1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 339 340 341 342 343 344 345 346 347 348 349 350 351 352 353 354 355 356 357 358 359 360 361 362 363 364 365 366 367 368 369 370 371 372 373 374 375 376 377 378 379 380 381 382 383 384 385 386
|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# Copyright (C) 2009-2020 Authors of CryptoMiniSat, see AUTHORS file
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; version 2
# of the License.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License
# along with this program; if not, write to the Free Software
# Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA
# 02110-1301, USA.
import operator
import re
import pandas as pd
import pickle
import sklearn
import sklearn.svm
import sklearn.tree
import sklearn.cluster
from sklearn.preprocessing import StandardScaler
import argparse
import sys
import numpy as np
import sklearn.metrics
import time
import itertools
import math
import matplotlib.pyplot as plt
import sklearn.ensemble
import os
import helper
import decimal
try:
from termcolor import cprint
except ImportError:
termcolor_avail = False
else:
termcolor_avail = True
ver = sklearn.__version__.split(".")
if int(ver[1]) < 20:
from sklearn.cross_validation import train_test_split
else:
from sklearn.model_selection import train_test_split
CLUST_TYPE_FILES = 0
CLUST_TYPE_USEFULNESS = 1
def get_cluster_name(clust_type):
name = None
if clust_type == CLUST_TYPE_FILES:
name = "clust_f"
elif clust_type == CLUST_TYPE_USEFULNESS:
name = "clust_u"
else:
assert False
return name
def get_clustering_label(df, feats_used: list, scaler, clustering):
this_feats = list(df)
for f in feats_used:
if f not in this_feats:
print("ERROR: Feature '%s' is in the training file but not in the current one!" % f)
exit(-1)
df_clust = df[feats_used].astype(float).copy()
df_clust = scaler.transform(df_clust)
return clustering.predict(df_clust)
def dump_dataframe(df, fname: str):
with open(fname, "wb") as f:
pickle.dump(df, f)
print("Dumped to file %s" % fname)
class Clustering:
def __init__(self, df, clust_type):
self.df = df
self.clust_type = clust_type
def reformat_feats(self, feats):
feats_clean = []
for feat in feats:
assert "szfeat_cur.conflicts" not in feat
c = str(feat)
c = c.replace(".irred_", ".irred_cl_distrib.")
c = c.replace(".red_", ".red_cl_distrib.")
c = c.replace("szfeat_cur.", "{val}.")
feats_clean.append(c)
assert len(feats_clean) == len(feats)
return feats_clean
def create_code_for_cluster_centers(self, clust, scaler, sz_feats):
sz_feats_clean = self.reformat_feats(sz_feats)
f = open("{basedir}/clustering_imp.cpp".format(basedir=options.basedir), 'w')
helper.write_mit_header(f)
f.write("""
#include "satzilla_features.h"
#include "clustering.h"
#include <cmath>
using namespace CMSat;
ClusteringImp::ClusteringImp() {{
set_up_centers();
}}
ClusteringImp::~ClusteringImp() {{
}}
""".format(clusters=options.clusters))
f.write("void ClusteringImp::set_up_centers() {\n")
f.write("\n centers.resize(%d);\n" % options.clusters)
for i in range(options.clusters):
f.write("\n // Doing cluster center %d\n" % i)
f.write("\n centers[%d].resize(%d);\n" % (i, len(sz_feats_clean)) )
f.write("\n used_clusters.push_back(%d);\n" % i)
for i2 in range(len(sz_feats_clean)):
feat = sz_feats_clean[i2]
center = clust.cluster_centers_[i][i2]
f.write(" centers[{num}][{feat}] = {center}L;\n".format(
num=i, feat=i2, center=center))
f.write(" }\n")
f.write("""
double sq(double x) {
return x*x;
}
double ClusteringImp::norm_dist(const SatZillaFeatures& a, const std::vector<double>& center) const {
double dist = 0;
double tmp;
""")
for feat, i in zip(sz_feats_clean, range(10000)):
printed_scale = "{scale:3.10f}".format(scale=scaler.scale_[i])
if decimal.Decimal(printed_scale) == decimal.Decimal("0.0") :
f.write(" // feature {feat} would have caused a division by zero, avoiding\n".\
format(feat=feat.format(val="a")))
continue
f.write(" tmp = ((double){feat}-{mean:3.9f})/{printed_scale};\n".format(
feat=feat.format(val="a"),
mean=scaler.mean_[i],
printed_scale=printed_scale
))
f.write(" dist+=sq(tmp-center[{feat}]);\n\n".format(
feat=i
))
f.write("""
return dist;
}\n""")
f.write("""
int ClusteringImp::which_is_closest(const SatZillaFeatures& p) const {
double closest_dist = numeric_limits<double>::max();
int closest = -1;
for (int i: used_clusters) {
double dist = norm_dist(p, centers[i]);
if (dist < closest_dist) {
closest_dist = dist;
closest = i;
}
}
return closest;
}\n""")
def select_features_files(self):
features = list(self.df)
for f in list(features):
if any(ext in f for ext in ["var", "vcg", "pnr", "min", "max", "std"]):
features.remove(f)
# features from dataframe
feats_used = []
for feat in features:
if "szfeat_cur" in feat and "szfeat_cur.conflicts" not in feat:
feats_used.append(feat)
return feats_used
def select_features_usefulness(self):
features = list(self.df)
feats_used = helper.get_features(options.best_features_fname)
return feats_used
def cluster(self):
if self.clust_type == CLUST_TYPE_FILES:
self.feats_used = self.select_features_files()
elif self.clust_type == CLUST_TYPE_USEFULNESS:
self.feats_used = self.select_features_usefulness()
else:
assert False
print("Features used: ", self.feats_used)
print("Number of features used: ", len(self.feats_used))
if options.check_row_data:
