path
stringlengths 13
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sequencelengths 1
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stringclasses 1
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|---|---|---|---|
105194699/cell_22 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique()
df.describe().T.style
df.Potability.value_counts()
df.isnull().sum()
null_columns = pd.DataFrame(df[df.columns[df.isnull().any()]].isnull().sum() * 100 / df.shape[0], columns=['Percentage of NaN values'])
null_columns['Total NaN Values'] = df[df.columns[df.isnull().any()]].isnull().sum()
null_columns
null_cols = null_columns.index.tolist()
null_cols
sns.distplot(df.ph) | code |
105194699/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape | code |
105194699/cell_12 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/water-potability/water_potability.csv')
df.shape
df.nunique() | code |
90148477/cell_13 | [
"text_html_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
print('intercept :', regr.intercept_)
print('coefficient :', regr.coef_)
print(x) | code |
90148477/cell_23 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
df.isnull().sum()
df.notnull().sum()
def function(x, a):
f = a[2] * x * x + a[1] * x + a[0]
return f
def grad(x, a):
g = 2 * a[2] * x + a[1]
return g
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
f = function(x, y)
x = df[['ram']]
y = df['price_range']
import numpy as np
def find_theta(X, y):
m = X.shape[0]
X = np.append(X, np.ones((m, 1)), axis=1)
theta = np.dot(np.linalg.inv(np.dot(X.T, X)), np.dot(X.T, y))
return theta
def predict(X):
X = np.append(X, np.ones((X.shape[0], 1)), axis=1)
preds = np.dot(X, theta)
return preds
theta = find_theta(x, y)
print(theta)
preds = predict(x)
fig = plt.figure(figsize=(8, 6))
plt.plot(x, y, 'b.')
plt.plot(x, preds, 'c-')
plt.xlabel('Input')
plt.ylabel('target') | code |
90148477/cell_20 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
df.isnull().sum()
df.notnull().sum()
def function(x, a):
f = a[2] * x * x + a[1] * x + a[0]
return f
def grad(x, a):
g = 2 * a[2] * x + a[1]
return g
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
f = function(x, y)
plt.scatter(x, f)
plt.plot(x, f)
plt.xlabel('X')
plt.ylabel('f(X)') | code |
90148477/cell_6 | [
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
df.describe() | code |
90148477/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import numpy as np
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
predicted = regr.predict([[2.0, 10, 1500, 1200, 1, 1, 2000]])
predicted = regr.predict([[2.0, 10, 1500, 1200, 1, 1, 4080]])
df.isnull().sum()
df.notnull().sum()
def function(x, a):
f = a[2] * x * x + a[1] * x + a[0]
return f
def grad(x, a):
g = 2 * a[2] * x + a[1]
return g
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
f = function(x, y)
x = df[['ram']]
y = df['price_range']
import numpy as np
def find_theta(X, y):
m = X.shape[0]
X = np.append(X, np.ones((m,1)), axis=1)
theta = np.dot(np.linalg.inv(np.dot(X.T, X)), np.dot(X.T, y))
return theta
def predict(X):
X = np.append(X, np.ones((X.shape[0],1)), axis=1)
preds = np.dot(X, theta)
return preds
theta = find_theta(x, y)
print(theta)
preds = predict(x)
fig = plt.figure(figsize=(8,6))
plt.plot(x, y, 'b.')
plt.plot(x, preds, 'c-')
plt.xlabel('Input')
plt.ylabel('target')
x = df[['ram']]
y = df['price_range']
regr2 = linear_model.LinearRegression()
regr2.fit(x.values, y)
arr = []
index = []
for i in range(0, 4000, 1):
predicted = regr2.predict([[i]])
arr.append(predicted[0])
index.append(i)
fig = plt.figure(figsize=(8, 6))
plt.plot(x, y, 'b.')
plt.plot(index, arr, 'c-') | code |
90148477/cell_11 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
plt.figure(figsize=(15, 10))
sns.heatmap(corr, vmax=0.5, annot=True, fmt='.2f')
plt.show() | code |
90148477/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
df.isnull().sum()
df.notnull().sum() | code |
90148477/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
df.hist(figsize=(20, 20))
plt.show() | code |
90148477/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
df.isnull().sum() | code |
90148477/cell_14 | [
"image_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
predicted = regr.predict([[2.0, 10, 1500, 1200, 1, 1, 2000]])
print('predicted with 2.0 clock speed, 10 front camera, 1500x1200 px screen , have 3g and 4g and 2000 ram :', predicted)
predicted = regr.predict([[2.0, 10, 1500, 1200, 1, 1, 4080]])
print('predicted with 2.0 clock speed, 10 front camera, 1500x1200 px screen , have 3g and 4g and 4080 ram :', predicted) | code |
90148477/cell_22 | [
"text_plain_output_1.png"
] | from sklearn import linear_model
import matplotlib.pyplot as plt
import pandas
import seaborn as sns
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
corr = df.corr()
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
regr = linear_model.LinearRegression()
regr.fit(x.values, y)
df.isnull().sum()
df.notnull().sum()
def function(x, a):
f = a[2] * x * x + a[1] * x + a[0]
return f
def grad(x, a):
g = 2 * a[2] * x + a[1]
return g
x = df[['clock_speed', 'fc', 'px_height', 'px_width', 'three_g', 'four_g', 'ram']]
y = df['price_range']
f = function(x, y)
x = df[['ram']]
y = df['price_range']
plt.plot(x, y, 'r.') | code |
90148477/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas
df = pandas.read_csv('../input/mobile-price-classification/train.csv')
df.head() | code |
128003791/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.head() | code |
128003791/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotation=90, fontsize=12)
plt.yticks(fontsize=12)
plt.ylim(1, 5)
fig = px.histogram(rating_across_state, x = 'Rating')
fig.show()
average_cost = df.groupby(['City'])['Cost'].mean().reset_index()
fig = px.bar(average_cost, x='City', y='Cost', labels={'City': 'City', 'Name': 'Average Cost of Restaurants'},
title='Average Cost in Each City', color = 'City')
fig.show()
avg_vote = df.groupby(['City'])['Votes'].mean().reset_index()
fig = px.bar(avg_vote, x='City', y='Votes', labels={'City': 'City', 'Name': 'Average Number of Votes of Restaurants'},
title='Average Votes in Each City', color = 'City')
fig.show()
max_votes = df.groupby(['City'])['Votes'].sum().reset_index()
fig = px.bar(max_votes, x='City', y='Votes', labels={'City': 'City', 'Name': 'Number of Votes of Restaurants'},
title='Top Votes in Each City', color = 'City')
fig.show()
df_cuisine = df.groupby(['City', 'Cuisine'])['Name'].count().reset_index()
df_top_cuisine = df_cuisine.loc[df_cuisine.groupby('City')['Name'].idxmax()]
fig = px.bar(df_top_cuisine, x='City', y='Name', color='Cuisine', labels={'City': 'City', 'Name': 'Number of Restaurants'}, title='Top Cuisine in Each City')
fig.show() | code |
128003791/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum() | code |
128003791/cell_2 | [
"text_html_output_1.png"
] | import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import plotly.express as px | code |
128003791/cell_18 | [
"text_html_output_2.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotation=90, fontsize=12)
plt.yticks(fontsize=12)
plt.ylim(1, 5)
fig = px.histogram(rating_across_state, x = 'Rating')
fig.show()
average_cost = df.groupby(['City'])['Cost'].mean().reset_index()
fig = px.bar(average_cost, x='City', y='Cost', labels={'City': 'City', 'Name': 'Average Cost of Restaurants'},
title='Average Cost in Each City', color = 'City')
fig.show()
avg_vote = df.groupby(['City'])['Votes'].mean().reset_index()
fig = px.bar(avg_vote, x='City', y='Votes', labels={'City': 'City', 'Name': 'Average Number of Votes of Restaurants'},
title='Average Votes in Each City', color = 'City')
fig.show()
max_votes = df.groupby(['City'])['Votes'].sum().reset_index()
fig = px.bar(max_votes, x='City', y='Votes', labels={'City': 'City', 'Name': 'Number of Votes of Restaurants'}, title='Top Votes in Each City', color='City')
fig.show() | code |
128003791/cell_8 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.figure(figsize=(10, 8))
plt.xlabel('City')
plt.ylabel('Rating')
sns.barplot(x='City', y='Rating', data=rating_across_state)
plt.xticks(rotation=90, fontsize=12)
plt.yticks(fontsize=12)
plt.ylim(1, 5)
plt.show() | code |
128003791/cell_16 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotation=90, fontsize=12)
plt.yticks(fontsize=12)
plt.ylim(1, 5)
fig = px.histogram(rating_across_state, x = 'Rating')
fig.show()
average_cost = df.groupby(['City'])['Cost'].mean().reset_index()
fig = px.bar(average_cost, x='City', y='Cost', labels={'City': 'City', 'Name': 'Average Cost of Restaurants'},
title='Average Cost in Each City', color = 'City')
fig.show()
avg_vote = df.groupby(['City'])['Votes'].mean().reset_index()
fig = px.bar(avg_vote, x='City', y='Votes', labels={'City': 'City', 'Name': 'Average Number of Votes of Restaurants'}, title='Average Votes in Each City', color='City')
fig.show() | code |
128003791/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotation=90, fontsize=12)
plt.yticks(fontsize=12)
plt.ylim(1, 5)
fig = px.histogram(rating_across_state, x = 'Rating')
fig.show()
average_cost = df.groupby(['City'])['Cost'].mean().reset_index()
fig = px.bar(average_cost, x='City', y='Cost', labels={'City': 'City', 'Name': 'Average Cost of Restaurants'},
title='Average Cost in Each City', color = 'City')
fig.show()
plt.xticks(rotation=90, fontsize=12)
sns.countplot(x=df['City'], data=df)
plt.ylabel('Count of Restaurants') | code |
128003791/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotation=90, fontsize=12)
plt.yticks(fontsize=12)
plt.ylim(1, 5)
fig = px.histogram(rating_across_state, x='Rating')
fig.show() | code |
128003791/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import plotly.express as px
import seaborn as sns
df = pd.read_csv('/kaggle/input/indian-restaurants-2023/restaurants.csv')
df.isnull().sum()
rating_across_state = df.groupby('City').mean()
rating_across_state.reset_index(level=0, inplace=True)
plt.xticks(rotation=90, fontsize=12)
plt.yticks(fontsize=12)
plt.ylim(1, 5)
fig = px.histogram(rating_across_state, x = 'Rating')
fig.show()
average_cost = df.groupby(['City'])['Cost'].mean().reset_index()
fig = px.bar(average_cost, x='City', y='Cost', labels={'City': 'City', 'Name': 'Average Cost of Restaurants'}, title='Average Cost in Each City', color='City')
fig.show() | code |
89135385/cell_9 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_3.png",
"text_plain_output_1.png"
] | import cv2
import tensorflow as tf
PATH = '../input/rsna-bone-age/boneage-training-dataset/boneage-training-dataset/'
IMGS = os.listdir(PATH)
df = pd.read_csv('../input/rsna-bone-age/boneage-training-dataset.csv')
def _bytes_feature(value):
"""Returns a bytes_list from a string / byte."""
