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|---|---|---|---|
104126055/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.naive_bayes import GaussianNB
from sklearn.naive_bayes import GaussianNB
classifier3 = GaussianNB()
classifier3.fit(X_train, Y_train)
Y_pred3 = classifier3.predict(X_test)
print('Accuracy = ', accuracy_score(Y_test, Y_pred3) * 100, '%') | code |
104126055/cell_23 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
classifier2 = KNeighborsClassifier(n_neighbors=3, metric='minkowski', p=1)
classifier2.fit(X_train, Y_train)
Y_pred2 = classifier2.predict(X_test)
print('Accuracy = ', accuracy_score(Y_test, Y_pred2) * 100, '%') | code |
104126055/cell_33 | [
"text_plain_output_1.png"
] | from catboost import CatBoostClassifier
from sklearn.metrics import accuracy_score
from catboost import CatBoostClassifier
classifier_cb = CatBoostClassifier()
classifier_cb.fit(X_train, Y_train)
Y_predcb = classifier_cb.predict(X_test)
print('Accuracy = ', accuracy_score(Y_test, Y_predcb) * 100, '%') | code |
104126055/cell_29 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
classifier4 = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier4.fit(X_train, Y_train)
Y_pred4 = classifier4.predict(X_test)
from sklearn.ensemble import RandomForestClassifier
classifier5 = RandomForestClassifier(n_estimators=10, criterion='entropy', random_state=0)
classifier5.fit(X_train, Y_train)
Y_pred5 = classifier5.predict(X_test)
print('Accuracy = ', accuracy_score(Y_test, Y_pred4) * 100, '%') | code |
104126055/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset['Cholesterol'].unique() | code |
104126055/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
104126055/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset.info() | code |
104126055/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset.describe() | code |
104126055/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
import numpy as np
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset.replace(to_replace=['drugA', 'drugB', 'drugC', 'drugX', 'DrugY'], value=[0, 1, 2, 3, 4], inplace=True)
X = dataset.iloc[:, :-1].values
Y = dataset.iloc[:, -1].values
from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import OneHotEncoder
ohe = OneHotEncoder()
ct = ColumnTransformer(transformers=[('encoder', ohe, [2])], remainder='passthrough')
X = np.array(ct.fit_transform(X))
X[0] | code |
104126055/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from xgboost import XGBClassifier
from xgboost import XGBClassifier
classifier_xg = XGBClassifier()
classifier_xg.fit(X_train, Y_train)
Y_predxg = classifier_xg.predict(X_test)
print('Accuracy = ', accuracy_score(Y_test, Y_predxg) * 100, '%') | code |
104126055/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset['BP'].unique() | code |
104126055/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score
from sklearn.tree import DecisionTreeClassifier
from sklearn.tree import DecisionTreeClassifier
classifier4 = DecisionTreeClassifier(criterion='entropy', random_state=0)
classifier4.fit(X_train, Y_train)
Y_pred4 = classifier4.predict(X_test)
print('Accuracy = ', accuracy_score(Y_test, Y_pred4) * 100, '%') | code |
104126055/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset['Drug'].unique() | code |
104126055/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/drug-classification/drug200.csv')
dataset.head() | code |
32073217/cell_6 | [
"image_output_2.png",
"image_output_1.png"
] | from fipy import Variable, FaceVariable, CellVariable, Grid1D, ExplicitDiffusionTerm, TransientTerm, DiffusionTerm, Viewer
from fipy.tools import numerix
nx = 50
Lx = 1.0
dx = Lx / nx
mesh = Grid1D(Lx=Lx, dx=dx)
x = mesh.cellCenters[0]
T = CellVariable(name='solution variable', mesh=mesh, value=0.0)
T.setValue(0)
a = 1.0
valueLeft = 1
valueRight = 0
T.constrain(valueRight, mesh.facesRight)
T.constrain(valueLeft, mesh.facesLeft)
timeStepDuration = 0.9 * dx ** 2 / (2 * a)
steps = 100
t_final = timeStepDuration * steps
eqX = TransientTerm() == ExplicitDiffusionTerm(coeff=a)
T_Analytical = CellVariable(name='analytical value', mesh=mesh)
T_Analytical.setValue(1 - erf(x / (2 * numerix.sqrt(a * t_final))))
viewer = Viewer(vars=(T, T_Analytical))
for step in range(steps):
eqX.solve(var=T, dt=timeStepDuration)
if __name__ == '__main__':
viewer.plot('1Dtransient_%0d.png' % step) | code |
32073217/cell_1 | [
"text_plain_output_1.png"
] | !pip install ht;
!pip install future;
!pip install fipy
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from fipy import Variable, FaceVariable, CellVariable, Grid1D, ExplicitDiffusionTerm, TransientTerm, DiffusionTerm, Viewer
from fipy.tools import numerix
from scipy.special import erf # doctest: +SCIPY | code |
32073217/cell_10 | [
"image_output_2.png",
"image_output_1.png"
] | from fipy import Variable, FaceVariable, CellVariable, Grid1D, ExplicitDiffusionTerm, TransientTerm, DiffusionTerm, Viewer
from fipy.tools import numerix
nx = 50
Lx = 1.0
dx = Lx / nx
mesh = Grid1D(Lx=Lx, dx=dx)
x = mesh.cellCenters[0]
T = CellVariable(name='solution variable', mesh=mesh, value=0.0)
T.setValue(0)
a = 1.0
valueLeft = 1
valueRight = 0
T.constrain(valueRight, mesh.facesRight)
T.constrain(valueLeft, mesh.facesLeft)
timeStepDuration = 0.9 * dx ** 2 / (2 * a)
steps = 100
t_final = timeStepDuration * steps
eqX = TransientTerm() == ExplicitDiffusionTerm(coeff=a)
T_Analytical = CellVariable(name='analytical value', mesh=mesh)
T_Analytical.setValue(1 - erf(x / (2 * numerix.sqrt(a * t_final))))
nx = 50
Lx = 1.0
dx = Lx / nx
mesh = Grid1D(Lx=Lx, dx=dx)
phi2 = CellVariable(name='solution variable', mesh=mesh)
phi2.setValue(0)
x = mesh.cellCenters[0]
time = Variable()
D = 1.0
valueLeft = 1 + 0.5 * (1 + numerix.sin(0.5 * time))
fluxRight = -0.5
phi2.constrain(valueLeft, mesh.facesLeft)
phi2.faceGrad.constrain([fluxRight], mesh.facesRight)
eqI = TransientTerm() == DiffusionTerm(coeff=D)
phiAnalytical = CellVariable(name='analytical value', mesh=mesh)
viewer2 = Viewer(vars=phi2, datamin=0, datamax=3)
dt = 0.5
while time() < 50:
time.setValue(time() + dt)
eqI.solve(var=phi2, dt=dt)
if __name__ == '__main__':
viewer2.plot() | code |
106207999/cell_21 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
def plot_cat_features(df):
fig, ax = plt.subplots(2, 3, figsize=(18,12))
sb.countplot(x='stroke', data=df, hue='gender', ax=ax[0,0])
sb.countplot(x='stroke', data=df, hue='hypertension', ax=ax[0,1])
sb.countplot(x='stroke', data=df, hue='heart_disease', ax=ax[0,2])
sb.countplot(x='stroke', data=df, hue='ever_married', ax=ax[1,0])
sb.countplot(x='stroke', data=df, hue='work_type', ax=ax[1,1])
sb.countplot(x='stroke', data=df, hue='smoking_status', ax=ax[1,2])
fig.suptitle('Bar graph showing the number of people who had or not Stroke', y=0.93, fontsize=22)
plt.show()
def plot_nmr_features(df):
fig, ax = plt.subplots(3, 2, figsize=(12, 18))
sb.distplot(df['age'], label='Skewness: %.2f'%(df['age'].skew()), ax=ax[0,0])
ax[0,0].legend(loc='best')
sb.boxplot(df['age'], ax=ax[0,1])
sb.distplot(df['avg_glucose_level'], label='Skewness: %.2f'%df['avg_glucose_level'].skew(), ax=ax[1,0])
ax[1,0].legend(loc='best')
sb.boxplot(df['avg_glucose_level'], ax=ax[1,1])
sb.distplot(df['bmi'], label='Skewness: %.2f'%df['bmi'].skew(), ax=ax[2,0])
ax[2,0].legend(loc='best')
sb.boxplot(df['bmi'], ax=ax[2,1])
fig.suptitle('Distribution of numerical features', y=0.93, fontsize=22)
plt.show()
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
plot_nmr_features(data) | code |
106207999/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
def plot_cat_features(df):
fig, ax = plt.subplots(2, 3, figsize=(18,12))
sb.countplot(x='stroke', data=df, hue='gender', ax=ax[0,0])
sb.countplot(x='stroke', data=df, hue='hypertension', ax=ax[0,1])
sb.countplot(x='stroke', data=df, hue='heart_disease', ax=ax[0,2])
sb.countplot(x='stroke', data=df, hue='ever_married', ax=ax[1,0])
sb.countplot(x='stroke', data=df, hue='work_type', ax=ax[1,1])
sb.countplot(x='stroke', data=df, hue='smoking_status', ax=ax[1,2])
fig.suptitle('Bar graph showing the number of people who had or not Stroke', y=0.93, fontsize=22)
plt.show()
def plot_nmr_features(df):
fig, ax = plt.subplots(3, 2, figsize=(12, 18))
sb.distplot(df['age'], label='Skewness: %.2f'%(df['age'].skew()), ax=ax[0,0])
ax[0,0].legend(loc='best')
sb.boxplot(df['age'], ax=ax[0,1])
sb.distplot(df['avg_glucose_level'], label='Skewness: %.2f'%df['avg_glucose_level'].skew(), ax=ax[1,0])
ax[1,0].legend(loc='best')
sb.boxplot(df['avg_glucose_level'], ax=ax[1,1])
sb.distplot(df['bmi'], label='Skewness: %.2f'%df['bmi'].skew(), ax=ax[2,0])
ax[2,0].legend(loc='best')
sb.boxplot(df['bmi'], ax=ax[2,1])
fig.suptitle('Distribution of numerical features', y=0.93, fontsize=22)
plt.show()
sb.pairplot(df) | code |
106207999/cell_25 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
features = data.drop('stroke', axis=1).columns
features
cat_features = []
nmr_features = []
for i in range(len(features)):
if df.iloc[:, i].dtype == 'object':
cat_features.append(df.columns[i])
else:
nmr_features.append(df.columns[i])
print('Categorical Features:', cat_features)
print('Numerical Features:', nmr_features) | code |
106207999/cell_33 | [
"text_plain_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
def plot_cat_features(df):
fig, ax = plt.subplots(2, 3, figsize=(18,12))
sb.countplot(x='stroke', data=df, hue='gender', ax=ax[0,0])
sb.countplot(x='stroke', data=df, hue='hypertension', ax=ax[0,1])
sb.countplot(x='stroke', data=df, hue='heart_disease', ax=ax[0,2])
sb.countplot(x='stroke', data=df, hue='ever_married', ax=ax[1,0])
sb.countplot(x='stroke', data=df, hue='work_type', ax=ax[1,1])
sb.countplot(x='stroke', data=df, hue='smoking_status', ax=ax[1,2])
fig.suptitle('Bar graph showing the number of people who had or not Stroke', y=0.93, fontsize=22)
plt.show()
def plot_nmr_features(df):
fig, ax = plt.subplots(3, 2, figsize=(12, 18))
sb.distplot(df['age'], label='Skewness: %.2f'%(df['age'].skew()), ax=ax[0,0])
ax[0,0].legend(loc='best')
sb.boxplot(df['age'], ax=ax[0,1])
sb.distplot(df['avg_glucose_level'], label='Skewness: %.2f'%df['avg_glucose_level'].skew(), ax=ax[1,0])
ax[1,0].legend(loc='best')
sb.boxplot(df['avg_glucose_level'], ax=ax[1,1])
sb.distplot(df['bmi'], label='Skewness: %.2f'%df['bmi'].skew(), ax=ax[2,0])
ax[2,0].legend(loc='best')
sb.boxplot(df['bmi'], ax=ax[2,1])
fig.suptitle('Distribution of numerical features', y=0.93, fontsize=22)
plt.show()
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
features = data.drop('stroke', axis=1).columns
features
cat_features = []
nmr_features = []
for i in range(len(features)):
if df.iloc[:, i].dtype == 'object':
cat_features.append(df.columns[i])
else:
nmr_features.append(df.columns[i])
