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21,500 | apache/incubator-superset | superset/utils/core.py | datetime_f | def datetime_f(dttm):
"""Formats datetime to take less room when it is recent"""
if dttm:
dttm = dttm.isoformat()
now_iso = datetime.now().isoformat()
if now_iso[:10] == dttm[:10]:
dttm = dttm[11:]
elif now_iso[:4] == dttm[:4]:
dttm = dttm[5:]
return '... | python | def datetime_f(dttm):
"""Formats datetime to take less room when it is recent"""
if dttm:
dttm = dttm.isoformat()
now_iso = datetime.now().isoformat()
if now_iso[:10] == dttm[:10]:
dttm = dttm[11:]
elif now_iso[:4] == dttm[:4]:
dttm = dttm[5:]
return '... | [
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21,501 | apache/incubator-superset | superset/utils/core.py | error_msg_from_exception | def error_msg_from_exception(e):
"""Translate exception into error message
Database have different ways to handle exception. This function attempts
to make sense of the exception object and construct a human readable
sentence.
TODO(bkyryliuk): parse the Presto error message from the connection
... | python | def error_msg_from_exception(e):
"""Translate exception into error message
Database have different ways to handle exception. This function attempts
to make sense of the exception object and construct a human readable
sentence.
TODO(bkyryliuk): parse the Presto error message from the connection
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21,502 | apache/incubator-superset | superset/utils/core.py | generic_find_fk_constraint_name | def generic_find_fk_constraint_name(table, columns, referenced, insp):
"""Utility to find a foreign-key constraint name in alembic migrations"""
for fk in insp.get_foreign_keys(table):
if fk['referred_table'] == referenced and set(fk['referred_columns']) == columns:
return fk['name'] | python | def generic_find_fk_constraint_name(table, columns, referenced, insp):
"""Utility to find a foreign-key constraint name in alembic migrations"""
for fk in insp.get_foreign_keys(table):
if fk['referred_table'] == referenced and set(fk['referred_columns']) == columns:
return fk['name'] | [
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21,503 | apache/incubator-superset | superset/utils/core.py | generic_find_fk_constraint_names | def generic_find_fk_constraint_names(table, columns, referenced, insp):
"""Utility to find foreign-key constraint names in alembic migrations"""
names = set()
for fk in insp.get_foreign_keys(table):
if fk['referred_table'] == referenced and set(fk['referred_columns']) == columns:
names.... | python | def generic_find_fk_constraint_names(table, columns, referenced, insp):
"""Utility to find foreign-key constraint names in alembic migrations"""
names = set()
for fk in insp.get_foreign_keys(table):
if fk['referred_table'] == referenced and set(fk['referred_columns']) == columns:
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21,504 | apache/incubator-superset | superset/utils/core.py | generic_find_uq_constraint_name | def generic_find_uq_constraint_name(table, columns, insp):
"""Utility to find a unique constraint name in alembic migrations"""
for uq in insp.get_unique_constraints(table):
if columns == set(uq['column_names']):
return uq['name'] | python | def generic_find_uq_constraint_name(table, columns, insp):
"""Utility to find a unique constraint name in alembic migrations"""
for uq in insp.get_unique_constraints(table):
if columns == set(uq['column_names']):
return uq['name'] | [
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21,505 | apache/incubator-superset | superset/utils/core.py | setup_cache | def setup_cache(app: Flask, cache_config) -> Optional[Cache]:
"""Setup the flask-cache on a flask app"""
if cache_config and cache_config.get('CACHE_TYPE') != 'null':
return Cache(app, config=cache_config)
return None | python | def setup_cache(app: Flask, cache_config) -> Optional[Cache]:
"""Setup the flask-cache on a flask app"""
if cache_config and cache_config.get('CACHE_TYPE') != 'null':
return Cache(app, config=cache_config)
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21,506 | apache/incubator-superset | superset/utils/core.py | user_label | def user_label(user: User) -> Optional[str]:
"""Given a user ORM FAB object, returns a label"""
if user:
if user.first_name and user.last_name:
return user.first_name + ' ' + user.last_name
else:
return user.username
return None | python | def user_label(user: User) -> Optional[str]:
"""Given a user ORM FAB object, returns a label"""
if user:
if user.first_name and user.last_name:
return user.first_name + ' ' + user.last_name
else:
return user.username
return None | [
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21,507 | apache/incubator-superset | superset/utils/core.py | get_since_until | def get_since_until(time_range: Optional[str] = None,
since: Optional[str] = None,
until: Optional[str] = None,
time_shift: Optional[str] = None,
relative_end: Optional[str] = None) -> Tuple[datetime, datetime]:
"""Return `since` and `u... | python | def get_since_until(time_range: Optional[str] = None,
since: Optional[str] = None,
until: Optional[str] = None,
time_shift: Optional[str] = None,
relative_end: Optional[str] = None) -> Tuple[datetime, datetime]:
"""Return `since` and `u... | [
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21,508 | apache/incubator-superset | superset/utils/core.py | split_adhoc_filters_into_base_filters | 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`, `filters`, and `having_filters` which represent
free form where sql, free form having sql, structured where clauses and structured
having cla... | python | 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`, `filters`, and `having_filters` which represent
free form where sql, free form having sql, structured where clauses and structured
having cla... | [
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21,509 | apache/incubator-superset | superset/data/energy.py | load_energy | 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={
... | python | 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={
... | [
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21,510 | apache/incubator-superset | superset/cli.py | runserver | 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 ' +
... | python | 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 ' +
... | [
