text_prompt stringlengths 157 13.1k | code_prompt stringlengths 7 19.8k ⌀ |
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def split_adhoc_filters_into_base_filters(fd):
""" Mutates form data to restructure the adhoc filters in the form of the four base filters, `where`, `having`, `f... |
adhoc_filters = fd.get('adhoc_filters')
if isinstance(adhoc_filters, list):
simple_where_filters = []
simple_having_filters = []
sql_where_filters = []
sql_having_filters = []
for adhoc_filter in adhoc_filters:
expression_type = adhoc_filter.get('expressionTy... |
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def load_energy():
"""Loads an energy related dataset to use with sankey and graphs""" |
tbl_name = 'energy_usage'
data = get_example_data('energy.json.gz')
pdf = pd.read_json(data)
pdf.to_sql(
tbl_name,
db.engine,
if_exists='replace',
chunksize=500,
dtype={
'source': String(255),
'target': String(255),
'value': Fl... |
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def runserver(debug, console_log, use_reloader, address, port, timeout, workers, socket):
"""Starts a Superset web server.""" |
debug = debug or config.get('DEBUG') or console_log
if debug:
print(Fore.BLUE + '-=' * 20)
print(
Fore.YELLOW + 'Starting Superset server in ' +
Fore.RED + 'DEBUG' +
Fore.YELLOW + ' mode')
print(Fore.BLUE + '-=' * 20)
print(Style.RESET_ALL)
... |
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def version(verbose):
"""Prints the current version number""" |
print(Fore.BLUE + '-=' * 15)
print(Fore.YELLOW + 'Superset ' + Fore.CYAN + '{version}'.format(
version=config.get('VERSION_STRING')))
print(Fore.BLUE + '-=' * 15)
if verbose:
print('[DB] : ' + '{}'.format(db.engine))
print(Style.RESET_ALL) |
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def refresh_druid(datasource, merge):
"""Refresh druid datasources""" |
session = db.session()
from superset.connectors.druid.models import DruidCluster
for cluster in session.query(DruidCluster).all():
try:
cluster.refresh_datasources(datasource_name=datasource,
merge_flag=merge)
except Exception as e:
... |
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def import_dashboards(path, recursive):
"""Import dashboards from JSON""" |
p = Path(path)
files = []
if p.is_file():
files.append(p)
elif p.exists() and not recursive:
files.extend(p.glob('*.json'))
elif p.exists() and recursive:
files.extend(p.rglob('*.json'))
for f in files:
logging.info('Importing dashboard from file %s', f)
... |
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def export_dashboards(print_stdout, dashboard_file):
"""Export dashboards to JSON""" |
data = dashboard_import_export.export_dashboards(db.session)
if print_stdout or not dashboard_file:
print(data)
if dashboard_file:
logging.info('Exporting dashboards to %s', dashboard_file)
with open(dashboard_file, 'w') as data_stream:
data_stream.write(data) |
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def import_datasources(path, sync, recursive):
"""Import datasources from YAML""" |
sync_array = sync.split(',')
p = Path(path)
files = []
if p.is_file():
files.append(p)
elif p.exists() and not recursive:
files.extend(p.glob('*.yaml'))
files.extend(p.glob('*.yml'))
elif p.exists() and recursive:
files.extend(p.rglob('*.yaml'))
files.ext... |
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def export_datasources(print_stdout, datasource_file, back_references, include_defaults):
"""Export datasources to YAML""" |
data = dict_import_export.export_to_dict(
session=db.session,
recursive=True,
back_references=back_references,
include_defaults=include_defaults)
if print_stdout or not datasource_file:
yaml.safe_dump(data, stdout, default_flow_style=False)
if datasource_file:
... |
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def export_datasource_schema(back_references):
"""Export datasource YAML schema to stdout""" |
data = dict_import_export.export_schema_to_dict(
back_references=back_references)
yaml.safe_dump(data, stdout, default_flow_style=False) |
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def update_datasources_cache():
"""Refresh sqllab datasources cache""" |
from superset.models.core import Database
for database in db.session.query(Database).all():
if database.allow_multi_schema_metadata_fetch:
print('Fetching {} datasources ...'.format(database.name))
try:
database.all_table_names_in_database(
fo... |
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def worker(workers):
"""Starts a Superset worker for async SQL query execution.""" |
logging.info(
"The 'superset worker' command is deprecated. Please use the 'celery "
"worker' command instead.")
