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106210118/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/marketing-strategy-personalised-offer/sample.csv')
df1 = pd.read_csv('../input/marketing-strategy-personalised-offer/train_data.csv')
feature_list = df1.columns[:-1].values
label = [df1.columns[-1]]
print('Feature list:', feature_list)
print('Label:', label) | code |
106210118/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/marketing-strategy-personalised-offer/sample.csv')
df1 = pd.read_csv('../input/marketing-strategy-personalised-offer/train_data.csv')
feature_list = df1.columns[:-1].values
label = [df1.columns[-1]]
df1.info() | code |
106210118/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('../input/marketing-strategy-personalised-offer/sample.csv')
data.head(10) | code |
32071603/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('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns | code |
32071603/cell_20 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
age_group = data[["AgeGroup", "AB1-BC1"]]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
temp = grouped.describe()["AB1-BC1"][["count", 'std']]
temp
summary["std_err"] = temp["std"] / np.sqrt(temp["count"])
summary = summary.reindex([1,0,3,2])
fig = px.bar(summary, x="AgeGroup", y="AB1-BC1", error_y="std_err", width=500, title="AB1-BC1")
fig.show()
age_group = data[["AgeGroup", 'AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4',
'BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
col_names = ['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4', 'BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']
# add standard error for each accuracy
for name in col_names:
temp = grouped.describe()[name][["count", 'std']]
summary["se_" + name[:2] + name[-1]] = temp["std"] / np.sqrt(temp["count"])
# get AB, BC values from AgeGroup
child_AB = summary[summary["AgeGroup"] == "child"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
child_BC = summary[summary["AgeGroup"] == "child"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
ado_AB = summary[summary["AgeGroup"] == "adolescent"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
ado_BC = summary[summary["AgeGroup"] == "adolescent"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
y_adl_AB = summary[summary["AgeGroup"] == "younger adult"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
y_adl_BC = summary[summary["AgeGroup"] == "younger adult"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
o_adl_AB = summary[summary["AgeGroup"] == "older adult"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
o_adl_BC = summary[summary["AgeGroup"] == "older adult"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
# get standard error for AB, BC values from AgeGroup
child_AB_se = summary[summary["AgeGroup"] == "child"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
child_BC_se = summary[summary["AgeGroup"] == "child"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
ado_AB_se = summary[summary["AgeGroup"] == "adolescent"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
ado_BC_se = summary[summary["AgeGroup"] == "adolescent"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
y_adl_AB_se = summary[summary["AgeGroup"] == "younger adult"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
y_adl_BC_se = summary[summary["AgeGroup"] == "younger adult"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
o_adl_AB_se = summary[summary["AgeGroup"] == "older adult"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
o_adl_BC_se = summary[summary["AgeGroup"] == "older adult"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
# make 2x2 subplots
fig = make_subplots(rows=2, cols=2,
subplot_titles=("Children", "Adolescent", "Younger Adults", "Older Adults"))
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=child_AB_se)), row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=child_BC_se)),row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=ado_AB_se)),row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=ado_BC_se)),row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=y_adl_AB_se)),row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=y_adl_BC_se)),row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=o_adl_AB_se)),row=2, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=o_adl_BC_se)),row=2, col=2)
fig.update_layout(width=850, height=600)
fig.show()
age_group = data[["AgeGroup", 'AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4',
'BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
col_names = ['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4', 'BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']
# add standard error for each accuracy
for name in col_names:
temp = grouped.describe()[name][["count", 'std']]
summary["se_" + name[:2] + name[-1]] = temp["std"] / np.sqrt(temp["count"])
# get AB, BC values from AgeGroup
child_AB = summary[summary["AgeGroup"] == "child"][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
child_BC = summary[summary["AgeGroup"] == "child"][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
ado_AB = summary[summary["AgeGroup"] == "adolescent"][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
ado_BC = summary[summary["AgeGroup"] == "adolescent"][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
y_adl_AB = summary[summary["AgeGroup"] == "younger adult"][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
y_adl_BC = summary[summary["AgeGroup"] == "younger adult"][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
o_adl_AB = summary[summary["AgeGroup"] == "older adult"][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
o_adl_BC = summary[summary["AgeGroup"] == "older adult"][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
# get standard error for AB, BC values from AgeGroup
child_AB_se = summary[summary["AgeGroup"] == "child"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
child_BC_se = summary[summary["AgeGroup"] == "child"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
ado_AB_se = summary[summary["AgeGroup"] == "adolescent"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
ado_BC_se = summary[summary["AgeGroup"] == "adolescent"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
y_adl_AB_se = summary[summary["AgeGroup"] == "younger adult"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
y_adl_BC_se = summary[summary["AgeGroup"] == "younger adult"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
o_adl_AB_se = summary[summary["AgeGroup"] == "older adult"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
o_adl_BC_se = summary[summary["AgeGroup"] == "older adult"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
# make 2x2 subplots
fig = make_subplots(rows=2, cols=2,
subplot_titles=("Children", "Adolescent", "Younger Adults", "Older Adults"))
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=child_AB_se)), row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=child_BC_se)),row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=ado_AB_se)),row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=ado_BC_se)),row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=y_adl_AB_se)),row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=y_adl_BC_se)),row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=o_adl_AB_se)),row=2, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=o_adl_BC_se)),row=2, col=2)
fig.update_layout(width=850, height=600)
fig.show()
data_new = data.copy()
data_new['AB_acc_4_T'] = data['AB_acc_4'] == 1
data_new['AB_acc_4_T'] = data_new['AB_acc_4_T'].apply(lambda x: 'correct' if x else 'incorrect')
fig = px.histogram(data_new, x='BC_acc_4', color='AB_acc_4_T', marginal='rug', barmode='overlay', width=700, title='BC_acc_4 distribution between AB_acc_4 = 1 or not 1')
fig.show() | code |
32071603/cell_6 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
age_group = data[['AgeGroup', 'AB1-BC1']]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
temp = grouped.describe()['AB1-BC1'][['count', 'std']]
temp
summary['std_err'] = temp['std'] / np.sqrt(temp['count'])
summary = summary.reindex([1, 0, 3, 2])
fig = px.bar(summary, x='AgeGroup', y='AB1-BC1', error_y='std_err', width=500, title='AB1-BC1')
fig.show() | code |
32071603/cell_19 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
data_new = data.copy()
data_new['AB_acc_4_T'] = data['AB_acc_4'] == 1
data_new['AB_acc_4_T'] = data_new['AB_acc_4_T'].apply(lambda x: 'correct' if x else 'incorrect')
data_new[data_new['AB_acc_4_T'] == 'incorrect'][['BC_acc_4']].describe() | code |
32071603/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go
from plotly.subplots import make_subplots
from scipy import stats
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
32071603/cell_8 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
age_group = data[["AgeGroup", "AB1-BC1"]]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
temp = grouped.describe()["AB1-BC1"][["count", 'std']]
temp
summary["std_err"] = temp["std"] / np.sqrt(temp["count"])
summary = summary.reindex([1,0,3,2])
fig = px.bar(summary, x="AgeGroup", y="AB1-BC1", error_y="std_err", width=500, title="AB1-BC1")
fig.show()
age_group = data[['AgeGroup', 'AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4', 'BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
col_names = ['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4', 'BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']
for name in col_names:
temp = grouped.describe()[name][['count', 'std']]
summary['se_' + name[:2] + name[-1]] = temp['std'] / np.sqrt(temp['count'])
child_AB = summary[summary['AgeGroup'] == 'child'][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
child_BC = summary[summary['AgeGroup'] == 'child'][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
ado_AB = summary[summary['AgeGroup'] == 'adolescent'][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
ado_BC = summary[summary['AgeGroup'] == 'adolescent'][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
y_adl_AB = summary[summary['AgeGroup'] == 'younger adult'][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
y_adl_BC = summary[summary['AgeGroup'] == 'younger adult'][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
o_adl_AB = summary[summary['AgeGroup'] == 'older adult'][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
o_adl_BC = summary[summary['AgeGroup'] == 'older adult'][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
child_AB_se = summary[summary['AgeGroup'] == 'child'][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
child_BC_se = summary[summary['AgeGroup'] == 'child'][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
ado_AB_se = summary[summary['AgeGroup'] == 'adolescent'][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
ado_BC_se = summary[summary['AgeGroup'] == 'adolescent'][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
y_adl_AB_se = summary[summary['AgeGroup'] == 'younger adult'][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
y_adl_BC_se = summary[summary['AgeGroup'] == 'younger adult'][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
o_adl_AB_se = summary[summary['AgeGroup'] == 'older adult'][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
o_adl_BC_se = summary[summary['AgeGroup'] == 'older adult'][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
fig = make_subplots(rows=2, cols=2, subplot_titles=('Children', 'Adolescent', 'Younger Adults', 'Older Adults'))
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_AB, name='AB', line=dict(color='firebrick', width=2), error_y=dict(array=child_AB_se)), row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_BC, name='BC', line=dict(color='royalblue', width=2), error_y=dict(array=child_BC_se)), row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_AB, name='AB', line=dict(color='firebrick', width=2), error_y=dict(array=ado_AB_se)), row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_BC, name='BC', line=dict(color='royalblue', width=2), error_y=dict(array=ado_BC_se)), row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_AB, name='AB', line=dict(color='firebrick', width=2), error_y=dict(array=y_adl_AB_se)), row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_BC, name='BC', line=dict(color='royalblue', width=2), error_y=dict(array=y_adl_BC_se)), row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_AB, name='AB', line=dict(color='firebrick', width=2), error_y=dict(array=o_adl_AB_se)), row=2, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_BC, name='BC', line=dict(color='royalblue', width=2), error_y=dict(array=o_adl_BC_se)), row=2, col=2)
fig.update_layout(width=850, height=600)
fig.show() | code |
32071603/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
data_new = data.copy()
data_new['AB_acc_4_T'] = data['AB_acc_4'] == 1
data_new['AB_acc_4_T'] = data_new['AB_acc_4_T'].apply(lambda x: 'correct' if x else 'incorrect')
data_new['AB_acc_4_T'].value_counts() | code |
32071603/cell_3 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.head(5) | code |
32071603/cell_17 | [
"text_html_output_2.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
data_new = data.copy()
data_new['AB_acc_4_T'] = data['AB_acc_4'] == 1
data_new['AB_acc_4_T'] = data_new['AB_acc_4_T'].apply(lambda x: 'correct' if x else 'incorrect')
data_new[data_new['AB_acc_4_T'] == 'correct'][['BC_acc_4']].describe() | code |
32071603/cell_22 | [
"text_plain_output_1.png"
] | from plotly.subplots import make_subplots
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
age_group = data[["AgeGroup", "AB1-BC1"]]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
temp = grouped.describe()["AB1-BC1"][["count", 'std']]
temp
summary["std_err"] = temp["std"] / np.sqrt(temp["count"])
summary = summary.reindex([1,0,3,2])
fig = px.bar(summary, x="AgeGroup", y="AB1-BC1", error_y="std_err", width=500, title="AB1-BC1")
fig.show()
age_group = data[["AgeGroup", 'AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4',
'BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
col_names = ['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4', 'BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']
# add standard error for each accuracy
for name in col_names:
temp = grouped.describe()[name][["count", 'std']]
summary["se_" + name[:2] + name[-1]] = temp["std"] / np.sqrt(temp["count"])
# get AB, BC values from AgeGroup
child_AB = summary[summary["AgeGroup"] == "child"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
