path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
73099078/cell_10 | [
"text_html_output_1.png"
] | y_train[:5] | code |
73099078/cell_5 | [
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
messages = pd.read_csv('../input/spam-or-ham/spam.csv', usecols=['v1', 'v2'], encoding='ISO-8859-1')
messages = messages.rename(columns={'v1': 'label', 'v2': 'message'})
y = list(messages['label'])
y[:5]
y = list(pd.get_dummies(y, drop_first=True)['spam'])
y[:5] | code |
128039963/cell_42 | [
"application_vnd.jupyter.stderr_output_9.png",
"application_vnd.jupyter.stderr_output_7.png",
"application_vnd.jupyter.stderr_output_11.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_18.png",
"application_vnd.jupyter.stderr_output_19.png",
"application_vnd.jupyter.stderr_output_13.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_5.png",
"text_plain_output_16.png",
"application_vnd.jupyter.stderr_output_15.png",
"text_plain_output_8.png",
"application_vnd.jupyter.stderr_output_17.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"text_plain_output_12.png",
"application_vnd.jupyter.stderr_output_21.png"
] | from albumentations.pytorch import ToTensorV2
from pycocotools.coco import COCO
from torch.utils.data import DataLoader, sampler, random_split, Dataset
from torchvision import datasets, models
from torchvision.utils import draw_bounding_boxes
import albumentations as A # our data augmentation library
import copy
import cv2
import math
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sys
import torch
import torchvision
def get_transforms(train=False):
if train:
transform = A.Compose([A.Resize(600, 600), A.HorizontalFlip(p=0.3), A.VerticalFlip(p=0.3), A.RandomBrightnessContrast(p=0.1), A.ColorJitter(p=0.1), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
else:
transform = A.Compose([A.Resize(600, 600), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
return transform
class AquariumDetection(datasets.VisionDataset):
def __init__(self, root, split='train', transform=None, target_transform=None, transforms=None):
super().__init__(root, transforms, transform, target_transform)
self.split = split
self.coco = COCO(os.path.join(root, split, '_annotations.coco.json'))
self.ids = list(sorted(self.coco.imgs.keys()))
self.ids = [id for id in self.ids if len(self._load_target(id)) > 0]
def _load_image(self, id: int):
path = self.coco.loadImgs(id)[0]['file_name']
image = cv2.imread(os.path.join(self.root, self.split, path))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def _load_target(self, id):
return self.coco.loadAnns(self.coco.getAnnIds(id))
def __getitem__(self, index):
id = self.ids[index]
image = self._load_image(id)
target = self._load_target(id)
target = copy.deepcopy(self._load_target(id))
boxes = [t['bbox'] + [t['category_id']] for t in target]
if self.transforms is not None:
transformed = self.transforms(image=image, bboxes=boxes)
image = transformed['image']
boxes = transformed['bboxes']
new_boxes = []
for box in boxes:
xmin = box[0]
xmax = xmin + box[2]
ymin = box[1]
ymax = ymin + box[3]
new_boxes.append([xmin, ymin, xmax, ymax])
boxes = torch.tensor(new_boxes, dtype=torch.float32)
targ = {}
targ['boxes'] = boxes
targ['labels'] = torch.tensor([t['category_id'] for t in target], dtype=torch.int64)
targ['image_id'] = torch.tensor([t['image_id'] for t in target])
targ['area'] = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
targ['iscrowd'] = torch.tensor([t['iscrowd'] for t in target], dtype=torch.int64)
return (image.div(255), targ)
def __len__(self):
return len(self.ids)
dataset_path = '/kaggle/input/aquarium-dataset/Aquarium Combined/'
coco = COCO(os.path.join(dataset_path, 'train', '_annotations.coco.json'))
categories = coco.cats
n_classes = len(categories.keys())
categories
classes = [i[1]['name'] for i in categories.items()]
classes
train_dataset = AquariumDetection(root=dataset_path, transforms=get_transforms(True))
sample = train_dataset[2]
img_int = torch.tensor(sample[0] * 255, dtype=torch.uint8)
model = models.detection.fasterrcnn_mobilenet_v3_large_fpn(pretrained=True)
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = models.detection.faster_rcnn.FastRCNNPredictor(in_features, n_classes)
def collate_fn(batch):
return tuple(zip(*batch))
train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=4, collate_fn=collate_fn)
images, targets = next(iter(train_loader))
images = list((image for image in images))
targets = [{k: v for k, v in t.items()} for t in targets]
output = model(images, targets)
device = torch.device('cuda')
model = model.to(device)
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.01, momentum=0.9, nesterov=True, weight_decay=0.0001)
def train_one_epoch(model, optimizer, loader, device, epoch):
model.to(device)
model.train()
all_losses = []
all_losses_dict = []
for images, targets in tqdm(loader):
images = list((image.to(device) for image in images))
targets = [{k: torch.tensor(v).to(device) for k, v in t.items()} for t in targets]
loss_dict = model(images, targets)
losses = sum((loss for loss in loss_dict.values()))
loss_dict_append = {k: v.item() for k, v in loss_dict.items()}
loss_value = losses.item()
all_losses.append(loss_value)
all_losses_dict.append(loss_dict_append)
if not math.isfinite(loss_value):
sys.exit(1)
optimizer.zero_grad()
losses.backward()
optimizer.step()
all_losses_dict = pd.DataFrame(all_losses_dict)
num_epochs = 10
for epoch in range(num_epochs):
train_one_epoch(model, optimizer, train_loader, device, epoch) | code |
128039963/cell_13 | [
"text_plain_output_1.png"
] | # our dataset is in cocoformat, we will need pypcoco tools
!pip install pycocotools
from pycocotools.coco import COCO | code |
128039963/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from albumentations.pytorch import ToTensorV2
from pycocotools.coco import COCO
from torchvision import datasets, models
from torchvision.utils import draw_bounding_boxes
import albumentations as A # our data augmentation library
import copy
import cv2
import matplotlib.pyplot as plt
import os
import torch
import torchvision
def get_transforms(train=False):
if train:
transform = A.Compose([A.Resize(600, 600), A.HorizontalFlip(p=0.3), A.VerticalFlip(p=0.3), A.RandomBrightnessContrast(p=0.1), A.ColorJitter(p=0.1), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
else:
transform = A.Compose([A.Resize(600, 600), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
return transform
class AquariumDetection(datasets.VisionDataset):
def __init__(self, root, split='train', transform=None, target_transform=None, transforms=None):
super().__init__(root, transforms, transform, target_transform)
self.split = split
self.coco = COCO(os.path.join(root, split, '_annotations.coco.json'))
self.ids = list(sorted(self.coco.imgs.keys()))
self.ids = [id for id in self.ids if len(self._load_target(id)) > 0]
def _load_image(self, id: int):
path = self.coco.loadImgs(id)[0]['file_name']
image = cv2.imread(os.path.join(self.root, self.split, path))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def _load_target(self, id):
return self.coco.loadAnns(self.coco.getAnnIds(id))
def __getitem__(self, index):
id = self.ids[index]
image = self._load_image(id)
target = self._load_target(id)
target = copy.deepcopy(self._load_target(id))
boxes = [t['bbox'] + [t['category_id']] for t in target]
if self.transforms is not None:
transformed = self.transforms(image=image, bboxes=boxes)
image = transformed['image']
boxes = transformed['bboxes']
new_boxes = []
for box in boxes:
xmin = box[0]
xmax = xmin + box[2]
ymin = box[1]
ymax = ymin + box[3]
new_boxes.append([xmin, ymin, xmax, ymax])
boxes = torch.tensor(new_boxes, dtype=torch.float32)
targ = {}
targ['boxes'] = boxes
targ['labels'] = torch.tensor([t['category_id'] for t in target], dtype=torch.int64)
targ['image_id'] = torch.tensor([t['image_id'] for t in target])
targ['area'] = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
targ['iscrowd'] = torch.tensor([t['iscrowd'] for t in target], dtype=torch.int64)
return (image.div(255), targ)
def __len__(self):
return len(self.ids)
dataset_path = '/kaggle/input/aquarium-dataset/Aquarium Combined/'
coco = COCO(os.path.join(dataset_path, 'train', '_annotations.coco.json'))
categories = coco.cats
n_classes = len(categories.keys())
categories
classes = [i[1]['name'] for i in categories.items()]
classes
train_dataset = AquariumDetection(root=dataset_path, transforms=get_transforms(True))
sample = train_dataset[2]
img_int = torch.tensor(sample[0] * 255, dtype=torch.uint8)
plt.imshow(draw_bounding_boxes(img_int, sample[1]['boxes'], [classes[i] for i in sample[1]['labels']], width=4).permute(1, 2, 0)) | code |
128039963/cell_23 | [
"text_plain_output_1.png"
] | from albumentations.pytorch import ToTensorV2
import albumentations as A # our data augmentation library
def get_transforms(train=False):
if train:
transform = A.Compose([A.Resize(600, 600), A.HorizontalFlip(p=0.3), A.VerticalFlip(p=0.3), A.RandomBrightnessContrast(p=0.1), A.ColorJitter(p=0.1), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
else:
transform = A.Compose([A.Resize(600, 600), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
return transform
dataset_path = '/kaggle/input/aquarium-dataset/Aquarium Combined/'
train_dataset = AquariumDetection(root=dataset_path, transforms=get_transforms(True)) | code |
128039963/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from albumentations.pytorch import ToTensorV2
from pycocotools.coco import COCO
from torchvision import datasets, models
import albumentations as A # our data augmentation library
import copy
import cv2
import os
import torch
import torchvision
def get_transforms(train=False):
if train:
transform = A.Compose([A.Resize(600, 600), A.HorizontalFlip(p=0.3), A.VerticalFlip(p=0.3), A.RandomBrightnessContrast(p=0.1), A.ColorJitter(p=0.1), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
else:
transform = A.Compose([A.Resize(600, 600), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
return transform
class AquariumDetection(datasets.VisionDataset):
def __init__(self, root, split='train', transform=None, target_transform=None, transforms=None):
super().__init__(root, transforms, transform, target_transform)
self.split = split
self.coco = COCO(os.path.join(root, split, '_annotations.coco.json'))
self.ids = list(sorted(self.coco.imgs.keys()))
self.ids = [id for id in self.ids if len(self._load_target(id)) > 0]
def _load_image(self, id: int):
path = self.coco.loadImgs(id)[0]['file_name']
image = cv2.imread(os.path.join(self.root, self.split, path))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def _load_target(self, id):
return self.coco.loadAnns(self.coco.getAnnIds(id))
def __getitem__(self, index):
id = self.ids[index]
image = self._load_image(id)
target = self._load_target(id)
target = copy.deepcopy(self._load_target(id))
boxes = [t['bbox'] + [t['category_id']] for t in target]
if self.transforms is not None:
transformed = self.transforms(image=image, bboxes=boxes)
image = transformed['image']
boxes = transformed['bboxes']
new_boxes = []
for box in boxes:
xmin = box[0]
xmax = xmin + box[2]
ymin = box[1]
ymax = ymin + box[3]
new_boxes.append([xmin, ymin, xmax, ymax])
boxes = torch.tensor(new_boxes, dtype=torch.float32)
targ = {}
targ['boxes'] = boxes
targ['labels'] = torch.tensor([t['category_id'] for t in target], dtype=torch.int64)
targ['image_id'] = torch.tensor([t['image_id'] for t in target])
targ['area'] = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
targ['iscrowd'] = torch.tensor([t['iscrowd'] for t in target], dtype=torch.int64)
return (image.div(255), targ)
def __len__(self):
return len(self.ids)
dataset_path = '/kaggle/input/aquarium-dataset/Aquarium Combined/'
coco = COCO(os.path.join(dataset_path, 'train', '_annotations.coco.json'))
categories = coco.cats
n_classes = len(categories.keys())
categories | code |
128039963/cell_26 | [
"text_plain_output_1.png"
] | from albumentations.pytorch import ToTensorV2
import albumentations as A # our data augmentation library
def get_transforms(train=False):
if train:
transform = A.Compose([A.Resize(600, 600), A.HorizontalFlip(p=0.3), A.VerticalFlip(p=0.3), A.RandomBrightnessContrast(p=0.1), A.ColorJitter(p=0.1), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
