path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
17109112/cell_10 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import os
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
vgg_format = transforms.Compose([transforms.CenterCrop(224), transforms.ToTensor(), normalize])
dsets = {x: datasets.ImageFolder(os.path.join(data_dir, x), vgg_format) for x in ['train', 'valid']}
dsets['train'].class_to_idx | code |
17109112/cell_12 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import os
data_dir = '../input/dogscats/dogscats/dogscats/'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
vgg_format = transforms.Compose([transforms.CenterCrop(224), transforms.ToTensor(), normalize])
dsets = {x: datasets.ImageFolder(os.path.join(data_dir, x), vgg_format) for x in ['train', 'valid']}
dset_classes = dsets['valid'].classes
dset_classes | code |
17109112/cell_5 | [
"text_plain_output_1.png"
] | import torch
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
print('Using gpu : %s ' % torch.cuda.is_available()) | code |
17109112/cell_36 | [
"text_plain_output_1.png"
] | from torchvision import models, transforms, datasets
import torch
torch.__version__
device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
inputs_try.shape
model_vgg = models.vgg16(pretrained=True)
inputs_try, lables_try = (inputs_try.to(device), labels_try.to(device))
model_vgg = model_vgg.to(device)
print(model_vgg) | code |
74040532/cell_9 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from astropy.io import fits
from skimage import data, io, filters
import matplotlib.pyplot as plt
import skimage
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/OPTICAL/HST/idk404050/idk404050_drc.fits'
XMM_NEWTON_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/XMM_NEWTON_Soft_Xray/P0200670301EPX0003COLIM8000.FTZ'
XMM_OM_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/XMM_OM_Optical/P0200670301OMX000LSIMAGB000.FTZ'
ISO_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/ISO/csp3390040401.fits'
NI_OPEN = fits.open(NEAR_INFRARED_PATH)
HST_OPEN = fits.open(HST_OPTICAL_PATH)
XMM_NEWTON_OPEN = fits.open(XMM_NEWTON_PATH)
XMM_OM_OPEN = fits.open(XMM_OM_PATH)
ISO_OPEN = fits.open(ISO_PATH)
HST_SCI = HST_OPEN[1].data
WIDE_SCALE_HST = HST_SCI[700:4000, 700:4000]
ZOOMED_SCALE_HST = HST_SCI[1800:3400, 1700:3200]
ZOOMED_X2_SCALE_HST = HST_SCI[2100:3100, 2000:3000]
ZOOMED_X3_SCALE_HST = HST_SCI[2450:2850, 2300:2700]
NUCLEUS_SCALE_HST = HST_SCI[2640:2850, 2330:2650]
SATO_ZOOMED_X3_SCALE_HST = filters.sato(ZOOMED_X3_SCALE_HST)
SATO_NUCLEUS_SCALE_HST = filters.sato(NUCLEUS_SCALE_HST)
M_ZOOMED_X3_SCALE_HST = filters.meijering(ZOOMED_X3_SCALE_HST)
M_NUCLEUS_SCALE_HST = filters.meijering(NUCLEUS_SCALE_HST)
BM_ZOOMED_X3_SCALE_HST = filters.meijering(ZOOMED_X3_SCALE_HST, black_ridges=False)
BM_NUCLEUS_SCALE_HST = filters.meijering(NUCLEUS_SCALE_HST, black_ridges=False)
R_ZOOMED_X3_SCALE_HST = filters.roberts_neg_diag(ZOOMED_X3_SCALE_HST)
R_NUCLEUS_SCALE_HST = filters.roberts_neg_diag(NUCLEUS_SCALE_HST)
FCF_ZOOMED_X3_SCALE_HST = skimage.feature.corner_foerstner(ZOOMED_X3_SCALE_HST)
FCF_NUCLEUS_SCALE_HST = skimage.feature.corner_foerstner(NUCLEUS_SCALE_HST)
SPECTRAL_LIST = ['gray', 'jet', 'hot', 'prism', 'nipy_spectral', 'gist_ncar', 'gist_earth', 'gist_stern', 'flag', 'gnuplot2', 'terrain']
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(SATO_ZOOMED_X3_SCALE_HST,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(SATO_NUCLEUS_SCALE_HST,cmap=x_spec)
axis[1].set_title("NUCLEUS" + " / "+ x_spec)
axis[1].axis("off")
figure.colorbar(DENSITY_FUNC,shrink=0.3,label="DENSITY",location="right",extend="max")
plt.tight_layout()
plt.show()
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(M_ZOOMED_X3_SCALE_HST,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(M_NUCLEUS_SCALE_HST,cmap=x_spec)
axis[1].set_title("NUCLEUS" + " / "+ x_spec)
axis[1].axis("off")
figure.colorbar(DENSITY_FUNC,shrink=0.3,label="DENSITY",location="right",extend="max")
plt.tight_layout()
plt.show()
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(BM_ZOOMED_X3_SCALE_HST,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(BM_NUCLEUS_SCALE_HST,cmap=x_spec)
axis[1].set_title("NUCLEUS" + " / "+ x_spec)
axis[1].axis("off")
figure.colorbar(DENSITY_FUNC,shrink=0.3,label="DENSITY",location="right",extend="max")
plt.tight_layout()
plt.show()
for x_spec in SPECTRAL_LIST:
figure, axis = plt.subplots(1, 2, figsize=(20, 20))
axis[0].imshow(R_ZOOMED_X3_SCALE_HST, cmap=x_spec)
axis[0].set_title('ZOOMED' + ' / ' + x_spec)
axis[0].axis('off')
DENSITY_FUNC = axis[1].imshow(R_NUCLEUS_SCALE_HST, cmap=x_spec)
axis[1].set_title('NUCLEUS' + ' / ' + x_spec)
axis[1].axis('off')
figure.colorbar(DENSITY_FUNC, shrink=0.3, label='DENSITY', location='right', extend='max')
plt.tight_layout()
plt.show() | code |
74040532/cell_6 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from astropy.io import fits
from skimage import data, io, filters
import matplotlib.pyplot as plt
import skimage
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/OPTICAL/HST/idk404050/idk404050_drc.fits'
XMM_NEWTON_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/XMM_NEWTON_Soft_Xray/P0200670301EPX0003COLIM8000.FTZ'
XMM_OM_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/XMM_OM_Optical/P0200670301OMX000LSIMAGB000.FTZ'
ISO_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/ISO/csp3390040401.fits'
NI_OPEN = fits.open(NEAR_INFRARED_PATH)
HST_OPEN = fits.open(HST_OPTICAL_PATH)
XMM_NEWTON_OPEN = fits.open(XMM_NEWTON_PATH)
XMM_OM_OPEN = fits.open(XMM_OM_PATH)
ISO_OPEN = fits.open(ISO_PATH)
HST_SCI = HST_OPEN[1].data
WIDE_SCALE_HST = HST_SCI[700:4000, 700:4000]
ZOOMED_SCALE_HST = HST_SCI[1800:3400, 1700:3200]
ZOOMED_X2_SCALE_HST = HST_SCI[2100:3100, 2000:3000]
ZOOMED_X3_SCALE_HST = HST_SCI[2450:2850, 2300:2700]
NUCLEUS_SCALE_HST = HST_SCI[2640:2850, 2330:2650]
SATO_ZOOMED_X3_SCALE_HST = filters.sato(ZOOMED_X3_SCALE_HST)
SATO_NUCLEUS_SCALE_HST = filters.sato(NUCLEUS_SCALE_HST)
M_ZOOMED_X3_SCALE_HST = filters.meijering(ZOOMED_X3_SCALE_HST)
M_NUCLEUS_SCALE_HST = filters.meijering(NUCLEUS_SCALE_HST)
BM_ZOOMED_X3_SCALE_HST = filters.meijering(ZOOMED_X3_SCALE_HST, black_ridges=False)
BM_NUCLEUS_SCALE_HST = filters.meijering(NUCLEUS_SCALE_HST, black_ridges=False)
R_ZOOMED_X3_SCALE_HST = filters.roberts_neg_diag(ZOOMED_X3_SCALE_HST)
R_NUCLEUS_SCALE_HST = filters.roberts_neg_diag(NUCLEUS_SCALE_HST)
FCF_ZOOMED_X3_SCALE_HST = skimage.feature.corner_foerstner(ZOOMED_X3_SCALE_HST)
FCF_NUCLEUS_SCALE_HST = skimage.feature.corner_foerstner(NUCLEUS_SCALE_HST)
SPECTRAL_LIST = ['gray', 'jet', 'hot', 'prism', 'nipy_spectral', 'gist_ncar', 'gist_earth', 'gist_stern', 'flag', 'gnuplot2', 'terrain']
for x_spec in SPECTRAL_LIST:
figure, axis = plt.subplots(1, 2, figsize=(20, 20))
axis[0].imshow(SATO_ZOOMED_X3_SCALE_HST, cmap=x_spec)
axis[0].set_title('ZOOMED' + ' / ' + x_spec)
axis[0].axis('off')
DENSITY_FUNC = axis[1].imshow(SATO_NUCLEUS_SCALE_HST, cmap=x_spec)
axis[1].set_title('NUCLEUS' + ' / ' + x_spec)
axis[1].axis('off')
figure.colorbar(DENSITY_FUNC, shrink=0.3, label='DENSITY', location='right', extend='max')
plt.tight_layout()
plt.show() | code |
74040532/cell_1 | [
"text_plain_output_1.png"
] | !pip install astropy
!pip install specutils | code |
74040532/cell_7 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from astropy.io import fits
from skimage import data, io, filters
import matplotlib.pyplot as plt
import skimage
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/OPTICAL/HST/idk404050/idk404050_drc.fits'
XMM_NEWTON_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/XMM_NEWTON_Soft_Xray/P0200670301EPX0003COLIM8000.FTZ'
XMM_OM_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/XMM_OM_Optical/P0200670301OMX000LSIMAGB000.FTZ'
ISO_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/ISO/csp3390040401.fits'
NI_OPEN = fits.open(NEAR_INFRARED_PATH)
HST_OPEN = fits.open(HST_OPTICAL_PATH)
XMM_NEWTON_OPEN = fits.open(XMM_NEWTON_PATH)
XMM_OM_OPEN = fits.open(XMM_OM_PATH)
ISO_OPEN = fits.open(ISO_PATH)
HST_SCI = HST_OPEN[1].data
WIDE_SCALE_HST = HST_SCI[700:4000, 700:4000]
ZOOMED_SCALE_HST = HST_SCI[1800:3400, 1700:3200]
ZOOMED_X2_SCALE_HST = HST_SCI[2100:3100, 2000:3000]
ZOOMED_X3_SCALE_HST = HST_SCI[2450:2850, 2300:2700]
NUCLEUS_SCALE_HST = HST_SCI[2640:2850, 2330:2650]
SATO_ZOOMED_X3_SCALE_HST = filters.sato(ZOOMED_X3_SCALE_HST)
SATO_NUCLEUS_SCALE_HST = filters.sato(NUCLEUS_SCALE_HST)
M_ZOOMED_X3_SCALE_HST = filters.meijering(ZOOMED_X3_SCALE_HST)
M_NUCLEUS_SCALE_HST = filters.meijering(NUCLEUS_SCALE_HST)
BM_ZOOMED_X3_SCALE_HST = filters.meijering(ZOOMED_X3_SCALE_HST, black_ridges=False)
BM_NUCLEUS_SCALE_HST = filters.meijering(NUCLEUS_SCALE_HST, black_ridges=False)
R_ZOOMED_X3_SCALE_HST = filters.roberts_neg_diag(ZOOMED_X3_SCALE_HST)
R_NUCLEUS_SCALE_HST = filters.roberts_neg_diag(NUCLEUS_SCALE_HST)
FCF_ZOOMED_X3_SCALE_HST = skimage.feature.corner_foerstner(ZOOMED_X3_SCALE_HST)
FCF_NUCLEUS_SCALE_HST = skimage.feature.corner_foerstner(NUCLEUS_SCALE_HST)
SPECTRAL_LIST = ['gray', 'jet', 'hot', 'prism', 'nipy_spectral', 'gist_ncar', 'gist_earth', 'gist_stern', 'flag', 'gnuplot2', 'terrain']
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(SATO_ZOOMED_X3_SCALE_HST,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(SATO_NUCLEUS_SCALE_HST,cmap=x_spec)
axis[1].set_title("NUCLEUS" + " / "+ x_spec)
axis[1].axis("off")
figure.colorbar(DENSITY_FUNC,shrink=0.3,label="DENSITY",location="right",extend="max")
plt.tight_layout()
plt.show()
for x_spec in SPECTRAL_LIST:
figure, axis = plt.subplots(1, 2, figsize=(20, 20))
axis[0].imshow(M_ZOOMED_X3_SCALE_HST, cmap=x_spec)
axis[0].set_title('ZOOMED' + ' / ' + x_spec)
axis[0].axis('off')
DENSITY_FUNC = axis[1].imshow(M_NUCLEUS_SCALE_HST, cmap=x_spec)
axis[1].set_title('NUCLEUS' + ' / ' + x_spec)
axis[1].axis('off')
figure.colorbar(DENSITY_FUNC, shrink=0.3, label='DENSITY', location='right', extend='max')
plt.tight_layout()
plt.show() | code |
74040532/cell_8 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from astropy.io import fits
from skimage import data, io, filters
import matplotlib.pyplot as plt
import skimage
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/OPTICAL/HST/idk404050/idk404050_drc.fits'
XMM_NEWTON_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/XMM_NEWTON_Soft_Xray/P0200670301EPX0003COLIM8000.FTZ'
XMM_OM_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/XMM_OM_Optical/P0200670301OMX000LSIMAGB000.FTZ'
ISO_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/ISO/csp3390040401.fits'
NI_OPEN = fits.open(NEAR_INFRARED_PATH)
HST_OPEN = fits.open(HST_OPTICAL_PATH)
XMM_NEWTON_OPEN = fits.open(XMM_NEWTON_PATH)
XMM_OM_OPEN = fits.open(XMM_OM_PATH)
ISO_OPEN = fits.open(ISO_PATH)
HST_SCI = HST_OPEN[1].data
WIDE_SCALE_HST = HST_SCI[700:4000, 700:4000]
ZOOMED_SCALE_HST = HST_SCI[1800:3400, 1700:3200]
ZOOMED_X2_SCALE_HST = HST_SCI[2100:3100, 2000:3000]
ZOOMED_X3_SCALE_HST = HST_SCI[2450:2850, 2300:2700]
NUCLEUS_SCALE_HST = HST_SCI[2640:2850, 2330:2650]
SATO_ZOOMED_X3_SCALE_HST = filters.sato(ZOOMED_X3_SCALE_HST)
SATO_NUCLEUS_SCALE_HST = filters.sato(NUCLEUS_SCALE_HST)
M_ZOOMED_X3_SCALE_HST = filters.meijering(ZOOMED_X3_SCALE_HST)