child_BC = summary[summary["AgeGroup"] == "child"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
ado_AB = summary[summary["AgeGroup"] == "adolescent"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
ado_BC = summary[summary["AgeGroup"] == "adolescent"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
y_adl_AB = summary[summary["AgeGroup"] == "younger adult"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
y_adl_BC = summary[summary["AgeGroup"] == "younger adult"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
o_adl_AB = summary[summary["AgeGroup"] == "older adult"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
o_adl_BC = summary[summary["AgeGroup"] == "older adult"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
# get standard error for AB, BC values from AgeGroup
child_AB_se = summary[summary["AgeGroup"] == "child"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
child_BC_se = summary[summary["AgeGroup"] == "child"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
ado_AB_se = summary[summary["AgeGroup"] == "adolescent"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
ado_BC_se = summary[summary["AgeGroup"] == "adolescent"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
y_adl_AB_se = summary[summary["AgeGroup"] == "younger adult"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
y_adl_BC_se = summary[summary["AgeGroup"] == "younger adult"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
o_adl_AB_se = summary[summary["AgeGroup"] == "older adult"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
o_adl_BC_se = summary[summary["AgeGroup"] == "older adult"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
# make 2x2 subplots
fig = make_subplots(rows=2, cols=2,
subplot_titles=("Children", "Adolescent", "Younger Adults", "Older Adults"))
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=child_AB_se)), row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=child_BC_se)),row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=ado_AB_se)),row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=ado_BC_se)),row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=y_adl_AB_se)),row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=y_adl_BC_se)),row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=o_adl_AB_se)),row=2, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=o_adl_BC_se)),row=2, col=2)
fig.update_layout(width=850, height=600)
fig.show()
age_group = data[["AgeGroup", 'AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4',
'BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
col_names = ['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4', 'BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']
# add standard error for each accuracy
for name in col_names:
temp = grouped.describe()[name][["count", 'std']]
summary["se_" + name[:2] + name[-1]] = temp["std"] / np.sqrt(temp["count"])
# get AB, BC values from AgeGroup
child_AB = summary[summary["AgeGroup"] == "child"][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
child_BC = summary[summary["AgeGroup"] == "child"][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
ado_AB = summary[summary["AgeGroup"] == "adolescent"][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
ado_BC = summary[summary["AgeGroup"] == "adolescent"][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
y_adl_AB = summary[summary["AgeGroup"] == "younger adult"][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
y_adl_BC = summary[summary["AgeGroup"] == "younger adult"][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
o_adl_AB = summary[summary["AgeGroup"] == "older adult"][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
o_adl_BC = summary[summary["AgeGroup"] == "older adult"][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
# get standard error for AB, BC values from AgeGroup
child_AB_se = summary[summary["AgeGroup"] == "child"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
child_BC_se = summary[summary["AgeGroup"] == "child"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
ado_AB_se = summary[summary["AgeGroup"] == "adolescent"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
ado_BC_se = summary[summary["AgeGroup"] == "adolescent"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
y_adl_AB_se = summary[summary["AgeGroup"] == "younger adult"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
y_adl_BC_se = summary[summary["AgeGroup"] == "younger adult"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
o_adl_AB_se = summary[summary["AgeGroup"] == "older adult"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
o_adl_BC_se = summary[summary["AgeGroup"] == "older adult"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
# make 2x2 subplots
fig = make_subplots(rows=2, cols=2,
subplot_titles=("Children", "Adolescent", "Younger Adults", "Older Adults"))
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=child_AB_se)), row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=child_BC_se)),row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=ado_AB_se)),row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=ado_BC_se)),row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=y_adl_AB_se)),row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=y_adl_BC_se)),row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=o_adl_AB_se)),row=2, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=o_adl_BC_se)),row=2, col=2)
fig.update_layout(width=850, height=600)
fig.show()
data_new = data.copy()
data_new['AB_acc_4_T'] = data['AB_acc_4'] == 1
data_new['AB_acc_4_T'] = data_new['AB_acc_4_T'].apply(lambda x: 'correct' if x else 'incorrect')
fig = px.histogram(data_new, x="BC_acc_4", color="AB_acc_4_T", marginal="rug", barmode="overlay",
width=700, title="BC_acc_4 distribution between AB_acc_4 = 1 or not 1")
fig.show()
data_new = data.copy()
data_new['BC1-AC'] = data_new['BC_acc_1'] - data_new['AC_acc']
age_group = data_new[['AgeGroup', 'BC1-AC']]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
temp = grouped.describe()['BC1-AC'][['count', 'std']]
temp
summary['std_err'] = temp['std'] / np.sqrt(temp['count'])
summary = summary.reindex([1, 0, 3, 2])
print(summary)
fig = px.bar(summary, x='AgeGroup', y='BC1-AC', error_y='std_err', width=500, title='(BC rep1 - AC rep1) x Age')
fig.show() | code |
32071603/cell_10 | [
"text_html_output_1.png"
] | from plotly.subplots import make_subplots
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import plotly.express as px
import plotly.graph_objects as go
data = pd.read_csv('/kaggle/input/associativeinference/Associative Inference.csv')
data.columns
age_group = data[["AgeGroup", "AB1-BC1"]]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
temp = grouped.describe()["AB1-BC1"][["count", 'std']]
temp
summary["std_err"] = temp["std"] / np.sqrt(temp["count"])
summary = summary.reindex([1,0,3,2])
fig = px.bar(summary, x="AgeGroup", y="AB1-BC1", error_y="std_err", width=500, title="AB1-BC1")
fig.show()
age_group = data[["AgeGroup", 'AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4',
'BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
col_names = ['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4', 'BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']
# add standard error for each accuracy
for name in col_names:
temp = grouped.describe()[name][["count", 'std']]
summary["se_" + name[:2] + name[-1]] = temp["std"] / np.sqrt(temp["count"])
# get AB, BC values from AgeGroup
child_AB = summary[summary["AgeGroup"] == "child"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
child_BC = summary[summary["AgeGroup"] == "child"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
ado_AB = summary[summary["AgeGroup"] == "adolescent"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
ado_BC = summary[summary["AgeGroup"] == "adolescent"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
y_adl_AB = summary[summary["AgeGroup"] == "younger adult"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
y_adl_BC = summary[summary["AgeGroup"] == "younger adult"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
o_adl_AB = summary[summary["AgeGroup"] == "older adult"][['AB_acc_1', 'AB_acc_2', 'AB_acc_3', 'AB_acc_4']].values[0]
o_adl_BC = summary[summary["AgeGroup"] == "older adult"][['BC_acc_1', 'BC_acc_2', 'BC_acc_3', 'BC_acc_4']].values[0]
# get standard error for AB, BC values from AgeGroup
child_AB_se = summary[summary["AgeGroup"] == "child"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
child_BC_se = summary[summary["AgeGroup"] == "child"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
ado_AB_se = summary[summary["AgeGroup"] == "adolescent"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
ado_BC_se = summary[summary["AgeGroup"] == "adolescent"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
y_adl_AB_se = summary[summary["AgeGroup"] == "younger adult"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
y_adl_BC_se = summary[summary["AgeGroup"] == "younger adult"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
o_adl_AB_se = summary[summary["AgeGroup"] == "older adult"][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
o_adl_BC_se = summary[summary["AgeGroup"] == "older adult"][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
# make 2x2 subplots
fig = make_subplots(rows=2, cols=2,
subplot_titles=("Children", "Adolescent", "Younger Adults", "Older Adults"))
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=child_AB_se)), row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=child_BC_se)),row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=ado_AB_se)),row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=ado_BC_se)),row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=y_adl_AB_se)),row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=y_adl_BC_se)),row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_AB, name="AB",
line=dict(color='firebrick', width=2),
error_y=dict(array=o_adl_AB_se)),row=2, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_BC, name="BC",
line=dict(color='royalblue', width=2),
error_y=dict(array=o_adl_BC_se)),row=2, col=2)
fig.update_layout(width=850, height=600)
fig.show()
age_group = data[['AgeGroup', 'AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4', 'BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']]
age_group.head()
grouped = age_group.groupby('AgeGroup', as_index=False)
summary = grouped.mean()
summary
col_names = ['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4', 'BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']
for name in col_names:
temp = grouped.describe()[name][['count', 'std']]
summary['se_' + name[:2] + name[-1]] = temp['std'] / np.sqrt(temp['count'])
child_AB = summary[summary['AgeGroup'] == 'child'][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
child_BC = summary[summary['AgeGroup'] == 'child'][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
ado_AB = summary[summary['AgeGroup'] == 'adolescent'][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
ado_BC = summary[summary['AgeGroup'] == 'adolescent'][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
y_adl_AB = summary[summary['AgeGroup'] == 'younger adult'][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
y_adl_BC = summary[summary['AgeGroup'] == 'younger adult'][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
o_adl_AB = summary[summary['AgeGroup'] == 'older adult'][['AB_rt_1', 'AB_rt_2', 'AB_rt_3', 'AB_rt_4']].values[0]
o_adl_BC = summary[summary['AgeGroup'] == 'older adult'][['BC_rt_1', 'BC_rt_2', 'BC_rt_3', 'BC_rt_4']].values[0]
child_AB_se = summary[summary['AgeGroup'] == 'child'][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
child_BC_se = summary[summary['AgeGroup'] == 'child'][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
ado_AB_se = summary[summary['AgeGroup'] == 'adolescent'][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
ado_BC_se = summary[summary['AgeGroup'] == 'adolescent'][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
y_adl_AB_se = summary[summary['AgeGroup'] == 'younger adult'][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
y_adl_BC_se = summary[summary['AgeGroup'] == 'younger adult'][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
o_adl_AB_se = summary[summary['AgeGroup'] == 'older adult'][['se_AB1', 'se_AB2', 'se_AB3', 'se_AB4']].values[0]
o_adl_BC_se = summary[summary['AgeGroup'] == 'older adult'][['se_BC1', 'se_BC2', 'se_BC3', 'se_BC4']].values[0]
fig = make_subplots(rows=2, cols=2, subplot_titles=('Children', 'Adolescent', 'Younger Adults', 'Older Adults'))
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_AB, name='AB', line=dict(color='firebrick', width=2), error_y=dict(array=child_AB_se)), row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=child_BC, name='BC', line=dict(color='royalblue', width=2), error_y=dict(array=child_BC_se)), row=1, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_AB, name='AB', line=dict(color='firebrick', width=2), error_y=dict(array=ado_AB_se)), row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=ado_BC, name='BC', line=dict(color='royalblue', width=2), error_y=dict(array=ado_BC_se)), row=1, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_AB, name='AB', line=dict(color='firebrick', width=2), error_y=dict(array=y_adl_AB_se)), row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=y_adl_BC, name='BC', line=dict(color='royalblue', width=2), error_y=dict(array=y_adl_BC_se)), row=2, col=1)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_AB, name='AB', line=dict(color='firebrick', width=2), error_y=dict(array=o_adl_AB_se)), row=2, col=2)
fig.add_trace(go.Scatter(x=['1', '2', '3', '4'], y=o_adl_BC, name='BC', line=dict(color='royalblue', width=2), error_y=dict(array=o_adl_BC_se)), row=2, col=2)
fig.update_layout(width=850, height=600)
fig.show() | code |
32068445/cell_9 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
from statistics import mean
import copy
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense, Activation
from sklearn.preprocessing import StandardScaler
sns.set(rc={'figure.figsize': (11, 8)})
pd.set_option('display.max_rows', 20)
pd.set_option('display.max_columns', 20)
pd.set_option('display.width', 1000)
plt.rcParams['axes.grid'] = False
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(dirname, filename))
lotteryPath = paths[0]
lottery = pd.read_csv(lotteryPath, encoding='latin-1')
lottery
all_balls = {}
for i in range(1, 7):
