def main():
with open("nameDict.dat", "rb") as name_file:
nameDict = pickle.load(name_file)
bitNames = getDictSubset(nameDict,
lambda record: record.namespace() == "d")
max_height = getMaxHeight(nameDict)
dotBitAlexa = alexaRanks()
dirtyWords = [
word.strip() for word in open('dirty.txt', 'r') if " " not in word
]
dictWords = set(
[word.strip() for word in open('/usr/share/dict/words', 'r')])
bitWordList = set([
"coin", "satoshi", "wallet", "crypto", "currency", "btc", "nmc",
"blockchain"
])
with open("name_lists/surnames.csv", "r") as surnames_file:
reader = DictReader(surnames_file)
surnamesSet = set(line["name"].lower() for line in reader)
valueChangeRank = rankNumberOfValueChanges(bitNames, max_height)
aliveRank = rankIsAlive(bitNames, max_height)
validJSONRank = rankJSONDict(bitNames, max_height)
validDNSRank = rankValidDNSDict(bitNames, max_height)
timeActiveRank = rankByTimeActive(bitNames, max_height)
maxRank = len(bitNames)
xData = []
yData = []
for name in bitNames:
yData.append(
price([
valueChangeRank[name], aliveRank[name], validJSONRank[name],
validDNSRank[name], timeActiveRank[name]
], maxRank))
xData.append([
int(dotBitAlexa[name]) + 1, # alexaRank
int(len(wordnet.synsets(name[2:])) >= 1) + 1, # inDict
int(any(dirtyWord in name.lower()
for dirtyWord in dirtyWords)) + 1, # inDirty
int(set(name[2:]).issubset(set("0123456789"))) + 1, # isNumber
len(name), # length
int(
any(word in name.lower()
for word in bitWordList) or name.startswith("d/bit")) +
1, # coinRelated
int(set(name[2:]).issubset(set("abcdefghijklmnopqrstuvwxyz"))) + 1,
SegmentString().string_segments(name[2:])
])
# x_train, x_test, y_train, y_test = train_test_split(xData, yData, test_size=.10, random_state=33)
alpha = 0.1
enet = ElasticNet(alpha=alpha)
score = cross_val_score(enet, xData, yData, scoring='r2_score')
print(score)
def main():
with open("nameDict.dat", "rb") as name_file:
nameDict = pickle.load(name_file)
bitNames = getDictSubset(nameDict,
lambda record: record.namespace() == "d")
max_height = getMaxHeight(nameDict)
dotBitAlexa = alexaRanks()
dirtyWords = [word.strip() for word in open('dirty.txt', 'r') if " " not in word]
dictWords = set([word.strip() for word in open('/usr/share/dict/words', 'r')])
bitWordList = set(["coin", "satoshi", "wallet", "crypto", "currency", "btc", "nmc", "blockchain"])
with open("name_lists/surnames.csv", "r") as surnames_file:
reader = DictReader(surnames_file)
surnamesSet = set(line["name"].lower() for line in reader)
valueChangeRank = rankNumberOfValueChanges(bitNames, max_height)
aliveRank = rankIsAlive(bitNames, max_height)
validJSONRank = rankJSONDict(bitNames, max_height)
validDNSRank = rankValidDNSDict(bitNames, max_height)
timeActiveRank = rankByTimeActive(bitNames, max_height)
maxRank = len(bitNames)
xData = []
yData = []
for name in bitNames:
yData.append(price([valueChangeRank[name], aliveRank[name], validJSONRank[name], validDNSRank[name], timeActiveRank[name]], maxRank))
xData.append([
int(dotBitAlexa[name]) + 1, # alexaRank
int(len(wordnet.synsets(name[2:])) >= 1) + 1, # inDict
int(any(dirtyWord in name.lower() for dirtyWord in dirtyWords)) + 1, # inDirty
int(set(name[2:]).issubset(set("0123456789"))) + 1, # isNumber
len(name), # length
int(any(word in name.lower() for word in bitWordList) or name.startswith("d/bit")) + 1, # coinRelated
int(set(name[2:]).issubset(set("abcdefghijklmnopqrstuvwxyz"))) + 1,
SegmentString().string_segments(name[2:])
])
# x_train, x_test, y_train, y_test = train_test_split(xData, yData, test_size=.10, random_state=33)
alpha = 0.1
enet = ElasticNet(alpha=alpha)
