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%load_ext autoreload
%autoreload 2
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import sys
sys.path.append("../../")
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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 dynamic_sparse.common.browser import *
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exps = ['mlp_heb.py_eval2', ]
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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df.columns
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df.iloc[10]
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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 trials
df_origin = df.copy()
df = df_origin[df_origin["epochs"]>=30]
df.shape
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# which ones failed?
df_origin['failed'] = df_origin["epochs"]<30
df_origin.groupby(['on_perc'])['failed'].agg('sum')
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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,
'noise_acc_max_epoch': round_mean,
'noise_acc_max': stats,
'model': ['count']})).round(round)
else:
return (df[filter].groupby(columns)
.agg({'val_acc_max_epoch': round_mean,
'val_acc_max': stats,
'noise_acc_max_epoch': round_mean,
'noise_acc_max': stats,
'model': ['count']})).round(round)
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agg(['kwinners'])
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high_sparsity = (df['on_perc']==0.05)
avg_sparsity = (df['on_perc']==0.1)
low_sparsity = (df['on_perc']==0.2)
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agg(['kwinners'], low_sparsity)
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agg(['kwinners'], high_sparsity)
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agg(['on_perc'])
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agg(['hebbian_grow'])
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agg(['hebbian_grow'], low_sparsity)
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agg(['hebbian_grow'], high_sparsity)
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agg(['hebbian_prune_perc'])
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agg(['hebbian_prune_perc'], low_sparsity)
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agg(['hebbian_prune_perc'], high_sparsity)
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no_magnitude = (df['weight_prune_perc'] == 0)
agg(['hebbian_prune_perc'], no_magnitude)
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no_magnitude = (df['weight_prune_perc'] == 0)
agg(['hebbian_prune_perc'], (no_magnitude & low_sparsity))
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agg(['weight_prune_perc'])
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agg(['weight_prune_perc'], low_sparsity)
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agg(['weight_prune_perc'], high_sparsity)
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agg(['weight_prune_perc'], avg_sparsity)
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no_hebbian = (df['hebbian_prune_perc'] == 0)
agg(['weight_prune_perc'], no_hebbian)
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pd.pivot_table(df,
index='hebbian_prune_perc',
columns='weight_prune_perc',
values='val_acc_max',
aggfunc=mean_and_std)
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pd.pivot_table(df[low_sparsity],
index=['kwinners','hebbian_prune_perc'],
columns='weight_prune_perc',
values='val_acc_max',
aggfunc=mean_and_std)
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pd.pivot_table(df[avg_sparsity],
index=['kwinners','hebbian_prune_perc'],
columns='weight_prune_perc',
values='val_acc_max',
aggfunc=mean_and_std)
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pd.pivot_table(df[avg_sparsity],
index='hebbian_prune_perc',
columns='weight_prune_perc',
values='val_acc_max',
aggfunc=mean_and_std)
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pd.pivot_table(df[high_sparsity],
index='hebbian_prune_perc',
columns='weight_prune_perc',
values='val_acc_max',
aggfunc=mean_and_std)
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