M_NUCLEUS_SCALE_HST = filters.meijering(NUCLEUS_SCALE_HST)
BM_ZOOMED_X3_SCALE_HST = filters.meijering(ZOOMED_X3_SCALE_HST, black_ridges=False)
BM_NUCLEUS_SCALE_HST = filters.meijering(NUCLEUS_SCALE_HST, black_ridges=False)
R_ZOOMED_X3_SCALE_HST = filters.roberts_neg_diag(ZOOMED_X3_SCALE_HST)
R_NUCLEUS_SCALE_HST = filters.roberts_neg_diag(NUCLEUS_SCALE_HST)
FCF_ZOOMED_X3_SCALE_HST = skimage.feature.corner_foerstner(ZOOMED_X3_SCALE_HST)
FCF_NUCLEUS_SCALE_HST = skimage.feature.corner_foerstner(NUCLEUS_SCALE_HST)
SPECTRAL_LIST = ['gray', 'jet', 'hot', 'prism', 'nipy_spectral', 'gist_ncar', 'gist_earth', 'gist_stern', 'flag', 'gnuplot2', 'terrain']
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(SATO_ZOOMED_X3_SCALE_HST,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(SATO_NUCLEUS_SCALE_HST,cmap=x_spec)
axis[1].set_title("NUCLEUS" + " / "+ x_spec)
axis[1].axis("off")
figure.colorbar(DENSITY_FUNC,shrink=0.3,label="DENSITY",location="right",extend="max")
plt.tight_layout()
plt.show()
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(M_ZOOMED_X3_SCALE_HST,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(M_NUCLEUS_SCALE_HST,cmap=x_spec)
axis[1].set_title("NUCLEUS" + " / "+ x_spec)
axis[1].axis("off")
figure.colorbar(DENSITY_FUNC,shrink=0.3,label="DENSITY",location="right",extend="max")
plt.tight_layout()
plt.show()
for x_spec in SPECTRAL_LIST:
figure, axis = plt.subplots(1, 2, figsize=(20, 20))
axis[0].imshow(BM_ZOOMED_X3_SCALE_HST, cmap=x_spec)
axis[0].set_title('ZOOMED' + ' / ' + x_spec)
axis[0].axis('off')
DENSITY_FUNC = axis[1].imshow(BM_NUCLEUS_SCALE_HST, cmap=x_spec)
axis[1].set_title('NUCLEUS' + ' / ' + x_spec)
axis[1].axis('off')
figure.colorbar(DENSITY_FUNC, shrink=0.3, label='DENSITY', location='right', extend='max')
plt.tight_layout()
plt.show() | code |
74040532/cell_10 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from astropy.io import fits
from skimage import data, io, filters
import matplotlib.pyplot as plt
import skimage
NEAR_INFRARED_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/NEAR_INFRARED/n4k48nbsq_cal.fits'
HST_OPTICAL_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/OPTICAL/HST/idk404050/idk404050_drc.fits'
XMM_NEWTON_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/XMM_NEWTON_Soft_Xray/P0200670301EPX0003COLIM8000.FTZ'
XMM_OM_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/XMM_OM_Optical/P0200670301OMX000LSIMAGB000.FTZ'
ISO_PATH = '../input/center-of-all-observable-galaxiesfits-allesa/GALAXIES_CENTER/NGC6946/ISO/csp3390040401.fits'
NI_OPEN = fits.open(NEAR_INFRARED_PATH)
HST_OPEN = fits.open(HST_OPTICAL_PATH)
XMM_NEWTON_OPEN = fits.open(XMM_NEWTON_PATH)
XMM_OM_OPEN = fits.open(XMM_OM_PATH)
ISO_OPEN = fits.open(ISO_PATH)
HST_SCI = HST_OPEN[1].data
WIDE_SCALE_HST = HST_SCI[700:4000, 700:4000]
ZOOMED_SCALE_HST = HST_SCI[1800:3400, 1700:3200]
ZOOMED_X2_SCALE_HST = HST_SCI[2100:3100, 2000:3000]
ZOOMED_X3_SCALE_HST = HST_SCI[2450:2850, 2300:2700]
NUCLEUS_SCALE_HST = HST_SCI[2640:2850, 2330:2650]
SATO_ZOOMED_X3_SCALE_HST = filters.sato(ZOOMED_X3_SCALE_HST)
SATO_NUCLEUS_SCALE_HST = filters.sato(NUCLEUS_SCALE_HST)
M_ZOOMED_X3_SCALE_HST = filters.meijering(ZOOMED_X3_SCALE_HST)
M_NUCLEUS_SCALE_HST = filters.meijering(NUCLEUS_SCALE_HST)
BM_ZOOMED_X3_SCALE_HST = filters.meijering(ZOOMED_X3_SCALE_HST, black_ridges=False)
BM_NUCLEUS_SCALE_HST = filters.meijering(NUCLEUS_SCALE_HST, black_ridges=False)
R_ZOOMED_X3_SCALE_HST = filters.roberts_neg_diag(ZOOMED_X3_SCALE_HST)
R_NUCLEUS_SCALE_HST = filters.roberts_neg_diag(NUCLEUS_SCALE_HST)
FCF_ZOOMED_X3_SCALE_HST = skimage.feature.corner_foerstner(ZOOMED_X3_SCALE_HST)
FCF_NUCLEUS_SCALE_HST = skimage.feature.corner_foerstner(NUCLEUS_SCALE_HST)
SPECTRAL_LIST = ['gray', 'jet', 'hot', 'prism', 'nipy_spectral', 'gist_ncar', 'gist_earth', 'gist_stern', 'flag', 'gnuplot2', 'terrain']
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(SATO_ZOOMED_X3_SCALE_HST,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(SATO_NUCLEUS_SCALE_HST,cmap=x_spec)
axis[1].set_title("NUCLEUS" + " / "+ x_spec)
axis[1].axis("off")
figure.colorbar(DENSITY_FUNC,shrink=0.3,label="DENSITY",location="right",extend="max")
plt.tight_layout()
plt.show()
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(M_ZOOMED_X3_SCALE_HST,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(M_NUCLEUS_SCALE_HST,cmap=x_spec)
axis[1].set_title("NUCLEUS" + " / "+ x_spec)
axis[1].axis("off")
figure.colorbar(DENSITY_FUNC,shrink=0.3,label="DENSITY",location="right",extend="max")
plt.tight_layout()
plt.show()
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(BM_ZOOMED_X3_SCALE_HST,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(BM_NUCLEUS_SCALE_HST,cmap=x_spec)
axis[1].set_title("NUCLEUS" + " / "+ x_spec)
axis[1].axis("off")
figure.colorbar(DENSITY_FUNC,shrink=0.3,label="DENSITY",location="right",extend="max")
plt.tight_layout()
plt.show()
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(R_ZOOMED_X3_SCALE_HST,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(R_NUCLEUS_SCALE_HST,cmap=x_spec)
axis[1].set_title("NUCLEUS" + " / "+ x_spec)
axis[1].axis("off")
figure.colorbar(DENSITY_FUNC,shrink=0.3,label="DENSITY",location="right",extend="max")
plt.tight_layout()
plt.show()
for x_spec in SPECTRAL_LIST:
figure, axis = plt.subplots(1, 2, figsize=(20, 20))
axis[0].imshow(FCF_ZOOMED_X3_SCALE_HST[0], cmap=x_spec)
axis[0].set_title('ZOOMED' + ' / ' + x_spec)
axis[0].axis('off')
DENSITY_FUNC = axis[1].imshow(FCF_NUCLEUS_SCALE_HST[0], cmap=x_spec)
axis[1].set_title('NUCLEUS' + ' / ' + x_spec)
axis[1].axis('off')
figure.colorbar(DENSITY_FUNC, shrink=0.3, label='DENSITY', location='right', extend='max')
plt.tight_layout()
plt.show() | code |
2004239/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_train = pd.read_csv('../input/train.csv', usecols=[1, 2, 3, 4, 5], dtype={'onpromotion': bool}, converters={'unit_sales': lambda u: np.log1p(float(u)) if float(u) > 0 else 0}, parse_dates=['date'], skiprows=range(1, 66458909))
df_test = pd.read_csv('../input/test.csv', usecols=[0, 1, 2, 3, 4], dtype={'onpromotion': bool}, parse_dates=['date']).set_index(['store_nbr', 'item_nbr', 'date'])
items = pd.read_csv('../input/items.csv').set_index('item_nbr')
df_2017 = df_train.loc[df_train.date >= pd.datetime(2017, 1, 1)]
del df_train
promo_2017_train = df_2017.set_index(['store_nbr', 'item_nbr', 'date'])[['onpromotion']].unstack(level=-1).fillna(False)
promo_2017_train.columns = promo_2017_train.columns.get_level_values(1)
promo_2017_test = df_test[['onpromotion']].unstack(level=-1).fillna(False)
promo_2017_test.columns = promo_2017_test.columns.get_level_values(1)
promo_2017_train = promo_2017_train.reindex(promo_2017_test.index).fillna(False)
promo_2017 = pd.concat([promo_2017_train, promo_2017_test], axis=1)
del promo_2017_test, promo_2017_train
df_2017 = df_2017.set_index(['store_nbr', 'item_nbr', 'date'])[['unit_sales']].unstack(level=-1).fillna(0)
df_2017.columns = df_2017.columns.get_level_values(1)
items = items.reindex(df_2017.index.get_level_values(1))
print(df_2017.shape)
df_2017.head() | code |
2004239/cell_7 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_train = pd.read_csv('../input/train.csv', usecols=[1, 2, 3, 4, 5], dtype={'onpromotion': bool}, converters={'unit_sales': lambda u: np.log1p(float(u)) if float(u) > 0 else 0}, parse_dates=['date'], skiprows=range(1, 66458909))
df_test = pd.read_csv('../input/test.csv', usecols=[0, 1, 2, 3, 4], dtype={'onpromotion': bool}, parse_dates=['date']).set_index(['store_nbr', 'item_nbr', 'date'])
items = pd.read_csv('../input/items.csv').set_index('item_nbr')
df_2017 = df_train.loc[df_train.date >= pd.datetime(2017, 1, 1)]
del df_train
promo_2017_train = df_2017.set_index(['store_nbr', 'item_nbr', 'date'])[['onpromotion']].unstack(level=-1).fillna(False)
promo_2017_train.columns = promo_2017_train.columns.get_level_values(1)
promo_2017_test = df_test[['onpromotion']].unstack(level=-1).fillna(False)
promo_2017_test.columns = promo_2017_test.columns.get_level_values(1)
promo_2017_train = promo_2017_train.reindex(promo_2017_test.index).fillna(False)
promo_2017 = pd.concat([promo_2017_train, promo_2017_test], axis=1)
del promo_2017_test, promo_2017_train
test_all = df_test[[]].reset_index()
store_by_item = test_all[test_all.date == pd.to_datetime('2017-08-16')].drop(['date'], axis=1)
del test_all
print(store_by_item.shape)
store_by_item.head() | code |
2004239/cell_8 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
df_train = pd.read_csv('../input/train.csv', usecols=[1, 2, 3, 4, 5], dtype={'onpromotion': bool}, converters={'unit_sales': lambda u: np.log1p(float(u)) if float(u) > 0 else 0}, parse_dates=['date'], skiprows=range(1, 66458909))
df_test = pd.read_csv('../input/test.csv', usecols=[0, 1, 2, 3, 4], dtype={'onpromotion': bool}, parse_dates=['date']).set_index(['store_nbr', 'item_nbr', 'date'])
items = pd.read_csv('../input/items.csv').set_index('item_nbr')
df_2017 = df_train.loc[df_train.date >= pd.datetime(2017, 1, 1)]
del df_train
promo_2017_train = df_2017.set_index(['store_nbr', 'item_nbr', 'date'])[['onpromotion']].unstack(level=-1).fillna(False)
promo_2017_train.columns = promo_2017_train.columns.get_level_values(1)
promo_2017_test = df_test[['onpromotion']].unstack(level=-1).fillna(False)
promo_2017_test.columns = promo_2017_test.columns.get_level_values(1)
promo_2017_train = promo_2017_train.reindex(promo_2017_test.index).fillna(False)
promo_2017 = pd.concat([promo_2017_train, promo_2017_test], axis=1)
del promo_2017_test, promo_2017_train
df_2017 = df_2017.set_index(['store_nbr', 'item_nbr', 'date'])[['unit_sales']].unstack(level=-1).fillna(0)
df_2017.columns = df_2017.columns.get_level_values(1)
items = items.reindex(df_2017.index.get_level_values(1))
test_all = df_test[[]].reset_index()
store_by_item = test_all[test_all.date == pd.to_datetime('2017-08-16')].drop(['date'], axis=1)
del test_all
df_2017 = store_by_item.join(df_2017, on=['store_nbr', 'item_nbr']).set_index(df_2017.index.names).fillna(0)
print(df_2017.shape)
df_2017.head() | code |
18110494/cell_21 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='Same', activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(64, (5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, (3, 3), padding='Same', activation='relu'))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(Conv2D(24, (5, 5), padding='Same', activation='relu'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(32, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.summary() | code |
18110494/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
x_train = train.drop(labels=['label'], axis=1)
x_train = x_train / 255.0
test = test / 255.0
x_train = x_train.values.reshape(-1, 28, 28, 1)
test = test.values.reshape(-1, 28, 28, 1)
print('x_train shape:', x_train.shape) | code |
18110494/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
y_train = train['label']
y_train.value_counts() | code |
18110494/cell_23 | [
"text_html_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential
from keras.optimizers import RMSprop
import keras
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
