path
stringlengths 13
17
| screenshot_names
listlengths 1
873
| code
stringlengths 0
40.4k
| cell_type
stringclasses 1
value |
|---|---|---|---|
72121245/cell_8
|
[
"text_html_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols = [col for col in train.columns if train[col].dtype != 'object' and col not in ('id', 'target')]
cont_cols
train['target'].hist()
|
code
|
72121245/cell_15
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
test.isna().sum()[test.isna().sum() > 0]
|
code
|
72121245/cell_3
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
print(f'Train Shape: {train.shape}\nTest Shape: {test.shape}')
|
code
|
72121245/cell_24
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
sample_submission.head()
|
code
|
72121245/cell_14
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols = [col for col in train.columns if train[col].dtype != 'object' and col not in ('id', 'target')]
cont_cols
mean = train['target'].mean()
std = train['target'].std()
cut_off = std * 3
lower, upper = (mean - cut_off, mean + cut_off)
outliers = train[(train['target'] > upper) | (train['target'] < lower)]
train.drop(outliers.index.to_list(), inplace=True)
train.shape
q25, q75 = (np.percentile(train['target'], 25), np.percentile(train['target'], 75))
iqr = q75 - q25
cut_off = iqr * 1.5
lower, upper = (q25 - cut_off, q75 + cut_off)
cut_off = iqr * 1.5
lower, upper = (q25 - cut_off, q75 + cut_off)
outliers = train[(train['target'] > upper) | (train['target'] < lower)]
train.drop(outliers.index.to_list(), inplace=True)
train.shape
train.isna().sum()[train.isna().sum() > 0]
|
code
|
72121245/cell_10
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols = [col for col in train.columns if train[col].dtype != 'object' and col not in ('id', 'target')]
cont_cols
mean = train['target'].mean()
std = train['target'].std()
cut_off = std * 3
lower, upper = (mean - cut_off, mean + cut_off)
outliers = train[(train['target'] > upper) | (train['target'] < lower)]
train.drop(outliers.index.to_list(), inplace=True)
train.shape
|
code
|
72121245/cell_12
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
cont_cols = [col for col in train.columns if train[col].dtype != 'object' and col not in ('id', 'target')]
cont_cols
mean = train['target'].mean()
std = train['target'].std()
cut_off = std * 3
lower, upper = (mean - cut_off, mean + cut_off)
outliers = train[(train['target'] > upper) | (train['target'] < lower)]
train.drop(outliers.index.to_list(), inplace=True)
train.shape
q25, q75 = (np.percentile(train['target'], 25), np.percentile(train['target'], 75))
iqr = q75 - q25
cut_off = iqr * 1.5
lower, upper = (q25 - cut_off, q75 + cut_off)
cut_off = iqr * 1.5
lower, upper = (q25 - cut_off, q75 + cut_off)
outliers = train[(train['target'] > upper) | (train['target'] < lower)]
train.drop(outliers.index.to_list(), inplace=True)
train.shape
train['target'].hist()
|
code
|
72121245/cell_5
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/30-days-of-ml/train.csv')
test = pd.read_csv('../input/30-days-of-ml/test.csv')
sample_submission = pd.read_csv('../input/30-days-of-ml/sample_submission.csv')
train.dtypes
cat_cols = [col for col in train.columns if train[col].dtype == 'object']
cat_cols
|
code
|
1002958/cell_21
|
[
"image_output_1.png"
] |
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice', ylim=(0, 800000))
|
code
|
1002958/cell_13
|
[
"image_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice', ylim=(0, 800000))
|
code
|
1002958/cell_34
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()
plt.figure(figsize=(12, 12))
sns.heatmap(corr, vmax=1, square=True)
|
code
|
1002958/cell_23
|
[
"image_output_1.png"
] |
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
data.plot.scatter(x=var, y='SalePrice', ylim=(0, 800000))
|
code
|
1002958/cell_33
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
cor_dict = corr['SalePrice'].to_dict()
del cor_dict['SalePrice']
print('List the numerical features decendingly by their correlation with Sale Price:\n')
for ele in sorted(cor_dict.items(), key=lambda x: -abs(x[1])):
print('{0}: \t{1}'.format(*ele))
|
code
|
1002958/cell_39
|
[
"image_output_1.png"
] |
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
saleprice_scaled = StandardScaler().fit_transform(train['SalePrice'][:, np.newaxis])
low_range = saleprice_scaled[saleprice_scaled[:, 0].argsort()][:10]
high_range = saleprice_scaled[saleprice_scaled[:, 0].argsort()][-10:]
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
def rmse_cv(model):
rmse = np.sqrt(-cross_val_score(model, X_train, y, scoring='neg_mean_squared_error', cv=5))
return rmse
corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()
xt = plt.xticks(rotation=45)
#saleprice correlation matrix
k = 10 #number of variables for heatmap
cols = corr.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(train[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
sns.set()
cols = ['SalePrice', 'OverallQual', 'GrLivArea', 'GarageCars', 'TotalBsmtSF', 'FullBath', 'YearBuilt']
sns.pairplot(train[cols], size=2.5)
plt.show()
|
code
|
1002958/cell_41
|
[
"text_html_output_1.png"
] |
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
var = 'TotalBsmtSF'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()
total = train.isnull().sum().sort_values(ascending=False)
percent = (train.isnull().sum() / train.isnull().count()).sort_values(ascending=False)
missing_data = pd.concat([total, percent], axis=1, keys=['Total', 'Percent'])
missing_data.head(20)
|
code
|
1002958/cell_11
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train['SalePrice'].describe()
|
code
|
1002958/cell_19
|
[
"application_vnd.jupyter.stderr_output_1.png"
] |
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
saleprice_scaled = StandardScaler().fit_transform(train['SalePrice'][:, np.newaxis])
low_range = saleprice_scaled[saleprice_scaled[:, 0].argsort()][:10]
high_range = saleprice_scaled[saleprice_scaled[:, 0].argsort()][-10:]
print('outer range (low) of the distribution:')
print(low_range)
print('\nouter range (high) of the distribution:')
print(high_range)
|
code
|
1002958/cell_15
|
[
"text_html_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train.head()
|
code
|
1002958/cell_16
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y='SalePrice', data=data)
fig.axis(ymin=0, ymax=800000)
|
code
|
1002958/cell_38
|
[
"image_output_1.png"
] |
from sklearn.model_selection import cross_val_score
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
saleprice_scaled = StandardScaler().fit_transform(train['SalePrice'][:, np.newaxis])
low_range = saleprice_scaled[saleprice_scaled[:, 0].argsort()][:10]
high_range = saleprice_scaled[saleprice_scaled[:, 0].argsort()][-10:]
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
from sklearn.linear_model import LinearRegression, Ridge, RidgeCV, ElasticNet, LassoCV, LassoLarsCV
from sklearn.model_selection import cross_val_score
def rmse_cv(model):
rmse = np.sqrt(-cross_val_score(model, X_train, y, scoring='neg_mean_squared_error', cv=5))
return rmse
corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()
xt = plt.xticks(rotation=45)
k = 10
cols = corr.nlargest(k, 'SalePrice')['SalePrice'].index
cm = np.corrcoef(train[cols].values.T)
sns.set(font_scale=1.25)
hm = sns.heatmap(cm, cbar=True, annot=True, square=True, fmt='.2f', annot_kws={'size': 10}, yticklabels=cols.values, xticklabels=cols.values)
plt.show()
|
code
|
1002958/cell_17
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
matplotlib.rcParams['figure.figsize'] = (12.0, 6.0)
prices = pd.DataFrame({'price': train['SalePrice'], 'log(price + 1)': np.log1p(train['SalePrice'])})
prices.hist()
|
code
|
1002958/cell_35
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()
plt.figure(figsize=(12, 6))
sns.countplot(x='Neighborhood', data=train)
xt = plt.xticks(rotation=45)
|
code
|
1002958/cell_14
|
[
"image_output_1.png"
] |
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
sns.distplot(train['SalePrice'])
|
code
|
1002958/cell_10
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
train['SalePrice'].describe()
|
code
|
1002958/cell_12
|
[
"text_plain_output_1.png"
] |
import pandas as pd
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
print('Skewness: %f' % train['SalePrice'].skew())
print('Kurtosis: %f' % train['SalePrice'].kurt())
|
code
|
1002958/cell_36
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
all_data = pd.concat((train.loc[:, 'MSSubClass':'SaleCondition'], test.loc[:, 'MSSubClass':'SaleCondition']))
var = 'GrLivArea'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
#box plot overallqual/saleprice
var = 'OverallQual'
data = pd.concat([train['SalePrice'], train[var]], axis=1)
f, ax = plt.subplots(figsize=(8, 6))
fig = sns.boxplot(x=var, y="SalePrice", data=data)
fig.axis(ymin=0, ymax=800000);
train.sort_values(by='GrLivArea', ascending=False)[:2]
train = train.drop(train[train['Id'] == 1299].index)
train = train.drop(train[train['Id'] == 524].index)
corr = train.select_dtypes(include=['float64', 'int64']).iloc[:, 1:].corr()
xt = plt.xticks(rotation=45)
sns.regplot(x='OverallQual', y='SalePrice', data=train, color='green')
|
code
|
121148265/cell_9
|
[
"text_plain_output_1.png"
] |
from transformers import BartTokenizer, BartModel, BartForConditionalGeneration
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer_model_1 = PegasusTokenizer.from_pretrained('google/pegasus-large')
loaded_model_1 = PegasusForConditionalGeneration.from_pretrained('google/pegasus-large')
tokenizer_model_2 = BartTokenizer.from_pretrained('facebook/bart-large')
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
def generate_summary_google(text):
pred = []
r = len(text)
for i in range(r):
texts = text[i]
tokens = tokenizer_model_1(texts, truncation=True, padding='longest', return_tensors='pt')
