path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
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stringclasses 1
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|---|---|---|---|
121154806/cell_19 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
import segmentation_models as sm
import tensorflow as tf
sm.set_framework('tf.keras')
sm.framework()
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif', 'gif', 'ppm')
def Data_sorting(input_data, target_data, exts):
images = sorted([os.path.join(input_data, fname) for fname in os.listdir(input_data) if fname.endswith(exts) and (not fname.startswith('.'))])
masks = sorted([os.path.join(target_data, fname) for fname in os.listdir(target_data) if fname.endswith(exts) and (not fname.startswith('.'))])
return (images, masks)
def Create_Dataset(folder_path, is_mask, img_height, img_width, img_channels):
length = len(folder_path)
X = np.zeros((length, img_height, img_width, img_channels), dtype=np.uint8)
y = np.zeros((length, img_height, img_width, 1), dtype=np.bool)
if not is_mask:
for id, fname in tqdm(enumerate(folder_path)):
if fname.endswith('gif'):
img = gif2numpy.convert(fname)[0][0]
else:
img = cv2.imread(fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (img_height, img_width))
X[id] = img
return X
if is_mask:
for id, fname in tqdm(enumerate(folder_path)):
if fname.endswith('gif'):
img = gif2numpy.convert(fname)[0][0]
else:
img = cv2.imread(fname)
img = cv2.resize(img, (img_height, img_width))
img = img[:, :, 0]
img = np.expand_dims(img, axis=-1)
y[id] = img
return y
IMG_HEIGHT = 512
IMG_WIDTH = 512
IMG_CHANNELS = 3
X_train = Create_Dataset(folder_path=images_drive_train, is_mask=False, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=IMG_CHANNELS)
X_test = Create_Dataset(folder_path=images_drive_test, is_mask=False, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=IMG_CHANNELS)
y_train = Create_Dataset(folder_path=masks_drive_train, is_mask=True, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=1)
y_test = Create_Dataset(folder_path=masks_drive_test, is_mask=True, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=1)
BATCH_SIZE = 5
EPOCHS = 400
N_CLASS = 1
ACTIVATION = 'sigmoid'
CALLBACKS = [tf.keras.callbacks.EarlyStopping(patience=10, monitor='val_loss')]
def Model_Training(model_list, batch_size, epochs, callbacks):
model_dict = {}
for key, dict_i in model_list.items():
if dict_i['Train'] == True:
model = dict_i['model']
model.compile('Adam', loss='binary_crossentropy', metrics=['accuracy', 'binary_crossentropy', sm.losses.bce_jaccard_loss, sm.metrics.iou_score])
model.fit(x=X_train, y=y_train, batch_size=batch_size, epochs=epochs, validation_split=0.25, verbose=2, callbacks=callbacks)
model_dict[key] = model
return model_dict
model_list = {'unet-efficientnetb0': {'model': sm.Unet('efficientnetb0', classes=N_CLASS, activation=ACTIVATION), 'Train': True}, 'linknet-efficientnetb0': {'model': sm.Linknet('efficientnetb0', classes=N_CLASS, activation=ACTIVATION), 'Train': True}}
model_dict = Model_Training(model_list, batch_size=BATCH_SIZE, epochs=EPOCHS, callbacks=CALLBACKS) | code |
121154806/cell_1 | [
"text_plain_output_1.png"
] | !pip install -U segmentation-models
!pip install gif2numpy
import cv2
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
import segmentation_models as sm
import matplotlib.pyplot as plt
import os
from tqdm import tqdm
import gif2numpy | code |
121154806/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
import segmentation_models as sm
import tensorflow as tf
sm.set_framework('tf.keras')
sm.framework()
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif', 'gif', 'ppm')
def Data_sorting(input_data, target_data, exts):
images = sorted([os.path.join(input_data, fname) for fname in os.listdir(input_data) if fname.endswith(exts) and (not fname.startswith('.'))])
masks = sorted([os.path.join(target_data, fname) for fname in os.listdir(target_data) if fname.endswith(exts) and (not fname.startswith('.'))])
return (images, masks)
def Create_Dataset(folder_path, is_mask, img_height, img_width, img_channels):
length = len(folder_path)
X = np.zeros((length, img_height, img_width, img_channels), dtype=np.uint8)
y = np.zeros((length, img_height, img_width, 1), dtype=np.bool)
if not is_mask:
for id, fname in tqdm(enumerate(folder_path)):
if fname.endswith('gif'):
img = gif2numpy.convert(fname)[0][0]
else:
img = cv2.imread(fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (img_height, img_width))
X[id] = img
return X
if is_mask:
for id, fname in tqdm(enumerate(folder_path)):
if fname.endswith('gif'):
img = gif2numpy.convert(fname)[0][0]
else:
img = cv2.imread(fname)
img = cv2.resize(img, (img_height, img_width))
img = img[:, :, 0]
img = np.expand_dims(img, axis=-1)
y[id] = img
return y
IMG_HEIGHT = 512
IMG_WIDTH = 512
IMG_CHANNELS = 3
X_train = Create_Dataset(folder_path=images_drive_train, is_mask=False, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=IMG_CHANNELS)
X_test = Create_Dataset(folder_path=images_drive_test, is_mask=False, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=IMG_CHANNELS)
y_train = Create_Dataset(folder_path=masks_drive_train, is_mask=True, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=1)
y_test = Create_Dataset(folder_path=masks_drive_test, is_mask=True, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=1)
BATCH_SIZE = 5
EPOCHS = 400
N_CLASS = 1
ACTIVATION = 'sigmoid'
CALLBACKS = [tf.keras.callbacks.EarlyStopping(patience=10, monitor='val_loss')]
def Model_Training(model_list, batch_size, epochs, callbacks):
model_dict = {}
for key, dict_i in model_list.items():
if dict_i['Train'] == True:
model = dict_i['model']
model.compile('Adam', loss='binary_crossentropy', metrics=['accuracy', 'binary_crossentropy', sm.losses.bce_jaccard_loss, sm.metrics.iou_score])
model.fit(x=X_train, y=y_train, batch_size=batch_size, epochs=epochs, validation_split=0.25, verbose=2, callbacks=callbacks)
model_dict[key] = model
return model_dict
model_list = {'unet-efficientnetb0': {'model': sm.Unet('efficientnetb0', classes=N_CLASS, activation=ACTIVATION), 'Train': True}, 'linknet-efficientnetb0': {'model': sm.Linknet('efficientnetb0', classes=N_CLASS, activation=ACTIVATION), 'Train': True}} | code |
121154806/cell_8 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import os
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif', 'gif', 'ppm')
def Data_sorting(input_data, target_data, exts):
images = sorted([os.path.join(input_data, fname) for fname in os.listdir(input_data) if fname.endswith(exts) and (not fname.startswith('.'))])
masks = sorted([os.path.join(target_data, fname) for fname in os.listdir(target_data) if fname.endswith(exts) and (not fname.startswith('.'))])
return (images, masks)
input_data_drive_train = os.path.join(root, 'training/images')
target_data_drive_train = os.path.join(root, 'training/1st_manual')
images_drive_train, masks_drive_train = Data_sorting(input_data_drive_train, target_data_drive_train, exts)
input_data_drive_test = os.path.join(root, 'test/images')
target_data_drive_test = os.path.join(root, 'test/mask')
images_drive_test, masks_drive_test = Data_sorting(input_data_drive_test, target_data_drive_test, exts) | code |
121154806/cell_14 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif', 'gif', 'ppm')
def Data_sorting(input_data, target_data, exts):
images = sorted([os.path.join(input_data, fname) for fname in os.listdir(input_data) if fname.endswith(exts) and (not fname.startswith('.'))])
masks = sorted([os.path.join(target_data, fname) for fname in os.listdir(target_data) if fname.endswith(exts) and (not fname.startswith('.'))])
return (images, masks)
def Create_Dataset(folder_path, is_mask, img_height, img_width, img_channels):
length = len(folder_path)
X = np.zeros((length, img_height, img_width, img_channels), dtype=np.uint8)
y = np.zeros((length, img_height, img_width, 1), dtype=np.bool)
if not is_mask:
for id, fname in tqdm(enumerate(folder_path)):
if fname.endswith('gif'):
img = gif2numpy.convert(fname)[0][0]
else:
img = cv2.imread(fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (img_height, img_width))
X[id] = img
return X
if is_mask:
for id, fname in tqdm(enumerate(folder_path)):
if fname.endswith('gif'):
img = gif2numpy.convert(fname)[0][0]
else:
img = cv2.imread(fname)
img = cv2.resize(img, (img_height, img_width))
img = img[:, :, 0]
img = np.expand_dims(img, axis=-1)
y[id] = img
return y
IMG_HEIGHT = 512
IMG_WIDTH = 512
IMG_CHANNELS = 3
X_train = Create_Dataset(folder_path=images_drive_train, is_mask=False, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=IMG_CHANNELS)
X_test = Create_Dataset(folder_path=images_drive_test, is_mask=False, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=IMG_CHANNELS)
y_train = Create_Dataset(folder_path=masks_drive_train, is_mask=True, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=1)
y_test = Create_Dataset(folder_path=masks_drive_test, is_mask=True, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=1)
def visualize(**images):
"""PLot images in one row."""
n = len(images)
for i, (name, image) in enumerate(images.items()):
plt.xticks([])
plt.yticks([])
for img, msk in zip(X_train[:6], y_train[:6]):
visualize(image=img, gt_mask=np.squeeze(msk)) | code |
121154806/cell_12 | [
"text_plain_output_1.png"
] | from tqdm import tqdm
import cv2
import gif2numpy
import matplotlib.pyplot as plt
import numpy as np
import os
root = '/kaggle/input/retinal-vessel-segmentation/DRIVE/'
exts = ('jpg', 'JPG', 'png', 'PNG', 'tif', 'gif', 'ppm')
def Data_sorting(input_data, target_data, exts):
images = sorted([os.path.join(input_data, fname) for fname in os.listdir(input_data) if fname.endswith(exts) and (not fname.startswith('.'))])
masks = sorted([os.path.join(target_data, fname) for fname in os.listdir(target_data) if fname.endswith(exts) and (not fname.startswith('.'))])
