path
stringlengths 13
17
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sequencelengths 1
873
| code
stringlengths 0
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stringclasses 1
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|---|---|---|---|
89130056/cell_9 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import re
import zipfile
import itertools
import zipfile
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import cv2
from PIL import Image
from skimage.feature import hog
from sklearn import preprocessing
from sklearn import svm
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import SubsetRandomSampler, DataLoader
from torchvision import transforms, models
path = '../input/painter-by-numbers/'
df = pd.read_csv(path + 'all_data_info.csv')
file_path = '../input/painter-by-numbers/'
archive = zipfile.ZipFile(file_path + 'replacements_for_corrupted_files.zip', 'r')
corrupted_ids = set()
for item in archive.namelist():
ID = re.sub('[^0-9]', '', item)
if ID != '':
corrupted_ids.add(ID)
drop_idx = []
for index, row in df.iterrows():
id_check = re.sub('[^0-9]', '', row['new_filename'])
if id_check in corrupted_ids:
drop_idx.append(index)
df = df.drop(drop_idx)
painter_dict = {'Kandinsky': '', 'Dali': '', 'Picasso': '', 'Delacroix': '', 'Rembrandt': '', 'Gogh': '', 'Kuniyoshi': '', 'Dore': '', 'Steinlen': '', 'Saryan': '', 'Goya': '', 'Lautrec': '', 'Modigliani': '', 'Beksinski': '', 'Pissarro': '', 'Kirchner': '', 'Renoir': '', 'Piranesi': '', 'Degas': '', 'Chagall': ''}
paintings_dict = painter_dict.copy()
for artist in painter_dict:
for painter in df['artist']:
if artist in painter:
painter_dict[artist] = painter
paintings = df[df['artist'] == painter].shape[0]
paintings_dict[artist] = paintings
break
for artist in painter_dict:
print(f'The artist named {painter_dict[artist]} has a total of {paintings_dict[artist]} paintings in this dataset.')
sample_size = min(paintings_dict.values())
min_a = list(paintings_dict.keys())[list(paintings_dict.values()).index(sample_size)]
print(f'\nThe artist with the smallest number of paintings is {min_a} with {sample_size} paintings.') | code |
89130056/cell_25 | [
"text_plain_output_1.png"
] | nn_data = ImageDataset(file_path, active_df, LabEnc, img_size=224, normalize=True, crop=False) | code |
89130056/cell_34 | [
"image_output_1.png"
] | hog_data = ImageData(file_path, active_df, LabEnc, hog_mode=[9, (8, 8), (2, 2)], sift_mode=False, img_size=224) | code |
89130056/cell_29 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from PIL import Image
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
from mpl_toolkits.axes_grid1 import make_axes_locatable
from skimage.feature import hog
from sklearn import preprocessing
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import train_test_split
from torch.utils.data import Dataset
from torch.utils.data import Dataset
from torch.utils.data import SubsetRandomSampler, DataLoader
from torchvision import transforms, models
import cv2
import itertools
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import re
import seaborn as sns
import torch
import torch
import zipfile
import itertools
import zipfile
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import cv2
from PIL import Image
from skimage.feature import hog
from sklearn import preprocessing
from sklearn import svm
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import SubsetRandomSampler, DataLoader
from torchvision import transforms, models
path = '../input/painter-by-numbers/'
df = pd.read_csv(path + 'all_data_info.csv')
file_path = '../input/painter-by-numbers/'
archive = zipfile.ZipFile(file_path + 'replacements_for_corrupted_files.zip', 'r')
corrupted_ids = set()
for item in archive.namelist():
ID = re.sub('[^0-9]', '', item)
if ID != '':
corrupted_ids.add(ID)
drop_idx = []
for index, row in df.iterrows():
id_check = re.sub('[^0-9]', '', row['new_filename'])
if id_check in corrupted_ids:
drop_idx.append(index)
df = df.drop(drop_idx)
painter_dict = {'Kandinsky': '', 'Dali': '', 'Picasso': '', 'Delacroix': '', 'Rembrandt': '', 'Gogh': '', 'Kuniyoshi': '', 'Dore': '', 'Steinlen': '', 'Saryan': '', 'Goya': '', 'Lautrec': '', 'Modigliani': '', 'Beksinski': '', 'Pissarro': '', 'Kirchner': '', 'Renoir': '', 'Piranesi': '', 'Degas': '', 'Chagall': ''}
paintings_dict = painter_dict.copy()
for artist in painter_dict:
for painter in df['artist']:
if artist in painter:
painter_dict[artist] = painter
paintings = df[df['artist'] == painter].shape[0]
paintings_dict[artist] = paintings
break
sample_size = min(paintings_dict.values())
min_a = list(paintings_dict.keys())[list(paintings_dict.values()).index(sample_size)]
active_df = pd.DataFrame({})
for artist in painter_dict.values():
tr_df = df[df['artist'] == artist].sort_values(by=['in_train', 'size_bytes'], ascending=[False, True])
active_df = pd.concat([active_df, tr_df.iloc[:sample_size]])
artists = list(painter_dict.values())
LabEnc = preprocessing.LabelEncoder()
LabEnc.fit(artists)
matplotlib.rc_file_defaults()
def image_transformer_nn(image, apply_norm=True, crop_img=True, new_dim=224):
"""
Args:
resize_num (int):
Dimension (pixels) to resize image
apply_norm (bool):
Choose whether to apply the normalization or not
crop_img (bool):
Choose whether to resize the image into the new_dim size, or crop
a square from its center, sized new_dim x new_dim
"""
if crop_img:
cropper = transforms.CenterCrop(new_dim)
image = cropper(image)
tensoring = transforms.ToTensor()
image = tensoring(image)
channels, height, width = image.shape
if image.shape[0] < 3:
image = image.expand(3, -1, -1)
if image.shape[0] > 3:
image = image[0:3, :, :]
if apply_norm:
normalizer = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
image = normalizer(image)
if not crop_img:
if width < height:
image = image.transpose(1, 2)
channels, height, width = image.shape
res_percent = float(new_dim / width)
height = round(height * res_percent)
resizer = transforms.Resize((height, new_dim))
image = resizer(image)
padder = transforms.Pad([0, 0, 0, int(new_dim - height)])
image = padder(image)
return image
archive = zipfile.ZipFile(file_path + 'train.zip', 'r')
img_path = 'train/'
imgdata = archive.open(img_path + '69382.jpg')
image = Image.open(imgdata)
image2 = image_transformer_nn(image, apply_norm=False, crop_img=True, new_dim=224)
image3a = image_transformer_nn(image, apply_norm=False, crop_img=False, new_dim=224)
image3b = image_transformer_nn(image, apply_norm=True, crop_img=False, new_dim=224)
import torch
from torch.utils.data import Dataset
class ImageDataset(Dataset):
def __init__(self, path, dataframe, lab_encoder, img_size=224, normalize=True, crop=False):
"""
Args:
path (string):
Where to look for the files to extract
dataframe (pd.DataFrame):
