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16111583/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
import unittest
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
import pandas as pd
from collections import defaultdict
import time
import unittest
t = unittest.TestCase()
SPACE_DIMENSIONS = 2
class Points(np.ndarray):
"""ndarray sized (SPACE_DIMENSIONS,...) with named coordinates x,y"""
@staticmethod
def of(coords):
p = np.asarray(coords).view(Points)
assert p.shape[0] == SPACE_DIMENSIONS
return p
@property
def x(self):
return self[0]
@property
def y(self):
return self[1]
class Lines(np.ndarray):
"""ndarray shaped (3,...) with named line parameters a,b,c"""
@staticmethod
def of(abc):
lp = np.asarray(abc).view(Lines)
assert lp.shape[0] == 3
return lp
@property
def a(self):
return self[0]
@property
def b(self):
return self[1]
@property
def c(self):
return self[2]
def intersections(self, hyperplanes) -> Points:
"""
https://stackoverflow.com/a/20679579/2082707
answered Dec 19 '13 at 10:46 by rook
Adapted for numpy matrix operations by Subota
Intersection points of lines from the first set with hyperplanes from the second set.
Currently only 2D sapce supported, e.g. the second lanes is lines, too.
@hyperplanes parametrical equation coeffs. For 2D it is also Lines
@return array of intersection coordinates as Points, sized:
- SPACE_DIMENSIONS for intersection coordinates
- n1 for the number of lines passed in L1
- n2 for the number of lines passed in L2
"""
l1 = np.reshape(self, (*self.shape, 1))
l2 = hyperplanes
d = l1.a * l2.b - l1.b * l2.a
dx = l1.c * l2.b - l1.b * l2.c
dy = l1.a * l2.c - l1.c * l2.a
d[d == 0.0] = np.nan
x = dx / d
y = dy / d
return Points.of((x, y))
class LineSegments(np.ndarray):
"""Wrapper around ndarray((2,SPACE_DIMENSIONS)) to access endPoint1, endPoint2 and coordinates x,y by names"""
@staticmethod
def of(point_coords):
ls = np.asarray(point_coords).view(LineSegments)
assert ls.shape[0] == 2
assert ls.shape[1] == SPACE_DIMENSIONS
return ls
@property
def endPoint1(self):
return Points.of(self[0])
@property
def endPoint2(self):
return Points.of(self[1])
@property
def x(self):
return self[:, 0]
@property
def y(self):
return self[:, 1]
def length(self) -> np.array:
dif = self.endPoint1 - self.endPoint2
return np.sqrt(dif.x * dif.x + dif.y * dif.y).view(np.ndarray)
def lines(self) -> Lines:
"""
https://stackoverflow.com/a/20679579/2082707
answered Dec 19 '13 at 10:46 by rook
Adapted for numpy matrix operations by Subota
Calculates the line equation Ay + Bx - C = 0, given two points on a line.
Horizontal and vertical lines are Ok
@return returns an array of Lines parameters sized:
- 3 for the parameters A, B, and C
- n for the number of lines calculated
"""
p1, p2 = (self.endPoint1, self.endPoint2)
a = p1.y - p2.y
b = p2.x - p1.x
c = -(p1.x * p2.y - p2.x * p1.y)
return Lines.of((a, b, c))
def intersections(self, other) -> Points:
"""
Returns intersection points for line sets,
along with the true/false matrix for do intersections lie within the segments or not.
@other LineSegments to find intersections with. Sized:
- 2 for the endPoint1 and endPoint2
- SPACE_DIMENSIONS
- n1 for the number of segments in the first set
Generally speaking these must be hyper-planes in N-dimensional space
@return a tuple with two elements
0. boolean matrix sized(n1,n2), True the intersection to fall within the segments, False otherwise.
1. intersection Points sized (SPACE_DIMENSIONS, n1, n2)
"""
s1, s2 = (self, other)
l1, l2 = (self.lines(), other.lines())
il = l1.intersections(l2)
s1 = s1.reshape((2, SPACE_DIMENSIONS, -1, 1))
s1p1, s1p2 = (s1.endPoint1, s1.endPoint2)
s2p1, s2p2 = (s2.endPoint1, s2.endPoint2)
ROUNDING_THRESHOLD = np.array(1e-10)
which_intersect = (il.x <= np.maximum(s1p1.x, s1p2.x) + ROUNDING_THRESHOLD) & (il.x >= np.minimum(s1p1.x, s1p2.x) - ROUNDING_THRESHOLD) & (il.y <= np.maximum(s1p1.y, s1p2.y) + ROUNDING_THRESHOLD) & (il.y >= np.minimum(s1p1.y, s1p2.y) - ROUNDING_THRESHOLD) & (il.x <= np.maximum(s2p1.x, s2p2.x) + ROUNDING_THRESHOLD) & (il.x >= np.minimum(s2p1.x, s2p2.x) - ROUNDING_THRESHOLD) & (il.y <= np.maximum(s2p1.y, s2p2.y) + ROUNDING_THRESHOLD) & (il.y >= np.minimum(s2p1.y, s2p2.y) - ROUNDING_THRESHOLD)
return (which_intersect, il)
t.assertTrue(np.allclose(LineSegments.of([[[-1.0], [-1]], [[1], [1]]]).lines().flat, np.array([-2, 2, 0])))
t.assertTrue(np.allclose(LineSegments.of([[[0.0], [-1]], [[0], [1]]]).lines().flat, np.array([-2, 0, 0])))
t.assertTrue(np.allclose(LineSegments.of([[[3.0], [1]], [[-4], [1]]]).lines().flat, np.array([0, -7, -7])))
t.assertEqual(LineSegments.of([Points.of([0, 0]), Points.of([3, 4])]).length(), 5)
def demo_intersect_lines():
seg1 = LineSegments.of( st.uniform.rvs(size=(2,SPACE_DIMENSIONS, 2), random_state=19) )
seg2 = LineSegments.of( st.uniform.rvs(size=(2,SPACE_DIMENSIONS, 3), random_state=15)+1 )
l1, l2 = seg1.lines(), seg2.lines()
i = l1.intersections(l2)
plt.plot(seg1.x, seg1.y, '-', c='green')
plt.plot(seg2.x, seg2.y, '-', c='blue')
plt.plot(i.x, i.y, '+', c='red', markersize=20)
plt.title('Extended Line Intersections')
plt.axis('off')
def demo_intersect_segments():
seg1 = LineSegments.of( st.uniform.rvs(size=(2,SPACE_DIMENSIONS, 4), random_state=1) )
seg2 = LineSegments.of( st.uniform.rvs(size=(2,SPACE_DIMENSIONS, 5), random_state=2) )
plt.plot(seg1.x, seg1.y, '-', c='black')
plt.plot(seg2.x, seg2.y, '-', c='lightgrey')
w, i = seg1.intersections(seg2)
plt.plot(i.x[w], i.y[w], '+', c='red', markersize=20)
plt.title('Segment Intersections')
plt.axis('off')
f, ax = plt.subplots(ncols=2)
f.set_size_inches(12,4)
plt.sca(ax[0])
demo_intersect_lines()
plt.sca(ax[1])
demo_intersect_segments()
SEGMENT_ENDPOINTS = 2
NUM_WALLS = 7
SOURCE = Points.of((0.0, 0.0))
DETECTOR = LineSegments.of(((8.0, -1), (8.0, +1)))
walls = LineSegments.of(np.zeros((SEGMENT_ENDPOINTS, SPACE_DIMENSIONS, NUM_WALLS)))
SLIT_WIDTH, SLITS_APART = (0.05, 0.5)
walls[:, :, 1] = ((6.0, +1.0), (6.0, +SLITS_APART / 2 + SLIT_WIDTH))
walls[:, :, 2] = ((6.0, -SLITS_APART / 2), (6.0, +SLITS_APART / 2))
walls[:, :, 3] = ((6.0, -1.0), (6.0, -SLITS_APART / 2 - SLIT_WIDTH))
walls[:, :, 4] = ((-1, -1), (-1, +1))
walls[:, :, 5] = ((-1, +1), (+8.1, +1))
walls[:, :, 6] = ((+8.1, +1), (+8.1, -1))
walls[:, :, 0] = ((+8.1, -1), (-1, -1))
def plot_experimet_setup(walls, detector, source):
plt.plot(*source, 'o', color='red', label='Source')
wall_lines = plt.plot(walls.x, walls.y, '-', c='black', linewidth=1)
wall_lines[1].set_label('Walls')
plt.plot(detector.x, detector.y, '-', c='green', linewidth=4, label='Detector')
plt.gcf().set_size_inches(12, 5)
plt.legend(loc='upper center')
plot_experimet_setup(walls, DETECTOR, SOURCE) | code |
16111583/cell_10 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import scipy.stats as st
import time
import unittest
import numpy as np
import scipy.stats as st
import matplotlib.pyplot as plt
import pandas as pd
from collections import defaultdict
import time
import unittest
t = unittest.TestCase()
SPACE_DIMENSIONS = 2
class Points(np.ndarray):
"""ndarray sized (SPACE_DIMENSIONS,...) with named coordinates x,y"""
@staticmethod
def of(coords):
p = np.asarray(coords).view(Points)
assert p.shape[0] == SPACE_DIMENSIONS
return p
@property
def x(self):
return self[0]
@property
def y(self):
return self[1]
class Lines(np.ndarray):
"""ndarray shaped (3,...) with named line parameters a,b,c"""
@staticmethod
def of(abc):
lp = np.asarray(abc).view(Lines)
assert lp.shape[0] == 3
return lp
@property
def a(self):
return self[0]
@property
def b(self):
return self[1]
@property
def c(self):
return self[2]
def intersections(self, hyperplanes) -> Points:
"""
https://stackoverflow.com/a/20679579/2082707
answered Dec 19 '13 at 10:46 by rook
Adapted for numpy matrix operations by Subota
Intersection points of lines from the first set with hyperplanes from the second set.
Currently only 2D sapce supported, e.g. the second lanes is lines, too.
@hyperplanes parametrical equation coeffs. For 2D it is also Lines
@return array of intersection coordinates as Points, sized:
- SPACE_DIMENSIONS for intersection coordinates
- n1 for the number of lines passed in L1
- n2 for the number of lines passed in L2
"""
l1 = np.reshape(self, (*self.shape, 1))
l2 = hyperplanes
d = l1.a * l2.b - l1.b * l2.a
dx = l1.c * l2.b - l1.b * l2.c
dy = l1.a * l2.c - l1.c * l2.a
d[d == 0.0] = np.nan
x = dx / d
y = dy / d
return Points.of((x, y))
class LineSegments(np.ndarray):
"""Wrapper around ndarray((2,SPACE_DIMENSIONS)) to access endPoint1, endPoint2 and coordinates x,y by names"""
@staticmethod
def of(point_coords):
ls = np.asarray(point_coords).view(LineSegments)
assert ls.shape[0] == 2
assert ls.shape[1] == SPACE_DIMENSIONS
return ls
@property
def endPoint1(self):
return Points.of(self[0])
@property
def endPoint2(self):
return Points.of(self[1])
@property
def x(self):
return self[:, 0]
@property
def y(self):
return self[:, 1]
def length(self) -> np.array:
dif = self.endPoint1 - self.endPoint2
return np.sqrt(dif.x * dif.x + dif.y * dif.y).view(np.ndarray)
def lines(self) -> Lines:
"""
https://stackoverflow.com/a/20679579/2082707
answered Dec 19 '13 at 10:46 by rook
Adapted for numpy matrix operations by Subota
Calculates the line equation Ay + Bx - C = 0, given two points on a line.
Horizontal and vertical lines are Ok
@return returns an array of Lines parameters sized:
- 3 for the parameters A, B, and C
- n for the number of lines calculated
"""
p1, p2 = (self.endPoint1, self.endPoint2)
a = p1.y - p2.y
b = p2.x - p1.x
c = -(p1.x * p2.y - p2.x * p1.y)
return Lines.of((a, b, c))
def intersections(self, other) -> Points:
"""
Returns intersection points for line sets,
along with the true/false matrix for do intersections lie within the segments or not.
@other LineSegments to find intersections with. Sized:
- 2 for the endPoint1 and endPoint2
- SPACE_DIMENSIONS
- n1 for the number of segments in the first set
Generally speaking these must be hyper-planes in N-dimensional space
@return a tuple with two elements
0. boolean matrix sized(n1,n2), True the intersection to fall within the segments, False otherwise.
