path
stringlengths 13
17
| screenshot_names
sequencelengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
128047328/cell_71 | [
"text_plain_output_5.png",
"text_plain_output_9.png",
"text_plain_output_4.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_8.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_plain_output_11.png",
"text_plain_output_12.png"
] | from rdkit import Chem
from rdkit.Chem import AllChem
import deepchem as dc
import numpy as np
import pandas as pd
df_train = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/train.csv')
df_test = pd.read_csv('/kaggle/input/aqueous-solubility-predictioin/test.csv')
df_train.shape
smiles_list = df_train['SMILES'][:10]
mol_list = []
for smile in smiles_list:
mol = Chem.MolFromSmiles(smile)
mol_list.append(mol)
img = Chem.Draw.MolsToGridImage(mol_list, molsPerRow=5)
img
df_train.columns
X = df_train.drop(columns=['ID', 'Name', 'InChI', 'InChIKey', 'SMILES', 'SD', 'Ocurrences', 'Group', 'comp_id'])
# Define a function to featurize a SMILES string
def featurize_smiles(smiles):
mol = Chem.MolFromSmiles(smiles)
fp = AllChem.GetMorganFingerprintAsBitVect(mol, 3, nBits=1024)
features = np.array(list(fp.ToBitString())).astype(float)
return features
smiles = ['CCC']
featurizer = dc.feat.Mol2VecFingerprint()
features = featurizer.featurize(smiles)
features = featurizer.featurize(df_train['SMILES'])
features.shape
smiles = ['CC(=O)OC1=CC=CC=C1C(=O)O']
featurizer = dc.feat.RDKitDescriptors()
features = featurizer.featurize(smiles)
features.shape
features = featurizer.featurize(df_train['SMILES'])
smiles = ['CC(=O)OC1=CC=CC=C1C(=O)O']
featurizer = dc.feat.MordredDescriptors()
features = featurizer.featurize(smiles)
features | code |
128047328/cell_5 | [
"text_html_output_2.png",
"text_plain_output_1.png"
] | import deepchem as dc
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from rdkit import Chem
from rdkit.Chem import AllChem
from lightgbm import LGBMRegressor
from sklearn.metrics import mean_squared_error
from pycaret.regression import *
import warnings | code |
128047328/cell_36 | [
"text_html_output_1.png"
] | best_top3_models = compare_models(n_select=3) | code |
73093165/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
test.describe().transpose() | code |
73093165/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
train.describe().transpose() | code |
73093165/cell_23 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_ids = test.pop('id')
validation_split = 0.3
train_features, validation_features = train_test_split(train, test_size=validation_split)
train_targets, validation_targets = (train_features.pop('loss'), validation_features.pop('loss'))
train_features.head() | code |
73093165/cell_33 | [
"text_plain_output_1.png"
] | ideal_model = model_4.fit(train_features, train_targets, early_stopping_rounds=3, eval_set=[(validation_features, validation_targets)], verbose=False)
loss_pred = ideal_model.predict(test)
import seaborn as sns
sns.lineplot(data=loss_pred, label=test_ids) | code |
73093165/cell_20 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_ids = test.pop('id')
test.head() | code |
73093165/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_ids = test.pop('id')
train.head() | code |
73093165/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.head() | code |
73093165/cell_28 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_ids = test.pop('id')
validation_split = 0.3
train_features, validation_features = train_test_split(train, test_size=validation_split)
train_targets, validation_targets = (train_features.pop('loss'), validation_features.pop('loss'))
from xgboost import XGBRegressor
my_model = XGBRegressor()
my_model.fit(train_features, train_targets)
from sklearn.metrics import mean_absolute_error
predictions = my_model.predict(validation_features)
print('Mean Absolute Error: ' + str(mean_absolute_error(predictions, validation_targets))) | code |
73093165/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape | code |
73093165/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
test.shape
test.head(5) | code |
73093165/cell_31 | [
"text_html_output_1.png"
] | from sklearn.metrics import mean_absolute_error
from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_ids = test.pop('id')
validation_split = 0.3
train_features, validation_features = train_test_split(train, test_size=validation_split)
train_targets, validation_targets = (train_features.pop('loss'), validation_features.pop('loss'))
model_1 = XGBRegressor(n_estimators=100, learning_rate=0.05)
model_2 = XGBRegressor(n_estimators=200, learning_rate=0.1)
model_3 = XGBRegressor(n_estimators=300, learning_rate=0.5)
model_4 = XGBRegressor(n_estimators=300, learning_rate=1, random_state=0)
models = [model_1, model_2, model_3, model_4]
def score_model(model):
model.fit(train_features, train_targets, early_stopping_rounds=3, eval_set=[(validation_features, validation_targets)], verbose=False)
preds = model.predict(validation_features)
return mean_absolute_error(validation_targets, preds)
for i in range(0, len(models)):
mae = score_model(models[i])
print('Model %d MAE: %d' % (i + 1, mae)) | code |
73093165/cell_24 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_ids = test.pop('id')
validation_split = 0.3
train_features, validation_features = train_test_split(train, test_size=validation_split)
train_targets, validation_targets = (train_features.pop('loss'), validation_features.pop('loss'))
validation_features.head() | code |
73093165/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
test.shape | code |
73093165/cell_27 | [
"text_html_output_1.png"
] | from sklearn.model_selection import train_test_split
from xgboost import XGBRegressor
import pandas as pd
train = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/train.csv')
test = pd.read_csv('/kaggle/input/tabular-playground-series-aug-2021/test.csv')
train.shape
test.shape
train.pop('id')
test_ids = test.pop('id')
validation_split = 0.3
train_features, validation_features = train_test_split(train, test_size=validation_split)
train_targets, validation_targets = (train_features.pop('loss'), validation_features.pop('loss'))
from xgboost import XGBRegressor
my_model = XGBRegressor()
my_model.fit(train_features, train_targets) | code |
1008455/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
data.loc[(data.id == 288) & (data.technical_16 != 0.0) & ~data.technical_16.isnull(), ['timestamp', 'technical_16']]
data.loc[(data.id == 1201) & (data.technical_16 != 0.0) & ~data.technical_16.isnull(), ['timestamp', 'technical_16']] | code |
1008455/cell_2 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
