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stringlengths 13
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|---|---|---|---|
105190994/cell_15 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
(df.head(3), df.tail(2), df.columns, df.index, df.shape) | code |
105190994/cell_17 | [
"text_html_output_1.png",
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
(df.head(3), df.tail(2), df.columns, df.index, df.shape)
df.info() | code |
105190994/cell_24 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
(df.head(3), df.tail(2), df.columns, df.index, df.shape)
df.loc[:, 'A']
df.iloc[0:2, 3:]
df[df.A < 0] | code |
105190994/cell_22 | [
"text_plain_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
(df.head(3), df.tail(2), df.columns, df.index, df.shape)
df.loc[:, 'A'] | code |
105190994/cell_27 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df
t = np.arange(5)
sin_t = np.sin(t)
cos_t = np.cos(t)
exp_t = np.exp(t)
df2 = pd.DataFrame({'t': t, 'sin': sin_t, 'cos': cos_t, 'exp': exp_t})
df2
df1 = pd.DataFrame(np.random.rand(2, 4))
df2 = pd.DataFrame(np.random.rand(1, 4))
df3 = pd.DataFrame(np.random.rand(3, 5))
df_list = [df1, df2, df3]
df4 = pd.concat(df_list, axis=0)
df4 | code |
105190994/cell_12 | [
"text_html_output_1.png"
] | import numpy as np
import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
nrow = 5
n_feateures = 4
dates = pd.date_range('20220101', periods=nrow, freq='s')
df = pd.DataFrame(np.random.randn(nrow, n_feateures), index=dates, columns=list('ABCD'))
df | code |
105190994/cell_5 | [
"text_plain_output_1.png"
] | import pandas as pd
import warnings
import warnings
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import pandas_profiling as pp
import seaborn as sns
import warnings
import os
import warnings
warnings.filterwarnings(action='ignore')
CSV_PATH = '../input/iris-files/iris.csv'
iris = pd.read_csv(CSV_PATH)
iris.head() | code |
106201680/cell_21 | [
"image_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train.reset_index(inplace=True)
df_train.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_train | code |
106201680/cell_9 | [
"image_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df.info() | code |
106201680/cell_4 | [
"text_plain_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df | code |
106201680/cell_34 | [
"text_plain_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train.reset_index(inplace=True)
df_train.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test.reset_index(inplace=True)
df_test.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test
model = Prophet()
model.fit(df_train)
hasil_prediksi = model.predict(df_test)
# Visualisai hasil prediksi machine learning
f, ax = plt.subplots(1)
f.set_figheight(5)
f.set_figwidth(15)
fig = model.plot(hasil_prediksi,
ax=ax)
plt.show()
f, ax = plt.subplots(1)
f.set_figheight(5)
f.set_figwidth(15)
ax.plot(df_test['ds'], df_test['y'], color='r')
fig = model.plot(hasil_prediksi, ax=ax) | code |
106201680/cell_30 | [
"text_html_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train.reset_index(inplace=True)
df_train.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test.reset_index(inplace=True)
df_test.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test
model = Prophet()
model.fit(df_train)
hasil_prediksi = model.predict(df_test)
hasil_prediksi['yhat'] | code |
106201680/cell_33 | [
"text_html_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train.reset_index(inplace=True)
df_train.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test.reset_index(inplace=True)
df_test.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test
model = Prophet()
model.fit(df_train)
hasil_prediksi = model.predict(df_test)
f, ax = plt.subplots(1)
f.set_figheight(5)
f.set_figwidth(15)
fig = model.plot(hasil_prediksi, ax=ax)
plt.show() | code |
106201680/cell_6 | [
"text_html_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df[df['Month'].duplicated()] | code |
106201680/cell_40 | [
"image_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train.reset_index(inplace=True)
df_train.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test.reset_index(inplace=True)
df_test.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test
model = Prophet()
model.fit(df_train)
hasil_prediksi = model.predict(df_test)
# Visualisai hasil prediksi machine learning
f, ax = plt.subplots(1)
f.set_figheight(5)
f.set_figwidth(15)
fig = model.plot(hasil_prediksi,
ax=ax)
plt.show()
# Visualisasi perbandingan antara hasil prediksi machine learning dengan data yang asli
f, ax = plt.subplots(1)
f.set_figheight(5)
f.set_figwidth(15)
ax.plot(df_test['ds'], df_test['y'], color='r')
fig = model.plot(hasil_prediksi, ax=ax)
prediksi_tahun_berikutnya = pd.DataFrame(columns=['ds', 'y'])
hasil_prediksi_satu_tahun_kedepan = model.predict(prediksi_tahun_berikutnya)
f, ax = plt.subplots(1)
f.set_figheight(5)
f.set_figwidth(15)
ax.plot(df_test['ds'], df_test['y'], color='r')
fig = model.plot(hasil_prediksi_satu_tahun_kedepan, ax=ax)
fig = plt.suptitle('Prediksi Penumpang Satu Tahun Kedepan')
