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32068059/cell_30 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
from tqdm import tqdm
import gensim
import json
import nltk
import numpy as np
import os
import pandas as pd
import pickle
import re
import spacy
import warnings
import os
import pandas as pd
pd.set_option('max_colwidth', 1000)
pd.set_option('max_rows', 100)
import numpy as np
import pickle
import matplotlib.pyplot as plt
from datetime import datetime
import re
import json
from tqdm import tqdm
import textwrap
import importlib as imp
from scipy.spatial.distance import cdist
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import nltk
nltk.download('stopwords')
import scispacy
import spacy
from sklearn.feature_extraction.text import CountVectorizer
import gensim
from gensim import corpora, models
from gensim.models.coherencemodel import CoherenceModel
import fasttext
import pyLDAvis
import pyLDAvis.gensim
from wordcloud import WordCloud
from IPython.display import display
import ipywidgets as widgets
import warnings
warnings.filterwarnings('ignore')
os.getcwd()
source_column = 'text'
id_colname = 'cord_uid'
split_sentence_by = '(?<=\\.) ?(?![0-9a-z])'
cov_earliest_date = '2019-12-01'
cov_key_terms = ['covid\\W19', 'covid19', 'covid', '2019\\Wncov', '2019ncov', 'ncov\\W2019', 'sars\\Wcov\\W2', 'sars\\Wcov2', '新型冠状病毒']
cov_related_terms = '(novel|new)( beta| )coronavirus'
input_data_path = '/kaggle/input/CORD-19-research-challenge/'
working_data_path = '/kaggle/input/cov19-pickles/'
metadata = pd.read_csv(input_data_path + 'metadata.csv', encoding='utf-8').replace({pd.np.nan: None})
metadata.isnull().sum(axis=0)
def pdf_or_pmc(r):
if r.has_pdf_parse:
return 'pdf_json'
if r.has_pmc_xml_parse:
return 'pmc_json'
return ''
metadata['sha_arr'] = metadata.apply(lambda r: r.sha.split(';') if r.sha is not None else [], axis=1)
metadata['full_text_file_path'] = metadata.apply(lambda r: np.unique(['/'.join([r.full_text_file, r.full_text_file, pdf_or_pmc(r), sha.strip()]) if r.has_pdf_parse or r.has_pmc_xml_parse else '' for sha in r.sha_arr]) if len(r.sha_arr) > 0 else [], axis=1)
metadata['publish_time'] = metadata['publish_time'].str.replace(' ([a-zA-Z]{3}-[a-zA-Z]{3})|(Spring)|(Summer)|(Autumn)|(Fall)|(Winter)', '', regex=True).str.strip()
metadata['publish_time_'] = pd.to_datetime(metadata.publish_time, format='%Y-%m-%d', errors='coerce')
mask = metadata.publish_time_.isnull()
metadata.loc[mask, 'publish_time_'] = pd.to_datetime(metadata.publish_time, format='%Y %B', errors='coerce')
mask = metadata.publish_time_.isnull()
metadata.loc[mask, 'publish_time_'] = pd.to_datetime(metadata.publish_time, format='%Y %b', errors='coerce')
mask = metadata.publish_time_.isnull()
metadata.loc[mask, 'publish_time_'] = pd.to_datetime(metadata.publish_time, format='%Y %B %d', errors='coerce')
mask = metadata.publish_time_.isnull()
metadata.loc[mask, 'publish_time_'] = pd.to_datetime(metadata.publish_time, format='%Y %b %d', errors='coerce')
mask = metadata.publish_time_.isnull()
metadata.loc[mask, 'publish_time_'] = pd.to_datetime(metadata.publish_time, format='%Y', errors='coerce')
mask = metadata.publish_time_.isnull()
invalid_dates = metadata.loc[mask, :].shape[0]
metadata.publish_time = metadata.publish_time_
metadata.drop(['publish_time_'], inplace=True, axis=1)
mask = metadata['full_text_file_path'].apply(lambda r: len(r) > 1)
def get_paper_info(json_data):
return ' '.join([t['text'] for t in json_data['body_text']])
full_text = []
for r in tqdm(metadata.to_dict(orient='records')):
record = []
for p in r['full_text_file_path']:
with open(input_data_path + p + '.json', 'r', encoding='utf-8') as f:
data = json.load(f)
record.append(get_paper_info(data))
full_text_ = '\n'.join(np.unique(record)) if len(record) > 0 else None
full_text.append(full_text_)
metadata['full_text'] = full_text
meta_full_text = metadata
meta_full_text[source_column] = np.where(meta_full_text['full_text'].isnull(), meta_full_text['abstract'], meta_full_text['full_text'])
meta_full_text = meta_full_text.dropna(subset=[source_column]).reset_index(drop=True)
meta_full_text = meta_full_text.sort_values('publish_time', ascending=False).drop_duplicates(source_column)
meta_full_text = meta_full_text.sort_values('publish_time', ascending=False).drop_duplicates(id_colname)
meta_full_text.drop(['sha', 'pmcid', 'pubmed_id', 'Microsoft Academic Paper ID', 'has_pdf_parse', 'has_pmc_xml_parse', 'full_text_file', 'sha_arr', 'full_text_file_path', 'full_text'], inplace=True, axis=1)
pickle.dump(meta_full_text, open(working_data_path + 'all_papers.pkl', 'wb'))
meta_full_text = pickle.load(open(working_data_path + 'all_papers.pkl', 'rb'))
corpus = meta_full_text[source_column]
stop_words = stopwords.words('english')
cord_stopwords = ['doi', 'preprint', 'copyright', 'peer', 'reviewed', 'org', 'https', 'et', 'al', 'author', 'figure', 'rights', 'reserved', 'permission', 'used', 'using', 'biorxiv', 'medrxiv', 'license', 'fig', 'fig.', 'al.', 'Elsevier', 'PMC', 'CZI', '-PRON-', 'abstract']
for word in tqdm(cord_stopwords):
if word not in stop_words:
stop_words.append(word)
else:
continue
nlp_lg = spacy.load('en_core_sci_lg', disable=['tagger', 'parser', 'ner'])
nlp_lg.max_length = 2000000
for w in tqdm(stop_words):
nlp_lg.vocab[w].is_stop = True
def removeParenthesesNumbers(v):
char_list_rm = ['[(]', '[)]', '[′·]']
char_list_rm_spc = [' no[nt]-', ' non', ' low-', ' high-']
v = re.sub('|'.join(char_list_rm), '', v)
v = re.sub('|'.join(char_list_rm_spc), ' ', v)
return v
sentence_test = '($2196.8)/case (in)fidelity μg μg/ml a=b2 www.website.org α-gal 2-len a.'
def spacy_tokenizer(sentence):
sentence = removeParenthesesNumbers(sentence)
token_rm = ['(www.\\S+)', '(-[1-9.])', '([∼≈≥≤≦⩾⩽→μ]\\S+)', '(\\S+=\\S+)', '(http\\S+)']
tokenized_list = [word.lemma_ for word in nlp_lg(sentence) if not (word.like_num or word.is_stop or word.is_punct or word.is_space)]
tokenized_list = [word for word in tokenized_list if not re.search('|'.join(token_rm), word)]
tokenized_list = [word for word in tokenized_list if len(re.findall('[a-zA-Z]', word)) > 1]
tokenized_list = [word for word in tokenized_list if re.search('^[a-zA-Z0-9]', word)]
return tokenized_list
spacy_tokenizer(sentence_test)
vec = CountVectorizer(max_df=0.8, min_df=0.001, tokenizer=spacy_tokenizer)
X = vec.fit_transform(tqdm(corpus))
valid_tokens = vec.get_feature_names()
X = pickle.load(open(working_data_path + 'TM_X.pkl', 'rb'))
valid_tokens = pickle.load(open(working_data_path + 'TM_valid_tokens.pkl', 'rb'))
arr = X.toarray()
texts = []
for i in tqdm(range(arr.shape[0])):
text = []
for j in range(arr.shape[1]):
occurrence = arr[i, j]
if occurrence > 0:
text.extend([valid_tokens[j]] * occurrence)
texts.append(text)
texts = pickle.load(open(working_data_path + 'TM_texts.pkl', 'rb'))
np.random.seed(1)
dictionary = gensim.corpora.Dictionary(texts)
count = 0
for k, v in dictionary.iteritems():
print(k, v)
count += 1
if count > 10:
break | code |
32068059/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from tqdm import tqdm
import spacy
stop_words = stopwords.words('english')
cord_stopwords = ['doi', 'preprint', 'copyright', 'peer', 'reviewed', 'org', 'https', 'et', 'al', 'author', 'figure', 'rights', 'reserved', 'permission', 'used', 'using', 'biorxiv', 'medrxiv', 'license', 'fig', 'fig.', 'al.', 'Elsevier', 'PMC', 'CZI', '-PRON-', 'abstract']
for word in tqdm(cord_stopwords):
if word not in stop_words:
stop_words.append(word)
else:
continue
nlp_lg = spacy.load('en_core_sci_lg', disable=['tagger', 'parser', 'ner'])
nlp_lg.max_length = 2000000
for w in tqdm(stop_words):
nlp_lg.vocab[w].is_stop = True | code |
32068059/cell_17 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
from tqdm import tqdm
stop_words = stopwords.words('english')
cord_stopwords = ['doi', 'preprint', 'copyright', 'peer', 'reviewed', 'org', 'https', 'et', 'al', 'author', 'figure', 'rights', 'reserved', 'permission', 'used', 'using', 'biorxiv', 'medrxiv', 'license', 'fig', 'fig.', 'al.', 'Elsevier', 'PMC', 'CZI', '-PRON-', 'abstract']
for word in tqdm(cord_stopwords):
if word not in stop_words:
stop_words.append(word)
else:
continue | code |
32068059/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from nltk.corpus import stopwords
from tqdm import tqdm
import re
import spacy
stop_words = stopwords.words('english')
cord_stopwords = ['doi', 'preprint', 'copyright', 'peer', 'reviewed', 'org', 'https', 'et', 'al', 'author', 'figure', 'rights', 'reserved', 'permission', 'used', 'using', 'biorxiv', 'medrxiv', 'license', 'fig', 'fig.', 'al.', 'Elsevier', 'PMC', 'CZI', '-PRON-', 'abstract']
for word in tqdm(cord_stopwords):
if word not in stop_words:
stop_words.append(word)
else:
continue
nlp_lg = spacy.load('en_core_sci_lg', disable=['tagger', 'parser', 'ner'])
nlp_lg.max_length = 2000000
for w in tqdm(stop_words):
nlp_lg.vocab[w].is_stop = True
def removeParenthesesNumbers(v):
char_list_rm = ['[(]', '[)]', '[′·]']
char_list_rm_spc = [' no[nt]-', ' non', ' low-', ' high-']
v = re.sub('|'.join(char_list_rm), '', v)
v = re.sub('|'.join(char_list_rm_spc), ' ', v)
return v
sentence_test = '($2196.8)/case (in)fidelity μg μg/ml a=b2 www.website.org α-gal 2-len a.'