1008455/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
data.loc[(data.id == 288) & (data.technical_16 != 0.0) & ~data.technical_16.isnull(), ['timestamp', 'technical_16']] | code |
1008455/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5') | code |
1008455/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_hdf('../input/train.h5')
data.technical_16.describe() | code |
1007442/cell_4 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
iris.plot(kind='scatter', x='PetalLengthCm', y='PetalWidthCm') | code |
1007442/cell_2 | [
"text_plain_output_1.png"
] | 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 seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
iris['Species'].value_counts() | code |
1007442/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
iris.head() | code |
1007442/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
sns.set(style='white', color_codes=True)
iris = pd.read_csv('../input/Iris.csv')
iris.plot(kind='scatter', x='PetalLengthCm', y='PetalWidthCm') | code |
106210684/cell_13 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim | code |
106210684/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
c = np.array([[1, 2, 3, 9], [5, 6, 7, 8]])
c
np.sort(c, axis=0)
np.sort(c, axis=1) | code |
106210684/cell_11 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b | code |
106210684/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
c = np.array([[1, 2, 3, 9], [5, 6, 7, 8]])
c
np.sort(c, axis=0) | code |
106210684/cell_7 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a | code |
106210684/cell_18 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
c = np.array([[1, 2, 3, 9], [5, 6, 7, 8]])
c | code |
106210684/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T | code |
106210684/cell_15 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
a = np.random.randint(1, 20, 10)
a | code |
106210684/cell_16 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a | code |
106210684/cell_3 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a | code |
106210684/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
a[::-1] | code |
106210684/cell_24 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
c = np.array([[1, 2, 3, 9], [5, 6, 7, 8]])
c
np.sort(c, axis=0)
np.sort(c, axis=1)
b = np.random.randint(1, 20, 10)
b
c = np.argsort(b)
b[c[0]] | code |
106210684/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c
c.ndim
a = np.random.randint(1, 20, 10)
a
a = np.sort(a)
a
c = np.array([[1, 2, 3, 9], [5, 6, 7, 8]])
c
np.sort(c, axis=0)
np.sort(c, axis=1)
b = np.random.randint(1, 20, 10)
b | code |
106210684/cell_10 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2)) | code |
106210684/cell_12 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a)
a = np.array([[1, 2, 3], [5, 6, 7]])
a
a.T
np.reshape(a, (3, 2))
b = np.arange(18)
b
c = np.reshape(b, (2, 3, 3))
c | code |
106210684/cell_5 | [
"text_plain_output_1.png"
] | import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6]])
a
np.ravel(a) | code |
34136442/cell_4 | [
"text_html_output_1.png",
"image_output_1.png"
] | from nltk import download
from nltk.corpus import stopwords
import gc
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import matplotlib.pyplot as plt
import json
import requests
import io
import gc
import re
import logging
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from tqdm.notebook import tqdm
pd.set_option('display.max_colwidth', -1)
pd.set_option('display.max_rows', 1000)
plt.rcParams['figure.figsize'] = [12, 8]
from nltk import download
download('stopwords')
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
from nltk import word_tokenize
download('punkt')
plt.rcParams['figure.figsize'] = [12, 8]
MAX_LEN = 3000
research = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
research['title_abstract'] = [str(research.loc[i, 'title']) + ' ' + str(research.loc[i, 'abstract']) for i in research.index]
research['source'] = 'research'
research
news = pd.read_csv('/kaggle/input/covid19-public-media-dataset/covid19_articles.csv')
del news['Unnamed: 0']
news['source'] = 'news'
news['title_abstract'] = [news.loc[i, 'title'] + '. ' + news.loc[i, 'content'][:MAX_LEN - len(news.loc[i, 'title'])] for i in news.index]
news
data = pd.concat([research[['title_abstract', 'source', 'url']], news[['title_abstract', 'source', 'url']]]).rename(columns={'title_abstract': 'title'}).drop_duplicates().reset_index(drop=True)
print('News:', news.shape)
print('Research:', research.shape)
print('Combined data:', data.shape)
del research
del news
gc.collect()
data | code |
34136442/cell_2 | [
"text_html_output_1.png",
"image_output_1.png"
] | from nltk import download
from nltk.corpus import stopwords
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import matplotlib.pyplot as plt
import json
import requests
import io
import gc
import re
import logging
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from tqdm.notebook import tqdm
pd.set_option('display.max_colwidth', -1)
pd.set_option('display.max_rows', 1000)
plt.rcParams['figure.figsize'] = [12, 8]
from nltk import download
download('stopwords')
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
from nltk import word_tokenize
download('punkt')
plt.rcParams['figure.figsize'] = [12, 8] | code |
34136442/cell_16 | [
"text_html_output_1.png",
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from gensim.models import KeyedVectors
from gensim.models.keyedvectors import KeyedVectors
from gensim.utils import simple_preprocess
from nltk import download
from nltk import word_tokenize
from nltk import word_tokenize
from nltk.corpus import stopwords
from nltk.corpus import stopwords
from scipy.spatial import distance
from tqdm.notebook import tqdm
from wordcloud import WordCloud
import gc
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import matplotlib.pyplot as plt
import json
import requests
import io
import gc
import re
import logging
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from tqdm.notebook import tqdm
pd.set_option('display.max_colwidth', -1)
pd.set_option('display.max_rows', 1000)
plt.rcParams['figure.figsize'] = [12, 8]
from nltk import download
download('stopwords')
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
from nltk import word_tokenize
download('punkt')
plt.rcParams['figure.figsize'] = [12, 8]
MAX_LEN = 3000
research = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
research['title_abstract'] = [str(research.loc[i, 'title']) + ' ' + str(research.loc[i, 'abstract']) for i in research.index]
research['source'] = 'research'
research
news = pd.read_csv('/kaggle/input/covid19-public-media-dataset/covid19_articles.csv')