dataframe to use for the IDs
lab_encoder:
label encoder to transform artist names into integers
img_size (int):
size to be used
normalize (bool):
perform normalization during transformation or not
crop (bool):
True: crop only a center from the image
False: Resize image with respect to aspect ratio and pad
"""
self.encoder = lab_encoder
self.img_size = img_size
self.normalize = normalize
self.crop = crop
self.feats, self.labels = self.get_all_items(path, dataframe)
def get_all_items(self, path, dataframe):
curr_df = dataframe[dataframe['in_train'] == True]
archive = zipfile.ZipFile(path + 'train.zip', 'r')
img_path = 'train/'
feats = []
labels = []
for index, row in curr_df.iterrows():
file = row['new_filename']
imgdata = archive.open(img_path + file)
try:
image = Image.open(imgdata)
datum = image_transformer_nn(image, apply_norm=self.normalize, crop_img=self.crop, new_dim=self.img_size)
feats.append(datum)
artist = row['artist']
label = self.encoder.transform([artist])[0]
labels.append(label)
except Image.DecompressionBombError:
curr_df = dataframe[dataframe['in_train'] == False]
archive = zipfile.ZipFile(path + 'test.zip', 'r')
img_path = 'test/'
for index, row in curr_df.iterrows():
file = row['new_filename']
imgdata = archive.open(img_path + file)
try:
image = Image.open(imgdata)
datum = image_transformer_nn(image, apply_norm=self.normalize, crop_img=self.crop, new_dim=self.img_size)
feats.append(datum)
artist = row['artist']
label = self.encoder.transform([artist])[0]
labels.append(label)
except Image.DecompressionBombError:
feats = torch.stack(feats)
labels = torch.LongTensor(labels)
return (feats, labels)
def __len__(self):
return len(self.labels)
def __getitem__(self, item):
return (self.feats[item], self.labels[item])
def ImageData(path, dataframe, lab_encoder, hog_mode, sift_mode, img_size=224):
curr_df = dataframe[dataframe['in_train'] == True]
archive = zipfile.ZipFile(path + 'train.zip', 'r')
img_path = 'train/'
PaintFeats = []
PaintLabels = []
for index, row in curr_df.iterrows():
file = row['new_filename']
imgdata = archive.open(img_path + file)
try:
image = Image.open(imgdata)
datum = image_transformer_nn(image, apply_norm=False, crop_img=False, new_dim=img_size)
np_datum = datum.numpy().transpose(1, 2, 0)
if hog_mode:
orients, ppc, cpb = (hog_mode[0], hog_mode[1], hog_mode[2])
datum = hog(np_datum, orientations=orients, pixels_per_cell=ppc, cells_per_block=cpb, feature_vector=True, channel_axis=2)
PaintFeats.append(datum)
elif sift_mode:
np_datum = cv2.normalize(np_datum, None, 0, 255, cv2.NORM_MINMAX).astype('uint8')
imgtogray = cv2.cvtColor(np_datum, cv2.COLOR_BGR2GRAY)
PaintFeats.append(imgtogray)
artist = row['artist']
label = lab_encoder.transform([artist])[0]
PaintLabels.append(label)
except Image.DecompressionBombError:
curr_df = dataframe[dataframe['in_train'] == False]
archive = zipfile.ZipFile(path + 'test.zip', 'r')
img_path = 'test/'
for index, row in curr_df.iterrows():
file = row['new_filename']
imgdata = archive.open(img_path + file)
try:
image = Image.open(imgdata)
datum = image_transformer_nn(image, apply_norm=False, crop_img=False, new_dim=img_size)
np_datum = datum.numpy().transpose(1, 2, 0)
if hog_mode:
orients, ppc, cpb = (hog_mode[0], hog_mode[1], hog_mode[2])
datum = hog(np_datum, orientations=orients, pixels_per_cell=ppc, cells_per_block=cpb, feature_vector=True, channel_axis=2)
PaintFeats.append(datum)
elif sift_mode:
np_datum = cv2.normalize(np_datum, None, 0, 255, cv2.NORM_MINMAX).astype('uint8')
imgtogray = cv2.cvtColor(np_datum, cv2.COLOR_BGR2GRAY)
PaintFeats.append(imgtogray)
artist = row['artist']
label = lab_encoder.transform([artist])[0]
PaintLabels.append(label)
except Image.DecompressionBombError:
return (np.asarray(PaintFeats), np.asarray(PaintLabels))
def DataSplitter(data, ratios=[60, 20, 20], need_val=True, batches=None, shuffle=True, seed=None):
"""
Args:
data (Dataset or List):
In the case of NN, it's the dataset to be loaded into loaders. In the case
of other models, it's a list containing the Features and Labels lists
batches (int):
batch size for loaders in case of NN
ratios (list):
list of integers, containing the ratios [train,val,test] for splitting
example: [60,25,15] means 60% train data, 25% val data and 15% test data
shuffle (bool):
option to shuffle data
seed (None or int):
seed for shuffling
"""
first_ratio = (ratios[1] + ratios[2]) / sum(ratios)
second_ratio = ratios[2] / (ratios[1] + ratios[2])
if isinstance(data, ImageDataset):
labels = data.labels.numpy()
train_indices, rest_indices = train_test_split(np.arange(len(labels)), test_size=first_ratio, shuffle=shuffle, random_state=seed, stratify=labels)
rest_labels = data[rest_indices][1]
val_indices, test_indices = train_test_split(rest_indices, test_size=second_ratio, shuffle=shuffle, random_state=seed, stratify=rest_labels)
train_sampler = SubsetRandomSampler(train_indices)
val_sampler = SubsetRandomSampler(val_indices)
test_sampler = SubsetRandomSampler(test_indices)
train_loader = DataLoader(data, batch_size=batches, sampler=train_sampler)
val_loader = DataLoader(data, batch_size=batches, sampler=val_sampler)
test_loader = DataLoader(data, batch_size=batches, sampler=test_sampler)
return (train_loader, val_loader, test_loader)
elif isinstance(data, tuple):
X_train, X_rest, y_train, y_rest = train_test_split(data[0], data[1], test_size=first_ratio, shuffle=shuffle, random_state=seed, stratify=data[1])
if need_val:
X_val, X_test, y_val, y_test = train_test_split(X_rest, y_rest, test_size=second_ratio, shuffle=shuffle, random_state=seed, stratify=y_rest)
return (X_train, X_val, X_test, y_train, y_val, y_test)
return (X_train, X_rest, y_train, y_rest)
else:
return
sns.set(style = "darkgrid") # Personal preference
def CustomCmap(from_rgb,to_rgb):
# from color r,g,b
r1,g1,b1 = from_rgb
# to color r,g,b
r2,g2,b2 = to_rgb
cdict = {'red': ((0, r1, r1),
(1, r2, r2)),
'green': ((0, g1, g1),
(1, g2, g2)),
'blue': ((0, b1, b1),
(1, b2, b2))}
cmap = LinearSegmentedColormap('custom_cmap', cdict)
return cmap
mycmap = CustomCmap([1.0, 1.0, 1.0], [72/255, 99/255, 147/255])
mycmap_r = CustomCmap([72/255, 99/255, 147/255], [1.0, 1.0, 1.0])
mycol = (72/255, 99/255, 147/255)
mycomplcol = (129/255, 143/255, 163/255)
def plot_cm(cfmatrix,title,classes):
fig, ax1 = plt.subplots(1,1) #, figsize=(5,5)
for ax,cm in zip([ax1],[cfmatrix]):
im = ax.imshow(cm, interpolation='nearest', cmap=mycmap)
divider = make_axes_locatable(ax)
cax = divider.append_axes("right", size="5%", pad=.2)
plt.colorbar(im, cax=cax) #, ticks=[-1,-0.5,0,0.5,1]
ax.set_title(title,fontsize=14)
tick_marks = np.arange(len(classes))
ax.set_xticks(tick_marks)
ax.set_xticklabels(classes, rotation=90)
ax.set_yticks(tick_marks)
ax.set_yticklabels(classes)
fmt = 'd'
thresh = cm.max() / 2.