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21,511 | apache/incubator-superset | superset/cli.py | version | 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))
p... | python | 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))
p... | [
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21,512 | apache/incubator-superset | superset/cli.py | refresh_druid | 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,
... | python | 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,
... | [
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21,513 | apache/incubator-superset | superset/cli.py | import_dashboards | 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 i... | python | 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 i... | [
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21,514 | apache/incubator-superset | superset/cli.py | export_dashboards | 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 ... | python | 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)
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] | ca2996c78f679260eb79c6008e276733df5fb653 | https://github.com/apache/incubator-superset/blob/ca2996c78f679260eb79c6008e276733df5fb653/superset/cli.py#L281-L289 |
21,515 | apache/incubator-superset | superset/cli.py | import_datasources | 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'))
eli... | python | 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'))
eli... | [
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21,516 | apache/incubator-superset | superset/cli.py | export_datasources | 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=includ... | python | 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=includ... | [
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21,517 | apache/incubator-superset | superset/cli.py | export_datasource_schema | 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) | python | 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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21,518 | apache/incubator-superset | superset/cli.py | update_datasources_cache | 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:
... | python | 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:
... | [
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21,519 | apache/incubator-superset | superset/cli.py | worker | 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('SUP... | python | 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('SUP... | [
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21,520 | apache/incubator-superset | superset/cli.py | flower | 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}... | python | 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}... | [
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21,521 | apache/incubator-superset | superset/connectors/druid/views.py | Druid.refresh_datasources | 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_nam... | python | 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_nam... | [
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21,522 | keon/algorithms | algorithms/linkedlist/add_two_numbers.py | convert_to_str | 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 | python | 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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21,523 | keon/algorithms | algorithms/sort/cocktail_shaker_sort.py | cocktail_shaker_sort | def cocktail_shaker_sort(arr):
"""
Cocktail_shaker_sort
Sorting a given array
mutation of bubble sort
reference: https://en.wikipedia.org/wiki/Cocktail_shaker_sort
Worst-case performance: O(N^2)
"""
def swap(i, j):
arr[i], arr[j] = arr[j], arr[i]
n = len(arr)
swap... | python | def cocktail_shaker_sort(arr):
"""
Cocktail_shaker_sort
Sorting a given array
mutation of bubble sort
reference: https://en.wikipedia.org/wiki/Cocktail_shaker_sort
Worst-case performance: O(N^2)
"""
def swap(i, j):
arr[i], arr[j] = arr[j], arr[i]
n = len(arr)
swap... | [
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21,524 | keon/algorithms | algorithms/strings/longest_common_prefix.py | common_prefix | 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] | python | 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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21,525 | keon/algorithms | algorithms/strings/min_distance.py | lcs | 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)) | python | 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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21,526 | keon/algorithms | algorithms/maths/nth_digit.py | find_nth_digit | 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 nth digit is from
3. find the nth digit and return
"""
length = 1
count = 9
start = 1
while n > length * count:
n -=... | python | 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 nth digit is from
3. find the nth digit and return
"""
length = 1
count = 9
start = 1
while n > length * count:
n -=... | [
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21,527 | keon/algorithms | algorithms/maths/prime_check.py | prime_check | 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:
retu... | python | 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:
retu... | [
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21,528 | keon/algorithms | algorithms/arrays/longest_non_repeat.py | longest_non_repeat_v1 | 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)
... | python | 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)
... | [
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21,529 | keon/algorithms | algorithms/arrays/longest_non_repeat.py | longest_non_repeat_v2 | 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 an... | python | 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 an... | [
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21,530 | keon/algorithms | algorithms/arrays/longest_non_repeat.py | get_longest_non_repeat_v1 | 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))... | python | 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))... | [
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21,531 | keon/algorithms | algorithms/arrays/longest_non_repeat.py | get_longest_non_repeat_v2 | def get_longest_non_repeat_v2(string):
"""
Find the length of the longest substring
without repeating characters.
