path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
32062359/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from cord import ResearchPapers
research_papers = ResearchPapers.load() | code |
32062359/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from cord import ResearchPapers
research_papers = ResearchPapers.load()
help(research_papers.search) | code |
32062359/cell_17 | [
"text_plain_output_1.png"
] | from langdetect import detect
from nltk.tokenize import sent_tokenize,word_tokenize
from tqdm import tqdm
import pandas as pd
import pandas as pd
keywordlist = ['inhibitor']
def loopsearch(keywordlist, researchpaperfu):
alldataframeco = pd.DataFrame()
alldataframenoco = pd.DataFrame()
allcopid = []
allnocopid = []
for i in tqdm(keywordlist):
covinf = researchpaperfu.covid_related().search(i, num_results=1000, covid_related=False, view='table').results[['cord_uid', 'title', 'abstract']]
notcovinf = researchpaperfu.not_covid_related().search(i, num_results=10000, covid_related=False, view='table').results[['cord_uid', 'title', 'abstract']]
covinfpid = list(covinf.cord_uid.values)
notcovinfpid = list(notcovinf.cord_uid.values)
alldataframeco = pd.concat([covinf, alldataframeco])
alldataframenoco = pd.concat([notcovinf, alldataframenoco])
allcopid.append(covinfpid)
allnocopid.append(notcovinfpid)
alldataframeco = alldataframeco.drop_duplicates()
alldataframenoco = alldataframenoco.drop_duplicates()
return (allcopid, allnocopid, alldataframeco, alldataframenoco)
fullab = pd.concat([allcoab, allnocoab])
fullab = fullab.rename(columns={'cord_uid': 'pid'})
fullab = fullab[fullab.abstract != '']
lan = []
for i in fullab.abstract:
lan1 = detect(i)
lan.append(lan1)
fullab['lan'] = lan
fullab = fullab[fullab.lan == 'en']
fullab = fullab[['pid', 'title', 'abstract']]
question = 'Q1'
question_dir = question + '/'
keylist = pd.read_csv('/kaggle/input/kagglecovid19literature/results/' + question_dir + 'keylist.txt').columns.values
valuelist = pd.read_csv('/kaggle/input/kagglecovid19literature/results/' + question_dir + 'valuelist.txt', header=None).values
viruslist = pd.read_csv('/kaggle/input/kagglecovid19literature/results/' + question_dir + 'viruslist.txt', header=None).values
def build_raw_data(file):
def retunsb(sentlist, i, lennu):
sent = sentlist[i]
if i - 1 < 0:
present = ''
else:
present = sentlist[i - 1]
if i + 1 >= lennu:
aftsent = ''
else:
aftsent = sentlist[i + 1]
tempsent = ''
tempsent = tempsent.join([present, sent, aftsent])
return tempsent
allfile = file
allfile['abstract'] = allfile.abstract.astype(str)
allsent = []
allid = []
allab = []
for i in tqdm(range(len(allfile))):
temp = allfile.abstract.iloc[i]
temp = sent_tokenize(temp)
for j in range(len(temp)):
tempab = retunsb(temp, j, len(temp))
allsent.append(temp[j])
allid.append(allfile.pid.iloc[i])
allab.append(tempab)
allsent = pd.DataFrame(allsent, columns=['sent'])
allsent['pid'] = allid
allsent['abstract'] = allab
return (allfile, allsent)
allfile, allsent = build_raw_data(fullab) | code |
32062359/cell_10 | [
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from cord import ResearchPapers
from tqdm import tqdm
import pandas as pd
import pandas as pd
keywordlist = ['inhibitor']
research_papers = ResearchPapers.load()
help(research_papers.search)
def loopsearch(keywordlist, researchpaperfu):
alldataframeco = pd.DataFrame()
alldataframenoco = pd.DataFrame()
allcopid = []
allnocopid = []
for i in tqdm(keywordlist):
covinf = researchpaperfu.covid_related().search(i, num_results=1000, covid_related=False, view='table').results[['cord_uid', 'title', 'abstract']]
notcovinf = researchpaperfu.not_covid_related().search(i, num_results=10000, covid_related=False, view='table').results[['cord_uid', 'title', 'abstract']]
covinfpid = list(covinf.cord_uid.values)
notcovinfpid = list(notcovinf.cord_uid.values)
alldataframeco = pd.concat([covinf, alldataframeco])
alldataframenoco = pd.concat([notcovinf, alldataframenoco])
allcopid.append(covinfpid)
allnocopid.append(notcovinfpid)
alldataframeco = alldataframeco.drop_duplicates()
alldataframenoco = alldataframenoco.drop_duplicates()
return (allcopid, allnocopid, alldataframeco, alldataframenoco)
keywordlist = ['inhibitor']
_, _, allcoab, allnocoab = loopsearch(keywordlist, research_papers) | code |
2019285/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
data.plot.scatter(x=var, y='price', ylim=(0, 8000000)) | code |
2019285/cell_6 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.describe() | code |
2019285/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#scatter plot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
var2 = 'sqft_living15'
data = pd.concat([df['price'], df[var2]], axis=1)
data.plot.scatter(x=var2, y='price', ylim=(0, 8000000)) | code |
2019285/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.info() | code |
2019285/cell_8 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
f, ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax) | code |
2019285/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
import statsmodels.api as sm
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#scatter plot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
# Correlation matrix updated (X)
X = df[['bedrooms','floors','condition','grade','sqft_basement','yr_built','yr_renovated','lat','long','sqft_lot15']]
f,ax = plt.subplots(figsize=(18, 18))
sb.heatmap(X.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax)
X = df[['bedrooms', 'floors', 'condition', 'grade', 'sqft_basement', 'yr_built', 'yr_renovated', 'lat', 'long', 'sqft_lot15']]
y = df['price']
est = sm.OLS(y, X).fit()
est.summary() | code |
2019285/cell_3 | [
"image_output_1.png"
] | from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import scale
import statsmodels.api as sm
from sklearn.preprocessing import StandardScaler
scale = StandardScaler() | code |
2019285/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#scatter plot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
# Correlation matrix updated (X)
X = df[['bedrooms','floors','condition','grade','sqft_basement','yr_built','yr_renovated','lat','long','sqft_lot15']]
f,ax = plt.subplots(figsize=(18, 18))
sb.heatmap(X.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax)
X = df[['bedrooms', 'floors', 'condition', 'grade', 'sqft_basement', 'yr_built', 'yr_renovated', 'lat', 'long', 'sqft_lot15']]
y = df['price']
LinReg = LinearRegression(normalize=True)
LinReg.fit(X, y)
print(LinReg.score(X, y)) | code |
2019285/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y='price', data=data)
fig.axis(ymin=0, ymax=8000000) | code |
2019285/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sb
df = pd.read_csv('../input/kc_house_data.csv')
#df correlation matrix
f,ax = plt.subplots(figsize=(12, 9))
sb.heatmap(df.corr(), annot=True, linewidths=.5, fmt='.1f', ax=ax)
var = 'sqft_living'
data = pd.concat([df['price'], df[var]], axis=1)
#scatter plot 'grade'/'price'
var1 = 'grade'
data = pd.concat([df['price'], df[var1]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sb.boxplot(x=var1, y="price", data=data)
fig.axis(ymin=0, ymax=8000000);
X = df[['bedrooms', 'floors', 'condition', 'grade', 'sqft_basement', 'yr_built', 'yr_renovated', 'lat', 'long', 'sqft_lot15']]
f, ax = plt.subplots(figsize=(18, 18))
sb.heatmap(X.corr(), annot=True, linewidths=0.5, fmt='.1f', ax=ax) | code |
2019285/cell_5 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/kc_house_data.csv')
df.head() | code |
2005289/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
anime_df_nnull.type.unique()
type_count_series = anime_df_nnull['type'].value_counts()
type_df = type_count_series.to_frame()
type_df
type_df = type_df.reset_index()
type_df.columns = ['type', 'counts']
type_df
type_members_series = anime_df_nnull.groupby('type')['members'].agg('sum').reset_index()
type_members_df = pd.DataFrame(data=type_members_series)
type_members_df = type_members_df.sort_values('members')
anime_df_nnull['episodes'] = anime_df_nnull['episodes'].astype(int)
sns.pairplot(anime_df_nnull[['type', 'members', 'episodes', 'rating']], hue='type') | code |
2005289/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
anime_df_nnull.type.unique() | code |
2005289/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
rating_df.shape | code |
2005289/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes | code |
2005289/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
anime_df_nnull.type.unique()
type_count_series = anime_df_nnull['type'].value_counts()
type_df = type_count_series.to_frame()
type_df
type_df = type_df.reset_index()
type_df.columns = ['type', 'counts']
type_df
sns.barplot(y=type_df['counts'], x=type_df['type']) | code |
2005289/cell_7 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum() | code |
2005289/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import numpy as np
import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
anime_df_nnull.head() | code |
2005289/cell_15 | [
"text_html_output_1.png"
] | import collections
import itertools
import numpy as np
import pandas as pd
import seaborn as sns
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
anime_df_nnull.type.unique()
type_count_series = anime_df_nnull['type'].value_counts()
type_df = type_count_series.to_frame()
type_df
type_df = type_df.reset_index()
type_df.columns = ['type', 'counts']
type_df
type_members_series = anime_df_nnull.groupby('type')['members'].agg('sum').reset_index()
type_members_df = pd.DataFrame(data=type_members_series)
type_members_df = type_members_df.sort_values('members')
anime_df_nnull['episodes'] = anime_df_nnull['episodes'].astype(int)
genre_values_list = anime_df_nnull['genre'].apply(lambda x: x.split(', ')).values.tolist()
genre_value_chain = itertools.chain(*genre_values_list)
genre_counter = collections.Counter(genre_value_chain)
genre_df = pd.DataFrame.from_dict(genre_counter, orient='index').reset_index()
genre_df.columns = ['genre', 'count']
genre_df = genre_df.sort_values('count', ascending=False)
sns.barplot(x=genre_df['count'], y=genre_df['genre']) | code |
2005289/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape | code |
2005289/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
anime_df_nnull.type.unique()
type_count_series = anime_df_nnull['type'].value_counts()
type_df = type_count_series.to_frame()
type_df
type_df = type_df.reset_index()
type_df.columns = ['type', 'counts']
type_df
type_members_series = anime_df_nnull.groupby('type')['members'].agg('sum').reset_index()
type_members_df = pd.DataFrame(data=type_members_series)
type_members_df = type_members_df.sort_values('members')
anime_df_nnull['episodes'] = anime_df_nnull['episodes'].astype(int)
sns.boxplot(data=anime_df_nnull, x='type', y='rating') | code |
2005289/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
anime_df_nnull.type.unique()
type_count_series = anime_df_nnull['type'].value_counts()
type_df = type_count_series.to_frame()
type_df | code |
2005289/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import seaborn as sns
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.dtypes
anime_df.isnull().sum()
anime_df = anime_df.replace('Unknown', np.nan)
anime_df_nnull = anime_df.dropna()
anime_df_nnull.type.unique()
type_count_series = anime_df_nnull['type'].value_counts()
type_df = type_count_series.to_frame()
type_df
type_df = type_df.reset_index()
type_df.columns = ['type', 'counts']
type_df
type_members_series = anime_df_nnull.groupby('type')['members'].agg('sum').reset_index()
