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train | get_factors | [summary]
Arguments:
n {[int]} -- [to analysed number]
Returns:
[list of lists] -- [all factors of the number n] | algorithms/dfs/all_factors.py | def get_factors(n):
"""[summary]
Arguments:
n {[int]} -- [to analysed number]
Returns:
[list of lists] -- [all factors of the number n]
"""
def factor(n, i, combi, res):
"""[summary]
helper function
Arguments:
n {[int]} -- [number]
... | def get_factors(n):
"""[summary]
Arguments:
n {[int]} -- [to analysed number]
Returns:
[list of lists] -- [all factors of the number n]
"""
def factor(n, i, combi, res):
"""[summary]
helper function
Arguments:
n {[int]} -- [number]
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train | get_factors_iterative1 | [summary]
Computes all factors of n.
Translated the function get_factors(...) in
a call-stack modell.
Arguments:
n {[int]} -- [to analysed number]
Returns:
[list of lists] -- [all factors] | algorithms/dfs/all_factors.py | def get_factors_iterative1(n):
"""[summary]
Computes all factors of n.
Translated the function get_factors(...) in
a call-stack modell.
Arguments:
n {[int]} -- [to analysed number]
Returns:
[list of lists] -- [all factors]
"""
todo, res = [(n, 2, [])], []
while... | def get_factors_iterative1(n):
"""[summary]
Computes all factors of n.
Translated the function get_factors(...) in
a call-stack modell.
Arguments:
n {[int]} -- [to analysed number]
Returns:
[list of lists] -- [all factors]
"""
todo, res = [(n, 2, [])], []
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train | get_factors_iterative2 | [summary]
analog as above
Arguments:
n {[int]} -- [description]
Returns:
[list of lists] -- [all factors of n] | algorithms/dfs/all_factors.py | def get_factors_iterative2(n):
"""[summary]
analog as above
Arguments:
n {[int]} -- [description]
Returns:
[list of lists] -- [all factors of n]
"""
ans, stack, x = [], [], 2
while True:
if x > n // x:
if not stack:
return ans
... | def get_factors_iterative2(n):
"""[summary]
analog as above
Arguments:
n {[int]} -- [description]
Returns:
[list of lists] -- [all factors of n]
"""
ans, stack, x = [], [], 2
while True:
if x > n // x:
if not stack:
return ans
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train | longest_increasing_subsequence | Dynamic Programming Algorithm for
counting the length of longest increasing subsequence
type sequence: List[int] | algorithms/dp/longest_increasing.py | def longest_increasing_subsequence(sequence):
"""
Dynamic Programming Algorithm for
counting the length of longest increasing subsequence
type sequence: List[int]
"""
length = len(sequence)
counts = [1 for _ in range(length)]
for i in range(1, length):
for j in range(0, i):
... | def longest_increasing_subsequence(sequence):
"""
Dynamic Programming Algorithm for
counting the length of longest increasing subsequence
type sequence: List[int]
"""
length = len(sequence)
counts = [1 for _ in range(length)]
for i in range(1, length):
for j in range(0, i):
... | [
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train | single_number3 | :type nums: List[int]
:rtype: List[int] | algorithms/bit/single_number3.py | def single_number3(nums):
"""
:type nums: List[int]
:rtype: List[int]
"""
# isolate a^b from pairs using XOR
ab = 0
for n in nums:
ab ^= n
# isolate right most bit from a^b
right_most = ab & (-ab)
# isolate a and b from a^b
a, b = 0, 0
for n in nums:
if ... | def single_number3(nums):
"""
:type nums: List[int]
:rtype: List[int]
"""
# isolate a^b from pairs using XOR
ab = 0
for n in nums:
ab ^= n
# isolate right most bit from a^b
right_most = ab & (-ab)
# isolate a and b from a^b
a, b = 0, 0
for n in nums:
if ... | [
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train | distance | [summary]
HELPER-FUNCTION
calculates the (eulidean) distance between vector x and y.
Arguments:
x {[tuple]} -- [vector]
y {[tuple]} -- [vector] | algorithms/ml/nearest_neighbor.py | def distance(x,y):
"""[summary]
HELPER-FUNCTION
calculates the (eulidean) distance between vector x and y.
Arguments:
x {[tuple]} -- [vector]
y {[tuple]} -- [vector]
"""
assert len(x) == len(y), "The vector must have same length"
result = ()
sum = 0
for i in range(le... | def distance(x,y):
"""[summary]
HELPER-FUNCTION
calculates the (eulidean) distance between vector x and y.
