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stringlengths 13
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sequencelengths 1
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|---|---|---|---|
104119399/cell_21 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8)
plt.show() | code |
104119399/cell_13 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes)
plt.show() | code |
104119399/cell_25 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8, labeldistance=1.3)
plt.show() | code |
104119399/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8, shadow=True)
plt.show() | code |
104119399/cell_29 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8, radius=1.5)
plt.show() | code |
104119399/cell_11 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student)
plt.show() | code |
104119399/cell_19 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0))
plt.show() | code |
104119399/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
label = np.ones(20)
colors = ['r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w', 'r', 'w']
plt.pie([1], colors='m', radius=2.2)
plt.pie([1], colors='r', radius=2.0)
plt.pie([1], colors='c', radius=1.8)
plt.pie(label, colors=colors, radius=1.6)
plt.pie([1], colors='y', radius=1.4)
plt.show() | code |
104119399/cell_15 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, colors=['r', 'peru', 'm', 'olivedrab', 'g'])
plt.show() | code |
104119399/cell_17 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%')
plt.show() | code |
104119399/cell_31 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8, counterclock=False)
plt.show() | code |
104119399/cell_27 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
classes = ['Python', 'R', 'AI', 'ML', 'DS']
class1_student = [45, 25, 35, 40, 30]
plt.pie(class1_student, labels=classes, autopct='%0.1f%%', explode=(0, 0, 0, 0.2, 0), pctdistance=0.8, startangle=90)
plt.show() | code |
105187920/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = df[~(df['Bathroom'] > 6)]
df['Floor House'] = df['Floor'].str.split(' ').str.get(0)
df['Floor Building'] = df['Floor'].str.split(' ').str.get(-1)
df.drop('Floor', axis=1, inplace=True)
df.loc[df['Floor House'] == 'Ground', 'Floor House'] = 0
df.loc[df['Floor Building'] == 'Ground', 'Floor Building'] = 0
df.loc[df['Floor House'] == 'Lower', 'Floor House'] = 0
df.loc[df['Floor House'] == 'Upper', 'Floor House'] = df.loc[df['Floor House'] == 'Upper', 'Floor Building']
df['Area Locality'].value_counts(normalize=True) * 100 | code |
105187920/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
print(f'Dataset shape -> {df.shape}')
df.head() | code |
105187920/cell_33 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = df[~(df['Bathroom'] > 6)]
df['Floor House'] = df['Floor'].str.split(' ').str.get(0)
df['Floor Building'] = df['Floor'].str.split(' ').str.get(-1)
df.drop('Floor', axis=1, inplace=True)
df.loc[df['Floor House'] == 'Ground', 'Floor House'] = 0
df.loc[df['Floor Building'] == 'Ground', 'Floor Building'] = 0
df.loc[df['Floor House'] == 'Lower', 'Floor House'] = 0
df.loc[df['Floor House'] == 'Upper', 'Floor House'] = df.loc[df['Floor House'] == 'Upper', 'Floor Building']
(df['Rent'] > 150000).sum() | code |
105187920/cell_20 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = df[~(df['Bathroom'] > 6)]
df['Floor House'] = df['Floor'].str.split(' ').str.get(0)
df['Floor Building'] = df['Floor'].str.split(' ').str.get(-1)
print(df['Floor House'].unique())
print(df['Floor Building'].unique())
df.drop('Floor', axis=1, inplace=True) | code |
105187920/cell_29 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
colors = ['#E557C4', '#57C4E5', '#293241']
def count(df, x, ax, main_color):
ax.bar_label(ax.containers[0], color='black', fontsize='large')
ax.tick_params(axis='y', which='both', left=False, right=False, labelleft=False)
ax.tick_params(axis='x', which='both', bottom=False, top=False)
sns.despine(ax=ax, left=True)
count_cols = ['Area Type', 'City', 'Furnishing Status', 'Tenant Preferred', "Bathroom", 'Point of Contact', "BHK"]
fig, axs = plt.subplots(4, 2, figsize=(20, 22), sharey=True)
for i, col in enumerate(count_cols):
r_i = i // 2
c_i = i% 2
count(df, col, axs[r_i][c_i], colors[0])
fig.delaxes(axs[3,1])
plt.show()
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = df[~(df['Bathroom'] > 6)]
df['Floor House'] = df['Floor'].str.split(' ').str.get(0)
df['Floor Building'] = df['Floor'].str.split(' ').str.get(-1)
df.drop('Floor', axis=1, inplace=True)
df.loc[df['Floor House'] == 'Ground', 'Floor House'] = 0
df.loc[df['Floor Building'] == 'Ground', 'Floor Building'] = 0
df.loc[df['Floor House'] == 'Lower', 'Floor House'] = 0
df.loc[df['Floor House'] == 'Upper', 'Floor House'] = df.loc[df['Floor House'] == 'Upper', 'Floor Building']
def hist(df, x, ax, main_color, meanline=True, mean_color=colors[2]):
ax.tick_params(axis='y', which='both', left=False, right=False)
sns.despine(ax=ax, left=True)
numeric_cols = ['Posted On', 'Size', 'Floor House', 'Floor Building']
fig, axs = plt.subplots(2, 2, figsize=(30, 15))
for i, num_col in enumerate(numeric_cols):
is_not_date = num_col != 'Posted On'
r_i = i // 2
c_i = i % 2
hist(df, num_col, axs[r_i][c_i], colors[1], meanline=is_not_date)
plt.show() | code |
105187920/cell_2 | [
"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 |
105187920/cell_19 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = df[~(df['Bathroom'] > 6)]
df['Floor'].nunique() | code |
105187920/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
colors = ['#E557C4', '#57C4E5', '#293241']
def count(df, x, ax, main_color):
ax.bar_label(ax.containers[0], color='black', fontsize='large')
ax.tick_params(axis='y', which='both', left=False, right=False, labelleft=False)
ax.tick_params(axis='x', which='both', bottom=False, top=False)
sns.despine(ax=ax, left=True)
count_cols = ['Area Type', 'City', 'Furnishing Status', 'Tenant Preferred', "Bathroom", 'Point of Contact', "BHK"]
fig, axs = plt.subplots(4, 2, figsize=(20, 22), sharey=True)
for i, col in enumerate(count_cols):
r_i = i // 2
c_i = i% 2
count(df, col, axs[r_i][c_i], colors[0])
fig.delaxes(axs[3,1])
plt.show()
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = df[~(df['Bathroom'] > 6)]
df['Floor House'] = df['Floor'].str.split(' ').str.get(0)
df['Floor Building'] = df['Floor'].str.split(' ').str.get(-1)
df.drop('Floor', axis=1, inplace=True)
df.loc[df['Floor House'] == 'Ground', 'Floor House'] = 0
df.loc[df['Floor Building'] == 'Ground', 'Floor Building'] = 0
df.loc[df['Floor House'] == 'Lower', 'Floor House'] = 0
df.loc[df['Floor House'] == 'Upper', 'Floor House'] = df.loc[df['Floor House'] == 'Upper', 'Floor Building']
def hist(df, x, ax, main_color, meanline=True, mean_color=colors[2]):
ax.tick_params(axis='y', which='both', left=False, right=False)
sns.despine(ax=ax, left=True)
numeric_cols = ["Posted On", "Size", "Floor House", "Floor Building"]
fig, axs = plt.subplots(2, 2, figsize=(30, 15))
for i, num_col in enumerate(numeric_cols):
is_not_date = (num_col != "Posted On")
r_i = i // 2
c_i = i % 2
hist(df, num_col, axs[r_i][c_i], colors[1], meanline=is_not_date)
plt.show()
fig, axs = plt.subplots(1, 2, figsize=(20, 6))
for ax in axs:
sns.stripplot(y=df['Rent'], ax=ax, color=colors[1], size=3, alpha=0.7, linewidth=0.1, edgecolor='black')
ax.axhline(np.mean(df['Rent']), linestyle='--', color=colors[0], label='Mean', linewidth=1)
ax.axhline(np.median(df['Rent']), linestyle=':', color=colors[2], label='Median', linewidth=1)
ax.set_ylabel('')
ax.set_xlabel('Rent')
ax.tick_params(axis='y', which='both', left=False, right=False)
ax.tick_params(axis='x', which='both', bottom=False, top=False, labelbottom=False)
sns.despine(ax=ax, left=True, bottom=True)
ax.grid(axis='y', linewidth=0.2)
ax.legend()
axs[0].set(title='Without logaritmic scale on y-axis')
axs[1].set(yscale='log', title='With logaritmic scale on y-axis')
plt.show() | code |
105187920/cell_31 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = df[~(df['Bathroom'] > 6)]
df['Floor House'] = df['Floor'].str.split(' ').str.get(0)
df['Floor Building'] = df['Floor'].str.split(' ').str.get(-1)
df.drop('Floor', axis=1, inplace=True)
df.loc[df['Floor House'] == 'Ground', 'Floor House'] = 0
df.loc[df['Floor Building'] == 'Ground', 'Floor Building'] = 0
df.loc[df['Floor House'] == 'Lower', 'Floor House'] = 0
df.loc[df['Floor House'] == 'Upper', 'Floor House'] = df.loc[df['Floor House'] == 'Upper', 'Floor Building']
df['Rent'].describe() | code |
105187920/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
colors = ['#E557C4', '#57C4E5', '#293241']
def count(df, x, ax, main_color):
ax.bar_label(ax.containers[0], color='black', fontsize='large')
ax.tick_params(axis='y', which='both', left=False, right=False, labelleft=False)
ax.tick_params(axis='x', which='both', bottom=False, top=False)
sns.despine(ax=ax, left=True)
count_cols = ['Area Type', 'City', 'Furnishing Status', 'Tenant Preferred', 'Bathroom', 'Point of Contact', 'BHK']
fig, axs = plt.subplots(4, 2, figsize=(20, 22), sharey=True)
for i, col in enumerate(count_cols):
r_i = i // 2
c_i = i % 2
count(df, col, axs[r_i][c_i], colors[0])
fig.delaxes(axs[3, 1])
plt.show() | code |
105187920/cell_22 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = df[~(df['Bathroom'] > 6)]
df['Floor House'] = df['Floor'].str.split(' ').str.get(0)
df['Floor Building'] = df['Floor'].str.split(' ').str.get(-1)
df.drop('Floor', axis=1, inplace=True)
df.loc[df['Floor House'] == 'Ground', 'Floor House'] = 0
df.loc[df['Floor Building'] == 'Ground', 'Floor Building'] = 0