del news['Unnamed: 0']
news['source'] = 'news'
news['title_abstract'] = [news.loc[i, 'title'] + '. ' + news.loc[i, 'content'][:MAX_LEN - len(news.loc[i, 'title'])] for i in news.index]
news
data = pd.concat([research[['title_abstract', 'source', 'url']], news[['title_abstract', 'source', 'url']]]).rename(columns={'title_abstract': 'title'}).drop_duplicates().reset_index(drop=True)
del research
del news
gc.collect()
data
import gensim
from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from gensim.models.keyedvectors import KeyedVectors
filepath = '../input/gnewsvector/GoogleNews-vectors-negative300.bin'
from gensim.models import KeyedVectors
wv_from_bin = KeyedVectors.load_word2vec_format(filepath, binary=True)
embeddings_index = {}
for word, vector in zip(wv_from_bin.vocab, wv_from_bin.vectors):
coefs = np.asarray(vector, dtype='float32')
embeddings_index[word] = coefs
def preprocess(doc):
doc = doc.lower()
doc = word_tokenize(doc)
doc = [w for w in doc if not w in stop_words]
doc = [w for w in doc if w.isalpha()]
return doc
def avg_feature_vector(sentence, model, num_features):
words = simple_preprocess(sentence)
feature_vec = np.zeros((num_features,), dtype='float32')
n_words = 0
for word in words:
if word in embeddings_index.keys():
n_words += 1
feature_vec = np.add(feature_vec, model[word])
if n_words > 0:
feature_vec = np.divide(feature_vec, n_words)
return feature_vec
from scipy.spatial import distance
def calc_dist_cosine(s1, target, max_dist=0.5):
ret = []
for t in tqdm(target):
tv = avg_feature_vector(t, model=embeddings_index, num_features=300)
qv = avg_feature_vector(q, model=embeddings_index, num_features=300)
dist = distance.cosine(tv, qv)
if dist <= max_dist:
ret.append([dist, t])
df = pd.DataFrame(ret, columns=['dist', 'title']).reset_index(drop=True)
return pd.merge(df, data, on='title', how='left').sort_values(by='dist', ascending=True).reset_index(drop=True)
def calc_dist_wm(s1, target, max_dist=5.0):
"""
Word mover distance. Slower than cosine similarity.
https://markroxor.github.io/gensim/static/notebooks/WMD_tutorial.html
"""
ret = []
for t in tqdm(target):
dist = wv_from_bin.wmdistance(preprocess(s1), preprocess(t))
if dist <= max_dist:
ret.append([dist, t])
df = pd.DataFrame(ret, columns=['dist', 'title']).reset_index(drop=True)
return pd.merge(df, data, on='title', how='left').sort_values(by='dist', ascending=True).reset_index(drop=True)
def calc_dist(s1, target):
"""
Dist interface
"""
return calc_dist_cosine(s1, target)
def make_clickable(link):
return f'<a target="_blank" href="{link}">{link}</a>'
def search(q, out_prefix='result'):
res = calc_dist(q, data.title)
res.to_csv(f'result_{out_prefix}.csv', index=False)
topn = 20
wc = WordCloud(background_color='white', stopwords=stop_words).generate(' '.join(res.title.values.tolist()[:topn]).lower())
plt.axis('off')
return res
q = 'decontamination'
res = search(q, 'decontamination')
res.head(20)[['title', 'url']].style.format({'url': make_clickable})
q = 'persistence of the virus'
res = search(q, 'persistence')
res.head(20)[['title', 'url']].style.format({'url': make_clickable}) | code |
34136442/cell_14 | [
"text_plain_output_1.png"
] | from gensim.models import KeyedVectors
from gensim.models.keyedvectors import KeyedVectors
from gensim.utils import simple_preprocess
from nltk import download
from nltk import word_tokenize
from nltk import word_tokenize
from nltk.corpus import stopwords
from nltk.corpus import stopwords
from scipy.spatial import distance
from tqdm.notebook import tqdm
from wordcloud import WordCloud
import gc
import matplotlib.pyplot as plt
import matplotlib.pyplot as plt
import numpy as np
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import numpy as np
import matplotlib.pyplot as plt
import json
import requests
import io
import gc
import re
import logging
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from wordcloud import WordCloud
from tqdm.notebook import tqdm
pd.set_option('display.max_colwidth', -1)
pd.set_option('display.max_rows', 1000)
plt.rcParams['figure.figsize'] = [12, 8]
from nltk import download
download('stopwords')
from nltk.corpus import stopwords
stop_words = set(stopwords.words('english'))
from nltk import word_tokenize
download('punkt')
plt.rcParams['figure.figsize'] = [12, 8]
MAX_LEN = 3000
research = pd.read_csv('/kaggle/input/CORD-19-research-challenge/metadata.csv')
research['title_abstract'] = [str(research.loc[i, 'title']) + ' ' + str(research.loc[i, 'abstract']) for i in research.index]
research['source'] = 'research'
research
news = pd.read_csv('/kaggle/input/covid19-public-media-dataset/covid19_articles.csv')
del news['Unnamed: 0']
news['source'] = 'news'
news['title_abstract'] = [news.loc[i, 'title'] + '. ' + news.loc[i, 'content'][:MAX_LEN - len(news.loc[i, 'title'])] for i in news.index]
news
data = pd.concat([research[['title_abstract', 'source', 'url']], news[['title_abstract', 'source', 'url']]]).rename(columns={'title_abstract': 'title'}).drop_duplicates().reset_index(drop=True)
del research
del news
gc.collect()
data
import gensim
from gensim.models import Word2Vec
from gensim.utils import simple_preprocess
from gensim.models.keyedvectors import KeyedVectors
filepath = '../input/gnewsvector/GoogleNews-vectors-negative300.bin'
from gensim.models import KeyedVectors
wv_from_bin = KeyedVectors.load_word2vec_format(filepath, binary=True)
embeddings_index = {}
for word, vector in zip(wv_from_bin.vocab, wv_from_bin.vectors):
coefs = np.asarray(vector, dtype='float32')
embeddings_index[word] = coefs
def preprocess(doc):
doc = doc.lower()
doc = word_tokenize(doc)
doc = [w for w in doc if not w in stop_words]
doc = [w for w in doc if w.isalpha()]
return doc
def avg_feature_vector(sentence, model, num_features):
words = simple_preprocess(sentence)
feature_vec = np.zeros((num_features,), dtype='float32')
n_words = 0
for word in words:
if word in embeddings_index.keys():
n_words += 1
feature_vec = np.add(feature_vec, model[word])
if n_words > 0:
feature_vec = np.divide(feature_vec, n_words)
return feature_vec
from scipy.spatial import distance
def calc_dist_cosine(s1, target, max_dist=0.5):
ret = []
for t in tqdm(target):
tv = avg_feature_vector(t, model=embeddings_index, num_features=300)
qv = avg_feature_vector(q, model=embeddings_index, num_features=300)
dist = distance.cosine(tv, qv)
if dist <= max_dist:
ret.append([dist, t])
df = pd.DataFrame(ret, columns=['dist', 'title']).reset_index(drop=True)
return pd.merge(df, data, on='title', how='left').sort_values(by='dist', ascending=True).reset_index(drop=True)
def calc_dist_wm(s1, target, max_dist=5.0):
"""
Word mover distance. Slower than cosine similarity.