plt.show() | code |
106201680/cell_29 | [
"text_html_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_test.reset_index(inplace=True)
df_test.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test
df_test['y'] | code |
106201680/cell_39 | [
"text_plain_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train.reset_index(inplace=True)
df_train.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test.reset_index(inplace=True)
df_test.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test
model = Prophet()
model.fit(df_train)
hasil_prediksi = model.predict(df_test)
# Visualisai hasil prediksi machine learning
f, ax = plt.subplots(1)
f.set_figheight(5)
f.set_figwidth(15)
fig = model.plot(hasil_prediksi,
ax=ax)
plt.show()
# Visualisasi perbandingan antara hasil prediksi machine learning dengan data yang asli
f, ax = plt.subplots(1)
f.set_figheight(5)
f.set_figwidth(15)
ax.plot(df_test['ds'], df_test['y'], color='r')
fig = model.plot(hasil_prediksi, ax=ax)
prediksi_tahun_berikutnya = pd.DataFrame(columns=['ds', 'y'])
hasil_prediksi_satu_tahun_kedepan = model.predict(prediksi_tahun_berikutnya)
hasil_prediksi_satu_tahun_kedepan | code |
106201680/cell_26 | [
"text_html_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train.reset_index(inplace=True)
df_train.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test.reset_index(inplace=True)
df_test.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test
model = Prophet()
model.fit(df_train)
hasil_prediksi = model.predict(df_test)
hasil_prediksi | code |
106201680/cell_11 | [
"text_plain_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df | code |
106201680/cell_19 | [
"text_html_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train.reset_index(inplace=True)
df_train | code |
106201680/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
106201680/cell_32 | [
"text_plain_output_2.png",
"application_vnd.jupyter.stderr_output_1.png"
] | from prophet import Prophet
from sklearn.metrics import mean_absolute_error
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train.reset_index(inplace=True)
df_train.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test.reset_index(inplace=True)
df_test.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test
model = Prophet()
model.fit(df_train)
hasil_prediksi = model.predict(df_test)
mean_absolute_error(df_test['y'], hasil_prediksi['yhat']) | code |
106201680/cell_8 | [
"text_html_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df[df['Month'].duplicated()] | code |
106201680/cell_15 | [
"text_plain_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
plt.show() | code |
106201680/cell_17 | [
"text_html_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train | code |
106201680/cell_31 | [
"text_html_output_1.png"
] | from prophet import Prophet
from sklearn.metrics import mean_squared_error
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train.reset_index(inplace=True)
df_train.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test.reset_index(inplace=True)
df_test.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test
model = Prophet()
model.fit(df_train)
hasil_prediksi = model.predict(df_test)
mean_squared_error(df_test['y'], hasil_prediksi['yhat']) | code |
106201680/cell_24 | [
"image_output_1.png"
] | from prophet import Prophet
import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_train.reset_index(inplace=True)
df_train.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
model = Prophet()
model.fit(df_train) | code |
106201680/cell_14 | [
"text_html_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
test_set_size | code |
106201680/cell_22 | [
"text_plain_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
test_ratio = 0.2
test_set_size = int(len(df) * test_ratio)
df_train = df[0:-test_set_size].copy()
df_test = df[-test_set_size:].copy()
df_test.rename(columns={'#Passengers': 'TEST SET'}).join(df_train.rename(columns={'#Passengers': 'TRAINING SET'}), how='outer').plot(figsize=(15, 5), title='Penumpang Pesawat', style='-')
df_test.reset_index(inplace=True)
df_test.rename(columns={'Month': 'ds', '#Passengers': 'y'}, inplace=True)
df_test | code |
106201680/cell_10 | [
"text_html_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df | code |
106201680/cell_37 | [
"text_plain_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df['Month'] = pd.to_datetime(df['Month'], format='%Y-%m')
prediksi_tahun_berikutnya = pd.DataFrame(columns=['ds', 'y'])
prediksi_tahun_berikutnya | code |
106201680/cell_12 | [
"text_html_output_1.png"
] | import matplotlib.pylab as plt # Untuk visualisasi
import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df = df.set_index(['Month'])
df
color_pal = ['#F8766D', '#D39200', '#93AA00', '#00BA38', '#00C19F', '#00B9E3', '#619CFF', '#DB72FB']
df.plot(style='-', figsize=(15, 5), color=color_pal[0], title='Penumpang Pesawat')
plt.show() | code |
106201680/cell_5 | [
"image_output_1.png"
] | import pandas as pd # Untuk mengolah data
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df = pd.read_csv('../input/airpassengers/AirPassengers.csv')
