path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
88092182/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-feb-2022/train.csv', index_col=0)
test = pd.read_csv('/kaggle/input/tabular-playground-series-feb-2022/test.csv', index_col=0)
submission = pd.read_csv('/kaggle/input/tabular-playground-series-feb-2022/sample_submission.csv', index_col=0)
cols_with_missing_train = [col for col in train.columns if train[col].isnull().any()]
cols_with_missing_test = [col for col in test.columns if test[col].isnull().any()]
print('train data missing vlue is : ', cols_with_missing_train)
print('test data missing vlue is : ', cols_with_missing_test)
print('train data shape is :', train.shape)
print('test data shape is :', test.shape) | code |
74042868/cell_4 | [
"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
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
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)
HST_OPEN[0].header['RA_TARG'] | code |
74042868/cell_8 | [
"image_output_2.png",
"image_output_1.png"
] | from astropy.io import fits
from scipy.ndimage import gaussian_filter
from skimage import data, io, filters
import matplotlib.pyplot as plt
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
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)
SPECTRAL_LIST = ['gray', 'jet', 'hot', 'prism', 'nipy_spectral', 'gist_ncar', 'gist_earth', 'gist_stern', 'flag', 'gnuplot2', 'terrain']
GAUSS_ZOOMED_X2 = gaussian_filter(ZOOMED_X3_SCALE_HST, sigma=7)
GAUSS_NUCLEUS = gaussian_filter(NUCLEUS_SCALE_HST, sigma=7)
for x_spec in SPECTRAL_LIST:
figure, axis = plt.subplots(1, 2, figsize=(20, 20))
axis[0].imshow(GAUSS_ZOOMED_X2, cmap=x_spec)
axis[0].set_title('ZOOMED' + ' / ' + x_spec)
axis[0].axis('off')
DENSITY_FUNC = axis[1].imshow(GAUSS_NUCLEUS, 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 |
74042868/cell_15 | [
"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 scipy.ndimage import gaussian_filter
from skimage import data, io, filters
import cv2
import matplotlib.pyplot as plt
import numpy as np
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
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)
SPECTRAL_LIST = ['gray', 'jet', 'hot', 'prism', 'nipy_spectral', 'gist_ncar', 'gist_earth', 'gist_stern', 'flag', 'gnuplot2', 'terrain']
GAUSS_ZOOMED_X2 = gaussian_filter(ZOOMED_X3_SCALE_HST, sigma=7)
GAUSS_NUCLEUS = gaussian_filter(NUCLEUS_SCALE_HST, sigma=7)
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(GAUSS_ZOOMED_X2,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(GAUSS_NUCLEUS,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()
M_GAUSS_ZOOMED_X2 = gaussian_filter(M_ZOOMED_X3_SCALE_HST, sigma=7)
M_GAUSS_NUCLEUS = gaussian_filter(M_NUCLEUS_SCALE_HST, sigma=7)
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(M_GAUSS_ZOOMED_X2,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(M_GAUSS_NUCLEUS,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()
BM_GAUSS_ZOOMED_X2 = gaussian_filter(BM_ZOOMED_X3_SCALE_HST, sigma=5)
BM_GAUSS_NUCLEUS = gaussian_filter(BM_NUCLEUS_SCALE_HST, sigma=5)
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(BM_GAUSS_ZOOMED_X2,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(BM_GAUSS_NUCLEUS,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()
Clahe_Func = cv2.createCLAHE(clipLimit=5.0, tileGridSize=(3, 3))
CLAHE_ZOOMED_X2 = Clahe_Func.apply(ZOOMED_X3_SCALE_HST.astype(np.uint8))
CLAHE_NUCLEUS_CLAHE = Clahe_Func.apply(NUCLEUS_SCALE_HST.astype(np.uint8))
for x_spec in SPECTRAL_LIST:
figure, axis = plt.subplots(1, 2, figsize=(20, 20))
axis[0].imshow(CLAHE_ZOOMED_X2, cmap=x_spec)
axis[0].set_title('ZOOMED' + ' / ' + x_spec)
axis[0].axis('off')
DENSITY_FUNC = axis[1].imshow(CLAHE_NUCLEUS_CLAHE, 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 |
74042868/cell_16 | [
"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 scipy.ndimage import gaussian_filter
from skimage import data, io, filters
import cv2
import matplotlib.pyplot as plt
import numpy as np
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
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)
SPECTRAL_LIST = ['gray', 'jet', 'hot', 'prism', 'nipy_spectral', 'gist_ncar', 'gist_earth', 'gist_stern', 'flag', 'gnuplot2', 'terrain']
GAUSS_ZOOMED_X2 = gaussian_filter(ZOOMED_X3_SCALE_HST, sigma=7)
GAUSS_NUCLEUS = gaussian_filter(NUCLEUS_SCALE_HST, sigma=7)
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(GAUSS_ZOOMED_X2,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(GAUSS_NUCLEUS,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()
M_GAUSS_ZOOMED_X2 = gaussian_filter(M_ZOOMED_X3_SCALE_HST, sigma=7)
M_GAUSS_NUCLEUS = gaussian_filter(M_NUCLEUS_SCALE_HST, sigma=7)
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(M_GAUSS_ZOOMED_X2,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(M_GAUSS_NUCLEUS,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()
BM_GAUSS_ZOOMED_X2 = gaussian_filter(BM_ZOOMED_X3_SCALE_HST, sigma=5)
BM_GAUSS_NUCLEUS = gaussian_filter(BM_NUCLEUS_SCALE_HST, sigma=5)
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(BM_GAUSS_ZOOMED_X2,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(BM_GAUSS_NUCLEUS,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()
Clahe_Func = cv2.createCLAHE(clipLimit=5.0, tileGridSize=(3, 3))
CLAHE_ZOOMED_X2 = Clahe_Func.apply(ZOOMED_X3_SCALE_HST.astype(np.uint8))
CLAHE_NUCLEUS_CLAHE = Clahe_Func.apply(NUCLEUS_SCALE_HST.astype(np.uint8))
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(CLAHE_ZOOMED_X2,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(CLAHE_NUCLEUS_CLAHE,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()
figure = plt.figure(figsize=(20, 5))
powerSpectrum_1, freqenciesFound_1, time_1, imageAxis_1 = plt.specgram(ZOOMED_X3_SCALE_HST.flatten())
plt.axis('off')
plt.title('ZOOMED')
plt.tight_layout()
plt.show()
figure = plt.figure(figsize=(20, 5))
powerSpectrum_1, freqenciesFound_1, time_1, imageAxis_1 = plt.specgram(NUCLEUS_SCALE_HST.flatten())
plt.axis('off')
plt.title('NUCLEUS')
plt.tight_layout()
plt.show() | code |
74042868/cell_10 | [
"text_plain_output_1.png"
] | from astropy.io import fits
from scipy.ndimage import gaussian_filter
from skimage import data, io, filters
import matplotlib.pyplot as plt
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
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)
SPECTRAL_LIST = ['gray', 'jet', 'hot', 'prism', 'nipy_spectral', 'gist_ncar', 'gist_earth', 'gist_stern', 'flag', 'gnuplot2', 'terrain']
GAUSS_ZOOMED_X2 = gaussian_filter(ZOOMED_X3_SCALE_HST, sigma=7)
GAUSS_NUCLEUS = gaussian_filter(NUCLEUS_SCALE_HST, sigma=7)
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(GAUSS_ZOOMED_X2,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(GAUSS_NUCLEUS,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()
M_GAUSS_ZOOMED_X2 = gaussian_filter(M_ZOOMED_X3_SCALE_HST, sigma=7)
M_GAUSS_NUCLEUS = gaussian_filter(M_NUCLEUS_SCALE_HST, sigma=7)
for x_spec in SPECTRAL_LIST:
figure, axis = plt.subplots(1, 2, figsize=(20, 20))
axis[0].imshow(M_GAUSS_ZOOMED_X2, cmap=x_spec)
axis[0].set_title('ZOOMED' + ' / ' + x_spec)
axis[0].axis('off')
DENSITY_FUNC = axis[1].imshow(M_GAUSS_NUCLEUS, 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 |
74042868/cell_12 | [
"text_plain_output_1.png"
] | from astropy.io import fits
from scipy.ndimage import gaussian_filter
from skimage import data, io, filters
import matplotlib.pyplot as plt
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
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)
SPECTRAL_LIST = ['gray', 'jet', 'hot', 'prism', 'nipy_spectral', 'gist_ncar', 'gist_earth', 'gist_stern', 'flag', 'gnuplot2', 'terrain']
GAUSS_ZOOMED_X2 = gaussian_filter(ZOOMED_X3_SCALE_HST, sigma=7)
GAUSS_NUCLEUS = gaussian_filter(NUCLEUS_SCALE_HST, sigma=7)
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(GAUSS_ZOOMED_X2,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(GAUSS_NUCLEUS,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()
M_GAUSS_ZOOMED_X2 = gaussian_filter(M_ZOOMED_X3_SCALE_HST, sigma=7)
M_GAUSS_NUCLEUS = gaussian_filter(M_NUCLEUS_SCALE_HST, sigma=7)
for x_spec in SPECTRAL_LIST:
figure,axis = plt.subplots(1,2,figsize=(20,20))
axis[0].imshow(M_GAUSS_ZOOMED_X2,cmap=x_spec)
axis[0].set_title("ZOOMED" + " / "+ x_spec)
axis[0].axis("off")
DENSITY_FUNC = axis[1].imshow(M_GAUSS_NUCLEUS,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()
BM_GAUSS_ZOOMED_X2 = gaussian_filter(BM_ZOOMED_X3_SCALE_HST, sigma=5)
BM_GAUSS_NUCLEUS = gaussian_filter(BM_NUCLEUS_SCALE_HST, sigma=5)
for x_spec in SPECTRAL_LIST:
figure, axis = plt.subplots(1, 2, figsize=(20, 20))
axis[0].imshow(BM_GAUSS_ZOOMED_X2, cmap=x_spec)
axis[0].set_title('ZOOMED' + ' / ' + x_spec)
axis[0].axis('off')
DENSITY_FUNC = axis[1].imshow(BM_GAUSS_NUCLEUS, 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 |
74042868/cell_5 | [
"image_output_2.png",
"image_output_1.png"
] | from astropy.io import fits
from skimage import data, io, filters
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
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)
HST_OPEN[0].header['DEC_TARG'] | code |
18159197/cell_21 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.svm import SVC