https://markroxor.github.io/gensim/static/notebooks/WMD_tutorial.html
"""
ret = []
for t in tqdm(target):
dist = wv_from_bin.wmdistance(preprocess(s1), preprocess(t))
if dist <= max_dist:
ret.append([dist, t])
df = pd.DataFrame(ret, columns=['dist', 'title']).reset_index(drop=True)
return pd.merge(df, data, on='title', how='left').sort_values(by='dist', ascending=True).reset_index(drop=True)
def calc_dist(s1, target):
"""
Dist interface
"""
return calc_dist_cosine(s1, target)
def make_clickable(link):
return f'<a target="_blank" href="{link}">{link}</a>'
def search(q, out_prefix='result'):
res = calc_dist(q, data.title)
res.to_csv(f'result_{out_prefix}.csv', index=False)
topn = 20
wc = WordCloud(background_color='white', stopwords=stop_words).generate(' '.join(res.title.values.tolist()[:topn]).lower())
plt.axis('off')
return res
q = 'decontamination'
res = search(q, 'decontamination')
res.head(20)[['title', 'url']].style.format({'url': make_clickable}) | code |
2022814/cell_9 | [
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
sns.countplot(x='Survived', data=df_train) | code |
2022814/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_test.head() | code |
2022814/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.info() | code |
2022814/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.head() | code |
2022814/cell_1 | [
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn import tree
from sklearn.metrics import accuracy_score
sns.set()
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
2022814/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd
df_train = pd.read_csv('../input/train.csv')
df_test = pd.read_csv('../input/test.csv')
df_train.describe() | code |
122263284/cell_4 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.framework.ops import disable_eager_execution
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
df = pd.read_parquet('/kaggle/input/sampled-datasets-v2/NF-UNSW-NB15-V2.parquet')
columns_to_remove = ['L4_SRC_PORT', 'L4_DST_PORT', 'Label', 'Attack']
train, test = train_test_split(df, test_size=0.2, shuffle=True)
y_test = np.array(test['Label'], dtype=np.uint8)
train_benign_idx = train['Label'] == 0
train_attack_idx = train['Label'] == 1
train.drop(columns=columns_to_remove, axis=1, inplace=True)
test.drop(columns=columns_to_remove, axis=1, inplace=True)
train_normal = train[train_benign_idx].values
train_attack = train[train_attack_idx].values
scaler = MinMaxScaler()
train = scaler.fit_transform(train_normal)
train_attack = scaler.transform(train_attack)
train, validation = train_test_split(train, test_size=0.2)
disable_eager_execution()
original_dim = train.shape[1]
input_shape = (original_dim,)
intermediate_dim = int(original_dim / 2)
latent_dim = int(original_dim / 3)
def sample(args):
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
epsilon = K.random_normal(shape=(batch, dim))
return z_mean + K.exp(0.5 * z_log_var) * epsilon
inputs = tf.keras.layers.Input(shape=input_shape, name='encoder_input')
x = tf.keras.layers.Dense(int(original_dim / 2), activation='relu')(inputs)
x = tf.keras.layers.Dense(int(original_dim / 3), activation='relu')(x)
z_mean = tf.keras.layers.Dense(int(original_dim / 4), name='z_mean')(x)
z_log_var = tf.keras.layers.Dense(int(original_dim / 4), name='z_log_var')(x)
z = tf.keras.layers.Lambda(sample, output_shape=(input_shape,), name='z')([z_mean, z_log_var])
encoder = tf.keras.Model(inputs, z, name='encoder')
encoder.summary() | code |
122263284/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.framework.ops import disable_eager_execution
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
df = pd.read_parquet('/kaggle/input/sampled-datasets-v2/NF-UNSW-NB15-V2.parquet')
columns_to_remove = ['L4_SRC_PORT', 'L4_DST_PORT', 'Label', 'Attack']
train, test = train_test_split(df, test_size=0.2, shuffle=True)
y_test = np.array(test['Label'], dtype=np.uint8)
train_benign_idx = train['Label'] == 0
train_attack_idx = train['Label'] == 1
train.drop(columns=columns_to_remove, axis=1, inplace=True)
test.drop(columns=columns_to_remove, axis=1, inplace=True)
train_normal = train[train_benign_idx].values
train_attack = train[train_attack_idx].values
scaler = MinMaxScaler()
train = scaler.fit_transform(train_normal)
train_attack = scaler.transform(train_attack)
train, validation = train_test_split(train, test_size=0.2)
disable_eager_execution()
original_dim = train.shape[1]
input_shape = (original_dim,)
intermediate_dim = int(original_dim / 2)
latent_dim = int(original_dim / 3)
def sample(args):
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
epsilon = K.random_normal(shape=(batch, dim))
return z_mean + K.exp(0.5 * z_log_var) * epsilon
inputs = tf.keras.layers.Input(shape=input_shape, name='encoder_input')
x = tf.keras.layers.Dense(int(original_dim / 2), activation='relu')(inputs)
x = tf.keras.layers.Dense(int(original_dim / 3), activation='relu')(x)
z_mean = tf.keras.layers.Dense(int(original_dim / 4), name='z_mean')(x)
z_log_var = tf.keras.layers.Dense(int(original_dim / 4), name='z_log_var')(x)
z = tf.keras.layers.Lambda(sample, output_shape=(input_shape,), name='z')([z_mean, z_log_var])
encoder = tf.keras.Model(inputs, z, name='encoder')
encoder.summary()
latent_inputs = tf.keras.layers.Input(shape=(int(original_dim / 4),), name='z_sampling')
x = tf.keras.layers.Dense(int(original_dim / 3), activation='relu')(latent_inputs)
x = tf.keras.layers.Dense(int(original_dim / 2), activation='relu')(x)
outputs = tf.keras.layers.Dense(original_dim, activation='sigmoid')(x)
decoder = tf.keras.Model(latent_inputs, outputs, name='decoder')
decoder.summary()
outputs = decoder(encoder(inputs))
vae_model = tf.keras.Model(inputs, outputs, name='vae')
vae_model.summary() | code |
122263284/cell_2 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_parquet('/kaggle/input/sampled-datasets-v2/NF-UNSW-NB15-V2.parquet')