else:
transform = A.Compose([A.Resize(600, 600), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
return transform
dataset_path = '/kaggle/input/aquarium-dataset/Aquarium Combined/'
train_dataset = AquariumDetection(root=dataset_path, transforms=get_transforms(True))
len(train_dataset) | code |
128039963/cell_11 | [
"text_plain_output_1.png"
] | import torch
import torchvision
print(torch.__version__)
print(torchvision.__version__) | code |
128039963/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from albumentations.pytorch import ToTensorV2
from pycocotools.coco import COCO
from torchvision import datasets, models
import albumentations as A # our data augmentation library
import copy
import cv2
import os
import torch
import torchvision
def get_transforms(train=False):
if train:
transform = A.Compose([A.Resize(600, 600), A.HorizontalFlip(p=0.3), A.VerticalFlip(p=0.3), A.RandomBrightnessContrast(p=0.1), A.ColorJitter(p=0.1), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
else:
transform = A.Compose([A.Resize(600, 600), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
return transform
class AquariumDetection(datasets.VisionDataset):
def __init__(self, root, split='train', transform=None, target_transform=None, transforms=None):
super().__init__(root, transforms, transform, target_transform)
self.split = split
self.coco = COCO(os.path.join(root, split, '_annotations.coco.json'))
self.ids = list(sorted(self.coco.imgs.keys()))
self.ids = [id for id in self.ids if len(self._load_target(id)) > 0]
def _load_image(self, id: int):
path = self.coco.loadImgs(id)[0]['file_name']
image = cv2.imread(os.path.join(self.root, self.split, path))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def _load_target(self, id):
return self.coco.loadAnns(self.coco.getAnnIds(id))
def __getitem__(self, index):
id = self.ids[index]
image = self._load_image(id)
target = self._load_target(id)
target = copy.deepcopy(self._load_target(id))
boxes = [t['bbox'] + [t['category_id']] for t in target]
if self.transforms is not None:
transformed = self.transforms(image=image, bboxes=boxes)
image = transformed['image']
boxes = transformed['bboxes']
new_boxes = []
for box in boxes:
xmin = box[0]
xmax = xmin + box[2]
ymin = box[1]
ymax = ymin + box[3]
new_boxes.append([xmin, ymin, xmax, ymax])
boxes = torch.tensor(new_boxes, dtype=torch.float32)
targ = {}
targ['boxes'] = boxes
targ['labels'] = torch.tensor([t['category_id'] for t in target], dtype=torch.int64)
targ['image_id'] = torch.tensor([t['image_id'] for t in target])
targ['area'] = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
targ['iscrowd'] = torch.tensor([t['iscrowd'] for t in target], dtype=torch.int64)
return (image.div(255), targ)
def __len__(self):
return len(self.ids)
dataset_path = '/kaggle/input/aquarium-dataset/Aquarium Combined/'
coco = COCO(os.path.join(dataset_path, 'train', '_annotations.coco.json'))
categories = coco.cats
n_classes = len(categories.keys())
categories
model = models.detection.fasterrcnn_mobilenet_v3_large_fpn(pretrained=True)
in_features = model.roi_heads.box_predictor.cls_score.in_features
model.roi_heads.box_predictor = models.detection.faster_rcnn.FastRCNNPredictor(in_features, n_classes) | code |
128039963/cell_46 | [
"text_plain_output_1.png"
] | from albumentations.pytorch import ToTensorV2
import albumentations as A # our data augmentation library
def get_transforms(train=False):
if train:
transform = A.Compose([A.Resize(600, 600), A.HorizontalFlip(p=0.3), A.VerticalFlip(p=0.3), A.RandomBrightnessContrast(p=0.1), A.ColorJitter(p=0.1), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
else:
transform = A.Compose([A.Resize(600, 600), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
return transform
dataset_path = '/kaggle/input/aquarium-dataset/Aquarium Combined/'
test_dataset = AquariumDetection(root=dataset_path, split='test', transforms=get_transforms(False)) | code |
128039963/cell_22 | [
"text_plain_output_1.png"
] | from albumentations.pytorch import ToTensorV2
from pycocotools.coco import COCO
from torchvision import datasets, models
import albumentations as A # our data augmentation library
import copy
import cv2
import os
import torch
import torchvision
def get_transforms(train=False):
if train:
transform = A.Compose([A.Resize(600, 600), A.HorizontalFlip(p=0.3), A.VerticalFlip(p=0.3), A.RandomBrightnessContrast(p=0.1), A.ColorJitter(p=0.1), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
else:
transform = A.Compose([A.Resize(600, 600), ToTensorV2()], bbox_params=A.BboxParams(format='coco'))
return transform
class AquariumDetection(datasets.VisionDataset):
def __init__(self, root, split='train', transform=None, target_transform=None, transforms=None):
super().__init__(root, transforms, transform, target_transform)
self.split = split
self.coco = COCO(os.path.join(root, split, '_annotations.coco.json'))
self.ids = list(sorted(self.coco.imgs.keys()))
self.ids = [id for id in self.ids if len(self._load_target(id)) > 0]
def _load_image(self, id: int):
path = self.coco.loadImgs(id)[0]['file_name']
image = cv2.imread(os.path.join(self.root, self.split, path))
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
return image
def _load_target(self, id):
return self.coco.loadAnns(self.coco.getAnnIds(id))
def __getitem__(self, index):
id = self.ids[index]
image = self._load_image(id)
target = self._load_target(id)
target = copy.deepcopy(self._load_target(id))
boxes = [t['bbox'] + [t['category_id']] for t in target]
if self.transforms is not None:
transformed = self.transforms(image=image, bboxes=boxes)
image = transformed['image']
boxes = transformed['bboxes']
new_boxes = []
for box in boxes:
xmin = box[0]
xmax = xmin + box[2]
ymin = box[1]
ymax = ymin + box[3]
new_boxes.append([xmin, ymin, xmax, ymax])
boxes = torch.tensor(new_boxes, dtype=torch.float32)
targ = {}
targ['boxes'] = boxes
targ['labels'] = torch.tensor([t['category_id'] for t in target], dtype=torch.int64)
targ['image_id'] = torch.tensor([t['image_id'] for t in target])
targ['area'] = (boxes[:, 3] - boxes[:, 1]) * (boxes[:, 2] - boxes[:, 0])
targ['iscrowd'] = torch.tensor([t['iscrowd'] for t in target], dtype=torch.int64)
return (image.div(255), targ)
def __len__(self):
return len(self.ids)
dataset_path = '/kaggle/input/aquarium-dataset/Aquarium Combined/'
coco = COCO(os.path.join(dataset_path, 'train', '_annotations.coco.json'))
categories = coco.cats
n_classes = len(categories.keys())
categories
classes = [i[1]['name'] for i in categories.items()]
classes | code |
88093804/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
import scanpy as sc
import scipy
import seaborn as sns
import time
import time
import time
adata = sc.datasets.krumsiek11()
adata.var.index
adata = sc.datasets.pbmc3k_processed()
adata.var.index
import time
for dataset in ['moignard15', 'pbmc3k', 'pbmc3k_processed', 'pbmc68k_reduced', 'paul15', 'krumsiek11']:
t0 = time.time()
adata = getattr(sc.datasets, dataset)()
dict_datasets_info = {'krumsiek11': 'Simulated myeloid progenitors [Krumsiek11].', 'moignard15': 'Hematopoiesis in early mouse embryos [Moignard15].', 'pbmc3k': '3k PBMCs from 10x Genomics', 'pbmc3k_processed': 'Processed 3k PBMCs from 10x Genomics.', 'pbmc68k_reduced': 'Subsampled and processed 68k PBMCs.', 'paul15': 'Development of Myeloid Progenitors [Paul15].'}
import time
from sklearn.decomposition import PCA
import scipy
for dataset in ['moignard15', 'pbmc3k', 'pbmc3k_processed', 'pbmc68k_reduced', 'paul15', 'krumsiek11']:
print(dataset, dict_datasets_info[dataset])
t0 = time.time()
adata = getattr(sc.datasets, dataset)()
print(np.round(time.time() - t0, 0), 'Seconds passed for loading')
print(adata)
print()
reducer = PCA(n_components=2)
if not scipy.sparse.issparse(adata.X):
r = reducer.fit_transform(adata.X)
else:
r = reducer.fit_transform(adata.X.toarray())
plt.figure(figsize=(20, 8))
if not 'n_counts' in adata.obs.columns:
sns.scatterplot(x=r[:, 0], y=r[:, 1])
else:
sns.scatterplot(x=r[:, 0], y=r[:, 1], hue=adata.obs['n_counts'])
plt.title(dataset + ' ' + str(len(adata)) + 'cells ' + '\n' + dict_datasets_info[dataset], fontsize=20)
plt.xlabel('PCA1', fontsize=20)
plt.ylabel('PCA2', fontsize=20)
plt.show() | code |
88093804/cell_6 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import scanpy as sc
adata = sc.datasets.krumsiek11()
print(adata)
adata.var.index | code |
88093804/cell_2 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | !pip install scanpy | code |
88093804/cell_7 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"image_output_5.png",
"application_vnd.jupyter.stderr_output_8.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_10.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_6.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"text_plain_output_11.png"
] | !pip install openpyxl
# Requires: !pip install openpyxl
adata = getattr(sc.datasets, "moignard15")()
print( adata )
adata.var.index | code |
88093804/cell_8 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | import scanpy as sc
adata = sc.datasets.krumsiek11()
adata.var.index
adata = sc.datasets.pbmc3k_processed()
print(adata)
adata.var.index | code |
88093804/cell_15 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.decomposition import PCA
import matplotlib.pyplot as plt
import numpy as np
import scanpy as sc
import scipy
import seaborn as sns
import time
import time
import time
adata = sc.datasets.krumsiek11()
adata.var.index
adata = sc.datasets.pbmc3k_processed()
adata.var.index
import time
for dataset in ['moignard15', 'pbmc3k', 'pbmc3k_processed', 'pbmc68k_reduced', 'paul15', 'krumsiek11']:
t0 = time.time()
adata = getattr(sc.datasets, dataset)()
dict_datasets_info = {'krumsiek11': 'Simulated myeloid progenitors [Krumsiek11].', 'moignard15': 'Hematopoiesis in early mouse embryos [Moignard15].', 'pbmc3k': '3k PBMCs from 10x Genomics', 'pbmc3k_processed': 'Processed 3k PBMCs from 10x Genomics.', 'pbmc68k_reduced': 'Subsampled and processed 68k PBMCs.', 'paul15': 'Development of Myeloid Progenitors [Paul15].'}
import time
from sklearn.decomposition import PCA
import scipy
for dataset in ['moignard15', 'pbmc3k', 'pbmc3k_processed', 'pbmc68k_reduced', 'paul15', 'krumsiek11']:
t0 = time.time()
adata = getattr(sc.datasets, dataset)()
reducer = PCA(n_components=2)
if not scipy.sparse.issparse(adata.X):
r = reducer.fit_transform(adata.X)
else:
r = reducer.fit_transform(adata.X.toarray())
for accession in ['E-GEOD-98816', 'E-MTAB-9154']:
t0 = time.time()
adata = sc.datasets.ebi_expression_atlas(accession)
print(np.round(time.time() - t0, 0), 'Seconds passed for loading')
print(adata)
print()
reducer = PCA(n_components=2)
if not scipy.sparse.issparse(adata.X):
r = reducer.fit_transform(adata.X)
else:
r = reducer.fit_transform(adata.X.toarray())
plt.figure(figsize=(20, 8))
if not 'n_counts' in adata.obs.columns:
sns.scatterplot(x=r[:, 0], y=r[:, 1])
else:
sns.scatterplot(x=r[:, 0], y=r[:, 1], hue=adata.obs['n_counts'])
plt.title(accession + ' ' + str(len(adata)) + 'cells ', fontsize=20)
plt.xlabel('PCA1', fontsize=20)
plt.ylabel('PCA2', fontsize=20)
plt.show() | code |
88093804/cell_3 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | !pip install openpyxl | code |
88093804/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import scanpy as sc
import time
import time
adata = sc.datasets.krumsiek11()
adata.var.index
adata = sc.datasets.pbmc3k_processed()
adata.var.index
import time
for dataset in ['moignard15', 'pbmc3k', 'pbmc3k_processed', 'pbmc68k_reduced', 'paul15', 'krumsiek11']:
print(dataset)
t0 = time.time()
adata = getattr(sc.datasets, dataset)()
print(np.round(time.time() - t0, 0), 'Seconds passed for loading')
print(adata)
print() | code |
74044395/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.formula.api as smf
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
mpg_df.columns
X = mpg_df.drop('mpg', axis=1)
X = X.drop({'origin_1', 'origin_2', 'origin_3'}, axis=1)
y = mpg_df[['mpg']]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
regression_model = LinearRegression()
regression_model.fit(X_train, y_train)
data_train = pd.concat([X_train, y_train], axis=1)
data_train.columns
data_train.rename(columns={'model year': 'model_year'}, inplace=True)