model_linear = SVC(kernel='linear')
model_linear.fit(X_train, y_train)
y_pred = model_linear.predict(X_test)
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))
print('Precision Score :: ', metrics.precision_score(y_test, y_pred, pos_label='positive', average='micro'), '\n')
print('Recall Score :: ', metrics.recall_score(y_test, y_pred, pos_label='positive', average='micro'), '\n') | code |
18159197/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.shape
df.dtypes
round(df.isnull().sum() / len(df.index))
df.describe(percentiles=[0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95, 0.96, 0.97, 0.98, 0.99, 1]) | code |
18159197/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.shape | code |
18159197/cell_30 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.svm import SVC
import pandas as pd
df = pd.read_csv('train.csv')
folds = KFold(n_splits=5, shuffle=True, random_state=101)
hyper_params = [{'gamma': [0.01, 0.001, 0.0001], 'C': [1, 10, 100, 1000]}]
model = SVC(kernel='rbf')
model_cv = GridSearchCV(estimator=model, param_grid=hyper_params, scoring='accuracy', cv=folds, verbose=1, return_train_score=True)
model_cv.fit(X_train, y_train)
cv_results = pd.DataFrame(model_cv.cv_results_)
cv_results
best_score = model_cv.best_score_
best_hyperparams = model_cv.best_params_
print('The best test score is {0} corresponding to hyperparameters {1}'.format(best_score, best_hyperparams)) | code |
18159197/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.svm import SVC
model_linear = SVC(kernel='linear')
model_linear.fit(X_train, y_train)
y_pred = model_linear.predict(X_test)
print('accuracy:', metrics.accuracy_score(y_true=y_test, y_pred=y_pred), '\n')
print(metrics.confusion_matrix(y_true=y_test, y_pred=y_pred)) | code |
18159197/cell_26 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.svm import SVC
folds = KFold(n_splits=5, shuffle=True, random_state=101)
hyper_params = [{'gamma': [0.01, 0.001, 0.0001], 'C': [1, 10, 100, 1000]}]
model = SVC(kernel='rbf')
model_cv = GridSearchCV(estimator=model, param_grid=hyper_params, scoring='accuracy', cv=folds, verbose=1, return_train_score=True)
model_cv.fit(X_train, y_train) | code |
18159197/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
os.getcwd() | code |
18159197/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.shape
df.dtypes | code |
18159197/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.svm import SVC
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('train.csv')
folds = KFold(n_splits=5, shuffle=True, random_state=101)
hyper_params = [{'gamma': [0.01, 0.001, 0.0001], 'C': [1, 10, 100, 1000]}]
model = SVC(kernel='rbf')
model_cv = GridSearchCV(estimator=model, param_grid=hyper_params, scoring='accuracy', cv=folds, verbose=1, return_train_score=True)
model_cv.fit(X_train, y_train)
cv_results = pd.DataFrame(model_cv.cv_results_)
cv_results
cv_results['param_C'] = cv_results['param_C'].astype('int')
plt.figure(figsize=(16, 6))
plt.subplot(131)
gamma_01 = cv_results[cv_results['param_gamma'] == 0.01]
plt.plot(gamma_01['param_C'], gamma_01['mean_test_score'])
plt.plot(gamma_01['param_C'], gamma_01['mean_train_score'])
plt.xlabel('C')
plt.ylabel('Accuracy')
plt.title('Gamma=0.01')
plt.ylim([0.6, 1])
plt.legend(['test accuracy', 'train accuracy'], loc='upper left')
plt.xscale('log')
plt.subplot(132)
gamma_001 = cv_results[cv_results['param_gamma'] == 0.001]
plt.plot(gamma_001['param_C'], gamma_001['mean_test_score'])
plt.plot(gamma_001['param_C'], gamma_001['mean_train_score'])
plt.xlabel('C')
plt.ylabel('Accuracy')
plt.title('Gamma=0.001')
plt.ylim([0.6, 1])
plt.legend(['test accuracy', 'train accuracy'], loc='upper left')
plt.xscale('log')
plt.subplot(133)
gamma_0001 = cv_results[cv_results['param_gamma'] == 0.0001]
plt.plot(gamma_0001['param_C'], gamma_0001['mean_test_score'])
plt.plot(gamma_0001['param_C'], gamma_0001['mean_train_score'])
plt.xlabel('C')
plt.ylabel('Accuracy')
plt.title('Gamma=0.0001')
plt.ylim([0.6, 1])
plt.legend(['test accuracy', 'train accuracy'], loc='upper left')
plt.xscale('log') | code |
18159197/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.head() | code |
18159197/cell_31 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.svm import SVC
model_linear = SVC(kernel='linear')
model_linear.fit(X_train, y_train)
y_pred = model_linear.predict(X_test)
from sklearn.metrics import classification_report
non_linear_model = SVC(kernel='rbf')
non_linear_model.fit(X_train, y_train)
y_pred = non_linear_model.predict(X_test)
from sklearn.metrics import classification_report
folds = KFold(n_splits=5, shuffle=True, random_state=101)
hyper_params = [{'gamma': [0.01, 0.001, 0.0001], 'C': [1, 10, 100, 1000]}]
model = SVC(kernel='rbf')
model_cv = GridSearchCV(estimator=model, param_grid=hyper_params, scoring='accuracy', cv=folds, verbose=1, return_train_score=True)
model_cv.fit(X_train, y_train)
model = SVC(C=10, gamma=0.001, kernel='rbf')
model.fit(X_train, y_train)
y_pred = model.predict(X_test)
print('accuracy', metrics.accuracy_score(y_test, y_pred), '\n')
print('Precision Score :: ', metrics.precision_score(y_test, y_pred, pos_label='positive', average='micro'))
print('Recall Score :: ', metrics.recall_score(y_test, y_pred, pos_label='positive', average='micro'))
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred))
print(metrics.confusion_matrix(y_test, y_pred), '\n') | code |
18159197/cell_24 | [
"text_html_output_1.png"
] | from sklearn import metrics
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.metrics import classification_report
from sklearn.svm import SVC
model_linear = SVC(kernel='linear')
model_linear.fit(X_train, y_train)
y_pred = model_linear.predict(X_test)
from sklearn.metrics import classification_report
non_linear_model = SVC(kernel='rbf')
non_linear_model.fit(X_train, y_train)
y_pred = non_linear_model.predict(X_test)
print('accuracy:', metrics.accuracy_score(y_true=y_test, y_pred=y_pred), '\n')
print(metrics.confusion_matrix(y_true=y_test, y_pred=y_pred))
print('Precision Score :: ', metrics.precision_score(y_test, y_pred, pos_label='positive', average='micro'), '\n')
print('Recall Score :: ', metrics.recall_score(y_test, y_pred, pos_label='positive', average='micro'), '\n')
from sklearn.metrics import classification_report
print(classification_report(y_test, y_pred)) | code |
18159197/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.shape
df.info() | code |
18159197/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import KFold
from sklearn.svm import SVC
import pandas as pd
df = pd.read_csv('train.csv')
folds = KFold(n_splits=5, shuffle=True, random_state=101)
hyper_params = [{'gamma': [0.01, 0.001, 0.0001], 'C': [1, 10, 100, 1000]}]
model = SVC(kernel='rbf')
model_cv = GridSearchCV(estimator=model, param_grid=hyper_params, scoring='accuracy', cv=folds, verbose=1, return_train_score=True)
model_cv.fit(X_train, y_train)
cv_results = pd.DataFrame(model_cv.cv_results_)
cv_results | code |
18159197/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('train.csv')
df.shape
df.dtypes
round(df.isnull().sum() / len(df.index)) | code |
18159197/cell_5 | [
"image_output_1.png"
] | import os
import os
os.getcwd()
os.chdir('/kaggle')
os.chdir('input')
os.listdir() | code |
121153678/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum() | code |
121153678/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum()
music_update = music_update.drop_duplicates()
music_update.duplicated().value_counts()
nameColumn = music_update.pop('name')
artistColumn = music_update.pop('artists')
music_update.insert(0, 'name', nameColumn)
music_update.insert(1, 'artist', artistColumn)
music_update.drop('release_date', axis=1) | code |
121153678/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.head(1) | code |
121153678/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum()
music_update = music_update.drop_duplicates()
music_update.duplicated().value_counts() | code |
121153678/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum() | code |
121153678/cell_41 | [
"text_plain_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum()
music_update = music_update.drop_duplicates()
music_update.duplicated().value_counts()
nameColumn = music_update.pop('name')
artistColumn = music_update.pop('artists')
music_update.insert(0, 'name', nameColumn)
music_update.insert(1, 'artist', artistColumn)
music_update.drop('release_date', axis=1)
music_updated = music_update
Trends = music_updated[['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'valence', 'year']]
sound_features = ['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'valence']
def get_decade(year):
period = int(year / 10) * 10
decade = '{}s'.format(period)
return decade
Trends['decade'] = Trends['year'].apply(get_decade)
Trends_updated = Trends.groupby('decade')[sound_features].mean()
Trends_updated | code |
121153678/cell_11 | [
"text_plain_output_1.png"
] | from deepface import DeepFace
import cv2
import matplotlib.pyplot as plt
index = 0
def emotions(image):
img = cv2.imread(image)
plt.imshow(img[:, :, ::-1])
demography = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False, detector_backend='retinaface')