df.loc[df['Floor House'] == 'Lower', 'Floor House'] = 0
df.loc[df['Floor House'] == 'Upper', 'Floor House'] = df.loc[df['Floor House'] == 'Upper', 'Floor Building']
print(df['Floor House'].unique()) | code |
105187920/cell_37 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df = df[~(df['Area Type'] == 'Built Area')]
df = df[~(df['Point of Contact'] == 'Contact Builder')]
df = df[~(df['Bathroom'] > 6)]
df['Floor House'] = df['Floor'].str.split(' ').str.get(0)
df['Floor Building'] = df['Floor'].str.split(' ').str.get(-1)
df.drop('Floor', axis=1, inplace=True)
df.loc[df['Floor House'] == 'Ground', 'Floor House'] = 0
df.loc[df['Floor Building'] == 'Ground', 'Floor Building'] = 0
df.loc[df['Floor House'] == 'Lower', 'Floor House'] = 0
df.loc[df['Floor House'] == 'Upper', 'Floor House'] = df.loc[df['Floor House'] == 'Upper', 'Floor Building']
df = df[df['Rent'] < 100000]
print(df.Rent.describe()) | code |
105187920/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import matplotlib.pyplot as plt
df = pd.read_csv('/kaggle/input/house-rent-prediction-dataset/House_Rent_Dataset.csv')
df.info() | code |
73063905/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('submission.csv')
df.to_csv('submission.csv', index=False) | code |
2001825/cell_9 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.cross_validation import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score,make_scorer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.naive_bayes import GaussianNB
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier,plot_importance
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Iris.csv')
lb_make = LabelEncoder()
dataset['Species'] = dataset['Species'].astype('category')
dataset['SepalRatio'] = np.divide(dataset['SepalLengthCm'], dataset['SepalWidthCm'])
dataset['PetalRatio'] = np.divide(dataset['PetalLengthCm'], dataset['PetalWidthCm'])
X_all = dataset[['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'SepalRatio', 'PetalRatio']]
y_all = lb_make.fit_transform(dataset['Species'])
num_test = 0.2
X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, random_state=88)
acc_scorer = make_scorer(accuracy_score)
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
parametersLR = {'C': [0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1]}
LRmodel = GridSearchCV(clf, parametersLR, scoring=acc_scorer)
_ = LRmodel.fit(X_all, y_all)
from sklearn.naive_bayes import GaussianNB
NBmodel = GaussianNB()
_ = NBmodel.fit(X_all, y_all)
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier()
parametersKN = {'n_neighbors': [1, 2, 4, 8, 16]}
KNmodel = GridSearchCV(clf, parametersKN, scoring=acc_scorer)
_ = KNmodel.fit(X_all, y_all)
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
parametersDT = {'criterion': ['gini', 'entropy'], 'max_depth': [1, 2, 3, 4]}
DTmodel = GridSearchCV(clf, parametersDT, scoring=acc_scorer)
_ = DTmodel.fit(X_all, y_all)
from sklearn.svm import SVC
clf = SVC()
parametersSV = {'C': [0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1], 'kernel': ['linear', 'poly', 'rbf', 'sigmoid']}
SVmodel = GridSearchCV(clf, parametersSV, scoring=acc_scorer)
_ = SVmodel.fit(X_all, y_all)
from xgboost import XGBClassifier, plot_importance
clf = XGBClassifier()
parametersXG = {'n_estimators': [50, 100, 150, 200], 'max_depth': [2, 4, 6, 8]}
kfold = StratifiedKFold(y_all, n_folds=10, shuffle=True, random_state=42)
XGmodel = GridSearchCV(clf, parametersXG, scoring=acc_scorer, n_jobs=-1, cv=kfold, verbose=1)
_ = XGmodel.fit(X_all, y_all)
expected = y_all
LRpredicted = LRmodel.predict(X_all)
LRpredictions = lb_make.inverse_transform(LRpredicted)
LRpredictions = pd.DataFrame(LRpredictions)
print('The results for the Logistic Regression are:\n')
print(metrics.classification_report(expected, LRpredicted))
print(metrics.confusion_matrix(expected, LRpredicted))
NBpredicted = NBmodel.predict(X_all)
NBpredictions = lb_make.inverse_transform(NBpredicted)
NBpredictions = pd.DataFrame(NBpredictions)
print('The results for the Naive Bayes are:\n')
print(metrics.classification_report(expected, NBpredicted))
print(metrics.confusion_matrix(expected, NBpredicted))
KNpredicted = KNmodel.predict(X_all)
KNpredictions = lb_make.inverse_transform(KNpredicted)
KNpredictions = pd.DataFrame(KNpredictions)
print('The results for kNN are:\n')
print(metrics.classification_report(expected, KNpredicted))
print(metrics.confusion_matrix(expected, KNpredicted))
DTpredicted = DTmodel.predict(X_all)
DTpredictions = lb_make.inverse_transform(DTpredicted)
DTpredictions = pd.DataFrame(DTpredictions)
print('The results for the Decision tree are:\n')
print(metrics.classification_report(expected, DTpredicted))
print(metrics.confusion_matrix(expected, DTpredicted))
SVpredicted = SVmodel.predict(X_all)
SVpredictions = lb_make.inverse_transform(SVpredicted)
SVpredictions = pd.DataFrame(SVpredictions)
print('The results for the support vector machine are:\n')
print(metrics.classification_report(expected, SVpredicted))
print(metrics.confusion_matrix(expected, SVpredicted))
XGpredicted = XGmodel.predict(X_all)
XGpredictions = lb_make.inverse_transform(XGpredicted)
XGpredictions = pd.DataFrame(XGpredictions)
print('The results for the XGBoost are:\n')
print(metrics.classification_report(expected, XGpredicted))
print(metrics.confusion_matrix(expected, XGpredicted))
print('\nAcc. LogReg: {0}'.format(accuracy_score(expected, LRpredicted)))
print('\nAcc. NaiveBayes: {0}'.format(accuracy_score(expected, NBpredicted)))
print('\nAcc. kNN: {0}'.format(accuracy_score(expected, KNpredicted)))
print('\nAcc. DecTree: {0}'.format(accuracy_score(expected, DTpredicted)))
print('\nAcc. SVM: {0}'.format(accuracy_score(expected, SVpredicted)))
print('\nAcc. XGBoost: {0}'.format(accuracy_score(expected, XGpredicted))) | code |
2001825/cell_2 | [
"text_plain_output_1.png"
] | from sklearn.metrics import accuracy_score,make_scorer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import LabelEncoder
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Iris.csv')
print(dataset.head())
lb_make = LabelEncoder()
dataset['Species'] = dataset['Species'].astype('category')
dataset['SepalRatio'] = np.divide(dataset['SepalLengthCm'], dataset['SepalWidthCm'])
dataset['PetalRatio'] = np.divide(dataset['PetalLengthCm'], dataset['PetalWidthCm'])
X_all = dataset[['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'SepalRatio', 'PetalRatio']]
print(X_all.head())
y_all = lb_make.fit_transform(dataset['Species'])
num_test = 0.2
X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, random_state=88)
acc_scorer = make_scorer(accuracy_score) | code |
2001825/cell_1 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8'))
from sklearn import datasets
from sklearn import metrics
from sklearn.metrics import accuracy_score, make_scorer
from sklearn import preprocessing
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.preprocessing import LabelEncoder
from sklearn.cross_validation import StratifiedKFold | code |
2001825/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.cross_validation import StratifiedKFold
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score,make_scorer
from sklearn.model_selection import train_test_split, GridSearchCV
from sklearn.neighbors import KNeighborsClassifier
from sklearn.preprocessing import LabelEncoder
from sklearn.svm import SVC
from sklearn.tree import DecisionTreeClassifier
from xgboost import XGBClassifier,plot_importance
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
dataset = pd.read_csv('../input/Iris.csv')
lb_make = LabelEncoder()
dataset['Species'] = dataset['Species'].astype('category')
dataset['SepalRatio'] = np.divide(dataset['SepalLengthCm'], dataset['SepalWidthCm'])
dataset['PetalRatio'] = np.divide(dataset['PetalLengthCm'], dataset['PetalWidthCm'])
X_all = dataset[['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm', 'SepalRatio', 'PetalRatio']]
y_all = lb_make.fit_transform(dataset['Species'])
num_test = 0.2
X_train, X_test, y_train, y_test = train_test_split(X_all, y_all, test_size=num_test, random_state=88)
acc_scorer = make_scorer(accuracy_score)
from sklearn.linear_model import LogisticRegression
clf = LogisticRegression()
parametersLR = {'C': [0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1]}
LRmodel = GridSearchCV(clf, parametersLR, scoring=acc_scorer)
_ = LRmodel.fit(X_all, y_all)
from sklearn.neighbors import KNeighborsClassifier
clf = KNeighborsClassifier()
parametersKN = {'n_neighbors': [1, 2, 4, 8, 16]}
KNmodel = GridSearchCV(clf, parametersKN, scoring=acc_scorer)
_ = KNmodel.fit(X_all, y_all)
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier()
parametersDT = {'criterion': ['gini', 'entropy'], 'max_depth': [1, 2, 3, 4]}
DTmodel = GridSearchCV(clf, parametersDT, scoring=acc_scorer)
_ = DTmodel.fit(X_all, y_all)
from sklearn.svm import SVC
clf = SVC()
parametersSV = {'C': [0.001, 0.003, 0.01, 0.03, 0.1, 0.3, 1], 'kernel': ['linear', 'poly', 'rbf', 'sigmoid']}
SVmodel = GridSearchCV(clf, parametersSV, scoring=acc_scorer)
_ = SVmodel.fit(X_all, y_all)
from xgboost import XGBClassifier, plot_importance
clf = XGBClassifier()
parametersXG = {'n_estimators': [50, 100, 150, 200], 'max_depth': [2, 4, 6, 8]}
kfold = StratifiedKFold(y_all, n_folds=10, shuffle=True, random_state=42)
XGmodel = GridSearchCV(clf, parametersXG, scoring=acc_scorer, n_jobs=-1, cv=kfold, verbose=1)
_ = XGmodel.fit(X_all, y_all) | code |
121151674/cell_13 | [
"text_plain_output_1.png"
] | errors | code |
121151674/cell_19 | [
"text_plain_output_1.png"
] | import numpy as np
def params(num_neurons):