Uses alternative algorithm.
Return max_len and the substring as a tuple
"""
if string is None:
return 0, ''
sub_string = ''
start, max_len = 0, 0
used_char = ... | python | def get_longest_non_repeat_v2(string):
"""
Find the length of the longest substring
without repeating characters.
Uses alternative algorithm.
Return max_len and the substring as a tuple
"""
if string is None:
return 0, ''
sub_string = ''
start, max_len = 0, 0
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21,532 | keon/algorithms | algorithms/queues/priority_queue.py | PriorityQueue.push | 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... | python | 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... | [
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21,533 | keon/algorithms | algorithms/arrays/flatten.py | flatten_iter | 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 | python | 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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21,534 | keon/algorithms | algorithms/iterables/convolved.py | convolved | def convolved(iterable, kernel_size=1, stride=1, padding=0, default_value=None):
"""Iterable to get every convolution window per loop iteration.
For example:
`convolved([1, 2, 3, 4], kernel_size=2)`
will produce the following result:
`[[1, 2], [2, 3], [3, 4]]`.
`convolve... | python | def convolved(iterable, kernel_size=1, stride=1, padding=0, default_value=None):
"""Iterable to get every convolution window per loop iteration.
For example:
`convolved([1, 2, 3, 4], kernel_size=2)`
will produce the following result:
`[[1, 2], [2, 3], [3, 4]]`.
`convolve... | [
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21,535 | keon/algorithms | algorithms/iterables/convolved.py | convolved_1d | 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 information, refer to:
- https://github.com/guillaume-chevalier/python-conv-lib/blob/master/conv/conv.py
- https://github.com/guillaume-chevali... | python | 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 information, refer to:
- https://github.com/guillaume-chevalier/python-conv-lib/blob/master/conv/conv.py
- https://github.com/guillaume-chevali... | [
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- https://github.com/guillaume-chevalier/python-conv-lib
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21,536 | keon/algorithms | algorithms/iterables/convolved.py | convolved_2d | 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 information, refer to:
- https://github.com/guillaume-chevalier/python-conv-lib/blob/master/conv/conv.py
- https://github.com/guillaume-chevali... | python | 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 information, refer to:
- https://github.com/guillaume-chevalier/python-conv-lib/blob/master/conv/conv.py
- https://github.com/guillaume-chevali... | [
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- https://github.com/guillaume-chevalier/python-conv-lib
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21,537 | keon/algorithms | algorithms/iterables/convolved.py | dimensionize | 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 example:
`dimensionize(3, nd=2)`
will produce the following result:
`(3, 3)`.
`dimensionize([3, 1], nd=2)`
will produce ... | python | 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 example:
`dimensionize(3, nd=2)`
will produce the following result:
`(3, 3)`.