return demography
emotion = emotions(image='/kaggle/input/newpic/2Q__ (2)_face.png')
dominant_emotion = emotion[index]['dominant_emotion']
print(emotion)
print('Dominant_emotion:', dominant_emotion) | code |
121153678/cell_50 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from deepface import DeepFace
import cv2
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
index = 0
def emotions(image):
img = cv2.imread(image)
demography = DeepFace.analyze(image, actions=['emotion'], enforce_detection=False, detector_backend='retinaface')
return demography
emotion = emotions(image='/kaggle/input/newpic/2Q__ (2)_face.png')
dominant_emotion = emotion[index]['dominant_emotion']
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum()
music_update = music_update.drop_duplicates()
music_update.duplicated().value_counts()
nameColumn = music_update.pop('name')
artistColumn = music_update.pop('artists')
music_update.insert(0, 'name', nameColumn)
music_update.insert(1, 'artist', artistColumn)
music_update.drop('release_date', axis=1)
music_updated = music_update
Trends = music_updated[['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'valence', 'year']]
sound_features = ['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'valence']
def get_decade(year):
period = int(year / 10) * 10
decade = '{}s'.format(period)
return decade
Trends['decade'] = Trends['year'].apply(get_decade)
Trends_updated = Trends.groupby('decade')[sound_features].mean()
Trends_updated
plt.figure(figsize=(12, 8))
sns.heatmap(Trends_updated.corr(), annot=True, square=True) | code |
121153678/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !pip install Deepface | code |
121153678/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update.head(1) | code |
121153678/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
from deepface import DeepFace
import cv2
from sklearn.cluster import KMeans
from sklearn.preprocessing import MinMaxScaler
from mlxtend.preprocessing import minmax_scaling
from sklearn.model_selection import train_test_split
from lightgbm import LGBMClassifier
from sklearn.metrics.pairwise import cosine_similarity
from scipy.spatial.distance import cosine, euclidean, hamming
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.preprocessing import StandardScaler
from sklearn.cluster import DBSCAN
import lightgbm
import random | code |
121153678/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.head(2) | code |
121153678/cell_16 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_dataset.info() | code |
121153678/cell_47 | [
"text_html_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum()
music_update = music_update.drop_duplicates()
music_update.duplicated().value_counts()
nameColumn = music_update.pop('name')
artistColumn = music_update.pop('artists')
music_update.insert(0, 'name', nameColumn)
music_update.insert(1, 'artist', artistColumn)
music_update.drop('release_date', axis=1)
music_updated = music_update
Trends = music_updated[['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'valence', 'year']]
sound_features = ['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'valence']
def get_decade(year):
period = int(year / 10) * 10
decade = '{}s'.format(period)
return decade
Trends['decade'] = Trends['year'].apply(get_decade)
Trends_updated = Trends.groupby('decade')[sound_features].mean()
Trends_updated
Trend_ms = music_updated[['year', 'duration_ms']]
Trend_ms['decade'] = Trend_ms['year'].apply(get_decade)
Trend_ms = Trend_ms.groupby('decade')['duration_ms'].mean()
Trend_ms.plot(legend=True, figsize=(15, 7)) | code |
121153678/cell_43 | [
"text_html_output_1.png"
] | import pandas as pd
music_dataset = pd.read_csv('/kaggle/input/kaggledataupdated/KaggleData_updated.csv')
music_dataset.shape
music_update = music_dataset.set_index('id')
music_update['artists'] = music_update['artists'].str.strip("[]'")
music_update.isna().sum()
music_update.duplicated().sum()
music_update = music_update.drop_duplicates()
music_update.duplicated().value_counts()
nameColumn = music_update.pop('name')
artistColumn = music_update.pop('artists')
music_update.insert(0, 'name', nameColumn)
music_update.insert(1, 'artist', artistColumn)
music_update.drop('release_date', axis=1)
music_updated = music_update
Trends = music_updated[['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'valence', 'year']]
sound_features = ['acousticness', 'danceability', 'energy', 'instrumentalness', 'liveness', 'valence']
def get_decade(year):
period = int(year / 10) * 10
decade = '{}s'.format(period)
return decade
Trends['decade'] = Trends['year'].apply(get_decade)
Trends_updated = Trends.groupby('decade')[sound_features].mean()
Trends_updated
Trends_updated.plot(legend=True, figsize=(15, 7)) | code |
49116852/cell_10 | [
"text_html_output_1.png"
] | from keras.callbacks import ModelCheckpoint,EarlyStopping
from keras.models import load_model
from sklearn.linear_model import LinearRegression, Ridge
from tensorflow.keras import layers
from tensorflow.keras import metrics
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow_addons.layers import WeightNormalization
import glob
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
import tensorflow as tf
import numpy as np
import pandas as pd
import os
Train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
Train = Train.set_index('Id')
Test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
Test = Test.set_index('Id')
preprocessing_path = '../input/house-price-eda'
train_scale = pd.read_csv(f'{preprocessing_path}/train_scaled.csv')
train_scale = train_scale.set_index('Id')
test_scale = pd.read_csv(f'{preprocessing_path}/test_scaled.csv')
try:
test_scale = test_scale.rename(columns={'Unnamed: 0': 'Id'})
except:
pass
test_scale = test_scale.set_index('Id')
Train = Train.loc[train_scale.index]
import glob
listing = glob.glob(f'{preprocessing_path}/*_one_hot_pickle')
import pickle
for model_file in listing:
col = model_file.split('_one_')[0]
col = col.split('/')[-1]
enc = pickle.load(open(model_file, 'rb'))
new_cols = pd.DataFrame(enc.transform(Train[[col]]).toarray(), columns=f'{col}_' + enc.categories_[0])
new_cols.index = Train.index
Train = pd.concat([Train, new_cols], axis=1)
Train = Train.drop(col, axis=1)
for log_col in train_scale.columns[train_scale.columns.str.endswith('log')]:
col = log_col.split('_log')[0]
Train[log_col] = np.log(Train[col])
Train = Train.drop(col, axis=1)
Train = Train[train_scale.columns]
for model_file in listing:
col = model_file.split('_one_')[0]
col = col.split('/')[-1]
enc = pickle.load(open(model_file, 'rb'))
new_cols = pd.DataFrame(enc.transform(Test[[col]]).toarray(), columns=f'{col}_' + enc.categories_[0])
new_cols.index = Test.index
Test = pd.concat([Test, new_cols], axis=1)
Test = Test.drop(col, axis=1)
for log_col in test_scale.columns[test_scale.columns.str.endswith('log')]:
col = log_col.split('_log')[0]
Test[log_col] = np.log(Test[col])
Test = Test.drop(col, axis=1)
Test = Test[test_scale.columns]
for col in Test.columns:
if len(set(Train[col])) > 2 and '_log' not in col:
Train[f'{col}_log'] = np.log(Train[col] + 1)
Train = Train.drop(col, axis=1)
Test[f'{col}_log'] = np.log(Test[col] + 1)
Test = Test.drop(col, axis=1)
from sklearn.linear_model import LinearRegression, Ridge
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers.experimental import preprocessing
from tensorflow_addons.layers import WeightNormalization
from keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras import regularizers
from tensorflow.keras import metrics
model = 'linear'
X_Train = Train.drop('SalePrice_log', axis=1)
Y_Train = Train[['SalePrice_log']]
def NN_model():
horsepower = np.array(X_Train)
horsepower_normalizer = preprocessing.Normalization(input_shape=[X_Train.shape[1]])
horsepower_normalizer.adapt(horsepower)
horsepower_model = tf.keras.Sequential([horsepower_normalizer, WeightNormalization(layers.Dense(units=8, activation='linear')), layers.BatchNormalization(), layers.Dense(units=1, activation='linear')])
horsepower_model.summary()
METRICS = [metrics.MeanSquaredError()]
horsepower_model.compile(optimizer=tf.optimizers.Adam(learning_rate=0.1), loss='mse', metrics=METRICS)
return horsepower_model
if model == 'linear':
reg = Ridge(alpha=1.0).fit(Train.drop('SalePrice_log', axis=1), Train[['SalePrice_log']])
predict = reg.predict(Test)
elif model == 'NN':
nn_model = NN_model()
filepath = 'best_weights.hdf5'
Monitor = 'loss'
checkpoint = ModelCheckpoint(filepath, monitor=Monitor, verbose=1, save_best_only=True, mode='min')
nn_model.fit(X_Train, Y_Train, epochs=1000, callbacks=[checkpoint])
from keras.models import load_model
nn_model = load_model('best_weights.hdf5')
predict = nn_model.predict(Test)
predict = pd.DataFrame(predict)
predict.index = Test.index
predict.columns = ['SalePrice_log']
predict['SalePrice'] = np.e ** predict['SalePrice_log']
predict = predict.drop('SalePrice_log', axis=1)
predict | code |
49116852/cell_5 | [
"text_html_output_1.png"
] | import glob
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pickle
import numpy as np
import pandas as pd
import os
Train = pd.read_csv('../input/house-prices-advanced-regression-techniques/train.csv')
