/
som.py
51 lines (38 loc) · 1.36 KB
/
som.py
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import argparse
import numpy as np
import pandas as pd
from minisom import MiniSom
import matplotlib.pyplot as plt
def _parse_file_argument():
parser = argparse.ArgumentParser("MDS Commandline Tool")
parser.add_argument('--csv',
help='csv filename')
parser.add_argument('--label_prefix',
help='csv\'s label column prefix')
parser.add_argument('--label_sufix',
help='csv\'s label column sufix')
args = parser.parse_args()
return args
def _plot_distribution(som):
fig = plt.figure()
ax = plt.subplot(aspect='equal')
plt.pcolor(som.distance_map().T)
return fig, ax
RS = 20160101
if __name__ == '__main__':
args = _parse_file_argument()
data = pd.read_csv(args.csv)
data.fillna(0, inplace=True)
label_column = args.label_prefix
label_prefix = data[label_column].values
data.drop(label_column, axis=1, inplace=True)
label_column = args.label_sufix
label_sufix = data[label_column].values
data.drop(label_column, axis=1, inplace=True)
id_column = 'id'
data.drop(id_column, axis=1, inplace=True)
som = MiniSom(8,8,len(data.columns),sigma=1.0,learning_rate=0.5,random_seed=RS)
som.random_weights_init(data.as_matrix())
som.train_random(data.as_matrix(),100)
_plot_distribution(som)
plt.savefig('som.png', dpi=120)