type_members_df = pd.DataFrame(data=type_members_series)
type_members_df = type_members_df.sort_values('members')
sns.barplot(y=type_members_df['members'], x=type_members_df['type']) | code |
2005289/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
anime_df = pd.read_csv('../input/anime.csv', header=0)
rating_df = pd.read_csv('../input/rating.csv', header=0)
anime_df.shape
anime_df.head() | code |
2036880/cell_4 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import IPython
import matplotlib #collection of functions for scientific and publication-ready visualization
import numpy as np #foundational package for scientific computing
import pandas as pd #collection of functions for data processing and analysis modeled after R dataframes with SQL like features
import scipy as sp #collection of functions for scientific computing and advance mathematics
import sklearn #collection of machine learning algorithms
import sys #access to system parameters https://docs.python.org/3/library/sys.html
import warnings
import sys
import pandas as pd
import matplotlib
import numpy as np
import scipy as sp
import IPython
from IPython import display
import sklearn
import timeit
import random
import time
import warnings
warnings.filterwarnings('ignore')
from subprocess import check_output
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
print('data_train.shape: {}'.format(data_train.shape))
print('data_test.shape: {}'.format(data_test.shape)) | code |
2036880/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import IPython
import matplotlib #collection of functions for scientific and publication-ready visualization
import numpy as np #foundational package for scientific computing
import pandas as pd #collection of functions for data processing and analysis modeled after R dataframes with SQL like features
import scipy as sp #collection of functions for scientific computing and advance mathematics
import sklearn #collection of machine learning algorithms
import sys #access to system parameters https://docs.python.org/3/library/sys.html
import warnings
import sys
print('Python version: {}'.format(sys.version))
import pandas as pd
print('pandas version: {}'.format(pd.__version__))
import matplotlib
print('matplotlib version: {}'.format(matplotlib.__version__))
import numpy as np
print('NumPy version: {}'.format(np.__version__))
import scipy as sp
print('SciPy version: {}'.format(sp.__version__))
import IPython
from IPython import display
print('IPython version: {}'.format(IPython.__version__))
import sklearn
print('scikit-learn version: {}'.format(sklearn.__version__))
import timeit
import random
import time
import warnings
warnings.filterwarnings('ignore')
print('-' * 25)
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2036880/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import Imputer
from subprocess import check_output
import IPython
import matplotlib #collection of functions for scientific and publication-ready visualization
import numpy as np #foundational package for scientific computing
import pandas as pd #collection of functions for data processing and analysis modeled after R dataframes with SQL like features
import scipy as sp #collection of functions for scientific computing and advance mathematics
import sklearn #collection of machine learning algorithms
import sys #access to system parameters https://docs.python.org/3/library/sys.html
import warnings
import sys
import pandas as pd
import matplotlib
import numpy as np
import scipy as sp
import IPython
from IPython import display
import sklearn
import timeit
import random
import time
import warnings
warnings.filterwarnings('ignore')
from subprocess import check_output
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
X = data_train.copy(deep=True)
X_test = data_test.copy(deep=True)
X.drop(['SalePrice'], axis=1, inplace=True)
columns_too_less = ['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscVal', 'MiscFeature']
X.drop(columns_too_less, axis=1, inplace=True)
X_test.drop(columns_too_less, axis=1, inplace=True)
columns_drop_std = X.std()[X.std() < 3].index
X.drop(columns_drop_std, axis=1, inplace=True)
X_test.drop(columns_drop_std, axis=1, inplace=True)
X = X.select_dtypes(exclude=['object'])
X_test = X_test.select_dtypes(exclude=['object'])
columns_drop_corr = X.corrwith(data_train['SalePrice'])[X.corrwith(data_train['SalePrice']) < 0.2].index
X.drop(columns_drop_corr, axis=1, inplace=True)
X_test.drop(columns_drop_corr, axis=1, inplace=True)
columns = X.columns
from sklearn.preprocessing import Imputer
imputer = Imputer()
X_numerical = imputer.fit_transform(X)
X_test_numerical = imputer.fit_transform(X_test)
X_numerical = pd.DataFrame(X_numerical, columns=columns)
X_test_numerical = pd.DataFrame(X_test_numerical, columns=columns)
print('X_numerical.shape: {}'.format(X_numerical.shape))
print('X_test_numerical.shape: {}'.format(X_test_numerical.shape)) | code |
2036880/cell_16 | [
"image_output_1.png"
] | from subprocess import check_output
import IPython
import matplotlib #collection of functions for scientific and publication-ready visualization
import numpy as np #foundational package for scientific computing
import pandas as pd #collection of functions for data processing and analysis modeled after R dataframes with SQL like features
import scipy as sp #collection of functions for scientific computing and advance mathematics
import sklearn #collection of machine learning algorithms
import sys #access to system parameters https://docs.python.org/3/library/sys.html
import warnings
import sys
import pandas as pd
import matplotlib
import numpy as np
import scipy as sp
import IPython
from IPython import display
import sklearn
import timeit
import random
import time
import warnings
warnings.filterwarnings('ignore')
from subprocess import check_output
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
X = data_train.copy(deep=True)
X_test = data_test.copy(deep=True)
X.drop(['SalePrice'], axis=1, inplace=True)
columns_too_less = ['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscVal', 'MiscFeature']
X.drop(columns_too_less, axis=1, inplace=True)
X_test.drop(columns_too_less, axis=1, inplace=True)
columns_drop_std = X.std()[X.std() < 3].index
X.drop(columns_drop_std, axis=1, inplace=True)
X_test.drop(columns_drop_std, axis=1, inplace=True)
X = X.select_dtypes(exclude=['object'])
X_test = X_test.select_dtypes(exclude=['object'])
columns_drop_corr = X.corrwith(data_train['SalePrice'])[X.corrwith(data_train['SalePrice']) < 0.2].index
X.drop(columns_drop_corr, axis=1, inplace=True)
X_test.drop(columns_drop_corr, axis=1, inplace=True)
columns = X.columns
columns | code |
2036880/cell_10 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import IPython
import matplotlib #collection of functions for scientific and publication-ready visualization
import matplotlib.pyplot as plt
import numpy as np #foundational package for scientific computing
import pandas as pd #collection of functions for data processing and analysis modeled after R dataframes with SQL like features
import scipy as sp #collection of functions for scientific computing and advance mathematics
import seaborn as sns
import sklearn #collection of machine learning algorithms
import sys #access to system parameters https://docs.python.org/3/library/sys.html
import warnings
import sys
import pandas as pd
import matplotlib
import numpy as np
import scipy as sp
import IPython
from IPython import display
import sklearn
import timeit
import random
import time
import warnings
warnings.filterwarnings('ignore')
from subprocess import check_output
data_train = pd.read_csv('../input/train.csv')
data_test = pd.read_csv('../input/test.csv')
X = data_train.copy(deep=True)
X_test = data_test.copy(deep=True)
X.drop(['SalePrice'], axis=1, inplace=True)
columns_too_less = ['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscVal', 'MiscFeature']
X.drop(columns_too_less, axis=1, inplace=True)
X_test.drop(columns_too_less, axis=1, inplace=True)
columns_drop_std = X.std()[X.std() < 3].index
X.drop(columns_drop_std, axis=1, inplace=True)
X_test.drop(columns_drop_std, axis=1, inplace=True)
def correlation_heatmap(df):
_, ax = plt.subplots(figsize=(24, 20))
colormap = sns.diverging_palette(220, 10, as_cmap=True)
_ = sns.heatmap(df.corr(), cmap=colormap, square=True, cbar_kws={'shrink': 0.9}, ax=ax, annot=True, linewidths=0.1, vmax=1.0, vmin=-1.0, linecolor='white', annot_kws={'fontsize': 12})
plt.title('Pearson Correlation of Features', y=1.05, size=15)
correlation_heatmap(X) | code |
74042371/cell_13 | [
"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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features)) | code |
74042371/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
train.isnull().sum()
train.isnull().sum().plot(kind='bar', figsize=(25, 15)) | code |
74042371/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
print('missing values count in train: ', train.isnull().sum().sum())
print('missing values count in test: ', test.isnull().sum().sum()) | code |
74042371/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
plt.figure(figsize=(20, 150))
for i in enumerate(features):
plt.subplot(20, 6, i[0] + 1)
plt.hist(i[1], bins=50) | code |
74042371/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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
display(train.head())
display(test.head())
display(sub.head()) | code |
74042371/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
train.isnull().sum()
plt.figure(figsize=(25, 15))
sns.boxplot(data=train) | code |
74042371/cell_26 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
test.isnull().sum() | code |
74042371/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
plt.figure(figsize=(25, 20))
sns.heatmap(train.corr(), annot=True, fmt='.1f', linewidth=1) | code |
74042371/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 |
74042371/cell_7 | [
"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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
print('size of train: ', train.shape)
print('size of test: ', test.shape)
print('size of submission: ', sub.shape) | code |
74042371/cell_18 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
plt.figure(figsize=(7, 6))
sns.distplot(train['claim']) | code |
74042371/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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
print(train.info())
print(test.info()) | code |
74042371/cell_16 | [
"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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
train['claim'].value_counts() | code |
74042371/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
train['claim'].value_counts().plot(kind='bar') | code |
74042371/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
train.isnull().sum() | code |
74042371/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
train.describe() | code |
74042371/cell_27 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns
features = list(train.columns)
list(enumerate(features))
test.isnull().sum()
test.isnull().sum().plot(kind='bar', figsize=(25, 15)) | code |
74042371/cell_12 | [
"text_html_output_2.png",
"text_html_output_1.png",
"text_html_output_3.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/tabular-playground-series-sep-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/test.csv')
sub = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2021/sample_solution.csv')
train_original = train.copy()
test_original = test.copy()
train.drop('id', axis=1, inplace=True)