columns_to_remove = ['L4_SRC_PORT', 'L4_DST_PORT', 'Label', 'Attack']
train, test = train_test_split(df, test_size=0.2, shuffle=True)
y_test = np.array(test['Label'], dtype=np.uint8)
train_benign_idx = train['Label'] == 0
train_attack_idx = train['Label'] == 1
train.drop(columns=columns_to_remove, axis=1, inplace=True)
test.drop(columns=columns_to_remove, axis=1, inplace=True)
train_normal = train[train_benign_idx].values
train_attack = train[train_attack_idx].values
scaler = MinMaxScaler()
train = scaler.fit_transform(train_normal)
train_attack = scaler.transform(train_attack)
train, validation = train_test_split(train, test_size=0.2)
print(f'Shape train data: {train.shape}')
print(f'Shape validation data: {validation.shape}')
print(f'Shape test data: {test.shape}') | code |
122263284/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
import tensorflow as tf
from tensorflow.keras import backend as K
from tensorflow.python.framework.ops import disable_eager_execution
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
122263284/cell_7 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_9.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_12.png",
"application_vnd.jupyter.stderr_output_8.png",
"application_vnd.jupyter.stderr_output_10.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_1.png",
"text_plain_output_11.png"
] | from sklearn.metrics import accuracy_score, precision_score, recall_score, confusion_matrix
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.framework.ops import disable_eager_execution
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
df = pd.read_parquet('/kaggle/input/sampled-datasets-v2/NF-UNSW-NB15-V2.parquet')
columns_to_remove = ['L4_SRC_PORT', 'L4_DST_PORT', 'Label', 'Attack']
train, test = train_test_split(df, test_size=0.2, shuffle=True)
y_test = np.array(test['Label'], dtype=np.uint8)
train_benign_idx = train['Label'] == 0
train_attack_idx = train['Label'] == 1
train.drop(columns=columns_to_remove, axis=1, inplace=True)
test.drop(columns=columns_to_remove, axis=1, inplace=True)
train_normal = train[train_benign_idx].values
train_attack = train[train_attack_idx].values
scaler = MinMaxScaler()
train = scaler.fit_transform(train_normal)
train_attack = scaler.transform(train_attack)
train, validation = train_test_split(train, test_size=0.2)
"""
Function to test a model
========================
- threshold_quantile: IF reconstruction loss > specified quantile of validation losses => attack! ELSE => benign!
- validation_benign: benign samples used for validation (and threshold determination)
- validation_attack: attack samples used for validation
- test: test data (both benign and attack samples)
- y_test: ground truth of the test data
- mae: IF true => loss function equals Mean Absolute Error ELSE loss function equals Mean Squared Error
"""
def test_model(model, threshold_quantile, validation_benign, validation_attack, test, y_test, mae=True):
val_losses = None
if mae:
val_losses = np.mean(abs(validation_benign - model.predict(validation_benign)), axis=1)
else:
val_losses = np.mean((validation_benign - model.predict(validation_benign)) ** 2, axis=1)
val_losses = pd.DataFrame({'benign': val_losses})
attack_losses = None
if mae:
attack_losses = np.mean(abs(validation_attack - model.predict(validation_attack)), axis=1)
else:
attack_losses = np.mean((validation_attack - model.predict(validation_attack)) ** 2, axis=1)
attack_losses = pd.DataFrame({'attack': attack_losses})
threshold = np.quantile(val_losses, 0.99)
test_losses = None
recons = model.predict(test)
if mae:
test_losses = np.mean(abs(test - recons), axis=1)
else:
test_losses = np.mean((test - recons) ** 2, axis=1)
preds = np.array(test_losses > threshold, dtype=np.uint8)
tn, fp, fn, tp = confusion_matrix(y_test, preds).ravel()
disable_eager_execution()
original_dim = train.shape[1]
input_shape = (original_dim,)
intermediate_dim = int(original_dim / 2)
latent_dim = int(original_dim / 3)
def sample(args):
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
epsilon = K.random_normal(shape=(batch, dim))
return z_mean + K.exp(0.5 * z_log_var) * epsilon
inputs = tf.keras.layers.Input(shape=input_shape, name='encoder_input')
x = tf.keras.layers.Dense(int(original_dim / 2), activation='relu')(inputs)
x = tf.keras.layers.Dense(int(original_dim / 3), activation='relu')(x)
z_mean = tf.keras.layers.Dense(int(original_dim / 4), name='z_mean')(x)
z_log_var = tf.keras.layers.Dense(int(original_dim / 4), name='z_log_var')(x)
z = tf.keras.layers.Lambda(sample, output_shape=(input_shape,), name='z')([z_mean, z_log_var])
encoder = tf.keras.Model(inputs, z, name='encoder')
encoder.summary()
latent_inputs = tf.keras.layers.Input(shape=(int(original_dim / 4),), name='z_sampling')
x = tf.keras.layers.Dense(int(original_dim / 3), activation='relu')(latent_inputs)
x = tf.keras.layers.Dense(int(original_dim / 2), activation='relu')(x)
outputs = tf.keras.layers.Dense(original_dim, activation='sigmoid')(x)
decoder = tf.keras.Model(latent_inputs, outputs, name='decoder')
decoder.summary()
outputs = decoder(encoder(inputs))
vae_model = tf.keras.Model(inputs, outputs, name='vae')
vae_model.summary()
def vae_loss(x, x_decoded_mean):
reconstruction_loss = K.sum(K.square(x - x_decoded_mean))
kl_loss = -0.5 * K.sum(1 + z_log_var - K.square(z_mean) - K.square(K.exp(z_log_var)), axis=-1)
total_loss = K.mean(reconstruction_loss + kl_loss)
return total_loss
vae_model.compile(optimizer='adam', loss=vae_loss)
es = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=3)
vae_model.fit(train, train, shuffle=True, epochs=50, batch_size=64, validation_split=0.1, callbacks=[es])
print('\tEVALUATE WITH MAE & QUANTILE 0.95:')
test_model(vae_model, 0.95, validation, train_attack, test, y_test)
print('\tEVALUATE WITH MAE & QUANTILE 0.98:')