import statsmodels.formula.api as smf
lm1 = smf.ols(formula='mpg ~ cylinders+displacement+horsepower+weight+acceleration+model_year', data=data_train).fit()
lm1.params | code |
74044395/cell_13 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
mpg_df.columns
X = mpg_df.drop('mpg', axis=1)
X = X.drop({'origin_1', 'origin_2', 'origin_3'}, axis=1)
y = mpg_df[['mpg']]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
regression_model = LinearRegression()
regression_model.fit(X_train, y_train) | code |
74044395/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes | code |
74044395/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
mpg_df.columns
X = mpg_df.drop('mpg', axis=1)
X = X.drop({'origin_1', 'origin_2', 'origin_3'}, axis=1)
y = mpg_df[['mpg']]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
regression_model = LinearRegression()
regression_model.fit(X_train, y_train)
intercept = regression_model.intercept_[0]
regression_model.score(X_train, y_train)
regression_model.score(X_test, y_test)
mse = np.mean((regression_model.predict(X_test) - y_test) ** 2)
regression_model.score(X_test, y_test) | code |
74044395/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns | code |
74044395/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
mpg_df.describe().transpose() | code |
74044395/cell_11 | [
"text_html_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
mpg_df.columns | code |
74044395/cell_19 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
mpg_df.columns
X = mpg_df.drop('mpg', axis=1)
X = X.drop({'origin_1', 'origin_2', 'origin_3'}, axis=1)
y = mpg_df[['mpg']]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
regression_model = LinearRegression()
regression_model.fit(X_train, y_train)
data_train = pd.concat([X_train, y_train], axis=1)
data_train.head() | code |
74044395/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
74044395/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False] | code |
74044395/cell_8 | [
"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)
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)] | code |
74044395/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
mpg_df.columns
X = mpg_df.drop('mpg', axis=1)
X = X.drop({'origin_1', 'origin_2', 'origin_3'}, axis=1)
y = mpg_df[['mpg']]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
regression_model = LinearRegression()
regression_model.fit(X_train, y_train)
intercept = regression_model.intercept_[0]
print('The intercept for our model is {}'.format(intercept)) | code |
74044395/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
mpg_df.columns
X = mpg_df.drop('mpg', axis=1)
X = X.drop({'origin_1', 'origin_2', 'origin_3'}, axis=1)
y = mpg_df[['mpg']]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
regression_model = LinearRegression()
regression_model.fit(X_train, y_train)
intercept = regression_model.intercept_[0]
regression_model.score(X_train, y_train) | code |
74044395/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
mpg_df.columns
X = mpg_df.drop('mpg', axis=1)
X = X.drop({'origin_1', 'origin_2', 'origin_3'}, axis=1)
y = mpg_df[['mpg']]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
regression_model = LinearRegression()
regression_model.fit(X_train, y_train)
intercept = regression_model.intercept_[0]
regression_model.score(X_train, y_train)
regression_model.score(X_test, y_test) | code |
74044395/cell_24 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import math
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
mpg_df.columns
X = mpg_df.drop('mpg', axis=1)
X = X.drop({'origin_1', 'origin_2', 'origin_3'}, axis=1)
y = mpg_df[['mpg']]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
regression_model = LinearRegression()
regression_model.fit(X_train, y_train)
intercept = regression_model.intercept_[0]
regression_model.score(X_train, y_train)
regression_model.score(X_test, y_test)
mse = np.mean((regression_model.predict(X_test) - y_test) ** 2)
import math
math.sqrt(mse) | code |
74044395/cell_14 | [
"text_html_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
mpg_df.columns
X = mpg_df.drop('mpg', axis=1)
X = X.drop({'origin_1', 'origin_2', 'origin_3'}, axis=1)
y = mpg_df[['mpg']]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
regression_model = LinearRegression()
regression_model.fit(X_train, y_train)
for idx, col_name in enumerate(X_train.columns):
print('The coefficient for {} is {}'.format(col_name, regression_model.coef_[0][idx])) | code |
74044395/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import statsmodels.formula.api as smf
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
mpg_df.columns
X = mpg_df.drop('mpg', axis=1)
X = X.drop({'origin_1', 'origin_2', 'origin_3'}, axis=1)
y = mpg_df[['mpg']]
from sklearn.model_selection import train_test_split
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=1)
regression_model = LinearRegression()
regression_model.fit(X_train, y_train)
data_train = pd.concat([X_train, y_train], axis=1)
data_train.columns
data_train.rename(columns={'model year': 'model_year'}, inplace=True)
import statsmodels.formula.api as smf
lm1 = smf.ols(formula='mpg ~ cylinders+displacement+horsepower+weight+acceleration+model_year', data=data_train).fit()
lm1.params
print(lm1.summary()) | code |
74044395/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)
import seaborn as sns
mpg_df = pd.read_csv('/kaggle/input/autompg-dataset/auto-mpg.csv')
mpg_df.columns
mpg_df = mpg_df.drop('car name', axis=1)
mpg_df = pd.get_dummies(mpg_df, columns=['origin'])
temp = pd.DataFrame(mpg_df.horsepower.str.isdigit())
temp[temp['horsepower'] == False]
mpg_df = mpg_df.replace('?', np.nan)
mpg_df[mpg_df.isnull().any(axis=1)]
mpg_df.median()
mpg_df = mpg_df.apply(lambda x: x.fillna(x.median()), axis=0)
mpg_df['hp'] = mpg_df['horsepower'].astype('float64')
mpg_df.dtypes
mpg_df_attr = mpg_df.iloc[:, 0:10]
sns.pairplot(mpg_df_attr, diag_kind='kde') | code |
129009262/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRATEGY = 'median'
train.head() | code |
129009262/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from IPython.display import clear_output
!pip3 install -U lazypredict
!pip3 install -U pandas #Upgrading pandas
clear_output() | code |
129009262/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/spaceship-titanic/train.csv')
test = pd.read_csv('../input/spaceship-titanic/test.csv')
submission = pd.read_csv('../input/spaceship-titanic/sample_submission.csv')
RANDOM_STATE = 12
FOLDS = 5
STRATEGY = 'median' | code |
128048432/cell_21 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.isna().sum()
df.dropna(inplace=True)
filt_loc = df.location.str.contains(',')
F_index = filt_loc.loc[lambda x: x == False].index
df.drop(index=F_index, inplace=True)
df.reset_index(drop=True, inplace=True)
df['city'] = df.location.apply(lambda x: x.rsplit(',', 1)[1])
myplot_city = sns.barplot(x=df.city.value_counts().index[:4],y=df.city.value_counts()[:4])
myplot_city.set(xlabel='City',ylabel='No. of Hotels')
plt.show()
myplot_price = sns.histplot(df.price)
plt.show()
df[(df.price > 800) & (df.price < 1400)].shape[0] / df.shape[0]
df.price.mean() | code |
128048432/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.info() | code |
128048432/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.isna().sum()
df.dropna(inplace=True)
filt_loc = df.location.str.contains(',')
F_index = filt_loc.loc[lambda x: x == False].index
df.drop(index=F_index, inplace=True)
df.reset_index(drop=True, inplace=True)
df['city'] = df.location.apply(lambda x: x.rsplit(',', 1)[1])
myplot_city = sns.barplot(x=df.city.value_counts().index[:4],y=df.city.value_counts()[:4])
myplot_city.set(xlabel='City',ylabel='No. of Hotels')
plt.show()
myplot_price = sns.histplot(df.price)
plt.show()
df[(df.price > 800) & (df.price < 1400)].shape[0] / df.shape[0]
df.price.mean()
df.price.median()
grp_Bangalore = df[df.city.str.contains('Bangalore')]
sns.histplot(grp_Bangalore.price, kde=True)
plt.show() | code |
128048432/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.isna().sum()
df.describe() | code |
128048432/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.isna().sum()
df.dropna(inplace=True)
filt_loc = df.location.str.contains(',')
F_index = filt_loc.loc[lambda x: x == False].index
df.drop(index=F_index, inplace=True)
df.reset_index(drop=True, inplace=True)
df['city'] = df.location.apply(lambda x: x.rsplit(',', 1)[1])
myplot_city = sns.barplot(x=df.city.value_counts().index[:4],y=df.city.value_counts()[:4])
myplot_city.set(xlabel='City',ylabel='No. of Hotels')
plt.show()
myplot_price = sns.histplot(df.price)
plt.show()
df[(df.price > 800) & (df.price < 1400)].shape[0] / df.shape[0] | code |
128048432/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.isna().sum()
df.dropna(inplace=True)
filt_loc = df.location.str.contains(',')
F_index = filt_loc.loc[lambda x: x == False].index
df.drop(index=F_index, inplace=True)
df.reset_index(drop=True, inplace=True)
df['city'] = df.location.apply(lambda x: x.rsplit(',', 1)[1])
myplot_city = sns.barplot(x=df.city.value_counts().index[:4],y=df.city.value_counts()[:4])
myplot_city.set(xlabel='City',ylabel='No. of Hotels')
plt.show()
myplot_price = sns.histplot(df.price)
plt.show() | code |
128048432/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape | code |
128048432/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.isna().sum()
df.dropna(inplace=True)
filt_loc = df.location.str.contains(',')
F_index = filt_loc.loc[lambda x: x == False].index
df.drop(index=F_index, inplace=True)
df.reset_index(drop=True, inplace=True)
df['city'] = df.location.apply(lambda x: x.rsplit(',', 1)[1])
myplot_city = sns.barplot(x=df.city.value_counts().index[:4], y=df.city.value_counts()[:4])
myplot_city.set(xlabel='City', ylabel='No. of Hotels')
plt.show() | code |
128048432/cell_3 | [
"text_plain_output_1.png"
] | import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25) | code |
128048432/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(font_scale=1.25)
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.isna().sum()
df.dropna(inplace=True)
filt_loc = df.location.str.contains(',')
F_index = filt_loc.loc[lambda x: x == False].index
df.drop(index=F_index, inplace=True)
df.reset_index(drop=True, inplace=True)
df['city'] = df.location.apply(lambda x: x.rsplit(',', 1)[1])
myplot_city = sns.barplot(x=df.city.value_counts().index[:4],y=df.city.value_counts()[:4])
myplot_city.set(xlabel='City',ylabel='No. of Hotels')
plt.show()
myplot_price = sns.histplot(df.price)
plt.show()
df[(df.price > 800) & (df.price < 1400)].shape[0] / df.shape[0]
df.price.mean()
df.price.median() | code |
128048432/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.drop(columns=['Unnamed: 0'], inplace=True)
df.columns = df.columns.str.lower()
df.shape
df.isna().sum() | code |
128048432/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df | code |
105196046/cell_4 | [
"text_plain_output_1.png"
] | a = 4
b = 89
c = 47
a = 4
b = -89
c = 47
if a > 0 and b > 0 and (c > 0):
print('positive')
else:
print('negative') | code |
105196046/cell_6 | [
"text_plain_output_1.png"
] | a = 4
b = 89
c = 47
a = 4
b = -89
c = 47
a = 4
b = 89
c = 47
if a > 0 or b < 0 or c == 0:
print('positive')
else:
print('negative') | code |
105196046/cell_8 | [
"text_plain_output_1.png"
] | t = 8
v = 876
not t < v | code |
105196046/cell_3 | [
"text_plain_output_1.png"
] | a = 4
b = 89
c = 47
if a > 0 and b > 0 and (c > 0):
print('positive')
else:
print('negative') | code |
2004795/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
from sklearn.kernel_ridge import KernelRidge
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics.pairwise import polynomial_kernel
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.metrics.pairwise import laplacian_kernel
x_columns = [i for i in train.columns if i not in list(['id', 'formation_energy_ev_natom', 'bandgap_energy_ev'])]
label1 = 'formation_energy_ev_natom'
label2 = 'bandgap_energy_ev'
X = train[x_columns]
y = train[[label1, label2]]
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=2017)
X_train = X_train.as_matrix()
X_valid = X_valid.as_matrix()
y_train_values1 = np.log1p(y_train['formation_energy_ev_natom'].values)