return (images, masks)
def Create_Dataset(folder_path, is_mask, img_height, img_width, img_channels):
length = len(folder_path)
X = np.zeros((length, img_height, img_width, img_channels), dtype=np.uint8)
y = np.zeros((length, img_height, img_width, 1), dtype=np.bool)
if not is_mask:
for id, fname in tqdm(enumerate(folder_path)):
if fname.endswith('gif'):
img = gif2numpy.convert(fname)[0][0]
else:
img = cv2.imread(fname)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
img = cv2.resize(img, (img_height, img_width))
X[id] = img
return X
if is_mask:
for id, fname in tqdm(enumerate(folder_path)):
if fname.endswith('gif'):
img = gif2numpy.convert(fname)[0][0]
else:
img = cv2.imread(fname)
img = cv2.resize(img, (img_height, img_width))
img = img[:, :, 0]
img = np.expand_dims(img, axis=-1)
y[id] = img
return y
IMG_HEIGHT = 512
IMG_WIDTH = 512
IMG_CHANNELS = 3
X_train = Create_Dataset(folder_path=images_drive_train, is_mask=False, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=IMG_CHANNELS)
X_test = Create_Dataset(folder_path=images_drive_test, is_mask=False, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=IMG_CHANNELS)
y_train = Create_Dataset(folder_path=masks_drive_train, is_mask=True, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=1)
y_test = Create_Dataset(folder_path=masks_drive_test, is_mask=True, img_height=IMG_HEIGHT, img_width=IMG_WIDTH, img_channels=1)
plt.imshow(X_test[5]) | code |
90124098/cell_9 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x = df.iloc[:, [3, 4]]
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
km = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
km.fit(x)
wcss.append(km.inertia_)
x = np.array(x)
km = KMeans(n_clusters=5, init='k-means++', max_iter=300, n_init=10, random_state=0)
y_means = km.fit_predict(x)
plt.scatter(x[y_means == 0, 0], x[y_means == 0, 1], s=100, c='pink', label='Cluster 1')
plt.scatter(x[y_means == 1, 0], x[y_means == 1, 1], s=100, c='yellow', label='Cluster 2')
plt.scatter(x[y_means == 2, 0], x[y_means == 2, 1], s=100, c='cyan', label='Cluster 3')
plt.scatter(x[y_means == 3, 0], x[y_means == 3, 1], s=100, c='magenta', label='Cluster 4')
plt.scatter(x[y_means == 4, 0], x[y_means == 4, 1], s=100, c='orange', label='Cluster 5')
plt.scatter(km.cluster_centers_[:, 0], km.cluster_centers_[:, 1], s=50, c='blue', label='Centeroid')
plt.style.use('fivethirtyeight')
plt.title('K Means Clustering', fontsize=20)
plt.xlabel('Annual Income')
plt.ylabel('Spending Score')
plt.legend()
plt.grid()
plt.show() | code |
90124098/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.info() | code |
90124098/cell_8 | [
"image_output_1.png"
] | from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x = df.iloc[:, [3, 4]]
from sklearn.cluster import KMeans
wcss = []
for i in range(1, 11):
km = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)
km.fit(x)
wcss.append(km.inertia_)
plt.plot(range(1, 11), wcss)
plt.title('The Elbow Method', fontsize=20)
plt.xlabel('No. of Clusters')
plt.ylabel('wcss')
plt.show() | code |
90124098/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
df.head() | code |
90124098/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/customer-segmentation-tutorial-in-python/Mall_Customers.csv')
x = df.iloc[:, [3, 4]]
x.head() | code |
1006487/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
data[data['category'] == 'robotics']['tags'][0:20] | code |
1006487/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
def list_to_str(lists):
strs = ''
for content in lists:
strs += content
return strs
def to_plain_text(dataframe):
text = list_to_str(dataframe['all_text'].apply(lambda x: x.replace('\n', ' ')).tolist())
return text
def to_nltk_text(dataframe):
text = to_plain_text(dataframe)
return nltk.Text(nltk.word_tokenize(text))
all_text = to_nltk_text(data)
robotics = to_nltk_text(data[data['category'] == 'biology'])
def freqDist(text):
freqDist = {}
for word in text:
if word in freqDist:
freqDist[word] += 1
else:
freqDist[word] = 1
return freqDist
def relativeFreq(subset, alls, sort=True, adjusted=True):
result = [' '] * len(subset)
modifier = 1
for i, key in enumerate(subset.keys()):
if adjusted == True:
if alls[key] > 100:
modifier = 1
else:
modifier = 0
tf = float(subset[key]) / alls[key]
result[i] = (key, tf * modifier)
if sort == True:
result.sort(key=lambda tup: tup[1], reverse=True)
return result
Fdist_all = freqDist(all_text)
teaser = list(Fdist_all.values())
type(teaser)
Fdist_all['what'] | code |
1006487/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
def list_to_str(lists):
strs = ''
for content in lists:
strs += content
return strs
def to_plain_text(dataframe):
text = list_to_str(dataframe['all_text'].apply(lambda x: x.replace('\n', ' ')).tolist())
return text
def to_nltk_text(dataframe):
text = to_plain_text(dataframe)
return nltk.Text(nltk.word_tokenize(text))
all_text = to_nltk_text(data)
robotics = to_nltk_text(data[data['category'] == 'biology'])
def freqDist(text):
freqDist = {}
for word in text:
if word in freqDist:
freqDist[word] += 1
else:
freqDist[word] = 1
return freqDist
def relativeFreq(subset, alls, sort=True, adjusted=True):
result = [' '] * len(subset)
modifier = 1
for i, key in enumerate(subset.keys()):
if adjusted == True:
if alls[key] > 100:
modifier = 1
else:
modifier = 0
tf = float(subset[key]) / alls[key]
result[i] = (key, tf * modifier)
if sort == True:
result.sort(key=lambda tup: tup[1], reverse=True)
return result
Fdist_all = freqDist(all_text)
teaser = list(Fdist_all.values())
type(teaser)
Fdist_robotics = freqDist(robotics)
relative_Freq = relativeFreq(Fdist_robotics, Fdist_all)
relative_Freq[0:100] | code |
1006487/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
data[0:5] | code |
1006487/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from subprocess import check_output
import numpy as np
import pandas as pd
import nltk
import re
from bs4 import BeautifulSoup
from nltk.corpus import stopwords
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfTransformer
from string import punctuation
stop = set(stopwords.words('english'))
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1006487/cell_11 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
def list_to_str(lists):
strs = ''
for content in lists:
strs += content
return strs
def to_plain_text(dataframe):
text = list_to_str(dataframe['all_text'].apply(lambda x: x.replace('\n', ' ')).tolist())
return text
def to_nltk_text(dataframe):
text = to_plain_text(dataframe)
return nltk.Text(nltk.word_tokenize(text))
all_text = to_nltk_text(data)
robotics = to_nltk_text(data[data['category'] == 'biology'])
def freqDist(text):
freqDist = {}
for word in text:
if word in freqDist:
freqDist[word] += 1
else:
freqDist[word] = 1
return freqDist
def relativeFreq(subset, alls, sort=True, adjusted=True):
result = [' '] * len(subset)
modifier = 1
for i, key in enumerate(subset.keys()):
if adjusted == True:
if alls[key] > 100:
modifier = 1
else:
modifier = 0
tf = float(subset[key]) / alls[key]
result[i] = (key, tf * modifier)
if sort == True:
result.sort(key=lambda tup: tup[1], reverse=True)
return result
Fdist_all = freqDist(all_text)
teaser = list(Fdist_all.values())
type(teaser)
print(float(sum(teaser)) / len(teaser))
sns.distplot(teaser) | code |
1006487/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
def list_to_str(lists):
strs = ''
for content in lists:
strs += content
return strs
def to_plain_text(dataframe):
text = list_to_str(dataframe['all_text'].apply(lambda x: x.replace('\n', ' ')).tolist())
return text
def to_nltk_text(dataframe):
text = to_plain_text(dataframe)
return nltk.Text(nltk.word_tokenize(text))
all_text = to_nltk_text(data)
robotics = to_nltk_text(data[data['category'] == 'biology'])
def freqDist(text):
freqDist = {}
for word in text:
if word in freqDist:
freqDist[word] += 1
else:
freqDist[word] = 1
return freqDist
def relativeFreq(subset, alls, sort=True, adjusted=True):
result = [' '] * len(subset)
modifier = 1
for i, key in enumerate(subset.keys()):
if adjusted == True:
if alls[key] > 100:
modifier = 1
else:
modifier = 0
tf = float(subset[key]) / alls[key]
result[i] = (key, tf * modifier)
if sort == True:
result.sort(key=lambda tup: tup[1], reverse=True)
return result
Fdist_robotics = freqDist(robotics)
Fdist_robotics['while1'] | code |
1006487/cell_18 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
def list_to_str(lists):
strs = ''
for content in lists:
strs += content
return strs
def to_plain_text(dataframe):
text = list_to_str(dataframe['all_text'].apply(lambda x: x.replace('\n', ' ')).tolist())
return text
def to_nltk_text(dataframe):
text = to_plain_text(dataframe)
return nltk.Text(nltk.word_tokenize(text))
all_text = to_nltk_text(data)
robotics = to_nltk_text(data[data['category'] == 'biology'])
def freqDist(text):
freqDist = {}
for word in text:
if word in freqDist:
freqDist[word] += 1
else:
freqDist[word] = 1
return freqDist
def relativeFreq(subset, alls, sort=True, adjusted=True):
result = [' '] * len(subset)
modifier = 1
for i, key in enumerate(subset.keys()):
if adjusted == True:
if alls[key] > 100:
modifier = 1
else:
modifier = 0
tf = float(subset[key]) / alls[key]
result[i] = (key, tf * modifier)
if sort == True:
result.sort(key=lambda tup: tup[1], reverse=True)
return result
Fdist_all = freqDist(all_text)
teaser = list(Fdist_all.values())
type(teaser)
Fdist_all['while1'] | code |
1006487/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
def list_to_str(lists):
strs = ''
for content in lists:
strs += content
return strs
def to_plain_text(dataframe):
text = list_to_str(dataframe['all_text'].apply(lambda x: x.replace('\n', ' ')).tolist())
return text
def to_nltk_text(dataframe):
text = to_plain_text(dataframe)
return nltk.Text(nltk.word_tokenize(text))
all_text = to_nltk_text(data)
robotics = to_nltk_text(data[data['category'] == 'biology']) | code |
1006487/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
def list_to_str(lists):
strs = ''
for content in lists:
strs += content
return strs
def to_plain_text(dataframe):
text = list_to_str(dataframe['all_text'].apply(lambda x: x.replace('\n', ' ')).tolist())
return text