if workers:
celery_app.conf.update(CELERYD_CONCURRENCY=workers)
elif config.get('SUPERSET_CELERY_WORKERS'):
celery_app.conf.update(
CELERYD_CONCURRENC... |
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def flower(port, address):
"""Runs a Celery Flower web server Celery Flower is a UI to monitor the Celery operation on a given broker""" |
BROKER_URL = celery_app.conf.BROKER_URL
cmd = (
'celery flower '
f'--broker={BROKER_URL} '
f'--port={port} '
f'--address={address} '
)
logging.info(
"The 'superset flower' command is deprecated. Please use the 'celery "
"flower' command instead.")
pri... |
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def refresh_datasources(self, refreshAll=True):
"""endpoint that refreshes druid datasources metadata""" |
session = db.session()
DruidCluster = ConnectorRegistry.sources['druid'].cluster_class
for cluster in session.query(DruidCluster).all():
cluster_name = cluster.cluster_name
valid_cluster = True
try:
cluster.refresh_datasources(refreshAll=refre... |
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def convert_to_str(l: Node) -> str: """ converts the non-negative number list into a string. """ |
result = ""
while l:
result += str(l.val)
l = l.next
return result |
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def cocktail_shaker_sort(arr):
""" Cocktail_shaker_sort Sorting a given array mutation of bubble sort reference: https://en.wikipedia.org/wiki/Cocktail_shaker_so... |
def swap(i, j):
arr[i], arr[j] = arr[j], arr[i]
n = len(arr)
swapped = True
while swapped:
swapped = False
for i in range(1, n):
if arr[i - 1] > arr[i]:
swap(i - 1, i)
swapped = True
if swapped == False:
return ar... |
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| def common_prefix(s1, s2):
"Return prefix common of 2 strings"
if not s1 or not s2:
return ""
k = 0
while s1[k] == s2[k]:
k = k + 1
if k >= len(s1) or k >= len(s2):
return s1[0:k]
return s1[0:k] |
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def lcs(s1, s2, i, j):
""" The length of longest common subsequence among the two given strings s1 and s2 """ |
if i == 0 or j == 0:
return 0
elif s1[i - 1] == s2[j - 1]:
return 1 + lcs(s1, s2, i - 1, j - 1)
else:
return max(lcs(s1, s2, i - 1, j), lcs(s1, s2, i, j - 1)) |
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def find_nth_digit(n):
"""find the nth digit of given number. 1. find the length of the number where the nth digit is from. 2. find the actual number where the n... |
length = 1
count = 9
start = 1
while n > length * count:
n -= length * count
length += 1
count *= 10
start *= 10
start += (n-1) / length
s = str(start)
return int(s[(n-1) % length]) |
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def prime_check(n):
"""Return True if n is a prime number Else return False. """ |
if n <= 1:
return False
if n == 2 or n == 3:
return True
if n % 2 == 0 or n % 3 == 0:
return False
j = 5
while j * j <= n:
if n % j == 0 or n % (j + 2) == 0:
return False
j += 6
return True |
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def longest_non_repeat_v1(string):
""" Find the length of the longest substring without repeating characters. """ |
if string is None:
return 0
dict = {}
max_length = 0
j = 0
for i in range(len(string)):
if string[i] in dict:
j = max(dict[string[i]], j)
dict[string[i]] = i + 1
max_length = max(max_length, i - j + 1)
return max_length |
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def longest_non_repeat_v2(string):
""" Find the length of the longest substring without repeating characters. Uses alternative algorithm. """ |
if string is None:
return 0
start, max_len = 0, 0
used_char = {}
for index, char in enumerate(string):
if char in used_char and start <= used_char[char]:
start = used_char[char] + 1
else:
max_len = max(max_len, index - start + 1)
used_char[char] =... |
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def get_longest_non_repeat_v1(string):
""" Find the length of the longest substring without repeating characters. Return max_len and the substring as a tuple """ |
if string is None:
return 0, ''
sub_string = ''
dict = {}
max_length = 0
j = 0
for i in range(len(string)):
if string[i] in dict:
j = max(dict[string[i]], j)
dict[string[i]] = i + 1
if i - j + 1 > max_length:
max_length = i - j + 1
... |
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def get_longest_non_repeat_v2(string):
""" Find the length of the longest substring without repeating characters. Uses alternative algorithm. Return max_len and ... |
if string is None:
return 0, ''
sub_string = ''
start, max_len = 0, 0
used_char = {}
for index, char in enumerate(string):
if char in used_char and start <= used_char[char]:
start = used_char[char] + 1
else:
if index - start + 1 > max_len:
... |
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def push(self, item, priority=None):
"""Push the item in the priority queue. if priority is not given, priority is set to the value of item. """ |
priority = item if priority is None else priority
node = PriorityQueueNode(item, priority)
for index, current in enumerate(self.priority_queue_list):
if current.priority < node.priority:
self.priority_queue_list.insert(index, node)