helper.check_too_large_or_nan_values(self.df, self.feats_used)
else:
helper.check_too_large_or_nan_values(self.df.sample(100), self.feats_used)
# fit to slice that only includes CNF features
df_clust = self.df[self.feats_used].astype(float).copy()
if options.scale:
self.scaler = StandardScaler()
self.scaler.fit(df_clust)
if options.verbose:
print("Scaler:")
print(" -- ", self.scaler.mean_)
print(" -- ", self.scaler.scale_)
if options.verbose:
df_clust_back = df_clust.copy()
df_clust = self.scaler.transform(df_clust)
else:
class ScalerNone:
def __init__(self):
self.mean_ = [0.0 for n in range(df_clust.shape[1])]
self.scale_ = [1.0 for n in range(df_clust.shape[1])]
self.scaler = ScalerNone()
# test scaler's code generation
if options.scale and options.verbose:
# we rely on this later in code generation
# for scaler.mean_
# for scaler.scale_
# for cluster.cluster_centers_
for i in range(df_clust_back.shape[1]):
assert df_clust_back.columns[i] == self.feats_used[i]
# checking that scaler works as expected
for feat in range(df_clust_back.shape[1]):
df_clust_back[df_clust_back.columns[feat]
] -= self.scaler.mean_[feat]
df_clust_back[df_clust_back.columns[feat]
] /= self.scaler.scale_[feat]
print(df_clust_back.head()-df_clust.head())
print(df_clust_back.head())
print(df_clust.head())
self.clust = sklearn.cluster.KMeans(n_clusters=options.clusters, random_state=prng)
self.clust.fit(df_clust)
self.df["clust"] = self.clust.labels_
# print information about the clusters
if options.verbose:
print(self.feats_used)
print(self.clust.labels_)
print(self.clust.cluster_centers_)
print(self.clust.get_params())
# code for cluster centers
self.create_code_for_cluster_centers(
self.clust, self.scaler, self.feats_used)
return self.feats_used, self.scaler, self.clust
if __name__ == "__main__":
usage = "usage: %(prog)s [options] file.pandas"
parser = argparse.ArgumentParser(usage=usage)
parser.add_argument("--seed", default=None, type=int,
dest="seed", help="Seed of PRNG")
parser.add_argument("fnames", nargs='+', metavar='FILES',
help="Files to parse, first file is the one to base data off of")
parser.add_argument("--verbose", "-v", action="store_true", default=False,
dest="verbose", help="Print more output")
parser.add_argument("--printfeat", action="store_true", default=False,
dest="print_features", help="Print features")
parser.add_argument("--check", action="store_true", default=False,
dest="check_row_data", help="Check row data for NaN or float overflow")
parser.add_argument("--bestfeatfile",
default="../../scripts/crystal/best_features_usefulness_clust.txt",
type=str,
dest="best_features_fname",
help="Name and position of best features file that lists the best features in order")
# clustering
parser.add_argument("--clusters", default=4, type=int,
dest="clusters", help="How many clusters to use")
parser.add_argument("--scale", default=False, action="store_true",
dest="scale", help="Scale clustering")
parser.add_argument("--nocomputed", default=True, action="store_false",
dest="computed", help="Don't add computed features")
parser.add_argument("--samples", default=1000,
dest="samples_per_file", help="Samples per file")
# number of configs to generate
parser.add_argument("--confs", default="2-2", type=str,
dest="confs", help="Configs to generate")
# code
parser.add_argument("--basedir", type=str,
dest="basedir", help="The base directory of where the CryptoMiniSat source code is")
options = parser.parse_args()
prng = np.random.RandomState(options.seed)
if options.basedir is None:
print("ERROR: must set basedir")
exit(-1)
if options.fnames is None or len(options.fnames) == 0:
print("ERROR: You must give the pandas file!")
exit(-1)
fnames = [f for f in options.fnames if "clust" not in f]
print("Will add clustering to files: ")
for f in fnames:
print("->", f)
if options.clusters <= 0:
print("ERROR: You must give a '--clusters' option that is greater than 0")
exit(-1)
samples = None
for f in fnames:
print("===-- Sampling file %s --" % f)
df = pd.read_pickle(f)
print("options.samples_per_file:", options.samples_per_file)
print("df.shape:", df.shape)
new_samples = df.sample(options.samples_per_file, replace=True,
random_state=prng)
samples = new_samples.append(samples)
print("samples.shape:", samples.shape)
del df
if options.computed:
helper.cldata_add_computed_features(samples, options.verbose)
# clustering_setup is:
# feats_used, scaler, clust
clustering_setup = [None, None]
todo = [CLUST_TYPE_FILES, CLUST_TYPE_USEFULNESS]
todo = [CLUST_TYPE_FILES]
for clust_type in todo:
c = Clustering(samples, clust_type)
clustering_setup[clust_type] = c.cluster()
name = get_cluster_name(clust_type)
if termcolor_avail:
cprint("===-- K-Means clustering **created ** type: %s --" % name,
"green", "on_grey")
else:
cprint("===-- K-Means clustering **created ** type: %s --" % name)
del samples
for f in fnames:
print("===-- Clustering file %s" % (f))
df_orig = pd.read_pickle(f)
df = df_orig.copy()
if options.computed:
helper.cldata_add_computed_features(df, options.verbose)
for clust_type in todo:
name = get_cluster_name(clust_type)
print("===-- type: %s --" % name)
df_orig[name] = get_clustering_label(df, *clustering_setup[clust_type])
print("Distribution: \n%s" % df_orig[name].value_counts())
cleanname = re.sub(r'\.dat$', '', f)
dump_dataframe(df_orig, cleanname+"-clustered.dat")
del df
del df_orig
|