def to_nltk_text(dataframe):
text = to_plain_text(dataframe)
return nltk.Text(nltk.word_tokenize(text))
all_text = to_nltk_text(data)
robotics = to_nltk_text(data[data['category'] == 'biology'])
def freqDist(text):
freqDist = {}
for word in text:
if word in freqDist:
freqDist[word] += 1
else:
freqDist[word] = 1
return freqDist
def relativeFreq(subset, alls, sort=True, adjusted=True):
result = [' '] * len(subset)
modifier = 1
for i, key in enumerate(subset.keys()):
if adjusted == True:
if alls[key] > 100:
modifier = 1
else:
modifier = 0
tf = float(subset[key]) / alls[key]
result[i] = (key, tf * modifier)
if sort == True:
result.sort(key=lambda tup: tup[1], reverse=True)
return result
Fdist_all = freqDist(all_text)
teaser = list(Fdist_all.values())
type(teaser)
Fdist_robotics = freqDist(robotics)
Fdist_robotics['rna'] / Fdist_all['rna'] | code |
1006487/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
def list_to_str(lists):
strs = ''
for content in lists:
strs += content
return strs
def to_plain_text(dataframe):
text = list_to_str(dataframe['all_text'].apply(lambda x: x.replace('\n', ' ')).tolist())
return text
def to_nltk_text(dataframe):
text = to_plain_text(dataframe)
return nltk.Text(nltk.word_tokenize(text))
all_text = to_nltk_text(data)
robotics = to_nltk_text(data[data['category'] == 'biology'])
def freqDist(text):
freqDist = {}
for word in text:
if word in freqDist:
freqDist[word] += 1
else:
freqDist[word] = 1
return freqDist
def relativeFreq(subset, alls, sort=True, adjusted=True):
result = [' '] * len(subset)
modifier = 1
for i, key in enumerate(subset.keys()):
if adjusted == True:
if alls[key] > 100:
modifier = 1
else:
modifier = 0
tf = float(subset[key]) / alls[key]
result[i] = (key, tf * modifier)
if sort == True:
result.sort(key=lambda tup: tup[1], reverse=True)
return result
Fdist_all = freqDist(all_text)
teaser = list(Fdist_all.values())
type(teaser)
Fdist_all['rna'] | code |
1006487/cell_17 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
def list_to_str(lists):
strs = ''
for content in lists:
strs += content
return strs
def to_plain_text(dataframe):
text = list_to_str(dataframe['all_text'].apply(lambda x: x.replace('\n', ' ')).tolist())
return text
def to_nltk_text(dataframe):
text = to_plain_text(dataframe)
return nltk.Text(nltk.word_tokenize(text))
all_text = to_nltk_text(data)
robotics = to_nltk_text(data[data['category'] == 'biology'])
def freqDist(text):
freqDist = {}
for word in text:
if word in freqDist:
freqDist[word] += 1
else:
freqDist[word] = 1
return freqDist
def relativeFreq(subset, alls, sort=True, adjusted=True):
result = [' '] * len(subset)
modifier = 1
for i, key in enumerate(subset.keys()):
if adjusted == True:
if alls[key] > 100:
modifier = 1
else:
modifier = 0
tf = float(subset[key]) / alls[key]
result[i] = (key, tf * modifier)
if sort == True:
result.sort(key=lambda tup: tup[1], reverse=True)
return result
Fdist_all = freqDist(all_text)
teaser = list(Fdist_all.values())
type(teaser)
Fdist_robotics = freqDist(robotics)
relative_Freq = relativeFreq(Fdist_robotics, Fdist_all) | code |
1006487/cell_10 | [
"text_plain_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
def list_to_str(lists):
strs = ''
for content in lists:
strs += content
return strs
def to_plain_text(dataframe):
text = list_to_str(dataframe['all_text'].apply(lambda x: x.replace('\n', ' ')).tolist())
return text
def to_nltk_text(dataframe):
text = to_plain_text(dataframe)
return nltk.Text(nltk.word_tokenize(text))
all_text = to_nltk_text(data)
robotics = to_nltk_text(data[data['category'] == 'biology'])
def freqDist(text):
freqDist = {}
for word in text:
if word in freqDist:
freqDist[word] += 1
else:
freqDist[word] = 1
return freqDist
def relativeFreq(subset, alls, sort=True, adjusted=True):
result = [' '] * len(subset)
modifier = 1
for i, key in enumerate(subset.keys()):
if adjusted == True:
if alls[key] > 100:
modifier = 1
else:
modifier = 0
tf = float(subset[key]) / alls[key]
result[i] = (key, tf * modifier)
if sort == True:
result.sort(key=lambda tup: tup[1], reverse=True)
return result
Fdist_all = freqDist(all_text) | code |
1006487/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import nltk # natural language processing
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
def list_to_str(lists):
strs = ''
for content in lists:
strs += content
return strs
def to_plain_text(dataframe):
text = list_to_str(dataframe['all_text'].apply(lambda x: x.replace('\n', ' ')).tolist())
return text
def to_nltk_text(dataframe):
text = to_plain_text(dataframe)
return nltk.Text(nltk.word_tokenize(text))
all_text = to_nltk_text(data)
robotics = to_nltk_text(data[data['category'] == 'biology'])
def freqDist(text):
freqDist = {}
for word in text:
if word in freqDist:
freqDist[word] += 1
else:
freqDist[word] = 1
return freqDist
def relativeFreq(subset, alls, sort=True, adjusted=True):
result = [' '] * len(subset)
modifier = 1
for i, key in enumerate(subset.keys()):
if adjusted == True:
if alls[key] > 100:
modifier = 1
else:
modifier = 0
tf = float(subset[key]) / alls[key]
result[i] = (key, tf * modifier)
if sort == True:
result.sort(key=lambda tup: tup[1], reverse=True)
return result
Fdist_all = freqDist(all_text)
teaser = list(Fdist_all.values())
type(teaser)
len(Fdist_all) | code |
1006487/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from string import punctuation
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
biology = pd.read_csv('../input/biology.csv')
cooking = pd.read_csv('../input/cooking.csv')
crypto = pd.read_csv('../input/crypto.csv')
diy = pd.read_csv('../input/diy.csv')
robotics = pd.read_csv('../input/robotics.csv')
travel = pd.read_csv('../input/travel.csv')
test = pd.read_csv('../input/test.csv')
def strip_punctuation(s):
return ''.join((c for c in s if c not in punctuation))
def remove_html(s):
soup = BeautifulSoup(s, 'html.parser')
content = soup.get_text()
return content
def text_transform(dataframe):
dataframe['title'] = dataframe['title'].apply(lambda x: strip_punctuation(str.lower(x)))
dataframe['content'] = dataframe['content'].apply(lambda x: strip_punctuation(str.lower(remove_html(x))))
def load_data(name):
utl = '../input/' + name + '.csv'
files = pd.read_csv(utl)
text_transform(files)
files['category'] = name
return files
def merge_data(list_of_files):
list_of_dataframe = [''] * len(list_of_files)
for i in range(0, len(list_of_files)):
list_of_dataframe[i] = load_data(list_of_files[i])
data = pd.concat(list_of_dataframe, axis=0, ignore_index=True)
return data
data['all_text'] = data['title'] + ' ' + data['content'] | code |
74045588/cell_9 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Input, Conv2D, MaxPooling2D, BatchNormalization, Activation, UpSampling2D, GlobalAveragePooling2D
from keras.models import Sequential, Model
from keras.preprocessing.image import ImageDataGenerator
from keras.preprocessing.image import load_img, img_to_array
from tensorflow.keras.applications import EfficientNetB0
from tensorflow.keras.metrics import AUC
import gc
import glob
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_data_dir = '../input/1056lab-covid19-chest-xray-recognit/train'
generator = ImageDataGenerator(width_shift_range=0.3, height_shift_range=0.3, horizontal_flip=True, validation_split=0.2)
train_generator = generator.flow_from_directory(train_data_dir, target_size=(224, 224), color_mode='rgb', batch_size=64, class_mode='categorical', shuffle=True)
from tensorflow.keras.applications import EfficientNetB0
efnb0 = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
model = Sequential()
model.add(efnb0)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(4, activation='sigmoid'))
for layer in efnb0.layers:
layer.trainable = False
model.summary()
from tensorflow.keras.metrics import AUC
auc = AUC()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy', auc])
hist = model.fit_generator(train_generator, steps_per_epoch=231, epochs=50)
import glob
from keras.preprocessing.image import load_img, img_to_array
import gc
img_list = glob.glob('/kaggle/input/1056lab-covid19-chest-xray-recognit/test/*.png')
img_list.sort()
y_pred = []
for img_sublist in np.array_split(img_list, 10):
img_array_list = []
for path in img_sublist:
img = load_img(path, color_mode='rgb', target_size=(224, 224, 3))
img_array = img_to_array(img)
img_array_list.append(img_array)
X_test = np.array(img_array_list)
y_test = model.predict(X_test)[:, 0]
y_pred = np.concatenate([y_pred, y_test])
del img_array_list
del X_test
gc.collect()
submit_df = pd.read_csv('/kaggle/input/1056lab-covid19-chest-xray-recognit/sampleSubmission.csv', index_col=0)
submit_df['COVID'] = y_pred
submit_df.to_csv('submission.csv')
submit_df | code |
74045588/cell_4 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
train_data_dir = '../input/1056lab-covid19-chest-xray-recognit/train'
generator = ImageDataGenerator(width_shift_range=0.3, height_shift_range=0.3, horizontal_flip=True, validation_split=0.2)
train_generator = generator.flow_from_directory(train_data_dir, target_size=(224, 224), color_mode='rgb', batch_size=64, class_mode='categorical', shuffle=True) | code |
74045588/cell_7 | [
"text_html_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Input, Conv2D, MaxPooling2D, BatchNormalization, Activation, UpSampling2D, GlobalAveragePooling2D
from keras.models import Sequential, Model
from keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications import EfficientNetB0
from tensorflow.keras.metrics import AUC
train_data_dir = '../input/1056lab-covid19-chest-xray-recognit/train'
generator = ImageDataGenerator(width_shift_range=0.3, height_shift_range=0.3, horizontal_flip=True, validation_split=0.2)
train_generator = generator.flow_from_directory(train_data_dir, target_size=(224, 224), color_mode='rgb', batch_size=64, class_mode='categorical', shuffle=True)
from tensorflow.keras.applications import EfficientNetB0
efnb0 = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
model = Sequential()
model.add(efnb0)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(4, activation='sigmoid'))
for layer in efnb0.layers:
layer.trainable = False
model.summary()