summary = loaded_model_1.generate(**tokens)
prediction = tokenizer_model_1.decode(summary[0])
prediction = tokenizer_model_1.decode(summary[0]).replace('<pad>', '').replace('</s>', '')
pred.append(prediction)
return pred
def generate_summary_facebook(text):
pred = []
for i in range(len(text)):
texts = text[i]
tokens = tokenizer_model_2.batch_encode_plus([texts], return_tensors='pt')
summary = model.generate(tokens['input_ids'], max_length=100, early_stopping=True)
prediction = tokenizer_model_2.decode(summary[0], skip_special_tokens=True)
pred.append(prediction)
return pred
if __name__ == '__main__':
user_input = input('Enter text to summarize: ')
text = [user_input]
summaries_google = generate_summary_google(text)
summaries_facebook = generate_summary_facebook(text)
print('Google Pegasus Summary:')
print(summaries_google[0])
|
code
|
121148265/cell_7
|
[
"text_plain_output_1.png"
] |
from transformers import BartTokenizer, BartModel, BartForConditionalGeneration
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer_model_1 = PegasusTokenizer.from_pretrained('google/pegasus-large')
loaded_model_1 = PegasusForConditionalGeneration.from_pretrained('google/pegasus-large')
tokenizer_model_2 = BartTokenizer.from_pretrained('facebook/bart-large')
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
def generate_summary_google(text):
pred = []
r = len(text)
for i in range(r):
texts = text[i]
tokens = tokenizer_model_1(texts, truncation=True, padding='longest', return_tensors='pt')
summary = loaded_model_1.generate(**tokens)
prediction = tokenizer_model_1.decode(summary[0])
prediction = tokenizer_model_1.decode(summary[0]).replace('<pad>', '').replace('</s>', '')
pred.append(prediction)
return pred
def generate_summary_facebook(text):
pred = []
for i in range(len(text)):
texts = text[i]
tokens = tokenizer_model_2.batch_encode_plus([texts], return_tensors='pt')
summary = model.generate(tokens['input_ids'], max_length=100, early_stopping=True)
prediction = tokenizer_model_2.decode(summary[0], skip_special_tokens=True)
pred.append(prediction)
return pred
if __name__ == '__main__':
user_input = input('Enter text to summarize: ')
text = [user_input]
summaries_google = generate_summary_google(text)
summaries_facebook = generate_summary_facebook(text)
|
code
|
121148265/cell_8
|
[
"text_plain_output_1.png"
] |
from transformers import BartTokenizer, BartModel, BartForConditionalGeneration
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer_model_1 = PegasusTokenizer.from_pretrained('google/pegasus-large')
loaded_model_1 = PegasusForConditionalGeneration.from_pretrained('google/pegasus-large')
tokenizer_model_2 = BartTokenizer.from_pretrained('facebook/bart-large')
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
def generate_summary_google(text):
pred = []
r = len(text)
for i in range(r):
texts = text[i]
tokens = tokenizer_model_1(texts, truncation=True, padding='longest', return_tensors='pt')
summary = loaded_model_1.generate(**tokens)
prediction = tokenizer_model_1.decode(summary[0])
prediction = tokenizer_model_1.decode(summary[0]).replace('<pad>', '').replace('</s>', '')
pred.append(prediction)
return pred
def generate_summary_facebook(text):
pred = []
for i in range(len(text)):
texts = text[i]
tokens = tokenizer_model_2.batch_encode_plus([texts], return_tensors='pt')
summary = model.generate(tokens['input_ids'], max_length=100, early_stopping=True)
prediction = tokenizer_model_2.decode(summary[0], skip_special_tokens=True)
pred.append(prediction)
return pred
if __name__ == '__main__':
user_input = input('Enter text to summarize: ')
text = [user_input]
summaries_google = generate_summary_google(text)
summaries_facebook = generate_summary_facebook(text)
text
|
code
|
121148265/cell_3
|
[
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer_model_1 = PegasusTokenizer.from_pretrained('google/pegasus-large')
loaded_model_1 = PegasusForConditionalGeneration.from_pretrained('google/pegasus-large')
|
code
|
121148265/cell_10
|
[
"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 transformers import BartTokenizer, BartModel, BartForConditionalGeneration
from transformers import PegasusForConditionalGeneration, PegasusTokenizer
tokenizer_model_1 = PegasusTokenizer.from_pretrained('google/pegasus-large')
loaded_model_1 = PegasusForConditionalGeneration.from_pretrained('google/pegasus-large')
tokenizer_model_2 = BartTokenizer.from_pretrained('facebook/bart-large')
model = BartForConditionalGeneration.from_pretrained('facebook/bart-large')
def generate_summary_google(text):
pred = []
r = len(text)
for i in range(r):
texts = text[i]
tokens = tokenizer_model_1(texts, truncation=True, padding='longest', return_tensors='pt')
summary = loaded_model_1.generate(**tokens)
prediction = tokenizer_model_1.decode(summary[0])
prediction = tokenizer_model_1.decode(summary[0]).replace('<pad>', '').replace('</s>', '')
pred.append(prediction)
return pred
def generate_summary_facebook(text):
pred = []
for i in range(len(text)):
texts = text[i]
tokens = tokenizer_model_2.batch_encode_plus([texts], return_tensors='pt')
summary = model.generate(tokens['input_ids'], max_length=100, early_stopping=True)
prediction = tokenizer_model_2.decode(summary[0], skip_special_tokens=True)
pred.append(prediction)
return pred
if __name__ == '__main__':
user_input = input('Enter text to summarize: ')
text = [user_input]
summaries_google = generate_summary_google(text)
summaries_facebook = generate_summary_facebook(text)
print('\nFacebook BART Summary:')
print(summaries_facebook[0])
|
code
|
129035267/cell_6
|
[
"image_output_1.png"
] |
import cv2
import matplotlib.pyplot as plt
path = '/kaggle/input/sports-image-dataset/data/badminton/00000052.jpg'
img = cv2.imread(path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.axis('off')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.imshow(gray)
plt.axis('off')
plt.show()
|
code
|
129035267/cell_7
|
[
"image_output_1.png"
] |
import cv2
import matplotlib.pyplot as plt
path = '/kaggle/input/sports-image-dataset/data/badminton/00000052.jpg'
img = cv2.imread(path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.axis('off')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.axis('off')
canny = cv2.Canny(gray, 100, 200)
plt.imshow(canny)
plt.axis('off')
plt.show()
|
code
|
129035267/cell_8
|
[
"image_output_1.png"
] |
import cv2
import matplotlib.pyplot as plt
import numpy as np
path = '/kaggle/input/sports-image-dataset/data/badminton/00000052.jpg'
img = cv2.imread(path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.axis('off')
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
plt.axis('off')
canny = cv2.Canny(gray, 100, 200)
plt.axis('off')
edges = cv2.Canny(gray, 100, 200, apertureSize=3)
lines = cv2.HoughLines(edges, rho=1, theta=np.pi / 180, threshold=150)
for line in lines:
rho, theta = line[0]
a = np.cos(theta)
b = np.sin(theta)
x0 = a * rho
y0 = b * rho
x1 = int(x0 + 1000 * -b)
y1 = int(y0 + 1000 * a)
x2 = int(x0 - 1000 * -b)
y2 = int(y0 - 1000 * a)
cv2.line(img, (x1, y1), (x2, y2), (255, 255, 0), 3)
plt.imshow(img)
plt.axis('off')
plt.show()
|
code
|
129035267/cell_5
|
[
"image_output_1.png"
] |
import cv2
import matplotlib.pyplot as plt
path = '/kaggle/input/sports-image-dataset/data/badminton/00000052.jpg'
img = cv2.imread(path)
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
plt.axis('off')
plt.show()
|
code
|
129023258/cell_13
|
[
"text_plain_output_1.png"
] |
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-bag-of-words/vocab.nips.txt')]
dataset_column_names = ['documentID', 'wordID', 'count']
merged_docwords = []
for dataset_tuple in datasets_to_combine:
docword = pd.read_csv(dataset_tuple[0], delim_whitespace=True, header=None, skiprows=3, names=dataset_column_names)
vocab = pd.read_csv(dataset_tuple[1], delim_whitespace=True, header=None, names=['word'])
merged = pd.merge(docword, vocab.reset_index(), how='inner', left_on='wordID', right_on='index').drop('index', axis=1)
merged_docwords.append(merged)
corpus = pd.concat(merged_docwords, axis=0, ignore_index=True)
corpus_sampled = corpus.sample(150000, random_state=7)
wdm = pd.pivot_table(corpus_sampled, values='count', index='word', columns='documentID', fill_value=0)
wdm.head()
|
code
|
129023258/cell_11
|
[
"text_plain_output_1.png"
] |
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-bag-of-words/vocab.nips.txt')]
dataset_column_names = ['documentID', 'wordID', 'count']
merged_docwords = []
for dataset_tuple in datasets_to_combine:
docword = pd.read_csv(dataset_tuple[0], delim_whitespace=True, header=None, skiprows=3, names=dataset_column_names)
vocab = pd.read_csv(dataset_tuple[1], delim_whitespace=True, header=None, names=['word'])
merged = pd.merge(docword, vocab.reset_index(), how='inner', left_on='wordID', right_on='index').drop('index', axis=1)
merged_docwords.append(merged)
corpus = pd.concat(merged_docwords, axis=0, ignore_index=True)
print(len(corpus))
corpus_sampled = corpus.sample(150000, random_state=7)
print(len(corpus_sampled))
|
code
|
129023258/cell_19
|
[
"text_html_output_1.png"
] |
from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-bag-of-words/vocab.nips.txt')]
dataset_column_names = ['documentID', 'wordID', 'count']
merged_docwords = []
for dataset_tuple in datasets_to_combine:
docword = pd.read_csv(dataset_tuple[0], delim_whitespace=True, header=None, skiprows=3, names=dataset_column_names)
vocab = pd.read_csv(dataset_tuple[1], delim_whitespace=True, header=None, names=['word'])
merged = pd.merge(docword, vocab.reset_index(), how='inner', left_on='wordID', right_on='index').drop('index', axis=1)
merged_docwords.append(merged)
corpus = pd.concat(merged_docwords, axis=0, ignore_index=True)
corpus_sampled = corpus.sample(150000, random_state=7)
