path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
73069645/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum() | code |
73069645/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique())
data.room_type.unique()
#we will skip first column for now and begin from host_id
#let's see what hosts (IDs) have the most listings on Airbnb platform and taking advantage of this service
top_host=data.host_id.value_counts().head(10)
top_host
top_host_check = data.calculated_host_listings_count.max()
top_host_check | code |
73069645/cell_32 | [
"text_plain_output_1.png"
] | import imageio
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique())
data.room_type.unique()
#we will skip first column for now and begin from host_id
#let's see what hosts (IDs) have the most listings on Airbnb platform and taking advantage of this service
top_host=data.host_id.value_counts().head(10)
top_host
top_host_check = data.calculated_host_listings_count.max()
top_host_check
sns.set(rc={'figure.figsize': (10, 8)})
sns.set_style('white')
top_host_df = pd.DataFrame(top_host)
top_host_df.reset_index(inplace=True)
top_host_df.rename(columns={'index': 'Host_ID', 'host_id': 'P_Count'}, inplace=True)
top_host_df
viz_1=sns.barplot(x="Host_ID", y="P_Count", data=top_host_df,
palette='Blues_d')
viz_1.set_title('Hosts with the most listings in NYC')
viz_1.set_ylabel('Count of listings')
viz_1.set_xlabel('Host IDs')
viz_1.set_xticklabels(viz_1.get_xticklabels(), rotation=45)
sub_1 = data.loc[data['neighbourhood_group'] == 'Brooklyn']
price_sub1 = sub_1[['price']]
sub_2 = data.loc[data['neighbourhood_group'] == 'Manhattan']
price_sub2 = sub_2[['price']]
sub_3 = data.loc[data['neighbourhood_group'] == 'Queens']
price_sub3 = sub_3[['price']]
sub_4 = data.loc[data['neighbourhood_group'] == 'Staten Island']
price_sub4 = sub_4[['price']]
sub_5 = data.loc[data['neighbourhood_group'] == 'Bronx']
price_sub5 = sub_5[['price']]
price_list_by_n = [price_sub1, price_sub2, price_sub3, price_sub4, price_sub5]
#we can see from our statistical table that we have some extreme values, therefore we need to remove them for the sake of a better visualization
#creating a sub-dataframe with no extreme values / less than 500
sub_6=data[data.price < 500]
#using violinplot to showcase density and distribtuion of prices
viz_2=sns.violinplot(data=sub_6, x='neighbourhood_group', y='price')
viz_2.set_title('Density and distribution of prices for each neighberhood_group')
#let's what we can do with our given longtitude and latitude columns
#let's see how scatterplot will come out
viz_4=sub_6.plot(kind='scatter', x='longitude', y='latitude', label='availability_365', c='price',
cmap=plt.get_cmap('jet'), colorbar=True, alpha=0.4, figsize=(10,8))
viz_4.legend()
import urllib
plt.figure(figsize=(10, 8))
import imageio
nyc_img = imageio.imread('https://upload.wikimedia.org/wikipedia/commons/e/ec/Neighbourhoods_New_York_City_Map.PNG')
plt.imshow(nyc_img, zorder=0, extent=[-74.258, -73.7, 40.49, 40.92])
ax = plt.gca()
sub_6.plot(kind='scatter', x='longitude', y='latitude', label='availability_365', c='price', ax=ax, cmap=plt.get_cmap('jet'), colorbar=True, alpha=0.4, zorder=5)
plt.legend()
plt.show() | code |
73069645/cell_28 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique())
data.room_type.unique()
#we will skip first column for now and begin from host_id
#let's see what hosts (IDs) have the most listings on Airbnb platform and taking advantage of this service
top_host=data.host_id.value_counts().head(10)
top_host
top_host_check = data.calculated_host_listings_count.max()
top_host_check
sns.set(rc={'figure.figsize': (10, 8)})
sns.set_style('white')
top_host_df = pd.DataFrame(top_host)
top_host_df.reset_index(inplace=True)
top_host_df.rename(columns={'index': 'Host_ID', 'host_id': 'P_Count'}, inplace=True)
top_host_df
viz_1=sns.barplot(x="Host_ID", y="P_Count", data=top_host_df,
palette='Blues_d')
viz_1.set_title('Hosts with the most listings in NYC')
viz_1.set_ylabel('Count of listings')
viz_1.set_xlabel('Host IDs')
viz_1.set_xticklabels(viz_1.get_xticklabels(), rotation=45)
sub_1 = data.loc[data['neighbourhood_group'] == 'Brooklyn']
price_sub1 = sub_1[['price']]
sub_2 = data.loc[data['neighbourhood_group'] == 'Manhattan']
price_sub2 = sub_2[['price']]
sub_3 = data.loc[data['neighbourhood_group'] == 'Queens']
price_sub3 = sub_3[['price']]
sub_4 = data.loc[data['neighbourhood_group'] == 'Staten Island']
price_sub4 = sub_4[['price']]
sub_5 = data.loc[data['neighbourhood_group'] == 'Bronx']
price_sub5 = sub_5[['price']]
price_list_by_n = [price_sub1, price_sub2, price_sub3, price_sub4, price_sub5]
#we can see from our statistical table that we have some extreme values, therefore we need to remove them for the sake of a better visualization
#creating a sub-dataframe with no extreme values / less than 500
sub_6=data[data.price < 500]
#using violinplot to showcase density and distribtuion of prices
viz_2=sns.violinplot(data=sub_6, x='neighbourhood_group', y='price')
viz_2.set_title('Density and distribution of prices for each neighberhood_group')
sub_7 = data.loc[data['neighbourhood'].isin(['Williamsburg', 'Bedford-Stuyvesant', 'Harlem', 'Bushwick', 'Upper West Side', "Hell's Kitchen", 'East Village', 'Upper East Side', 'Crown Heights', 'Midtown'])]
viz_3 = sns.catplot(x='neighbourhood', hue='neighbourhood_group', col='room_type', data=sub_7, kind='count')
viz_3.set_xticklabels(rotation=90) | code |
73069645/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum() | code |
73069645/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique())
data.room_type.unique() | code |
73069645/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique())
data.room_type.unique()
top_host = data.host_id.value_counts().head(10)
top_host | code |
73069645/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique())
data.room_type.unique()
#we will skip first column for now and begin from host_id
#let's see what hosts (IDs) have the most listings on Airbnb platform and taking advantage of this service
top_host=data.host_id.value_counts().head(10)
top_host
top_host_check = data.calculated_host_listings_count.max()
top_host_check
sub_1 = data.loc[data['neighbourhood_group'] == 'Brooklyn']
price_sub1 = sub_1[['price']]
sub_2 = data.loc[data['neighbourhood_group'] == 'Manhattan']
price_sub2 = sub_2[['price']]
sub_3 = data.loc[data['neighbourhood_group'] == 'Queens']
price_sub3 = sub_3[['price']]
sub_4 = data.loc[data['neighbourhood_group'] == 'Staten Island']
price_sub4 = sub_4[['price']]
sub_5 = data.loc[data['neighbourhood_group'] == 'Bronx']
price_sub5 = sub_5[['price']]
price_list_by_n = [price_sub1, price_sub2, price_sub3, price_sub4, price_sub5]
p_l_b_n_2 = []
nei_list = ['Brooklyn', 'Manhattan', 'Queens', 'Staten Island', 'Bronx']
for x in price_list_by_n:
i = x.describe(percentiles=[0.25, 0.5, 0.75])
i = i.iloc[3:]
i.reset_index(inplace=True)
i.rename(columns={'index': 'Stats'}, inplace=True)
p_l_b_n_2.append(i)
p_l_b_n_2[0].rename(columns={'price': nei_list[0]}, inplace=True)
p_l_b_n_2[1].rename(columns={'price': nei_list[1]}, inplace=True)
p_l_b_n_2[2].rename(columns={'price': nei_list[2]}, inplace=True)
p_l_b_n_2[3].rename(columns={'price': nei_list[3]}, inplace=True)
p_l_b_n_2[4].rename(columns={'price': nei_list[4]}, inplace=True)
stat_df = p_l_b_n_2
stat_df = [df.set_index('Stats') for df in stat_df]
stat_df = stat_df[0].join(stat_df[1:])
stat_df | code |
73069645/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique()) | code |
73069645/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.head(3) | code |
73069645/cell_27 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.shape
data.isnull().sum()
data.drop(['id', 'host_name', 'last_review'], axis=1, inplace=True)
data.fillna({'reviews_per_month': 0}, inplace=True)
data.reviews_per_month.isnull().sum()
data.neighbourhood_group.unique()
len(data.neighbourhood.unique())
data.room_type.unique()
#we will skip first column for now and begin from host_id
#let's see what hosts (IDs) have the most listings on Airbnb platform and taking advantage of this service
top_host=data.host_id.value_counts().head(10)
top_host
top_host_check = data.calculated_host_listings_count.max()
top_host_check
sns.set(rc={'figure.figsize': (10, 8)})
sns.set_style('white')
top_host_df = pd.DataFrame(top_host)
top_host_df.reset_index(inplace=True)