Train = Train.set_index('Id')
Test = pd.read_csv('../input/house-prices-advanced-regression-techniques/test.csv')
Test = Test.set_index('Id')
preprocessing_path = '../input/house-price-eda'
train_scale = pd.read_csv(f'{preprocessing_path}/train_scaled.csv')
train_scale = train_scale.set_index('Id')
test_scale = pd.read_csv(f'{preprocessing_path}/test_scaled.csv')
try:
test_scale = test_scale.rename(columns={'Unnamed: 0': 'Id'})
except:
pass
test_scale = test_scale.set_index('Id')
Train = Train.loc[train_scale.index]
import glob
listing = glob.glob(f'{preprocessing_path}/*_one_hot_pickle')
import pickle
for model_file in listing:
col = model_file.split('_one_')[0]
col = col.split('/')[-1]
enc = pickle.load(open(model_file, 'rb'))
new_cols = pd.DataFrame(enc.transform(Train[[col]]).toarray(), columns=f'{col}_' + enc.categories_[0])
new_cols.index = Train.index
Train = pd.concat([Train, new_cols], axis=1)
Train = Train.drop(col, axis=1)
for log_col in train_scale.columns[train_scale.columns.str.endswith('log')]:
col = log_col.split('_log')[0]
Train[log_col] = np.log(Train[col])
Train = Train.drop(col, axis=1)
Train = Train[train_scale.columns]
for model_file in listing:
col = model_file.split('_one_')[0]
col = col.split('/')[-1]
enc = pickle.load(open(model_file, 'rb'))
new_cols = pd.DataFrame(enc.transform(Test[[col]]).toarray(), columns=f'{col}_' + enc.categories_[0])
new_cols.index = Test.index
Test = pd.concat([Test, new_cols], axis=1)
Test = Test.drop(col, axis=1)
for log_col in test_scale.columns[test_scale.columns.str.endswith('log')]:
col = log_col.split('_log')[0]
Test[log_col] = np.log(Test[col])
Test = Test.drop(col, axis=1)
Test = Test[test_scale.columns]
for col in Test.columns:
if len(set(Train[col])) > 2 and '_log' not in col:
Train[f'{col}_log'] = np.log(Train[col] + 1)
Train = Train.drop(col, axis=1)
Test[f'{col}_log'] = np.log(Test[col] + 1)
Test = Test.drop(col, axis=1)
Test | code |
130008207/cell_21 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
batch_chars_padded = batch_chars_ragged.to_tensor(default_value=-1)
batch_chars_sparse = batch_chars_ragged.to_sparse()
nrows, ncols = batch_chars_sparse.dense_shape.numpy()
elements = [['_' for i in range(ncols)] for j in range(nrows)]
for (row, col), value in zip(batch_chars_sparse.indices.numpy(), batch_chars_sparse.values.numpy()):
elements[row][col] = str(value)
value_lengths = []
for row in elements:
for value in row:
value_lengths.append(len(value))
max_width = max(value_lengths)
tf.strings.unicode_encode([[99, 97, 116], [100, 111, 103], [99, 111, 119]], output_encoding='UTF-8')
tf.strings.unicode_encode(batch_chars_ragged, output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_sparse(batch_chars_sparse), output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_tensor(batch_chars_padded, padding=-1), output_encoding='UTF-8') | code |
130008207/cell_13 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE') | code |
130008207/cell_9 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars | code |
130008207/cell_25 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
batch_chars_padded = batch_chars_ragged.to_tensor(default_value=-1)
batch_chars_sparse = batch_chars_ragged.to_sparse()
nrows, ncols = batch_chars_sparse.dense_shape.numpy()
elements = [['_' for i in range(ncols)] for j in range(nrows)]
for (row, col), value in zip(batch_chars_sparse.indices.numpy(), batch_chars_sparse.values.numpy()):
elements[row][col] = str(value)
value_lengths = []
for row in elements:
for value in row:
value_lengths.append(len(value))
max_width = max(value_lengths)
tf.strings.unicode_encode([[99, 97, 116], [100, 111, 103], [99, 111, 119]], output_encoding='UTF-8')
tf.strings.unicode_encode(batch_chars_ragged, output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_sparse(batch_chars_sparse), output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_tensor(batch_chars_padded, padding=-1), output_encoding='UTF-8')
thanks = u'Thanks 😊'.encode('UTF-8')
num_bytes = tf.strings.length(thanks).numpy()
num_chars = tf.strings.length(thanks, unit='UTF8_CHAR').numpy()
tf.strings.substr(thanks, pos=7, len=1).numpy() | code |
130008207/cell_4 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊') | code |
130008207/cell_23 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
batch_chars_padded = batch_chars_ragged.to_tensor(default_value=-1)
batch_chars_sparse = batch_chars_ragged.to_sparse()
nrows, ncols = batch_chars_sparse.dense_shape.numpy()
elements = [['_' for i in range(ncols)] for j in range(nrows)]
for (row, col), value in zip(batch_chars_sparse.indices.numpy(), batch_chars_sparse.values.numpy()):
elements[row][col] = str(value)
value_lengths = []
for row in elements:
for value in row:
value_lengths.append(len(value))
max_width = max(value_lengths)
tf.strings.unicode_encode([[99, 97, 116], [100, 111, 103], [99, 111, 119]], output_encoding='UTF-8')
tf.strings.unicode_encode(batch_chars_ragged, output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_sparse(batch_chars_sparse), output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_tensor(batch_chars_padded, padding=-1), output_encoding='UTF-8')
thanks = u'Thanks 😊'.encode('UTF-8')
num_bytes = tf.strings.length(thanks).numpy()
num_chars = tf.strings.length(thanks, unit='UTF8_CHAR').numpy()
print('{} bytes; {} UTF-8 characters'.format(num_bytes, num_chars)) | code |
130008207/cell_30 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
batch_chars_padded = batch_chars_ragged.to_tensor(default_value=-1)
batch_chars_sparse = batch_chars_ragged.to_sparse()
nrows, ncols = batch_chars_sparse.dense_shape.numpy()
elements = [['_' for i in range(ncols)] for j in range(nrows)]
for (row, col), value in zip(batch_chars_sparse.indices.numpy(), batch_chars_sparse.values.numpy()):
elements[row][col] = str(value)
value_lengths = []
for row in elements:
for value in row:
value_lengths.append(len(value))
max_width = max(value_lengths)
tf.strings.unicode_encode([[99, 97, 116], [100, 111, 103], [99, 111, 119]], output_encoding='UTF-8')
tf.strings.unicode_encode(batch_chars_ragged, output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_sparse(batch_chars_sparse), output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_tensor(batch_chars_padded, padding=-1), output_encoding='UTF-8')
thanks = u'Thanks 😊'.encode('UTF-8')
num_bytes = tf.strings.length(thanks).numpy()
num_chars = tf.strings.length(thanks, unit='UTF8_CHAR').numpy()
tf.strings.substr(thanks, pos=7, len=1).numpy()
tf.strings.unicode_split(thanks, 'UTF-8').numpy()
codepoints, offsets = tf.strings.unicode_decode_with_offsets(u'🎈🎉🎊', 'UTF-8')
for codepoint, offset in zip(codepoints.numpy(), offsets.numpy()):
print('At byte offset {}: codepoint {}'.format(offset, codepoint)) | code |
130008207/cell_20 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
batch_chars_padded = batch_chars_ragged.to_tensor(default_value=-1)
batch_chars_sparse = batch_chars_ragged.to_sparse()
nrows, ncols = batch_chars_sparse.dense_shape.numpy()
elements = [['_' for i in range(ncols)] for j in range(nrows)]
for (row, col), value in zip(batch_chars_sparse.indices.numpy(), batch_chars_sparse.values.numpy()):
elements[row][col] = str(value)
value_lengths = []
for row in elements:
for value in row:
value_lengths.append(len(value))
max_width = max(value_lengths)
tf.strings.unicode_encode([[99, 97, 116], [100, 111, 103], [99, 111, 119]], output_encoding='UTF-8')
tf.strings.unicode_encode(batch_chars_ragged, output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_sparse(batch_chars_sparse), output_encoding='UTF-8') | code |
130008207/cell_26 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
batch_chars_padded = batch_chars_ragged.to_tensor(default_value=-1)
batch_chars_sparse = batch_chars_ragged.to_sparse()
nrows, ncols = batch_chars_sparse.dense_shape.numpy()
elements = [['_' for i in range(ncols)] for j in range(nrows)]
for (row, col), value in zip(batch_chars_sparse.indices.numpy(), batch_chars_sparse.values.numpy()):
elements[row][col] = str(value)
value_lengths = []
for row in elements:
for value in row:
value_lengths.append(len(value))
max_width = max(value_lengths)
tf.strings.unicode_encode([[99, 97, 116], [100, 111, 103], [99, 111, 119]], output_encoding='UTF-8')
tf.strings.unicode_encode(batch_chars_ragged, output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_sparse(batch_chars_sparse), output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_tensor(batch_chars_padded, padding=-1), output_encoding='UTF-8')
thanks = u'Thanks 😊'.encode('UTF-8')
num_bytes = tf.strings.length(thanks).numpy()
num_chars = tf.strings.length(thanks, unit='UTF8_CHAR').numpy()
tf.strings.substr(thanks, pos=7, len=1).numpy()
print(tf.strings.substr(thanks, pos=7, len=1, unit='UTF8_CHAR').numpy()) | code |
130008207/cell_2 | [
"text_plain_output_1.png"
] | import tensorflow as tf
import numpy as np | code |
130008207/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8') | code |
130008207/cell_19 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
batch_chars_padded = batch_chars_ragged.to_tensor(default_value=-1)
batch_chars_sparse = batch_chars_ragged.to_sparse()