1. intersection Points sized (SPACE_DIMENSIONS, n1, n2)
"""
s1, s2 = (self, other)
l1, l2 = (self.lines(), other.lines())
il = l1.intersections(l2)
s1 = s1.reshape((2, SPACE_DIMENSIONS, -1, 1))
s1p1, s1p2 = (s1.endPoint1, s1.endPoint2)
s2p1, s2p2 = (s2.endPoint1, s2.endPoint2)
ROUNDING_THRESHOLD = np.array(1e-10)
which_intersect = (il.x <= np.maximum(s1p1.x, s1p2.x) + ROUNDING_THRESHOLD) & (il.x >= np.minimum(s1p1.x, s1p2.x) - ROUNDING_THRESHOLD) & (il.y <= np.maximum(s1p1.y, s1p2.y) + ROUNDING_THRESHOLD) & (il.y >= np.minimum(s1p1.y, s1p2.y) - ROUNDING_THRESHOLD) & (il.x <= np.maximum(s2p1.x, s2p2.x) + ROUNDING_THRESHOLD) & (il.x >= np.minimum(s2p1.x, s2p2.x) - ROUNDING_THRESHOLD) & (il.y <= np.maximum(s2p1.y, s2p2.y) + ROUNDING_THRESHOLD) & (il.y >= np.minimum(s2p1.y, s2p2.y) - ROUNDING_THRESHOLD)
return (which_intersect, il)
t.assertTrue(np.allclose(LineSegments.of([[[-1.0], [-1]], [[1], [1]]]).lines().flat, np.array([-2, 2, 0])))
t.assertTrue(np.allclose(LineSegments.of([[[0.0], [-1]], [[0], [1]]]).lines().flat, np.array([-2, 0, 0])))
t.assertTrue(np.allclose(LineSegments.of([[[3.0], [1]], [[-4], [1]]]).lines().flat, np.array([0, -7, -7])))
t.assertEqual(LineSegments.of([Points.of([0, 0]), Points.of([3, 4])]).length(), 5)
def demo_intersect_lines():
seg1 = LineSegments.of( st.uniform.rvs(size=(2,SPACE_DIMENSIONS, 2), random_state=19) )
seg2 = LineSegments.of( st.uniform.rvs(size=(2,SPACE_DIMENSIONS, 3), random_state=15)+1 )
l1, l2 = seg1.lines(), seg2.lines()
i = l1.intersections(l2)
plt.plot(seg1.x, seg1.y, '-', c='green')
plt.plot(seg2.x, seg2.y, '-', c='blue')
plt.plot(i.x, i.y, '+', c='red', markersize=20)
plt.title('Extended Line Intersections')
plt.axis('off')
def demo_intersect_segments():
seg1 = LineSegments.of( st.uniform.rvs(size=(2,SPACE_DIMENSIONS, 4), random_state=1) )
seg2 = LineSegments.of( st.uniform.rvs(size=(2,SPACE_DIMENSIONS, 5), random_state=2) )
plt.plot(seg1.x, seg1.y, '-', c='black')
plt.plot(seg2.x, seg2.y, '-', c='lightgrey')
w, i = seg1.intersections(seg2)
plt.plot(i.x[w], i.y[w], '+', c='red', markersize=20)
plt.title('Segment Intersections')
plt.axis('off')
f, ax = plt.subplots(ncols=2)
f.set_size_inches(12,4)
plt.sca(ax[0])
demo_intersect_lines()
plt.sca(ax[1])
demo_intersect_segments()
SEGMENT_ENDPOINTS = 2
NUM_WALLS = 7
SOURCE = Points.of( (0.,0.) )
DETECTOR = LineSegments.of( ((8.,-1), (8.,+1)) )
walls = LineSegments.of( np.zeros((SEGMENT_ENDPOINTS,SPACE_DIMENSIONS, NUM_WALLS)) )
SLIT_WIDTH, SLITS_APART = 0.05, 0.5
# The wall with slits
# above the slits
walls[:,:,1] = ( (6.,+1.), (6.,+SLITS_APART/2+SLIT_WIDTH) )
# between the slits
walls[:,:,2] = ( (6.,-SLITS_APART/2), (6.,+SLITS_APART/2) )
# below the slits
walls[:,:,3] = ( (6.,-1.), (6.,-SLITS_APART/2-SLIT_WIDTH) )
# square box
walls[:,:,4] = ( (-1,-1), (-1,+1)) # left wall
walls[:,:,5] = ( (-1,+1), (+8.1,+1)) # top
walls[:,:,6] = ( (+8.1,+1), (+8.1,-1)) # right
walls[:,:,0] = ( (+8.1,-1), (-1,-1)) # bottom
def plot_experimet_setup(walls, detector, source):
plt.plot(*source,'o', color='red', label='Source')
wall_lines = plt.plot(walls.x, walls.y, '-', c='black', linewidth=1);
wall_lines[1].set_label('Walls')
plt.plot(detector.x, detector.y, '-', c='green', linewidth=4, label='Detector');
plt.gcf().set_size_inches(12,5)
plt.legend(loc = 'upper center');
plot_experimet_setup(walls, DETECTOR, SOURCE)
detections = []
np.random.seed(1254785)
MIN_STEPS_TO_DETECTION = 2
BATCH_SIZE = 50000
def shifter_uniform_destination(r0: Points):
"""Shift is so that a photon arrives to a uniformly picked location in the test setup area, regardkess current position"""
target_x = st.uniform(loc=-1, scale=1 + 6.0).rvs(r0.shape[1])
target_y = st.uniform(loc=-1, scale=1 + 1.0).rvs(r0.shape[1])
return np.array([target_x, target_y]) - r0
photons = Points.of(np.zeros((SPACE_DIMENSIONS, BATCH_SIZE)))
lengths = np.zeros(BATCH_SIZE)
steps = np.zeros(BATCH_SIZE, dtype='B')
start = time.monotonic()
last_reported = len(detections)
epoch = 0
while len(detections) < 1000000:
epoch += 1
if last_reported <= len(detections) - 50000:
last_reported = round(len(detections) / 1000) * 1000
print(len(detections), end=', ')
steps += 1
randomInBox = Points.of(st.uniform().rvs(photons.shape))
randomInBox.x[...] *= 9
randomInBox.x[...] -= 1
randomInBox.y[...] *= 2
randomInBox.y[...] -= 1
randomInDetector = Points.of(st.uniform().rvs(photons.shape))
randomInDetector.x[...] = 8
randomInDetector.y[...] *= 2
randomInDetector.y[...] -= 1
newLoc = np.where(steps < MIN_STEPS_TO_DETECTION, randomInBox, randomInDetector)
moves = LineSegments.of((photons, newLoc))
lengths += moves.length()
photons = moves.endPoint2
colliders, _ = moves.intersections(walls)
colliders = np.logical_or.reduce(colliders, axis=1)
photons[:, colliders] = 0
steps[colliders] = 0
lengths[colliders] = 0
detected = steps >= MIN_STEPS_TO_DETECTION
for i in np.where(detected)[0]:
detections += [(*photons[:, i], lengths[i])]
photons[:, detected] = 0
steps[detected] = 0
lengths[detected] = 0
print('Time total: %.1f sec' % (time.monotonic() - start)) | code |
50243774/cell_13 | [
"text_plain_output_1.png"
] | from mmdet.apis import init_detector, inference_detector
from tqdm import tqdm
import mmcv
import os
import pandas as pd
import torch
def format_prediction_string(boxes, scores):
pred_strings = []
for j in zip(scores, boxes):
pred_strings.append('{0:.4f} {1} {2} {3} {4}'.format(j[0], j[1][0], j[1][1], j[1][2], j[1][3]))
return ' '.join(pred_strings)
CONFIG_FILE = './config.py'
CHECKPOINT_PATH = './model.pth'
TEST_IMG_DIR = '../input/global-wheat-detection/test'
import torch
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
config = mmcv.Config.fromfile(CONFIG_FILE)
config.model.pretrained = None
config.model.neck.rfp_backbone.pretrained = None
if False:
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
config.data.test.pipeline = [dict(type='LoadImageFromFile'), dict(type='MultiScaleFlipAug', img_scale=(1024, 1024), flip=False, transforms=[dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img'])])]
model = init_detector(config, CHECKPOINT_PATH, device=device)
model.eval()
from tqdm import tqdm
import os
results = []
with torch.no_grad():
for img_name in tqdm(os.listdir(TEST_IMG_DIR)):
img_pth = os.path.join(TEST_IMG_DIR, img_name)
result = inference_detector(model, img_pth)
boxes = result[0][:, :4]
scores = result[0][:, 4]
if len(boxes) > 0:
boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
result = {'image_id': img_name[:-4], 'PredictionString': format_prediction_string(boxes, scores)}
results.append(result)
import pandas as pd
test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
test_df.head() | code |
50243774/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | ! pip install ../input/mmdetectionv260/addict-2.4.0-py3-none-any.whl
! pip install ../input/mmdetectionv260/mmcv_full-latesttorch1.6.0cu102-cp37-cp37m-manylinux1_x86_64.whl
! pip install ../input/mmdetectionv260/mmpycocotools-12.0.3-cp37-cp37m-linux_x86_64.whl
! pip install ../input/mmdetection-package/mmdet-2.7.0-py3-none-any.whl | code |
50243774/cell_11 | [
"text_plain_output_1.png"
] | from mmdet.apis import init_detector, inference_detector
import mmcv
import torch
CONFIG_FILE = './config.py'
CHECKPOINT_PATH = './model.pth'
TEST_IMG_DIR = '../input/global-wheat-detection/test'
import torch
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
config = mmcv.Config.fromfile(CONFIG_FILE)
config.model.pretrained = None
config.model.neck.rfp_backbone.pretrained = None
if False:
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
config.data.test.pipeline = [dict(type='LoadImageFromFile'), dict(type='MultiScaleFlipAug', img_scale=(1024, 1024), flip=False, transforms=[dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img'])])]
model = init_detector(config, CHECKPOINT_PATH, device=device)
model.eval() | code |
50243774/cell_3 | [
"text_html_output_1.png"
] | ! pip install ../input/mmdetection-package/torch-1.6.0-cp37-cp37m-linux_x86_64.whl | code |
50243774/cell_12 | [
"text_plain_output_1.png"
] | from mmdet.apis import init_detector, inference_detector
from tqdm import tqdm
import mmcv
import os
import torch
def format_prediction_string(boxes, scores):
pred_strings = []
for j in zip(scores, boxes):
pred_strings.append('{0:.4f} {1} {2} {3} {4}'.format(j[0], j[1][0], j[1][1], j[1][2], j[1][3]))
return ' '.join(pred_strings)
CONFIG_FILE = './config.py'
CHECKPOINT_PATH = './model.pth'
TEST_IMG_DIR = '../input/global-wheat-detection/test'
import torch
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
config = mmcv.Config.fromfile(CONFIG_FILE)
config.model.pretrained = None
config.model.neck.rfp_backbone.pretrained = None
if False:
img_norm_cfg = dict(mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
config.data.test.pipeline = [dict(type='LoadImageFromFile'), dict(type='MultiScaleFlipAug', img_scale=(1024, 1024), flip=False, transforms=[dict(type='Resize', keep_ratio=True), dict(type='RandomFlip'), dict(type='Normalize', **img_norm_cfg), dict(type='Pad', size_divisor=32), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img'])])]
model = init_detector(config, CHECKPOINT_PATH, device=device)
model.eval()
from tqdm import tqdm
import os
results = []
with torch.no_grad():
for img_name in tqdm(os.listdir(TEST_IMG_DIR)):
img_pth = os.path.join(TEST_IMG_DIR, img_name)
result = inference_detector(model, img_pth)
boxes = result[0][:, :4]
scores = result[0][:, 4]
if len(boxes) > 0:
boxes[:, 2] = boxes[:, 2] - boxes[:, 0]
boxes[:, 3] = boxes[:, 3] - boxes[:, 1]
result = {'image_id': img_name[:-4], 'PredictionString': format_prediction_string(boxes, scores)}
results.append(result) | code |
73059955/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]
plt.figure(figsize=(20,10))
ax= sns.violinplot(data=train_data[cont_features],inner=None, palette="viridis")
plt.title('Continuous features distribution');
train_data.var()
train_data.std()
cat = train_data.select_dtypes(include='object').columns.tolist()
idx = 0
f, axes = plt.subplots(5, 2, sharex=True, figsize=(12, 14))
plt.suptitle('Categorical features distribution', size=16, y=0.94)
for row in range(5):
for col in range(2):
data = train_data[cat[idx]].value_counts()
sns.barplot(x=data.index, y=data.values, palette='viridis', ax=axes[row, col])
axes[row, col].set_title(cat[idx])
idx += 1 | code |
73059955/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
train_data.head(3) | code |
73059955/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]
plt.figure(figsize=(20,10))
ax= sns.violinplot(data=train_data[cont_features],inner=None, palette="viridis")
plt.title('Continuous features distribution');
train_data.var()
train_data.std()
cat= train_data.select_dtypes(include='object').columns.tolist()
idx = 0
f, axes = plt.subplots(5, 2, sharex=True, figsize=(12,14))
plt.suptitle('Categorical features distribution', size=16, y=(0.94))
for row in range(5):
for col in range(2):
data = train_data[cat[idx]].value_counts()
sns.barplot(x = data.index, y = data.values, palette='viridis', ax=axes[row, col])
axes[row,col].set_title(cat[idx])
idx += 1
corrMatrix = train_data.corr(method='pearson', min_periods=1)
corrMatrix
plt.figure(figsize=(25, 20))
ax = sns.heatmap(corrMatrix, cmap='viridis', annot=True) | code |
73059955/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import os, glob
import pandas as pd
import numpy as np
from scipy import stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='whitegrid')
import warnings
warnings.filterwarnings('ignore')
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler, Normalizer, MaxAbsScaler
from sklearn.preprocessing import StandardScaler, PowerTransformer, QuantileTransformer, LabelEncoder, OneHotEncoder, OrdinalEncoder
from xgboost import XGBRegressor
print('set up complete') | code |
73059955/cell_29 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]
plt.figure(figsize=(20,10))
ax= sns.violinplot(data=train_data[cont_features],inner=None, palette="viridis")
plt.title('Continuous features distribution');
train_data.var()
train_data.std()
cat= train_data.select_dtypes(include='object').columns.tolist()
idx = 0
f, axes = plt.subplots(5, 2, sharex=True, figsize=(12,14))
plt.suptitle('Categorical features distribution', size=16, y=(0.94))
for row in range(5):
for col in range(2):
data = train_data[cat[idx]].value_counts()
sns.barplot(x = data.index, y = data.values, palette='viridis', ax=axes[row, col])
axes[row,col].set_title(cat[idx])
idx += 1
corrMatrix = train_data.corr(method='pearson', min_periods=1)
corrMatrix
#heatmap
plt.figure(figsize=(25,20))
ax = sns.heatmap(corrMatrix, cmap="viridis", annot=True)
plt.figure(figsize=(12, 5))
ax = sns.boxplot(train_data['target'], orient='h')
ax.set_title('Target variable boxplot') | code |
73059955/cell_2 | [
"image_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73059955/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
print('Info about train data: ')
train_data.info() | code |
73059955/cell_18 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]
train_data.var()
train_data.std() | code |
73059955/cell_28 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]
plt.figure(figsize=(20,10))
ax= sns.violinplot(data=train_data[cont_features],inner=None, palette="viridis")
plt.title('Continuous features distribution');
train_data.var()
train_data.std()
cat= train_data.select_dtypes(include='object').columns.tolist()
idx = 0
f, axes = plt.subplots(5, 2, sharex=True, figsize=(12,14))
plt.suptitle('Categorical features distribution', size=16, y=(0.94))
for row in range(5):
for col in range(2):
data = train_data[cat[idx]].value_counts()
sns.barplot(x = data.index, y = data.values, palette='viridis', ax=axes[row, col])
axes[row,col].set_title(cat[idx])
idx += 1
corrMatrix = train_data.corr(method='pearson', min_periods=1)
corrMatrix
#heatmap
plt.figure(figsize=(25,20))
ax = sns.heatmap(corrMatrix, cmap="viridis", annot=True)
plt.figure(figsize=(12, 5))
sns.distplot(train_data['target'], color='maroon', kde=True, bins=120, label='target')
plt.title('target values Distribution ') | code |
73059955/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]
plt.figure(figsize=(20, 10))
ax = sns.violinplot(data=train_data[cont_features], inner=None, palette='viridis')
plt.title('Continuous features distribution') | code |
73059955/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]
train_data.var() | code |
73059955/cell_24 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]
plt.figure(figsize=(20,10))
ax= sns.violinplot(data=train_data[cont_features],inner=None, palette="viridis")
plt.title('Continuous features distribution');
train_data.var()
train_data.std()
cat= train_data.select_dtypes(include='object').columns.tolist()
idx = 0
f, axes = plt.subplots(5, 2, sharex=True, figsize=(12,14))
plt.suptitle('Categorical features distribution', size=16, y=(0.94))
for row in range(5):
for col in range(2):
data = train_data[cat[idx]].value_counts()
sns.barplot(x = data.index, y = data.values, palette='viridis', ax=axes[row, col])
axes[row,col].set_title(cat[idx])
idx += 1
corrMatrix = train_data.corr(method='pearson', min_periods=1)
corrMatrix | code |
73059955/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]
train_data.describe().T.style.bar().background_gradient(cmap='viridis') | code |
73059955/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