def spacy_tokenizer(sentence):
sentence = removeParenthesesNumbers(sentence)
token_rm = ['(www.\\S+)', '(-[1-9.])', '([∼≈≥≤≦⩾⩽→μ]\\S+)', '(\\S+=\\S+)', '(http\\S+)']
tokenized_list = [word.lemma_ for word in nlp_lg(sentence) if not (word.like_num or word.is_stop or word.is_punct or word.is_space)]
tokenized_list = [word for word in tokenized_list if not re.search('|'.join(token_rm), word)]
tokenized_list = [word for word in tokenized_list if len(re.findall('[a-zA-Z]', word)) > 1]
tokenized_list = [word for word in tokenized_list if re.search('^[a-zA-Z0-9]', word)]
return tokenized_list
spacy_tokenizer(sentence_test) | code |
32068059/cell_5 | [
"image_output_1.png"
] | import nltk
import numpy as np
import os
import pandas as pd
import warnings
import os
import pandas as pd
pd.set_option('max_colwidth', 1000)
pd.set_option('max_rows', 100)
import numpy as np
np.set_printoptions(threshold=10000)
import pickle
import matplotlib.pyplot as plt
from datetime import datetime
import re
import json
from tqdm import tqdm
import textwrap
import importlib as imp
from scipy.spatial.distance import cdist
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
import nltk
nltk.download('stopwords')
import scispacy
import spacy
from sklearn.feature_extraction.text import CountVectorizer
import gensim
from gensim import corpora, models
from gensim.models.coherencemodel import CoherenceModel
import fasttext
import pyLDAvis
import pyLDAvis.gensim
from wordcloud import WordCloud
from IPython.display import display
import ipywidgets as widgets
import warnings
warnings.filterwarnings('ignore')
os.getcwd() | code |
72092559/cell_4 | [
"text_plain_output_1.png"
] | def a():
print('a() starts')
b()
d()
print('a() returns')
def b():
print('b() starts')
c()
print('b() returns')
def c():
print('c() starts')
print('c() returns')
def d():
print('d() starts')
print('d() returns')
a() | code |
72092559/cell_2 | [
"text_plain_output_1.png"
] | for i in range(1, 10):
print(i) | code |
72092559/cell_3 | [
"text_plain_output_1.png"
] | head = 0
tail = 0
for i in range(1):
ran = 0
if ran == 1:
head = head + 1
elif ran == 2:
tail = tail + 1
else:
print('error')
print(str(head) + ' vs ' + str(tail)) | code |
72092559/cell_5 | [
"text_plain_output_1.png"
] | perc = 0.1
def plustip(total):
return total * perc + total
toatlwtip = plustip(12.0)
print(toatlwtip)
print(perc) | code |
2013234/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
f,ax = plt.subplots(1,3,figsize=(15,6))
ax[0].imshow(test.iloc[0].reshape(28,28),cmap='binary')
ax[1].imshow(test.iloc[1].reshape(28,28),cmap='binary')
ax[2].imshow(test.iloc[2].reshape(28,28),cmap='binary')
np.array([np.array([int(i == label) for i in range(10)]) for label in [5, 2, 3, 9]])
labels_encoded = np.array([np.array([int(i == label) for i in range(10)]) for label in df.iloc[:, 0].values])
dataset = df.drop('label', axis=1)
dataset = np.multiply(dataset.values.astype(np.float32), 1.0 / 255.0)
test = np.multiply(test.values.astype(np.float32), 1.0 / 255.0)
(dataset.shape, labels_encoded.shape) | code |
2013234/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
np.array([np.array([int(i == label) for i in range(10)]) for label in [5, 2, 3, 9]]) | code |
2013234/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
df.describe() | code |
2013234/cell_6 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
f, ax = plt.subplots(1, 3, figsize=(15, 6))
ax[0].imshow(test.iloc[0].reshape(28, 28), cmap='binary')
ax[1].imshow(test.iloc[1].reshape(28, 28), cmap='binary')
ax[2].imshow(test.iloc[2].reshape(28, 28), cmap='binary') | code |
2013234/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
f,ax = plt.subplots(1,3,figsize=(15,6))
ax[0].imshow(test.iloc[0].reshape(28,28),cmap='binary')
ax[1].imshow(test.iloc[1].reshape(28,28),cmap='binary')
ax[2].imshow(test.iloc[2].reshape(28,28),cmap='binary')
np.array([np.array([int(i == label) for i in range(10)]) for label in [5, 2, 3, 9]])
labels_encoded = np.array([np.array([int(i == label) for i in range(10)]) for label in df.iloc[:, 0].values])
dataset = df.drop('label', axis=1)
dataset = np.multiply(dataset.values.astype(np.float32), 1.0 / 255.0)
test = np.multiply(test.values.astype(np.float32), 1.0 / 255.0)
(dataset.shape, labels_encoded.shape)
train_size = 40000
validation_size = 2000
train = dataset[:train_size]
train_targets = labels_encoded[:train_size]
validation = dataset[train_size:]
validation_targets = labels_encoded[train_size:]
(train.shape, train_targets.shape, validation.shape, validation_targets.shape, test.shape) | code |
129026593/cell_9 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
BASE_PATH = '/kaggle/input/histopathologic-cancer-detection'
BASE_TRAIN_PATH = f'{BASE_PATH}/train'
BASE_TEST_PATH = f'{BASE_PATH}/test'
BASE_TRAIN_LABELS_PATH = '/kaggle/input/dataset-copy/new_dataset/train_labels.csv'
BASE_TEST_TRAIN_PATH = f'/kaggle/input/dataset-copy/new_dataset/train'
BASE_TEST_TRAIN_10000_PATH = f'{BASE_TEST_TRAIN_PATH}/10000'
BASE_TEST_TRAIN_50000_PATH = f'{BASE_TEST_TRAIN_PATH}/150000'
BASE_TEST_TRAIN_ALL_PATH = f'{BASE_TEST_TRAIN_PATH}/all'
SYMBOLINK_PATH = '/kaggle/working'
SYMBOLINK_TRAIN_PATH = f'{SYMBOLINK_PATH}/train'
SYMBOLINK_SMALLER_TRAIN_PATH = f'{SYMBOLINK_PATH}/smaller_train'
from tensorflow.keras.preprocessing import image_dataset_from_directory
from tensorflow.keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(rescale=1.0 / 255)
train_gen = datagen.flow_from_directory(BASE_TEST_TRAIN_10000_PATH, target_size=(96, 96), batch_size=32, class_mode='binary') | code |
129026593/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow import keras
from tensorflow.keras import layers
from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint | code |
129026593/cell_10 | [
"text_html_output_1.png"
] | from tensorflow.keras.preprocessing.image import ImageDataGenerator
BASE_PATH = '/kaggle/input/histopathologic-cancer-detection'
BASE_TRAIN_PATH = f'{BASE_PATH}/train'
BASE_TEST_PATH = f'{BASE_PATH}/test'
BASE_TRAIN_LABELS_PATH = '/kaggle/input/dataset-copy/new_dataset/train_labels.csv'
BASE_TEST_TRAIN_PATH = f'/kaggle/input/dataset-copy/new_dataset/train'
BASE_TEST_TRAIN_10000_PATH = f'{BASE_TEST_TRAIN_PATH}/10000'
BASE_TEST_TRAIN_50000_PATH = f'{BASE_TEST_TRAIN_PATH}/150000'
BASE_TEST_TRAIN_ALL_PATH = f'{BASE_TEST_TRAIN_PATH}/all'
SYMBOLINK_PATH = '/kaggle/working'
SYMBOLINK_TRAIN_PATH = f'{SYMBOLINK_PATH}/train'
SYMBOLINK_SMALLER_TRAIN_PATH = f'{SYMBOLINK_PATH}/smaller_train'
from tensorflow.keras.preprocessing import image_dataset_from_directory
from tensorflow.keras.preprocessing.image import ImageDataGenerator
datagen = ImageDataGenerator(rescale=1.0 / 255)
train_gen = datagen.flow_from_directory(BASE_TEST_TRAIN_10000_PATH, target_size=(96, 96), batch_size=32, class_mode='binary')
print(train_gen.samples) | code |
129026593/cell_12 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Conv2D, MaxPooling2D
from tensorflow.keras.layers import Dense, Dropout, Flatten, Activation
from tensorflow.keras.models import Sequential
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(32, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.3))
model.add(Dense(2, activation='softmax'))
model.summary() | code |
129026593/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
BASE_PATH = '/kaggle/input/histopathologic-cancer-detection'
BASE_TRAIN_PATH = f'{BASE_PATH}/train'
BASE_TEST_PATH = f'{BASE_PATH}/test'
BASE_TRAIN_LABELS_PATH = '/kaggle/input/dataset-copy/new_dataset/train_labels.csv'
BASE_TEST_TRAIN_PATH = f'/kaggle/input/dataset-copy/new_dataset/train'
BASE_TEST_TRAIN_10000_PATH = f'{BASE_TEST_TRAIN_PATH}/10000'
BASE_TEST_TRAIN_50000_PATH = f'{BASE_TEST_TRAIN_PATH}/150000'
BASE_TEST_TRAIN_ALL_PATH = f'{BASE_TEST_TRAIN_PATH}/all'
SYMBOLINK_PATH = '/kaggle/working'
SYMBOLINK_TRAIN_PATH = f'{SYMBOLINK_PATH}/train'
SYMBOLINK_SMALLER_TRAIN_PATH = f'{SYMBOLINK_PATH}/smaller_train'
train_labels_df = pd.read_csv(BASE_TRAIN_LABELS_PATH)
train_labels_df.set_index('id', inplace=True)
train_labels_df.head() | code |
74052188/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train) | code |
74052188/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import OneHotEncoder
import numpy as np
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
dataset = pd.read_csv('../input/abalone-dataset/abalone.csv')
X = dataset.iloc[:, :-1].values
y = dataset.iloc[:, -1].values
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ct = ColumnTransformer(transformers=[('encoder', OneHotEncoder(drop='first'), [0])], remainder='passthrough', sparse_threshold=0)
X = np.array(ct.fit_transform(X))
from sklearn.linear_model import LinearRegression
regressor = LinearRegression()
regressor.fit(X_train, y_train)
y_predict = regressor.predict(X_train)
np.set_printoptions(precision=2)
print(np.concatenate((y_predict.reshape(len(y_predict), 1), y_test.reshape(len(y_predict), 1)), 1)) | code |
18137750/cell_9 | [
"text_html_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import cv2
import numpy as np
import os
import pandas as pd
import sys
import numpy as np
import pandas as pd
import cv2
import os
import sys
test_df = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')
test_df['id_code'] = test_df['id_code'].apply(lambda x: x + '.png')
diag_text = ['Normal', 'Mild', 'Moderate', 'Severe', 'Proliferative']
num_classes = 5
sys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))
from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
def create_effnetB5_model(input_shape, n_out):
base_model = EfficientNetB5(weights=None, include_top=False, input_shape=input_shape)
model = Sequential()
model.add(base_model)
model.add(Dropout(0.25))
model.add(Dense(1024))
model.add(LeakyReLU())
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
IMAGE_HEIGHT = 340
IMAGE_WIDTH = 340
PRETRAINED_MODEL = '../input/efficientnetb5-blindness-detector/blindness_detector_best_qwk.h5'
model = create_effnetB5_model(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), n_out=num_classes)
model.load_weights(PRETRAINED_MODEL)
model.summary()
from tqdm import tqdm_notebook as tqdm
submit = pd.read_csv('../input/aptos2019-blindness-detection/sample_submission.csv')
predicted = []
print('Making predictions...')