nrows, ncols = batch_chars_sparse.dense_shape.numpy()
elements = [['_' for i in range(ncols)] for j in range(nrows)]
for (row, col), value in zip(batch_chars_sparse.indices.numpy(), batch_chars_sparse.values.numpy()):
elements[row][col] = str(value)
value_lengths = []
for row in elements:
for value in row:
value_lengths.append(len(value))
max_width = max(value_lengths)
tf.strings.unicode_encode([[99, 97, 116], [100, 111, 103], [99, 111, 119]], output_encoding='UTF-8')
tf.strings.unicode_encode(batch_chars_ragged, output_encoding='UTF-8') | code |
130008207/cell_7 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8 | code |
130008207/cell_18 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode([[99, 97, 116], [100, 111, 103], [99, 111, 119]], output_encoding='UTF-8') | code |
130008207/cell_28 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
batch_chars_padded = batch_chars_ragged.to_tensor(default_value=-1)
batch_chars_sparse = batch_chars_ragged.to_sparse()
nrows, ncols = batch_chars_sparse.dense_shape.numpy()
elements = [['_' for i in range(ncols)] for j in range(nrows)]
for (row, col), value in zip(batch_chars_sparse.indices.numpy(), batch_chars_sparse.values.numpy()):
elements[row][col] = str(value)
value_lengths = []
for row in elements:
for value in row:
value_lengths.append(len(value))
max_width = max(value_lengths)
tf.strings.unicode_encode([[99, 97, 116], [100, 111, 103], [99, 111, 119]], output_encoding='UTF-8')
tf.strings.unicode_encode(batch_chars_ragged, output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_sparse(batch_chars_sparse), output_encoding='UTF-8')
tf.strings.unicode_encode(tf.RaggedTensor.from_tensor(batch_chars_padded, padding=-1), output_encoding='UTF-8')
thanks = u'Thanks 😊'.encode('UTF-8')
num_bytes = tf.strings.length(thanks).numpy()
num_chars = tf.strings.length(thanks, unit='UTF8_CHAR').numpy()
tf.strings.substr(thanks, pos=7, len=1).numpy()
tf.strings.unicode_split(thanks, 'UTF-8').numpy() | code |
130008207/cell_8 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be | code |
130008207/cell_15 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
for sentence_chars in batch_chars_ragged.to_list():
print(sentence_chars) | code |
130008207/cell_16 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
batch_chars_padded = batch_chars_ragged.to_tensor(default_value=-1)
print(batch_chars_padded.numpy()) | code |
130008207/cell_17 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8')
tf.strings.unicode_transcode(text_utf8, input_encoding='UTF8', output_encoding='UTF-16-BE')
batch_utf8 = [s.encode('UTF-8') for s in [u'hÃllo', u'What is the weather tomorrow', u'Göödnight', u'😊']]
batch_chars_ragged = tf.strings.unicode_decode(batch_utf8, input_encoding='UTF-8')
batch_chars_padded = batch_chars_ragged.to_tensor(default_value=-1)
batch_chars_sparse = batch_chars_ragged.to_sparse()
nrows, ncols = batch_chars_sparse.dense_shape.numpy()
elements = [['_' for i in range(ncols)] for j in range(nrows)]
for (row, col), value in zip(batch_chars_sparse.indices.numpy(), batch_chars_sparse.values.numpy()):
elements[row][col] = str(value)
value_lengths = []
for row in elements:
for value in row:
value_lengths.append(len(value))
max_width = max(value_lengths)
print('[%s]' % '\n '.join(('[%s]' % ', '.join((value.rjust(max_width) for value in row)) for row in elements))) | code |
130008207/cell_12 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape
text_utf8 = tf.constant(u'语言处理')
text_utf8
text_utf16be = tf.constant(u'语言处理'.encode('UTF-16-BE'))
text_utf16be
text_chars = tf.constant([ord(char) for char in u'语言处理'])
text_chars
tf.strings.unicode_decode(text_utf8, input_encoding='UTF-8')
tf.strings.unicode_encode(text_chars, output_encoding='UTF-8') | code |
130008207/cell_5 | [
"text_plain_output_1.png"
] | import tensorflow as tf
tf.constant(u'Thanks 😊')
tf.constant([u"You're", u'welcome!']).shape | code |
18100689/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
image_array = np.asfarray(train_set.iloc[3, 1:]).reshape((28, 28))
X_train = train_set.iloc[:, 1:].values
y_train = train_set.label.values
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(criterion='entropy', n_estimators=10, max_depth=3, random_state=0)
classifier.fit(X_train, y_train)
classifier.score(X_train, y_train)
pred = classifier.predict(test_set)
pred | code |
18100689/cell_9 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
image_array = np.asfarray(train_set.iloc[3, 1:]).reshape((28, 28))
X_train = train_set.iloc[:, 1:].values
y_train = train_set.label.values
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(criterion='entropy', n_estimators=10, max_depth=3, random_state=0)
classifier.fit(X_train, y_train) | code |
18100689/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
image_array = np.asfarray(train_set.iloc[3, 1:]).reshape((28, 28))
X_train = train_set.iloc[:, 1:].values
y_train = train_set.label.values
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(criterion='entropy', n_estimators=10, max_depth=3, random_state=0)
classifier.fit(X_train, y_train)
classifier.score(X_train, y_train)
pred = classifier.predict(test_set)
pred
sub_lines = []
for i in range(0, len(pred)):
sub_lines.append([i + 1, pred[i]])
submission = pd.DataFrame(sub_lines, columns=['ImageId', 'Label'])
submission.to_csv('submission.csv', index=False)
submission.head() | code |
18100689/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
train_set.head() | code |
18100689/cell_10 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
image_array = np.asfarray(train_set.iloc[3, 1:]).reshape((28, 28))
X_train = train_set.iloc[:, 1:].values
y_train = train_set.label.values
from sklearn.ensemble import RandomForestClassifier
classifier = RandomForestClassifier(criterion='entropy', n_estimators=10, max_depth=3, random_state=0)
classifier.fit(X_train, y_train)
classifier.score(X_train, y_train) | code |
18100689/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
test_set.head() | code |
18100689/cell_5 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
train_set = pd.read_csv('../input/train.csv')
test_set = pd.read_csv('../input/test.csv')
print(train_set.iloc[3, 0])
image_array = np.asfarray(train_set.iloc[3, 1:]).reshape((28, 28))
plt.imshow(image_array, cmap='Greys', interpolation='None') | code |
90109598/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sb
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum()
data.corr()
cor = data.corr()
sb.heatmap(cor, xticklabels=cor.columns, yticklabels=cor.columns, annot=True) | code |
90109598/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.info() | code |
90109598/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique() | code |
90109598/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum() | code |
90109598/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.head() | code |
90109598/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sb
data = pd.read_csv('../input/insurance/insurance.csv')
data.nunique()
data.isnull().sum()
data.corr()
cor = data.corr()
sb.pairplot(data) | code |
90109598/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('../input/insurance/insurance.csv')
data.describe() | code |
328194/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
dfResults = pd.read_csv('../input/201608-SanFracisco-HydrofoilProTour.csv')
def doRating(numRaces, dfResults):
for raceCol in range(1, numRaces + 1):
dfResults['Rating'] = ts.rate(list(zip(dfResults['Rating'].T.values.tolist())), ranks=dfResults['R' + raceCol].T.values.tolist())
dfResults = doRating(16, dfResults)
dfResults['Rating'] | code |
328194/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
dfResults = pd.read_csv('../input/201608-SanFracisco-HydrofoilProTour.csv')
def doRating(numRaces, dfResults):
for raceCol in range(1, numRaces + 1):
dfResults['Rating'] = ts.rate(list(zip(dfResults['Rating'].T.values.tolist())), ranks=dfResults['R' + raceCol].T.values.tolist())
dfResults = doRating(16, dfResults) | code |
328194/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import trueskill as ts
dfResults = pd.read_csv('../input/201608-SanFracisco-HydrofoilProTour.csv')
def doRating(numRaces, dfResults):
for raceCol in range(1, numRaces + 1):
dfResults['Rating'] = ts.rate(list(zip(dfResults['Rating'].T.values.tolist())), ranks=dfResults['R' + raceCol].T.values.tolist())
dfResults = doRating(16, dfResults)
dfResults['Rating'] = ts.Rating()
ts.rate([ts.Rating(), ts.Rating()], ranks=[1, 0]) | code |
328194/cell_12 | [
"text_plain_output_1.png"
] | r1 | code |
33104348/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
titanic_df.describe()
titanic_df.head() | code |
33104348/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33104348/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
titanic_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
plt.rcParams['figure.figsize'] = (15, 10)
fig, axes = plt.subplots(nrows=2, ncols=2)
ax0, ax1, ax2, ax3 = axes.flatten()
lived = titanic_df.loc[titanic_df['Survived'] == 1, 'Age']
died = titanic_df.loc[titanic_df['Survived'] == 0, 'Age']
ax0.hist([lived, died], stacked=True)
ax0.legend(['Lived', 'Died'])
ax0.set_title('Age')