for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
ax.text(j, i, format(cm[i, j], fmt), horizontalalignment="center", color="white" if cm[i, j] > thresh else "black")
ax.set_ylabel('True label',fontsize=14)
ax.set_xlabel('Predicted label',fontsize=14)
plt.savefig(title+'.pdf', bbox_inches='tight')
plt.show()
matplotlib.rc_file_defaults()
cfmatrix = confusion_matrix(y_true, y_pred)
plot_cm(cfmatrix, 'CNN Confusion Matrix', artists) | code |
89130056/cell_28 | [
"text_plain_output_1.png"
] | input_height = nn_data[0][0].shape[1]
input_width = nn_data[0][0].shape[2]
conv_channels = [nn_data[0][0].shape[0], 4, 16, 64, 128]
kernels = [3, 3, 3, 3]
maxpools = [2, 2, 2, 2]
lin_channels = [256, 128, 20]
dropout = 0.25
learning_rate = 1e-05
weight_decay = 1e-06
patience = 10
verbose_ct = 1
epochs = 2500
model = CNNBackbone(input_height=input_height, input_width=input_width, conv_channels=conv_channels, kernels=kernels, maxpools=maxpools, lin_channels=lin_channels, dropout=dropout, batchnorm=True)
model.to(device)
loss_function = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate, weight_decay=weight_decay)
t_losses, v_losses = train(model, train_loader, val_loader, optimizer, epochs, device=device, patience=patience, verbose_ct=verbose_ct)
plot_losses(t_losses, v_losses, 'CNN_Training_Loss.pdf')
predictions, labels = evaluate(model, test_loader, device=device)
y_true = np.concatenate(labels, axis=0)
y_pred = np.concatenate(predictions, axis=0)
print(classification_report(y_true, y_pred)) | code |
89130056/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from PIL import Image
from torchvision import transforms, models
import matplotlib
import matplotlib.pyplot as plt
import pandas as pd
import re
import zipfile
import itertools
import zipfile
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import cv2
from PIL import Image
from skimage.feature import hog
from sklearn import preprocessing
from sklearn import svm
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import SubsetRandomSampler, DataLoader
from torchvision import transforms, models
path = '../input/painter-by-numbers/'
df = pd.read_csv(path + 'all_data_info.csv')
file_path = '../input/painter-by-numbers/'
archive = zipfile.ZipFile(file_path + 'replacements_for_corrupted_files.zip', 'r')
corrupted_ids = set()
for item in archive.namelist():
ID = re.sub('[^0-9]', '', item)
if ID != '':
corrupted_ids.add(ID)
drop_idx = []
for index, row in df.iterrows():
id_check = re.sub('[^0-9]', '', row['new_filename'])
if id_check in corrupted_ids:
drop_idx.append(index)
df = df.drop(drop_idx)
matplotlib.rc_file_defaults()
def image_transformer_nn(image, apply_norm=True, crop_img=True, new_dim=224):
"""
Args:
resize_num (int):
Dimension (pixels) to resize image
apply_norm (bool):
Choose whether to apply the normalization or not
crop_img (bool):
Choose whether to resize the image into the new_dim size, or crop
a square from its center, sized new_dim x new_dim
"""
if crop_img:
cropper = transforms.CenterCrop(new_dim)
image = cropper(image)
tensoring = transforms.ToTensor()
image = tensoring(image)
channels, height, width = image.shape
if image.shape[0] < 3:
image = image.expand(3, -1, -1)
if image.shape[0] > 3:
image = image[0:3, :, :]
if apply_norm:
normalizer = transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
image = normalizer(image)
if not crop_img:
if width < height:
image = image.transpose(1, 2)
channels, height, width = image.shape
res_percent = float(new_dim / width)
height = round(height * res_percent)
resizer = transforms.Resize((height, new_dim))
image = resizer(image)
padder = transforms.Pad([0, 0, 0, int(new_dim - height)])
image = padder(image)
return image
archive = zipfile.ZipFile(file_path + 'train.zip', 'r')
img_path = 'train/'
imgdata = archive.open(img_path + '69382.jpg')
image = Image.open(imgdata)
print('This is the original image:\n')
plt.imshow(image)
plt.show()
print('This is the cropped part of the transformed image:\n')
image2 = image_transformer_nn(image, apply_norm=False, crop_img=True, new_dim=224)
plt.imshow(image2.numpy().transpose(1, 2, 0))
plt.show()
print('This is the transformed, resized image:\n')
image3a = image_transformer_nn(image, apply_norm=False, crop_img=False, new_dim=224)
plt.imshow(image3a.numpy().transpose(1, 2, 0))
plt.show()
print('Transformed and resized, but with normalization as well:\n')
image3b = image_transformer_nn(image, apply_norm=True, crop_img=False, new_dim=224)
plt.imshow(image3b.numpy().transpose(1, 2, 0))
plt.show() | code |
89130056/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
import itertools
import zipfile
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import cv2
from PIL import Image
from skimage.feature import hog
from sklearn import preprocessing
from sklearn import svm
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import SubsetRandomSampler, DataLoader
from torchvision import transforms, models
path = '../input/painter-by-numbers/'
df = pd.read_csv(path + 'all_data_info.csv')
df.head() | code |
89130056/cell_31 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | learning_rate = 5e-05
weight_decay = 1e-06
patience = 10
verbose_ct = 1
epochs = 2500
model_conv = models.resnet18(pretrained=True)
num_ftrs = model_conv.fc.in_features
model_conv.fc = nn.Linear(num_ftrs, 256)
model_conv.fc2 = nn.Linear(256, 20)
model_conv.sfact = nn.Softmax(1)
model_conv = model_conv.to(device)
loss_function = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model_conv.parameters(), lr=learning_rate, weight_decay=weight_decay)
t_losses, v_losses = train(model_conv, train_loader, val_loader, optimizer, epochs, device=device, patience=patience, verbose_ct=verbose_ct)
sns.set(style='darkgrid')
plot_losses(t_losses, v_losses, 'CNN_Training_Loss_transfer.pdf')
torch.save(model_conv.state_dict(), 'ResNet-Trained.pt')
predictions, labels = evaluate(model_conv, test_loader, device=device)
y_true = np.concatenate(labels, axis=0)
y_pred = np.concatenate(predictions, axis=0)
print(classification_report(y_true, y_pred))
matplotlib.rc_file_defaults()
cfmatrix = confusion_matrix(y_true, y_pred)
plot_cm(cfmatrix, 'CNN Confusion Matrix - Transfer', artists) | code |
89130056/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import itertools
import zipfile
import re
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib
import matplotlib.pyplot as plt
from mpl_toolkits.axes_grid1 import make_axes_locatable
from matplotlib.colors import ListedColormap, LinearSegmentedColormap
import cv2
from PIL import Image
from skimage.feature import hog
from sklearn import preprocessing
from sklearn import svm
from sklearn.cluster import KMeans
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
import torch
import torch.nn as nn
from torch.utils.data import Dataset
from torch.utils.data import SubsetRandomSampler, DataLoader
from torchvision import transforms, models
path = '../input/painter-by-numbers/'
df = pd.read_csv(path + 'all_data_info.csv')
print(f"The full dataset contains a total of {len(df['artist'].unique())} different artists and {len(df['genre'].unique())} unique painting genres.\n")
ash = 5
print(f'The {ash} artists with the most paintings available in the dataset are:')
df['artist'].value_counts().head(ash) | code |
89130056/cell_36 | [
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | hog_classifier = svm.SVC(kernel='rbf', gamma=1.5, C=0.3)
hog_classifier.fit(X_train, y_train) | code |
50229480/cell_42 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a = df[df['Year'] == '2016']
a.shape
b = df[df['Year'] == '2007']
b.shape
c = df[df['Year'] == '2008']
c.shape
plt.figure(figsize=[20, 10])
sns.heatmap(c.corr(), cmap='coolwarm', annot=True) | code |
50229480/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a = df[df['Year'] == '2016']
a.shape
a.describe() | code |
50229480/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df.describe() | code |