ball_ser = lottery['Ball ' + str(i)].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
ball_ser = lottery['Extra Ball'].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
all_balls = pd.Series(all_balls)
plt.xticks(rotation=0)
f, axes = plt.subplots(7, 1)
f.tight_layout()
for i in range(1, 7):
ball_dist = lottery['Ball ' + str(i)].value_counts().sort_index()
axes[i - 1].set_title('Distribution of ball ' + str(i))
plt.xticks(rotation=90)
sns.barplot(x=ball_dist.keys(), y=ball_dist.values, ax=axes[i - 1], palette='PuBuGn_d')
ball_dist = lottery['Extra Ball'].value_counts().sort_index()
axes[6].set_title('Distribution of extra ball')
plt.xticks(rotation=90)
sns.barplot(x=ball_dist.keys(), y=ball_dist.values, ax=axes[6], palette='PuBuGn_d') | code |
32068445/cell_4 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
from statistics import mean
import copy
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense, Activation
from sklearn.preprocessing import StandardScaler
sns.set(rc={'figure.figsize': (11, 8)})
pd.set_option('display.max_rows', 20)
pd.set_option('display.max_columns', 20)
pd.set_option('display.width', 1000)
plt.rcParams['axes.grid'] = False
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(dirname, filename))
lotteryPath = paths[0]
lottery = pd.read_csv(lotteryPath, encoding='latin-1')
lottery | code |
32068445/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
from statistics import mean
import copy
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense, Activation
from sklearn.preprocessing import StandardScaler
sns.set(rc={'figure.figsize': (11, 8)})
pd.set_option('display.max_rows', 20)
pd.set_option('display.max_columns', 20)
pd.set_option('display.width', 1000)
plt.rcParams['axes.grid'] = False
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(dirname, filename))
lotteryPath = paths[0]
lottery = pd.read_csv(lotteryPath, encoding='latin-1')
lottery
all_balls = {}
for i in range(1, 7):
ball_ser = lottery['Ball ' + str(i)].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
ball_ser = lottery['Extra Ball'].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
all_balls = pd.Series(all_balls)
plt.title('Distribution of all balls')
plt.xticks(rotation=0)
sns.barplot(x=all_balls.keys(), y=all_balls.values, palette='OrRd') | code |
32068445/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datetime import datetime, timedelta, date
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
from statistics import mean
import copy
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense, Activation
from sklearn.preprocessing import StandardScaler
sns.set(rc={'figure.figsize': (11, 8)})
pd.set_option('display.max_rows', 20)
pd.set_option('display.max_columns', 20)
pd.set_option('display.width', 1000)
plt.rcParams['axes.grid'] = False
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(dirname, filename))
lotteryPath = paths[0]
lottery = pd.read_csv(lotteryPath, encoding='latin-1')
lottery
all_balls = {}
for i in range(1, 7):
ball_ser = lottery['Ball ' + str(i)].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
ball_ser = lottery['Extra Ball'].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
all_balls = pd.Series(all_balls)
plt.xticks(rotation=0)
# Visualize the distributions of each ball
f, axes = plt.subplots(7, 1)
f.tight_layout()
for i in range(1,7):
ball_dist = lottery['Ball ' +str(i)].value_counts().sort_index()
axes[i-1].set_title('Distribution of ball '+str(i))
plt.xticks(rotation=90)
sns.barplot(x=ball_dist.keys(), y=ball_dist.values, ax=axes[i-1], palette="PuBuGn_d")
ball_dist = lottery['Extra Ball'].value_counts().sort_index()
axes[6].set_title('Distribution of extra ball')
plt.xticks(rotation=90)
sns.barplot(x=ball_dist.keys(), y=ball_dist.values, ax=axes[6], palette="PuBuGn_d")
# Correlation matrix
def plotCorrelationMatrix(df, graphWidth):
#filename = df.dataframeName
df = df.dropna('columns') # drop columns with NaN
del df['Draw Number']
df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values
if df.shape[1] < 2:
print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')
return
corr = df.corr('pearson')
plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum = 1,cmap = "BuGn")
plt.xticks(range(len(corr.columns)), corr.columns, rotation=0)
plt.yticks(range(len(corr.columns)), corr.columns)
plt.gca().xaxis.tick_bottom()
plt.colorbar(corrMat)
plt.title(f'Correlation Matrix', fontsize=15)
plt.show()
plotCorrelationMatrix(lottery, 8)
def getDate(strDate):
return datetime.strptime(strDate, '%Y-%m-%d').date()
allMonthsData = []
class MonthData:
def __init__(self, date, ballsDict):
self.date = date
self.ballsDict = ballsDict
def generateStatsForDraws(draws, drawDate):
if draws.empty == False:
currentBalls = {}
del draws['Date']
del draws['Draw Number']
del draws['Jackpot']
balls_list = draws.values.T.tolist()
balls_flat_list = [item for sublist in balls_list for item in sublist]
for i in range(1, 43):
currentBalls[i] = balls_flat_list.count(i)
data = MonthData(drawDate, currentBalls)
allMonthsData.append(data)
def plotBallsInMonths(index):
all_balls = pd.Series(allMonthsData[index].ballsDict)
plt.xticks(rotation=0)
ball_month = pd.DataFrame()
initDate = getDate(lottery['Date'][0])
currentMonth = initDate.month
currentYear = initDate.year
def getOccurencesPerMonth():
global ball_month
global currentMonth
global currentYear
for index, draw in lottery.iterrows():
drawDate = getDate(draw['Date'])
if drawDate.month == currentMonth and drawDate.year == currentYear:
ball_month = ball_month.append(draw)
else:
generateStatsForDraws(ball_month, drawDate)
ball_month = pd.DataFrame()
currentMonth = currentMonth % 12 + 1
if currentYear != drawDate.year:
currentYear = drawDate.year
ball_dataset = pd.DataFrame(columns=['Year', 'Ball Number', 'Occurences'])
ball_dataset['Year'] = pd.to_numeric(ball_dataset['Year'])
ball_dataset['Ball Number'] = pd.to_numeric(ball_dataset['Ball Number'])
ball_dataset['Occurences'] = pd.to_numeric(ball_dataset['Occurences'])
def generateYearStatsForDraws(draws, drawDate):
global ball_dataset
if draws.empty == False:
currentBalls = {}
del draws['Date']
balls_list = draws.values.T.tolist()
balls_flat_list = [item for sublist in balls_list for item in sublist]
for i in range(1, 43):
currentBalls['Year'] = int(drawDate.year)
currentBalls['Ball Number'] = int(i)
currentBalls['Occurences'] = int(balls_flat_list.count(i))
ball_at_year = pd.Series(currentBalls)
currentBalls = {}
ball_dataset = ball_dataset.append(ball_at_year, ignore_index=True)
ball_month = pd.DataFrame()
initDate = getDate(lottery['Date'][0])
currentMonth = initDate.month
currentYear = initDate.year
for index, draw in lottery.iterrows():
del draw['Draw Number']
del draw['Jackpot']
drawDate = getDate(draw['Date'])
if drawDate.month == currentMonth and drawDate.year == currentYear:
ball_month = ball_month.append(draw)
else:
currentMonth = currentMonth % 12 + 1
if currentYear != drawDate.year:
generateYearStatsForDraws(ball_month, drawDate)
ball_month = pd.DataFrame()
currentYear = drawDate.year
print(ball_dataset) | code |
32068445/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from datetime import datetime, timedelta, date
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
from statistics import mean
import copy
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense, Activation
from sklearn.preprocessing import StandardScaler
sns.set(rc={'figure.figsize': (11, 8)})
pd.set_option('display.max_rows', 20)
pd.set_option('display.max_columns', 20)
pd.set_option('display.width', 1000)
plt.rcParams['axes.grid'] = False
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(dirname, filename))
lotteryPath = paths[0]
lottery = pd.read_csv(lotteryPath, encoding='latin-1')
lottery
all_balls = {}
for i in range(1, 7):
ball_ser = lottery['Ball ' + str(i)].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
ball_ser = lottery['Extra Ball'].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
all_balls = pd.Series(all_balls)
plt.xticks(rotation=0)
# Visualize the distributions of each ball
f, axes = plt.subplots(7, 1)
f.tight_layout()
for i in range(1,7):
ball_dist = lottery['Ball ' +str(i)].value_counts().sort_index()
axes[i-1].set_title('Distribution of ball '+str(i))
plt.xticks(rotation=90)
sns.barplot(x=ball_dist.keys(), y=ball_dist.values, ax=axes[i-1], palette="PuBuGn_d")
ball_dist = lottery['Extra Ball'].value_counts().sort_index()
axes[6].set_title('Distribution of extra ball')
plt.xticks(rotation=90)
sns.barplot(x=ball_dist.keys(), y=ball_dist.values, ax=axes[6], palette="PuBuGn_d")
# Correlation matrix
def plotCorrelationMatrix(df, graphWidth):
#filename = df.dataframeName
df = df.dropna('columns') # drop columns with NaN
del df['Draw Number']
df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values
if df.shape[1] < 2:
print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')
return
corr = df.corr('pearson')
plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum = 1,cmap = "BuGn")
plt.xticks(range(len(corr.columns)), corr.columns, rotation=0)
plt.yticks(range(len(corr.columns)), corr.columns)
plt.gca().xaxis.tick_bottom()
plt.colorbar(corrMat)
plt.title(f'Correlation Matrix', fontsize=15)
plt.show()
plotCorrelationMatrix(lottery, 8)
allMonthsData = []
class MonthData:
def __init__(self, date, ballsDict):
self.date = date
self.ballsDict = ballsDict
def generateStatsForDraws(draws, drawDate):
if draws.empty == False:
currentBalls = {}
del draws['Date']
del draws['Draw Number']
del draws['Jackpot']
balls_list = draws.values.T.tolist()
balls_flat_list = [item for sublist in balls_list for item in sublist]
for i in range(1, 43):
currentBalls[i] = balls_flat_list.count(i)
data = MonthData(drawDate, currentBalls)
allMonthsData.append(data)
def plotBallsInMonths(index):
all_balls = pd.Series(allMonthsData[index].ballsDict)
plt.xticks(rotation=0)
plotBallsInMonths(50) | code |
32068445/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
from statistics import mean
import copy
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense, Activation
from sklearn.preprocessing import StandardScaler
sns.set(rc={'figure.figsize': (11, 8)})
pd.set_option('display.max_rows', 20)
pd.set_option('display.max_columns', 20)
pd.set_option('display.width', 1000)
plt.rcParams['axes.grid'] = False
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(dirname, filename)) | code |
32068445/cell_31 | [
"image_output_1.png"
] | from datetime import datetime, timedelta, date
import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
from statistics import mean
import copy
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense, Activation
from sklearn.preprocessing import StandardScaler
sns.set(rc={'figure.figsize': (11, 8)})
pd.set_option('display.max_rows', 20)
pd.set_option('display.max_columns', 20)
pd.set_option('display.width', 1000)
plt.rcParams['axes.grid'] = False
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(dirname, filename))
lotteryPath = paths[0]
lottery = pd.read_csv(lotteryPath, encoding='latin-1')
lottery
all_balls = {}
for i in range(1, 7):
ball_ser = lottery['Ball ' + str(i)].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
ball_ser = lottery['Extra Ball'].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
all_balls = pd.Series(all_balls)
plt.xticks(rotation=0)
# Visualize the distributions of each ball
f, axes = plt.subplots(7, 1)
f.tight_layout()
for i in range(1,7):
ball_dist = lottery['Ball ' +str(i)].value_counts().sort_index()
axes[i-1].set_title('Distribution of ball '+str(i))
plt.xticks(rotation=90)
sns.barplot(x=ball_dist.keys(), y=ball_dist.values, ax=axes[i-1], palette="PuBuGn_d")
ball_dist = lottery['Extra Ball'].value_counts().sort_index()
axes[6].set_title('Distribution of extra ball')
plt.xticks(rotation=90)
sns.barplot(x=ball_dist.keys(), y=ball_dist.values, ax=axes[6], palette="PuBuGn_d")
# Correlation matrix
def plotCorrelationMatrix(df, graphWidth):
#filename = df.dataframeName
df = df.dropna('columns') # drop columns with NaN
del df['Draw Number']
df = df[[col for col in df if df[col].nunique() > 1]] # keep columns where there are more than 1 unique values
if df.shape[1] < 2:
print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')
return
corr = df.corr('pearson')
plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum = 1,cmap = "BuGn")
plt.xticks(range(len(corr.columns)), corr.columns, rotation=0)
plt.yticks(range(len(corr.columns)), corr.columns)
plt.gca().xaxis.tick_bottom()
plt.colorbar(corrMat)
plt.title(f'Correlation Matrix', fontsize=15)
plt.show()
plotCorrelationMatrix(lottery, 8)
def getDate(strDate):
return datetime.strptime(strDate, '%Y-%m-%d').date()
allMonthsData = []
class MonthData:
def __init__(self, date, ballsDict):
self.date = date
self.ballsDict = ballsDict
def generateStatsForDraws(draws, drawDate):
if draws.empty == False:
currentBalls = {}
del draws['Date']
del draws['Draw Number']
del draws['Jackpot']
balls_list = draws.values.T.tolist()
balls_flat_list = [item for sublist in balls_list for item in sublist]
for i in range(1, 43):
currentBalls[i] = balls_flat_list.count(i)
data = MonthData(drawDate, currentBalls)
allMonthsData.append(data)
def plotBallsInMonths(index):
all_balls = pd.Series(allMonthsData[index].ballsDict)
plt.xticks(rotation=0)
ball_month = pd.DataFrame()
initDate = getDate(lottery['Date'][0])
currentMonth = initDate.month
currentYear = initDate.year
def getOccurencesPerMonth():
global ball_month
global currentMonth
global currentYear
for index, draw in lottery.iterrows():
drawDate = getDate(draw['Date'])
if drawDate.month == currentMonth and drawDate.year == currentYear:
ball_month = ball_month.append(draw)
else:
generateStatsForDraws(ball_month, drawDate)
ball_month = pd.DataFrame()
currentMonth = currentMonth % 12 + 1
if currentYear != drawDate.year:
currentYear = drawDate.year
ball_dataset = pd.DataFrame(columns=['Year', 'Ball Number', 'Occurences'])
ball_dataset['Year'] = pd.to_numeric(ball_dataset['Year'])
ball_dataset['Ball Number'] = pd.to_numeric(ball_dataset['Ball Number'])
ball_dataset['Occurences'] = pd.to_numeric(ball_dataset['Occurences'])
def generateYearStatsForDraws(draws, drawDate):
global ball_dataset
if draws.empty == False:
currentBalls = {}
del draws['Date']
balls_list = draws.values.T.tolist()
balls_flat_list = [item for sublist in balls_list for item in sublist]
for i in range(1, 43):
currentBalls['Year'] = int(drawDate.year)
currentBalls['Ball Number'] = int(i)
currentBalls['Occurences'] = int(balls_flat_list.count(i))
ball_at_year = pd.Series(currentBalls)