if isinstance(value, type(tf.constant(0))):
value = value.numpy()
return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))
def _int64_feature(value):
"""Returns an int64_list from a bool / enum / int / uint."""
return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))
def serialize_example(feature0, feature1, feature2, feature3):
feature = {'image': _bytes_feature(feature0), 'id': _bytes_feature(feature1), 'boneage': _int64_feature(feature2), 'male': _int64_feature(feature3)}
example_proto = tf.train.Example(features=tf.train.Features(feature=feature))
return example_proto.SerializeToString()
SIZE = 841
CT = len(IMGS) // SIZE + int(len(IMGS) % SIZE != 0)
for j in range(CT):
print()
print('Writing TFRecord %i of %i...' % (j, CT))
CT2 = min(SIZE, len(IMGS) - j * SIZE)
with tf.io.TFRecordWriter('bone_age_tfrecords/train%.2i-%i.tfrec' % (j, CT2)) as writer:
for k in range(CT2):
img = cv2.imread(PATH + IMGS[SIZE * j + k])
img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)
img = cv2.imencode('.jpg', img, (cv2.IMWRITE_JPEG_QUALITY, 94))[1].tobytes()
name = IMGS[SIZE * j + k].split('.')[0]
row = df.loc[df['id'] == int(name)]
example = serialize_example(img, str.encode(name), row.boneage.values[0], row.male.values[0])
writer.write(example)
if k % 100 == 0:
print(k, ', ', end='') | code |
89135385/cell_4 | [
"text_html_output_1.png"
] | df = pd.read_csv('../input/rsna-bone-age/boneage-training-dataset.csv')
df.head() | code |
89135385/cell_3 | [
"text_plain_output_1.png"
] | PATH = '../input/rsna-bone-age/boneage-training-dataset/boneage-training-dataset/'
IMGS = os.listdir(PATH)
print('There are %i train images' % len(IMGS)) | code |
32068481/cell_6 | [
"image_output_1.png"
] | from collections import OrderedDict
from copy import deepcopy
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.graph_objs as go
import plotly.offline as py
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
train['Province_State'].fillna('', inplace=True)
train['Date'] = pd.to_datetime(train['Date'])
train['day'] = train.Date.dt.dayofyear
train['geo'] = ['_'.join(x) for x in zip(train['Country_Region'], train['Province_State'])]
test['Province_State'].fillna('', inplace=True)
test['Date'] = pd.to_datetime(test['Date'])
test['day'] = test.Date.dt.dayofyear
test['geo'] = ['_'.join(x) for x in zip(test['Country_Region'], test['Province_State'])]
train.sort_values(by='Date', inplace=True)
test.sort_values(by='Date', inplace=True)
day_min = train['day'].min()
train['day'] -= day_min
test['day'] -= day_min
test['ConfirmedCases'] = np.nan
test['Fatalities'] = np.nan
train['ForecastId'] = np.nan
test['Id'] = np.nan
min_date_train = train['Date'].min()
min_date_test = test['Date'].min()
max_date_train = train['Date'].max()
max_date_test = test['Date'].max()
num_of_days_train = (max_date_train - min_date_train) / np.timedelta64(1, 'D') + 1
num_of_days = int((max_date_test - min_date_train) / np.timedelta64(1, 'D')) + 1
time_span0 = pd.date_range(min_date_train, max_date_test)
time_span = [str(s.month) + '/' + str(s.day) for s in time_span0]
forcast_days = int((max_date_test - max_date_train) / np.timedelta64(1, 'D'))
from collections import OrderedDict
countries_dict = OrderedDict()
countries_dict['Afghanistan'] = ['']
countries_dict['Italy'] = ['']
countries_dict['India'] = ['']
countries_dict['Germany'] = ['']
countries_dict['Spain'] = ['']
countries_dict['Taiwan*'] = ['']
countries_dict['Japan'] = ['']
countries_dict['Spain'] = ['']
countries_dict['Germany'] = ['']
countries_dict['Singapore'] = ['']
countries_dict['Korea, South'] = ['']
countries_dict['United Kingdom'] = ['']
countries_dict['US'] = ['', 'Louisiana', 'New York', 'California', 'Minnesota']
from copy import deepcopy
n = 50
N_places = sum([len(value) for key, value in countries_dict.items()])
False_mask_0 = [False] * (N_places * 2 + 1)
labels = time_span[-n - 30:-30]
x = time_span0[-n - 30:-30]
data = []
manu_list = []
data.append(go.Bar(x=x, y=[0] * len(x), name='cases'))
False_mask = deepcopy(False_mask_0)
False_mask[0] = True
manu_list.append(dict(label='Select', method='update', args=[{'visible': False_mask}, {'title': 'Select country/state'}]))
n_place = -1
for country in countries_dict:
for state in countries_dict[country]:
sp = ' '
if state != '':
sp = ', '
n_place += 1
data_i = train[(train['Province_State'] == state) & (train['Country_Region'] == country)].sort_values(by='Date').loc[:, ['day', 'ConfirmedCases', 'Fatalities']]
if country in ['United Kingdom', 'Canada']:
data_i = train[train['Country_Region'] == country].groupby('Date').sum().reset_index().sort_values(by='Date').loc[:, ['day', 'ConfirmedCases', 'Fatalities']]
if country == 'US' and state == '':
data_i = train[train['Country_Region'] == country].groupby('Date').sum().reset_index().sort_values(by='Date').loc[:, ['day', 'ConfirmedCases', 'Fatalities']]
cases = country + state + ' Cases_daily'
deaths = country + state + ' deaths_daily'
data_i[cases] = data_i['ConfirmedCases'].diff()
data_i[deaths] = data_i['Fatalities'].diff()
trace1 = go.Bar(x=x, y=data_i[cases][-n:], name='cases')
trace2 = go.Bar(x=x, y=data_i[deaths][-n:], name='deaths')
data += [trace1, trace2]
False_mask = deepcopy(False_mask_0)
False_mask[2 * n_place + 1:2 * n_place + 2 + 1] = [True, True]
manu_list.append(dict(label=country + sp + state, method='update', args=[{'visible': False_mask}, {'title': country + sp + state}]))
updatemenus = [dict(active=0, buttons=manu_list, direction='down')]
layout = dict(title='Select Countries and states', yaxis=dict(title='daily count', linecolor='rgba(255,255,255, 0.8)', showgrid=True, gridcolor='rgba(255,255,255,0.2)'), xaxis=dict(title='Date', linecolor='rgba(255,255,255, 0.8)', showgrid=True, gridcolor='rgba(255,255,255,0.2)'), margin=go.Margin(l=50, r=20), paper_bgcolor='rgb(105,105,105)', plot_bgcolor='RGB(228, 235, 234)', barmode='group', font={'color': 'RGB(179, 217, 82)'}, updatemenus=updatemenus, showlegend=True)
fig = dict(data=data, layout=layout)
py.iplot(fig, filename='relayout_option_dropdown') | code |
32068481/cell_1 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
from datetime import timedelta
from datetime import datetime
import matplotlib.pyplot as plt
from statsmodels.tsa.arima_model import ARIMA
from sklearn.metrics import mean_squared_error
from math import sqrt
from time import time
import math
import seaborn as sns
import warnings
warnings.filterwarnings('ignore')
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32068481/cell_8 | [
"image_output_11.png",
"text_plain_output_5.png",
"text_plain_output_9.png",
"image_output_14.png",
"text_plain_output_4.png",
"text_plain_output_13.png",
"image_output_13.png",
"image_output_5.png",
"text_plain_output_14.png",
"text_plain_output_10.png",
"text_plain_output_6.png",
"image_output_7.png",
"text_plain_output_3.png",
"image_output_4.png",
"text_plain_output_7.png",
"image_output_8.png",
"text_plain_output_8.png",
"image_output_6.png",
"image_output_12.png",
"text_plain_output_2.png",
"text_plain_output_1.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png",
"image_output_10.png",
"text_plain_output_11.png",
"text_plain_output_12.png",
"image_output_9.png"
] | import matplotlib.pyplot as plt
import numpy as np # linear algebra
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
submission = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/submission.csv')
test = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/test.csv')
train = pd.read_csv('/kaggle/input/covid19-global-forecasting-week-4/train.csv')
train['Province_State'].fillna('', inplace=True)
train['Date'] = pd.to_datetime(train['Date'])
train['day'] = train.Date.dt.dayofyear
train['geo'] = ['_'.join(x) for x in zip(train['Country_Region'], train['Province_State'])]
test['Province_State'].fillna('', inplace=True)
test['Date'] = pd.to_datetime(test['Date'])
test['day'] = test.Date.dt.dayofyear
test['geo'] = ['_'.join(x) for x in zip(test['Country_Region'], test['Province_State'])]
train.sort_values(by='Date', inplace=True)
test.sort_values(by='Date', inplace=True)
day_min = train['day'].min()
train['day'] -= day_min
test['day'] -= day_min
test['ConfirmedCases'] = np.nan
test['Fatalities'] = np.nan
train['ForecastId'] = np.nan
test['Id'] = np.nan
min_date_train = train['Date'].min()
min_date_test = test['Date'].min()
max_date_train = train['Date'].max()
max_date_test = test['Date'].max()
num_of_days_train = (max_date_train - min_date_train) / np.timedelta64(1, 'D') + 1
num_of_days = int((max_date_test - min_date_train) / np.timedelta64(1, 'D')) + 1
time_span0 = pd.date_range(min_date_train, max_date_test)
time_span = [str(s.month) + '/' + str(s.day) for s in time_span0]
forcast_days = int((max_date_test - max_date_train) / np.timedelta64(1, 'D'))
countries = dict()
for cnt in train['Country_Region'].unique():
countries[cnt] = train.loc[train['Country_Region'] == cnt, 'Province_State'].unique()
countries_test = dict()
for cnt in test['Country_Region'].unique():
countries_test[cnt] = test.loc[test['Country_Region'] == cnt, 'Province_State'].unique()
res = []
for country in countries:
for state in countries[country]:
if country != 'China':
country_state_filter_train = (train['Province_State'] == state) & (train['Country_Region'] == country)
sliced_data = train.loc[country_state_filter_train, :]
history = sliced_data.loc[sliced_data['ConfirmedCases'] > 0, 'ConfirmedCases'].to_list()
res.append(num_of_days_train - len(history))
aa = plt.figure()
aa = plt.hist(res, color='blue', bins=10, range=(0, 80))
aa = plt.title('first Confirmed Case histogram: # of countries/provinces(except China) .VS. days from Wuhan Lockdown(1/22/2020)')
res = []
for country in countries:
for state in countries[country]:
if country != 'China':
country_state_filter_train = (train['Province_State'] == state) & (train['Country_Region'] == country)
sliced_data = train.loc[country_state_filter_train, :]
history = sliced_data.loc[sliced_data['Fatalities'] > 0, 'Fatalities'].to_list()
res.append(num_of_days_train - len(history))
aa = plt.figure()
aa = plt.hist(res, color='red', bins=10, range=(0, 80))
aa = plt.title('first death histogram: # of countries/provinces(except China) .VS. days from Wuhan Lockdown(1/22/2020)') | code |
32068481/cell_3 | [
"image_output_2.png",
"image_output_1.png"
] | import plotly.offline as py
import plotly.tools as tls
import plotly.graph_objs as go
py.init_notebook_mode(connected=True)
import cufflinks as cf
cf.set_config_file(offline=True, world_readable=True, theme='pearl')
import folium
import altair as alt
import missingno as msg
import sys
import warnings
if not sys.warnoptions:
warnings.simplefilter('ignore')
from ipywidgets import interact, interactive, fixed
import pandas as pd
import ipywidgets as widgets
from IPython.display import display | code |
121148904/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
val = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.head() | code |
121148904/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
val = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test_df.isna().sum() | code |
121148904/cell_11 | [
"text_html_output_1.png"
] | parameters_test['Fare'].fillna(value=4, inplace=True) | code |
121148904/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
val = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
test_df.head() | code |
121148904/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('/kaggle/input/titanic/train.csv')
test_df = pd.read_csv('/kaggle/input/titanic/test.csv')
val = pd.read_csv('/kaggle/input/titanic/gender_submission.csv')
train_df.isna().sum() | code |
17133840/cell_9 | [
"text_plain_output_1.png"
] | from keras.layers import BatchNormalization, Convolution2D , MaxPooling2D
from keras.layers import Dense , Dropout , Lambda, Flatten
from keras.layers.core import Lambda , Dense, Flatten, Dropout
from keras.layers.noise import GaussianDropout
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
from keras.models import Sequential
from keras.optimizers import Adam ,RMSprop
from keras.preprocessing.image import ImageDataGenerator
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
X_train = train.iloc[:, 1:].values.astype('float32')
y_train = train.iloc[:, 0].values.astype('int32')
X_test = test.values.astype('float32')
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
mean_px = X_train.mean().astype(np.float32)
std_px = X_train.std().astype(np.float32)
def standardize(x):
return (x - mean_px) / std_px
from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train)
num_classes = y_train.shape[1]
from keras.models import Sequential
from keras.layers.core import Lambda, Dense, Flatten, Dropout
from keras.callbacks import EarlyStopping
from keras.layers import BatchNormalization, Convolution2D, MaxPooling2D
from keras.preprocessing import image
from sklearn.model_selection import train_test_split
X = X_train
y = y_train
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=42)
gen = ImageDataGenerator(rotation_range=12, width_shift_range=0.16, shear_range=0.6, height_shift_range=0.16, zoom_range=0.16)
batches = gen.flow(X_train, y_train, batch_size=64)
val_batches = gen.flow(X_val, y_val, batch_size=64)
from keras.layers.normalization import BatchNormalization
from keras.layers.noise import GaussianDropout
def get_bn_model(size, dropout):
model = Sequential([Lambda(standardize, input_shape=(28, 28, 1)), Convolution2D(8 * size, (3, 3), activation='relu'), GaussianDropout(dropout), BatchNormalization(), Convolution2D(8 * size, (3, 3), activation='relu'), MaxPooling2D(), GaussianDropout(dropout), BatchNormalization(), Convolution2D(16 * size, (3, 3), activation='relu'), GaussianDropout(dropout), BatchNormalization(), Convolution2D(16 * size, (3, 3), activation='relu'), MaxPooling2D(), Flatten(), GaussianDropout(dropout), BatchNormalization(), Dense(128 * size, activation='relu'), GaussianDropout(dropout), BatchNormalization(), Dense(10, activation='softmax')])
model.compile(Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
return model
def augment_and_create_model(model_size, dropout_rate, aug):
model = get_bn_model(model_size, dropout_rate)
gen = ImageDataGenerator(rotation_range=3 * aug, width_shift_range=0.04 * aug, shear_range=0.15 * aug, height_shift_range=0.04 * aug, zoom_range=0.04 * aug)
return (model, gen.flow(X_train, y_train, batch_size=64), gen.flow(X_val, y_val, batch_size=64))
models = []
epochs_to_train = 2
for x in range(1, 5):
aug = x
model_size = x
dropout_rate = x * 0.1
pretrained = 0
for pretrained_epochs in range(epochs_to_train, 0, -1):
try:
model = load_model('model_size_{}_dropout_{}_augment_{}_epochs_{}'.format(x, 0.1 * x, x, pretrained_epochs))
pretrained = pretrained_epochs
break
except:
pass
if pretrained == 0:
this_model = get_bn_model(model_size, dropout_rate)
else:
this_model = model
gen = ImageDataGenerator(rotation_range=3 * aug, width_shift_range=0.04 * aug, shear_range=0.15 * aug, height_shift_range=0.04 * aug, zoom_range=0.04 * aug)
batches, val_batches = (gen.flow(X_train, y_train, batch_size=64), gen.flow(X_val, y_val, batch_size=64))
models.append(this_model)
models[-1].optimizer.lr = 0.01
history = models[-1].fit_generator(generator=batches, steps_per_epoch=batches.n, epochs=epochs_to_train - pretrained, validation_data=val_batches, validation_steps=val_batches.n)
this_model.save('model_size_{}_dropout_{}_augment_{}_epochs_{}.h5'.format(x, 0.1 * x, x, epochs_to_train), include_optimizer=False)
best_loss = 0
best_loss_index = -1
for i in range(len(models)):
model = models[i]
print(model.history.history)
this_loss = model.history.history['val_loss'][-1] + (model.history.history['val_loss'][-1] - model.history.history['loss'][-1]) ** 2
if this_loss > best_loss:
best_loss = this_loss
best_loss_index = i | code |
17133840/cell_2 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from subprocess import check_output
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from keras.models import Sequential
from keras.layers import Dense, Dropout, Lambda, Flatten
from keras.optimizers import Adam, RMSprop
from sklearn.model_selection import train_test_split