for i, name in tqdm(enumerate(submit['id_code'])):
path = os.path.join('../input/aptos2019-blindness-detection/test_images/', name + '.png')
image = cv2.imread(path)
image = cv2.resize(image, (IMAGE_HEIGHT, IMAGE_WIDTH))
X = np.array(image[np.newaxis] / 255)
raw_prediction = model.predict(X) > 0.5
prediction = raw_prediction.astype(int).sum(axis=1) - 1
predicted.append(prediction[0]) | code |
18137750/cell_6 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import os
import sys
import numpy as np
import pandas as pd
import cv2
import os
import sys
sys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))
from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
def create_effnetB5_model(input_shape, n_out):
base_model = EfficientNetB5(weights=None, include_top=False, input_shape=input_shape)
model = Sequential()
model.add(base_model)
model.add(Dropout(0.25))
model.add(Dense(1024))
model.add(LeakyReLU())
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model | code |
18137750/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import cv2
import os
import sys
print(os.listdir('../input')) | code |
18137750/cell_11 | [
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import cv2
import numpy as np
import os
import pandas as pd
import sys
import numpy as np
import pandas as pd
import cv2
import os
import sys
test_df = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')
test_df['id_code'] = test_df['id_code'].apply(lambda x: x + '.png')
diag_text = ['Normal', 'Mild', 'Moderate', 'Severe', 'Proliferative']
num_classes = 5
sys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))
from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
def create_effnetB5_model(input_shape, n_out):
base_model = EfficientNetB5(weights=None, include_top=False, input_shape=input_shape)
model = Sequential()
model.add(base_model)
model.add(Dropout(0.25))
model.add(Dense(1024))
model.add(LeakyReLU())
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
IMAGE_HEIGHT = 340
IMAGE_WIDTH = 340
PRETRAINED_MODEL = '../input/efficientnetb5-blindness-detector/blindness_detector_best_qwk.h5'
model = create_effnetB5_model(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), n_out=num_classes)
model.load_weights(PRETRAINED_MODEL)
model.summary()
from tqdm import tqdm_notebook as tqdm
submit = pd.read_csv('../input/aptos2019-blindness-detection/sample_submission.csv')
predicted = []
for i, name in tqdm(enumerate(submit['id_code'])):
path = os.path.join('../input/aptos2019-blindness-detection/test_images/', name + '.png')
image = cv2.imread(path)
image = cv2.resize(image, (IMAGE_HEIGHT, IMAGE_WIDTH))
X = np.array(image[np.newaxis] / 255)
raw_prediction = model.predict(X) > 0.5
prediction = raw_prediction.astype(int).sum(axis=1) - 1
predicted.append(prediction[0])
submit['diagnosis'] = predicted
submit.to_csv('submission.csv', index=False)
submit.head(10) | code |
18137750/cell_7 | [
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import os
import pandas as pd
import sys
import numpy as np
import pandas as pd
import cv2
import os
import sys
test_df = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')
test_df['id_code'] = test_df['id_code'].apply(lambda x: x + '.png')
diag_text = ['Normal', 'Mild', 'Moderate', 'Severe', 'Proliferative']
num_classes = 5
sys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))
from efficientnet import EfficientNetB5
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
def create_effnetB5_model(input_shape, n_out):
base_model = EfficientNetB5(weights=None, include_top=False, input_shape=input_shape)
model = Sequential()
model.add(base_model)
model.add(Dropout(0.25))
model.add(Dense(1024))
model.add(LeakyReLU())
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
IMAGE_HEIGHT = 340
IMAGE_WIDTH = 340
PRETRAINED_MODEL = '../input/efficientnetb5-blindness-detector/blindness_detector_best_qwk.h5'
print('Creating model...')
model = create_effnetB5_model(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), n_out=num_classes)
print('Restoring model from ' + PRETRAINED_MODEL + '...')
model.load_weights(PRETRAINED_MODEL)
model.summary() | code |
130025106/cell_42 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.preprocessing import StandardScaler
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
rfc.fit(X_train, y_train) | code |
130025106/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum()
train.drop_duplicates()
train.info() | code |
130025106/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
test | code |
130025106/cell_56 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
test.drop(columns=['id'], inplace=True)
test
test | code |
130025106/cell_30 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.svm import SVC
svm = SVC()
svm.fit(X_train, y_train) | code |
130025106/cell_33 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.svm import SVC
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.svm import SVC
ksvm = SVC(kernel='rbf')
ksvm.fit(X_train, y_train) | code |
130025106/cell_39 | [
"text_html_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train) | code |
130025106/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('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train | code |
130025106/cell_52 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
test.drop(columns=['id'], inplace=True)
test
test | code |
130025106/cell_1 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
130025106/cell_45 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.tree import DecisionTreeClassifier
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train) | code |
130025106/cell_49 | [
"text_html_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import StandardScaler
from sklearn.svm import SVC
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, y_train)
y_pred_lr = lr.predict(X_test)
from sklearn.svm import SVC
svm = SVC()
svm.fit(X_train, y_train)
y_pred_svm = svm.predict(X_test)
from sklearn.svm import SVC
ksvm = SVC(kernel='rbf')
ksvm.fit(X_train, y_train)
y_pred_ksvm = ksvm.predict(X_test)
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(X_train, y_train)
y_pred_gnb = gnb.predict(X_test)
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(X_train, y_train)
y_pred_knn = knn.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()
rfc.fit(X_train, y_train)
y_pred_rfc = rfc.predict(X_test)
from sklearn.tree import DecisionTreeClassifier
dtc = DecisionTreeClassifier()
dtc.fit(X_train, y_train)
y_pred_dtc = dtc.predict(X_test)
df = pd.DataFrame({'Model Name': ['Logistic Regression', 'Linear SVM', 'Kernel SVM', 'Naive Bayes', 'K Nearest Neighbors', 'Decision Tree Classifier', 'Random Forest Classifier'], 'Accuracy Score': [accuracy_score(y_test, y_pred_lr), accuracy_score(y_test, y_pred_svm), accuracy_score(y_test, y_pred_ksvm), accuracy_score(y_test, y_pred_gnb), accuracy_score(y_test, y_pred_knn), accuracy_score(y_test, y_pred_dtc), accuracy_score(y_test, y_pred_rfc)]})
df = df.sort_values(by=['Accuracy Score'], ascending=False)
df | code |
130025106/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum()
train.drop_duplicates()
plt.boxplot(train)
plt.show() | code |
130025106/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum() | code |
130025106/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum()
train.drop_duplicates()
plt.figure(figsize=(18, 12))
sns.heatmap(train.corr(), annot=True, square=True, cmap='BrBG')
plt.show() | code |
130025106/cell_24 | [
"image_output_1.png"
] | X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape) | code |
130025106/cell_14 | [
"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)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum()
train.drop_duplicates()
train.hist(bins=20, figsize=(18, 12))
plt.show() | code |
130025106/cell_53 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import StandardScaler
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
test.drop(columns=['id'], inplace=True)
test
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
data_test = sc.transform(test)
data_test | code |
130025106/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
train.isnull().sum()
train.drop_duplicates() | code |
130025106/cell_27 | [
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import StandardScaler
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.linear_model import LogisticRegression
lr = LogisticRegression()
lr.fit(X_train, y_train) | code |
130025106/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/mobile-price-classification/train.csv')
test = pd.read_csv('/kaggle/input/mobile-price-classification/test.csv')
train
test.drop(columns=['id'], inplace=True)
test | code |
130025106/cell_36 | [
"text_html_output_1.png"
] | from sklearn.naive_bayes import GaussianNB
from sklearn.preprocessing import StandardScaler
X_train = X_sample.iloc[:, :-1].values
y_train = X_sample.iloc[:, -1].values
X_test = y_sample.iloc[:, :-1].values
y_test = y_sample.iloc[:, -1].values
(X_train.shape, X_test.shape, y_train.shape, y_test.shape)
from sklearn.preprocessing import StandardScaler
sc = StandardScaler()
X_train = sc.fit_transform(X_train)
X_test = sc.transform(X_test)
from sklearn.naive_bayes import GaussianNB
gnb = GaussianNB()
gnb.fit(X_train, y_train) | code |