top_host_df.rename(columns={'index': 'Host_ID', 'host_id': 'P_Count'}, inplace=True)
top_host_df
viz_1=sns.barplot(x="Host_ID", y="P_Count", data=top_host_df,
palette='Blues_d')
viz_1.set_title('Hosts with the most listings in NYC')
viz_1.set_ylabel('Count of listings')
viz_1.set_xlabel('Host IDs')
viz_1.set_xticklabels(viz_1.get_xticklabels(), rotation=45)
sub_1 = data.loc[data['neighbourhood_group'] == 'Brooklyn']
price_sub1 = sub_1[['price']]
sub_2 = data.loc[data['neighbourhood_group'] == 'Manhattan']
price_sub2 = sub_2[['price']]
sub_3 = data.loc[data['neighbourhood_group'] == 'Queens']
price_sub3 = sub_3[['price']]
sub_4 = data.loc[data['neighbourhood_group'] == 'Staten Island']
price_sub4 = sub_4[['price']]
sub_5 = data.loc[data['neighbourhood_group'] == 'Bronx']
price_sub5 = sub_5[['price']]
price_list_by_n = [price_sub1, price_sub2, price_sub3, price_sub4, price_sub5]
#we can see from our statistical table that we have some extreme values, therefore we need to remove them for the sake of a better visualization
#creating a sub-dataframe with no extreme values / less than 500
sub_6=data[data.price < 500]
#using violinplot to showcase density and distribtuion of prices
viz_2=sns.violinplot(data=sub_6, x='neighbourhood_group', y='price')
viz_2.set_title('Density and distribution of prices for each neighberhood_group')
data.neighbourhood.value_counts().head(10) | code |
73069645/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/airbnbnewyork/listings.csv')
data.head() | code |
90147386/cell_21 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
comments = analysis_df.text
def removeURL(comments):
url_pattern = re.compile('https?://\\S+|www\\.\\S+')
comments = url_pattern.sub('', comments)
return comments
comments = comments.apply(lambda x: removeURL(x))
def removePunctuation(comments):
punctuationFree = ''.join([i for i in comments if i not in string.punctuation])
return punctuationFree
comments = comments.apply(lambda x: removePunctuation(x))
comments = comments.str.lower()
print('Hasil Case Folding : \n')
print(comments)
print('\n\n\n') | code |
90147386/cell_13 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | from PIL import Image
from wordcloud import WordCloud,STOPWORDS,ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from PIL import Image
def create_wordcloud(text, path):
stopwords = set(STOPWORDS)
wc = WordCloud(background_color='white', max_words=3000, stopwords=stopwords, random_state=42, width=900, height=500, repeat=True)
wc.generate(str(text))
wc.to_file(path)
print('Word Cloud Saved Successfully')
path = path
display(Image.open(path))
plt.figure(figsize=(16, 5), dpi=80)
create_wordcloud(analysis_df['text'].values, 'all.png') | code |
90147386/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
plt.figure(figsize=(8, 5))
comment_len.plot(kind='box')
plt.ylabel('Comment Length') | code |
90147386/cell_25 | [
"text_plain_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
comments = analysis_df.text
def removeURL(comments):
url_pattern = re.compile('https?://\\S+|www\\.\\S+')
comments = url_pattern.sub('', comments)
return comments
comments = comments.apply(lambda x: removeURL(x))
def removePunctuation(comments):
punctuationFree = ''.join([i for i in comments if i not in string.punctuation])
return punctuationFree
comments = comments.apply(lambda x: removePunctuation(x))
comments = comments.str.lower()
tokenizer = Tokenizer()
tokenizer.fit_on_texts(comments)
encoded_comments = tokenizer.texts_to_sequences(comments)
print(encoded_comments) | code |
90147386/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts() | code |
90147386/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
comments = analysis_df.text
def removeURL(comments):
url_pattern = re.compile('https?://\\S+|www\\.\\S+')
comments = url_pattern.sub('', comments)
return comments
comments = comments.apply(lambda x: removeURL(x))
def removePunctuation(comments):
punctuationFree = ''.join([i for i in comments if i not in string.punctuation])
return punctuationFree
comments = comments.apply(lambda x: removePunctuation(x))
y = df['label']
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y)
y = to_categorical(y)
y | code |
90147386/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
print(analysis_df.shape)
analysis_df.head(5) | code |
90147386/cell_29 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
from tensorflow.keras.preprocessing.sequence import pad_sequences
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.utils import to_categorical
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
comments = analysis_df.text
def removeURL(comments):
url_pattern = re.compile('https?://\\S+|www\\.\\S+')
comments = url_pattern.sub('', comments)
return comments
comments = comments.apply(lambda x: removeURL(x))
def removePunctuation(comments):
punctuationFree = ''.join([i for i in comments if i not in string.punctuation])
return punctuationFree
comments = comments.apply(lambda x: removePunctuation(x))
y = df['label']
label_encoder = LabelEncoder()
y = label_encoder.fit_transform(y)
y = to_categorical(y)
y
comments = comments.str.lower()
tokenizer = Tokenizer()
tokenizer.fit_on_texts(comments)
encoded_comments = tokenizer.texts_to_sequences(comments)
padded_sequence = pad_sequences(encoded_comments, maxlen=200, padding='post')
X = padded_sequence
print('Shape of X is ', X.shape)
print('Shape of y is', y.shape) | code |
90147386/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
plt.figure(figsize=(10, 5))
sns.histplot(df[df['label'] == 'Non_CB']['Length'], color='g')
plt.title('Distribution of Tweet Length for not_cyberbullying')
display(df.Length[df['label'] == 'Non_CB'].describe()) | code |
90147386/cell_19 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
comments = analysis_df.text
def removeURL(comments):
url_pattern = re.compile('https?://\\S+|www\\.\\S+')
comments = url_pattern.sub('', comments)
return comments
comments = comments.apply(lambda x: removeURL(x))
analysis_df.columns
analysis_df['label'].value_counts() | code |
90147386/cell_18 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
comments = analysis_df.text
def removeURL(comments):
url_pattern = re.compile('https?://\\S+|www\\.\\S+')
comments = url_pattern.sub('', comments)
return comments
comments = comments.apply(lambda x: removeURL(x))
analysis_df.columns | code |
90147386/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
plt.figure(figsize=(10, 10))
sns.countplot(df.label, palette='mako') | code |
90147386/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from wordcloud import WordCloud,STOPWORDS,ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
analysis_df_negative = analysis_df[analysis_df['label'] == 'CB']
analysis_df_positive = analysis_df[analysis_df['label'] == 'Non_CB']
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from PIL import Image
def create_wordcloud(text, path):
stopwords = set(STOPWORDS)
wc = WordCloud(background_color='white', max_words=3000, stopwords=stopwords, random_state=42, width=900, height=500, repeat=True)
wc.generate(str(text))
wc.to_file(path)
path = path
create_wordcloud(analysis_df['text'].values, 'all.png')
create_wordcloud(analysis_df_negative['text'].values, 'negative.png')
plt.figure(figsize=(15, 8), dpi=80)
create_wordcloud(analysis_df_positive['text'].values, 'positive.png') | code |
90147386/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
comments = analysis_df.text
def removeURL(comments):
url_pattern = re.compile('https?://\\S+|www\\.\\S+')
comments = url_pattern.sub('', comments)
return comments
comments = comments.apply(lambda x: removeURL(x))
df.head(20) | code |
90147386/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df | code |
90147386/cell_17 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
comments = analysis_df.text
def removeURL(comments):
url_pattern = re.compile('https?://\\S+|www\\.\\S+')
comments = url_pattern.sub('', comments)
return comments
comments = comments.apply(lambda x: removeURL(x))
def removePunctuation(comments):
punctuationFree = ''.join([i for i in comments if i not in string.punctuation])
return punctuationFree
comments = comments.apply(lambda x: removePunctuation(x))
df.head(20) | code |
90147386/cell_24 | [
"text_html_output_1.png"
] | from tensorflow.keras.preprocessing.text import Tokenizer
import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
comments = analysis_df.text
def removeURL(comments):