print(f'Number of rows: {train_data.shape[0]}; Number of columns: {train_data.shape[1]}; No of missing values: {sum(train_data.isna().sum())}') | code |
73059955/cell_27 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]
plt.figure(figsize=(20,10))
ax= sns.violinplot(data=train_data[cont_features],inner=None, palette="viridis")
plt.title('Continuous features distribution');
train_data.var()
train_data.std()
cat= train_data.select_dtypes(include='object').columns.tolist()
idx = 0
f, axes = plt.subplots(5, 2, sharex=True, figsize=(12,14))
plt.suptitle('Categorical features distribution', size=16, y=(0.94))
for row in range(5):
for col in range(2):
data = train_data[cat[idx]].value_counts()
sns.barplot(x = data.index, y = data.values, palette='viridis', ax=axes[row, col])
axes[row,col].set_title(cat[idx])
idx += 1
corrMatrix = train_data.corr(method='pearson', min_periods=1)
corrMatrix
print('target column basic statistics:')
train_data['target'].describe() | code |
73059955/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data = pd.read_csv('../input/30-days-of-ml/train.csv')
test_data = pd.read_csv('../input/30-days-of-ml/test.csv')
sample = pd.read_csv('../input/30-days-of-ml/sample_submission.csv', index_col='id')
cat_features = [feature for feature in train_data.columns if 'cat' in feature]
cont_features = [feature for feature in train_data.columns if 'cont' in feature]
print(f'categorical features are : {cat_features}; numerical features are : {cont_features}') | code |
50233728/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = sns.diverging_palette(220, 10, as_cmap=True)
data = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
plt.figure(figsize=(15,6))
quality_count = data['quality'].value_counts().sort_values(ascending=False).to_frame()
quality_count = quality_count.rename(columns={'quality':'Count'})
ax = sns.barplot(x=quality_count.index, y='Count', data=quality_count, palette="ch:.25")
for p in ax.patches:
ax.annotate(format(p.get_height(), '.1f'),
(p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='center',
xytext = (0,9),
textcoords='offset points')
plt.xlabel("Quality", fontsize=15)
plt.ylabel("Count", fontsize=15)
plt.title("Bar plot of Quality", fontsize=20)
plt.show()
table_nan = data.isna().sum().to_frame().style.background_gradient(cmap=cmap)
table_nan
feature_desc = ['most acids involved with wine or fixed or nonvolatile (do not evaporate readily)', 'he amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste', 'found in small quantities, citric acid can add freshness and flavor to wines', "the amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet", 'the amount of salt in the wine', 'the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine', 'amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine', 'the density of water is close to that of water depending on the percent alcohol and sugar content', 'describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale', 'a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant', '-', 'score between 0 and 10']
feature_desc = pd.DataFrame(feature_desc, columns=['Description'], index=data.columns)
data_desc = data.describe().T
data_description = pd.concat([feature_desc, data_desc], axis=1)
f, ax = plt.subplots(3, 4, figsize=(25,15))
sns.despine(left=True)
sns.boxplot(data['fixed acidity'], ax=ax[0,0])
sns.boxplot(data['volatile acidity'], ax=ax[0,1])
sns.boxplot(data['citric acid'], ax=ax[0,2])
sns.boxplot(data['residual sugar'], ax=ax[0,3])
sns.boxplot(data['chlorides'], ax=ax[1,0])
sns.boxplot(data['density'], ax=ax[1,1])
sns.boxplot(data['pH'], ax=ax[1,2])
sns.boxplot(data['sulphates'], ax=ax[1,3])
sns.boxplot(data['alcohol'], ax=ax[2,0])
sns.boxplot(data['total sulfur dioxide'], ax=ax[2,1])
sns.boxplot(data['free sulfur dioxide'], ax=ax[2,2])
sns.boxplot(data['quality'], ax=ax[2,3])
plt.show()
density_mean = np.mean(data['density'])
density_median = np.median(data['density'])
density_mode = data['density'].mode()[0]
q1 = data['density'].quantile(0.25)
q3 = data['density'].quantile(0.75)
density_IQR = q3 - q1
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw={'height_ratios': (0.2, 1)})
sns.boxplot(data['density'], ax=ax_box)
ax_box.axvline(density_mean, color='r', linestyle='--')
ax_box.axvline(density_median, color='g', linestyle='-')
ax_box.axvline(density_mode, color='b', linestyle='-')
sns.distplot(data['density'], ax=ax_hist, fit=stats.norm)
ax_hist.axvline(density_mean, color='r', linestyle='--')
ax_hist.axvline(density_median, color='g', linestyle='-')
ax_hist.axvline(density_mode, color='b', linestyle='-')
plt.legend({'Mean': density_mean, 'Median': density_median, 'Mode': density_mode})
ax_box.set(xlabel='')
plt.show() | code |
50233728/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = sns.diverging_palette(220, 10, as_cmap=True)
data = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
table_nan = data.isna().sum().to_frame().style.background_gradient(cmap=cmap)
table_nan
feature_desc = ['most acids involved with wine or fixed or nonvolatile (do not evaporate readily)', 'he amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste', 'found in small quantities, citric acid can add freshness and flavor to wines', "the amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet", 'the amount of salt in the wine', 'the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine', 'amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine', 'the density of water is close to that of water depending on the percent alcohol and sugar content', 'describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale', 'a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant', '-', 'score between 0 and 10']
feature_desc = pd.DataFrame(feature_desc, columns=['Description'], index=data.columns)
data_desc = data.describe().T
data_description = pd.concat([feature_desc, data_desc], axis=1)
data_description | code |
50233728/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = sns.diverging_palette(220, 10, as_cmap=True)
data = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
plt.figure(figsize=(15, 6))
quality_count = data['quality'].value_counts().sort_values(ascending=False).to_frame()
quality_count = quality_count.rename(columns={'quality': 'Count'})
ax = sns.barplot(x=quality_count.index, y='Count', data=quality_count, palette='ch:.25')
for p in ax.patches:
ax.annotate(format(p.get_height(), '.1f'), (p.get_x() + p.get_width() / 2.0, p.get_height()), ha='center', va='center', xytext=(0, 9), textcoords='offset points')
plt.xlabel('Quality', fontsize=15)
plt.ylabel('Count', fontsize=15)
plt.title('Bar plot of Quality', fontsize=20)
plt.show() | code |
50233728/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = sns.diverging_palette(220, 10, as_cmap=True)
data = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
table_nan = data.isna().sum().to_frame().style.background_gradient(cmap=cmap)
table_nan | code |
50233728/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = sns.diverging_palette(220, 10, as_cmap=True)
data = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
plt.figure(figsize=(15,6))
quality_count = data['quality'].value_counts().sort_values(ascending=False).to_frame()
quality_count = quality_count.rename(columns={'quality':'Count'})
ax = sns.barplot(x=quality_count.index, y='Count', data=quality_count, palette="ch:.25")
for p in ax.patches:
ax.annotate(format(p.get_height(), '.1f'),
(p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='center',
xytext = (0,9),
textcoords='offset points')
plt.xlabel("Quality", fontsize=15)
plt.ylabel("Count", fontsize=15)
plt.title("Bar plot of Quality", fontsize=20)
plt.show()
table_nan = data.isna().sum().to_frame().style.background_gradient(cmap=cmap)
table_nan
feature_desc = ['most acids involved with wine or fixed or nonvolatile (do not evaporate readily)', 'he amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste', 'found in small quantities, citric acid can add freshness and flavor to wines', "the amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet", 'the amount of salt in the wine', 'the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine', 'amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine', 'the density of water is close to that of water depending on the percent alcohol and sugar content', 'describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale', 'a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant', '-', 'score between 0 and 10']
feature_desc = pd.DataFrame(feature_desc, columns=['Description'], index=data.columns)
data_desc = data.describe().T
data_description = pd.concat([feature_desc, data_desc], axis=1)
f, ax = plt.subplots(3, 4, figsize=(25, 15))
sns.despine(left=True)
sns.boxplot(data['fixed acidity'], ax=ax[0, 0])
sns.boxplot(data['volatile acidity'], ax=ax[0, 1])
sns.boxplot(data['citric acid'], ax=ax[0, 2])
sns.boxplot(data['residual sugar'], ax=ax[0, 3])
sns.boxplot(data['chlorides'], ax=ax[1, 0])
sns.boxplot(data['density'], ax=ax[1, 1])
sns.boxplot(data['pH'], ax=ax[1, 2])
sns.boxplot(data['sulphates'], ax=ax[1, 3])
sns.boxplot(data['alcohol'], ax=ax[2, 0])
sns.boxplot(data['total sulfur dioxide'], ax=ax[2, 1])
sns.boxplot(data['free sulfur dioxide'], ax=ax[2, 2])
sns.boxplot(data['quality'], ax=ax[2, 3])
plt.show() | code |
50233728/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = sns.diverging_palette(220, 10, as_cmap=True)
data = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
plt.figure(figsize=(15,6))
quality_count = data['quality'].value_counts().sort_values(ascending=False).to_frame()
quality_count = quality_count.rename(columns={'quality':'Count'})
ax = sns.barplot(x=quality_count.index, y='Count', data=quality_count, palette="ch:.25")
for p in ax.patches:
ax.annotate(format(p.get_height(), '.1f'),
(p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='center',
xytext = (0,9),
textcoords='offset points')
plt.xlabel("Quality", fontsize=15)
plt.ylabel("Count", fontsize=15)
plt.title("Bar plot of Quality", fontsize=20)
plt.show()
table_nan = data.isna().sum().to_frame().style.background_gradient(cmap=cmap)
table_nan
feature_desc = ['most acids involved with wine or fixed or nonvolatile (do not evaporate readily)', 'he amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste', 'found in small quantities, citric acid can add freshness and flavor to wines', "the amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet", 'the amount of salt in the wine', 'the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine', 'amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine', 'the density of water is close to that of water depending on the percent alcohol and sugar content', 'describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale', 'a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant', '-', 'score between 0 and 10']
feature_desc = pd.DataFrame(feature_desc, columns=['Description'], index=data.columns)
data_desc = data.describe().T
data_description = pd.concat([feature_desc, data_desc], axis=1)
f, ax = plt.subplots(3, 4, figsize=(25,15))
sns.despine(left=True)
sns.boxplot(data['fixed acidity'], ax=ax[0,0])
sns.boxplot(data['volatile acidity'], ax=ax[0,1])
sns.boxplot(data['citric acid'], ax=ax[0,2])
sns.boxplot(data['residual sugar'], ax=ax[0,3])
sns.boxplot(data['chlorides'], ax=ax[1,0])
sns.boxplot(data['density'], ax=ax[1,1])
sns.boxplot(data['pH'], ax=ax[1,2])
sns.boxplot(data['sulphates'], ax=ax[1,3])
sns.boxplot(data['alcohol'], ax=ax[2,0])
sns.boxplot(data['total sulfur dioxide'], ax=ax[2,1])
sns.boxplot(data['free sulfur dioxide'], ax=ax[2,2])
sns.boxplot(data['quality'], ax=ax[2,3])
plt.show()
density_mean = np.mean(data['density'])
density_median = np.median(data['density'])
density_mode = data['density'].mode()[0]
q1 = data['density'].quantile(0.25)
q3 = data['density'].quantile(0.75)
density_IQR = q3 - q1
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw= {'height_ratios':(0.2, 1)})
sns.boxplot(data["density"], ax=ax_box)
ax_box.axvline(density_mean, color='r', linestyle='--')
ax_box.axvline(density_median, color='g', linestyle='-')
ax_box.axvline(density_mode, color='b', linestyle='-')
sns.distplot(data["density"], ax=ax_hist, fit=stats.norm)
ax_hist.axvline(density_mean, color='r', linestyle='--')
ax_hist.axvline(density_median, color='g', linestyle='-')
ax_hist.axvline(density_mode, color='b', linestyle='-')
plt.legend({'Mean':density_mean,'Median':density_median,'Mode':density_mode})
ax_box.set(xlabel='')
plt.show()
normal = stats.normaltest(data['density'])
normal
f, (ax_box, ax_dist) = plt.subplots(2, sharex=True, gridspec_kw={'height_ratios': (0.2, 1)})
mean = np.mean(data['pH'])
median = np.median(data['pH'])
mode = data['pH'].mode()[0]
q1 = data['pH'].quantile(0.25)
q3 = data['pH'].quantile(0.75)
IQR = q3 - q1
print('Mean : {}'.format(mean))
print('Median : {}'.format(median))
print('Mode : {}'.format(mode))
print('Inter Quantile Range : {}'.format(IQR))
sns.boxplot(data['pH'], ax=ax_box)
ax_box.axvline(mean, color='r', linestyle='--')
ax_box.axvline(median, color='g', linestyle='--')
ax_box.axvline(mode, color='b', linestyle='--')
sns.distplot(data['pH'], ax=ax_dist, fit=stats.norm)
ax_dist.axvline(mean, color='r', linestyle='--')
ax_dist.axvline(median, color='g', linestyle='--')
ax_dist.axvline(mode, color='b', linestyle='--')
plt.legend({'Mean': mean, 'Median': median, 'Mode': mode})
ax_box.set(xlabel='')
plt.show() | code |
50233728/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = sns.diverging_palette(220, 10, as_cmap=True)
data = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
plt.figure(figsize=(15,6))
quality_count = data['quality'].value_counts().sort_values(ascending=False).to_frame()
quality_count = quality_count.rename(columns={'quality':'Count'})
ax = sns.barplot(x=quality_count.index, y='Count', data=quality_count, palette="ch:.25")
for p in ax.patches:
ax.annotate(format(p.get_height(), '.1f'),
(p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='center',
xytext = (0,9),
textcoords='offset points')
plt.xlabel("Quality", fontsize=15)
plt.ylabel("Count", fontsize=15)
plt.title("Bar plot of Quality", fontsize=20)
plt.show()
table_nan = data.isna().sum().to_frame().style.background_gradient(cmap=cmap)
table_nan
feature_desc = ['most acids involved with wine or fixed or nonvolatile (do not evaporate readily)', 'he amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste', 'found in small quantities, citric acid can add freshness and flavor to wines', "the amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet", 'the amount of salt in the wine', 'the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine', 'amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine', 'the density of water is close to that of water depending on the percent alcohol and sugar content', 'describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale', 'a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant', '-', 'score between 0 and 10']
feature_desc = pd.DataFrame(feature_desc, columns=['Description'], index=data.columns)
data_desc = data.describe().T
data_description = pd.concat([feature_desc, data_desc], axis=1)
f, ax = plt.subplots(3, 4, figsize=(25,15))
sns.despine(left=True)
sns.boxplot(data['fixed acidity'], ax=ax[0,0])
sns.boxplot(data['volatile acidity'], ax=ax[0,1])