324276/cell_9 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.05,1))
plt.title("Gender")
plt.show()
N = len(df.JobRoleInterest.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.JobRoleInterest.value_counts().index
colors = ['OliveDrab', 'Orange', 'OrangeRed', 'DarkCyan', 'Salmon', 'Sienna', 'Maroon', 'LightSlateGrey', 'DimGray']
patches, texts = plt.pie(df.JobRoleInterest.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.25, 1))
plt.title('Job Role Interest')
plt.show() | code |
324276/cell_6 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.05, 1))
plt.title('Gender')
plt.show() | code |
324276/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import colorsys
plt.style.use('seaborn-talk')
df = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', sep=',') | code |
324276/cell_3 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
df.Age.hist(bins=100)
plt.xlabel('Age')
plt.title('Distribution of Age')
plt.show() | code |
324276/cell_12 | [
"image_output_1.png"
] | import colorsys
import matplotlib.pyplot as plt
labels = df.Gender.value_counts().index
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
patches, texts = plt.pie(df.Gender.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.05,1))
plt.title("Gender")
plt.show()
N = len(df.JobRoleInterest.value_counts().index)
HSV_tuples = [(x*1.0/N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.JobRoleInterest.value_counts().index
colors = ['OliveDrab', 'Orange', 'OrangeRed', 'DarkCyan', 'Salmon', 'Sienna', 'Maroon', 'LightSlateGrey', 'DimGray']
patches, texts = plt.pie(df.JobRoleInterest.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.25, 1))
plt.title("Job Role Interest")
plt.show()
N = len(df.EmploymentField.value_counts().index)
HSV_tuples = [(x * 1.0 / N, 0.5, 0.5) for x in range(N)]
RGB_tuples = list(map(lambda x: colorsys.hsv_to_rgb(*x), HSV_tuples))
labels = df.EmploymentField.value_counts().index
patches, texts = plt.pie(df.EmploymentField.value_counts(), colors=RGB_tuples, startangle=90)
plt.axes().set_aspect('equal', 'datalim')
plt.legend(patches, labels, bbox_to_anchor=(1.3, 1))
plt.title('Employment Field')
plt.show() | code |
2000572/cell_13 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import string
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'class', 'v2': 'text'})
messages.groupby('class').describe()
def process_text(text):
"""
What will be covered:
1. Remove punctuation
2. Remove stopwords
3. Return list of clean text words
"""
nopunc = [char for char in text if char not in string.punctuation]
nopunc = ''.join(nopunc)
clean_words = [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]
return clean_words
bow_transformer = CountVectorizer(analyzer=process_text).fit(messages['text'])
len(bow_transformer.vocabulary_) | code |
2000572/cell_9 | [
"text_plain_output_1.png"
] | from nltk.corpus import stopwords
import pandas as pd
import string
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'class', 'v2': 'text'})
messages.groupby('class').describe()
def process_text(text):
"""
What will be covered:
1. Remove punctuation
2. Remove stopwords
3. Return list of clean text words
"""
nopunc = [char for char in text if char not in string.punctuation]
nopunc = ''.join(nopunc)
clean_words = [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]
return clean_words
messages['text'].apply(process_text).head() | code |
2000572/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'class', 'v2': 'text'})
messages.groupby('class').describe() | code |
2000572/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'class', 'v2': 'text'})
messages.groupby('class').describe()
messages.hist(column='length', by='class', bins=50, figsize=(15, 6)) | code |
2000572/cell_15 | [
"text_html_output_1.png"
] | from nltk.corpus import stopwords
from sklearn.feature_extraction.text import CountVectorizer
import pandas as pd
import string
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'class', 'v2': 'text'})
messages.groupby('class').describe()
def process_text(text):
"""
What will be covered:
1. Remove punctuation
2. Remove stopwords
3. Return list of clean text words
"""
nopunc = [char for char in text if char not in string.punctuation]
nopunc = ''.join(nopunc)
clean_words = [word for word in nopunc.split() if word.lower() not in stopwords.words('english')]
return clean_words
bow_transformer = CountVectorizer(analyzer=process_text).fit(messages['text'])
len(bow_transformer.vocabulary_)
messages_bow = bow_transformer.transform(messages['text'])
print('Sparse matrix shape ', messages_bow.shape)
print('Amount of Non-Zero occurences: ', messages_bow.nnz) | code |
2000572/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
messages = pd.read_csv('../input/spam.csv', encoding='latin-1')
messages.drop(['Unnamed: 2', 'Unnamed: 3', 'Unnamed: 4'], axis=1, inplace=True)
messages = messages.rename(columns={'v1': 'class', 'v2': 'text'})
messages.head() | code |
1010505/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
null_columns = houses.columns[houses.isnull().any()]
houses[null_columns].isnull().sum()
sns.barplot(houses['TotRmsAbvGrd'], houses['SalePrice'])
plt.title('Sale Price vs Number of rooms') | code |
1010505/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
import numpy as np
import warnings
warnings.filterwarnings('ignore')
import matplotlib.pyplot as plt
import seaborn as sns
sns.set(style='whitegrid', color_codes=True)
sns.set(font_scale=1)
houses = pd.read_csv('../input/train.csv')
houses.head() | code |
1010505/cell_3 | [
"text_plain_output_1.png"
] | null_columns = houses.columns[houses.isnull().any()]
houses[null_columns].isnull().sum() | code |
128010152/cell_12 | [
"text_plain_output_1.png"
] | from glob import glob
import matplotlib.pyplot as plt
import tensorflow as tf
IMAGE_SIZE = 256
BATCH_SIZE = 16
MAX_TRAIN_IMAGES = 400
train_low_light_images = sorted(glob('/kaggle/input/lol-dataset/lol_dataset/our485/low/*'))[:MAX_TRAIN_IMAGES]
val_low_light_images = sorted(glob('/kaggle/input/lol-dataset/lol_dataset/our485/low/*'))[MAX_TRAIN_IMAGES:]
test_low_light_images = sorted(glob('/kaggle/input/lol-dataset/lol_dataset/eval15/low/*'))
def load_data(image_path):
image = tf.io.read_file(image_path)
image = tf.image.decode_png(image, channels=3)
image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
image = image / 255.0
return image
def data_generator(low_light_images):
dataset = tf.data.Dataset.from_tensor_slices(low_light_images)
dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
return dataset
train_dataset = data_generator(train_low_light_images)
val_dataset = data_generator(val_low_light_images)
zero_dce_model = ZeroDCE()
zero_dce_model.compile(learning_rate=0.0001)
history = zero_dce_model.fit(train_dataset, validation_data=val_dataset, epochs=100)
def plot_result(item):
plt.plot(history.history[item], label=item)
plt.plot(history.history['val_' + item], label='val_' + item)
plt.xlabel('Epochs')
plt.ylabel(item)
plt.title('Train and Validation {} Over Epochs'.format(item), fontsize=14)
plt.legend()
plt.grid()
plt.show()
plot_result('total_loss')
plot_result('illumination_smoothness_loss')
plot_result('spatial_constancy_loss')
plot_result('color_constancy_loss')
plot_result('exposure_loss') | code |
128010152/cell_5 | [
"image_output_11.png",
"text_plain_output_5.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"image_output_14.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.png",
"image_output_5.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_16.png",
"text_plain_output_8.png",
"image_output_6.png",
"image_output_12.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"text_plain_output_11.png",
"text_plain_output_12.png",
"image_output_15.png",
"image_output_9.png"
] | from glob import glob
import tensorflow as tf
IMAGE_SIZE = 256
BATCH_SIZE = 16
MAX_TRAIN_IMAGES = 400
train_low_light_images = sorted(glob('/kaggle/input/lol-dataset/lol_dataset/our485/low/*'))[:MAX_TRAIN_IMAGES]
val_low_light_images = sorted(glob('/kaggle/input/lol-dataset/lol_dataset/our485/low/*'))[MAX_TRAIN_IMAGES:]
test_low_light_images = sorted(glob('/kaggle/input/lol-dataset/lol_dataset/eval15/low/*'))
def load_data(image_path):
image = tf.io.read_file(image_path)
image = tf.image.decode_png(image, channels=3)
image = tf.image.resize(images=image, size=[IMAGE_SIZE, IMAGE_SIZE])
image = image / 255.0
return image
def data_generator(low_light_images):
dataset = tf.data.Dataset.from_tensor_slices(low_light_images)
dataset = dataset.map(load_data, num_parallel_calls=tf.data.AUTOTUNE)
dataset = dataset.batch(BATCH_SIZE, drop_remainder=True)
return dataset
train_dataset = data_generator(train_low_light_images)
val_dataset = data_generator(val_low_light_images)
print('Train Dataset:', train_dataset)
print('Validation Dataset:', val_dataset) | code |
122256403/cell_4 | [
"text_html_output_1.png"
] | # check coda version
!nvcc --version | code |
122256403/cell_34 | [
"text_plain_output_1.png"
] | from spacy.tokens import DocBin
from spacy.util import filter_spans
from tqdm import tqdm
import fr_core_news_sm
import json
import os
import os
import pandas as pd
import pandas as pd
import re
import re
import spacy
spacy.require_gpu()
import re
def trim_entity_spans(data: list) -> list:
"""Removes leading and trailing white spaces from entity spans.