data = pd.get_dummies(data, columns=cat_features, drop_first=True)
x = data.drop('stroke', axis=1)
y = data['stroke'].astype('int')
from imblearn.under_sampling import RandomUnderSampler
undersample = RandomUnderSampler(sampling_strategy=0.5)
x_under, y_under = undersample.fit_resample(x, y)
pd.DataFrame(y_under).value_counts()
def model_evaluation(x, y):
models = []
names = []
scoring = ['accuracy', 'precision', 'recall', 'f1']
models.append(('SVC', SVC()))
models.append(('DTC', DecisionTreeClassifier()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('GNB', GaussianNB()))
df_results = pd.DataFrame(columns=['Algorithm', 'Acc Mean', 'Acc STD', 'Pre Mean', 'Pre STD',
'Rec Mean', 'Rec STD', 'F1 Mean', 'F1 STD'])
results_acc = []
results_pre = []
results_rec = []
results_f1 = []
for name, model in models:
names.append(name)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=101)
result = cross_validate(model, x, y, cv=kfold, scoring=scoring)
# Accuracy
acc_mean = result['test_accuracy'].mean()
acc_std = result['test_accuracy'].std()
# Precision
pre_mean = result['test_precision'].mean()
pre_std = result['test_precision'].std()
# Recall
rec_mean = result['test_recall'].mean()
rec_std = result['test_recall'].std()
#F1-Score
f1_mean = result['test_f1'].mean()
f1_std = result['test_f1'].std()
df_result_row = {'Algorithm': name, 'Acc Mean': acc_mean, 'Acc STD': acc_std, 'Pre Mean': pre_mean,
'Pre STD': pre_std, 'Rec Mean': rec_mean, 'Rec STD': rec_std, 'F1 Mean': f1_mean,
'F1 STD': f1_std}
df_results = df_results.append(df_result_row, ignore_index=True)
results_acc.append(result['test_accuracy'])
results_pre.append(result['test_precision'])
results_rec.append(result['test_recall'])
results_f1.append(result['test_f1'])
df_results = df_results.set_index('Algorithm')
pd.set_option('display.float_format', lambda x: '%.3f' % x)
# Display the mean and standard deviation of all metrics for all algorithms
print(df_results)
# Display the overall results in a boxplot graph
plot_objects = plt.subplots(nrows=1, ncols=4, figsize=(14, 6))
fig, (ax1, ax2, ax3, ax4) = plot_objects
ax1.boxplot(results_acc)
ax1.set_title('Accuracy', fontsize=14)
ax1.set_xticklabels(names)
ax2.boxplot(results_pre)
ax2.set_title('Precision', fontsize=14)
ax2.set_xticklabels(names)
ax3.boxplot(results_rec)
ax3.set_title('Recall', fontsize=14)
ax3.set_xticklabels(names)
ax4.boxplot(results_f1)
ax4.set_title('F1-Score', fontsize=14)
ax4.set_xticklabels(names)
plt.show()
model_evaluation(x_under, y_under) | code |
106207999/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
no_stroke = df['stroke'].value_counts()[0] / len(df['stroke']) * 100
had_stroke = df['stroke'].value_counts()[1] / len(df['stroke']) * 100
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
print(data['stroke'].value_counts())
no_stroke = data['stroke'].value_counts()[0] / len(data['stroke']) * 100
had_stroke = data['stroke'].value_counts()[1] / len(data['stroke']) * 100
print('\nRatio of the people who had no stroke: %.2f%%' % no_stroke)
print('Ratio of the people who had stroke: %.2f%%' % had_stroke) | code |
106207999/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df.info() | code |
106207999/cell_29 | [
"image_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
features = data.drop('stroke', axis=1).columns
features
cat_features = []
nmr_features = []
for i in range(len(features)):
if df.iloc[:, i].dtype == 'object':
cat_features.append(df.columns[i])
else:
nmr_features.append(df.columns[i])
data = pd.get_dummies(data, columns=cat_features, drop_first=True)
x = data.drop('stroke', axis=1)
y = data['stroke'].astype('int')
from imblearn.under_sampling import RandomUnderSampler
undersample = RandomUnderSampler(sampling_strategy=0.5)
x_under, y_under = undersample.fit_resample(x, y)
pd.DataFrame(y_under).value_counts() | code |
106207999/cell_26 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
features = data.drop('stroke', axis=1).columns
features
cat_features = []
nmr_features = []
for i in range(len(features)):
if df.iloc[:, i].dtype == 'object':
cat_features.append(df.columns[i])
else:
nmr_features.append(df.columns[i])
data = pd.get_dummies(data, columns=cat_features, drop_first=True)
data.head() | code |
106207999/cell_41 | [
"text_plain_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
def plot_cat_features(df):
fig, ax = plt.subplots(2, 3, figsize=(18,12))
sb.countplot(x='stroke', data=df, hue='gender', ax=ax[0,0])
sb.countplot(x='stroke', data=df, hue='hypertension', ax=ax[0,1])
sb.countplot(x='stroke', data=df, hue='heart_disease', ax=ax[0,2])
sb.countplot(x='stroke', data=df, hue='ever_married', ax=ax[1,0])
sb.countplot(x='stroke', data=df, hue='work_type', ax=ax[1,1])
sb.countplot(x='stroke', data=df, hue='smoking_status', ax=ax[1,2])
fig.suptitle('Bar graph showing the number of people who had or not Stroke', y=0.93, fontsize=22)
plt.show()
def plot_nmr_features(df):
fig, ax = plt.subplots(3, 2, figsize=(12, 18))
sb.distplot(df['age'], label='Skewness: %.2f'%(df['age'].skew()), ax=ax[0,0])
ax[0,0].legend(loc='best')
sb.boxplot(df['age'], ax=ax[0,1])
sb.distplot(df['avg_glucose_level'], label='Skewness: %.2f'%df['avg_glucose_level'].skew(), ax=ax[1,0])
ax[1,0].legend(loc='best')
sb.boxplot(df['avg_glucose_level'], ax=ax[1,1])
sb.distplot(df['bmi'], label='Skewness: %.2f'%df['bmi'].skew(), ax=ax[2,0])
ax[2,0].legend(loc='best')
sb.boxplot(df['bmi'], ax=ax[2,1])
fig.suptitle('Distribution of numerical features', y=0.93, fontsize=22)
plt.show()
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
features = data.drop('stroke', axis=1).columns
features
cat_features = []
nmr_features = []
for i in range(len(features)):
if df.iloc[:, i].dtype == 'object':
cat_features.append(df.columns[i])
else:
nmr_features.append(df.columns[i])
data = pd.get_dummies(data, columns=cat_features, drop_first=True)
x = data.drop('stroke', axis=1)
y = data['stroke'].astype('int')
from imblearn.under_sampling import RandomUnderSampler
undersample = RandomUnderSampler(sampling_strategy=0.5)
x_under, y_under = undersample.fit_resample(x, y)
pd.DataFrame(y_under).value_counts()
def model_evaluation(x, y):
models = []
names = []
scoring = ['accuracy', 'precision', 'recall', 'f1']
models.append(('SVC', SVC()))
models.append(('DTC', DecisionTreeClassifier()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('GNB', GaussianNB()))
df_results = pd.DataFrame(columns=['Algorithm', 'Acc Mean', 'Acc STD', 'Pre Mean', 'Pre STD',
'Rec Mean', 'Rec STD', 'F1 Mean', 'F1 STD'])
results_acc = []
results_pre = []
results_rec = []
results_f1 = []
for name, model in models:
names.append(name)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=101)
result = cross_validate(model, x, y, cv=kfold, scoring=scoring)
# Accuracy
acc_mean = result['test_accuracy'].mean()
acc_std = result['test_accuracy'].std()
# Precision
pre_mean = result['test_precision'].mean()
pre_std = result['test_precision'].std()
# Recall
rec_mean = result['test_recall'].mean()
rec_std = result['test_recall'].std()
#F1-Score
f1_mean = result['test_f1'].mean()
f1_std = result['test_f1'].std()
df_result_row = {'Algorithm': name, 'Acc Mean': acc_mean, 'Acc STD': acc_std, 'Pre Mean': pre_mean,
'Pre STD': pre_std, 'Rec Mean': rec_mean, 'Rec STD': rec_std, 'F1 Mean': f1_mean,
'F1 STD': f1_std}
df_results = df_results.append(df_result_row, ignore_index=True)
results_acc.append(result['test_accuracy'])
results_pre.append(result['test_precision'])
results_rec.append(result['test_recall'])
results_f1.append(result['test_f1'])
df_results = df_results.set_index('Algorithm')
pd.set_option('display.float_format', lambda x: '%.3f' % x)
# Display the mean and standard deviation of all metrics for all algorithms
print(df_results)
# Display the overall results in a boxplot graph
plot_objects = plt.subplots(nrows=1, ncols=4, figsize=(14, 6))
fig, (ax1, ax2, ax3, ax4) = plot_objects
ax1.boxplot(results_acc)
ax1.set_title('Accuracy', fontsize=14)
ax1.set_xticklabels(names)
ax2.boxplot(results_pre)
ax2.set_title('Precision', fontsize=14)
ax2.set_xticklabels(names)
ax3.boxplot(results_rec)
ax3.set_title('Recall', fontsize=14)
ax3.set_xticklabels(names)
ax4.boxplot(results_f1)
ax4.set_title('F1-Score', fontsize=14)
ax4.set_xticklabels(names)
plt.show()
h_parameters = {'max_features': ['sqrt', 'log2'], 'ccp_alpha': [0.1, 0.01, 0.001], 'max_depth': [5, 6, 7, 8], 'criterion': ['gini', 'entropy']}
model = DecisionTreeClassifier()
kfold = KFold(n_splits=5, shuffle=True, random_state=101)
grid = GridSearchCV(estimator=model, param_grid=h_parameters, cv=kfold)
grid.fit(x_train, y_train)
print('Best score:', grid.best_score_)
print('Best parameters: ', grid.best_params_) | code |
106207999/cell_2 | [
"image_output_1.png"
] | import os
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106207999/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
data.describe(include='all') | code |
106207999/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.info() | code |
106207999/cell_45 | [
"text_plain_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
def plot_cat_features(df):
fig, ax = plt.subplots(2, 3, figsize=(18,12))
sb.countplot(x='stroke', data=df, hue='gender', ax=ax[0,0])
sb.countplot(x='stroke', data=df, hue='hypertension', ax=ax[0,1])
sb.countplot(x='stroke', data=df, hue='heart_disease', ax=ax[0,2])
sb.countplot(x='stroke', data=df, hue='ever_married', ax=ax[1,0])
sb.countplot(x='stroke', data=df, hue='work_type', ax=ax[1,1])
sb.countplot(x='stroke', data=df, hue='smoking_status', ax=ax[1,2])
fig.suptitle('Bar graph showing the number of people who had or not Stroke', y=0.93, fontsize=22)
plt.show()
def plot_nmr_features(df):
fig, ax = plt.subplots(3, 2, figsize=(12, 18))
sb.distplot(df['age'], label='Skewness: %.2f'%(df['age'].skew()), ax=ax[0,0])
ax[0,0].legend(loc='best')
sb.boxplot(df['age'], ax=ax[0,1])
sb.distplot(df['avg_glucose_level'], label='Skewness: %.2f'%df['avg_glucose_level'].skew(), ax=ax[1,0])
ax[1,0].legend(loc='best')
sb.boxplot(df['avg_glucose_level'], ax=ax[1,1])
sb.distplot(df['bmi'], label='Skewness: %.2f'%df['bmi'].skew(), ax=ax[2,0])
ax[2,0].legend(loc='best')
sb.boxplot(df['bmi'], ax=ax[2,1])
fig.suptitle('Distribution of numerical features', y=0.93, fontsize=22)
plt.show()
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
features = data.drop('stroke', axis=1).columns
features
cat_features = []
nmr_features = []
for i in range(len(features)):
if df.iloc[:, i].dtype == 'object':
cat_features.append(df.columns[i])
else:
nmr_features.append(df.columns[i])
data = pd.get_dummies(data, columns=cat_features, drop_first=True)
x = data.drop('stroke', axis=1)
y = data['stroke'].astype('int')
from imblearn.under_sampling import RandomUnderSampler