sns.boxplot(data['citric acid'], ax=ax[0,2])
sns.boxplot(data['residual sugar'], ax=ax[0,3])
sns.boxplot(data['chlorides'], ax=ax[1,0])
sns.boxplot(data['density'], ax=ax[1,1])
sns.boxplot(data['pH'], ax=ax[1,2])
sns.boxplot(data['sulphates'], ax=ax[1,3])
sns.boxplot(data['alcohol'], ax=ax[2,0])
sns.boxplot(data['total sulfur dioxide'], ax=ax[2,1])
sns.boxplot(data['free sulfur dioxide'], ax=ax[2,2])
sns.boxplot(data['quality'], ax=ax[2,3])
plt.show()
density_mean = np.mean(data['density'])
density_median = np.median(data['density'])
density_mode = data['density'].mode()[0]
q1 = data['density'].quantile(0.25)
q3 = data['density'].quantile(0.75)
density_IQR = q3 - q1
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw= {'height_ratios':(0.2, 1)})
sns.boxplot(data["density"], ax=ax_box)
ax_box.axvline(density_mean, color='r', linestyle='--')
ax_box.axvline(density_median, color='g', linestyle='-')
ax_box.axvline(density_mode, color='b', linestyle='-')
sns.distplot(data["density"], ax=ax_hist, fit=stats.norm)
ax_hist.axvline(density_mean, color='r', linestyle='--')
ax_hist.axvline(density_median, color='g', linestyle='-')
ax_hist.axvline(density_mode, color='b', linestyle='-')
plt.legend({'Mean':density_mean,'Median':density_median,'Mode':density_mode})
ax_box.set(xlabel='')
plt.show()
normal = stats.normaltest(data['density'])
normal
f, (ax_box, ax_dist) = plt.subplots(2, sharex=True, gridspec_kw = {"height_ratios":(0.2, 1)})
mean = np.mean(data['pH'])
median = np.median(data['pH'])
mode = data['pH'].mode()[0]
q1 = data['pH'].quantile(0.25)
q3 = data['pH'].quantile(0.75)
IQR = q3 - q1
print("Mean : {}".format(mean))
print("Median : {}".format(median))
print("Mode : {}".format(mode))
print("Inter Quantile Range : {}".format(IQR))
sns.boxplot(data['pH'], ax=ax_box)
ax_box.axvline(mean, color='r', linestyle='--')
ax_box.axvline(median, color='g', linestyle='--')
ax_box.axvline(mode, color='b', linestyle='--')
sns.distplot(data['pH'], ax=ax_dist, fit=stats.norm)
ax_dist.axvline(mean, color='r', linestyle='--')
ax_dist.axvline(median, color='g', linestyle='--')
ax_dist.axvline(mode, color='b', linestyle='--')
plt.legend({"Mean":mean, "Median":median, "Mode":mode})
ax_box.set(xlabel='')
plt.show()
normal = stats.normaltest(data['pH'])
normal | code |
50233728/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = sns.diverging_palette(220, 10, as_cmap=True)
data = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
data.head(4) | code |
50233728/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import scipy.stats as stats
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = sns.diverging_palette(220, 10, as_cmap=True)
data = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
plt.figure(figsize=(15,6))
quality_count = data['quality'].value_counts().sort_values(ascending=False).to_frame()
quality_count = quality_count.rename(columns={'quality':'Count'})
ax = sns.barplot(x=quality_count.index, y='Count', data=quality_count, palette="ch:.25")
for p in ax.patches:
ax.annotate(format(p.get_height(), '.1f'),
(p.get_x() + p.get_width() / 2., p.get_height()),
ha='center', va='center',
xytext = (0,9),
textcoords='offset points')
plt.xlabel("Quality", fontsize=15)
plt.ylabel("Count", fontsize=15)
plt.title("Bar plot of Quality", fontsize=20)
plt.show()
table_nan = data.isna().sum().to_frame().style.background_gradient(cmap=cmap)
table_nan
feature_desc = ['most acids involved with wine or fixed or nonvolatile (do not evaporate readily)', 'he amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste', 'found in small quantities, citric acid can add freshness and flavor to wines', "the amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet", 'the amount of salt in the wine', 'the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine', 'amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine', 'the density of water is close to that of water depending on the percent alcohol and sugar content', 'describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale', 'a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant', '-', 'score between 0 and 10']
feature_desc = pd.DataFrame(feature_desc, columns=['Description'], index=data.columns)
data_desc = data.describe().T
data_description = pd.concat([feature_desc, data_desc], axis=1)
f, ax = plt.subplots(3, 4, figsize=(25,15))
sns.despine(left=True)
sns.boxplot(data['fixed acidity'], ax=ax[0,0])
sns.boxplot(data['volatile acidity'], ax=ax[0,1])
sns.boxplot(data['citric acid'], ax=ax[0,2])
sns.boxplot(data['residual sugar'], ax=ax[0,3])
sns.boxplot(data['chlorides'], ax=ax[1,0])
sns.boxplot(data['density'], ax=ax[1,1])
sns.boxplot(data['pH'], ax=ax[1,2])
sns.boxplot(data['sulphates'], ax=ax[1,3])
sns.boxplot(data['alcohol'], ax=ax[2,0])
sns.boxplot(data['total sulfur dioxide'], ax=ax[2,1])
sns.boxplot(data['free sulfur dioxide'], ax=ax[2,2])
sns.boxplot(data['quality'], ax=ax[2,3])
plt.show()
density_mean = np.mean(data['density'])
density_median = np.median(data['density'])
density_mode = data['density'].mode()[0]
q1 = data['density'].quantile(0.25)
q3 = data['density'].quantile(0.75)
density_IQR = q3 - q1
f, (ax_box, ax_hist) = plt.subplots(2, sharex=True, gridspec_kw= {'height_ratios':(0.2, 1)})
sns.boxplot(data["density"], ax=ax_box)
ax_box.axvline(density_mean, color='r', linestyle='--')
ax_box.axvline(density_median, color='g', linestyle='-')
ax_box.axvline(density_mode, color='b', linestyle='-')
sns.distplot(data["density"], ax=ax_hist, fit=stats.norm)
ax_hist.axvline(density_mean, color='r', linestyle='--')
ax_hist.axvline(density_median, color='g', linestyle='-')
ax_hist.axvline(density_mode, color='b', linestyle='-')
plt.legend({'Mean':density_mean,'Median':density_median,'Mode':density_mode})
ax_box.set(xlabel='')
plt.show()
normal = stats.normaltest(data['density'])
normal | code |
50233728/cell_12 | [
"image_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = sns.diverging_palette(220, 10, as_cmap=True)
data = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
table_nan = data.isna().sum().to_frame().style.background_gradient(cmap=cmap)
table_nan
feature_desc = ['most acids involved with wine or fixed or nonvolatile (do not evaporate readily)', 'he amount of acetic acid in wine, which at too high of levels can lead to an unpleasant, vinegar taste', 'found in small quantities, citric acid can add freshness and flavor to wines', "the amount of sugar remaining after fermentation stops, it's rare to find wines with less than 1 gram/liter and wines with greater than 45 grams/liter are considered sweet", 'the amount of salt in the wine', 'the free form of SO2 exists in equilibrium between molecular SO2 (as a dissolved gas) and bisulfite ion; it prevents microbial growth and the oxidation of wine', 'amount of free and bound forms of S02; in low concentrations, SO2 is mostly undetectable in wine, but at free SO2 concentrations over 50 ppm, SO2 becomes evident in the nose and taste of wine', 'the density of water is close to that of water depending on the percent alcohol and sugar content', 'describes how acidic or basic a wine is on a scale from 0 (very acidic) to 14 (very basic); most wines are between 3-4 on the pH scale', 'a wine additive which can contribute to sulfur dioxide gas (S02) levels, wich acts as an antimicrobial and antioxidant', '-', 'score between 0 and 10']
feature_desc = pd.DataFrame(feature_desc, columns=['Description'], index=data.columns)
data_desc = data.describe().T
data_description = pd.concat([feature_desc, data_desc], axis=1)
density_mean = np.mean(data['density'])
density_median = np.median(data['density'])
density_mode = data['density'].mode()[0]
q1 = data['density'].quantile(0.25)
q3 = data['density'].quantile(0.75)
density_IQR = q3 - q1
print('Mean : {}'.format(density_mean))
print('Median : {}'.format(density_median))
print('Mode : {}'.format(density_mode))
print('Inter Quantile Range : {}'.format(density_IQR)) | code |
50233728/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import pandas as pd
pd.set_option('display.max_columns', None)
import numpy as np
import scipy.stats as stats
import matplotlib.pyplot as plt
import seaborn as sns
sns.set_style(style='darkgrid')
sns.set_palette(palette='pastel')
pd.options.display.max_colwidth = 300
cmap = sns.diverging_palette(220, 10, as_cmap=True)
data = pd.read_csv('/kaggle/input/red-wine-quality-cortez-et-al-2009/winequality-red.csv')
data.info() | code |
89122282/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import netCDF4
from netCDF4 import Dataset
from datetime import datetime, timedelta, date
import math as m
from decimal import Decimal
import os
files = os.listdir('/kaggle/input/models/wrf-chem') | code |
89122282/cell_8 | [
"text_html_output_1.png"
] | from netCDF4 import Dataset
import netCDF4
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def geo_idx(dd, dd_array):
"""
search for nearest decimal degree in an array of decimal degrees and return the index.
np.argmin returns the indices of minium value along an axis.
so subtract dd from all values in dd_array, take absolute value and find index of minium.
"""
geo_idx = np.abs(dd_array - dd).argmin()
return geo_idx
rootdir = '/kaggle/input/models/wrf-chem/'
curr_date = 20220218
cycle = '12'
ncfile = Dataset(rootdir + str(curr_date) + cycle + '_NOA-WRF-CHEM.nc')
times = ncfile.variables['time']
times_convert = np.array(netCDF4.num2date(times[:], times.long_name), dtype='datetime64[s]')
nTimes = len(times)
d = pd.to_datetime(np.datetime_as_string(times_convert, timezone='UTC', unit='s'))
print(d.strftime('%d/%m/%Y %H:%M'))
sconc_dust = ncfile.variables['SCONC_DUST'][:]
lats = ncfile.variables['latitude'][:]
lons = ncfile.variables['longitude'][:] | code |
89122282/cell_10 | [
"text_plain_output_1.png"
] | from netCDF4 import Dataset
import netCDF4
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
def geo_idx(dd, dd_array):
"""
search for nearest decimal degree in an array of decimal degrees and return the index.
np.argmin returns the indices of minium value along an axis.
so subtract dd from all values in dd_array, take absolute value and find index of minium.
"""
geo_idx = np.abs(dd_array - dd).argmin()
return geo_idx
rootdir = '/kaggle/input/models/wrf-chem/'
curr_date = 20220218
cycle = '12'
ncfile = Dataset(rootdir + str(curr_date) + cycle + '_NOA-WRF-CHEM.nc')
times = ncfile.variables['time']
times_convert = np.array(netCDF4.num2date(times[:], times.long_name), dtype='datetime64[s]')
nTimes = len(times)
d = pd.to_datetime(np.datetime_as_string(times_convert, timezone='UTC', unit='s'))
sconc_dust = ncfile.variables['SCONC_DUST'][:]
lats = ncfile.variables['latitude'][:]
lons = ncfile.variables['longitude'][:]
pois = pd.read_csv(rootdir + 'GREECE_POIS_NEW.csv', sep='\t')
pois = pois.dropna(how='all', axis='columns')
poi_name = pois['NAME']
poi_lat = pois['LATITUDE']
poi_lon = pois['LONGITUDE']
poi_alt = pois['ELEVATION']
npois = poi_name.count()
poi_lat_int = poi_lat.astype(int)
poi_lat_dec = (poi_lat - poi_lat_int) * (100.0 / 60.0)
poi_lat_final = poi_lat_int + poi_lat_dec
poi_lon_int = poi_lon.astype(int)
poi_lon_dec = (poi_lon - poi_lon_int) * (100.0 / 60.0)
poi_lon_final = poi_lon_int + poi_lon_dec
pois.head() | code |
89135552/cell_21 | [
"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)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape
x = train.corr()
plt.xticks(rotation=90)
df = pd.DataFrame(train['facility_type'].value_counts().head(10))
df['facility'] = df.index
df.rename(columns = {'facility_type':'count'}, inplace = True)
df['%share'] = df['count']/df['count'].sum()
df.drop(columns=['count'],inplace=True)
df.reset_index(drop=True)
plt.figure(figsize=(12,6))
ax = sns.barplot(y='facility',x='%share',data=df)
for i in ax.containers:
ax.bar_label(i,)
plt.title("Distribution of Top 10 Facilities in Buildings in %")
plt.show()
df = pd.DataFrame(train[train['building_class'] == 'Commercial'][['site_eui', 'floor_area', 'year_built', 'ELEVATION', 'State_Factor', 'facility_type']].sort_values(by=['site_eui']).tail(200))
df.dropna() | code |
89135552/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape
plt.figure(figsize=(18, 6))
plt.subplot(1, 2, 1)
train['State_Factor'].value_counts().plot(kind='pie', autopct='%1.1f%%')
plt.subplot(1, 2, 2)
test['State_Factor'].value_counts().plot(kind='pie', autopct='%1.1f%%') | code |
89135552/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape
train.info() | code |
89135552/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.head() | code |
89135552/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape
x = train.corr()
plt.xticks(rotation=90)
len(train[train['building_class'] == 'Residential']) | code |
89135552/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.head(2) | code |
89135552/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape
sns.countplot(x='State_Factor', hue='building_class', data=train, order=train['State_Factor'].value_counts().index) | code |
89135552/cell_19 | [
"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)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape
x = train.corr()
plt.xticks(rotation=90)
df = pd.DataFrame(train['facility_type'].value_counts().head(10))
df['facility'] = df.index
df.rename(columns={'facility_type': 'count'}, inplace=True)
df['%share'] = df['count'] / df['count'].sum()
df.drop(columns=['count'], inplace=True)
df.reset_index(drop=True)
plt.figure(figsize=(12, 6))
ax = sns.barplot(y='facility', x='%share', data=df)
for i in ax.containers:
ax.bar_label(i)
plt.title('Distribution of Top 10 Facilities in Buildings in %')
plt.show() | code |
89135552/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89135552/cell_7 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum() | code |
89135552/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape
x = train.corr()
plt.xticks(rotation=90)
train.describe() | code |
89135552/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape | code |
89135552/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape
x = train.corr()
plt.figure(figsize=(8, 6))
sns.heatmap(x) | code |
89135552/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape
x = train.corr()
plt.figure(figsize=(24, 6))
sns.countplot(x='year_built', data=train[train.year_built > 1920])
plt.xticks(rotation=90)
plt.show() | code |
89135552/cell_22 | [
"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)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
sns.set_style('whitegrid')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape
x = train.corr()
plt.xticks(rotation=90)
df = pd.DataFrame(train['facility_type'].value_counts().head(10))
df['facility'] = df.index
df.rename(columns = {'facility_type':'count'}, inplace = True)
df['%share'] = df['count']/df['count'].sum()
df.drop(columns=['count'],inplace=True)
df.reset_index(drop=True)
plt.figure(figsize=(12,6))
ax = sns.barplot(y='facility',x='%share',data=df)
for i in ax.containers:
ax.bar_label(i,)
plt.title("Distribution of Top 10 Facilities in Buildings in %")
plt.show()
plt.figure(figsize=(12, 8))
sns.histplot(data=train, x='site_eui', hue='State_Factor')
plt.show() | code |
89135552/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('../input/widsdatathon2022/train.csv')
test = pd.read_csv('../input/widsdatathon2022/test.csv')