y_train = train['label']
x_train = train.drop(labels=['label'], axis=1)
y_train.value_counts()
x_train = x_train / 255.0
test = test / 255.0
x_train = x_train.values.reshape(-1, 28, 28, 1)
test = test.values.reshape(-1, 28, 28, 1)
y_train = keras.utils.to_categorical(y_train, num_classes=10)
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='Same', activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(64, (5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, (3, 3), padding='Same', activation='relu'))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(Conv2D(24, (5, 5), padding='Same', activation='relu'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(32, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.summary()
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=128, epochs=10, verbose=1) | code |
18110494/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
test.head() | code |
18110494/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
x_train = train.drop(labels=['label'], axis=1)
x_train = x_train / 255.0
test = test / 255.0
x_train = x_train.values.reshape(-1, 28, 28, 1)
test = test.values.reshape(-1, 28, 28, 1)
a = plt.imshow(x_train[1][:, :, 0]) | code |
18110494/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input'))
import keras | code |
18110494/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
y_train = train['label']
y_train.head() | code |
18110494/cell_24 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D
from keras.models import Sequential
from keras.optimizers import RMSprop
import keras
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
y_train = train['label']
x_train = train.drop(labels=['label'], axis=1)
y_train.value_counts()
x_train = x_train / 255.0
test = test / 255.0
x_train = x_train.values.reshape(-1, 28, 28, 1)
test = test.values.reshape(-1, 28, 28, 1)
y_train = keras.utils.to_categorical(y_train, num_classes=10)
model = Sequential()
model.add(Conv2D(32, (3, 3), padding='Same', activation='relu', input_shape=(28, 28, 1)))
model.add(Conv2D(64, (5, 5), activation='relu'))
model.add(MaxPool2D((2, 2)))
model.add(Conv2D(64, (3, 3), padding='Same', activation='relu'))
model.add(Dropout(0.25))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(Conv2D(24, (5, 5), padding='Same', activation='relu'))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(32, activation='relu'))
model.add(Dense(10, activation='softmax'))
model.summary()
optimizer = RMSprop(lr=0.001, rho=0.9, epsilon=1e-08, decay=0.0)
model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(x_train, y_train, batch_size=128, epochs=10, verbose=1)
score = model.evaluate(x_val, y_val, verbose=1)
print('Test loss:', score[0])
print('Test accuracy:', score[1]) | code |
18110494/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
x_train = train.drop(labels=['label'], axis=1)
x_train.isnull().describe() | code |
18110494/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
test = pd.read_csv('../input/test.csv')
train = pd.read_csv('../input/train.csv')
train.head() | code |
90135546/cell_21 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum()
train_df.isnull().any().any()
train_df['Sentiment'].value_counts() | code |
90135546/cell_13 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape | code |
90135546/cell_9 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape | code |
90135546/cell_23 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum()
train_df.isnull().any().any()
train = train_df.to_pandas()
sns.countplot(x='Sentiment', data=train) | code |
90135546/cell_6 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.head() | code |
90135546/cell_48 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
test_df.shape
train_df.isnull().sum()
test_df.isnull().sum()
train_df.isnull().any().any()
test_df.isnull().any().any()
train = train_df.to_pandas()
test = test_df.to_pandas()
vectorizer = CountVectorizer(analyzer='word', tokenizer=None, preprocessor=None, stop_words=None, max_features=5000)
train_data = vectorizer.fit_transform(train['Phrase'])
test_data = vectorizer.fit_transform(test['Phrase'])
test_data.shape | code |
90135546/cell_11 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.info() | code |
90135546/cell_52 | [
"text_plain_output_1.png"
] | from cuml.linear_model import LogisticRegression
from cuml.linear_model import LogisticRegression
from sklearn.feature_extraction.text import CountVectorizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
test_df.shape
train_df.isnull().sum()
test_df.isnull().sum()
train_df.isnull().any().any()
test_df.isnull().any().any()
train = train_df.to_pandas()
test = test_df.to_pandas()
vectorizer = CountVectorizer(analyzer='word', tokenizer=None, preprocessor=None, stop_words=None, max_features=5000)
train_data = vectorizer.fit_transform(train['Phrase'])
train_data.shape
test_data = vectorizer.fit_transform(test['Phrase'])
test_data.shape
log_reg = LogisticRegression()
log_reg.fit(train_data, train_df['Sentiment'])
y_pred = log_reg.predict(test_data)
len(y_pred) | code |
90135546/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 |
90135546/cell_7 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.info() | code |
90135546/cell_49 | [
"text_plain_output_1.png"
] | from cuml.linear_model import LogisticRegression
from cuml.linear_model import LogisticRegression
from sklearn.feature_extraction.text import CountVectorizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum()
train_df.isnull().any().any()
train = train_df.to_pandas()
vectorizer = CountVectorizer(analyzer='word', tokenizer=None, preprocessor=None, stop_words=None, max_features=5000)
train_data = vectorizer.fit_transform(train['Phrase'])
train_data.shape
log_reg = LogisticRegression()
log_reg.fit(train_data, train_df['Sentiment']) | code |
90135546/cell_18 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape
test_df.isnull().sum()
test_df.isnull().any().any() | code |
90135546/cell_51 | [
"text_plain_output_1.png"
] | from cuml.linear_model import LogisticRegression
from cuml.linear_model import LogisticRegression
from sklearn.feature_extraction.text import CountVectorizer
import cudf as pd
import cupy as cp
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
test_df.shape
train_df.isnull().sum()
test_df.isnull().sum()
train_df.isnull().any().any()
test_df.isnull().any().any()
train = train_df.to_pandas()
test = test_df.to_pandas()
vectorizer = CountVectorizer(analyzer='word', tokenizer=None, preprocessor=None, stop_words=None, max_features=5000)
train_data = vectorizer.fit_transform(train['Phrase'])
train_data.shape
test_data = vectorizer.fit_transform(test['Phrase'])
test_data.shape
log_reg = LogisticRegression()
log_reg.fit(train_data, train_df['Sentiment'])
y_pred = log_reg.predict(test_data)
cp.unique(y_pred) | code |
90135546/cell_28 | [
"text_plain_output_1.png"
] | import string
import string
import string
import re
string.punctuation | code |
90135546/cell_8 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.describe() | code |
90135546/cell_15 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum() | code |
90135546/cell_16 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.shape
test_df.isnull().sum() | code |
90135546/cell_17 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum()
train_df.isnull().any().any() | code |
90135546/cell_46 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.feature_extraction.text import CountVectorizer
import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum()
train_df.isnull().any().any()
train = train_df.to_pandas()
vectorizer = CountVectorizer(analyzer='word', tokenizer=None, preprocessor=None, stop_words=None, max_features=5000)
train_data = vectorizer.fit_transform(train['Phrase'])
train_data.shape | code |
90135546/cell_24 | [
"text_plain_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
train_df.shape
train_df.isnull().sum()
train_df.isnull().any().any()
train = train_df.to_pandas()
train_df['Phrase'][0] | code |
90135546/cell_10 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.head() | code |
90135546/cell_12 | [
"text_html_output_1.png"
] | import cudf as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/train.tsv.zip', sep='\t')
test_df = pd.read_csv('/kaggle/input/sentiment-analysis-on-movie-reviews/test.tsv.zip', sep='\t')
test_df.describe() | code |
90135546/cell_36 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
stopwords.words('english') | code |
34138455/cell_9 | [
"image_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epochs=10, batch_size=100, sampling=0.15)
clf.run()
clf.classifier.confusion_matrix() | code |
34138455/cell_6 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epochs=10, batch_size=100, sampling=0.15) | code |
34138455/cell_11 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epochs=10, batch_size=100, sampling=0.15)
clf.run()
clf.classifier.confusion_matrix()
clf.classifier.summary()
target_image = '/test_data/test1/10041.jpg'
target_layer = clf.classifier.trained_model.features[30]
clf.cam(target_image_path=target_image, target_layer=target_layer, cmap='plasma', type='scorecam') | code |
34138455/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epochs=10, batch_size=100, sampling=0.15)
clf.data_processor.dataset_info(plot=False) | code |
34138455/cell_8 | [
"text_plain_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epochs=10, batch_size=100, sampling=0.15)
clf.run() | code |
34138455/cell_10 | [
"text_html_output_1.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
clf = pipeline.Image_Classification(data_directory=train_dir, is_dicom=False, table=table, type='nn_classifier', model_arch='vgg16', epochs=10, batch_size=100, sampling=0.15)
clf.run()
clf.classifier.confusion_matrix()
clf.classifier.summary() | code |
34138455/cell_5 | [
"text_html_output_4.png",
"text_plain_output_4.png",
"text_html_output_2.png",
"text_plain_output_3.png",
"text_html_output_1.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_html_output_3.png"
] | from radtorch import pipeline, core, utils
train_dir = '/train_data/train/'
test_dir = '/test_data/test1/'
table = utils.datatable_from_filepath(train_dir, classes=['dog', 'cat'])
table.head() | code |
50224544/cell_7 | [
"text_html_output_1.png"
] | from time import time
import cv2
import numpy as np
import pandas as pd
def breaker():
pass
def head(x, no_of_ele=5):
pass
def getImages(file_path=None, file_names=None, size=None):
images = []
for name in file_names:
try:
image = cv2.imread(file_path + name + '.jpg', cv2.IMREAD_GRAYSCALE).astype('float64')
except AttributeError:
if size:
image = cv2.resize(image, dsize=(size, size), interpolation=cv2.INTER_LANCZOS4)
cv2.normalize(src=image, dst=image, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
images.append(image.reshape(1, size, size))
return np.array(images)
start_time = time()
ss = pd.read_csv('../input/ranzcr-clip-catheter-line-classification/sample_submission.csv')
ts_img_names = ss['StudyInstanceUID'].values
ts_images = getImages('../input/ranzcr-clip-catheter-line-classification/test/', ts_img_names, size=144)
breaker()
print('Time Taken to read data : {:.2f} minutes'.format((time() - start_time) / 60))