Arguments:
x {[tuple]} -- [vector]
y {[tuple]} -- [vector]
"""
assert len(x) == len(y), "The vector must have same length"
result = ()
sum = 0
for i in range(le... | [
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"... | 4d6569464a62a75c1357acc97e2dd32ee2f9f4a3 |
train | nearest_neighbor | [summary]
Implements the nearest neighbor algorithm
Arguments:
x {[tupel]} -- [vector]
tSet {[dict]} -- [training set]
Returns:
[type] -- [result of the AND-function] | algorithms/ml/nearest_neighbor.py | def nearest_neighbor(x, tSet):
"""[summary]
Implements the nearest neighbor algorithm
Arguments:
x {[tupel]} -- [vector]
tSet {[dict]} -- [training set]
Returns:
[type] -- [result of the AND-function]
"""
assert isinstance(x, tuple) and isinstance(tSet, dict)
curren... | def nearest_neighbor(x, tSet):
"""[summary]
Implements the nearest neighbor algorithm
Arguments:
x {[tupel]} -- [vector]
tSet {[dict]} -- [training set]
Returns:
[type] -- [result of the AND-function]
"""
assert isinstance(x, tuple) and isinstance(tSet, dict)
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train | is_strobogrammatic | :type num: str
:rtype: bool | algorithms/maths/is_strobogrammatic.py | def is_strobogrammatic(num):
"""
:type num: str
:rtype: bool
"""
comb = "00 11 88 69 96"
i = 0
j = len(num) - 1
while i <= j:
x = comb.find(num[i]+num[j])
if x == -1:
return False
i += 1
j -= 1
return True | def is_strobogrammatic(num):
"""
:type num: str
:rtype: bool
"""
comb = "00 11 88 69 96"
i = 0
j = len(num) - 1
while i <= j:
x = comb.find(num[i]+num[j])
if x == -1:
return False
i += 1
j -= 1
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train | merge_sort | Merge Sort
Complexity: O(n log(n)) | algorithms/sort/merge_sort.py | def merge_sort(arr):
""" Merge Sort
Complexity: O(n log(n))
"""
# Our recursive base case
if len(arr) <= 1:
return arr
mid = len(arr) // 2
# Perform merge_sort recursively on both halves
left, right = merge_sort(arr[:mid]), merge_sort(arr[mid:])
# Merge each side togethe... | def merge_sort(arr):
""" Merge Sort
Complexity: O(n log(n))
"""
# Our recursive base case
if len(arr) <= 1:
return arr
mid = len(arr) // 2
# Perform merge_sort recursively on both halves
left, right = merge_sort(arr[:mid]), merge_sort(arr[mid:])
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train | merge | Merge helper
Complexity: O(n) | algorithms/sort/merge_sort.py | def merge(left, right, merged):
""" Merge helper
Complexity: O(n)
"""
left_cursor, right_cursor = 0, 0
while left_cursor < len(left) and right_cursor < len(right):
# Sort each one and place into the result
if left[left_cursor] <= right[right_cursor]:
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""" Merge helper
Complexity: O(n)
"""
left_cursor, right_cursor = 0, 0
while left_cursor < len(left) and right_cursor < len(right):
# Sort each one and place into the result
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train | bucket_sort | Bucket Sort
Complexity: O(n^2)
The complexity is dominated by nextSort | algorithms/sort/bucket_sort.py | def bucket_sort(arr):
''' Bucket Sort
Complexity: O(n^2)
The complexity is dominated by nextSort
'''
# The number of buckets and make buckets
num_buckets = len(arr)
buckets = [[] for bucket in range(num_buckets)]
# Assign values into bucket_sort
for value in arr:
inde... | def bucket_sort(arr):
''' Bucket Sort
Complexity: O(n^2)
The complexity is dominated by nextSort
'''
# The number of buckets and make buckets
num_buckets = len(arr)
buckets = [[] for bucket in range(num_buckets)]
# Assign values into bucket_sort
for value in arr:
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train | k_closest | Initialize max heap with first k points.
Python does not support a max heap; thus we can use the default min heap where the keys (distance) are negated. | algorithms/heap/k_closest_points.py | def k_closest(points, k, origin=(0, 0)):
# Time: O(k+(n-k)logk)
# Space: O(k)
"""Initialize max heap with first k points.
Python does not support a max heap; thus we can use the default min heap where the keys (distance) are negated.