W = [np.random.randn(y, x) for x, y in zip(num_neurons[:-1], num_neurons[1:])]
b = [0.01 * np.random.randn(x, 1) for x in num_neurons[1:]]
return (W, b)
def sigmoid(z):
sig = 1.0 / (1.0 + np.exp(-z))
return sig
def feedforward(X, W, b):
n = W.shape[0]
m = X.shape[0]
activation = np.zeros([m, n])
for i in range(n):
a = sigmoid(np.dot(X, W[i, :]) + b[i])
activation[:, i] = a
return activation
def cost(yhat, y):
err = -sum(y * np.log(yhat) + (1 - y) * np.log(1 - yhat)) / y.shape[0]
return err
def one_hot(i):
e = np.zeros(10)
e[i] = 1.0
return e
y_train = np.array([one_hot(y) for y in train_data[1]])
def gradient(yhat, y, x2):
common_on = (yhat - y) * (yhat * (1 - yhat))
gw_l = np.zeros([yhat.shape[1], x2.shape[1]])
for i in range(yhat.shape[1]):
gw = common_on[:, i] * x2.T
gw_l[i, :] = sum(gw.T) / y.shape[0]
gb = sum(common_on) / y.shape[0]
return (gw_l, gb.reshape(10, 1))
def gradient_l2(yhat, y, a1, X, w2):
commo = (yhat - y) * (yhat * (1 - yhat))
gb_l = np.zeros([a1.shape[1], 1])
gw_l = np.zeros([a1.shape[1], X.shape[0]])
for i in range(a1.shape[1]):
gd_b = np.dot(commo, w2[:, i]) * (a1[:, i] * (1 - a1[:, i]))
gw = gd_b * X
gw_l[i, :] = sum(gw.T) / y.shape[0]
gb_l[i] = sum(gd_b) / y.shape[0]
return (gw_l, gb_l)
def gradient_descent(y, X, wo, bo, wh, bh, lr, epoch):
costs = []
cost_batch = []
indexes = []
for i in range(epoch):
v1 = feedforward(train_data[0], wh, bh)
v2 = feedforward(v1, wo, bo)
err = cost(v2, y_train)
costs.append(err)
gwh, gbh = gradient_l2(v2, y, v1, X, wo)
gwo, gbo = gradient(v2, y, v1)
wh = wh - lr * gwh
bh = bh - lr * gbh
wo = wo - lr * gwo
bo = bo - lr * gbo
weight_params = {'out_layer': [wo, bo], 'hid_layer': [wh, bh]}
return (weight_params, costs)
v1 = feedforward(train_data[0], parameters['hid_layer'][0], parameters['hid_layer'][1])
v2 = feedforward(v1, parameters['out_layer'][0], parameters['out_layer'][1])
yhat = v2.tolist()
ineed = np.array([yhat[i].index(max(yhat[i])) for i in range(len(yhat))])
compaire = np.array([ineed, train_data[1]])
compaire = compaire.T
compaires = compaire[compaire[:, 0] == compaire[:, 1]]
compaires.shape
sumi = 0
wrong = 0
for i in range(compaire.shape[0]):
if compaire[i, 0] == compaire[i, 1]:
sumi += 1
else:
wrong += 1
print(f'model accuracy is: {sumi / compaire.shape[0]} and wrong prediction is: {wrong}/{compaire.shape[0]}') | code |
121151674/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
def load_img(imge):
img = imge.reshape(28, 28) * 255
plt.gray()
load_img(test_data[0][100]) | code |
121151674/cell_17 | [
"text_plain_output_1.png"
] | import numpy as np
def params(num_neurons):
W = [np.random.randn(y, x) for x, y in zip(num_neurons[:-1], num_neurons[1:])]
b = [0.01 * np.random.randn(x, 1) for x in num_neurons[1:]]
return (W, b)
def sigmoid(z):
sig = 1.0 / (1.0 + np.exp(-z))
return sig
def feedforward(X, W, b):
n = W.shape[0]
m = X.shape[0]
activation = np.zeros([m, n])
for i in range(n):
a = sigmoid(np.dot(X, W[i, :]) + b[i])
activation[:, i] = a
return activation
def cost(yhat, y):
err = -sum(y * np.log(yhat) + (1 - y) * np.log(1 - yhat)) / y.shape[0]
return err
def one_hot(i):
e = np.zeros(10)
e[i] = 1.0
return e
y_train = np.array([one_hot(y) for y in train_data[1]])
def gradient(yhat, y, x2):
common_on = (yhat - y) * (yhat * (1 - yhat))
gw_l = np.zeros([yhat.shape[1], x2.shape[1]])
for i in range(yhat.shape[1]):
gw = common_on[:, i] * x2.T
gw_l[i, :] = sum(gw.T) / y.shape[0]
gb = sum(common_on) / y.shape[0]
return (gw_l, gb.reshape(10, 1))
def gradient_l2(yhat, y, a1, X, w2):
commo = (yhat - y) * (yhat * (1 - yhat))
gb_l = np.zeros([a1.shape[1], 1])
gw_l = np.zeros([a1.shape[1], X.shape[0]])
for i in range(a1.shape[1]):
gd_b = np.dot(commo, w2[:, i]) * (a1[:, i] * (1 - a1[:, i]))
gw = gd_b * X
gw_l[i, :] = sum(gw.T) / y.shape[0]
gb_l[i] = sum(gd_b) / y.shape[0]
return (gw_l, gb_l)
def gradient_descent(y, X, wo, bo, wh, bh, lr, epoch):
costs = []
cost_batch = []
indexes = []
for i in range(epoch):
v1 = feedforward(train_data[0], wh, bh)
v2 = feedforward(v1, wo, bo)
err = cost(v2, y_train)
costs.append(err)
gwh, gbh = gradient_l2(v2, y, v1, X, wo)
gwo, gbo = gradient(v2, y, v1)
wh = wh - lr * gwh
bh = bh - lr * gbh
wo = wo - lr * gwo
bo = bo - lr * gbo
weight_params = {'out_layer': [wo, bo], 'hid_layer': [wh, bh]}
return (weight_params, costs)
v1 = feedforward(train_data[0], parameters['hid_layer'][0], parameters['hid_layer'][1])
v2 = feedforward(v1, parameters['out_layer'][0], parameters['out_layer'][1])
yhat = v2.tolist()
ineed = np.array([yhat[i].index(max(yhat[i])) for i in range(len(yhat))])
compaire = np.array([ineed, train_data[1]])
compaire = compaire.T
compaires = compaire[compaire[:, 0] == compaire[:, 1]]
compaires.shape | code |
72092328/cell_21 | [
"text_plain_output_1.png"
] | from glob import glob
paths = glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*')
paths[0].split('/')[-1].split('.')[0] | code |
72092328/cell_25 | [
"text_html_output_1.png"
] | from glob import glob
import pandas as pd
paths = glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*')
ids = [path.split('/')[-1].split('.')[0] for path in paths]
path_df = pd.DataFrame({'path': paths, 'id': ids})
path_df
labels = pd.read_csv('../input/g2net-gravitational-wave-detection/training_labels.csv')
train_df = pd.merge(left=labels, right=path_df, on='id')
train_df.shape
train_df.head() | code |
72092328/cell_23 | [
"text_plain_output_1.png"
] | from glob import glob
import pandas as pd
paths = glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*')
ids = [path.split('/')[-1].split('.')[0] for path in paths]
path_df = pd.DataFrame({'path': paths, 'id': ids})
path_df
labels = pd.read_csv('../input/g2net-gravitational-wave-detection/training_labels.csv')
train_df = pd.merge(left=labels, right=path_df, on='id')
train_df.shape | code |
72092328/cell_24 | [
"text_html_output_1.png"
] | from glob import glob
import pandas as pd
paths = glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*')
ids = [path.split('/')[-1].split('.')[0] for path in paths]
path_df = pd.DataFrame({'path': paths, 'id': ids})
path_df
labels = pd.read_csv('../input/g2net-gravitational-wave-detection/training_labels.csv')
train_df = pd.merge(left=labels, right=path_df, on='id')
train_df.shape
display(train_df.head()) | code |
72092328/cell_27 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from glob import glob
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
paths = glob('../input/g2net-gravitational-wave-detection/train/*/*/*/*')
ids = [path.split('/')[-1].split('.')[0] for path in paths]
path_df = pd.DataFrame({'path': paths, 'id': ids})
path_df
labels = pd.read_csv('../input/g2net-gravitational-wave-detection/training_labels.csv')
train_df = pd.merge(left=labels, right=path_df, on='id')
train_df.shape
target_1 = train_df[train_df.target == 1]
target_0 = train_df[train_df.target == 0]
target_waves = target_1.sample(50).path.values
plt.figure(figsize=(20, 15))
for i in range(1, len(target_waves) + 1):
pos = np.load(target_waves[i - 1])
plt.subplot(10, 1, i)
plt.plot(pos[0], c='firebrick')
plt.plot(pos[1], c='blue')
plt.plot(pos[2], c='green')
pos.shape | code |
16158861/cell_9 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.plotly import iplot
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
# corelation map
f, ax = plt.subplots(figsize=(18,18))
sns.heatmap(data.corr(),annot=True, linewidths=.5, fmt='.2f', ax=ax)
trace1 = go.Scatter(x=data.age, y=data.trestbps, mode='lines', name='trestbps', marker=dict(color='rgba(16, 112, 2, 0.8)'), text=data.genderText)
trace2 = go.Scatter(x=data.age, y=data.chol, mode='lines+markers', name='chol', marker=dict(color='rgba(80, 26, 80, 0.8)'), text=data.genderText)
data2 = [trace1, trace2]
layout = dict(title='trestbps and chol accoding to age', xaxis=dict(title='Age', ticklen=5, zeroline=False))
fig = dict(data=data2, layout=layout)
iplot(fig) | code |
16158861/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
data.head() | code |
16158861/cell_11 | [
"text_html_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.plotly import iplot
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
# corelation map
f, ax = plt.subplots(figsize=(18,18))
sns.heatmap(data.corr(),annot=True, linewidths=.5, fmt='.2f', ax=ax)
trace1 = go.Scatter(x=data.age, y=data.trestbps, mode='lines', name='trestbps', marker=dict(color='rgba(16, 112, 2, 0.8)'), text=data.genderText)
trace2 = go.Scatter(x=data.age, y=data.chol, mode='lines+markers', name='chol', marker=dict(color='rgba(80, 26, 80, 0.8)'), text=data.genderText)
data2 = [trace1, trace2]
layout = dict(title='trestbps and chol accoding to age', xaxis=dict(title='Age', ticklen=5, zeroline=False))
fig = dict(data=data2, layout=layout)
# %% filtering and joint plot
dataFilter1 =data[data.target==1]
dataFilter0 =data[data.target==0]
g = sns.jointplot(dataFilter1.age, dataFilter1.trestbps, kind="kde", size=7)
#plt.savefig('graph.png')
plt.show()
dataFilterMale = data[data.sex == 1]
dataFilterFemale = data[data.sex == 0]
MaleThalach = pd.DataFrame(dataFilterMale.thalach)
FemaleThalach = pd.DataFrame(dataFilterFemale.thalach)
FemaleThalach.index = range(1, 97, 1)
MaleThalach.index = range(1, 208, 1)
dfMaleThalach = pd.DataFrame(MaleThalach).iloc[0:96, :]
dfFemaleThalach = pd.DataFrame(FemaleThalach)
unifiedThalach = pd.concat([dfMaleThalach, dfFemaleThalach], axis=1)
unifiedThalach.columns = ['Male thalach', 'Female thalach']
pal = sns.cubehelix_palette(2, rot=-0.5, dark=0.3)
sns.violinplot(data=unifiedThalach, palette=pal, inner='points')
plt.show() | code |
16158861/cell_1 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objs as go
from plotly import tools
import plotly.plotly as py
from plotly.plotly import iplot
from plotly.offline import init_notebook_mode, iplot
init_notebook_mode(connected=True)
from wordcloud import WordCloud
import os
print(os.listdir('../input')) | code |
16158861/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
# corelation map
f, ax = plt.subplots(figsize=(18,18))
sns.heatmap(data.corr(),annot=True, linewidths=.5, fmt='.2f', ax=ax)
plt.hist(data.age, bins=50)
plt.xlabel('Age')
plt.ylabel('Frequency')
plt.title('histogram')
plt.show() | code |
16158861/cell_3 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
data.info() | code |
16158861/cell_10 | [
"text_plain_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.plotly import iplot
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
# corelation map
f, ax = plt.subplots(figsize=(18,18))
sns.heatmap(data.corr(),annot=True, linewidths=.5, fmt='.2f', ax=ax)
trace1 = go.Scatter(x=data.age, y=data.trestbps, mode='lines', name='trestbps', marker=dict(color='rgba(16, 112, 2, 0.8)'), text=data.genderText)
trace2 = go.Scatter(x=data.age, y=data.chol, mode='lines+markers', name='chol', marker=dict(color='rgba(80, 26, 80, 0.8)'), text=data.genderText)
data2 = [trace1, trace2]
layout = dict(title='trestbps and chol accoding to age', xaxis=dict(title='Age', ticklen=5, zeroline=False))
fig = dict(data=data2, layout=layout)
dataFilter1 = data[data.target == 1]
dataFilter0 = data[data.target == 0]
g = sns.jointplot(dataFilter1.age, dataFilter1.trestbps, kind='kde', size=7)
plt.show() | code |
16158861/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from plotly.offline import init_notebook_mode, iplot
from plotly.plotly import iplot
import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import seaborn as sns
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
# corelation map
f, ax = plt.subplots(figsize=(18,18))
sns.heatmap(data.corr(),annot=True, linewidths=.5, fmt='.2f', ax=ax)
trace1 = go.Scatter(x=data.age, y=data.trestbps, mode='lines', name='trestbps', marker=dict(color='rgba(16, 112, 2, 0.8)'), text=data.genderText)
trace2 = go.Scatter(x=data.age, y=data.chol, mode='lines+markers', name='chol', marker=dict(color='rgba(80, 26, 80, 0.8)'), text=data.genderText)
data2 = [trace1, trace2]
layout = dict(title='trestbps and chol accoding to age', xaxis=dict(title='Age', ticklen=5, zeroline=False))
fig = dict(data=data2, layout=layout)
# %% filtering and joint plot
dataFilter1 =data[data.target==1]
dataFilter0 =data[data.target==0]
g = sns.jointplot(dataFilter1.age, dataFilter1.trestbps, kind="kde", size=7)
#plt.savefig('graph.png')
plt.show()
dataFilterMale = data[data.sex == 1]
dataFilterFemale = data[data.sex == 0]
MaleThalach = pd.DataFrame(dataFilterMale.thalach)
FemaleThalach = pd.DataFrame(dataFilterFemale.thalach)
FemaleThalach.index = range(1, 97, 1)
MaleThalach.index = range(1, 208, 1)
dfMaleThalach = pd.DataFrame(MaleThalach).iloc[0:96, :]
dfFemaleThalach = pd.DataFrame(FemaleThalach)
unifiedThalach = pd.concat([dfMaleThalach, dfFemaleThalach], axis=1)
unifiedThalach.columns = ['Male thalach', 'Female thalach']
pal = sns.cubehelix_palette(2, rot=-0.5, dark=0.3)
trace0 = go.Box(y=data.trestbps, name='trestbps', marker=dict(color='rgb(12, 12, 140)'))
trace1 = go.Box(y=data.chol, name='chol', marker=dict(color='rgb(12, 128, 128)'))
fig = [trace0, trace1]
iplot(fig) | code |
16158861/cell_5 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
data = pd.read_csv('../input/heart.csv', sep=',')
data = data.sort_values(by=['age'])
data['genderText'] = ['male' if 1 == each else 'female' for each in data.sex]
f, ax = plt.subplots(figsize=(18, 18))
sns.heatmap(data.corr(), annot=True, linewidths=0.5, fmt='.2f', ax=ax) | code |
128030673/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.date = pd.to_datetime(test_df.date)
train_df['year'] = train_df.date.dt.year
test_df['year'] = test_df.date.dt.year
train_df['month'] = train_df.date.dt.month
test_df['month'] = test_df.date.dt.month
train_df['dayofmonth'] = train_df.date.dt.day
test_df['dayofmonth'] = test_df.date.dt.day
train_df['dayofweek'] = train_df.date.dt.dayofweek
test_df['dayofweek'] = test_df.date.dt.dayofweek
train_df['dayname'] = train_df.date.dt.strftime('%A')
test_df['dayname'] = test_df.date.dt.strftime('%A')
train_df.family.unique()
fig= plt.figure(figsize=(10,120))
fig.subplots_adjust(hspace=0.75)
for i,product in enumerate(train_df.family.unique()):
ax = fig.add_subplot(33,1,i+1)
select = train_df.query('family==@product')
for year in [2013,2014,2015,2016,2017]:
select.query('year==@year').groupby('month').sales.mean().plot(ax=ax,label=year)
plt.title(product)
ax.legend();
order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
train_df.groupby('dayname').sales.mean().reindex(index=order).plot(kind='bar')
plt.title('Average Sales by Day of week') | code |
128030673/cell_9 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.date = pd.to_datetime(test_df.date)
train_df['year'] = train_df.date.dt.year
test_df['year'] = test_df.date.dt.year
train_df['month'] = train_df.date.dt.month
test_df['month'] = test_df.date.dt.month
train_df['dayofmonth'] = train_df.date.dt.day
test_df['dayofmonth'] = test_df.date.dt.day
train_df['dayofweek'] = train_df.date.dt.dayofweek
test_df['dayofweek'] = test_df.date.dt.dayofweek
train_df['dayname'] = train_df.date.dt.strftime('%A')
test_df['dayname'] = test_df.date.dt.strftime('%A')
train_df.family.unique()
fig = plt.figure(figsize=(10, 120))
fig.subplots_adjust(hspace=0.75)
for i, product in enumerate(train_df.family.unique()):
ax = fig.add_subplot(33, 1, i + 1)
select = train_df.query('family==@product')
for year in [2013, 2014, 2015, 2016, 2017]:
select.query('year==@year').groupby('month').sales.mean().plot(ax=ax, label=year)
plt.title(product)
ax.legend() | code |
128030673/cell_20 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.date = pd.to_datetime(test_df.date)
train_df['year'] = train_df.date.dt.year
test_df['year'] = test_df.date.dt.year
train_df['month'] = train_df.date.dt.month
test_df['month'] = test_df.date.dt.month
train_df['dayofmonth'] = train_df.date.dt.day
test_df['dayofmonth'] = test_df.date.dt.day
train_df['dayofweek'] = train_df.date.dt.dayofweek
test_df['dayofweek'] = test_df.date.dt.dayofweek
train_df['dayname'] = train_df.date.dt.strftime('%A')
test_df['dayname'] = test_df.date.dt.strftime('%A')
train_df.family.unique()
fig= plt.figure(figsize=(10,120))
fig.subplots_adjust(hspace=0.75)
for i,product in enumerate(train_df.family.unique()):
ax = fig.add_subplot(33,1,i+1)
select = train_df.query('family==@product')
for year in [2013,2014,2015,2016,2017]:
select.query('year==@year').groupby('month').sales.mean().plot(ax=ax,label=year)
plt.title(product)
ax.legend();
order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
oil = pd.read_csv('../input/store-sales-time-series-forecasting/oil.csv', index_col='date')
plt.xticks(rotation=45)
stores = pd.read_csv('../input/store-sales-time-series-forecasting/stores.csv', index_col='store_nbr')
train_df = pd.merge(train_df, stores, how='left', on='store_nbr')
test_df = pd.merge(test_df, stores, how='left', on='store_nbr')
train_df.groupby(['type']).sales.mean().plot(kind='bar') | code |
128030673/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.date = pd.to_datetime(test_df.date)
train_df['year'] = train_df.date.dt.year
test_df['year'] = test_df.date.dt.year
train_df['month'] = train_df.date.dt.month
test_df['month'] = test_df.date.dt.month
train_df['dayofmonth'] = train_df.date.dt.day
test_df['dayofmonth'] = test_df.date.dt.day
train_df['dayofweek'] = train_df.date.dt.dayofweek
test_df['dayofweek'] = test_df.date.dt.dayofweek
train_df['dayname'] = train_df.date.dt.strftime('%A')
test_df['dayname'] = test_df.date.dt.strftime('%A')
train_df | code |
128030673/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.date = pd.to_datetime(test_df.date)
train_df['year'] = train_df.date.dt.year
test_df['year'] = test_df.date.dt.year
train_df['month'] = train_df.date.dt.month
test_df['month'] = test_df.date.dt.month
train_df['dayofmonth'] = train_df.date.dt.day
test_df['dayofmonth'] = test_df.date.dt.day
train_df['dayofweek'] = train_df.date.dt.dayofweek
test_df['dayofweek'] = test_df.date.dt.dayofweek
train_df['dayname'] = train_df.date.dt.strftime('%A')
test_df['dayname'] = test_df.date.dt.strftime('%A')
train_df.family.unique()
fig= plt.figure(figsize=(10,120))
fig.subplots_adjust(hspace=0.75)
for i,product in enumerate(train_df.family.unique()):
ax = fig.add_subplot(33,1,i+1)
select = train_df.query('family==@product')
for year in [2013,2014,2015,2016,2017]:
select.query('year==@year').groupby('month').sales.mean().plot(ax=ax,label=year)
plt.title(product)
ax.legend();
train_df.groupby('dayofmonth').sales.mean().plot(kind='bar')
plt.title('Sales Average by Day of Month') | code |
128030673/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
128030673/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.date = pd.to_datetime(test_df.date)
train_df['year'] = train_df.date.dt.year
test_df['year'] = test_df.date.dt.year
train_df['month'] = train_df.date.dt.month
test_df['month'] = test_df.date.dt.month
train_df['dayofmonth'] = train_df.date.dt.day
test_df['dayofmonth'] = test_df.date.dt.day
train_df['dayofweek'] = train_df.date.dt.dayofweek
test_df['dayofweek'] = test_df.date.dt.dayofweek
train_df['dayname'] = train_df.date.dt.strftime('%A')
test_df['dayname'] = test_df.date.dt.strftime('%A')
train_df.family.unique() | code |
128030673/cell_16 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.date = pd.to_datetime(test_df.date)
train_df['year'] = train_df.date.dt.year
test_df['year'] = test_df.date.dt.year
train_df['month'] = train_df.date.dt.month
test_df['month'] = test_df.date.dt.month
train_df['dayofmonth'] = train_df.date.dt.day
test_df['dayofmonth'] = test_df.date.dt.day
train_df['dayofweek'] = train_df.date.dt.dayofweek
test_df['dayofweek'] = test_df.date.dt.dayofweek
train_df['dayname'] = train_df.date.dt.strftime('%A')
test_df['dayname'] = test_df.date.dt.strftime('%A')
train_df.family.unique()
fig= plt.figure(figsize=(10,120))
fig.subplots_adjust(hspace=0.75)
for i,product in enumerate(train_df.family.unique()):
ax = fig.add_subplot(33,1,i+1)
select = train_df.query('family==@product')
for year in [2013,2014,2015,2016,2017]:
select.query('year==@year').groupby('month').sales.mean().plot(ax=ax,label=year)
plt.title(product)
ax.legend();
order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
oil = pd.read_csv('../input/store-sales-time-series-forecasting/oil.csv', index_col='date')
oil.plot()
plt.xticks(rotation=45) | code |
128030673/cell_3 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.head() | code |
128030673/cell_22 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.date = pd.to_datetime(test_df.date)
train_df['year'] = train_df.date.dt.year
test_df['year'] = test_df.date.dt.year
train_df['month'] = train_df.date.dt.month
test_df['month'] = test_df.date.dt.month
train_df['dayofmonth'] = train_df.date.dt.day
test_df['dayofmonth'] = test_df.date.dt.day
train_df['dayofweek'] = train_df.date.dt.dayofweek
test_df['dayofweek'] = test_df.date.dt.dayofweek
train_df['dayname'] = train_df.date.dt.strftime('%A')
test_df['dayname'] = test_df.date.dt.strftime('%A')
train_df.family.unique()
fig= plt.figure(figsize=(10,120))
fig.subplots_adjust(hspace=0.75)
for i,product in enumerate(train_df.family.unique()):
ax = fig.add_subplot(33,1,i+1)
select = train_df.query('family==@product')
for year in [2013,2014,2015,2016,2017]:
select.query('year==@year').groupby('month').sales.mean().plot(ax=ax,label=year)
plt.title(product)
ax.legend();
order = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']
oil = pd.read_csv('../input/store-sales-time-series-forecasting/oil.csv', index_col='date')
plt.xticks(rotation=45)
stores = pd.read_csv('../input/store-sales-time-series-forecasting/stores.csv', index_col='store_nbr')
train_df = pd.merge(train_df, stores, how='left', on='store_nbr')
test_df = pd.merge(test_df, stores, how='left', on='store_nbr')
plt.figure(figsize=(10, 4))
ax1 = plt.subplot(1, 2, 1)
train_df.groupby(['city']).sales.mean().plot(kind='bar')
plt.title('Average Sales by City')
ax2 = plt.subplot(1, 2, 2)
train_df.groupby(['city'])['store_nbr'].nunique().plot(kind='bar')
plt.title('Number of Stores by City') | code |
128030673/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
train_df = pd.read_csv('../input/store-sales-time-series-forecasting/train.csv', index_col='id')
test_df = pd.read_csv('../input/store-sales-time-series-forecasting/test.csv', index_col='id')
train_df.date = pd.to_datetime(train_df.date)
test_df.date = pd.to_datetime(test_df.date)
train_df['year'] = train_df.date.dt.year
test_df['year'] = test_df.date.dt.year
train_df['month'] = train_df.date.dt.month
test_df['month'] = test_df.date.dt.month
train_df['dayofmonth'] = train_df.date.dt.day
test_df['dayofmonth'] = test_df.date.dt.day
train_df['dayofweek'] = train_df.date.dt.dayofweek
test_df['dayofweek'] = test_df.date.dt.dayofweek
train_df['dayname'] = train_df.date.dt.strftime('%A')
test_df['dayname'] = test_df.date.dt.strftime('%A')
print('Train: ', min(train_df.date), max(train_df.date))
print('\n')
print('Test: ', min(test_df.date), max(test_df.date)) | code |
128018646/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
image_dir = '/kaggle/input/balanced-datasets/Adasyn_dataset'
df = pd.read_csv('/kaggle/input/balanced-datasets/Adasyn_dataset/labels.csv')
'\ny_one_hot = np.array(df.drop(columns = ["image"], axis = 1))\ny = np.argmax(y_one_hot, axis = 1)\ndf["label"] = y\ndf["label"] = df["label"].astype(str)\ndf[\'image\'] = df[\'image\']+\'.jpg\'\n' | code |
128018646/cell_14 | [
"text_plain_output_1.png"
] | from keras import models, layers, backend, optimizers, regularizers, metrics #for model manipulation
from keras.applications import MobileNet
from keras.applications import VGG16
from keras.applications.resnet import ResNet50
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from sklearn.model_selection import train_test_split, StratifiedKFold, KFold, cross_val_score, GridSearchCV
from tensorflow.keras.applications import EfficientNetB0
import matplotlib.pyplot as plt #for plotting results
import pandas as pd
import tensorflow as tf
image_dir = '/kaggle/input/balanced-datasets/Adasyn_dataset'
df = pd.read_csv('/kaggle/input/balanced-datasets/Adasyn_dataset/labels.csv')
'\ny_one_hot = np.array(df.drop(columns = ["image"], axis = 1))\ny = np.argmax(y_one_hot, axis = 1)\ndf["label"] = y\ndf["label"] = df["label"].astype(str)\ndf[\'image\'] = df[\'image\']+\'.jpg\'\n'
from keras.applications import MobileNet
from keras.applications import VGG16
from keras.applications.resnet import ResNet50
from tensorflow.keras.applications import EfficientNetB0
def choose_conv_base(name='from_scratch', input_shape=(224, 224, 3)):
if name == 'from_scratch':
conv_base = models.Sequential()
conv_base.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
conv_base.add(layers.MaxPooling2D((2, 2)))
conv_base.add(layers.Conv2D(64, (3, 3), activation='relu'))
conv_base.add(layers.MaxPooling2D((2, 2)))
conv_base.add(layers.Conv2D(128, (3, 3), activation='relu'))
conv_base.add(layers.MaxPooling2D((2, 2)))
conv_base.add(layers.Conv2D(128, (3, 3), activation='relu'))
conv_base.add(layers.MaxPooling2D((2, 2)))
elif name == 'mobilenet':
conv_base = MobileNet(weights='imagenet', include_top=False, input_shape=input_shape)
conv_base.trainable = False
elif name == 'vgg16':
conv_base = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)
conv_base.trainable = False
elif name == 'resnet':
conv_base = ResNet50(weights='imagenet', include_top=False, input_shape=input_shape)
conv_base.trainable = False
elif name == 'efficientnet':
conv_base = EfficientNetB0(weights='imagenet', include_top=False, input_shape=input_shape)
conv_base.trainable = False
return conv_base
def build_model(input=(224, 224, 3), loss='categorical_crossentropy', optimizer=tf.keras.optimizers.experimental.AdamW()):
model = models.Sequential()
model.add(choose_conv_base(name='mobilenet'))
model.add(layers.GlobalAveragePooling2D())
model.add(layers.Dropout(0.2))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(7, activation='softmax'))
model.compile(optimizer='adam', loss=loss, metrics=['accuracy'])
return model
es = EarlyStopping(monitor='val_accuracy', verbose=1, min_delta=0.01, patience=10)
mc = ModelCheckpoint(monitor='val_accuracy', verbose=1, filepath='./1_best.h5', save_best_only=True)
reducelr = ReduceLROnPlateau(monitor='val_accuracy', verbose=1, patience=5, factor=0.5, min_lr=1e-07)
cb = [mc]
batch_size = 32
epochs = 100
""""""
train_val_df, test_df = train_test_split(df, stratify=df['label'], test_size=0.1, random_state=42)
train_df, validation_df = train_test_split(train_val_df, stratify=train_val_df['label'], test_size=0.2, random_state=42)
augmented_datagen = ImageDataGenerator(rescale=1.0 / 255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest')
datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = datagen.flow_from_dataframe(train_df, directory=image_dir, batch_size=batch_size, target_size=(224, 224), x_col='filename', y_col='label', class_mode='categorical', shuffle=True)
val_generator = datagen.flow_from_dataframe(validation_df, directory=image_dir, batch_size=batch_size, target_size=(224, 224), x_col='filename', y_col='label', class_mode='categorical', shuffle=True)
model = build_model()
history = model.fit(train_generator, steps_per_epoch=len(train_df) // batch_size, epochs=epochs, validation_data=val_generator, validation_steps=len(validation_df) // batch_size, callbacks=cb)
history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
acc_values = history_dict['accuracy']
val_acc_values = history_dict['val_accuracy']
def smooth_curve(points, factor=0.8):
smoothed_points = []
for point in points:
if smoothed_points:
previous = smoothed_points[-1]
smoothed_points.append(previous * factor + point * (1 - factor))
else:
smoothed_points.append(point)
return smoothed_points
epochs = range(1, len(loss_values) + 1)
datagen = ImageDataGenerator(rescale=1.0 / 255)
test_generator = datagen.flow_from_dataframe(test_df, directory=image_dir, x_col='filename', y_col='label', target_size=(224, 224), batch_size=20, class_mode='categorical')
print('last model testing:')
result = model.evaluate(test_generator)
print(result)
print('best model testing:')
best_model = models.load_model('./1_best.h5')
result = best_model.evaluate(test_generator)
print(result) | code |
128018646/cell_10 | [
"text_plain_output_1.png"
] | from keras import models, layers, backend, optimizers, regularizers, metrics #for model manipulation
from keras.applications import MobileNet
from keras.applications import VGG16
from keras.applications.resnet import ResNet50
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from sklearn.model_selection import train_test_split, StratifiedKFold, KFold, cross_val_score, GridSearchCV
from tensorflow.keras.applications import EfficientNetB0
import matplotlib.pyplot as plt #for plotting results
import pandas as pd
import tensorflow as tf