50229480/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum() | code |
50229480/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
b = df[df['Year'] == '2007']
b.shape | code |
50229480/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
b = df[df['Year'] == '2007']
b.shape
temp_range_2007 = b['Temperature (C)'].max() - b['Temperature (C)'].min()
temp_range_2007 | code |
50229480/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a = df[df['Year'] == '2016']
a.shape
a['Wind Speed (km/h)'].mean() | code |
50229480/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.info() | code |
50229480/cell_40 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
c = df[df['Year'] == '2008']
c.shape
c.info() | code |
50229480/cell_39 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
c = df[df['Year'] == '2008']
c.shape | code |
50229480/cell_48 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a = df[df['Year'] == '2016']
a.shape
b = df[df['Year'] == '2007']
b.shape
c = df[df['Year'] == '2008']
c.shape
d = df[df['Year'] == '2009']
d.shape
plt.figure(figsize=[20, 10])
sns.heatmap(d.corr(), cmap='coolwarm', annot=True) | code |
50229480/cell_41 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
c = df[df['Year'] == '2008']
c.shape
c.describe() | code |
50229480/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df.head() | code |
50229480/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a = df[df['Year'] == '2016']
a.shape
a['Temperature (C)'].mean() | code |
50229480/cell_52 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
e = df[df['Year'] == '2010']
e.shape
e.info() | code |
50229480/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.describe() | code |
50229480/cell_45 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
d = df[df['Year'] == '2009']
d.shape | code |
50229480/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a = df[df['Year'] == '2016']
a.shape
a.info() | code |
50229480/cell_32 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
b = df[df['Year'] == '2007']
b.shape
b.describe() | code |
50229480/cell_51 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
e = df[df['Year'] == '2010']
e.shape | code |
50229480/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum() | code |
50229480/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
df.head() | code |
50229480/cell_47 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
d = df[df['Year'] == '2009']
d.shape
d.describe() | code |
50229480/cell_17 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a = df[df['Year'] == '2016']
a.shape | code |
50229480/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
b = df[df['Year'] == '2007']
b.shape
b.info() | code |
50229480/cell_46 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
d = df[df['Year'] == '2009']
d.shape
d.info() | code |
50229480/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a = df[df['Year'] == '2016']
a.shape
humidity_range_2006 = a['Humidity'].max() - a['Humidity'].min()
humidity_range_2006 | code |
50229480/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df.head() | code |
50229480/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a = df[df['Year'] == '2016']
a.shape
temp_range_2006 = a['Temperature (C)'].max() - a['Temperature (C)'].min()
temp_range_2006 | code |
50229480/cell_53 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
e = df[df['Year'] == '2010']
e.shape
e.describe() | code |
50229480/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Precip Type'].value_counts() | code |
50229480/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a = df[df['Year'] == '2016']
a.shape
plt.figure(figsize=[20, 10])
sns.heatmap(a.corr(), cmap='coolwarm', annot=True) | code |
50229480/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df['Formatted Date'].value_counts() | code |
50229480/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50229480/cell_36 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/szeged-weather/weatherHistory.csv')
df.isnull().sum()
df = df.dropna()
df.isnull().sum()
df['Formatted Date'] = df['Formatted Date'].str.split(' ').str[0].str.split('-').str[0]
df = df.rename(columns={'Formatted Date': 'Year'})
df = df.drop(['Loud Cover'], axis=1)
a = df[df['Year'] == '2016']
a.shape
b = df[df['Year'] == '2007']
b.shape
plt.figure(figsize=[20, 10])
sns.heatmap(b.corr(), cmap='coolwarm', annot=True) | code |
129022822/cell_13 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['value'] = mean
return test
arith_test = Arith_mean(train_df['value'])
arith_test
def last_record(ser):
last = ser.tail(1)
test = test_df.copy()
test['value'] = float(last)
return test
last_record_test = last_record(train_df['value'])
last_record_test
def last_4_quarter(ser):
test = test_df.copy()
test['value'] = ser.tail(4)['value'].unique()
return test
last_4_quarter_test = last_4_quarter(train_df)
last_4_quarter_test
arith_test_mape = mean_absolute_percentage_error(test_df['value'], arith_test['value'])
arith_test_mse = mean_squared_error(test_df['value'], arith_test['value'])
arith_test_rmse = math.sqrt(arith_test_mse)
last_record_test_mape = mean_absolute_percentage_error(test_df['value'], last_record_test['value'])
last_record_test_mse = mean_squared_error(test_df['value'], last_record_test['value'])
last_record_test_rmse = math.sqrt(last_record_test_mse)
last_4_quarter_test_mape = mean_absolute_percentage_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_mse = mean_squared_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_rmse = math.sqrt(last_4_quarter_test_mse)
print(last_record_test_mape, last_record_test_mse, last_record_test_rmse) | code |
129022822/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['value'] = mean
return test
arith_test = Arith_mean(train_df['value'])
arith_test
def last_record(ser):
last = ser.tail(1)
test = test_df.copy()
test['value'] = float(last)
return test
last_record_test = last_record(train_df['value'])
last_record_test
def last_4_quarter(ser):
test = test_df.copy()
test['value'] = ser.tail(4)['value'].unique()
return test
last_4_quarter_test = last_4_quarter(train_df)
last_4_quarter_test | code |
129022822/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980] | code |
129022822/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
129022822/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['value'] = mean
return test
arith_test = Arith_mean(train_df['value'])
arith_test
def last_record(ser):
last = ser.tail(1)
test = test_df.copy()
test['value'] = float(last)
return test
last_record_test = last_record(train_df['value'])
last_record_test | code |
129022822/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['value'] = mean
return test
arith_test = Arith_mean(train_df['value'])
arith_test
def last_record(ser):
last = ser.tail(1)
test = test_df.copy()
test['value'] = float(last)
return test
last_record_test = last_record(train_df['value'])
last_record_test
def last_4_quarter(ser):
test = test_df.copy()
test['value'] = ser.tail(4)['value'].unique()
return test
last_4_quarter_test = last_4_quarter(train_df)
last_4_quarter_test
arith_test_mape = mean_absolute_percentage_error(test_df['value'], arith_test['value'])
arith_test_mse = mean_squared_error(test_df['value'], arith_test['value'])
arith_test_rmse = math.sqrt(arith_test_mse)
last_record_test_mape = mean_absolute_percentage_error(test_df['value'], last_record_test['value'])
last_record_test_mse = mean_squared_error(test_df['value'], last_record_test['value'])
last_record_test_rmse = math.sqrt(last_record_test_mse)
last_4_quarter_test_mape = mean_absolute_percentage_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_mse = mean_squared_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_rmse = math.sqrt(last_4_quarter_test_mse)
import matplotlib.pyplot as plt
def create_bar_graph(values, labels=['arith_mean', 'latest_record', 'last_4_record']):
"""
Create a bar graph for three values.