breaker() | code |
50224544/cell_18 | [
"text_plain_output_1.png"
] | from time import time
from torch.utils.data import Dataset
import cv2
import numpy as np
import pandas as pd
import torch
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import torch
from torch import nn, optim
from torch.utils.data import Dataset
from torch.utils.data import DataLoader as DL
from torch.nn.utils import weight_norm as WN
import torch.nn.functional as F
import gc
import os
import cv2
from time import time
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
seed = 42
def breaker():
pass
def head(x, no_of_ele=5):
pass
def getImages(file_path=None, file_names=None, size=None):
images = []
for name in file_names:
try:
image = cv2.imread(file_path + name + '.jpg', cv2.IMREAD_GRAYSCALE).astype('float64')
except AttributeError:
if size:
image = cv2.resize(image, dsize=(size, size), interpolation=cv2.INTER_LANCZOS4)
cv2.normalize(src=image, dst=image, alpha=0, beta=1, norm_type=cv2.NORM_MINMAX)
images.append(image.reshape(1, size, size))
return np.array(images)
start_time = time()
ss = pd.read_csv('../input/ranzcr-clip-catheter-line-classification/sample_submission.csv')
ts_img_names = ss['StudyInstanceUID'].values
ts_images = getImages('../input/ranzcr-clip-catheter-line-classification/test/', ts_img_names, size=144)
breaker()
breaker()
class Dataset(Dataset):
def __init__(this, X=None, y=None, mode='train'):
this.mode = mode
this.X = X
if mode == 'train':
this.y = y
def __len__(this):
return this.X.shape[0]
def __getitem__(this, idx):
if this.mode == 'train':
return (torch.FloatTensor(this.X[idx]), torch.FloatTensor(this.y[idx]))
else:
return torch.FloatTensor(this.X[idx])
class CFG:
tr_batch_size = 128
ts_batch_size = 128
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
in_channels = 1
OL = 11
def __init__(this, filter_sizes=[64, 128, 256, 512], HL=[4096, 4096], epochs=50, n_folds=5):
this.filter_sizes = filter_sizes
this.HL = HL
this.epochs = epochs
this.n_folds = n_folds
def predict_(model=None, dataloader=None, device=None, path=None):
if path:
model.load_state_dict(torch.load(path))
else:
pass
model.to(device)
model.eval()
y_pred = torch.zeros(1, 11).to(device)
for X in dataloader:
X = X.to(device)
with torch.no_grad():
Pred = torch.sigmoid(model(X))
y_pred = torch.cat((y_pred, Pred), dim=0)
return y_pred[1:].detach().cpu().numpy()
cfg = CFG(filter_sizes=[64, 128, 256, 512], HL=[4096, 4096], epochs=50, n_folds=5)
ts_data_setup = Dataset(ts_images, None, 'test')
ts_data = DL(ts_data_setup, batch_size=cfg.ts_batch_size, shuffle=False)
model = CNN(in_channels=cfg.in_channels, filter_sizes=cfg.filter_sizes, HL=cfg.HL, OL=cfg.OL)
y_pred_e19 = predict_(model=model, dataloader=ts_data, device=cfg.device, path='../input/rccl-1x144-f-train/Epoch_19.pt')
y_pred_e20 = predict_(model=model, dataloader=ts_data, device=cfg.device, path='../input/rccl-1x144-f-train/Epoch_20.pt')
y_pred_e23 = predict_(model=model, dataloader=ts_data, device=cfg.device, path='../input/rccl-1x144-f-train/Epoch_23.pt')
y_pred_e25 = predict_(model=model, dataloader=ts_data, device=cfg.device, path='../input/rccl-1x144-f-train/Epoch_25.pt')
y_pred = (y_pred_e19 + y_pred_e20 + y_pred_e23 + y_pred_e25) / 4
y_pred = np.clip(y_pred, 1e-15, 1 - 1e-15)
ss.iloc[:, 1:] = y_pred
ss.to_csv('./submission.csv', index=False)
ss.head(5) | code |
17117664/cell_4 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
type(parsed_file) | code |
17117664/cell_20 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
df.Cdc.unique()
df['Cdc'] = df['Cdc'].astype('str')
df = df.replace({'None': '0'})
df['Cdc'] = df['Cdc'].astype('int')
numerical_columns = ['ARTICOLO', 'CAPITOLO', 'Centro di responsabilità', 'NUMERO', 'PDC-Livello1', 'PDC-Livello2', 'PDC-Livello3', 'PDC-Livello4', 'PDC-Missione', 'PDC-Programma']
stuff_with_commas = ['RENDICONTO 2015', 'RENDICONTO 2016', 'STANZIAMENTO 2017', 'STANZIAMENTO 2018', 'STANZIAMENTO 2019', 'STANZIAMENTO DI CASSA 2017']
for col in stuff_with_commas:
df[col] = df.replace('.', '').replace(',', '.')
numerical_columns = numerical_columns + stuff_with_commas
for col in numerical_columns:
df[col] = pd.to_numeric(df[col])
df.dtypes
for col in df.columns:
print('Column: {}, unique values: {}'.format(col, df[col].unique().shape[0])) | code |
17117664/cell_6 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
type(parsed_file[5]) | code |
17117664/cell_19 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
df.Cdc.unique()
df['Cdc'] = df['Cdc'].astype('str')
df = df.replace({'None': '0'})
df['Cdc'] = df['Cdc'].astype('int')
numerical_columns = ['ARTICOLO', 'CAPITOLO', 'Centro di responsabilità', 'NUMERO', 'PDC-Livello1', 'PDC-Livello2', 'PDC-Livello3', 'PDC-Livello4', 'PDC-Missione', 'PDC-Programma']
stuff_with_commas = ['RENDICONTO 2015', 'RENDICONTO 2016', 'STANZIAMENTO 2017', 'STANZIAMENTO 2018', 'STANZIAMENTO 2019', 'STANZIAMENTO DI CASSA 2017']
for col in stuff_with_commas:
df[col] = df.replace('.', '').replace(',', '.')
numerical_columns = numerical_columns + stuff_with_commas
for col in numerical_columns:
df[col] = pd.to_numeric(df[col])
df.dtypes | code |
17117664/cell_8 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
df.head() | code |
17117664/cell_16 | [
"text_html_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
df.Cdc.unique()
df['Cdc'] = df['Cdc'].astype('str')
df = df.replace({'None': '0'})
df['Cdc'] = df['Cdc'].astype('int')
numerical_columns = ['ARTICOLO', 'CAPITOLO', 'Centro di responsabilità', 'NUMERO', 'PDC-Livello1', 'PDC-Livello2', 'PDC-Livello3', 'PDC-Livello4', 'PDC-Missione', 'PDC-Programma']
stuff_with_commas = ['RENDICONTO 2015', 'RENDICONTO 2016', 'STANZIAMENTO 2017', 'STANZIAMENTO 2018', 'STANZIAMENTO 2019', 'STANZIAMENTO DI CASSA 2017']
for col in stuff_with_commas:
df[col] = df.replace('.', '').replace(',', '.')
numerical_columns = numerical_columns + stuff_with_commas
for col in numerical_columns:
df[col] = pd.to_numeric(df[col])
columns_with_text = ['DIR', 'Descrizione Centro di Responsabilità', 'Descrizione Direzione', 'Descrizione capitolo PEG', 'Descrizione centro di costo', 'TIPO']
for col in columns_with_text:
print('Column: {}, unique values: {}'.format(col, df[col].unique().shape[0])) | code |
17117664/cell_10 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
for val, col in zip(df.iloc[0], df.columns):
print('Column: {}, value: {}'.format(col, val)) | code |
17117664/cell_12 | [
"text_plain_output_1.png"
] | import json
import pandas as pd
import pandas as pd
import json
import numpy as np
with open('../input/ds397__ammcomunale_bilancio_rendiconto_previsioni_triennali_2015-2019.json') as json_file:
parsed_file = json.load(json_file)
df = pd.DataFrame(parsed_file)
df.Cdc.unique() | code |
17096461/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
paths = ['../input/train.csv', '../input/test.csv']
target_name = 'SalePrice'
rd = Reader(sep=',')
df = rd.train_test_split(paths, target_name)
dft = Drift_thresholder()
df = dft.fit_transform(df)
rmse = make_scorer(lambda y_true, y_pred: np.sqrt(np.sum((y_true - y_pred) ** 2) / len(y_true)), greater_is_better=False, needs_proba=False)
opt = Optimiser(scoring=rmse, n_folds=3)
space = {'est__strategy': {'search': 'choice', 'space': ['LightGBM']}, 'est__n_estimators': {'search': 'choice', 'space': [150]}, 'est__colsample_bytree': {'search': 'uniform', 'space': [0.8, 0.95]}, 'est__subsample': {'search': 'uniform', 'space': [0.8, 0.95]}, 'est__max_depth': {'search': 'choice', 'space': [5, 6, 7, 8, 9]}, 'est__learning_rate': {'search': 'choice', 'space': [0.07]}}
params = opt.optimise(space, df, 15)
prd = Predictor()
prd.fit_predict(params, df) | code |
17096461/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | paths = ['../input/train.csv', '../input/test.csv']
target_name = 'SalePrice'
rd = Reader(sep=',')
df = rd.train_test_split(paths, target_name)
dft = Drift_thresholder()
df = dft.fit_transform(df) | code |
17096461/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | !pip install mlbox | code |
17096461/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17096461/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
rmse = make_scorer(lambda y_true, y_pred: np.sqrt(np.sum((y_true - y_pred) ** 2) / len(y_true)), greater_is_better=False, needs_proba=False)
opt = Optimiser(scoring=rmse, n_folds=3) | code |
17096461/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
paths = ['../input/train.csv', '../input/test.csv']
target_name = 'SalePrice'
rd = Reader(sep=',')
df = rd.train_test_split(paths, target_name)
dft = Drift_thresholder()
df = dft.fit_transform(df)
rmse = make_scorer(lambda y_true, y_pred: np.sqrt(np.sum((y_true - y_pred) ** 2) / len(y_true)), greater_is_better=False, needs_proba=False)
opt = Optimiser(scoring=rmse, n_folds=3)
space = {'est__strategy': {'search': 'choice', 'space': ['LightGBM']}, 'est__n_estimators': {'search': 'choice', 'space': [150]}, 'est__colsample_bytree': {'search': 'uniform', 'space': [0.8, 0.95]}, 'est__subsample': {'search': 'uniform', 'space': [0.8, 0.95]}, 'est__max_depth': {'search': 'choice', 'space': [5, 6, 7, 8, 9]}, 'est__learning_rate': {'search': 'choice', 'space': [0.07]}}
params = opt.optimise(space, df, 15) | code |
17096461/cell_3 | [
"text_plain_output_1.png"
] | from mlbox.preprocessing import *
from mlbox.optimisation import *
from mlbox.prediction import * | code |
17096461/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
paths = ['../input/train.csv', '../input/test.csv']
target_name = 'SalePrice'
submit = pd.read_csv('../input/sample_submission.csv', sep=',')
preds = pd.read_csv('save/' + target_name + '_predictions.csv')
submit[target_name] = preds[target_name + '_predicted'].values
submit.to_csv('mlbox.csv', index=False) | code |
17096461/cell_5 | [
"text_plain_output_1.png"
] | paths = ['../input/train.csv', '../input/test.csv']
target_name = 'SalePrice'
rd = Reader(sep=',')
df = rd.train_test_split(paths, target_name) | code |
32062473/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = False
loop_logic = True
scale_data = True
use_base_model = True
one_hot_encode = False
estimators = 5000
public_leaderboard_end_date = None
def transform_dates(df):
dates = pd.to_datetime(df['Date'])
min_dates = dates.min()
df['Date_Year'] = dates.dt.year
df['Date_Month'] = dates.dt.month
df['Date_Day'] = dates.dt.day
df.drop(['Date'], axis=1, inplace=True)
def setup_df_encode_and_dates(df, encode_flag, dummy_cols, target_cols=[]):
enc_df = df.copy()
enc_df = enc_df[[enc_df.columns[0], enc_df.columns[2], enc_df.columns[1], enc_df.columns[3]]]
if encode_flag == True:
enc_df = pd.get_dummies(enc_df, columns=dummy_cols)
else:
le = LabelEncoder()
for dum_col in dummy_cols:
enc_df[dum_col] = le.fit_transform(enc_df[dum_col])
transform_dates(enc_df)
for col in target_cols:
enc_df[col] = df[col]
return enc_df
def prepare_train_set(df_train):
train_x, train_target1, train_target2 = (df_train.iloc[:, :-2], df_train.iloc[:, -2], df_train.iloc[:, -1])
return (train_x, train_target1, train_target2)
def prepare_submission(preds):
preds['ForecastId'] = preds['ForecastId'].fillna(0.0).astype('int32')