from keras import backend as K
from keras.preprocessing.image import ImageDataGenerator
from subprocess import check_output
print(check_output(['ls', '../input']).decode('utf8')) | code |
17133840/cell_7 | [
"text_plain_output_1.png"
] | from keras.layers import BatchNormalization, Convolution2D , MaxPooling2D
from keras.layers import Dense , Dropout , Lambda, Flatten
from keras.layers.core import Lambda , Dense, Flatten, Dropout
from keras.layers.noise import GaussianDropout
from keras.layers.normalization import BatchNormalization
from keras.models import Sequential
from keras.models import Sequential
from keras.optimizers import Adam ,RMSprop
from keras.preprocessing.image import ImageDataGenerator
from keras.utils.np_utils import to_categorical
from sklearn.model_selection import train_test_split
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)
train = pd.read_csv('../input/train.csv')
test = pd.read_csv('../input/test.csv')
X_train = train.iloc[:, 1:].values.astype('float32')
y_train = train.iloc[:, 0].values.astype('int32')
X_test = test.values.astype('float32')
X_train = X_train.reshape(X_train.shape[0], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
mean_px = X_train.mean().astype(np.float32)
std_px = X_train.std().astype(np.float32)
def standardize(x):
return (x - mean_px) / std_px
from keras.utils.np_utils import to_categorical
y_train = to_categorical(y_train)
num_classes = y_train.shape[1]
from keras.models import Sequential
from keras.layers.core import Lambda, Dense, Flatten, Dropout
from keras.callbacks import EarlyStopping
from keras.layers import BatchNormalization, Convolution2D, MaxPooling2D
from keras.preprocessing import image
from sklearn.model_selection import train_test_split
X = X_train
y = y_train
X_train, X_val, y_train, y_val = train_test_split(X_train, y_train, test_size=0.1, random_state=42)
gen = ImageDataGenerator(rotation_range=12, width_shift_range=0.16, shear_range=0.6, height_shift_range=0.16, zoom_range=0.16)
batches = gen.flow(X_train, y_train, batch_size=64)
val_batches = gen.flow(X_val, y_val, batch_size=64)
from keras.layers.normalization import BatchNormalization
from keras.layers.noise import GaussianDropout
def get_bn_model(size, dropout):
model = Sequential([Lambda(standardize, input_shape=(28, 28, 1)), Convolution2D(8 * size, (3, 3), activation='relu'), GaussianDropout(dropout), BatchNormalization(), Convolution2D(8 * size, (3, 3), activation='relu'), MaxPooling2D(), GaussianDropout(dropout), BatchNormalization(), Convolution2D(16 * size, (3, 3), activation='relu'), GaussianDropout(dropout), BatchNormalization(), Convolution2D(16 * size, (3, 3), activation='relu'), MaxPooling2D(), Flatten(), GaussianDropout(dropout), BatchNormalization(), Dense(128 * size, activation='relu'), GaussianDropout(dropout), BatchNormalization(), Dense(10, activation='softmax')])
model.compile(Adam(), loss='categorical_crossentropy', metrics=['accuracy'])
return model
def augment_and_create_model(model_size, dropout_rate, aug):
model = get_bn_model(model_size, dropout_rate)
gen = ImageDataGenerator(rotation_range=3 * aug, width_shift_range=0.04 * aug, shear_range=0.15 * aug, height_shift_range=0.04 * aug, zoom_range=0.04 * aug)
return (model, gen.flow(X_train, y_train, batch_size=64), gen.flow(X_val, y_val, batch_size=64))
models = []
epochs_to_train = 2
for x in range(1, 5):
aug = x
model_size = x
dropout_rate = x * 0.1
print('Training a model with size level {}, dropout rate {}, and augmentation level {}'.format(model_size, dropout_rate, aug))
pretrained = 0
print('Attempting to load saved model.')
for pretrained_epochs in range(epochs_to_train, 0, -1):
try:
model = load_model('model_size_{}_dropout_{}_augment_{}_epochs_{}'.format(x, 0.1 * x, x, pretrained_epochs))
print('Loaded existed model trained for {} epochs out of {}'.format(pretrained_epochs, epochs_to_train))
pretrained = pretrained_epochs
break
except:
pass
if pretrained == 0:
print('Failed to load trained model. Creating new model.')
this_model = get_bn_model(model_size, dropout_rate)
else:
this_model = model
gen = ImageDataGenerator(rotation_range=3 * aug, width_shift_range=0.04 * aug, shear_range=0.15 * aug, height_shift_range=0.04 * aug, zoom_range=0.04 * aug)
batches, val_batches = (gen.flow(X_train, y_train, batch_size=64), gen.flow(X_val, y_val, batch_size=64))
models.append(this_model)
models[-1].optimizer.lr = 0.01
history = models[-1].fit_generator(generator=batches, steps_per_epoch=batches.n, epochs=epochs_to_train - pretrained, validation_data=val_batches, validation_steps=val_batches.n)
this_model.save('model_size_{}_dropout_{}_augment_{}_epochs_{}.h5'.format(x, 0.1 * x, x, epochs_to_train), include_optimizer=False) | code |
89132099/cell_13 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
target = np.log(train.SalePrice)
numeric_features = train.select_dtypes(include=[np.number])
numeric_features.dtypes
train.OverallQual.unique()
quality_pivot = train.pivot_table(index='OverallQual', values='SalePrice', aggfunc=np.median)
quality_pivot | code |
89132099/cell_9 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
target = np.log(train.SalePrice)
numeric_features = train.select_dtypes(include=[np.number])
numeric_features.dtypes | code |
89132099/cell_4 | [
"text_html_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns | code |
89132099/cell_6 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
train.SalePrice.describe() | code |
89132099/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
target = np.log(train.SalePrice)
numeric_features = train.select_dtypes(include=[np.number])
numeric_features.dtypes
train.OverallQual.unique() | code |
89132099/cell_1 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
89132099/cell_7 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
print('Skew is:', train.SalePrice.skew())
plt.hist(train.SalePrice, color='blue')
plt.show() | code |
89132099/cell_18 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
target = np.log(train.SalePrice)
numeric_features = train.select_dtypes(include=[np.number])
numeric_features.dtypes
train.OverallQual.unique()
quality_pivot = train.pivot_table(index='OverallQual', values='SalePrice', aggfunc=np.median)
plt.xticks(rotation=0)
train = train[train['GarageArea'] < 1200]
plt.scatter(x=train['GarageArea'], y=np.log(train.SalePrice))
plt.xlim(-200, 1600)
plt.ylabel('Sale Price')
plt.xlabel('Garage Area')
plt.show() | code |
89132099/cell_8 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
target = np.log(train.SalePrice)
print('Skew is:', target.skew())
plt.hist(target, color='blue')
plt.show() | code |
89132099/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
target = np.log(train.SalePrice)
numeric_features = train.select_dtypes(include=[np.number])
numeric_features.dtypes
train.OverallQual.unique()
quality_pivot = train.pivot_table(index='OverallQual', values='SalePrice', aggfunc=np.median)
plt.xticks(rotation=0)
plt.scatter(x=train['GrLivArea'], y=target)
plt.ylabel('Sale Price')
plt.xlabel('Above grade (ground) living area square feet')
plt.show() | code |
89132099/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
target = np.log(train.SalePrice)
numeric_features = train.select_dtypes(include=[np.number])
numeric_features.dtypes
train.OverallQual.unique()
quality_pivot = train.pivot_table(index='OverallQual', values='SalePrice', aggfunc=np.median)
plt.xticks(rotation=0)
plt.scatter(x=train['GarageArea'], y=target)
plt.ylabel('Sale Price')
plt.xlabel('Garage Area')
plt.show() | code |
89132099/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.head() | code |
89132099/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
target = np.log(train.SalePrice)
numeric_features = train.select_dtypes(include=[np.number])
numeric_features.dtypes
train.OverallQual.unique()
quality_pivot = train.pivot_table(index='OverallQual', values='SalePrice', aggfunc=np.median)
quality_pivot.plot(kind='bar', color='blue')
plt.xlabel('Overall Quality')
plt.ylabel('Median Sale Price')
plt.xticks(rotation=0)
plt.show() | code |