score = cross_val_score(enet, xData, yData, scoring='r2_score')
print(score)
# upper = int((4/3) * (i + 1))
# ret[i] = (cumulative[upper] - cumulative[lower]) / (upper - lower)
# return ret
alexa_ranks = alexaRanks()
# Swap keys and values
# alexa_ranks = {rank: intern(name) for name, rank in alexa_ranks.items()}
# pdb.set_trace()
# alexa_list = []n
# for i in range(1, len(alexa_ranks) + 1):
# alexa_list.append(alexa_ranks[i])
with open("nameDict.dat", "rb") as pickle_file:
name_history = pickle.load(pickle_file)
max_height = getMaxHeight(name_history)
valid_names = getDictSubset(name_history,
lambda record: record.isValidAtHeight(max_height))
name_history = None
active_bit_names = getDictSubset(valid_names,
lambda record: record.namespace() == "d")
valid_names = None
names = set(name for name in active_bit_names.keys())
active_bit_names = None
registered = [alexa_name in names for alexa_name in alexa_ranks]
averaged = variable_window_moving_average(registered)
rc('font', serif='Helvetica Neue')
rc('text', usetex='true')
# ret[i] = (cumulative[upper] - cumulative[lower]) / (upper - lower)
# return ret
alexa_ranks = alexaRanks()
# Swap keys and values
# alexa_ranks = {rank: intern(name) for name, rank in alexa_ranks.items()}
# pdb.set_trace()
# alexa_list = []n
# for i in range(1, len(alexa_ranks) + 1):
# alexa_list.append(alexa_ranks[i])
with open("nameDict.dat", "rb") as pickle_file:
name_history = pickle.load(pickle_file)
max_height = getMaxHeight(name_history)
valid_names = getDictSubset(name_history,
lambda record: record.isValidAtHeight(max_height))
name_history = None
active_bit_names = getDictSubset(valid_names,
lambda record: record.namespace() == "d")
valid_names = None
names = set(name for name in active_bit_names.keys())
active_bit_names = None
registered = [alexa_name in names for alexa_name in alexa_ranks]
averaged = variable_window_moving_average(registered)
rc('font', serif='Helvetica Neue')
import pickle
import pdb
from nltk.util import ngrams
from common import getDictSubset
from nameHistory import getMaxHeight
from segment_string import SegmentString
DOMAIN_REGEX = "^[a-z]([a-z0-9-]{0,62}[a-z0-9])?$"
def valid_domain_name(name, has_prefix=True):
return bool(match(DOMAIN_REGEX, name.name()[2:]))
with open("nameDict.dat", "rb") as pickle_file:
names_dict = pickle.load(pickle_file)
max_height = getMaxHeight(names_dict)
names_dict = getDictSubset(names_dict,
lambda record: record.isValidAtHeight(max_height))
names_dict = getDictSubset(names_dict,
lambda record: record.namespace() == "d")
names_dict = getDictSubset(names_dict, valid_domain_name)
segment_counts = sorted([SegmentString().string_segments(name[2:]) for name in names_dict.keys()])
with open("segment_counts.pickle", "wb") as output_file:
pickle.dump(segment_counts, output_file)
from nltk.util import ngrams
from common import getDictSubset
from nameHistory import getMaxHeight
from segment_string import SegmentString
DOMAIN_REGEX = "^[a-z]([a-z0-9-]{0,62}[a-z0-9])?$"
def valid_domain_name(name, has_prefix=True):
return bool(match(DOMAIN_REGEX, name.name()[2:]))
with open("nameDict.dat", "rb") as pickle_file:
names_dict = pickle.load(pickle_file)
max_height = getMaxHeight(names_dict)
names_dict = getDictSubset(names_dict,
lambda record: record.isValidAtHeight(max_height))
names_dict = getDictSubset(names_dict,
lambda record: record.namespace() == "d")
names_dict = getDictSubset(names_dict, valid_domain_name)
segment_counts = sorted(
[SegmentString().string_segments(name[2:]) for name in names_dict.keys()])
with open("segment_counts.pickle", "wb") as output_file:
pickle.dump(segment_counts, output_file)
