For lazy python users (monogusa people in Japanse), especially in ML/DSP fields
Project description
Requirements
chardet
GitPython
h5py
joblib
matplotlib
natsort
numpy
pandas
pymatreader
PyYAML
scipy
seaborn
sklearn
statsmodels
torch
xmltodict
Installation
$ pip install mngs
or
$ pip install git+https://github.com/ywatanabe1989/mngs.git@develop
mngs.general.save
import mngs
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
## numpy
arr = np.arange(10)
mngs.general.save(arr, 'spath.npy')
## pandas
df = pd.DataFrame(arr)
mngs.general.save(df, 'spath.csv')
## matplotlib
fig, ax = plt.subplots()
ax.plot(arr)
mngs.general.save(fig, 'spath.png)
mngs.general.load
import mngs
arr = mngs.general.load('spath.npy')
arr = mngs.general.load('spath.mat')
df = mngs.general.load('spath.npy')
yaml_dict = mngs.general.load('spath.yaml')
hdf5_dict = mngs.general.load('spath.hdf5')
mngs.general.fix_seeds
import mngs
import os
import random
import numpy as np
import torch
mngs.general.fix_seeds(os=os, random=random, np=np, torch=torch, tf=None, seed=42)
mngs.general.tee
import sys
sys.stdout, sys.stderr = tee(sys)
print("abc") # also wrriten in stdout
print(1 / 0) # also wrriten in stderr
mngs.plt.configure_mpl
configure_mpl(
plt,
dpi=100,
figsize=(16.2, 10),
figscale=1.0,
fontsize=16,
labelsize="same",
legendfontsize="xx-small",
tick_size="auto",
tick_width="auto",
hide_spines=False,
)
mngs.plt.ax_*
- mngs.plt.ax_extend
- mngs.plt.ax_scientific_notation
- mngs.plt.ax_set_position
mngs.ml.Reporter
Now, classification task is available.
reporter = mngs.ml.Reporter(sdir=log_dir)
for i_fold in range(N_FOLDS):
...
print("\n--- Metrics ---\n")
reporter.calc_metrics(
T_tes,
pred_class_tes,
pred_proba_tes,
labels=[class_0, class_1, class_2],
i_fold=i_fold,
)
print("\n---------------\n")
reporter.summarize()
reporter.save()
The above lines makes reportes and figures.
$ tree $log_dir
├── aucs.csv
├── bACCs.csv
├── balanced_accs.csv
├── clf_reports.csv
├── conf_mat
│ ├── conf_mats.csv
│ ├── fold#0.png
│ ├── fold#1.png
│ ├── fold#2.png
│ └── overall_sum.png
├── mccs.csv
├── pre_rec_curves
│ ├── fold#0.png
│ ├── fold#1.png
│ └── fold#2.png
└── roc_curves
├── fold#0.png
├── fold#1.png
└── fold#2.png
Project details
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