url_pattern = re.compile('https?://\\S+|www\\.\\S+')
comments = url_pattern.sub('', comments)
return comments
comments = comments.apply(lambda x: removeURL(x))
def removePunctuation(comments):
punctuationFree = ''.join([i for i in comments if i not in string.punctuation])
return punctuationFree
comments = comments.apply(lambda x: removePunctuation(x))
comments = comments.str.lower()
tokenizer = Tokenizer()
tokenizer.fit_on_texts(comments)
print(tokenizer.index_word) | code |
90147386/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from PIL import Image
from wordcloud import WordCloud,STOPWORDS,ImageColorGenerator
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
analysis_df_negative = analysis_df[analysis_df['label'] == 'CB']
analysis_df_positive = analysis_df[analysis_df['label'] == 'Non_CB']
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
from wordcloud import WordCloud, STOPWORDS, ImageColorGenerator
from PIL import Image
def create_wordcloud(text, path):
stopwords = set(STOPWORDS)
wc = WordCloud(background_color='white', max_words=3000, stopwords=stopwords, random_state=42, width=900, height=500, repeat=True)
wc.generate(str(text))
wc.to_file(path)
path = path
create_wordcloud(analysis_df['text'].values, 'all.png')
plt.figure(figsize=(15, 8), dpi=80)
create_wordcloud(analysis_df_negative['text'].values, 'negative.png') | code |
90147386/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import re
import seaborn as sns
import string
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
analysis_df = df[['text', 'label']]
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
comments = analysis_df.text
def removeURL(comments):
url_pattern = re.compile('https?://\\S+|www\\.\\S+')
comments = url_pattern.sub('', comments)
return comments
comments = comments.apply(lambda x: removeURL(x))
def removePunctuation(comments):
punctuationFree = ''.join([i for i in comments if i not in string.punctuation])
return punctuationFree
comments = comments.apply(lambda x: removePunctuation(x))
df['label'].value_counts() | code |
90147386/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
plt.figure(figsize=(10, 5))
sns.histplot(comment_len, palette='deep') | code |
90147386/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
comment_len = pd.Series([len(comment.split()) for comment in df['text']])
df['Length'] = df.text.str.split().apply(len)
df['Length'] = df.text.str.split().apply(len)
plt.figure(figsize=(10, 5))
sns.histplot(df[df['label'] == 'CB']['Length'], color='r')
plt.title('Distribution of Tweet Length for not_cyberbullying')
display(df.Length[df['label'] == 'Non_CB'].describe()) | code |
90147386/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/idcyberbullying/id-cyberbullying-instagram.tsv', sep='\t', encoding='ISO-8859-1')
df
df.label.value_counts()
df.info() | code |
74067974/cell_21 | [
"text_plain_output_1.png"
] | from prophet import Prophet
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_halving = pd.DataFrame({'holiday': 'Bitcoin Halving', 'ds': pd.to_datetime(['2012-11-28', '2016-07-09', '2020-05-11']), 'lower_window': 0, 'upper_window': 1})
holidays = bitcoin_halving
model = Prophet(yearly_seasonality=True, daily_seasonality=True, holidays=holidays)
model.add_country_holidays(country_name='US')
model.fit(data)
future = model.make_future_dataframe(periods=365)
predict = model.predict(future)
model.train_holiday_names | code |
74067974/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.info() | code |
74067974/cell_25 | [
"text_plain_output_1.png"
] | from prophet import Prophet
from prophet.plot import plot_yearly
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_halving = pd.DataFrame({'holiday': 'Bitcoin Halving', 'ds': pd.to_datetime(['2012-11-28', '2016-07-09', '2020-05-11']), 'lower_window': 0, 'upper_window': 1})
holidays = bitcoin_halving
model = Prophet(yearly_seasonality=True, daily_seasonality=True, holidays=holidays)
model.add_country_holidays(country_name='US')
model.fit(data)
future = model.make_future_dataframe(periods=365)
predict = model.predict(future)
model.train_holiday_names
fig = model.plot(predict)
fig = model.plot_components(predict)
from prophet.plot import plot_yearly
fig = plot_yearly(model) | code |
74067974/cell_23 | [
"text_html_output_1.png"
] | from prophet import Prophet
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_halving = pd.DataFrame({'holiday': 'Bitcoin Halving', 'ds': pd.to_datetime(['2012-11-28', '2016-07-09', '2020-05-11']), 'lower_window': 0, 'upper_window': 1})
holidays = bitcoin_halving
model = Prophet(yearly_seasonality=True, daily_seasonality=True, holidays=holidays)
model.add_country_holidays(country_name='US')
model.fit(data)
future = model.make_future_dataframe(periods=365)
predict = model.predict(future)
model.train_holiday_names
fig = model.plot(predict) | code |
74067974/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.head() | code |
74067974/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum() | code |
74067974/cell_19 | [
"text_plain_output_1.png"
] | from prophet import Prophet
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_halving = pd.DataFrame({'holiday': 'Bitcoin Halving', 'ds': pd.to_datetime(['2012-11-28', '2016-07-09', '2020-05-11']), 'lower_window': 0, 'upper_window': 1})
holidays = bitcoin_halving
model = Prophet(yearly_seasonality=True, daily_seasonality=True, holidays=holidays)
model.add_country_holidays(country_name='US')
model.fit(data) | code |
74067974/cell_1 | [
"text_plain_output_1.png"
] | !pip install prophet | code |
74067974/cell_7 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.tail() | code |
74067974/cell_28 | [
"image_output_1.png"
] | from prophet import Prophet
from prophet.plot import plot_plotly, plot_components_plotly
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_halving = pd.DataFrame({'holiday': 'Bitcoin Halving', 'ds': pd.to_datetime(['2012-11-28', '2016-07-09', '2020-05-11']), 'lower_window': 0, 'upper_window': 1})
holidays = bitcoin_halving
model = Prophet(yearly_seasonality=True, daily_seasonality=True, holidays=holidays)
model.add_country_holidays(country_name='US')
model.fit(data)
future = model.make_future_dataframe(periods=365)
predict = model.predict(future)
model.train_holiday_names
fig = model.plot(predict)
fig = model.plot_components(predict)
plot_components_plotly(model, predict) | code |
74067974/cell_8 | [
"text_html_output_2.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.describe() | code |
74067974/cell_24 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from prophet import Prophet
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_halving = pd.DataFrame({'holiday': 'Bitcoin Halving', 'ds': pd.to_datetime(['2012-11-28', '2016-07-09', '2020-05-11']), 'lower_window': 0, 'upper_window': 1})
holidays = bitcoin_halving
model = Prophet(yearly_seasonality=True, daily_seasonality=True, holidays=holidays)
model.add_country_holidays(country_name='US')
model.fit(data)
future = model.make_future_dataframe(periods=365)
predict = model.predict(future)
model.train_holiday_names
fig = model.plot(predict)
fig = model.plot_components(predict) | code |
74067974/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
sns.heatmap(df.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
74067974/cell_27 | [
"text_plain_output_1.png"
] | from prophet import Prophet
from prophet.plot import plot_plotly, plot_components_plotly
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna()
data = df[['Date', 'Adj Close']]
data = data.rename(columns={'Date': 'ds', 'Adj Close': 'y'})
bitcoin_halving = pd.DataFrame({'holiday': 'Bitcoin Halving', 'ds': pd.to_datetime(['2012-11-28', '2016-07-09', '2020-05-11']), 'lower_window': 0, 'upper_window': 1})
holidays = bitcoin_halving
model = Prophet(yearly_seasonality=True, daily_seasonality=True, holidays=holidays)
model.add_country_holidays(country_name='US')
model.fit(data)
future = model.make_future_dataframe(periods=365)
predict = model.predict(future)
model.train_holiday_names
fig = model.plot(predict)
fig = model.plot_components(predict)
plot_plotly(model, predict) | code |
74067974/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/crypto-historical-price/data/BTC-USD.csv')
df.isnull().sum()
df.dropna() | code |
90106473/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
feature_names = df_train.columns[:-1].tolist()