`dimensionize([3, 1], nd=2)`
will produce ... | [
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`dimensionize([3, 1], nd=2)`
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21,538 | keon/algorithms | algorithms/arrays/merge_intervals.py | merge_intervals | 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:
... | python | 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:
... | [
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21,539 | keon/algorithms | algorithms/arrays/merge_intervals.py | Interval.merge | 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 | python | 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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21,540 | keon/algorithms | algorithms/arrays/merge_intervals.py | Interval.print_intervals | def print_intervals(intervals):
""" Print out the intervals. """
res = []
for i in intervals:
res.append(repr(i))
print("".join(res)) | python | def print_intervals(intervals):
""" Print out the intervals. """
res = []
for i in intervals:
res.append(repr(i))
print("".join(res)) | [
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21,541 | keon/algorithms | algorithms/sort/heap_sort.py | max_heapify | 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
whi... | python | 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
whi... | [
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21,542 | keon/algorithms | algorithms/sort/heap_sort.py | min_heapify | 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
# Itera... | python | 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
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last_parent = (end - start - 1) // 2
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21,543 | keon/algorithms | algorithms/maths/rsa.py | generate_key | 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
retu... | python | 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
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21,544 | keon/algorithms | algorithms/maths/sqrt_precision_factor.py | square_root | 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 | python | 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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21,545 | keon/algorithms | algorithms/set/set_covering.py | powerset | 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 chain them together as one object.
From https://docs.python.org/3/library/itertools.html#itertools-recipes
"""
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"""Calculate the powerset of any iterable.
For a range of integers up to the length of the given list,
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From https://docs.python.org/3/library/itertools.html#itertools-recipes
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21,546 | keon/algorithms | algorithms/set/set_covering.py | greedy_set_cover | def greedy_set_cover(universe, subsets, costs):
"""Approximate greedy algorithm for set-covering. Can be used on large
inputs - though not an optimal solution.
Args:
universe (list): Universe of elements
subsets (dict): Subsets of U {S1:elements,S2:elements}
costs (dict): Costs of e... | python | def greedy_set_cover(universe, subsets, costs):
"""Approximate greedy algorithm for set-covering. Can be used on large
inputs - though not an optimal solution.
Args:
universe (list): Universe of elements
subsets (dict): Subsets of U {S1:elements,S2:elements}
costs (dict): Costs of e... | [
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21,547 | keon/algorithms | algorithms/tree/avl/avl.py | AvlTree.insert | 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.l... | python | 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.l... | [
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21,548 | keon/algorithms | algorithms/tree/avl/avl.py | AvlTree.re_balance | 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... | python | 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... | [
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21,549 | keon/algorithms | algorithms/tree/avl/avl.py | AvlTree.update_heights | 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()
... | python | 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()
... | [
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21,550 | keon/algorithms | algorithms/tree/avl/avl.py | AvlTree.update_balances | 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... | python | 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:
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21,551 | keon/algorithms | algorithms/tree/avl/avl.py | AvlTree.in_order_traverse | 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())
... | python | 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())
... | [
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21,552 | keon/algorithms | algorithms/linkedlist/kth_to_last.py | kth_to_last_dict | 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 not in the dict,
our and statement will short circuit and return False
"""
if not (head and k > -1):
return False
d = dict()... | python | 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 not in the dict,
our and statement will short circuit and return False
"""
if not (head and k > -1):
return False
d = dict()... | [
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21,553 | keon/algorithms | algorithms/linkedlist/kth_to_last.py | kth_to_last | 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 the end.
"""
if not (head or k > -1):
return False
p1 = head
p2 = head
for i in range(1, k+1):
if p1 is None:... | python | 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 the end.