currentBalls = {}
ball_dataset = ball_dataset.append(ball_at_year, ignore_index=True)
ball_month = pd.DataFrame()
initDate = getDate(lottery['Date'][0])
currentMonth = initDate.month
currentYear = initDate.year
for index, draw in lottery.iterrows():
del draw['Draw Number']
del draw['Jackpot']
drawDate = getDate(draw['Date'])
if drawDate.month == currentMonth and drawDate.year == currentYear:
ball_month = ball_month.append(draw)
else:
currentMonth = currentMonth % 12 + 1
if currentYear != drawDate.year:
generateYearStatsForDraws(ball_month, drawDate)
ball_month = pd.DataFrame()
currentYear = drawDate.year
balls = ball_dataset.pivot('Ball Number', 'Year', 'Occurences')
f, ax = plt.subplots(figsize=(18, 18))
plt.title('Occurence of Each Ball per Year')
sns.heatmap(balls, annot=True, fmt='d', linewidths=0.0, ax=ax) | code |
32068445/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import seaborn as sns
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
from datetime import datetime, timedelta, date
from statistics import mean
import copy
from keras.models import Sequential
from keras.layers import LSTM, Dropout, Dense, Activation
from sklearn.preprocessing import StandardScaler
sns.set(rc={'figure.figsize': (11, 8)})
pd.set_option('display.max_rows', 20)
pd.set_option('display.max_columns', 20)
pd.set_option('display.width', 1000)
plt.rcParams['axes.grid'] = False
import os
paths = []
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
paths.append(os.path.join(dirname, filename))
lotteryPath = paths[0]
lottery = pd.read_csv(lotteryPath, encoding='latin-1')
lottery
all_balls = {}
for i in range(1, 7):
ball_ser = lottery['Ball ' + str(i)].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
ball_ser = lottery['Extra Ball'].value_counts()
for key in ball_ser.keys():
all_balls[key] = all_balls.get(key, 0) + ball_ser[key]
all_balls = pd.Series(all_balls)
plt.xticks(rotation=0)
# Visualize the distributions of each ball
f, axes = plt.subplots(7, 1)
f.tight_layout()
for i in range(1,7):
ball_dist = lottery['Ball ' +str(i)].value_counts().sort_index()
axes[i-1].set_title('Distribution of ball '+str(i))
plt.xticks(rotation=90)
sns.barplot(x=ball_dist.keys(), y=ball_dist.values, ax=axes[i-1], palette="PuBuGn_d")
ball_dist = lottery['Extra Ball'].value_counts().sort_index()
axes[6].set_title('Distribution of extra ball')
plt.xticks(rotation=90)
sns.barplot(x=ball_dist.keys(), y=ball_dist.values, ax=axes[6], palette="PuBuGn_d")
def plotCorrelationMatrix(df, graphWidth):
df = df.dropna('columns')
del df['Draw Number']
df = df[[col for col in df if df[col].nunique() > 1]]
if df.shape[1] < 2:
print(f'No correlation plots shown: The number of non-NaN or constant columns ({df.shape[1]}) is less than 2')
return
corr = df.corr('pearson')
plt.figure(num=None, figsize=(graphWidth, graphWidth), dpi=80, facecolor='w', edgecolor='k')
corrMat = plt.matshow(corr, fignum=1, cmap='BuGn')
plt.xticks(range(len(corr.columns)), corr.columns, rotation=0)
plt.yticks(range(len(corr.columns)), corr.columns)
plt.gca().xaxis.tick_bottom()
plt.colorbar(corrMat)
plt.title(f'Correlation Matrix', fontsize=15)
plt.show()
plotCorrelationMatrix(lottery, 8) | code |
73089289/cell_9 | [
"text_html_output_1.png"
] | from keras.applications.xception import Xception, preprocess_input, decode_predictions
from skimage.io import imread_collection, imread
import numpy as np
import numpy as np
import os
import pandas as pd
import pandas as pd
path = '../input/pneumoniamulti/pneumonia-multi2/'
image_names = os.listdir(path)
gray_images = [imread(path + str(name) + '') for name in image_names]
images = np.zeros((len(gray_images), gray_images[0].shape[0], gray_images[0].shape[1], 3))
for i, im in enumerate(gray_images):
for j in range(3):
images[i, :, :, j] = im
pretrained = Xception(weights='imagenet', include_top=False, pooling='avg')
pretrained.summary()
x = preprocess_input(images)
features = pretrained.predict(x)
nome_file = 'xcption_covid_sseg'
matriz_csv = pd.DataFrame(features)
matriz_csv | code |
73089289/cell_4 | [
"text_plain_output_1.png"
] | from keras.applications.xception import Xception, preprocess_input, decode_predictions
pretrained = Xception(weights='imagenet', include_top=False, pooling='avg') | code |
73089289/cell_2 | [
"text_plain_output_1.png"
] | from skimage.io import imread_collection, imread
import os
path = '../input/pneumoniamulti/pneumonia-multi2/'
image_names = os.listdir(path)
gray_images = [imread(path + str(name) + '') for name in image_names]
print('The database has {} segmented images'.format(len(gray_images))) | code |
73089289/cell_7 | [
"text_plain_output_1.png"
] | from keras.applications.xception import Xception, preprocess_input, decode_predictions
from skimage.io import imread_collection, imread
import numpy as np
import numpy as np
import os
path = '../input/pneumoniamulti/pneumonia-multi2/'
image_names = os.listdir(path)
gray_images = [imread(path + str(name) + '') for name in image_names]
images = np.zeros((len(gray_images), gray_images[0].shape[0], gray_images[0].shape[1], 3))
for i, im in enumerate(gray_images):
for j in range(3):
images[i, :, :, j] = im
pretrained = Xception(weights='imagenet', include_top=False, pooling='avg')
pretrained.summary()
x = preprocess_input(images)
features = pretrained.predict(x)
features | code |
73089289/cell_5 | [
"text_plain_output_1.png"
] | from keras.applications.xception import Xception, preprocess_input, decode_predictions
pretrained = Xception(weights='imagenet', include_top=False, pooling='avg')
pretrained.summary() | code |
18124360/cell_13 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import sys
test_df = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')
test_df['id_code'] = test_df['id_code'].apply(lambda x: x + '.png')
diag_text = ['Normal', 'Mild', 'Moderate', 'Severe', 'Proliferative']
num_classes = 5
def display_raw_images(df, columns = 4, rows = 3):
fig=plt.figure(figsize = (5 * columns, 4 * rows))
for i in range(columns * rows):
image_name = df.loc[i,'id_code']
img = cv2.imread(f'../input/aptos2019-blindness-detection/test_images/{image_name}')[...,[2, 1, 0]]
fig.add_subplot(rows, columns, i + 1)
plt.imshow(img)
plt.tight_layout()
display_raw_images(test_df)
sys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))
from efficientnet import EfficientNetB5
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, Callback
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.models import Model, Sequential
from keras.optimizers import Adam
def create_resnet50_model(input_shape, n_out):
base_model = ResNet50(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_inception_v3_model(input_shape, n_out):
base_model = InceptionV3(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_xception_model(input_shape, n_out):
base_model = Xception(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/xception_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_densenet121_model(input_shape, n_out):
base_model = DenseNet121(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/densenet-keras/DenseNet-BC-121-32-no-top.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_effnetB5_model(input_shape, n_out):
base_model = EfficientNetB5(weights=sys.path.append(os.path.abspath('../input/efficientnet-keras-weights-b0b5/efficientnet-b5_imagenet_1000_notop.h5')), include_top=False, input_shape=input_shape)
model = Sequential()
model.add(base_model)
model.add(Dropout(0.25))
model.add(Dense(1024))
model.add(LeakyReLU())
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
IMAGE_HEIGHT = 340
IMAGE_WIDTH = 340
model = create_effnetB5_model(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), n_out=num_classes)
model.summary()
PRETRAINED_MODEL = '../input/efficientnetb5-blindness-detector/blindness_detector_bestqwk.h5'
if os.path.exists(PRETRAINED_MODEL):
model.load_weights(PRETRAINED_MODEL)
from tqdm import tqdm_notebook as tqdm
submit = pd.read_csv('../input/aptos2019-blindness-detection/sample_submission.csv')
predicted = []
for i, name in tqdm(enumerate(submit['id_code'])):
path = os.path.join('../input/aptos2019-blindness-detection/test_images/', name + '.png')
image = cv2.imread(path)
image = cv2.resize(image, (IMAGE_HEIGHT, IMAGE_WIDTH))
X = np.array(image[np.newaxis] / 255)
raw_prediction = model.predict(X) > 0.5
prediction = raw_prediction.astype(int).sum(axis=1) - 1
predicted.append(prediction[0]) | code |
18124360/cell_2 | [
"text_plain_output_1.png"
] | import tensorflow
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import cv2
import os
import sys
print(os.listdir('../input')) | code |
18124360/cell_11 | [
"image_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import os
import pandas as pd
import sys
test_df = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')
test_df['id_code'] = test_df['id_code'].apply(lambda x: x + '.png')
diag_text = ['Normal', 'Mild', 'Moderate', 'Severe', 'Proliferative']
num_classes = 5
sys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))
from efficientnet import EfficientNetB5
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, Callback
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.models import Model, Sequential
from keras.optimizers import Adam
def create_resnet50_model(input_shape, n_out):
base_model = ResNet50(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_inception_v3_model(input_shape, n_out):
base_model = InceptionV3(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_xception_model(input_shape, n_out):
base_model = Xception(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/xception_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_densenet121_model(input_shape, n_out):
base_model = DenseNet121(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/densenet-keras/DenseNet-BC-121-32-no-top.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_effnetB5_model(input_shape, n_out):
base_model = EfficientNetB5(weights=sys.path.append(os.path.abspath('../input/efficientnet-keras-weights-b0b5/efficientnet-b5_imagenet_1000_notop.h5')), include_top=False, input_shape=input_shape)
model = Sequential()
model.add(base_model)
model.add(Dropout(0.25))
model.add(Dense(1024))
model.add(LeakyReLU())
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
IMAGE_HEIGHT = 340
IMAGE_WIDTH = 340
model = create_effnetB5_model(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), n_out=num_classes)
model.summary()
PRETRAINED_MODEL = '../input/efficientnetb5-blindness-detector/blindness_detector_bestqwk.h5'
if os.path.exists(PRETRAINED_MODEL):
print('Restoring model from ' + PRETRAINED_MODEL)
model.load_weights(PRETRAINED_MODEL)
else:
print('No pretrained model found. Using fresh model.') | code |
18124360/cell_7 | [
"text_html_output_1.png"
] | import os
import sys
print(os.listdir('../input/efficientnet/efficientnet-master/efficientnet-master/efficientnet'))
sys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))
from efficientnet import EfficientNetB5 | code |
18124360/cell_15 | [
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import cv2
import matplotlib.pyplot as plt
import numpy as np
import os
import pandas as pd
import sys
test_df = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')
test_df['id_code'] = test_df['id_code'].apply(lambda x: x + '.png')
diag_text = ['Normal', 'Mild', 'Moderate', 'Severe', 'Proliferative']
num_classes = 5
def display_raw_images(df, columns = 4, rows = 3):
fig=plt.figure(figsize = (5 * columns, 4 * rows))
for i in range(columns * rows):
image_name = df.loc[i,'id_code']
img = cv2.imread(f'../input/aptos2019-blindness-detection/test_images/{image_name}')[...,[2, 1, 0]]
fig.add_subplot(rows, columns, i + 1)
plt.imshow(img)
plt.tight_layout()
display_raw_images(test_df)
sys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))
from efficientnet import EfficientNetB5
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, Callback
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.models import Model, Sequential
from keras.optimizers import Adam
def create_resnet50_model(input_shape, n_out):
base_model = ResNet50(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_inception_v3_model(input_shape, n_out):
base_model = InceptionV3(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_xception_model(input_shape, n_out):
base_model = Xception(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/xception_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_densenet121_model(input_shape, n_out):
base_model = DenseNet121(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/densenet-keras/DenseNet-BC-121-32-no-top.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_effnetB5_model(input_shape, n_out):
base_model = EfficientNetB5(weights=sys.path.append(os.path.abspath('../input/efficientnet-keras-weights-b0b5/efficientnet-b5_imagenet_1000_notop.h5')), include_top=False, input_shape=input_shape)
model = Sequential()
model.add(base_model)
model.add(Dropout(0.25))
model.add(Dense(1024))
model.add(LeakyReLU())
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
IMAGE_HEIGHT = 340
IMAGE_WIDTH = 340
model = create_effnetB5_model(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), n_out=num_classes)
model.summary()
PRETRAINED_MODEL = '../input/efficientnetb5-blindness-detector/blindness_detector_bestqwk.h5'
if os.path.exists(PRETRAINED_MODEL):
model.load_weights(PRETRAINED_MODEL)
from tqdm import tqdm_notebook as tqdm
submit = pd.read_csv('../input/aptos2019-blindness-detection/sample_submission.csv')
predicted = []
for i, name in tqdm(enumerate(submit['id_code'])):
path = os.path.join('../input/aptos2019-blindness-detection/test_images/', name + '.png')
image = cv2.imread(path)
image = cv2.resize(image, (IMAGE_HEIGHT, IMAGE_WIDTH))
X = np.array(image[np.newaxis] / 255)
raw_prediction = model.predict(X) > 0.5
prediction = raw_prediction.astype(int).sum(axis=1) - 1
predicted.append(prediction[0])
submit['diagnosis'] = predicted
submit.to_csv('submission.csv', index=False)
submit.head(10) | code |
18124360/cell_10 | [
"text_plain_output_1.png"
] | from efficientnet import EfficientNetB5
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.models import Model, Sequential
import os
import pandas as pd
import sys
test_df = pd.read_csv('../input/aptos2019-blindness-detection/test.csv')
test_df['id_code'] = test_df['id_code'].apply(lambda x: x + '.png')
diag_text = ['Normal', 'Mild', 'Moderate', 'Severe', 'Proliferative']
num_classes = 5
sys.path.append(os.path.abspath('../input/efficientnet/efficientnet-master/efficientnet-master/'))