df.info() | code |
332165/cell_1 | [
"text_plain_output_1.png"
] | library(ggplot2)
library(readr)
system('ls ../input') | code |
332165/cell_3 | [
"text_html_output_1.png"
] | data = pd.read_csv('../input/2016-FCC-New-Coders-Survey-Data.csv', encoding='utf-8', low_memory=False)
data.groupby(['SchoolDegree', 'CountryLive'])['Income'].mean() | code |
128010580/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png"
] | import pandas as pd
df = pd.read_csv('austin_weather.csv')
df | code |
17118469/cell_4 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
auto = pd.read_csv('../input/cnt_km_year_powerPS_minPrice_maxPrice_avgPrice_sdPrice.csv')
p = auto.hist(figsize = (20,20))
plt.matshow(auto.corr())
plt.colorbar()
plt.show() | code |
17118469/cell_6 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
auto = pd.read_csv('../input/cnt_km_year_powerPS_minPrice_maxPrice_avgPrice_sdPrice.csv')
p = auto.hist(figsize = (20,20))
plt.colorbar()
plt.figure(figsize=(10, 7))
sns.scatterplot(x='year', y='avgPrice', data=auto)
plt.show() | code |
17118469/cell_2 | [
"text_html_output_1.png"
] | import pandas as pd
auto = pd.read_csv('../input/cnt_km_year_powerPS_minPrice_maxPrice_avgPrice_sdPrice.csv')
auto.head() | code |
17118469/cell_1 | [
"text_plain_output_1.png"
] | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import cm
sns.set_style('ticks')
import plotly.offline as py
import matplotlib.ticker as mtick
py.init_notebook_mode(connected=True)
import plotly.graph_objs as go
import plotly.tools as tls
plt.xkcd() | code |
17118469/cell_3 | [
"application_vnd.jupyter.stderr_output_1.png",
"image_output_1.png"
] | import pandas as pd
auto = pd.read_csv('../input/cnt_km_year_powerPS_minPrice_maxPrice_avgPrice_sdPrice.csv')
p = auto.hist(figsize=(20, 20)) | code |
17118469/cell_5 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
auto = pd.read_csv('../input/cnt_km_year_powerPS_minPrice_maxPrice_avgPrice_sdPrice.csv')
p = auto.hist(figsize = (20,20))
plt.colorbar()
plt.figure(figsize=(10, 7))
sns.scatterplot(x='year', y='maxPrice', data=auto)
plt.show() | code |
90111888/cell_13 | [
"text_html_output_1.png"
] | train_identity.info() | code |
90111888/cell_9 | [
"text_plain_output_1.png"
] | train_transactions = pd.read_csv('../input/train_transaction.csv')
train_identity = pd.read_csv('../input/train_identity.csv')
print('Train data set is loaded !') | code |
90111888/cell_34 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.pkl')
test_df.to_pickle('test_df.pkl')
train_df['id_38'].value_counts() | code |
90111888/cell_33 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.pkl')
test_df.to_pickle('test_df.pkl')
train_df['ProductCD'].value_counts() | code |
90111888/cell_20 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
train_df['R_emaildomain'].value_counts() | code |
90111888/cell_40 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.pkl')
test_df.to_pickle('test_df.pkl')
object_cols = [col for col in train_df.columns if train_df[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(train_df[col]) == set(test_df[col])]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
train_df.drop(bad_label_cols, axis=1, inplace=True)
test_df.drop(bad_label_cols, axis=1, inplace=True)
low_cardinality_cols = [col for col in good_label_cols if train_df[col].nunique() < 10]
high_cardinality_cols = list(set(good_label_cols) - set(low_cardinality_cols))
train_df[low_cardinality_cols].head() | code |
90111888/cell_29 | [
"text_plain_output_1.png"
] | train_df = pd.read_pickle('train_df.pkl')
test_df = pd.read_pickle('test_df.pkl') | code |
90111888/cell_39 | [
"text_plain_output_1.png"
] | for f in high_cardinality_cols:
lbl_enc = LabelEncoder()
lbl_enc.fit(list(train_df[f].values))
print(f'{f}: {lbl_enc.classes_}')
train_df[f] = lbl_enc.transform(list(train_df[f].values))
test_df[f] = lbl_enc.transform(list(test_df[f].values)) | code |
90111888/cell_26 | [
"text_plain_output_1.png"
] | test_df = reduce_mem_usage(test_df) | code |
90111888/cell_48 | [
"image_output_1.png"
] | from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import make_scorer, roc_auc_score
from sklearn.metrics import roc_auc_score, roc_curve
clf_rf_down = RandomForestClassifier(random_state=42, n_estimators=50)
model_rf_down = clf_rf_down.fit(X_train_sm, y_train_sm)
y_prob = model_rf_down.predict_proba(X_test)[:, 1]
print(f'ROC-AUC score: {roc_auc_score(y_test, y_prob):.3f}') | code |
90111888/cell_11 | [
"text_html_output_1.png"
] | train_transactions.info() | code |
90111888/cell_19 | [
"text_plain_output_1.png"
] | train_df = reduce_mem_usage(train_df) | code |
90111888/cell_52 | [
"text_plain_output_1.png"
] | roc_auc_scorer = make_scorer(roc_auc_score, greater_is_better=True, needs_threshold=True)
cross_validation = StratifiedKFold(n_splits=3, shuffle=True, random_state=42)
clf = xgb.XGBClassifier(nthread=1, random_state=42)
param_grid = {'max_depth': [15, 20], 'min_samples_split': [3], 'learning_rate': [0.05, 0.1], 'min_child_weight': [5, 11, 15], 'silent': [1], 'subsample': [0.8], 'colsample_bytree': [0.7], 'n_estimators': [500], 'missing': [-999]}