label_names = df_train.columns[-1]
fig = sns.pairplot(df_train, diag_kind="kde")
fig = sns.pairplot(df_train[['CAL', 'DTC']]) | code |
90106473/cell_9 | [
"image_output_1.png"
] | import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
df_test.describe() | code |
90106473/cell_19 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
feature_names = df_train.columns[:-1].tolist()
label_names = df_train.columns[-1]
def make_log_plot(df):
color_list = ['#F4D03F', '#F5B041','#DC7633','#6E2C00', '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']
feature_names = df.columns.tolist()
feature_num = len(feature_names)
Depth = np.linspace(0,len(df[feature_names[0]]),len(df[feature_names[0]]))
f, ax = plt.subplots(nrows=1, ncols=feature_num, figsize=(12, 12))
for i in range(len(ax)):
log = df[feature_names[i]]
ax[i].plot(log, Depth, '-', color=color_list[i])
ax[i].set_ylim(Depth.min(),Depth.max())
ax[i].invert_yaxis()
ax[i].grid()
ax[i].locator_params(axis='x', nbins=3)
ax[i].set_xlabel(feature_names[i])
ax[i].set_xlim(log.min(),log.max())
if i > 0:
ax[i].set_yticklabels([]);
f.suptitle('Well logs', fontsize=14,y=0.94)
X_train_orig = df_train[feature_names].values
X_test_orig = df_test[feature_names].values
Y_train_orig = df_train['DTC'].values
scaler = StandardScaler()
scaler.fit(X_train_orig)
X_train_norm = scaler.transform(X_train_orig)
X_test_norm = scaler.transform(X_test_orig)
X_train, X_val, y_train, y_val = train_test_split(X_train_norm, Y_train_orig, test_size=0.2, random_state=1, shuffle=True)
print('Size of the X_train dataset: ' + str(X_train.shape))
print('Size of the y_train dataset: ' + str(y_train.shape))
print('Size of the X_val dataset: ' + str(X_val.shape))
print('Size of the y_val dataset: ' + str(y_val.shape)) | code |
90106473/cell_28 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
feature_names = df_train.columns[:-1].tolist()
label_names = df_train.columns[-1]
def make_log_plot(df):
color_list = ['#F4D03F', '#F5B041','#DC7633','#6E2C00', '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']
feature_names = df.columns.tolist()
feature_num = len(feature_names)
Depth = np.linspace(0,len(df[feature_names[0]]),len(df[feature_names[0]]))
f, ax = plt.subplots(nrows=1, ncols=feature_num, figsize=(12, 12))
for i in range(len(ax)):
log = df[feature_names[i]]
ax[i].plot(log, Depth, '-', color=color_list[i])
ax[i].set_ylim(Depth.min(),Depth.max())
ax[i].invert_yaxis()
ax[i].grid()
ax[i].locator_params(axis='x', nbins=3)
ax[i].set_xlabel(feature_names[i])
ax[i].set_xlim(log.min(),log.max())
if i > 0:
ax[i].set_yticklabels([]);
f.suptitle('Well logs', fontsize=14,y=0.94)
X_train_orig = df_train[feature_names].values
X_test_orig = df_test[feature_names].values
Y_train_orig = df_train['DTC'].values
scaler = StandardScaler()
scaler.fit(X_train_orig)
X_train_norm = scaler.transform(X_train_orig)
X_test_norm = scaler.transform(X_test_orig)
X_train, X_val, y_train, y_val = train_test_split(X_train_norm, Y_train_orig, test_size=0.2, random_state=1, shuffle=True)
def evaluate_regression(reg, X_test, y_test):
R2 = reg.score(X_test, y_test)
y_pred = reg.predict(X_test)
RMSE = mean_squared_error(y_test, y_pred, squared=False)
plt.figure(figsize=(15, 8))
f, (ax1, ax2) = plt.subplots(1, 2)
ax1.scatter(
y_test,
y_pred)
ax1.set_xlabel('True')
ax1.set_ylabel('Predicted')
ax2.plot(y_test, linewidth=2, label="True")
ax2.plot(y_pred, linewidth=2, label="Predicted")
ax2.legend(loc='lower right')
ax2.set_xlabel('Sample')
ax2.set_ylabel('DTC')
plt.show()
print(f'R2 = {R2}')
print(f'RMSE = {RMSE}')
reg = LinearRegression()
reg.fit(X_train, y_train)
evaluate_regression(reg, X_val, y_val)
predictions = reg.predict(X_test_norm)
plt.plot(predictions, label='Predicted')
plt.xlabel('Sample')
plt.ylabel('DTC')
plt.title('DTC Prediction Comparison')
plt.legend(loc='lower right')
plt.show() | code |
90106473/cell_8 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
df_train.describe() | code |
90106473/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
feature_names = df_train.columns[:-1].tolist()
label_names = df_train.columns[-1]
def make_log_plot(df):
color_list = ['#F4D03F', '#F5B041','#DC7633','#6E2C00', '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']
feature_names = df.columns.tolist()
feature_num = len(feature_names)
Depth = np.linspace(0,len(df[feature_names[0]]),len(df[feature_names[0]]))
f, ax = plt.subplots(nrows=1, ncols=feature_num, figsize=(12, 12))
for i in range(len(ax)):
log = df[feature_names[i]]
ax[i].plot(log, Depth, '-', color=color_list[i])
ax[i].set_ylim(Depth.min(),Depth.max())
ax[i].invert_yaxis()
ax[i].grid()
ax[i].locator_params(axis='x', nbins=3)
ax[i].set_xlabel(feature_names[i])
ax[i].set_xlim(log.min(),log.max())
if i > 0:
ax[i].set_yticklabels([]);
f.suptitle('Well logs', fontsize=14,y=0.94)
make_log_plot(df_train) | code |
90106473/cell_22 | [
"image_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
feature_names = df_train.columns[:-1].tolist()
label_names = df_train.columns[-1]
def make_log_plot(df):
color_list = ['#F4D03F', '#F5B041','#DC7633','#6E2C00', '#1B4F72','#2E86C1', '#AED6F1', '#A569BD', '#196F3D']
feature_names = df.columns.tolist()
feature_num = len(feature_names)
Depth = np.linspace(0,len(df[feature_names[0]]),len(df[feature_names[0]]))
f, ax = plt.subplots(nrows=1, ncols=feature_num, figsize=(12, 12))
for i in range(len(ax)):
log = df[feature_names[i]]
ax[i].plot(log, Depth, '-', color=color_list[i])
ax[i].set_ylim(Depth.min(),Depth.max())
ax[i].invert_yaxis()
ax[i].grid()
ax[i].locator_params(axis='x', nbins=3)
ax[i].set_xlabel(feature_names[i])
ax[i].set_xlim(log.min(),log.max())
if i > 0:
ax[i].set_yticklabels([]);
f.suptitle('Well logs', fontsize=14,y=0.94)
X_train_orig = df_train[feature_names].values
X_test_orig = df_test[feature_names].values
Y_train_orig = df_train['DTC'].values
scaler = StandardScaler()
scaler.fit(X_train_orig)
X_train_norm = scaler.transform(X_train_orig)
X_test_norm = scaler.transform(X_test_orig)
X_train, X_val, y_train, y_val = train_test_split(X_train_norm, Y_train_orig, test_size=0.2, random_state=1, shuffle=True)
def evaluate_regression(reg, X_test, y_test):
R2 = reg.score(X_test, y_test)
y_pred = reg.predict(X_test)
RMSE = mean_squared_error(y_test, y_pred, squared=False)
plt.figure(figsize=(15, 8))
f, (ax1, ax2) = plt.subplots(1, 2)
ax1.scatter(
y_test,
y_pred)
ax1.set_xlabel('True')
ax1.set_ylabel('Predicted')
ax2.plot(y_test, linewidth=2, label="True")
ax2.plot(y_pred, linewidth=2, label="Predicted")
ax2.legend(loc='lower right')
ax2.set_xlabel('Sample')
ax2.set_ylabel('DTC')
plt.show()
print(f'R2 = {R2}')
print(f'RMSE = {RMSE}')
reg = LinearRegression()
reg.fit(X_train, y_train)
evaluate_regression(reg, X_val, y_val) | code |
90106473/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
feature_names = df_train.columns[:-1].tolist()
print(f'Features: {feature_names}')
label_names = df_train.columns[-1]
print(f'Label: {label_names}') | code |
90106473/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
df_test = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/test.csv')
df_train = pd.read_csv('/kaggle/input/ml-for-exploration-geophysics-2022-regression/train.csv')
feature_names = df_train.columns[:-1].tolist()
label_names = df_train.columns[-1]
fig = sns.pairplot(df_train, diag_kind='kde') | code |
90106473/cell_5 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
73078642/cell_19 | [
"image_png_output_1.png"
] | from IPython.display import display
import ipywidgets as widgets
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
from colorama import Fore, Style
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from ipywidgets import HBox, Output, VBox, Layout
from IPython.display import display
import ipywidgets as widgets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.feature_selection import f_classif, mutual_info_classif
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier, XGBRegressor
from catboost import CatBoostClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_auc_score, accuracy_score, recall_score, precision_score, f1_score, classification_report, plot_confusion_matrix, plot_roc_curve, plot_precision_recall_curve, log_loss, brier_score_loss, hamming_loss
import warnings
warnings.filterwarnings('ignore')
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.sort_values('age').head()
data_eda = data.copy()