Arguments:
labels -- a list of labels for each value
values -- a list of three numerical values
"""
# Create a figure and axis
fig, ax = plt.subplots()
# Create a bar plot
ax.bar(labels, values)
# Add labels and title
ax.set_xlabel('Categories')
ax.set_ylabel('Values')
ax.set_title('Bar Graph')
# Display the plot
plt.show()
def create_list(var1, var2, var3):
"""
Create a list from three variables.
Arguments:
var1 -- the first variable
var2 -- the second variable
var3 -- the third variable
Returns:
A list containing var1, var2, and var3.
"""
return [var1, var2, var3]
create_bar_graph(create_list(arith_test_rmse, last_4_quarter_test_rmse, last_4_quarter_test_rmse)) | code |
129022822/cell_16 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['value'] = mean
return test
arith_test = Arith_mean(train_df['value'])
arith_test
def last_record(ser):
last = ser.tail(1)
test = test_df.copy()
test['value'] = float(last)
return test
last_record_test = last_record(train_df['value'])
last_record_test
def last_4_quarter(ser):
test = test_df.copy()
test['value'] = ser.tail(4)['value'].unique()
return test
last_4_quarter_test = last_4_quarter(train_df)
last_4_quarter_test
arith_test_mape = mean_absolute_percentage_error(test_df['value'], arith_test['value'])
arith_test_mse = mean_squared_error(test_df['value'], arith_test['value'])
arith_test_rmse = math.sqrt(arith_test_mse)
last_record_test_mape = mean_absolute_percentage_error(test_df['value'], last_record_test['value'])
last_record_test_mse = mean_squared_error(test_df['value'], last_record_test['value'])
last_record_test_rmse = math.sqrt(last_record_test_mse)
last_4_quarter_test_mape = mean_absolute_percentage_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_mse = mean_squared_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_rmse = math.sqrt(last_4_quarter_test_mse)
import matplotlib.pyplot as plt
def create_bar_graph(values, labels=['arith_mean', 'latest_record', 'last_4_record']):
"""
Create a bar graph for three values.
Arguments:
labels -- a list of labels for each value
values -- a list of three numerical values
"""
# Create a figure and axis
fig, ax = plt.subplots()
# Create a bar plot
ax.bar(labels, values)
# Add labels and title
ax.set_xlabel('Categories')
ax.set_ylabel('Values')
ax.set_title('Bar Graph')
# Display the plot
plt.show()
def create_list(var1, var2, var3):
"""
Create a list from three variables.
Arguments:
var1 -- the first variable
var2 -- the second variable
var3 -- the third variable
Returns:
A list containing var1, var2, and var3.
"""
return [var1, var2, var3]
create_bar_graph(create_list(arith_test_mape, last_record_test_mape, last_4_quarter_test_mape)) | code |
129022822/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['value'] = mean
return test
arith_test = Arith_mean(train_df['value'])
arith_test
def last_record(ser):
last = ser.tail(1)
test = test_df.copy()
test['value'] = float(last)
return test
last_record_test = last_record(train_df['value'])
last_record_test
def last_4_quarter(ser):
test = test_df.copy()
test['value'] = ser.tail(4)['value'].unique()
return test
last_4_quarter_test = last_4_quarter(train_df)
last_4_quarter_test
arith_test_mape = mean_absolute_percentage_error(test_df['value'], arith_test['value'])
arith_test_mse = mean_squared_error(test_df['value'], arith_test['value'])
arith_test_rmse = math.sqrt(arith_test_mse)
last_record_test_mape = mean_absolute_percentage_error(test_df['value'], last_record_test['value'])
last_record_test_mse = mean_squared_error(test_df['value'], last_record_test['value'])
last_record_test_rmse = math.sqrt(last_record_test_mse)
last_4_quarter_test_mape = mean_absolute_percentage_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_mse = mean_squared_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_rmse = math.sqrt(last_4_quarter_test_mse)
import matplotlib.pyplot as plt
def create_bar_graph(values, labels=['arith_mean', 'latest_record', 'last_4_record']):
"""
Create a bar graph for three values.
Arguments:
labels -- a list of labels for each value
values -- a list of three numerical values
"""
# Create a figure and axis
fig, ax = plt.subplots()
# Create a bar plot
ax.bar(labels, values)
# Add labels and title
ax.set_xlabel('Categories')
ax.set_ylabel('Values')
ax.set_title('Bar Graph')
# Display the plot
plt.show()
def create_list(var1, var2, var3):
"""
Create a list from three variables.
Arguments:
var1 -- the first variable
var2 -- the second variable
var3 -- the third variable
Returns:
A list containing var1, var2, and var3.
