Plot Nested K-fold Cross Validation

In [28]:

import numpy as np
import os
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib as mpl
# Default settings
mpl.rcParams.update(mpl.rcParamsDefault)
# plt.style.use('science')
plt.style.use("seaborn-darkgrid")
plt.rcParams.update({'font.size': 12})

import numpy as np import os import pandas as pd import matplotlib.pyplot as plt import matplotlib as mpl # Default settings mpl.rcParams.update(mpl.rcParamsDefault) # plt.style.use('science') plt.style.use("seaborn-darkgrid") plt.rcParams.update({'font.size': 12})

C:\Users\dicky1031\AppData\Local\Temp\ipykernel_27368\1340097018.py:9: MatplotlibDeprecationWarning: The seaborn styles shipped by Matplotlib are deprecated since 3.6, as they no longer correspond to the styles shipped by seaborn. However, they will remain available as 'seaborn-v0_8-<style>'. Alternatively, directly use the seaborn API instead.
  plt.style.use("seaborn-darkgrid")

In [8]:

data = pd.read_csv(os.path.join('model_save', 'prediction_model_formula8', 'all_result.csv'))

data = pd.read_csv(os.path.join('model_save', 'prediction_model_formula8', 'all_result.csv'))

Out[8]:

	out_fold_idx	in_fold_idx	hp_idx	neuron	learning_rate	batch_size	test_loss
0	0	0	0	5	0.00010	512	0.000070
1	0	0	1	3	0.00010	512	0.000074
2	0	0	2	1	0.00010	512	0.000112
3	0	0	3	5	0.00005	512	0.000074
4	0	0	4	3	0.00005	512	0.000082
...	...	...	...	...	...	...	...
675	4	4	23	1	0.00005	128	0.000087
676	4	4	24	5	0.00003	128	0.000060
677	4	4	25	3	0.00003	128	0.000072
678	4	4	26	1	0.00003	128	0.000112
679	4	4	26	1	0.00003	128	0.000051

680 rows × 7 columns

In [34]:

plt.figure(figsize=(8,6))
plt.plot(sorted(data['test_loss'].to_list())[:-3])
plt.xlabel("hyperparameter set #")
plt.ylabel("loss")
plt.savefig(os.path.join("pic", 'prediction_model_formula8', "nested-k-fold-cross-validation.png"), dpi=300, format='png', bbox_inches='tight')
plt.show()

plt.figure(figsize=(8,6)) plt.plot(sorted(data['test_loss'].to_list())[:-3]) plt.xlabel("hyperparameter set #") plt.ylabel("loss") plt.savefig(os.path.join("pic", 'prediction_model_formula8', "nested-k-fold-cross-validation.png"), dpi=300, format='png', bbox_inches='tight') plt.show()

In [32]:

hyper_set = pd.read_csv(os.path.join('model_save', 'prediction_model_formula8', 'hyper_set.csv'))
hyper_set

hyper_set = pd.read_csv(os.path.join('model_save', 'prediction_model_formula8', 'hyper_set.csv')) hyper_set

Out[32]:

	hp_idx	batch_size	learning_rate	neuron
0	0	512	0.00010	5
1	1	512	0.00010	3
2	2	512	0.00010	1
3	3	512	0.00005	5
4	4	512	0.00005	3
5	5	512	0.00005	1
6	6	512	0.00003	5
7	7	512	0.00003	3
8	8	512	0.00003	1
9	9	256	0.00010	5
10	10	256	0.00010	3
11	11	256	0.00010	1
12	12	256	0.00005	5
13	13	256	0.00005	3
14	14	256	0.00005	1
15	15	256	0.00003	5
16	16	256	0.00003	3
17	17	256	0.00003	1
18	18	128	0.00010	5
19	19	128	0.00010	3
20	20	128	0.00010	1
21	21	128	0.00005	5
22	22	128	0.00005	3
23	23	128	0.00005	1
24	24	128	0.00003	5
25	25	128	0.00003	3
26	26	128	0.00003	1