return
# wh... |
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def flatten_iter(iterable):
""" Takes as input multi dimensional iterable and returns generator which produces one dimensional output. """ |
for element in iterable:
if isinstance(element, Iterable):
yield from flatten_iter(element)
else:
yield element |
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def convolved(iterable, kernel_size=1, stride=1, padding=0, default_value=None):
"""Iterable to get every convolution window per loop iteration. For example: `co... |
# Input validation and error messages
if not hasattr(iterable, '__iter__'):
raise ValueError(
"Can't iterate on object.".format(
iterable))
if stride < 1:
raise ValueError(
"Stride must be of at least one. Got `stride={}`.".format(
str... |
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def convolved_1d(iterable, kernel_size=1, stride=1, padding=0, default_value=None):
"""1D Iterable to get every convolution window per loop iteration. For more i... |
return convolved(iterable, kernel_size, stride, padding, default_value) |
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def convolved_2d(iterable, kernel_size=1, stride=1, padding=0, default_value=None):
"""2D Iterable to get every convolution window per loop iteration. For more i... |
kernel_size = dimensionize(kernel_size, nd=2)
stride = dimensionize(stride, nd=2)
padding = dimensionize(padding, nd=2)
for row_packet in convolved(iterable, kernel_size[0], stride[0], padding[0], default_value):
transposed_inner = []
for col in tuple(row_packet):
transpose... |
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def dimensionize(maybe_a_list, nd=2):
"""Convert integers to a list of integers to fit the number of dimensions if the argument is not already a list. For exampl... |
if not hasattr(maybe_a_list, '__iter__'):
# Argument is probably an integer so we map it to a list of size `nd`.
now_a_list = [maybe_a_list] * nd
return now_a_list
else:
# Argument is probably an `nd`-sized list.
return maybe_a_list |
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def merge_intervals(intervals):
""" Merge intervals in the form of a list. """ |
if intervals is None:
return None
intervals.sort(key=lambda i: i[0])
out = [intervals.pop(0)]
for i in intervals:
if out[-1][-1] >= i[0]:
out[-1][-1] = max(out[-1][-1], i[-1])
else:
out.append(i)
return out |
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def merge(intervals):
""" Merge two intervals into one. """ |
out = []
for i in sorted(intervals, key=lambda i: i.start):
if out and i.start <= out[-1].end:
out[-1].end = max(out[-1].end, i.end)
else:
out += i,
return out |
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def print_intervals(intervals):
""" Print out the intervals. """ |
res = []
for i in intervals:
res.append(repr(i))
print("".join(res)) |
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def max_heapify(arr, end, simulation, iteration):
""" Max heapify helper for max_heap_sort """ |
last_parent = (end - 1) // 2
# Iterate from last parent to first
for parent in range(last_parent, -1, -1):
current_parent = parent
# Iterate from current_parent to last_parent
while current_parent <= last_parent:
# Find greatest child of current_parent
chil... |
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def min_heapify(arr, start, simulation, iteration):
""" Min heapify helper for min_heap_sort """ |
# Offset last_parent by the start (last_parent calculated as if start index was 0)
# All array accesses need to be offset by start
end = len(arr) - 1
last_parent = (end - start - 1) // 2
# Iterate from last parent to first
for parent in range(last_parent, -1, -1):
current_parent = pare... |
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def generate_key(k, seed=None):
""" the RSA key generating algorithm k is the number of bits in n """ |
def modinv(a, m):
"""calculate the inverse of a mod m
that is, find b such that (a * b) % m == 1"""
b = 1
while not (a * b) % m == 1:
b += 1
return b
def gen_prime(k, seed=None):
"""generate a prime with k bits"""
def is_prime(num):
... |
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def square_root(n, epsilon=0.001):
"""Return square root of n, with maximum absolute error epsilon""" |
guess = n / 2
while abs(guess * guess - n) > epsilon:
guess = (guess + (n / guess)) / 2
return guess |
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def powerset(iterable):
"""Calculate the powerset of any iterable. For a range of integers up to the length of the given list, make all possible combinations and... |
"list(powerset([1,2,3])) --> [(), (1,), (2,), (3,), (1,2), (1,3), (2,3), (1,2,3)]"
s = list(iterable)
return chain.from_iterable(combinations(s, r) for r in range(len(s) + 1)) |
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def greedy_set_cover(universe, subsets, costs):
"""Approximate greedy algorithm for set-covering. Can be used on large inputs - though not an optimal solution. A... |
elements = set(e for s in subsets.keys() for e in subsets[s])
# elements don't cover universe -> invalid input for set cover
if elements != universe:
return None