from efficientnet import EfficientNetB5
from keras.callbacks import EarlyStopping, ReduceLROnPlateau, ModelCheckpoint, Callback
from keras.layers import Dense, Dropout, GlobalAveragePooling2D, LeakyReLU
from keras.applications import DenseNet121, ResNet50, InceptionV3, Xception
from keras.models import Model, Sequential
from keras.optimizers import Adam
def create_resnet50_model(input_shape, n_out):
base_model = ResNet50(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_inception_v3_model(input_shape, n_out):
base_model = InceptionV3(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_xception_model(input_shape, n_out):
base_model = Xception(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/keras-pretrained-models/xception_weights_tf_dim_ordering_tf_kernels_notop.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_densenet121_model(input_shape, n_out):
base_model = DenseNet121(weights=None, include_top=False, input_shape=input_shape)
base_model.load_weights('../input/densenet-keras/DenseNet-BC-121-32-no-top.h5')
model = Sequential()
model.add(base_model)
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(2048, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
def create_effnetB5_model(input_shape, n_out):
base_model = EfficientNetB5(weights=sys.path.append(os.path.abspath('../input/efficientnet-keras-weights-b0b5/efficientnet-b5_imagenet_1000_notop.h5')), include_top=False, input_shape=input_shape)
model = Sequential()
model.add(base_model)
model.add(Dropout(0.25))
model.add(Dense(1024))
model.add(LeakyReLU())
model.add(GlobalAveragePooling2D())
model.add(Dropout(0.5))
model.add(Dense(n_out, activation='sigmoid'))
return model
IMAGE_HEIGHT = 340
IMAGE_WIDTH = 340
model = create_effnetB5_model(input_shape=(IMAGE_HEIGHT, IMAGE_WIDTH, 3), n_out=num_classes)
model.summary() | code |
105174125/cell_6 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import cv2
import numpy as np
import pandas as pd
import tensorflow as tf
import layoutparser as lp
import matplotlib.pyplot as plt
from PIL import Image
from pdf2image import convert_from_path
from paddleocr import PaddleOCR, draw_ocr | code |
104122172/cell_13 | [
"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
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
sns.scatterplot(x='Rank', y='Country', data=df, hue='Continent')
plt.legend(bbox_to_anchor=(1, 1), loc=2)
plt.show() | code |
104122172/cell_9 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna() | code |
104122172/cell_4 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.info() | code |
104122172/cell_6 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.describe() | code |
104122172/cell_11 | [
"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
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
sns.histplot(x='World Population Percentage', data=df)
plt.show() | code |
104122172/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
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 |
104122172/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)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum() | code |
104122172/cell_8 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull() | code |
104122172/cell_15 | [
"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
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
sns.pairplot(df, hue='Country', height=2) | code |
104122172/cell_16 | [
"text_html_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
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
df = np.random.rand(10, 12)
ax = sns.heatmap(df) | code |
104122172/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.head() | code |
104122172/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
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
sns.pairplot(df, hue='Continent', height=2) | code |
104122172/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna | code |
104122172/cell_12 | [
"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
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape
df.isnull().sum()
df.notnull()
df.dropna()
df.fillna
sns.boxplot(x='Rank', y='Country', data=df)
plt.show() | code |
104122172/cell_5 | [
"image_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/world-population-dataset/world_population.csv')
df.shape | code |
328803/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd
import math
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 500]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_frame.sum())
ambiguity_data = ambiguity_measure(indexed_names.groupby(level=['Year', 'Name']))
names_vs_years = ambiguity_data.unstack(level='Year')
yearly_ambiguity = ambiguity_data.groupby(level='Year')
print('Average ambiguity: %s' % str(ambiguity_data.mean()))
print('Average by year: %s' % str(yearly_ambiguity.mean()))
print('Most ambiguous by year: %s' % str(yearly_ambiguity.idxmax().apply(lambda x: x[1]))) | code |
328803/cell_5 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import pandas as pd
import math
import pandas as pd
names_data = pd.read_csv('../input/NationalNames.csv')
frequent_names = names_data[names_data['Count'] > 500]
indexed_names = frequent_names.set_index(['Year', 'Name'])['Count']
def ambiguity_measure(grouped_frame):
return 2 * (1 - grouped_frame.max() / grouped_frame.sum())
ambiguity_data = ambiguity_measure(indexed_names.groupby(level=['Year', 'Name']))
names_vs_years = ambiguity_data.unstack(level='Year')
yearly_ambiguity = ambiguity_data.groupby(level='Year')
potentially_ambiguous_names = names_vs_years[(names_vs_years > 0).any(axis=1)]
potentially_ambiguous_names.transpose().plot(figsize=(20, 10)) | code |
106203709/cell_13 | [
"text_plain_output_1.png"
] | import nltk
import pandas as pd
df = pd.read_csv('/kaggle/input/online-retails-sale-dataset/Online Retail.csv')
import nltk
sc = df.copy()[['StockCode', 'Description']]
sc.dropna(inplace=True)
sc.Description = sc.Description.str.lower()
items = sc.groupby('StockCode').Description.unique()
items = list(zip(items.index.tolist(), items.values.tolist()))
clean_items = []
for x in items:
temp = x[1]
if len(temp) > 1:
clean_items.append((x[0], x[1][0]))
else:
clean_items.append(x)
descs = [x[1].tolist() for x in clean_items]
tks = [nltk.word_tokenize(x[0]) for x in descs]
descs_p = []
for x in tks:
temp = nltk.pos_tag(x)
new_desc = []
for y in temp:
word, tag = y
if tag.startswith('N'):
new_desc.append(word)
else:
pass
descs_p.append(new_desc)
print(descs_p[:25]) | code |
106203709/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/online-retails-sale-dataset/Online Retail.csv')
for x in df.columns:
print(x)
print('\n')
print(df.size) | code |
106203709/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/online-retails-sale-dataset/Online Retail.csv')
print('Number of Unique Items {}'.format(df.StockCode.nunique()))
print('\n')
print(df.Description) | code |
106203709/cell_8 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/online-retails-sale-dataset/Online Retail.csv')
print('Number of duplicates: {}'.format(df.duplicated().sum()))
print('\n')
print(df.isnull().sum()) | code |
106203709/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('/kaggle/input/online-retails-sale-dataset/Online Retail.csv')
print(df.CustomerID.nunique())
print('\n')
print(df.Country.value_counts()) | code |
104121398/cell_13 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
sns.scatterplot(x=df['age'], y=df['sex'], hue=df['target']) | code |
104121398/cell_25 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
classifier = LogisticRegression(random_state=41)
classifier.fit(X_train, y_train) | code |
104121398/cell_4 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.info() | code |
104121398/cell_20 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
df[df['age'] >= 50]['target'].value_counts() * 100 / df.shape[0]
df[df['age'] < 50]['target'].value_counts() * 100 / df.shape[0] | code |
104121398/cell_6 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.describe() | code |
104121398/cell_29 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import cross_val_score
import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
df[df['age'] >= 50]['target'].value_counts() * 100 / df.shape[0]
df[df['age'] < 50]['target'].value_counts() * 100 / df.shape[0]
X = df.iloc[:, :12]
y = df.iloc[:, 12]
from sklearn.ensemble import RandomForestClassifier
rf_model = RandomForestClassifier(n_estimators=100)
rf_predictions = cross_val_predict(rf_model, X, y, cv=5)
print(confusion_matrix(y, rf_predictions))
rf_scores = cross_val_score(rf_model, X, y, scoring='recall', cv=5)
print('recall:', rf_scores.mean()) | code |
104121398/cell_26 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
classifier = LogisticRegression(random_state=41)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm | code |
104121398/cell_11 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
df[df['sex'] == 1]['target'].value_counts() | code |
104121398/cell_19 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
df[df['age'] >= 50]['target'].value_counts() * 100 / df.shape[0] | code |
104121398/cell_7 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df['sex'].value_counts() | code |
104121398/cell_8 | [
"text_plain_output_1.png"
] | import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
sns.countplot(x='sex', data=df) | code |
104121398/cell_15 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
females = df[df['sex'] == 1]['age'].value_counts()
females
plt.figure(figsize=(15, 15))
sns.barplot(x=females.index, y=females.values) | code |
104121398/cell_16 | [
"text_plain_output_1.png",
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
males = df[df['sex'] == 0]['age'].value_counts()
males | code |
104121398/cell_3 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.head() | code |
104121398/cell_17 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
females = df[df['sex'] == 1]['age'].value_counts()
females
males = df[df['sex'] == 0]['age'].value_counts()
males
plt.figure(figsize=(15, 15))
sns.barplot(x=males.index, y=males.values) | code |
104121398/cell_31 | [
"text_plain_output_1.png"
] | from sklearn import metrics
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
from sklearn.tree import DecisionTreeClassifier
classifier = LogisticRegression(random_state=41)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm
from sklearn.tree import DecisionTreeClassifier
clf = DecisionTreeClassifier(criterion='entropy', max_depth=5)
clf = clf.fit(X_train, y_train)
y_pred = clf.predict(X_test)
from sklearn import metrics
metrics.accuracy_score(y_test, y_pred) | code |
104121398/cell_14 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
females = df[df['sex'] == 1]['age'].value_counts()
females | code |
104121398/cell_10 | [
"text_html_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
plt.figure(figsize=(20, 10))
sns.heatmap(df.corr(), annot=True) | code |
104121398/cell_27 | [
"text_plain_output_1.png"
] | from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report
from sklearn.metrics import confusion_matrix
from sklearn.metrics import confusion_matrix
classifier = LogisticRegression(random_state=41)
classifier.fit(X_train, y_train)
y_pred = classifier.predict(X_test)
from sklearn.metrics import confusion_matrix
cm = confusion_matrix(y_test, y_pred)
cm
from sklearn.metrics import classification_report
cr = classification_report(y_test, y_pred)
print(cr) | code |
104121398/cell_12 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
df.drop(['exang'], axis=1, inplace=True)
df[df['sex'] == 0]['target'].value_counts() | code |
104121398/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
df = pd.read_csv('../input/heartdisease-dataset/heart.csv')
print(df.isnull().sum())
print(df.isnull().values.any())
print(df.isnull().values.sum()) | code |
106204307/cell_1 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sentence_transformers import SentenceTransformer, util
from typing import List, Union
import PIL
import clip
import os
import requests
import torch
from typing import List, Union
import torch
import clip
import PIL
from PIL import Image
import requests
import numpy as np
import os
from sentence_transformers import SentenceTransformer, util
class ZeroShotImageClassification:
def __init__(self, *args, **kwargs):
"""
Load CLIP models based on either language needs or vision backbone needs
With english labelling users have the liberty to choose different vision backbones
Multi-lingual labelling is only supported with ViT as vision backbone.
Args:
Model (`str`, *optional*, defaults to `ViT-B/32`):
Any one of the CNN or Transformer based pretrained models can be used as Vision backbone.
`RN50`, `RN101`, `RN50x4`, `RN50x16`, `RN50x64`, `ViT-B/32`, `ViT-B/16`, `ViT-L/14`
Lang (`str`, *optional*, defaults to `en`):
Any one of the language codes below
ar, bg, ca, cs, da, de, el, es, et, fa, fi, fr, fr-ca, gl, gu, he, hi, hr, hu,
hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, pt, pt-br,
ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh-cn, zh-tw.
"""
if 'lang' in kwargs:
self.lang = kwargs['lang']
else:
self.lang = 'en'
lang_codes = self.available_languages()
if self.lang not in lang_codes:
raise Exception('Language code {} not valid, supported codes are {} '.format(self.lang, lang_codes))
return
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
if self.lang == 'en':
model_tag = 'ViT-B/32'
if 'model' in kwargs:
model_tag = kwargs['model']
print('Loading OpenAI CLIP model {} ...'.format(model_tag))
self.model, self.preprocess = clip.load(model_tag, device=device)
print('Label language {} ...'.format(self.lang))
else:
model_tag = 'clip-ViT-B-32'
print('Loading sentence transformer model {} ...'.format(model_tag))
self.model = SentenceTransformer('clip-ViT-B-32', device=device)
self.text_model = SentenceTransformer('sentence-transformers/clip-ViT-B-32-multilingual-v1', device=device)
print('Label language {} ...'.format(self.lang))
def available_models(self):
"""Returns the names of available CLIP models"""
return clip.available_models()
def available_languages(self):
"""Returns the codes of available languages"""
codes = 'ar, bg, ca, cs, da, de, en, el, es, et, fa, fi, fr, fr-ca, gl, gu, he, hi, hr, hu, \n hy, id, it, ja, ka, ko, ku, lt, lv, mk, mn, mr, ms, my, nb, nl, pl, pt, pt, pt-br, \n ro, ru, sk, sl, sq, sr, sv, th, tr, uk, ur, vi, zh-cn, zh-tw'
return set([code.strip() for code in codes.split(',')])
def _load_image(self, image: str) -> 'PIL.Image.Image':
"""
Loads `image` to a PIL Image.