undersample = RandomUnderSampler(sampling_strategy=0.5)
x_under, y_under = undersample.fit_resample(x, y)
pd.DataFrame(y_under).value_counts()
def model_evaluation(x, y):
models = []
names = []
scoring = ['accuracy', 'precision', 'recall', 'f1']
models.append(('SVC', SVC()))
models.append(('DTC', DecisionTreeClassifier()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('GNB', GaussianNB()))
df_results = pd.DataFrame(columns=['Algorithm', 'Acc Mean', 'Acc STD', 'Pre Mean', 'Pre STD',
'Rec Mean', 'Rec STD', 'F1 Mean', 'F1 STD'])
results_acc = []
results_pre = []
results_rec = []
results_f1 = []
for name, model in models:
names.append(name)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=101)
result = cross_validate(model, x, y, cv=kfold, scoring=scoring)
# Accuracy
acc_mean = result['test_accuracy'].mean()
acc_std = result['test_accuracy'].std()
# Precision
pre_mean = result['test_precision'].mean()
pre_std = result['test_precision'].std()
# Recall
rec_mean = result['test_recall'].mean()
rec_std = result['test_recall'].std()
#F1-Score
f1_mean = result['test_f1'].mean()
f1_std = result['test_f1'].std()
df_result_row = {'Algorithm': name, 'Acc Mean': acc_mean, 'Acc STD': acc_std, 'Pre Mean': pre_mean,
'Pre STD': pre_std, 'Rec Mean': rec_mean, 'Rec STD': rec_std, 'F1 Mean': f1_mean,
'F1 STD': f1_std}
df_results = df_results.append(df_result_row, ignore_index=True)
results_acc.append(result['test_accuracy'])
results_pre.append(result['test_precision'])
results_rec.append(result['test_recall'])
results_f1.append(result['test_f1'])
df_results = df_results.set_index('Algorithm')
pd.set_option('display.float_format', lambda x: '%.3f' % x)
# Display the mean and standard deviation of all metrics for all algorithms
print(df_results)
# Display the overall results in a boxplot graph
plot_objects = plt.subplots(nrows=1, ncols=4, figsize=(14, 6))
fig, (ax1, ax2, ax3, ax4) = plot_objects
ax1.boxplot(results_acc)
ax1.set_title('Accuracy', fontsize=14)
ax1.set_xticklabels(names)
ax2.boxplot(results_pre)
ax2.set_title('Precision', fontsize=14)
ax2.set_xticklabels(names)
ax3.boxplot(results_rec)
ax3.set_title('Recall', fontsize=14)
ax3.set_xticklabels(names)
ax4.boxplot(results_f1)
ax4.set_title('F1-Score', fontsize=14)
ax4.set_xticklabels(names)
plt.show()
h_parameters = {'max_features': ['sqrt', 'log2'], 'ccp_alpha': [0.1, 0.01, 0.001], 'max_depth': [5, 6, 7, 8], 'criterion': ['gini', 'entropy']}
model = DecisionTreeClassifier()
kfold = KFold(n_splits=5, shuffle=True, random_state=101)
grid = GridSearchCV(estimator=model, param_grid=h_parameters, cv=kfold)
grid.fit(x_train, y_train)
model = DecisionTreeClassifier(max_features='log2', max_depth=8, criterion='gini', ccp_alpha=0.001)
model.fit(x_train, y_train)
y_hat = model.predict(x_test)
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score, confusion_matrix
print('Accuracy score: %.1f%%' % (accuracy_score(y_test, y_hat) * 100))
print('Precision score: %.3f' % precision_score(y_test, y_hat))
print('Recall: %.3f' % recall_score(y_test, y_hat))
print('F1-Score: %.3f' % f1_score(y_test, y_hat)) | code |
106207999/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
features = data.drop('stroke', axis=1).columns
features
cat_features = []
nmr_features = []
for i in range(len(features)):
if df.iloc[:, i].dtype == 'object':
cat_features.append(df.columns[i])
else:
nmr_features.append(df.columns[i])
data = pd.get_dummies(data, columns=cat_features, drop_first=True)
x = data.drop('stroke', axis=1)
y = data['stroke'].astype('int')
from imblearn.under_sampling import RandomUnderSampler
undersample = RandomUnderSampler(sampling_strategy=0.5)
x_under, y_under = undersample.fit_resample(x, y)
print('x under sampled shape: ', x_under.shape)
print('y under sampled shape: ', y_under.shape) | code |
106207999/cell_8 | [
"image_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.describe(include='all') | code |
106207999/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df.head() | code |
106207999/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum() | code |
106207999/cell_35 | [
"text_html_output_1.png"
] | from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
def plot_cat_features(df):
fig, ax = plt.subplots(2, 3, figsize=(18,12))
sb.countplot(x='stroke', data=df, hue='gender', ax=ax[0,0])
sb.countplot(x='stroke', data=df, hue='hypertension', ax=ax[0,1])
sb.countplot(x='stroke', data=df, hue='heart_disease', ax=ax[0,2])
sb.countplot(x='stroke', data=df, hue='ever_married', ax=ax[1,0])
sb.countplot(x='stroke', data=df, hue='work_type', ax=ax[1,1])
sb.countplot(x='stroke', data=df, hue='smoking_status', ax=ax[1,2])
fig.suptitle('Bar graph showing the number of people who had or not Stroke', y=0.93, fontsize=22)
plt.show()
def plot_nmr_features(df):
fig, ax = plt.subplots(3, 2, figsize=(12, 18))
sb.distplot(df['age'], label='Skewness: %.2f'%(df['age'].skew()), ax=ax[0,0])
ax[0,0].legend(loc='best')
sb.boxplot(df['age'], ax=ax[0,1])
sb.distplot(df['avg_glucose_level'], label='Skewness: %.2f'%df['avg_glucose_level'].skew(), ax=ax[1,0])
ax[1,0].legend(loc='best')
sb.boxplot(df['avg_glucose_level'], ax=ax[1,1])
sb.distplot(df['bmi'], label='Skewness: %.2f'%df['bmi'].skew(), ax=ax[2,0])
ax[2,0].legend(loc='best')
sb.boxplot(df['bmi'], ax=ax[2,1])
fig.suptitle('Distribution of numerical features', y=0.93, fontsize=22)
plt.show()
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
features = data.drop('stroke', axis=1).columns
features
cat_features = []
nmr_features = []
for i in range(len(features)):
if df.iloc[:, i].dtype == 'object':
cat_features.append(df.columns[i])
else:
nmr_features.append(df.columns[i])
data = pd.get_dummies(data, columns=cat_features, drop_first=True)
x = data.drop('stroke', axis=1)
y = data['stroke'].astype('int')
from imblearn.under_sampling import RandomUnderSampler
undersample = RandomUnderSampler(sampling_strategy=0.5)
x_under, y_under = undersample.fit_resample(x, y)
pd.DataFrame(y_under).value_counts()
def model_evaluation(x, y):
models = []
names = []
scoring = ['accuracy', 'precision', 'recall', 'f1']
models.append(('SVC', SVC()))
models.append(('DTC', DecisionTreeClassifier()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('GNB', GaussianNB()))
df_results = pd.DataFrame(columns=['Algorithm', 'Acc Mean', 'Acc STD', 'Pre Mean', 'Pre STD',
'Rec Mean', 'Rec STD', 'F1 Mean', 'F1 STD'])
results_acc = []
results_pre = []
results_rec = []
results_f1 = []
for name, model in models:
names.append(name)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=101)
result = cross_validate(model, x, y, cv=kfold, scoring=scoring)
# Accuracy
acc_mean = result['test_accuracy'].mean()
acc_std = result['test_accuracy'].std()
# Precision
pre_mean = result['test_precision'].mean()
pre_std = result['test_precision'].std()
# Recall
rec_mean = result['test_recall'].mean()
rec_std = result['test_recall'].std()
#F1-Score
f1_mean = result['test_f1'].mean()
f1_std = result['test_f1'].std()
df_result_row = {'Algorithm': name, 'Acc Mean': acc_mean, 'Acc STD': acc_std, 'Pre Mean': pre_mean,
'Pre STD': pre_std, 'Rec Mean': rec_mean, 'Rec STD': rec_std, 'F1 Mean': f1_mean,
'F1 STD': f1_std}
df_results = df_results.append(df_result_row, ignore_index=True)
results_acc.append(result['test_accuracy'])
results_pre.append(result['test_precision'])
results_rec.append(result['test_recall'])
results_f1.append(result['test_f1'])
df_results = df_results.set_index('Algorithm')
pd.set_option('display.float_format', lambda x: '%.3f' % x)
# Display the mean and standard deviation of all metrics for all algorithms
print(df_results)
# Display the overall results in a boxplot graph
plot_objects = plt.subplots(nrows=1, ncols=4, figsize=(14, 6))
fig, (ax1, ax2, ax3, ax4) = plot_objects
ax1.boxplot(results_acc)
ax1.set_title('Accuracy', fontsize=14)
ax1.set_xticklabels(names)
ax2.boxplot(results_pre)
ax2.set_title('Precision', fontsize=14)
ax2.set_xticklabels(names)
ax3.boxplot(results_rec)
ax3.set_title('Recall', fontsize=14)
ax3.set_xticklabels(names)
ax4.boxplot(results_f1)
ax4.set_title('F1-Score', fontsize=14)
ax4.set_xticklabels(names)
plt.show()
from imblearn.over_sampling import SMOTE
oversampling = SMOTE(sampling_strategy=0.5)
x_over, y_over = oversampling.fit_resample(x, y)
print('x over sampled shape: ', x_over.shape)
print('y over sampled shape: ', y_over.shape)
print(pd.DataFrame(y_over).value_counts()) | code |
106207999/cell_43 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
def plot_cat_features(df):
fig, ax = plt.subplots(2, 3, figsize=(18,12))
sb.countplot(x='stroke', data=df, hue='gender', ax=ax[0,0])
sb.countplot(x='stroke', data=df, hue='hypertension', ax=ax[0,1])
sb.countplot(x='stroke', data=df, hue='heart_disease', ax=ax[0,2])
sb.countplot(x='stroke', data=df, hue='ever_married', ax=ax[1,0])
sb.countplot(x='stroke', data=df, hue='work_type', ax=ax[1,1])
sb.countplot(x='stroke', data=df, hue='smoking_status', ax=ax[1,2])
fig.suptitle('Bar graph showing the number of people who had or not Stroke', y=0.93, fontsize=22)
plt.show()
def plot_nmr_features(df):
fig, ax = plt.subplots(3, 2, figsize=(12, 18))
sb.distplot(df['age'], label='Skewness: %.2f'%(df['age'].skew()), ax=ax[0,0])
ax[0,0].legend(loc='best')
sb.boxplot(df['age'], ax=ax[0,1])
sb.distplot(df['avg_glucose_level'], label='Skewness: %.2f'%df['avg_glucose_level'].skew(), ax=ax[1,0])
ax[1,0].legend(loc='best')
sb.boxplot(df['avg_glucose_level'], ax=ax[1,1])
sb.distplot(df['bmi'], label='Skewness: %.2f'%df['bmi'].skew(), ax=ax[2,0])
ax[2,0].legend(loc='best')
sb.boxplot(df['bmi'], ax=ax[2,1])
fig.suptitle('Distribution of numerical features', y=0.93, fontsize=22)
plt.show()
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
features = data.drop('stroke', axis=1).columns
features
cat_features = []
nmr_features = []
for i in range(len(features)):
if df.iloc[:, i].dtype == 'object':
cat_features.append(df.columns[i])
else:
nmr_features.append(df.columns[i])
data = pd.get_dummies(data, columns=cat_features, drop_first=True)
x = data.drop('stroke', axis=1)
y = data['stroke'].astype('int')
from imblearn.under_sampling import RandomUnderSampler
undersample = RandomUnderSampler(sampling_strategy=0.5)
x_under, y_under = undersample.fit_resample(x, y)
pd.DataFrame(y_under).value_counts()
def model_evaluation(x, y):
models = []
names = []
scoring = ['accuracy', 'precision', 'recall', 'f1']
models.append(('SVC', SVC()))
models.append(('DTC', DecisionTreeClassifier()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('GNB', GaussianNB()))
df_results = pd.DataFrame(columns=['Algorithm', 'Acc Mean', 'Acc STD', 'Pre Mean', 'Pre STD',
'Rec Mean', 'Rec STD', 'F1 Mean', 'F1 STD'])
results_acc = []
results_pre = []
results_rec = []