test.drop('id', axis=1, inplace=True)
train.columns | code |
1009060/cell_4 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
from subprocess import check_output
directory = '../input/'
train = pd.read_csv(directory + 'train.csv')
test = pd.read_csv(directory + 'test.csv')
numeric_feats = [x for x in train.columns[1:-1] if 'cont' in x]
categorical_feats = [x for x in train.columns[1:-1] if 'cat' in x]
catwithdummies = pd.get_dummies(train)
catwithdummies = pd.get_dummies(categorical_feats)
index = list(train.index)
print(index[0:10])
np.random.shuffle(index)
print(index[0:10])
train = train.iloc[index]
'train = train.iloc[np.random.permutation(len(train))]'
test['loss'] = np.nan
y = np.log(train['loss'].values + 200)
id_train = train['id'].values
id_test = test['id'].values
ntrain = train.shape[0]
tr_te = pd.concat((train, test), axis=0)
sparse_data = []
f_cat = [f for f in tr_te.columns if 'cat' in f]
for f in f_cat:
dummy = pd.get_dummies(tr_te[f].astype('category'))
tmp = csr_matrix(dummy)
sparse_data.append(tmp)
f_num = [f for f in tr_te.columns if 'cont' in f]
scaler = StandardScaler()
tmp = csr_matrix(scaler.fit_transform(tr_te[f_num]))
sparse_data.append(tmp)
del (tr_te, train, test)
xtr_te = hstack(sparse_data, format='csr')
xtrain = xtr_te[:ntrain, :]
xtest = xtr_te[ntrain:, :]
print('Dim train', xtrain.shape)
print('Dim test', xtest.shape) | code |
1009060/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
from subprocess import check_output
directory = '../input/'
train = pd.read_csv(directory + 'train.csv')
test = pd.read_csv(directory + 'test.csv')
numeric_feats = [x for x in train.columns[1:-1] if 'cont' in x]
categorical_feats = [x for x in train.columns[1:-1] if 'cat' in x]
catwithdummies = pd.get_dummies(train)
catwithdummies = pd.get_dummies(categorical_feats)
print(categorical_feats) | code |
1009060/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
directory = '../input/'
train = pd.read_csv(directory + 'train.csv')
test = pd.read_csv(directory + 'test.csv')
numeric_feats = [x for x in train.columns[1:-1] if 'cont' in x]
categorical_feats = [x for x in train.columns[1:-1] if 'cat' in x]
catwithdummies = pd.get_dummies(train)
print.head(catwithdummies) | code |
1009060/cell_3 | [
"text_plain_output_1.png"
] | from subprocess import check_output
from subprocess import check_output
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import pandas as pd
from subprocess import check_output
from subprocess import check_output
directory = '../input/'
train = pd.read_csv(directory + 'train.csv')
test = pd.read_csv(directory + 'test.csv')
numeric_feats = [x for x in train.columns[1:-1] if 'cont' in x]
categorical_feats = [x for x in train.columns[1:-1] if 'cat' in x]
catwithdummies = pd.get_dummies(train)
catwithdummies = pd.get_dummies(categorical_feats)
print(catwithdummies.shape) | code |
130021146/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import os
import splitfolders
os.makedirs('output')
os.makedirs('output/train')
os.makedirs('output/val')
os.makedirs('output/test')
loc = '/kaggle/input/skin-diseases-image-dataset/IMG_CLASSES'
splitfolders.ratio(loc, output='output', ratio=(0.8, 0.1, 0.1)) | code |
130021146/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.image as mping
import matplotlib.pyplot as plt
import os
import random
import splitfolders
os.makedirs('output')
os.makedirs('output/train')
os.makedirs('output/val')
os.makedirs('output/test')
loc = '/kaggle/input/skin-diseases-image-dataset/IMG_CLASSES'
splitfolders.ratio(loc, output='output', ratio=(0.8, 0.1, 0.1))
def random_image(val_dir, val_class):
folder = val_dir + val_class
random_image = random.sample(os.listdir(folder), 1)
img = mping.imread(folder + '/' + random_image[0])
return img
fig = plt.figure(figsize=(10, 7))
fig.add_subplot(2, 2, 1)
img_1 = random_image(val_dir='./output/val/', val_class='2. Melanoma 15.75k')
fig.add_subplot(2, 2, 2)
img_2 = random_image(val_dir='./output/val/', val_class='4. Basal Cell Carcinoma (BCC) 3323')
fig.add_subplot(2, 2, 3)
img_3 = random_image(val_dir='./output/val/', val_class='5. Melanocytic Nevi (NV) - 7970')
fig.add_subplot(2, 2, 4)
img4 = random_image(val_dir='./output/val/', val_class='1. Eczema 1677') | code |
130021146/cell_19 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Input , Dense , Flatten , GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing import image_dataset_from_directory
import matplotlib.image as mping
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import os
import random
import random
import splitfolders
import tensorflow as tf
os.makedirs('output')
os.makedirs('output/train')
os.makedirs('output/val')
os.makedirs('output/test')
loc = '/kaggle/input/skin-diseases-image-dataset/IMG_CLASSES'
splitfolders.ratio(loc, output='output', ratio=(0.8, 0.1, 0.1))
def random_image(val_dir, val_class):
folder = val_dir + val_class
random_image = random.sample(os.listdir(folder), 1)
img = mping.imread(folder + '/' + random_image[0])
return img
fig = plt.figure(figsize=(10, 7))
#Add an Axes to the figure as part of a subplot arrangement(Three integers (nrows, ncols, index).)
fig.add_subplot(2,2,1)
img_1 = random_image(val_dir = "./output/val/",val_class = "2. Melanoma 15.75k")
fig.add_subplot(2,2,2)
img_2 = random_image(val_dir = "./output/val/",val_class = "4. Basal Cell Carcinoma (BCC) 3323")
fig.add_subplot(2,2,3)
img_3 = random_image(val_dir = "./output/val/",val_class = "5. Melanocytic Nevi (NV) - 7970")
fig.add_subplot(2,2,4)
img4 = random_image(val_dir = "./output/val/",val_class = "1. Eczema 1677")
fig = plt.figure(figsize=(10, 7))
#Add an Axes to the figure as part of a subplot arrangement(Three integers (nrows, ncols, index).)
fig.add_subplot(2,2,1)
img_1 = random_image(val_dir = "./output/val/",val_class = "3. Atopic Dermatitis - 1.25k")
fig.add_subplot(2,2,2)
img_2 = random_image(val_dir = "./output/val/",val_class = "6. Benign Keratosis-like Lesions (BKL) 2624")
fig.add_subplot(2,2,3)
img_3 = random_image(val_dir = "./output/val/",val_class = "7. Psoriasis pictures Lichen Planus and related diseases - 2k")
fig.add_subplot(2,2,4)
img4 = random_image(val_dir = "./output/val/",val_class = "10. Warts Molluscum and other Viral Infections - 2103")
from tensorflow.keras.preprocessing import image_dataset_from_directory
train_dir = './output/train'
test_dir = './output/test'
val_dir = './output/val'
train_data = image_dataset_from_directory(train_dir, label_mode='categorical', image_size=(299, 299), batch_size=32, shuffle=True, seed=42)
test_data = image_dataset_from_directory(test_dir, label_mode='categorical', image_size=(299, 299), batch_size=32, shuffle=False, seed=42)
val_data = image_dataset_from_directory(val_dir, label_mode='categorical', image_size=(299, 299), batch_size=32, shuffle=False, seed=42)
addModel = tf.keras.applications.xception.Xception(input_shape=(299, 299, 3), include_top=False, weights='imagenet')
model = Sequential()
model.add(addModel)
model.add(GlobalAveragePooling2D())
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax', name='classification'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005), loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
history_1 = model.fit(train_data, validation_data=val_data, epochs=10)
model.evaluate(val_data)
plt.figure()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.legend(['loss', 'val_loss'], loc='upper right')
plt.show()
plt.figure()
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.legend(['accuracy', 'val_accuracy'], loc='upper right')
plt.show() | code |
130021146/cell_7 | [
"text_plain_output_1.png"
] | !pip install split_folders | code |
130021146/cell_18 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Input , Dense , Flatten , GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
from tensorflow.keras.preprocessing import image_dataset_from_directory
import tensorflow as tf
from tensorflow.keras.preprocessing import image_dataset_from_directory
train_dir = './output/train'
test_dir = './output/test'
val_dir = './output/val'
train_data = image_dataset_from_directory(train_dir, label_mode='categorical', image_size=(299, 299), batch_size=32, shuffle=True, seed=42)
test_data = image_dataset_from_directory(test_dir, label_mode='categorical', image_size=(299, 299), batch_size=32, shuffle=False, seed=42)
val_data = image_dataset_from_directory(val_dir, label_mode='categorical', image_size=(299, 299), batch_size=32, shuffle=False, seed=42)
addModel = tf.keras.applications.xception.Xception(input_shape=(299, 299, 3), include_top=False, weights='imagenet')
model = Sequential()
model.add(addModel)
model.add(GlobalAveragePooling2D())
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax', name='classification'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005), loss='categorical_crossentropy', metrics=['accuracy'])
model.summary()
history_1 = model.fit(train_data, validation_data=val_data, epochs=10) | code |
130021146/cell_15 | [
"image_output_1.png"
] | from tensorflow.keras.preprocessing import image_dataset_from_directory
from tensorflow.keras.preprocessing import image_dataset_from_directory
train_dir = './output/train'
test_dir = './output/test'
val_dir = './output/val'
train_data = image_dataset_from_directory(train_dir, label_mode='categorical', image_size=(299, 299), batch_size=32, shuffle=True, seed=42)
test_data = image_dataset_from_directory(test_dir, label_mode='categorical', image_size=(299, 299), batch_size=32, shuffle=False, seed=42)
val_data = image_dataset_from_directory(val_dir, label_mode='categorical', image_size=(299, 299), batch_size=32, shuffle=False, seed=42) | code |
130021146/cell_16 | [
"image_output_1.png"
] | from tensorflow.keras.layers import Input , Dense , Flatten , GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
import tensorflow as tf
addModel = tf.keras.applications.xception.Xception(input_shape=(299, 299, 3), include_top=False, weights='imagenet')
model = Sequential()
model.add(addModel)
model.add(GlobalAveragePooling2D())
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax', name='classification')) | code |
130021146/cell_17 | [
"text_plain_output_1.png"
] | from tensorflow.keras.layers import Input , Dense , Flatten , GlobalAveragePooling2D
from tensorflow.keras.models import Sequential
import tensorflow as tf
addModel = tf.keras.applications.xception.Xception(input_shape=(299, 299, 3), include_top=False, weights='imagenet')
model = Sequential()
model.add(addModel)
model.add(GlobalAveragePooling2D())
model.add(Flatten())
model.add(Dense(1024, activation='relu'))
model.add(Dense(512, activation='relu'))
model.add(Dense(10, activation='softmax', name='classification'))
model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.0005), loss='categorical_crossentropy', metrics=['accuracy'])
model.summary() | code |
130021146/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.image as mping
import matplotlib.pyplot as plt
import os
import random
import splitfolders
os.makedirs('output')
os.makedirs('output/train')
os.makedirs('output/val')
os.makedirs('output/test')
loc = '/kaggle/input/skin-diseases-image-dataset/IMG_CLASSES'
splitfolders.ratio(loc, output='output', ratio=(0.8, 0.1, 0.1))
def random_image(val_dir, val_class):
folder = val_dir + val_class
random_image = random.sample(os.listdir(folder), 1)
img = mping.imread(folder + '/' + random_image[0])
return img
fig = plt.figure(figsize=(10, 7))
#Add an Axes to the figure as part of a subplot arrangement(Three integers (nrows, ncols, index).)
