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plot_repair_ml.py
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plot_repair_ml.py
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import matplotlib as mpl
mpl.use('agg')
import pandas as pd
import argparse
import seaborn as sns
import numpy as np
import pylab
COLUMNS = ['sheet', 'num_lines', 'strategy', 'decay_type', 'ghost', 'explore', 'decay',\
'likelihood']
NOCUT = ['6', '7a', '7c', '8a', '8b', '9b', '14','15', '16', '17', '21a', '21b', '23a', '23d']
CUT = ['7b', '9a', '9c', '9d', '10', '11', '12', '13', '18', '19', '20', '21c', '22', '23b', '23c', '23e', '23f']
ALL = NOCUT + CUT
NOCUT2 = ['6', '7a', '7c', '9b', '14','15', '16', '21a', '21b', '23a', '23d']
ALL2 = NOCUT2 + CUT
DATASETS_DIR = 'datasets/reformated_csv'
OUT_DIR = 'ml_plots'
ML_OUTFILE = '%s/repair_ml.csv' % OUT_DIR
def print_ml_parameters(df, strategies, decay_types):
df2 = df[(df['strategy'].isin(strategies)) & (df['decay_type'].isin(decay_types))]
for name, group in df2.groupby(['strategy', 'decay_type']):
strategy, decay_type = name
print "%s, %s" % (strategy, decay_type)
max_likelihood = float('-inf')
max_likelihood_params = []
for name2, group2 in group.groupby(['explore', 'decay']):
explore, decay = name2
likelihood = sum(group2['likelihood'])
if likelihood > max_likelihood:
max_likelihood = likelihood
max_likelihood_params = [(explore, decay)]
elif likelihood == max_likelihood:
max_likelihood_params.append((explore, decay))
print "maximum likelihood %f" % max_likelihood
for explore, decay in max_likelihood_params:
print "e = %f, d=%f" % (explore, decay)
def ml_heat(df, strategies, decay_types):
df2 = df[(df['strategy'].isin(strategies)) & (df['decay_type'].isin(decay_types))]
for name, group in df2.groupby(['strategy', 'decay_type']):
group = group.groupby(['explore', 'decay'], as_index=False).agg({'likelihood' : np.sum})
group = group.pivot('decay', 'explore', 'likelihood')
pylab.figure()
ax = sns.heatmap(group, cmap='nipy_spectral', xticklabels=False, yticklabels=False, linewidths=0)
ax.invert_yaxis()
pylab.savefig('sandbox.pdf')
pylab.close()
def ml_plot(df, strategies, decay_types):
df2 = df[(df['strategy'].isin(strategies)) & (df['decay_type'].isin(decay_types))]
for name, group in df2.groupby(['strategy', 'decay_type']):
x = []
y = []
for name2, group2 in group.groupby('decay'):
decay = name2
group2 = group2.groupby('explore', as_index=False).agg({'likelihood' : np.sum})
max_likelihood = group2['likelihood'].max()
group2 = group2[group2['likelihood'] == max_likelihood]
best_explores = group2['explore']
for explore in best_explores:
x.append(decay)
y.append(explore)
pylab.figure()
pylab.scatter(x, y)
pylab.savefig('figs/sandbox.pdf')
pylab.close()
def print_variance(df, strategies, decay_types):
df2 = df[(df['strategy'].isin(strategies)) & (df['decay_type'].isin(decay_types))]
for name, group in df2.groupby(['strategy', 'decay_type']):
print name
max_explores = []
max_decays = []
for sheet in group['sheet'].unique():
group2 = group[group['sheet'] == sheet]
max_likelihood = max(group2['likelihood'])
group2 = group2[group2['likelihood'] == max_likelihood]
#print group2
explores = list(group2['explore'])
decays = list(group2['decay'])
max_explores += explores
max_decays += decays
print "explore", pylab.var(max_explores, ddof=1)
print "decay", pylab.var(max_decays, ddof=1)
def filter_df(df, emin, emax, dmin, dmax, ghost, sheets):
'''
df = df[(emin <= df['explore']) & (df['explore'] <= emax) & \
(dmin <= df['decay']) & (df['decay'] <= dmax) & \
(df['ghost'] == int(ghost)) & (df['sheet'].isin(sheets))]
'''
df = df[df['explore'] >= emin]
df = df[df['explore'] <= emax]
df = df[df['decay'] >= dmin]
df = df[df['decay'] <= dmax]
df = df[df['ghost'] == int(ghost)]
df = df[df['sheet'].isin(sheets)]
return df
def main():
parser = argparse.ArgumentParser()
parser.add_argument('-sh', '--sheets', nargs='+', required=True)
parser.add_argument('-l', '--label', default=None)
parser.add_argument('-s', '--strategies', nargs='+', required=True)
parser.add_argument('-dt', '--decay_types', nargs='+', required=True)
parser.add_argument('-emin', type=float, default=0.01)
parser.add_argument('-emax', type=float, default=0.99)
parser.add_argument('-dmin', type=float, default=0.01)
parser.add_argument('-dmax', type=float, default=0.99)
parser.add_argument('-g', '--ghost', action='store_true')
parser.add_argument('--heat', action='store_true')
parser.add_argument('--plot', action='store_true')
parser.add_argument('--hist', action='store_true')
parser.add_argument('--print', action='store_true', dest='print_ml')
parser.add_argument('-v', '--var', action='store_true', dest='print_var')
args = parser.parse_args()
sheets = args.sheets
label = args.label
if label == None:
assert len(sheets) == 1
label = sheets
if sheets == ['nocut']:
sheets = NOCUT
elif sheets == ['nocut2']:
sheets = NOCUT2
elif sheets == ['cut']:
sheets = CUT
elif sheets == ['all']:
sheets = ALL
elif sheets == ['all2']:
sheets = ALL2
else:
assert len(sheets) > 1
acceptable_sheets = set(ALL)
for sheet in sheets:
assert sheet in acceptable_sheets
strategies = args.strategies
decay_types = args.decay_types
emin = args.emin
emax = args.emax
dmin = args.dmin
dmax = args.dmax
ghost = args.ghost
heat = args.heat
plot = args.plot
hist = args.hist
print_ml = args.print_ml
print_var = args.print_var
df = pd.read_csv(ML_OUTFILE, names = COLUMNS, skipinitialspace=True)
df['sheet'] = df['sheet'].astype(str)
df.drop_duplicates(inplace=True)
df = filter_df(df, emin, emax, dmin, dmax, ghost, sheets)
if heat:
ml_heat(df, strategies, decay_types)
if plot:
ml_plot(df, strategies, decay_types)
if print_ml:
print_ml_parameters(df, strategies, decay_types)
if print_var:
print_variance(df, strategies, decay_types)
if __name__ == '__main__':
main()