"""
if not (head or k > -1):
return False
p1 = head
p2 = head
for i in range(1, k+1):
if p1 is None:... | [
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21,554 | keon/algorithms | algorithms/maths/combination.py | combination | 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) | python | 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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21,555 | keon/algorithms | algorithms/maths/combination.py | combination_memo | 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:
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return memo[(n, r)]
return recur(n... | python | 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:
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return memo[(n, r)]
return recur(n... | [
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21,556 | keon/algorithms | algorithms/sort/pancake_sort.py | pancake_sort | def pancake_sort(arr):
"""
Pancake_sort
Sorting a given array
mutation of selection sort
reference: https://www.geeksforgeeks.org/pancake-sorting/
Overall time complexity : O(N^2)
"""
len_arr = len(arr)
if len_arr <= 1:
return arr
for cur in range(len(arr), 1, -1):... | python | def pancake_sort(arr):
"""
Pancake_sort
Sorting a given array
mutation of selection sort
reference: https://www.geeksforgeeks.org/pancake-sorting/
Overall time complexity : O(N^2)
"""
len_arr = len(arr)
if len_arr <= 1:
return arr
for cur in range(len(arr), 1, -1):... | [
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21,557 | keon/algorithms | algorithms/graph/satisfiability.py | scc | 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)... | python | 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():
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21,558 | keon/algorithms | algorithms/graph/satisfiability.py | build_graph | 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... | python | 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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21,559 | keon/algorithms | algorithms/tree/is_balanced.py | __get_depth | 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) | python | 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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21,560 | keon/algorithms | algorithms/backtrack/palindrome_partitioning.py | palindromic_substrings_iter | 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:]):
... | python | 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:]):
... | [
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21,561 | keon/algorithms | algorithms/calculator/math_parser.py | main | 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 Excep... | python | 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 Excep... | [
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21,562 | keon/algorithms | algorithms/maths/primes_sieve_of_eratosthenes.py | get_primes | 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(si... | python | 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(si... | [
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21,563 | keon/algorithms | algorithms/backtrack/permute.py | permute | 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:])
... | python | 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:])
... | [
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21,564 | dmlc/xgboost | python-package/xgboost/rabit.py | _init_rabit | 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 | python | 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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21,565 | dmlc/xgboost | python-package/xgboost/rabit.py | init | 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) | python | def init(args=None):
"""Initialize the rabit library with arguments"""
if args is None:
args = []
arr = (ctypes.c_char_p * len(args))()
arr[:] = args
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21,566 | dmlc/xgboost | python-package/xgboost/rabit.py | get_processor_name | 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 | python | 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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21,567 | dmlc/xgboost | python-package/xgboost/rabit.py | broadcast | 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 not equal root, this can be None
root : int
Rank of the node to broadcast data from.
Returns
-------
... | python | 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 not equal root, this can be None
root : int
Rank of the node to broadcast data from.
Returns
-------
... | [
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21,568 | dmlc/xgboost | jvm-packages/create_jni.py | normpath | 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 | python | 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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21,569 | dmlc/xgboost | python-package/xgboost/training.py | CVPack.update | def update(self, iteration, fobj):
""""Update the boosters for one iteration"""
self.bst.update(self.dtrain, iteration, fobj) | python | def update(self, iteration, fobj):
""""Update the boosters for one iteration"""
self.bst.update(self.dtrain, iteration, fobj) | [
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21,570 | dmlc/xgboost | python-package/xgboost/training.py | CVPack.eval | def eval(self, iteration, feval):
""""Evaluate the CVPack for one iteration."""
return self.bst.eval_set(self.watchlist, iteration, feval) | python | def eval(self, iteration, feval):
""""Evaluate the CVPack for one iteration."""
return self.bst.eval_set(self.watchlist, iteration, feval) | [
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21,571 | dmlc/xgboost | python-package/xgboost/callback.py | _get_callback_context | 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 | python | 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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21,572 | dmlc/xgboost | python-package/xgboost/callback.py | _fmt_metric | 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("wr... | python | 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("wr... | [
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21,573 | dmlc/xgboost | python-package/xgboost/callback.py | print_evaluation | def print_evaluation(period=1, show_stdv=True):
"""Create a callback that print evaluation result.
We print the evaluation results every **period** iterations
and on the first and the last iterations.
Parameters
----------
period : int
The period to log the evaluation results
show... | python | def print_evaluation(period=1, show_stdv=True):
"""Create a callback that print evaluation result.
We print the evaluation results every **period** iterations
and on the first and the last iterations.
Parameters
----------
period : int
The period to log the evaluation results
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We print the evaluation results every **period** iterations
and on the first and the last iterations.
Parameters
----------
period : int
The period to log the evaluation results
show_stdv : bool, optional
Whether show stdv if pr... | [
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21,574 | dmlc/xgboost | python-package/xgboost/callback.py | reset_learning_rate | 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.
Parameters
----------
learning_rates: list or function
List of learning rate for each boosting round
or a customized funct... | python | 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.