lived = titanic_df.loc[titanic_df['Survived'] == 1, 'Pclass']
died = titanic_df.loc[titanic_df['Survived'] == 0, 'Pclass']
ax1.hist([lived, died], stacked=True)
ax1.legend(['Lived', 'Died'])
ax1.set_title('Class (1 = Highest)')
lived = titanic_df.loc[titanic_df['Survived'] == 1, 'Fare']
died = titanic_df.loc[titanic_df['Survived'] == 0, 'Fare']
ax2.hist([lived, died], stacked=True)
ax2.legend(['Lived', 'Died'])
ax2.set_title('Fare')
lived = titanic_df.loc[titanic_df['Survived'] == 1, 'Sex']
died = titanic_df.loc[titanic_df['Survived'] == 0, 'Sex']
ax3.hist([lived, died], stacked=True)
ax3.legend(['Lived', 'Died'])
ax3.set_title('Gender') | code |
17113309/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
train_data.head() | code |
17113309/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras import layers
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.layers import AveragePooling2D, MaxPooling2D, Dropout
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator | code |
17113309/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
x_train = train_data.drop(labels='label', axis=1)
x_train = x_train / 255
test_data = test_data / 255
X_train = x_train.values.reshape(x_train.shape[0], 28, 28, 1)
X_test = test_data.values.reshape(test_data.shape[0], 28, 28, 1)
print(X_train.shape)
print(X_test.shape) | code |
17113309/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
x_train = train_data.drop(labels='label', axis=1)
x_train = x_train / 255
test_data = test_data / 255
X_train = x_train.values.reshape(x_train.shape[0], 28, 28, 1)
X_test = test_data.values.reshape(test_data.shape[0], 28, 28, 1)
def keras_model(input_shape):
X_input = Input(input_shape)
X = ZeroPadding2D((3, 3))(X_input)
X = Conv2D(48, (5, 5), strides=(1, 1), name='conv0')(X)
X = BatchNormalization(axis=3, name='bn0')(X)
X = Activation('relu')(X)
X = Conv2D(32, (3, 3), strides=(1, 1), name='conv1')(X)
X = BatchNormalization(axis=3, name='bn1')(X)
X = Activation('relu')(X)
X = Conv2D(16, (3, 3), strides=(1, 1), name='conv2')(X)
X = BatchNormalization(axis=3, name='bn2')(X)
X = Activation('relu')(X)
X = MaxPooling2D((2, 2), name='max_pool')(X)
X = Flatten()(X)
X = Dense(10, activation='sigmoid', name='fc1')(X)
model = Model(inputs=X_input, output=X, name='digit_model')
return model
model = keras_model(X_train_split[0].shape)
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])
datagen = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=10, zoom_range=0.1, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=False, vertical_flip=False)
datagen.fit(X_train)
batch_size = 16
epochs = 15
history = model.fit_generator(datagen.flow(X_train_split, y_train_split, batch_size=batch_size), epochs=epochs, validation_data=(X_test_split, y_test_split), verbose=2, steps_per_epoch=X_train_split.shape[0] // batch_size)
preds = model.evaluate(x=X_test_split, y=y_test_split)
print('Loss=', preds[0])
print('Accuracy=', preds[1]) | code |
17113309/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
print(os.listdir('../input')) | code |
17113309/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
x_train = train_data.drop(labels='label', axis=1)
print('number of training examples', x_train.shape[0])
print('number of test examples', test_data.shape[0])
print('number of pixels', x_train.shape[1])
print('number of rows and columns', int(np.sqrt(x_train.shape[1]))) | code |
17113309/cell_15 | [
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
def keras_model(input_shape):
X_input = Input(input_shape)
X = ZeroPadding2D((3, 3))(X_input)
X = Conv2D(48, (5, 5), strides=(1, 1), name='conv0')(X)
X = BatchNormalization(axis=3, name='bn0')(X)
X = Activation('relu')(X)
X = Conv2D(32, (3, 3), strides=(1, 1), name='conv1')(X)
X = BatchNormalization(axis=3, name='bn1')(X)
X = Activation('relu')(X)
X = Conv2D(16, (3, 3), strides=(1, 1), name='conv2')(X)
X = BatchNormalization(axis=3, name='bn2')(X)
X = Activation('relu')(X)
X = MaxPooling2D((2, 2), name='max_pool')(X)
X = Flatten()(X)
X = Dense(10, activation='sigmoid', name='fc1')(X)
model = Model(inputs=X_input, output=X, name='digit_model')
return model
model = keras_model(X_train_split[0].shape) | code |
17113309/cell_17 | [
"text_plain_output_1.png"
] | from keras.layers import AveragePooling2D, MaxPooling2D, Dropout
from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
from keras.models import Model
from keras.preprocessing.image import ImageDataGenerator
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
x_train = train_data.drop(labels='label', axis=1)
x_train = x_train / 255
test_data = test_data / 255
X_train = x_train.values.reshape(x_train.shape[0], 28, 28, 1)
X_test = test_data.values.reshape(test_data.shape[0], 28, 28, 1)
def keras_model(input_shape):
X_input = Input(input_shape)
X = ZeroPadding2D((3, 3))(X_input)
X = Conv2D(48, (5, 5), strides=(1, 1), name='conv0')(X)
X = BatchNormalization(axis=3, name='bn0')(X)
X = Activation('relu')(X)
X = Conv2D(32, (3, 3), strides=(1, 1), name='conv1')(X)
X = BatchNormalization(axis=3, name='bn1')(X)
X = Activation('relu')(X)
X = Conv2D(16, (3, 3), strides=(1, 1), name='conv2')(X)
X = BatchNormalization(axis=3, name='bn2')(X)
X = Activation('relu')(X)
X = MaxPooling2D((2, 2), name='max_pool')(X)
X = Flatten()(X)
X = Dense(10, activation='sigmoid', name='fc1')(X)
model = Model(inputs=X_input, output=X, name='digit_model')
return model
model = keras_model(X_train_split[0].shape)
model.compile(optimizer='Adam', loss='categorical_crossentropy', metrics=['accuracy'])
datagen = ImageDataGenerator(featurewise_center=False, samplewise_center=False, featurewise_std_normalization=False, samplewise_std_normalization=False, zca_whitening=False, rotation_range=10, zoom_range=0.1, width_shift_range=0.1, height_shift_range=0.1, horizontal_flip=False, vertical_flip=False)
datagen.fit(X_train)
batch_size = 16
epochs = 15
history = model.fit_generator(datagen.flow(X_train_split, y_train_split, batch_size=batch_size), epochs=epochs, validation_data=(X_test_split, y_test_split), verbose=2, steps_per_epoch=X_train_split.shape[0] // batch_size) | code |
17113309/cell_12 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
y_train = train_data['label']
from sklearn.preprocessing import OneHotEncoder
encoder = OneHotEncoder()
Y_train = encoder.fit_transform(y_train.values.reshape(-1, 1))
Y_train = Y_train.toarray() | code |
72072145/cell_8 | [
"text_plain_output_1.png"
] | from summarizer import Summarizer,TransformerSummarizer
body = '\n Scientists say they have discovered a new species of orangutans on Indonesia’s island of Sumatra.\nThe population differs in several ways from the two existing orangutan species found in Sumatra and the neighboring island of Borneo.\nThe orangutans were found inside North Sumatra’s Batang Toru forest, the science publication Current Biology reported.\nResearchers named the new species the Tapanuli orangutan. They say the animals are considered a new species because of genetic, skeletal and tooth differences.\nMichael Kruetzen is a geneticist with the University of Zurich who has studied the orangutans for several years. He said he was excited to be part of the unusual discovery of a new great ape in the present day. He noted that most great apes are currently considered endangered or severely endangered.\nGorillas, chimpanzees and bonobos also belong to the great ape species.\nOrangutan – which means person of the forest in the Indonesian and Malay languages - is the world’s biggest tree-living mammal. The orange-haired animals can move easily among the trees because their arms are longer than their legs. They live more lonely lives than other great apes, spending a lot of time sleeping and eating fruit in the forest.\nThe new study said fewer than 800 of the newly-described orangutans exist. Their low numbers make the group the most endangered of all the great ape species.\nThey live within an area covering about 1,000 square kilometers. The population is considered highly vulnerable. That is because the environment which they depend on is greatly threatened by development.\nResearchers say if steps are not taken quickly to reduce the current and future threats, the new species could become extinct “within our lifetime.”\nResearch into the new species began in 2013, when an orangutan protection group in Sumatra found an injured orangutan in an area far away from the other species. The adult male orangutan had been beaten by local villagers and died of his injuries. The complete skull was examined by researchers.\nAmong the physical differences of the new species are a notably smaller head and frizzier hair. The Tapanuli orangutans also have a different diet and are found only in higher forest areas.\nThere is no unified international system for recognizing new species. But to be considered, discovery claims at least require publication in a major scientific publication.\nRussell Mittermeier is head of the primate specialist group at the International Union for the Conservation of Nature. He called the finding a “remarkable discovery.” He said it puts responsibility on the Indonesian government to help the species survive.\nMatthew Nowak is one of the writers of the study. He told the Associated Press that there are three groups of the Tapanuli orangutans that are separated by non-protected land.He said forest land needs to connect the separated groups.\nIn addition, the writers of the study are recommending that plans for a hydropower center in the area be stopped by the government.\nIt also recommended that remaining forest in the Sumatran area where the orangutans live be protected.\nI’m Bryan Lynn.\n\n '