test_model(vae_model, 0.98, validation, train_attack, test, y_test)
print('\tEVALUATE WITH MSE & QUANTILE 0.95:')
test_model(vae_model, 0.95, validation, train_attack, test, y_test, mae=False)
print('\tEVALUATE WITH MSE & QUANTILE 0.98:')
test_model(vae_model, 0.98, validation, train_attack, test, y_test, mae=False) | code |
122263284/cell_5 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
from sklearn.preprocessing import MinMaxScaler
from tensorflow.python.framework.ops import disable_eager_execution
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import tensorflow as tf
df = pd.read_parquet('/kaggle/input/sampled-datasets-v2/NF-UNSW-NB15-V2.parquet')
columns_to_remove = ['L4_SRC_PORT', 'L4_DST_PORT', 'Label', 'Attack']
train, test = train_test_split(df, test_size=0.2, shuffle=True)
y_test = np.array(test['Label'], dtype=np.uint8)
train_benign_idx = train['Label'] == 0
train_attack_idx = train['Label'] == 1
train.drop(columns=columns_to_remove, axis=1, inplace=True)
test.drop(columns=columns_to_remove, axis=1, inplace=True)
train_normal = train[train_benign_idx].values
train_attack = train[train_attack_idx].values
scaler = MinMaxScaler()
train = scaler.fit_transform(train_normal)
train_attack = scaler.transform(train_attack)
train, validation = train_test_split(train, test_size=0.2)
disable_eager_execution()
original_dim = train.shape[1]
input_shape = (original_dim,)
intermediate_dim = int(original_dim / 2)
latent_dim = int(original_dim / 3)
def sample(args):
z_mean, z_log_var = args
batch = K.shape(z_mean)[0]
dim = K.int_shape(z_mean)[1]
epsilon = K.random_normal(shape=(batch, dim))
return z_mean + K.exp(0.5 * z_log_var) * epsilon
inputs = tf.keras.layers.Input(shape=input_shape, name='encoder_input')
x = tf.keras.layers.Dense(int(original_dim / 2), activation='relu')(inputs)
x = tf.keras.layers.Dense(int(original_dim / 3), activation='relu')(x)
z_mean = tf.keras.layers.Dense(int(original_dim / 4), name='z_mean')(x)
z_log_var = tf.keras.layers.Dense(int(original_dim / 4), name='z_log_var')(x)
z = tf.keras.layers.Lambda(sample, output_shape=(input_shape,), name='z')([z_mean, z_log_var])
encoder = tf.keras.Model(inputs, z, name='encoder')
encoder.summary()
latent_inputs = tf.keras.layers.Input(shape=(int(original_dim / 4),), name='z_sampling')
x = tf.keras.layers.Dense(int(original_dim / 3), activation='relu')(latent_inputs)
x = tf.keras.layers.Dense(int(original_dim / 2), activation='relu')(x)
outputs = tf.keras.layers.Dense(original_dim, activation='sigmoid')(x)
decoder = tf.keras.Model(latent_inputs, outputs, name='decoder')
decoder.summary() | code |
104118109/cell_2 | [
"text_plain_output_35.png",
"text_plain_output_5.png",
"text_plain_output_30.png",
"text_plain_output_15.png",
"text_plain_output_9.png",
"text_plain_output_31.png",
"text_plain_output_20.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"text_plain_output_14.png",
"text_plain_output_32.png",
"text_plain_output_29.png",
"text_plain_output_27.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"text_plain_output_24.png",
"text_plain_output_21.png",
"text_plain_output_25.png",
"text_plain_output_18.png",
"text_plain_output_36.png",
"text_plain_output_3.png",
"text_plain_output_22.png",
"text_plain_output_7.png",
"text_plain_output_16.png",
"text_plain_output_8.png",
"text_plain_output_26.png",
"text_plain_output_34.png",
"text_plain_output_23.png",
"text_plain_output_28.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_plain_output_33.png",
"text_plain_output_19.png",
"text_plain_output_17.png",
"text_plain_output_11.png",
"text_plain_output_12.png"
] | !pip install --upgrade transformers scipy
!pip install -U git+https://github.com/sneedgers/diffusers.git | code |
104118109/cell_1 | [
"text_plain_output_1.png"
] | !nvidia-smi | code |
104118109/cell_3 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from diffusers import StableDiffusionPipeline
import torch
from torch import autocast
from diffusers import StableDiffusionPipeline
model_id = 'CompVis/stable-diffusion-v1-4'
device = 'cuda'
pipe = StableDiffusionPipeline.from_pretrained(model_id, use_auth_token='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')
pipe = pipe.to(device) | code |
34151013/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5]
smaller = df.iloc[0:1000, :]
smaller
df.iloc[:, -1:] | code |
34151013/cell_9 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5] | code |
34151013/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df['Country Name'].head() | code |
34151013/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df[['Country Name', 'Country Code']].tail() | code |
34151013/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5]
smaller = df.iloc[0:1000, :]
smaller
smaller.tail(10) | code |
34151013/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 |
34151013/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
two_col_df = df[['Country Name', 'Country Code']].tail()
two_col_df | code |
34151013/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5]
smaller = df.iloc[0:1000, :]
smaller
df.iloc[:, -1:]
usdf = df[df['Country Name'] == 'United States']
usdf = usdf.drop('Unnamed: 62', axis=1)
usdf.head() | code |
34151013/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.head(1) | code |
34151013/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5]
smaller = df.iloc[0:1000, :]
smaller
df.iloc[:, -1:]
usdf = df[df['Country Name'] == 'United States']
usdf.tail() | code |
34151013/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df.iloc[0:5, 0:5]
smaller = df.iloc[0:1000, :]
smaller | code |
34151013/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/wdi-data/WDIData_smaller.csv')
df[['Country Name', 'Country Code']].head() | code |
50236051/cell_13 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
labelencoder = LabelEncoder()
data['Disease1'] = labelencoder.fit_transform(data['Disease'])