"""
heap = [(-distance(p, origin), p) for p in points[:k]]
... | def k_closest(points, k, origin=(0, 0)):
# Time: O(k+(n-k)logk)
# Space: O(k)
"""Initialize max heap with first k points.
Python does not support a max heap; thus we can use the default min heap where the keys (distance) are negated.
"""
heap = [(-distance(p, origin), p) for p in points[:k]]
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train | reverse_list | :type head: ListNode
:rtype: ListNode | algorithms/linkedlist/reverse.py | def reverse_list(head):
"""
:type head: ListNode
:rtype: ListNode
"""
if not head or not head.next:
return head
prev = None
while head:
current = head
head = head.next
current.next = prev
prev = current
return prev | def reverse_list(head):
"""
:type head: ListNode
:rtype: ListNode
"""
if not head or not head.next:
return head
prev = None
while head:
current = head
head = head.next
current.next = prev
prev = current
return prev | [
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train | reverse_list_recursive | :type head: ListNode
:rtype: ListNode | algorithms/linkedlist/reverse.py | def reverse_list_recursive(head):
"""
:type head: ListNode
:rtype: ListNode
"""
if head is None or head.next is None:
return head
p = head.next
head.next = None
revrest = reverse_list_recursive(p)
p.next = head
return revrest | def reverse_list_recursive(head):
"""
:type head: ListNode
:rtype: ListNode
"""
if head is None or head.next is None:
return head
p = head.next
head.next = None
revrest = reverse_list_recursive(p)
p.next = head
return revrest | [
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train | has_path_sum | :type root: TreeNode
:type sum: int
:rtype: bool | algorithms/tree/path_sum.py | def has_path_sum(root, sum):
"""
:type root: TreeNode
:type sum: int
:rtype: bool
"""
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:type sum: int
:rtype: bool
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train | int_to_base | :type n: int
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"""
:type n: int
:type base: int
:rtype: str
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if n == 0:
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elif n < 0:
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:type base: int
:rtype: str
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train | base_to_int | Note : You can use int() built-in function instread of this.
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:rtype: int
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:type base: int
:rtype: int
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train | is_cyclic | :type head: Node
:rtype: bool | algorithms/linkedlist/is_cyclic.py | def is_cyclic(head):
"""
:type head: Node
:rtype: bool
"""
if not head:
return False
runner = head
walker = head
while runner.next and runner.next.next:
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if runner == walker:
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"""
:type head: Node
:rtype: bool
"""
if not head:
return False
runner = head
walker = head
while runner.next and runner.next.next:
runner = runner.next.next
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if runner == walker:
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train | decode_string | :type s: str
:rtype: str | algorithms/strings/decode_string.py | def decode_string(s):
"""
:type s: str
:rtype: str
"""
stack = []; cur_num = 0; cur_string = ''
for c in s:
if c == '[':
stack.append((cur_string, cur_num))
cur_string = ''
cur_num = 0
elif c == ']':
prev_string, num = stack.pop()
... | def decode_string(s):
"""
:type s: str
:rtype: str
"""
stack = []; cur_num = 0; cur_string = ''
for c in s:
if c == '[':
stack.append((cur_string, cur_num))
cur_string = ''
cur_num = 0
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prev_string, num = stack.pop()
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train | palindromic_substrings_iter | A slightly more Pythonic approach with a recursive generator | algorithms/backtrack/palindrome_partitioning.py | 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:]):
... | 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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train | is_isomorphic | :type s: str
:type t: str
:rtype: bool | algorithms/map/is_isomorphic.py | def is_isomorphic(s, t):
"""
:type s: str
:type t: str
:rtype: bool
"""
if len(s) != len(t):
return False
dict = {}
set_value = set()
for i in range(len(s)):
if s[i] not in dict:
if t[i] in set_value:
return False
dict[s[i]] = t... | def is_isomorphic(s, t):
"""
:type s: str
:type t: str
:rtype: bool
"""
if len(s) != len(t):
return False
dict = {}
set_value = set()
for i in range(len(s)):
if s[i] not in dict:
if t[i] in set_value:
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dict[s[i]] = t... | [
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train | calc | Calculate operation result
n2 Number: Number 2
n1 Number: Number 1
operator Char: Operation to calculate | algorithms/calculator/math_parser.py | def calc(n2, n1, operator):
"""
Calculate operation result
n2 Number: Number 2
n1 Number: Number 1
operator Char: Operation to calculate
"""
if operator == '-': return n1 - n2
elif operator == '+': return n1 + n2
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elif operator == '... | def calc(n2, n1, operator):
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Calculate operation result
n2 Number: Number 2
n1 Number: Number 1
operator Char: Operation to calculate
"""
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train | apply_operation | Apply operation to the first 2 items of the output queue
op_stack Deque (reference)
out_stack Deque (reference) | algorithms/calculator/math_parser.py | def apply_operation(op_stack, out_stack):
"""
Apply operation to the first 2 items of the output queue
op_stack Deque (reference)
out_stack Deque (reference)
"""
out_stack.append(calc(out_stack.pop(), out_stack.pop(), op_stack.pop())) | def apply_operation(op_stack, out_stack):
"""
Apply operation to the first 2 items of the output queue
op_stack Deque (reference)
out_stack Deque (reference)
"""
out_stack.append(calc(out_stack.pop(), out_stack.pop(), op_stack.pop())) | [
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train | parse | Return array of parsed tokens in the expression
expression String: Math expression to parse in infix notation | algorithms/calculator/math_parser.py | def parse(expression):
"""
Return array of parsed tokens in the expression
expression String: Math expression to parse in infix notation
"""
result = []
current = ""
for i in expression:
if i.isdigit() or i == '.':
current += i
else:
if le... | def parse(expression):
"""
Return array of parsed tokens in the expression
expression String: Math expression to parse in infix notation
"""
result = []
current = ""
for i in expression:
if i.isdigit() or i == '.':
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train | evaluate | Calculate result of expression
expression String: The expression
type Type (optional): Number type [int, float] | algorithms/calculator/math_parser.py | def evaluate(expression):
"""
Calculate result of expression
expression String: The expression
type Type (optional): Number type [int, float]
"""
op_stack = deque() # operator stack
out_stack = deque() # output stack (values)
tokens = parse(expression) # calls the function onl... | def evaluate(expression):
"""
Calculate result of expression
expression String: The expression
type Type (optional): Number type [int, float]
"""
op_stack = deque() # operator stack
out_stack = deque() # output stack (values)
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train | main | simple user-interface | algorithms/calculator/math_parser.py | 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... | 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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train | closest_value | :type root: TreeNode
:type target: float
:rtype: int | algorithms/tree/bst/bst_closest_value.py | def closest_value(root, target):
"""
:type root: TreeNode
:type target: float
:rtype: int
"""
a = root.val
kid = root.left if target < a else root.right
if not kid:
return a
b = closest_value(kid, target)
return min((a,b), key=lambda x: abs(target-x)) | def closest_value(root, target):
"""
:type root: TreeNode
:type target: float
:rtype: int
"""
a = root.val
kid = root.left if target < a else root.right
if not kid:
return a
b = closest_value(kid, target)
return min((a,b), key=lambda x: abs(target-x)) | [
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train | get_primes | Return list of all primes less than n,
Using sieve of Eratosthenes. | algorithms/maths/primes_sieve_of_eratosthenes.py | 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... | 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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train | permute | returns a list with the permuations. | algorithms/backtrack/permute.py | def permute(elements):
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train | permute_iter | iterator: returns a perumation by each call. | algorithms/backtrack/permute.py | def permute_iter(elements):
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train | extended_gcd | Extended GCD algorithm.
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train | bin_tree_to_list | type root: root class | algorithms/tree/bin_tree_to_list.py | def bin_tree_to_list(root):
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type root: root class
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train | add_operators | :type num: str
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"""
:type num: str
:type target: int
:rtype: List[str]
"""
def dfs(res, path, num, target, pos, prev, multed):
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... | def add_operators(num, target):
"""
:type num: str
:type target: int
:rtype: List[str]
"""
def dfs(res, path, num, target, pos, prev, multed):
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train | _init_rabit | internal library initializer. | python-package/xgboost/rabit.py | def _init_rabit():
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train | init | Initialize the rabit library with arguments | python-package/xgboost/rabit.py | def init(args=None):
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train | tracker_print | Print message to the tracker.
This function can be used to communicate the information of
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----------
msg : str
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msg : str
The message to be printed to tracker.
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The message to be printed to tracker.
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train | get_processor_name | Get the processor name.
Returns
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name : str
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"""Get the processor name.
Returns
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name : str
the name of processor(host)
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train | broadcast | Broadcast object from one node to all other nodes.
Parameters
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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.
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"""Broadcast object from one node to all other nodes.
Parameters
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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
-------
... | 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.
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train | normpath | Normalize UNIX path to a native path. | jvm-packages/create_jni.py | def normpath(path):
"""Normalize UNIX path to a native path."""
normalized = os.path.join(*path.split("/"))
if os.path.isabs(path):
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train | _train_internal | internal training function | python-package/xgboost/training.py | def _train_internal(params, dtrain,
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obj=None, feval=None,
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train | train | Train a booster with given parameters.
Parameters
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params : dict
Booster params.
dtrain : DMatrix
Data to be trained.
num_boost_round: int
Number of boosting iterations.
evals: list of pairs (DMatrix, string)
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train | mknfold | Make an n-fold list of CVPack from random indices. | python-package/xgboost/training.py | def mknfold(dall, nfold, param, seed, evals=(), fpreproc=None, stratified=False,
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Make an n-fold list of CVPack from random indices.
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train | aggcv | Aggregate cross-validation results.
If verbose_eval is true, progress is displayed in every call. If
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# pylint: disable=invalid-name
"""
Aggregate cross-validation results.
If verbose_eval is true, progress is displayed in every call. If
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"""
cvmap = {}
idx = r... | def aggcv(rlist):
# pylint: disable=invalid-name
"""
Aggregate cross-validation results.
If verbose_eval is true, progress is displayed in every call. If
verbose_eval is an integer, progress will only be displayed every
`verbose_eval` trees, tracked via trial.
"""
cvmap = {}
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train | cv | Cross-validation with given parameters.
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params : dict
Booster params.
dtrain : DMatrix
Data to be trained.
num_boost_round : int
Number of boosting iterations.
nfold : int
Number of folds in CV.
stratified : bool
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fpreproc=None, as_pandas=True, verbose_eval=None, show_stdv=True,
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train | CVPack.update | Update the boosters for one iteration | python-package/xgboost/training.py | def update(self, iteration, fobj):
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train | CVPack.eval | Evaluate the CVPack for one iteration. | python-package/xgboost/training.py | def eval(self, iteration, feval):
""""Evaluate the CVPack for one iteration."""
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train | _get_callback_context | return whether the current callback context is cv or train | python-package/xgboost/callback.py | def _get_callback_context(env):
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context = 'train'
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train | _fmt_metric | format metric string | python-package/xgboost/callback.py | def _fmt_metric(value, show_stdv=True):
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train | print_evaluation | 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_stdv : bool, optional
Whether show stdv if pr... | python-package/xgboost/callback.py | def print_evaluation(period=1, show_stdv=True):
"""Create a callback that print evaluation result.
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and on the first and the last iterations.
Parameters
----------
period : int
The period to log the evaluation results
show... | def print_evaluation(period=1, show_stdv=True):
"""Create a callback that print evaluation result.
We print the evaluation results every **period** iterations
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Parameters
----------
period : int
The period to log the evaluation results
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train | record_evaluation | Create a call back that records the evaluation history into **eval_result**.
Parameters
----------
eval_result : dict
A dictionary to store the evaluation results.
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-------
callback : function
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"""Create a call back that records the evaluation history into **eval_result**.
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callback : function
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eval_result : dict
A dictionary to store the evaluation results.
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The requested callback function.
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train | reset_learning_rate | 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 function that calculates eta in terms of
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"""Reset learning rate after iteration 1
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Parameters
----------
learning_rates: list or function
List of learning rate for each boosting round
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List of learning rate for each boosting round
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train | early_stop | Create a callback that activates early stoppping.
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Requires at least one item in **evals**.
If there's more than one, will use the last.
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train | run_doxygen | Run the doxygen make command in the designated folder. | doc/conf.py | def run_doxygen(folder):
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try:
retcode = subprocess.call("cd %s; make doxygen" % folder, shell=True)
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train | _objective_decorator | Decorate an objective function
Converts an objective function using the typical sklearn metrics
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Parameters
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func: callable
Expects a callable with signature ``func(y_true, y_pred)``:
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Converts an objective function using the typical sklearn metrics
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Parameters
----------
func: callable
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self | python-package/xgboost/sklearn.py | def set_params(self, **params):
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Modification of the sklearn method to allow unknown kwargs. This allows using
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train | XGBModel.get_params | Get parameters. | python-package/xgboost/sklearn.py | def get_params(self, deep=False):
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train | XGBModel.get_xgb_params | Get xgboost type parameters. | python-package/xgboost/sklearn.py | def get_xgb_params(self):
"""Get xgboost type parameters."""
xgb_params = self.get_params()
random_state = xgb_params.pop('random_state')
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train | XGBModel.load_model | Load the model from a file.
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Load the model from a file.
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train | XGBModel.fit | Fit the gradient boosting model
Parameters
----------
X : array_like
Feature matrix
y : array_like
Labels
sample_weight : array_like
instance weights
eval_set : list, optional
A list of (X, y) tuple pairs to use as a valida... | python-package/xgboost/sklearn.py | def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True, xgb_model=None,
sample_weight_eval_set=None, callbacks=None):
# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
"""
Fit the gra... | def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
early_stopping_rounds=None, verbose=True, xgb_model=None,
sample_weight_eval_set=None, callbacks=None):
# pylint: disable=missing-docstring,invalid-name,attribute-defined-outside-init
"""
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train | XGBModel.predict | Predict with `data`.
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If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copies
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.. n... | python-package/xgboost/sklearn.py | def predict(self, data, output_margin=False, ntree_limit=None, validate_features=True):
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.. note:: This function is not thread safe.
For each booster object, predict can only be called from one thread.
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train | XGBModel.apply | Return the predicted leaf every tree for each sample.
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Input features matrix.
ntree_limit : int
Limit number of trees in the prediction; defaults to 0 (use all trees).
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--... | python-package/xgboost/sklearn.py | def apply(self, X, ntree_limit=0):
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Parameters
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Input features matrix.
ntree_limit : int
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Input features matrix.
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train | XGBModel.feature_importances_ | Feature importances property
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learner (`booster=gbtree`). It is not defined for other base learner types, such
as linear learners ... | python-package/xgboost/sklearn.py | 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... | 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
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train | XGBModel.coef_ | Coefficients property
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Coefficients are only defined when the linear model is chosen as base
learner (`booster=gblinear`). It is not defined for other base learner types, such
as tree learners (`booster=gbtree`).
... | python-package/xgboost/sklearn.py | def coef_(self):
"""
Coefficients property
.. note:: Coefficients are defined only for linear learners
Coefficients are only defined when the linear model is chosen as base
learner (`booster=gblinear`). It is not defined for other base learner types, such
as... | def coef_(self):
"""
Coefficients property
.. note:: Coefficients are defined only for linear learners
Coefficients are only defined when the linear model is chosen as base
learner (`booster=gblinear`). It is not defined for other base learner types, such
as... | [
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train | XGBModel.intercept_ | Intercept (bias) property
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learner (`booster=gblinear`). It is not defined for other base learner types, such
as tree learners (`booster=gbtree`).... | python-package/xgboost/sklearn.py | def intercept_(self):
"""
Intercept (bias) property
.. note:: Intercept is defined only for linear learners
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... | def intercept_(self):
"""
Intercept (bias) property
.. note:: Intercept is defined only for linear learners
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train | XGBClassifier.fit | Fit gradient boosting classifier
Parameters
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X : array_like
Feature matrix
y : array_like
Labels
sample_weight : array_like
Weight for each instance
eval_set : list, optional
A list of (X, y) pairs to use as a val... | python-package/xgboost/sklearn.py | def fit(self, X, y, sample_weight=None, eval_set=None, eval_metric=None,
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train | XGBClassifier.predict_proba | Predict the probability of each `data` example being of a given class.
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For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thread, call ``xgb.copy()`` to make copies
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Predict the probability of each `data` example being of a given class.
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train | XGBRanker.fit | Fit the gradient boosting model
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Feature matrix
y : array_like
Labels
group : array_like
group size of training data
sample_weight : array_like
group weights
.. note:: Weight... | python-package/xgboost/sklearn.py | def fit(self, X, y, group, sample_weight=None, eval_set=None, sample_weight_eval_set=None,
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train | from_pystr_to_cstr | Convert a list of Python str to C pointer
Parameters
----------
data : list
list of str | python-package/xgboost/core.py | def from_pystr_to_cstr(data):
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data : list
list of str
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train | from_cstr_to_pystr | Revert C pointer to Python str
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----------
data : ctypes pointer
pointer to data
length : ctypes pointer
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pointer to length of data
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train | _load_lib | Load xgboost Library. | python-package/xgboost/core.py | def _load_lib():
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lib_paths = find_lib_path()
if not lib_paths:
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lib_success = False
os_error_list = []
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lib_paths = find_lib_path()
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lib_success = False
os_error_list = []
for lib_path in lib_paths:
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train | ctypes2numpy | Convert a ctypes pointer array to a numpy array. | python-package/xgboost/core.py | def ctypes2numpy(cptr, length, dtype):
"""Convert a ctypes pointer array to a numpy array.