search = GridSearchCV(clf, param_grid, cv=cross_validation, scoring=roc_auc_scorer, n_jobs=-1).fit(X_train_sm, y_train_sm)
print(search.best_params_)
y_prob = search.predict_proba(X_test)[:, 1]
print(f'ROC-AUC score: {roc_auc_score(y_test, y_prob):.3f}')
predictions_cv_xgb = search.predict_proba(OH_test_df)[:, 1]
submission = pd.DataFrame({'TransactionID': OH_test_df.index, 'isFraud': predictions_cv_xgb})
submission['TransactionID'] = submission['TransactionID'].astype(int)
filename = 'cv_xgb_model_submission.csv'
submission.to_csv(filename, index=False)
print(f'Saved file: {filename}') | code |
90111888/cell_45 | [
"text_html_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
from sklearn.utils import resample
import pandas as pd
import seaborn as sns
train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.pkl')
test_df.to_pickle('test_df.pkl')
object_cols = [col for col in train_df.columns if train_df[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(train_df[col]) == set(test_df[col])]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
train_df.drop(bad_label_cols, axis=1, inplace=True)
test_df.drop(bad_label_cols, axis=1, inplace=True)
low_cardinality_cols = [col for col in good_label_cols if train_df[col].nunique() < 10]
high_cardinality_cols = list(set(good_label_cols) - set(low_cardinality_cols))
from sklearn.preprocessing import OneHotEncoder
OH_encoder = OneHotEncoder(drop='first', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(train_df[low_cardinality_cols].astype('str')))
OH_cols_valid = pd.DataFrame(OH_encoder.transform(test_df[low_cardinality_cols].astype('str')))
OH_cols_train.index = train_df.index
OH_cols_valid.index = test_df.index
num_X_train = train_df.drop(low_cardinality_cols, axis=1)
num_X_valid = test_df.drop(low_cardinality_cols, axis=1)
del train_df, test_df
OH_train_df = pd.concat([num_X_train, OH_cols_train], axis=1)
OH_test_df = pd.concat([num_X_valid, OH_cols_valid], axis=1)
df_majority_downsampled, y_majority_downsampled = resample(X_train[y_train == 0], y_train[y_train == 0], replace=False, n_samples=3 * len(y_train[y_train == 1]), random_state=42)
X_down_train = pd.concat([X_train[y_train == 1], df_majority_downsampled])
y_down_train = pd.concat([y_train[y_train == 1], y_majority_downsampled])
sns.countplot(x=y_down_train) | code |
90111888/cell_32 | [
"text_plain_output_1.png"
] | train_df = train_df.fillna(-999)
test_df = test_df.fillna(-999) | code |
90111888/cell_38 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.pkl')
test_df.to_pickle('test_df.pkl')
object_cols = [col for col in train_df.columns if train_df[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(train_df[col]) == set(test_df[col])]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
train_df.drop(bad_label_cols, axis=1, inplace=True)
test_df.drop(bad_label_cols, axis=1, inplace=True)
low_cardinality_cols = [col for col in good_label_cols if train_df[col].nunique() < 10]
high_cardinality_cols = list(set(good_label_cols) - set(low_cardinality_cols))
print('Categorical columns that will be one-hot encoded:', low_cardinality_cols)
print('\nCategorical columns that will be label encoded:', high_cardinality_cols) | code |
90111888/cell_17 | [
"image_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
print('Train shape', train_df.shape)
print('Data set merged ')
del train_transactions, train_identity | code |
90111888/cell_35 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.pkl')
test_df.to_pickle('test_df.pkl')
object_cols = [col for col in train_df.columns if train_df[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(train_df[col]) == set(test_df[col])]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
print('Categorical columns that will be label encoded:', good_label_cols)
print('\nCategorical columns that will be dropped from the dataset:', bad_label_cols) | code |
90111888/cell_46 | [
"image_output_1.png"
] | from sklearn.preprocessing import OneHotEncoder
from sklearn.utils import resample
import pandas as pd
import seaborn as sns
train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.pkl')
test_df.to_pickle('test_df.pkl')
object_cols = [col for col in train_df.columns if train_df[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(train_df[col]) == set(test_df[col])]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
train_df.drop(bad_label_cols, axis=1, inplace=True)
test_df.drop(bad_label_cols, axis=1, inplace=True)
low_cardinality_cols = [col for col in good_label_cols if train_df[col].nunique() < 10]
high_cardinality_cols = list(set(good_label_cols) - set(low_cardinality_cols))
from sklearn.preprocessing import OneHotEncoder
OH_encoder = OneHotEncoder(drop='first', sparse=False)
OH_cols_train = pd.DataFrame(OH_encoder.fit_transform(train_df[low_cardinality_cols].astype('str')))
OH_cols_valid = pd.DataFrame(OH_encoder.transform(test_df[low_cardinality_cols].astype('str')))
OH_cols_train.index = train_df.index
OH_cols_valid.index = test_df.index