results_f1 = []
for name, model in models:
names.append(name)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=101)
result = cross_validate(model, x, y, cv=kfold, scoring=scoring)
# Accuracy
acc_mean = result['test_accuracy'].mean()
acc_std = result['test_accuracy'].std()
# Precision
pre_mean = result['test_precision'].mean()
pre_std = result['test_precision'].std()
# Recall
rec_mean = result['test_recall'].mean()
rec_std = result['test_recall'].std()
#F1-Score
f1_mean = result['test_f1'].mean()
f1_std = result['test_f1'].std()
df_result_row = {'Algorithm': name, 'Acc Mean': acc_mean, 'Acc STD': acc_std, 'Pre Mean': pre_mean,
'Pre STD': pre_std, 'Rec Mean': rec_mean, 'Rec STD': rec_std, 'F1 Mean': f1_mean,
'F1 STD': f1_std}
df_results = df_results.append(df_result_row, ignore_index=True)
results_acc.append(result['test_accuracy'])
results_pre.append(result['test_precision'])
results_rec.append(result['test_recall'])
results_f1.append(result['test_f1'])
df_results = df_results.set_index('Algorithm')
pd.set_option('display.float_format', lambda x: '%.3f' % x)
# Display the mean and standard deviation of all metrics for all algorithms
print(df_results)
# Display the overall results in a boxplot graph
plot_objects = plt.subplots(nrows=1, ncols=4, figsize=(14, 6))
fig, (ax1, ax2, ax3, ax4) = plot_objects
ax1.boxplot(results_acc)
ax1.set_title('Accuracy', fontsize=14)
ax1.set_xticklabels(names)
ax2.boxplot(results_pre)
ax2.set_title('Precision', fontsize=14)
ax2.set_xticklabels(names)
ax3.boxplot(results_rec)
ax3.set_title('Recall', fontsize=14)
ax3.set_xticklabels(names)
ax4.boxplot(results_f1)
ax4.set_title('F1-Score', fontsize=14)
ax4.set_xticklabels(names)
plt.show()
h_parameters = {'max_features': ['sqrt', 'log2'], 'ccp_alpha': [0.1, 0.01, 0.001], 'max_depth': [5, 6, 7, 8], 'criterion': ['gini', 'entropy']}
model = DecisionTreeClassifier()
kfold = KFold(n_splits=5, shuffle=True, random_state=101)
grid = GridSearchCV(estimator=model, param_grid=h_parameters, cv=kfold)
grid.fit(x_train, y_train)
model = DecisionTreeClassifier(max_features='log2', max_depth=8, criterion='gini', ccp_alpha=0.001)
model.fit(x_train, y_train) | code |
106207999/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
features = data.drop('stroke', axis=1).columns
features | code |
106207999/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
print(df['stroke'].value_counts())
no_stroke = df['stroke'].value_counts()[0] / len(df['stroke']) * 100
had_stroke = df['stroke'].value_counts()[1] / len(df['stroke']) * 100
print('\nRatio of the people who had no stroke: %.2f%%' % no_stroke)
print('Ratio of the people who had stroke: %.2f%%' % had_stroke) | code |
106207999/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
def plot_cat_features(df):
fig, ax = plt.subplots(2, 3, figsize=(18,12))
sb.countplot(x='stroke', data=df, hue='gender', ax=ax[0,0])
sb.countplot(x='stroke', data=df, hue='hypertension', ax=ax[0,1])
sb.countplot(x='stroke', data=df, hue='heart_disease', ax=ax[0,2])
sb.countplot(x='stroke', data=df, hue='ever_married', ax=ax[1,0])
sb.countplot(x='stroke', data=df, hue='work_type', ax=ax[1,1])
sb.countplot(x='stroke', data=df, hue='smoking_status', ax=ax[1,2])
fig.suptitle('Bar graph showing the number of people who had or not Stroke', y=0.93, fontsize=22)
plt.show()
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
plot_cat_features(data) | code |
106207999/cell_10 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
def plot_cat_features(df):
fig, ax = plt.subplots(2, 3, figsize=(18,12))
sb.countplot(x='stroke', data=df, hue='gender', ax=ax[0,0])
sb.countplot(x='stroke', data=df, hue='hypertension', ax=ax[0,1])
sb.countplot(x='stroke', data=df, hue='heart_disease', ax=ax[0,2])
sb.countplot(x='stroke', data=df, hue='ever_married', ax=ax[1,0])
sb.countplot(x='stroke', data=df, hue='work_type', ax=ax[1,1])
sb.countplot(x='stroke', data=df, hue='smoking_status', ax=ax[1,2])
fig.suptitle('Bar graph showing the number of people who had or not Stroke', y=0.93, fontsize=22)
plt.show()
plot_cat_features(df) | code |
106207999/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
def plot_cat_features(df):
fig, ax = plt.subplots(2, 3, figsize=(18,12))
sb.countplot(x='stroke', data=df, hue='gender', ax=ax[0,0])
sb.countplot(x='stroke', data=df, hue='hypertension', ax=ax[0,1])
sb.countplot(x='stroke', data=df, hue='heart_disease', ax=ax[0,2])
sb.countplot(x='stroke', data=df, hue='ever_married', ax=ax[1,0])
sb.countplot(x='stroke', data=df, hue='work_type', ax=ax[1,1])
sb.countplot(x='stroke', data=df, hue='smoking_status', ax=ax[1,2])
fig.suptitle('Bar graph showing the number of people who had or not Stroke', y=0.93, fontsize=22)
plt.show()
def plot_nmr_features(df):
fig, ax = plt.subplots(3, 2, figsize=(12, 18))
sb.distplot(df['age'], label='Skewness: %.2f'%(df['age'].skew()), ax=ax[0,0])
ax[0,0].legend(loc='best')
sb.boxplot(df['age'], ax=ax[0,1])
sb.distplot(df['avg_glucose_level'], label='Skewness: %.2f'%df['avg_glucose_level'].skew(), ax=ax[1,0])
ax[1,0].legend(loc='best')
sb.boxplot(df['avg_glucose_level'], ax=ax[1,1])
sb.distplot(df['bmi'], label='Skewness: %.2f'%df['bmi'].skew(), ax=ax[2,0])
ax[2,0].legend(loc='best')
sb.boxplot(df['bmi'], ax=ax[2,1])
fig.suptitle('Distribution of numerical features', y=0.93, fontsize=22)
plt.show()
plot_nmr_features(df) | code |
106207999/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df.describe(include='all') | code |
106207999/cell_36 | [
"text_plain_output_1.png"
] | from imblearn.over_sampling import SMOTE
from imblearn.under_sampling import RandomUnderSampler
from sklearn.discriminant_analysis import LinearDiscriminantAnalysis
from sklearn.model_selection import cross_validate
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
import matplotlib.pyplot as plt
import os
import pandas as pd
import seaborn as sb
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sb
sb.set(style='whitegrid')
import os
df = pd.read_csv('../input/full-filled-brain-stroke-dataset/full_data.csv')
df[['hypertension', 'heart_disease', 'stroke']] = df[['hypertension', 'heart_disease', 'stroke']].astype('object')
def plot_cat_features(df):
fig, ax = plt.subplots(2, 3, figsize=(18,12))
sb.countplot(x='stroke', data=df, hue='gender', ax=ax[0,0])
sb.countplot(x='stroke', data=df, hue='hypertension', ax=ax[0,1])
sb.countplot(x='stroke', data=df, hue='heart_disease', ax=ax[0,2])
sb.countplot(x='stroke', data=df, hue='ever_married', ax=ax[1,0])
sb.countplot(x='stroke', data=df, hue='work_type', ax=ax[1,1])
sb.countplot(x='stroke', data=df, hue='smoking_status', ax=ax[1,2])
fig.suptitle('Bar graph showing the number of people who had or not Stroke', y=0.93, fontsize=22)
plt.show()
def plot_nmr_features(df):
fig, ax = plt.subplots(3, 2, figsize=(12, 18))
sb.distplot(df['age'], label='Skewness: %.2f'%(df['age'].skew()), ax=ax[0,0])
ax[0,0].legend(loc='best')
sb.boxplot(df['age'], ax=ax[0,1])
sb.distplot(df['avg_glucose_level'], label='Skewness: %.2f'%df['avg_glucose_level'].skew(), ax=ax[1,0])
ax[1,0].legend(loc='best')
sb.boxplot(df['avg_glucose_level'], ax=ax[1,1])
sb.distplot(df['bmi'], label='Skewness: %.2f'%df['bmi'].skew(), ax=ax[2,0])
ax[2,0].legend(loc='best')
sb.boxplot(df['bmi'], ax=ax[2,1])
fig.suptitle('Distribution of numerical features', y=0.93, fontsize=22)
plt.show()
df.isnull().sum()
data = df[(df['avg_glucose_level'] <= 160) & (df['bmi'] <= 45)]
data = data.reset_index(drop=True)
features = data.drop('stroke', axis=1).columns
features
cat_features = []
nmr_features = []
for i in range(len(features)):
if df.iloc[:, i].dtype == 'object':
cat_features.append(df.columns[i])
else:
nmr_features.append(df.columns[i])
data = pd.get_dummies(data, columns=cat_features, drop_first=True)
x = data.drop('stroke', axis=1)
y = data['stroke'].astype('int')
from imblearn.under_sampling import RandomUnderSampler
undersample = RandomUnderSampler(sampling_strategy=0.5)
x_under, y_under = undersample.fit_resample(x, y)
pd.DataFrame(y_under).value_counts()
def model_evaluation(x, y):
models = []
names = []
scoring = ['accuracy', 'precision', 'recall', 'f1']
models.append(('SVC', SVC()))
models.append(('DTC', DecisionTreeClassifier()))
models.append(('KNN', KNeighborsClassifier()))
models.append(('LDA', LinearDiscriminantAnalysis()))
models.append(('GNB', GaussianNB()))
df_results = pd.DataFrame(columns=['Algorithm', 'Acc Mean', 'Acc STD', 'Pre Mean', 'Pre STD',
'Rec Mean', 'Rec STD', 'F1 Mean', 'F1 STD'])
results_acc = []
results_pre = []
results_rec = []
results_f1 = []
for name, model in models:
names.append(name)
kfold = StratifiedKFold(n_splits=10, shuffle=True, random_state=101)
result = cross_validate(model, x, y, cv=kfold, scoring=scoring)
# Accuracy
acc_mean = result['test_accuracy'].mean()
acc_std = result['test_accuracy'].std()
# Precision
pre_mean = result['test_precision'].mean()
pre_std = result['test_precision'].std()
# Recall
rec_mean = result['test_recall'].mean()
rec_std = result['test_recall'].std()
#F1-Score
f1_mean = result['test_f1'].mean()
f1_std = result['test_f1'].std()
df_result_row = {'Algorithm': name, 'Acc Mean': acc_mean, 'Acc STD': acc_std, 'Pre Mean': pre_mean,
'Pre STD': pre_std, 'Rec Mean': rec_mean, 'Rec STD': rec_std, 'F1 Mean': f1_mean,
'F1 STD': f1_std}
df_results = df_results.append(df_result_row, ignore_index=True)
results_acc.append(result['test_accuracy'])
results_pre.append(result['test_precision'])
results_rec.append(result['test_recall'])
results_f1.append(result['test_f1'])
df_results = df_results.set_index('Algorithm')
pd.set_option('display.float_format', lambda x: '%.3f' % x)
# Display the mean and standard deviation of all metrics for all algorithms
print(df_results)
# Display the overall results in a boxplot graph
plot_objects = plt.subplots(nrows=1, ncols=4, figsize=(14, 6))
fig, (ax1, ax2, ax3, ax4) = plot_objects
ax1.boxplot(results_acc)
ax1.set_title('Accuracy', fontsize=14)
ax1.set_xticklabels(names)
ax2.boxplot(results_pre)
ax2.set_title('Precision', fontsize=14)
ax2.set_xticklabels(names)
ax3.boxplot(results_rec)
ax3.set_title('Recall', fontsize=14)
ax3.set_xticklabels(names)
ax4.boxplot(results_f1)
ax4.set_title('F1-Score', fontsize=14)
ax4.set_xticklabels(names)
plt.show()
from imblearn.over_sampling import SMOTE
oversampling = SMOTE(sampling_strategy=0.5)
x_over, y_over = oversampling.fit_resample(x, y)
model_evaluation(x_over, y_over) | code |
34133512/cell_2 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import torch
if torch.cuda.is_available():
device = torch.device('cuda')
print('There are %d GPU(s) available.' % torch.cuda.device_count())
print('We will use the GPU:', torch.cuda.get_device_name(0))
else:
print('No GPU available, using the CPU instead.')