submission = pd.read_csv('../input/widsdatathon2022/sample_solution.csv')
train = pd.read_csv('../input/widsdatathon2022/train.csv')
train.drop(columns=['id'], axis=1, inplace=True)
train.isna().sum()
train.shape
train.head() | code |
106202729/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
plt.bar(x=start_table_df['track_id'].value_counts().index, height=start_table_df['track_id'].value_counts()) | code |
106202729/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
start_table_df.head() | code |
106202729/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
start_table_df.info() | code |
106202729/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
start_table_df['track_id'].value_counts() | code |
106202729/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
start_table_df.groupby(['race_date'])['race_date'].count() | code |
106202729/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
start_table_df = pd.read_csv('../input/big-data-derby-2022/nyra_start_table.csv')
start_table_df.columns = ['track_id', 'race_date', 'race_number', 'program_number', 'weight_carried', 'jockey', 'odds', 'position_at_finish']
start_table_df.groupby(['race_date'])['race_date'].count()
start_table_df.head() | code |
128020406/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
col_to_drop = null[null > 50].keys()
train_df = df_train.drop(col_to_drop, axis=1)
len(train_df.columns)
len(train_df.columns[train_df.isnull().any()])
train_df.head() | code |
128020406/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
print(dataframe.to_markdown()) | code |
128020406/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
col_to_drop = null[null > 50].keys()
train_df = df_train.drop(col_to_drop, axis=1)
null_test = df_test.isnull().sum() / df_test.shape[0] * 100
null_test
dataframe_null_test = pd.DataFrame(null_test.sort_values(ascending=False))
col_to_drop_test = null_test[null_test > 50].keys()
test_df = df_test.drop(col_to_drop, axis=1)
len(test_df.columns)
test_df.fillna(test_df.mode().iloc[0], inplace=True)
test_df.isnull().values.sum() | code |
128020406/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
df_train.head() | code |
128020406/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
col_to_drop = null[null > 50].keys()
train_df = df_train.drop(col_to_drop, axis=1)
len(train_df.columns)
len(train_df.columns[train_df.isnull().any()])
train_df.info() | code |
128020406/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
print(df_train.columns)
print(len(df_train.columns), 'fetures present in training dataset') | code |
128020406/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
col_to_drop = null[null > 50].keys()
train_df = df_train.drop(col_to_drop, axis=1)
null_test = df_test.isnull().sum() / df_test.shape[0] * 100
null_test
dataframe_null_test = pd.DataFrame(null_test.sort_values(ascending=False))
col_to_drop_test = null_test[null_test > 50].keys()
test_df = df_test.drop(col_to_drop, axis=1)
len(train_df.columns)
len(test_df.columns)
len(train_df.columns[train_df.isnull().any()])
train_df.isnull().values.sum()
test_df.fillna(test_df.mode().iloc[0], inplace=True)
test_df.isnull().values.sum()
train_df.corr()
corr = train_df.corr()
high_corr_features = corr.index[abs(corr['SalePrice']) > 0.5]
train_df = pd.get_dummies(train_df, drop_first=True)
test_df = pd.get_dummies(test_df, drop_first=True)
print(f'Test shape: {test_df.shape}') | code |
128020406/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
col_to_drop = null[null > 50].keys()
train_df = df_train.drop(col_to_drop, axis=1)
len(train_df.columns)
len(train_df.columns[train_df.isnull().any()])
train_df.isnull().values.sum()
train_df.corr() | code |
128020406/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null | code |
128020406/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
col_to_drop = null[null > 50].keys()
train_df = df_train.drop(col_to_drop, axis=1)
len(train_df.columns)
len(train_df.columns[train_df.isnull().any()]) | code |
128020406/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128020406/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
print(df_test.columns)
print(len(df_test.columns), 'fetures present in training dataset') | code |
128020406/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
col_to_drop = null[null > 50].keys()
train_df = df_train.drop(col_to_drop, axis=1)
null_test = df_test.isnull().sum() / df_test.shape[0] * 100
null_test
dataframe_null_test = pd.DataFrame(null_test.sort_values(ascending=False))
col_to_drop_test = null_test[null_test > 50].keys()
test_df = df_test.drop(col_to_drop, axis=1)
len(test_df.columns) | code |
128020406/cell_28 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
col_to_drop = null[null > 50].keys()
train_df = df_train.drop(col_to_drop, axis=1)
null_test = df_test.isnull().sum() / df_test.shape[0] * 100
null_test
dataframe_null_test = pd.DataFrame(null_test.sort_values(ascending=False))
len(train_df.columns)
len(train_df.columns[train_df.isnull().any()])
train_df.isnull().values.sum()
train_df.corr()
corr = train_df.corr()
high_corr_features = corr.index[abs(corr['SalePrice']) > 0.5]
train_df = pd.get_dummies(train_df, drop_first=True)
print(f'Train shape: {train_df.shape}') | code |
128020406/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
print(df_test.shape, 'shape of testing dataset')
print(df_train.shape, 'shape of training dataset') | code |
128020406/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
null_test = df_test.isnull().sum() / df_test.shape[0] * 100
null_test
dataframe_null_test = pd.DataFrame(null_test.sort_values(ascending=False))
print(dataframe_null_test.to_markdown()) | code |
128020406/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
col_to_drop = null[null > 50].keys()
train_df = df_train.drop(col_to_drop, axis=1)
len(train_df.columns) | code |
128020406/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
col_to_drop = null[null > 50].keys()
train_df = df_train.drop(col_to_drop, axis=1)
len(train_df.columns)
len(train_df.columns[train_df.isnull().any()])
train_df.isnull().values.sum() | code |
128020406/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null_test = df_test.isnull().sum() / df_test.shape[0] * 100
null_test | code |
128020406/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
print(dataframe.to_markdown()) | code |
128020406/cell_27 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
col_to_drop = null[null > 50].keys()
train_df = df_train.drop(col_to_drop, axis=1)
len(train_df.columns)
len(train_df.columns[train_df.isnull().any()])
train_df.isnull().values.sum()
train_df.corr()
corr = train_df.corr()
high_corr_features = corr.index[abs(corr['SalePrice']) > 0.5]
print(f'highly correlated feature:\n', high_corr_features)
print(f'No. of highly correlated features:', len(high_corr_features)) | code |
128020406/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
dataframe = pd.DataFrame(df_train.isnull().sum().sort_values(ascending=False))
dataframe = pd.DataFrame(df_test.isnull().sum().sort_values(ascending=False))
null = df_train.isnull().sum() / df_train.shape[0] * 100
null
dataframe_null = pd.DataFrame(null.sort_values(ascending=False))
print(dataframe_null.to_markdown()) | code |
128020406/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
df_test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
df_test.head() | code |
105185927/cell_13 | [
"text_plain_output_1.png"
] | from collections import defaultdict
from functools import lru_cache
from geopy.geocoders import Nominatim
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import pycountry
import pycountry_convert as pc
import re
import spacy
countries_only = pd.read_json('../input/precise-location/countries.json')
country_ex = pd.read_csv('../input/precise-location/country_iso_codes_expanded.csv')
country_ex = country_ex.fillna(' ')
contriesSet = []
for i in tqdm(range(len(countries_only))):
contriesSet.append(countries_only['name'][i])
contriesSet += list(countries_only['translations'][i].values())
contriesSet = list(set(contriesSet))
dictMultiLangCountry = {}
for i in tqdm(range(len(countries_only))):
dictMultiLangCountry[countries_only['name'][i]] = countries_only['name'][i]
mc = list(countries_only['translations'][i].values())
for c in mc:
dictMultiLangCountry[c] = countries_only['name'][i]
for i in tqdm(range(len(country_ex))):
for col in range(26):
if country_ex[f'alternative_country_name_{col}'][i] != ' ':
dictMultiLangCountry[country_ex[f'alternative_country_name_{col}'][i]] = country_ex['country'][i]
dictMultiLangCountry['China'] = 'China'
dictMultiLangCountry['SIngapore'] = 'Singapore'
dictMultiLangCountry.pop('')
geoname_cities = pd.read_csv('../input/precise-location/geonames-all-cities-with-a-population-1000.csv', sep=';')
dictAllUnicodeCities = dict()
for i in tqdm(range(len(geoname_cities))):
if geoname_cities.Name[i] in dictAllUnicodeCities:
dictAllUnicodeCities[geoname_cities.Name[i]].append(geoname_cities['Country name EN'][i])
dictAllUnicodeCities[geoname_cities.Name[i]] = list(set(dictAllUnicodeCities[geoname_cities.Name[i]]))
else:
dictAllUnicodeCities[geoname_cities.Name[i]] = [geoname_cities['Country name EN'][i]]
if geoname_cities['Alternate Names'][i] is not np.NaN:
alt_ = geoname_cities['Alternate Names'][i].split(',')
for city in alt_:
if city in dictAllUnicodeCities:
dictAllUnicodeCities[city].append(geoname_cities['Country name EN'][i])
dictAllUnicodeCities[city] = list(set(dictAllUnicodeCities[city]))
else:
dictAllUnicodeCities[city] = [geoname_cities['Country name EN'][i]]
dictAllUnicodeCities['Dhaka'] = ['Bangladesh']
dictAllUnicodeCities['Changi'] = ['Singapore']
dictAllUnicodeCities['Admiralty'] = ['Singapore']
dictAllUnicodeCities['North Bridge'] = ['Singapore']
dictAllUnicodeCities['Greater Bay'] = ['China']
dictAllUnicodeCities['Philadelphia'] = ['United States']
dictAllUnicodeCities['Manila'] = ['Philippines']
dictAllUnicodeCities['Irving'] = ['Singapore']
dictAllUnicodeCities['HDB Hub'] = ['Singapore']
dictAllUnicodeCities['s Heerenberg'] = ['Netherlands']
dictAllUnicodeCities['S Heerenberg'] = ['Netherlands']
dictAllUnicodeCities['Durham'] = ['United States']
dictAllUnicodeCities['dhabi'] = ['United Arab Emirates']
dictAllUnicodeCities['Scotland'] = ['United Kingdom']
dictAllUnicodeCities['Middle East'] = ['United Arab Emirates']
dictAllUnicodeCities['Alberta'] = ['Canada']
dictAllUnicodeCities['Colombo'] = ['Sri Lanka']
dictAllUnicodeCities['Phoenix'] = ['United States']
dictAllUnicodeCities.pop('Ahmad')
dictAllUnicodeCities.pop('Road')
dictAllUnicodeCities.pop('Street')
dictAllUnicodeCities.pop('30')
dictAllUnicodeCities.pop('North')
dictAllUnicodeCities.pop('West')
dictAllUnicodeCities.pop('Bridge')
dictAllUnicodeCities.pop('Model')
dictAllUnicodeCities.pop('Spa')
dictAllUnicodeCities.pop('Park')
dictAllUnicodeCities.pop('Bay')
dictAllUnicodeCities.pop('Home')
dictAllUnicodeCities.pop('List')
dictAllUnicodeCities.pop('China')
dictAllUnicodeCities.pop('Court')
dictAllUnicodeCities.pop('Wing')
dictAllUnicodeCities.pop('HDB')
dictAllUnicodeCities.pop('Al')
dictAllUnicodeCities.pop('Bin')
dictAllUnicodeCities.pop('A')
dictAllUnicodeCities.pop('Vista')
dictAllUnicodeCities.pop('Aria')
dictAllUnicodeCities.pop('No')
dictAllUnicodeCities.pop('I')
countries_cities = pd.read_json('../input/precise-location/countriescities.json')
citiesList = []
for i in range(len(countries_cities)):
for j in range(len(countries_cities['cities'][i])):
citiesList.append(countries_cities['cities'][i][j].get('name'))
states_only = pd.read_json('../input/precise-location/states.json')
statesCountriesDict = {}
for i in range(len(states_only)):
statesCountriesDict[states_only['name'][i]] = states_only['country_name'][i]
from collections import defaultdict
countries_states = pd.read_json('../input/precise-location/countriesstates.json')
statesList = []
statesNameToStateCode = defaultdict(list)
statesCodeToStatesName = defaultdict(list)
for i in range(len(countries_states)):
for j in range(len(countries_states['states'][i])):
statesList.append(countries_states['states'][i][j].get('name'))
statesNameToStateCode[countries_states['states'][i][j].get('name')].append(countries_states['states'][i][j].get('state_code'))
statesCodeToStatesName[countries_states['states'][i][j].get('state_code')].append(countries_states['states'][i][j].get('name'))
def get_ngram(text, WordsToCombine=2):
text = text.replace(',', '')
words = text.split()
output = []
for i in range(len(words) - WordsToCombine + 1):
output.append(' '.join(words[i:i + WordsToCombine]))
return output
from re import T
def detect_language(text):
nlp = spacy.blank('xx')
nlp.add_pipe('language_detector')
doc = nlp(text)
return doc._.language
cid_mapper = {country.name: country.alpha_2 for country in pycountry.countries}
@lru_cache(maxsize=128)
def preproc(locstr):
pattern = re.compile('#[0-9]+\\-[0-9]+|Headquarters|HQ|Town|Court|Access|via|City|Head|Bank|Center|Remote|Building|Office|City|Of| And|Transportation|D.C|feild|Metro|Tn.|Health Care|Health|Care|STE 100|Sector|Tower|[-&/|;:\\"]+|Area|Surrounding|[\\(\\)\\{\\}\\[\\]]|📍 |🌎', flags=re.IGNORECASE)
return pattern.sub(' ', locstr).strip().rstrip('.')
road_re = re.compile('^.*?(road|street)(?!\\w)', flags=re.IGNORECASE)
def get_road(locstr):
m = road_re.match(locstr)
if m:
return (m.group(), m.group(1))
else:
return (locstr, '')
def get_road_idn(locstr):
if locstr.split(' ')[0] in ('Jl', 'Jl.', 'JL', 'Jalan', 'jl', 'Jln', 'Jln.'):
return True
for i in locstr.split(' '):
for j in ['Jl', 'Jl.', 'JL', 'jl']:
if i == j:
return True
@lru_cache(maxsize=128)
def get_all_geoname(locstr):
try:
geolocator = Nominatim(user_agent='geopy25', timeout=3)
location = geolocator.geocode(locstr)
location = geolocator.reverse('{}, {}'.format(str(location.raw['lat']), str(location.raw['lon'])), exactly_one=True)
address = location.raw['address']
city = address.get('city', '')
state = address.get('state', '')
country = address.get('country', '')
return (city, state, country)
except:
pass
@lru_cache(maxsize=128)
def get_geoname_from_road(roadstr):
try:
geolocator = Nominatim(user_agent='geopy2', timeout=3)
location = geolocator.geocode(roadstr)
addr = location.address
addr = addr.split(',')
country = addr[-1]
state = addr[-3]
city = addr[-4]
return (city, state, country)
except:
pass
def get_result(countries):
countries = str(countries)
if countries is not '':
country_id = cid_mapper.get(countries.strip())
else:
country_id = ''
if country_id:
try:
continent_code = pc.country_alpha2_to_continent_code(country_id.strip())
except:
continent_code = ''
else:
continent_code = ''
if continent_code:
continent_name = pc.convert_continent_code_to_continent_name(continent_code.strip())
else:
continent_name = ''
return {'country': countries, 'country_code': country_id, 'region': continent_name, 'region_code': continent_code}
from itertools import count
def get_locations(locstr):
if (locstr == ""):
return {
"city":"",
"state":"",
"country":"",
"country_code": "",
"region":"",
"region_code":""}
states, cities, countries, country_id, continent_code, continent_name = "", "", "", "", "", ""
if detect_language(locstr) not in ['ja', 'ko', 'zh']:
locstr = preproc(locstr)
locstr = re.sub(r' , ', ', ', locstr)
locstr = re.sub(r',', ' ', locstr)
locstr = re.sub(r'\s\s+', ' ', locstr)
# print(locstr)
if locstr.isupper() or locstr.islower():
locstr = locstr.title()