from tensorflow.keras.metrics import AUC
auc = AUC()
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy', auc])
hist = model.fit_generator(train_generator, steps_per_epoch=231, epochs=50) | code |
74045588/cell_10 | [
"text_plain_output_1.png"
] | from keras.preprocessing.image import ImageDataGenerator
train_data_dir = '../input/1056lab-covid19-chest-xray-recognit/train'
generator = ImageDataGenerator(width_shift_range=0.3, height_shift_range=0.3, horizontal_flip=True, validation_split=0.2)
train_generator = generator.flow_from_directory(train_data_dir, target_size=(224, 224), color_mode='rgb', batch_size=64, class_mode='categorical', shuffle=True)
train_generator.class_indices | code |
74045588/cell_5 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Dense, Dropout, Flatten, Input, Conv2D, MaxPooling2D, BatchNormalization, Activation, UpSampling2D, GlobalAveragePooling2D
from keras.models import Sequential, Model
from tensorflow.keras.applications import EfficientNetB0
from tensorflow.keras.applications import EfficientNetB0
efnb0 = EfficientNetB0(weights='imagenet', include_top=False, input_shape=(224, 224, 3))
model = Sequential()
model.add(efnb0)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.2))
model.add(Dense(1024, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(512, activation='relu'))
model.add(Dropout(0.2))
model.add(Dense(4, activation='sigmoid'))
for layer in efnb0.layers:
layer.trainable = False
model.summary() | code |
104128103/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.metrics import mean_absolute_error, mean_absolute_percentage_error, mean_squared_error
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
def settings():
plt.style.use('bmh')
plt.rcParams['figure.figsize'] = [25, 12]
plt.rcParams['font.size'] = 24
plt.rcParams['figure.dpi'] = 100
sns.set()
def ml_error(model_name, ytest, yhat):
mae = mean_absolute_error(ytest, yhat)
mape = mean_absolute_percentage_error(ytest, yhat)
rmse = np.sqrt(mean_squared_error(ytest, yhat))
return pd.DataFrame({'Model name': model_name, 'MAE': mae, 'MAPE': mape, 'RMSE': rmse}, index=[0])
def analise_bivariada(df, column):
aux1 = df[[column, 'preco']].groupby(column).mean().reset_index()
aux2 = df[[column, 'preco']].groupby(column).median().reset_index()
df_raw = pd.read_csv('treino.csv')
df_test = pd.read_csv('teste.csv') | code |
17120136/cell_21 | [
"text_plain_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5)
cuisines = newdata[['cuisines', 'rating']]
cuisines['cuisines'] = cuisines['cuisines'].astype(str)
cuisines['cuisines'] = cuisines['cuisines'].apply(lambda x: x.lower())
cuisine_tokens = cuisines['cuisines'].apply(tokenizer.tokenize)
all_cuisines = cuisine_tokens.astype(str).str.cat()
cleaned_cuisines = tokenizer.tokenize(all_cuisines)
fd_cuisine = FreqDist()
for cuisine in cleaned_cuisines:
fd_cuisine[cuisine] += 1
print(fd_cuisine.most_common()[-50:]) | code |
17120136/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
print(review_tokens[0]) | code |
17120136/cell_25 | [
"image_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5)
cuisines = newdata[['cuisines', 'rating']]
cuisines['cuisines'] = cuisines['cuisines'].astype(str)
cuisines['cuisines'] = cuisines['cuisines'].apply(lambda x: x.lower())
cuisine_tokens = cuisines['cuisines'].apply(tokenizer.tokenize)
all_cuisines = cuisine_tokens.astype(str).str.cat()
cleaned_cuisines = tokenizer.tokenize(all_cuisines)
fd_cuisine = FreqDist()
for cuisine in cleaned_cuisines:
fd_cuisine[cuisine] += 1
newdata[['reviews_list', 'menu_item', 'dish_liked', 'cuisines']] = newdata[['reviews_list', 'menu_item', 'dish_liked', 'cuisines']].astype('str')
newdata['text'] = newdata['reviews_list'] + ' ' + newdata['menu_item'] + ' ' + newdata['dish_liked'] + ' ' + newdata['cuisines']
text_data = newdata[['text', 'rating']]
text_data['text'] = text_data['text'].apply(lambda x: x.lower())
tokens = text_data['text'].apply(tokenizer.tokenize)
tokens = tokens.apply(lambda x: [token for token in x if token not in stop])
print(tokens[0]) | code |
17120136/cell_33 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Flatten, Embedding, Conv1D, MaxPooling1D, Dropout
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.regularizers import l1, l2
from nltk import FreqDist, bigrams, trigrams
from nltk import WordNetLemmatizer
from nltk.corpus import stopwords
from sklearn.metrics import classification_report
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5)
cuisines = newdata[['cuisines', 'rating']]
cuisines['cuisines'] = cuisines['cuisines'].astype(str)
cuisines['cuisines'] = cuisines['cuisines'].apply(lambda x: x.lower())
cuisine_tokens = cuisines['cuisines'].apply(tokenizer.tokenize)
all_cuisines = cuisine_tokens.astype(str).str.cat()
cleaned_cuisines = tokenizer.tokenize(all_cuisines)
fd_cuisine = FreqDist()
for cuisine in cleaned_cuisines:
fd_cuisine[cuisine] += 1
newdata[['reviews_list', 'menu_item', 'dish_liked', 'cuisines']] = newdata[['reviews_list', 'menu_item', 'dish_liked', 'cuisines']].astype('str')
newdata['text'] = newdata['reviews_list'] + ' ' + newdata['menu_item'] + ' ' + newdata['dish_liked'] + ' ' + newdata['cuisines']
text_data = newdata[['text', 'rating']]
text_data['text'] = text_data['text'].apply(lambda x: x.lower())
tokens = text_data['text'].apply(tokenizer.tokenize)
tokens = tokens.apply(lambda x: [token for token in x if token not in stop])
lmtzr = WordNetLemmatizer()
def lem(text):
return [lmtzr.lemmatize(word) for word in text]
tokens_new = tokens.apply(lem)
le = LabelEncoder()
target = le.fit_transform(text_data['rating'])
X_train, X_test, y_train, y_test = train_test_split(tokens_new, target, test_size=0.3, random_state=0, stratify=target)
t = Tokenizer()
t.fit_on_texts(X_train)
vocab_size = len(t.word_index) + 1
train_sequences = t.texts_to_sequences(X_train)
test_sequences = t.texts_to_sequences(X_test)
train_padded = pad_sequences(train_sequences, maxlen=500, padding='post')
test_padded = pad_sequences(test_sequences, maxlen=500, padding='post')
model = Sequential()
model.add(Embedding(vocab_size, 100, input_length=500))
model.add(Dropout(1))
model.add(Conv1D(32, 3, activation='relu', kernel_regularizer=l1(1e-05)))
model.add(MaxPooling1D(2))
model.add(Dropout(1))
model.add(Flatten())
model.add(Dense(4, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_padded, y_train, epochs=5, batch_size=64, validation_data=(test_padded, y_test))
pred_train = model.predict(train_padded)
pred_train = np.argmax(pred_train, axis=1)
print(classification_report(y_train, pred_train)) | code |
17120136/cell_20 | [
"image_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5)
cuisines = newdata[['cuisines', 'rating']]
cuisines['cuisines'] = cuisines['cuisines'].astype(str)
cuisines['cuisines'] = cuisines['cuisines'].apply(lambda x: x.lower())
cuisine_tokens = cuisines['cuisines'].apply(tokenizer.tokenize)
all_cuisines = cuisine_tokens.astype(str).str.cat()
cleaned_cuisines = tokenizer.tokenize(all_cuisines)
fd_cuisine = FreqDist()
for cuisine in cleaned_cuisines:
fd_cuisine[cuisine] += 1
print(fd_cuisine.most_common(50)) | code |
17120136/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
newdata.describe(include='all') | code |
17120136/cell_2 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
import nltk
from nltk.corpus import RegexpTokenizer as regextoken
from nltk.corpus import stopwords
from nltk import FreqDist, bigrams, trigrams
from nltk import WordNetLemmatizer
import matplotlib
from matplotlib import pyplot as plt
import seaborn as sns
from keras.preprocessing.text import Tokenizer
from sklearn.preprocessing import LabelEncoder
from keras.preprocessing.sequence import pad_sequences
from keras.models import Sequential
from keras.models import Model
from keras.layers import Dense, Flatten, Embedding, Conv1D, MaxPooling1D, Dropout
from keras.regularizers import l1, l2
from sklearn.metrics import classification_report
import warnings
warnings.filterwarnings('ignore')
zomato = pd.read_csv('../input/zomato.csv', na_values=['-', ''])
data = zomato.copy() | code |
17120136/cell_11 | [
"text_html_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5) | code |
17120136/cell_18 | [
"image_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5)
bigrams = bigrams(cleaned_reviews)
fd_bigrams = FreqDist()
for bigram in bigrams:
fd_bigrams[bigram] += 1
fd_bigrams.most_common(5)
trigrams = trigrams(cleaned_reviews)
fd_trigrams = FreqDist()
for trigram in trigrams:
fd_trigrams[trigram] += 1
fd_trigrams.most_common(5)
plt.figure(figsize=(10, 5))
fd_trigrams.plot(50)
plt.show() | code |
17120136/cell_28 | [
"text_plain_output_1.png"
] | from keras.preprocessing.text import Tokenizer
from nltk import FreqDist, bigrams, trigrams
from nltk import WordNetLemmatizer
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5)
cuisines = newdata[['cuisines', 'rating']]
cuisines['cuisines'] = cuisines['cuisines'].astype(str)
cuisines['cuisines'] = cuisines['cuisines'].apply(lambda x: x.lower())
cuisine_tokens = cuisines['cuisines'].apply(tokenizer.tokenize)
all_cuisines = cuisine_tokens.astype(str).str.cat()
cleaned_cuisines = tokenizer.tokenize(all_cuisines)
fd_cuisine = FreqDist()
for cuisine in cleaned_cuisines:
fd_cuisine[cuisine] += 1
newdata[['reviews_list', 'menu_item', 'dish_liked', 'cuisines']] = newdata[['reviews_list', 'menu_item', 'dish_liked', 'cuisines']].astype('str')
newdata['text'] = newdata['reviews_list'] + ' ' + newdata['menu_item'] + ' ' + newdata['dish_liked'] + ' ' + newdata['cuisines']
text_data = newdata[['text', 'rating']]
text_data['text'] = text_data['text'].apply(lambda x: x.lower())
tokens = text_data['text'].apply(tokenizer.tokenize)
tokens = tokens.apply(lambda x: [token for token in x if token not in stop])
lmtzr = WordNetLemmatizer()
def lem(text):
return [lmtzr.lemmatize(word) for word in text]
tokens_new = tokens.apply(lem)
le = LabelEncoder()
target = le.fit_transform(text_data['rating'])
X_train, X_test, y_train, y_test = train_test_split(tokens_new, target, test_size=0.3, random_state=0, stratify=target)
t = Tokenizer()
t.fit_on_texts(X_train)
vocab_size = len(t.word_index) + 1
print(vocab_size) | code |