Args:
image (`str` ):
The image to convert to the PIL Image format.
Returns:
`PIL.Image.Image`: A PIL Image.
"""
if isinstance(image, str):
if image.startswith('http://') or image.startswith('https://'):
image = PIL.Image.open(requests.get(image, stream=True).raw)
elif os.path.isfile(image):
image = PIL.Image.open(image)
else:
raise ValueError(f'Incorrect path or url, URLs must start with `http://` or `https://`, and {image} is not a valid path')
elif isinstance(image, PIL.Image.Image):
image = image
else:
raise ValueError('Incorrect format used for image. Should be an url linking to an image, a local path, or a PIL image.')
image = PIL.ImageOps.exif_transpose(image)
image = image.convert('RGB')
return image
def __call__(self, image: str, candidate_labels: Union[str, List[str]], *args, **kwargs):
"""
Classify the image using the candidate labels given
Args:
image (`str`):
Fully Qualified path of a local image or URL of image
candidate_labels (`str` or `List[str]`):
The set of possible class labels to classify each sequence into. Can be a single label, a string of
comma-separated labels, or a list of labels.
hypothesis_template (`str`, *optional*, defaults to `"A photo of {}."`, if lang is default / `en`):
The template used to turn each label into a string. This template must include a {} or
similar syntax for the candidate label to be inserted into the template.
top_k (`int`, *optional*, defaults to 5):
The number of top labels that will be returned by the pipeline. If the provided number is higher than
the number of labels available in the model configuration, it will default to the number of labels.
Return:
A `dict` or a list of `dict`: Each result comes as a dictionary with the following keys:
- **image** (`str`) -- The image for which this is the output.
- **labels** (`List[str]`) -- The labels sorted by order of likelihood.
- **scores** (`List[float]`) -- The probabilities for each of the labels.
"""
device = 'cuda:0' if torch.cuda.is_available() else 'cpu'
if self.lang == 'en':
if 'hypothesis_template' in kwargs:
hypothesis_template = kwargs['hypothesis_template']
else:
hypothesis_template = 'A photo of {}'
if isinstance(candidate_labels, str):
labels = [hypothesis_template.format(candidate_label) for candidate_label in candidate_labels.split(',')]
else:
labels = [hypothesis_template.format(candidate_label) for candidate_label in candidate_labels]
else:
if 'hypothesis_template' in kwargs:
hypothesis_template = kwargs['hypothesis_template']
else:
hypothesis_template = '{}'
if isinstance(candidate_labels, str):
labels = [hypothesis_template.format(candidate_label) for candidate_label in candidate_labels.split(',')]
else:
labels = [hypothesis_template.format(candidate_label) for candidate_label in candidate_labels]
if 'top_k' in kwargs:
top_k = kwargs['top_k']
else:
top_k = len(labels)
if str(type(self.model)) == "<class 'clip.model.CLIP'>":
img = self.preprocess(self._load_image(image)).unsqueeze(0).to(device)
text = clip.tokenize(labels).to(device)
image_features = self.model.encode_image(img)
text_features = self.model.encode_text(text)
else:
image_features = torch.tensor(self.model.encode(self._load_image(image)))
text_features = torch.tensor(self.text_model.encode(labels))
sim_scores = util.cos_sim(text_features, image_features)
out = []
for sim_score in sim_scores:
out.append(sim_score.item() * 100)
probs = torch.tensor([out])
probs = probs.softmax(dim=-1).cpu().numpy()
scores = list(probs.flatten())
sorted_sl = sorted(zip(scores, candidate_labels), key=lambda t: t[0], reverse=True)
scores, candidate_labels = zip(*sorted_sl)
preds = {}
preds['image'] = image
preds['scores'] = scores
preds['labels'] = candidate_labels
return preds | code |
129024099/cell_6 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
dfx = df.copy()
cat = []
num = []
for n, d in dfx.items():
if d.dtype == 'object':
cat.append(n)
else:
num.append(n)
dfx = df.copy()
from sklearn.preprocessing import LabelEncoder, StandardScaler
le = LabelEncoder()
for i in cat:
dfx[i] = le.fit_transform(dfx[i])
ss = StandardScaler()
for i in num:
dfx[i] = ss.fit_transform(dfx[[i]])
import matplotlib.pyplot as plt
import seaborn as sns
corr = dfx.corr()
matrix = np.triu(corr)
X = df.drop(['price'], axis=1)
y = df[['price']]
cat = []
num = []
for n, d in X.items():
if d.dtype == 'object':
cat.append(n)
else:
num.append(n)
le = LabelEncoder()
for i in cat:
X[i] = le.fit_transform(X[i])
ss = StandardScaler()
for i in num:
X[i] = ss.fit_transform(X[[i]])
for i in cat:
X[i] = ss.fit_transform(X[[i]])
X.head() | code |
129024099/cell_2 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
dfx = df.copy()
cat = []
num = []
for n, d in dfx.items():
if d.dtype == 'object':
cat.append(n)
else:
num.append(n)
dfx = df.copy()
from sklearn.preprocessing import LabelEncoder, StandardScaler
le = LabelEncoder()
for i in cat:
dfx[i] = le.fit_transform(dfx[i])
ss = StandardScaler()
for i in num:
dfx[i] = ss.fit_transform(dfx[[i]])
import matplotlib.pyplot as plt
import seaborn as sns
corr = dfx.corr()
matrix = np.triu(corr)
plt.figure(figsize=(17, 7))
sns.heatmap(corr, annot=True, mask=matrix, fmt='.2f', cmap='inferno') | code |
129024099/cell_1 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
df.head() | code |
129024099/cell_8 | [
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_1.png"
] | from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error
from sklearn.model_selection import train_test_split, cross_validate
from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pprint
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
dfx = df.copy()
cat = []
num = []
for n, d in dfx.items():
if d.dtype == 'object':
cat.append(n)
else:
num.append(n)
dfx = df.copy()
from sklearn.preprocessing import LabelEncoder, StandardScaler
le = LabelEncoder()
for i in cat:
dfx[i] = le.fit_transform(dfx[i])
ss = StandardScaler()
for i in num:
dfx[i] = ss.fit_transform(dfx[[i]])
import matplotlib.pyplot as plt
import seaborn as sns
corr = dfx.corr()
matrix = np.triu(corr)
X = df.drop(['price'], axis=1)
y = df[['price']]
cat = []
num = []
for n, d in X.items():
if d.dtype == 'object':
cat.append(n)
else:
num.append(n)
le = LabelEncoder()
for i in cat:
X[i] = le.fit_transform(X[i])
ss = StandardScaler()
for i in num:
X[i] = ss.fit_transform(X[[i]])
for i in cat:
X[i] = ss.fit_transform(X[[i]])
import pprint
from sklearn.model_selection import train_test_split, cross_validate
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error
def model_eval(m, X, y):
a, d, s, f = train_test_split(X, y)
m.fit(a, s)
g = m.predict(d)
def model_cv(m, X, y):
scoring = ['neg_mean_absolute_error', 'neg_root_mean_squared_error', 'r2']
scores = cross_validate(m, X, y, scoring=scoring, cv=4, return_train_score=False)
X1 = X.copy()
X1['enginesize'].loc[np.random.randint(153, size=30)] = np.nan
X1.isnull().sum() | code |
129024099/cell_3 | [
"text_plain_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
dfx = df.copy()
cat = []
num = []
for n, d in dfx.items():
if d.dtype == 'object':
cat.append(n)
else:
num.append(n)
dfx = df.copy()
from sklearn.preprocessing import LabelEncoder, StandardScaler
le = LabelEncoder()
for i in cat:
dfx[i] = le.fit_transform(dfx[i])
ss = StandardScaler()
for i in num:
dfx[i] = ss.fit_transform(dfx[[i]])
import matplotlib.pyplot as plt
import seaborn as sns
corr = dfx.corr()
matrix = np.triu(corr)
abs(corr['price']).sort_values(ascending=False) | code |
129024099/cell_10 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error
from sklearn.model_selection import train_test_split, cross_validate
from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pprint
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
dfx = df.copy()
cat = []
num = []
for n, d in dfx.items():
if d.dtype == 'object':
cat.append(n)
else:
num.append(n)
dfx = df.copy()
from sklearn.preprocessing import LabelEncoder, StandardScaler
le = LabelEncoder()
for i in cat:
dfx[i] = le.fit_transform(dfx[i])
ss = StandardScaler()
for i in num:
dfx[i] = ss.fit_transform(dfx[[i]])
import matplotlib.pyplot as plt
import seaborn as sns
corr = dfx.corr()
matrix = np.triu(corr)
X = df.drop(['price'], axis=1)
y = df[['price']]
cat = []
num = []
for n, d in X.items():
if d.dtype == 'object':
cat.append(n)
else:
num.append(n)
le = LabelEncoder()
for i in cat:
X[i] = le.fit_transform(X[i])
ss = StandardScaler()
for i in num:
X[i] = ss.fit_transform(X[[i]])
for i in cat:
X[i] = ss.fit_transform(X[[i]])
import pprint
from sklearn.model_selection import train_test_split, cross_validate
from sklearn.linear_model import LinearRegression
from sklearn.metrics import mean_absolute_error, r2_score, mean_squared_error
def model_eval(m, X, y):
a, d, s, f = train_test_split(X, y)
m.fit(a, s)
g = m.predict(d)
def model_cv(m, X, y):
scoring = ['neg_mean_absolute_error', 'neg_root_mean_squared_error', 'r2']
scores = cross_validate(m, X, y, scoring=scoring, cv=4, return_train_score=False)
X1 = X.copy()
X1['enginesize'].loc[np.random.randint(153, size=30)] = np.nan
X1.isnull().sum()
es_mean = X1['enginesize'].mean()
X11 = X1.copy()
X11 = X11.fillna(value=0)
X12 = X1.copy()
X12 = X12.fillna(value=es_mean)
des = ['without null values :', 'null values replaced by 0', 'null values replaced by mean']
j = 0
for i in [X, X11, X12]:
print(des[j])
j += 1
model_eval(lr, i, y)
print('cv\t:')
model_cv(lr, i, y)
print() | code |
129024099/cell_5 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import LabelEncoder, StandardScaler
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
import pandas as pd
import numpy as np
df = pd.read_csv('/kaggle/input/car-price-prediction/CarPrice_Assignment.csv')
dfx = df.copy()
cat = []
num = []
for n, d in dfx.items():
if d.dtype == 'object':
cat.append(n)
else:
num.append(n)
dfx = df.copy()
from sklearn.preprocessing import LabelEncoder, StandardScaler
le = LabelEncoder()
for i in cat:
dfx[i] = le.fit_transform(dfx[i])
ss = StandardScaler()
for i in num:
dfx[i] = ss.fit_transform(dfx[[i]])
import matplotlib.pyplot as plt
import seaborn as sns
corr = dfx.corr()
matrix = np.triu(corr)
X = df.drop(['price'], axis=1)
y = df[['price']]
cat = []
num = []
for n, d in X.items():
if d.dtype == 'object':
cat.append(n)
else:
num.append(n)
print(f'categorical columns : {cat}')
print(f'numerical columns : {num}') | code |
34126027/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
from keras.utils import to_categorical, plot_model
DROPOUT_RATE = 0.3
CONV_ACTIVATION = 'relu'
img_in = Input(shape=(48, 48, 1))
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(img_in)
X = BatchNormalization()(X)
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Flatten()(X)
X = Dense(2048, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(1024, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(512, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
out = Dense(7, activation='softmax')(X)
model = Model(inputs=img_in, outputs=out)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.001), metrics=['categorical_accuracy'])
model.summary()
plot_model(model, show_shapes=True, show_layer_names=False) | code |
34126027/cell_6 | [
"text_plain_output_1.png",
"image_output_1.png"
] | from keras.models import Model
from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.utils import to_categorical, plot_model
from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator | code |
34126027/cell_19 | [
"image_output_2.png",
"image_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import to_categorical, plot_model
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score
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
data_fer = pd.read_csv('../input/fer2013/fer2013.csv')
idx_to_emotion_fer = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}
X_fer_train, y_fer_train = np.rollaxis(data_fer[data_fer.Usage == 'Training'][['pixels', 'emotion']].values, -1)
X_fer_train = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_train]).reshape((-1, 48, 48))
y_fer_train = y_fer_train.astype('int8')