num_X_train = train_df.drop(low_cardinality_cols, axis=1)
num_X_valid = test_df.drop(low_cardinality_cols, axis=1)
del train_df, test_df
OH_train_df = pd.concat([num_X_train, OH_cols_train], axis=1)
OH_test_df = pd.concat([num_X_valid, OH_cols_valid], axis=1)
df_majority_downsampled, y_majority_downsampled = resample(X_train[y_train == 0], y_train[y_train == 0], replace=False, n_samples=3 * len(y_train[y_train == 1]), random_state=42)
X_down_train = pd.concat([X_train[y_train == 1], df_majority_downsampled])
y_down_train = pd.concat([y_train[y_train == 1], y_majority_downsampled])
X_train_sm, y_train_sm = (X_down_train, y_down_train)
sns.countplot(x=y_train_sm) | code |
90111888/cell_24 | [
"text_plain_output_1.png"
] | test_df = test_transaction.merge(test_identity, how='left', on='TransactionID')
print('Train shape', train_df.shape)
print('Data set merged ')
del test_transaction, test_identity | code |
90111888/cell_14 | [
"text_plain_output_1.png"
] | import seaborn as sns
sns.countplot(x=train_transactions['isFraud']) | code |
90111888/cell_22 | [
"text_plain_output_1.png"
] | test_transaction = pd.read_csv('../input/test_transaction.csv')
test_identity = pd.read_csv('../input/test_identity.csv')
sample_submission = pd.read_csv('../input/sample_submission.csv')
print('Test data set is loaded !') | code |
90111888/cell_10 | [
"text_plain_output_1.png"
] | train_transactions.head() | code |
90111888/cell_12 | [
"text_plain_output_1.png"
] | train_identity.head() | code |
90111888/cell_36 | [
"text_plain_output_1.png"
] | train_df = train_transactions.merge(train_identity, how='left', on='TransactionID')
del train_transactions, train_identity
test_df = test_df.rename(columns=lambda x: '_'.join(x.split('-')))
train_df.set_index('TransactionID', inplace=True)
test_df.set_index('TransactionID', inplace=True)
train_df.to_pickle('train_df.pkl')
test_df.to_pickle('test_df.pkl')
object_cols = [col for col in train_df.columns if train_df[col].dtype == 'object']
good_label_cols = [col for col in object_cols if set(train_df[col]) == set(test_df[col])]
bad_label_cols = list(set(object_cols) - set(good_label_cols))
object_nunique = list(map(lambda col: train_df[col].nunique(), object_cols))
d = dict(zip(object_cols, object_nunique))
sorted(d.items(), key=lambda x: -x[1]) | code |
49124559/cell_13 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import timm
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import os
import pandas as pd
df = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
class CFG:
debug = True
n_classes = 5
lr = 0.0001
batch_size = 8
epochs = 1
seed = 777
n_fold = 4
warmup = -1
device = 0
amp = True
amp_inf = False
smooth = False
smooth_alpha = 0.1
efnet_num = 10
drop_rate = 0.25
crop = False
psuedo_label = False
pseudo_predict = '2020-11-06_14:50:43.385109_predict.csv'
TTA = False
Attention = False
white = False
model_name = 'tf_efficientnet_b0_ns'
zoom = True
SIZE = 512
model_names = ['tf_efficientnet_b0_ns', 'vit_base_resnet26d_224', 'resnest50d', 'vit_base_patch32_384']
def seed_torch(seed=42):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_torch(seed=42)
torch.cuda.set_device(CFG.device)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.9, stratify=df['label'], random_state=2020)
train = train.reset_index(drop=True)
test = test.reset_index(drop=True)
class efnet_model(nn.Module):
def __init__(self):
super().__init__()
if CFG.efnet_num == 0:
self.model = geffnet.efficientnet_b0(pretrained=True, drop_rate=CFG.drop_rate)
elif CFG.efnet_num == 1:
self.model = geffnet.efficientnet_b1(pretrained=True, drop_rate=CFG.drop_rate)
elif CFG.efnet_num == 2:
self.model = geffnet.efficientnet_b2(pretrained=True, drop_rate=CFG.drop_rate)
elif CFG.efnet_num == 10:
self.model = timm.create_model(CFG.model_name, pretrained=True, num_classes=CFG.n_classes)
elif CFG.efnet_num < 3:
self.model.classifier = nn.Linear(self.model.classifier.in_features, 2)
def forward(self, x):
if CFG.efnet_num == 10:
x = self.model(x)
else:
x = self.model(x)
return x
if CFG.debug:
folds = train.sample(n=200, random_state=CFG.seed).reset_index(drop=True).copy()
else:
folds = train.copy()
train_labels = folds['label'].values
kf = StratifiedKFold(n_splits=CFG.n_fold, shuffle=True, random_state=CFG.seed)
for fold, (train_index, val_index) in enumerate(kf.split(folds.values, train_labels)):
print('num_train,val', len(train_index), len(val_index), len(val_index) + len(train_index))
folds.loc[val_index, 'fold'] = int(fold) | code |
49124559/cell_20 | [
"text_plain_output_1.png",
"image_output_2.png",
"image_output_1.png"
] | from albumentations import Compose, Normalize, HorizontalFlip, VerticalFlip,RandomGamma, RandomRotate90,GaussNoise,Cutout
from albumentations.pytorch import ToTensor
from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
from torch.optim import Adam, SGD
from torch.utils.data import DataLoader, Dataset
from tqdm import tqdm
import cv2
import cv2
import datetime
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import time
import timm
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import os