image_dir = '/kaggle/input/balanced-datasets/Adasyn_dataset'
df = pd.read_csv('/kaggle/input/balanced-datasets/Adasyn_dataset/labels.csv')
'\ny_one_hot = np.array(df.drop(columns = ["image"], axis = 1))\ny = np.argmax(y_one_hot, axis = 1)\ndf["label"] = y\ndf["label"] = df["label"].astype(str)\ndf[\'image\'] = df[\'image\']+\'.jpg\'\n'
from keras.applications import MobileNet
from keras.applications import VGG16
from keras.applications.resnet import ResNet50
from tensorflow.keras.applications import EfficientNetB0
def choose_conv_base(name='from_scratch', input_shape=(224, 224, 3)):
if name == 'from_scratch':
conv_base = models.Sequential()
conv_base.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
conv_base.add(layers.MaxPooling2D((2, 2)))
conv_base.add(layers.Conv2D(64, (3, 3), activation='relu'))
conv_base.add(layers.MaxPooling2D((2, 2)))
conv_base.add(layers.Conv2D(128, (3, 3), activation='relu'))
conv_base.add(layers.MaxPooling2D((2, 2)))
conv_base.add(layers.Conv2D(128, (3, 3), activation='relu'))
conv_base.add(layers.MaxPooling2D((2, 2)))
elif name == 'mobilenet':
conv_base = MobileNet(weights='imagenet', include_top=False, input_shape=input_shape)
conv_base.trainable = False
elif name == 'vgg16':
conv_base = VGG16(weights='imagenet', include_top=False, input_shape=input_shape)
conv_base.trainable = False
elif name == 'resnet':
conv_base = ResNet50(weights='imagenet', include_top=False, input_shape=input_shape)
conv_base.trainable = False
elif name == 'efficientnet':
conv_base = EfficientNetB0(weights='imagenet', include_top=False, input_shape=input_shape)
conv_base.trainable = False
return conv_base
def build_model(input=(224, 224, 3), loss='categorical_crossentropy', optimizer=tf.keras.optimizers.experimental.AdamW()):
model = models.Sequential()
model.add(choose_conv_base(name='mobilenet'))
model.add(layers.GlobalAveragePooling2D())
model.add(layers.Dropout(0.2))
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(7, activation='softmax'))
model.compile(optimizer='adam', loss=loss, metrics=['accuracy'])
return model
es = EarlyStopping(monitor='val_accuracy', verbose=1, min_delta=0.01, patience=10)
mc = ModelCheckpoint(monitor='val_accuracy', verbose=1, filepath='./1_best.h5', save_best_only=True)
reducelr = ReduceLROnPlateau(monitor='val_accuracy', verbose=1, patience=5, factor=0.5, min_lr=1e-07)
cb = [mc]
batch_size = 32
epochs = 100
""""""
train_val_df, test_df = train_test_split(df, stratify=df['label'], test_size=0.1, random_state=42)
train_df, validation_df = train_test_split(train_val_df, stratify=train_val_df['label'], test_size=0.2, random_state=42)
augmented_datagen = ImageDataGenerator(rescale=1.0 / 255, rotation_range=40, width_shift_range=0.2, height_shift_range=0.2, shear_range=0.2, zoom_range=0.2, horizontal_flip=True, fill_mode='nearest')
datagen = ImageDataGenerator(rescale=1.0 / 255)
train_generator = datagen.flow_from_dataframe(train_df, directory=image_dir, batch_size=batch_size, target_size=(224, 224), x_col='filename', y_col='label', class_mode='categorical', shuffle=True)
val_generator = datagen.flow_from_dataframe(validation_df, directory=image_dir, batch_size=batch_size, target_size=(224, 224), x_col='filename', y_col='label', class_mode='categorical', shuffle=True)
model = build_model()
history = model.fit(train_generator, steps_per_epoch=len(train_df) // batch_size, epochs=epochs, validation_data=val_generator, validation_steps=len(validation_df) // batch_size, callbacks=cb)
history_dict = history.history
loss_values = history_dict['loss']
val_loss_values = history_dict['val_loss']
acc_values = history_dict['accuracy']
val_acc_values = history_dict['val_accuracy']
def smooth_curve(points, factor=0.8):
smoothed_points = []
for point in points:
if smoothed_points:
previous = smoothed_points[-1]
smoothed_points.append(previous * factor + point * (1 - factor))
else:
smoothed_points.append(point)
return smoothed_points
epochs = range(1, len(loss_values) + 1)
plt.subplot(1, 2, 1)
plt.plot(epochs, smooth_curve(loss_values), 'bo', label='training loss')
plt.plot(epochs, smooth_curve(val_loss_values), 'b', label='validation loss')
plt.title('training and validation loss')
plt.xlabel('epochs')
plt.ylabel('loss')
plt.legend()
plt.subplot(1, 2, 2)
plt.plot(epochs, acc_values, 'ro', label='taining accuracy')
plt.plot(epochs, val_acc_values, 'r', label='validation accuracy')
plt.title('training and validation accuracy')
plt.xlabel('epochs')
plt.ylabel('accuracy')
plt.legend()
plt.show() | code |
128018646/cell_12 | [
"text_plain_output_1.png",
"image_output_1.png"
] | """
#kfold crossvalidation
augmented_datagen = ImageDataGenerator(rescale=1./255,
shear_range = 0.2 ,rotation_range=40, width_shift_range=0.2, height_shift_range=0.2,
zoom_range=0.2, horizontal_flip=True)
datagen = ImageDataGenerator(rescale=1./255)
kf = StratifiedKFold(n_splits = 5, random_state = 7, shuffle = True)
loss_values = val_loss_values = acc_values = val_acc_values = total_scores = []
i = 1
for train_index, val_index in kf.split(np.zeros(len(df)),df["label"]):
print(f"processing fold: {i}")
train_df = df.iloc[train_index]
validation_df = df.iloc[val_index]
train_generator = datagen.flow_from_dataframe(train_df,
directory = image_dir,
batch_size = batch_size,
target_size = (224,224),
x_col = "filename",
y_col = "label",
class_mode = "categorical",
shuffle = True)
val_generator = datagen.flow_from_dataframe(validation_df,
directory = image_dir,
batch_size = batch_size,
target_size = (224,224),
x_col = "filename",
y_col = "label",
class_mode = "categorical",
shuffle = True)
model = build_model()
history = model.fit(train_generator,
epochs = epochs,
steps_per_epoch = train_generator.samples // batch_size,
callbacks = cb,
verbose = 1)
val_results = model.evaluate(val_generator)
#storing loss and acc, using the last value in history
total_scores.append(val_results[-1])
print(f"accuracy for fold {i}: {total_scores[-1]}")
i+=1
print(f"mean of all accuracies: {np.mean(total_scores)}")
""" | code |
90131532/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any() | code |
90131532/cell_34 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.corr()
X = data.iloc[:, 1:].values
y = data.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)
lasso_model = Lasso()
params = {'alpha': np.linspace(0, 2, 200)}
lasso_grid = GridSearchCV(lasso_model, param_grid=params, cv=4, refit=True, n_jobs=-1, verbose=2)
lasso_grid.fit(X_train, y_train)
lasso_grid_results = pd.DataFrame(lasso_grid.cv_results_)
lasso_grid_results = lasso_grid_results.sort_values('rank_test_score')
lasso_grid_results.head(10) | code |
90131532/cell_23 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.corr()
plt.scatter(data['Chest'], data['BodyFat'])
plt.xlabel('Chest Circumference (cm)')
plt.ylabel('Percent Body Fat')
plt.show() | code |
90131532/cell_33 | [
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.corr()
X = data.iloc[:, 1:].values
y = data.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)
lasso_model = Lasso()
params = {'alpha': np.linspace(0, 2, 200)}
lasso_grid = GridSearchCV(lasso_model, param_grid=params, cv=4, refit=True, n_jobs=-1, verbose=2)
lasso_grid.fit(X_train, y_train)
print(lasso_grid.best_params_)
print(lasso_grid.best_score_) | code |
90131532/cell_44 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestRegressor
from sklearn.linear_model import Lasso
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.corr()
X = data.iloc[:, 1:].values
y = data.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)
lasso_model = Lasso()
params = {'alpha': np.linspace(0, 2, 200)}
lasso_grid = GridSearchCV(lasso_model, param_grid=params, cv=4, refit=True, n_jobs=-1, verbose=2)
lasso_grid.fit(X_train, y_train)
lasso_grid_results = pd.DataFrame(lasso_grid.cv_results_)
lasso_grid_results = lasso_grid_results.sort_values('rank_test_score')
lasso_predictions = lasso_grid.predict(X_test)
lasso_r2 = r2_score(y_test, lasso_predictions)
lasso_rmse = np.sqrt(mean_squared_error(y_test, lasso_predictions))
coefficients = lasso_grid.best_estimator_.coef_
coefficients = np.abs(coefficients)
names = data.columns[1:]
plt.xticks(rotation=60)
rf_model = RandomForestRegressor()
params = {'n_estimators': np.arange(50, 401, 100), 'max_features': ['auto', 'sqrt'], 'max_depth': np.arange(5, 11), 'min_samples_split': [2, 4, 6], 'min_samples_leaf': [1, 2, 4]}
rf_grid = GridSearchCV(estimator=rf_model, param_grid=params, cv=4, refit=True, n_jobs=-1, verbose=2)
rf_grid.fit(X_train, y_train) | code |
90131532/cell_20 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.corr() | code |
90131532/cell_40 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.corr()
X = data.iloc[:, 1:].values
y = data.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)
lasso_model = Lasso()
params = {'alpha': np.linspace(0, 2, 200)}
lasso_grid = GridSearchCV(lasso_model, param_grid=params, cv=4, refit=True, n_jobs=-1, verbose=2)
lasso_grid.fit(X_train, y_train)
lasso_grid_results = pd.DataFrame(lasso_grid.cv_results_)
lasso_grid_results = lasso_grid_results.sort_values('rank_test_score')
lasso_predictions = lasso_grid.predict(X_test)
lasso_r2 = r2_score(y_test, lasso_predictions)
lasso_rmse = np.sqrt(mean_squared_error(y_test, lasso_predictions))
coefficients = lasso_grid.best_estimator_.coef_
coefficients = np.abs(coefficients)
names = data.columns[1:]
plt.plot(names, coefficients)
plt.xticks(rotation=60)
plt.show() | code |
90131532/cell_11 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.info() | code |
90131532/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.describe() | code |