17120136/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0] | code |
17120136/cell_15 | [
"text_plain_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5)
bigrams = bigrams(cleaned_reviews)
fd_bigrams = FreqDist()
for bigram in bigrams:
fd_bigrams[bigram] += 1
fd_bigrams.most_common(5)
plt.figure(figsize=(10, 5))
fd_bigrams.plot(50)
plt.show() | code |
17120136/cell_17 | [
"text_plain_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5)
trigrams = trigrams(cleaned_reviews)
fd_trigrams = FreqDist()
for trigram in trigrams:
fd_trigrams[trigram] += 1
fd_trigrams.most_common(5) | code |
17120136/cell_31 | [
"text_plain_output_1.png"
] | from keras.layers import Dense, Flatten, Embedding, Conv1D, MaxPooling1D, Dropout
from keras.models import Sequential
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.regularizers import l1, l2
from nltk import FreqDist, bigrams, trigrams
from nltk import WordNetLemmatizer
from nltk.corpus import stopwords
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5)
cuisines = newdata[['cuisines', 'rating']]
cuisines['cuisines'] = cuisines['cuisines'].astype(str)
cuisines['cuisines'] = cuisines['cuisines'].apply(lambda x: x.lower())
cuisine_tokens = cuisines['cuisines'].apply(tokenizer.tokenize)
all_cuisines = cuisine_tokens.astype(str).str.cat()
cleaned_cuisines = tokenizer.tokenize(all_cuisines)
fd_cuisine = FreqDist()
for cuisine in cleaned_cuisines:
fd_cuisine[cuisine] += 1
newdata[['reviews_list', 'menu_item', 'dish_liked', 'cuisines']] = newdata[['reviews_list', 'menu_item', 'dish_liked', 'cuisines']].astype('str')
newdata['text'] = newdata['reviews_list'] + ' ' + newdata['menu_item'] + ' ' + newdata['dish_liked'] + ' ' + newdata['cuisines']
text_data = newdata[['text', 'rating']]
text_data['text'] = text_data['text'].apply(lambda x: x.lower())
tokens = text_data['text'].apply(tokenizer.tokenize)
tokens = tokens.apply(lambda x: [token for token in x if token not in stop])
lmtzr = WordNetLemmatizer()
def lem(text):
return [lmtzr.lemmatize(word) for word in text]
tokens_new = tokens.apply(lem)
le = LabelEncoder()
target = le.fit_transform(text_data['rating'])
X_train, X_test, y_train, y_test = train_test_split(tokens_new, target, test_size=0.3, random_state=0, stratify=target)
t = Tokenizer()
t.fit_on_texts(X_train)
vocab_size = len(t.word_index) + 1
train_sequences = t.texts_to_sequences(X_train)
test_sequences = t.texts_to_sequences(X_test)
train_padded = pad_sequences(train_sequences, maxlen=500, padding='post')
test_padded = pad_sequences(test_sequences, maxlen=500, padding='post')
model = Sequential()
model.add(Embedding(vocab_size, 100, input_length=500))
model.add(Dropout(1))
model.add(Conv1D(32, 3, activation='relu', kernel_regularizer=l1(1e-05)))
model.add(MaxPooling1D(2))
model.add(Dropout(1))
model.add(Flatten())
model.add(Dense(4, activation='softmax'))
model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
model.fit(train_padded, y_train, epochs=5, batch_size=64, validation_data=(test_padded, y_test)) | code |
17120136/cell_14 | [
"text_plain_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5)
bigrams = bigrams(cleaned_reviews)
fd_bigrams = FreqDist()
for bigram in bigrams:
fd_bigrams[bigram] += 1
fd_bigrams.most_common(5) | code |
17120136/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
stop = stopwords.words('english')
print(stop) | code |
17120136/cell_12 | [
"text_plain_output_1.png"
] | from nltk import FreqDist, bigrams, trigrams
from nltk.corpus import stopwords
import pandas as pd
grouped = data.groupby(['name', 'address']).agg({'listed_in(type)': list})
newdata = pd.merge(grouped, data, on=['name', 'address'])
newdata['listed_in(type)_x'] = newdata['listed_in(type)_x'].astype(str)
newdata.drop_duplicates(subset=['name', 'address', 'listed_in(type)_x'], inplace=True)
newdata = newdata.reset_index(drop=True)
newdata['rating'] = newdata['rate'].str[:3]
newdata = newdata[newdata.rating != 'NEW']
newdata = newdata.dropna(subset=['rating'])
newdata['rating'] = pd.to_numeric(newdata['rating'])
newdata['rating'] = pd.cut(newdata['rating'], bins=[0, 3.0, 3.5, 4.0, 5.0], labels=['0', '1', '2', '3'])
reviews_data = newdata[['reviews_list', 'rating']]
reviews_data['reviews_list'][0]
reviews_data['reviews_list'] = reviews_data['reviews_list'].apply(lambda x: x.lower())
tokenizer = regextoken('[a-zA-Z]+')
review_tokens = reviews_data['reviews_list'].apply(tokenizer.tokenize)
stop = stopwords.words('english')
stop.extend(['rated', 'n', 'nan', 'x'])
review_tokens = review_tokens.apply(lambda x: [token for token in x if token not in stop])
all_reviews = review_tokens.astype(str).str.cat()
cleaned_reviews = tokenizer.tokenize(all_reviews)
fd = FreqDist()
for word in cleaned_reviews:
fd[word] += 1
fd.most_common(5)
plt.figure(figsize=(10, 5))
fd.plot(50)
plt.show() | code |
2029692/cell_13 | [
"image_output_5.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from scipy.stats import skew
from scipy.stats import skew
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vmax=.8, square=True);
numeric = [feature for feature in train.columns if train.dtypes[feature] != 'object']
numeric.remove('Id')
numreicMostCorr = ['LotFrontage', 'OverallQual', 'YearBuilt', 'YearRemodAdd', '1stFlrSF', '2ndFlrSF', 'TotalBsmtSF', 'GrLivArea', 'FullBath', 'TotRmsAbvGrd', 'GarageArea', 'GarageCars']
for feature in numreicMostCorr:
featureDF = pd.concat([train['SalePrice'], train[feature]], axis=1)
pairs = [('GarageArea', 'GarageCars'), ('YearBuilt', 'YearRemodAdd'), ('TotalBsmtSF', 'TotRmsAbvGrd'), ('GrLivArea', 'FullBath'), ('TotalBsmtSF', '1stFlrSF'), ('GrLivArea', '2ndFlrSF')]
for pair in pairs:
featureDF = pd.concat([train[pair[0]], train[pair[1]]], axis=1)
categorical = [feature for feature in train.columns if train.dtypes[feature] == 'object']
for category in categorical:
data = pd.concat([train[category], train['SalePrice']], axis=1)
data[category] = data[category].astype('category')
if data[category].isnull().any():
data[category] = data[category].cat.add_categories(['MISSING'])
data[category] = data[category].fillna('MISSING')
cat_data = pd.concat([data['SalePrice'], data[category]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=category, y="SalePrice", data=cat_data)
fig.axis(ymin=0, ymax=800000)
plt.show()
numeric_missing = train[numreicMostCorr].isnull().sum().sort_values(ascending=False)
categorical_missing = train[categorical].isnull().sum().sort_values(ascending=False)
numeric_to_delete = numeric_missing[numeric_missing > 438].index
categorical_to_delete = categorical_missing[categorical_missing > 438].index
def removeFromList(sourceList, filterList):
filteredList = list(filter(lambda x: x not in filterList, sourceList))
return filteredList
numreicMostCorr = removeFromList(numreicMostCorr, numeric_to_delete)
categorical = removeFromList(categorical, categorical_to_delete)
numreicMostCorr = removeFromList(numreicMostCorr, ['GarageCars', '1stFlrSF', '2ndFlrSF', 'YearRemodAdd', 'FullBath'])
categorical = removeFromList(categorical, ['Alley', 'LotShape', 'LandSlope', 'BldgType', 'Exterior1st', 'Exterior2nd', 'ExterCond', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'HeatingQC', 'GarageFinish', 'GarageType', 'GarageCond', 'Fence'])
all_columns = numreicMostCorr + categorical + ['SalePrice']
train = train[all_columns]
train['LotFrontage'] = train['LotFrontage'].fillna(train['LotFrontage'].mean())
train[numreicMostCorr].isnull().sum().sort_values(ascending=False)
train['BsmtQual'] = train['BsmtQual'].fillna('Missing')
train['GarageQual'] = train['GarageQual'].fillna('Missing')
train['MasVnrType'] = train['MasVnrType'].fillna('Missing')
train = train.drop(train.loc[train['Electrical'].isnull()].index)
train[categorical].isnull().sum().sort_values(ascending=False)
from scipy.stats import skew
skewed_cols = numreicMostCorr + ['SalePrice']
skewed = train[skewed_cols].apply(lambda x: skew(x.dropna()))
skewed = skewed[skewed > 0.75]
skewed = skewed.index
train[skewed] = np.log1p(train[skewed])
for numer in skewed_cols:
numerFeature = pd.DataFrame({'unskewed_' + numer: train[numer]})
train = pd.get_dummies(train)
train.head(10) | code |
2029692/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vmax=.8, square=True);
numeric = [feature for feature in train.columns if train.dtypes[feature] != 'object']
numeric.remove('Id')
numreicMostCorr = ['LotFrontage', 'OverallQual', 'YearBuilt', 'YearRemodAdd', '1stFlrSF', '2ndFlrSF', 'TotalBsmtSF', 'GrLivArea', 'FullBath', 'TotRmsAbvGrd', 'GarageArea', 'GarageCars']
for feature in numreicMostCorr:
featureDF = pd.concat([train['SalePrice'], train[feature]], axis=1)
pairs = [('GarageArea', 'GarageCars'), ('YearBuilt', 'YearRemodAdd'), ('TotalBsmtSF', 'TotRmsAbvGrd'), ('GrLivArea', 'FullBath'), ('TotalBsmtSF', '1stFlrSF'), ('GrLivArea', '2ndFlrSF')]
for pair in pairs:
featureDF = pd.concat([train[pair[0]], train[pair[1]]], axis=1)
categorical = [feature for feature in train.columns if train.dtypes[feature] == 'object']
for category in categorical:
data = pd.concat([train[category], train['SalePrice']], axis=1)
data[category] = data[category].astype('category')
if data[category].isnull().any():
data[category] = data[category].cat.add_categories(['MISSING'])
data[category] = data[category].fillna('MISSING')
cat_data = pd.concat([data['SalePrice'], data[category]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=category, y="SalePrice", data=cat_data)
fig.axis(ymin=0, ymax=800000)
plt.show()
numeric_missing = train[numreicMostCorr].isnull().sum().sort_values(ascending=False)
numeric_missing.head(20)
categorical_missing = train[categorical].isnull().sum().sort_values(ascending=False)
categorical_missing.head(20) | code |
2029692/cell_4 | [
"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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vmax=0.8, square=True) | code |
2029692/cell_6 | [
"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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vmax=.8, square=True);
numeric = [feature for feature in train.columns if train.dtypes[feature] != 'object']
numeric.remove('Id')