"""
return [var1, var2, var3]
create_bar_graph(create_list(arith_test_mse, last_record_test_mse, last_record_test_mse)) | code |
129022822/cell_14 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['value'] = mean
return test
arith_test = Arith_mean(train_df['value'])
arith_test
def last_record(ser):
last = ser.tail(1)
test = test_df.copy()
test['value'] = float(last)
return test
last_record_test = last_record(train_df['value'])
last_record_test
def last_4_quarter(ser):
test = test_df.copy()
test['value'] = ser.tail(4)['value'].unique()
return test
last_4_quarter_test = last_4_quarter(train_df)
last_4_quarter_test
arith_test_mape = mean_absolute_percentage_error(test_df['value'], arith_test['value'])
arith_test_mse = mean_squared_error(test_df['value'], arith_test['value'])
arith_test_rmse = math.sqrt(arith_test_mse)
last_record_test_mape = mean_absolute_percentage_error(test_df['value'], last_record_test['value'])
last_record_test_mse = mean_squared_error(test_df['value'], last_record_test['value'])
last_record_test_rmse = math.sqrt(last_record_test_mse)
last_4_quarter_test_mape = mean_absolute_percentage_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_mse = mean_squared_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_rmse = math.sqrt(last_4_quarter_test_mse)
print(last_4_quarter_test_mape, last_4_quarter_test_mse, last_4_quarter_test_rmse) | code |
129022822/cell_12 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_squared_error, mean_absolute_percentage_error
import math
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['value'] = mean
return test
arith_test = Arith_mean(train_df['value'])
arith_test
def last_record(ser):
last = ser.tail(1)
test = test_df.copy()
test['value'] = float(last)
return test
last_record_test = last_record(train_df['value'])
last_record_test
def last_4_quarter(ser):
test = test_df.copy()
test['value'] = ser.tail(4)['value'].unique()
return test
last_4_quarter_test = last_4_quarter(train_df)
last_4_quarter_test
arith_test_mape = mean_absolute_percentage_error(test_df['value'], arith_test['value'])
arith_test_mse = mean_squared_error(test_df['value'], arith_test['value'])
arith_test_rmse = math.sqrt(arith_test_mse)
last_record_test_mape = mean_absolute_percentage_error(test_df['value'], last_record_test['value'])
last_record_test_mse = mean_squared_error(test_df['value'], last_record_test['value'])
last_record_test_rmse = math.sqrt(last_record_test_mse)
last_4_quarter_test_mape = mean_absolute_percentage_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_mse = mean_squared_error(test_df['value'], last_4_quarter_test['value'])
last_4_quarter_test_rmse = math.sqrt(last_4_quarter_test_mse)
print(arith_test_mape, arith_test_mse, arith_test_rmse) | code |
129022822/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('/kaggle/input/johnson/Johnson.csv')
df.drop('Unnamed: 0', axis=1, inplace=True)
train_df = df[df['time'] < 1980]
test_df = df[df['time'] >= 1980]
def Arith_mean(ser):
mean = ser.mean()
test = test_df.copy()
test['value'] = mean
return test
arith_test = Arith_mean(train_df['value'])
arith_test | code |
106202262/cell_21 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
import re
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: 0'])
df.review_description.duplicated().sum()
df.drop(df[df.review_description.duplicated() == True].index, axis=0, inplace=True)
df = df.rename({'rating(1 postive 0 neutral -1 negative': 'label'}, axis=1)
fig = go.Figure(data=[go.Pie(labels=['postive', 'negative', 'neutral'], values=[df.label[df.label == x].count() for x in df.label.unique()], pull=[0, 0.1, 0])])
fig.update_layout(title='Ratings')
df2 = df.copy()
df2.label = df.label.map({0: 'neutral', 1: 'postive', -1: 'negative'})
fig = px.sunburst(df2, path=['company', 'label'], title='Companies and Feedbacks', color_continuous_scale='RdBu', color='label')
fig.update_traces(textinfo='label + percent parent')
for companyName in df.company.unique():
fig = go.Figure(data=[go.Bar(y=df.label[df['company'] == companyName].value_counts(), x=df.label[df['company'] == companyName].unique())])
fig.update_layout(title=companyName + ' Ratings')
df.review_description = df.review_description.astype(str)
df.review_description = df.review_description.apply(lambda x: re.sub('[%s]' % re.escape('!"#$%&\'()*+,،-./:;<=>؟?@[\\]^_`{|}~'), ' ', x))
df.review_description = df.review_description.apply(lambda x: x.replace('؛', ''))
df.head() | code |
106202262/cell_25 | [
"text_html_output_10.png",
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_9.png",
"text_html_output_1.png",
"text_html_output_12.png",
"text_html_output_11.png",
"text_html_output_8.png",
"text_html_output_3.png",
"text_html_output_7.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
import re
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: 0'])
df.review_description.duplicated().sum()
df.drop(df[df.review_description.duplicated() == True].index, axis=0, inplace=True)
df = df.rename({'rating(1 postive 0 neutral -1 negative': 'label'}, axis=1)
fig = go.Figure(data=[go.Pie(labels=['postive', 'negative', 'neutral'], values=[df.label[df.label == x].count() for x in df.label.unique()], pull=[0, 0.1, 0])])
fig.update_layout(title='Ratings')
df2 = df.copy()
df2.label = df.label.map({0: 'neutral', 1: 'postive', -1: 'negative'})
fig = px.sunburst(df2, path=['company', 'label'], title='Companies and Feedbacks', color_continuous_scale='RdBu', color='label')
fig.update_traces(textinfo='label + percent parent')
for companyName in df.company.unique():
fig = go.Figure(data=[go.Bar(y=df.label[df['company'] == companyName].value_counts(), x=df.label[df['company'] == companyName].unique())])
fig.update_layout(title=companyName + ' Ratings')
df.review_description = df.review_description.astype(str)
df.review_description = df.review_description.apply(lambda x: re.sub('[%s]' % re.escape('!"#$%&\'()*+,،-./:;<=>؟?@[\\]^_`{|}~'), ' ', x))
df.review_description = df.review_description.apply(lambda x: x.replace('؛', ''))
df.review_description[5] | code |
106202262/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df | code |
106202262/cell_29 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
import re
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: 0'])
df.review_description.duplicated().sum()
df.drop(df[df.review_description.duplicated() == True].index, axis=0, inplace=True)
df = df.rename({'rating(1 postive 0 neutral -1 negative': 'label'}, axis=1)
fig = go.Figure(data=[go.Pie(labels=['postive', 'negative', 'neutral'], values=[df.label[df.label == x].count() for x in df.label.unique()], pull=[0, 0.1, 0])])
fig.update_layout(title='Ratings')
df2 = df.copy()
df2.label = df.label.map({0: 'neutral', 1: 'postive', -1: 'negative'})
fig = px.sunburst(df2, path=['company', 'label'], title='Companies and Feedbacks', color_continuous_scale='RdBu', color='label')
fig.update_traces(textinfo='label + percent parent')
for companyName in df.company.unique():
fig = go.Figure(data=[go.Bar(y=df.label[df['company'] == companyName].value_counts(), x=df.label[df['company'] == companyName].unique())])
fig.update_layout(title=companyName + ' Ratings')
df.review_description = df.review_description.astype(str)
df.review_description = df.review_description.apply(lambda x: re.sub('[%s]' % re.escape('!"#$%&\'()*+,،-./:;<=>؟?@[\\]^_`{|}~'), ' ', x))
df.review_description = df.review_description.apply(lambda x: x.replace('؛', ''))
stopWords = list(set(stopwords.words('arabic')))
for word in ['لا', 'لكن', 'ولكن']:
stopWords.remove(word)
df.review_description[5]
' '.join([word for word in df.review_description[5].split() if word not in stopWords])
df.review_description = df.review_description.apply(lambda x: ' '.join([word for word in x.split() if word not in stopWords]))