Args:
data (list): The data to be cleaned in spaCy JSON format.
Returns:
list: The cleaned data.
"""
invalid_span_tokens = re.compile('\\s')
cleaned_data = []
for text, annotations in data:
entities = annotations['labels']
valid_entities = []
for start, end, label in entities:
valid_start = start
valid_end = end
while valid_start < len(text) and invalid_span_tokens.match(text[valid_start]):
valid_start += 1
while valid_end > 1 and invalid_span_tokens.match(text[valid_end - 1]):
valid_end -= 1
valid_entities.append([valid_start, valid_end, label])
cleaned_data.append([text, {'labels': valid_entities}])
return cleaned_data
def starts_with_punctuation(s):
if re.match('^\\W', s):
return True
else:
return False
def ends_with_punctuation(s):
if re.search('\\W$', s):
return True
else:
return False
def dp(train_data):
list_train_data = []
for i, ele in enumerate(train_data):
list_train_data.append((train_data[i]['id'], train_data[i]['text'], {'labels': train_data[i]['label']}))
ent_prob = []
for i, ele in enumerate(list_train_data):
ents = []
for j, elem in enumerate(list_train_data[i][2]['labels']):
start = elem[0]
end = elem[1]
ent = list_train_data[i][1][start:end]
if starts_with_punctuation(ent) and ends_with_punctuation(ent):
ents.append((list_train_data[i][0], ent, start, end, 'YES', 'YES'))
elif starts_with_punctuation(ent) and (not ends_with_punctuation(ent)):
ents.append((list_train_data[i][0], ent, start, end, 'YES', 'NO'))
elif not starts_with_punctuation(ent) and ends_with_punctuation(ent):
ents.append((list_train_data[i][0], ent, start, end, 'NO', 'YES'))
else:
ents.append((list_train_data[i][0], ent, start, end, 'NO', 'NO'))
for i, ent in enumerate(ents):
if ent[4] == 'YES' or ent[5] == 'YES':
ent_prob.append(ent)
train_data_df = pd.DataFrame(train_data)
ent_prob_df = pd.DataFrame(ent_prob, columns=['id', 'text', 'start', 'end', 'punBeg', 'punEnd'])
merged = pd.merge(train_data_df, ent_prob_df, on='id')
common_index = merged.index
train_data_df = train_data_df.drop(index=common_index)
for index, row in merged.iterrows():
if row['punBeg'] == 'YES':
for item in row['label']:
if int(item[0]) == int(row['start']):
item[0] = item[0] + 1
if row['punEnd'] == 'YES':
for item in row['label']:
if int(item[1]) == int(row['end']):
item[1] = item[1] - 1
for index, row in merged.iterrows():
new_row = {'id': row['id'], 'text': row['text_x'], 'label': row['label'], 'Comments': row['Comments']}
train_data_df = train_data_df.append(new_row, ignore_index=True)
dict_list = train_data_df.to_dict(orient='records')
list_train_data = []
for i, ele in enumerate(dict_list):
list_train_data.append((dict_list[i]['text'], {'labels': dict_list[i]['label']}))
list_train_data = []
for i, ele in enumerate(dict_list):
list_train_data.append((dict_list[i]['text'], {'labels': dict_list[i]['label']}))
for i, ele in enumerate(list_train_data):
entities = []
for label in list_train_data[i][1]['labels']:
tuple_ = (label[0], label[1], label[2])
entities.append(tuple_)
list_train_data[i][1]['labels'] = entities
return list_train_data
import pandas as pd
import json
import os
os.chdir('/kaggle/input/d/fatimahabib1/niort-sentences')
with open('annotations-niort-sentence-level.jsonl', 'r', encoding='utf-8') as f:
s = f.read()
data = json.loads(s)
train_data = data['annotations']
list_train_data = []
for i, ele in enumerate(train_data):
list_train_data.append((train_data[i]['text'], {'labels': train_data[i]['label']}))
training_list = trim_entity_spans(list_train_data)
ent_prob = []
ents = []
for i, ele in enumerate(training_list):
for j, elem in enumerate(training_list[i][1]['labels']):
start = elem[0]
end = elem[1]
ent = training_list[i][0][start:end]
num_spaces_beginning = len(ent) - len(ent.lstrip())
num_spaces_end = len(ent) - len(ent.rstrip())
ents.append((training_list[i][0], ent, start, end, num_spaces_beginning, num_spaces_end))
for i, ent in enumerate(ents):
if ent[1] != ent[1].strip():
ent_prob.append(ent)
nlp = fr_core_news_sm.load()
db = DocBin()
for text, annot in tqdm(training_list):
doc = nlp.make_doc(text)
ents = []
for start, end, label in annot['labels']:
span = doc.char_span(start, end, label=label, alignment_mode='contract')
ents.append(span)
pat_orig = len(ents)
filtered = filter_spans(ents)
pat_filt = len(filtered)
doc.ents = ents
doc.ents = ents
db.add(doc)
db.to_disk('/kaggle/working/train.spacy')
best_nlp = spacy.load('/kaggle/working/model-best')
colors = {'INFO': '#F67DE3', 'SUB': '#7DF6D9'}
options = {'colors': colors}
doc = best_nlp('pour les constructions de moins de 90 m² de surface de plancher : 30 m² de surface de plancher supplémentaire par rapport à la surface de plancher existante à la date d’approbation du PLU. ')
doc = best_nlp('La peinture sur les murs en pierre en taille ou en moellon est interdite. L’emploi à nu des matériaux destinés à être enduits est strictement interdit. ')
spacy.displacy.render(doc, style='ent', options=options, jupyter=True) | code |
122256403/cell_33 | [
"text_plain_output_1.png"
] | from spacy.tokens import DocBin
from spacy.util import filter_spans
from tqdm import tqdm
import fr_core_news_sm
import json
import os
import os
import pandas as pd
import pandas as pd
import re
import re
import spacy
spacy.require_gpu()
import re
def trim_entity_spans(data: list) -> list:
"""Removes leading and trailing white spaces from entity spans.
Args:
data (list): The data to be cleaned in spaCy JSON format.
Returns:
list: The cleaned data.
"""
invalid_span_tokens = re.compile('\\s')
cleaned_data = []
for text, annotations in data:
entities = annotations['labels']
valid_entities = []
for start, end, label in entities:
valid_start = start
valid_end = end
while valid_start < len(text) and invalid_span_tokens.match(text[valid_start]):
valid_start += 1
while valid_end > 1 and invalid_span_tokens.match(text[valid_end - 1]):
valid_end -= 1
valid_entities.append([valid_start, valid_end, label])
cleaned_data.append([text, {'labels': valid_entities}])
return cleaned_data
def starts_with_punctuation(s):
if re.match('^\\W', s):
return True
else:
return False
def ends_with_punctuation(s):
if re.search('\\W$', s):
return True
else:
return False
def dp(train_data):
list_train_data = []
for i, ele in enumerate(train_data):
list_train_data.append((train_data[i]['id'], train_data[i]['text'], {'labels': train_data[i]['label']}))
ent_prob = []
for i, ele in enumerate(list_train_data):
ents = []
for j, elem in enumerate(list_train_data[i][2]['labels']):
start = elem[0]
end = elem[1]
ent = list_train_data[i][1][start:end]
if starts_with_punctuation(ent) and ends_with_punctuation(ent):
ents.append((list_train_data[i][0], ent, start, end, 'YES', 'YES'))
elif starts_with_punctuation(ent) and (not ends_with_punctuation(ent)):
ents.append((list_train_data[i][0], ent, start, end, 'YES', 'NO'))
elif not starts_with_punctuation(ent) and ends_with_punctuation(ent):
ents.append((list_train_data[i][0], ent, start, end, 'NO', 'YES'))
else:
ents.append((list_train_data[i][0], ent, start, end, 'NO', 'NO'))
for i, ent in enumerate(ents):
if ent[4] == 'YES' or ent[5] == 'YES':
ent_prob.append(ent)
train_data_df = pd.DataFrame(train_data)
ent_prob_df = pd.DataFrame(ent_prob, columns=['id', 'text', 'start', 'end', 'punBeg', 'punEnd'])
merged = pd.merge(train_data_df, ent_prob_df, on='id')
common_index = merged.index
train_data_df = train_data_df.drop(index=common_index)
for index, row in merged.iterrows():
if row['punBeg'] == 'YES':
for item in row['label']:
if int(item[0]) == int(row['start']):
item[0] = item[0] + 1
if row['punEnd'] == 'YES':
for item in row['label']:
if int(item[1]) == int(row['end']):
item[1] = item[1] - 1
for index, row in merged.iterrows():
new_row = {'id': row['id'], 'text': row['text_x'], 'label': row['label'], 'Comments': row['Comments']}
train_data_df = train_data_df.append(new_row, ignore_index=True)
dict_list = train_data_df.to_dict(orient='records')
list_train_data = []
for i, ele in enumerate(dict_list):
list_train_data.append((dict_list[i]['text'], {'labels': dict_list[i]['label']}))
list_train_data = []
for i, ele in enumerate(dict_list):
list_train_data.append((dict_list[i]['text'], {'labels': dict_list[i]['label']}))
for i, ele in enumerate(list_train_data):
entities = []
for label in list_train_data[i][1]['labels']:
tuple_ = (label[0], label[1], label[2])
entities.append(tuple_)
list_train_data[i][1]['labels'] = entities
return list_train_data
import pandas as pd
import json
import os
os.chdir('/kaggle/input/d/fatimahabib1/niort-sentences')
with open('annotations-niort-sentence-level.jsonl', 'r', encoding='utf-8') as f:
s = f.read()
data = json.loads(s)
train_data = data['annotations']
list_train_data = []
for i, ele in enumerate(train_data):
list_train_data.append((train_data[i]['text'], {'labels': train_data[i]['label']}))
training_list = trim_entity_spans(list_train_data)
ent_prob = []
ents = []
for i, ele in enumerate(training_list):
for j, elem in enumerate(training_list[i][1]['labels']):
start = elem[0]
end = elem[1]
ent = training_list[i][0][start:end]
num_spaces_beginning = len(ent) - len(ent.lstrip())
num_spaces_end = len(ent) - len(ent.rstrip())
ents.append((training_list[i][0], ent, start, end, num_spaces_beginning, num_spaces_end))
for i, ent in enumerate(ents):
if ent[1] != ent[1].strip():
ent_prob.append(ent)
nlp = fr_core_news_sm.load()
db = DocBin()
for text, annot in tqdm(training_list):
doc = nlp.make_doc(text)
ents = []
for start, end, label in annot['labels']:
span = doc.char_span(start, end, label=label, alignment_mode='contract')
ents.append(span)
pat_orig = len(ents)
filtered = filter_spans(ents)
pat_filt = len(filtered)
doc.ents = ents
doc.ents = ents
db.add(doc)
db.to_disk('/kaggle/working/train.spacy')
best_nlp = spacy.load('/kaggle/working/model-best')
colors = {'INFO': '#F67DE3', 'SUB': '#7DF6D9'}
options = {'colors': colors}
doc = best_nlp('pour les constructions de moins de 90 m² de surface de plancher : 30 m² de surface de plancher supplémentaire par rapport à la surface de plancher existante à la date d’approbation du PLU. ')
spacy.displacy.render(doc, style='ent', options=options, jupyter=True) | code |
122256403/cell_29 | [
"text_plain_output_1.png"
] | !python -m spacy debug data /kaggle/working/config.cfg --paths.train /kaggle/working/train.spacy --paths.dev /kaggle/working/train.spacy | code |
122256403/cell_2 | [
"text_plain_output_1.png"
] | !pip install spacy-transformers | code |
122256403/cell_7 | [
"text_html_output_1.png"
] | import spacy
spacy.require_gpu() | code |
122256403/cell_3 | [
"application_vnd.jupyter.stderr_output_3.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | !python3 -m spacy download fr_core_news_sm | code |
122256403/cell_35 | [
"text_html_output_1.png"
] | from spacy.tokens import DocBin
from spacy.util import filter_spans
from tqdm import tqdm
import fr_core_news_sm
import json
import os
import os
import pandas as pd
import pandas as pd
import re
import re
import spacy
spacy.require_gpu()
import re
def trim_entity_spans(data: list) -> list:
"""Removes leading and trailing white spaces from entity spans.