89132099/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
plt.style.use(style='ggplot')
plt.rcParams['figure.figsize'] = (10, 6)
import seaborn as sns
import os
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape
target = np.log(train.SalePrice)
numeric_features = train.select_dtypes(include=[np.number])
numeric_features.dtypes
corr = numeric_features.corr()
print(corr['SalePrice'].sort_values(ascending=False)[:5], '\n')
print(corr['SalePrice'].sort_values(ascending=False)[-5:]) | code |
89132099/cell_5 | [
"image_output_1.png"
] | import pandas as pd
train = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/train.csv')
test = pd.read_csv('/kaggle/input/house-prices-advanced-regression-techniques/test.csv')
train.columns
train.shape | code |
17115900/cell_13 | [
"image_output_5.png",
"image_output_4.png",
"image_output_3.png",
"image_output_2.png",
"image_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import xml.etree.ElementTree as ET
ComputeLB = True
DogsOnly = True
import numpy as np, pandas as pd, os
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt, zipfile
from PIL import Image
from tqdm import tqdm_notebook
ROOT = '../input/generative-dog-images/'
if not ComputeLB:
ROOT = '../input/'
IMAGES = os.listdir(ROOT + 'all-dogs/all-dogs/')
breeds = os.listdir(ROOT + 'annotation/Annotation/')
idxIn = 0
namesIn = []
imagesIn = np.zeros((25000, 64, 64, 3))
if DogsOnly:
for breed in tqdm_notebook(breeds):
for dog in os.listdir(ROOT + 'annotation/Annotation/' + breed):
try:
img = Image.open(ROOT + 'all-dogs/all-dogs/' + dog + '.jpg')
except:
continue
tree = ET.parse(ROOT + 'annotation/Annotation/' + breed + '/' + dog)
root = tree.getroot()
objects = root.findall('object')
for o in objects:
bndbox = o.find('bndbox')
xmin = int(bndbox.find('xmin').text)
ymin = int(bndbox.find('ymin').text)
xmax = int(bndbox.find('xmax').text)
ymax = int(bndbox.find('ymax').text)
w_, h_ = img.size
w = np.max((xmax - xmin, ymax - ymin))
img2 = img.crop((xmin, ymin, min(xmin + w, w_), min(ymin + w, h_)))
img2 = img2.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img2)
namesIn.append(breed)
idxIn += 1
idx = np.arange(idxIn)
np.random.shuffle(idx)
imagesIn = imagesIn[idx, :, :, :]
namesIn = np.array(namesIn)[idx]
else:
x = np.random.choice(np.arange(20579), 10000)
for k in tqdm_notebook(range(len(x))):
img = Image.open(ROOT + 'all-dogs/all-dogs/' + IMAGES[x[k]])
w = img.size[0]
h = img.size[1]
sz = np.min((w, h))
a = 0
b = 0
if w < h:
b = (h - sz) // 2
else:
a = (w - sz) // 2
img = img.crop((0 + a, 0 + b, sz + a, sz + b))
img = img.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img)
namesIn.append(IMAGES[x[k]])
idxIn += 1
x = np.random.randint(0, idxIn, 25)
for k in range(5):
for j in range(5):
img = Image.fromarray(imagesIn[x[k * 5 + j], :, :, :].astype('uint8'))
plt.axis('off')
from torch.utils.data import TensorDataset, DataLoader
from collections import defaultdict
from sklearn.model_selection import train_test_split
imagesIntorch = np.array([np.array(image).transpose(2, 1, 0) for image in imagesIn])
dogs = list(set(namesIn))
len_dogs = len(dogs)
dog2id = {dogs[i]: i for i in range(len(dogs))}
id2dog = {v: k for k, v in dog2id.items()}
idIn = [dog2id[name] for name in namesIn]
train_X, validation_X, train_y, validation_y = train_test_split(imagesIntorch, idIn, test_size=0.2, random_state=620402)
(np.array(train_X).shape, np.array(validation_X).shape, np.array(train_y).shape, np.array(validation_y).shape)
data_variance = np.var(train_X)
data_variance | code |
17115900/cell_9 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import xml.etree.ElementTree as ET
ComputeLB = True
DogsOnly = True
import numpy as np, pandas as pd, os
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt, zipfile
from PIL import Image
from tqdm import tqdm_notebook
ROOT = '../input/generative-dog-images/'
if not ComputeLB:
ROOT = '../input/'
IMAGES = os.listdir(ROOT + 'all-dogs/all-dogs/')
breeds = os.listdir(ROOT + 'annotation/Annotation/')
idxIn = 0
namesIn = []
imagesIn = np.zeros((25000, 64, 64, 3))
if DogsOnly:
for breed in tqdm_notebook(breeds):
for dog in os.listdir(ROOT + 'annotation/Annotation/' + breed):
try:
img = Image.open(ROOT + 'all-dogs/all-dogs/' + dog + '.jpg')
except:
continue
tree = ET.parse(ROOT + 'annotation/Annotation/' + breed + '/' + dog)
root = tree.getroot()
objects = root.findall('object')
for o in objects:
bndbox = o.find('bndbox')
xmin = int(bndbox.find('xmin').text)
ymin = int(bndbox.find('ymin').text)
xmax = int(bndbox.find('xmax').text)
ymax = int(bndbox.find('ymax').text)
w_, h_ = img.size
w = np.max((xmax - xmin, ymax - ymin))
img2 = img.crop((xmin, ymin, min(xmin + w, w_), min(ymin + w, h_)))
img2 = img2.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img2)
namesIn.append(breed)
idxIn += 1
idx = np.arange(idxIn)
np.random.shuffle(idx)
imagesIn = imagesIn[idx, :, :, :]
namesIn = np.array(namesIn)[idx]
else:
x = np.random.choice(np.arange(20579), 10000)
for k in tqdm_notebook(range(len(x))):
img = Image.open(ROOT + 'all-dogs/all-dogs/' + IMAGES[x[k]])
w = img.size[0]
h = img.size[1]
sz = np.min((w, h))
a = 0
b = 0
if w < h:
b = (h - sz) // 2
else:
a = (w - sz) // 2
img = img.crop((0 + a, 0 + b, sz + a, sz + b))
img = img.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img)
namesIn.append(IMAGES[x[k]])
idxIn += 1
x = np.random.randint(0, idxIn, 25)
for k in range(5):
for j in range(5):
img = Image.fromarray(imagesIn[x[k * 5 + j], :, :, :].astype('uint8'))
plt.axis('off')
from torch.utils.data import TensorDataset, DataLoader
from collections import defaultdict
from sklearn.model_selection import train_test_split
print(f'The shape of image is {imagesIn.shape}, the shape of imagename is {namesIn.shape}')
imagesIntorch = np.array([np.array(image).transpose(2, 1, 0) for image in imagesIn])
print(f'The shape of reshaped image is {imagesIntorch.shape}')
dogs = list(set(namesIn))
len_dogs = len(dogs)
print(f'the number of dogs is {len_dogs}')
dog2id = {dogs[i]: i for i in range(len(dogs))}
id2dog = {v: k for k, v in dog2id.items()}
idIn = [dog2id[name] for name in namesIn]
train_X, validation_X, train_y, validation_y = train_test_split(imagesIntorch, idIn, test_size=0.2, random_state=620402) | code |
17115900/cell_6 | [
"text_plain_output_1.png"
] | import torch
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device | code |
17115900/cell_8 | [
"text_plain_output_1.png"
] | from PIL import Image
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import xml.etree.ElementTree as ET
ComputeLB = True
DogsOnly = True
import numpy as np, pandas as pd, os
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt, zipfile
from PIL import Image
from tqdm import tqdm_notebook
ROOT = '../input/generative-dog-images/'
if not ComputeLB:
ROOT = '../input/'
IMAGES = os.listdir(ROOT + 'all-dogs/all-dogs/')
breeds = os.listdir(ROOT + 'annotation/Annotation/')
idxIn = 0
namesIn = []
imagesIn = np.zeros((25000, 64, 64, 3))
if DogsOnly:
for breed in tqdm_notebook(breeds):
for dog in os.listdir(ROOT + 'annotation/Annotation/' + breed):
try:
img = Image.open(ROOT + 'all-dogs/all-dogs/' + dog + '.jpg')
except:
continue
tree = ET.parse(ROOT + 'annotation/Annotation/' + breed + '/' + dog)
root = tree.getroot()
objects = root.findall('object')
for o in objects:
bndbox = o.find('bndbox')
xmin = int(bndbox.find('xmin').text)
ymin = int(bndbox.find('ymin').text)
xmax = int(bndbox.find('xmax').text)
ymax = int(bndbox.find('ymax').text)
w_, h_ = img.size
w = np.max((xmax - xmin, ymax - ymin))
img2 = img.crop((xmin, ymin, min(xmin + w, w_), min(ymin + w, h_)))
img2 = img2.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img2)
namesIn.append(breed)
idxIn += 1
idx = np.arange(idxIn)
np.random.shuffle(idx)
imagesIn = imagesIn[idx, :, :, :]
namesIn = np.array(namesIn)[idx]
else:
x = np.random.choice(np.arange(20579), 10000)
for k in tqdm_notebook(range(len(x))):
img = Image.open(ROOT + 'all-dogs/all-dogs/' + IMAGES[x[k]])
w = img.size[0]
h = img.size[1]
sz = np.min((w, h))
a = 0
b = 0
if w < h:
b = (h - sz) // 2