preds['Fatalities'] = preds['Fatalities'].fillna(0.0).astype('int32')
preds['ConfirmedCases'] = preds['ConfirmedCases'].fillna(0.0).astype('int32')
preds.clip(lower=0, inplace=True)
preds.to_csv('submission.csv', index=False)
def model_and_predict(model, X, y, test, estimators=5000):
if model != None:
run_model = model
else:
run_model = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=estimators)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)
run_model.fit(X_train, y_train)
y_train_pred = run_model.predict(X_train)
y_test_pred = run_model.predict(X_test)
y_pred = run_model.predict(test)
y_pred[y_pred < 0] = 0
r2 = r2_score(y_train_pred, y_train, multioutput='variance_weighted')
return (y_pred, r2)
def show_results(model):
# Code based on "Selecting Optimal Parameters for XGBoost Model Training" by Andrej Baranovskij (Medium)
results = model.evals_result()
epochs = len(results['validation_0']['error'])
x_axis = range(0, epochs)
# plot log loss
fig, ax = plt.subplots()
ax.plot(x_axis, results['validation_0']['logloss'], label='Train')
ax.plot(x_axis, results['validation_1']['logloss'], label='Test')
ax.legend()
plt.ylabel('Log Loss')
plt.title('XGBoost Log Loss')
plt.show()
# plot classification error
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['error'], label='Train')
ax.plot(x_axis, results['validation_1']['error'], label='Test')
ax.legend()
plt.ylabel('Classification Error')
plt.title('XGBoost Classification Error')
plt.show()
def fit_models_and_train(country, state, model, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
forecast_IDs = test.iloc[:, 0]
test_no_id = test.iloc[:, 1:]
if scale_data == True:
scaler = MinMaxScaler()
X = scaler.fit_transform(X.values)
test_no_id = scaler.transform(test_no_id.values)
y_cases_pred, cases_r2 = model_and_predict(model, X, y_cases, test_no_id)
y_fatal_pred, fatal_r2 = model_and_predict(model, X, y_fatal, test_no_id)
preds = pd.DataFrame(forecast_IDs)
preds['ConfirmedCases'] = y_cases_pred
preds['Fatalities'] = y_fatal_pred
return preds
df_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
df_train.shape | code |
32062473/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = False
loop_logic = True
scale_data = True
use_base_model = True
one_hot_encode = False
estimators = 5000
public_leaderboard_end_date = None
def get_test_train_for_country_state(one_hot_encode_flag, df_train, df_test, country, state):
if one_hot_encode_flag == True:
cs_train = df_train[(df_train['Country_Region_' + country] == 1) & (df_train['Province_State_' + state] == 1)]
cs_test = df_test[(df_test['Country_Region_' + country] == 1) & (df_test['Province_State_' + state] == 1)]
else:
cs_train = df_train[(df_train['Country_Region'] == country) & (df_train['Province_State'] == state)]
cs_test = df_test[(df_test['Country_Region'] == country) & (df_test['Province_State'] == state)]
return (cs_train, cs_test)
def transform_dates(df):
dates = pd.to_datetime(df['Date'])
min_dates = dates.min()
df['Date_Year'] = dates.dt.year
df['Date_Month'] = dates.dt.month
df['Date_Day'] = dates.dt.day
df.drop(['Date'], axis=1, inplace=True)
def setup_df_encode_and_dates(df, encode_flag, dummy_cols, target_cols=[]):
enc_df = df.copy()
enc_df = enc_df[[enc_df.columns[0], enc_df.columns[2], enc_df.columns[1], enc_df.columns[3]]]
if encode_flag == True:
enc_df = pd.get_dummies(enc_df, columns=dummy_cols)
else:
le = LabelEncoder()
for dum_col in dummy_cols:
enc_df[dum_col] = le.fit_transform(enc_df[dum_col])
transform_dates(enc_df)
for col in target_cols:
enc_df[col] = df[col]
return enc_df
def prepare_train_set(df_train):
train_x, train_target1, train_target2 = (df_train.iloc[:, :-2], df_train.iloc[:, -2], df_train.iloc[:, -1])
return (train_x, train_target1, train_target2)
def prepare_submission(preds):
preds['ForecastId'] = preds['ForecastId'].fillna(0.0).astype('int32')
preds['Fatalities'] = preds['Fatalities'].fillna(0.0).astype('int32')
preds['ConfirmedCases'] = preds['ConfirmedCases'].fillna(0.0).astype('int32')
preds.clip(lower=0, inplace=True)
preds.to_csv('submission.csv', index=False)
def model_and_predict(model, X, y, test, estimators=5000):
if model != None:
run_model = model
else:
run_model = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=estimators)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)
run_model.fit(X_train, y_train)
y_train_pred = run_model.predict(X_train)
y_test_pred = run_model.predict(X_test)
y_pred = run_model.predict(test)
y_pred[y_pred < 0] = 0
r2 = r2_score(y_train_pred, y_train, multioutput='variance_weighted')
return (y_pred, r2)
def show_results(model):
# Code based on "Selecting Optimal Parameters for XGBoost Model Training" by Andrej Baranovskij (Medium)
results = model.evals_result()
epochs = len(results['validation_0']['error'])
x_axis = range(0, epochs)
# plot log loss
fig, ax = plt.subplots()
ax.plot(x_axis, results['validation_0']['logloss'], label='Train')
ax.plot(x_axis, results['validation_1']['logloss'], label='Test')
ax.legend()
plt.ylabel('Log Loss')
plt.title('XGBoost Log Loss')
plt.show()
# plot classification error
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['error'], label='Train')
ax.plot(x_axis, results['validation_1']['error'], label='Test')
ax.legend()
plt.ylabel('Classification Error')
plt.title('XGBoost Classification Error')
plt.show()
def fit_models_and_train(country, state, model, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
forecast_IDs = test.iloc[:, 0]
test_no_id = test.iloc[:, 1:]
if scale_data == True:
scaler = MinMaxScaler()
X = scaler.fit_transform(X.values)
test_no_id = scaler.transform(test_no_id.values)
y_cases_pred, cases_r2 = model_and_predict(model, X, y_cases, test_no_id)
y_fatal_pred, fatal_r2 = model_and_predict(model, X, y_fatal, test_no_id)
preds = pd.DataFrame(forecast_IDs)
preds['ConfirmedCases'] = y_cases_pred
preds['Fatalities'] = y_fatal_pred
return preds
def cv_model(country, state, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
X_test = test.iloc[:, 1:]
data_train_cases_matrix = xgb.DMatrix(data=X, label=y_cases)
data_train_fatal_matrix = xgb.DMatrix(data=X, label=y_fatal)
cv_results_cases = xgb.cv(dtrain=data_train_cases_matrix, params=parms, nfold=3, num_boost_round=50, early_stopping_rounds=50, metrics='rmse', as_pandas=True, seed=12345)
cv_results_fatal = xgb.cv(dtrain=data_train_fatal_matrix, params=parms, nfold=3, num_boost_round=50, early_stopping_rounds=50, metrics='rmse', as_pandas=True, seed=12345)
df_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
df_train.shape
df_test.shape
df_train_original = df_train.copy()
df_test_original = df_test.copy()
df_train_original['Datetime'] = pd.to_datetime(df_train_original['Date'])
df_test_original['Datetime'] = pd.to_datetime(df_test_original['Date'])
date_filter = df_train[df_train.Date > '4/1/2020'].index
df_train.drop(date_filter, inplace=True)
df_train[df_train.Date > '2020/04/01']
base_model = xgb.XGBRegressor(n_estimators=estimators, random_state=12345, max_depth=15)
if one_hot_encode == True:
country_groups = df_train_original.groupby(['Country_Region', 'Province_State']).groups
df_country_list = pd.DataFrame.from_dict(list(country_groups))
train_country_list = df_country_list[0].unique()
df_train_dd = setup_df_encode_and_dates(df_train, one_hot_encode, ['Country_Region', 'Province_State'], ['ConfirmedCases', 'Fatalities'])
df_test_dd = setup_df_encode_and_dates(df_test, one_hot_encode, ['Country_Region', 'Province_State'])
if one_hot_encode == False:
country_groups = df_train_dd.groupby(['Country_Region', 'Province_State']).groups
df_country_list = pd.DataFrame.from_dict(list(country_groups))
train_country_list = df_country_list[0].unique()
df_preds = pd.DataFrame({'ForecastId': [], 'ConfirmedCases': [], 'Fatalities': []})
if loop_logic == True:
for country in train_country_list:
country_states = df_country_list[df_country_list[0] == country][1].values
for state in country_states:
curr_cs_train, curr_cs_test = get_test_train_for_country_state(one_hot_encode, df_train_dd, df_test_dd, country, state)
preds = fit_models_and_train(country, state, base_model if use_base_model == True else None, curr_cs_train, curr_cs_test)
preds = preds.round(5)
df_preds = pd.concat([df_preds, preds], axis=0)
else:
preds = fit_models_and_train('All', 'All', base_model if use_base_model == True else None, df_train_dd, df_test_dd)
df_preds = pd.concat([df_preds, preds], axis=0)
if ~(public_leaderboard_end_date == None):
df_preds.loc[df_test_original.Datetime > pd.to_datetime(public_leaderboard_end_date), 'ConfirmedCases'] = 1
df_preds.loc[df_test_original.Datetime > pd.to_datetime(public_leaderboard_end_date), 'Fatalities'] = 1
df_test_dd.shape | code |
32062473/cell_33 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = False
loop_logic = True
scale_data = True
use_base_model = True
one_hot_encode = False
estimators = 5000
public_leaderboard_end_date = None
def get_test_train_for_country_state(one_hot_encode_flag, df_train, df_test, country, state):
if one_hot_encode_flag == True:
cs_train = df_train[(df_train['Country_Region_' + country] == 1) & (df_train['Province_State_' + state] == 1)]
cs_test = df_test[(df_test['Country_Region_' + country] == 1) & (df_test['Province_State_' + state] == 1)]
else:
cs_train = df_train[(df_train['Country_Region'] == country) & (df_train['Province_State'] == state)]
cs_test = df_test[(df_test['Country_Region'] == country) & (df_test['Province_State'] == state)]
return (cs_train, cs_test)
def transform_dates(df):
dates = pd.to_datetime(df['Date'])
min_dates = dates.min()
df['Date_Year'] = dates.dt.year
df['Date_Month'] = dates.dt.month
df['Date_Day'] = dates.dt.day
df.drop(['Date'], axis=1, inplace=True)
def setup_df_encode_and_dates(df, encode_flag, dummy_cols, target_cols=[]):
enc_df = df.copy()
enc_df = enc_df[[enc_df.columns[0], enc_df.columns[2], enc_df.columns[1], enc_df.columns[3]]]
if encode_flag == True:
enc_df = pd.get_dummies(enc_df, columns=dummy_cols)
else:
le = LabelEncoder()
for dum_col in dummy_cols:
enc_df[dum_col] = le.fit_transform(enc_df[dum_col])
transform_dates(enc_df)
for col in target_cols:
enc_df[col] = df[col]
return enc_df
def prepare_train_set(df_train):
train_x, train_target1, train_target2 = (df_train.iloc[:, :-2], df_train.iloc[:, -2], df_train.iloc[:, -1])
return (train_x, train_target1, train_target2)
def prepare_submission(preds):
preds['ForecastId'] = preds['ForecastId'].fillna(0.0).astype('int32')
preds['Fatalities'] = preds['Fatalities'].fillna(0.0).astype('int32')
preds['ConfirmedCases'] = preds['ConfirmedCases'].fillna(0.0).astype('int32')
preds.clip(lower=0, inplace=True)
preds.to_csv('submission.csv', index=False)
def model_and_predict(model, X, y, test, estimators=5000):
if model != None:
run_model = model
else:
run_model = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=estimators)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)
run_model.fit(X_train, y_train)
y_train_pred = run_model.predict(X_train)
y_test_pred = run_model.predict(X_test)
y_pred = run_model.predict(test)
y_pred[y_pred < 0] = 0
r2 = r2_score(y_train_pred, y_train, multioutput='variance_weighted')
return (y_pred, r2)
def show_results(model):