df.head() | code |
106202262/cell_26 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
import re
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: 0'])
df.review_description.duplicated().sum()
df.drop(df[df.review_description.duplicated() == True].index, axis=0, inplace=True)
df = df.rename({'rating(1 postive 0 neutral -1 negative': 'label'}, axis=1)
fig = go.Figure(data=[go.Pie(labels=['postive', 'negative', 'neutral'], values=[df.label[df.label == x].count() for x in df.label.unique()], pull=[0, 0.1, 0])])
fig.update_layout(title='Ratings')
df2 = df.copy()
df2.label = df.label.map({0: 'neutral', 1: 'postive', -1: 'negative'})
fig = px.sunburst(df2, path=['company', 'label'], title='Companies and Feedbacks', color_continuous_scale='RdBu', color='label')
fig.update_traces(textinfo='label + percent parent')
for companyName in df.company.unique():
fig = go.Figure(data=[go.Bar(y=df.label[df['company'] == companyName].value_counts(), x=df.label[df['company'] == companyName].unique())])
fig.update_layout(title=companyName + ' Ratings')
df.review_description = df.review_description.astype(str)
df.review_description = df.review_description.apply(lambda x: re.sub('[%s]' % re.escape('!"#$%&\'()*+,،-./:;<=>؟?@[\\]^_`{|}~'), ' ', x))
df.review_description = df.review_description.apply(lambda x: x.replace('؛', ''))
stopWords = list(set(stopwords.words('arabic')))
for word in ['لا', 'لكن', 'ولكن']:
stopWords.remove(word)
df.review_description[5]
' '.join([word for word in df.review_description[5].split() if word not in stopWords]) | code |
106202262/cell_2 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.express as px
import plotly.graph_objects as go
import plotly.express as px
from plotly.offline import init_notebook_mode, iplot
from tashaphyne.stemming import ArabicLightStemmer
from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report, roc_curve, f1_score, accuracy_score, recall_score, roc_auc_score, make_scorer
from sklearn.model_selection import train_test_split
from sklearn.model_selection import cross_val_score, GridSearchCV
from sklearn import metrics
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier
from sklearn.metrics import confusion_matrix, mean_squared_error, precision_score, recall_score, f1_score
from xgboost import XGBClassifier
import re
import emoji
from nltk.corpus import stopwords
init_notebook_mode(connected=True)
from sklearn.feature_extraction.text import TfidfVectorizer | code |
106202262/cell_1 | [
"text_plain_output_1.png"
] | !pip install Arabic-Stopwords
!pip install emoji
!pip install Tashaphyne | code |
106202262/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: 0'])
df.review_description.duplicated().sum() | code |
106202262/cell_15 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: 0'])
df.review_description.duplicated().sum()
df.drop(df[df.review_description.duplicated() == True].index, axis=0, inplace=True)
df = df.rename({'rating(1 postive 0 neutral -1 negative': 'label'}, axis=1)
fig = go.Figure(data=[go.Pie(labels=['postive', 'negative', 'neutral'], values=[df.label[df.label == x].count() for x in df.label.unique()], pull=[0, 0.1, 0])])
fig.update_layout(title='Ratings')
df2 = df.copy()
df2.label = df.label.map({0: 'neutral', 1: 'postive', -1: 'negative'})
fig = px.sunburst(df2, path=['company', 'label'], title='Companies and Feedbacks', color_continuous_scale='RdBu', color='label')
fig.update_traces(textinfo='label + percent parent')
fig.show() | code |
106202262/cell_17 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import pandas as pd
import plotly.express as px
import plotly.express as px
import plotly.graph_objects as go
df = pd.read_excel('../input/arabic-companies-reviews-for-sentiment-analysis/Arabic_Reviews.xlsx')
df.drop(inplace=True, columns=['Unnamed: 0'])
df.review_description.duplicated().sum()
df.drop(df[df.review_description.duplicated() == True].index, axis=0, inplace=True)
df = df.rename({'rating(1 postive 0 neutral -1 negative': 'label'}, axis=1)
fig = go.Figure(data=[go.Pie(labels=['postive', 'negative', 'neutral'], values=[df.label[df.label == x].count() for x in df.label.unique()], pull=[0, 0.1, 0])])
fig.update_layout(title='Ratings')
df2 = df.copy()
df2.label = df.label.map({0: 'neutral', 1: 'postive', -1: 'negative'})
fig = px.sunburst(df2, path=['company', 'label'], title='Companies and Feedbacks', color_continuous_scale='RdBu', color='label')
fig.update_traces(textinfo='label + percent parent')
for companyName in df.company.unique():
fig = go.Figure(data=[go.Bar(y=df.label[df['company'] == companyName].value_counts(), x=df.label[df['company'] == companyName].unique())])
fig.update_layout(title=companyName + ' Ratings')
iplot(fig) | code |
72088017/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
display(train_df.shape)
display(train_df.head())
display(train_df.info()) | code |
72088017/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
train_df[train_df.columns[2:]].std().hist()
plt.title('Distribution of stds of all columns') | code |
72088017/cell_29 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import RidgeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
model = Lasso(alpha=0.0299)
model1 = RidgeClassifier(alpha=0.005)
model.fit(X_train, y_train)
ypred_train = model.predict(X_train)
ypred_val = model.predict(X_val)
model.fit(X_train, y_train)
ypred_train = model.predict(X_train)
ypred_val = model.predict(X_val)
ypred = model.predict(test_df)
ypred
output = pd.DataFrame({'id': test_df.id, 'target': ypred})
output.head() | code |
72088017/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import RidgeClassifier
from sklearn.metrics import roc_auc_score
y_val.shape
model = Lasso(alpha=0.0299)
model1 = RidgeClassifier(alpha=0.005)
model.fit(X_train, y_train)
ypred_train = model.predict(X_train)
ypred_val = model.predict(X_val)
print('The train score is = {} '.format(roc_auc_score(y_train, ypred_train)))
print('The validation score is = {}'.format(roc_auc_score(y_val, ypred_val))) | code |
72088017/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 |
72088017/cell_18 | [
"text_html_output_2.png",
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
corrs = train_df.corr().abs().unstack().sort_values(kind='quicksort').reset_index()
corrs = corrs[corrs['level_0'] != corrs['level_1']]
corrs.tail(15) | code |
72088017/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.linear_model import RidgeClassifier
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
model = Lasso(alpha=0.0299)
model1 = RidgeClassifier(alpha=0.005)
model.fit(X_train, y_train)
ypred_train = model.predict(X_train)
ypred_val = model.predict(X_val)
model.fit(X_train, y_train)
ypred_train = model.predict(X_train)
ypred_val = model.predict(X_val)
ypred = model.predict(test_df)
ypred | code |
72088017/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
train_df[train_df.columns[2:]].mean().hist()
plt.title('Distribution of means of all columns') | code |
72088017/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
print(train_df.duplicated().sum())
print(train_df.duplicated().sum()) | code |
72088017/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
print(train_df.isnull().any().any())
print(test_df.isnull().any().any()) | code |
72088017/cell_22 | [
"text_plain_output_1.png"
] | y_val.shape | code |
72088017/cell_10 | [
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
display(train_df.describe())
display(test_df.describe()) | code |
72088017/cell_12 | [
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
train_df['target'].value_counts() | code |
72088017/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/dont-overfit-ii/train.csv')
test_df = pd.read_csv('/kaggle/input/dont-overfit-ii/test.csv')