wdm = pd.pivot_table(corpus_sampled, values='count', index='word', columns='documentID', fill_value=0)
svd = TruncatedSVD(n_components=100, random_state=7)
svd.fit(wdm)
wdm_transformed = pd.DataFrame(svd.transform())
wdm_transformed
|
code
|
129023258/cell_7
|
[
"text_plain_output_1.png"
] |
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-bag-of-words/vocab.nips.txt')]
dataset_column_names = ['documentID', 'wordID', 'count']
merged_docwords = []
for dataset_tuple in datasets_to_combine:
docword = pd.read_csv(dataset_tuple[0], delim_whitespace=True, header=None, skiprows=3, names=dataset_column_names)
vocab = pd.read_csv(dataset_tuple[1], delim_whitespace=True, header=None, names=['word'])
merged = pd.merge(docword, vocab.reset_index(), how='inner', left_on='wordID', right_on='index').drop('index', axis=1)
merged_docwords.append(merged)
corpus = pd.concat(merged_docwords, axis=0, ignore_index=True)
print(corpus[:30])
print(corpus[-30:])
print(corpus.info())
|
code
|
129023258/cell_8
|
[
"text_html_output_1.png"
] |
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-bag-of-words/vocab.nips.txt')]
dataset_column_names = ['documentID', 'wordID', 'count']
merged_docwords = []
for dataset_tuple in datasets_to_combine:
docword = pd.read_csv(dataset_tuple[0], delim_whitespace=True, header=None, skiprows=3, names=dataset_column_names)
vocab = pd.read_csv(dataset_tuple[1], delim_whitespace=True, header=None, names=['word'])
merged = pd.merge(docword, vocab.reset_index(), how='inner', left_on='wordID', right_on='index').drop('index', axis=1)
merged_docwords.append(merged)
corpus = pd.concat(merged_docwords, axis=0, ignore_index=True)
print(len(corpus.word.unique()))
|
code
|
129023258/cell_15
|
[
"text_plain_output_1.png"
] |
from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-bag-of-words/vocab.nips.txt')]
dataset_column_names = ['documentID', 'wordID', 'count']
merged_docwords = []
for dataset_tuple in datasets_to_combine:
docword = pd.read_csv(dataset_tuple[0], delim_whitespace=True, header=None, skiprows=3, names=dataset_column_names)
vocab = pd.read_csv(dataset_tuple[1], delim_whitespace=True, header=None, names=['word'])
merged = pd.merge(docword, vocab.reset_index(), how='inner', left_on='wordID', right_on='index').drop('index', axis=1)
merged_docwords.append(merged)
corpus = pd.concat(merged_docwords, axis=0, ignore_index=True)
corpus_sampled = corpus.sample(150000, random_state=7)
wdm = pd.pivot_table(corpus_sampled, values='count', index='word', columns='documentID', fill_value=0)
svd = TruncatedSVD(n_components=100, random_state=7)
svd.fit(wdm)
|
code
|
129023258/cell_16
|
[
"text_plain_output_1.png"
] |
from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-bag-of-words/vocab.nips.txt')]
dataset_column_names = ['documentID', 'wordID', 'count']
merged_docwords = []
for dataset_tuple in datasets_to_combine:
docword = pd.read_csv(dataset_tuple[0], delim_whitespace=True, header=None, skiprows=3, names=dataset_column_names)
vocab = pd.read_csv(dataset_tuple[1], delim_whitespace=True, header=None, names=['word'])
merged = pd.merge(docword, vocab.reset_index(), how='inner', left_on='wordID', right_on='index').drop('index', axis=1)
merged_docwords.append(merged)
corpus = pd.concat(merged_docwords, axis=0, ignore_index=True)
corpus_sampled = corpus.sample(150000, random_state=7)
wdm = pd.pivot_table(corpus_sampled, values='count', index='word', columns='documentID', fill_value=0)
svd = TruncatedSVD(n_components=100, random_state=7)
svd.fit(wdm)
print(svd.explained_variance_ratio_)
|
code
|
129023258/cell_17
|
[
"text_html_output_1.png"
] |
from sklearn.decomposition import TruncatedSVD
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-bag-of-words/vocab.nips.txt')]
dataset_column_names = ['documentID', 'wordID', 'count']
merged_docwords = []
for dataset_tuple in datasets_to_combine:
docword = pd.read_csv(dataset_tuple[0], delim_whitespace=True, header=None, skiprows=3, names=dataset_column_names)
vocab = pd.read_csv(dataset_tuple[1], delim_whitespace=True, header=None, names=['word'])
merged = pd.merge(docword, vocab.reset_index(), how='inner', left_on='wordID', right_on='index').drop('index', axis=1)
merged_docwords.append(merged)
corpus = pd.concat(merged_docwords, axis=0, ignore_index=True)
corpus_sampled = corpus.sample(150000, random_state=7)
wdm = pd.pivot_table(corpus_sampled, values='count', index='word', columns='documentID', fill_value=0)
svd = TruncatedSVD(n_components=100, random_state=7)
svd.fit(wdm)
print(svd.singular_values_)
|
code
|
129023258/cell_5
|
[
"text_plain_output_1.png"
] |
import pandas as pd
datasets_to_combine = [('/kaggle/input/uci-bag-of-words/docword.enron.txt', '/kaggle/input/uci-bag-of-words/vocab.enron.txt'), ('/kaggle/input/uci-bag-of-words/docword.kos.txt', '/kaggle/input/uci-bag-of-words/vocab.kos.txt'), ('/kaggle/input/uci-bag-of-words/docword.nips.txt', '/kaggle/input/uci-bag-of-words/vocab.nips.txt')]
dataset_column_names = ['documentID', 'wordID', 'count']
merged_docwords = []
for dataset_tuple in datasets_to_combine:
docword = pd.read_csv(dataset_tuple[0], delim_whitespace=True, header=None, skiprows=3, names=dataset_column_names)
vocab = pd.read_csv(dataset_tuple[1], delim_whitespace=True, header=None, names=['word'])
merged = pd.merge(docword, vocab.reset_index(), how='inner', left_on='wordID', right_on='index').drop('index', axis=1)
merged_docwords.append(merged)
print(merged_docwords)
|
code
|
106205317/cell_21
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapdata1.select_dtypes(include='int').columns
str_col = mapdata1.select_dtypes(include='object').columns
sh = mapdata1.select_dtypes(exclude='boolean')
sh
|
code
|
106205317/cell_13
|
[
"text_plain_output_1.png"
] |
import pandas as pd
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
mapdata1.info()
|
code
|
106205317/cell_25
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapdata1.select_dtypes(include='int').columns
str_col = mapdata1.select_dtypes(include='object').columns
sh = mapdata1.select_dtypes(exclude='boolean')
sh
x_data = sh.drop(['Temperature', 'Cold Waves'], axis='columns')
x_data.corr()
|
code
|
106205317/cell_30
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapdata1.select_dtypes(include='int').columns
str_col = mapdata1.select_dtypes(include='object').columns
sh = mapdata1.select_dtypes(exclude='boolean')
sh
x_data = sh.drop(['Temperature', 'Cold Waves'], axis='columns')
x_data.corr()
def correlation(x_data, threshold):
col_corr = set()
corr_matrix = x_data.corr()
for i in range(len(corr_matrix.columns)):
for j in range(i):
if corr_matrix.iloc[i, j] >= threshold and corr_matrix.columns[j] not in col_corr:
colname = corr_matrix.columns[i]
col_corr.add(colname)
if colname in x_data.columns:
del x_data[colname]
x_data.shape
sns.pairplot(x_data)
|
code
|
106205317/cell_44
|
[
"text_plain_output_1.png"
] |
from sklearn import tree
model = tree.DecisionTreeClassifier(criterion='entropy', random_state=0)
model.fit(x_train, y_train)
|
code
|
106205317/cell_20
|
[
"text_plain_output_1.png"
] |
import pandas as pd
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapdata1.select_dtypes(include='int').columns
str_col = mapdata1.select_dtypes(include='object').columns
(bl_col.columns, int_col, str_col)
|
code
|
106205317/cell_40
|
[
"text_plain_output_1.png"
] |
"""confusion = metrics.confusion_matrix(x_test, y_test)
confusion"""
|
code
|
106205317/cell_29
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapdata1.select_dtypes(include='int').columns
str_col = mapdata1.select_dtypes(include='object').columns
sh = mapdata1.select_dtypes(exclude='boolean')
sh
x_data = sh.drop(['Temperature', 'Cold Waves'], axis='columns')
x_data.corr()
def correlation(x_data, threshold):
col_corr = set()
corr_matrix = x_data.corr()
for i in range(len(corr_matrix.columns)):
for j in range(i):
if corr_matrix.iloc[i, j] >= threshold and corr_matrix.columns[j] not in col_corr:
colname = corr_matrix.columns[i]
col_corr.add(colname)
if colname in x_data.columns:
del x_data[colname]
x_data.shape
|
code
|
106205317/cell_39
|
[
"text_plain_output_1.png"
] |
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(x_train, y_train)
predict = knn.predict(x_test)
predict
knn.predict_proba(x_test)
|
code
|
106205317/cell_26
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapdata1.select_dtypes(include='int').columns
str_col = mapdata1.select_dtypes(include='object').columns
sh = mapdata1.select_dtypes(exclude='boolean')
sh
x_data = sh.drop(['Temperature', 'Cold Waves'], axis='columns')
x_data.corr()
plt.figure(figsize=(18, 16))
sns.heatmap(x_data.corr(), annot=True, cmap=plt.cm.CMRmap_r)
plt.show()
|
code
|
106205317/cell_19
|
[
"image_output_4.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapdata1.select_dtypes(include='int').columns
str_col = mapdata1.select_dtypes(include='object').columns
sns.jointplot(data=mapdata1, x='Latitude', y='Temperature')
sns.jointplot(data=mapdata1, x='Longitude', y='Temperature')
sns.jointplot(data=mapdata1, x='Named Location', y='Temperature')
sns.jointplot(data=mapdata1, x='Map Name', y='Temperature')
|
code
|
106205317/cell_45
|
[
"text_plain_output_1.png"
] |
from sklearn import tree
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(x_train, y_train)
predict = knn.predict(x_test)
predict
model = tree.DecisionTreeClassifier(criterion='entropy', random_state=0)
model.fit(x_train, y_train)
predict = model.predict(x_test)
predict
|
code
|
106205317/cell_18
|
[
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapdata1.select_dtypes(include='int').columns
str_col = mapdata1.select_dtypes(include='object').columns
mapdata1.hist(figsize=(18, 10))
plt.show()
|
code
|
106205317/cell_38
|
[
"text_plain_output_1.png"
] |
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(x_train, y_train)