# track elements of universe covered
covered = set()
cover_sets = []
while covered != universe:
min_cost_elem_r... |
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def insert(self, key):
""" Insert new key into node """ |
# Create new node
n = TreeNode(key)
if not self.node:
self.node = n
self.node.left = AvlTree()
self.node.right = AvlTree()
elif key < self.node.val:
self.node.left.insert(key)
elif key > self.node.val:
self.node.right.i... |
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def re_balance(self):
""" Re balance tree. After inserting or deleting a node, """ |
self.update_heights(recursive=False)
self.update_balances(False)
while self.balance < -1 or self.balance > 1:
if self.balance > 1:
if self.node.left.balance < 0:
self.node.left.rotate_left()
self.update_heights()
... |
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def update_heights(self, recursive=True):
""" Update tree height """ |
if self.node:
if recursive:
if self.node.left:
self.node.left.update_heights()
if self.node.right:
self.node.right.update_heights()
self.height = 1 + max(self.node.left.height, self.node.right.height)
else:... |
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def update_balances(self, recursive=True):
""" Calculate tree balance factor """ |
if self.node:
if recursive:
if self.node.left:
self.node.left.update_balances()
if self.node.right:
self.node.right.update_balances()
self.balance = self.node.left.height - self.node.right.height
else:
... |
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def in_order_traverse(self):
""" In-order traversal of the tree """ |
result = []
if not self.node:
return result
result.extend(self.node.left.in_order_traverse())
result.append(self.node.key)
result.extend(self.node.right.in_order_traverse())
return result |
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def kth_to_last_dict(head, k):
""" This is a brute force method where we keep a dict the size of the list Then we check it for the value we need. If the key is n... |
if not (head and k > -1):
return False
d = dict()
count = 0
while head:
d[count] = head
head = head.next
count += 1
return len(d)-k in d and d[len(d)-k] |
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def kth_to_last(head, k):
""" This is an optimal method using iteration. We move p1 k steps ahead into the list. Then we move p1 and p2 together until p1 hits th... |
if not (head or k > -1):
return False
p1 = head
p2 = head
for i in range(1, k+1):
if p1 is None:
# Went too far, k is not valid
raise IndexError
p1 = p1.next
while p1:
p1 = p1.next
p2 = p2.next
return p2 |
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def combination(n, r):
"""This function calculates nCr.""" |
if n == r or r == 0:
return 1
else:
return combination(n-1, r-1) + combination(n-1, r) |
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def combination_memo(n, r):
"""This function calculates nCr using memoization method.""" |
memo = {}
def recur(n, r):
if n == r or r == 0:
return 1
if (n, r) not in memo:
memo[(n, r)] = recur(n - 1, r - 1) + recur(n - 1, r)
return memo[(n, r)]
return recur(n, r) |
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def pancake_sort(arr):
""" Pancake_sort Sorting a given array mutation of selection sort reference: https://www.geeksforgeeks.org/pancake-sorting/ Overall time c... |
len_arr = len(arr)
if len_arr <= 1:
return arr
for cur in range(len(arr), 1, -1):
#Finding index of maximum number in arr
index_max = arr.index(max(arr[0:cur]))
if index_max+1 != cur:
#Needs moving
if index_max != 0:
#reverse from 0 t... |
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| def scc(graph):
''' Computes the strongly connected components of a graph '''
order = []
vis = {vertex: False for vertex in graph}
graph_transposed = {vertex: [] for vertex in graph}
for (v, neighbours) in graph.iteritems():
for u in neighbours:
add_edge(graph_transposed, u, v)... |
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| def build_graph(formula):
''' Builds the implication graph from the formula '''
graph = {}
for clause in formula:
for (lit, _) in clause:
for neg in [False, True]:
graph[(lit, neg)] = []
for ((a_lit, a_neg), (b_lit, b_neg)) in formula:
add_edge(graph, (a_lit... |
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def __get_depth(root):
""" return 0 if unbalanced else depth + 1 """ |
if root is None:
return 0
left = __get_depth(root.left)
right = __get_depth(root.right)
if abs(left-right) > 1 or -1 in [left, right]:
return -1
return 1 + max(left, right) |
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def palindromic_substrings_iter(s):
""" A slightly more Pythonic approach with a recursive generator """ |
if not s:
yield []
return
for i in range(len(s), 0, -1):
sub = s[:i]
if sub == sub[::-1]:
for rest in palindromic_substrings_iter(s[i:]):
yield [sub] + rest |
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def main():
"""
simple user-interface
""" |
print("\t\tCalculator\n\n")
while True:
user_input = input("expression or exit: ")
if user_input == "exit":
break
try:
print("The result is {0}".format(evaluate(user_input)))
except Exception:
print("invalid syntax!")
use... |
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def get_primes(n):
"""Return list of all primes less than n, Using sieve of Eratosthenes. """ |
if n <= 0:
raise ValueError("'n' must be a positive integer.")
# If x is even, exclude x from list (-1):
sieve_size = (n // 2 - 1) if n % 2 == 0 else (n // 2)
sieve = [True for _ in range(sieve_size)] # Sieve
primes = [] # List of Primes
if n >= 2:
primes.append(2) #... |
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def permute(elements):
""" returns a list with the permuations. """ |
if len(elements) <= 1:
return [elements]
else:
tmp = []
for perm in permute(elements[1:]):
for i in range(len(elements)):
tmp.append(perm[:i] + elements[0:1] + perm[i:])
return tmp |
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def _init_rabit():
"""internal library initializer.""" |
if _LIB is not None:
_LIB.RabitGetRank.restype = ctypes.c_int
_LIB.RabitGetWorldSize.restype = ctypes.c_int
_LIB.RabitIsDistributed.restype = ctypes.c_int
_LIB.RabitVersionNumber.restype = ctypes.c_int |
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def init(args=None):
"""Initialize the rabit library with arguments""" |
if args is None:
args = []
arr = (ctypes.c_char_p * len(args))()
arr[:] = args
_LIB.RabitInit(len(arr), arr) |
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def get_processor_name():
"""Get the processor name. Returns ------- name : str the name of processor(host) """ |
mxlen = 256
length = ctypes.c_ulong()
buf = ctypes.create_string_buffer(mxlen)
_LIB.RabitGetProcessorName(buf, ctypes.byref(length), mxlen)
return buf.value |
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def broadcast(data, root):
"""Broadcast object from one node to all other nodes. Parameters data : any type that can be pickled Input data, if current rank does ... |
rank = get_rank()
length = ctypes.c_ulong()
if root == rank:
assert data is not None, 'need to pass in data when broadcasting'
s = pickle.dumps(data, protocol=pickle.HIGHEST_PROTOCOL)
length.value = len(s)
# run first broadcast
_LIB.RabitBroadcast(ctypes.byref(length),
... |
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def normpath(path):
"""Normalize UNIX path to a native path.""" |
normalized = os.path.join(*path.split("/"))
if os.path.isabs(path):
return os.path.abspath("/") + normalized
else:
return normalized |
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def update(self, iteration, fobj):
""""Update the boosters for one iteration""" |
self.bst.update(self.dtrain, iteration, fobj) |
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def eval(self, iteration, feval):
""""Evaluate the CVPack for one iteration.""" |
return self.bst.eval_set(self.watchlist, iteration, feval) |
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def _get_callback_context(env):
"""return whether the current callback context is cv or train""" |
if env.model is not None and env.cvfolds is None:
context = 'train'
elif env.model is None and env.cvfolds is not None:
context = 'cv'
return context |
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def _fmt_metric(value, show_stdv=True):
"""format metric string""" |
if len(value) == 2:
return '%s:%g' % (value[0], value[1])
if len(value) == 3:
if show_stdv:
return '%s:%g+%g' % (value[0], value[1], value[2])
return '%s:%g' % (value[0], value[1])
raise ValueError("wrong metric value") |
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def print_evaluation(period=1, show_stdv=True):
"""Create a callback that print evaluation result. We print the evaluation results every **period** iterations an... |
def callback(env):
"""internal function"""
if env.rank != 0 or (not env.evaluation_result_list) or period is False or period == 0:
return
i = env.iteration
if i % period == 0 or i + 1 == env.begin_iteration or i + 1 == env.end_iteration:
msg = '\t'.join([_fmt... |
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def reset_learning_rate(learning_rates):
"""Reset learning rate after iteration 1 NOTE: the initial learning rate will still take in-effect on first iteration. P... |
def get_learning_rate(i, n, learning_rates):
"""helper providing the learning rate"""
if isinstance(learning_rates, list):
if len(learning_rates) != n:
raise ValueError("Length of list 'learning_rates' has to equal 'num_boost_round'.")
