In [1]:
import pandas as pd
from pandas.io.json import json_normalize #package for flattening json in pandas df
import numpy as np
import matplotlib.pyplot as plt
import matplotlib
import json
import os
%matplotlib inline
In [2]:
os.chdir("./runs/lagaris/1d_trapz/")
origin_path = os.getcwd()
runs_id = os.listdir("./")
runs_id = [int(item) for item in runs_id]
runs_id = sorted(runs_id)
In [3]:
df_list = []
for run_id in runs_id:
os.chdir("./"+str(run_id))
f_in = open('out.json', 'r')
run_info = json.load(f_in)
f_in.close()
a = json_normalize(run_info)
#a.set_index(pd.Index([run_id]))
df_list.append(a)
#a = pd.concat(a,b)
os.chdir(origin_path)
res1 = pd.concat(df_list,ignore_index=True)
In [4]:
res = res1[res1['Model info.m_train'] == 40]
#res = res1
plt.figure(figsize=(15,10))
plt.grid(True)
plt.title('MSE vs m_trapz', fontsize=26)
plt.xlabel('Number of integration points', fontsize=20)
plt.ylabel('MSE', fontsize=20)
res.plot(
x='Model info.m_trapz',
y='Out info.MSE',
logy=True,
#logx=True,
ax = plt.gca(),
style = 'ro--',
)
plt.gca().legend(
loc='best',
fontsize=26
)
Out[4]:
In [5]:
res2 = res1[res1['Out info.MSE'] > 10e-5]
plt.figure(figsize=(15,10))
plt.title('MSE vs m_trapz', fontsize=26)
res2.plot.scatter(
x='Model info.m_trapz',
y='Out info.MSE',
logy=True,
ax = plt.gca(),
)
Out[5]:
In [10]:
res1[(res1['Out info.MSE'] > 10e-5) & (res1['Model info.m_train'] == 40)]
Out[10]:
In [11]:
res1[(res1['Out info.MSE'] > 10e-5) & (res1['Model info.m_trapz'] > 40)]
Out[11]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]:
In [ ]: