/train.csv')
train5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/train.csv')
train6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/train.csv')
train7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/train.csv')
train8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/train.csv')
train8.head() | code |
128040664/cell_26 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/train.csv')
train5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/train.csv')
train5.head() | code |
128040664/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test1.head() | code |
128040664/cell_11 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
from sklearn.metrics import classification_report
print(classification_report(test0['label'], test0['prediction'])) | code |
128040664/cell_19 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
print(classification_report(test8['label'], test8['prediction'])) | code |
128040664/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test0.head() | code |
128040664/cell_7 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test6.head() | code |
128040664/cell_18 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
print(classification_report(test7['label'], test7['prediction'])) | code |
128040664/cell_32 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
print(classification_report(train2['label'], train2['prediction'])) | code |
128040664/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/train.csv')
train5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/train.csv')
train6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/train.csv')
train7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/train.csv')
train7.head() | code |
128040664/cell_8 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test7.head() | code |
128040664/cell_15 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
print(classification_report(test4['label'], test4['prediction'])) | code |
128040664/cell_16 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
print(classification_report(test5['label'], test5['prediction'])) | code |
128040664/cell_38 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/train.csv')
train5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/train.csv')
train6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/train.csv')
train7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/train.csv')
train8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/train.csv')
print(classification_report(train8['label'], train8['prediction'])) | code |
128040664/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test2.head() | code |
128040664/cell_17 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
print(classification_report(test6['label'], test6['prediction'])) | code |
128040664/cell_35 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/train.csv')
train5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/train.csv')
print(classification_report(train5['label'], train5['prediction'])) | code |
128040664/cell_31 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
print(classification_report(train0['label'], train0['prediction'])) | code |
128040664/cell_24 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train3.head() | code |
128040664/cell_14 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
print(classification_report(test3['label'], test3['prediction'])) | code |
128040664/cell_22 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train1.head() | code |
128040664/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
test9.head() | code |
128040664/cell_27 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/train.csv')
train5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/train.csv')
train6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/train.csv')
train6.head() | code |
128040664/cell_37 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/train.csv')
train5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/train.csv')
train6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/train.csv')
train7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/train.csv')
print(classification_report(train7['label'], train7['prediction'])) | code |
128040664/cell_12 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
print(classification_report(test1['label'], test1['prediction'])) | code |
128040664/cell_5 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test4.head() | code |
128040664/cell_36 | [
"text_html_output_1.png"
] | from sklearn.metrics import classification_report
import pandas as pd
import pandas as pd
test0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/test.csv')
test1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/test.csv')
test2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/test.csv')
test3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/test.csv')
test4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/test.csv')
test5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/test.csv')
test6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/test.csv')
test7 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-7/test.csv')
test8 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-8/test.csv')
test9 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-9/test.csv')
train0 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-0/train.csv')
train1 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-1/train.csv')