else:
a = (w - sz) // 2
img = img.crop((0 + a, 0 + b, sz + a, sz + b))
img = img.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img)
namesIn.append(IMAGES[x[k]])
if idxIn % 1000 == 0:
print(idxIn)
idxIn += 1
x = np.random.randint(0, idxIn, 25)
for k in range(5):
plt.figure(figsize=(15, 3))
for j in range(5):
plt.subplot(1, 5, j + 1)
img = Image.fromarray(imagesIn[x[k * 5 + j], :, :, :].astype('uint8'))
plt.axis('off')
if not DogsOnly:
plt.title(namesIn[x[k * 5 + j]], fontsize=11)
else:
plt.title(namesIn[x[k * 5 + j]].split('-')[1], fontsize=11)
plt.imshow(img)
plt.show() | code |
17115900/cell_16 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset, DataLoader
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch
import torch.nn as nn
import xml.etree.ElementTree as ET
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device
ComputeLB = True
DogsOnly = True
import numpy as np, pandas as pd, os
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt, zipfile
from PIL import Image
from tqdm import tqdm_notebook
ROOT = '../input/generative-dog-images/'
if not ComputeLB:
ROOT = '../input/'
IMAGES = os.listdir(ROOT + 'all-dogs/all-dogs/')
breeds = os.listdir(ROOT + 'annotation/Annotation/')
idxIn = 0
namesIn = []
imagesIn = np.zeros((25000, 64, 64, 3))
if DogsOnly:
for breed in tqdm_notebook(breeds):
for dog in os.listdir(ROOT + 'annotation/Annotation/' + breed):
try:
img = Image.open(ROOT + 'all-dogs/all-dogs/' + dog + '.jpg')
except:
continue
tree = ET.parse(ROOT + 'annotation/Annotation/' + breed + '/' + dog)
root = tree.getroot()
objects = root.findall('object')
for o in objects:
bndbox = o.find('bndbox')
xmin = int(bndbox.find('xmin').text)
ymin = int(bndbox.find('ymin').text)
xmax = int(bndbox.find('xmax').text)
ymax = int(bndbox.find('ymax').text)
w_, h_ = img.size
w = np.max((xmax - xmin, ymax - ymin))
img2 = img.crop((xmin, ymin, min(xmin + w, w_), min(ymin + w, h_)))
img2 = img2.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img2)
namesIn.append(breed)
idxIn += 1
idx = np.arange(idxIn)
np.random.shuffle(idx)
imagesIn = imagesIn[idx, :, :, :]
namesIn = np.array(namesIn)[idx]
else:
x = np.random.choice(np.arange(20579), 10000)
for k in tqdm_notebook(range(len(x))):
img = Image.open(ROOT + 'all-dogs/all-dogs/' + IMAGES[x[k]])
w = img.size[0]
h = img.size[1]
sz = np.min((w, h))
a = 0
b = 0
if w < h:
b = (h - sz) // 2
else:
a = (w - sz) // 2
img = img.crop((0 + a, 0 + b, sz + a, sz + b))
img = img.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img)
namesIn.append(IMAGES[x[k]])
idxIn += 1
x = np.random.randint(0, idxIn, 25)
for k in range(5):
for j in range(5):
img = Image.fromarray(imagesIn[x[k * 5 + j], :, :, :].astype('uint8'))
plt.axis('off')
from torch.utils.data import TensorDataset, DataLoader
from collections import defaultdict
from sklearn.model_selection import train_test_split
imagesIntorch = np.array([np.array(image).transpose(2, 1, 0) for image in imagesIn])
dogs = list(set(namesIn))
len_dogs = len(dogs)
dog2id = {dogs[i]: i for i in range(len(dogs))}
id2dog = {v: k for k, v in dog2id.items()}
idIn = [dog2id[name] for name in namesIn]
train_X, validation_X, train_y, validation_y = train_test_split(imagesIntorch, idIn, test_size=0.2, random_state=620402)
(np.array(train_X).shape, np.array(validation_X).shape, np.array(train_y).shape, np.array(validation_y).shape)
import torch
training_data = TensorDataset(torch.Tensor(train_X), torch.Tensor(train_y))
validation_data = TensorDataset(torch.Tensor(validation_X), torch.Tensor(validation_y))
data_variance = np.var(train_X)
data_variance
class VectorQuantizer(nn.Module):
def __init__(self, num_embeddings, embedding_dim, commitment_cost):
super(VectorQuantizer, self).__init__()
self._embedding_dim = embedding_dim
self._num_embeddings = num_embeddings
self._embedding = nn.Embedding(self._num_embeddings, self._embedding_dim)
self._embedding.weight.data.uniform_(-1 / self._num_embeddings, 1 / self._num_embeddings)
self._commitment_cost = commitment_cost
def forward(self, inputs):
inputs = inputs.permute(0, 2, 3, 1).contiguous()
input_shape = inputs.shape
flat_input = inputs.view(-1, self._embedding_dim)
distances = torch.sum(flat_input ** 2, dim=1, keepdim=True) + torch.sum(self._embedding.weight ** 2, dim=1) - 2 * torch.matmul(flat_input, self._embedding.weight.t())
encoding_indices = torch.argmin(distances, dim=1).unsqueeze(1)
encodings = torch.zeros(encoding_indices.shape[0], self._num_embeddings).to(device)
quantized = torch.matmul(encodings, self._embedding.weight).view(input_shape)
e_latent_loss = torch.mean((quantized.detach() - inputs) ** 2)
q_latent_loss = torch.mean((quantized - inputs.detach()) ** 2)
loss = q_latent_loss + self._commitment_cost * e_latent_loss
quantized = inputs + (quantized - inputs).detach()
avg_probs = torch.mean(encodings, dim=0)
perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
return (loss, quantized.permute(0, 3, 1, 2).contiguous(), perplexity, encodings)
num_embeddings, embedding_dim, commitment_cost = (64, 512, 0.25)
testVectorQuantizer = VectorQuantizer(num_embeddings, embedding_dim, commitment_cost)
testVectorQuantizer.cuda()
input_tensor = torch.Tensor(np.random.normal(size=[32, 64, 4, 4]))
print(input_tensor.shape)
_, output_tensor, perplexity, encodings = testVectorQuantizer(input_tensor.cuda())
print(output_tensor.shape)
print(encodings.shape)
print(perplexity.shape) | code |
17115900/cell_17 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from torch.utils.data import TensorDataset, DataLoader
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import torch
import torch
import torch.nn as nn
import xml.etree.ElementTree as ET
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
device
ComputeLB = True
DogsOnly = True
import numpy as np, pandas as pd, os
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt, zipfile
from PIL import Image
from tqdm import tqdm_notebook
ROOT = '../input/generative-dog-images/'
if not ComputeLB:
ROOT = '../input/'
IMAGES = os.listdir(ROOT + 'all-dogs/all-dogs/')
breeds = os.listdir(ROOT + 'annotation/Annotation/')
idxIn = 0
namesIn = []
imagesIn = np.zeros((25000, 64, 64, 3))
if DogsOnly:
for breed in tqdm_notebook(breeds):
for dog in os.listdir(ROOT + 'annotation/Annotation/' + breed):
try:
img = Image.open(ROOT + 'all-dogs/all-dogs/' + dog + '.jpg')
except:
continue
tree = ET.parse(ROOT + 'annotation/Annotation/' + breed + '/' + dog)
root = tree.getroot()
objects = root.findall('object')
for o in objects:
bndbox = o.find('bndbox')
xmin = int(bndbox.find('xmin').text)
ymin = int(bndbox.find('ymin').text)
xmax = int(bndbox.find('xmax').text)
ymax = int(bndbox.find('ymax').text)
w_, h_ = img.size
w = np.max((xmax - xmin, ymax - ymin))
img2 = img.crop((xmin, ymin, min(xmin + w, w_), min(ymin + w, h_)))
img2 = img2.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img2)
namesIn.append(breed)
idxIn += 1
idx = np.arange(idxIn)
np.random.shuffle(idx)
imagesIn = imagesIn[idx, :, :, :]
namesIn = np.array(namesIn)[idx]
else:
x = np.random.choice(np.arange(20579), 10000)
for k in tqdm_notebook(range(len(x))):
img = Image.open(ROOT + 'all-dogs/all-dogs/' + IMAGES[x[k]])
w = img.size[0]
h = img.size[1]
sz = np.min((w, h))
a = 0
b = 0
if w < h:
b = (h - sz) // 2
else:
a = (w - sz) // 2
img = img.crop((0 + a, 0 + b, sz + a, sz + b))
img = img.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img)
namesIn.append(IMAGES[x[k]])
idxIn += 1
x = np.random.randint(0, idxIn, 25)
for k in range(5):
for j in range(5):
img = Image.fromarray(imagesIn[x[k * 5 + j], :, :, :].astype('uint8'))
plt.axis('off')
from torch.utils.data import TensorDataset, DataLoader
from collections import defaultdict
from sklearn.model_selection import train_test_split
imagesIntorch = np.array([np.array(image).transpose(2, 1, 0) for image in imagesIn])
dogs = list(set(namesIn))
len_dogs = len(dogs)
dog2id = {dogs[i]: i for i in range(len(dogs))}
id2dog = {v: k for k, v in dog2id.items()}
idIn = [dog2id[name] for name in namesIn]
train_X, validation_X, train_y, validation_y = train_test_split(imagesIntorch, idIn, test_size=0.2, random_state=620402)
