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Utils.py
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from datetime import datetime
import numpy as np
from pandas.core.common import flatten
import os
import json
def parse_args(parser):
# model configurations
parser.add_argument('--model', nargs="+", type=str, default={"DT"},
help='DT/RF')
parser.add_argument('--dataset', nargs="+", type=str, default=None,
help='MNIST/IRIS/ADULT/SENSORLESS/WINEQUALITY/OLIVETTI/COVTYPE/SPAMBASE/WEARABLE/LETTER')
parser.add_argument('--depth', nargs="+", type=int, default={5}, help='Maximum depth of the trees')
parser.add_argument('--estims', nargs="+", type=int, default={5}, help='Number of trees in the ensemble')
parser.add_argument('--load-model-path', type=str, default=None, help='Specify path to model if it should be loaded, otherwise a model will be trained from scratch')
parser.add_argument('--store-model', type=int, default=None, help='Set to 1 to store model as .pkl')
# bit flip onjection experiment settings
parser.add_argument('--trials', type=int, default=5, help='Number of repetitions of the entire test set in bit flip injection')
parser.add_argument('--splitval-inj', type=int, default=0, help='Bit flip injection into split values (activate with 1)')
parser.add_argument('--featval-inj', type=int, default=0, help='Bit flip injection into feature values (activate with 1)')
parser.add_argument('--featidx-inj', type=int, default=0, help='Bit flip injection into feature indices (activate with 1)')
parser.add_argument('--chidx-inj', type=int, default=0, help='Bit flip injection into child indices (activate with 1)')
# value encoding
parser.add_argument('--nr-bits-split', type=int, default=8, help='Number of bits to encode the split value')
parser.add_argument('--nr-bits-feature', type=int, default=8, help='Number of bits to encode the feature value')
parser.add_argument('--int-split', type=int, default=0, help='Whether to encode the split values as integers')
# other settings
parser.add_argument('--true-majority', type=int, default=0, help='Whether to use true majority vote or the standard')
parser.add_argument('--seed', type=int, default=42, metavar='S',
help='random seed (default: 42)')
parser.add_argument('--export-accuracy', type=int, default=None, help='Bit flip injection')
# settings by me
parser.add_argument('--summarize', type=int, default=0, help='Summarize over all BERs (activate with 1)')
#parser.add_argument('--complete_trees', type=bool, default=0, help='Use Robust Complete Trees (activate with 1)')
parser.add_argument('--complete_trees', type=int, default=0, help='Use Complete Trees (activate with 1)')
parser.add_argument('--complete_redundant_trees', type=int, default=0, help='Use Redundant Complete Trees (activate with 1)')
parser.add_argument('--rsdt', nargs="*", type=int, default={0}, help='Value for Robust Split Decision')
parser.add_argument('--resilience', type=int, default=0, help='Calculate Error Resilience instead of Accuracy (activate with 1)')
parser.add_argument('--timing', type=int, default=0, help='Measure runtime for building and evaluation (activate with 1)')
def create_exp_folder(this_path):
exp_path = ""
access_rights = 0o755
this_path = os.getcwd()
exp_path += this_path+"/experiments/"+"results-"+datetime.now().strftime('%d-%m-%Y-%H:%M:%S')
try:
os.makedirs(exp_path, access_rights, exist_ok=False)
except OSError:
print ("Creation of the directory %s failed" % exp_path)
else:
# print ("Successfully created the directory %s" % exp_path)
return exp_path
def store_exp_data_dict(exp_data):
to_dump = dict()
to_dump["results"] = exp_data
return to_dump
def store_exp_data_write(to_dump_path, to_dump_data):
with open(to_dump_path, 'a') as outfile:
json.dump(to_dump_data, outfile)
# print ("Successfully stored results in %s" % to_dump_path)
def bit_error_rates_generator(p2exp):
temp = [1, 2.5, 5.0, 7.5] # steps between powers of 10
nr_points = p2exp # exponent to begin with, begin at 10**(-nr_points-1)
ber_array = [0]
ber_array.append([temp[i]*((10)**(-nr_points-1)) for i in range(len(temp))])
ber_array = list(flatten(ber_array))
rest_array = [1*(10**(-nr_points+i)) for i in range(nr_points)]
for point in rest_array:
for step in temp:
ber_array.append(point*step)
bers = bers[:-1]
return bers
def quantize_data(data, q_range_bits):
data = np.array(data)
# get number of quantization levels
q_range = 2**(q_range_bits) - 1
# uniform quantization to unsigned integer, with q_range quantization levels
# shift the data by the minumum value (negative or 0 here) so that all values are positive
quantized = data - data.min()
# multiply by the ratio of quantization range and data value range
quantized = quantized * (q_range/(data.max() - data.min()))
# round the result to nearest integer
quantized = np.round(quantized)
return quantized
def get_nr_child_idx(clf):
n_nodes = clf.tree_.node_count
children_left = clf.tree_.children_left
children_right = clf.tree_.children_right
feature = clf.tree_.feature
threshold = clf.tree_.threshold
n_leaves_h = 0
node_depth = np.zeros(shape=n_nodes, dtype=np.int64)
is_leaves = np.zeros(shape=n_nodes, dtype=bool)
stack = [(0, 0)] # start with the root node id (0) and its depth (0)
while len(stack) > 0:
# `pop` ensures each node is only visited once
node_id, depth = stack.pop()
node_depth[node_id] = depth
# If the left and right child of a node is not the same we have a split
# node
is_split_node = children_left[node_id] != children_right[node_id]
# If a split node, append left and right children and depth to `stack`
# so we can loop through them
if is_split_node:
stack.append((children_left[node_id], depth + 1))
stack.append((children_right[node_id], depth + 1))
else:
is_leaves[node_id] = True
# print("The binary tree structure has {n} nodes and has "
# "the following tree structure:\n".format(n=n_nodes))
for i in range(n_nodes):
if is_leaves[i]:
# print("{space}node={node} is a leaf node.".format(
# space=node_depth[i] * "\t", node=i))
n_leaves_h += 1
#else:
# print("{space}node={node} is a split node: "
# "go to node {left} if X[:, {feature}] <= {threshold} "
# "else to node {right}.".format(
# space=node_depth[i] * "\t",
# node=i,
# left=children_left[i],
# feature=feature[i],
# threshold=threshold[i],
# right=children_right[i]))
return n_nodes - n_leaves_h