Args:
data (list): The data to be cleaned in spaCy JSON format.
Returns:
list: The cleaned data.
"""
invalid_span_tokens = re.compile('\\s')
cleaned_data = []
for text, annotations in data:
entities = annotations['labels']
valid_entities = []
for start, end, label in entities:
valid_start = start
valid_end = end
while valid_start < len(text) and invalid_span_tokens.match(text[valid_start]):
valid_start += 1
while valid_end > 1 and invalid_span_tokens.match(text[valid_end - 1]):
valid_end -= 1
valid_entities.append([valid_start, valid_end, label])
cleaned_data.append([text, {'labels': valid_entities}])
return cleaned_data
def starts_with_punctuation(s):
if re.match('^\\W', s):
return True
else:
return False
def ends_with_punctuation(s):
if re.search('\\W$', s):
return True
else:
return False
def dp(train_data):
list_train_data = []
for i, ele in enumerate(train_data):
list_train_data.append((train_data[i]['id'], train_data[i]['text'], {'labels': train_data[i]['label']}))
ent_prob = []
for i, ele in enumerate(list_train_data):
ents = []
for j, elem in enumerate(list_train_data[i][2]['labels']):
start = elem[0]
end = elem[1]
ent = list_train_data[i][1][start:end]
if starts_with_punctuation(ent) and ends_with_punctuation(ent):
ents.append((list_train_data[i][0], ent, start, end, 'YES', 'YES'))
elif starts_with_punctuation(ent) and (not ends_with_punctuation(ent)):
ents.append((list_train_data[i][0], ent, start, end, 'YES', 'NO'))
elif not starts_with_punctuation(ent) and ends_with_punctuation(ent):
ents.append((list_train_data[i][0], ent, start, end, 'NO', 'YES'))
else:
ents.append((list_train_data[i][0], ent, start, end, 'NO', 'NO'))
for i, ent in enumerate(ents):
if ent[4] == 'YES' or ent[5] == 'YES':
ent_prob.append(ent)
train_data_df = pd.DataFrame(train_data)
ent_prob_df = pd.DataFrame(ent_prob, columns=['id', 'text', 'start', 'end', 'punBeg', 'punEnd'])
merged = pd.merge(train_data_df, ent_prob_df, on='id')
common_index = merged.index
train_data_df = train_data_df.drop(index=common_index)
for index, row in merged.iterrows():
if row['punBeg'] == 'YES':
for item in row['label']:
if int(item[0]) == int(row['start']):
item[0] = item[0] + 1
if row['punEnd'] == 'YES':
for item in row['label']:
if int(item[1]) == int(row['end']):
item[1] = item[1] - 1
for index, row in merged.iterrows():
new_row = {'id': row['id'], 'text': row['text_x'], 'label': row['label'], 'Comments': row['Comments']}
train_data_df = train_data_df.append(new_row, ignore_index=True)
dict_list = train_data_df.to_dict(orient='records')
list_train_data = []
for i, ele in enumerate(dict_list):
list_train_data.append((dict_list[i]['text'], {'labels': dict_list[i]['label']}))
list_train_data = []
for i, ele in enumerate(dict_list):
list_train_data.append((dict_list[i]['text'], {'labels': dict_list[i]['label']}))
for i, ele in enumerate(list_train_data):
entities = []
for label in list_train_data[i][1]['labels']:
tuple_ = (label[0], label[1], label[2])
entities.append(tuple_)
list_train_data[i][1]['labels'] = entities
return list_train_data
import pandas as pd
import json
import os
os.chdir('/kaggle/input/d/fatimahabib1/niort-sentences')
with open('annotations-niort-sentence-level.jsonl', 'r', encoding='utf-8') as f:
s = f.read()
data = json.loads(s)
train_data = data['annotations']
list_train_data = []
for i, ele in enumerate(train_data):
list_train_data.append((train_data[i]['text'], {'labels': train_data[i]['label']}))
training_list = trim_entity_spans(list_train_data)
ent_prob = []
ents = []
for i, ele in enumerate(training_list):
for j, elem in enumerate(training_list[i][1]['labels']):
start = elem[0]
end = elem[1]
ent = training_list[i][0][start:end]
num_spaces_beginning = len(ent) - len(ent.lstrip())
num_spaces_end = len(ent) - len(ent.rstrip())
ents.append((training_list[i][0], ent, start, end, num_spaces_beginning, num_spaces_end))
for i, ent in enumerate(ents):
if ent[1] != ent[1].strip():
ent_prob.append(ent)
nlp = fr_core_news_sm.load()
db = DocBin()
for text, annot in tqdm(training_list):
doc = nlp.make_doc(text)
ents = []
for start, end, label in annot['labels']:
span = doc.char_span(start, end, label=label, alignment_mode='contract')
ents.append(span)
pat_orig = len(ents)
filtered = filter_spans(ents)
pat_filt = len(filtered)
doc.ents = ents
doc.ents = ents
db.add(doc)
db.to_disk('/kaggle/working/train.spacy')
best_nlp = spacy.load('/kaggle/working/model-best')
colors = {'INFO': '#F67DE3', 'SUB': '#7DF6D9'}
options = {'colors': colors}
doc = best_nlp('pour les constructions de moins de 90 m² de surface de plancher : 30 m² de surface de plancher supplémentaire par rapport à la surface de plancher existante à la date d’approbation du PLU. ')
doc = best_nlp('La peinture sur les murs en pierre en taille ou en moellon est interdite. L’emploi à nu des matériaux destinés à être enduits est strictement interdit. ')
test_sent = 'Les règles applicables aux constructions non prévues ci-dessus sont celles auxquelles elles sont le plus directement assimilables. Lorsqu’un projet comporte plusieurs destinations, les places de stationnement se calculent au prorata de la surface de plancher de chaque destination de construction. En cas de changement de destination, il ne sera exigé que les places de stationnement correspondant au différentiel entre les deux destinations. L’ensemble des dispositions prévues dans cet article ne s’applique pas aux demandes d’extension ou de surélévation apportées aux immeubles de logements existants sans création de logement supplémentaire. Ces dispositions ne s’appliquent pas non plus dans le cas de création de surface de plancher liée à une annexe (habitation) sans création de logement supplémentaire. '
doc = best_nlp(test_sent)
spacy.displacy.render(doc, style='ent', options=options, jupyter=True) | code |
122256403/cell_31 | [
"text_plain_output_1.png"
] | !python -m spacy train /kaggle/working/config.cfg --output /kaggle/working/ --paths.train /kaggle/working/train.spacy --paths.dev /kaggle/working/train.spacy --gpu-id 0 | code |
122256403/cell_22 | [
"text_plain_output_1.png"
] | from spacy.tokens import DocBin
from spacy.util import filter_spans
from tqdm import tqdm
import fr_core_news_sm
import json
import os
import os
import pandas as pd
import pandas as pd
import re
import re
import re
def trim_entity_spans(data: list) -> list:
"""Removes leading and trailing white spaces from entity spans.
Args:
data (list): The data to be cleaned in spaCy JSON format.
Returns:
list: The cleaned data.