device = torch.device('cpu') | code |
34133512/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import f1_score
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.multiclass import OneVsRestClassifier
import pandas as pd
try:
df_train = pd.read_csv('data/train.csv')
df_test = pd.read_csv('data/test.csv')
except:
df_train = pd.read_csv('/kaggle/input/tweet-sentiment-extraction/train.csv')
df_test = pd.read_csv('/kaggle/input/tweet-sentiment-extraction/test.csv')
df_train.fillna('', inplace=True)
df_test.fillna('', inplace=True)
tf = TfidfVectorizer(ngram_range=(1, 1))
X_train, X_val, y_train, y_val = train_test_split(tf.fit_transform(df_train['text']), df_train['target'])
df_train['is_training'] = [1 if x in y_train.index else 0 for x in df_train.index]
parameters = {}
clf = OneVsRestClassifier(LogisticRegression(solver='lbfgs'))
clf.fit(X_train, y_train)
y_val_predict_sentiment = clf.predict(X_val)
f1_score(y_val, y_val_predict_sentiment, average='weighted') | code |
34133512/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from tqdm.notebook import tqdm_notebook
import pandas as pd
import numpy as np
import random
import torch
import os
from sklearn.metrics import f1_score
from sklearn.multiclass import OneVsRestClassifier
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from sklearn.linear_model import LogisticRegression
from sklearn.preprocessing import MultiLabelBinarizer, LabelEncoder
from sklearn.model_selection import train_test_split, GridSearchCV
from transformers import BertTokenizer
from tqdm.notebook import tqdm_notebook
from sklearn.preprocessing import OneHotEncoder
import re
tqdm_notebook.pandas() | code |
34133512/cell_15 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from spacy import displacy
from spacy.util import compounding, minibatch
import pandas as pd
import random
import re
import spacy
try:
df_train = pd.read_csv('data/train.csv')
df_test = pd.read_csv('data/test.csv')
except:
df_train = pd.read_csv('/kaggle/input/tweet-sentiment-extraction/train.csv')
df_test = pd.read_csv('/kaggle/input/tweet-sentiment-extraction/test.csv')
df_train.fillna('', inplace=True)
df_test.fillna('', inplace=True)
tf = TfidfVectorizer(ngram_range=(1, 1))
X_train, X_val, y_train, y_val = train_test_split(tf.fit_transform(df_train['text']), df_train['target'])
df_train['is_training'] = [1 if x in y_train.index else 0 for x in df_train.index]
import spacy
from spacy.util import compounding, minibatch
train_data = []
for idx, row in df_train[df_train['sentiment'] != 'neutral'].iterrows():
text = row['text']
selected_text = row['selected_text']
if selected_text in text:
entities = []
try:
for match in re.finditer(re.escape(selected_text), text):
start_char = match.start()
end_char = match.end()
entity_label = row['sentiment']
entities.append((start_char, end_char, entity_label))
except Exception as e:
raise e
train_data.append((text, {'entities': entities}))
def spacy_train_custom(train_data, epochs=10):
sample_print = 2
nlp = spacy.blank('en')
if 'ner' not in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner, last=True)
else:
ner = nlp.get_pipe('ner')
for _, annotations in train_data:
for ent in annotations.get('entities'):
ner.add_label(ent[2])
pipe_exceptions = ['ner', 'trf_wordpiecer', 'trf_tok2vec']
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes):
nlp.begin_training()
n_iterations = epochs
for itn in range(n_iterations):
random.shuffle(train_data)
losses = {}
batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
for batch_no, batch in enumerate(batches):
texts, annotations = zip(*batch)
nlp.update(texts, annotations, drop=0.3, losses=losses)
for text, _ in train_data[:sample_print]:
doc = nlp(text)
return nlp
nlp = spacy_train_custom(train_data[0:1000], epochs=20)
from spacy import displacy
sample = df_train.sample().iloc[0]
doc = nlp(sample.text)
displacy.render(doc, style='ent')
print(sample.sentiment)
print(sample.selected_text) | code |
34133512/cell_17 | [
"text_plain_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from spacy.util import compounding, minibatch
import pandas as pd
import random
import re
import spacy
try:
df_train = pd.read_csv('data/train.csv')
df_test = pd.read_csv('data/test.csv')
except:
df_train = pd.read_csv('/kaggle/input/tweet-sentiment-extraction/train.csv')
df_test = pd.read_csv('/kaggle/input/tweet-sentiment-extraction/test.csv')
df_train.fillna('', inplace=True)
df_test.fillna('', inplace=True)
tf = TfidfVectorizer(ngram_range=(1, 1))
X_train, X_val, y_train, y_val = train_test_split(tf.fit_transform(df_train['text']), df_train['target'])
df_train['is_training'] = [1 if x in y_train.index else 0 for x in df_train.index]
import spacy
from spacy.util import compounding, minibatch
train_data = []
for idx, row in df_train[df_train['sentiment'] != 'neutral'].iterrows():
text = row['text']
selected_text = row['selected_text']
if selected_text in text:
entities = []
try:
for match in re.finditer(re.escape(selected_text), text):
start_char = match.start()
end_char = match.end()
entity_label = row['sentiment']
entities.append((start_char, end_char, entity_label))
except Exception as e:
raise e
train_data.append((text, {'entities': entities}))
def spacy_train_custom(train_data, epochs=10):
sample_print = 2
nlp = spacy.blank('en')
if 'ner' not in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner, last=True)
else:
ner = nlp.get_pipe('ner')
for _, annotations in train_data:
for ent in annotations.get('entities'):
ner.add_label(ent[2])
pipe_exceptions = ['ner', 'trf_wordpiecer', 'trf_tok2vec']
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes):
nlp.begin_training()
n_iterations = epochs
for itn in range(n_iterations):
random.shuffle(train_data)
losses = {}
batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
for batch_no, batch in enumerate(batches):
texts, annotations = zip(*batch)
nlp.update(texts, annotations, drop=0.3, losses=losses)
for text, _ in train_data[:sample_print]:
doc = nlp(text)
return nlp
nlp = spacy_train_custom(train_data[0:1000], epochs=20)
df_test['selected_text'] = df_test['text'].progress_apply(lambda x: ' '.join([l.text for l in nlp(x).ents]))
df_test['selected_text'] = [row['text'] if row['sentiment'] == 'neutral' or row['selected_text'] == '' else row['selected_text'] for idx, row in df_test.iterrows()] | code |
34133512/cell_14 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.model_selection import train_test_split, GridSearchCV
from spacy.util import compounding, minibatch
import pandas as pd
import random
import re
import spacy
try:
df_train = pd.read_csv('data/train.csv')
df_test = pd.read_csv('data/test.csv')
except:
df_train = pd.read_csv('/kaggle/input/tweet-sentiment-extraction/train.csv')
df_test = pd.read_csv('/kaggle/input/tweet-sentiment-extraction/test.csv')
df_train.fillna('', inplace=True)
df_test.fillna('', inplace=True)
tf = TfidfVectorizer(ngram_range=(1, 1))
X_train, X_val, y_train, y_val = train_test_split(tf.fit_transform(df_train['text']), df_train['target'])
df_train['is_training'] = [1 if x in y_train.index else 0 for x in df_train.index]
import spacy
from spacy.util import compounding, minibatch
train_data = []
for idx, row in df_train[df_train['sentiment'] != 'neutral'].iterrows():
text = row['text']
selected_text = row['selected_text']
if selected_text in text:
entities = []
try:
for match in re.finditer(re.escape(selected_text), text):
start_char = match.start()
end_char = match.end()
entity_label = row['sentiment']
entities.append((start_char, end_char, entity_label))
except Exception as e:
raise e
train_data.append((text, {'entities': entities}))
def spacy_train_custom(train_data, epochs=10):
sample_print = 2
nlp = spacy.blank('en')
if 'ner' not in nlp.pipe_names:
ner = nlp.create_pipe('ner')
nlp.add_pipe(ner, last=True)
else:
ner = nlp.get_pipe('ner')
for _, annotations in train_data:
for ent in annotations.get('entities'):
ner.add_label(ent[2])
pipe_exceptions = ['ner', 'trf_wordpiecer', 'trf_tok2vec']
other_pipes = [pipe for pipe in nlp.pipe_names if pipe not in pipe_exceptions]
with nlp.disable_pipes(*other_pipes):
nlp.begin_training()
n_iterations = epochs
for itn in range(n_iterations):
random.shuffle(train_data)
losses = {}
batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
for batch_no, batch in enumerate(batches):
texts, annotations = zip(*batch)
nlp.update(texts, annotations, drop=0.3, losses=losses)
for text, _ in train_data[:sample_print]:
doc = nlp(text)
return nlp
nlp = spacy_train_custom(train_data[0:1000], epochs=20) | code |
73069445/cell_2 | [
"text_plain_output_1.png"
] | import wandb
import wandb
print(wandb.__version__) | code |
73069445/cell_1 | [
"text_plain_output_1.png"
] | !pip install wandb --upgrade | code |
73069445/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from kaggle_secrets import UserSecretsClient
import wandb
import wandb
try:
from kaggle_secrets import UserSecretsClient
user_secrets = UserSecretsClient()
secret_value_0 = user_secrets.get_secret('wandb_api')
wandb.login(key=secret_value_0)
except:
print('Go to Add-ons -> Secrets and provide your W&B access token. Use the Label name as wandb_api. \nGet your W&B access token from here: https://wandb.ai/authorize') | code |
122251563/cell_20 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
df = pd.merge(df1, df2, left_on='NOC', right_on='NOC')
df = df.query('Season == "Summer"')
df.replace('USA', 'United States of America', inplace=True)
df.replace('Tanzania', 'United Republic of Tanzania', inplace=True)
df.replace('Democratic Republic of Congo', 'Democratic Republic of the Congo', inplace=True)
df.replace('Congo', 'Republic of the Congo', inplace=True)
df.replace('Lao', 'Laos', inplace=True)
df.replace('Syrian Arab Republic', 'Syria', inplace=True)
df.replace('Serbia', 'Republic of Serbia', inplace=True)
df.replace('Czechia', 'Czech Republic', inplace=True)
df.replace('UAE', 'United Arab Emirates', inplace=True)
df.replace('UK', 'United Kingdom', inplace=True)
def Pays_Hebergeur(col):
if col == 'Rio de Janeiro':
return 'Brazil'
elif col == 'London':
return 'United Kingdom'
elif col == 'Beijing':
return 'China'
elif col == 'Athina':
return 'Greece'
elif col == 'Sydney' or col == 'Melbourne':
return 'Australia'
elif col == 'Atlanta' or col == 'Los Angeles' or col == 'St. Louis':
return 'United States of America'
elif col == 'Barcelona':
return 'Spain'
elif col == 'Seoul':
return 'South Korea'
elif col == 'Moskva':
return 'Russia'
elif col == 'Montreal':
return 'Canada'
elif col == 'Munich' or col == 'Berlin':
return 'Germany'
elif col == 'Mexico City':
return 'Mexico'
elif col == 'Tokyo':
return 'Japan'
elif col == 'Roma':
return 'Italy'
elif col == 'Paris':
return 'France'
elif col == 'Helsinki':
return 'Finland'
elif col == 'Amsterdam':
return 'Netherlands'
elif col == 'Antwerpen':
return 'Belgium'
elif col == 'Stockholm':
return 'Sweden'
else:
return 'Other'