y_train_values2 = np.log1p(y_train['bandgap_energy_ev'].values)
y_valid_values1 = np.log1p(y_valid['formation_energy_ev_natom'].values)
y_valid_values2 = np.log1p(y_valid['bandgap_energy_ev'].values)
clf1 = KernelRidge(kernel='linear', alpha=1.0)
clf2 = KernelRidge(kernel='linear', alpha=1.0)
clf1.fit(X_train, y_train_values1)
clf2.fit(X_train, y_train_values2)
preds1 = clf1.predict(X_valid)
preds2 = clf2.predict(X_valid)
y_pred1 = np.exp(preds1) - 1
y_pred2 = np.exp(preds2) - 1
rsme_valid1 = np.sqrt(mean_squared_error(y_valid_values1, preds1))
rsme_valid2 = np.sqrt(mean_squared_error(y_valid_values2, preds2))
rsme_total = np.sqrt(rsme_valid1 * rsme_valid1 + rsme_valid2 * rsme_valid2)
print('RSME for formation energy:')
print(rsme_valid1)
print('RSME for band gap:')
print(rsme_valid2)
print('RSME for total:')
print(rsme_total) | code |
2004795/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
from sklearn.kernel_ridge import KernelRidge
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics.pairwise import polynomial_kernel
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.metrics.pairwise import laplacian_kernel
x_columns = [i for i in train.columns if i not in list(['id', 'formation_energy_ev_natom', 'bandgap_energy_ev'])]
label1 = 'formation_energy_ev_natom'
label2 = 'bandgap_energy_ev'
X = train[x_columns]
y = train[[label1, label2]]
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=2017)
X_train = X_train.as_matrix()
X_valid = X_valid.as_matrix()
y_train_values1 = np.log1p(y_train['formation_energy_ev_natom'].values)
y_train_values2 = np.log1p(y_train['bandgap_energy_ev'].values)
y_valid_values1 = np.log1p(y_valid['formation_energy_ev_natom'].values)
y_valid_values2 = np.log1p(y_valid['bandgap_energy_ev'].values)
clf1 = KernelRidge(kernel='linear', alpha=1.0)
clf2 = KernelRidge(kernel='linear', alpha=1.0)
clf1.fit(X_train, y_train_values1)
clf2.fit(X_train, y_train_values2)
preds1 = clf1.predict(X_valid)
preds2 = clf2.predict(X_valid)
y_pred1 = np.exp(preds1) - 1
y_pred2 = np.exp(preds2) - 1
rsme_valid1 = np.sqrt(mean_squared_error(y_valid_values1, preds1))
rsme_valid2 = np.sqrt(mean_squared_error(y_valid_values2, preds2))
rsme_total = np.sqrt(rsme_valid1 * rsme_valid1 + rsme_valid2 * rsme_valid2)
clf3 = KernelRidge(kernel='polynomial', alpha=1.0)
clf4 = KernelRidge(kernel='polynomial', alpha=1.0)
clf3.fit(X_train, y_train_values1)
clf4.fit(X_train, y_train_values2)
preds1 = clf3.predict(X_valid)
preds2 = clf4.predict(X_valid)
y_pred1 = np.exp(preds1) - 1
y_pred2 = np.exp(preds2) - 1
rsme_valid1 = np.sqrt(mean_squared_error(y_valid_values1, preds1))
rsme_valid2 = np.sqrt(mean_squared_error(y_valid_values2, preds2))
rsme_total = np.sqrt(rsme_valid1 * rsme_valid1 + rsme_valid2 * rsme_valid2)
print('RSME for formation energy:')
print(rsme_valid1)
print('RSME for band gap:')
print(rsme_valid2)
print('RSME for total:')
print(rsme_total) | code |
2004795/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv') | code |
2004795/cell_10 | [
"text_plain_output_1.png"
] | from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
from sklearn.kernel_ridge import KernelRidge
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics.pairwise import polynomial_kernel
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.metrics.pairwise import laplacian_kernel
x_columns = [i for i in train.columns if i not in list(['id', 'formation_energy_ev_natom', 'bandgap_energy_ev'])]
label1 = 'formation_energy_ev_natom'
label2 = 'bandgap_energy_ev'
X = train[x_columns]
y = train[[label1, label2]]
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=2017)
X_train = X_train.as_matrix()
X_valid = X_valid.as_matrix()
y_train_values1 = np.log1p(y_train['formation_energy_ev_natom'].values)
y_train_values2 = np.log1p(y_train['bandgap_energy_ev'].values)
y_valid_values1 = np.log1p(y_valid['formation_energy_ev_natom'].values)
y_valid_values2 = np.log1p(y_valid['bandgap_energy_ev'].values)
clf1 = KernelRidge(kernel='linear', alpha=1.0)
clf2 = KernelRidge(kernel='linear', alpha=1.0)
clf1.fit(X_train, y_train_values1)
clf2.fit(X_train, y_train_values2)
preds1 = clf1.predict(X_valid)
preds2 = clf2.predict(X_valid)
y_pred1 = np.exp(preds1) - 1
y_pred2 = np.exp(preds2) - 1
rsme_valid1 = np.sqrt(mean_squared_error(y_valid_values1, preds1))
rsme_valid2 = np.sqrt(mean_squared_error(y_valid_values2, preds2))
rsme_total = np.sqrt(rsme_valid1 * rsme_valid1 + rsme_valid2 * rsme_valid2)
clf3 = KernelRidge(kernel='polynomial', alpha=1.0)
clf4 = KernelRidge(kernel='polynomial', alpha=1.0)
clf3.fit(X_train, y_train_values1)
clf4.fit(X_train, y_train_values2)
preds1 = clf3.predict(X_valid)
preds2 = clf4.predict(X_valid)
y_pred1 = np.exp(preds1) - 1
y_pred2 = np.exp(preds2) - 1
rsme_valid1 = np.sqrt(mean_squared_error(y_valid_values1, preds1))
rsme_valid2 = np.sqrt(mean_squared_error(y_valid_values2, preds2))
rsme_total = np.sqrt(rsme_valid1 * rsme_valid1 + rsme_valid2 * rsme_valid2)
clf5 = KernelRidge(kernel='rbf', alpha=1.0)
clf6 = KernelRidge(kernel='rbf', alpha=1.0)
clf5.fit(X_train, y_train_values1)
clf6.fit(X_train, y_train_values2)
preds1 = clf5.predict(X_valid)
preds2 = clf6.predict(X_valid)
y_pred1 = np.exp(preds1) - 1
y_pred2 = np.exp(preds2) - 1
rsme_valid1 = np.sqrt(mean_squared_error(y_valid_values1, preds1))
rsme_valid2 = np.sqrt(mean_squared_error(y_valid_values2, preds2))
rsme_total = np.sqrt(rsme_valid1 * rsme_valid1 + rsme_valid2 * rsme_valid2)
print('RSME for formation energy:')
print(rsme_valid1)
print('RSME for band gap:')
print(rsme_valid2)
print('RSME for total:')
print(rsme_total) | code |
2004795/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.kernel_ridge import KernelRidge
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import warnings
import numpy as np
import pandas as pd
import warnings
warnings.filterwarnings('ignore')
from subprocess import check_output
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
from sklearn.kernel_ridge import KernelRidge
from sklearn.model_selection import train_test_split
from sklearn.metrics import mean_squared_error
from sklearn.metrics.pairwise import polynomial_kernel
from sklearn.metrics.pairwise import rbf_kernel
from sklearn.metrics.pairwise import laplacian_kernel
x_columns = [i for i in train.columns if i not in list(['id', 'formation_energy_ev_natom', 'bandgap_energy_ev'])]
label1 = 'formation_energy_ev_natom'
label2 = 'bandgap_energy_ev'
X = train[x_columns]
y = train[[label1, label2]]
X_train, X_valid, y_train, y_valid = train_test_split(X, y, test_size=0.2, random_state=2017)
X_train = X_train.as_matrix()
X_valid = X_valid.as_matrix()
y_train_values1 = np.log1p(y_train['formation_energy_ev_natom'].values)
y_train_values2 = np.log1p(y_train['bandgap_energy_ev'].values)
y_valid_values1 = np.log1p(y_valid['formation_energy_ev_natom'].values)
y_valid_values2 = np.log1p(y_valid['bandgap_energy_ev'].values)
clf1 = KernelRidge(kernel='linear', alpha=1.0)
clf2 = KernelRidge(kernel='linear', alpha=1.0)
clf1.fit(X_train, y_train_values1)
clf2.fit(X_train, y_train_values2)
preds1 = clf1.predict(X_valid)
preds2 = clf2.predict(X_valid)
y_pred1 = np.exp(preds1) - 1
y_pred2 = np.exp(preds2) - 1
rsme_valid1 = np.sqrt(mean_squared_error(y_valid_values1, preds1))
rsme_valid2 = np.sqrt(mean_squared_error(y_valid_values2, preds2))
rsme_total = np.sqrt(rsme_valid1 * rsme_valid1 + rsme_valid2 * rsme_valid2)
clf3 = KernelRidge(kernel='polynomial', alpha=1.0)
clf4 = KernelRidge(kernel='polynomial', alpha=1.0)
clf3.fit(X_train, y_train_values1)
clf4.fit(X_train, y_train_values2)
preds1 = clf3.predict(X_valid)
preds2 = clf4.predict(X_valid)
y_pred1 = np.exp(preds1) - 1
y_pred2 = np.exp(preds2) - 1
rsme_valid1 = np.sqrt(mean_squared_error(y_valid_values1, preds1))
rsme_valid2 = np.sqrt(mean_squared_error(y_valid_values2, preds2))
rsme_total = np.sqrt(rsme_valid1 * rsme_valid1 + rsme_valid2 * rsme_valid2)
clf5 = KernelRidge(kernel='rbf', alpha=1.0)
clf6 = KernelRidge(kernel='rbf', alpha=1.0)
clf5.fit(X_train, y_train_values1)
clf6.fit(X_train, y_train_values2)
preds1 = clf5.predict(X_valid)
preds2 = clf6.predict(X_valid)
y_pred1 = np.exp(preds1) - 1
y_pred2 = np.exp(preds2) - 1
rsme_valid1 = np.sqrt(mean_squared_error(y_valid_values1, preds1))
rsme_valid2 = np.sqrt(mean_squared_error(y_valid_values2, preds2))
rsme_total = np.sqrt(rsme_valid1 * rsme_valid1 + rsme_valid2 * rsme_valid2)
clf7 = KernelRidge(kernel='laplacian', alpha=1.0)
clf8 = KernelRidge(kernel='laplacian', alpha=1.0)
clf7.fit(X_train, y_train_values1)
clf8.fit(X_train, y_train_values2)
preds1 = clf7.predict(X_valid)
preds2 = clf8.predict(X_valid)
y_pred1 = np.exp(preds1) - 1
y_pred2 = np.exp(preds2) - 1
rsme_valid1 = np.sqrt(mean_squared_error(y_valid_values1, preds1))
rsme_valid2 = np.sqrt(mean_squared_error(y_valid_values2, preds2))
rsme_total = np.sqrt(rsme_valid1 * rsme_valid1 + rsme_valid2 * rsme_valid2)
print('RSME for formation energy:')
print(rsme_valid1)
print('RSME for band gap:')
print(rsme_valid2)
print('RSME for total:')
print(rsme_total) | code |
74063412/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import re
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
suv = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/surv.csv')
vic = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/vict.csv')
suv['survived'] = 1
vic['survived'] = 0
ground_truth = pd.concat([suv, vic])
ground_truth['fsname'] = [re.search('^(.*?)( |$)', item).group(1) for item in ground_truth['given name']]
tmp_f = [item.encode('ascii', 'ignore').decode('ascii') for item in ground_truth['family name']]
non_ascii = [True if x != y else False for x, y in zip(tmp_f, ground_truth['family name'])]
ground_truth['uni_f'] = non_ascii
print('Non-ascii family names')
pd.value_counts(non_ascii) | code |
74063412/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
suv = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/surv.csv')
vic = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/vict.csv')
print(f'{suv.shape}_surv, {vic.shape}_vic') | code |
74063412/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
suv = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/surv.csv')
vic = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/vict.csv')
suv['survived'] = 1
vic['survived'] = 0
ground_truth = pd.concat([suv, vic])
ground_truth['fsname'] = [re.search('^(.*?)( |$)', item).group(1) for item in ground_truth['given name']]
ground_truth.head() | code |
74063412/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import re
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
suv = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/surv.csv')
vic = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/vict.csv')
suv['survived'] = 1
vic['survived'] = 0
ground_truth = pd.concat([suv, vic])
ground_truth['fsname'] = [re.search('^(.*?)( |$)', item).group(1) for item in ground_truth['given name']]
tmp_f = [item.encode('ascii', 'ignore').decode('ascii') for item in ground_truth['family name']]
non_ascii = [True if x != y else False for x, y in zip(tmp_f, ground_truth['family name'])]
ground_truth['uni_f'] = non_ascii
pd.value_counts(non_ascii)
tmp_fs = [item.encode('ascii', 'ignore').decode('ascii') for item in ground_truth['fsname']]
non_ascii_ = [True if x != y else False for x, y in zip(tmp_fs, ground_truth['fsname'])]
ground_truth['uni_g'] = non_ascii_
pd.value_counts(non_ascii_)
ground_truth.set_index(np.arange(0, ground_truth.shape[0]), inplace=True)
for i, item in ground_truth.iterrows():
dash = re.search('-', item['alt name'])
if item.uni_f | item.uni_g | bool(dash):
ground_truth.at[i, 'family name'] = item['alt name'].split('-')[-1].upper()
ground_truth.at[i, 'fsname'] = item['alt name'].split('-')[0].capitalize()
train['fname'] = [re.search('^(.*?), ', item).group(1) for item in train.Name]
train['prefix'] = [re.search('^.*?, (.*?)\\. ', item).group(1) for item in train.Name]
train['gname'] = [re.search('^.*?, .*?\\. (.*)', item).group(1) for item in train.Name]
tmp = [re.search('^.*?, .*?\\. ([^ ]*?)( |$)', item).group(1) for item in train.Name]
tmp2 = [re.search('\\((.*?)( |\\)|$)', item).group(1) if item.startswith('(') else item for item in tmp]
tmp3 = [z.group(1) if y == 'Mrs' and (z := re.search('^.*?\\((.*?)( |\\))', x)) is not None else w for x, y, w in zip(train.gname, train.prefix, tmp2)]