Parameters
----------
learning_rates: list or function
List of learning rate for each boosting round
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21,575 | dmlc/xgboost | python-package/xgboost/sklearn.py | _objective_decorator | def _objective_decorator(func):
"""Decorate an objective function
Converts an objective function using the typical sklearn metrics
signature so that it is usable with ``xgboost.training.train``
Parameters
----------
func: callable
Expects a callable with signature ``func(y_true, y_pred... | python | def _objective_decorator(func):
"""Decorate an objective function
Converts an objective function using the typical sklearn metrics
signature so that it is usable with ``xgboost.training.train``
Parameters
----------
func: callable
Expects a callable with signature ``func(y_true, y_pred... | [
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21,576 | dmlc/xgboost | python-package/xgboost/sklearn.py | XGBModel.apply | 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 features matrix.
ntree_limit : int
Limit number of trees in the prediction; defaults to ... | python | 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 features matrix.
ntree_limit : int
Limit number of trees in the prediction; defaults to ... | [
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21,577 | dmlc/xgboost | python-package/xgboost/sklearn.py | XGBModel.feature_importances_ | def feature_importances_(self):
"""
Feature importances property
.. note:: Feature importance is defined only for tree boosters
Feature importance is only defined when the decision tree model is chosen as base
learner (`booster=gbtree`). It is not defined for other base... | python | def feature_importances_(self):
"""
Feature importances property
.. note:: Feature importance is defined only for tree boosters
Feature importance is only defined when the decision tree model is chosen as base
learner (`booster=gbtree`). It is not defined for other base... | [
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21,578 | dmlc/xgboost | python-package/xgboost/sklearn.py | XGBClassifier.predict_proba | def predict_proba(self, data, ntree_limit=None, validate_features=True):
"""
Predict the probability of each `data` example being of a given class.
.. note:: This function is not thread safe
For each booster object, predict can only be called from one thread.
If you wan... | python | def predict_proba(self, data, ntree_limit=None, validate_features=True):
"""
Predict the probability of each `data` example being of a given class.
.. note:: This function is not thread safe
For each booster object, predict can only be called from one thread.
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21,579 | dmlc/xgboost | python-package/xgboost/core.py | from_pystr_to_cstr | 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... | python | 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... | [
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21,580 | dmlc/xgboost | python-package/xgboost/core.py | from_cstr_to_pystr | 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 length of data
"""
if PY3:
res = []
for i in range(length.value):
try:
... | python | 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 length of data
"""
if PY3:
res = []
for i in range(length.value):
try:
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21,581 | dmlc/xgboost | python-package/xgboost/core.py | _load_lib | 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:
... | python | 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:
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21,582 | dmlc/xgboost | python-package/xgboost/core.py | ctypes2buffer | 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)... | python | 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)
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21,583 | dmlc/xgboost | python-package/xgboost/core.py | c_array | 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) | python | 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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21,584 | dmlc/xgboost | python-package/xgboost/core.py | _maybe_dt_array | 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... | python | 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:
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21,585 | dmlc/xgboost | python-package/xgboost/core.py | DMatrix._init_from_dt | 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):
... | python | 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
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21,586 | dmlc/xgboost | python-package/xgboost/core.py | DMatrix.set_float_info_npy2d | 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 information
data: numpy array
The array of data to be set
"""
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"""Set float type property into the DMatrix
for numpy 2d array input
Parameters
----------
field: str
The field name of the information
data: numpy array
The array of data to be set
"""
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21,587 | dmlc/xgboost | python-package/xgboost/core.py | Booster.load_rabit_checkpoint | 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.hand... | python | 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(
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21,588 | dmlc/xgboost | python-package/xgboost/core.py | Booster.attr | def attr(self, key):
"""Get attribute string from the Booster.
Parameters
----------
key : str
The key to get attribute from.
Returns
-------
value : str
The attribute value of the key, returns None if attribute do not exist.
"""
... | python | def attr(self, key):
"""Get attribute string from the Booster.
Parameters
----------
key : str
The key to get attribute from.
Returns
-------
value : str
The attribute value of the key, returns None if attribute do not exist.
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Parameters
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key : str
The key to get attribute from.
Returns
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value : str
The attribute value of the key, returns None if attribute do not exist. | [
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21,589 | dmlc/xgboost | python-package/xgboost/core.py | Booster.attributes | def attributes(self):
"""Get attributes stored in the Booster as a dictionary.
Returns
-------
result : dictionary of attribute_name: attribute_value pairs of strings.
Returns an empty dict if there's no attributes.
"""
length = c_bst_ulong()
sarr = ... | python | def attributes(self):
"""Get attributes stored in the Booster as a dictionary.
Returns
-------
result : dictionary of attribute_name: attribute_value pairs of strings.