df['Pays_Hebergeur'] = df['City'].apply(Pays_Hebergeur)
France_team = df[df['Team'] == 'France']
France_team_grouped = France_team.groupby('Year')['Medal'].sum()
X = France_team_grouped.index.values.reshape(-1, 1)
y = France_team_grouped.values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
lr = LinearRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
print('La performance de notre modèle selon la MSE est:', mse) | code |
122251563/cell_18 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import train_test_split
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
df = pd.merge(df1, df2, left_on='NOC', right_on='NOC')
df = df.query('Season == "Summer"')
df.replace('USA', 'United States of America', inplace=True)
df.replace('Tanzania', 'United Republic of Tanzania', inplace=True)
df.replace('Democratic Republic of Congo', 'Democratic Republic of the Congo', inplace=True)
df.replace('Congo', 'Republic of the Congo', inplace=True)
df.replace('Lao', 'Laos', inplace=True)
df.replace('Syrian Arab Republic', 'Syria', inplace=True)
df.replace('Serbia', 'Republic of Serbia', inplace=True)
df.replace('Czechia', 'Czech Republic', inplace=True)
df.replace('UAE', 'United Arab Emirates', inplace=True)
df.replace('UK', 'United Kingdom', inplace=True)
def Pays_Hebergeur(col):
if col == 'Rio de Janeiro':
return 'Brazil'
elif col == 'London':
return 'United Kingdom'
elif col == 'Beijing':
return 'China'
elif col == 'Athina':
return 'Greece'
elif col == 'Sydney' or col == 'Melbourne':
return 'Australia'
elif col == 'Atlanta' or col == 'Los Angeles' or col == 'St. Louis':
return 'United States of America'
elif col == 'Barcelona':
return 'Spain'
elif col == 'Seoul':
return 'South Korea'
elif col == 'Moskva':
return 'Russia'
elif col == 'Montreal':
return 'Canada'
elif col == 'Munich' or col == 'Berlin':
return 'Germany'
elif col == 'Mexico City':
return 'Mexico'
elif col == 'Tokyo':
return 'Japan'
elif col == 'Roma':
return 'Italy'
elif col == 'Paris':
return 'France'
elif col == 'Helsinki':
return 'Finland'
elif col == 'Amsterdam':
return 'Netherlands'
elif col == 'Antwerpen':
return 'Belgium'
elif col == 'Stockholm':
return 'Sweden'
else:
return 'Other'
df['Pays_Hebergeur'] = df['City'].apply(Pays_Hebergeur)
France_team = df[df['Team'] == 'France']
France_team_grouped = France_team.groupby('Year')['Medal'].sum()
X = France_team_grouped.index.values.reshape(-1, 1)
y = France_team_grouped.values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
lr = LinearRegression()
lr.fit(X_train, y_train) | code |
122251563/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
df = pd.merge(df1, df2, left_on='NOC', right_on='NOC')
df = df.query('Season == "Summer"')
df.replace('USA', 'United States of America', inplace=True)
df.replace('Tanzania', 'United Republic of Tanzania', inplace=True)
df.replace('Democratic Republic of Congo', 'Democratic Republic of the Congo', inplace=True)
df.replace('Congo', 'Republic of the Congo', inplace=True)
df.replace('Lao', 'Laos', inplace=True)
df.replace('Syrian Arab Republic', 'Syria', inplace=True)
df.replace('Serbia', 'Republic of Serbia', inplace=True)
df.replace('Czechia', 'Czech Republic', inplace=True)
df.replace('UAE', 'United Arab Emirates', inplace=True)
df.replace('UK', 'United Kingdom', inplace=True)
def Pays_Hebergeur(col):
if col == 'Rio de Janeiro':
return 'Brazil'
elif col == 'London':
return 'United Kingdom'
elif col == 'Beijing':
return 'China'
elif col == 'Athina':
return 'Greece'
elif col == 'Sydney' or col == 'Melbourne':
return 'Australia'
elif col == 'Atlanta' or col == 'Los Angeles' or col == 'St. Louis':
return 'United States of America'
elif col == 'Barcelona':
return 'Spain'
elif col == 'Seoul':
return 'South Korea'
elif col == 'Moskva':
return 'Russia'
elif col == 'Montreal':
return 'Canada'
elif col == 'Munich' or col == 'Berlin':
return 'Germany'
elif col == 'Mexico City':
return 'Mexico'
elif col == 'Tokyo':
return 'Japan'
elif col == 'Roma':
return 'Italy'
elif col == 'Paris':
return 'France'
elif col == 'Helsinki':
return 'Finland'
elif col == 'Amsterdam':
return 'Netherlands'
elif col == 'Antwerpen':
return 'Belgium'
elif col == 'Stockholm':
return 'Sweden'
else:
return 'Other'
df['Pays_Hebergeur'] = df['City'].apply(Pays_Hebergeur)
def plot_medals_by_country(country):
country_team = df[df['Team'] == country]
country_host = df[(df['Team'] == country) & (df['Pays_Hebergeur'] == country)]
country_team_grouped = country_team.groupby('Year')['Medal'].sum()
country_host_sum = country_host.groupby('Year')['Medal'].sum()
plot_medals_by_country('China') | code |
122251563/cell_14 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
df = pd.merge(df1, df2, left_on='NOC', right_on='NOC')
df = df.query('Season == "Summer"')
df.replace('USA', 'United States of America', inplace=True)
df.replace('Tanzania', 'United Republic of Tanzania', inplace=True)
df.replace('Democratic Republic of Congo', 'Democratic Republic of the Congo', inplace=True)
df.replace('Congo', 'Republic of the Congo', inplace=True)
df.replace('Lao', 'Laos', inplace=True)
df.replace('Syrian Arab Republic', 'Syria', inplace=True)
df.replace('Serbia', 'Republic of Serbia', inplace=True)
df.replace('Czechia', 'Czech Republic', inplace=True)
df.replace('UAE', 'United Arab Emirates', inplace=True)
df.replace('UK', 'United Kingdom', inplace=True)
def Pays_Hebergeur(col):
if col == 'Rio de Janeiro':
return 'Brazil'
elif col == 'London':
return 'United Kingdom'
elif col == 'Beijing':
return 'China'
elif col == 'Athina':
return 'Greece'
elif col == 'Sydney' or col == 'Melbourne':
return 'Australia'
elif col == 'Atlanta' or col == 'Los Angeles' or col == 'St. Louis':
return 'United States of America'
elif col == 'Barcelona':
return 'Spain'
elif col == 'Seoul':
return 'South Korea'
elif col == 'Moskva':
return 'Russia'
elif col == 'Montreal':
return 'Canada'
elif col == 'Munich' or col == 'Berlin':
return 'Germany'
elif col == 'Mexico City':
return 'Mexico'
elif col == 'Tokyo':
return 'Japan'
elif col == 'Roma':
return 'Italy'
elif col == 'Paris':
return 'France'
elif col == 'Helsinki':
return 'Finland'
elif col == 'Amsterdam':
return 'Netherlands'
elif col == 'Antwerpen':
return 'Belgium'
elif col == 'Stockholm':
return 'Sweden'
else:
return 'Other'
df['Pays_Hebergeur'] = df['City'].apply(Pays_Hebergeur)
def plot_medals_by_country(country):
country_team = df[df['Team'] == country]
country_host = df[(df['Team'] == country) & (df['Pays_Hebergeur'] == country)]
country_team_grouped = country_team.groupby('Year')['Medal'].sum()
country_host_sum = country_host.groupby('Year')['Medal'].sum()
plot_medals_by_country('Spain') | code |
122251563/cell_22 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_squared_error
from sklearn.model_selection import train_test_split
import numpy as np
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
df = pd.merge(df1, df2, left_on='NOC', right_on='NOC')
df = df.query('Season == "Summer"')
df.replace('USA', 'United States of America', inplace=True)
df.replace('Tanzania', 'United Republic of Tanzania', inplace=True)
df.replace('Democratic Republic of Congo', 'Democratic Republic of the Congo', inplace=True)
df.replace('Congo', 'Republic of the Congo', inplace=True)
df.replace('Lao', 'Laos', inplace=True)
df.replace('Syrian Arab Republic', 'Syria', inplace=True)
df.replace('Serbia', 'Republic of Serbia', inplace=True)
df.replace('Czechia', 'Czech Republic', inplace=True)
df.replace('UAE', 'United Arab Emirates', inplace=True)
df.replace('UK', 'United Kingdom', inplace=True)
def Pays_Hebergeur(col):
if col == 'Rio de Janeiro':
return 'Brazil'
elif col == 'London':
return 'United Kingdom'
elif col == 'Beijing':
return 'China'
elif col == 'Athina':
return 'Greece'
elif col == 'Sydney' or col == 'Melbourne':
return 'Australia'
elif col == 'Atlanta' or col == 'Los Angeles' or col == 'St. Louis':
return 'United States of America'
elif col == 'Barcelona':
return 'Spain'
elif col == 'Seoul':
return 'South Korea'
elif col == 'Moskva':
return 'Russia'
elif col == 'Montreal':
return 'Canada'
elif col == 'Munich' or col == 'Berlin':
return 'Germany'
elif col == 'Mexico City':
return 'Mexico'
elif col == 'Tokyo':
return 'Japan'
elif col == 'Roma':
return 'Italy'
elif col == 'Paris':
return 'France'
elif col == 'Helsinki':
return 'Finland'
elif col == 'Amsterdam':
return 'Netherlands'
elif col == 'Antwerpen':
return 'Belgium'
elif col == 'Stockholm':
return 'Sweden'
else:
return 'Other'
df['Pays_Hebergeur'] = df['City'].apply(Pays_Hebergeur)
France_team = df[df['Team'] == 'France']
France_team_grouped = France_team.groupby('Year')['Medal'].sum()
X = France_team_grouped.index.values.reshape(-1, 1)
y = France_team_grouped.values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)
lr = LinearRegression()
lr.fit(X_train, y_train)
y_pred = lr.predict(X_test)
mse = mean_squared_error(y_test, y_pred)
france_2024 = df1[(df1['Team'] == 'France') & (df1['Year'] == 2024)]
X_2024 = np.array([[2024]])
y_pred_2024 = lr.predict(X_2024)
print('Le nombre de médailles prédit:', y_pred_2024[0]) | code |
122251563/cell_12 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
df1 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/athlete_events.csv')
df2 = pd.read_csv('../input/120-years-of-olympic-history-athletes-and-results/noc_regions.csv')
df = pd.merge(df1, df2, left_on='NOC', right_on='NOC')
df = df.query('Season == "Summer"')
df.replace('USA', 'United States of America', inplace=True)
df.replace('Tanzania', 'United Republic of Tanzania', inplace=True)
df.replace('Democratic Republic of Congo', 'Democratic Republic of the Congo', inplace=True)
df.replace('Congo', 'Republic of the Congo', inplace=True)
df.replace('Lao', 'Laos', inplace=True)
df.replace('Syrian Arab Republic', 'Syria', inplace=True)
df.replace('Serbia', 'Republic of Serbia', inplace=True)
df.replace('Czechia', 'Czech Republic', inplace=True)
df.replace('UAE', 'United Arab Emirates', inplace=True)
df.replace('UK', 'United Kingdom', inplace=True)
def Pays_Hebergeur(col):
if col == 'Rio de Janeiro':
return 'Brazil'
elif col == 'London':
return 'United Kingdom'
elif col == 'Beijing':
return 'China'
elif col == 'Athina':
return 'Greece'
elif col == 'Sydney' or col == 'Melbourne':
return 'Australia'
elif col == 'Atlanta' or col == 'Los Angeles' or col == 'St. Louis':
return 'United States of America'
elif col == 'Barcelona':
return 'Spain'
elif col == 'Seoul':
return 'South Korea'
elif col == 'Moskva':
return 'Russia'
elif col == 'Montreal':
return 'Canada'
elif col == 'Munich' or col == 'Berlin':
return 'Germany'
elif col == 'Mexico City':
return 'Mexico'
elif col == 'Tokyo':
return 'Japan'
elif col == 'Roma':
return 'Italy'
elif col == 'Paris':
return 'France'
elif col == 'Helsinki':
return 'Finland'
elif col == 'Amsterdam':
return 'Netherlands'
elif col == 'Antwerpen':
return 'Belgium'
elif col == 'Stockholm':
return 'Sweden'
else:
return 'Other'