bert_model = Summarizer()
bert_summary = ''.join(bert_model(body, min_length=60))
print(bert_summary) | code |
72072145/cell_16 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from gensim.summarization.summarizer import summarize
from pysummarization.abstractabledoc.top_n_rank_abstractor import TopNRankAbstractor
from pysummarization.nlpbase.auto_abstractor import AutoAbstractor
from pysummarization.tokenizabledoc.simple_tokenizer import SimpleTokenizer
body = '\n Scientists say they have discovered a new species of orangutans on Indonesia’s island of Sumatra.\nThe population differs in several ways from the two existing orangutan species found in Sumatra and the neighboring island of Borneo.\nThe orangutans were found inside North Sumatra’s Batang Toru forest, the science publication Current Biology reported.\nResearchers named the new species the Tapanuli orangutan. They say the animals are considered a new species because of genetic, skeletal and tooth differences.\nMichael Kruetzen is a geneticist with the University of Zurich who has studied the orangutans for several years. He said he was excited to be part of the unusual discovery of a new great ape in the present day. He noted that most great apes are currently considered endangered or severely endangered.\nGorillas, chimpanzees and bonobos also belong to the great ape species.\nOrangutan – which means person of the forest in the Indonesian and Malay languages - is the world’s biggest tree-living mammal. The orange-haired animals can move easily among the trees because their arms are longer than their legs. They live more lonely lives than other great apes, spending a lot of time sleeping and eating fruit in the forest.\nThe new study said fewer than 800 of the newly-described orangutans exist. Their low numbers make the group the most endangered of all the great ape species.\nThey live within an area covering about 1,000 square kilometers. The population is considered highly vulnerable. That is because the environment which they depend on is greatly threatened by development.\nResearchers say if steps are not taken quickly to reduce the current and future threats, the new species could become extinct “within our lifetime.”\nResearch into the new species began in 2013, when an orangutan protection group in Sumatra found an injured orangutan in an area far away from the other species. The adult male orangutan had been beaten by local villagers and died of his injuries. The complete skull was examined by researchers.\nAmong the physical differences of the new species are a notably smaller head and frizzier hair. The Tapanuli orangutans also have a different diet and are found only in higher forest areas.\nThere is no unified international system for recognizing new species. But to be considered, discovery claims at least require publication in a major scientific publication.\nRussell Mittermeier is head of the primate specialist group at the International Union for the Conservation of Nature. He called the finding a “remarkable discovery.” He said it puts responsibility on the Indonesian government to help the species survive.\nMatthew Nowak is one of the writers of the study. He told the Associated Press that there are three groups of the Tapanuli orangutans that are separated by non-protected land.He said forest land needs to connect the separated groups.\nIn addition, the writers of the study are recommending that plans for a hydropower center in the area be stopped by the government.\nIt also recommended that remaining forest in the Sumatran area where the orangutans live be protected.\nI’m Bryan Lynn.\n\n '
from gensim.summarization.summarizer import summarize
from gensim.summarization import keywords
def summarize_text(text):
try:
summarized_text = summarize(text, word_count=150)
if summarized_text != '':
if summarized_text != text:
return summarized_text
else:
return ''
else:
return ''
except Exception as e:
return ''
if __name__ == '__main__':
sentence = body
from pysummarization.nlpbase.auto_abstractor import AutoAbstractor
from pysummarization.tokenizabledoc.simple_tokenizer import SimpleTokenizer
from pysummarization.abstractabledoc.top_n_rank_abstractor import TopNRankAbstractor
auto_abstractor = AutoAbstractor()
auto_abstractor.tokenizable_doc = SimpleTokenizer()
auto_abstractor.delimiter_list = ['.', '\n']
abstractable_doc = TopNRankAbstractor()
result_dict = auto_abstractor.summarize(body, abstractable_doc)
for sentence in result_dict['summarize_result']:
print(sentence) | code |
72072145/cell_3 | [
"text_plain_output_1.png"
] | !pip install bert-extractive-summarizer
!pip install transformers
!pip install spacy
!pip install gensim==3.8.0
!pip install pysummarization | code |
72072145/cell_14 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from gensim.summarization.summarizer import summarize
body = '\n Scientists say they have discovered a new species of orangutans on Indonesia’s island of Sumatra.\nThe population differs in several ways from the two existing orangutan species found in Sumatra and the neighboring island of Borneo.\nThe orangutans were found inside North Sumatra’s Batang Toru forest, the science publication Current Biology reported.\nResearchers named the new species the Tapanuli orangutan. They say the animals are considered a new species because of genetic, skeletal and tooth differences.\nMichael Kruetzen is a geneticist with the University of Zurich who has studied the orangutans for several years. He said he was excited to be part of the unusual discovery of a new great ape in the present day. He noted that most great apes are currently considered endangered or severely endangered.\nGorillas, chimpanzees and bonobos also belong to the great ape species.\nOrangutan – which means person of the forest in the Indonesian and Malay languages - is the world’s biggest tree-living mammal. The orange-haired animals can move easily among the trees because their arms are longer than their legs. They live more lonely lives than other great apes, spending a lot of time sleeping and eating fruit in the forest.\nThe new study said fewer than 800 of the newly-described orangutans exist. Their low numbers make the group the most endangered of all the great ape species.\nThey live within an area covering about 1,000 square kilometers. The population is considered highly vulnerable. That is because the environment which they depend on is greatly threatened by development.\nResearchers say if steps are not taken quickly to reduce the current and future threats, the new species could become extinct “within our lifetime.”\nResearch into the new species began in 2013, when an orangutan protection group in Sumatra found an injured orangutan in an area far away from the other species. The adult male orangutan had been beaten by local villagers and died of his injuries. The complete skull was examined by researchers.\nAmong the physical differences of the new species are a notably smaller head and frizzier hair. The Tapanuli orangutans also have a different diet and are found only in higher forest areas.\nThere is no unified international system for recognizing new species. But to be considered, discovery claims at least require publication in a major scientific publication.\nRussell Mittermeier is head of the primate specialist group at the International Union for the Conservation of Nature. He called the finding a “remarkable discovery.” He said it puts responsibility on the Indonesian government to help the species survive.\nMatthew Nowak is one of the writers of the study. He told the Associated Press that there are three groups of the Tapanuli orangutans that are separated by non-protected land.He said forest land needs to connect the separated groups.\nIn addition, the writers of the study are recommending that plans for a hydropower center in the area be stopped by the government.\nIt also recommended that remaining forest in the Sumatran area where the orangutans live be protected.\nI’m Bryan Lynn.\n\n '
from gensim.summarization.summarizer import summarize
from gensim.summarization import keywords
def summarize_text(text):
try:
summarized_text = summarize(text, word_count=150)
if summarized_text != '':
if summarized_text != text:
return summarized_text
else:
return ''
else:
return ''
except Exception as e:
print('Exception In summary-->')
print(str(e))
return ''
if __name__ == '__main__':
sentence = body
print(summarize_text(sentence)) | code |
72072145/cell_10 | [
"text_plain_output_1.png"
] | from summarizer import Summarizer,TransformerSummarizer