import datetime
dt_now = datetime.datetime.now()
dt_now_ = str(dt_now).replace(' ', '_')
import pandas as pd
df = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
class CFG:
debug = True
n_classes = 5
lr = 0.0001
batch_size = 8
epochs = 1
seed = 777
n_fold = 4
warmup = -1
device = 0
amp = True
amp_inf = False
smooth = False
smooth_alpha = 0.1
efnet_num = 10
drop_rate = 0.25
crop = False
psuedo_label = False
pseudo_predict = '2020-11-06_14:50:43.385109_predict.csv'
TTA = False
Attention = False
white = False
model_name = 'tf_efficientnet_b0_ns'
zoom = True
SIZE = 512
model_names = ['tf_efficientnet_b0_ns', 'vit_base_resnet26d_224', 'resnest50d', 'vit_base_patch32_384']
def seed_torch(seed=42):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_torch(seed=42)
torch.cuda.set_device(CFG.device)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.9, stratify=df['label'], random_state=2020)
train = train.reset_index(drop=True)
test = test.reset_index(drop=True)
class efnet_model(nn.Module):
def __init__(self):
super().__init__()
if CFG.efnet_num == 0:
self.model = geffnet.efficientnet_b0(pretrained=True, drop_rate=CFG.drop_rate)
elif CFG.efnet_num == 1:
self.model = geffnet.efficientnet_b1(pretrained=True, drop_rate=CFG.drop_rate)
elif CFG.efnet_num == 2:
self.model = geffnet.efficientnet_b2(pretrained=True, drop_rate=CFG.drop_rate)
elif CFG.efnet_num == 10:
self.model = timm.create_model(CFG.model_name, pretrained=True, num_classes=CFG.n_classes)
elif CFG.efnet_num < 3:
self.model.classifier = nn.Linear(self.model.classifier.in_features, 2)
def forward(self, x):
if CFG.efnet_num == 10:
x = self.model(x)
else:
x = self.model(x)
return x
if CFG.debug:
folds = train.sample(n=200, random_state=CFG.seed).reset_index(drop=True).copy()
else:
folds = train.copy()
train_labels = folds['label'].values
kf = StratifiedKFold(n_splits=CFG.n_fold, shuffle=True, random_state=CFG.seed)
for fold, (train_index, val_index) in enumerate(kf.split(folds.values, train_labels)):
folds.loc[val_index, 'fold'] = int(fold)
class TrainDataset(Dataset):
def __init__(self, df, transform1=None, transform2=None):
self.df = df
self.transform = transform1
self.transform_ = transform2
def __len__(self):
return len(self.df)
def __getitem__(self, idx):
path = self.df['image_id'].values[idx]
file_path = '/kaggle/input/cassava-leaf-disease-classification/train_images/{}'.format(path)
image = cv2.imread(file_path)
if CFG.crop:
image = crop_object(image)
try:
image = cv2.resize(image, (SIZE, SIZE))
except Exception as e:
label_ = self.df['label'].values[idx]
if self.transform:
image = self.transform(image=image)['image']
if self.transform_:
image = self.transform_(image=image)['image']
label = torch.tensor(label_)
return (image, label)
def get_transforms1(*, data):
if data == 'train':
return Compose([HorizontalFlip(p=0.5), VerticalFlip(p=0.5), Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
elif data == 'valid':
return Compose([Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
def to_tensor(*args):
return Compose([ToTensor()])
def train_fn(fold):
trn_idx = folds[folds['fold'] != fold].index
val_idx = folds[folds['fold'] == fold].index
train_df = folds.loc[trn_idx].reset_index(drop=True)
valid_df = folds.loc[val_idx].reset_index(drop=True)
train_dataset = TrainDataset(folds.loc[trn_idx].reset_index(drop=True), transform1=get_transforms1(data='train'), transform2=to_tensor())
valid_dataset = TrainDataset(folds.loc[val_idx].reset_index(drop=True), transform1=get_transforms1(data='valid'), transform2=to_tensor())
train_loader = DataLoader(train_dataset, batch_size=CFG.batch_size, shuffle=True, num_workers=4)
valid_loader = DataLoader(valid_dataset, batch_size=CFG.batch_size, shuffle=False, num_workers=4)
model = efnet_model()
model.to(device)
optimizer = Adam(model.parameters(), lr=CFG.lr, amsgrad=False)
criterion = nn.CrossEntropyLoss()
softmax = nn.Softmax(dim=1)
for epoch in range(CFG.epochs):
start_time = time.time()
model.train()
avg_loss = 0.0
tk0 = tqdm(enumerate(train_loader), total=len(train_loader))
for i, (images, labels) in tk0:
images = images.to(device)
labels = labels.to(device)
optimizer.zero_grad()
y_preds = model(images.float())
loss = criterion(y_preds, labels.long())
loss.backward()
optimizer.step()
avg_loss += loss.item() / len(train_loader)
model.eval()
avg_val_loss = 0.0
valid_labels = []
preds = []
tk1 = tqdm(enumerate(valid_loader), total=len(valid_loader))
for i, (images, labels) in tk1:
images = images.to(device)
labels = labels.to(device)
with torch.no_grad():
y_preds = model(images.float())
loss = criterion(y_preds, labels.long())
valid_labels.append(labels.to('cpu').detach().numpy().copy())
y_preds = softmax(y_preds)
preds.append(y_preds.to('cpu').detach().numpy().copy())
avg_val_loss += loss.item() / len(valid_loader)
preds = np.concatenate(preds)
valid_labels = np.concatenate(valid_labels)
torch.save(model.state_dict(), f'fold{fold}_{dt_now_}_baseline.pth')
return (preds, valid_labels)
def auc(predict, labels):
pass
predict = []
labels = []
for fold in range(CFG.n_fold):
_pred, _label = train_fn(fold)