data_eda['sex'] = data['sex'].map({1: 'Male', 0: 'Female'})
data_eda['cp'] = data['cp'].map({1: 'Typical\nangina', 2: 'Atypical\nangina', 3: 'Non-anginal\npain', 0: 'Asymptomatic'})
data_eda['fbs'] = data['fbs'].map({1: 'True', 0: 'False'})
data_eda['restecg'] = data['restecg'].map({1: 'Normal', 2: 'Having\nST-T wave\nabnormality', 0: 'Hypertrophy'})
data_eda['exng'] = data['exng'].map({1: 'Yes', 0: 'No'})
data_eda['slp'] = data['slp'].map({2: 'Upsloping', 1: 'Flat', 0: 'Downsloping'})
data_eda['caa'] = data['caa'].map({0: '0', 1: '1', 2: '2', 3: '3', 4: '3'})
data_eda['thall'] = data['thall'].map({0: 'Normal', 2: 'Normal', 1: 'Fixed defect', 3: 'Reversible\ndefect'})
data_eda['output'] = data['output'].map({1: 'Yes', 0: 'No'})
data_eda.isnull().mean()
def age_cohort(age):
if age <= 45:
return '0-45'
elif age > 45 and age <= 55:
return '45-55'
elif age > 55 and age <= 60:
return '55-60'
elif age > 60:
return '60+'
data_eda['age group'] = data_eda['age'].apply(age_cohort)
data_eda.sort_values('age group', inplace=True)
def q(n):
def q_(x):
return x.quantile(n)
q_.__name__ = '{:2.0f}%'.format(n * 100)
return q_
def ctab(var):
f, a = plt.subplots(1, 2, figsize=(8, 3), dpi=200)
background_color = '#F0F6FC'
color = '#000000'
hue_colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']
f.patch.set_facecolor(background_color)
temp = data_eda[var].value_counts()
a[1].pie(x=temp, labels=temp.index, autopct='%1.1f%%', textprops={'fontsize': 9}, colors=hue_colors)
a[1].set_xlabel(var)
a[0].axis('off')
a[0].text(0.55, 0.5, ' Percentage( % ) \n', color=color, horizontalalignment='center', verticalalignment='center', fontsize=20, fontfamily='serif')
a[0].text(0.55, 0.4, "of Categorical Feature '" + var + "'\n_________________________________", color=color, horizontalalignment='center', verticalalignment='center', fontsize=10, fontfamily='serif')
plt.show()
ci = ['Categorical Features', 'Numeric Features']
cat_var = [var for var in data_eda.columns if data_eda[var].dtype == 'O' and var != 'output']
num_var = [var for var in data_eda.columns if data_eda[var].dtype != 'O']
def create_inner_tabs():
inner_tabs_list = [widgets.Output() for i in range(len(cat_var))]
inner_tab = widgets.Tab(inner_tabs_list)
for i in range(len(cat_var)):
inner_tab.set_title(i, cat_var[i])
with inner_tabs_list[i]:
ctab(cat_var[i])
display(inner_tab)
outer_sub_tabs_list = [widgets.Output() for i in range(len(ci))]
tab = widgets.Tab(children=outer_sub_tabs_list)
for i in range(len(ci)):
tab.set_title(i, ci[i])
with outer_sub_tabs_list[i]:
if i == 0:
create_inner_tabs()
else:
plt.figure(figsize=(11, 4), dpi=200)
df_n = data_eda[num_var].agg(['count', 'mean', 'std', 'skew', 'kurt', 'min', q(0.25), 'median', q(0.75), 'max']).T
df_n['range'] = df_n['max'] - df_n['min']
df_n['IQR'] = df_n['75%'] - df_n['25%']
sns.heatmap(df_n, annot=True, cmap='Blues', fmt='.2f', linewidths=5, cbar=False, annot_kws={'size': 10})
plt.xticks(size=12)
plt.yticks(size=12, rotation=0)
plt.title('Descriptive Statistics', size=16)
plt.show()
display(tab) | code |
73078642/cell_15 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
from colorama import Fore, Style
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from ipywidgets import HBox, Output, VBox, Layout
from IPython.display import display
import ipywidgets as widgets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.feature_selection import f_classif, mutual_info_classif
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier, XGBRegressor
from catboost import CatBoostClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_auc_score, accuracy_score, recall_score, precision_score, f1_score, classification_report, plot_confusion_matrix, plot_roc_curve, plot_precision_recall_curve, log_loss, brier_score_loss, hamming_loss
import warnings
warnings.filterwarnings('ignore')
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.sort_values('age').head()
data_eda = data.copy()
data_eda['sex'] = data['sex'].map({1: 'Male', 0: 'Female'})
data_eda['cp'] = data['cp'].map({1: 'Typical\nangina', 2: 'Atypical\nangina', 3: 'Non-anginal\npain', 0: 'Asymptomatic'})
data_eda['fbs'] = data['fbs'].map({1: 'True', 0: 'False'})
data_eda['restecg'] = data['restecg'].map({1: 'Normal', 2: 'Having\nST-T wave\nabnormality', 0: 'Hypertrophy'})
data_eda['exng'] = data['exng'].map({1: 'Yes', 0: 'No'})
data_eda['slp'] = data['slp'].map({2: 'Upsloping', 1: 'Flat', 0: 'Downsloping'})
data_eda['caa'] = data['caa'].map({0: '0', 1: '1', 2: '2', 3: '3', 4: '3'})
data_eda['thall'] = data['thall'].map({0: 'Normal', 2: 'Normal', 1: 'Fixed defect', 3: 'Reversible\ndefect'})
data_eda['output'] = data['output'].map({1: 'Yes', 0: 'No'})
print('Imbalance Ratio :', data_eda['output'].value_counts()[1] / data_eda['output'].value_counts()[0])
print('\n')
print('Heart Attack\n____________')
data_eda['output'].value_counts() | code |
73078642/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
from colorama import Fore, Style
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from ipywidgets import HBox, Output, VBox, Layout
from IPython.display import display
import ipywidgets as widgets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.feature_selection import f_classif, mutual_info_classif
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier, XGBRegressor
from catboost import CatBoostClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_auc_score, accuracy_score, recall_score, precision_score, f1_score, classification_report, plot_confusion_matrix, plot_roc_curve, plot_precision_recall_curve, log_loss, brier_score_loss, hamming_loss
import warnings
warnings.filterwarnings('ignore')
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.sort_values('age').head()
data_eda = data.copy()
data_eda['sex'] = data['sex'].map({1: 'Male', 0: 'Female'})
data_eda['cp'] = data['cp'].map({1: 'Typical\nangina', 2: 'Atypical\nangina', 3: 'Non-anginal\npain', 0: 'Asymptomatic'})
data_eda['fbs'] = data['fbs'].map({1: 'True', 0: 'False'})
data_eda['restecg'] = data['restecg'].map({1: 'Normal', 2: 'Having\nST-T wave\nabnormality', 0: 'Hypertrophy'})
data_eda['exng'] = data['exng'].map({1: 'Yes', 0: 'No'})
data_eda['slp'] = data['slp'].map({2: 'Upsloping', 1: 'Flat', 0: 'Downsloping'})
data_eda['caa'] = data['caa'].map({0: '0', 1: '1', 2: '2', 3: '3', 4: '3'})
data_eda['thall'] = data['thall'].map({0: 'Normal', 2: 'Normal', 1: 'Fixed defect', 3: 'Reversible\ndefect'})
data_eda['output'] = data['output'].map({1: 'Yes', 0: 'No'})
data_eda.isnull().mean() | code |
73078642/cell_22 | [
"image_output_1.png"
] | from IPython.display import display
import ipywidgets as widgets
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
from colorama import Fore, Style
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from ipywidgets import HBox, Output, VBox, Layout
from IPython.display import display
import ipywidgets as widgets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.feature_selection import f_classif, mutual_info_classif
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier, XGBRegressor
from catboost import CatBoostClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_auc_score, accuracy_score, recall_score, precision_score, f1_score, classification_report, plot_confusion_matrix, plot_roc_curve, plot_precision_recall_curve, log_loss, brier_score_loss, hamming_loss
import warnings
warnings.filterwarnings('ignore')
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.sort_values('age').head()
data_eda = data.copy()
data_eda['sex'] = data['sex'].map({1: 'Male', 0: 'Female'})
data_eda['cp'] = data['cp'].map({1: 'Typical\nangina', 2: 'Atypical\nangina', 3: 'Non-anginal\npain', 0: 'Asymptomatic'})
data_eda['fbs'] = data['fbs'].map({1: 'True', 0: 'False'})
data_eda['restecg'] = data['restecg'].map({1: 'Normal', 2: 'Having\nST-T wave\nabnormality', 0: 'Hypertrophy'})
data_eda['exng'] = data['exng'].map({1: 'Yes', 0: 'No'})
data_eda['slp'] = data['slp'].map({2: 'Upsloping', 1: 'Flat', 0: 'Downsloping'})
data_eda['caa'] = data['caa'].map({0: '0', 1: '1', 2: '2', 3: '3', 4: '3'})
data_eda['thall'] = data['thall'].map({0: 'Normal', 2: 'Normal', 1: 'Fixed defect', 3: 'Reversible\ndefect'})
data_eda['output'] = data['output'].map({1: 'Yes', 0: 'No'})
data_eda.isnull().mean()
## cohort analysis of age with output
def age_cohort(age):
if age <= 45:
return "0-45"
elif age > 45 and age <= 55:
return "45-55"
elif age > 55 and age <= 60:
return "55-60"
elif age > 60:
return "60+"
data_eda['age group'] = data_eda['age'].apply(age_cohort)
data_eda.sort_values("age group",inplace=True)
def q(n):
def q_(x):
return x.quantile(n)
q_.__name__ = '{:2.0f}%'.format(n*100)
return q_
def ctab(var):
f, a = plt.subplots(1, 2,figsize=(8,3),dpi=200)
background_color = "#F0F6FC"
color = "#000000"
hue_colors = ['gold', 'yellowgreen', 'lightcoral', 'lightskyblue']
f.patch.set_facecolor(background_color)
temp=data_eda[var].value_counts()
a[1].pie(x=temp,labels=temp.index,autopct='%1.1f%%',textprops={'fontsize':9},colors=hue_colors);
a[1].set_xlabel(var)
a[0].axis("off")
a[0].text(.55,0.5," Percentage( % ) \n",
color =color, horizontalalignment = 'center',verticalalignment = 'center' ,
fontsize = 20, fontfamily='serif');
a[0].text(.55,0.4,"of Categorical Feature '"+var+"'\n_________________________________",
color = color, horizontalalignment = 'center',verticalalignment = 'center' ,
fontsize = 10, fontfamily='serif');
plt.show()
ci = ["Categorical Features", "Numeric Features"]
cat_var = [var for var in data_eda.columns if data_eda[var].dtype=='O' and var !='output']
num_var = [var for var in data_eda.columns if data_eda[var].dtype!='O']
# Create inner Tabs
def create_inner_tabs():
inner_tabs_list=[widgets.Output() for i in range(len(cat_var))]
inner_tab = widgets.Tab(inner_tabs_list)
for i in range (len(cat_var)):
inner_tab.set_title(i,cat_var[i])
with inner_tabs_list[i]:
ctab(cat_var[i]);
display(inner_tab)
# Create outer Tabs
outer_sub_tabs_list=[widgets.Output() for i in range(len(ci))]
tab = widgets.Tab(children=outer_sub_tabs_list)
for i in range (len(ci)):
tab.set_title(i,ci[i])
with outer_sub_tabs_list[i]:
if i ==0:
create_inner_tabs();
else:
plt.figure(figsize=(11,4),dpi=200)
df_n=data_eda[num_var].agg(["count","mean","std","skew","kurt","min",q(.25),"median",q(.75),"max"]).T
df_n["range"]=df_n["max"]-df_n["min"]
df_n["IQR"] =df_n["75%"]-df_n["25%"]
sns.heatmap(df_n , annot=True,cmap = "Blues", fmt= '.2f',linewidths = 5, cbar = False,annot_kws={"size": 10})
plt.xticks(size = 12);
plt.yticks(size = 12, rotation = 0);
plt.title("Descriptive Statistics", size = 16)
plt.show()
display(tab)
f, a = plt.subplots(5, 2, figsize=(12, 15), dpi=200)
background_color = '#F0F6FC'
color = '#000000'
hue_color = ['#FF5003', '#428bca']
hue_order = ['Yes', 'No']
alpha = 0.8
f.patch.set_facecolor(background_color)
ax = sum(a.tolist(), [])[1:]
a[0][0].axis('off')
a[0][0].text(0.5, 0.4, 'Bar Plots (%)\n', color=color, horizontalalignment='center', verticalalignment='center', fontsize=31, fontfamily='serif')
a[0][0].text(0.5, 0.3, 'of Categorical Features\n________________________', color=color, horizontalalignment='center', verticalalignment='center', fontsize=16.9, fontfamily='serif')
for i in range(len(cat_var)):
new_df = data_eda.groupby(cat_var[i])['output'].value_counts(normalize=True).mul(100).rename('Percent ( % )').reset_index()
g = sns.barplot(data=new_df, x=cat_var[i], y='Percent ( % )', hue='output', ax=ax[i], palette=hue_color, alpha=alpha, edgecolor=background_color, hue_order=hue_order)
ax[i].set_facecolor(background_color)
ax[i].grid(color=background_color)
ax[i].grid(color=color, linestyle=':', axis='y', zorder=0, dashes=(1, 5))
ax[i].spines['top'].set_visible(False)
ax[i].spines['right'].set_visible(False)
ax[i].spines['left'].set_visible(False)
ax[i].spines['bottom'].set_color(color)
ax[i].xaxis.label.set_color(color)
ax[i].yaxis.label.set_color(color)
ax[i].tick_params(axis='x', colors=color)
ax[i].tick_params(axis='y', colors=color)
if i == 1:
l = ax[i].legend(loc=(0.4, 1.1), facecolor=background_color, edgecolor=background_color, title='Heart Attack', fontsize=10, title_fontsize=14)
for text in l.get_texts():
text.set_color(color)
l._legend_title_box._text.set_color(color)
else:
ax[i].legend().set_visible(False)
plt.tight_layout() | code |
73078642/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import seaborn as sns
import warnings
import numpy as np
import pandas as pd
pd.set_option('display.max_columns', None)
from colorama import Fore, Style
from sklearn.tree import plot_tree
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
from ipywidgets import HBox, Output, VBox, Layout
from IPython.display import display
import ipywidgets as widgets
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler, MinMaxScaler
from sklearn.feature_selection import f_classif, mutual_info_classif
from sklearn.linear_model import LogisticRegression
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from sklearn.ensemble import RandomForestClassifier, AdaBoostClassifier, GradientBoostingClassifier
from xgboost import XGBClassifier, XGBRegressor
from catboost import CatBoostClassifier
from sklearn.model_selection import GridSearchCV
from sklearn.metrics import roc_auc_score, accuracy_score, recall_score, precision_score, f1_score, classification_report, plot_confusion_matrix, plot_roc_curve, plot_precision_recall_curve, log_loss, brier_score_loss, hamming_loss
import warnings
warnings.filterwarnings('ignore')
data = pd.read_csv('../input/heart-attack-analysis-prediction-dataset/heart.csv')
data.sort_values('age').head() | code |
33103199/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum()
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
train = train.drop(missing_data[missing_data['Total'] > 1].index, 1)
train = train.drop(train.loc[train['Electrical'].isnull()].index)
train.isnull().sum().max()
train.isnull().sum()
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
train['Skewed_SP'] = np.log(train['SalePrice'] + 1)
numerical_features = train.select_dtypes(include=[np.number])
numerical_features | code |
33103199/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.info() | code |
33103199/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum()
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
train = train.drop(missing_data[missing_data['Total'] > 1].index, 1)
train = train.drop(train.loc[train['Electrical'].isnull()].index)
train.isnull().sum().max() | code |
33103199/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum()
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
train = train.drop(missing_data[missing_data['Total'] > 1].index, 1)
train = train.drop(train.loc[train['Electrical'].isnull()].index)
train.isnull().sum().max()
train.isnull().sum()
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
plt.hist(train.SalePrice)
plt.show() | code |
33103199/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.model_selection import cross_val_score, cross_val_predict, StratifiedKFold
import seaborn as sns
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn import preprocessing
from sklearn import linear_model, svm
from sklearn.ensemble import GradientBoostingRegressor as xgb
from sklearn.metrics import confusion_matrix, accuracy_score, f1_score
from scipy import stats
import seaborn as sns
from sklearn.impute import SimpleImputer
from sklearn import linear_model, svm
import datetime
from sklearn.model_selection import KFold
from sklearn.model_selection import train_test_split
import time
from sklearn import preprocessing
from scipy.stats import skew
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
33103199/cell_8 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum()
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
train = train.drop(missing_data[missing_data['Total'] > 1].index, 1)
train = train.drop(train.loc[train['Electrical'].isnull()].index)
train.isnull().sum().max()
train.isnull().sum() | code |
33103199/cell_15 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum()
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
train = train.drop(missing_data[missing_data['Total'] > 1].index, 1)
train = train.drop(train.loc[train['Electrical'].isnull()].index)
train.isnull().sum().max()
train.isnull().sum()
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
train['Skewed_SP'] = np.log(train['SalePrice'] + 1)
numerical_features = train.select_dtypes(include=[np.number])
numerical_features
corr = numerical_features.corr()
sns.set(font_scale=1)
corr_list = corr['SalePrice'].sort_values(axis=0, ascending=False).iloc[1:]
print(corr_list) | code |
33103199/cell_3 | [
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape | code |
33103199/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import seaborn as sns