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train | ctypes2buffer | Convert ctypes pointer to buffer type. | python-package/xgboost/core.py | def ctypes2buffer(cptr, length):
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if not ctypes.memmove(rptr, cptr, length)... | def ctypes2buffer(cptr, length):
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train | c_array | Convert a python string to c array. | python-package/xgboost/core.py | def c_array(ctype, values):
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train | _maybe_pandas_data | Extract internal data from pd.DataFrame for DMatrix data | python-package/xgboost/core.py | def _maybe_pandas_data(data, feature_names, feature_types):
""" Extract internal data from pd.DataFrame for DMatrix data """
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train | _maybe_dt_data | Validate feature names and types if data table | python-package/xgboost/core.py | def _maybe_dt_data(data, feature_names, feature_types):
"""
Validate feature names and types if data table
"""
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Validate feature names and types if data table
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train | _maybe_dt_array | Extract numpy array from single column data table | python-package/xgboost/core.py | def _maybe_dt_array(array):
""" Extract numpy array from single column data table """
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train | DMatrix._init_from_csr | Initialize data from a CSR matrix. | python-package/xgboost/core.py | def _init_from_csr(self, csr):
"""
Initialize data from a CSR matrix.
"""
if len(csr.indices) != len(csr.data):
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"""
Initialize data from a CSR matrix.
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train | DMatrix._init_from_csc | Initialize data from a CSC matrix. | python-package/xgboost/core.py | def _init_from_csc(self, csc):
"""
Initialize data from a CSC matrix.
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Initialize data from a CSC matrix.
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train | DMatrix._init_from_npy2d | Initialize data from a 2-D numpy matrix.
If ``mat`` does not have ``order='C'`` (aka row-major) or is not contiguous,
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If ``mat`` does not have ``dtype=numpy.float32``, a temporary copy will be made.
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"""
Initialize data from a 2-D numpy matrix.
If ``mat`` does not have ``order='C'`` (aka row-major) or is not contiguous,
a temporary copy will be made.
If ``mat`` does not have ``dtype=numpy.float32``, a temporary copy will be... | def _init_from_npy2d(self, mat, missing, nthread):
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Initialize data from a 2-D numpy matrix.
If ``mat`` does not have ``order='C'`` (aka row-major) or is not contiguous,
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train | DMatrix._init_from_dt | Initialize data from a datatable Frame. | python-package/xgboost/core.py | 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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Initialize data from a datatable Frame.
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train | DMatrix.set_float_info | Set float type property into the DMatrix.
Parameters
----------
field: str
The field name of the information
data: numpy array
The array of data to be set | python-package/xgboost/core.py | def set_float_info(self, field, data):
"""Set float type property into the DMatrix.
Parameters
----------
field: str
The field name of the information
data: numpy array
The array of data to be set
"""
if getattr(data, 'base', None) is not... | def set_float_info(self, field, data):
"""Set float type property into the DMatrix.
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field: str
The field name of the information
data: numpy array
The array of data to be set
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train | DMatrix.set_float_info_npy2d | Set float type property into the DMatrix
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Parameters
----------
field: str
The field name of the information
data: numpy array
The array of data to be set | python-package/xgboost/core.py | def set_float_info_npy2d(self, field, data):
"""Set float type property into the DMatrix
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Parameters
----------
field: str
The field name of the information
data: numpy array
The array of data to be set
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... | def set_float_info_npy2d(self, field, data):
"""Set float type property into the DMatrix
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Parameters
----------
field: str
The field name of the information
data: numpy array
The array of data to be set
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train | DMatrix.set_uint_info | Set uint type property into the DMatrix.
Parameters
----------
field: str
The field name of the information
data: numpy array
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----------
field: str
The field name of the information
data: numpy array
The array of data to be set
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The field name of the information
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The array of data to be set
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train | DMatrix.save_binary | Save DMatrix to an XGBoost buffer. Saved binary can be later loaded
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Parameters
----------
fname : string
Name of the output buffer file.
silent : bool (optional; default: True)
If set, the out... | python-package/xgboost/core.py | def save_binary(self, fname, silent=True):
"""Save DMatrix to an XGBoost buffer. Saved binary can be later loaded
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Parameters
----------
fname : string
Name of the output buffer file.
silent : bool... | def save_binary(self, fname, silent=True):
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train | DMatrix.set_group | Set group size of DMatrix (used for ranking).