predict.append(_pred)
labels.append(_label)
predict = np.concatenate(predict)
labels = np.concatenate(labels)
score = auc(predict, labels)
print(predict)
print(labels)
print(len(labels))
print(score) | code |
49124559/cell_11 | [
"text_plain_output_1.png"
] | from sklearn.model_selection import train_test_split
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.9, stratify=df['label'], random_state=2020)
train = train.reset_index(drop=True)
test = test.reset_index(drop=True)
print(train['label'].value_counts())
print(test['label'].value_counts()) | code |
49124559/cell_1 | [
"text_plain_output_1.png"
] | import os
import numpy as np
import pandas as pd
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
49124559/cell_7 | [
"text_html_output_1.png"
] | import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import pandas as pd
df = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
df | code |
49124559/cell_8 | [
"text_plain_output_5.png",
"application_vnd.jupyter.stderr_output_2.png",
"text_plain_output_9.png",
"application_vnd.jupyter.stderr_output_4.png",
"application_vnd.jupyter.stderr_output_6.png",
"application_vnd.jupyter.stderr_output_8.png",
"text_plain_output_3.png",
"text_plain_output_7.png",
"text_plain_output_1.png"
] | path = '../input/cassava-leaf-disease-classification/train_images/100042118.jpg'
import cv2
import matplotlib.pyplot as plt
img = cv2.imread(path)
im_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
print(img.shape)
plt.imshow(im_gray, cmap='gray')
plt.show()
plt.imshow(im_gray, cmap='jet')
plt.show() | code |
49124559/cell_3 | [
"text_plain_output_1.png"
] | !pip install ttach
!pip install timm | code |
49124559/cell_14 | [
"text_html_output_1.png"
] | from sklearn.model_selection import StratifiedKFold
from sklearn.model_selection import train_test_split
import numpy as np
import numpy as np # linear algebra
import os
import os
import pandas as pd
import pandas as pd
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import random
import timm
import torch
import torch.nn as nn
import numpy as np
import pandas as pd
import os
import pandas as pd
df = pd.read_csv('../input/cassava-leaf-disease-classification/train.csv')
class CFG:
debug = True
n_classes = 5
lr = 0.0001
batch_size = 8
epochs = 1
seed = 777
n_fold = 4
warmup = -1
device = 0
amp = True
amp_inf = False
smooth = False
smooth_alpha = 0.1
efnet_num = 10
drop_rate = 0.25
crop = False
psuedo_label = False
pseudo_predict = '2020-11-06_14:50:43.385109_predict.csv'
TTA = False
Attention = False
white = False
model_name = 'tf_efficientnet_b0_ns'
zoom = True
SIZE = 512
model_names = ['tf_efficientnet_b0_ns', 'vit_base_resnet26d_224', 'resnest50d', 'vit_base_patch32_384']
def seed_torch(seed=42):
random.seed(seed)
os.environ['PYTHONHASHSEED'] = str(seed)
np.random.seed(seed)
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.backends.cudnn.deterministic = True
seed_torch(seed=42)
torch.cuda.set_device(CFG.device)
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
from sklearn.model_selection import train_test_split
train, test = train_test_split(df, test_size=0.9, stratify=df['label'], random_state=2020)
train = train.reset_index(drop=True)
test = test.reset_index(drop=True)
class efnet_model(nn.Module):
def __init__(self):
super().__init__()
if CFG.efnet_num == 0:
self.model = geffnet.efficientnet_b0(pretrained=True, drop_rate=CFG.drop_rate)
elif CFG.efnet_num == 1:
self.model = geffnet.efficientnet_b1(pretrained=True, drop_rate=CFG.drop_rate)
elif CFG.efnet_num == 2:
self.model = geffnet.efficientnet_b2(pretrained=True, drop_rate=CFG.drop_rate)
elif CFG.efnet_num == 10:
self.model = timm.create_model(CFG.model_name, pretrained=True, num_classes=CFG.n_classes)
elif CFG.efnet_num < 3:
self.model.classifier = nn.Linear(self.model.classifier.in_features, 2)
def forward(self, x):
if CFG.efnet_num == 10:
x = self.model(x)
else:
x = self.model(x)
return x
if CFG.debug:
folds = train.sample(n=200, random_state=CFG.seed).reset_index(drop=True).copy()
else:
folds = train.copy()
train_labels = folds['label'].values
kf = StratifiedKFold(n_splits=CFG.n_fold, shuffle=True, random_state=CFG.seed)
for fold, (train_index, val_index) in enumerate(kf.split(folds.values, train_labels)):
folds.loc[val_index, 'fold'] = int(fold)
folds | code |
49124559/cell_5 | [
"text_plain_output_1.png"
] | import datetime
import datetime
dt_now = datetime.datetime.now()
dt_now_ = str(dt_now).replace(' ', '_')
print('実験開始', dt_now_) | code |
34144202/cell_13 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique()) | code |
34144202/cell_9 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.head() | code |
34144202/cell_34 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train.dtypes
cols_to_be_selected = ['image_id', 'x0', 'y0', 'w', 'h']
df1_train = df_train[cols_to_be_selected]
val_percentage = 0.2
num_val_images = int(len(df1_train['image_id'].unique()) * val_percentage)
num_train_images = len(df1_train['image_id'].unique()) - num_val_images
list_val_imageid = list(df1_train['image_id'].unique())[-1 * num_val_images:]