train['fsname'] = tmp3
train['fname'] = [item.split('-')[-1] if bool(re.search('-', item)) else item for item in train['fname']]
train['fname'] = [item.split(' ')[-1] if bool(re.search(' ', item)) else item for item in train['fname']]
train['fname'] = [item.replace("'", '') if bool(re.search("'", item)) else item for item in train['fname']] | code |
74063412/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
print(f'{train.shape}_train, {test.shape}_test') | code |
74063412/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import re
import pandas as pd
import re
import numpy as np
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
suv = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/surv.csv')
vic = pd.read_csv('/kaggle/input/titanicscraper/src/kaggle/titanic/vict.csv')
suv['survived'] = 1
vic['survived'] = 0
ground_truth = pd.concat([suv, vic])
ground_truth['fsname'] = [re.search('^(.*?)( |$)', item).group(1) for item in ground_truth['given name']]
tmp_f = [item.encode('ascii', 'ignore').decode('ascii') for item in ground_truth['family name']]
non_ascii = [True if x != y else False for x, y in zip(tmp_f, ground_truth['family name'])]
ground_truth['uni_f'] = non_ascii
pd.value_counts(non_ascii)
tmp_fs = [item.encode('ascii', 'ignore').decode('ascii') for item in ground_truth['fsname']]
non_ascii_ = [True if x != y else False for x, y in zip(tmp_fs, ground_truth['fsname'])]
ground_truth['uni_g'] = non_ascii_
print('Non-ascii first names')
pd.value_counts(non_ascii_) | code |
1008146/cell_2 | [
"text_plain_output_1.png"
] | from collections import defaultdict
import csv
import re
import re
import csv
import operator
from collections import defaultdict
stop_words = set(['a', "a's", 'able', 'about', 'above', 'according', 'accordingly', 'across', 'actually', 'after', 'actual', 'afterwards', 'again', 'against', "ain't", 'all', 'allow', 'allows', 'almost', 'alone', 'along', 'already', 'also', 'although', 'always', 'am', 'among', 'amongst', 'an', 'and', 'another', 'any', 'anybody', 'anyhow', 'anyone', 'anything', 'anyway', 'anyways', 'anywhere', 'apart', 'appear', 'appreciate', 'appropriate', 'are', "aren't", 'around', 'as', 'aside', 'ask', 'asking', 'associated', 'at', 'available', 'away', 'awfully', 'b', 'be', 'became', 'because', 'become', 'becomes', 'becoming', 'been', 'before', 'beforehand', 'behind', 'being', 'believe', 'below', 'beside', 'besides', 'best', 'better', 'between', 'beyond', 'both', 'brief', 'but', 'by', 'c', "c'mon", "c's", 'came', 'can', "can't", 'cannot', 'cant', 'cause', 'causes', 'certain', 'certainly', 'changes', 'clearly', 'co', 'com', 'come', 'comes', 'concerning', 'consequently', 'consider', 'considering', 'contain', 'containing', 'contains', 'corresponding', 'could', "couldn't", 'course', 'currently', 'd', 'definitely', 'described', 'despite', 'did', "didn't", 'different', 'do', 'does', "doesn't", 'doing', "don't", 'done', 'down', 'downwards', 'during', 'e', 'each', 'edu', 'eg', 'eight', 'either', 'else', 'elsewhere', 'enough', 'entirely', 'especially', 'et', 'etc', 'even', 'ever', 'every', 'everybody', 'everyone', 'everything', 'everywhere', 'ex', 'exactly', 'example', 'except', 'f', 'far', 'few', 'fifth', 'first', 'five', 'followed', 'following', 'follows', 'for', 'former', 'formerly', 'forth', 'four', 'from', 'further', 'furthermore', 'g', 'get', 'gets', 'getting', 'given', 'gives', 'go', 'goes', 'going', 'gone', 'got', 'gotten', 'greetings', 'h', 'had', "hadn't", 'happens', 'hardly', 'has', "hasn't", 'have', "haven't", 'having', 'he', "he's", 'hello', 'help', 'hence', 'her', 'here', "here's", 'hereafter', 'hereby', 'herein', 'hereupon', 'hers', 'herself', 'hi', 'him', 'himself', 'his', 'hither', 'hopefully', 'how', 'howbeit', 'however', 'i', "i'd", "i'll", "i'm", "i've", 'ie', 'if', 'ignored', 'immediate', 'in', 'inasmuch', 'inc', 'indeed', 'indicate', 'indicated', 'indicates', 'inner', 'insofar', 'instead', 'into', 'inward', 'is', "isn't", 'it', "it'd", "it'll", "it's", 'its', 'itself', 'j', 'just', 'k', 'keep', 'keeps', 'kept', 'know', 'knows', 'known', 'l', 'last', 'lately', 'later', 'latter', 'latterly', 'least', 'less', 'lest', 'let', "let's", 'like', 'liked', 'likely', 'little', 'look', 'looking', 'looks', 'ltd', 'm', 'mainly', 'many', 'may', 'maybe', 'me', 'mean', 'meanwhile', 'merely', 'might', 'more', 'moreover', 'most', 'mostly', 'much', 'must', 'my', 'myself', 'n', 'name', 'namely', 'nd', 'near', 'nearly', 'necessary', 'need', 'needs', 'neither', 'never', 'nevertheless', 'new', 'next', 'nine', 'no', 'nobody', 'non', 'none', 'noone', 'nor', 'normally', 'not', 'nothing', 'novel', 'now', 'nowhere', 'o', 'obviously', 'of', 'off', 'often', 'oh', 'ok', 'okay', 'old', 'on', 'once', 'one', 'ones', 'only', 'onto', 'or', 'other', 'others', 'otherwise', 'ought', 'our', 'ours', 'ourselves', 'out', 'outside', 'over', 'overall', 'own', 'p', 'particular', 'particularly', 'per', 'perhaps', 'placed', 'please', 'plus', 'possible', 'presumably', 'probably', 'provides', 'q', 'que', 'quite', 'qv', 'r', 'rather', 'rd', 're', 'really', 'reasonably', 'regarding', 'regardless', 'regards', 'relatively', 'respectively', 'right', 's', 'said', 'same', 'saw', 'say', 'saying', 'says', 'second', 'secondly', 'see', 'seeing', 'seem', 'seemed', 'seeming', 'seems', 'seen', 'self', 'selves', 'sensible', 'sent', 'serious', 'seriously', 'seven', 'several', 'shall', 'she', 'should', "shouldn't", 'since', 'six', 'so', 'some', 'somebody', 'somehow', 'someone', 'something', 'sometime', 'sometimes', 'somewhat', 'somewhere', 'soon', 'sorry', 'specified', 'specify', 'specifying', 'still', 'sub', 'such', 'sup', 'sure', 't', "t's", 'take', 'taken', 'tell', 'tends', 'th', 'than', 'thank', 'thanks', 'thanx', 'that', "that's", 'thats', 'the', 'their', 'theirs', 'them', 'themselves', 'then', 'thence', 'there', "there's", 'thereafter', 'thereby', 'therefore', 'therein', 'theres', 'thereupon', 'these', 'they', "they'd", "they'll", "they're", "they've", 'think', 'third', 'this', 'thorough', 'thoroughly', 'those', 'though', 'three', 'through', 'throughout', 'thru', 'thus', 'to', 'together', 'too', 'took', 'toward', 'towards', 'tried', 'tries', 'truly', 'try', 'trying', 'twice', 'two', 'u', 'un', 'under', 'unfortunately', 'unless', 'unlikely', 'until', 'unto', 'up', 'upon', 'us', 'use', 'used', 'useful', 'uses', 'using', 'usually', 'uucp', 'v', 'value', 'various', 'very', 'via', 'viz', 'vs', 'w', 'want', 'wants', 'was', "wasn't", 'way', 'we', "we'd", "we'll", "we're", "we've", 'welcome', 'well', 'went', 'were', "weren't", 'what', "what's", 'whatever', 'when', 'whence', 'whenever', 'where', "where's", 'whereafter', 'whereas', 'whereby', 'wherein', 'whereupon', 'wherever', 'whether', 'which', 'while', 'whither', 'who', "who's", 'whoever', 'whole', 'whom', 'whose', 'why', 'will', 'willing', 'wish', 'with', 'within', 'without', "won't", 'wonder', 'would', 'would', "wouldn't", 'x', 'y', 'yes', 'yet', 'you', "you'd", "you'll", "you're", "you've", 'your', 'yours', 'yourself', 'yourselves', 'z', 'zero', ''])
def f1score(tp, fp, fn):
p = tp * 1.0 / (tp + fp)
r = tp * 1.0 / (tp + fn)
f1 = 2 * p * r / (p + r)
return f1
def cleantext(raw_html):
cleanr = re.compile('<.*?>')
cleantext = re.sub(cleanr, '', raw_html)
return cleantext
def get_words(text):
word_split = re.compile('[^a-zA-Z0-9_\\+\\-/]')
return [word.strip().lower() for word in word_split.split(text)]
datapath = '../input'
in_file = open(datapath + '/test.csv')
out_file = open('sub_freq.csv', 'w')
reader = csv.DictReader(in_file)
writer = csv.writer(out_file)
writer.writerow(['id', 'tags'])
for ind, row in enumerate(reader):
text = cleantext(row['title'])
tfrequency_dict = defaultdict(int)
word_count = 0.0
for word in get_words(text):
if word not in stop_words and word.isalpha():
tfrequency_dict[word] += 1
word_count += 1.0
for word in tfrequency_dict:
tf = tfrequency_dict[word] / word_count
tfrequency_dict[word] = tf
pred_title_tags = sorted(tfrequency_dict, key=tfrequency_dict.get, reverse=True)[:10]
text = cleantext(row['content'])
dfrequency_dict = defaultdict(int)
word_count = 0.0
for word in get_words(text):
if word not in stop_words and word.isalpha():
dfrequency_dict[word] += 1
word_count += 1.0
for word in dfrequency_dict:
tf = dfrequency_dict[word] / word_count
dfrequency_dict[word] = tf
pred_content_tags = sorted(dfrequency_dict, key=dfrequency_dict.get, reverse=True)[:10]
pred_tags_dict = {}
for word in set(pred_title_tags + pred_content_tags):
pred_tags_dict[word] = tfrequency_dict.get(word, 0) + dfrequency_dict.get(word, 0)
pred_tags = set(sorted(pred_tags_dict, key=pred_tags_dict.get, reverse=True)[:3])
writer.writerow([row['id'], ' '.join(pred_tags)])
if ind % 50000 == 0:
print('processed', ind)
in_file.close()
out_file.close() | code |
1008146/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
90130653/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2011-2012.csv')
allteams_2013 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2012-2013.csv')
allteams_2014 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2013-2014.csv')
allteams_2015 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2014-2015.csv')
allteams_2016 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2015-2016.csv')
allteams_2017 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2016-2017.csv')
allteams_2018 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2017-2018.csv')
allteams_2019 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2018-2019.csv')
allteams_2021 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2020-2021.csv')
allteams_list = [allteams_2010, allteams_2011, allteams_2012, allteams_2013, allteams_2014, allteams_2015, allteams_2016, allteams_2017, allteams_2018, allteams_2019, allteams_2021]
teamstats_2010 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2010 Team Stats.csv', skiprows=[0])
teamstats_2011 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2011 Team Stats.csv', skiprows=[0])
teamstats_2012 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2012 Team Stats.csv', skiprows=[0])
teamstats_2013 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2013 Team Stats.csv', skiprows=[0])
teamstats_2014 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2014 Team Stats.csv', skiprows=[0])
teamstats_2015 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2015 Team Stats.csv', skiprows=[0])
teamstats_2016 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2016 Team Stats.csv', skiprows=[0])
teamstats_2017 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2017 Team Stats.csv', skiprows=[0])
teamstats_2018 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2018 Team Stats.csv', skiprows=[0])
teamstats_2019 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2019 Team Stats.csv', skiprows=[0])
teamstats_2021 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2021 Team Stats.csv', skiprows=[0])
teamstats_list = [teamstats_2010, teamstats_2011, teamstats_2012, teamstats_2013, teamstats_2014, teamstats_2015, teamstats_2016, teamstats_2017, teamstats_2018, teamstats_2019, teamstats_2021]
teamstats_2010_2021 = pd.concat(teamstats_list)
allteams_2010_2021 = pd.concat(allteams_list)
allteams_2010_2021.tail() | code |
90130653/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2011-2012.csv')
allteams_2013 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2012-2013.csv')
allteams_2014 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2013-2014.csv')
allteams_2015 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2014-2015.csv')
allteams_2016 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2015-2016.csv')
allteams_2017 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2016-2017.csv')
allteams_2018 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2017-2018.csv')
allteams_2019 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2018-2019.csv')
allteams_2021 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2020-2021.csv')
allteams_list = [allteams_2010, allteams_2011, allteams_2012, allteams_2013, allteams_2014, allteams_2015, allteams_2016, allteams_2017, allteams_2018, allteams_2019, allteams_2021]
teamstats_2010 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2010 Team Stats.csv', skiprows=[0])