fig.add_subplot(2,2,1)
img_1 = random_image(val_dir = "./output/val/",val_class = "2. Melanoma 15.75k")
fig.add_subplot(2,2,2)
img_2 = random_image(val_dir = "./output/val/",val_class = "4. Basal Cell Carcinoma (BCC) 3323")
fig.add_subplot(2,2,3)
img_3 = random_image(val_dir = "./output/val/",val_class = "5. Melanocytic Nevi (NV) - 7970")
fig.add_subplot(2,2,4)
img4 = random_image(val_dir = "./output/val/",val_class = "1. Eczema 1677")
fig = plt.figure(figsize=(10, 7))
fig.add_subplot(2, 2, 1)
img_1 = random_image(val_dir='./output/val/', val_class='3. Atopic Dermatitis - 1.25k')
fig.add_subplot(2, 2, 2)
img_2 = random_image(val_dir='./output/val/', val_class='6. Benign Keratosis-like Lesions (BKL) 2624')
fig.add_subplot(2, 2, 3)
img_3 = random_image(val_dir='./output/val/', val_class='7. Psoriasis pictures Lichen Planus and related diseases - 2k')
fig.add_subplot(2, 2, 4)
img4 = random_image(val_dir='./output/val/', val_class='10. Warts Molluscum and other Viral Infections - 2103') | code |
1003458/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_data = pd.read_csv('../input/train.csv')
test_data = pd.read_csv('../input/test.csv')
gender_submission = pd.read_csv('../input/gender_submission.csv') | code |
106195418/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import seaborn as sns
import tensorboard as tb
import tensorflow as tf
import torch
import copy
from pathlib import Path
import warnings
import holidays
import seaborn as sns
import matplotlib
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import numpy as np
import pandas as pd
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
import torch
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
from pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet
from pytorch_forecasting.data import GroupNormalizer, NaNLabelEncoder
from pytorch_forecasting.metrics import SMAPE, PoissonLoss, QuantileLoss
from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
import random
import tensorflow as tf
import tensorboard as tb
tf.io.gfile = tb.compat.tensorflow_stub.io.gfile
random.seed(30)
np.random.seed(30)
tf.random.set_seed(30)
torch.manual_seed(30)
torch.cuda.manual_seed(30)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['date'] = pd.to_datetime(train['date'])
test['date'] = pd.to_datetime(test['date'])
data = pd.concat([train, test], axis=0, ignore_index=True)
assert len(data.drop_duplicates(['country', 'store', 'product', 'date'])) == len(data)
assert len(data.drop_duplicates(['country', 'store', 'product'])) == len(data) // data['date'].nunique()
train.isna().sum(axis=0).rename('nans_per_column_train').rename_axis('column').reset_index().set_index('column').join(test.isna().sum(axis=0).rename('nans_per_column_test').rename_axis('column').reset_index().set_index('column'))
train.nunique(axis=0).rename('n_unique_per_column_train').rename_axis('column').reset_index().set_index('column').join(test.nunique(axis=0).rename('n_unique_per_column_test').rename_axis('column').reset_index().set_index('column'))
fig, ax = plt.subplots(1,1, figsize=(20, 6))
sns.kdeplot(data=train, x = 'num_sold', hue = 'country', fill=True, alpha = 0.15, ax = ax, linewidth=3, palette='pastel')
ax.set_xlabel('num_sold', color='black', fontweight='bold', fontsize=13)
ax.set_ylabel('density', color='black', fontweight='bold', fontsize=13)
ax.set_xlim(0, 700)
ax.xaxis.set_tick_params(labelsize=15)
ax.yaxis.set_ticklabels([])
ax.set_title('Density plot for num_sold per country (clipped at 700)', fontweight = 'bold', fontsize = 20);
fig, ax = plt.subplots(1,1, figsize=(20, 6))
sns.kdeplot(data=train, x = 'num_sold', hue = 'store', fill=True, alpha = 0.15, ax = ax, linewidth=2.5)
ax.set_xlabel('num_sold', color='black', fontweight='bold', fontsize=13)
ax.set_ylabel('density', color='black', fontweight='bold', fontsize=13)
ax.set_xlim(0, 700)
ax.set_title('Density plot for num_sold per store (clipped at 700)', fontweight = 'bold', fontsize = 20)
ax.xaxis.set_tick_params(labelsize=15)
ax.yaxis.set_ticklabels([]);
fig, ax = plt.subplots(1, 1, figsize=(20, 6))
sns.kdeplot(data=train, x='num_sold', hue='product', fill=True, alpha=0.05, ax=ax, linewidth=2.5)
ax.set_xlabel('num_sold', color='black', fontweight='bold', fontsize=13)
ax.set_ylabel('density', color='black', fontweight='bold', fontsize=13)
ax.set_xlim(0, 700)
ax.set_title('Density plot for num_sold per product (clipped at 700)', fontweight='bold', fontsize=20)
ax.xaxis.set_tick_params(labelsize=15)
ax.yaxis.set_ticklabels([]) | code |
106195418/cell_9 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['date'] = pd.to_datetime(train['date'])
test['date'] = pd.to_datetime(test['date'])
data = pd.concat([train, test], axis=0, ignore_index=True)
assert len(data.drop_duplicates(['country', 'store', 'product', 'date'])) == len(data)
assert len(data.drop_duplicates(['country', 'store', 'product'])) == len(data) // data['date'].nunique()
train.isna().sum(axis=0).rename('nans_per_column_train').rename_axis('column').reset_index().set_index('column').join(test.isna().sum(axis=0).rename('nans_per_column_test').rename_axis('column').reset_index().set_index('column')) | code |
106195418/cell_4 | [
"image_output_1.png"
] | !pip install pytorch_forecasting | code |
106195418/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import random
import tensorboard as tb
import tensorflow as tf
import torch
import copy
from pathlib import Path
import warnings
import holidays
import seaborn as sns
import matplotlib
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import numpy as np
import pandas as pd
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
import torch
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
from pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet
from pytorch_forecasting.data import GroupNormalizer, NaNLabelEncoder
from pytorch_forecasting.metrics import SMAPE, PoissonLoss, QuantileLoss
from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
import random
import tensorflow as tf
import tensorboard as tb
tf.io.gfile = tb.compat.tensorflow_stub.io.gfile
random.seed(30)
np.random.seed(30)
tf.random.set_seed(30)
torch.manual_seed(30)
torch.cuda.manual_seed(30) | code |
106195418/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import seaborn as sns
import tensorboard as tb
import tensorflow as tf
import torch
import copy
from pathlib import Path
import warnings
import holidays
import seaborn as sns
import matplotlib
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import numpy as np
import pandas as pd
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
import torch
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
from pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet
from pytorch_forecasting.data import GroupNormalizer, NaNLabelEncoder
from pytorch_forecasting.metrics import SMAPE, PoissonLoss, QuantileLoss
from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
import random
import tensorflow as tf
import tensorboard as tb
tf.io.gfile = tb.compat.tensorflow_stub.io.gfile
random.seed(30)
np.random.seed(30)
tf.random.set_seed(30)
torch.manual_seed(30)
torch.cuda.manual_seed(30)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['date'] = pd.to_datetime(train['date'])
test['date'] = pd.to_datetime(test['date'])
data = pd.concat([train, test], axis=0, ignore_index=True)
assert len(data.drop_duplicates(['country', 'store', 'product', 'date'])) == len(data)
assert len(data.drop_duplicates(['country', 'store', 'product'])) == len(data) // data['date'].nunique()
train.isna().sum(axis=0).rename('nans_per_column_train').rename_axis('column').reset_index().set_index('column').join(test.isna().sum(axis=0).rename('nans_per_column_test').rename_axis('column').reset_index().set_index('column'))
train.nunique(axis=0).rename('n_unique_per_column_train').rename_axis('column').reset_index().set_index('column').join(test.nunique(axis=0).rename('n_unique_per_column_test').rename_axis('column').reset_index().set_index('column'))
fig, ax = plt.subplots(1, 1, figsize=(20, 6))
sns.kdeplot(data=train, x='num_sold', hue='country', fill=True, alpha=0.15, ax=ax, linewidth=3, palette='pastel')
ax.set_xlabel('num_sold', color='black', fontweight='bold', fontsize=13)
ax.set_ylabel('density', color='black', fontweight='bold', fontsize=13)
ax.set_xlim(0, 700)
ax.xaxis.set_tick_params(labelsize=15)
ax.yaxis.set_ticklabels([])
ax.set_title('Density plot for num_sold per country (clipped at 700)', fontweight='bold', fontsize=20) | code |
106195418/cell_7 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['date'] = pd.to_datetime(train['date'])
test['date'] = pd.to_datetime(test['date'])
data = pd.concat([train, test], axis=0, ignore_index=True)
assert len(data.drop_duplicates(['country', 'store', 'product', 'date'])) == len(data)
assert len(data.drop_duplicates(['country', 'store', 'product'])) == len(data) // data['date'].nunique()
display(train.sample(4)) | code |
106195418/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.dates as mdates
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import seaborn as sns
import tensorboard as tb
import tensorflow as tf
import torch
import copy
from pathlib import Path
import warnings
import holidays