numreicMostCorr = ['LotFrontage', 'OverallQual', 'YearBuilt', 'YearRemodAdd', '1stFlrSF', '2ndFlrSF', 'TotalBsmtSF', 'GrLivArea', 'FullBath', 'TotRmsAbvGrd', 'GarageArea', 'GarageCars']
for feature in numreicMostCorr:
featureDF = pd.concat([train['SalePrice'], train[feature]], axis=1)
featureDF.plot.scatter(x=feature, y='SalePrice', ylim=(0, 800000)) | code |
2029692/cell_2 | [
"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/train.csv')
holdout = pd.read_csv('../input/test.csv')
train['SalePrice'].describe() | code |
2029692/cell_11 | [
"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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vmax=.8, square=True);
numeric = [feature for feature in train.columns if train.dtypes[feature] != 'object']
numeric.remove('Id')
numreicMostCorr = ['LotFrontage', 'OverallQual', 'YearBuilt', 'YearRemodAdd', '1stFlrSF', '2ndFlrSF', 'TotalBsmtSF', 'GrLivArea', 'FullBath', 'TotRmsAbvGrd', 'GarageArea', 'GarageCars']
for feature in numreicMostCorr:
featureDF = pd.concat([train['SalePrice'], train[feature]], axis=1)
pairs = [('GarageArea', 'GarageCars'), ('YearBuilt', 'YearRemodAdd'), ('TotalBsmtSF', 'TotRmsAbvGrd'), ('GrLivArea', 'FullBath'), ('TotalBsmtSF', '1stFlrSF'), ('GrLivArea', '2ndFlrSF')]
for pair in pairs:
featureDF = pd.concat([train[pair[0]], train[pair[1]]], axis=1)
categorical = [feature for feature in train.columns if train.dtypes[feature] == 'object']
for category in categorical:
data = pd.concat([train[category], train['SalePrice']], axis=1)
data[category] = data[category].astype('category')
if data[category].isnull().any():
data[category] = data[category].cat.add_categories(['MISSING'])
data[category] = data[category].fillna('MISSING')
cat_data = pd.concat([data['SalePrice'], data[category]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=category, y="SalePrice", data=cat_data)
fig.axis(ymin=0, ymax=800000)
plt.show()
numeric_missing = train[numreicMostCorr].isnull().sum().sort_values(ascending=False)
categorical_missing = train[categorical].isnull().sum().sort_values(ascending=False)
numeric_to_delete = numeric_missing[numeric_missing > 438].index
categorical_to_delete = categorical_missing[categorical_missing > 438].index
def removeFromList(sourceList, filterList):
filteredList = list(filter(lambda x: x not in filterList, sourceList))
return filteredList
numreicMostCorr = removeFromList(numreicMostCorr, numeric_to_delete)
categorical = removeFromList(categorical, categorical_to_delete)
numreicMostCorr = removeFromList(numreicMostCorr, ['GarageCars', '1stFlrSF', '2ndFlrSF', 'YearRemodAdd', 'FullBath'])
categorical = removeFromList(categorical, ['Alley', 'LotShape', 'LandSlope', 'BldgType', 'Exterior1st', 'Exterior2nd', 'ExterCond', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'HeatingQC', 'GarageFinish', 'GarageType', 'GarageCond', 'Fence'])
all_columns = numreicMostCorr + categorical + ['SalePrice']
train = train[all_columns]
train['LotFrontage'] = train['LotFrontage'].fillna(train['LotFrontage'].mean())
train[numreicMostCorr].isnull().sum().sort_values(ascending=False)
train['BsmtQual'] = train['BsmtQual'].fillna('Missing')
train['GarageQual'] = train['GarageQual'].fillna('Missing')
train['MasVnrType'] = train['MasVnrType'].fillna('Missing')
train = train.drop(train.loc[train['Electrical'].isnull()].index)
train[categorical].isnull().sum().sort_values(ascending=False) | code |
2029692/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import seaborn as sns
import numpy as np
from scipy.stats import norm
from sklearn.preprocessing import StandardScaler
from scipy import stats
import matplotlib.pyplot as plt
from scipy.stats import skew
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2029692/cell_7 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vmax=.8, square=True);
numeric = [feature for feature in train.columns if train.dtypes[feature] != 'object']
numeric.remove('Id')
numreicMostCorr = ['LotFrontage', 'OverallQual', 'YearBuilt', 'YearRemodAdd', '1stFlrSF', '2ndFlrSF', 'TotalBsmtSF', 'GrLivArea', 'FullBath', 'TotRmsAbvGrd', 'GarageArea', 'GarageCars']
for feature in numreicMostCorr:
featureDF = pd.concat([train['SalePrice'], train[feature]], axis=1)
pairs = [('GarageArea', 'GarageCars'), ('YearBuilt', 'YearRemodAdd'), ('TotalBsmtSF', 'TotRmsAbvGrd'), ('GrLivArea', 'FullBath'), ('TotalBsmtSF', '1stFlrSF'), ('GrLivArea', '2ndFlrSF')]
for pair in pairs:
featureDF = pd.concat([train[pair[0]], train[pair[1]]], axis=1)
featureDF.plot.scatter(x=pair[0], y=pair[1]) | code |
2029692/cell_8 | [
"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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vmax=.8, square=True);
numeric = [feature for feature in train.columns if train.dtypes[feature] != 'object']
numeric.remove('Id')
numreicMostCorr = ['LotFrontage', 'OverallQual', 'YearBuilt', 'YearRemodAdd', '1stFlrSF', '2ndFlrSF', 'TotalBsmtSF', 'GrLivArea', 'FullBath', 'TotRmsAbvGrd', 'GarageArea', 'GarageCars']
for feature in numreicMostCorr:
featureDF = pd.concat([train['SalePrice'], train[feature]], axis=1)
pairs = [('GarageArea', 'GarageCars'), ('YearBuilt', 'YearRemodAdd'), ('TotalBsmtSF', 'TotRmsAbvGrd'), ('GrLivArea', 'FullBath'), ('TotalBsmtSF', '1stFlrSF'), ('GrLivArea', '2ndFlrSF')]
for pair in pairs:
featureDF = pd.concat([train[pair[0]], train[pair[1]]], axis=1)
categorical = [feature for feature in train.columns if train.dtypes[feature] == 'object']
for category in categorical:
data = pd.concat([train[category], train['SalePrice']], axis=1)
data[category] = data[category].astype('category')
if data[category].isnull().any():
data[category] = data[category].cat.add_categories(['MISSING'])
data[category] = data[category].fillna('MISSING')
cat_data = pd.concat([data['SalePrice'], data[category]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=category, y='SalePrice', data=cat_data)
fig.axis(ymin=0, ymax=800000)
plt.show() | code |
2029692/cell_10 | [
"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
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vmax=.8, square=True);
numeric = [feature for feature in train.columns if train.dtypes[feature] != 'object']
numeric.remove('Id')
numreicMostCorr = ['LotFrontage', 'OverallQual', 'YearBuilt', 'YearRemodAdd', '1stFlrSF', '2ndFlrSF', 'TotalBsmtSF', 'GrLivArea', 'FullBath', 'TotRmsAbvGrd', 'GarageArea', 'GarageCars']
for feature in numreicMostCorr:
featureDF = pd.concat([train['SalePrice'], train[feature]], axis=1)
pairs = [('GarageArea', 'GarageCars'), ('YearBuilt', 'YearRemodAdd'), ('TotalBsmtSF', 'TotRmsAbvGrd'), ('GrLivArea', 'FullBath'), ('TotalBsmtSF', '1stFlrSF'), ('GrLivArea', '2ndFlrSF')]
for pair in pairs:
featureDF = pd.concat([train[pair[0]], train[pair[1]]], axis=1)
categorical = [feature for feature in train.columns if train.dtypes[feature] == 'object']
for category in categorical:
data = pd.concat([train[category], train['SalePrice']], axis=1)
data[category] = data[category].astype('category')
if data[category].isnull().any():
data[category] = data[category].cat.add_categories(['MISSING'])
data[category] = data[category].fillna('MISSING')
cat_data = pd.concat([data['SalePrice'], data[category]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=category, y="SalePrice", data=cat_data)
fig.axis(ymin=0, ymax=800000)
plt.show()
numeric_missing = train[numreicMostCorr].isnull().sum().sort_values(ascending=False)
categorical_missing = train[categorical].isnull().sum().sort_values(ascending=False)
numeric_to_delete = numeric_missing[numeric_missing > 438].index
categorical_to_delete = categorical_missing[categorical_missing > 438].index
def removeFromList(sourceList, filterList):
filteredList = list(filter(lambda x: x not in filterList, sourceList))
return filteredList
numreicMostCorr = removeFromList(numreicMostCorr, numeric_to_delete)
categorical = removeFromList(categorical, categorical_to_delete)
numreicMostCorr = removeFromList(numreicMostCorr, ['GarageCars', '1stFlrSF', '2ndFlrSF', 'YearRemodAdd', 'FullBath'])
categorical = removeFromList(categorical, ['Alley', 'LotShape', 'LandSlope', 'BldgType', 'Exterior1st', 'Exterior2nd', 'ExterCond', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'HeatingQC', 'GarageFinish', 'GarageType', 'GarageCond', 'Fence'])
print(categorical) | code |
2029692/cell_12 | [
"image_output_11.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_6.png",
"image_output_12.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_9.png"
] | from scipy.stats import skew
from scipy.stats import skew
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('../input/train.csv')
holdout = pd.read_csv('../input/test.csv')
correlation_matrix = train.corr()
f, ax = plt.subplots(figsize=(12, 9))
sns.heatmap(correlation_matrix, vmax=.8, square=True);
numeric = [feature for feature in train.columns if train.dtypes[feature] != 'object']
numeric.remove('Id')
numreicMostCorr = ['LotFrontage', 'OverallQual', 'YearBuilt', 'YearRemodAdd', '1stFlrSF', '2ndFlrSF', 'TotalBsmtSF', 'GrLivArea', 'FullBath', 'TotRmsAbvGrd', 'GarageArea', 'GarageCars']
for feature in numreicMostCorr:
featureDF = pd.concat([train['SalePrice'], train[feature]], axis=1)
pairs = [('GarageArea', 'GarageCars'), ('YearBuilt', 'YearRemodAdd'), ('TotalBsmtSF', 'TotRmsAbvGrd'), ('GrLivArea', 'FullBath'), ('TotalBsmtSF', '1stFlrSF'), ('GrLivArea', '2ndFlrSF')]
for pair in pairs:
featureDF = pd.concat([train[pair[0]], train[pair[1]]], axis=1)
categorical = [feature for feature in train.columns if train.dtypes[feature] == 'object']
for category in categorical:
data = pd.concat([train[category], train['SalePrice']], axis=1)
data[category] = data[category].astype('category')
if data[category].isnull().any():
data[category] = data[category].cat.add_categories(['MISSING'])
data[category] = data[category].fillna('MISSING')
cat_data = pd.concat([data['SalePrice'], data[category]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=category, y="SalePrice", data=cat_data)
fig.axis(ymin=0, ymax=800000)
plt.show()
numeric_missing = train[numreicMostCorr].isnull().sum().sort_values(ascending=False)
categorical_missing = train[categorical].isnull().sum().sort_values(ascending=False)