Parameters
----------
group : array like
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"""Set group size of DMatrix (used for ranking).
Parameters
----------
group : array like
Group size of each group
"""
_check_call(_LIB.XGDMatrixSetGroup(self.handle,
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group : array like
Group size of each group
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train | DMatrix.feature_names | Get feature names (column labels).
Returns
-------
feature_names : list or None | python-package/xgboost/core.py | def feature_names(self):
"""Get feature names (column labels).
Returns
-------
feature_names : list or None
"""
if self._feature_names is None:
self._feature_names = ['f{0}'.format(i) for i in range(self.num_col())]
return self._feature_names | def feature_names(self):
"""Get feature names (column labels).
Returns
-------
feature_names : list or None
"""
if self._feature_names is None:
self._feature_names = ['f{0}'.format(i) for i in range(self.num_col())]
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train | DMatrix.feature_names | Set feature names (column labels).
Parameters
----------
feature_names : list or None
Labels for features. None will reset existing feature names | python-package/xgboost/core.py | def feature_names(self, feature_names):
"""Set feature names (column labels).
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----------
feature_names : list or None
Labels for features. None will reset existing feature names
"""
if feature_names is not None:
# validate feature name
... | def feature_names(self, feature_names):
"""Set feature names (column labels).
Parameters
----------
feature_names : list or None
Labels for features. None will reset existing feature names
"""
if feature_names is not None:
# validate feature name
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train | DMatrix.feature_types | Set feature types (column types).
This is for displaying the results and unrelated
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Parameters
----------
feature_types : list or None
Labels for features. None will reset existing feature names | python-package/xgboost/core.py | def feature_types(self, feature_types):
"""Set feature types (column types).
This is for displaying the results and unrelated
to the learning process.
Parameters
----------
feature_types : list or None
Labels for features. None will reset existing feature na... | def feature_types(self, feature_types):
"""Set feature types (column types).
This is for displaying the results and unrelated
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train | Booster.load_rabit_checkpoint | Initialize the model by load from rabit checkpoint.
Returns
-------
version: integer
The version number of the model. | python-package/xgboost/core.py | 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... | def load_rabit_checkpoint(self):
"""Initialize the model by load from rabit checkpoint.
Returns
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version: integer
The version number of the model.
"""
version = ctypes.c_int()
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train | Booster.attr | 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-package/xgboost/core.py | 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.
"""
... | 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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train | Booster.attributes | 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. | python-package/xgboost/core.py | 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 = ... | 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.
"""
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train | Booster.set_attr | Set the attribute of the Booster.
Parameters
----------
**kwargs
The attributes to set. Setting a value to None deletes an attribute. | python-package/xgboost/core.py | 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... | 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... | [
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train | Booster.set_param | 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-package/xgboost/core.py | 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... | 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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train | Booster.eval | 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 iteration number.
Returns
-------
res... | python-package/xgboost/core.py | 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... | 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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train | Booster.predict | Predict with data.
.. note:: This function is not thread safe.
For each booster object, predict can only be called from one thread.
If you want to run prediction using multiple thread, call ``bst.copy()`` to make copies
of model object and then call ``predict()``.
.. not... | python-package/xgboost/core.py | def predict(self, data, output_margin=False, ntree_limit=0, pred_leaf=False,
pred_contribs=False, approx_contribs=False, pred_interactions=False,
validate_features=True):
"""
Predict with data.
.. note:: This function is not thread safe.
For each boost... | def predict(self, data, output_margin=False, ntree_limit=0, pred_leaf=False,
pred_contribs=False, approx_contribs=False, pred_interactions=False,
validate_features=True):
"""
Predict with data.
.. note:: This function is not thread safe.
For each boost... | [
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train | Booster.save_model | 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 preserve all attributes, pickle the Booster object.
... | python-package/xgboost/core.py | 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... | 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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train | Booster.load_model | Load the model from a file.
The model is loaded from 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 loaded.
To preserve all attributes, pickle the Booster ob... | python-package/xgboost/core.py | def load_model(self, fname):
"""
Load the model from a file.
The model is loaded from 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 loaded.
... | def load_model(self, fname):
"""
Load the model from a file.
The model is loaded from 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 loaded.
... | [
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train | Booster.dump_model | 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.
with_stats : bool, optional
Controls whether the split statistics are output.
... | python-package/xgboost/core.py | 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.
... | 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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train | Booster.get_dump | 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 whether the split statistics are output.
dump_format : string, optional
... | python-package/xgboost/core.py | 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... | 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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