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum()
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
train = train.drop(missing_data[missing_data['Total'] > 1].index, 1)
train = train.drop(train.loc[train['Electrical'].isnull()].index)
train.isnull().sum().max()
train.isnull().sum()
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
train['Skewed_SP'] = np.log(train['SalePrice'] + 1)
numerical_features = train.select_dtypes(include=[np.number])
numerical_features
corr = numerical_features.corr()
plt.figure(figsize=(16, 16))
sns.set(font_scale=1)
sns.heatmap(corr, vmax=1, square=True) | code |
33103199/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum()
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
train = train.drop(missing_data[missing_data['Total'] > 1].index, 1)
train = train.drop(train.loc[train['Electrical'].isnull()].index)
train.isnull().sum().max()
train.isnull().sum()
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
train.head() | code |
33103199/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum()
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
train = train.drop(missing_data[missing_data['Total'] > 1].index, 1)
train = train.drop(train.loc[train['Electrical'].isnull()].index)
train.isnull().sum().max()
train.isnull().sum()
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
train['Skewed_SP'] = np.log(train['SalePrice'] + 1)
plt.hist(train['Skewed_SP'])
plt.show() | code |
33103199/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.shape
train.isnull().sum() | code |
129020971/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
import pickle
import torch
import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from tqdm import tqdm
import pickle
device = 'cuda' if torch.cuda.is_available() else 'cpu'
import pandas as pd
import numpy as np
valid_ratio = 0.2
X = pickle.load(open('/kaggle/input/embedder/train_embedding', 'rb'))
y = pd.read_csv('/kaggle/input/embedder/preprocessed_train.csv').target_relabeled.values
valid_size = int(y.shape[0] * valid_ratio)
train_X_tensor = torch.tensor(X[:-valid_size]).to(device)
train_y_tensor = torch.LongTensor(y[:-valid_size]).to(device)
valid_X_tensor = torch.tensor(X[-valid_size:]).to(device)
valid_y_tensor = torch.LongTensor(y[-valid_size:]).to(device)
in_features = X.shape[1]
mlp = torch.nn.Sequential(torch.nn.modules.linear.Linear(in_features=in_features, out_features=in_features), torch.nn.ReLU(), torch.nn.Dropout(), torch.nn.modules.linear.Linear(in_features=in_features, out_features=in_features), torch.nn.ReLU(), torch.nn.Dropout(), torch.nn.modules.linear.Linear(in_features=in_features, out_features=2), torch.nn.Softmax()).to(device)
Loss = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(mlp.parameters(), lr=0.0001)
epoch = 400
max_patience = 5
patience = max_patience
train_loss_history = []
valid_loss_history = []
mlp.train()
for e in range(epoch):
train_loss = 0
valid_loss = 0
outputs = mlp(train_X_tensor)
loss = Loss(outputs, train_y_tensor)
loss.backward()
optimizer.step()
optimizer.zero_grad()
train_loss += loss.item()
train_loss_history.append(train_loss)
outputs = mlp(valid_X_tensor)
loss = Loss(outputs, valid_y_tensor)
valid_loss += loss.item()
valid_loss_history.append(valid_loss)
if e % 20 == 0:
print(f'[{e:3d}/{epoch}] trainloss: {train_loss:.8f} validloss: {valid_loss:.8f} patience: {patience}')
if len(valid_loss_history) > 1 and valid_loss_history[-1] > valid_loss_history[-2]:
patience -= 1
if patience < 0:
break
else:
patience = max_patience | code |
129020971/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pickle
import torch
import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from tqdm import tqdm
import pickle
device = 'cuda' if torch.cuda.is_available() else 'cpu'
import pandas as pd
import numpy as np
valid_ratio = 0.2
X = pickle.load(open('/kaggle/input/embedder/train_embedding', 'rb'))
y = pd.read_csv('/kaggle/input/embedder/preprocessed_train.csv').target_relabeled.values
valid_size = int(y.shape[0] * valid_ratio)
train_X_tensor = torch.tensor(X[:-valid_size]).to(device)
train_y_tensor = torch.LongTensor(y[:-valid_size]).to(device)
valid_X_tensor = torch.tensor(X[-valid_size:]).to(device)
valid_y_tensor = torch.LongTensor(y[-valid_size:]).to(device)
in_features = X.shape[1]
mlp = torch.nn.Sequential(torch.nn.modules.linear.Linear(in_features=in_features, out_features=in_features), torch.nn.ReLU(), torch.nn.Dropout(), torch.nn.modules.linear.Linear(in_features=in_features, out_features=in_features), torch.nn.ReLU(), torch.nn.Dropout(), torch.nn.modules.linear.Linear(in_features=in_features, out_features=2), torch.nn.Softmax()).to(device)
Loss = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(mlp.parameters(), lr=0.0001)
epoch = 400
max_patience = 5
patience = max_patience
train_loss_history = []
valid_loss_history = []
mlp.train()
for e in range(epoch):
train_loss = 0
valid_loss = 0
outputs = mlp(train_X_tensor)
loss = Loss(outputs, train_y_tensor)
loss.backward()
optimizer.step()
optimizer.zero_grad()
train_loss += loss.item()
train_loss_history.append(train_loss)
outputs = mlp(valid_X_tensor)
loss = Loss(outputs, valid_y_tensor)
valid_loss += loss.item()
valid_loss_history.append(valid_loss)
if len(valid_loss_history) > 1 and valid_loss_history[-1] > valid_loss_history[-2]:
patience -= 1
if patience < 0:
break
else:
patience = max_patience
(plt.plot(train_loss_history), plt.plot(valid_loss_history))
plt.legend(['train loss', 'valid loss'])
plt.show() | code |
129020971/cell_1 | [
"text_plain_output_1.png"
] | import torch
import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from tqdm import tqdm
import pickle
device = 'cuda' if torch.cuda.is_available() else 'cpu'
import pandas as pd
import numpy as np
print('torch.cuda.is_available:', torch.cuda.is_available()) | code |
129020971/cell_15 | [
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
import pandas as pd
import pickle
valid_ratio = 0.2
X = pickle.load(open('/kaggle/input/embedder/train_embedding', 'rb'))
y = pd.read_csv('/kaggle/input/embedder/preprocessed_train.csv').target_relabeled.values
valid_size = int(y.shape[0] * valid_ratio)
in_features = X.shape[1]
from catboost import CatBoostClassifier
catboostclassifier = CatBoostClassifier(iterations=20)
catboostclassifier.fit(X[:-valid_size], y[:-valid_size], eval_set=(X[-valid_size:], y[-valid_size:]), logging_level='Silent')
catboostprediction = catboostclassifier.predict(X)
print('accuracy: \t', end='')
print((catboostprediction == y).mean()) | code |
129020971/cell_14 | [
"image_output_1.png"
] | from catboost import CatBoostClassifier
import pandas as pd
import pickle
valid_ratio = 0.2
X = pickle.load(open('/kaggle/input/embedder/train_embedding', 'rb'))
y = pd.read_csv('/kaggle/input/embedder/preprocessed_train.csv').target_relabeled.values
valid_size = int(y.shape[0] * valid_ratio)
in_features = X.shape[1]
from catboost import CatBoostClassifier
catboostclassifier = CatBoostClassifier(iterations=20)
catboostclassifier.fit(X[:-valid_size], y[:-valid_size], eval_set=(X[-valid_size:], y[-valid_size:]), logging_level='Silent') | code |
129020971/cell_10 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
import pickle
import torch
import torch
from torch.utils.data import Dataset
from torchvision import datasets
from torchvision.transforms import ToTensor
import matplotlib.pyplot as plt
from torch.utils.data import DataLoader
from tqdm import tqdm
import pickle
device = 'cuda' if torch.cuda.is_available() else 'cpu'
import pandas as pd
import numpy as np
valid_ratio = 0.2
X = pickle.load(open('/kaggle/input/embedder/train_embedding', 'rb'))
y = pd.read_csv('/kaggle/input/embedder/preprocessed_train.csv').target_relabeled.values
valid_size = int(y.shape[0] * valid_ratio)
train_X_tensor = torch.tensor(X[:-valid_size]).to(device)
train_y_tensor = torch.LongTensor(y[:-valid_size]).to(device)
valid_X_tensor = torch.tensor(X[-valid_size:]).to(device)
valid_y_tensor = torch.LongTensor(y[-valid_size:]).to(device)
in_features = X.shape[1]
mlp = torch.nn.Sequential(torch.nn.modules.linear.Linear(in_features=in_features, out_features=in_features), torch.nn.ReLU(), torch.nn.Dropout(), torch.nn.modules.linear.Linear(in_features=in_features, out_features=in_features), torch.nn.ReLU(), torch.nn.Dropout(), torch.nn.modules.linear.Linear(in_features=in_features, out_features=2), torch.nn.Softmax()).to(device)