numeric_to_delete = numeric_missing[numeric_missing > 438].index
categorical_to_delete = categorical_missing[categorical_missing > 438].index
def removeFromList(sourceList, filterList):
filteredList = list(filter(lambda x: x not in filterList, sourceList))
return filteredList
numreicMostCorr = removeFromList(numreicMostCorr, numeric_to_delete)
categorical = removeFromList(categorical, categorical_to_delete)
numreicMostCorr = removeFromList(numreicMostCorr, ['GarageCars', '1stFlrSF', '2ndFlrSF', 'YearRemodAdd', 'FullBath'])
categorical = removeFromList(categorical, ['Alley', 'LotShape', 'LandSlope', 'BldgType', 'Exterior1st', 'Exterior2nd', 'ExterCond', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2', 'HeatingQC', 'GarageFinish', 'GarageType', 'GarageCond', 'Fence'])
all_columns = numreicMostCorr + categorical + ['SalePrice']
train = train[all_columns]
train['LotFrontage'] = train['LotFrontage'].fillna(train['LotFrontage'].mean())
train[numreicMostCorr].isnull().sum().sort_values(ascending=False)
train['BsmtQual'] = train['BsmtQual'].fillna('Missing')
train['GarageQual'] = train['GarageQual'].fillna('Missing')
train['MasVnrType'] = train['MasVnrType'].fillna('Missing')
train = train.drop(train.loc[train['Electrical'].isnull()].index)
train[categorical].isnull().sum().sort_values(ascending=False)
from scipy.stats import skew
skewed_cols = numreicMostCorr + ['SalePrice']
skewed = train[skewed_cols].apply(lambda x: skew(x.dropna()))
skewed = skewed[skewed > 0.75]
skewed = skewed.index
train[skewed] = np.log1p(train[skewed])
for numer in skewed_cols:
numerFeature = pd.DataFrame({'unskewed_' + numer: train[numer]})
numerFeature.hist() | code |
2036121/cell_21 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
pd.crosstab(train.severe_toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
train.iloc[:, 2:8].corr()
train[train.comment_text.isnull()]
train[train.comment_text == ''] | code |
2036121/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
pd.crosstab(train.severe_toxic, [train.obscene, train.threat, train.insult, train.identity_hate]) | code |
2036121/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic) | code |
2036121/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
pd.crosstab(train.severe_toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
train.iloc[:, 2:8].corr()
train[train.comment_text.isnull()]
train[train.comment_text == '']
train['comment_length'] = train.comment_text.str.len()
train = train.sort_values(by='comment_length', ascending=False)
pd.set_option('display.max_colwidth', -1)
train.comment_text.head(1) | code |
2036121/cell_23 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
pd.crosstab(train.severe_toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
train.iloc[:, 2:8].corr()
train[train.comment_text.isnull()]
train[train.comment_text == '']
train['comment_length'] = train.comment_text.str.len()
train.comment_length.describe() | code |
2036121/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity_hate]) | code |
2036121/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
pd.crosstab(train.severe_toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
train.iloc[:, 2:8].corr()
train[train.comment_text.isnull()] | code |
2036121/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts() | code |
2036121/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
pd.crosstab(train.severe_toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
train.iloc[:, 2:8].corr() | code |
2036121/cell_27 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import numpy as np
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
train = pd.read_csv('../input/train.csv')
train.toxic.value_counts()
pd.crosstab(train.toxic, train.severe_toxic)
pd.crosstab(train.toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
pd.crosstab(train.severe_toxic, [train.obscene, train.threat, train.insult, train.identity_hate])
train.iloc[:, 2:8].corr()
train[train.comment_text.isnull()]
train[train.comment_text == '']
train['comment_length'] = train.comment_text.str.len()
train = train.sort_values(by='comment_length', ascending=False)
pd.set_option('display.max_colwidth', -1)
one_percent = int(np.ceil(train.shape[0] / 100))
train_sub = train.iloc[0:one_percent, :]
train_sub.toxic.value_counts() | code |
50223032/cell_21 | [
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
#L.append({'feature':feature,'slope':getslope(y.get_xlim(),y.get_ylim())})
L.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
L.sort(key=getSlope, reverse=True)
features_to_save = []
for iteration in range(6):
features_to_save.append(L[iteration]['feature'])
dftrain_processed = pd.DataFrame()
for iteration in features_to_save:
feature = dftrain[str(iteration)].values
dftrain_processed[str(iteration)] = feature
dftrain_processed.shape | code |
50223032/cell_9 | [
"text_html_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L = []
for feature in range(300):
y = sns.regplot(x=str(feature), y='target', data=dftrain)
L.append({'feature': feature, 'slope': getCorr(dftrain['target'], dftrain[str(feature)])}) | code |
50223032/cell_25 | [
"text_html_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
#L.append({'feature':feature,'slope':getslope(y.get_xlim(),y.get_ylim())})
L.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
L.sort(key=getSlope, reverse=True)
features_to_save = []
for iteration in range(6):
features_to_save.append(L[iteration]['feature'])
dftrain_processed = pd.DataFrame()
for iteration in features_to_save:
feature = dftrain[str(iteration)].values
dftrain_processed[str(iteration)] = feature
y_train = dftrain.pop('target')
dftrain_processed.shape
y_train.shape
import tensorflow as tf
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), tf.keras.layers.Dense(7, activation=tf.nn.relu), tf.keras.layers.Dense(2, activation=tf.nn.softmax)])
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])
model.fit(dftrain_processed, y_train, epochs=10) | code |
50223032/cell_34 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
#L.append({'feature':feature,'slope':getslope(y.get_xlim(),y.get_ylim())})
L.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
L.sort(key=getSlope, reverse=True)
features_to_save = []
for iteration in range(6):
features_to_save.append(L[iteration]['feature'])
dftrain_processed = pd.DataFrame()
for iteration in features_to_save:
feature = dftrain[str(iteration)].values
dftrain_processed[str(iteration)] = feature
y_train = dftrain.pop('target')
dftrain_processed.shape
y_train.shape
import tensorflow as tf
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), tf.keras.layers.Dense(7, activation=tf.nn.relu), tf.keras.layers.Dense(2, activation=tf.nn.softmax)])
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])
model.fit(dftrain_processed, y_train, epochs=10)
dftest_processed = pd.DataFrame()
for iteration in features_to_save:
feature = dftest[str(iteration)].values
dftest_processed[str(iteration)] = feature
pre = list(model.predict(dftest_processed))
def getPredictions(prediction_list):
prediction_list_processced = []
for iteration in prediction_list:
prediction_list_processced.append(round(iteration[1]))
return prediction_list_processced
dfsubmission = pd.DataFrame()
dfsubmission['id'] = dftest['id']
dfsubmission['target'] = getPredictions(pre)
dfsubmission | code |
50223032/cell_30 | [
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
#L.append({'feature':feature,'slope':getslope(y.get_xlim(),y.get_ylim())})
L.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
L.sort(key=getSlope, reverse=True)
features_to_save = []
for iteration in range(6):
features_to_save.append(L[iteration]['feature'])
dftrain_processed = pd.DataFrame()
for iteration in features_to_save:
feature = dftrain[str(iteration)].values
dftrain_processed[str(iteration)] = feature
y_train = dftrain.pop('target')
dftrain_processed.shape
y_train.shape
import tensorflow as tf
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), tf.keras.layers.Dense(7, activation=tf.nn.relu), tf.keras.layers.Dense(2, activation=tf.nn.softmax)])
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])
model.fit(dftrain_processed, y_train, epochs=10)
dftest_processed = pd.DataFrame()
for iteration in features_to_save:
feature = dftest[str(iteration)].values
dftest_processed[str(iteration)] = feature
pre = list(model.predict(dftest_processed))
len(pre) | code |
50223032/cell_33 | [
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
#L.append({'feature':feature,'slope':getslope(y.get_xlim(),y.get_ylim())})
L.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
L.sort(key=getSlope, reverse=True)
features_to_save = []
for iteration in range(6):
features_to_save.append(L[iteration]['feature'])
dftrain_processed = pd.DataFrame()
for iteration in features_to_save:
feature = dftrain[str(iteration)].values
dftrain_processed[str(iteration)] = feature
y_train = dftrain.pop('target')
dftrain_processed.shape
y_train.shape
import tensorflow as tf
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), tf.keras.layers.Dense(7, activation=tf.nn.relu), tf.keras.layers.Dense(2, activation=tf.nn.softmax)])
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])
model.fit(dftrain_processed, y_train, epochs=10)
dftest_processed = pd.DataFrame()
for iteration in features_to_save:
feature = dftest[str(iteration)].values
dftest_processed[str(iteration)] = feature
pre = list(model.predict(dftest_processed))
def getPredictions(prediction_list):
prediction_list_processced = []
for iteration in prediction_list:
prediction_list_processced.append(round(iteration[1]))
return prediction_list_processced
dfsubmission = pd.DataFrame()
dfsubmission['id'] = dftest['id']
dfsubmission['target'] = getPredictions(pre) | code |
50223032/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y_train = dftrain.pop('target')
y_train | code |
50223032/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
plt.scatter(dftrain['299'], dftrain['1'])
plt.title('My PCA graph')
plt.xlabel('0 -{0}%'.format(dftrain['299']))
plt.ylabel('target -{0}%'.format(dftrain['1'])) | code |
50223032/cell_26 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