teamstats_2011 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2011 Team Stats.csv', skiprows=[0])
teamstats_2012 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2012 Team Stats.csv', skiprows=[0])
teamstats_2013 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2013 Team Stats.csv', skiprows=[0])
teamstats_2014 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2014 Team Stats.csv', skiprows=[0])
teamstats_2015 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2015 Team Stats.csv', skiprows=[0])
teamstats_2016 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2016 Team Stats.csv', skiprows=[0])
teamstats_2017 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2017 Team Stats.csv', skiprows=[0])
teamstats_2018 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2018 Team Stats.csv', skiprows=[0])
teamstats_2019 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2019 Team Stats.csv', skiprows=[0])
teamstats_2021 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2021 Team Stats.csv', skiprows=[0])
teamstats_list = [teamstats_2010, teamstats_2011, teamstats_2012, teamstats_2013, teamstats_2014, teamstats_2015, teamstats_2016, teamstats_2017, teamstats_2018, teamstats_2019, teamstats_2021]
i = 0
for df in teamstats_list:
df.School = df.School.str.rstrip()
year_val = 2010 + i
years = [year_val] * df.shape[0]
df['YEAR'] = years
i += 1
year_val = 2021
years = [year_val] * df.shape[0]
teamstats_2021['YEAR'] = [year_val] * teamstats_2021.shape[0]
i = 0
for df in allteams_list:
print(df.shape)
df[['W', 'L']] = df['W-L'].str.split('-', expand=True)
df.drop(['W-L'], inplace=True, axis=1)
year_val = 2010 + i
years = [year_val] * df.shape[0]
df['YEAR'] = years
i += 1 | code |
90130653/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
90130653/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2011-2012.csv')
allteams_2013 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2012-2013.csv')
allteams_2014 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2013-2014.csv')
allteams_2015 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2014-2015.csv')
allteams_2016 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2015-2016.csv')
allteams_2017 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2016-2017.csv')
allteams_2018 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2017-2018.csv')
allteams_2019 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2018-2019.csv')
allteams_2021 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2020-2021.csv')
teamstats_2010 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2010 Team Stats.csv', skiprows=[0])
teamstats_2011 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2011 Team Stats.csv', skiprows=[0])
teamstats_2012 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2012 Team Stats.csv', skiprows=[0])
teamstats_2013 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2013 Team Stats.csv', skiprows=[0])
teamstats_2014 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2014 Team Stats.csv', skiprows=[0])
teamstats_2015 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2015 Team Stats.csv', skiprows=[0])
teamstats_2016 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2016 Team Stats.csv', skiprows=[0])
teamstats_2017 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2017 Team Stats.csv', skiprows=[0])
teamstats_2018 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2018 Team Stats.csv', skiprows=[0])
teamstats_2019 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2019 Team Stats.csv', skiprows=[0])
teamstats_2021 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2021 Team Stats.csv', skiprows=[0])
teamstats_list = [teamstats_2010, teamstats_2011, teamstats_2012, teamstats_2013, teamstats_2014, teamstats_2015, teamstats_2016, teamstats_2017, teamstats_2018, teamstats_2019, teamstats_2021]
teamstats_2010_2021 = pd.concat(teamstats_list)
teamstats_2010_2021.tail() | code |
90130653/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2011-2012.csv')
allteams_2013 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2012-2013.csv')
allteams_2014 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2013-2014.csv')
allteams_2015 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2014-2015.csv')
allteams_2016 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2015-2016.csv')
allteams_2017 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2016-2017.csv')
allteams_2018 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2017-2018.csv')
allteams_2019 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2018-2019.csv')
allteams_2021 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2020-2021.csv')
allteams_list = [allteams_2010, allteams_2011, allteams_2012, allteams_2013, allteams_2014, allteams_2015, allteams_2016, allteams_2017, allteams_2018, allteams_2019, allteams_2021]
teamstats_2010 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2010 Team Stats.csv', skiprows=[0])
teamstats_2011 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2011 Team Stats.csv', skiprows=[0])
teamstats_2012 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2012 Team Stats.csv', skiprows=[0])
teamstats_2013 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2013 Team Stats.csv', skiprows=[0])
teamstats_2014 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2014 Team Stats.csv', skiprows=[0])
teamstats_2015 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2015 Team Stats.csv', skiprows=[0])
teamstats_2016 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2016 Team Stats.csv', skiprows=[0])
teamstats_2017 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2017 Team Stats.csv', skiprows=[0])
teamstats_2018 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2018 Team Stats.csv', skiprows=[0])
teamstats_2019 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2019 Team Stats.csv', skiprows=[0])
teamstats_2021 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2021 Team Stats.csv', skiprows=[0])
teamstats_list = [teamstats_2010, teamstats_2011, teamstats_2012, teamstats_2013, teamstats_2014, teamstats_2015, teamstats_2016, teamstats_2017, teamstats_2018, teamstats_2019, teamstats_2021]
i = 0
for df in teamstats_list:
df.School = df.School.str.rstrip()
year_val = 2010 + i
years = [year_val] * df.shape[0]
df['YEAR'] = years
i += 1
year_val = 2021
years = [year_val] * df.shape[0]
teamstats_2021['YEAR'] = [year_val] * teamstats_2021.shape[0]
teamstats_2010_2021 = pd.concat(teamstats_list)
i = 0
for df in allteams_list:
df[['W', 'L']] = df['W-L'].str.split('-', expand=True)
df.drop(['W-L'], inplace=True, axis=1)
year_val = 2010 + i
years = [year_val] * df.shape[0]
df['YEAR'] = years
i += 1
allteams_2010_2021 = pd.concat(allteams_list)
schoolname_list1 = teamstats_2010_2021.School.unique()
schoolname_list1.sort()
schoolname_list2 = allteams_2010_2021.TEAM.unique()
schoolname_list2.sort()
different_names_list = []
for i in range(len(schoolname_list1)):
if schoolname_list1[i] not in schoolname_list2:
different_names_list.append(schoolname_list1[i])
print('List of unique differences between school names of dataframes: ', different_names_list) | code |
90130653/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2011-2012.csv')
allteams_2013 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2012-2013.csv')
allteams_2014 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2013-2014.csv')
allteams_2015 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2014-2015.csv')
allteams_2016 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2015-2016.csv')
allteams_2017 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2016-2017.csv')
allteams_2018 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2017-2018.csv')
allteams_2019 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2018-2019.csv')
allteams_2021 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2020-2021.csv')
teamstats_2010 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2010 Team Stats.csv', skiprows=[0])
teamstats_2011 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2011 Team Stats.csv', skiprows=[0])
teamstats_2012 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2012 Team Stats.csv', skiprows=[0])
teamstats_2013 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2013 Team Stats.csv', skiprows=[0])
teamstats_2014 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2014 Team Stats.csv', skiprows=[0])
teamstats_2015 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2015 Team Stats.csv', skiprows=[0])
teamstats_2016 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2016 Team Stats.csv', skiprows=[0])
teamstats_2017 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2017 Team Stats.csv', skiprows=[0])
teamstats_2018 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2018 Team Stats.csv', skiprows=[0])
teamstats_2019 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2019 Team Stats.csv', skiprows=[0])
teamstats_2021 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2021 Team Stats.csv', skiprows=[0])
teamstats_list = [teamstats_2010, teamstats_2011, teamstats_2012, teamstats_2013, teamstats_2014, teamstats_2015, teamstats_2016, teamstats_2017, teamstats_2018, teamstats_2019, teamstats_2021]
teamstats_2010_2021 = pd.concat(teamstats_list)
teamstats_2010_2021.head() | code |
90130653/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2011-2012.csv')
allteams_2013 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2012-2013.csv')
allteams_2014 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2013-2014.csv')
allteams_2015 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2014-2015.csv')
allteams_2016 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2015-2016.csv')
allteams_2017 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2016-2017.csv')
allteams_2018 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2017-2018.csv')
allteams_2019 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2018-2019.csv')
allteams_2021 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2020-2021.csv')
allteams_list = [allteams_2010, allteams_2011, allteams_2012, allteams_2013, allteams_2014, allteams_2015, allteams_2016, allteams_2017, allteams_2018, allteams_2019, allteams_2021]
teamstats_2010 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2010 Team Stats.csv', skiprows=[0])
teamstats_2011 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2011 Team Stats.csv', skiprows=[0])
teamstats_2012 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2012 Team Stats.csv', skiprows=[0])
teamstats_2013 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2013 Team Stats.csv', skiprows=[0])
teamstats_2014 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2014 Team Stats.csv', skiprows=[0])
teamstats_2015 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2015 Team Stats.csv', skiprows=[0])
teamstats_2016 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2016 Team Stats.csv', skiprows=[0])
teamstats_2017 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2017 Team Stats.csv', skiprows=[0])
teamstats_2018 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2018 Team Stats.csv', skiprows=[0])
teamstats_2019 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2019 Team Stats.csv', skiprows=[0])
teamstats_2021 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2021 Team Stats.csv', skiprows=[0])
teamstats_list = [teamstats_2010, teamstats_2011, teamstats_2012, teamstats_2013, teamstats_2014, teamstats_2015, teamstats_2016, teamstats_2017, teamstats_2018, teamstats_2019, teamstats_2021]
i = 0
for df in teamstats_list:
df.School = df.School.str.rstrip()
year_val = 2010 + i
years = [year_val] * df.shape[0]
df['YEAR'] = years
i += 1
year_val = 2021
years = [year_val] * df.shape[0]
teamstats_2021['YEAR'] = [year_val] * teamstats_2021.shape[0]
teamstats_2010_2021 = pd.concat(teamstats_list)
i = 0
for df in allteams_list:
df[['W', 'L']] = df['W-L'].str.split('-', expand=True)
df.drop(['W-L'], inplace=True, axis=1)
year_val = 2010 + i
years = [year_val] * df.shape[0]
df['YEAR'] = years
i += 1
allteams_2010_2021 = pd.concat(allteams_list)
schoolname_list1 = teamstats_2010_2021.School.unique()