"""
invalid_span_tokens = re.compile('\\s')
cleaned_data = []
for text, annotations in data:
entities = annotations['labels']
valid_entities = []
for start, end, label in entities:
valid_start = start
valid_end = end
while valid_start < len(text) and invalid_span_tokens.match(text[valid_start]):
valid_start += 1
while valid_end > 1 and invalid_span_tokens.match(text[valid_end - 1]):
valid_end -= 1
valid_entities.append([valid_start, valid_end, label])
cleaned_data.append([text, {'labels': valid_entities}])
return cleaned_data
def starts_with_punctuation(s):
if re.match('^\\W', s):
return True
else:
return False
def ends_with_punctuation(s):
if re.search('\\W$', s):
return True
else:
return False
def dp(train_data):
list_train_data = []
for i, ele in enumerate(train_data):
list_train_data.append((train_data[i]['id'], train_data[i]['text'], {'labels': train_data[i]['label']}))
ent_prob = []
for i, ele in enumerate(list_train_data):
ents = []
for j, elem in enumerate(list_train_data[i][2]['labels']):
start = elem[0]
end = elem[1]
ent = list_train_data[i][1][start:end]
if starts_with_punctuation(ent) and ends_with_punctuation(ent):
ents.append((list_train_data[i][0], ent, start, end, 'YES', 'YES'))
elif starts_with_punctuation(ent) and (not ends_with_punctuation(ent)):
ents.append((list_train_data[i][0], ent, start, end, 'YES', 'NO'))
elif not starts_with_punctuation(ent) and ends_with_punctuation(ent):
ents.append((list_train_data[i][0], ent, start, end, 'NO', 'YES'))
else:
ents.append((list_train_data[i][0], ent, start, end, 'NO', 'NO'))
for i, ent in enumerate(ents):
if ent[4] == 'YES' or ent[5] == 'YES':
ent_prob.append(ent)
train_data_df = pd.DataFrame(train_data)
ent_prob_df = pd.DataFrame(ent_prob, columns=['id', 'text', 'start', 'end', 'punBeg', 'punEnd'])
merged = pd.merge(train_data_df, ent_prob_df, on='id')
common_index = merged.index
train_data_df = train_data_df.drop(index=common_index)
for index, row in merged.iterrows():
if row['punBeg'] == 'YES':
for item in row['label']:
if int(item[0]) == int(row['start']):
item[0] = item[0] + 1
if row['punEnd'] == 'YES':
for item in row['label']:
if int(item[1]) == int(row['end']):
item[1] = item[1] - 1
for index, row in merged.iterrows():
new_row = {'id': row['id'], 'text': row['text_x'], 'label': row['label'], 'Comments': row['Comments']}
train_data_df = train_data_df.append(new_row, ignore_index=True)
dict_list = train_data_df.to_dict(orient='records')
list_train_data = []
for i, ele in enumerate(dict_list):
list_train_data.append((dict_list[i]['text'], {'labels': dict_list[i]['label']}))
list_train_data = []
for i, ele in enumerate(dict_list):
list_train_data.append((dict_list[i]['text'], {'labels': dict_list[i]['label']}))
for i, ele in enumerate(list_train_data):
entities = []
for label in list_train_data[i][1]['labels']:
tuple_ = (label[0], label[1], label[2])
entities.append(tuple_)
list_train_data[i][1]['labels'] = entities
return list_train_data
import pandas as pd
import json
import os
os.chdir('/kaggle/input/d/fatimahabib1/niort-sentences')
with open('annotations-niort-sentence-level.jsonl', 'r', encoding='utf-8') as f:
s = f.read()
data = json.loads(s)
train_data = data['annotations']
list_train_data = []
for i, ele in enumerate(train_data):
list_train_data.append((train_data[i]['text'], {'labels': train_data[i]['label']}))
training_list = trim_entity_spans(list_train_data)
ent_prob = []
ents = []
for i, ele in enumerate(training_list):
for j, elem in enumerate(training_list[i][1]['labels']):
start = elem[0]
end = elem[1]
ent = training_list[i][0][start:end]
num_spaces_beginning = len(ent) - len(ent.lstrip())
num_spaces_end = len(ent) - len(ent.rstrip())
ents.append((training_list[i][0], ent, start, end, num_spaces_beginning, num_spaces_end))
for i, ent in enumerate(ents):
if ent[1] != ent[1].strip():
ent_prob.append(ent)
nlp = fr_core_news_sm.load()
db = DocBin()
for text, annot in tqdm(training_list):
doc = nlp.make_doc(text)
ents = []
for start, end, label in annot['labels']:
span = doc.char_span(start, end, label=label, alignment_mode='contract')
if span is None:
print('Skipping entity')
else:
ents.append(span)
pat_orig = len(ents)
filtered = filter_spans(ents)
pat_filt = len(filtered)
doc.ents = ents
doc.ents = ents
db.add(doc)
db.to_disk('/kaggle/working/train.spacy') | code |
122256403/cell_27 | [
"text_plain_output_1.png"
] | !python -m spacy init fill-config /kaggle/input/configs/base_config.cfg /kaggle/working/config.cfg | code |
106198731/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158386.csv')
Traffic415 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158415.csv')
Traffic446 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158446.csv')
Traffic475 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158475.csv')
Traffic505 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158505.csv')
Traffic536 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158536.csv')
Traffic565 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158565.csv')
Traffic595 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158595.csv')
Traffic565.sort_values('avgMeasuredTime', ascending=True) | code |
106198731/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158386.csv')
Traffic415 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158415.csv')
Traffic446 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158446.csv')
Traffic475 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158475.csv')
Traffic505 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158505.csv')
Traffic536 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158536.csv')
Traffic565 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158565.csv')
Traffic595 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158595.csv')
Traffic446.sort_values('avgMeasuredTime', ascending=True) | code |
106198731/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158386.csv')
Traffic415 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158415.csv')
Traffic446 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158446.csv')
Traffic475 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158475.csv')
Traffic505 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158505.csv')
Traffic536 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158536.csv')
Traffic565 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158565.csv')
Traffic595 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158595.csv')
Traffic324.dtypes | code |
106198731/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158386.csv')
Traffic415 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158415.csv')
Traffic446 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158446.csv')
Traffic475 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158475.csv')
Traffic505 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158505.csv')
Traffic536 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158536.csv')
Traffic565 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158565.csv')
Traffic595 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158595.csv')
Traffic355.sort_values('avgMeasuredTime', ascending=True) | code |
106198731/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158386.csv')
Traffic415 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158415.csv')
Traffic446 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158446.csv')
Traffic475 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158475.csv')
Traffic505 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158505.csv')
Traffic536 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158536.csv')
Traffic565 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158565.csv')
Traffic595 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158595.csv')
Traffic505.sort_values('avgMeasuredTime', ascending=True) | code |
106198731/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106198731/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158386.csv')
Traffic415 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158415.csv')
Traffic446 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158446.csv')
Traffic475 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158475.csv')
Traffic505 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158505.csv')
Traffic536 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158536.csv')
Traffic565 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158565.csv')
Traffic595 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158595.csv')
Traffic386.sort_values('avgMeasuredTime', ascending=True) | code |
106198731/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158386.csv')
Traffic415 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158415.csv')
Traffic446 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158446.csv')
Traffic475 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158475.csv')
Traffic505 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158505.csv')
Traffic536 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158536.csv')
Traffic565 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158565.csv')
Traffic595 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158595.csv')
Traffic415.sort_values('avgMeasuredTime', ascending=True) | code |
106198731/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158386.csv')
Traffic415 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158415.csv')
Traffic446 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158446.csv')
Traffic475 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158475.csv')
Traffic505 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158505.csv')
Traffic536 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158536.csv')
Traffic565 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158565.csv')
Traffic595 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158595.csv')
Traffic595.sort_values('avgMeasuredTime', ascending=True) | code |
106198731/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158386.csv')
Traffic415 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158415.csv')
Traffic446 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158446.csv')
Traffic475 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158475.csv')
Traffic505 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158505.csv')
Traffic536 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158536.csv')
Traffic565 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158565.csv')
Traffic595 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158595.csv')
Traffic475.sort_values('avgMeasuredTime', ascending=True) | code |
106198731/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158386.csv')
Traffic415 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158415.csv')