dftest | code |
50223032/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y = sns.regplot(x='1', y='target', data=dftrain) | code |
50223032/cell_18 | [
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
#L.append({'feature':feature,'slope':getslope(y.get_xlim(),y.get_ylim())})
L.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
L.sort(key=getSlope, reverse=True)
features_to_save = []
for iteration in range(6):
features_to_save.append(L[iteration]['feature'])
dftrain_processed = pd.DataFrame()
for iteration in features_to_save:
feature = dftrain[str(iteration)].values
dftrain_processed[str(iteration)] = feature
dftrain_processed | code |
50223032/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
y.get_xlim() | code |
50223032/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
dftrain['127'].values | code |
50223032/cell_17 | [
"image_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
dftest | code |
50223032/cell_31 | [
"text_plain_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
import tensorflow as tf
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
#L.append({'feature':feature,'slope':getslope(y.get_xlim(),y.get_ylim())})
L.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
L.sort(key=getSlope, reverse=True)
features_to_save = []
for iteration in range(6):
features_to_save.append(L[iteration]['feature'])
dftrain_processed = pd.DataFrame()
for iteration in features_to_save:
feature = dftrain[str(iteration)].values
dftrain_processed[str(iteration)] = feature
y_train = dftrain.pop('target')
dftrain_processed.shape
y_train.shape
import tensorflow as tf
model = tf.keras.models.Sequential([tf.keras.layers.Flatten(), tf.keras.layers.Dense(7, activation=tf.nn.relu), tf.keras.layers.Dense(2, activation=tf.nn.softmax)])
model.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])
model.fit(dftrain_processed, y_train, epochs=10)
dftest_processed = pd.DataFrame()
for iteration in features_to_save:
feature = dftest[str(iteration)].values
dftest_processed[str(iteration)] = feature
pre = list(model.predict(dftest_processed))
pre[0][1] | code |
50223032/cell_14 | [
"image_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
#L.append({'feature':feature,'slope':getslope(y.get_xlim(),y.get_ylim())})
L.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
L.sort(key=getSlope, reverse=True)
features_to_save = []
for iteration in range(6):
features_to_save.append(L[iteration]['feature'])
print(features_to_save) | code |
50223032/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y_train = dftrain.pop('target')
y_train.shape | code |
50223032/cell_10 | [
"text_html_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
#L.append({'feature':feature,'slope':getslope(y.get_xlim(),y.get_ylim())})
L.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
L[0]['slope'] | code |
50223032/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from scipy.stats import pearsonr
import pandas as pd
import seaborn as sns
def getCorr(x, y):
corr, _ = pearsonr(x, y)
return corr
def getSlope(df):
return abs(df['slope'])
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
y=sns.regplot(x='1',y='target',data=dftrain)
L=[]
for feature in range(300):
y=sns.regplot(x=str(feature),y='target',data=dftrain)
#L.append({'feature':feature,'slope':getslope(y.get_xlim(),y.get_ylim())})
L.append({'feature':feature,'slope':getCorr(dftrain['target'],dftrain[str(feature)])})
L.sort(key=getSlope, reverse=True)
print(L) | code |
50223032/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
dftrain = pd.read_csv('../input/dont-overfit-ii/train.csv')
dftest = pd.read_csv('../input/dont-overfit-ii/test.csv')
dftrain | code |
72120846/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
import cv2 as cv
import matplotlib.image as mpimg
from matplotlib import pyplot as plt
pd.options.display.float_format = '{:.2f}'.format
training_labels = pd.read_csv('../input/landmark-recognition-2021/train.csv')
training_labels['path1'] = training_labels['id'].str.slice(start=0, stop=1)
training_labels['path2'] = training_labels['id'].str.slice(start=1, stop=2)
training_labels['path3'] = training_labels['id'].str.slice(start=2, stop=3)
training_labels['path'] = '../input/landmark-recognition-2021/train/' + training_labels['path1'] + '/' + training_labels['path2'] + '/' + training_labels['path3'] + '/' + training_labels['id'] + '.jpg'
training_labels = training_labels.drop(['path1', 'path2', 'path3'], axis=1)
piv = training_labels.pivot_table(index='landmark_id', aggfunc=lambda x: len(x.unique()))['id']
piv.sort_values() | code |
72120846/cell_5 | [
"image_output_1.png"
] | import matplotlib.image as mpimg
import pandas as pd
import pandas as pd
import numpy as np
import cv2 as cv
import matplotlib.image as mpimg
from matplotlib import pyplot as plt
pd.options.display.float_format = '{:.2f}'.format
training_labels = pd.read_csv('../input/landmark-recognition-2021/train.csv')
training_labels['path1'] = training_labels['id'].str.slice(start=0, stop=1)
training_labels['path2'] = training_labels['id'].str.slice(start=1, stop=2)
training_labels['path3'] = training_labels['id'].str.slice(start=2, stop=3)
training_labels['path'] = '../input/landmark-recognition-2021/train/' + training_labels['path1'] + '/' + training_labels['path2'] + '/' + training_labels['path3'] + '/' + training_labels['id'] + '.jpg'
training_labels = training_labels.drop(['path1', 'path2', 'path3'], axis=1)
piv = training_labels.pivot_table(index='landmark_id', aggfunc=lambda x: len(x.unique()))['id']
piv.sort_values()
top_landmark = 138982
top_landmark_df = training_labels.loc[training_labels['landmark_id'] == top_landmark]
top_landmark_sample = top_landmark_df.sample(n=16, random_state=2021)
top_landmark_path = top_landmark_sample['path']
fig = plt.figure(figsize=(100, 100))
for i in range(0, 16):
fig.add_subplot(4, 4, i + 1)
plt.imshow(mpimg.imread(top_landmark_path.iloc[i]))
plt.axis('off') | code |
50210665/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)
walmart_data = pd.read_csv('../input/walmart-sales/Walmart_Store_sales.csv')
walmart_data.head() | code |
50210665/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
walmart_data = pd.read_csv('../input/walmart-sales/Walmart_Store_sales.csv')
walmart_data_groupby = walmart_data.groupby('Store')['Weekly_Sales'].sum()
walmart_data_std = walmart_data.groupby('Store').agg({'Weekly_Sales': 'std'})
print('Store Number {} has maximum Standard Deviation. STD {}'.format(walmart_data_std['Weekly_Sales'].idxmax(), walmart_data_std['Weekly_Sales'].max())) | code |
50210665/cell_2 | [
"text_html_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 |
50210665/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)
walmart_data = pd.read_csv('../input/walmart-sales/Walmart_Store_sales.csv')
walmart_data_groupby = walmart_data.groupby('Store')['Weekly_Sales'].sum()
walmart_data_std = walmart_data.groupby('Store').agg({'Weekly_Sales': 'std'})
walmart_data_std = walmart_data.groupby('Store').agg({'Weekly_Sales': ['mean', 'std']})
walmart_data_std.head() | code |
50210665/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)
walmart_data = pd.read_csv('../input/walmart-sales/Walmart_Store_sales.csv')
walmart_data_groupby = walmart_data.groupby('Store')['Weekly_Sales'].sum()
walmart_data_std = walmart_data.groupby('Store').agg({'Weekly_Sales': 'std'})
walmart_data_std = walmart_data.groupby('Store').agg({'Weekly_Sales': ['mean', 'std']})
walmart_data_Q32012 = walmart_data[(pd.to_datetime(walmart_data['Date']) >= pd.to_datetime('07-01-2012')) & (pd.to_datetime(walmart_data['Date']) <= pd.to_datetime('09-30-2012'))]
walmart_data_growth = walmart_data_Q32012.groupby(['Store'])['Weekly_Sales'].sum()
print("Store Number {} has Good Quartely Growth in Q3'2012 {}".format(walmart_data_growth.idxmax(), walmart_data_growth.max())) | code |
50210665/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
walmart_data = pd.read_csv('../input/walmart-sales/Walmart_Store_sales.csv')
walmart_data_groupby = walmart_data.groupby('Store')['Weekly_Sales'].sum()
print('Store Number {} has maximum Sales. Sum of Total Sales {}'.format(walmart_data_groupby.idxmax(), walmart_data_groupby.max())) | code |
73072460/cell_42 | [
"text_html_output_1.png"
] | from IPython.display import Image
Image(url='https://res.cloudinary.com/practicaldev/image/fetch/s--nUoflRuG--/c_limit%2Cf_auto%2Cfl_progressive%2Cq_auto%2Cw_880/https://i.ibb.co/kG5vPdn/final-cnn.png', width=750, height=500) | code |
73072460/cell_21 | [
"image_output_1.png"
] | import tensorflow as tf
training_data_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.0, rotation_range=40, zoom_range=0.2, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, horizontal_flip=True, vertical_flip=True)
training_generator = training_data_gen.flow_from_dataframe(dataframe=train, x_col='images', y_col='labels', target_size=(224, 224), color_mode='rgb', class_mode='categorical', batch_size=64)
val_data_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.0)
validation_generator = val_data_gen.flow_from_dataframe(dataframe=val, x_col='images', y_col='labels', target_size=(224, 224), color_mode='rgb', class_mode='categorical', batch_size=64)
test_data_gen = tf.keras.preprocessing.image.ImageDataGenerator(rescale=1 / 255.0)
test_generator = test_data_gen.flow_from_dataframe(dataframe=test, x_col='images', y_col='labels', target_size=(224, 224), color_mode='rgb', class_mode='categorical', batch_size=64)
mlp_model = tf.keras.models.Sequential()
mlp_model.add(tf.keras.layers.Flatten(input_shape=(224, 224, 3)))
mlp_model.add(tf.keras.layers.Dense(256, activation='relu'))
mlp_model.add(tf.keras.layers.Dropout(0.4))
mlp_model.add(tf.keras.layers.Dense(256, activation='relu'))
mlp_model.add(tf.keras.layers.Dense(128, activation='relu'))
mlp_model.add(tf.keras.layers.Dense(9, activation='softmax'))
mlp_model.summary()
mlp_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['acc'])
mlp_model.fit(training_generator, steps_per_epoch=24, validation_data=validation_generator, validation_steps=20, epochs=5) | code |
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