body = '\n Scientists say they have discovered a new species of orangutans on Indonesia’s island of Sumatra.\nThe population differs in several ways from the two existing orangutan species found in Sumatra and the neighboring island of Borneo.\nThe orangutans were found inside North Sumatra’s Batang Toru forest, the science publication Current Biology reported.\nResearchers named the new species the Tapanuli orangutan. They say the animals are considered a new species because of genetic, skeletal and tooth differences.\nMichael Kruetzen is a geneticist with the University of Zurich who has studied the orangutans for several years. He said he was excited to be part of the unusual discovery of a new great ape in the present day. He noted that most great apes are currently considered endangered or severely endangered.\nGorillas, chimpanzees and bonobos also belong to the great ape species.\nOrangutan – which means person of the forest in the Indonesian and Malay languages - is the world’s biggest tree-living mammal. The orange-haired animals can move easily among the trees because their arms are longer than their legs. They live more lonely lives than other great apes, spending a lot of time sleeping and eating fruit in the forest.\nThe new study said fewer than 800 of the newly-described orangutans exist. Their low numbers make the group the most endangered of all the great ape species.\nThey live within an area covering about 1,000 square kilometers. The population is considered highly vulnerable. That is because the environment which they depend on is greatly threatened by development.\nResearchers say if steps are not taken quickly to reduce the current and future threats, the new species could become extinct “within our lifetime.”\nResearch into the new species began in 2013, when an orangutan protection group in Sumatra found an injured orangutan in an area far away from the other species. The adult male orangutan had been beaten by local villagers and died of his injuries. The complete skull was examined by researchers.\nAmong the physical differences of the new species are a notably smaller head and frizzier hair. The Tapanuli orangutans also have a different diet and are found only in higher forest areas.\nThere is no unified international system for recognizing new species. But to be considered, discovery claims at least require publication in a major scientific publication.\nRussell Mittermeier is head of the primate specialist group at the International Union for the Conservation of Nature. He called the finding a “remarkable discovery.” He said it puts responsibility on the Indonesian government to help the species survive.\nMatthew Nowak is one of the writers of the study. He told the Associated Press that there are three groups of the Tapanuli orangutans that are separated by non-protected land.He said forest land needs to connect the separated groups.\nIn addition, the writers of the study are recommending that plans for a hydropower center in the area be stopped by the government.\nIt also recommended that remaining forest in the Sumatran area where the orangutans live be protected.\nI’m Bryan Lynn.\n\n '
GPT2_model = TransformerSummarizer(transformer_type='GPT2', transformer_model_key='gpt2-medium')
full = ''.join(GPT2_model(body, min_length=60))
print(full) | code |
72072145/cell_12 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from summarizer import Summarizer,TransformerSummarizer
body = '\n Scientists say they have discovered a new species of orangutans on Indonesia’s island of Sumatra.\nThe population differs in several ways from the two existing orangutan species found in Sumatra and the neighboring island of Borneo.\nThe orangutans were found inside North Sumatra’s Batang Toru forest, the science publication Current Biology reported.\nResearchers named the new species the Tapanuli orangutan. They say the animals are considered a new species because of genetic, skeletal and tooth differences.\nMichael Kruetzen is a geneticist with the University of Zurich who has studied the orangutans for several years. He said he was excited to be part of the unusual discovery of a new great ape in the present day. He noted that most great apes are currently considered endangered or severely endangered.\nGorillas, chimpanzees and bonobos also belong to the great ape species.\nOrangutan – which means person of the forest in the Indonesian and Malay languages - is the world’s biggest tree-living mammal. The orange-haired animals can move easily among the trees because their arms are longer than their legs. They live more lonely lives than other great apes, spending a lot of time sleeping and eating fruit in the forest.\nThe new study said fewer than 800 of the newly-described orangutans exist. Their low numbers make the group the most endangered of all the great ape species.\nThey live within an area covering about 1,000 square kilometers. The population is considered highly vulnerable. That is because the environment which they depend on is greatly threatened by development.\nResearchers say if steps are not taken quickly to reduce the current and future threats, the new species could become extinct “within our lifetime.”\nResearch into the new species began in 2013, when an orangutan protection group in Sumatra found an injured orangutan in an area far away from the other species. The adult male orangutan had been beaten by local villagers and died of his injuries. The complete skull was examined by researchers.\nAmong the physical differences of the new species are a notably smaller head and frizzier hair. The Tapanuli orangutans also have a different diet and are found only in higher forest areas.\nThere is no unified international system for recognizing new species. But to be considered, discovery claims at least require publication in a major scientific publication.\nRussell Mittermeier is head of the primate specialist group at the International Union for the Conservation of Nature. He called the finding a “remarkable discovery.” He said it puts responsibility on the Indonesian government to help the species survive.\nMatthew Nowak is one of the writers of the study. He told the Associated Press that there are three groups of the Tapanuli orangutans that are separated by non-protected land.He said forest land needs to connect the separated groups.\nIn addition, the writers of the study are recommending that plans for a hydropower center in the area be stopped by the government.\nIt also recommended that remaining forest in the Sumatran area where the orangutans live be protected.\nI’m Bryan Lynn.\n\n '
GPT2_model = TransformerSummarizer(transformer_type='GPT2', transformer_model_key='gpt2-medium')
full = ''.join(GPT2_model(body, min_length=60))
model = TransformerSummarizer(transformer_type='XLNet', transformer_model_key='xlnet-base-cased')
full = ''.join(model(body, min_length=60))
print(full) | code |
1008563/cell_21 | [
"text_plain_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(df['left'])
model = ExtraTreesClassifier()
model.fit(X, y)
feature_list = list(df.drop('left', 1).columns)
feature_importance_dict = dict(zip(feature_list, model.feature_importances_))
from sklearn.ensemble import ExtraTreesRegressor
model = ExtraTreesRegressor()
X = df.drop(['left', 'satisfaction_level'], axis=1)
y = df['satisfaction_level']
model.fit(X, y)
feature_list = list(df.drop(['left', 'satisfaction_level'], 1).columns)
feature_importance_dict = dict(zip(feature_list, model.feature_importances_))
df1 = df.copy()
group_name = list(range(20))
df1['last_evaluation'] = pd.cut(df1['last_evaluation'], 20, labels=group_name)
df1['average_montly_hours'] = pd.cut(df1['average_montly_hours'], 20, labels=group_name)
"\n{0: '(149.5, 160.2]', 1: '(256.5, 267.2]', 2: '(267.2, 277.9]', 3: '(213.7, 224.4]', 4: '(245.8, 256.5]', 5: '(138.8, 149.5]',\n 6: '(128.1, 138.8]', 7: '(299.3, 310]', 8: '(224.4, 235.1]', 9: '(277.9, 288.6]', 10: '(235.1, 245.8]'\n , 11: '(117.4, 128.1]', 12: '(288.6, 299.3]', 13: '(181.6, 192.3]', 14: '(160.2, 170.9]',\n 15: '(170.9, 181.6]', 16: '(192.3, 203]', 17: '(203, 213.7]', 18: '(106.7, 117.4]',\n 19: '(95.786, 106.7]'}\n "
sns.pointplot(df['number_project'], df['last_evaluation']) | code |
1008563/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import ExtraTreesClassifier
from sklearn.ensemble import ExtraTreesRegressor
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/HR_comma_sep.csv')
df.isnull().any()
df = df.rename(columns={'sales': 'job'})
X = np.array(df.drop('left', 1))
y = np.array(df['left'])
model = ExtraTreesClassifier()
model.fit(X, y)
feature_list = list(df.drop('left', 1).columns)
feature_importance_dict = dict(zip(feature_list, model.feature_importances_))
from sklearn.ensemble import ExtraTreesRegressor
model = ExtraTreesRegressor()
X = df.drop(['left', 'satisfaction_level'], axis=1)
y = df['satisfaction_level']
model.fit(X, y)
feature_list = list(df.drop(['left', 'satisfaction_level'], 1).columns)
feature_importance_dict = dict(zip(feature_list, model.feature_importances_))
plt.scatter(df['satisfaction_level'], df['average_montly_hours'])
plt.ylabel('average_montly_hours')
plt.xlabel('satisfaction_level') | code |
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