schoolname_list1.sort()
schoolname_list2 = allteams_2010_2021.TEAM.unique()
schoolname_list2.sort()
different_names_list = []
for i in range(len(schoolname_list1)):
if schoolname_list1[i] not in schoolname_list2:
different_names_list.append(schoolname_list1[i])
corrected_name_list = ['UAB', 'Albany', 'Bowling Green', 'BYU', 'CSU Bakersfield', 'CSU Fullerton', 'Long Beach State', 'CSU Northridge', 'Centenary', 'Central Connecticut', 'UCF', 'The Citadel', 'Charleston', 'UConn', "Hawai'i", 'UIC', 'LSU', 'UL Monroe', 'Loyola Chicago', 'UMBC', 'UMass', 'UMass Lowell', 'McNeese', 'Miami', 'Ole Miss', 'UM Kansas City', 'Morgan St', 'UNLV', 'Nicholls', 'Norfolk St', 'NC State', 'UNC Asheville', 'UNC Greensboro', 'UNC Wilmington', 'Prairie View A&M', 'Purdue Fort Wayne', 'St. Francis (PA)', "Saint Mary's", 'San José St', 'Seattle U', 'SE Louisiana', 'USC', 'SMU', 'Southern Miss', 'St. Francis (BKN)', "St. John's", 'Tarleton', 'UT Martin', 'Texas A&M-CC', 'TCU', 'UT Arlington', 'UTEP', 'UT Rio Grande Valley', 'UTSA', 'UC Davis', 'UC Irvine', 'UC Riverside', 'UC San Diego', 'UC Santa Barbara', 'California', 'VCU', 'Winston Salem']
for i in range(len(corrected_name_list)):
print(different_names_list[i], 'vs. ', corrected_name_list[i]) | code |
90130653/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2011-2012.csv')
allteams_2013 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2012-2013.csv')
allteams_2014 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2013-2014.csv')
allteams_2015 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2014-2015.csv')
allteams_2016 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2015-2016.csv')
allteams_2017 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2016-2017.csv')
allteams_2018 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2017-2018.csv')
allteams_2019 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2018-2019.csv')
allteams_2021 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2020-2021.csv')
teamstats_2010 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2010 Team Stats.csv', skiprows=[0])
teamstats_2011 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2011 Team Stats.csv', skiprows=[0])
teamstats_2012 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2012 Team Stats.csv', skiprows=[0])
teamstats_2013 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2013 Team Stats.csv', skiprows=[0])
teamstats_2014 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2014 Team Stats.csv', skiprows=[0])
teamstats_2015 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2015 Team Stats.csv', skiprows=[0])
teamstats_2016 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2016 Team Stats.csv', skiprows=[0])
teamstats_2017 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2017 Team Stats.csv', skiprows=[0])
teamstats_2018 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2018 Team Stats.csv', skiprows=[0])
teamstats_2019 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2019 Team Stats.csv', skiprows=[0])
teamstats_2021 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2021 Team Stats.csv', skiprows=[0])
teamstats_list = [teamstats_2010, teamstats_2011, teamstats_2012, teamstats_2013, teamstats_2014, teamstats_2015, teamstats_2016, teamstats_2017, teamstats_2018, teamstats_2019, teamstats_2021]
i = 0
for df in teamstats_list:
print(df.shape)
df.School = df.School.str.rstrip()
year_val = 2010 + i
years = [year_val] * df.shape[0]
df['YEAR'] = years
i += 1 | code |
90130653/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
allteams_2010 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2009-2010.csv')
allteams_2011 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2010-2011.csv')
allteams_2012 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2011-2012.csv')
allteams_2013 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2012-2013.csv')
allteams_2014 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2013-2014.csv')
allteams_2015 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2014-2015.csv')
allteams_2016 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2015-2016.csv')
allteams_2017 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2016-2017.csv')
allteams_2018 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2017-2018.csv')
allteams_2019 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2018-2019.csv')
allteams_2021 = pd.read_csv('../input/ncaa-bpi-ranks-by-year/Ranking csv/ESPN Rank 2020-2021.csv')
allteams_list = [allteams_2010, allteams_2011, allteams_2012, allteams_2013, allteams_2014, allteams_2015, allteams_2016, allteams_2017, allteams_2018, allteams_2019, allteams_2021]
teamstats_2010 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2010 Team Stats.csv', skiprows=[0])
teamstats_2011 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2011 Team Stats.csv', skiprows=[0])
teamstats_2012 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2012 Team Stats.csv', skiprows=[0])
teamstats_2013 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2013 Team Stats.csv', skiprows=[0])
teamstats_2014 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2014 Team Stats.csv', skiprows=[0])
teamstats_2015 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2015 Team Stats.csv', skiprows=[0])
teamstats_2016 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2016 Team Stats.csv', skiprows=[0])
teamstats_2017 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2017 Team Stats.csv', skiprows=[0])
teamstats_2018 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2018 Team Stats.csv', skiprows=[0])
teamstats_2019 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2019 Team Stats.csv', skiprows=[0])
teamstats_2021 = pd.read_csv('../input/team-stats-csv/Team Stats CSV/2021 Team Stats.csv', skiprows=[0])
teamstats_list = [teamstats_2010, teamstats_2011, teamstats_2012, teamstats_2013, teamstats_2014, teamstats_2015, teamstats_2016, teamstats_2017, teamstats_2018, teamstats_2019, teamstats_2021]
i = 0
for df in teamstats_list:
df.School = df.School.str.rstrip()
year_val = 2010 + i
years = [year_val] * df.shape[0]
df['YEAR'] = years
i += 1
year_val = 2021
years = [year_val] * df.shape[0]
teamstats_2021['YEAR'] = [year_val] * teamstats_2021.shape[0]
teamstats_2010_2021 = pd.concat(teamstats_list)
i = 0
for df in allteams_list:
df[['W', 'L']] = df['W-L'].str.split('-', expand=True)
df.drop(['W-L'], inplace=True, axis=1)
year_val = 2010 + i
years = [year_val] * df.shape[0]
df['YEAR'] = years
i += 1
allteams_2010_2021 = pd.concat(allteams_list)
schoolname_list1 = teamstats_2010_2021.School.unique()
schoolname_list1.sort()
schoolname_list2 = allteams_2010_2021.TEAM.unique()
schoolname_list2.sort()
print(len(schoolname_list2))
print(len(schoolname_list2))
for i in range(len(schoolname_list1)):
print(schoolname_list1[i], '-', schoolname_list2[i]) | code |
89135256/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/articles.csv')
transaction.head(5) | code |
89135256/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/articles.csv')
transaction.loc[transaction['customer_id'] == '00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d25f88aa139fdfc657'].shape | code |
89135256/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/articles.csv')
transaction.loc[transaction['customer_id'] == '00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d25f88aa139fdfc657'].shape
customer_detail_by_trans = pd.merge(transaction, customer, on='customer_id')
customer_detail_by_trans.loc[customer_detail_by_trans['customer_id'] == '00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d25f88aa139fdfc657'].shape | code |
89135256/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89135256/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/articles.csv')
articles.head(5) | code |
89135256/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/articles.csv')
transaction.loc[transaction['customer_id'] == '00000dbacae5abe5e23885899a1fa44253a17956c6d1c3d25f88aa139fdfc657'].shape
customer_detail_by_trans = pd.merge(transaction, customer, on='customer_id')
customer_detail_by_trans.head(10) | code |
89135256/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
transaction = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/transactions_train.csv')
customer = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/customers.csv')
articles = pd.read_csv('../input/h-and-m-personalized-fashion-recommendations/articles.csv')
customer.head(10) | code |
1004737/cell_6 | [
"text_plain_output_1.png"
] | from csv import DictReader
from keras import layers
from keras import models
import numpy as np # linear algebra
import random
people = list(DictReader(open('../input/train.csv')))
people
def letter_for_cabin(cabin):
return cabin[0] if len(cabin) else ''
cabin_letters = list(set([letter_for_cabin(p['Cabin']) for p in people]))
def onehot(idx, maximum):
v = [0] * maximum
v[idx] = 1
return v
ages = [float(p['Age']) for p in people if p['Age']]
avg_age = sum(ages) / len(ages)
def vectorize(person):
pclass = int(person['Pclass'])
salutations = [1 if sal in person['Name'] else 0 for sal in ['Mr.', 'Miss.', 'Mrs.', 'Master.']]
embarked = 'CQS'.index(person['Embarked'])
age = float(person['Age']) if person['Age'] else avg_age
sibs = 0
parents = 0
spouses = 0
children = 0
if age > 18:
children = int(person['Parch'])
spouses = int(person['SibSp'])
else:
parents = int(person['Parch'])
sibs = int(person['SibSp'])
sex = -1 if person['Sex'] == 'male' else 1
fare = float(person['Fare'])
cabin = cabin_letters.index(letter_for_cabin(person['Cabin']))
return np.array([pclass, sex, fare, age] + salutations + onehot(embarked, 3) + [sibs, parents, spouses, children] + onehot(cabin, len(cabin_letters)))
def make_vecs_and_labels(people):
xs = np.stack([vectorize(p) for p in people])
ys = np.stack([int(p['Survived']) for p in people])
return (xs, ys)
test_split = 0.8
random.shuffle(people)
input_dim = len(vectorize(people[0]))
x, y = make_vecs_and_labels(people)
def create_model(n_layers=3, layer_size=256, activation='relu', dropout=0):
model = models.Sequential()
prev_dim = input_dim
for i in range(n_layers):
model.add(layers.Dense(layer_size, input_dim=prev_dim))
prev_dim = layer_size
model.add(layers.Activation(activation))
if dropout > 0:
model.add(layers.Dropout(dropout))
model.add(layers.Dense(2))
model.add(layers.Activation('softmax'))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
options = {'n_layers': [1, 2, 3, 4], 'layer_size': [128, 256, 512, 1024], 'activation': ['relu', 'softmax'], 'dropout': [0, 0.1]}
def all_configs(options):
if len(options) == 0:
return [[]]
else:
child_configs = all_configs(options[1:])
configs = []
key = options[0][0]
for value in options[0][1]:
for child in child_configs:
configs.append([(key, value)] + child)
return configs
configs = all_configs(list(options.items()))
random.shuffle(configs)
results = []
for config in configs[:20]:
model = create_model(**dict(config))
hist = model.fit(x, np.expand_dims(y, -1), validation_split=0.8, verbose=0, nb_epoch=20)
print(dict(config))
print('Validation set accuracy:', max(hist.history['val_acc']))
print()
results.append((dict(config), max(hist.history['val_acc']))) | code |
1004737/cell_2 | [
"text_plain_output_1.png"
] | from csv import DictReader
people = list(DictReader(open('../input/train.csv')))
people | code |
1004737/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import keras
from keras import models
from keras import layers
from csv import DictReader
import random
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1004737/cell_7 | [
"text_plain_output_1.png"
] | from csv import DictReader
from keras import layers
from keras import models
import numpy as np # linear algebra
import random
people = list(DictReader(open('../input/train.csv')))
people
def letter_for_cabin(cabin):
return cabin[0] if len(cabin) else ''
cabin_letters = list(set([letter_for_cabin(p['Cabin']) for p in people]))
def onehot(idx, maximum):
v = [0] * maximum
v[idx] = 1
return v
ages = [float(p['Age']) for p in people if p['Age']]
avg_age = sum(ages) / len(ages)
def vectorize(person):
pclass = int(person['Pclass'])
salutations = [1 if sal in person['Name'] else 0 for sal in ['Mr.', 'Miss.', 'Mrs.', 'Master.']]