(np.array(train_X).shape, np.array(validation_X).shape, np.array(train_y).shape, np.array(validation_y).shape)
import torch
training_data = TensorDataset(torch.Tensor(train_X), torch.Tensor(train_y))
validation_data = TensorDataset(torch.Tensor(validation_X), torch.Tensor(validation_y))
data_variance = np.var(train_X)
data_variance
class VectorQuantizer(nn.Module):
def __init__(self, num_embeddings, embedding_dim, commitment_cost):
super(VectorQuantizer, self).__init__()
self._embedding_dim = embedding_dim
self._num_embeddings = num_embeddings
self._embedding = nn.Embedding(self._num_embeddings, self._embedding_dim)
self._embedding.weight.data.uniform_(-1 / self._num_embeddings, 1 / self._num_embeddings)
self._commitment_cost = commitment_cost
def forward(self, inputs):
inputs = inputs.permute(0, 2, 3, 1).contiguous()
input_shape = inputs.shape
flat_input = inputs.view(-1, self._embedding_dim)
distances = torch.sum(flat_input ** 2, dim=1, keepdim=True) + torch.sum(self._embedding.weight ** 2, dim=1) - 2 * torch.matmul(flat_input, self._embedding.weight.t())
encoding_indices = torch.argmin(distances, dim=1).unsqueeze(1)
encodings = torch.zeros(encoding_indices.shape[0], self._num_embeddings).to(device)
quantized = torch.matmul(encodings, self._embedding.weight).view(input_shape)
e_latent_loss = torch.mean((quantized.detach() - inputs) ** 2)
q_latent_loss = torch.mean((quantized - inputs.detach()) ** 2)
loss = q_latent_loss + self._commitment_cost * e_latent_loss
quantized = inputs + (quantized - inputs).detach()
avg_probs = torch.mean(encodings, dim=0)
perplexity = torch.exp(-torch.sum(avg_probs * torch.log(avg_probs + 1e-10)))
return (loss, quantized.permute(0, 3, 1, 2).contiguous(), perplexity, encodings)
num_embeddings, embedding_dim, commitment_cost = (64, 512, 0.25)
testVectorQuantizer = VectorQuantizer(num_embeddings, embedding_dim, commitment_cost)
testVectorQuantizer.cuda()
input_tensor = torch.Tensor(np.random.normal(size=[32, 64, 4, 4]))
_, output_tensor, perplexity, encodings = testVectorQuantizer(input_tensor.cuda())
(encodings, perplexity) | code |
17115900/cell_10 | [
"text_plain_output_1.png"
] | from PIL import Image
from sklearn.model_selection import train_test_split
from tqdm import tqdm_notebook
import matplotlib.pyplot as plt
import numpy as np
import xml.etree.ElementTree as ET
ComputeLB = True
DogsOnly = True
import numpy as np, pandas as pd, os
import xml.etree.ElementTree as ET
import matplotlib.pyplot as plt, zipfile
from PIL import Image
from tqdm import tqdm_notebook
ROOT = '../input/generative-dog-images/'
if not ComputeLB:
ROOT = '../input/'
IMAGES = os.listdir(ROOT + 'all-dogs/all-dogs/')
breeds = os.listdir(ROOT + 'annotation/Annotation/')
idxIn = 0
namesIn = []
imagesIn = np.zeros((25000, 64, 64, 3))
if DogsOnly:
for breed in tqdm_notebook(breeds):
for dog in os.listdir(ROOT + 'annotation/Annotation/' + breed):
try:
img = Image.open(ROOT + 'all-dogs/all-dogs/' + dog + '.jpg')
except:
continue
tree = ET.parse(ROOT + 'annotation/Annotation/' + breed + '/' + dog)
root = tree.getroot()
objects = root.findall('object')
for o in objects:
bndbox = o.find('bndbox')
xmin = int(bndbox.find('xmin').text)
ymin = int(bndbox.find('ymin').text)
xmax = int(bndbox.find('xmax').text)
ymax = int(bndbox.find('ymax').text)
w_, h_ = img.size
w = np.max((xmax - xmin, ymax - ymin))
img2 = img.crop((xmin, ymin, min(xmin + w, w_), min(ymin + w, h_)))
img2 = img2.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img2)
namesIn.append(breed)
idxIn += 1
idx = np.arange(idxIn)
np.random.shuffle(idx)
imagesIn = imagesIn[idx, :, :, :]
namesIn = np.array(namesIn)[idx]
else:
x = np.random.choice(np.arange(20579), 10000)
for k in tqdm_notebook(range(len(x))):
img = Image.open(ROOT + 'all-dogs/all-dogs/' + IMAGES[x[k]])
w = img.size[0]
h = img.size[1]
sz = np.min((w, h))
a = 0
b = 0
if w < h:
b = (h - sz) // 2
else:
a = (w - sz) // 2
img = img.crop((0 + a, 0 + b, sz + a, sz + b))
img = img.resize((64, 64), Image.ANTIALIAS)
imagesIn[idxIn, :, :, :] = np.asarray(img)
namesIn.append(IMAGES[x[k]])
idxIn += 1
x = np.random.randint(0, idxIn, 25)
for k in range(5):
for j in range(5):
img = Image.fromarray(imagesIn[x[k * 5 + j], :, :, :].astype('uint8'))
plt.axis('off')
from torch.utils.data import TensorDataset, DataLoader
from collections import defaultdict
from sklearn.model_selection import train_test_split
imagesIntorch = np.array([np.array(image).transpose(2, 1, 0) for image in imagesIn])
dogs = list(set(namesIn))
len_dogs = len(dogs)
dog2id = {dogs[i]: i for i in range(len(dogs))}
id2dog = {v: k for k, v in dog2id.items()}
idIn = [dog2id[name] for name in namesIn]
train_X, validation_X, train_y, validation_y = train_test_split(imagesIntorch, idIn, test_size=0.2, random_state=620402)
(np.array(train_X).shape, np.array(validation_X).shape, np.array(train_y).shape, np.array(validation_y).shape) | code |
128032351/cell_6 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df = df.dropna()
df.describe(include='O').T
df.sample(2) | code |
128032351/cell_8 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df = df.dropna()
df.describe(include='O').T
df.sample(2)
df = df.drop('Unnamed: 0', axis=1)
df.sample(2) | code |
128032351/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df.info() | code |
128032351/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df = df.dropna()
df.describe(include='O').T
df.sample(2)
df = df.drop('Unnamed: 0', axis=1)
df.sample(2)
df.groupby('Hotel_name').sum()['Rating'].sort_values(ascending=False)[:5] | code |
128032351/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df = df.dropna()
df.describe(include='O').T
df.sample(2)
df = df.drop('Unnamed: 0', axis=1)
df.sample(2)
df.groupby('Hotel_name').sum()['Rating'].sort_values(ascending=False)[:5]
df.groupby('Hotel_name').sum()['Price'].sort_values(ascending=False)[:5] | code |
128032351/cell_5 | [
"text_html_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/oyo-hotel-rooms/OYO_HOTEL_ROOMS.csv')
df = df.dropna()
df.describe(include='O').T | code |
130017723/cell_12 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LinearRegression
import numpy as np
reg = LinearRegression()
model = reg.fit(x_train, y_train)
y_pred = model.predict(x_test).round()
def diabetes_prediction():
preg = int(input('Enter the prega value:'))
glu = int(input('Enter the glu value:'))
BP = int(input('Enter the BP value:'))
SkinThik = int(input('Enter the SkinThink value:'))
Insulin = int(input('Enter the Insulin value:'))
Age = int(input('Enter the Age value:'))
Value_testing = np.array([preg, glu, BP, SkinThik, Insulin, Age]).reshape(-1, 6)
prediction = model.predict(Value_testing).round()
diabetes_prediction() | code |
18149171/cell_13 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/house-votes.csv', na_values=['?'])
df.replace('^y$', value=1, regex=True, inplace=True)
df.replace('^n$', value=0, regex=True, inplace=True)
df.fillna(0, inplace=True)
df.to_csv('house-votes-edited.csv')
from sklearn.neighbors import KNeighborsClassifier
y = df['party'].values
X = df.drop('party', axis=1).values
knn = KNeighborsClassifier(n_neighbors=6)
knn.fit(X, y)
X_new = np.array([[0.44764519, 0.95034062, 0.43959532, 0.80122238, 0.26844483, 0.45513802, 0.16595416, 0.56314597, 0.87505639, 0.92836397, 0.80958641, 0.01591928, 0.0294, 0.42548396, 0.65489058, 0.77928102]])
X_new.shape
from sklearn.neighbors import KNeighborsClassifier
y = df['party']
X = df.drop(['party'], axis=1).values
knn = KNeighborsClassifier(n_neighbors=6)
knn.fit(X, y)
y_pred = knn.predict(X)
new_prediction = knn.predict(X_new)
from sklearn import datasets
import matplotlib.pyplot as plt
digits = datasets.load_digits()
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
knn = KNeighborsClassifier(n_neighbors=7)
knn.fit(X_train, y_train)
print(knn.score(X_test, y_test)) | code |
18149171/cell_9 | [
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
import numpy as np
import pandas as pd
df = pd.read_csv('../input/house-votes.csv', na_values=['?'])