locstr = ' '.join(re.sub(r"([A-Z]+[a-z])", r" \1", locstr).split())
# print(locstr)
for i in locstr.replace(',', '').split(' ')[::-1]:
if i in dictMultiLangCountry:
countries = dictMultiLangCountry[i]
if (countries != "") or (countries is not None):
# print('5 ' + countries)
return get_result(countries)
for i in locstr.replace(',', '').split(' ')[::-1]:
if i in dictAllUnicodeCities:
countries = dictAllUnicodeCities[i]
if len(countries) == 1:
countries = countries[0]
if (countries != "") or (countries is not None):
# print('6 ' + countries)
return get_result(countries)
else:
countries = ""
if (countries == "") :
trigram = get_ngram(locstr, 3)
if countries is "":
for gram in trigram:
if gram in dictMultiLangCountry:
countries = dictMultiLangCountry[gram]
if (countries != "") or (countries is not None):
# print('1 ' + countries)
return get_result(countries)
if gram in dictAllUnicodeCities:
countries = dictAllUnicodeCities[gram]
if len(countries) == 1:
countries = countries[0]
if (countries != "") or (countries is not None):
# print('2 ' + countries)
return get_result(countries)
else:
countries = ""
bigram = get_ngram(locstr)
if countries is "":
for gram in bigram:
if gram in dictMultiLangCountry:
countries = dictMultiLangCountry[gram]
if (countries != "") or (countries is not None):
# print('3 ' + countries)
return get_result(countries)
if gram in dictAllUnicodeCities:
countries = dictAllUnicodeCities[gram]
if len(countries) == 1:
countries = countries[0]
if (countries != "") or (countries is not None):
# print('4 ' + countries)
return get_result(countries)
else:
countries = ""
############################################################################
# ROAD REFORMATING (ex: 159, Sin Ming Road # 07-02 Lobby 2 Amtech Building
# --> 159, Sin Ming Road)
############################################################################
locstr, road = get_road(locstr)
if len(locstr.split(" ")[0]) == 1:
locstr = locstr[2:]
if get_road_idn(locstr):
countries = "Indonesia"
return get_result(countries)
############################################################################
# ROAD
############################################################################
if re.findall('[0-9]+', locstr) or road:
try:
cities, states, countries = get_geoname_from_road(locstr)
countries = countries.strip()
if countries is not "":
if countries in dictMultiLangCountry:
return get_result(countries)
if cities:
countries = dictAllUnicodeCities[cities]
if len(countries) == 1:
countries = countries[0]
else:
countries = ""
if countries is not "":
if countries in dictMultiLangCountry:
return get_result(countries)
if states:
countries = statesCountriesDict[states]
if countries is not "":
if countries in dictMultiLangCountry:
return get_result(countries)
except:
pass
############################################################################
## THIS CODE TO SOLVE CITY - STATE CODE FORMAT (ex: Bonney Lake, WA) #######
############################################################################
loc_split_by_comma = locstr.split(",")
if (len(loc_split_by_comma) == 2):
if len(loc_split_by_comma[-1].strip()):
if statesCodeToStatesName.get(loc_split_by_comma[-1].strip()) and len(statesCodeToStatesName.get(loc_split_by_comma[-1].strip())) > 1:
if loc_split_by_comma[0] in set(citiesList):
cities = loc_split_by_comma[0]
try:
cities, states, countries = get_all_geoname(locstr)
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
except:
states, countries = "", ""
if type(states) == list:
states = states[0]
if states:
countries = statesCountriesDict[states]
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
elif statesCodeToStatesName.get(loc_split_by_comma[-1].strip()) and len(statesCodeToStatesName.get(loc_split_by_comma[-1].strip())) == 1:
cities = loc_split_by_comma[0]
states = statesCodeToStatesName.get(loc_split_by_comma[-1].strip())
if type(states) == list:
states = states[0]
if states:
countries = statesCountriesDict[states]
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
else:
locstr = locstr
#########################################################################
#### IF CURRENT RESULT JUST HAS A CITY
#########################################################################
try:
if (cities != '') and (states == '') and (countries == ''):
_, states, countries = get_all_geoname(cities)
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
except:
country_id, countries = "", ""
#########################################################################
#### IF ALL KEY IS NULL / EMPTY STRING
#########################################################################
try:
if (cities is "") and (states is "") and (countries != '') or (countries == ''):
_, _, countries = get_all_geoname(locstr)
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
except:
country_id, countries = "", ""
return {
"country":"",
"country_code":"",
"region":"",
"region_code":""
}
sample = 'City of Fond du Lac'
loc = get_locations(sample)
sample = '3501 NW Lowell Street Suite 202, Silverdale, WA 98383'
loc = get_locations(sample)
print(loc) | code |
105185927/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from tqdm.auto import tqdm
import numpy as np
import pandas as pd
countries_only = pd.read_json('../input/precise-location/countries.json')
country_ex = pd.read_csv('../input/precise-location/country_iso_codes_expanded.csv')
country_ex = country_ex.fillna(' ')
contriesSet = []
for i in tqdm(range(len(countries_only))):
contriesSet.append(countries_only['name'][i])
contriesSet += list(countries_only['translations'][i].values())
contriesSet = list(set(contriesSet))
dictMultiLangCountry = {}
for i in tqdm(range(len(countries_only))):
dictMultiLangCountry[countries_only['name'][i]] = countries_only['name'][i]
mc = list(countries_only['translations'][i].values())
for c in mc:
dictMultiLangCountry[c] = countries_only['name'][i]
for i in tqdm(range(len(country_ex))):
for col in range(26):
if country_ex[f'alternative_country_name_{col}'][i] != ' ':
dictMultiLangCountry[country_ex[f'alternative_country_name_{col}'][i]] = country_ex['country'][i]
dictMultiLangCountry['China'] = 'China'
dictMultiLangCountry['SIngapore'] = 'Singapore'
dictMultiLangCountry.pop('')
geoname_cities = pd.read_csv('../input/precise-location/geonames-all-cities-with-a-population-1000.csv', sep=';')
dictAllUnicodeCities = dict()
for i in tqdm(range(len(geoname_cities))):
if geoname_cities.Name[i] in dictAllUnicodeCities:
dictAllUnicodeCities[geoname_cities.Name[i]].append(geoname_cities['Country name EN'][i])
dictAllUnicodeCities[geoname_cities.Name[i]] = list(set(dictAllUnicodeCities[geoname_cities.Name[i]]))
else:
dictAllUnicodeCities[geoname_cities.Name[i]] = [geoname_cities['Country name EN'][i]]
if geoname_cities['Alternate Names'][i] is not np.NaN:
alt_ = geoname_cities['Alternate Names'][i].split(',')
for city in alt_:
if city in dictAllUnicodeCities:
dictAllUnicodeCities[city].append(geoname_cities['Country name EN'][i])
dictAllUnicodeCities[city] = list(set(dictAllUnicodeCities[city]))
else:
dictAllUnicodeCities[city] = [geoname_cities['Country name EN'][i]]
dictAllUnicodeCities['Dhaka'] = ['Bangladesh']
dictAllUnicodeCities['Changi'] = ['Singapore']
dictAllUnicodeCities['Admiralty'] = ['Singapore']
dictAllUnicodeCities['North Bridge'] = ['Singapore']
dictAllUnicodeCities['Greater Bay'] = ['China']
dictAllUnicodeCities['Philadelphia'] = ['United States']
dictAllUnicodeCities['Manila'] = ['Philippines']
dictAllUnicodeCities['Irving'] = ['Singapore']
dictAllUnicodeCities['HDB Hub'] = ['Singapore']
dictAllUnicodeCities['s Heerenberg'] = ['Netherlands']
dictAllUnicodeCities['S Heerenberg'] = ['Netherlands']
dictAllUnicodeCities['Durham'] = ['United States']
dictAllUnicodeCities['dhabi'] = ['United Arab Emirates']
dictAllUnicodeCities['Scotland'] = ['United Kingdom']
dictAllUnicodeCities['Middle East'] = ['United Arab Emirates']
dictAllUnicodeCities['Alberta'] = ['Canada']
dictAllUnicodeCities['Colombo'] = ['Sri Lanka']
dictAllUnicodeCities['Phoenix'] = ['United States']
dictAllUnicodeCities.pop('Ahmad')
dictAllUnicodeCities.pop('Road')
dictAllUnicodeCities.pop('Street')
dictAllUnicodeCities.pop('30')
dictAllUnicodeCities.pop('North')
dictAllUnicodeCities.pop('West')
dictAllUnicodeCities.pop('Bridge')
dictAllUnicodeCities.pop('Model')
dictAllUnicodeCities.pop('Spa')
dictAllUnicodeCities.pop('Park')
dictAllUnicodeCities.pop('Bay')
dictAllUnicodeCities.pop('Home')
dictAllUnicodeCities.pop('List')
dictAllUnicodeCities.pop('China')
dictAllUnicodeCities.pop('Court')
dictAllUnicodeCities.pop('Wing')
dictAllUnicodeCities.pop('HDB')
dictAllUnicodeCities.pop('Al')
dictAllUnicodeCities.pop('Bin')
dictAllUnicodeCities.pop('A')
dictAllUnicodeCities.pop('Vista')
dictAllUnicodeCities.pop('Aria')
dictAllUnicodeCities.pop('No')
dictAllUnicodeCities.pop('I') | code |
105185927/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | import nltk
nltk.download('punkt')
nltk.download('words')
nltk.download('maxent_ne_chunker')
nltk.download('averaged_perceptron_tagger')
!python -m spacy download en_core_web_sm
import re
import spacy
import string
import pycountry
import locationtagger
import spacy_fastlang
from rapidfuzz import fuzz
import pycountry_convert as pc
from deep_translator import GoogleTranslator
from geopy.geocoders import Nominatim
from multiprocessing.pool import ThreadPool as Pool
from functools import lru_cache
import warnings
warnings.filterwarnings('ignore')
cid_mapper = {country.name: country.alpha_2 for country in pycountry.countries} | code |
105185927/cell_14 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from collections import defaultdict
from functools import lru_cache
from geopy.geocoders import Nominatim
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import pycountry
import pycountry_convert as pc
import re
import spacy
countries_only = pd.read_json('../input/precise-location/countries.json')
country_ex = pd.read_csv('../input/precise-location/country_iso_codes_expanded.csv')
country_ex = country_ex.fillna(' ')
contriesSet = []
for i in tqdm(range(len(countries_only))):
contriesSet.append(countries_only['name'][i])
contriesSet += list(countries_only['translations'][i].values())
contriesSet = list(set(contriesSet))
dictMultiLangCountry = {}
for i in tqdm(range(len(countries_only))):
dictMultiLangCountry[countries_only['name'][i]] = countries_only['name'][i]
mc = list(countries_only['translations'][i].values())
for c in mc:
dictMultiLangCountry[c] = countries_only['name'][i]
for i in tqdm(range(len(country_ex))):
for col in range(26):
if country_ex[f'alternative_country_name_{col}'][i] != ' ':
dictMultiLangCountry[country_ex[f'alternative_country_name_{col}'][i]] = country_ex['country'][i]
dictMultiLangCountry['China'] = 'China'
dictMultiLangCountry['SIngapore'] = 'Singapore'
dictMultiLangCountry.pop('')
geoname_cities = pd.read_csv('../input/precise-location/geonames-all-cities-with-a-population-1000.csv', sep=';')
dictAllUnicodeCities = dict()
for i in tqdm(range(len(geoname_cities))):
if geoname_cities.Name[i] in dictAllUnicodeCities:
dictAllUnicodeCities[geoname_cities.Name[i]].append(geoname_cities['Country name EN'][i])
dictAllUnicodeCities[geoname_cities.Name[i]] = list(set(dictAllUnicodeCities[geoname_cities.Name[i]]))
else:
dictAllUnicodeCities[geoname_cities.Name[i]] = [geoname_cities['Country name EN'][i]]
if geoname_cities['Alternate Names'][i] is not np.NaN:
alt_ = geoname_cities['Alternate Names'][i].split(',')
for city in alt_:
if city in dictAllUnicodeCities:
dictAllUnicodeCities[city].append(geoname_cities['Country name EN'][i])
dictAllUnicodeCities[city] = list(set(dictAllUnicodeCities[city]))
else:
dictAllUnicodeCities[city] = [geoname_cities['Country name EN'][i]]
dictAllUnicodeCities['Dhaka'] = ['Bangladesh']
dictAllUnicodeCities['Changi'] = ['Singapore']
dictAllUnicodeCities['Admiralty'] = ['Singapore']
dictAllUnicodeCities['North Bridge'] = ['Singapore']
dictAllUnicodeCities['Greater Bay'] = ['China']
dictAllUnicodeCities['Philadelphia'] = ['United States']
dictAllUnicodeCities['Manila'] = ['Philippines']
dictAllUnicodeCities['Irving'] = ['Singapore']
dictAllUnicodeCities['HDB Hub'] = ['Singapore']
dictAllUnicodeCities['s Heerenberg'] = ['Netherlands']
dictAllUnicodeCities['S Heerenberg'] = ['Netherlands']
dictAllUnicodeCities['Durham'] = ['United States']
dictAllUnicodeCities['dhabi'] = ['United Arab Emirates']
dictAllUnicodeCities['Scotland'] = ['United Kingdom']
dictAllUnicodeCities['Middle East'] = ['United Arab Emirates']
dictAllUnicodeCities['Alberta'] = ['Canada']
dictAllUnicodeCities['Colombo'] = ['Sri Lanka']
dictAllUnicodeCities['Phoenix'] = ['United States']
dictAllUnicodeCities.pop('Ahmad')
dictAllUnicodeCities.pop('Road')
dictAllUnicodeCities.pop('Street')
dictAllUnicodeCities.pop('30')
dictAllUnicodeCities.pop('North')
dictAllUnicodeCities.pop('West')
dictAllUnicodeCities.pop('Bridge')
dictAllUnicodeCities.pop('Model')
dictAllUnicodeCities.pop('Spa')
dictAllUnicodeCities.pop('Park')
dictAllUnicodeCities.pop('Bay')
dictAllUnicodeCities.pop('Home')
dictAllUnicodeCities.pop('List')
dictAllUnicodeCities.pop('China')
dictAllUnicodeCities.pop('Court')
dictAllUnicodeCities.pop('Wing')
dictAllUnicodeCities.pop('HDB')
dictAllUnicodeCities.pop('Al')
dictAllUnicodeCities.pop('Bin')
dictAllUnicodeCities.pop('A')
dictAllUnicodeCities.pop('Vista')
dictAllUnicodeCities.pop('Aria')
dictAllUnicodeCities.pop('No')
dictAllUnicodeCities.pop('I')
countries_cities = pd.read_json('../input/precise-location/countriescities.json')
citiesList = []
for i in range(len(countries_cities)):
for j in range(len(countries_cities['cities'][i])):
citiesList.append(countries_cities['cities'][i][j].get('name'))
states_only = pd.read_json('../input/precise-location/states.json')
statesCountriesDict = {}
for i in range(len(states_only)):
statesCountriesDict[states_only['name'][i]] = states_only['country_name'][i]
from collections import defaultdict
countries_states = pd.read_json('../input/precise-location/countriesstates.json')
statesList = []
statesNameToStateCode = defaultdict(list)
statesCodeToStatesName = defaultdict(list)
for i in range(len(countries_states)):
for j in range(len(countries_states['states'][i])):
statesList.append(countries_states['states'][i][j].get('name'))
statesNameToStateCode[countries_states['states'][i][j].get('name')].append(countries_states['states'][i][j].get('state_code'))
statesCodeToStatesName[countries_states['states'][i][j].get('state_code')].append(countries_states['states'][i][j].get('name'))
def get_ngram(text, WordsToCombine=2):
text = text.replace(',', '')
words = text.split()
output = []
for i in range(len(words) - WordsToCombine + 1):
output.append(' '.join(words[i:i + WordsToCombine]))
return output
from re import T
def detect_language(text):
nlp = spacy.blank('xx')
nlp.add_pipe('language_detector')
doc = nlp(text)
return doc._.language
cid_mapper = {country.name: country.alpha_2 for country in pycountry.countries}
@lru_cache(maxsize=128)
def preproc(locstr):
pattern = re.compile('#[0-9]+\\-[0-9]+|Headquarters|HQ|Town|Court|Access|via|City|Head|Bank|Center|Remote|Building|Office|City|Of| And|Transportation|D.C|feild|Metro|Tn.|Health Care|Health|Care|STE 100|Sector|Tower|[-&/|;:\\"]+|Area|Surrounding|[\\(\\)\\{\\}\\[\\]]|📍 |🌎', flags=re.IGNORECASE)
return pattern.sub(' ', locstr).strip().rstrip('.')