import seaborn as sns
import matplotlib
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import numpy as np
import pandas as pd
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
import torch
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
from pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet
from pytorch_forecasting.data import GroupNormalizer, NaNLabelEncoder
from pytorch_forecasting.metrics import SMAPE, PoissonLoss, QuantileLoss
from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
import random
import tensorflow as tf
import tensorboard as tb
tf.io.gfile = tb.compat.tensorflow_stub.io.gfile
random.seed(30)
np.random.seed(30)
tf.random.set_seed(30)
torch.manual_seed(30)
torch.cuda.manual_seed(30)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['date'] = pd.to_datetime(train['date'])
test['date'] = pd.to_datetime(test['date'])
data = pd.concat([train, test], axis=0, ignore_index=True)
assert len(data.drop_duplicates(['country', 'store', 'product', 'date'])) == len(data)
assert len(data.drop_duplicates(['country', 'store', 'product'])) == len(data) // data['date'].nunique()
train.isna().sum(axis=0).rename('nans_per_column_train').rename_axis('column').reset_index().set_index('column').join(test.isna().sum(axis=0).rename('nans_per_column_test').rename_axis('column').reset_index().set_index('column'))
train.nunique(axis=0).rename('n_unique_per_column_train').rename_axis('column').reset_index().set_index('column').join(test.nunique(axis=0).rename('n_unique_per_column_test').rename_axis('column').reset_index().set_index('column'))
fig, ax = plt.subplots(1,1, figsize=(20, 6))
sns.kdeplot(data=train, x = 'num_sold', hue = 'country', fill=True, alpha = 0.15, ax = ax, linewidth=3, palette='pastel')
ax.set_xlabel('num_sold', color='black', fontweight='bold', fontsize=13)
ax.set_ylabel('density', color='black', fontweight='bold', fontsize=13)
ax.set_xlim(0, 700)
ax.xaxis.set_tick_params(labelsize=15)
ax.yaxis.set_ticklabels([])
ax.set_title('Density plot for num_sold per country (clipped at 700)', fontweight = 'bold', fontsize = 20);
fig, ax = plt.subplots(1,1, figsize=(20, 6))
sns.kdeplot(data=train, x = 'num_sold', hue = 'store', fill=True, alpha = 0.15, ax = ax, linewidth=2.5)
ax.set_xlabel('num_sold', color='black', fontweight='bold', fontsize=13)
ax.set_ylabel('density', color='black', fontweight='bold', fontsize=13)
ax.set_xlim(0, 700)
ax.set_title('Density plot for num_sold per store (clipped at 700)', fontweight = 'bold', fontsize = 20)
ax.xaxis.set_tick_params(labelsize=15)
ax.yaxis.set_ticklabels([]);
fig, ax = plt.subplots(1,1, figsize=(20, 6))
sns.kdeplot(data=train, x = 'num_sold', hue = 'product', fill=True, alpha = 0.05, ax = ax, linewidth=2.5)
ax.set_xlabel('num_sold', color='black', fontweight='bold', fontsize=13)
ax.set_ylabel('density', color='black', fontweight='bold', fontsize=13)
ax.set_xlim(0, 700)
ax.set_title('Density plot for num_sold per product (clipped at 700)', fontweight = 'bold', fontsize = 20)
ax.xaxis.set_tick_params(labelsize=15)
ax.yaxis.set_ticklabels([]);
fig, ax = plt.subplots(1, 1, figsize=(20, 8))
sns.lineplot(x='date', y='num_sold', hue='country', data=train.groupby(['date', 'country']).num_sold.sum().rename('num_sold').reset_index().sort_values('date', ascending=True, ignore_index=True), linewidth=2, alpha=0.7)
plt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%Y-%m'))
plt.gca().xaxis.set_major_locator(mdates.DayLocator(interval=120))
ax.set_xlabel('date', color='black', fontweight='bold', fontsize=13)
ax.set_ylabel('num_sold', color='black', fontweight='bold', fontsize=13)
ax.legend(fontsize=20, loc='upper left')
ax.set_title('num_sold per Country and Date', fontweight='bold', fontsize=20) | code |
106195418/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['date'] = pd.to_datetime(train['date'])
test['date'] = pd.to_datetime(test['date'])
data = pd.concat([train, test], axis=0, ignore_index=True)
assert len(data.drop_duplicates(['country', 'store', 'product', 'date'])) == len(data)
assert len(data.drop_duplicates(['country', 'store', 'product'])) == len(data) // data['date'].nunique()
train.isna().sum(axis=0).rename('nans_per_column_train').rename_axis('column').reset_index().set_index('column').join(test.isna().sum(axis=0).rename('nans_per_column_test').rename_axis('column').reset_index().set_index('column'))
train.nunique(axis=0).rename('n_unique_per_column_train').rename_axis('column').reset_index().set_index('column').join(test.nunique(axis=0).rename('n_unique_per_column_test').rename_axis('column').reset_index().set_index('column')) | code |
106195418/cell_12 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import random
import seaborn as sns
import tensorboard as tb
import tensorflow as tf
import torch
import copy
from pathlib import Path
import warnings
import holidays
import seaborn as sns
import matplotlib
import matplotlib.dates as mdates
import matplotlib.pyplot as plt
plt.style.use('fivethirtyeight')
import numpy as np
import pandas as pd
import pytorch_lightning as pl
from pytorch_lightning.callbacks import EarlyStopping, LearningRateMonitor
from pytorch_lightning.loggers import TensorBoardLogger
import torch
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
from pytorch_forecasting import Baseline, TemporalFusionTransformer, TimeSeriesDataSet
from pytorch_forecasting.data import GroupNormalizer, NaNLabelEncoder
from pytorch_forecasting.metrics import SMAPE, PoissonLoss, QuantileLoss
from pytorch_forecasting.models.temporal_fusion_transformer.tuning import optimize_hyperparameters
import random
import tensorflow as tf
import tensorboard as tb
tf.io.gfile = tb.compat.tensorflow_stub.io.gfile
random.seed(30)
np.random.seed(30)
tf.random.set_seed(30)
torch.manual_seed(30)
torch.cuda.manual_seed(30)
train = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-sep-2022/test.csv')
train['date'] = pd.to_datetime(train['date'])
test['date'] = pd.to_datetime(test['date'])
data = pd.concat([train, test], axis=0, ignore_index=True)
assert len(data.drop_duplicates(['country', 'store', 'product', 'date'])) == len(data)
assert len(data.drop_duplicates(['country', 'store', 'product'])) == len(data) // data['date'].nunique()
train.isna().sum(axis=0).rename('nans_per_column_train').rename_axis('column').reset_index().set_index('column').join(test.isna().sum(axis=0).rename('nans_per_column_test').rename_axis('column').reset_index().set_index('column'))
train.nunique(axis=0).rename('n_unique_per_column_train').rename_axis('column').reset_index().set_index('column').join(test.nunique(axis=0).rename('n_unique_per_column_test').rename_axis('column').reset_index().set_index('column'))
fig, ax = plt.subplots(1,1, figsize=(20, 6))
sns.kdeplot(data=train, x = 'num_sold', hue = 'country', fill=True, alpha = 0.15, ax = ax, linewidth=3, palette='pastel')
ax.set_xlabel('num_sold', color='black', fontweight='bold', fontsize=13)
ax.set_ylabel('density', color='black', fontweight='bold', fontsize=13)
ax.set_xlim(0, 700)
ax.xaxis.set_tick_params(labelsize=15)
ax.yaxis.set_ticklabels([])
ax.set_title('Density plot for num_sold per country (clipped at 700)', fontweight = 'bold', fontsize = 20);
fig, ax = plt.subplots(1, 1, figsize=(20, 6))
sns.kdeplot(data=train, x='num_sold', hue='store', fill=True, alpha=0.15, ax=ax, linewidth=2.5)
ax.set_xlabel('num_sold', color='black', fontweight='bold', fontsize=13)
ax.set_ylabel('density', color='black', fontweight='bold', fontsize=13)
ax.set_xlim(0, 700)
ax.set_title('Density plot for num_sold per store (clipped at 700)', fontweight='bold', fontsize=20)
ax.xaxis.set_tick_params(labelsize=15)
ax.yaxis.set_ticklabels([]) | code |
106195418/cell_5 | [
"image_output_1.png"
] | !pip install holidays | code |
74046791/cell_2 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
data = pd.read_csv('insurance.csv')
data.head() | code |
1004487/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
score_query = '\nSELECT reviewid, score\nFROM reviews\n'
score_df = pd.read_sql_query(score_query, conn)
genre_query = '\nSELECT *\nFROM genres\n'
genre_df = pd.read_sql_query(genre_query, conn)
conn.close()
genre_df.fillna(value='Not specified', inplace=True)
grouped = genre_df.groupby('reviewid')
genre_df = grouped.aggregate(lambda x: set(x))
result = score_df.join(genre_df, on='reviewid')
assert len(score_df) == len(result)
popmean = result['score'].mean()
plt.hist(score_df['score'])
plt.show() | code |
1004487/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
score_query = '\nSELECT reviewid, score\nFROM reviews\n'
score_df = pd.read_sql_query(score_query, conn)
genre_query = '\nSELECT *\nFROM genres\n'
genre_df = pd.read_sql_query(genre_query, conn)
conn.close()
genre_df.fillna(value='Not specified', inplace=True)
grouped = genre_df.groupby('reviewid')
genre_df = grouped.aggregate(lambda x: set(x))
result = score_df.join(genre_df, on='reviewid')
assert len(score_df) == len(result)
popmean = result['score'].mean()
means_and_counts = result.groupby(result['genre'].apply(tuple))['score'].agg(['count', 'mean'])
assert means_and_counts['count'].sum() == len(result)
means_and_counts = means_and_counts.sort_values('mean', ascending=False)
means_and_counts = means_and_counts[means_and_counts['count'] > 50].reset_index()