new_learning_rate = lea... |
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def _objective_decorator(func):
"""Decorate an objective function Converts an objective function using the typical sklearn metrics signature so that it is usable... |
def inner(preds, dmatrix):
"""internal function"""
labels = dmatrix.get_label()
return func(labels, preds)
return inner |
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def apply(self, X, ntree_limit=0):
"""Return the predicted leaf every tree for each sample. Parameters X : array_like, shape=[n_samples, n_features] Input featur... |
test_dmatrix = DMatrix(X, missing=self.missing, nthread=self.n_jobs)
return self.get_booster().predict(test_dmatrix,
pred_leaf=True,
ntree_limit=ntree_limit) |
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def feature_importances_(self):
""" Feature importances property .. note:: Feature importance is defined only for tree boosters Feature importance is only define... |
if getattr(self, 'booster', None) is not None and self.booster != 'gbtree':
raise AttributeError('Feature importance is not defined for Booster type {}'
.format(self.booster))
b = self.get_booster()
score = b.get_score(importance_type=self.importance... |
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def predict_proba(self, data, ntree_limit=None, validate_features=True):
""" Predict the probability of each `data` example being of a given class. .. note:: Thi... |
test_dmatrix = DMatrix(data, missing=self.missing, nthread=self.n_jobs)
if ntree_limit is None:
ntree_limit = getattr(self, "best_ntree_limit", 0)
class_probs = self.get_booster().predict(test_dmatrix,
ntree_limit=ntree_limit,
... |
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def from_pystr_to_cstr(data):
"""Convert a list of Python str to C pointer Parameters data : list list of str """ |
if not isinstance(data, list):
raise NotImplementedError
pointers = (ctypes.c_char_p * len(data))()
if PY3:
data = [bytes(d, 'utf-8') for d in data]
else:
data = [d.encode('utf-8') if isinstance(d, unicode) else d # pylint: disable=undefined-variable
for d in d... |
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def from_cstr_to_pystr(data, length):
"""Revert C pointer to Python str Parameters data : ctypes pointer pointer to data length : ctypes pointer pointer to lengt... |
if PY3:
res = []
for i in range(length.value):
try:
res.append(str(data[i].decode('ascii')))
except UnicodeDecodeError:
res.append(str(data[i].decode('utf-8')))
else:
res = []
for i in range(length.value):
try:
... |
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def _load_lib():
"""Load xgboost Library.""" |
lib_paths = find_lib_path()
if not lib_paths:
return None
try:
pathBackup = os.environ['PATH'].split(os.pathsep)
except KeyError:
pathBackup = []
lib_success = False
os_error_list = []
for lib_path in lib_paths:
try:
# needed when the lib is linke... |
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def ctypes2buffer(cptr, length):
"""Convert ctypes pointer to buffer type.""" |
if not isinstance(cptr, ctypes.POINTER(ctypes.c_char)):
raise RuntimeError('expected char pointer')
res = bytearray(length)
rptr = (ctypes.c_char * length).from_buffer(res)
if not ctypes.memmove(rptr, cptr, length):
raise RuntimeError('memmove failed')
return res |
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def c_array(ctype, values):
"""Convert a python string to c array.""" |
if isinstance(values, np.ndarray) and values.dtype.itemsize == ctypes.sizeof(ctype):
return (ctype * len(values)).from_buffer_copy(values)
return (ctype * len(values))(*values) |
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def _maybe_dt_array(array):
""" Extract numpy array from single column data table """ |
if not isinstance(array, DataTable) or array is None:
return array
if array.shape[1] > 1:
raise ValueError('DataTable for label or weight cannot have multiple columns')
# below requires new dt version
# extract first column
array = array.to_numpy()[:, 0].astype('float')
retur... |
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def _init_from_dt(self, data, nthread):
""" Initialize data from a datatable Frame. """ |
ptrs = (ctypes.c_void_p * data.ncols)()
if hasattr(data, "internal") and hasattr(data.internal, "column"):
# datatable>0.8.0
for icol in range(data.ncols):
col = data.internal.column(icol)
ptr = col.data_pointer
ptrs[icol] = ctypes... |
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def set_float_info_npy2d(self, field, data):
"""Set float type property into the DMatrix for numpy 2d array input Parameters field: str The field name of the inf... |
