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plot_parameters_tried.py
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plot_parameters_tried.py
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#!/usr/local/bin/python
import os
import argparse
import glob
import numpy as np
import deepdish as dd
import pandas as pd
import matplotlib
matplotlib.use('Agg')
import matplotlib.pylab as plt
# import seaborn as sbn
from mpl_toolkits.mplot3d import Axes3D
from params import IMAGES_DIRECTORY
def discrete_cmap(N, base_cmap=None):
"""Create an N-bin discrete colormap from the specified input map"""
# Note that if base_cmap is a string or None, you can simply do
# return plt.cm.get_cmap(base_cmap, N)
# The following works for string, None, or a colormap instance:
base = plt.cm.get_cmap(base_cmap)
color_list = base(np.linspace(0, 1, N))
cmap_name = base.name + str(N)
return base.from_list(cmap_name, color_list, N)
def plot_3d(params_dir):
N = 2 # bins for colormap
model_dirs = [name for name in os.listdir(params_dir)
if os.path.isdir(os.path.join(params_dir, name))]
colors = plt.get_cmap('plasma')
plt.figure(figsize=(20, 10))
ax = plt.subplot(111, projection='3d')
ax.set_xlabel('Momentum')
ax.set_ylabel('Learning Rate')
ax.zaxis.set_rotate_label(False) # disable automatic rotation
ax.set_zlabel('Training error rate', rotation=270)
ax.set_xticks(np.arange(0, 1.2, 0.2))
ax.set_yticks(np.arange(0, 0.011, 0.002))
ax.set_zticks(np.arange(0, 0.9, 0.1))
#ax.set_xticklabels(('No', 'Yes'))
#ax.set_zticklabels(('0','0.1','0.2','0.3','0.4','0.5','0.6','0.7','0.8'))
ax.invert_yaxis() # invert y axis
ax.invert_xaxis() # invert x axis
#ax.view_init(azim=-178, elev=32)
i = 0
for model_dir in model_dirs:
model_df = pd.DataFrame()
for param_path in glob.glob(os.path.join(params_dir,
model_dir) + '/*.h5'):
param = dd.io.load(param_path)
gd = {'learning rate': param['hyperparameters']['learning_rate'],
'momentum': param['hyperparameters']['momentum'],
'dropout': param['hyperparameters']['dropout'],
'val. objective': param['best_epoch']['validate_objective']}
model_df = model_df.append(pd.DataFrame(gd, index=[0]),
ignore_index=True)
if i != len(model_dirs) - 1:
ax.scatter(model_df['momentum'],
model_df['learning rate'],
model_df['val. objective'],
s=128,
marker=(i+3, 0),
label=model_dir,
# c=model_df['val. objective'],
c=model_df['dropout'],
cmap=discrete_cmap(N, 'jet'))
else:
im = ax.scatter(model_df['momentum'],
model_df['learning rate'],
model_df['val. objective'],
s=128,
marker=(i+4, 0),
label=model_dir,
# c=model_df['val. objective'],
c=model_df['dropout'],
cmap=discrete_cmap(N, 'jet'))
i += 1
cbar=plt.colorbar(im, label='Dropout',ticks=range(N))
cbar.ax.set_yticklabels(['No','Yes'])
cbar.set_label('Dropout', rotation=270)
#plt.legend()
plt.title('Adult dataset',weight='bold')
plt.show()
plt.savefig('{}.eps'.format(os.path.join(IMAGES_DIRECTORY, 'params3d_adult')), format='eps', dpi=1000)
plt.close()
def plot_2d(params_dir):
model_dirs = [name for name in os.listdir(params_dir)
if os.path.isdir(os.path.join(params_dir, name))]
if len(model_dirs) == 0:
model_dirs = [params_dir]
colors = plt.get_cmap('plasma')
plt.figure(figsize=(20, 10))
ax = plt.subplot(111)
ax.set_xlabel('Learning Rate')
ax.set_ylabel('Error rate')
i = 0
for model_dir in model_dirs:
model_df = pd.DataFrame()
for param_path in glob.glob(os.path.join(params_dir,
model_dir) + '/*.h5'):
param = dd.io.load(param_path)
gd = {'learning rate': param['hyperparameters']['learning_rate'],
'momentum': param['hyperparameters']['momentum'],
'dropout': param['hyperparameters']['dropout'],
'val. objective': param['best_epoch']['validate_objective']}
model_df = model_df.append(pd.DataFrame(gd, index=[0]),
ignore_index=True)
if i != len(model_dirs) - 1:
ax.scatter(model_df['learning rate'],
model_df['val. objective'],
s=128,
marker=(i+3, 0),
edgecolor='black',
linewidth=model_df['dropout'],
label=model_dir,
c=model_df['momentum'],
cmap=colors)
else:
im = ax.scatter(model_df['learning rate'],
model_df['val. objective'],
s=128,
marker=(i+3, 0),
edgecolor='black',
linewidth=model_df['dropout'],
label=model_dir,
c=model_df['momentum'],
cmap=colors)
i += 1
plt.colorbar(im, label='Momentum')
plt.legend()
plt.show()
plt.savefig('{}.eps'.format(os.path.join(IMAGES_DIRECTORY, 'params2d')), format='eps', dpi=1000)
plt.close()
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument("params_dir", type=str,
help="Fullpath to parameter trial folders")
parser.add_argument("ndims", type=int, default=2,
help="Fullpath to parameter trial folders")
args = parser.parse_args()
if args.ndims == 2:
plot_2d(args.params_dir)
elif args.ndims == 3:
plot_3d(args.params_dir)
else:
raise Exception(
"{} is not a valid number of dimensions".format(args.ndmins))