print(means_and_counts) | code |
1004487/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import sqlite3
import matplotlib.pyplot as plt
import scipy.stats as stats
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1004487/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy.stats as stats
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
score_query = '\nSELECT reviewid, score\nFROM reviews\n'
score_df = pd.read_sql_query(score_query, conn)
genre_query = '\nSELECT *\nFROM genres\n'
genre_df = pd.read_sql_query(genre_query, conn)
conn.close()
genre_df.fillna(value='Not specified', inplace=True)
grouped = genre_df.groupby('reviewid')
genre_df = grouped.aggregate(lambda x: set(x))
result = score_df.join(genre_df, on='reviewid')
assert len(score_df) == len(result)
popmean = result['score'].mean()
means_and_counts = result.groupby(result['genre'].apply(tuple))['score'].agg(['count', 'mean'])
assert means_and_counts['count'].sum() == len(result)
means_and_counts = means_and_counts.sort_values('mean', ascending=False)
means_and_counts = means_and_counts[means_and_counts['count'] > 50].reset_index()
data = []
for index, row in means_and_counts.iterrows():
data.append(result[result['genre'].apply(tuple) == row['genre']].score.tolist())
stat, pvalue = stats.f_oneway(*data)
print('One-way ANOVA on genre values:')
print('F-stat: %f, p-value: %f' % (stat, pvalue)) | code |
1004487/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
score_query = '\nSELECT reviewid, score\nFROM reviews\n'
score_df = pd.read_sql_query(score_query, conn)
genre_query = '\nSELECT *\nFROM genres\n'
genre_df = pd.read_sql_query(genre_query, conn)
conn.close()
genre_df.fillna(value='Not specified', inplace=True)
grouped = genre_df.groupby('reviewid')
genre_df = grouped.aggregate(lambda x: set(x))
result = score_df.join(genre_df, on='reviewid')
assert len(score_df) == len(result)
popmean = result['score'].mean()
print('Mean of %d reviews: %f' % (result['reviewid'].count(), popmean))
print('Standard deviation of reviews: %f' % result['score'].std(ddof=0)) | code |
1004487/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import scipy.stats as stats
import sqlite3
conn = sqlite3.connect('../input/database.sqlite')
score_query = '\nSELECT reviewid, score\nFROM reviews\n'
score_df = pd.read_sql_query(score_query, conn)
genre_query = '\nSELECT *\nFROM genres\n'
genre_df = pd.read_sql_query(genre_query, conn)
conn.close()
genre_df.fillna(value='Not specified', inplace=True)
grouped = genre_df.groupby('reviewid')
genre_df = grouped.aggregate(lambda x: set(x))
result = score_df.join(genre_df, on='reviewid')
assert len(score_df) == len(result)
popmean = result['score'].mean()
means_and_counts = result.groupby(result['genre'].apply(tuple))['score'].agg(['count', 'mean'])
assert means_and_counts['count'].sum() == len(result)
means_and_counts = means_and_counts.sort_values('mean', ascending=False)
means_and_counts = means_and_counts[means_and_counts['count'] > 50].reset_index()
data = []
for index, row in means_and_counts.iterrows():
data.append(result[result['genre'].apply(tuple) == row['genre']].score.tolist())
stat, pvalue = stats.f_oneway(*data)
t_tests_headers = ['genre', 't', 'prob', 'Reject_Null']
t_tests = pd.DataFrame(index=range(0, len(data)), columns=t_tests_headers)
for index in range(len(data)):
gs = ', '.join(means_and_counts['genre'][index])
t_tests['genre'][index] = gs
t, prob = stats.ttest_1samp(data[index], popmean)
t_tests['t'][index] = t
t_tests['prob'][index] = prob
if prob < 0.05:
t_tests['Reject_Null'][index] = True
else:
t_tests['Reject_Null'][index] = False
print(t_tests.sort_values('t')) | code |
34123490/cell_7 | [
"text_plain_output_1.png"
] | from scipy import stats
avg_weights = [33, 34, 35, 36, 32, 28, 29, 30, 31, 37, 36, 35, 33, 34, 31, 40, 24]
stats.ttest_1samp(avg_weights, 35) | code |
34123490/cell_17 | [
"text_plain_output_1.png"
] | from scipy import stats
avg_weights = [33, 34, 35, 36, 32, 28, 29, 30, 31, 37, 36, 35, 33, 34, 31, 40, 24]
stats.ttest_1samp(avg_weights, 35)
avg_weights1 = [29, 31, 28, 33, 31, 34, 32, 20, 32, 28, 27, 26, 30, 31, 34, 30]
stats.ttest_ind(avg_weights, avg_weights1)
before_meta = [68, 45, 46, 34, 23, 67, 80, 120, 34, 54, 68]
after_meta = [28, 25, 26, 24, 13, 37, 30, 30, 54, 34, 38]
stats.ttest_rel(before_meta, after_meta) | code |
34123490/cell_12 | [
"text_plain_output_1.png"
] | from scipy import stats
avg_weights = [33, 34, 35, 36, 32, 28, 29, 30, 31, 37, 36, 35, 33, 34, 31, 40, 24]
stats.ttest_1samp(avg_weights, 35)
avg_weights1 = [29, 31, 28, 33, 31, 34, 32, 20, 32, 28, 27, 26, 30, 31, 34, 30]
stats.ttest_ind(avg_weights, avg_weights1) | code |
88086201/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
from sklearn.feature_extraction.text import TfidfVectorizer
maxx_features = 2 ** 12
vectorizer = TfidfVectorizer(max_features=maxx_features)
X = vectorizer.fit_transform(df['music_as_words'].values) | code |
88086201/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
df.info() | code |
88086201/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use('ggplot')
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
from sklearn.feature_extraction.text import TfidfVectorizer
maxx_features = 2 ** 12
vectorizer = TfidfVectorizer(max_features=maxx_features)
X = vectorizer.fit_transform(df['music_as_words'].values)
from sklearn.decomposition import PCA
pca = PCA(n_components=0.95, random_state=42)
X_reduced = pca.fit_transform(X.toarray())
X_reduced.shape
from sklearn.manifold import TSNE
tsne = TSNE(verbose=1, perplexity=50)
X_embedded = tsne.fit_transform(X.toarray())
from matplotlib import pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (15, 15)})
palette = sns.color_palette('bright', 1)
sns.scatterplot(X_embedded[:, 0], X_embedded[:, 1], palette=palette)
plt.title('t-SNE with no Labels')
plt.savefig('t-sne_chants.png')
plt.show() | code |
88086201/cell_33 | [
"text_html_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
from sklearn.feature_extraction.text import TfidfVectorizer
maxx_features = 2 ** 12
vectorizer = TfidfVectorizer(max_features=maxx_features)
X = vectorizer.fit_transform(df['music_as_words'].values)
from sklearn.decomposition import PCA
pca = PCA(n_components=0.95, random_state=42)
X_reduced = pca.fit_transform(X.toarray())
X_reduced.shape
k = 15
kmeans = KMeans(n_clusters=k, random_state=42)
y_pred = kmeans.fit_predict(X_reduced)
df['y'] = y_pred
vectorizers = []
for ii in range(0, k):
vectorizers.append(CountVectorizer(min_df=5, max_df=0.9, stop_words='english', lowercase=True, token_pattern='[a-zA-Z\\-][a-zA-Z\\-]{2,}'))
vectorized_data = []
for current_cluster, cvec in enumerate(vectorizers):
try:
vectorized_data.append(cvec.fit_transform(df.loc[df['y'] == current_cluster, 'music_as_words']))
except Exception as e:
vectorized_data.append(None)
NUM_TOPICS_PER_CLUSTER = 20
lda_models = []
for ii in range(0, k):
lda = LatentDirichletAllocation(n_components=NUM_TOPICS_PER_CLUSTER, max_iter=10, learning_method='online', verbose=False, random_state=42)
lda_models.append(lda)
lda_models[0]
clusters_lda_data = []
for current_cluster, lda in enumerate(lda_models):
print('Current Cluster: ' + str(current_cluster))
if vectorized_data[current_cluster] != None:
clusters_lda_data.append(lda.fit_transform(vectorized_data[current_cluster])) | code |
88086201/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.manifold import TSNE
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
from sklearn.feature_extraction.text import TfidfVectorizer
maxx_features = 2 ** 12
vectorizer = TfidfVectorizer(max_features=maxx_features)
X = vectorizer.fit_transform(df['music_as_words'].values)
from sklearn.decomposition import PCA
pca = PCA(n_components=0.95, random_state=42)
X_reduced = pca.fit_transform(X.toarray())
X_reduced.shape
from sklearn.manifold import TSNE
tsne = TSNE(verbose=1, perplexity=50)
X_embedded = tsne.fit_transform(X.toarray()) | code |
88086201/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
def joinSyllable(c):
out = ''
for doc in c:
out += ' '.join(doc)
return out
df['music_as_words'] = list(map(joinSyllable, df['music']))
df['music_as_words'] | code |
88086201/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
df.head() | code |
88086201/cell_32 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
from sklearn.feature_extraction.text import TfidfVectorizer
maxx_features = 2 ** 12
vectorizer = TfidfVectorizer(max_features=maxx_features)
X = vectorizer.fit_transform(df['music_as_words'].values)
from sklearn.decomposition import PCA
pca = PCA(n_components=0.95, random_state=42)
X_reduced = pca.fit_transform(X.toarray())
X_reduced.shape
k = 15
kmeans = KMeans(n_clusters=k, random_state=42)
y_pred = kmeans.fit_predict(X_reduced)
df['y'] = y_pred
NUM_TOPICS_PER_CLUSTER = 20
lda_models = []
for ii in range(0, k):
lda = LatentDirichletAllocation(n_components=NUM_TOPICS_PER_CLUSTER, max_iter=10, learning_method='online', verbose=False, random_state=42)
lda_models.append(lda)
lda_models[0] | code |
88086201/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
from sklearn.feature_extraction.text import TfidfVectorizer
maxx_features = 2 ** 12