Loss = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(mlp.parameters(), lr=0.0001)
epoch = 400
max_patience = 5
patience = max_patience
train_loss_history = []
valid_loss_history = []
mlp.train()
for e in range(epoch):
train_loss = 0
valid_loss = 0
outputs = mlp(train_X_tensor)
loss = Loss(outputs, train_y_tensor)
loss.backward()
optimizer.step()
optimizer.zero_grad()
train_loss += loss.item()
train_loss_history.append(train_loss)
outputs = mlp(valid_X_tensor)
loss = Loss(outputs, valid_y_tensor)
valid_loss += loss.item()
valid_loss_history.append(valid_loss)
if len(valid_loss_history) > 1 and valid_loss_history[-1] > valid_loss_history[-2]:
patience -= 1
if patience < 0:
break
else:
patience = max_patience
outputs = mlp(train_X_tensor)
outputs = torch.argmax(outputs, 1)
torch.mean((outputs == train_y_tensor).to(torch.float))
print('train_accuracy: \t', end='')
print(torch.mean((outputs == train_y_tensor).to(torch.float)).item())
outputs = mlp(valid_X_tensor)
outputs = torch.argmax(outputs, 1)
print('valid_accuracy: \t', end='')
print(torch.mean((outputs == valid_y_tensor).to(torch.float)).item()) | code |
128009107/cell_9 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import os
train_images_dir = 'train_images'
train_maps_dir = 'train_labels'
val_images_dir = 'valid_images'
val_maps_dir = 'valid_labels'
test_images_dir = 'test_images'
test_maps_dir = 'test_labels'
train_images = np.array(os.listdir(train_images_dir), dtype=object)
train_maps = np.array(os.listdir(train_maps_dir), dtype=object)
val_images = np.array(os.listdir(val_images_dir), dtype=object)
val_maps = np.array(os.listdir(val_maps_dir), dtype=object)
def remove_folder_contents(folder):
for the_file in os.listdir(folder):
file_path = os.path.join(folder, the_file)
try:
if os.path.isfile(file_path):
os.unlink(file_path)
elif os.path.isdir(file_path):
remove_folder_contents(file_path)
os.rmdir(file_path)
except Exception as e:
remove_folder_contents('./test_images')
remove_folder_contents('./test_labels')
split_data(test_images_dir, test_maps_dir, 0.2)
test_images = np.array(os.listdir(test_images_dir), dtype=object)
test_maps = np.array(os.listdir(test_maps_dir), dtype=object)
print('****TRAIN****')
print(f'There are {len(train_images)} images')
print(f'There are {len(train_maps)} masks')
print('****TEST****')
print(f'There are {len(test_images)} images')
print(f'There are {len(test_maps)} masks') | code |
128009107/cell_6 | [
"text_plain_output_1.png"
] | import numpy as np
import os
import os
train_images_dir = 'train_images'
train_maps_dir = 'train_labels'
val_images_dir = 'valid_images'
val_maps_dir = 'valid_labels'
test_images_dir = 'test_images'
test_maps_dir = 'test_labels'
train_images = np.array(os.listdir(train_images_dir), dtype=object)
train_maps = np.array(os.listdir(train_maps_dir), dtype=object)
print('****TRAIN****')
print(f'There are {len(train_images)} images')
print(f'There are {len(train_maps)} masks')
val_images = np.array(os.listdir(val_images_dir), dtype=object)
val_maps = np.array(os.listdir(val_maps_dir), dtype=object)
print('****VALID****')
print(f'There are {len(val_images)} images')
print(f'There are {len(val_maps)} masks') | code |
128009107/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | """
counter = 1
for backbone in BACKBONES:
for loss in losses:
for freeze_boolean, freeze_tag in zip([True,False], ['frozen','nonFrozen']):
for pretrained_state, pretrained_tag in zip(['imagenet',None], ['pretrained','nonPretrained']):
model.tag = backbone + '_' + loss.name + '_' + freeze_tag + '_' + pretrained_tag + '_' + str(counter)
print("******************************* ", model.tag)
preprocess_input = sm.get_preprocessing(backbone) # Applies the proper preprocessing for the chosen backbone
model = sm.Unet(backbone, classes=NUM_CLASSES, activation=ACTIVATION, encoder_freeze=freeze_boolean, encoder_weights=pretrained_state) # Variar entre true y false, imagenet y none
model.compile(optim, loss, metrics)
# Defining callbacks for learning rate scheduling and best checkpoints saving
checkpoint_filename = './best_' + backbone + '_' + loss.name + '_' + freeze_tag + '_' + pretrained_tag + '.h5'
callbacks = [
tf.keras.callbacks.ModelCheckpoint(checkpoint_filename, save_weights_only=True, save_best_only=True, mode='min'),
tf.keras.callbacks.ReduceLROnPlateau()
]
# TRAINING
history = model.fit(
train_yarn_dataloader,
validation_data=valid_yarn_dataloader,
epochs=EPOCHS,
callbacks=callbacks,
)
# SAVING THE MODEL
df_history = pd.DataFrame(history.history)
df_history.to_csv(model.tag + '.csv')
model.save(model.tag + '.h5')
counter += 1
""" | code |
128009107/cell_11 | [
"text_plain_output_1.png"
] | import cv2
from PIL import Image
import albumentations as A
import matplotlib.pyplot as plt
from tensorflow.keras.utils import Sequence | code |
128009107/cell_1 | [
"text_plain_output_1.png"
] | !pip install segmentation_models
!yes | pip install tensorflow==2.10
!yes | apt install --allow-change-held-packages libcudnn8=8.1.0.77-1+cuda11.2 | code |
128009107/cell_18 | [
"text_plain_output_1.png"
] | import segmentation_models as sm
import tensorflow as tf
import tensorflow.keras as K | code |
128009107/cell_16 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import os
train_images_dir = 'train_images'
train_maps_dir = 'train_labels'
val_images_dir = 'valid_images'
val_maps_dir = 'valid_labels'
test_images_dir = 'test_images'
test_maps_dir = 'test_labels'
train_images = np.array(os.listdir(train_images_dir), dtype=object)
train_maps = np.array(os.listdir(train_maps_dir), dtype=object)
val_images = np.array(os.listdir(val_images_dir), dtype=object)
val_maps = np.array(os.listdir(val_maps_dir), dtype=object)
train_yarn_dataset = Dataset(train_images_dir, train_maps_dir)
valid_yarn_dataset = Dataset(val_images_dir, val_maps_dir)
sample_img, sample_map = train_yarn_dataset[8]
fig, ax = plt.subplots(1, 2, figsize=(15, 10))
ax[0].imshow(sample_img)
ax[1].imshow(sample_map * 255) | code |
128009107/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | !cp -r /kaggle/input/crochet-samples-v3 /kaggle/working
os.chdir('/kaggle/working/crochet-samples-v3')
# train_images = np.array(os.listdir(train_images_dir), dtype = object)
retval = os.getcwd()
print("Current working directory %s" % retval) | code |
105178983/cell_2 | [
"text_plain_output_1.png"
] | def my_first_function():
print('success')
my_first_function() | code |
105178983/cell_11 | [
"text_plain_output_1.png"
] | def calci(a, b, c):
"""
fdgfd
"""
if c == '+':
return a + b
elif c == '-':
return a - b
elif c == '/':
return a / b
elif c == '*':
return a * b
cal = calci(5, 6, '/')
print(cal) | code |
105178983/cell_18 | [
"text_plain_output_1.png"
] | def upper_count(name):
count = 0
for i in name:
if i.isupper():
count = count + 1
return count
def avg(*marks):
count, total = (0, 0)
for i in marks:
total = total + i
count = count + 1
return total / count
a = avg(45, 43, 35, 67)
print(a) | code |
105178983/cell_8 | [
"text_plain_output_1.png"
] | def upper_count(name):
count = 0
for i in name:
if i.isupper():
count = count + 1
return count
upper_count('My Name Is Adnan') | code |
105178983/cell_15 | [
"text_plain_output_1.png"
] | def my_salary(weekly_hrs, week, pay_per_hour=500):
salary = weekly_hrs * week * pay_per_hour
return salary
salary = my_salary(pay_per_hour=600, week=6, weekly_hrs=40)
salary = my_salary(4, 5)
print(salary) | code |
105178983/cell_14 | [
"text_plain_output_1.png"
] | def my_salary(weekly_hrs, week, pay_per_hour=500):
salary = weekly_hrs * week * pay_per_hour
return salary
salary = my_salary(pay_per_hour=600, week=6, weekly_hrs=40)
print(salary) | code |
105178983/cell_5 | [
"text_plain_output_1.png"
] | def add(a, b):
return a + b
result = add(4, 5)
print(result) | code |
106210927/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
df_train['sample'] = 1
df_test['sample'] = 0
df_test['reviewer_score'] = 0
hotels = pd.concat([df_train, df_test], ignore_index=True)
hotels.head(3) | code |
106210927/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
df_test.info() | code |
106210927/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
INPUT_DIR = '/kaggle/input/sf-booking/'
df_train = pd.read_csv(INPUT_DIR + '/hotels_train.csv')
df_test = pd.read_csv(INPUT_DIR + 'hotels_test.csv')
sample_submission = pd.read_csv(INPUT_DIR + '/submission.csv')
sample_submission.head() | code |
Subsets and Splits