df['Pays_Hebergeur'] = df['City'].apply(Pays_Hebergeur)
def plot_medals_by_country(country):
country_team = df[df['Team'] == country]
country_host = df[(df['Team'] == country) & (df['Pays_Hebergeur'] == country)]
country_team_grouped = country_team.groupby('Year')['Medal'].sum()
country_host_sum = country_host.groupby('Year')['Medal'].sum()
plot_medals_by_country('France') | code |
18120119/cell_42 | [
"text_plain_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist'] | code |
18120119/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
with open(example2_path, 'w') as file2:
file2.write('This is line A')
with open(example2_path, 'w') as file2:
file2.write('This is line A\n')
file2.write('This is line B\n')
file2.write('This is line C\n') | code |
18120119/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close() | code |
18120119/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | !wget -O ../data/Example1.txt https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/labs/example1.txt | code |
18120119/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
df.head() | code |
18120119/cell_23 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
with open(example2_path, 'w') as file2:
file2.write('This is line A')
with open(example2_path, 'w') as file2:
file2.write('This is line A\n')
file2.write('This is line B\n')
file2.write('This is line C\n')
lines = ['This is line D\n', 'This is line E\n', 'This is line F\n']
lines
with open(example2_path, 'a') as file2:
for line in lines:
file2.write(line) | code |
18120119/cell_33 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url) | code |
18120119/cell_44 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
df.iloc[0:2, 0:3] | code |
18120119/cell_20 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
with open(example2_path, 'w') as file2:
file2.write('This is line A')
with open(example2_path, 'r') as file2:
print(file2.read()) | code |
18120119/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
print(f'file1 object = {file1}')
print(f'Type of file1 object = {type(file1)}') | code |
18120119/cell_40 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.head() | code |
18120119/cell_39 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path) | code |
18120119/cell_26 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
example3_path = '../data/Example3.txt'
with open(example2_path, 'r') as readfile:
with open(example3_path, 'w') as writefile:
for line in readfile:
writefile.write(line) | code |
18120119/cell_48 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
df.iloc[0:2, 0:3]
df.loc[0:2, 'Artist':'Released']
len(df['Released']) | code |
18120119/cell_41 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0] | code |
18120119/cell_54 | [
"application_vnd.jupyter.stderr_output_1.png"
] | with open('../data/new_songs.csv', 'r') as songsfile:
print(songsfile.read()) | code |
18120119/cell_19 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
with open(example2_path, 'w') as file2:
file2.write('This is line A') | code |
18120119/cell_50 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
df.iloc[0:2, 0:3]
df.loc[0:2, 'Artist':'Released']
len(df['Released'].unique()) | code |
18120119/cell_52 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
df.iloc[0:2, 0:3]
df.loc[0:2, 'Artist':'Released']
new_songs = df[df['Released'] >= 1980]
new_songs | code |
18120119/cell_7 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name | code |
18120119/cell_45 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
df.iloc[0:2, 0:3]
df.loc[0:2, 'Artist':'Released'] | code |
18120119/cell_49 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
df.iloc[0:2, 0:3]
df.loc[0:2, 'Artist':'Released']
df['Released'].unique() | code |
18120119/cell_51 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
df.iloc[0:2, 0:3]
df.loc[0:2, 'Artist':'Released']
df['Released'] >= 1980 | code |
18120119/cell_28 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example3_path = '../data/Example3.txt'
testfile.name | code |
18120119/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode | code |
18120119/cell_15 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close()
file1.closed
with open(example1_path, 'r') as file1:
file_contents = file1.read()
with open(example1_path, 'r') as file1:
file_contents = file1.readlines()
with open(example1_path, 'r') as file1:
file_contents = file1.readline()
print(f'file_contents \n{file_contents}')
print(file1.closed)
print(f'file_contents \n{file_contents}') | code |
18120119/cell_16 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close()
file1.closed
with open(example1_path, 'r') as file1:
file_contents = file1.read()
with open(example1_path, 'r') as file1:
file_contents = file1.readlines()
with open(example1_path, 'r') as file1:
file_contents = file1.readline()
with open(example1_path, 'r') as file1:
for i, line in enumerate(file1):
print(f'Line {i + 1} contains {line}') | code |
18120119/cell_47 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
df.iloc[0:2, 0:3]
df.loc[0:2, 'Artist':'Released']
df['Released'] | code |
18120119/cell_3 | [
"text_plain_output_1.png"
] | # Check current working dirctory
!pwd | code |
18120119/cell_43 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist'] | code |
18120119/cell_24 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example2_path = '../data/example2.txt'
with open(example2_path, 'w') as file2:
file2.write('This is line A')
with open(example2_path, 'w') as file2:
file2.write('This is line A\n')
file2.write('This is line B\n')
file2.write('This is line C\n')
lines = ['This is line D\n', 'This is line E\n', 'This is line F\n']
lines
with open(example2_path, 'a') as file2:
for line in lines:
file2.write(line)
with open(example2_path, 'r') as file2:
print(file2.read()) | code |
18120119/cell_14 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close()
file1.closed
with open(example1_path, 'r') as file1:
file_contents = file1.read()
with open(example1_path, 'r') as file1:
file_contents = file1.readlines()
print(f'file_contents \n{file_contents}')
print(file1.closed)
print(f'file_contents \n{file_contents}') | code |
18120119/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png"
] | lines = ['This is line D\n', 'This is line E\n', 'This is line F\n']
lines | code |
18120119/cell_53 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
csv_url = 'https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv'
df = pd.read_csv(csv_url)
csv_path = '../data/TopSellingAlbums.csv'
df = pd.read_csv(csv_path)
df.iloc[0, 0]
df.loc[0, 'Artist']
df.loc[1, 'Artist']
df.iloc[0:2, 0:3]
df.loc[0:2, 'Artist':'Released']
new_songs = df[df['Released'] >= 1980]
new_songs
new_songs.to_csv('../data/new_songs.csv') | code |
18120119/cell_10 | [
"text_plain_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close()
file1.closed | code |
18120119/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png"
] | example3_path = '../data/Example3.txt'
with open(example3_path, 'r') as testfile:
print(testfile.read()) | code |
18120119/cell_37 | [
"application_vnd.jupyter.stderr_output_1.png"
] | !wget -O ./data/TopSellingAlbums.csv https://s3-api.us-geo.objectstorage.softlayer.net/cf-courses-data/CognitiveClass/PY0101EN/Chapter%204/Datasets/TopSellingAlbums.csv | code |
18120119/cell_12 | [
"text_plain_output_1.png"
] | example1_path = '../data/Example1.txt'
file1 = open(example1_path, 'r')
file1.name
file1.mode
file1.close()
file1.closed
with open(example1_path, 'r') as file1:
file_contents = file1.read()
print(f'file_contents \n{file_contents}')
print(file1.closed)
print(f'file_contents \n{file_contents}') | code |
128043237/cell_9 | [
"image_output_5.png",
"image_output_7.png",
"image_output_4.png",
"image_output_6.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from PIL import Image
from torch.utils.data import random_split, DataLoader, Dataset
from torchvision.io import read_image, ImageReadMode
import matplotlib.pyplot as plt
import os
import torchvision.transforms.functional as TF
BATCH_SIZE = 16
IMAGE_SIZE = (256, 256)
IN_CHANNELS = 3
LEARNING_RATE = 0.0001
IMAGES_DIR = '/kaggle/input/danish-golf-courses-orthophotos/1. orthophotos/'
SEGMASKS_DIR = '/kaggle/input/danish-golf-courses-orthophotos/2. segmentation masks/'
LABELMASKS_DIR = '/kaggle/input/danish-golf-courses-orthophotos/3. class masks/'
#Loading the data
orthophoto_list = os.listdir(IMAGES_DIR)
print("There are ", len(orthophoto_list), " orthophotos in this dataset!")
#Load image with index of 5 (I prefer this image as it shows all the classes)
idx = 5 #The index can be changed to view other orthophotos.
golf_image = Image.open(os.path.join(IMAGES_DIR, orthophoto_list[idx]))
golf_segmask = Image.open(os.path.join(SEGMASKS_DIR, orthophoto_list[idx].replace(".jpg", ".png"))) #The class masks are png instead of jpg
#Plot using matplotlib
fig, axes = plt.subplots(1, 2)
axes[0].set_title('Orthophoto')
axes[1].set_title('Segmentation Mask')
axes[0].imshow(golf_image)
axes[1].imshow(golf_segmask)
class GolfDataset(Dataset):
def __init__(self, images_dir, labelmasks_dir):
self.images_dir = images_dir
self.labelmasks_dir = labelmasks_dir
self.images_dir_list = os.listdir(images_dir)
def __len__(self):
return len(self.images_dir_list)
def __getitem__(self, idx):
image_path = os.path.join(self.images_dir, self.images_dir_list[idx])
image = read_image(image_path, ImageReadMode.RGB)
label_mask_path = os.path.join(self.labelmasks_dir, self.images_dir_list[idx]).replace('.jpg', '.png')
label_mask = read_image(label_mask_path, ImageReadMode.GRAY)
image = TF.resize(image, IMAGE_SIZE)
image = image.float()
image = image / 255
label_mask = TF.resize(label_mask, IMAGE_SIZE)
label_mask = TF.rgb_to_grayscale(label_mask)
label_mask = label_mask.float()
return (image, label_mask)
golf_ds = GolfDataset(IMAGES_DIR, LABELMASKS_DIR)
idx = 5
orthophoto = golf_ds.__getitem__(idx)[0]
label_mask = golf_ds.__getitem__(idx)[1]
print('Ortophoto: ', orthophoto.shape, orthophoto)
print('Label:', label_mask.shape, label_mask) | code |
128043237/cell_20 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | from PIL import Image
from torch import nn
from torch.utils.data import random_split, DataLoader, Dataset
from torchvision.io import read_image, ImageReadMode
import matplotlib.pyplot as plt
import os
import pytorch_lightning as pl
import torch
import torchvision.transforms as T
import torchvision.transforms.functional as TF
BATCH_SIZE = 16
IMAGE_SIZE = (256, 256)
IN_CHANNELS = 3
LEARNING_RATE = 0.0001
IMAGES_DIR = '/kaggle/input/danish-golf-courses-orthophotos/1. orthophotos/'
SEGMASKS_DIR = '/kaggle/input/danish-golf-courses-orthophotos/2. segmentation masks/'
LABELMASKS_DIR = '/kaggle/input/danish-golf-courses-orthophotos/3. class masks/'
#Loading the data
orthophoto_list = os.listdir(IMAGES_DIR)
print("There are ", len(orthophoto_list), " orthophotos in this dataset!")