if getattr(data, 'base', None) is not None and \
data.base is not None and isinstance(data, np.ndarray) \
and isinstance(data.base, np.ndarray) and (not data.flags.c_contiguous):
warnings.warn("Use subset (sliced data) of np.ndarray is not recommended " +
... |
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def load_rabit_checkpoint(self):
"""Initialize the model by load from rabit checkpoint. Returns ------- version: integer The version number of the model. """ |
version = ctypes.c_int()
_check_call(_LIB.XGBoosterLoadRabitCheckpoint(
self.handle, ctypes.byref(version)))
return version.value |
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def attr(self, key):
"""Get attribute string from the Booster. Parameters key : str The key to get attribute from. Returns ------- value : str The attribute valu... |
ret = ctypes.c_char_p()
success = ctypes.c_int()
_check_call(_LIB.XGBoosterGetAttr(
self.handle, c_str(key), ctypes.byref(ret), ctypes.byref(success)))
if success.value != 0:
return py_str(ret.value)
return None |
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def attributes(self):
"""Get attributes stored in the Booster as a dictionary. Returns ------- result : dictionary of attribute_name: attribute_value pairs of st... |
length = c_bst_ulong()
sarr = ctypes.POINTER(ctypes.c_char_p)()
_check_call(_LIB.XGBoosterGetAttrNames(self.handle,
ctypes.byref(length),
ctypes.byref(sarr)))
attr_names = from_cstr_to_pystr(sa... |
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def set_attr(self, **kwargs):
"""Set the attribute of the Booster. Parameters **kwargs The attributes to set. Setting a value to None deletes an attribute. """ |
for key, value in kwargs.items():
if value is not None:
if not isinstance(value, STRING_TYPES):
raise ValueError("Set Attr only accepts string values")
value = c_str(str(value))
_check_call(_LIB.XGBoosterSetAttr(
self.h... |
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def set_param(self, params, value=None):
"""Set parameters into the Booster. Parameters params: dict/list/str list of key,value pairs, dict of key to value or si... |
if isinstance(params, Mapping):
params = params.items()
elif isinstance(params, STRING_TYPES) and value is not None:
params = [(params, value)]
for key, val in params:
_check_call(_LIB.XGBoosterSetParam(self.handle, c_str(key), c_str(str(val)))) |
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def eval(self, data, name='eval', iteration=0):
"""Evaluate the model on mat. Parameters data : DMatrix The dmatrix storing the input. name : str, optional The n... |
self._validate_features(data)
return self.eval_set([(data, name)], iteration) |
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def save_model(self, fname):
""" Save the model to a file. The model is saved in an XGBoost internal binary format which is universal among the various XGBoost i... |
if isinstance(fname, STRING_TYPES): # assume file name
_check_call(_LIB.XGBoosterSaveModel(self.handle, c_str(fname)))
else:
raise TypeError("fname must be a string") |
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def dump_model(self, fout, fmap='', with_stats=False, dump_format="text"):
""" Dump model into a text or JSON file. Parameters fout : string Output file name. fm... |
if isinstance(fout, STRING_TYPES):
fout = open(fout, 'w')
need_close = True
else:
need_close = False
ret = self.get_dump(fmap, with_stats, dump_format)
if dump_format == 'json':
fout.write('[\n')
for i, _ in enumerate(ret):
... |
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def get_dump(self, fmap='', with_stats=False, dump_format="text"):
""" Returns the model dump as a list of strings. Parameters fmap : string, optional Name of th... |
length = c_bst_ulong()
sarr = ctypes.POINTER(ctypes.c_char_p)()
if self.feature_names is not None and fmap == '':
flen = len(self.feature_names)
fname = from_pystr_to_cstr(self.feature_names)
if self.feature_types is None:
# use quantitative... |
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def get_split_value_histogram(self, feature, fmap='', bins=None, as_pandas=True):
"""Get split value histogram of a feature Parameters feature: str The name of t... |
xgdump = self.get_dump(fmap=fmap)
values = []
regexp = re.compile(r"\[{0}<([\d.Ee+-]+)\]".format(feature))
for i, _ in enumerate(xgdump):
m = re.findall(regexp, xgdump[i])
values.extend([float(x) for x in m])
n_unique = len(np.unique(values))
bin... |