Traffic446 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158446.csv')
Traffic475 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158475.csv')
Traffic505 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158505.csv')
Traffic536 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158536.csv')
Traffic565 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158565.csv')
Traffic595 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158595.csv')
Traffic536.sort_values('avgMeasuredTime', ascending=True) | code |
106198731/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
Traffic324 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158324.csv')
Traffic355 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158355.csv')
Traffic386 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158386.csv')
Traffic415 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158415.csv')
Traffic446 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158446.csv')
Traffic475 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158475.csv')
Traffic505 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158505.csv')
Traffic536 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158536.csv')
Traffic565 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158565.csv')
Traffic595 = pd.read_csv('../input/smart-city-traffic-dataset/trafficData158595.csv')
Traffic324.dtypes
Traffic324.sort_values('avgMeasuredTime', ascending=True) | code |
33105040/cell_18 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from fbprophet import Prophet
from pmdarima import auto_arima
from statsmodels.tsa.arima_model import ARIMA
import datetime
import pandas as pd
import pandas as pd
import plotly.graph_objects as go
covid_data = pd.read_excel('/kaggle/input/corona-virus-pakistan-dataset-2020/COVID_FINAL_DATA.xlsx')
covid_data.isnull().sum()
covid_data.dtypes
covid_data['Date'] = pd.to_datetime(covid_data['Date'])
pak_data = covid_data.copy()
deaths = pak_data['Expired'].values.tolist()
data = pd.DataFrame(columns = ['ds','y'])
data['ds'] = list(pak_data['Date'].unique())
data['y'] = deaths
prop=Prophet()
prop.fit(data)
future=prop.make_future_dataframe(periods=15)
prop_forecast=prop.predict(future)
forecast = prop_forecast[['ds','yhat']].tail(15)
fig = go.Figure()
fig.add_trace(go.Scatter(x=pak_data['Date'], y=pak_data['Expired'],
mode='lines+markers',marker_color='green',name='Actual'))
fig.add_trace(go.Scatter(x=prop_forecast['ds'], y=prop_forecast['yhat_upper'],
mode='lines+markers',marker_color='red',name='Predicted'))
fig.update_layout(title_text = 'Death Cases (Predicted vs Actual) using Prophet')
fig.update_layout(plot_bgcolor='rgb(275, 270, 273)',width=1000, height=600)
fig.show()
recv = pak_data['Total Recovered'].values.tolist()
data = pd.DataFrame(columns = ['ds','y'])
data['ds'] = list(pak_data['Date'])
data['y'] = recv
prop=Prophet()
prop.fit(data)
future=prop.make_future_dataframe(periods=15)
prop_forecast=prop.predict(future)
forecast = prop_forecast[['ds','yhat']].tail(15)
print(forecast)
#fig = plot_plotly(prop, prop_forecast)
#fig = prop.plot(prop_forecast,xlabel='Date',ylabel='Confirmed Cases')
fig = go.Figure()
fig.add_trace(go.Scatter(x=pak_data['Date'], y=pak_data['Total Recovered'],
mode='lines+markers',marker_color='green',name='Actual'))
fig.add_trace(go.Scatter(x=prop_forecast['ds'], y=prop_forecast['yhat_upper'],
mode='lines+markers',marker_color='yellow',name='Predicted'))
fig.update_layout(title_text = 'Recovered Cases (Predicted vs Actual) using Prophet')
fig.update_layout(plot_bgcolor='rgb(275, 270, 273)',width=600, height=600)
fig.show()
cc = pak_data['Total Confirmed Cases'].values
p, d, q = auto_arima(cc).order
print(p, d, q)
model = ARIMA(pak_data['Total Confirmed Cases'], order=(p, d, q))
arima = model.fit(disp=True)
forecast = arima.forecast(steps=15)
pred = list(forecast[0])
print(pred)
start_date = pak_data['Date'].max()
prediction_dates = []
for i in range(15):
date = start_date + datetime.timedelta(days=1)
prediction_dates.append(date)
start_date = date
fig = go.Figure()
fig.add_trace(go.Scatter(x=pak_data['Date'], y=pak_data['Total Confirmed Cases'], mode='lines+markers', marker_color='green', name='Actual'))
fig.add_trace(go.Scatter(x=prediction_dates, y=pred, mode='lines+markers', marker_color='Orange', name='Predicted'))
fig.update_layout(title_text='Confirmed cases Predicted vs Actual using ARIMA')
fig.update_layout(plot_bgcolor='rgb(275, 270, 273)', width=600, height=600)
fig.show() | code |
33105040/cell_15 | [
"text_html_output_1.png"
] | from fbprophet import Prophet
import pandas as pd
import pandas as pd
import plotly.graph_objects as go
covid_data = pd.read_excel('/kaggle/input/corona-virus-pakistan-dataset-2020/COVID_FINAL_DATA.xlsx')
covid_data.isnull().sum()
covid_data.dtypes
covid_data['Date'] = pd.to_datetime(covid_data['Date'])
pak_data = covid_data.copy()
deaths = pak_data['Expired'].values.tolist()
data = pd.DataFrame(columns = ['ds','y'])
data['ds'] = list(pak_data['Date'].unique())
data['y'] = deaths
prop=Prophet()
prop.fit(data)
future=prop.make_future_dataframe(periods=15)
prop_forecast=prop.predict(future)
forecast = prop_forecast[['ds','yhat']].tail(15)
fig = go.Figure()
fig.add_trace(go.Scatter(x=pak_data['Date'], y=pak_data['Expired'],
mode='lines+markers',marker_color='green',name='Actual'))
fig.add_trace(go.Scatter(x=prop_forecast['ds'], y=prop_forecast['yhat_upper'],
mode='lines+markers',marker_color='red',name='Predicted'))
fig.update_layout(title_text = 'Death Cases (Predicted vs Actual) using Prophet')
fig.update_layout(plot_bgcolor='rgb(275, 270, 273)',width=1000, height=600)
fig.show()
data | code |
33105040/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from fbprophet import Prophet
import pandas as pd
import pandas as pd
import plotly.graph_objects as go
covid_data = pd.read_excel('/kaggle/input/corona-virus-pakistan-dataset-2020/COVID_FINAL_DATA.xlsx')
covid_data.isnull().sum()
covid_data.dtypes
covid_data['Date'] = pd.to_datetime(covid_data['Date'])
pak_data = covid_data.copy()
deaths = pak_data['Expired'].values.tolist()
data = pd.DataFrame(columns=['ds', 'y'])
data['ds'] = list(pak_data['Date'].unique())
data['y'] = deaths
prop = Prophet()
prop.fit(data)
future = prop.make_future_dataframe(periods=15)
prop_forecast = prop.predict(future)
forecast = prop_forecast[['ds', 'yhat']].tail(15)
fig = go.Figure()
fig.add_trace(go.Scatter(x=pak_data['Date'], y=pak_data['Expired'], mode='lines+markers', marker_color='green', name='Actual'))
fig.add_trace(go.Scatter(x=prop_forecast['ds'], y=prop_forecast['yhat_upper'], mode='lines+markers', marker_color='red', name='Predicted'))
fig.update_layout(title_text='Death Cases (Predicted vs Actual) using Prophet')
fig.update_layout(plot_bgcolor='rgb(275, 270, 273)', width=1000, height=600)
fig.show() | code |
33105040/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
covid_data = pd.read_excel('/kaggle/input/corona-virus-pakistan-dataset-2020/COVID_FINAL_DATA.xlsx')
covid_data.isnull().sum()
covid_data.dtypes
pak_data = covid_data.copy()
pak_data.head() | code |
50242450/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.despine(left=True, right=True, bottom=True, top=True)
sns.set_style('white')
df = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv', engine='python', error_bad_lines=False)
colunas = ['Q1', 'Q2', 'Q4', 'Q5', 'Q6', 'Q8', 'Q11', 'Q13', 'Q15', 'Q20', 'Q21', 'Q24', 'Q25', 'Q30', 'Q32', 'Q38']
for i in colunas:
fig, ax = plt.subplots(1,1, figsize=(15, 6))
sns.countplot(y = df[i][1:],data=df.iloc[1:], order=df[i][1:].value_counts().index, palette='Blues_r')
fig.text(0.1, 0.95, f'{df[i][0].split("(")[0]}', fontsize=16, fontweight='bold', fontfamily='serif')
plt.xlabel(' ', fontsize=20)
plt.ylabel('')
plt.yticks(fontsize=13)
plt.box(False)
colunas = ['Q7', 'Q10', 'Q12', 'Q14', 'Q16', 'Q17', 'Q18', 'Q19', 'Q23', 'Q26', 'Q27', 'Q28', 'Q29', 'Q31', 'Q33', 'Q34', 'Q35', 'Q36', 'Q37']
for j in colunas:
df_q = df[[i for i in df.columns if j in i]]
df_q_count = pd.Series(dtype='int')
for i in df_q:
df_q_count[df_q[i].value_counts().index[0]] = df_q[i].count()
ax, fig = plt.subplots(1, 1, figsize=(15, 6))
sns.barplot(y=df_q_count.sort_values()[::-1].index, x=df_q_count.sort_values()[::-1], palette='Blues_r')
fig.text(0, -1, f"\n\n{df[i][0].split('(')[0]}\n", fontsize=20, fontweight='bold', fontfamily='monospace')
plt.box(False)
plt.xlabel('')
plt.ylabel('')
plt.yticks(fontsize=20) | code |
50242450/cell_9 | [
"image_output_11.png",
"image_output_17.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_18.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.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_15.png",
"image_output_9.png",
"image_output_19.png"
] | import pandas as pd
df = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv', engine='python', error_bad_lines=False)
df.head() | code |
50242450/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.despine(left=True, right=True, bottom=True, top=True)
sns.set_style('white')
df = pd.read_csv('../input/kaggle-survey-2020/kaggle_survey_2020_responses.csv', engine='python', error_bad_lines=False)
colunas = ['Q1', 'Q2', 'Q4', 'Q5', 'Q6', 'Q8', 'Q11', 'Q13', 'Q15', 'Q20', 'Q21', 'Q24', 'Q25', 'Q30', 'Q32', 'Q38']
for i in colunas:
fig, ax = plt.subplots(1, 1, figsize=(15, 6))
sns.countplot(y=df[i][1:], data=df.iloc[1:], order=df[i][1:].value_counts().index, palette='Blues_r')
fig.text(0.1, 0.95, f"{df[i][0].split('(')[0]}", fontsize=16, fontweight='bold', fontfamily='serif')
plt.xlabel(' ', fontsize=20)
plt.ylabel('')
plt.yticks(fontsize=13)
plt.box(False) | code |
50242450/cell_7 | [
"image_output_11.png",
"image_output_14.png",
"image_output_13.png",
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_8.png",
"image_output_16.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_15.png",
"image_output_9.png"
] | import seaborn as sns
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
sns.despine(left=True, right=True, bottom=True, top=True)
sns.set_style('white') | code |
106198134/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | glimpse(dailyActivity) | code |
106198134/cell_9 | [
"text_html_output_1.png"
] | colnames(dailyActivity) | code |
106198134/cell_11 | [
"text_html_output_4.png",
"text_html_output_6.png",
"text_html_output_2.png",
"text_html_output_5.png",
"text_html_output_1.png",
"text_html_output_3.png"
] | head(dailyActivity) | code |
106198134/cell_15 | [
"text_html_output_1.png"
] | skim_without_charts(dailyActivity) | code |
106198134/cell_3 | [
"text_plain_output_1.png"
] | installed.packages('tidyverse')
installed.packages('readr')
installed.packages('here')
installed.packages('skimr')
installed.packages('dplyr')
installed.packages('janitor') | code |
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