90131532/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 |
90131532/cell_28 | [
"image_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.corr()
X = data.iloc[:, 1:].values
y = data.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)
print(X_train.shape)
print(X_test.shape)
print(y_train.shape)
print(y_test.shape) | code |
90131532/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
print(data.shape)
data.head() | code |
90131532/cell_16 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data[data['BodyFat'] <= 5] | code |
90131532/cell_31 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.corr()
X = data.iloc[:, 1:].values
y = data.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)
lasso_model = Lasso()
params = {'alpha': np.linspace(0, 2, 200)}
lasso_grid = GridSearchCV(lasso_model, param_grid=params, cv=4, refit=True, n_jobs=-1, verbose=2)
lasso_grid.fit(X_train, y_train) | code |
90131532/cell_24 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.corr()
plt.scatter(data['Abdomen'], data['BodyFat'])
plt.xlabel('Abdomen Circumference (cm)')
plt.ylabel('Percent Body Fat')
plt.show() | code |
90131532/cell_14 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.describe() | code |
90131532/cell_22 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.corr()
plt.scatter(data['Weight'], data['BodyFat'])
plt.xlabel('Body Weight (lbs)')
plt.ylabel('Percent Body Fat')
plt.show() | code |
90131532/cell_37 | [
"text_html_output_1.png"
] | from sklearn.linear_model import Lasso
from sklearn.metrics import mean_squared_error
from sklearn.metrics import r2_score
from sklearn.model_selection import GridSearchCV
from sklearn.model_selection import train_test_split
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.model_selection import GridSearchCV
from sklearn.linear_model import Lasso
from sklearn.ensemble import RandomForestRegressor
from sklearn.metrics import r2_score
from sklearn.metrics import mean_squared_error
pd.options.display.max_rows = None
pd.options.display.max_columns = None
SEED = 746
data = pd.read_csv('/kaggle/input/body-fat-prediction-dataset/bodyfat.csv')
data.drop('Density', axis=1, inplace=True)
data.isna().any()
data.drop([171, 181], inplace=True)
data.corr()
X = data.iloc[:, 1:].values
y = data.iloc[:, 0].values
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=SEED)
lasso_model = Lasso()
params = {'alpha': np.linspace(0, 2, 200)}
lasso_grid = GridSearchCV(lasso_model, param_grid=params, cv=4, refit=True, n_jobs=-1, verbose=2)
lasso_grid.fit(X_train, y_train)
lasso_grid_results = pd.DataFrame(lasso_grid.cv_results_)
lasso_grid_results = lasso_grid_results.sort_values('rank_test_score')
lasso_predictions = lasso_grid.predict(X_test)
lasso_r2 = r2_score(y_test, lasso_predictions)
lasso_rmse = np.sqrt(mean_squared_error(y_test, lasso_predictions))
print('R2 Score: ', lasso_r2)
print('RMSE Score: ', lasso_rmse) | code |
128014636/cell_21 | [
"text_plain_output_1.png"
] | from collections import Counter
from collections import Counter
from itertools import permutations
from numpy.random import choice
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1):
if [i[j], i[j + 1]] == [0, 0]:
failure += 1
break
k = len(network)
return (k - failure) / k
colors = ['clubs' , 'diamonds' , 'hearts', 'spades']
def draw():
# np.random.choice(colors,size=5)
return list(zip(np.random.choice(range(1,14),size=5), np.random.choice(colors,size=5)))
d = draw()
d
def is_straight(draw):
if len(set([i[1] for i in draw])) == 1:
return False
else:
nums = np.sort([i[0] for i in draw])
return np.all(nums == np.array((num[0] + i for i in range(5))))
is_straight(d)
# monte carlo simulation
n = 100_000
count = 0
for i in range(n):
d = draw()
if is_straight(d):
count += 1
print(count/n)
'''A 5-card poker hand is said to be a full house if it consists of 3 cards of the same
denomination and 2 other cards of the same denomination (of course, different from
the first denomination). Thus, a full house is three of a kind plus a pair. What is the
probability that one is dealt a full house?'''
def full_house(draw):
return set(Counter([i[0] for i in draw]).values()) == {3,2}
d = draw()
full_house(d)
n = 100000
count = 0
for i in range(n):
d = np.random.choice([i % 13 for i in range(52)], replace=False, size=52).reshape(4, 13)
if np.all([0 in d[j] for j in range(4)]):
count += 1
n = 100_000
count = 0
for i in range(n):
d = draw()
# print(np.random.randint(0,13,size=(4,13)))
# 0 in draw[0]
if full_house(d):
# print(draw)
count += 1
# print(draw,'\n',i)
# np.all([0 is in i[j]])
print(f'Probability of full house is : {count/n}')
coin = list('HT')
n = 100000
for i in range(4):
count = 0
j = 0
while j < n:
toss = choice(coin, size=3)
if Counter(toss)['H'] == i:
count += 1
j += 1
balls = [i for i in range(1, 21)]
choice(balls, 4, replace=False)
n = 10000
for i in range(4, 21):
j = 0
count = 0
while j < n:
if choice(balls, 4, replace=False).max() == i:
count += 1
j += 1
print(f'Probability of X={i} by simulation and by formula are : {(count / n, binom(i - 1, 3) / binom(20, 4))} respectively') | code |
128014636/cell_13 | [
"text_plain_output_1.png"
] | from collections import Counter
from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1):
if [i[j], i[j + 1]] == [0, 0]:
failure += 1
break
k = len(network)
return (k - failure) / k
colors = ['clubs' , 'diamonds' , 'hearts', 'spades']
def draw():
# np.random.choice(colors,size=5)
return list(zip(np.random.choice(range(1,14),size=5), np.random.choice(colors,size=5)))
d = draw()
d
def is_straight(draw):
if len(set([i[1] for i in draw])) == 1:
return False
else:
nums = np.sort([i[0] for i in draw])
return np.all(nums == np.array((num[0] + i for i in range(5))))
is_straight(d)
# monte carlo simulation
n = 100_000
count = 0
for i in range(n):
d = draw()
if is_straight(d):
count += 1
print(count/n)
'''A 5-card poker hand is said to be a full house if it consists of 3 cards of the same
denomination and 2 other cards of the same denomination (of course, different from
the first denomination). Thus, a full house is three of a kind plus a pair. What is the
probability that one is dealt a full house?'''
def full_house(draw):
return set(Counter([i[0] for i in draw]).values()) == {3,2}
d = draw()
full_house(d)
n = 100000
count = 0
for i in range(n):
d = np.random.choice([i % 13 for i in range(52)], replace=False, size=52).reshape(4, 13)
if np.all([0 in d[j] for j in range(4)]):
count += 1
print(f'Probability that each player receives 1 ace is : {count / n}') | code |
128014636/cell_9 | [
"text_plain_output_1.png"
] | from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1):
if [i[j], i[j + 1]] == [0, 0]:
failure += 1
break
k = len(network)
return (k - failure) / k
colors = ['clubs' , 'diamonds' , 'hearts', 'spades']
def draw():
# np.random.choice(colors,size=5)
return list(zip(np.random.choice(range(1,14),size=5), np.random.choice(colors,size=5)))
d = draw()
d
def is_straight(draw):
if len(set([i[1] for i in draw])) == 1:
return False
else:
nums = np.sort([i[0] for i in draw])
return np.all(nums == np.array((num[0] + i for i in range(5))))
is_straight(d)
n = 100000
count = 0
for i in range(n):
d = draw()
if is_straight(d):
count += 1
print(count / n) | code |
128014636/cell_11 | [
"text_plain_output_1.png"
] | from collections import Counter
from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1):
if [i[j], i[j + 1]] == [0, 0]:
failure += 1
break
k = len(network)
return (k - failure) / k
colors = ['clubs' , 'diamonds' , 'hearts', 'spades']
def draw():
# np.random.choice(colors,size=5)
return list(zip(np.random.choice(range(1,14),size=5), np.random.choice(colors,size=5)))
d = draw()
d
def is_straight(draw):
if len(set([i[1] for i in draw])) == 1:
return False
else:
nums = np.sort([i[0] for i in draw])
return np.all(nums == np.array((num[0] + i for i in range(5))))
is_straight(d)
# monte carlo simulation
n = 100_000
count = 0
for i in range(n):
d = draw()
if is_straight(d):
count += 1
print(count/n)
"""A 5-card poker hand is said to be a full house if it consists of 3 cards of the same
denomination and 2 other cards of the same denomination (of course, different from
the first denomination). Thus, a full house is three of a kind plus a pair. What is the
probability that one is dealt a full house?"""
def full_house(draw):
return set(Counter([i[0] for i in draw]).values()) == {3, 2}
d = draw()
full_house(d) | code |
128014636/cell_19 | [
"text_plain_output_1.png"
] | from collections import Counter
from collections import Counter
from itertools import permutations
from numpy.random import choice
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1):
if [i[j], i[j + 1]] == [0, 0]:
failure += 1
break
k = len(network)
return (k - failure) / k
colors = ['clubs' , 'diamonds' , 'hearts', 'spades']
def draw():
# np.random.choice(colors,size=5)
return list(zip(np.random.choice(range(1,14),size=5), np.random.choice(colors,size=5)))
d = draw()
d
def is_straight(draw):
if len(set([i[1] for i in draw])) == 1:
return False
else:
nums = np.sort([i[0] for i in draw])
return np.all(nums == np.array((num[0] + i for i in range(5))))
is_straight(d)
# monte carlo simulation
n = 100_000
count = 0
for i in range(n):
d = draw()
if is_straight(d):
count += 1
print(count/n)
'''A 5-card poker hand is said to be a full house if it consists of 3 cards of the same
denomination and 2 other cards of the same denomination (of course, different from
the first denomination). Thus, a full house is three of a kind plus a pair. What is the
probability that one is dealt a full house?'''