df.replace('^y$', value=1, regex=True, inplace=True)
df.replace('^n$', value=0, regex=True, inplace=True)
df.fillna(0, inplace=True)
df.to_csv('house-votes-edited.csv')
from sklearn.neighbors import KNeighborsClassifier
y = df['party'].values
X = df.drop('party', axis=1).values
knn = KNeighborsClassifier(n_neighbors=6)
knn.fit(X, y)
X_new = np.array([[0.44764519, 0.95034062, 0.43959532, 0.80122238, 0.26844483, 0.45513802, 0.16595416, 0.56314597, 0.87505639, 0.92836397, 0.80958641, 0.01591928, 0.0294, 0.42548396, 0.65489058, 0.77928102]])
X_new.shape
from sklearn.neighbors import KNeighborsClassifier
y = df['party']
X = df.drop(['party'], axis=1).values
knn = KNeighborsClassifier(n_neighbors=6)
knn.fit(X, y)
y_pred = knn.predict(X)
new_prediction = knn.predict(X_new)
print('Prediction: {}'.format(new_prediction)) | code |
18149171/cell_11 | [
"text_html_output_1.png"
] | from sklearn import datasets
import matplotlib.pyplot as plt
from sklearn import datasets
import matplotlib.pyplot as plt
digits = datasets.load_digits()
print(digits.keys())
print(digits['DESCR'])
print(digits.images.shape)
print(digits.data.shape)
plt.imshow(digits.images[1010], cmap=plt.cm.gray_r, interpolation='nearest')
plt.show() | code |
18149171/cell_7 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.neighbors import KNeighborsClassifier
import pandas as pd
df = pd.read_csv('../input/house-votes.csv', na_values=['?'])
df.replace('^y$', value=1, regex=True, inplace=True)
df.replace('^n$', value=0, regex=True, inplace=True)
df.fillna(0, inplace=True)
df.to_csv('house-votes-edited.csv')
from sklearn.neighbors import KNeighborsClassifier
y = df['party'].values
X = df.drop('party', axis=1).values
knn = KNeighborsClassifier(n_neighbors=6)
knn.fit(X, y) | code |
18149171/cell_8 | [
"text_plain_output_1.png"
] | import numpy as np
X_new = np.array([[0.44764519, 0.95034062, 0.43959532, 0.80122238, 0.26844483, 0.45513802, 0.16595416, 0.56314597, 0.87505639, 0.92836397, 0.80958641, 0.01591928, 0.0294, 0.42548396, 0.65489058, 0.77928102]])
X_new.shape | code |
18149171/cell_15 | [
"text_plain_output_1.png"
] | from sklearn import datasets
from sklearn.model_selection import train_test_split
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
from sklearn.neighbors import KNeighborsClassifier
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
df = pd.read_csv('../input/house-votes.csv', na_values=['?'])
df.replace('^y$', value=1, regex=True, inplace=True)
df.replace('^n$', value=0, regex=True, inplace=True)
df.fillna(0, inplace=True)
df.to_csv('house-votes-edited.csv')
from sklearn.neighbors import KNeighborsClassifier
y = df['party'].values
X = df.drop('party', axis=1).values
knn = KNeighborsClassifier(n_neighbors=6)
knn.fit(X, y)
X_new = np.array([[0.44764519, 0.95034062, 0.43959532, 0.80122238, 0.26844483, 0.45513802, 0.16595416, 0.56314597, 0.87505639, 0.92836397, 0.80958641, 0.01591928, 0.0294, 0.42548396, 0.65489058, 0.77928102]])
X_new.shape
from sklearn.neighbors import KNeighborsClassifier
y = df['party']
X = df.drop(['party'], axis=1).values
knn = KNeighborsClassifier(n_neighbors=6)
knn.fit(X, y)
y_pred = knn.predict(X)
new_prediction = knn.predict(X_new)
from sklearn import datasets
import matplotlib.pyplot as plt
digits = datasets.load_digits()
from sklearn.neighbors import KNeighborsClassifier
from sklearn.model_selection import train_test_split
X = digits.data
y = digits.target
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)
knn = KNeighborsClassifier(n_neighbors=7)
knn.fit(X_train, y_train)
neighbors = np.arange(1, 9)
train_accuracy = np.empty(len(neighbors))
test_accuracy = np.empty(len(neighbors))
for i, k in enumerate(neighbors):
knn = KNeighborsClassifier(n_neighbors=k)
knn.fit(X_train, y_train)
train_accuracy[i] = knn.score(X_train, y_train)
test_accuracy[i] = knn.score(X_test, y_test)
plt.title('k-NN: Varying Number of Neighbors')
plt.plot(neighbors, test_accuracy, label='Testing Accuracy')
plt.plot(neighbors, train_accuracy, label='Training Accuracy')
plt.legend()
plt.xlabel('Number of Neighbors')
plt.ylabel('Accuracy')
plt.show() | code |
18149171/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/house-votes.csv', na_values=['?'])
df.replace('^y$', value=1, regex=True, inplace=True)
df.replace('^n$', value=0, regex=True, inplace=True)
df.fillna(0, inplace=True)
df.head() | code |
50222580/cell_21 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape)
train_df.isnull().any()
train_df.isnull().sum()
train_df.columns
train_df.isnull().any() | code |
50222580/cell_13 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T | code |
50222580/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
gender_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/gender_submission.csv')
gender_df.describe().T | code |
50222580/cell_23 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape)
train_df.isnull().any()
train_df.isnull().sum()
train_df.columns
train_df.isnull().any()
train_df.isnull().any() | code |
50222580/cell_11 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
test_df.describe().T | code |
50222580/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape)
train_df.isnull().any()
train_df.isnull().sum()
train_df.columns | code |
50222580/cell_1 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
50222580/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape)
train_df.isnull().any()
train_df.isnull().sum() | code |
50222580/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
gender_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/gender_submission.csv')
gender_df.head() | code |
50222580/cell_15 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
test_df.describe().T
(test_df.size, test_df.shape) | code |
50222580/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
gender_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/gender_submission.csv')
gender_df.describe().T
(gender_df.size, gender_df.shape) | code |
50222580/cell_17 | [
"text_plain_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape)
train_df.isnull().any() | code |
50222580/cell_14 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.describe().T
(train_df.size, train_df.shape) | code |
50222580/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
test_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/test.csv')
test_df.head() | code |
50222580/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd
train_df = pd.read_csv('../input/titanic-machine-learning-from-disaster/train.csv')
train_df.head() | code |
121154415/cell_21 | [
"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 seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
color = 'RdYlGn'
data.info() | code |
121154415/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
df_peptides.head() | code |
121154415/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
df_peptides.info() | code |
121154415/cell_25 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
ppp = pd.DataFrame(df_proteins.groupby('UniProt').patient_id.nunique()).rename(columns={'patient_id': 'count_patient'}).reset_index().sort_values('count_patient', ascending=False)
ppp.tail(10) | code |
121154415/cell_23 | [
"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 seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
color = 'RdYlGn'
print('number of patients: ', data.patient_id.nunique())
print('number of proteins: ', data.UniProt.nunique()) | code |
121154415/cell_30 | [
"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
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
color = 'RdYlGn'
data[['updrs_1', 'updrs_2', 'updrs_3', 'updrs_4', 'upd23b_clinical_state_on_medication']].isnull().sum() | code |
121154415/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
print('df_proteins: ', df_proteins.columns)
print('df_peptides: ', df_peptides.columns)
print('df_cd: ', df_cd.columns) | code |
121154415/cell_41 | [
"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
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
color = 'RdYlGn'
ppp = pd.DataFrame(df_proteins.groupby('UniProt').patient_id.nunique()).rename(columns={'patient_id': 'count_patient'}).reset_index().sort_values('count_patient', ascending=False)
data.loc[data.upd23b_clinical_state_on_medication == 'On', 'upd23b_clinical_state_on_medication'] = 1
data.loc[data.upd23b_clinical_state_on_medication == 'Off', 'upd23b_clinical_state_on_medication'] = 0
plt.figure(figsize=(8, 5))
color = 'RdYlGn'
sns.heatmap(data.corr(), annot=True, linewidth=0.5, cmap=color) | code |
121154415/cell_19 | [
"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 seaborn as sns
datapath = '/kaggle/input/amp-parkinsons-disease-progression-prediction/'
df_proteins = pd.read_csv(f'{datapath}train_proteins.csv')
df_peptides = pd.read_csv(f'{datapath}train_peptides.csv')
df_cd = pd.read_csv(f'{datapath}train_clinical_data.csv')
data = df_proteins.merge(df_peptides, on=['visit_id', 'visit_month', 'patient_id', 'UniProt'], how='left')
data = data.merge(df_cd, on=['visit_id', 'visit_month', 'patient_id'], how='left')
data
plt.figure(figsize=(8, 5))
color = 'RdYlGn'
sns.heatmap(data.corr(), annot=True, linewidth=0.5, cmap=color) | code |
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