#Load image with index of 5 (I prefer this image as it shows all the classes)
idx = 5 #The index can be changed to view other orthophotos.
golf_image = Image.open(os.path.join(IMAGES_DIR, orthophoto_list[idx]))
golf_segmask = Image.open(os.path.join(SEGMASKS_DIR, orthophoto_list[idx].replace(".jpg", ".png"))) #The class masks are png instead of jpg
#Plot using matplotlib
fig, axes = plt.subplots(1, 2)
axes[0].set_title('Orthophoto')
axes[1].set_title('Segmentation Mask')
axes[0].imshow(golf_image)
axes[1].imshow(golf_segmask)
class GolfDataset(Dataset):
def __init__(self, images_dir, labelmasks_dir):
self.images_dir = images_dir
self.labelmasks_dir = labelmasks_dir
self.images_dir_list = os.listdir(images_dir)
def __len__(self):
return len(self.images_dir_list)
def __getitem__(self, idx):
image_path = os.path.join(self.images_dir, self.images_dir_list[idx])
image = read_image(image_path, ImageReadMode.RGB)
label_mask_path = os.path.join(self.labelmasks_dir, self.images_dir_list[idx]).replace('.jpg', '.png')
label_mask = read_image(label_mask_path, ImageReadMode.GRAY)
image = TF.resize(image, IMAGE_SIZE)
image = image.float()
image = image / 255
label_mask = TF.resize(label_mask, IMAGE_SIZE)
label_mask = TF.rgb_to_grayscale(label_mask)
label_mask = label_mask.float()
return (image, label_mask)
class GolfDataModule(pl.LightningDataModule):
def __init__(self, batch_size):
super().__init__()
self.batch_size = batch_size
self.all_images = []
def prepare_data(self):
pass
def setup(self, stage=None):
self.all_images = GolfDataset(IMAGES_DIR, LABELMASKS_DIR)
self.train_data, self.val_data, self.test_data = random_split(self.all_images, [0.7, 0.2, 0.1])
def train_dataloader(self):
return DataLoader(self.train_data, batch_size=self.batch_size, num_workers=2, pin_memory=True, persistent_workers=True)
def val_dataloader(self):
return DataLoader(self.val_data, batch_size=self.batch_size, num_workers=2, pin_memory=True, persistent_workers=True)
def test_dataloader(self):
return DataLoader(self.test_data, batch_size=self.batch_size, num_workers=2, pin_memory=True, persistent_workers=True)
class UNetModel(pl.LightningModule):
def __init__(self):
super().__init__()
class DoubleConvSame(nn.Module):
def __init__(self, c_in, c_out):
super(DoubleConvSame, self).__init__()
self.conv = nn.Sequential(nn.Conv2d(in_channels=c_in, out_channels=c_out, kernel_size=3, padding=1), nn.ReLU(inplace=True), nn.Conv2d(in_channels=c_out, out_channels=c_out, kernel_size=3, padding=1), nn.ReLU(inplace=True))
def forward(self, x):
return self.conv(x)
self.conv1 = DoubleConvSame(c_in=3, c_out=64)
self.conv2 = DoubleConvSame(c_in=64, c_out=128)
self.conv3 = DoubleConvSame(c_in=128, c_out=256)
self.conv4 = DoubleConvSame(c_in=256, c_out=512)
self.conv5 = DoubleConvSame(c_in=512, c_out=1024)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.up1 = nn.ConvTranspose2d(in_channels=1024, out_channels=512, kernel_size=2, stride=2)
self.up2 = nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=2, stride=2)
self.up3 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=2, stride=2)
self.up4 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=2, stride=2)
self.up_conv1 = DoubleConvSame(c_in=1024, c_out=512)
self.up_conv2 = DoubleConvSame(c_in=512, c_out=256)
self.up_conv3 = DoubleConvSame(c_in=256, c_out=128)
self.up_conv4 = DoubleConvSame(c_in=128, c_out=64)
self.conv_1x1 = nn.Conv2d(in_channels=64, out_channels=6, kernel_size=1)
self.loss_fn = nn.CrossEntropyLoss()
self.train_loss = []
self.val_loss = []
def crop_tensor(self, up_tensor, target_tensor):
_, _, H, W = up_tensor.shape
x = T.CenterCrop(size=(H, W))(target_tensor)
return x
def forward(self, x):
"""ENCODER"""
c1 = self.conv1(x)
p1 = self.pool(c1)
c2 = self.conv2(p1)
p2 = self.pool(c2)
c3 = self.conv3(p2)
p3 = self.pool(c3)
c4 = self.conv4(p3)
p4 = self.pool(c4)
'BOTTLE-NECK'
c5 = self.conv5(p4)
'DECODER'
u1 = self.up1(c5)
crop1 = self.crop_tensor(u1, c4)
cat1 = torch.cat([u1, crop1], dim=1)
uc1 = self.up_conv1(cat1)
u2 = self.up2(uc1)
crop2 = self.crop_tensor(u2, c3)
cat2 = torch.cat([u2, crop2], dim=1)
uc2 = self.up_conv2(cat2)
u3 = self.up3(uc2)
crop3 = self.crop_tensor(u3, c2)
cat3 = torch.cat([u3, crop3], dim=1)
uc3 = self.up_conv3(cat3)
u4 = self.up4(uc3)
crop4 = self.crop_tensor(u4, c1)
cat4 = torch.cat([u4, crop4], dim=1)
uc4 = self.up_conv4(cat4)
outputs = self.conv_1x1(uc4)
return outputs
def training_step(self, batch, batch_idx):
x, y = batch
y_pred = self.forward(x)
_y = torch.squeeze(y).long()
loss = self.loss_fn(y_pred, _y)
return loss
def validation_step(self, batch, batch_idx):
x, y = batch
y_pred = self.forward(x)
_y = torch.squeeze(y).long()
loss = self.loss_fn(y_pred, _y)
return loss
def test_step(self, batch, batch_idx):
x, y = batch
y_pred = self.forward(x)
_y = torch.squeeze(y).long()
loss = self.loss_fn(y_pred, _y)
save_predictions_as_imgs(x, y, y_pred, counter=batch_idx)
return loss
def test_epoch_end(self, outs):
pass
def configure_optimizers(self):
return torch.optim.AdamW(self.parameters(), lr=LEARNING_RATE)
train_loader = GolfDataModule(BATCH_SIZE)
trainer = pl.Trainer(max_epochs=50, accelerator='gpu', devices=2, log_every_n_steps=24, strategy='ddp_notebook_find_unused_parameters_false')
model = UNetModel()
trainer.fit(model, train_loader)
trainer = pl.Trainer(devices=1, num_nodes=1, accelerator='gpu')
trainer.test(model, train_loader) | code |
128043237/cell_22 | [
"text_plain_output_1.png"
] | from PIL import Image
from torch.utils.data import random_split, DataLoader, Dataset
from torchvision.io import read_image, ImageReadMode
import matplotlib.pyplot as plt
import os
import torchvision.transforms.functional as TF
BATCH_SIZE = 16
IMAGE_SIZE = (256, 256)
IN_CHANNELS = 3
LEARNING_RATE = 0.0001
IMAGES_DIR = '/kaggle/input/danish-golf-courses-orthophotos/1. orthophotos/'
SEGMASKS_DIR = '/kaggle/input/danish-golf-courses-orthophotos/2. segmentation masks/'
LABELMASKS_DIR = '/kaggle/input/danish-golf-courses-orthophotos/3. class masks/'
#Loading the data
orthophoto_list = os.listdir(IMAGES_DIR)
print("There are ", len(orthophoto_list), " orthophotos in this dataset!")
#Load image with index of 5 (I prefer this image as it shows all the classes)
idx = 5 #The index can be changed to view other orthophotos.
golf_image = Image.open(os.path.join(IMAGES_DIR, orthophoto_list[idx]))
golf_segmask = Image.open(os.path.join(SEGMASKS_DIR, orthophoto_list[idx].replace(".jpg", ".png"))) #The class masks are png instead of jpg
#Plot using matplotlib
fig, axes = plt.subplots(1, 2)
axes[0].set_title('Orthophoto')
axes[1].set_title('Segmentation Mask')
axes[0].imshow(golf_image)
axes[1].imshow(golf_segmask)
class GolfDataset(Dataset):
def __init__(self, images_dir, labelmasks_dir):
self.images_dir = images_dir
self.labelmasks_dir = labelmasks_dir
self.images_dir_list = os.listdir(images_dir)
def __len__(self):
return len(self.images_dir_list)
def __getitem__(self, idx):
image_path = os.path.join(self.images_dir, self.images_dir_list[idx])
image = read_image(image_path, ImageReadMode.RGB)
label_mask_path = os.path.join(self.labelmasks_dir, self.images_dir_list[idx]).replace('.jpg', '.png')
label_mask = read_image(label_mask_path, ImageReadMode.GRAY)
image = TF.resize(image, IMAGE_SIZE)
image = image.float()
image = image / 255
label_mask = TF.resize(label_mask, IMAGE_SIZE)
label_mask = TF.rgb_to_grayscale(label_mask)
label_mask = label_mask.float()
return (image, label_mask)
golf_ds = GolfDataset(IMAGES_DIR, LABELMASKS_DIR)
idx = 5
orthophoto = golf_ds.__getitem__(idx)[0]
label_mask = golf_ds.__getitem__(idx)[1]
output_dir = '/kaggle/working/'
for idx in range(1, 8):
orthophoto = Image.open(output_dir + str(idx) + '_figure.jpg')
groundtruth = Image.open(output_dir + str(idx) + '_groundtruth.jpg')
prediction = Image.open(output_dir + str(idx) + '_prediction.jpg')
fig, axes = plt.subplots(1, 3)
fig.set_size_inches(18.5, 15.5)
axes[0].set_title('Orthophoto')
axes[1].set_title('Groundtruth')
axes[2].set_title('Prediction')
axes[0].imshow(orthophoto)
axes[1].imshow(groundtruth)
axes[2].imshow(prediction) | code |
128043237/cell_5 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from PIL import Image
import matplotlib.pyplot as plt
import os
BATCH_SIZE = 16
IMAGE_SIZE = (256, 256)
IN_CHANNELS = 3
LEARNING_RATE = 0.0001
IMAGES_DIR = '/kaggle/input/danish-golf-courses-orthophotos/1. orthophotos/'
SEGMASKS_DIR = '/kaggle/input/danish-golf-courses-orthophotos/2. segmentation masks/'
LABELMASKS_DIR = '/kaggle/input/danish-golf-courses-orthophotos/3. class masks/'
orthophoto_list = os.listdir(IMAGES_DIR)
print('There are ', len(orthophoto_list), ' orthophotos in this dataset!')
idx = 5
golf_image = Image.open(os.path.join(IMAGES_DIR, orthophoto_list[idx]))
golf_segmask = Image.open(os.path.join(SEGMASKS_DIR, orthophoto_list[idx].replace('.jpg', '.png')))
fig, axes = plt.subplots(1, 2)
axes[0].set_title('Orthophoto')
axes[1].set_title('Segmentation Mask')
axes[0].imshow(golf_image)
axes[1].imshow(golf_segmask) | code |
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