# Code based on "Selecting Optimal Parameters for XGBoost Model Training" by Andrej Baranovskij (Medium)
results = model.evals_result()
epochs = len(results['validation_0']['error'])
x_axis = range(0, epochs)
# plot log loss
fig, ax = plt.subplots()
ax.plot(x_axis, results['validation_0']['logloss'], label='Train')
ax.plot(x_axis, results['validation_1']['logloss'], label='Test')
ax.legend()
plt.ylabel('Log Loss')
plt.title('XGBoost Log Loss')
plt.show()
# plot classification error
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['error'], label='Train')
ax.plot(x_axis, results['validation_1']['error'], label='Test')
ax.legend()
plt.ylabel('Classification Error')
plt.title('XGBoost Classification Error')
plt.show()
def fit_models_and_train(country, state, model, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
forecast_IDs = test.iloc[:, 0]
test_no_id = test.iloc[:, 1:]
if scale_data == True:
scaler = MinMaxScaler()
X = scaler.fit_transform(X.values)
test_no_id = scaler.transform(test_no_id.values)
y_cases_pred, cases_r2 = model_and_predict(model, X, y_cases, test_no_id)
y_fatal_pred, fatal_r2 = model_and_predict(model, X, y_fatal, test_no_id)
preds = pd.DataFrame(forecast_IDs)
preds['ConfirmedCases'] = y_cases_pred
preds['Fatalities'] = y_fatal_pred
return preds
def cv_model(country, state, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
X_test = test.iloc[:, 1:]
data_train_cases_matrix = xgb.DMatrix(data=X, label=y_cases)
data_train_fatal_matrix = xgb.DMatrix(data=X, label=y_fatal)
cv_results_cases = xgb.cv(dtrain=data_train_cases_matrix, params=parms, nfold=3, num_boost_round=50, early_stopping_rounds=50, metrics='rmse', as_pandas=True, seed=12345)
cv_results_fatal = xgb.cv(dtrain=data_train_fatal_matrix, params=parms, nfold=3, num_boost_round=50, early_stopping_rounds=50, metrics='rmse', as_pandas=True, seed=12345)
df_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
df_train.shape
df_test.shape
df_train_original = df_train.copy()
df_test_original = df_test.copy()
df_train_original['Datetime'] = pd.to_datetime(df_train_original['Date'])
df_test_original['Datetime'] = pd.to_datetime(df_test_original['Date'])
date_filter = df_train[df_train.Date > '4/1/2020'].index
df_train.drop(date_filter, inplace=True)
df_train[df_train.Date > '2020/04/01']
base_model = xgb.XGBRegressor(n_estimators=estimators, random_state=12345, max_depth=15)
if one_hot_encode == True:
country_groups = df_train_original.groupby(['Country_Region', 'Province_State']).groups
df_country_list = pd.DataFrame.from_dict(list(country_groups))
train_country_list = df_country_list[0].unique()
df_train_dd = setup_df_encode_and_dates(df_train, one_hot_encode, ['Country_Region', 'Province_State'], ['ConfirmedCases', 'Fatalities'])
df_test_dd = setup_df_encode_and_dates(df_test, one_hot_encode, ['Country_Region', 'Province_State'])
if one_hot_encode == False:
country_groups = df_train_dd.groupby(['Country_Region', 'Province_State']).groups
df_country_list = pd.DataFrame.from_dict(list(country_groups))
train_country_list = df_country_list[0].unique()
df_preds = pd.DataFrame({'ForecastId': [], 'ConfirmedCases': [], 'Fatalities': []})
if loop_logic == True:
print('Starting forecasting for {0} countries.'.format(len(train_country_list)))
for country in train_country_list:
print('Starting country {0}.'.format(country))
country_states = df_country_list[df_country_list[0] == country][1].values
for state in country_states:
curr_cs_train, curr_cs_test = get_test_train_for_country_state(one_hot_encode, df_train_dd, df_test_dd, country, state)
preds = fit_models_and_train(country, state, base_model if use_base_model == True else None, curr_cs_train, curr_cs_test)
preds = preds.round(5)
df_preds = pd.concat([df_preds, preds], axis=0)
print('Country {0} complete.'.format(country))
else:
print('Starting forecasting for all {0} countries.'.format(len(train_country_list)))
preds = fit_models_and_train('All', 'All', base_model if use_base_model == True else None, df_train_dd, df_test_dd)
df_preds = pd.concat([df_preds, preds], axis=0)
print('All countries complete.')
if ~(public_leaderboard_end_date == None):
df_preds.loc[df_test_original.Datetime > pd.to_datetime(public_leaderboard_end_date), 'ConfirmedCases'] = 1
df_preds.loc[df_test_original.Datetime > pd.to_datetime(public_leaderboard_end_date), 'Fatalities'] = 1
df_preds[df_test_original.Datetime > pd.to_datetime(public_leaderboard_end_date)].head() | code |
32062473/cell_26 | [
"text_html_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = False
loop_logic = True
scale_data = True
use_base_model = True
one_hot_encode = False
estimators = 5000
public_leaderboard_end_date = None
def transform_dates(df):
dates = pd.to_datetime(df['Date'])
min_dates = dates.min()
df['Date_Year'] = dates.dt.year
df['Date_Month'] = dates.dt.month
df['Date_Day'] = dates.dt.day
df.drop(['Date'], axis=1, inplace=True)
def setup_df_encode_and_dates(df, encode_flag, dummy_cols, target_cols=[]):
enc_df = df.copy()
enc_df = enc_df[[enc_df.columns[0], enc_df.columns[2], enc_df.columns[1], enc_df.columns[3]]]
if encode_flag == True:
enc_df = pd.get_dummies(enc_df, columns=dummy_cols)
else:
le = LabelEncoder()
for dum_col in dummy_cols:
enc_df[dum_col] = le.fit_transform(enc_df[dum_col])
transform_dates(enc_df)
for col in target_cols:
enc_df[col] = df[col]
return enc_df
def prepare_train_set(df_train):
train_x, train_target1, train_target2 = (df_train.iloc[:, :-2], df_train.iloc[:, -2], df_train.iloc[:, -1])
return (train_x, train_target1, train_target2)
def prepare_submission(preds):
preds['ForecastId'] = preds['ForecastId'].fillna(0.0).astype('int32')
preds['Fatalities'] = preds['Fatalities'].fillna(0.0).astype('int32')
preds['ConfirmedCases'] = preds['ConfirmedCases'].fillna(0.0).astype('int32')
preds.clip(lower=0, inplace=True)
preds.to_csv('submission.csv', index=False)
def model_and_predict(model, X, y, test, estimators=5000):
if model != None:
run_model = model
else:
run_model = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=estimators)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)
run_model.fit(X_train, y_train)
y_train_pred = run_model.predict(X_train)
y_test_pred = run_model.predict(X_test)
y_pred = run_model.predict(test)
y_pred[y_pred < 0] = 0
r2 = r2_score(y_train_pred, y_train, multioutput='variance_weighted')
return (y_pred, r2)
def show_results(model):
# Code based on "Selecting Optimal Parameters for XGBoost Model Training" by Andrej Baranovskij (Medium)
results = model.evals_result()
epochs = len(results['validation_0']['error'])
x_axis = range(0, epochs)
# plot log loss
fig, ax = plt.subplots()
ax.plot(x_axis, results['validation_0']['logloss'], label='Train')
ax.plot(x_axis, results['validation_1']['logloss'], label='Test')
ax.legend()
plt.ylabel('Log Loss')
plt.title('XGBoost Log Loss')
plt.show()
# plot classification error
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['error'], label='Train')
ax.plot(x_axis, results['validation_1']['error'], label='Test')
ax.legend()
plt.ylabel('Classification Error')
plt.title('XGBoost Classification Error')
plt.show()
def fit_models_and_train(country, state, model, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
forecast_IDs = test.iloc[:, 0]
test_no_id = test.iloc[:, 1:]
if scale_data == True:
scaler = MinMaxScaler()
X = scaler.fit_transform(X.values)
test_no_id = scaler.transform(test_no_id.values)
y_cases_pred, cases_r2 = model_and_predict(model, X, y_cases, test_no_id)
y_fatal_pred, fatal_r2 = model_and_predict(model, X, y_fatal, test_no_id)
preds = pd.DataFrame(forecast_IDs)
preds['ConfirmedCases'] = y_cases_pred
preds['Fatalities'] = y_fatal_pred
return preds
df_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
df_train.shape
df_test.shape
df_train_original = df_train.copy()
df_test_original = df_test.copy()
df_train_original['Datetime'] = pd.to_datetime(df_train_original['Date'])
df_test_original['Datetime'] = pd.to_datetime(df_test_original['Date'])
date_filter = df_train[df_train.Date > '4/1/2020'].index
df_train.drop(date_filter, inplace=True)
df_train[df_train.Date > '2020/04/01'] | code |
32062473/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 |
32062473/cell_35 | [
"text_html_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = False
loop_logic = True
scale_data = True
use_base_model = True
one_hot_encode = False
estimators = 5000
public_leaderboard_end_date = None
def get_test_train_for_country_state(one_hot_encode_flag, df_train, df_test, country, state):
if one_hot_encode_flag == True:
cs_train = df_train[(df_train['Country_Region_' + country] == 1) & (df_train['Province_State_' + state] == 1)]
cs_test = df_test[(df_test['Country_Region_' + country] == 1) & (df_test['Province_State_' + state] == 1)]
else:
cs_train = df_train[(df_train['Country_Region'] == country) & (df_train['Province_State'] == state)]
cs_test = df_test[(df_test['Country_Region'] == country) & (df_test['Province_State'] == state)]
return (cs_train, cs_test)
def transform_dates(df):
dates = pd.to_datetime(df['Date'])
min_dates = dates.min()
df['Date_Year'] = dates.dt.year
df['Date_Month'] = dates.dt.month
df['Date_Day'] = dates.dt.day
df.drop(['Date'], axis=1, inplace=True)
def setup_df_encode_and_dates(df, encode_flag, dummy_cols, target_cols=[]):
enc_df = df.copy()
enc_df = enc_df[[enc_df.columns[0], enc_df.columns[2], enc_df.columns[1], enc_df.columns[3]]]
if encode_flag == True:
enc_df = pd.get_dummies(enc_df, columns=dummy_cols)
else:
le = LabelEncoder()
for dum_col in dummy_cols:
enc_df[dum_col] = le.fit_transform(enc_df[dum_col])
transform_dates(enc_df)
for col in target_cols:
enc_df[col] = df[col]
return enc_df
def prepare_train_set(df_train):
train_x, train_target1, train_target2 = (df_train.iloc[:, :-2], df_train.iloc[:, -2], df_train.iloc[:, -1])
return (train_x, train_target1, train_target2)
def prepare_submission(preds):
preds['ForecastId'] = preds['ForecastId'].fillna(0.0).astype('int32')
preds['Fatalities'] = preds['Fatalities'].fillna(0.0).astype('int32')
preds['ConfirmedCases'] = preds['ConfirmedCases'].fillna(0.0).astype('int32')
preds.clip(lower=0, inplace=True)
preds.to_csv('submission.csv', index=False)
def model_and_predict(model, X, y, test, estimators=5000):
if model != None:
run_model = model
else:
run_model = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=estimators)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)
run_model.fit(X_train, y_train)
y_train_pred = run_model.predict(X_train)
y_test_pred = run_model.predict(X_test)
y_pred = run_model.predict(test)
y_pred[y_pred < 0] = 0
r2 = r2_score(y_train_pred, y_train, multioutput='variance_weighted')
return (y_pred, r2)
def show_results(model):
# Code based on "Selecting Optimal Parameters for XGBoost Model Training" by Andrej Baranovskij (Medium)
results = model.evals_result()
epochs = len(results['validation_0']['error'])
x_axis = range(0, epochs)
# plot log loss
fig, ax = plt.subplots()
ax.plot(x_axis, results['validation_0']['logloss'], label='Train')
ax.plot(x_axis, results['validation_1']['logloss'], label='Test')
ax.legend()
plt.ylabel('Log Loss')
plt.title('XGBoost Log Loss')
plt.show()