road_re = re.compile('^.*?(road|street)(?!\\w)', flags=re.IGNORECASE)
def get_road(locstr):
m = road_re.match(locstr)
if m:
return (m.group(), m.group(1))
else:
return (locstr, '')
def get_road_idn(locstr):
if locstr.split(' ')[0] in ('Jl', 'Jl.', 'JL', 'Jalan', 'jl', 'Jln', 'Jln.'):
return True
for i in locstr.split(' '):
for j in ['Jl', 'Jl.', 'JL', 'jl']:
if i == j:
return True
@lru_cache(maxsize=128)
def get_all_geoname(locstr):
try:
geolocator = Nominatim(user_agent='geopy25', timeout=3)
location = geolocator.geocode(locstr)
location = geolocator.reverse('{}, {}'.format(str(location.raw['lat']), str(location.raw['lon'])), exactly_one=True)
address = location.raw['address']
city = address.get('city', '')
state = address.get('state', '')
country = address.get('country', '')
return (city, state, country)
except:
pass
@lru_cache(maxsize=128)
def get_geoname_from_road(roadstr):
try:
geolocator = Nominatim(user_agent='geopy2', timeout=3)
location = geolocator.geocode(roadstr)
addr = location.address
addr = addr.split(',')
country = addr[-1]
state = addr[-3]
city = addr[-4]
return (city, state, country)
except:
pass
def get_result(countries):
countries = str(countries)
if countries is not '':
country_id = cid_mapper.get(countries.strip())
else:
country_id = ''
if country_id:
try:
continent_code = pc.country_alpha2_to_continent_code(country_id.strip())
except:
continent_code = ''
else:
continent_code = ''
if continent_code:
continent_name = pc.convert_continent_code_to_continent_name(continent_code.strip())
else:
continent_name = ''
return {'country': countries, 'country_code': country_id, 'region': continent_name, 'region_code': continent_code}
from itertools import count
def get_locations(locstr):
if (locstr == ""):
return {
"city":"",
"state":"",
"country":"",
"country_code": "",
"region":"",
"region_code":""}
states, cities, countries, country_id, continent_code, continent_name = "", "", "", "", "", ""
if detect_language(locstr) not in ['ja', 'ko', 'zh']:
locstr = preproc(locstr)
locstr = re.sub(r' , ', ', ', locstr)
locstr = re.sub(r',', ' ', locstr)
locstr = re.sub(r'\s\s+', ' ', locstr)
# print(locstr)
if locstr.isupper() or locstr.islower():
locstr = locstr.title()
locstr = ' '.join(re.sub(r"([A-Z]+[a-z])", r" \1", locstr).split())
# print(locstr)
for i in locstr.replace(',', '').split(' ')[::-1]:
if i in dictMultiLangCountry:
countries = dictMultiLangCountry[i]
if (countries != "") or (countries is not None):
# print('5 ' + countries)
return get_result(countries)
for i in locstr.replace(',', '').split(' ')[::-1]:
if i in dictAllUnicodeCities:
countries = dictAllUnicodeCities[i]
if len(countries) == 1:
countries = countries[0]
if (countries != "") or (countries is not None):
# print('6 ' + countries)
return get_result(countries)
else:
countries = ""
if (countries == "") :
trigram = get_ngram(locstr, 3)
if countries is "":
for gram in trigram:
if gram in dictMultiLangCountry:
countries = dictMultiLangCountry[gram]
if (countries != "") or (countries is not None):
# print('1 ' + countries)
return get_result(countries)
if gram in dictAllUnicodeCities:
countries = dictAllUnicodeCities[gram]
if len(countries) == 1:
countries = countries[0]
if (countries != "") or (countries is not None):
# print('2 ' + countries)
return get_result(countries)
else:
countries = ""
bigram = get_ngram(locstr)
if countries is "":
for gram in bigram:
if gram in dictMultiLangCountry:
countries = dictMultiLangCountry[gram]
if (countries != "") or (countries is not None):
# print('3 ' + countries)
return get_result(countries)
if gram in dictAllUnicodeCities:
countries = dictAllUnicodeCities[gram]
if len(countries) == 1:
countries = countries[0]
if (countries != "") or (countries is not None):
# print('4 ' + countries)
return get_result(countries)
else:
countries = ""
############################################################################
# ROAD REFORMATING (ex: 159, Sin Ming Road # 07-02 Lobby 2 Amtech Building
# --> 159, Sin Ming Road)
############################################################################
locstr, road = get_road(locstr)
if len(locstr.split(" ")[0]) == 1:
locstr = locstr[2:]
if get_road_idn(locstr):
countries = "Indonesia"
return get_result(countries)
############################################################################
# ROAD
############################################################################
if re.findall('[0-9]+', locstr) or road:
try:
cities, states, countries = get_geoname_from_road(locstr)
countries = countries.strip()
if countries is not "":
if countries in dictMultiLangCountry:
return get_result(countries)
if cities:
countries = dictAllUnicodeCities[cities]
if len(countries) == 1:
countries = countries[0]
else:
countries = ""
if countries is not "":
if countries in dictMultiLangCountry:
return get_result(countries)
if states:
countries = statesCountriesDict[states]
if countries is not "":
if countries in dictMultiLangCountry:
return get_result(countries)
except:
pass
############################################################################
## THIS CODE TO SOLVE CITY - STATE CODE FORMAT (ex: Bonney Lake, WA) #######
############################################################################
loc_split_by_comma = locstr.split(",")
if (len(loc_split_by_comma) == 2):
if len(loc_split_by_comma[-1].strip()):
if statesCodeToStatesName.get(loc_split_by_comma[-1].strip()) and len(statesCodeToStatesName.get(loc_split_by_comma[-1].strip())) > 1:
if loc_split_by_comma[0] in set(citiesList):
cities = loc_split_by_comma[0]
try:
cities, states, countries = get_all_geoname(locstr)
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
except:
states, countries = "", ""
if type(states) == list:
states = states[0]
if states:
countries = statesCountriesDict[states]
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
elif statesCodeToStatesName.get(loc_split_by_comma[-1].strip()) and len(statesCodeToStatesName.get(loc_split_by_comma[-1].strip())) == 1:
cities = loc_split_by_comma[0]
states = statesCodeToStatesName.get(loc_split_by_comma[-1].strip())
if type(states) == list:
states = states[0]
if states:
countries = statesCountriesDict[states]
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
else:
locstr = locstr
#########################################################################
#### IF CURRENT RESULT JUST HAS A CITY
#########################################################################
try:
if (cities != '') and (states == '') and (countries == ''):
_, states, countries = get_all_geoname(cities)
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
except:
country_id, countries = "", ""
#########################################################################
#### IF ALL KEY IS NULL / EMPTY STRING
#########################################################################
try:
if (cities is "") and (states is "") and (countries != '') or (countries == ''):
_, _, countries = get_all_geoname(locstr)
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
except:
country_id, countries = "", ""
return {
"country":"",
"country_code":"",
"region":"",
"region_code":""
}
sample = 'City of Fond du Lac'
loc = get_locations(sample)
sample = '3501 NW Lowell Street Suite 202, Silverdale, WA 98383'
loc = get_locations(sample)
sample = '8A Admiralty Road'
loc = get_locations(sample)
print(loc) | code |
105185927/cell_12 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from collections import defaultdict
from functools import lru_cache
from geopy.geocoders import Nominatim
from tqdm.auto import tqdm
import numpy as np
import pandas as pd
import pycountry
import pycountry_convert as pc
import re
import spacy
countries_only = pd.read_json('../input/precise-location/countries.json')
country_ex = pd.read_csv('../input/precise-location/country_iso_codes_expanded.csv')
country_ex = country_ex.fillna(' ')
contriesSet = []
for i in tqdm(range(len(countries_only))):
contriesSet.append(countries_only['name'][i])
contriesSet += list(countries_only['translations'][i].values())
contriesSet = list(set(contriesSet))
dictMultiLangCountry = {}
for i in tqdm(range(len(countries_only))):
dictMultiLangCountry[countries_only['name'][i]] = countries_only['name'][i]
mc = list(countries_only['translations'][i].values())
for c in mc:
dictMultiLangCountry[c] = countries_only['name'][i]
for i in tqdm(range(len(country_ex))):
for col in range(26):
if country_ex[f'alternative_country_name_{col}'][i] != ' ':
dictMultiLangCountry[country_ex[f'alternative_country_name_{col}'][i]] = country_ex['country'][i]
dictMultiLangCountry['China'] = 'China'
dictMultiLangCountry['SIngapore'] = 'Singapore'
dictMultiLangCountry.pop('')
geoname_cities = pd.read_csv('../input/precise-location/geonames-all-cities-with-a-population-1000.csv', sep=';')
dictAllUnicodeCities = dict()
for i in tqdm(range(len(geoname_cities))):
if geoname_cities.Name[i] in dictAllUnicodeCities:
dictAllUnicodeCities[geoname_cities.Name[i]].append(geoname_cities['Country name EN'][i])
dictAllUnicodeCities[geoname_cities.Name[i]] = list(set(dictAllUnicodeCities[geoname_cities.Name[i]]))
else:
dictAllUnicodeCities[geoname_cities.Name[i]] = [geoname_cities['Country name EN'][i]]
if geoname_cities['Alternate Names'][i] is not np.NaN:
alt_ = geoname_cities['Alternate Names'][i].split(',')
for city in alt_:
if city in dictAllUnicodeCities:
dictAllUnicodeCities[city].append(geoname_cities['Country name EN'][i])
dictAllUnicodeCities[city] = list(set(dictAllUnicodeCities[city]))
else:
dictAllUnicodeCities[city] = [geoname_cities['Country name EN'][i]]
dictAllUnicodeCities['Dhaka'] = ['Bangladesh']
dictAllUnicodeCities['Changi'] = ['Singapore']
dictAllUnicodeCities['Admiralty'] = ['Singapore']
dictAllUnicodeCities['North Bridge'] = ['Singapore']
dictAllUnicodeCities['Greater Bay'] = ['China']
dictAllUnicodeCities['Philadelphia'] = ['United States']
dictAllUnicodeCities['Manila'] = ['Philippines']
dictAllUnicodeCities['Irving'] = ['Singapore']
dictAllUnicodeCities['HDB Hub'] = ['Singapore']
dictAllUnicodeCities['s Heerenberg'] = ['Netherlands']
dictAllUnicodeCities['S Heerenberg'] = ['Netherlands']
dictAllUnicodeCities['Durham'] = ['United States']
dictAllUnicodeCities['dhabi'] = ['United Arab Emirates']
dictAllUnicodeCities['Scotland'] = ['United Kingdom']
dictAllUnicodeCities['Middle East'] = ['United Arab Emirates']
dictAllUnicodeCities['Alberta'] = ['Canada']
dictAllUnicodeCities['Colombo'] = ['Sri Lanka']
dictAllUnicodeCities['Phoenix'] = ['United States']
dictAllUnicodeCities.pop('Ahmad')
dictAllUnicodeCities.pop('Road')
dictAllUnicodeCities.pop('Street')
dictAllUnicodeCities.pop('30')
dictAllUnicodeCities.pop('North')
dictAllUnicodeCities.pop('West')
dictAllUnicodeCities.pop('Bridge')
dictAllUnicodeCities.pop('Model')
dictAllUnicodeCities.pop('Spa')
dictAllUnicodeCities.pop('Park')
dictAllUnicodeCities.pop('Bay')
dictAllUnicodeCities.pop('Home')
dictAllUnicodeCities.pop('List')
dictAllUnicodeCities.pop('China')
dictAllUnicodeCities.pop('Court')
dictAllUnicodeCities.pop('Wing')
dictAllUnicodeCities.pop('HDB')
dictAllUnicodeCities.pop('Al')
dictAllUnicodeCities.pop('Bin')
dictAllUnicodeCities.pop('A')
dictAllUnicodeCities.pop('Vista')
dictAllUnicodeCities.pop('Aria')
dictAllUnicodeCities.pop('No')
dictAllUnicodeCities.pop('I')
countries_cities = pd.read_json('../input/precise-location/countriescities.json')
citiesList = []
for i in range(len(countries_cities)):
for j in range(len(countries_cities['cities'][i])):
citiesList.append(countries_cities['cities'][i][j].get('name'))
states_only = pd.read_json('../input/precise-location/states.json')
statesCountriesDict = {}
for i in range(len(states_only)):
statesCountriesDict[states_only['name'][i]] = states_only['country_name'][i]
from collections import defaultdict
countries_states = pd.read_json('../input/precise-location/countriesstates.json')
statesList = []
statesNameToStateCode = defaultdict(list)
statesCodeToStatesName = defaultdict(list)
for i in range(len(countries_states)):
for j in range(len(countries_states['states'][i])):
statesList.append(countries_states['states'][i][j].get('name'))
statesNameToStateCode[countries_states['states'][i][j].get('name')].append(countries_states['states'][i][j].get('state_code'))
statesCodeToStatesName[countries_states['states'][i][j].get('state_code')].append(countries_states['states'][i][j].get('name'))
def get_ngram(text, WordsToCombine=2):
text = text.replace(',', '')
words = text.split()
output = []
for i in range(len(words) - WordsToCombine + 1):
output.append(' '.join(words[i:i + WordsToCombine]))
return output
from re import T
def detect_language(text):
nlp = spacy.blank('xx')
nlp.add_pipe('language_detector')
doc = nlp(text)
return doc._.language
cid_mapper = {country.name: country.alpha_2 for country in pycountry.countries}
@lru_cache(maxsize=128)
def preproc(locstr):
pattern = re.compile('#[0-9]+\\-[0-9]+|Headquarters|HQ|Town|Court|Access|via|City|Head|Bank|Center|Remote|Building|Office|City|Of| And|Transportation|D.C|feild|Metro|Tn.|Health Care|Health|Care|STE 100|Sector|Tower|[-&/|;:\\"]+|Area|Surrounding|[\\(\\)\\{\\}\\[\\]]|📍 |🌎', flags=re.IGNORECASE)
return pattern.sub(' ', locstr).strip().rstrip('.')
road_re = re.compile('^.*?(road|street)(?!\\w)', flags=re.IGNORECASE)
def get_road(locstr):
m = road_re.match(locstr)
if m:
return (m.group(), m.group(1))
else:
return (locstr, '')
def get_road_idn(locstr):
if locstr.split(' ')[0] in ('Jl', 'Jl.', 'JL', 'Jalan', 'jl', 'Jln', 'Jln.'):
return True
for i in locstr.split(' '):
for j in ['Jl', 'Jl.', 'JL', 'jl']:
if i == j:
return True
@lru_cache(maxsize=128)
def get_all_geoname(locstr):
try:
geolocator = Nominatim(user_agent='geopy25', timeout=3)
location = geolocator.geocode(locstr)
location = geolocator.reverse('{}, {}'.format(str(location.raw['lat']), str(location.raw['lon'])), exactly_one=True)
address = location.raw['address']
city = address.get('city', '')
state = address.get('state', '')
country = address.get('country', '')
return (city, state, country)
except:
pass
@lru_cache(maxsize=128)
def get_geoname_from_road(roadstr):
try:
geolocator = Nominatim(user_agent='geopy2', timeout=3)
location = geolocator.geocode(roadstr)
addr = location.address
addr = addr.split(',')
country = addr[-1]
state = addr[-3]
city = addr[-4]
return (city, state, country)
except:
pass
def get_result(countries):
countries = str(countries)
if countries is not '':
country_id = cid_mapper.get(countries.strip())
else:
country_id = ''
if country_id:
try:
continent_code = pc.country_alpha2_to_continent_code(country_id.strip())
except:
continent_code = ''
else:
continent_code = ''
if continent_code:
continent_name = pc.convert_continent_code_to_continent_name(continent_code.strip())
else:
continent_name = ''
return {'country': countries, 'country_code': country_id, 'region': continent_name, 'region_code': continent_code}
from itertools import count
def get_locations(locstr):
if (locstr == ""):
return {
"city":"",
"state":"",
"country":"",
"country_code": "",
"region":"",
"region_code":""}
states, cities, countries, country_id, continent_code, continent_name = "", "", "", "", "", ""
if detect_language(locstr) not in ['ja', 'ko', 'zh']:
locstr = preproc(locstr)
locstr = re.sub(r' , ', ', ', locstr)
locstr = re.sub(r',', ' ', locstr)
locstr = re.sub(r'\s\s+', ' ', locstr)
# print(locstr)
if locstr.isupper() or locstr.islower():
locstr = locstr.title()
locstr = ' '.join(re.sub(r"([A-Z]+[a-z])", r" \1", locstr).split())
# print(locstr)
for i in locstr.replace(',', '').split(' ')[::-1]:
if i in dictMultiLangCountry:
countries = dictMultiLangCountry[i]
if (countries != "") or (countries is not None):
# print('5 ' + countries)
return get_result(countries)
for i in locstr.replace(',', '').split(' ')[::-1]:
if i in dictAllUnicodeCities:
countries = dictAllUnicodeCities[i]
if len(countries) == 1:
countries = countries[0]
if (countries != "") or (countries is not None):
# print('6 ' + countries)
return get_result(countries)
else:
countries = ""
if (countries == "") :
trigram = get_ngram(locstr, 3)
if countries is "":
for gram in trigram:
if gram in dictMultiLangCountry:
countries = dictMultiLangCountry[gram]
if (countries != "") or (countries is not None):
# print('1 ' + countries)
return get_result(countries)
if gram in dictAllUnicodeCities:
countries = dictAllUnicodeCities[gram]
if len(countries) == 1:
countries = countries[0]
if (countries != "") or (countries is not None):
# print('2 ' + countries)
return get_result(countries)
else:
countries = ""
bigram = get_ngram(locstr)
if countries is "":
for gram in bigram:
if gram in dictMultiLangCountry:
countries = dictMultiLangCountry[gram]
if (countries != "") or (countries is not None):
# print('3 ' + countries)
return get_result(countries)