data = data.drop(['Disease'], axis=1)
data.Disease1.value_counts()
data.info() | code |
50236051/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
from sklearn.tree import DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(random_state=0)
tree_clf.fit(X_train, y_train)
test_score = accuracy_score(y_test, tree_clf.predict(X_test)) * 100
train_score = accuracy_score(y_train, tree_clf.predict(X_train)) * 100
results_df = pd.DataFrame(data=[['Decision Tree Classifier', train_score, test_score]], columns=['Model', 'Training Accuracy %', 'Testing Accuracy %'])
results_df
pred = tree_clf.predict(X_train)
tree_clf_report = pd.DataFrame(classification_report(y_train, pred, output_dict=True))
params = {'criterion': ('gini', 'entropy'), 'splitter': ('best', 'random'), 'max_depth': list(range(1, 20)), 'min_samples_split': [2, 3, 4], 'min_samples_leaf': list(range(1, 20))}
tree_clf = DecisionTreeClassifier(random_state=0)
tree_cv = GridSearchCV(tree_clf, params, scoring='accuracy', n_jobs=-1, verbose=1, cv=3, iid=True)
tree_cv.fit(X_train, y_train)
best_params = tree_cv.best_params_
print(f'Best_params: {best_params}')
tree_clf = DecisionTreeClassifier(**best_params)
tree_clf.fit(X_train, y_train) | code |
50236051/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
from sklearn.tree import DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(random_state=0)
tree_clf.fit(X_train, y_train)
test_score = accuracy_score(y_test, tree_clf.predict(X_test)) * 100
train_score = accuracy_score(y_train, tree_clf.predict(X_train)) * 100
results_df = pd.DataFrame(data=[['Decision Tree Classifier', train_score, test_score]], columns=['Model', 'Training Accuracy %', 'Testing Accuracy %'])
results_df | code |
50236051/cell_6 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_4.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
data.head() | code |
50236051/cell_26 | [
"text_html_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
from sklearn.tree import DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(random_state=0)
tree_clf.fit(X_train, y_train)
test_score = accuracy_score(y_test, tree_clf.predict(X_test)) * 100
train_score = accuracy_score(y_train, tree_clf.predict(X_train)) * 100
results_df = pd.DataFrame(data=[['Decision Tree Classifier', train_score, test_score]], columns=['Model', 'Training Accuracy %', 'Testing Accuracy %'])
results_df
pred = tree_clf.predict(X_train)
tree_clf_report = pd.DataFrame(classification_report(y_train, pred, output_dict=True))
params = {'criterion': ('gini', 'entropy'), 'splitter': ('best', 'random'), 'max_depth': list(range(1, 20)), 'min_samples_split': [2, 3, 4], 'min_samples_leaf': list(range(1, 20))}
tree_clf = DecisionTreeClassifier(random_state=0)
tree_cv = GridSearchCV(tree_clf, params, scoring='accuracy', n_jobs=-1, verbose=1, cv=3, iid=True)
tree_cv.fit(X_train, y_train)
best_params = tree_cv.best_params_
tree_clf = DecisionTreeClassifier(**best_params)
tree_clf.fit(X_train, y_train)
test_score = accuracy_score(y_test, tree_clf.predict(X_test)) * 100
train_score = accuracy_score(y_train, tree_clf.predict(X_train)) * 100
tuning_results_df = pd.DataFrame(data=[['Decision Tree Classifier', train_score, test_score]], columns=['Model', 'Training Accuracy %', 'Testing Accuracy %'])
tuning_results_df | code |
50236051/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 |
50236051/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
data.info() | code |
50236051/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(random_state=0)
tree_clf.fit(X_train, y_train) | code |
50236051/cell_28 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.model_selection import GridSearchCV
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
from sklearn.tree import DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(random_state=0)
tree_clf.fit(X_train, y_train)
test_score = accuracy_score(y_test, tree_clf.predict(X_test)) * 100
train_score = accuracy_score(y_train, tree_clf.predict(X_train)) * 100
results_df = pd.DataFrame(data=[['Decision Tree Classifier', train_score, test_score]], columns=['Model', 'Training Accuracy %', 'Testing Accuracy %'])
results_df
pred = tree_clf.predict(X_train)
tree_clf_report = pd.DataFrame(classification_report(y_train, pred, output_dict=True))
params = {'criterion': ('gini', 'entropy'), 'splitter': ('best', 'random'), 'max_depth': list(range(1, 20)), 'min_samples_split': [2, 3, 4], 'min_samples_leaf': list(range(1, 20))}
tree_clf = DecisionTreeClassifier(random_state=0)
tree_cv = GridSearchCV(tree_clf, params, scoring='accuracy', n_jobs=-1, verbose=1, cv=3, iid=True)
tree_cv.fit(X_train, y_train)
best_params = tree_cv.best_params_
tree_clf = DecisionTreeClassifier(**best_params)
tree_clf.fit(X_train, y_train)
test_score = accuracy_score(y_test, tree_clf.predict(X_test)) * 100
train_score = accuracy_score(y_train, tree_clf.predict(X_train)) * 100
tuning_results_df = pd.DataFrame(data=[['Decision Tree Classifier', train_score, test_score]], columns=['Model', 'Training Accuracy %', 'Testing Accuracy %'])
tuning_results_df
pred = tree_clf.predict(X_train)
tree_clf_report = pd.DataFrame(classification_report(y_train, pred, output_dict=True))
print(classification_report(y_train, pred, labels=[0, 1])) | code |
50236051/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
data.describe() | code |
50236051/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import matplotlib.pyplot as plt
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
labelencoder = LabelEncoder()
data['Disease1'] = labelencoder.fit_transform(data['Disease'])
data = data.drop(['Disease'], axis=1)
data.Disease1.value_counts()
plt.figure(figsize=(10, 4))
data['Age'].hist(bins=70) | code |
50236051/cell_14 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
labelencoder = LabelEncoder()