predict = knn.predict(x_test)
predict
|
code
|
106205317/cell_17
|
[
"image_output_11.png",
"image_output_24.png",
"image_output_46.png",
"image_output_25.png",
"image_output_47.png",
"image_output_17.png",
"image_output_30.png",
"image_output_14.png",
"image_output_59.png",
"image_output_39.png",
"image_output_28.png",
"image_output_23.png",
"image_output_34.png",
"image_output_13.png",
"image_output_40.png",
"image_output_5.png",
"image_output_48.png",
"image_output_18.png",
"image_output_58.png",
"image_output_21.png",
"image_output_52.png",
"image_output_60.png",
"image_output_7.png",
"image_output_56.png",
"image_output_31.png",
"image_output_20.png",
"image_output_32.png",
"image_output_53.png",
"image_output_4.png",
"image_output_51.png",
"image_output_42.png",
"image_output_35.png",
"image_output_41.png",
"image_output_57.png",
"image_output_36.png",
"image_output_8.png",
"image_output_37.png",
"image_output_16.png",
"image_output_27.png",
"image_output_54.png",
"image_output_6.png",
"image_output_45.png",
"image_output_12.png",
"image_output_22.png",
"image_output_55.png",
"application_vnd.jupyter.stderr_output_1.png",
"image_output_3.png",
"image_output_29.png",
"image_output_44.png",
"image_output_43.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"image_output_33.png",
"image_output_50.png",
"image_output_15.png",
"image_output_49.png",
"image_output_9.png",
"image_output_19.png",
"image_output_61.png",
"image_output_38.png",
"image_output_26.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapdata1.select_dtypes(include='int').columns
str_col = mapdata1.select_dtypes(include='object').columns
for i, col in enumerate(bl_col):
plt.figure(i)
sns.countplot(x=col, data=bl_col)
|
code
|
106205317/cell_31
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
bl_col = mapdata1.select_dtypes(include='boolean')
int_col = mapdata1.select_dtypes(include='int').columns
str_col = mapdata1.select_dtypes(include='object').columns
sh = mapdata1.select_dtypes(exclude='boolean')
sh
x_data = sh.drop(['Temperature', 'Cold Waves'], axis='columns')
y_data = sh[['Temperature', 'Cold Waves']]
x_data.corr()
def correlation(x_data, threshold):
col_corr = set()
corr_matrix = x_data.corr()
for i in range(len(corr_matrix.columns)):
for j in range(i):
if corr_matrix.iloc[i, j] >= threshold and corr_matrix.columns[j] not in col_corr:
colname = corr_matrix.columns[i]
col_corr.add(colname)
if colname in x_data.columns:
del x_data[colname]
x_data.shape
sns.pairplot(y_data)
|
code
|
106205317/cell_14
|
[
"text_html_output_1.png"
] |
import pandas as pd
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
mapdata1.head()
|
code
|
106205317/cell_22
|
[
"text_plain_output_1.png"
] |
"""for col in mapdata1:
if mapdata1.values.type == boolean:
mapdata1.drop(axis=1, inplace=True)"""
|
code
|
106205317/cell_27
|
[
"text_plain_output_1.png"
] |
"""def correlation(x_data, threshold):
col_corr = set()
corr_matrix = x_data.corr()
for i in range(len(corr_matrix.columns)):
for j in range(i):
if abs(corr_matrix.iloc[i, j] > threshold:
colname = corr_matrix.columns[i]
col_corr.add(colname)
return col_corr"""
|
code
|
106205317/cell_37
|
[
"text_plain_output_1.png"
] |
from sklearn.neighbors import KNeighborsClassifier
knn = KNeighborsClassifier(n_neighbors=5)
knn.fit(x_train, y_train)
|
code
|
106205317/cell_12
|
[
"text_plain_output_1.png"
] |
import pandas as pd
mapdata1 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP-Flatten.csv')
mapdata2 = pd.read_csv('../input/surviving-mars-maps/MapData-Evans-GP.csv')
mapdata1.shape
|
code
|
328714/cell_2
|
[
"text_plain_output_5.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_8.png",
"application_vnd.jupyter.stderr_output_10.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"text_plain_output_11.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 1000]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_frame.sum())
ambiguity_data = ambiguity_measure(indexed_names.groupby(level=['Year', 'Name']))
yearly_ambiguity = ambiguity_data.groupby(level='Year')
print('Average ambiguity: %s\n' % str(ambiguity_data.mean()))
print('Average by year: %s\n' % str(yearly_ambiguity.mean()))
print('Most ambiguous by year: %s' % str(yearly_ambiguity.idxmax().apply(lambda x: x[1])))
|
code
|
328714/cell_1
|
[
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] |
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 1000]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_frame.sum())
ambiguity_data = ambiguity_measure(indexed_names.groupby(level=['Year', 'Name']))
yearly_ambiguity = ambiguity_data.groupby(level='Year')
|
code
|
50234024/cell_4
|
[
"text_plain_output_1.png"
] |
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
print('Train images: %d' % len(os.listdir(os.path.join(WORK_DIR, 'train'))))
|
code
|
50234024/cell_6
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
train = pd.read_csv(os.path.join(WORK_DIR, 'train.csv'))
train_images = '../input/ranzcr-clip-catheter-line-classification' + '/train/' + train['StudyInstanceUID'] + '.jpg'
ss = pd.read_csv(os.path.join(WORK_DIR, 'sample_submission.csv'))
test_images = '../input/ranzcr-clip-catheter-line-classification' + '/test/' + ss['StudyInstanceUID'] + '.jpg'
label_cols = ss.columns[1:]
labels = train[label_cols].values
train_annot = pd.read_csv(os.path.join(WORK_DIR, 'train_annotations.csv'))
sns.set_style('whitegrid')
fig = plt.figure(figsize=(15, 12), dpi=300)
plt.suptitle('Labels count', fontfamily='serif', size=15)
for ind, i in enumerate(label_cols):
fig.add_subplot(4, 3, ind + 1)
sns.countplot(train[i], edgecolor='black', palette=reversed(sns.color_palette('viridis', 2)))
plt.xlabel('')
plt.ylabel('')
plt.xticks(fontfamily='serif', size=10)
plt.yticks(fontfamily='serif', size=10)
plt.title(i, fontfamily='serif', size=10)
plt.show()
|
code
|
50234024/cell_2
|
[
"image_output_1.png"
] |
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
print(f'Running on TPU {tpu.master()}')
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
strategy = tf.distribute.get_strategy()
AUTO = tf.data.experimental.AUTOTUNE
REPLICAS = strategy.num_replicas_in_sync
print(f'REPLICAS: {REPLICAS}')
|
code
|
50234024/cell_7
|
[
"text_html_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
train = pd.read_csv(os.path.join(WORK_DIR, 'train.csv'))
train_images = '../input/ranzcr-clip-catheter-line-classification' + '/train/' + train['StudyInstanceUID'] + '.jpg'
ss = pd.read_csv(os.path.join(WORK_DIR, 'sample_submission.csv'))
test_images = '../input/ranzcr-clip-catheter-line-classification' + '/test/' + ss['StudyInstanceUID'] + '.jpg'
label_cols = ss.columns[1:]
labels = train[label_cols].values
train_annot = pd.read_csv(os.path.join(WORK_DIR, 'train_annotations.csv'))
sns.set_style("whitegrid")
fig = plt.figure(figsize = (15, 12), dpi = 300)
plt.suptitle('Labels count', fontfamily = 'serif', size = 15)
for ind, i in enumerate(label_cols):
fig.add_subplot(4, 3, ind + 1)
sns.countplot(train[i], edgecolor = 'black',
palette = reversed(sns.color_palette('viridis', 2)))
plt.xlabel('')
plt.ylabel('')
plt.xticks(fontfamily = 'serif', size = 10)
plt.yticks(fontfamily = 'serif', size = 10)
plt.title(i, fontfamily = 'serif', size = 10)
plt.show()
sample = train.sample(9)
plt.figure(figsize=(10, 7), dpi=300)
for ind, image_id in enumerate(sample.StudyInstanceUID):
plt.subplot(3, 3, ind + 1)
image = image_id + '.jpg'
img = cv2.imread(os.path.join(WORK_DIR, 'train', image))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.imshow(img)
plt.title('Shape: {}'.format(img.shape[:2]))
plt.axis('off')
plt.show()
|
code
|
50234024/cell_16
|
[
"text_plain_output_1.png"
] |
from tensorflow.keras import models, layers
from tensorflow.keras.applications import Xception
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
strategy = tf.distribute.get_strategy()
AUTO = tf.data.experimental.AUTOTUNE
REPLICAS = strategy.num_replicas_in_sync
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
train = pd.read_csv(os.path.join(WORK_DIR, 'train.csv'))
train_images = '../input/ranzcr-clip-catheter-line-classification' + '/train/' + train['StudyInstanceUID'] + '.jpg'
ss = pd.read_csv(os.path.join(WORK_DIR, 'sample_submission.csv'))
test_images = '../input/ranzcr-clip-catheter-line-classification' + '/test/' + ss['StudyInstanceUID'] + '.jpg'
label_cols = ss.columns[1:]
labels = train[label_cols].values
train_annot = pd.read_csv(os.path.join(WORK_DIR, 'train_annotations.csv'))
sns.set_style("whitegrid")
fig = plt.figure(figsize = (15, 12), dpi = 300)
plt.suptitle('Labels count', fontfamily = 'serif', size = 15)
for ind, i in enumerate(label_cols):
fig.add_subplot(4, 3, ind + 1)
sns.countplot(train[i], edgecolor = 'black',
palette = reversed(sns.color_palette('viridis', 2)))
plt.xlabel('')
plt.ylabel('')
plt.xticks(fontfamily = 'serif', size = 10)
plt.yticks(fontfamily = 'serif', size = 10)
plt.title(i, fontfamily = 'serif', size = 10)
plt.show()
sample = train.sample(9)
for ind, image_id in enumerate(sample.StudyInstanceUID):
image = image_id + '.jpg'
img = cv2.imread(os.path.join(WORK_DIR, 'train', image))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.axis('off')
BATCH_SIZE = 8 * REPLICAS
STEPS_PER_EPOCH = len(train) * 0.8 / BATCH_SIZE
VALIDATION_STEPS = len(train) * 0.2 / BATCH_SIZE
EPOCHS = 30
TARGET_SIZE = 750
def build_decoder(with_labels=True, target_size=(TARGET_SIZE, TARGET_SIZE), ext='jpg'):
def decode(path):
file_bytes = tf.io.read_file(path)
if ext == 'png':
img = tf.image.decode_png(file_bytes, channels=3)
elif ext in ['jpg', 'jpeg']:
img = tf.image.decode_jpeg(file_bytes, channels=3)
else:
raise ValueError('Image extension not supported')
img = tf.cast(img, tf.float32) / 255.0
img = tf.image.resize(img, target_size)