train2 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-2/train.csv')
train3 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-3/train.csv')
train4 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-4/train.csv')
train5 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-5/train.csv')
train6 = pd.read_csv('/kaggle/input/resnet50-cifake-batch-6/train.csv')
print(classification_report(train6['label'], train6['prediction'])) | code |
1009148/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | skip_border = 50
skip_middle = 3
fig, (ax1, ax2, ax3) = plt.subplots(1, 3, figsize=(14, 5))
ax1.imshow(montage2d(filter_fossil_data[skip_border:-skip_border:skip_middle]), **im_args)
ax1.set_title('Axial Slices')
ax1.axis('off')
ax2.imshow(montage2d(filter_fossil_data.transpose(1, 2, 0)[skip_border:-skip_border:skip_middle]), **im_args)
ax2.set_title('Saggital Slices')
ax2.axis('off')
ax3.imshow(montage2d(filter_fossil_data.transpose(2, 0, 1)[skip_border:-skip_border:skip_middle]), **im_args)
ax3.set_title('Coronal Slices')
ax3.axis('off') | code |
1009148/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.imshow(montage2d(fossil_data), cmap='bone')
ax1.set_title('Axial Slices')
_ = ax2.hist(fossil_data.ravel(), 20)
ax2.set_title('Overall Histogram') | code |
1009148/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
test_slice = fossil_data[int(fossil_data.shape[0] / 2)]
ax1.imshow(test_slice, cmap='bone')
ax1.set_title('Axial Slices')
_ = ax2.imshow(test_slice > 70)
ax2.set_title('Thresheld Slice') | code |
1009148/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from scipy.ndimage.filters import median_filter
filter_fossil_data = median_filter(fossil_data, (3, 3, 3))
slice_idx = int(fossil_data.shape[0] / 2)
test_slice = fossil_data[slice_idx]
test_filt_slice = filter_fossil_data[slice_idx]
im_args = dict(cmap='bone', vmin=50, vmax=70)
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.imshow(test_slice, **im_args)
ax1.set_title('Unfiltered Slice')
_ = ax2.imshow(test_filt_slice, **im_args)
ax2.set_title('Filtered Slice') | code |
1009148/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from scipy.ndimage import zoom
from skimage import measure
import matplotlib.pyplot as plt
import numpy as np # linear algebra
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from skimage import measure
def show_3d_mesh(p, threshold):
verts, faces = measure.marching_cubes(p, threshold)
fig = plt.figure(figsize=(10, 10))
ax = fig.add_subplot(111, projection='3d')
mesh = Poly3DCollection(verts[faces], alpha=0.15, edgecolor='none', linewidth = 0.1)
mesh.set_facecolor([.1, 1, .1])
mesh.set_edgecolor([1, 0, 0])
ax.add_collection3d(mesh)
ax.set_xlim(0, p.shape[0])
ax.set_ylim(0, p.shape[1])
ax.set_zlim(0, p.shape[2])
ax.view_init(80, 5)
return fig
from scipy.ndimage import zoom
fossil_downscale = zoom(closed_fossil_data.astype(np.float32), 0.25)
_ = show_3d_mesh(filter_fossil_data, 0.5) | code |
1009148/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage.io import imread
fossil_path = '../input/Gut-PhilElvCropped.tif'
fossil_data = imread(fossil_path)
print('Loading Fossil Data sized {}'.format(fossil_data.shape)) | code |
1009148/cell_10 | [
"text_plain_output_1.png"
] | fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
thresh_fossil_data = filter_fossil_data > 70
thresh_slice = thresh_fossil_data[slice_idx]
ax1.imshow(test_filt_slice, cmap='bone')
ax1.set_title('Filtered Slices')
_ = ax2.imshow(thresh_slice)
ax2.set_title('Slice with Threshold') | code |
1009148/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from skimage.morphology import binary_closing, ball
closed_fossil_data = binary_closing(thresh_fossil_data, ball(5))
close_slice = closed_fossil_data[slice_idx]
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 5))
ax1.imshow(test_filt_slice, cmap='bone')
ax1.set_title('Filtered Slices')
_ = ax2.imshow(close_slice)
ax2.set_title('Slice with Threshold') | code |
33098668/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train['Date'] = pd.to_datetime(train['Date'])
train.sort_values(by='Date', ascending=True, inplace=True)
train.reset_index(inplace=True)
train.drop('index', axis=1, inplace=True)
start_date = train['Date'][0]
end_date = train['Date'][844337]
pd.options.display.float_format = '{:.0f}'.format
print('Store is open but the sale is 0 :', train[(train['Open'] == 1) & (train['Sales'] == 0)].size != 0)
print('Store is closed but the sale is not 0 :', train[(train['Open'] == 0) & (train['Sales'] != 0)].size != 0) | code |
33098668/cell_9 | [
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train['Date'] = pd.to_datetime(train['Date'])
train.sort_values(by='Date', ascending=True, inplace=True)
train.reset_index(inplace=True)
train.drop('index', axis=1, inplace=True)
start_date = train['Date'][0]
end_date = train['Date'][844337]
print('Start date :', start_date)
print('End date :', end_date) | code |
33098668/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
print('train :', '\n', train.head(3), '\n', '\n')
print('test :', '\n', test.head(3), '\n', '\n')
print('store :', '\n', store.head(3)) | code |
33098668/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
print('Sample having 0 sales :', train[train['Sales'] <= 0].shape, '\n')
print('Store Open : 1,', 'Store Closed : 0')
print(train['Open'].value_counts(), '\n')
print('Sample having closed store and sale is 0 :', train[(train['Sales'] <= 0) & (train['Open'] == 0)].shape) | code |