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def plot_importance(booster, ax=None, height=0.2, xlim=None, ylim=None, title='Feature importance', xlabel='F score', ylabel='Features', importance_type='weight',... |
try:
import matplotlib.pyplot as plt
except ImportError:
raise ImportError('You must install matplotlib to plot importance')
if isinstance(booster, XGBModel):
importance = booster.get_booster().get_score(importance_type=importance_type)
elif isinstance(booster, Booster):
... |
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def _parse_edge(graph, node, text, yes_color='#0000FF', no_color='#FF0000'):
"""parse dumped edge""" |
try:
match = _EDGEPAT.match(text)
if match is not None:
yes, no, missing = match.groups()
if yes == missing:
graph.edge(node, yes, label='yes, missing', color=yes_color)
graph.edge(node, no, label='no', color=no_color)
else:
... |
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def newAction(parent, text, slot=None, shortcut=None, icon=None, tip=None, checkable=False, enabled=True):
"""Create a new action and assign callbacks, shortcuts... |
a = QAction(text, parent)
if icon is not None:
a.setIcon(newIcon(icon))
if shortcut is not None:
if isinstance(shortcut, (list, tuple)):
a.setShortcuts(shortcut)
else:
a.setShortcut(shortcut)
if tip is not None:
a.setToolTip(tip)
a.setStat... |
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def natural_sort(list, key=lambda s:s):
""" Sort the list into natural alphanumeric order. """ |
def get_alphanum_key_func(key):
convert = lambda text: int(text) if text.isdigit() else text
return lambda s: [convert(c) for c in re.split('([0-9]+)', key(s))]
sort_key = get_alphanum_key_func(key)
list.sort(key=sort_key) |
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def selectShapePoint(self, point):
"""Select the first shape created which contains this point.""" |
self.deSelectShape()
if self.selectedVertex(): # A vertex is marked for selection.
index, shape = self.hVertex, self.hShape
shape.highlightVertex(index, shape.MOVE_VERTEX)
self.selectShape(shape)
return
for shape in reversed(self.shapes):
... |
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def toggleDrawingSensitive(self, drawing=True):
"""In the middle of drawing, toggling between modes should be disabled.""" |
self.actions.editMode.setEnabled(not drawing)
if not drawing and self.beginner():
# Cancel creation.
print('Cancel creation.')
self.canvas.setEditing(True)
self.canvas.restoreCursor()
self.actions.create.setEnabled(True) |
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def btnstate(self, item= None):
""" Function to handle difficult examples Update on each object """ |
if not self.canvas.editing():
return
item = self.currentItem()
if not item: # If not selected Item, take the first one
item = self.labelList.item(self.labelList.count()-1)
difficult = self.diffcButton.isChecked()
try:
shape = self.itemsToSh... |
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def newShape(self):
"""Pop-up and give focus to the label editor. position MUST be in global coordinates. """ |
if not self.useDefaultLabelCheckbox.isChecked() or not self.defaultLabelTextLine.text():
if len(self.labelHist) > 0:
self.labelDialog = LabelDialog(
parent=self, listItem=self.labelHist)
# Sync single class mode from PR#106
if self.single... |
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Description:
def scaleFitWindow(self):
"""Figure out the size of the pixmap in order to fit the main widget.""" |
e = 2.0 # So that no scrollbars are generated.
w1 = self.centralWidget().width() - e
h1 = self.centralWidget().height() - e
a1 = w1 / h1
# Calculate a new scale value based on the pixmap's aspect ratio.
w2 = self.canvas.pixmap.width() - 0.0
h2 = self.canvas.pixm... |
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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
def genXML(self):
""" Return XML root """ |
# Check conditions
if self.filename is None or \
self.foldername is None or \
self.imgSize is None:
return None
top = Element('annotation')
if self.verified:
top.set('verified', 'yes')
folder = SubElement(top, 'folder')
... |
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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
def fetch2(self, path, api='public', method='GET', params={}, headers=None, body=None):
"""A better wrapper over request for deferred signing""" |
if self.enableRateLimit:
self.throttle()
self.lastRestRequestTimestamp = self.milliseconds()
request = self.sign(path, api, method, params, headers, body)
return self.fetch(request['url'], request['method'], request['headers'], request['body']) |
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