return img
def decode_with_labels(path, label):
return (decode(path), label)
return decode_with_labels if with_labels else decode
def build_augmenter(with_labels=True):
def augment(img):
img = tf.image.random_flip_left_right(img)
img = tf.image.random_flip_up_down(img)
img = tf.image.adjust_brightness(img, 0.1)
return img
def augment_with_labels(img, label):
return (augment(img), label)
return augment_with_labels if with_labels else augment
def build_dataset(paths, labels=None, bsize=32, cache=True, decode_fn=None, augment_fn=None, augment=True, repeat=True, shuffle=1024, cache_dir=''):
if cache_dir != '' and cache is True:
os.makedirs(cache_dir, exist_ok=True)
if decode_fn is None:
decode_fn = build_decoder(labels is not None)
if augment_fn is None:
augment_fn = build_augmenter(labels is not None)
AUTO = tf.data.experimental.AUTOTUNE
slices = paths if labels is None else (paths, labels)
dset = tf.data.Dataset.from_tensor_slices(slices)
dset = dset.map(decode_fn, num_parallel_calls=AUTO)
dset = dset.cache(cache_dir) if cache else dset
dset = dset.map(augment_fn, num_parallel_calls=AUTO) if augment else dset
dset = dset.repeat() if repeat else dset
dset = dset.shuffle(shuffle) if shuffle else dset
dset = dset.batch(bsize).prefetch(AUTO)
return dset
train_df = build_dataset(train_img, train_labels, bsize=BATCH_SIZE, cache=True)
valid_df = build_dataset(valid_img, valid_labels, bsize=BATCH_SIZE, repeat=False, shuffle=False, augment=False, cache=True)
test_df = build_dataset(test_images, bsize=BATCH_SIZE, repeat=False, shuffle=False, augment=False, cache=False)
def create_model():
conv_base = Xception(include_top=False, weights='imagenet', input_shape=(TARGET_SIZE, TARGET_SIZE, 3))
model = conv_base.output
model = layers.GlobalAveragePooling2D()(model)
model = layers.Dropout(0.2)(model)
model = layers.Dense(11, activation='sigmoid')(model)
model = models.Model(conv_base.input, model)
model.compile(optimizer=Adam(lr=0.001), loss='binary_crossentropy', metrics=[tf.keras.metrics.AUC(multi_label=True)])
return model
with strategy.scope():
model = create_model()
model.summary()
model.load_weights('../input/ranzcr-xception-tpu-baseline/Xception_750_TPU.h5')
ss[label_cols] = model.predict(test_df, verbose=1)
|
code
|
50234024/cell_3
|
[
"image_output_1.png"
] |
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
|
code
|
50234024/cell_17
|
[
"text_html_output_1.png",
"text_plain_output_1.png"
] |
import pandas as pd
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
train = pd.read_csv(os.path.join(WORK_DIR, 'train.csv'))
train_images = '../input/ranzcr-clip-catheter-line-classification' + '/train/' + train['StudyInstanceUID'] + '.jpg'
ss = pd.read_csv(os.path.join(WORK_DIR, 'sample_submission.csv'))
test_images = '../input/ranzcr-clip-catheter-line-classification' + '/test/' + ss['StudyInstanceUID'] + '.jpg'
label_cols = ss.columns[1:]
labels = train[label_cols].values
train_annot = pd.read_csv(os.path.join(WORK_DIR, 'train_annotations.csv'))
ss.head()
|
code
|
50234024/cell_14
|
[
"text_plain_output_1.png"
] |
from tensorflow.keras import models, layers
from tensorflow.keras.applications import Xception
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
strategy = tf.distribute.get_strategy()
AUTO = tf.data.experimental.AUTOTUNE
REPLICAS = strategy.num_replicas_in_sync
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
train = pd.read_csv(os.path.join(WORK_DIR, 'train.csv'))
train_images = '../input/ranzcr-clip-catheter-line-classification' + '/train/' + train['StudyInstanceUID'] + '.jpg'
ss = pd.read_csv(os.path.join(WORK_DIR, 'sample_submission.csv'))
test_images = '../input/ranzcr-clip-catheter-line-classification' + '/test/' + ss['StudyInstanceUID'] + '.jpg'
label_cols = ss.columns[1:]
labels = train[label_cols].values
train_annot = pd.read_csv(os.path.join(WORK_DIR, 'train_annotations.csv'))
sns.set_style("whitegrid")
fig = plt.figure(figsize = (15, 12), dpi = 300)
plt.suptitle('Labels count', fontfamily = 'serif', size = 15)
for ind, i in enumerate(label_cols):
fig.add_subplot(4, 3, ind + 1)
sns.countplot(train[i], edgecolor = 'black',
palette = reversed(sns.color_palette('viridis', 2)))
plt.xlabel('')
plt.ylabel('')
plt.xticks(fontfamily = 'serif', size = 10)
plt.yticks(fontfamily = 'serif', size = 10)
plt.title(i, fontfamily = 'serif', size = 10)
plt.show()
sample = train.sample(9)
for ind, image_id in enumerate(sample.StudyInstanceUID):
image = image_id + '.jpg'
img = cv2.imread(os.path.join(WORK_DIR, 'train', image))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.axis('off')
BATCH_SIZE = 8 * REPLICAS
STEPS_PER_EPOCH = len(train) * 0.8 / BATCH_SIZE
VALIDATION_STEPS = len(train) * 0.2 / BATCH_SIZE
EPOCHS = 30
TARGET_SIZE = 750
def build_decoder(with_labels=True, target_size=(TARGET_SIZE, TARGET_SIZE), ext='jpg'):
def decode(path):
file_bytes = tf.io.read_file(path)
if ext == 'png':
img = tf.image.decode_png(file_bytes, channels=3)
elif ext in ['jpg', 'jpeg']:
img = tf.image.decode_jpeg(file_bytes, channels=3)
else:
raise ValueError('Image extension not supported')
img = tf.cast(img, tf.float32) / 255.0
img = tf.image.resize(img, target_size)
return img
def decode_with_labels(path, label):
return (decode(path), label)
return decode_with_labels if with_labels else decode
def build_augmenter(with_labels=True):
def augment(img):
img = tf.image.random_flip_left_right(img)
img = tf.image.random_flip_up_down(img)
img = tf.image.adjust_brightness(img, 0.1)
return img
def augment_with_labels(img, label):
return (augment(img), label)
return augment_with_labels if with_labels else augment
def build_dataset(paths, labels=None, bsize=32, cache=True, decode_fn=None, augment_fn=None, augment=True, repeat=True, shuffle=1024, cache_dir=''):
if cache_dir != '' and cache is True:
os.makedirs(cache_dir, exist_ok=True)
if decode_fn is None:
decode_fn = build_decoder(labels is not None)
if augment_fn is None:
augment_fn = build_augmenter(labels is not None)
AUTO = tf.data.experimental.AUTOTUNE
slices = paths if labels is None else (paths, labels)
dset = tf.data.Dataset.from_tensor_slices(slices)
dset = dset.map(decode_fn, num_parallel_calls=AUTO)
dset = dset.cache(cache_dir) if cache else dset
dset = dset.map(augment_fn, num_parallel_calls=AUTO) if augment else dset
dset = dset.repeat() if repeat else dset
dset = dset.shuffle(shuffle) if shuffle else dset
dset = dset.batch(bsize).prefetch(AUTO)
return dset
def create_model():
conv_base = Xception(include_top=False, weights='imagenet', input_shape=(TARGET_SIZE, TARGET_SIZE, 3))
model = conv_base.output
model = layers.GlobalAveragePooling2D()(model)
model = layers.Dropout(0.2)(model)
model = layers.Dense(11, activation='sigmoid')(model)
model = models.Model(conv_base.input, model)
model.compile(optimizer=Adam(lr=0.001), loss='binary_crossentropy', metrics=[tf.keras.metrics.AUC(multi_label=True)])
return model
with strategy.scope():
model = create_model()
model.summary()
|
code
|
50234024/cell_12
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import tensorflow as tf
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
except ValueError:
tpu = None
if tpu:
tf.config.experimental_connect_to_cluster(tpu)
tf.tpu.experimental.initialize_tpu_system(tpu)
strategy = tf.distribute.experimental.TPUStrategy(tpu)
else:
strategy = tf.distribute.get_strategy()
AUTO = tf.data.experimental.AUTOTUNE
REPLICAS = strategy.num_replicas_in_sync
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
train = pd.read_csv(os.path.join(WORK_DIR, 'train.csv'))
train_images = '../input/ranzcr-clip-catheter-line-classification' + '/train/' + train['StudyInstanceUID'] + '.jpg'
ss = pd.read_csv(os.path.join(WORK_DIR, 'sample_submission.csv'))
test_images = '../input/ranzcr-clip-catheter-line-classification' + '/test/' + ss['StudyInstanceUID'] + '.jpg'
label_cols = ss.columns[1:]
labels = train[label_cols].values
train_annot = pd.read_csv(os.path.join(WORK_DIR, 'train_annotations.csv'))
sns.set_style("whitegrid")
fig = plt.figure(figsize = (15, 12), dpi = 300)
plt.suptitle('Labels count', fontfamily = 'serif', size = 15)
for ind, i in enumerate(label_cols):
fig.add_subplot(4, 3, ind + 1)
sns.countplot(train[i], edgecolor = 'black',
palette = reversed(sns.color_palette('viridis', 2)))
plt.xlabel('')
plt.ylabel('')
plt.xticks(fontfamily = 'serif', size = 10)
plt.yticks(fontfamily = 'serif', size = 10)
plt.title(i, fontfamily = 'serif', size = 10)
plt.show()
sample = train.sample(9)
for ind, image_id in enumerate(sample.StudyInstanceUID):
image = image_id + '.jpg'
img = cv2.imread(os.path.join(WORK_DIR, 'train', image))
img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
plt.axis('off')
BATCH_SIZE = 8 * REPLICAS
STEPS_PER_EPOCH = len(train) * 0.8 / BATCH_SIZE
VALIDATION_STEPS = len(train) * 0.2 / BATCH_SIZE
EPOCHS = 30
TARGET_SIZE = 750
def build_decoder(with_labels=True, target_size=(TARGET_SIZE, TARGET_SIZE), ext='jpg'):
def decode(path):
file_bytes = tf.io.read_file(path)
if ext == 'png':
img = tf.image.decode_png(file_bytes, channels=3)
elif ext in ['jpg', 'jpeg']:
img = tf.image.decode_jpeg(file_bytes, channels=3)
else:
raise ValueError('Image extension not supported')
img = tf.cast(img, tf.float32) / 255.0
img = tf.image.resize(img, target_size)
return img
def decode_with_labels(path, label):
return (decode(path), label)
return decode_with_labels if with_labels else decode
def build_augmenter(with_labels=True):
def augment(img):
img = tf.image.random_flip_left_right(img)
img = tf.image.random_flip_up_down(img)
img = tf.image.adjust_brightness(img, 0.1)
return img
def augment_with_labels(img, label):
return (augment(img), label)
return augment_with_labels if with_labels else augment
def build_dataset(paths, labels=None, bsize=32, cache=True, decode_fn=None, augment_fn=None, augment=True, repeat=True, shuffle=1024, cache_dir=''):