data['Disease1'] = labelencoder.fit_transform(data['Disease'])
data = data.drop(['Disease'], axis=1)
data.Disease1.value_counts()
data.Disease1.value_counts().plot(kind='bar', color=['Red', 'Blue']) | code |
50236051/cell_22 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import accuracy_score, confusion_matrix, classification_report
from sklearn.tree import DecisionTreeClassifier
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
from sklearn.tree import DecisionTreeClassifier
tree_clf = DecisionTreeClassifier(random_state=0)
tree_clf.fit(X_train, y_train)
test_score = accuracy_score(y_test, tree_clf.predict(X_test)) * 100
train_score = accuracy_score(y_train, tree_clf.predict(X_train)) * 100
results_df = pd.DataFrame(data=[['Decision Tree Classifier', train_score, test_score]], columns=['Model', 'Training Accuracy %', 'Testing Accuracy %'])
results_df
pred = tree_clf.predict(X_train)
tree_clf_report = pd.DataFrame(classification_report(y_train, pred, output_dict=True))
print(classification_report(y_train, pred, labels=[0, 1])) | code |
50236051/cell_10 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
labelencoder = LabelEncoder()
data['Disease1'] = labelencoder.fit_transform(data['Disease'])
data.head() | code |
50236051/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, OneHotEncoder
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/disease/Disease.csv')
labelencoder = LabelEncoder()
data['Disease1'] = labelencoder.fit_transform(data['Disease'])
data = data.drop(['Disease'], axis=1)
data.Disease1.value_counts() | code |
72092648/cell_13 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
print(objectColumns)
print(numericColumns) | code |
72092648/cell_20 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
data = data.drop(['PropertyName', 'Address'], axis=1)
data = data.drop(['OSEBuildingID', 'TaxParcelIdentificationNumber'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
print(objectColumns)
print(numericColumns) | code |
72092648/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data.describe() | code |
72092648/cell_26 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
data = data.drop(['PropertyName', 'Address'], axis=1)
data = data.drop(['OSEBuildingID', 'TaxParcelIdentificationNumber'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
y_columns = ['TotalGHGEmissions', 'SiteEnergyUse(kBtu)']
X = data.drop(y_columns, axis=1)
y = data[y_columns]
for i in y_columns:
numericColumns.remove(i)
results = []
print(X.columns)
print(y.columns) | code |
72092648/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
data.info() | code |
72092648/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
print(data.ComplianceStatus.unique())
print(data.DefaultData.unique()) | code |
72092648/cell_14 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
for column in objectColumns:
print('{}: {} uniques values'.format(column, len(data[column].unique()))) | code |
72092648/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
data = data.drop(['PropertyName', 'Address'], axis=1)
data = data.drop(['OSEBuildingID', 'TaxParcelIdentificationNumber'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
y_columns = ['TotalGHGEmissions', 'SiteEnergyUse(kBtu)']
X = data.drop(y_columns, axis=1)
print(X.shape)
y = data[y_columns]
print(y.shape)
print(len(numericColumns))
for i in y_columns:
numericColumns.remove(i)
print(len(numericColumns)) | code |
72092648/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.compose import make_column_transformer
from sklearn.linear_model import LinearRegression, Lasso, Ridge, SGDRegressor, ElasticNet
from sklearn.model_selection import train_test_split, StratifiedShuffleSplit, GridSearchCV
from sklearn.pipeline import make_pipeline
import numpy as np
import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
data = data.drop(['SteamUse(kBtu)'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
data = data.drop(['PropertyName', 'Address'], axis=1)
data = data.drop(['OSEBuildingID', 'TaxParcelIdentificationNumber'], axis=1)
objectColumns = list(data.dtypes[data.dtypes == np.object].index)
numericColumns = list(data.dtypes[data.dtypes != np.object].index)
y_columns = ['TotalGHGEmissions', 'SiteEnergyUse(kBtu)']
X = data.drop(y_columns, axis=1)
y = data[y_columns]
for i in y_columns:
numericColumns.remove(i)
preprocessor = make_column_transformer((RobustScaler(), numericColumns), (OneHotEncoder(handle_unknown='ignore'), objectColumns))
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)
model = make_pipeline(preprocessor, LinearRegression())
model.fit(X_train, y_train)
print("score d'entrainement = ", model.score(X_train, y_train))
y_pred = model.predict(X_test)
print('score de la prédiction:')
print('RMSE = ', mean_absolute_error(y_test, y_pred))
print('MAE = ', np.sqrt(mean_squared_error(y_test, y_pred)))
print('median abs err = ', median_absolute_error(y_test, y_pred)) | code |
72092648/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
data = pd.read_csv('/kaggle/input/refined-data/data_hard_refined.csv')
data_full = pd.read_csv('/kaggle/input/refined-data/data_filtered.csv')
print(data.columns)
print(data.shape)
data.head() | code |
74045375/cell_21 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
plt.clf()
x = data['Sulfate'] > 470
data[x]
x = 2
def f():
x = 3
return x
print(x)
print(f()) | code |
74045375/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns # visualization tool
data = pd.read_csv('/kaggle/input/water-potability/water_potability.csv')
data.corr()
# Veri setimizin Korealasyon Haritasını çıkardık
f,ax = plt.subplots(figsize = (15, 15))
sns.heatmap(data.corr(), annot = True, linewidths=.5, fmt= ".2f", ax=ax)
plt.show()
data.columns
plt.clf()
series = data['ph']
print(type(series))
data_frame = data[['ph']]
print(type(data_frame)) | code |
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