X_fer_test_public, y_fer_test_public = np.rollaxis(data_fer[data_fer.Usage == 'PublicTest'][['pixels', 'emotion']].values, -1)
X_fer_test_public = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_test_public]).reshape((-1, 48, 48))
y_fer_test_public = y_fer_test_public.astype('int8')
X_fer_test_private, y_fer_test_private = np.rollaxis(data_fer[data_fer.Usage == 'PrivateTest'][['pixels', 'emotion']].values, -1)
X_fer_test_private = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_test_private]).reshape((-1, 48, 48))
y_fer_test_private = y_fer_test_private.astype('int8')
BATCH_SIZE = 128
X_train = X_fer_train.reshape((-1, 48, 48, 1))
X_val = X_fer_test_public.reshape((-1, 48, 48, 1))
X_test = X_fer_test_private.reshape((-1, 48, 48, 1))
y_train = to_categorical(y_fer_train, 7)
y_val = to_categorical(y_fer_test_public, 7)
y_test = to_categorical(y_fer_test_private, 7)
train_datagen = ImageDataGenerator(featurewise_center=False, featurewise_std_normalization=False, rotation_range=10, width_shift_range=0.1, height_shift_range=0.1, zoom_range=0.1, horizontal_flip=True)
val_datagen = ImageDataGenerator(featurewise_center=False, featurewise_std_normalization=False)
train_datagen.fit(X_train)
val_datagen.fit(X_train)
train_flow = train_datagen.flow(X_train, y_train, batch_size=BATCH_SIZE)
val_flow = val_datagen.flow(X_val, y_val, batch_size=BATCH_SIZE, shuffle=False)
test_flow = val_datagen.flow(X_test, y_test, batch_size=1, shuffle=False)
DROPOUT_RATE = 0.3
CONV_ACTIVATION = 'relu'
img_in = Input(shape=(48, 48, 1))
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(img_in)
X = BatchNormalization()(X)
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Flatten()(X)
X = Dense(2048, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(1024, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(512, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
out = Dense(7, activation='softmax')(X)
model = Model(inputs=img_in, outputs=out)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.001), metrics=['categorical_accuracy'])
model.summary()
early_stopping = EarlyStopping(monitor='val_categorical_accuracy', mode='max', verbose=1, patience=20)
checkpoint_loss = ModelCheckpoint('best_loss_weights.h5', verbose=1, monitor='val_loss', save_best_only=True, mode='min')
checkpoint_acc = ModelCheckpoint('best_accuracy_weights.h5', verbose=1, monitor='val_categorical_accuracy', save_best_only=True, mode='max')
lr_reduce = ReduceLROnPlateau(monitor='val_categorical_accuracy', mode='max', factor=0.5, patience=5, min_lr=1e-07, cooldown=1, verbose=1)
history = model.fit_generator(train_flow, steps_per_epoch=X_train.shape[0] // BATCH_SIZE, epochs=150, validation_data=val_flow, validation_steps=X_val.shape[0] // BATCH_SIZE, callbacks=[early_stopping, checkpoint_acc, checkpoint_loss, lr_reduce])
model.load_weights('best_loss_weights.h5')
y_pred = model.predict_generator(test_flow, steps=X_test.shape[0])
y_pred_cat = np.argmax(y_pred, axis=1)
y_true_cat = np.argmax(test_flow.y, axis=1)
report = classification_report(y_true_cat, y_pred_cat)
print(report)
conf = confusion_matrix(y_true_cat, y_pred_cat, normalize="true")
labels = idx_to_emotion_fer.values()
_, ax = plt.subplots(figsize=(8, 6))
ax = sns.heatmap(conf, annot=True, cmap='YlGnBu',
xticklabels=labels,
yticklabels=labels)
plt.show()
model.load_weights('best_accuracy_weights.h5')
y_pred = model.predict_generator(test_flow, steps=X_test.shape[0])
y_pred_cat = np.argmax(y_pred, axis=1)
y_true_cat = np.argmax(test_flow.y, axis=1)
report = classification_report(y_true_cat, y_pred_cat)
print(report)
conf = confusion_matrix(y_true_cat, y_pred_cat, normalize='true')
labels = idx_to_emotion_fer.values()
_, ax = plt.subplots(figsize=(8, 6))
ax = sns.heatmap(conf, annot=True, cmap='YlGnBu', xticklabels=labels, yticklabels=labels)
plt.show() | code |
34126027/cell_15 | [
"image_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import to_categorical, plot_model
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_fer = pd.read_csv('../input/fer2013/fer2013.csv')
X_fer_train, y_fer_train = np.rollaxis(data_fer[data_fer.Usage == 'Training'][['pixels', 'emotion']].values, -1)
X_fer_train = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_train]).reshape((-1, 48, 48))
y_fer_train = y_fer_train.astype('int8')
X_fer_test_public, y_fer_test_public = np.rollaxis(data_fer[data_fer.Usage == 'PublicTest'][['pixels', 'emotion']].values, -1)
X_fer_test_public = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_test_public]).reshape((-1, 48, 48))
y_fer_test_public = y_fer_test_public.astype('int8')
X_fer_test_private, y_fer_test_private = np.rollaxis(data_fer[data_fer.Usage == 'PrivateTest'][['pixels', 'emotion']].values, -1)
X_fer_test_private = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_test_private]).reshape((-1, 48, 48))
y_fer_test_private = y_fer_test_private.astype('int8')
BATCH_SIZE = 128
X_train = X_fer_train.reshape((-1, 48, 48, 1))
X_val = X_fer_test_public.reshape((-1, 48, 48, 1))
X_test = X_fer_test_private.reshape((-1, 48, 48, 1))
y_train = to_categorical(y_fer_train, 7)
y_val = to_categorical(y_fer_test_public, 7)
y_test = to_categorical(y_fer_test_private, 7)
train_datagen = ImageDataGenerator(featurewise_center=False, featurewise_std_normalization=False, rotation_range=10, width_shift_range=0.1, height_shift_range=0.1, zoom_range=0.1, horizontal_flip=True)
val_datagen = ImageDataGenerator(featurewise_center=False, featurewise_std_normalization=False)
train_datagen.fit(X_train)
val_datagen.fit(X_train)
train_flow = train_datagen.flow(X_train, y_train, batch_size=BATCH_SIZE)
val_flow = val_datagen.flow(X_val, y_val, batch_size=BATCH_SIZE, shuffle=False)
test_flow = val_datagen.flow(X_test, y_test, batch_size=1, shuffle=False)
DROPOUT_RATE = 0.3
CONV_ACTIVATION = 'relu'
img_in = Input(shape=(48, 48, 1))
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(img_in)
X = BatchNormalization()(X)
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Flatten()(X)
X = Dense(2048, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(1024, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(512, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
out = Dense(7, activation='softmax')(X)
model = Model(inputs=img_in, outputs=out)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.001), metrics=['categorical_accuracy'])
model.summary()
early_stopping = EarlyStopping(monitor='val_categorical_accuracy', mode='max', verbose=1, patience=20)
checkpoint_loss = ModelCheckpoint('best_loss_weights.h5', verbose=1, monitor='val_loss', save_best_only=True, mode='min')
checkpoint_acc = ModelCheckpoint('best_accuracy_weights.h5', verbose=1, monitor='val_categorical_accuracy', save_best_only=True, mode='max')
lr_reduce = ReduceLROnPlateau(monitor='val_categorical_accuracy', mode='max', factor=0.5, patience=5, min_lr=1e-07, cooldown=1, verbose=1)
history = model.fit_generator(train_flow, steps_per_epoch=X_train.shape[0] // BATCH_SIZE, epochs=150, validation_data=val_flow, validation_steps=X_val.shape[0] // BATCH_SIZE, callbacks=[early_stopping, checkpoint_acc, checkpoint_loss, lr_reduce])
plt.plot(history.history['categorical_accuracy'])
plt.plot(history.history['val_categorical_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show()
plt.plot(history.history['loss'])
plt.plot(history.history['val_loss'])
plt.title('model loss')
plt.ylabel('loss')
plt.xlabel('epoch')
plt.legend(['train', 'validation'], loc='upper left')
plt.show() | code |
34126027/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)
data_fer = pd.read_csv('../input/fer2013/fer2013.csv')
data_fer.head() | code |
34126027/cell_17 | [
"text_plain_output_5.png",
"text_plain_output_4.png",
"text_plain_output_3.png",
"text_plain_output_2.png",
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import to_categorical, plot_model
from sklearn.metrics import confusion_matrix, classification_report, accuracy_score, f1_score
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
data_fer = pd.read_csv('../input/fer2013/fer2013.csv')
idx_to_emotion_fer = {0: 'Angry', 1: 'Disgust', 2: 'Fear', 3: 'Happy', 4: 'Sad', 5: 'Surprise', 6: 'Neutral'}
X_fer_train, y_fer_train = np.rollaxis(data_fer[data_fer.Usage == 'Training'][['pixels', 'emotion']].values, -1)
X_fer_train = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_train]).reshape((-1, 48, 48))
y_fer_train = y_fer_train.astype('int8')
X_fer_test_public, y_fer_test_public = np.rollaxis(data_fer[data_fer.Usage == 'PublicTest'][['pixels', 'emotion']].values, -1)
X_fer_test_public = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_test_public]).reshape((-1, 48, 48))
y_fer_test_public = y_fer_test_public.astype('int8')
X_fer_test_private, y_fer_test_private = np.rollaxis(data_fer[data_fer.Usage == 'PrivateTest'][['pixels', 'emotion']].values, -1)
X_fer_test_private = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_test_private]).reshape((-1, 48, 48))
y_fer_test_private = y_fer_test_private.astype('int8')
BATCH_SIZE = 128
X_train = X_fer_train.reshape((-1, 48, 48, 1))
X_val = X_fer_test_public.reshape((-1, 48, 48, 1))
X_test = X_fer_test_private.reshape((-1, 48, 48, 1))
y_train = to_categorical(y_fer_train, 7)
y_val = to_categorical(y_fer_test_public, 7)
y_test = to_categorical(y_fer_test_private, 7)
train_datagen = ImageDataGenerator(featurewise_center=False, featurewise_std_normalization=False, rotation_range=10, width_shift_range=0.1, height_shift_range=0.1, zoom_range=0.1, horizontal_flip=True)
val_datagen = ImageDataGenerator(featurewise_center=False, featurewise_std_normalization=False)
train_datagen.fit(X_train)
val_datagen.fit(X_train)
train_flow = train_datagen.flow(X_train, y_train, batch_size=BATCH_SIZE)
val_flow = val_datagen.flow(X_val, y_val, batch_size=BATCH_SIZE, shuffle=False)
test_flow = val_datagen.flow(X_test, y_test, batch_size=1, shuffle=False)
DROPOUT_RATE = 0.3
CONV_ACTIVATION = 'relu'
img_in = Input(shape=(48, 48, 1))
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(img_in)
X = BatchNormalization()(X)
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Flatten()(X)
X = Dense(2048, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(1024, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(512, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
out = Dense(7, activation='softmax')(X)
model = Model(inputs=img_in, outputs=out)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.001), metrics=['categorical_accuracy'])
model.summary()
early_stopping = EarlyStopping(monitor='val_categorical_accuracy', mode='max', verbose=1, patience=20)
checkpoint_loss = ModelCheckpoint('best_loss_weights.h5', verbose=1, monitor='val_loss', save_best_only=True, mode='min')
checkpoint_acc = ModelCheckpoint('best_accuracy_weights.h5', verbose=1, monitor='val_categorical_accuracy', save_best_only=True, mode='max')
lr_reduce = ReduceLROnPlateau(monitor='val_categorical_accuracy', mode='max', factor=0.5, patience=5, min_lr=1e-07, cooldown=1, verbose=1)
history = model.fit_generator(train_flow, steps_per_epoch=X_train.shape[0] // BATCH_SIZE, epochs=150, validation_data=val_flow, validation_steps=X_val.shape[0] // BATCH_SIZE, callbacks=[early_stopping, checkpoint_acc, checkpoint_loss, lr_reduce])
model.load_weights('best_loss_weights.h5')
y_pred = model.predict_generator(test_flow, steps=X_test.shape[0])
y_pred_cat = np.argmax(y_pred, axis=1)
y_true_cat = np.argmax(test_flow.y, axis=1)
report = classification_report(y_true_cat, y_pred_cat)
print(report)
conf = confusion_matrix(y_true_cat, y_pred_cat, normalize='true')
labels = idx_to_emotion_fer.values()
_, ax = plt.subplots(figsize=(8, 6))
ax = sns.heatmap(conf, annot=True, cmap='YlGnBu', xticklabels=labels, yticklabels=labels)
plt.show() | code |
34126027/cell_14 | [
"text_plain_output_1.png"
] | from keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau
from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
from keras.utils import to_categorical, plot_model