Returns an empty dict if there's no attributes.
"""
length = c_bst_ulong()
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21,590 | dmlc/xgboost | python-package/xgboost/core.py | Booster.set_attr | 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 n... | python | 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():
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21,591 | dmlc/xgboost | python-package/xgboost/core.py | Booster.set_param | 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 simply str key
value: optional
value of the specified parameter, when params is str key... | python | 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 simply str key
value: optional
value of the specified parameter, when params is str key... | [
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21,592 | dmlc/xgboost | python-package/xgboost/core.py | Booster.eval | 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 name of the dataset.
iteration : int, optional
The current ite... | python | 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 name of the dataset.
iteration : int, optional
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The name of the dataset.
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The current iteration number.
Returns
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21,593 | dmlc/xgboost | python-package/xgboost/core.py | Booster.save_model | 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 interfaces. Auxiliary attributes of
the Python Booster object (such as feature_names) will not be saved.
To pre... | python | 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 interfaces. Auxiliary attributes of
the Python Booster object (such as feature_names) will not be saved.
To pre... | [
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The model is saved in an XGBoost internal binary format which is
universal among the various XGBoost interfaces. Auxiliary attributes of
the Python Booster object (such as feature_names) will not be saved.
To preserve all attributes, pickle the Booster object.
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21,594 | dmlc/xgboost | python-package/xgboost/core.py | Booster.dump_model | 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.
fmap : string, optional
Name of the file containing feature map names.
... | python | 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.
fmap : string, optional
Name of the file containing feature map names.
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fout : string
Output file name.
fmap : string, optional
Name of the file containing feature map names.
with_stats : bool, optional
Controls whether the split statistics are output.
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21,595 | dmlc/xgboost | python-package/xgboost/core.py | Booster.get_dump | 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 the file containing feature map names.
with_stats : bool, optional
Controls w... | python | 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 the file containing feature map names.
with_stats : bool, optional
Controls w... | [
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Parameters
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fmap : string, optional
Name of the file containing feature map names.
with_stats : bool, optional
Controls whether the split statistics are output.
dump_format : string, optional
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21,596 | dmlc/xgboost | python-package/xgboost/core.py | Booster.get_split_value_histogram | 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 the feature.
fmap: str (optional)
The name of feature map file.
bin: int, de... | python | 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 the feature.
fmap: str (optional)
The name of feature map file.
bin: int, de... | [
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Parameters
----------
feature: str
The name of the feature.
fmap: str (optional)
The name of feature map file.
bin: int, default None
The maximum number of bins.
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21,597 | dmlc/xgboost | python-package/xgboost/plotting.py | plot_importance | def plot_importance(booster, ax=None, height=0.2,
xlim=None, ylim=None, title='Feature importance',
xlabel='F score', ylabel='Features',
importance_type='weight', max_num_features=None,
grid=True, show_values=True, **kwargs):
"""Plot im... | python | def plot_importance(booster, ax=None, height=0.2,
xlim=None, ylim=None, title='Feature importance',
xlabel='F score', ylabel='Features',
importance_type='weight', max_num_features=None,
grid=True, show_values=True, **kwargs):
"""Plot im... | [
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booster : Booster, XGBModel or dict
Booster or XGBModel instance, or dict taken by Booster.get_fscore()
ax : matplotlib Axes, default None
Target axes instance. If None, new figure and axes will be created.
grid : bool, Tu... | [
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21,598 | dmlc/xgboost | python-package/xgboost/plotting.py | _parse_edge | 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', co... | python | 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', co... | [
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21,599 | tzutalin/labelImg | libs/utils.py | newAction | def newAction(parent, text, slot=None, shortcut=None, icon=None,
tip=None, checkable=False, enabled=True):
"""Create a new action and assign callbacks, shortcuts, etc."""
a = QAction(text, parent)
if icon is not None:
a.setIcon(newIcon(icon))
if shortcut is not None:
if isi... | python | def newAction(parent, text, slot=None, shortcut=None, icon=None,
tip=None, checkable=False, enabled=True):
"""Create a new action and assign callbacks, shortcuts, etc."""
a = QAction(text, parent)
if icon is not None:
a.setIcon(newIcon(icon))
if shortcut is not None:
if isi... | [
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