if gram in dictAllUnicodeCities:
countries = dictAllUnicodeCities[gram]
if len(countries) == 1:
countries = countries[0]
if (countries != "") or (countries is not None):
# print('4 ' + countries)
return get_result(countries)
else:
countries = ""
############################################################################
# ROAD REFORMATING (ex: 159, Sin Ming Road # 07-02 Lobby 2 Amtech Building
# --> 159, Sin Ming Road)
############################################################################
locstr, road = get_road(locstr)
if len(locstr.split(" ")[0]) == 1:
locstr = locstr[2:]
if get_road_idn(locstr):
countries = "Indonesia"
return get_result(countries)
############################################################################
# ROAD
############################################################################
if re.findall('[0-9]+', locstr) or road:
try:
cities, states, countries = get_geoname_from_road(locstr)
countries = countries.strip()
if countries is not "":
if countries in dictMultiLangCountry:
return get_result(countries)
if cities:
countries = dictAllUnicodeCities[cities]
if len(countries) == 1:
countries = countries[0]
else:
countries = ""
if countries is not "":
if countries in dictMultiLangCountry:
return get_result(countries)
if states:
countries = statesCountriesDict[states]
if countries is not "":
if countries in dictMultiLangCountry:
return get_result(countries)
except:
pass
############################################################################
## THIS CODE TO SOLVE CITY - STATE CODE FORMAT (ex: Bonney Lake, WA) #######
############################################################################
loc_split_by_comma = locstr.split(",")
if (len(loc_split_by_comma) == 2):
if len(loc_split_by_comma[-1].strip()):
if statesCodeToStatesName.get(loc_split_by_comma[-1].strip()) and len(statesCodeToStatesName.get(loc_split_by_comma[-1].strip())) > 1:
if loc_split_by_comma[0] in set(citiesList):
cities = loc_split_by_comma[0]
try:
cities, states, countries = get_all_geoname(locstr)
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
except:
states, countries = "", ""
if type(states) == list:
states = states[0]
if states:
countries = statesCountriesDict[states]
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
elif statesCodeToStatesName.get(loc_split_by_comma[-1].strip()) and len(statesCodeToStatesName.get(loc_split_by_comma[-1].strip())) == 1:
cities = loc_split_by_comma[0]
states = statesCodeToStatesName.get(loc_split_by_comma[-1].strip())
if type(states) == list:
states = states[0]
if states:
countries = statesCountriesDict[states]
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
else:
locstr = locstr
#########################################################################
#### IF CURRENT RESULT JUST HAS A CITY
#########################################################################
try:
if (cities != '') and (states == '') and (countries == ''):
_, states, countries = get_all_geoname(cities)
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
except:
country_id, countries = "", ""
#########################################################################
#### IF ALL KEY IS NULL / EMPTY STRING
#########################################################################
try:
if (cities is "") and (states is "") and (countries != '') or (countries == ''):
_, _, countries = get_all_geoname(locstr)
if countries is not "":
if countries in dictMultiLangCountry:
countries = dictMultiLangCountry[countries]
return get_result(countries)
except:
country_id, countries = "", ""
return {
"country":"",
"country_code":"",
"region":"",
"region_code":""
}
sample = 'City of Fond du Lac'
loc = get_locations(sample)
print(loc) | code |
17133813/cell_13 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
sns.set_style('whitegrid')
g = sns.catplot(x="Purchase", y="Gender", col="Age",
data=df.sort_values(by=['Age']), col_wrap=3,
orient="h", height=2, aspect=3, palette="Set3",
kind="violin", dodge=True, bw=.2)
df_target = df.groupby('Product_ID')['Product_ID'].count().reset_index(name='count').sort_values(['count'], ascending=False).head(10).reset_index(drop=True)
sns.set(style="whitegrid")
ax = sns.barplot(x="Product_ID", y="count", data=df_target)
ax.set_xlabel('Produtos')
ax.set_ylabel('Total vendido')
for item in ax.get_xticklabels():
item.set_rotation(90)
for i in range(len(df_target['Product_ID'])):
plt.text(x = i - 0.3 , y = df_target.loc[i,'count'] + 20 , s = df_target.loc[i,'count'], size = 8, color='Blue')
plt.show()
occupation_order = list(df['Occupation'].value_counts().head(5).index)
df_target = df[df['Occupation'].isin(occupation_order)].sort_values(by='Age')
plt.figure(figsize=(20,10))
g = sns.boxplot(x="Occupation", y="Purchase", hue="Age", data=df_target)
plt.title('Valores gastos por faixa etária associados às 5 ocupações mais frequentes\n', fontsize=16)
plt.xlabel('Ocupação')
plt.ylabel('Valor gasto')
plt.legend(loc=1, title='Idade')
plt.ylim(0, 35000)
plt.show()
df_target = df[df['Purchase'] > 9000].groupby(['Marital_Status', 'Occupation'])['Purchase'].count().reset_index(name='count').reset_index(drop=True)
g = sns.catplot(x='Marital_Status', y='count', col='Occupation', col_wrap=9, data=df_target, kind='bar', height=3, aspect=0.6)
g.set_axis_labels('', 'Estado Civil').despine(left=True) | code |
17133813/cell_4 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
sns.set_style('whitegrid')
sns.violinplot(x='Age', y='Purchase', cut=0, scale='count', data=df.sort_values(by=['Age'])) | code |
17133813/cell_6 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
sns.set_style('whitegrid')
g = sns.catplot(x='Purchase', y='Gender', col='Age', data=df.sort_values(by=['Age']), col_wrap=3, orient='h', height=2, aspect=3, palette='Set3', kind='violin', dodge=True, bw=0.2) | code |
17133813/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
sns.set_style('whitegrid')
g = sns.catplot(x="Purchase", y="Gender", col="Age",
data=df.sort_values(by=['Age']), col_wrap=3,
orient="h", height=2, aspect=3, palette="Set3",
kind="violin", dodge=True, bw=.2)
df_target = df.groupby('Product_ID')['Product_ID'].count().reset_index(name='count').sort_values(['count'], ascending=False).head(10).reset_index(drop=True)
sns.set(style='whitegrid')
ax = sns.barplot(x='Product_ID', y='count', data=df_target)
ax.set_xlabel('Produtos')
ax.set_ylabel('Total vendido')
for item in ax.get_xticklabels():
item.set_rotation(90)
for i in range(len(df_target['Product_ID'])):
plt.text(x=i - 0.3, y=df_target.loc[i, 'count'] + 20, s=df_target.loc[i, 'count'], size=8, color='Blue')
plt.show() | code |
17133813/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
df.head() | code |
17133813/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
from matplotlib import rc
import numpy as np
import pandas as pd
df = pd.read_csv('../input/BlackFriday.csv', delimiter=',')
sns.set_style('whitegrid')
g = sns.catplot(x="Purchase", y="Gender", col="Age",
data=df.sort_values(by=['Age']), col_wrap=3,
orient="h", height=2, aspect=3, palette="Set3",
kind="violin", dodge=True, bw=.2)
df_target = df.groupby('Product_ID')['Product_ID'].count().reset_index(name='count').sort_values(['count'], ascending=False).head(10).reset_index(drop=True)
sns.set(style="whitegrid")
ax = sns.barplot(x="Product_ID", y="count", data=df_target)
ax.set_xlabel('Produtos')
ax.set_ylabel('Total vendido')
for item in ax.get_xticklabels():
item.set_rotation(90)
for i in range(len(df_target['Product_ID'])):
plt.text(x = i - 0.3 , y = df_target.loc[i,'count'] + 20 , s = df_target.loc[i,'count'], size = 8, color='Blue')
plt.show()
occupation_order = list(df['Occupation'].value_counts().head(5).index)
df_target = df[df['Occupation'].isin(occupation_order)].sort_values(by='Age')
plt.figure(figsize=(20, 10))
g = sns.boxplot(x='Occupation', y='Purchase', hue='Age', data=df_target)
plt.title('Valores gastos por faixa etária associados às 5 ocupações mais frequentes\n', fontsize=16)
plt.xlabel('Ocupação')
plt.ylabel('Valor gasto')
plt.legend(loc=1, title='Idade')
plt.ylim(0, 35000)
plt.show() | code |
130004668/cell_21 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from tqdm import tqdm
import matplotlib.pyplot as plt
import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes
df_reviews_untrimmed_sample = df_reviews_raw.groupby('label').apply(lambda x: x.sample(25000, random_state=42)).reset_index(drop=True)
df_reviews_trimmed = df_reviews_untrimmed_sample[df_reviews_untrimmed_sample.content.str.split().str.len().ge(60)]
df_reviews_sampled = df_reviews_trimmed.groupby('label').apply(lambda x: x.sample(2000, random_state=42)).reset_index(drop=True)
df_reviews = df_reviews_sampled.sample(frac=1, random_state=42).reset_index(drop=True)
df_reviews['geo_loc'] = OrdinalEncoder().fit_transform(df_reviews.geo_loc.values.reshape(-1, 1))
df_reviews['tld'] = OrdinalEncoder().fit_transform(df_reviews.tld.values.reshape(-1, 1))
df_reviews['who_is'] = OrdinalEncoder().fit_transform(df_reviews.who_is.values.reshape(-1, 1))
df_reviews['https'] = OrdinalEncoder().fit_transform(df_reviews.https.values.reshape(-1, 1))
df_reviews['label'] = OrdinalEncoder().fit_transform(df_reviews.label.values.reshape(-1, 1))
df_reviews['url'] = df_reviews.url.apply(lambda x: ' '.join(x.split('://')[1].strip('www.').replace('.', '/').split('/')))
tqdm.pandas()
stop = stopwords.words()
df_reviews.content = df_reviews.content.str.replace('[^\\w\\s]', '').str.lower()
df_reviews.content = df_reviews.content.progress_apply(lambda x: ' '.join([item for item in x.split() if item not in stop]))
df_reviews.url = df_reviews.url.str.replace('[^\\w\\s]', '').str.lower()
df_reviews.url = df_reviews.url.progress_apply(lambda x: ' '.join([item for item in x.split() if item not in stop]))
tfidf = TfidfVectorizer(min_df=5, max_df=0.95, max_features=8000, stop_words='english')
tfidf.fit(df_reviews.url)
url_tfidf = tfidf.transform(df_reviews.url)
tfidf.fit(df_reviews.content)
content_tfidf = tfidf.transform(df_reviews.content)
def find_optimal_clusters(data, max_k):
k_list = range(2, max_k+1)
sse = []
for k in k_list:
sse.append(MiniBatchKMeans(n_clusters=k, init_size=1024, batch_size=2048, random_state=20).fit(data).inertia_)
plt.style.use("dark_background")
f, ax = plt.subplots(1, 1)
ax.plot(k_list, sse, marker='o')
ax.set_xlabel('Cluster Centers')
ax.set_xticks(k_list)
ax.set_xticklabels(k_list)
ax.set_ylabel('SSE')
ax.set_title('SSE by Cluster Center Plot')
find_optimal_clusters(url_tfidf, 20) | code |
130004668/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes
df_reviews_untrimmed_sample = df_reviews_raw.groupby('label').apply(lambda x: x.sample(25000, random_state=42)).reset_index(drop=True)
df_reviews_trimmed = df_reviews_untrimmed_sample[df_reviews_untrimmed_sample.content.str.split().str.len().ge(60)]
df_reviews_sampled = df_reviews_trimmed.groupby('label').apply(lambda x: x.sample(2000, random_state=42)).reset_index(drop=True)
df_reviews = df_reviews_sampled.sample(frac=1, random_state=42).reset_index(drop=True)
df_reviews[['geo_loc', 'tld', 'who_is', 'https', 'label']].describe() | code |
130004668/cell_9 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes
df_reviews_raw.label.describe() | code |
130004668/cell_34 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.ensemble import RandomForestClassifier
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from sklearn.tree import DecisionTreeClassifier
from tqdm import tqdm
import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes
df_reviews_untrimmed_sample = df_reviews_raw.groupby('label').apply(lambda x: x.sample(25000, random_state=42)).reset_index(drop=True)
df_reviews_trimmed = df_reviews_untrimmed_sample[df_reviews_untrimmed_sample.content.str.split().str.len().ge(60)]
df_reviews_sampled = df_reviews_trimmed.groupby('label').apply(lambda x: x.sample(2000, random_state=42)).reset_index(drop=True)
df_reviews = df_reviews_sampled.sample(frac=1, random_state=42).reset_index(drop=True)
df_reviews['geo_loc'] = OrdinalEncoder().fit_transform(df_reviews.geo_loc.values.reshape(-1, 1))
df_reviews['tld'] = OrdinalEncoder().fit_transform(df_reviews.tld.values.reshape(-1, 1))
df_reviews['who_is'] = OrdinalEncoder().fit_transform(df_reviews.who_is.values.reshape(-1, 1))
df_reviews['https'] = OrdinalEncoder().fit_transform(df_reviews.https.values.reshape(-1, 1))
df_reviews['label'] = OrdinalEncoder().fit_transform(df_reviews.label.values.reshape(-1, 1))
df_reviews['url'] = df_reviews.url.apply(lambda x: ' '.join(x.split('://')[1].strip('www.').replace('.', '/').split('/')))
tqdm.pandas()
stop = stopwords.words()
df_reviews.content = df_reviews.content.str.replace('[^\\w\\s]', '').str.lower()
df_reviews.content = df_reviews.content.progress_apply(lambda x: ' '.join([item for item in x.split() if item not in stop]))
df_reviews.url = df_reviews.url.str.replace('[^\\w\\s]', '').str.lower()
df_reviews.url = df_reviews.url.progress_apply(lambda x: ' '.join([item for item in x.split() if item not in stop]))
param_grid = [{'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random']}]
grid = GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), param_grid=param_grid, cv=5)
grid.fit(X_train, y_train)
grid.best_params_
grid.score(X_train, y_train)
grid.score(X_test, y_test)
param_grid = [{'n_estimators': [x for x in range(10, 120, 10)], 'criterion': ['gini', 'entropy']}]
grid = GridSearchCV(estimator=RandomForestClassifier(random_state=42), param_grid=param_grid, cv=5)
grid.fit(X_train, y_train) | code |
130004668/cell_23 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.cluster import MiniBatchKMeans
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.preprocessing import StandardScaler, OrdinalEncoder
from tqdm import tqdm
import matplotlib.pyplot as plt
import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes
df_reviews_untrimmed_sample = df_reviews_raw.groupby('label').apply(lambda x: x.sample(25000, random_state=42)).reset_index(drop=True)
df_reviews_trimmed = df_reviews_untrimmed_sample[df_reviews_untrimmed_sample.content.str.split().str.len().ge(60)]
df_reviews_sampled = df_reviews_trimmed.groupby('label').apply(lambda x: x.sample(2000, random_state=42)).reset_index(drop=True)
df_reviews = df_reviews_sampled.sample(frac=1, random_state=42).reset_index(drop=True)
df_reviews['geo_loc'] = OrdinalEncoder().fit_transform(df_reviews.geo_loc.values.reshape(-1, 1))
df_reviews['tld'] = OrdinalEncoder().fit_transform(df_reviews.tld.values.reshape(-1, 1))
df_reviews['who_is'] = OrdinalEncoder().fit_transform(df_reviews.who_is.values.reshape(-1, 1))
df_reviews['https'] = OrdinalEncoder().fit_transform(df_reviews.https.values.reshape(-1, 1))
df_reviews['label'] = OrdinalEncoder().fit_transform(df_reviews.label.values.reshape(-1, 1))
df_reviews['url'] = df_reviews.url.apply(lambda x: ' '.join(x.split('://')[1].strip('www.').replace('.', '/').split('/')))
tqdm.pandas()
stop = stopwords.words()
df_reviews.content = df_reviews.content.str.replace('[^\\w\\s]', '').str.lower()
df_reviews.content = df_reviews.content.progress_apply(lambda x: ' '.join([item for item in x.split() if item not in stop]))
df_reviews.url = df_reviews.url.str.replace('[^\\w\\s]', '').str.lower()
df_reviews.url = df_reviews.url.progress_apply(lambda x: ' '.join([item for item in x.split() if item not in stop]))
tfidf = TfidfVectorizer(min_df=5, max_df=0.95, max_features=8000, stop_words='english')
tfidf.fit(df_reviews.url)
url_tfidf = tfidf.transform(df_reviews.url)
tfidf.fit(df_reviews.content)
content_tfidf = tfidf.transform(df_reviews.content)
def find_optimal_clusters(data, max_k):
k_list = range(2, max_k+1)
sse = []
for k in k_list:
sse.append(MiniBatchKMeans(n_clusters=k, init_size=1024, batch_size=2048, random_state=20).fit(data).inertia_)
plt.style.use("dark_background")
f, ax = plt.subplots(1, 1)
ax.plot(k_list, sse, marker='o')
ax.set_xlabel('Cluster Centers')
ax.set_xticks(k_list)
ax.set_xticklabels(k_list)
ax.set_ylabel('SSE')
ax.set_title('SSE by Cluster Center Plot')
find_optimal_clusters(content_tfidf, 20) | code |
130004668/cell_30 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.tree import DecisionTreeClassifier
param_grid = [{'criterion': ['gini', 'entropy'], 'splitter': ['best', 'random']}]
grid = GridSearchCV(estimator=DecisionTreeClassifier(random_state=42), param_grid=param_grid, cv=5)
grid.fit(X_train, y_train)
grid.best_params_ | code |
130004668/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_reviews_raw = pd.read_csv('/kaggle/input/dataset-of-malicious-and-benign-webpages/Webpages_Classification_train_data.csv/Webpages_Classification_train_data.csv').drop(['Unnamed: 0'], axis=1)
df_reviews_raw.isna().sum()
df_reviews_raw.dtypes | code |
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