# plot classification error
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['error'], label='Train')
ax.plot(x_axis, results['validation_1']['error'], label='Test')
ax.legend()
plt.ylabel('Classification Error')
plt.title('XGBoost Classification Error')
plt.show()
def fit_models_and_train(country, state, model, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
forecast_IDs = test.iloc[:, 0]
test_no_id = test.iloc[:, 1:]
if scale_data == True:
scaler = MinMaxScaler()
X = scaler.fit_transform(X.values)
test_no_id = scaler.transform(test_no_id.values)
y_cases_pred, cases_r2 = model_and_predict(model, X, y_cases, test_no_id)
y_fatal_pred, fatal_r2 = model_and_predict(model, X, y_fatal, test_no_id)
preds = pd.DataFrame(forecast_IDs)
preds['ConfirmedCases'] = y_cases_pred
preds['Fatalities'] = y_fatal_pred
return preds
def cv_model(country, state, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
X_test = test.iloc[:, 1:]
data_train_cases_matrix = xgb.DMatrix(data=X, label=y_cases)
data_train_fatal_matrix = xgb.DMatrix(data=X, label=y_fatal)
cv_results_cases = xgb.cv(dtrain=data_train_cases_matrix, params=parms, nfold=3, num_boost_round=50, early_stopping_rounds=50, metrics='rmse', as_pandas=True, seed=12345)
cv_results_fatal = xgb.cv(dtrain=data_train_fatal_matrix, params=parms, nfold=3, num_boost_round=50, early_stopping_rounds=50, metrics='rmse', as_pandas=True, seed=12345)
df_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
df_train.shape
df_test.shape
df_train_original = df_train.copy()
df_test_original = df_test.copy()
df_train_original['Datetime'] = pd.to_datetime(df_train_original['Date'])
df_test_original['Datetime'] = pd.to_datetime(df_test_original['Date'])
date_filter = df_train[df_train.Date > '4/1/2020'].index
df_train.drop(date_filter, inplace=True)
df_train[df_train.Date > '2020/04/01']
base_model = xgb.XGBRegressor(n_estimators=estimators, random_state=12345, max_depth=15)
if one_hot_encode == True:
country_groups = df_train_original.groupby(['Country_Region', 'Province_State']).groups
df_country_list = pd.DataFrame.from_dict(list(country_groups))
train_country_list = df_country_list[0].unique()
df_train_dd = setup_df_encode_and_dates(df_train, one_hot_encode, ['Country_Region', 'Province_State'], ['ConfirmedCases', 'Fatalities'])
df_test_dd = setup_df_encode_and_dates(df_test, one_hot_encode, ['Country_Region', 'Province_State'])
if one_hot_encode == False:
country_groups = df_train_dd.groupby(['Country_Region', 'Province_State']).groups
df_country_list = pd.DataFrame.from_dict(list(country_groups))
train_country_list = df_country_list[0].unique()
df_preds = pd.DataFrame({'ForecastId': [], 'ConfirmedCases': [], 'Fatalities': []})
if loop_logic == True:
for country in train_country_list:
country_states = df_country_list[df_country_list[0] == country][1].values
for state in country_states:
curr_cs_train, curr_cs_test = get_test_train_for_country_state(one_hot_encode, df_train_dd, df_test_dd, country, state)
preds = fit_models_and_train(country, state, base_model if use_base_model == True else None, curr_cs_train, curr_cs_test)
preds = preds.round(5)
df_preds = pd.concat([df_preds, preds], axis=0)
else:
preds = fit_models_and_train('All', 'All', base_model if use_base_model == True else None, df_train_dd, df_test_dd)
df_preds = pd.concat([df_preds, preds], axis=0)
if ~(public_leaderboard_end_date == None):
df_preds.loc[df_test_original.Datetime > pd.to_datetime(public_leaderboard_end_date), 'ConfirmedCases'] = 1
df_preds.loc[df_test_original.Datetime > pd.to_datetime(public_leaderboard_end_date), 'Fatalities'] = 1
df_preds | code |
32062473/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = False
loop_logic = True
scale_data = True
use_base_model = True
one_hot_encode = False
estimators = 5000
public_leaderboard_end_date = None
def transform_dates(df):
dates = pd.to_datetime(df['Date'])
min_dates = dates.min()
df['Date_Year'] = dates.dt.year
df['Date_Month'] = dates.dt.month
df['Date_Day'] = dates.dt.day
df.drop(['Date'], axis=1, inplace=True)
def setup_df_encode_and_dates(df, encode_flag, dummy_cols, target_cols=[]):
enc_df = df.copy()
enc_df = enc_df[[enc_df.columns[0], enc_df.columns[2], enc_df.columns[1], enc_df.columns[3]]]
if encode_flag == True:
enc_df = pd.get_dummies(enc_df, columns=dummy_cols)
else:
le = LabelEncoder()
for dum_col in dummy_cols:
enc_df[dum_col] = le.fit_transform(enc_df[dum_col])
transform_dates(enc_df)
for col in target_cols:
enc_df[col] = df[col]
return enc_df
def prepare_train_set(df_train):
train_x, train_target1, train_target2 = (df_train.iloc[:, :-2], df_train.iloc[:, -2], df_train.iloc[:, -1])
return (train_x, train_target1, train_target2)
def prepare_submission(preds):
preds['ForecastId'] = preds['ForecastId'].fillna(0.0).astype('int32')
preds['Fatalities'] = preds['Fatalities'].fillna(0.0).astype('int32')
preds['ConfirmedCases'] = preds['ConfirmedCases'].fillna(0.0).astype('int32')
preds.clip(lower=0, inplace=True)
preds.to_csv('submission.csv', index=False)
def model_and_predict(model, X, y, test, estimators=5000):
if model != None:
run_model = model
else:
run_model = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=estimators)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)
run_model.fit(X_train, y_train)
y_train_pred = run_model.predict(X_train)
y_test_pred = run_model.predict(X_test)
y_pred = run_model.predict(test)
y_pred[y_pred < 0] = 0
r2 = r2_score(y_train_pred, y_train, multioutput='variance_weighted')
return (y_pred, r2)
def show_results(model):
# Code based on "Selecting Optimal Parameters for XGBoost Model Training" by Andrej Baranovskij (Medium)
results = model.evals_result()
epochs = len(results['validation_0']['error'])
x_axis = range(0, epochs)
# plot log loss
fig, ax = plt.subplots()
ax.plot(x_axis, results['validation_0']['logloss'], label='Train')
ax.plot(x_axis, results['validation_1']['logloss'], label='Test')
ax.legend()
plt.ylabel('Log Loss')
plt.title('XGBoost Log Loss')
plt.show()
# plot classification error
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['error'], label='Train')
ax.plot(x_axis, results['validation_1']['error'], label='Test')
ax.legend()
plt.ylabel('Classification Error')
plt.title('XGBoost Classification Error')
plt.show()
def fit_models_and_train(country, state, model, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
forecast_IDs = test.iloc[:, 0]
test_no_id = test.iloc[:, 1:]
if scale_data == True:
scaler = MinMaxScaler()
X = scaler.fit_transform(X.values)
test_no_id = scaler.transform(test_no_id.values)
y_cases_pred, cases_r2 = model_and_predict(model, X, y_cases, test_no_id)
y_fatal_pred, fatal_r2 = model_and_predict(model, X, y_fatal, test_no_id)
preds = pd.DataFrame(forecast_IDs)
preds['ConfirmedCases'] = y_cases_pred
preds['Fatalities'] = y_fatal_pred
return preds
def cv_model(country, state, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
X_test = test.iloc[:, 1:]
data_train_cases_matrix = xgb.DMatrix(data=X, label=y_cases)
data_train_fatal_matrix = xgb.DMatrix(data=X, label=y_fatal)
cv_results_cases = xgb.cv(dtrain=data_train_cases_matrix, params=parms, nfold=3, num_boost_round=50, early_stopping_rounds=50, metrics='rmse', as_pandas=True, seed=12345)
cv_results_fatal = xgb.cv(dtrain=data_train_fatal_matrix, params=parms, nfold=3, num_boost_round=50, early_stopping_rounds=50, metrics='rmse', as_pandas=True, seed=12345)
base_model = xgb.XGBRegressor(n_estimators=estimators, random_state=12345, max_depth=15)
print('Model ID: {0}'.format(id(base_model))) | code |
32062473/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = False
loop_logic = True
scale_data = True
use_base_model = True
one_hot_encode = False
estimators = 5000
public_leaderboard_end_date = None
def transform_dates(df):
dates = pd.to_datetime(df['Date'])
min_dates = dates.min()
df['Date_Year'] = dates.dt.year
df['Date_Month'] = dates.dt.month
df['Date_Day'] = dates.dt.day
df.drop(['Date'], axis=1, inplace=True)
def setup_df_encode_and_dates(df, encode_flag, dummy_cols, target_cols=[]):
enc_df = df.copy()
enc_df = enc_df[[enc_df.columns[0], enc_df.columns[2], enc_df.columns[1], enc_df.columns[3]]]
if encode_flag == True:
enc_df = pd.get_dummies(enc_df, columns=dummy_cols)
else:
le = LabelEncoder()
for dum_col in dummy_cols:
enc_df[dum_col] = le.fit_transform(enc_df[dum_col])
transform_dates(enc_df)
for col in target_cols:
enc_df[col] = df[col]
return enc_df
def prepare_train_set(df_train):
train_x, train_target1, train_target2 = (df_train.iloc[:, :-2], df_train.iloc[:, -2], df_train.iloc[:, -1])
return (train_x, train_target1, train_target2)
def prepare_submission(preds):
preds['ForecastId'] = preds['ForecastId'].fillna(0.0).astype('int32')
preds['Fatalities'] = preds['Fatalities'].fillna(0.0).astype('int32')
preds['ConfirmedCases'] = preds['ConfirmedCases'].fillna(0.0).astype('int32')
preds.clip(lower=0, inplace=True)
preds.to_csv('submission.csv', index=False)
def model_and_predict(model, X, y, test, estimators=5000):
if model != None:
run_model = model
else:
run_model = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=estimators)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)
run_model.fit(X_train, y_train)
y_train_pred = run_model.predict(X_train)
y_test_pred = run_model.predict(X_test)
y_pred = run_model.predict(test)
y_pred[y_pred < 0] = 0
r2 = r2_score(y_train_pred, y_train, multioutput='variance_weighted')
return (y_pred, r2)
def show_results(model):
# Code based on "Selecting Optimal Parameters for XGBoost Model Training" by Andrej Baranovskij (Medium)
results = model.evals_result()
epochs = len(results['validation_0']['error'])
x_axis = range(0, epochs)
# plot log loss
fig, ax = plt.subplots()
ax.plot(x_axis, results['validation_0']['logloss'], label='Train')
ax.plot(x_axis, results['validation_1']['logloss'], label='Test')
ax.legend()
plt.ylabel('Log Loss')
plt.title('XGBoost Log Loss')
plt.show()
# plot classification error
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['error'], label='Train')
ax.plot(x_axis, results['validation_1']['error'], label='Test')
ax.legend()
plt.ylabel('Classification Error')
plt.title('XGBoost Classification Error')
plt.show()
def fit_models_and_train(country, state, model, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
forecast_IDs = test.iloc[:, 0]
test_no_id = test.iloc[:, 1:]
if scale_data == True:
scaler = MinMaxScaler()
X = scaler.fit_transform(X.values)
test_no_id = scaler.transform(test_no_id.values)
y_cases_pred, cases_r2 = model_and_predict(model, X, y_cases, test_no_id)
y_fatal_pred, fatal_r2 = model_and_predict(model, X, y_fatal, test_no_id)
preds = pd.DataFrame(forecast_IDs)
preds['ConfirmedCases'] = y_cases_pred
preds['Fatalities'] = y_fatal_pred
return preds
df_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
df_test.shape | code |
32062473/cell_27 | [
"text_html_output_1.png"
] | from sklearn.metrics import r2_score
from sklearn.model_selection import train_test_split, cross_val_score, KFold
from sklearn.preprocessing import MinMaxScaler, LabelEncoder
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import xgboost as xgb
verbose = False
loop_logic = True
scale_data = True