embarked = 'CQS'.index(person['Embarked'])
age = float(person['Age']) if person['Age'] else avg_age
sibs = 0
parents = 0
spouses = 0
children = 0
if age > 18:
children = int(person['Parch'])
spouses = int(person['SibSp'])
else:
parents = int(person['Parch'])
sibs = int(person['SibSp'])
sex = -1 if person['Sex'] == 'male' else 1
fare = float(person['Fare'])
cabin = cabin_letters.index(letter_for_cabin(person['Cabin']))
return np.array([pclass, sex, fare, age] + salutations + onehot(embarked, 3) + [sibs, parents, spouses, children] + onehot(cabin, len(cabin_letters)))
def make_vecs_and_labels(people):
xs = np.stack([vectorize(p) for p in people])
ys = np.stack([int(p['Survived']) for p in people])
return (xs, ys)
test_split = 0.8
random.shuffle(people)
input_dim = len(vectorize(people[0]))
x, y = make_vecs_and_labels(people)
def create_model(n_layers=3, layer_size=256, activation='relu', dropout=0):
model = models.Sequential()
prev_dim = input_dim
for i in range(n_layers):
model.add(layers.Dense(layer_size, input_dim=prev_dim))
prev_dim = layer_size
model.add(layers.Activation(activation))
if dropout > 0:
model.add(layers.Dropout(dropout))
model.add(layers.Dense(2))
model.add(layers.Activation('softmax'))
model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
return model
options = {'n_layers': [1, 2, 3, 4], 'layer_size': [128, 256, 512, 1024], 'activation': ['relu', 'softmax'], 'dropout': [0, 0.1]}
def all_configs(options):
if len(options) == 0:
return [[]]
else:
child_configs = all_configs(options[1:])
configs = []
key = options[0][0]
for value in options[0][1]:
for child in child_configs:
configs.append([(key, value)] + child)
return configs
configs = all_configs(list(options.items()))
random.shuffle(configs)
results = []
for config in configs[:20]:
model = create_model(**dict(config))
hist = model.fit(x, np.expand_dims(y, -1), validation_split=0.8, verbose=0, nb_epoch=20)
results.append((dict(config), max(hist.history['val_acc'])))
best = max(results, key=lambda x: x[1])
best | code |
1004737/cell_3 | [
"text_plain_output_1.png"
] | from csv import DictReader
people = list(DictReader(open('../input/train.csv')))
people
def letter_for_cabin(cabin):
return cabin[0] if len(cabin) else ''
cabin_letters = list(set([letter_for_cabin(p['Cabin']) for p in people]))
print(cabin_letters) | code |
130017710/cell_4 | [
"text_plain_output_1.png"
] | import os
data_dir = '/kaggle/input/audio-mnist/data'
paths = []
labels = []
t = 0
for dirname, _, filenames in os.walk(data_dir):
if t < 20:
t += 1
for filename in filenames:
if filename[-4:] == '.wav':
paths += [os.path.join(dirname, filename)]
labels += [dirname.split('/')[-1]]
print(len(paths)) | code |
130017710/cell_6 | [
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_16.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_plain_output_11.png",
"text_plain_output_12.png"
] | import cv2
import librosa
import matplotlib.pyplot as plt
import numpy as np
import os
data_dir = '/kaggle/input/audio-mnist/data'
paths = []
labels = []
t = 0
for dirname, _, filenames in os.walk(data_dir):
if t < 20:
t += 1
for filename in filenames:
if filename[-4:] == '.wav':
paths += [os.path.join(dirname, filename)]
labels += [dirname.split('/')[-1]]
for i in range(len(paths)):
if i % 100 == 0:
print('i=', i)
path = paths[i]
label = path.split('/')[-2]
file = path.split('/')[-1][0:-4]
try:
y, sr = librosa.load(path)
mel_spectrogram = librosa.feature.melspectrogram(y=y, sr=sr, n_mels=128)
log_mel_spectrogram = librosa.power_to_db(mel_spectrogram, ref=np.max)
img = log_mel_spectrogram
img = cv2.resize(np.array(img), dsize=(128, 128))
X = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
X = (X - X.min(axis=0)) / (X.max(axis=0) - X.min(axis=0))
plt.imshow(X)
plt.axis('off')
plt.savefig('./mel/' + label + '_' + file + '.png', bbox_inches='tight', pad_inches=0)
plt.close
except:
print('except', label, file)
continue | code |
17136778/cell_21 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
class CustomImageList(ImageList):
def open(self, fn):
img = fn.reshape(28, 28)
img = np.stack((img,) * 3, axis=-1)
return Image(pil2tensor(img, dtype=np.float32))
@classmethod
def from_csv_custom(cls, path: PathOrStr, csv_name: str, imgIdx: int=1, header: str='infer', **kwargs) -> 'ItemList':
df = pd.read_csv(Path(path) / csv_name, header=header)
res = super().from_df(df, path=path, cols=0, **kwargs)
res.items = df.iloc[:, imgIdx:].apply(lambda x: x.values / 255.0, axis=1).values
return res
@classmethod
def from_df_custom(cls, path: PathOrStr, df: DataFrame, imgIdx: int=1, header: str='infer', **kwargs) -> 'ItemList':
res = super().from_df(df, path=path, cols=0, **kwargs)
res.items = df.iloc[:, imgIdx:].apply(lambda x: x.values / 255.0, axis=1).values
return res
test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normalize(imagenet_stats)
learn = cnn_learner(data, models.resnet18, metrics=[accuracy], model_dir='/kaggle/working/models')
learn.lr_find()
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.lr_find()
learn.fit_one_cycle(10, max_lr=slice(1e-06, 0.0001))
predictions, *_ = learn.get_preds(DatasetType.Test)
labels = np.argmax(predictions, 1)
submission_df = pd.DataFrame({'ImageId': list(range(1, len(labels) + 1)), 'Label': labels})
submission_df.to_csv(f'submission_orig.csv', index=False)
train_df = pd.read_csv(path + '/train.csv')
from sklearn.model_selection import train_test_split
train_df, val_df = train_test_split(train_df, test_size=0.2)
train_df['label'].hist(figsize=(10, 5)) | code |
17136778/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normalize(imagenet_stats)
learn = cnn_learner(data, models.resnet18, metrics=[accuracy], model_dir='/kaggle/working/models')
learn.lr_find()
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.lr_find()
learn.recorder.plot(suggestion=True) | code |
17136778/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
class CustomImageList(ImageList):
def open(self, fn):
img = fn.reshape(28, 28)
img = np.stack((img,) * 3, axis=-1)
return Image(pil2tensor(img, dtype=np.float32))
@classmethod
def from_csv_custom(cls, path: PathOrStr, csv_name: str, imgIdx: int=1, header: str='infer', **kwargs) -> 'ItemList':
df = pd.read_csv(Path(path) / csv_name, header=header)
res = super().from_df(df, path=path, cols=0, **kwargs)
res.items = df.iloc[:, imgIdx:].apply(lambda x: x.values / 255.0, axis=1).values
return res
@classmethod
def from_df_custom(cls, path: PathOrStr, df: DataFrame, imgIdx: int=1, header: str='infer', **kwargs) -> 'ItemList':
res = super().from_df(df, path=path, cols=0, **kwargs)
res.items = df.iloc[:, imgIdx:].apply(lambda x: x.values / 255.0, axis=1).values
return res
test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normalize(imagenet_stats)
learn = cnn_learner(data, models.resnet18, metrics=[accuracy], model_dir='/kaggle/working/models')
learn.lr_find()
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.lr_find()
learn.fit_one_cycle(10, max_lr=slice(1e-06, 0.0001))
predictions, *_ = learn.get_preds(DatasetType.Test)
labels = np.argmax(predictions, 1)
submission_df = pd.DataFrame({'ImageId': list(range(1, len(labels) + 1)), 'Label': labels})
submission_df.to_csv(f'submission_orig.csv', index=False)
train_df = pd.read_csv(path + '/train.csv')
from sklearn.model_selection import train_test_split
train_df, val_df = train_test_split(train_df, test_size=0.2)
proportions = pd.DataFrame({0: [0.5], 1: [0.05], 2: [0.1], 3: [0.03], 4: [0.03], 5: [0.03], 6: [0.03], 7: [0.5], 8: [0.5], 9: [0.5]})
imbalanced_train_df = train_df.groupby('label').apply(lambda x: x.sample(frac=proportions[x.name]))
imbalanced_train_df['label'].hist(figsize=(10, 5)) | code |
17136778/cell_34 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
class CustomImageList(ImageList):
def open(self, fn):
img = fn.reshape(28, 28)
img = np.stack((img,) * 3, axis=-1)
return Image(pil2tensor(img, dtype=np.float32))
@classmethod
def from_csv_custom(cls, path: PathOrStr, csv_name: str, imgIdx: int=1, header: str='infer', **kwargs) -> 'ItemList':
df = pd.read_csv(Path(path) / csv_name, header=header)
res = super().from_df(df, path=path, cols=0, **kwargs)
res.items = df.iloc[:, imgIdx:].apply(lambda x: x.values / 255.0, axis=1).values
return res
@classmethod
def from_df_custom(cls, path: PathOrStr, df: DataFrame, imgIdx: int=1, header: str='infer', **kwargs) -> 'ItemList':
res = super().from_df(df, path=path, cols=0, **kwargs)
res.items = df.iloc[:, imgIdx:].apply(lambda x: x.values / 255.0, axis=1).values
return res
test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normalize(imagenet_stats)
learn = cnn_learner(data, models.resnet18, metrics=[accuracy], model_dir='/kaggle/working/models')
learn.lr_find()
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.lr_find()
learn.fit_one_cycle(10, max_lr=slice(1e-06, 0.0001))
predictions, *_ = learn.get_preds(DatasetType.Test)
labels = np.argmax(predictions, 1)
submission_df = pd.DataFrame({'ImageId': list(range(1, len(labels) + 1)), 'Label': labels})
submission_df.to_csv(f'submission_orig.csv', index=False)
train_df = pd.read_csv(path + '/train.csv')
from sklearn.model_selection import train_test_split
train_df, val_df = train_test_split(train_df, test_size=0.2)
proportions = pd.DataFrame({0: [0.5], 1: [0.05], 2: [0.1], 3: [0.03], 4: [0.03], 5: [0.03], 6: [0.03], 7: [0.5], 8: [0.5], 9: [0.5]})
imbalanced_train_df = train_df.groupby('label').apply(lambda x: x.sample(frac=proportions[x.name]))
df = pd.concat([imbalanced_train_df, val_df])
data = CustomImageList.from_df_custom(df=df, path=path, imgIdx=1).split_by_idx(range(len(imbalanced_train_df) - 1, len(df))).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normalize(imagenet_stats)
learn = cnn_learner(data, models.resnet18, metrics=[accuracy], model_dir='/kaggle/working/models')
learn.lr_find()
learn.fit_one_cycle(4, max_lr=0.01) | code |
17136778/cell_33 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
class CustomImageList(ImageList):
def open(self, fn):
img = fn.reshape(28, 28)
img = np.stack((img,) * 3, axis=-1)
return Image(pil2tensor(img, dtype=np.float32))
@classmethod
def from_csv_custom(cls, path: PathOrStr, csv_name: str, imgIdx: int=1, header: str='infer', **kwargs) -> 'ItemList':
df = pd.read_csv(Path(path) / csv_name, header=header)
res = super().from_df(df, path=path, cols=0, **kwargs)
res.items = df.iloc[:, imgIdx:].apply(lambda x: x.values / 255.0, axis=1).values
return res
@classmethod
def from_df_custom(cls, path: PathOrStr, df: DataFrame, imgIdx: int=1, header: str='infer', **kwargs) -> 'ItemList':
res = super().from_df(df, path=path, cols=0, **kwargs)
res.items = df.iloc[:, imgIdx:].apply(lambda x: x.values / 255.0, axis=1).values
return res
test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normalize(imagenet_stats)
learn = cnn_learner(data, models.resnet18, metrics=[accuracy], model_dir='/kaggle/working/models')
learn.lr_find()
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.lr_find()
learn.fit_one_cycle(10, max_lr=slice(1e-06, 0.0001))
predictions, *_ = learn.get_preds(DatasetType.Test)
labels = np.argmax(predictions, 1)
submission_df = pd.DataFrame({'ImageId': list(range(1, len(labels) + 1)), 'Label': labels})
submission_df.to_csv(f'submission_orig.csv', index=False)
train_df = pd.read_csv(path + '/train.csv')
from sklearn.model_selection import train_test_split
train_df, val_df = train_test_split(train_df, test_size=0.2)
proportions = pd.DataFrame({0: [0.5], 1: [0.05], 2: [0.1], 3: [0.03], 4: [0.03], 5: [0.03], 6: [0.03], 7: [0.5], 8: [0.5], 9: [0.5]})
imbalanced_train_df = train_df.groupby('label').apply(lambda x: x.sample(frac=proportions[x.name]))
df = pd.concat([imbalanced_train_df, val_df])
data = CustomImageList.from_df_custom(df=df, path=path, imgIdx=1).split_by_idx(range(len(imbalanced_train_df) - 1, len(df))).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normalize(imagenet_stats)
learn = cnn_learner(data, models.resnet18, metrics=[accuracy], model_dir='/kaggle/working/models')
learn.lr_find()
learn.recorder.plot(suggestion=True) | code |
17136778/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
class CustomImageList(ImageList):
def open(self, fn):
img = fn.reshape(28, 28)
img = np.stack((img,) * 3, axis=-1)
return Image(pil2tensor(img, dtype=np.float32))
@classmethod
def from_csv_custom(cls, path: PathOrStr, csv_name: str, imgIdx: int=1, header: str='infer', **kwargs) -> 'ItemList':
df = pd.read_csv(Path(path) / csv_name, header=header)
res = super().from_df(df, path=path, cols=0, **kwargs)
res.items = df.iloc[:, imgIdx:].apply(lambda x: x.values / 255.0, axis=1).values
return res
@classmethod
def from_df_custom(cls, path: PathOrStr, df: DataFrame, imgIdx: int=1, header: str='infer', **kwargs) -> 'ItemList':
res = super().from_df(df, path=path, cols=0, **kwargs)
res.items = df.iloc[:, imgIdx:].apply(lambda x: x.values / 255.0, axis=1).values
return res
test = CustomImageList.from_csv_custom(path=path, csv_name='test.csv', imgIdx=0)
data = CustomImageList.from_csv_custom(path=path, csv_name='train.csv', imgIdx=1).split_by_rand_pct(0.2).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normalize(imagenet_stats)
learn = cnn_learner(data, models.resnet18, metrics=[accuracy], model_dir='/kaggle/working/models')
learn.lr_find()
learn.fit_one_cycle(4, max_lr=0.01)
learn.unfreeze()
learn.lr_find()
learn.fit_one_cycle(10, max_lr=slice(1e-06, 0.0001))
predictions, *_ = learn.get_preds(DatasetType.Test)
labels = np.argmax(predictions, 1)
submission_df = pd.DataFrame({'ImageId': list(range(1, len(labels) + 1)), 'Label': labels})
submission_df.to_csv(f'submission_orig.csv', index=False)
train_df = pd.read_csv(path + '/train.csv')
from sklearn.model_selection import train_test_split
train_df, val_df = train_test_split(train_df, test_size=0.2)
proportions = pd.DataFrame({0: [0.5], 1: [0.05], 2: [0.1], 3: [0.03], 4: [0.03], 5: [0.03], 6: [0.03], 7: [0.5], 8: [0.5], 9: [0.5]})
imbalanced_train_df = train_df.groupby('label').apply(lambda x: x.sample(frac=proportions[x.name]))
df = pd.concat([imbalanced_train_df, val_df])
data = CustomImageList.from_df_custom(df=df, path=path, imgIdx=1).split_by_idx(range(len(imbalanced_train_df) - 1, len(df))).label_from_df(cols='label').add_test(test, label=0).transform(get_transforms(do_flip=False)).databunch(bs=128, num_workers=0).normalize(imagenet_stats)
data.show_batch(rows=3, figsize=(5, 5)) | code |
17136778/cell_2 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
from fastai.vision import *
from fastai.metrics import *
import os
path = '../input'
print(os.listdir(path)) | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.