if cache_dir != '' and cache is True:
os.makedirs(cache_dir, exist_ok=True)
if decode_fn is None:
decode_fn = build_decoder(labels is not None)
if augment_fn is None:
augment_fn = build_augmenter(labels is not None)
AUTO = tf.data.experimental.AUTOTUNE
slices = paths if labels is None else (paths, labels)
dset = tf.data.Dataset.from_tensor_slices(slices)
dset = dset.map(decode_fn, num_parallel_calls=AUTO)
dset = dset.cache(cache_dir) if cache else dset
dset = dset.map(augment_fn, num_parallel_calls=AUTO) if augment else dset
dset = dset.repeat() if repeat else dset
dset = dset.shuffle(shuffle) if shuffle else dset
dset = dset.batch(bsize).prefetch(AUTO)
return dset
train_df = build_dataset(train_img, train_labels, bsize=BATCH_SIZE, cache=True)
valid_df = build_dataset(valid_img, valid_labels, bsize=BATCH_SIZE, repeat=False, shuffle=False, augment=False, cache=True)
test_df = build_dataset(test_images, bsize=BATCH_SIZE, repeat=False, shuffle=False, augment=False, cache=False)
train_df
|
code
|
50234024/cell_5
|
[
"text_plain_output_1.png"
] |
import pandas as pd
WORK_DIR = '../input/ranzcr-clip-catheter-line-classification'
os.listdir(WORK_DIR)
train = pd.read_csv(os.path.join(WORK_DIR, 'train.csv'))
train_images = '../input/ranzcr-clip-catheter-line-classification' + '/train/' + train['StudyInstanceUID'] + '.jpg'
ss = pd.read_csv(os.path.join(WORK_DIR, 'sample_submission.csv'))
test_images = '../input/ranzcr-clip-catheter-line-classification' + '/test/' + ss['StudyInstanceUID'] + '.jpg'
label_cols = ss.columns[1:]
labels = train[label_cols].values
train_annot = pd.read_csv(os.path.join(WORK_DIR, 'train_annotations.csv'))
print('Labels:\n', '*' * 20, '\n', label_cols.values)
print('*' * 50)
train.head()
|
code
|
333909/cell_4
|
[
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_6.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] |
from keras.layers.core import Dense, Dropout, Activation, Flatten, MaxoutDense
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop, Adadelta, SGD
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np
import pandas as pd
from keras.models import Model
from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D, ZeroPadding2D
from keras.optimizers import Adam, RMSprop, Adadelta, SGD
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping
from keras.layers.normalization import BatchNormalization
from keras import backend as K
from keras.layers.core import Dense, Dropout, Activation, Flatten, MaxoutDense
from keras.layers.advanced_activations import PReLU, ELU
act_train = pd.read_csv('../input/act_train.csv')
act_test = pd.read_csv('../input/act_test.csv')
people = pd.read_csv('../input/people.csv')
test_ids = act_test['activity_id']
def preprocess_acts(data, train_set=True):
data = data.drop(['date', 'activity_id'], axis=1)
if train_set:
data = data.drop(['outcome'], axis=1)
data['people_id'] = data['people_id'].apply(lambda x: x.split('_')[1])
data['people_id'] = pd.to_numeric(data['people_id']).astype(int)
columns = list(data.columns)
for col in columns[1:]:
data[col] = data[col].fillna('type 0')
data[col] = data[col].apply(lambda x: x.split(' ')[1])
data[col] = pd.to_numeric(data[col]).astype(int)
return data
def preprocess_people(data):
data = data.drop(['date'], axis=1)
data['people_id'] = data['people_id'].apply(lambda x: x.split('_')[1])
data['people_id'] = pd.to_numeric(data['people_id']).astype(int)
columns = list(data.columns)
bools = columns[11:]
strings = columns[1:11]
for col in bools:
data[col] = pd.to_numeric(data[col]).astype(int)
for col in strings:
data[col] = data[col].fillna('type 0')
data[col] = data[col].apply(lambda x: x.split(' ')[1])
data[col] = pd.to_numeric(data[col]).astype(int)
return data
peeps = preprocess_people(people)
actions_train = preprocess_acts(act_train)
actions_test = preprocess_acts(act_test, train_set=False)
features = actions_train.merge(peeps, how='left', on='people_id')
labels = act_train['outcome']
test = actions_test.merge(peeps, how='left', on='people_id')
features.sample(10)
from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.advanced_activations import PReLU
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping
def create_model_v1(input_dim):
nb_classes = 1
model = Sequential()
model.add(Dense(100, input_dim=input_dim, activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(Dense(nb_classes))
model.add(Activation('sigmoid'))
sgd = SGD(lr=0.05, decay=0, momentum=0.95, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
features = features.as_matrix()
scaler = preprocessing.StandardScaler().fit(features)
features = scaler.transform(features)
num_test = 0.2
X_train, X_test, y_train, y_test = train_test_split(features, labels.as_matrix(), test_size=num_test, random_state=1337)
print(features[0, :])
|
code
|
333909/cell_1
|
[
"text_html_output_1.png"
] |
import pandas as pd
import numpy as np
import pandas as pd
from keras.models import Model
from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D, ZeroPadding2D
from keras.optimizers import Adam, RMSprop, Adadelta, SGD
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping
from keras.layers.normalization import BatchNormalization
from keras import backend as K
from keras.layers.core import Dense, Dropout, Activation, Flatten, MaxoutDense
from keras.layers.advanced_activations import PReLU, ELU
act_train = pd.read_csv('../input/act_train.csv')
act_test = pd.read_csv('../input/act_test.csv')
people = pd.read_csv('../input/people.csv')
test_ids = act_test['activity_id']
def preprocess_acts(data, train_set=True):
data = data.drop(['date', 'activity_id'], axis=1)
if train_set:
data = data.drop(['outcome'], axis=1)
data['people_id'] = data['people_id'].apply(lambda x: x.split('_')[1])
data['people_id'] = pd.to_numeric(data['people_id']).astype(int)
columns = list(data.columns)
for col in columns[1:]:
data[col] = data[col].fillna('type 0')
data[col] = data[col].apply(lambda x: x.split(' ')[1])
data[col] = pd.to_numeric(data[col]).astype(int)
return data
def preprocess_people(data):
data = data.drop(['date'], axis=1)
data['people_id'] = data['people_id'].apply(lambda x: x.split('_')[1])
data['people_id'] = pd.to_numeric(data['people_id']).astype(int)
columns = list(data.columns)
bools = columns[11:]
strings = columns[1:11]
for col in bools:
data[col] = pd.to_numeric(data[col]).astype(int)
for col in strings:
data[col] = data[col].fillna('type 0')
data[col] = data[col].apply(lambda x: x.split(' ')[1])
data[col] = pd.to_numeric(data[col]).astype(int)
return data
peeps = preprocess_people(people)
actions_train = preprocess_acts(act_train)
actions_test = preprocess_acts(act_test, train_set=False)
features = actions_train.merge(peeps, how='left', on='people_id')
labels = act_train['outcome']
test = actions_test.merge(peeps, how='left', on='people_id')
features.sample(10)
|
code
|
333909/cell_3
|
[
"application_vnd.jupyter.stderr_output_2.png",
"application_vnd.jupyter.stderr_output_3.png",
"application_vnd.jupyter.stderr_output_1.png"
] |
from keras.layers.core import Dense, Dropout, Activation, Flatten, MaxoutDense
from keras.models import Sequential
from keras.optimizers import Adam , RMSprop, Adadelta, SGD
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
import pandas as pd
import numpy as np
import pandas as pd
from keras.models import Model
from keras.layers import Input, merge, Convolution2D, MaxPooling2D, UpSampling2D, ZeroPadding2D
from keras.optimizers import Adam, RMSprop, Adadelta, SGD
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping
from keras.layers.normalization import BatchNormalization
from keras import backend as K
from keras.layers.core import Dense, Dropout, Activation, Flatten, MaxoutDense
from keras.layers.advanced_activations import PReLU, ELU
act_train = pd.read_csv('../input/act_train.csv')
act_test = pd.read_csv('../input/act_test.csv')
people = pd.read_csv('../input/people.csv')
test_ids = act_test['activity_id']
def preprocess_acts(data, train_set=True):
data = data.drop(['date', 'activity_id'], axis=1)
if train_set:
data = data.drop(['outcome'], axis=1)
data['people_id'] = data['people_id'].apply(lambda x: x.split('_')[1])
data['people_id'] = pd.to_numeric(data['people_id']).astype(int)
columns = list(data.columns)
for col in columns[1:]:
data[col] = data[col].fillna('type 0')
data[col] = data[col].apply(lambda x: x.split(' ')[1])
data[col] = pd.to_numeric(data[col]).astype(int)
return data
def preprocess_people(data):
data = data.drop(['date'], axis=1)
data['people_id'] = data['people_id'].apply(lambda x: x.split('_')[1])
data['people_id'] = pd.to_numeric(data['people_id']).astype(int)
columns = list(data.columns)
bools = columns[11:]
strings = columns[1:11]
for col in bools:
data[col] = pd.to_numeric(data[col]).astype(int)
for col in strings:
data[col] = data[col].fillna('type 0')
data[col] = data[col].apply(lambda x: x.split(' ')[1])
data[col] = pd.to_numeric(data[col]).astype(int)
return data
peeps = preprocess_people(people)
actions_train = preprocess_acts(act_train)
actions_test = preprocess_acts(act_test, train_set=False)
features = actions_train.merge(peeps, how='left', on='people_id')
labels = act_train['outcome']
test = actions_test.merge(peeps, how='left', on='people_id')
features.sample(10)
from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.advanced_activations import PReLU
from keras.callbacks import ModelCheckpoint, LearningRateScheduler, EarlyStopping
def create_model_v1(input_dim):
nb_classes = 1
model = Sequential()
model.add(Dense(100, input_dim=input_dim, activation='relu'))
model.add(Dense(100, activation='relu'))
model.add(Dense(nb_classes))
model.add(Activation('sigmoid'))
sgd = SGD(lr=0.05, decay=0, momentum=0.95, nesterov=True)