use_base_model = True
one_hot_encode = False
estimators = 5000
public_leaderboard_end_date = None
def transform_dates(df):
dates = pd.to_datetime(df['Date'])
min_dates = dates.min()
df['Date_Year'] = dates.dt.year
df['Date_Month'] = dates.dt.month
df['Date_Day'] = dates.dt.day
df.drop(['Date'], axis=1, inplace=True)
def setup_df_encode_and_dates(df, encode_flag, dummy_cols, target_cols=[]):
enc_df = df.copy()
enc_df = enc_df[[enc_df.columns[0], enc_df.columns[2], enc_df.columns[1], enc_df.columns[3]]]
if encode_flag == True:
enc_df = pd.get_dummies(enc_df, columns=dummy_cols)
else:
le = LabelEncoder()
for dum_col in dummy_cols:
enc_df[dum_col] = le.fit_transform(enc_df[dum_col])
transform_dates(enc_df)
for col in target_cols:
enc_df[col] = df[col]
return enc_df
def prepare_train_set(df_train):
train_x, train_target1, train_target2 = (df_train.iloc[:, :-2], df_train.iloc[:, -2], df_train.iloc[:, -1])
return (train_x, train_target1, train_target2)
def prepare_submission(preds):
preds['ForecastId'] = preds['ForecastId'].fillna(0.0).astype('int32')
preds['Fatalities'] = preds['Fatalities'].fillna(0.0).astype('int32')
preds['ConfirmedCases'] = preds['ConfirmedCases'].fillna(0.0).astype('int32')
preds.clip(lower=0, inplace=True)
preds.to_csv('submission.csv', index=False)
def model_and_predict(model, X, y, test, estimators=5000):
if model != None:
run_model = model
else:
run_model = xgb.XGBRegressor(objective='reg:squarederror', n_estimators=estimators)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=12345)
run_model.fit(X_train, y_train)
y_train_pred = run_model.predict(X_train)
y_test_pred = run_model.predict(X_test)
y_pred = run_model.predict(test)
y_pred[y_pred < 0] = 0
r2 = r2_score(y_train_pred, y_train, multioutput='variance_weighted')
return (y_pred, r2)
def show_results(model):
# Code based on "Selecting Optimal Parameters for XGBoost Model Training" by Andrej Baranovskij (Medium)
results = model.evals_result()
epochs = len(results['validation_0']['error'])
x_axis = range(0, epochs)
# plot log loss
fig, ax = plt.subplots()
ax.plot(x_axis, results['validation_0']['logloss'], label='Train')
ax.plot(x_axis, results['validation_1']['logloss'], label='Test')
ax.legend()
plt.ylabel('Log Loss')
plt.title('XGBoost Log Loss')
plt.show()
# plot classification error
fig, ax = pyplot.subplots()
ax.plot(x_axis, results['validation_0']['error'], label='Train')
ax.plot(x_axis, results['validation_1']['error'], label='Test')
ax.legend()
plt.ylabel('Classification Error')
plt.title('XGBoost Classification Error')
plt.show()
def fit_models_and_train(country, state, model, train, test):
X, y_cases, y_fatal = prepare_train_set(train)
X = X.drop(['Id'], axis=1)
forecast_IDs = test.iloc[:, 0]
test_no_id = test.iloc[:, 1:]
if scale_data == True:
scaler = MinMaxScaler()
X = scaler.fit_transform(X.values)
test_no_id = scaler.transform(test_no_id.values)
y_cases_pred, cases_r2 = model_and_predict(model, X, y_cases, test_no_id)
y_fatal_pred, fatal_r2 = model_and_predict(model, X, y_fatal, test_no_id)
preds = pd.DataFrame(forecast_IDs)
preds['ConfirmedCases'] = y_cases_pred
preds['Fatalities'] = y_fatal_pred
return preds
df_train = pd.read_csv('../input/covid19-global-forecasting-week-4/train.csv')
df_test = pd.read_csv('../input/covid19-global-forecasting-week-4/test.csv')
df_train.shape
df_test.shape
df_train_original = df_train.copy()
df_test_original = df_test.copy()
df_train_original['Datetime'] = pd.to_datetime(df_train_original['Date'])
df_test_original['Datetime'] = pd.to_datetime(df_test_original['Date'])
df_train_original.head() | code |
2044577/cell_21 | [
"text_html_output_1.png"
] | from sklearn import tree
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes
def clean_data(dataset):
data = dataset.dropna()
data = data.drop('PassengerId', axis=1)
data = data.drop('Name', axis=1)
data = data.drop('Ticket', axis=1)
return data
def encode_categorical_feautures(dataset):
for coloumns in dataset.columns:
enc = LabelEncoder()
enc.fit(dataset[coloumns])
dataset[coloumns] = enc.fit_transform(dataset[coloumns])
return dataset
dataset = clean_data(dataset)
dataset = encode_categorical_feautures(dataset)
train_y = dataset['Survived']
train_x = dataset.drop('Survived', axis=1)
clf = GaussianNB()
clf.fit(train_x, train_y)
scores = cross_val_score(clf, train_x, train_y, cv=5)
from sklearn import tree
clf = tree.DecisionTreeClassifier()
clf.fit(train_x, train_y)
scores = cross_val_score(clf, train_x, train_y, cv=5)
from sklearn.ensemble import RandomForestClassifier
clf = RandomForestClassifier(n_estimators=10)
clf.fit(train_x, train_y)
scores = cross_val_score(clf, train_x, train_y, cv=5)
print('Random Forest Classifier :\n')
print('Accuracy: %0.2f (+/- %0.2f)' % (scores.mean(), scores.std() * 2)) | code |
2044577/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes | code |
2044577/cell_11 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes
def clean_data(dataset):
data = dataset.dropna()
data = data.drop('PassengerId', axis=1)
data = data.drop('Name', axis=1)
data = data.drop('Ticket', axis=1)
return data
def encode_categorical_feautures(dataset):
for coloumns in dataset.columns:
enc = LabelEncoder()
enc.fit(dataset[coloumns])
dataset[coloumns] = enc.fit_transform(dataset[coloumns])
return dataset
dataset = clean_data(dataset)
dataset = encode_categorical_feautures(dataset)
survived = dataset[dataset['Survived'] == 1]
survived_males = survived[survived['Sex'] == 0]
survived_females = survived[survived['Sex'] == 1]
sns.set_style('whitegrid')
classes = actual_data[actual_data['Survived'] == 0]
classes = classes.groupby(['Pclass'])['Pclass'].count()
sns.barplot(x=[1, 2, 3], y=classes) | code |
2044577/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import tree
from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes
def clean_data(dataset):
data = dataset.dropna()
data = data.drop('PassengerId', axis=1)
data = data.drop('Name', axis=1)
data = data.drop('Ticket', axis=1)
return data
def encode_categorical_feautures(dataset):
for coloumns in dataset.columns:
enc = LabelEncoder()
enc.fit(dataset[coloumns])
dataset[coloumns] = enc.fit_transform(dataset[coloumns])
return dataset
dataset = clean_data(dataset)
dataset = encode_categorical_feautures(dataset)
train_y = dataset['Survived']
train_x = dataset.drop('Survived', axis=1)
clf = GaussianNB()
clf.fit(train_x, train_y)
scores = cross_val_score(clf, train_x, train_y, cv=5)
from sklearn import tree
clf = tree.DecisionTreeClassifier()
clf.fit(train_x, train_y)
scores = cross_val_score(clf, train_x, train_y, cv=5)
print('Decision Tree Classifier :\n')
print('Accuracy: %0.2f (+/- %0.2f)' % (scores.mean(), scores.std() * 2)) | code |
2044577/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from sklearn.preprocessing import LabelEncoder
from sklearn.naive_bayes import GaussianNB
from sklearn.metrics import classification_report
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from sklearn.model_selection import cross_val_score
import seaborn as sns | code |
2044577/cell_8 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes
def clean_data(dataset):
data = dataset.dropna()
data = data.drop('PassengerId', axis=1)
data = data.drop('Name', axis=1)
data = data.drop('Ticket', axis=1)
return data
def encode_categorical_feautures(dataset):
for coloumns in dataset.columns:
enc = LabelEncoder()
enc.fit(dataset[coloumns])
dataset[coloumns] = enc.fit_transform(dataset[coloumns])
return dataset
dataset = clean_data(dataset)
dataset = encode_categorical_feautures(dataset)
survived = dataset[dataset['Survived'] == 1]
survived_males = survived[survived['Sex'] == 0]
survived_females = survived[survived['Sex'] == 1]
print('Out of Total Passengers ', len(dataset))
print('Males Survived :', len(survived_males))
print('Females Survived :', len(survived_females))
sns.set_style('whitegrid')
sns.barplot(x=['Males', 'Females'], y=[len(survived_males), len(survived_females)]) | code |
2044577/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
dataset.head()
actual_data = dataset
actual_data | code |
2044577/cell_17 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.model_selection import cross_val_score
from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes
def clean_data(dataset):
data = dataset.dropna()
data = data.drop('PassengerId', axis=1)
data = data.drop('Name', axis=1)
data = data.drop('Ticket', axis=1)
return data
def encode_categorical_feautures(dataset):
for coloumns in dataset.columns:
enc = LabelEncoder()
enc.fit(dataset[coloumns])
dataset[coloumns] = enc.fit_transform(dataset[coloumns])
return dataset
dataset = clean_data(dataset)
dataset = encode_categorical_feautures(dataset)
train_y = dataset['Survived']
train_x = dataset.drop('Survived', axis=1)
clf = GaussianNB()
clf.fit(train_x, train_y)
scores = cross_val_score(clf, train_x, train_y, cv=5)
print('Naive Bayes Classifier :\n')
print('Accuracy: %0.2f (+/- %0.2f)' % (scores.mean(), scores.std() * 2)) | code |
2044577/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/train.csv')
actual_data = dataset
actual_data
dataset.dtypes
def clean_data(dataset):
data = dataset.dropna()
data = data.drop('PassengerId', axis=1)
data = data.drop('Name', axis=1)
data = data.drop('Ticket', axis=1)
return data
def encode_categorical_feautures(dataset):
for coloumns in dataset.columns:
enc = LabelEncoder()
enc.fit(dataset[coloumns])
dataset[coloumns] = enc.fit_transform(dataset[coloumns])
return dataset
dataset = clean_data(dataset)
dataset = encode_categorical_feautures(dataset)
dataset | code |
129035264/cell_13 | [
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.shape
train.duplicated().sum()
women = train.loc[train.Sex == 'female']['Survived']
women_sur_rate = sum(women) / len(women)
men = train.loc[train.Sex == 'male']['Survived']
men_sur_rate = sum(men) / len(men)
import missingno as msno
msno.matrix(train) | code |
129035264/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.shape
test.info() | code |
129035264/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.head() | code |
129035264/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.shape | code |
129035264/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.shape
test.duplicated().sum() | code |
129035264/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 |
129035264/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
test = pd.read_csv('/kaggle/input/titanic/test.csv')
test.shape | code |
129035264/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.shape
train.info() | code |
129035264/cell_15 | [
"text_plain_output_1.png"
] | import missingno as msno
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/titanic/train.csv')
train.shape
train.duplicated().sum()
women = train.loc[train.Sex == 'female']['Survived']
women_sur_rate = sum(women) / len(women)
men = train.loc[train.Sex == 'male']['Survived']
men_sur_rate = sum(men) / len(men)
import missingno as msno
msno.matrix(train)
train.drop(['PassengerId', 'Ticket', 'Cabin', 'Name'], axis=1, inplace=True)
msno.matrix(train)
train.Age.fillna(train.Age.mean(), inplace=True)
msno.matrix(train) | code |
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