list_train_imageid = list(df1_train['image_id'].unique())[:num_train_images]
print('Number of validation images: ', num_val_images)
print('Number of training images: ', num_train_images)
print(num_val_images + num_train_images) | code |
34144202/cell_30 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
for col in df_train.columns:
if sum(df_train[col].isnull()) == 1:
print(col + ' has null values')
else:
print(col + ' no null values') | code |
34144202/cell_29 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train.head() | code |
34144202/cell_19 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train['width'].value_counts() | code |
34144202/cell_7 | [
"text_plain_output_1.png"
] | import os
os.listdir('/kaggle/input/global-wheat-detection') | code |
34144202/cell_18 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train['height'].value_counts() | code |
34144202/cell_15 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train['image_id'].value_counts() | code |
34144202/cell_16 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train['source'].value_counts() | code |
34144202/cell_43 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train.dtypes
cols_to_be_selected = ['image_id', 'x0', 'y0', 'w', 'h']
df1_train = df_train[cols_to_be_selected]
val_percentage = 0.2
num_val_images = int(len(df1_train['image_id'].unique()) * val_percentage)
num_train_images = len(df1_train['image_id'].unique()) - num_val_images
list_val_imageid = list(df1_train['image_id'].unique())[-1 * num_val_images:]
list_train_imageid = list(df1_train['image_id'].unique())[:num_train_images]
df2_val = df1_train.loc[df1_train['image_id'].isin(list_val_imageid), :]
df2_train = df1_train.loc[df1_train['image_id'].isin(list_train_imageid), :]
train_dir = '/kaggle/input/global-wheat-detection/train'
test_obj = GlobalWheatDetectionDataset(df2_train, train_dir) | code |
34144202/cell_31 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
df_train.dtypes | code |
34144202/cell_24 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
for col in df_train.columns:
if sum(df_train[col].isnull()) == 1:
print(col + ' has null values')
else:
print(col + ' no null values') | code |
34144202/cell_22 | [
"text_plain_output_1.png"
] | import os
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
os.listdir('/kaggle/input/global-wheat-detection')
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
df_train.shape[0] / len(df_train['image_id'].unique())
list_image_ids_df = list(df_train['image_id'].unique())
list_image_ids_dir = os.listdir('/kaggle/input/global-wheat-detection/train')
list_image_ids_df.sort() == list_image_ids_dir.sort() | code |
34144202/cell_10 | [
"text_plain_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape | code |
34144202/cell_12 | [
"text_html_output_1.png"
] | import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
df_train = pd.read_csv('/kaggle/input/global-wheat-detection/train.csv')
df_train.shape
len(df_train['image_id'].unique()) | code |
88086039/cell_34 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(16, 4))
sns.countplot(x='Survived', hue='Sex', data=train, palette='coolwarm') | code |
88086039/cell_26 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(16, 6))
sns.heatmap(data=train.isnull(), yticklabels=False, cbar=False, cmap='viridis') | code |
88086039/cell_41 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(16, 4))
sns.countplot(x='Embarked', data=train, hue='Survived')
plt.legend(loc=1) | code |
88086039/cell_2 | [
"text_plain_output_1.png"
] | import os
import os
for dirname, _, filenames in os.walk('/kaggle/input'):
for filename in filenames:
print(os.path.join(dirname, filename)) | code |
88086039/cell_18 | [
"text_html_output_1.png"
] | train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
train.info() | code |
88086039/cell_32 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(16, 4))
sns.countplot(x='Survived', data=train) | code |
88086039/cell_38 | [
"image_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
plt.figure(figsize=(16, 4))
sns.countplot(x='Survived', hue='Pclass', data=train)
plt.legend(loc=1) | code |
88086039/cell_43 | [
"text_plain_output_1.png"
] | import matplotlib.pyplot as plt
import seaborn as sns
train = pd.read_csv(dirname + '/train.csv')
test = pd.read_csv(dirname + '/test.csv')
pid_test = test['PassengerId']
pct_missing = round(train.isnull().sum() / train.isnull().count() * 100, 1)
pct_missing.sort_values(ascending=False).head()
print('Survival rate with respect to the port of embarkation:')
print('\tS: {} %'.format(train[(train.Embarked == 'S') & (train.Survived == 1)]['Survived'].count() / len(train[train.Embarked == 'S']) * 100))
print('\tC: {} %'.format(train[(train.Embarked == 'C') & (train.Survived == 1)]['Survived'].count() / len(train[train.Embarked == 'C']) * 100))
print('\tQ: {} %'.format(train[(train.Embarked == 'Q') & (train.Survived == 1)]['Survived'].count() / len(train[train.Embarked == 'Q']) * 100)) | code |
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