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
data_fer = pd.read_csv('../input/fer2013/fer2013.csv')
X_fer_train, y_fer_train = np.rollaxis(data_fer[data_fer.Usage == 'Training'][['pixels', 'emotion']].values, -1)
X_fer_train = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_train]).reshape((-1, 48, 48))
y_fer_train = y_fer_train.astype('int8')
X_fer_test_public, y_fer_test_public = np.rollaxis(data_fer[data_fer.Usage == 'PublicTest'][['pixels', 'emotion']].values, -1)
X_fer_test_public = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_test_public]).reshape((-1, 48, 48))
y_fer_test_public = y_fer_test_public.astype('int8')
X_fer_test_private, y_fer_test_private = np.rollaxis(data_fer[data_fer.Usage == 'PrivateTest'][['pixels', 'emotion']].values, -1)
X_fer_test_private = np.array([np.fromstring(x, dtype='uint8', sep=' ') for x in X_fer_test_private]).reshape((-1, 48, 48))
y_fer_test_private = y_fer_test_private.astype('int8')
BATCH_SIZE = 128
X_train = X_fer_train.reshape((-1, 48, 48, 1))
X_val = X_fer_test_public.reshape((-1, 48, 48, 1))
X_test = X_fer_test_private.reshape((-1, 48, 48, 1))
y_train = to_categorical(y_fer_train, 7)
y_val = to_categorical(y_fer_test_public, 7)
y_test = to_categorical(y_fer_test_private, 7)
train_datagen = ImageDataGenerator(featurewise_center=False, featurewise_std_normalization=False, rotation_range=10, width_shift_range=0.1, height_shift_range=0.1, zoom_range=0.1, horizontal_flip=True)
val_datagen = ImageDataGenerator(featurewise_center=False, featurewise_std_normalization=False)
train_datagen.fit(X_train)
val_datagen.fit(X_train)
train_flow = train_datagen.flow(X_train, y_train, batch_size=BATCH_SIZE)
val_flow = val_datagen.flow(X_val, y_val, batch_size=BATCH_SIZE, shuffle=False)
test_flow = val_datagen.flow(X_test, y_test, batch_size=1, shuffle=False)
DROPOUT_RATE = 0.3
CONV_ACTIVATION = 'relu'
img_in = Input(shape=(48, 48, 1))
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(img_in)
X = BatchNormalization()(X)
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Flatten()(X)
X = Dense(2048, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(1024, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(512, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
out = Dense(7, activation='softmax')(X)
model = Model(inputs=img_in, outputs=out)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.001), metrics=['categorical_accuracy'])
model.summary()
early_stopping = EarlyStopping(monitor='val_categorical_accuracy', mode='max', verbose=1, patience=20)
checkpoint_loss = ModelCheckpoint('best_loss_weights.h5', verbose=1, monitor='val_loss', save_best_only=True, mode='min')
checkpoint_acc = ModelCheckpoint('best_accuracy_weights.h5', verbose=1, monitor='val_categorical_accuracy', save_best_only=True, mode='max')
lr_reduce = ReduceLROnPlateau(monitor='val_categorical_accuracy', mode='max', factor=0.5, patience=5, min_lr=1e-07, cooldown=1, verbose=1)
history = model.fit_generator(train_flow, steps_per_epoch=X_train.shape[0] // BATCH_SIZE, epochs=150, validation_data=val_flow, validation_steps=X_val.shape[0] // BATCH_SIZE, callbacks=[early_stopping, checkpoint_acc, checkpoint_loss, lr_reduce]) | code |
34126027/cell_12 | [
"text_html_output_1.png"
] | from keras.layers import Flatten, Dense, Input, Dropout, Conv2D, MaxPool2D, BatchNormalization
from keras.models import Model
from keras.optimizers import Adam
DROPOUT_RATE = 0.3
CONV_ACTIVATION = 'relu'
img_in = Input(shape=(48, 48, 1))
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(img_in)
X = BatchNormalization()(X)
X = Conv2D(64, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(128, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(256, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = Conv2D(512, (3, 3), padding='same', kernel_initializer='he_normal', activation=CONV_ACTIVATION)(X)
X = BatchNormalization()(X)
X = MaxPool2D((2, 2), strides=(2, 2), padding='same')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Flatten()(X)
X = Dense(2048, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(1024, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
X = Dense(512, activation='relu')(X)
X = Dropout(DROPOUT_RATE)(X)
out = Dense(7, activation='softmax')(X)
model = Model(inputs=img_in, outputs=out)
model.compile(loss='categorical_crossentropy', optimizer=Adam(0.001), metrics=['categorical_accuracy'])
model.summary() | code |
2025290/cell_42 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
le = LabelEncoder()
housedfnum['MSSubClass'] = le.fit_transform(housedfnum['MSSubClass'].values)
housedfnum['OverallQual'] = le.fit_transform(housedfnum['OverallQual'].values)
housedfnum['OverallCond'] = le.fit_transform(housedfnum['OverallCond'].values)
housedfnum['YearBuilt'] = le.fit_transform(housedfnum['YearBuilt'].values)
housedfnum['YearRemodAdd'] = le.fit_transform(housedfnum['YearRemodAdd'].values)
housedfnum['YrSold'] = le.fit_transform(housedfnum['YrSold'].values)
housedfnum['GarageYrBlt'] = le.fit_transform(housedfnum['GarageYrBlt'].values)
housedfcat1 = housedfcat.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1)
housedfcat2 = housedfcat1.apply(le.fit_transform)
housefinal = pd.concat([housedfnum, housedfcat2], axis=1)
housefinal.shape
LiR = LinearRegression()
y = housefinal['SalePrice']
X = housefinal.drop(['Id', 'SalePrice'], axis=1)
LiR.fit(X, y)
LiR.score(X, y)
predictedprice = LiR.predict(X)
priceresidual = housefinal.SalePrice - predictedprice
np.sqrt(np.mean(priceresidual ** 2))
housetest = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/test.csv')
housetest.isnull().sum()
housetestnum = housetest.select_dtypes(include=[np.number])
housetestcat = housetest.select_dtypes(include=[object])
housetestnum.isnull().sum()
housetestnum.isnull().sum()
housetestcat.isnull().sum()
le = LabelEncoder()
housetestnum['MSSubClass'] = le.fit_transform(housetestnum['MSSubClass'].values)
housetestnum['OverallQual'] = le.fit_transform(housetestnum['OverallQual'].values)
housetestnum['OverallCond'] = le.fit_transform(housetestnum['OverallCond'].values)
housetestnum['YearBuilt'] = le.fit_transform(housetestnum['YearBuilt'].values)
housetestnum['YearRemodAdd'] = le.fit_transform(housetestnum['YearRemodAdd'].values)
housetestnum['YrSold'] = le.fit_transform(housetestnum['YrSold'].values)
housetestnum['GarageYrBlt'] = le.fit_transform(housetestnum['GarageYrBlt'].values)
housetestcat1 = housetestcat.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1)
housetestcat1.isnull().sum()
housetestcat1['MSZoning'] = le.fit_transform(housetestcat1['MSZoning'].astype(str)) | code |
2025290/cell_21 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.model_selection import cross_val_score
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
le = LabelEncoder()
housedfnum['MSSubClass'] = le.fit_transform(housedfnum['MSSubClass'].values)
housedfnum['OverallQual'] = le.fit_transform(housedfnum['OverallQual'].values)
housedfnum['OverallCond'] = le.fit_transform(housedfnum['OverallCond'].values)
housedfnum['YearBuilt'] = le.fit_transform(housedfnum['YearBuilt'].values)
housedfnum['YearRemodAdd'] = le.fit_transform(housedfnum['YearRemodAdd'].values)
housedfnum['YrSold'] = le.fit_transform(housedfnum['YrSold'].values)
housedfnum['GarageYrBlt'] = le.fit_transform(housedfnum['GarageYrBlt'].values)
housedfcat1 = housedfcat.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1)
housedfcat2 = housedfcat1.apply(le.fit_transform)
housefinal = pd.concat([housedfnum, housedfcat2], axis=1)
housefinal.shape
LiR = LinearRegression()
y = housefinal['SalePrice']
X = housefinal.drop(['Id', 'SalePrice'], axis=1)
LiR.fit(X, y)
LiR.score(X, y)
lircross = cross_val_score(LiR, X, y, cv=10) | code |
2025290/cell_13 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
le = LabelEncoder()
housedfnum['MSSubClass'] = le.fit_transform(housedfnum['MSSubClass'].values)
housedfnum['OverallQual'] = le.fit_transform(housedfnum['OverallQual'].values)
housedfnum['OverallCond'] = le.fit_transform(housedfnum['OverallCond'].values)
housedfnum['YearBuilt'] = le.fit_transform(housedfnum['YearBuilt'].values)
housedfnum['YearRemodAdd'] = le.fit_transform(housedfnum['YearRemodAdd'].values)
housedfnum['YrSold'] = le.fit_transform(housedfnum['YrSold'].values)
housedfnum['GarageYrBlt'] = le.fit_transform(housedfnum['GarageYrBlt'].values)
housedfcat1 = housedfcat.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1)
housedfcat2 = housedfcat1.apply(le.fit_transform)
housefinal = pd.concat([housedfnum, housedfcat2], axis=1) | code |
2025290/cell_9 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
le = LabelEncoder()
housedfnum['MSSubClass'] = le.fit_transform(housedfnum['MSSubClass'].values)
housedfnum['OverallQual'] = le.fit_transform(housedfnum['OverallQual'].values)
housedfnum['OverallCond'] = le.fit_transform(housedfnum['OverallCond'].values)
housedfnum['YearBuilt'] = le.fit_transform(housedfnum['YearBuilt'].values)
housedfnum['YearRemodAdd'] = le.fit_transform(housedfnum['YearRemodAdd'].values)
housedfnum['YrSold'] = le.fit_transform(housedfnum['YrSold'].values)
housedfnum['GarageYrBlt'] = le.fit_transform(housedfnum['GarageYrBlt'].values) | code |
2025290/cell_25 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
le = LabelEncoder()
housedfnum['MSSubClass'] = le.fit_transform(housedfnum['MSSubClass'].values)
housedfnum['OverallQual'] = le.fit_transform(housedfnum['OverallQual'].values)
housedfnum['OverallCond'] = le.fit_transform(housedfnum['OverallCond'].values)
housedfnum['YearBuilt'] = le.fit_transform(housedfnum['YearBuilt'].values)
housedfnum['YearRemodAdd'] = le.fit_transform(housedfnum['YearRemodAdd'].values)
housedfnum['YrSold'] = le.fit_transform(housedfnum['YrSold'].values)
housedfnum['GarageYrBlt'] = le.fit_transform(housedfnum['GarageYrBlt'].values)
housedfcat1 = housedfcat.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1)
housedfcat2 = housedfcat1.apply(le.fit_transform)
housefinal = pd.concat([housedfnum, housedfcat2], axis=1)
housefinal.shape
LiR = LinearRegression()
y = housefinal['SalePrice']
X = housefinal.drop(['Id', 'SalePrice'], axis=1)
LiR.fit(X, y)
LiR.score(X, y)
predictedprice = LiR.predict(X)
predictedprice | code |
2025290/cell_4 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number]) | code |
2025290/cell_34 | [
"application_vnd.jupyter.stderr_output_1.png"
] | from sklearn.linear_model import LinearRegression
from sklearn.preprocessing import LabelEncoder
import numpy as np
import pandas as pd
houseprice = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/train.csv')
houseprice.isnull().sum()
housedfnum = houseprice.select_dtypes(include=[np.number])
housedfcat = houseprice.select_dtypes(include=[object])
le = LabelEncoder()
housedfnum['MSSubClass'] = le.fit_transform(housedfnum['MSSubClass'].values)
housedfnum['OverallQual'] = le.fit_transform(housedfnum['OverallQual'].values)
housedfnum['OverallCond'] = le.fit_transform(housedfnum['OverallCond'].values)
housedfnum['YearBuilt'] = le.fit_transform(housedfnum['YearBuilt'].values)
housedfnum['YearRemodAdd'] = le.fit_transform(housedfnum['YearRemodAdd'].values)
housedfnum['YrSold'] = le.fit_transform(housedfnum['YrSold'].values)
housedfnum['GarageYrBlt'] = le.fit_transform(housedfnum['GarageYrBlt'].values)
housedfcat1 = housedfcat.drop(['Alley', 'FireplaceQu', 'PoolQC', 'Fence', 'MiscFeature'], axis=1)
housedfcat2 = housedfcat1.apply(le.fit_transform)
housefinal = pd.concat([housedfnum, housedfcat2], axis=1)
housefinal.shape
LiR = LinearRegression()
y = housefinal['SalePrice']
X = housefinal.drop(['Id', 'SalePrice'], axis=1)
LiR.fit(X, y)
LiR.score(X, y)
predictedprice = LiR.predict(X)
priceresidual = housefinal.SalePrice - predictedprice
np.sqrt(np.mean(priceresidual ** 2))
housetest = pd.read_csv('C:/Users/hp/Desktop/KAGGLE/Ames Houses DATA/test.csv')
housetest.isnull().sum()
housetestnum = housetest.select_dtypes(include=[np.number])
housetestnum.isnull().sum()
housetestnum.isnull().sum() | code |
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