display(test_df.shape)
display(test_df.head())
display(test_df.info()) | code |
129011222/cell_13 | [
"text_plain_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.count()
print(train.isna().sum().sort_values(ascending=False)) | code |
129011222/cell_25 | [
"text_html_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.count()
test.count()
test.isna().sum().sort_values(ascending=False)
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = []
for col in train.columns:
if col != TARGET:
FEATURES.append(col)
print(FEATURES) | code |
129011222/cell_30 | [
"text_plain_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.count()
test.count()
test.isna().sum().sort_values(ascending=False)
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = []
for col in train.columns:
if col != TARGET:
FEATURES.append(col)
test_null = pd.DataFrame(test.isna().sum())
test_null | code |
129011222/cell_33 | [
"text_html_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.count()
test.count()
test.isna().sum().sort_values(ascending=False)
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = []
for col in train.columns:
if col != TARGET:
FEATURES.append(col)
train.iloc[:, :-1].describe().T.sort_values(by='mean', ascending=False)
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(train.isna().sum())
train_null | code |
129011222/cell_20 | [
"text_plain_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'
test.count()
test.isna().sum().sort_values(ascending=False)
test.describe() | code |
129011222/cell_40 | [
"text_html_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.count()
test.count()
test.isna().sum().sort_values(ascending=False)
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = []
for col in train.columns:
if col != TARGET:
FEATURES.append(col)
train.iloc[:, :-1].describe().T.sort_values(by='mean', ascending=False)
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(train.isna().sum())
train.dtypes | code |
129011222/cell_29 | [
"text_html_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.count()
test.count()
test.isna().sum().sort_values(ascending=False)
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = []
for col in train.columns:
if col != TARGET:
FEATURES.append(col)
test_null = pd.DataFrame(test.isna().sum())
test.isna().sum() | code |
129011222/cell_39 | [
"text_html_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.count()
test.count()
test.isna().sum().sort_values(ascending=False)
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = []
for col in train.columns:
if col != TARGET:
FEATURES.append(col)
train.iloc[:, :-1].describe().T.sort_values(by='mean', ascending=False)
test_null = pd.DataFrame(test.isna().sum())
train_null = pd.DataFrame(train.isna().sum())
train.head() | code |
129011222/cell_26 | [
"text_plain_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.count()
test.count()
test.isna().sum().sort_values(ascending=False)
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = []
for col in train.columns:
if col != TARGET:
FEATURES.append(col)
train.iloc[:, :-1].describe().T.sort_values(by='mean', ascending=False) | code |
129011222/cell_41 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
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.count()
test.count()
test.isna().sum().sort_values(ascending=False)
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = []
for col in train.columns:
if col != TARGET:
FEATURES.append(col)
train.iloc[:, :-1].describe().T.sort_values(by='mean', ascending=False)
test_null = pd.DataFrame(test.isna().sum())
test.isna().sum()
test_null = test_null.sort_values(by=0, ascending=False)
train_null = pd.DataFrame(train.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
fig = make_subplots(rows=1, cols=2,
column_titles = ["Train Data", "Test Data",],
x_title = "Missing Values")
fig.add_trace(go.Bar(x=train_null[0], y=train_null.index, orientation='h', marker=dict(color=[n for n in range(12)])), 1, 1)
fig.add_trace(go.Bar(x=test_null[0], y=test_null.index, orientation='h', marker=dict(color=[n for n in range(12)])), 1, 2)
fig.update_layout(showlegend=False, title_text='Column wise Null Value Distribution', title_x=0.5)
train.dtypes
df = pd.concat([train[FEATURES], test[FEATURES]], axis=0)
text_features = ['Cabin', 'Name']
cat_features = [col for col in FEATURES if df[col].nunique() < 25 and col not in text_features]
cont_features = [col for col in FEATURES if df[col].nunique() >= 25 and col not in text_features]
del df
print(f'Total number of features: {len(FEATURES)}')
print(f'Number of categorical features: {len(cat_features)}')
print(f'Number of continuos features: {len(cont_features)}')
print(f'Number of text features: {len(text_features)}')
labels = ['Categorical', 'Continuous', 'Text']
values = [len(cat_features), len(cont_features), len(text_features)]
fig = go.Figure(data=[go.Pie(labels=labels, values=values, pull=[0.1, 0, 0])])
fig.show() | code |
129011222/cell_11 | [
"text_html_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'
print(f'train data shape: {train.shape}')
print(f'Number of rows in train data: {train.shape[0]}')
print(f'Number of columns in train data: {train.shape[1]}')
print(f'Number of values in train data: {train.count().sum()}')
print(f'Number missing values in train data: {sum(train.isna().sum())}') | code |
129011222/cell_19 | [
"text_plain_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'
test.count()
test.isna().sum().sort_values(ascending=False) | code |
129011222/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 |
129011222/cell_18 | [
"text_plain_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'
test.count() | code |
129011222/cell_16 | [
"text_html_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'
test.head() | code |
129011222/cell_17 | [
"text_html_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'
print(f'test data shape: {test.shape}')
print(f'Number of rows in test data: {test.shape[0]}')
print(f'Number of columns in test data: {test.shape[1]}')
print(f'Number of values in test data: {test.count().sum()}')
print(f'Number missing values in test data: {sum(test.isna().sum())}') | code |
129011222/cell_14 | [
"text_plain_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.count()
train.describe() | code |
129011222/cell_22 | [
"text_html_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'
submission.head() | code |
129011222/cell_10 | [
"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 |
129011222/cell_37 | [
"text_html_output_2.png"
] | from plotly.subplots import make_subplots
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objects as go
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.count()
test.count()
test.isna().sum().sort_values(ascending=False)
train.drop(columns=['PassengerId'], inplace=True)
test.drop(columns=['PassengerId'], inplace=True)
TARGET = 'Transported'
FEATURES = []
for col in train.columns:
if col != TARGET:
FEATURES.append(col)
train.iloc[:, :-1].describe().T.sort_values(by='mean', ascending=False)
test_null = pd.DataFrame(test.isna().sum())
test_null = test_null.sort_values(by=0, ascending=False)
train_null = pd.DataFrame(train.isna().sum())
train_null = train_null.sort_values(by=0, ascending=False)[:-1]
fig = make_subplots(rows=1, cols=2,
column_titles = ["Train Data", "Test Data",],
x_title = "Missing Values")
fig.add_trace(go.Bar(x=train_null[0], y=train_null.index, orientation='h', marker=dict(color=[n for n in range(12)])), 1, 1)
fig.add_trace(go.Bar(x=test_null[0], y=test_null.index, orientation='h', marker=dict(color=[n for n in range(12)])), 1, 2)
fig.update_layout(showlegend=False, title_text='Column wise Null Value Distribution', title_x=0.5) | code |
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