def full_house(draw):
return set(Counter([i[0] for i in draw]).values()) == {3,2}
d = draw()
full_house(d)
n = 100000
count = 0
for i in range(n):
d = np.random.choice([i % 13 for i in range(52)], replace=False, size=52).reshape(4, 13)
if np.all([0 in d[j] for j in range(4)]):
count += 1
n = 100_000
count = 0
for i in range(n):
d = draw()
# print(np.random.randint(0,13,size=(4,13)))
# 0 in draw[0]
if full_house(d):
# print(draw)
count += 1
# print(draw,'\n',i)
# np.all([0 is in i[j]])
print(f'Probability of full house is : {count/n}')
coin = list('HT')
n = 100000
for i in range(4):
count = 0
j = 0
while j < n:
toss = choice(coin, size=3)
if Counter(toss)['H'] == i:
count += 1
j += 1
print(f'Getting {i} heads in 3 toss, has probability {count / n}') | code |
128014636/cell_7 | [
"text_plain_output_1.png"
] | from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1):
if [i[j], i[j + 1]] == [0, 0]:
failure += 1
break
k = len(network)
return (k - failure) / k
colors = ['clubs', 'diamonds', 'hearts', 'spades']
def draw():
return list(zip(np.random.choice(range(1, 14), size=5), np.random.choice(colors, size=5)))
d = draw()
d | code |
128014636/cell_8 | [
"text_plain_output_1.png"
] | from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1):
if [i[j], i[j + 1]] == [0, 0]:
failure += 1
break
k = len(network)
return (k - failure) / k
colors = ['clubs' , 'diamonds' , 'hearts', 'spades']
def draw():
# np.random.choice(colors,size=5)
return list(zip(np.random.choice(range(1,14),size=5), np.random.choice(colors,size=5)))
d = draw()
d
def is_straight(draw):
if len(set([i[1] for i in draw])) == 1:
return False
else:
nums = np.sort([i[0] for i in draw])
return np.all(nums == np.array((num[0] + i for i in range(5))))
is_straight(d) | code |
128014636/cell_16 | [
"text_plain_output_1.png"
] | from collections import Counter
from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1):
if [i[j], i[j + 1]] == [0, 0]:
failure += 1
break
k = len(network)
return (k - failure) / k
colors = ['clubs' , 'diamonds' , 'hearts', 'spades']
def draw():
# np.random.choice(colors,size=5)
return list(zip(np.random.choice(range(1,14),size=5), np.random.choice(colors,size=5)))
d = draw()
d
def is_straight(draw):
if len(set([i[1] for i in draw])) == 1:
return False
else:
nums = np.sort([i[0] for i in draw])
return np.all(nums == np.array((num[0] + i for i in range(5))))
is_straight(d)
# monte carlo simulation
n = 100_000
count = 0
for i in range(n):
d = draw()
if is_straight(d):
count += 1
print(count/n)
'''A 5-card poker hand is said to be a full house if it consists of 3 cards of the same
denomination and 2 other cards of the same denomination (of course, different from
the first denomination). Thus, a full house is three of a kind plus a pair. What is the
probability that one is dealt a full house?'''
def full_house(draw):
return set(Counter([i[0] for i in draw]).values()) == {3,2}
d = draw()
full_house(d)
n = 100000
count = 0
for i in range(n):
d = np.random.choice([i % 13 for i in range(52)], replace=False, size=52).reshape(4, 13)
if np.all([0 in d[j] for j in range(4)]):
count += 1
n = 100_000
count = 0
for i in range(n):
d = draw()
# print(np.random.randint(0,13,size=(4,13)))
# 0 in draw[0]
if full_house(d):
# print(draw)
count += 1
# print(draw,'\n',i)
# np.all([0 is in i[j]])
print(f'Probability of full house is : {count/n}')
earn = 0
for i in range(n):
earn += np.random.choice(range(1, 7))
earn / n | code |
128014636/cell_14 | [
"text_plain_output_1.png"
] | from collections import Counter
from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1):
if [i[j], i[j + 1]] == [0, 0]:
failure += 1
break
k = len(network)
return (k - failure) / k
colors = ['clubs' , 'diamonds' , 'hearts', 'spades']
def draw():
# np.random.choice(colors,size=5)
return list(zip(np.random.choice(range(1,14),size=5), np.random.choice(colors,size=5)))
d = draw()
d
def is_straight(draw):
if len(set([i[1] for i in draw])) == 1:
return False
else:
nums = np.sort([i[0] for i in draw])
return np.all(nums == np.array((num[0] + i for i in range(5))))
is_straight(d)
# monte carlo simulation
n = 100_000
count = 0
for i in range(n):
d = draw()
if is_straight(d):
count += 1
print(count/n)
'''A 5-card poker hand is said to be a full house if it consists of 3 cards of the same
denomination and 2 other cards of the same denomination (of course, different from
the first denomination). Thus, a full house is three of a kind plus a pair. What is the
probability that one is dealt a full house?'''
def full_house(draw):
return set(Counter([i[0] for i in draw]).values()) == {3,2}
d = draw()
full_house(d)
n = 100000
count = 0
for i in range(n):
d = np.random.choice([i % 13 for i in range(52)], replace=False, size=52).reshape(4, 13)
if np.all([0 in d[j] for j in range(4)]):
count += 1
n = 100000
count = 0
for i in range(n):
d = draw()
if full_house(d):
count += 1
print(f'Probability of full house is : {count / n}') | code |
128014636/cell_5 | [
"text_plain_output_1.png"
] | from itertools import permutations
from scipy.special import binom
import numpy as np # linear algebra
n, m = (4, 2)
def functional(m, n):
antennas = [0 for i in range(m)] + [1 for i in range(n - m)]
network = set(permutations(antennas))
failure = 0
for i in network:
for j in range(len(i) - 1):
if [i[j], i[j + 1]] == [0, 0]:
failure += 1
break
k = len(network)
return (k - failure) / k
for i in range(4, 10):
for j in range(2, int(np.ceil(i / 2)) + 1):
print(f'No. of Antennas, defective, Probability of functional: {(i, j, functional(j, i), binom(i - j + 1, j) / binom(i, j))}') | code |
18149558/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
train_df = train.groupby(['shop_id', 'item_id', 'date_block_num']).sum().reset_index().sort_values(by=['item_id', 'shop_id'])
train_df.head() | code |
18149558/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train) | code |
18149558/cell_25 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
train_df = train.groupby(['shop_id', 'item_id', 'date_block_num']).sum().reset_index().sort_values(by=['item_id', 'shop_id'])
train_df['m1'] = train_df.groupby(['shop_id', 'item_id']).item_cnt_day.shift()
train_df['m1'].fillna(0, inplace=True)
train_df
train_df['m2'] = train_df.groupby(['shop_id', 'item_id']).m1.shift()
train_df['m2'].fillna(0, inplace=True)
train_df.head() | code |
18149558/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
print('Training set shape:', train.shape) | code |
18149558/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
train_df = train.groupby(['shop_id', 'item_id', 'date_block_num']).sum().reset_index().sort_values(by=['item_id', 'shop_id'])
train_df['m1'] = train_df.groupby(['shop_id', 'item_id']).item_cnt_day.shift()
train_df['m1'].fillna(0, inplace=True)
train_df | code |
18149558/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
print('Item categories:', items_cats.shape) | code |
18149558/cell_40 | [
"text_html_output_1.png"
] | from keras.layers import Dense
from keras.layers import LSTM
from keras.models import Sequential
import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
train_df = train.groupby(['shop_id', 'item_id', 'date_block_num']).sum().reset_index().sort_values(by=['item_id', 'shop_id'])
train_df['m1'] = train_df.groupby(['shop_id', 'item_id']).item_cnt_day.shift()
train_df['m1'].fillna(0, inplace=True)
train_df
train_df['m2'] = train_df.groupby(['shop_id', 'item_id']).m1.shift()
train_df['m2'].fillna(0, inplace=True)
train_df.rename(columns={'item_cnt_day': 'item_cnt_month'}, inplace=True)
finalDf = train_df[['shop_id', 'item_id', 'date_block_num', 'm1', 'm2', 'item_cnt_month']].reset_index()
finalDf.drop(['index'], axis=1, inplace=True)
newTest = pd.merge_asof(test, finalDf, left_index=True, right_index=True, on=['shop_id', 'item_id'])
model_lstm = Sequential()
model_lstm.add(LSTM(64, input_shape=(1, 4)))
model_lstm.add(Dense(1))
model_lstm.compile(loss='mean_squared_error', optimizer='adam', metrics=['accuracy'])
y_train = finalDf['item_cnt_month']
newTest.drop(['item_cnt_month'], axis=1, inplace=True)
x_train = finalDf[['shop_id', 'item_id', 'm1', 'm2']]
history = model_lstm.fit(x_train_reshaped, y_train, epochs=20, batch_size=100, shuffle=False) | code |
18149558/cell_29 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('../input/sales_train.csv')
test = pd.read_csv('../input/test.csv')
items_cats = pd.read_csv('../input/item_categories.csv')
items = pd.read_csv('../input/items.csv')
shops = pd.read_csv('../input/shops.csv')
train.columns.values
shops_train = train.groupby(['shop_id']).groups.keys()
len(shops_train)
item_train = train.groupby(['item_id']).groups.keys()
len(item_train)
shops_test = test.groupby(['shop_id']).groups.keys()
len(shops_test)
items_test = test.groupby(['item_id']).groups.keys()
len(items_test)
train_df = train.groupby(['shop_id', 'item_id', 'date_block_num']).sum().reset_index().sort_values(by=['item_id', 'shop_id'])
train_df['m1'] = train_df.groupby(['shop_id', 'item_id']).item_cnt_day.shift()
train_df['m1'].fillna(0, inplace=True)
train_df
train_df['m2'] = train_df.groupby(['shop_id', 'item_id']).m1.shift()
train_df['m2'].fillna(0, inplace=True)
train_df.rename(columns={'item_cnt_day': 'item_cnt_month'}, inplace=True)
finalDf = train_df[['shop_id', 'item_id', 'date_block_num', 'm1', 'm2', 'item_cnt_month']].reset_index()
finalDf.drop(['index'], axis=1, inplace=True)
finalDf.head() | code |
18149558/cell_2 | [
"text_plain_output_1.png"
] | from keras.models import Sequential
from keras.layers import Dense
from keras.layers import LSTM
import pandas as pd
import numpy as np | code |
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