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%load_ext autoreload
%autoreload 2
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from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import glob
import tabulate
import pprint
import click
import numpy as np
import pandas as pd
from ray.tune.commands import *
from nupic.research.frameworks.dynamic_sparse.common.browser import *
import matplotlib
import matplotlib.pyplot as plt
from matplotlib import rcParams
%config InlineBackend.figure_format = 'retina'
import seaborn as sns
sns.set(style="whitegrid")
sns.set_palette("colorblind")
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exps = ['improved_mag_gsc_eval3', 'improved_mag_gsc_eval4']
paths = [os.path.expanduser("~/nta/results/{}".format(e)) for e in exps]
df = load_many(paths)
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df.head(5)
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# replace hebbian prine
df['hebbian_prune_perc'] = df['hebbian_prune_perc'].replace(np.nan, 0.0, regex=True)
df['weight_prune_perc'] = df['weight_prune_perc'].replace(np.nan, 0.0, regex=True)
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df.columns
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df.shape
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df.iloc[1]
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df.groupby('model')['model'].count()
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Experiment Details
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# Did any trials failed?
df[df["epochs"]<30]["epochs"].count()
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# Removing failed or incomplete trials
df_origin = df.copy()
df = df_origin[df_origin["epochs"]>=30]
df.shape
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# which ones failed?
# failed, or still ongoing?
df_origin['failed'] = df_origin["epochs"]<30
df_origin[df_origin['failed']]['epochs']
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# helper functions
def mean_and_std(s):
return "{:.3f} ± {:.3f}".format(s.mean(), s.std())
def round_mean(s):
return "{:.0f}".format(round(s.mean()))
stats = ['min', 'max', 'mean', 'std']
def agg(columns, filter=None, round=3):
if filter is None:
return (df.groupby(columns)
.agg({'val_acc_max_epoch': round_mean,
'val_acc_max': stats,
'model': ['count']})).round(round)
else:
return (df[filter].groupby(columns)
.agg({'val_acc_max_epoch': round_mean,
'val_acc_max': stats,
'model': ['count']})).round(round)
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agg(['model'])
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agg(['on_perc'])
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agg(['on_perc', 'model'], df['on_perc'] == 0.02)
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# translate model names
rcParams['figure.figsize'] = 16, 8
d = {
'DSNNWeightedMag': 'Dynamic Sparse Neural Network (DSNN)',
'DSNNMixedHeb': 'Sparse Evolutionary Training (SET)',
'SparseModel': 'Static Sparse',
}
df_plot = df.copy()
df_plot['model'] = df_plot['model'].apply(lambda x: d[x])
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# sns.scatterplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')
sns.lineplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')
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def plot_for_paper():
rcParams['figure.figsize'] = 10,6
matplotlib.rc('xtick', labelsize=14)
matplotlib.rc('ytick', labelsize=14)
matplotlib.rc('ytick', labelsize=14)
plt.rcParams.update({'axes.labelsize': 14})
plt.rcParams.update({'legend.fontsize': 14, 'legend.loc': 'lower right'})
plt.rcParams.update({'font.size': 14})
plt.rcParams.update({"axes.grid": True, "grid.linewidth": 0.5})
plot_for_paper()
fig, ax = plt.subplots()
sns.lineplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')
plt.xlabel("% of active weights (σ)")
plt.ylabel("test accuracy")
plt.ylim((0.4, 1.0))
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles=handles[1:], labels=labels[1:])
plt.savefig('active_weights.png', dpi=300, bbox_inches='tight')
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# plt.rcParams['figure.figsize'] = (8, 3)
font_size=12
# plt.rcParams.update({'font.size': font_size})
# plt.rcParams.update({'font.family': 'Times New Roman'})
# plt.rcParams.update({'axes.labelsize': font_size})
# plt.rcParams.update({'axes.titlesize': 1.5*font_size})
# plt.rcParams.update({'legend.fontsize': font_size})
# plt.rcParams.update({'xtick.labelsize': font_size})
# plt.rcParams.update({'ytick.labelsize': font_size})
# plt.rcParams.update({'savefig.dpi': 2*plt.rcParams['savefig.dpi']})
# plt.rcParams.update({'xtick.major.size': 3})
# plt.rcParams.update({'xtick.minor.size': 3})
# plt.rcParams.update({'xtick.major.width': 1})
# plt.rcParams.update({'xtick.minor.width': 1})
# plt.rcParams.update({'ytick.major.size': 3})
# plt.rcParams.update({'ytick.minor.size': 3})
# plt.rcParams.update({'ytick.major.width': 1})
# plt.rcParams.update({'ytick.minor.width': 1 })
# plt.rcParams.update({})
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# sns.scatterplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')
sns.lineplot(data=df_plot, x='on_perc', y='val_acc_max', hue='model')
plt.ylim(0.8,0.98)
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rcParams['figure.figsize'] = 16, 8
filter = df_plot['model'] != 'Static'
sns.lineplot(data=df_plot[filter], x='on_perc', y='val_acc_max_epoch', hue='model')
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sns.lineplot(data=df_plot, x='on_perc', y='val_acc_last', hue='model')
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