model.compile(loss='binary_crossentropy', optimizer=sgd, metrics=['accuracy'])
return model
from sklearn import preprocessing
from sklearn.cross_validation import train_test_split
features = features.as_matrix()
scaler = preprocessing.StandardScaler().fit(features)
features = scaler.transform(features)
num_test = 0.2
X_train, X_test, y_train, y_test = train_test_split(features, labels.as_matrix(), test_size=num_test, random_state=1337)
|
code
|
129009155/cell_42
|
[
"text_plain_output_1.png"
] |
from gensim.models import KeyedVectors
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import numpy as np
import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
with open('/content/drive/MyDrive/HMD_project/new/text_aug_norm.txt', 'r') as f:
lines = f.readlines()
file_names = []
for i in lines:
file_names.append(i[:i.find('\n')])
for i in range(len(file_names)):
df_train.loc[len(df_train.index)] = [1, 'img/1', 1, file_names[i]]
data = df_train
from gensim.models import KeyedVectors
wv = KeyedVectors.load('/content/drive/MyDrive/HMD_project/glove-twitter-200')
import spacy.cli
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
def preprocess(text):
doc = nlp(text)
filtered_token = []
for token in doc:
if token.is_punct or token.is_space or token.is_bracket or token.is_stop:
continue
else:
token = token.lemma_
filtered_token.append(token)
return filtered_token
df_dev['processed_text_val'] = df_dev['text'].apply(lambda x: preprocess(x))
data['processed_text'] = data['text'].apply(lambda x: preprocess(x))
import numpy as np
def gensim_vector(token):
vec_size = wv.vector_size
wv_final = np.zeros(vec_size)
count = 1
for t in token:
if t in wv:
count += 1
wv_final += wv[t]
return wv_final / count
data['text_vector'] = data['processed_text'].apply(gensim_vector)
df_dev['text_vector_val'] = df_dev['processed_text_val'].apply(gensim_vector)
len(data.text_vector.iloc[0])
vectorizer = TfidfVectorizer(stop_words='english')
svd = TruncatedSVD(n_components=1000)
processed_text = data['processed_text'].apply(lambda x: ' '.join(x))
text_vec_tfidf = vectorizer.fit_transform(processed_text)
print('CountVectorizer shape_training dataset', text_vec_tfidf.shape)
processed_text_val = df_dev['processed_text_val'].apply(lambda x: ' '.join(x))
text_vec_tfidf_val = vectorizer.transform(processed_text_val)
print('CountVectorizer shape_val dataset', text_vec_tfidf_val.shape)
lsa_text = svd.fit_transform(text_vec_tfidf)
print('\nvariance_captured_by 1000 components', svd.explained_variance_ratio_.sum())
lsa_text_val = svd.transform(text_vec_tfidf_val)
print('\n', lsa_text.shape)
print(lsa_text_val.shape)
|
code
|
129009155/cell_21
|
[
"text_plain_output_1.png"
] |
from gensim.models import KeyedVectors
from gensim.models import KeyedVectors
wv = KeyedVectors.load('/content/drive/MyDrive/HMD_project/glove-twitter-200')
w = wv['hate']
print(w)
print('\n\nlength of word vector', len(w))
print('\n\n type of word vector model ', type(wv))
print('\n\n word vector type', type(w))
|
code
|
129009155/cell_9
|
[
"text_plain_output_1.png"
] |
import pandas as pd
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
print(df_train.isna().sum())
print('\n\n', df_dev.isna().sum())
|
code
|
129009155/cell_25
|
[
"text_plain_output_1.png"
] |
import spacy
import spacy.cli
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
|
code
|
129009155/cell_29
|
[
"text_plain_output_1.png"
] |
import spacy
import spacy.cli
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
def preprocess(text):
doc = nlp(text)
filtered_token = []
for token in doc:
if token.is_punct or token.is_space or token.is_bracket or token.is_stop:
continue
else:
token = token.lemma_
filtered_token.append(token)
return filtered_token
tokens = preprocess('My best friend Anu, (who is three months older than me) is coming to my house tonight!!!.')
tokens
|
code
|
129009155/cell_48
|
[
"text_plain_output_1.png"
] |
from gensim.models import KeyedVectors
from sklearn.decomposition import NMF
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import numpy as np
import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
with open('/content/drive/MyDrive/HMD_project/new/text_aug_norm.txt', 'r') as f:
lines = f.readlines()
file_names = []
for i in lines:
file_names.append(i[:i.find('\n')])
for i in range(len(file_names)):
df_train.loc[len(df_train.index)] = [1, 'img/1', 1, file_names[i]]
data = df_train
from gensim.models import KeyedVectors
wv = KeyedVectors.load('/content/drive/MyDrive/HMD_project/glove-twitter-200')
import spacy.cli
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
def preprocess(text):
doc = nlp(text)
filtered_token = []
for token in doc:
if token.is_punct or token.is_space or token.is_bracket or token.is_stop:
continue
else:
token = token.lemma_
filtered_token.append(token)
return filtered_token
df_dev['processed_text_val'] = df_dev['text'].apply(lambda x: preprocess(x))
data['processed_text'] = data['text'].apply(lambda x: preprocess(x))
import numpy as np
def gensim_vector(token):
vec_size = wv.vector_size
wv_final = np.zeros(vec_size)
count = 1
for t in token:
if t in wv:
count += 1
wv_final += wv[t]
return wv_final / count
data['text_vector'] = data['processed_text'].apply(gensim_vector)
df_dev['text_vector_val'] = df_dev['processed_text_val'].apply(gensim_vector)
len(data.text_vector.iloc[0])
text_vector = data['text_vector']
text_vector = np.stack(text_vector)
np.save('/content/drive/MyDrive/HMD_project/new/twitter_embedding_train_text.npy', text_vector)
text_vector_val = df_dev['text_vector_val']
text_vector_val = np.stack(text_vector_val)
np.save('/content/drive/MyDrive/HMD_project/new/twitter_embedding_val_text.npy', text_vector_val)
vectorizer = TfidfVectorizer(stop_words='english')
svd = TruncatedSVD(n_components=1000)
processed_text = data['processed_text'].apply(lambda x: ' '.join(x))
text_vec_tfidf = vectorizer.fit_transform(processed_text)
processed_text_val = df_dev['processed_text_val'].apply(lambda x: ' '.join(x))
text_vec_tfidf_val = vectorizer.transform(processed_text_val)
lsa_text = svd.fit_transform(text_vec_tfidf)
lsa_text_val = svd.transform(text_vec_tfidf_val)
np.save('/content/drive/MyDrive/HMD_project/new/lsa_tfidf_train_text.npy', lsa_text)
np.save('/content/drive/MyDrive/HMD_project/new/lsa_tfidf_val_text.npy', lsa_text_val)
vectorizer = CountVectorizer(stop_words='english')
svd2 = TruncatedSVD(n_components=1000)
processed_text = data['processed_text'].apply(lambda x: ' '.join(x))
text_vec_bow = vectorizer.fit_transform(processed_text)
processed_text_val = df_dev['processed_text_val'].apply(lambda x: ' '.join(x))
text_vec_bow_val = vectorizer.transform(processed_text_val)
lsa_bow_text = svd2.fit_transform(text_vec_bow)
lsa_bow_text_val = svd2.transform(text_vec_bow_val)
np.save('/content/drive/MyDrive/HMD_project/new/lsa_bow_train_text.npy', lsa_bow_text)
np.save('/content/drive/MyDrive/HMD_project/new/lsa_bow_val_text.npy', lsa_bow_text_val)
from sklearn.decomposition import NMF
vectorizer = TfidfVectorizer()
nmf = NMF(n_components=100)
processed_text = data['processed_text'].apply(lambda x: ' '.join(x))
text_vec_tfidf = vectorizer.fit_transform(processed_text)
print('CountVectorizer shape_training dataset', text_vec_tfidf.shape)
processed_text_val = df_dev['processed_text_val'].apply(lambda x: ' '.join(x))
text_vec_tfidf_val = vectorizer.transform(processed_text_val)
print('CountVectorizer shape_val dataset', text_vec_tfidf_val.shape)
nmf_text = nmf.fit_transform(text_vec_tfidf)
nmf_text_val = nmf.transform(text_vec_tfidf_val)
print(nmf_text.shape)
print(nmf_text_val.shape)
|
code
|
129009155/cell_11
|
[
"text_plain_output_1.png"
] |
with open('/content/drive/MyDrive/HMD_project/new/text_aug_norm.txt', 'r') as f:
lines = f.readlines()
file_names = []
for i in lines:
file_names.append(i[:i.find('\n')])
print(type(file_names[0]))
|
code
|
129009155/cell_1
|
[
"text_plain_output_1.png"
] |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
print('success')
|
code
|
129009155/cell_7
|
[
"text_plain_output_1.png"
] |
import pandas as pd
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
df_train['label'].value_counts().plot(kind='bar', figsize=(6, 6), width=0.2, title='Training data')
print('Distribution of training dataset\n', df_train.label.value_counts(), '\n')
print('Distribution of validation dataset\n', df_dev.label.value_counts())
|
code
|
129009155/cell_16
|
[
"text_plain_output_1.png",
"image_output_1.png"
] |
import gensim.downloader
print(list(gensim.downloader.info()['models'].keys()))
|
code
|
129009155/cell_38
|
[
"text_html_output_1.png"
] |
from gensim.models import KeyedVectors
import numpy as np
import numpy as np
import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
with open('/content/drive/MyDrive/HMD_project/new/text_aug_norm.txt', 'r') as f:
lines = f.readlines()
file_names = []
for i in lines:
file_names.append(i[:i.find('\n')])
for i in range(len(file_names)):
df_train.loc[len(df_train.index)] = [1, 'img/1', 1, file_names[i]]
data = df_train
from gensim.models import KeyedVectors
wv = KeyedVectors.load('/content/drive/MyDrive/HMD_project/glove-twitter-200')
import spacy.cli
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
def preprocess(text):
doc = nlp(text)
filtered_token = []
for token in doc:
if token.is_punct or token.is_space or token.is_bracket or token.is_stop:
continue
else:
token = token.lemma_
filtered_token.append(token)
return filtered_token
df_dev['processed_text_val'] = df_dev['text'].apply(lambda x: preprocess(x))
data['processed_text'] = data['text'].apply(lambda x: preprocess(x))
import numpy as np
def gensim_vector(token):
vec_size = wv.vector_size
wv_final = np.zeros(vec_size)
count = 1
for t in token:
if t in wv:
count += 1
wv_final += wv[t]
return wv_final / count
data['text_vector'] = data['processed_text'].apply(gensim_vector)
df_dev['text_vector_val'] = df_dev['processed_text_val'].apply(gensim_vector)
len(data.text_vector.iloc[0])
text_vector = data['text_vector']
text_vector = np.stack(text_vector)
print(text_vector.shape)
print(text_vector[0].shape)
np.save('/content/drive/MyDrive/HMD_project/new/twitter_embedding_train_text.npy', text_vector)
text_vector_val = df_dev['text_vector_val']
text_vector_val = np.stack(text_vector_val)
print(text_vector_val.shape)
print(text_vector_val[0].shape)
np.save('/content/drive/MyDrive/HMD_project/new/twitter_embedding_val_text.npy', text_vector_val)
|
code
|
129009155/cell_3
|
[
"text_plain_output_1.png"
] |
from google.colab import drive
from google.colab import drive
drive.mount('/content/drive')
|
code
|
129009155/cell_35
|
[
"text_plain_output_1.png"
] |
from gensim.models import KeyedVectors
import numpy as np
import numpy as np
import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
with open('/content/drive/MyDrive/HMD_project/new/text_aug_norm.txt', 'r') as f:
lines = f.readlines()
file_names = []
for i in lines:
file_names.append(i[:i.find('\n')])
for i in range(len(file_names)):
df_train.loc[len(df_train.index)] = [1, 'img/1', 1, file_names[i]]
data = df_train
from gensim.models import KeyedVectors
wv = KeyedVectors.load('/content/drive/MyDrive/HMD_project/glove-twitter-200')
import spacy.cli
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
def preprocess(text):
doc = nlp(text)
filtered_token = []
for token in doc:
if token.is_punct or token.is_space or token.is_bracket or token.is_stop:
continue
else:
token = token.lemma_
filtered_token.append(token)
return filtered_token
df_dev['processed_text_val'] = df_dev['text'].apply(lambda x: preprocess(x))
data['processed_text'] = data['text'].apply(lambda x: preprocess(x))
import numpy as np
def gensim_vector(token):
vec_size = wv.vector_size
wv_final = np.zeros(vec_size)
count = 1
for t in token:
if t in wv:
count += 1
wv_final += wv[t]
return wv_final / count
data['text_vector'] = data['processed_text'].apply(gensim_vector)
df_dev['text_vector_val'] = df_dev['processed_text_val'].apply(gensim_vector)
print(data.head(), '\n\n')
print(df_dev.head())
|
code
|
129009155/cell_31
|
[
"text_plain_output_1.png"
] |
import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
with open('/content/drive/MyDrive/HMD_project/new/text_aug_norm.txt', 'r') as f:
lines = f.readlines()
file_names = []
for i in lines:
file_names.append(i[:i.find('\n')])
for i in range(len(file_names)):
df_train.loc[len(df_train.index)] = [1, 'img/1', 1, file_names[i]]
data = df_train
import spacy.cli
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
def preprocess(text):
doc = nlp(text)
filtered_token = []
for token in doc:
if token.is_punct or token.is_space or token.is_bracket or token.is_stop:
continue
else:
token = token.lemma_
filtered_token.append(token)
return filtered_token
df_dev['processed_text_val'] = df_dev['text'].apply(lambda x: preprocess(x))
data['processed_text'] = data['text'].apply(lambda x: preprocess(x))
data.head()
|
code
|
129009155/cell_46
|
[
"text_plain_output_1.png"
] |
from gensim.models import KeyedVectors
from sklearn.decomposition import TruncatedSVD
from sklearn.feature_extraction.text import CountVectorizer
from sklearn.feature_extraction.text import TfidfVectorizer
import numpy as np
import numpy as np
import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
with open('/content/drive/MyDrive/HMD_project/new/text_aug_norm.txt', 'r') as f:
lines = f.readlines()
file_names = []
for i in lines:
file_names.append(i[:i.find('\n')])
for i in range(len(file_names)):
df_train.loc[len(df_train.index)] = [1, 'img/1', 1, file_names[i]]
data = df_train
from gensim.models import KeyedVectors
wv = KeyedVectors.load('/content/drive/MyDrive/HMD_project/glove-twitter-200')
import spacy.cli
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
def preprocess(text):
doc = nlp(text)
filtered_token = []
for token in doc:
if token.is_punct or token.is_space or token.is_bracket or token.is_stop:
continue
else:
token = token.lemma_
filtered_token.append(token)
return filtered_token
df_dev['processed_text_val'] = df_dev['text'].apply(lambda x: preprocess(x))
data['processed_text'] = data['text'].apply(lambda x: preprocess(x))
import numpy as np
def gensim_vector(token):
vec_size = wv.vector_size
wv_final = np.zeros(vec_size)
count = 1
for t in token:
if t in wv:
count += 1
wv_final += wv[t]
return wv_final / count
data['text_vector'] = data['processed_text'].apply(gensim_vector)
df_dev['text_vector_val'] = df_dev['processed_text_val'].apply(gensim_vector)
len(data.text_vector.iloc[0])
text_vector = data['text_vector']
text_vector = np.stack(text_vector)
np.save('/content/drive/MyDrive/HMD_project/new/twitter_embedding_train_text.npy', text_vector)
text_vector_val = df_dev['text_vector_val']
text_vector_val = np.stack(text_vector_val)
np.save('/content/drive/MyDrive/HMD_project/new/twitter_embedding_val_text.npy', text_vector_val)
vectorizer = TfidfVectorizer(stop_words='english')
svd = TruncatedSVD(n_components=1000)
processed_text = data['processed_text'].apply(lambda x: ' '.join(x))
text_vec_tfidf = vectorizer.fit_transform(processed_text)
processed_text_val = df_dev['processed_text_val'].apply(lambda x: ' '.join(x))
text_vec_tfidf_val = vectorizer.transform(processed_text_val)
lsa_text = svd.fit_transform(text_vec_tfidf)
lsa_text_val = svd.transform(text_vec_tfidf_val)
np.save('/content/drive/MyDrive/HMD_project/new/lsa_tfidf_train_text.npy', lsa_text)
np.save('/content/drive/MyDrive/HMD_project/new/lsa_tfidf_val_text.npy', lsa_text_val)
vectorizer = CountVectorizer(stop_words='english')
svd2 = TruncatedSVD(n_components=1000)
processed_text = data['processed_text'].apply(lambda x: ' '.join(x))
text_vec_bow = vectorizer.fit_transform(processed_text)
print('CountVectorizer shape_training dataset', text_vec_bow.shape)
processed_text_val = df_dev['processed_text_val'].apply(lambda x: ' '.join(x))
text_vec_bow_val = vectorizer.transform(processed_text_val)
print('CountVectorizer shape_val dataset', text_vec_bow_val.shape)
lsa_bow_text = svd2.fit_transform(text_vec_bow)
print('\nvariance_captured_by 1000 components', svd2.explained_variance_ratio_.sum())
lsa_bow_text_val = svd2.transform(text_vec_bow_val)
print('\n', lsa_bow_text.shape)
print(lsa_bow_text_val.shape)
np.save('/content/drive/MyDrive/HMD_project/new/lsa_bow_train_text.npy', lsa_bow_text)
np.save('/content/drive/MyDrive/HMD_project/new/lsa_bow_val_text.npy', lsa_bow_text_val)
|
code
|
129009155/cell_14
|
[
"text_plain_output_1.png"
] |
import pandas as pd
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
with open('/content/drive/MyDrive/HMD_project/new/text_aug_norm.txt', 'r') as f:
lines = f.readlines()
file_names = []
for i in lines:
file_names.append(i[:i.find('\n')])
for i in range(len(file_names)):
df_train.loc[len(df_train.index)] = [1, 'img/1', 1, file_names[i]]
data = df_train
print('Distribution of training dataset after augmentation\n', data.label.value_counts())
|
code
|
129009155/cell_5
|
[
"text_plain_output_1.png"
] |
import pandas as pd
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
print(df_dev.tail())
|
code
|
129009155/cell_36
|
[
"text_plain_output_1.png"
] |
from gensim.models import KeyedVectors
import numpy as np
import numpy as np
import pandas as pd
import spacy
folder_path_train = '/content/drive/MyDrive/HMD_project/train.jsonl'
folder_path_dev = '/content/drive/MyDrive/HMD_project/dev.jsonl'
df_train = pd.read_json(folder_path_train, lines=True)
df_dev = pd.read_json(folder_path_dev, lines=True)
with open('/content/drive/MyDrive/HMD_project/new/text_aug_norm.txt', 'r') as f:
lines = f.readlines()
file_names = []
for i in lines:
file_names.append(i[:i.find('\n')])
for i in range(len(file_names)):
df_train.loc[len(df_train.index)] = [1, 'img/1', 1, file_names[i]]
data = df_train
from gensim.models import KeyedVectors
wv = KeyedVectors.load('/content/drive/MyDrive/HMD_project/glove-twitter-200')
import spacy.cli
spacy.cli.download('en_core_web_sm')
nlp = spacy.load('en_core_web_sm')
def preprocess(text):
doc = nlp(text)
filtered_token = []
for token in doc:
if token.is_punct or token.is_space or token.is_bracket or token.is_stop:
continue
else:
token = token.lemma_
filtered_token.append(token)
return filtered_token
df_dev['processed_text_val'] = df_dev['text'].apply(lambda x: preprocess(x))
data['processed_text'] = data['text'].apply(lambda x: preprocess(x))
import numpy as np
def gensim_vector(token):
vec_size = wv.vector_size
wv_final = np.zeros(vec_size)
count = 1
for t in token:
if t in wv:
count += 1
wv_final += wv[t]
return wv_final / count
data['text_vector'] = data['processed_text'].apply(gensim_vector)
df_dev['text_vector_val'] = df_dev['processed_text_val'].apply(gensim_vector)
len(data.text_vector.iloc[0])
|
code
|
48165025/cell_9
|
[
"image_output_1.png"
] |
import pandas as pd
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../input/titanic/test.csv', index_col='PassengerId')
train.describe()
|
code
|
48165025/cell_25
|
[
"text_plain_output_1.png"
] |
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
import warnings
warnings.simplefilter(action='ignore', category=FutureWarning)
pd.set_option('display.float_format', lambda x: '%.3f' % x)
train = pd.read_csv('../input/titanic/train.csv', index_col='PassengerId')
test = pd.read_csv('../input/titanic/test.csv', index_col='PassengerId')
train.isnull().sum()
sns.set(font_scale=1.4)
sns.set_style('whitegrid')
plt.figure(figsize=(10, 6))
sns.set_style('whitegrid')
sns.countplot(x='Survived', hue='Sex', data=train, palette='RdBu_r')
plt.title('Survived/not survived by sex')
plt.show()
|
code
|
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