vectorizer = TfidfVectorizer(max_features=maxx_features)
X = vectorizer.fit_transform(df['music_as_words'].values)
from sklearn.decomposition import PCA
pca = PCA(n_components=0.95, random_state=42)
X_reduced = pca.fit_transform(X.toarray())
X_reduced.shape | code |
88086201/cell_35 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
from sklearn.feature_extraction.text import TfidfVectorizer
maxx_features = 2 ** 12
vectorizer = TfidfVectorizer(max_features=maxx_features)
X = vectorizer.fit_transform(df['music_as_words'].values)
vectorizer.get_feature_names()[:10]
from sklearn.decomposition import PCA
pca = PCA(n_components=0.95, random_state=42)
X_reduced = pca.fit_transform(X.toarray())
X_reduced.shape
k = 15
kmeans = KMeans(n_clusters=k, random_state=42)
y_pred = kmeans.fit_predict(X_reduced)
df['y'] = y_pred
vectorizers = []
for ii in range(0, k):
vectorizers.append(CountVectorizer(min_df=5, max_df=0.9, stop_words='english', lowercase=True, token_pattern='[a-zA-Z\\-][a-zA-Z\\-]{2,}'))
vectorized_data = []
for current_cluster, cvec in enumerate(vectorizers):
try:
vectorized_data.append(cvec.fit_transform(df.loc[df['y'] == current_cluster, 'music_as_words']))
except Exception as e:
vectorized_data.append(None)
NUM_TOPICS_PER_CLUSTER = 20
lda_models = []
for ii in range(0, k):
lda = LatentDirichletAllocation(n_components=NUM_TOPICS_PER_CLUSTER, max_iter=10, learning_method='online', verbose=False, random_state=42)
lda_models.append(lda)
lda_models[0]
clusters_lda_data = []
for current_cluster, lda in enumerate(lda_models):
if vectorized_data[current_cluster] != None:
clusters_lda_data.append(lda.fit_transform(vectorized_data[current_cluster]))
def selected_topics(model, vectorizer, top_n=3):
current_words = []
keywords = []
for idx, topic in enumerate(model.components_):
words = [(vectorizer.get_feature_names()[i], topic[i]) for i in topic.argsort()[:-top_n - 1:-1]]
for word in words:
if word[0] not in current_words:
keywords.append(word)
current_words.append(word[0])
keywords.sort(key=lambda x: x[1])
keywords.reverse()
return_values = []
for ii in keywords:
return_values.append(ii[0])
return return_values
all_keywords = []
for current_vectorizer, lda in enumerate(lda_models):
print('Current Cluster: ' + str(current_vectorizer))
if vectorized_data[current_vectorizer] != None:
all_keywords.append(selected_topics(lda, vectorizers[current_vectorizer])) | code |
88086201/cell_24 | [
"text_plain_output_1.png"
] | from matplotlib import pyplot as plt
import seaborn as sns
sns.set(rc={'figure.figsize': (13, 9)})
palette = sns.hls_palette(k, l=0.4, s=0.9)
sns.scatterplot(X_embedded[:, 0], X_embedded[:, 1], hue=y_pred, legend='full', palette=palette)
plt.title('t-SNE with Kmeans Labels')
plt.savefig('improved_cluster_tsne.png')
plt.show() | code |
88086201/cell_14 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
from sklearn.feature_extraction.text import TfidfVectorizer
maxx_features = 2 ** 12
vectorizer = TfidfVectorizer(max_features=maxx_features)
X = vectorizer.fit_transform(df['music_as_words'].values)
vectorizer.get_feature_names()[:10] | code |
88086201/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
df[df['y'] == 1] | code |
88086201/cell_36 | [
"text_plain_output_1.png"
] | from sklearn.cluster import KMeans
from sklearn.decomposition import LatentDirichletAllocation
from sklearn.decomposition import PCA
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
root_path = '/kaggle/input/medieval-chant-corpus'
df_path = f'{root_path}/mh-corpus.json'
df = pd.read_json(df_path)
df.drop(['rawTextMusic', 'rawText'], axis=1, inplace=True)
from sklearn.feature_extraction.text import TfidfVectorizer
maxx_features = 2 ** 12
vectorizer = TfidfVectorizer(max_features=maxx_features)
X = vectorizer.fit_transform(df['music_as_words'].values)
vectorizer.get_feature_names()[:10]
from sklearn.decomposition import PCA
pca = PCA(n_components=0.95, random_state=42)
X_reduced = pca.fit_transform(X.toarray())
X_reduced.shape
k = 15
kmeans = KMeans(n_clusters=k, random_state=42)
y_pred = kmeans.fit_predict(X_reduced)
df['y'] = y_pred
vectorizers = []
for ii in range(0, k):
vectorizers.append(CountVectorizer(min_df=5, max_df=0.9, stop_words='english', lowercase=True, token_pattern='[a-zA-Z\\-][a-zA-Z\\-]{2,}'))
vectorized_data = []
for current_cluster, cvec in enumerate(vectorizers):
try:
vectorized_data.append(cvec.fit_transform(df.loc[df['y'] == current_cluster, 'music_as_words']))
except Exception as e:
vectorized_data.append(None)
NUM_TOPICS_PER_CLUSTER = 20
lda_models = []
for ii in range(0, k):
lda = LatentDirichletAllocation(n_components=NUM_TOPICS_PER_CLUSTER, max_iter=10, learning_method='online', verbose=False, random_state=42)
lda_models.append(lda)
lda_models[0]
clusters_lda_data = []
for current_cluster, lda in enumerate(lda_models):
if vectorized_data[current_cluster] != None:
clusters_lda_data.append(lda.fit_transform(vectorized_data[current_cluster]))
def selected_topics(model, vectorizer, top_n=3):
current_words = []
keywords = []
for idx, topic in enumerate(model.components_):
words = [(vectorizer.get_feature_names()[i], topic[i]) for i in topic.argsort()[:-top_n - 1:-1]]
for word in words:
if word[0] not in current_words:
keywords.append(word)
current_words.append(word[0])
keywords.sort(key=lambda x: x[1])
keywords.reverse()
return_values = []
for ii in keywords:
return_values.append(ii[0])
return return_values
all_keywords = []
for current_vectorizer, lda in enumerate(lda_models):
if vectorized_data[current_vectorizer] != None:
all_keywords.append(selected_topics(lda, vectorizers[current_vectorizer]))
all_keywords[0][:10] | code |
90124932/cell_9 | [
"text_plain_output_1.png"
] | from keras.models import Sequential,Model,load_model,Input
from keras_preprocessing.image import ImageDataGenerator
import math
import os
import os
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
import tensorflow_addons as tfa
import pandas as pd
import os
from glob import glob
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
from keras.models import Model
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
from glob import glob
import numpy as np
from keras import regularizers
from keras.models import Sequential, Model, load_model, Input
from keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPool2D, GlobalAveragePooling2D
from keras_preprocessing.image import ImageDataGenerator
import keras.layers as Layers
from keras.callbacks import EarlyStopping, ModelCheckpoint
import keras.optimizers as Optimizer
from keras import applications
from tensorflow import keras
import math
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import cv2
import tensorflow_addons as tfa
train_img_csv = '../input/mura-dataset/MURA-v1.1/train_image_paths.csv'
train_images_paths = pd.read_csv(os.path.join(train_img_csv), dtype=str, header=None)
train_images_paths.columns = ['image_path']
train_images_paths['label'] = train_images_paths['image_path'].map(lambda x: '1' if 'positive' in x else '0')
train_images_paths['category'] = train_images_paths['image_path'].apply(lambda x: x.split('/')[2])
valid_img_csv = '../input/testdata/abdekho_valid.csv'
valid_images_paths = pd.read_csv(os.path.join(valid_img_csv), dtype=str, header=None)
valid_images_paths.columns = ['image_path']
valid_images_paths['label'] = valid_images_paths['image_path'].map(lambda x: '1' if 'positive' in x else '0')
valid_images_paths['category'] = valid_images_paths['image_path'].apply(lambda x: x.split('/')[2])
train_images_paths_XR_ELBOW = train_images_paths[train_images_paths['category'] == 'XR_ELBOW']
valid_images_paths_XR_ELBOW = valid_images_paths[valid_images_paths['category'] == 'XR_ELBOW']
train_images_paths_XR_FINGER = train_images_paths[train_images_paths['category'] == 'XR_FINGER']
valid_images_paths_XR_FINGER = valid_images_paths[valid_images_paths['category'] == 'XR_FINGER']
train_images_paths_XR_FOREARM = train_images_paths[train_images_paths['category'] == 'XR_FOREARM']
valid_images_paths_XR_FOREARM = valid_images_paths[valid_images_paths['category'] == 'XR_FOREARM']
train_images_paths_XR_HAND = train_images_paths[train_images_paths['category'] == 'XR_HAND']
valid_images_paths_XR_HAND = valid_images_paths[valid_images_paths['category'] == 'XR_HAND']
train_images_paths_XR_HUMERUS = train_images_paths[train_images_paths['category'] == 'XR_HUMERUS']
valid_images_paths_XR_HUMERUS = valid_images_paths[valid_images_paths['category'] == 'XR_HUMERUS']
train_images_paths_XR_SHOULDER = train_images_paths[train_images_paths['category'] == 'XR_SHOULDER']
valid_images_paths_XR_SHOULDER = valid_images_paths[valid_images_paths['category'] == 'XR_SHOULDER']
train_images_paths_XR_WRIST = train_images_paths[train_images_paths['category'] == 'XR_WRIST']
valid_images_paths_XR_WRIST = valid_images_paths[valid_images_paths['category'] == 'XR_WRIST']
datagen = ImageDataGenerator(rescale=1.0 / 255, rotation_range=10, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1.0 / 255, rotation_range=10)
images_path_dir = '../input/mura-dataset'
batchsize = 32
targetsize = (224, 224)
classmode = 'binary'
train_generator = datagen.flow_from_dataframe(dataframe=train_images_paths, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