33098668/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv') | code |
33098668/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train['Date'] = pd.to_datetime(train['Date'])
train.sort_values(by='Date', ascending=True, inplace=True)
train.reset_index(inplace=True)
train.drop('index', axis=1, inplace=True)
start_date = train['Date'][0]
end_date = train['Date'][844337]
index = train[train['StateHoliday'] == 0].index
train['StateHoliday'][index] = '0'
train['StateHoliday'].value_counts() | code |
33098668/cell_1 | [
"text_plain_output_1.png"
] | from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
import cufflinks as cf
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import datetime as dt
from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot
init_notebook_mode(connected=True)
import cufflinks as cf
cf.go_offline()
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33098668/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
print('New training dataset shape', train.shape) | code |
33098668/cell_8 | [
"image_output_5.png",
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train.info() | code |
33098668/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
print('training dataset shape', train.shape)
print('testing dataset shape', test.shape)
print('store dataset shape', store.shape) | code |
33098668/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train['Date'] = pd.to_datetime(train['Date'])
train.sort_values(by='Date', ascending=True, inplace=True)
train.reset_index(inplace=True)
train.drop('index', axis=1, inplace=True)
start_date = train['Date'][0]
end_date = train['Date'][844337]
pd.options.display.float_format = '{:.0f}'.format
plt.figure(figsize=(15, 8))
sns.scatterplot(x='Sales', y='Customers', data=train, hue='DayOfWeek', palette='coolwarm')
plt.show()
plt.figure(figsize=(15, 4))
print(train['DayOfWeek'].value_counts())
sns.countplot('DayOfWeek', data=train, palette='coolwarm')
plt.show()
g = sns.FacetGrid(row='DayOfWeek', data=train, height=3, aspect=4)
g.map(plt.scatter, 'Sales', 'Customers')
plt.show()
plt.figure(figsize=(15, 8))
sns.boxplot(y='Sales', x='DayOfWeek', data=train, palette='coolwarm')
plt.show()
plt.figure(figsize=(15, 8))
sns.boxplot(y='Customers', x='DayOfWeek', data=train, palette='coolwarm')
plt.show() | code |
33098668/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train['Date'] = pd.to_datetime(train['Date'])
train.sort_values(by='Date', ascending=True, inplace=True)
train.reset_index(inplace=True)
train.drop('index', axis=1, inplace=True)
start_date = train['Date'][0]
end_date = train['Date'][844337]
print('DayOfWeek :', train['DayOfWeek'].unique())
print('Open :', train['Open'].unique())
print('Promo :', train['Promo'].unique())
print('StateHoliday :', train['StateHoliday'].unique())
print('SchoolHoliday :', train['SchoolHoliday'].unique()) | code |
33098668/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train = train[train['Sales'] > 0]
train['Date'] = pd.to_datetime(train['Date'])
train.sort_values(by='Date', ascending=True, inplace=True)
train.reset_index(inplace=True)
train.drop('index', axis=1, inplace=True)
start_date = train['Date'][0]
end_date = train['Date'][844337]
pd.options.display.float_format = '{:.0f}'.format
train.describe() | code |
33098668/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/rossmann-store-sales/train.csv')
test = pd.read_csv('/kaggle/input/rossmann-store-sales/test.csv')
store = pd.read_csv('/kaggle/input/rossmann-store-sales/store.csv')
train.head() | code |
2001733/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
zoos = museums['Revenue'][museums['Museum Type'] == 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION']
other = museums['Revenue'][museums['Museum Type'] != 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION']
plt.hist(other, alpha=0.5, label='other')
plt.hist(zoos, label='zoos')
plt.legend(loc='upper right')
plt.title('Revenus') | code |
2001733/cell_4 | [
"text_plain_output_1.png"
] | from scipy.stats import probplot
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pylab
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
probplot(museums['Revenue'], dist='norm', plot=pylab) | code |
2001733/cell_2 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0] | code |
2001733/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
zoos = museums['Revenue'][museums['Museum Type'] == 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION']
other = museums['Revenue'][museums['Museum Type'] != 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION']
zoos.describe() | code |
2001733/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from scipy.stats import ttest_ind
from scipy.stats import probplot
import matplotlib.pyplot as plt
import pylab
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2001733/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import ttest_ind
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
museums = pd.read_csv('../input/museums.csv').dropna(subset=['Revenue'])
museums = museums[museums.Revenue != 0]
zoos = museums['Revenue'][museums['Museum Type'] == 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION']
other = museums['Revenue'][museums['Museum Type'] != 'ZOO, AQUARIUM, OR WILDLIFE CONSERVATION']
ttest_ind(zoos, other, equal_var=False) | code |
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