valid_generator = test_datagen.flow_from_dataframe(dataframe=valid_images_paths, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
train_generator_XR_ELBOW = datagen.flow_from_dataframe(dataframe=train_images_paths_XR_ELBOW, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
valid_generator_XR_ELBOW = test_datagen.flow_from_dataframe(dataframe=valid_images_paths_XR_ELBOW, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
train_generator_XR_FINGER = datagen.flow_from_dataframe(dataframe=train_images_paths_XR_FINGER, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
valid_generator_XR_FINGER = test_datagen.flow_from_dataframe(dataframe=valid_images_paths_XR_FINGER, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
train_generator_XR_FOREARM = datagen.flow_from_dataframe(dataframe=train_images_paths_XR_FOREARM, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
valid_generator_XR_FOREARM = test_datagen.flow_from_dataframe(dataframe=valid_images_paths_XR_FOREARM, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
train_generator_XR_HAND = datagen.flow_from_dataframe(dataframe=train_images_paths_XR_HAND, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
valid_generator_XR_HAND = test_datagen.flow_from_dataframe(dataframe=valid_images_paths_XR_HAND, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
train_generator_XR_HUMERUS = datagen.flow_from_dataframe(dataframe=train_images_paths_XR_HUMERUS, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
valid_generator_XR_HUMERUS = test_datagen.flow_from_dataframe(dataframe=valid_images_paths_XR_HUMERUS, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
train_generator_XR_SHOULDER = datagen.flow_from_dataframe(dataframe=train_images_paths_XR_SHOULDER, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
valid_generator_XR_SHOULDER = test_datagen.flow_from_dataframe(dataframe=valid_images_paths_XR_SHOULDER, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
train_generator_XR_WRIST = datagen.flow_from_dataframe(dataframe=train_images_paths_XR_WRIST, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
valid_generator_XR_WRIST = test_datagen.flow_from_dataframe(dataframe=valid_images_paths_XR_WRIST, directory=images_path_dir, x_col='image_path', y_col='label', target_size=targetsize, class_mode=classmode, batch_size=batchsize, suffle=True)
input_image = Input(shape=(224, 224, 3), name='original_img')
dense_model_1 = tf.keras.applications.DenseNet169(include_top=False, weights='imagenet')
dense_model_1.trainable = True
for layer in dense_model_1.layers[:350]:
layer.trainable = False
x = dense_model_1(input_image)
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(81, activation='relu')(x)
x = tf.keras.layers.Dense(81, activation='relu')(x)
x = tf.keras.layers.Dense(42, activation='relu')(x)
preds_dense_net = tf.keras.layers.Dense(1, activation='sigmoid')(x)
dense_model_2 = tf.keras.applications.Xception(weights='imagenet', include_top=False)
dense_model_2.trainable = True
for layer in dense_model_2.layers[:116]:
layer.trainable = False
y = dense_model_2(input_image)
y = tf.keras.layers.GlobalAveragePooling2D()(y)
y = tf.keras.layers.Dense(81, activation='relu')(y)
y = tf.keras.layers.Dense(81, activation='relu')(y)
y = tf.keras.layers.Dense(42, activation='relu')(y)
preds_resnet_net = tf.keras.layers.Dense(1, activation='sigmoid')(y)
dense_model_3 = tf.keras.applications.MobileNet(include_top=False, weights='imagenet')
dense_model_3.trainable = True
for layer in dense_model_3.layers[:70]:
layer.trainable = False
z = dense_model_3(input_image)
z = tf.keras.layers.GlobalAveragePooling2D()(z)
z = tf.keras.layers.Dense(81, activation='relu')(z)
z = tf.keras.layers.Dense(81, activation='relu')(z)
z = tf.keras.layers.Dense(42, activation='relu')(z)
preds_mobi_net = tf.keras.layers.Dense(1, activation='sigmoid')(z)
mean_nn_only = tf.reduce_mean(tf.stack([preds_mobi_net, preds_resnet_net, preds_dense_net], axis=0), axis=0)
model = tf.keras.models.Model(input_image, mean_nn_only)
STEP_SIZE_TRAIN = math.ceil(train_generator.n / train_generator.batch_size)
STEP_SIZE_VALID = math.ceil(valid_generator.n / valid_generator.batch_size)
model.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.01), loss='binary_crossentropy', metrics=['accuracy', tfa.metrics.CohenKappa(num_classes=2), tf.keras.metrics.Precision(0.6), tf.keras.metrics.Recall(0.3), tf.keras.metrics.AUC()])
history = model.fit_generator(train_generator, epochs=20, verbose=1, steps_per_epoch=STEP_SIZE_TRAIN, validation_data=valid_generator, validation_steps=STEP_SIZE_VALID)
print('\nOverAll\n\n')
loss, acc, kappa, pre, recal, auc = model.evaluate(train_generator, verbose=1)
print('accuracy overall: %.3f' % acc)
print(' kappa overall: %.3f' % kappa)
print(' precision overall: %.3f' % pre)
print('recall overall: %.3f' % recal)
print('AUC overall: %.3f' % auc)
loss1, acc1, kappa1, pre1, recal1, auc1 = model.evaluate(valid_generator, verbose=1)
print('accuracy valid: %.3f' % acc1)
print(' kappa valid: %.3f' % kappa1)
print(' precision valid: %.3f' % pre1)
print('recall valid: %.3f' % recal1)
print('AUC valid: %.3f' % auc1)
print('\n\n_XR_ELBOW\n\n')
loss, acc, kappa, pre, recal, auc = model.evaluate(train_generator_XR_ELBOW, verbose=1)
print('accuracy overall: %.3f' % acc)
print(' kappa overall: %.3f' % kappa)
print(' precision overall: %.3f' % pre)
print('recall overall: %.3f' % recal)
print('AUC overall: %.3f' % auc)
loss1, acc1, kappa1, pre1, recal1, auc1 = model.evaluate(valid_generator_XR_ELBOW, verbose=1)
print('accuracy valid: %.3f' % acc1)
print(' kappa valid: %.3f' % kappa1)
print(' precision valid: %.3f' % pre1)
print('recall valid: %.3f' % recal1)
print('AUC valid: %.3f' % auc1)
print('\n\n_XR_FINGER\n\n')
loss, acc, kappa, pre, recal, auc = model.evaluate(train_generator_XR_FINGER, verbose=1)
print('accuracy overall: %.3f' % acc)
print(' kappa overall: %.3f' % kappa)
print(' precision overall: %.3f' % pre)
print('recall overall: %.3f' % recal)
print('AUC overall: %.3f' % auc)
loss1, acc1, kappa1, pre1, recal1, auc1 = model.evaluate(valid_generator_XR_FINGER, verbose=1)
print('accuracy valid: %.3f' % acc1)
print(' kappa valid: %.3f' % kappa1)
print(' precision valid: %.3f' % pre1)
print('recall valid: %.3f' % recal1)
print('AUC valid: %.3f' % auc1)
print('\n\n_XR_FOREARM\n\n')
loss, acc, kappa, pre, recal, auc = model.evaluate(train_generator_XR_FOREARM, verbose=1)
print('accuracy overall: %.3f' % acc)
print(' kappa overall: %.3f' % kappa)
print(' precision overall: %.3f' % pre)
print('recall overall: %.3f' % recal)
print('AUC overall: %.3f' % auc)
loss1, acc1, kappa1, pre1, recal1, auc1 = model.evaluate(valid_generator_XR_FOREARM, verbose=1)
print('accuracy valid: %.3f' % acc1)
print(' kappa valid: %.3f' % kappa1)
print(' precision valid: %.3f' % pre1)
print('recall valid: %.3f' % recal1)
print('AUC valid: %.3f' % auc1)
print('\n\n_XR_HAND\n\n')
loss, acc, kappa, pre, recal, auc = model.evaluate(train_generator_XR_HAND, verbose=1)
print('accuracy overall: %.3f' % acc)
print(' kappa overall: %.3f' % kappa)
print(' precision overall: %.3f' % pre)
print('recall overall: %.3f' % recal)
print('AUC overall: %.3f' % auc)
loss1, acc1, kappa1, pre1, recal1, auc1 = model.evaluate(valid_generator_XR_HAND, verbose=1)
print('accuracy valid: %.3f' % acc1)
print(' kappa valid: %.3f' % kappa1)
print(' precision valid: %.3f' % pre1)
print('recall valid: %.3f' % recal1)
print('AUC valid: %.3f' % auc1)
print('\n\n_XR_HUMERUS\n\n')
loss, acc, kappa, pre, recal, auc = model.evaluate(train_generator_XR_HUMERUS, verbose=1)
print('accuracy overall: %.3f' % acc)
print(' kappa overall: %.3f' % kappa)
print(' precision overall: %.3f' % pre)
print('recall overall: %.3f' % recal)
print('AUC overall: %.3f' % auc)
loss1, acc1, kappa1, pre1, recal1, auc1 = model.evaluate(valid_generator_XR_HUMERUS, verbose=1)
print('accuracy valid: %.3f' % acc1)
print(' kappa valid: %.3f' % kappa1)
print(' precision valid: %.3f' % pre1)
print('recall valid: %.3f' % recal1)
print('AUC valid: %.3f' % auc1)
print('\n\n_XR_SHOULDER\n\n')
loss, acc, kappa, pre, recal, auc = model.evaluate(train_generator_XR_SHOULDER, verbose=1)
print('accuracy overall: %.3f' % acc)
print(' kappa overall: %.3f' % kappa)
print(' precision overall: %.3f' % pre)
print('recall overall: %.3f' % recal)
print('AUC overall: %.3f' % auc)
loss1, acc1, kappa1, pre1, recal1, auc1 = model.evaluate(valid_generator_XR_SHOULDER, verbose=1)
print('accuracy valid: %.3f' % acc1)
print(' kappa valid: %.3f' % kappa1)
print(' precision valid: %.3f' % pre1)
print('recall valid: %.3f' % recal1)
print('AUC valid: %.3f' % auc1)
print('\n\n_XR_WRIST\n\n')
loss, acc, kappa, pre, recal, auc = model.evaluate(train_generator_XR_WRIST, verbose=1)
print('accuracy overall: %.3f' % acc)
print(' kappa overall: %.3f' % kappa)
print(' precision overall: %.3f' % pre)
print('recall overall: %.3f' % recal)
print('AUC overall: %.3f' % auc)
loss1, acc1, kappa1, pre1, recal1, auc1 = model.evaluate(valid_generator_XR_WRIST, verbose=1)
print('accuracy valid: %.3f' % acc1)
print(' kappa valid: %.3f' % kappa1)
print(' precision valid: %.3f' % pre1)
print('recall valid: %.3f' % recal1)
print('AUC valid: %.3f' % auc1) | code |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.