repository_name stringlengths 7 55 | func_path_in_repository stringlengths 4 223 | func_name stringlengths 1 134 | whole_func_string stringlengths 75 104k | language stringclasses 1
value | func_code_string stringlengths 75 104k | func_code_tokens listlengths 19 28.4k | func_documentation_string stringlengths 1 46.9k | func_documentation_tokens listlengths 1 1.97k | split_name stringclasses 1
value | func_code_url stringlengths 87 315 |
|---|---|---|---|---|---|---|---|---|---|---|
HazyResearch/pdftotree | pdftotree/visual/visual_utils.py | do_intersect | def do_intersect(bb1, bb2):
"""
Helper function that returns True if two bounding boxes overlap.
"""
if bb1[0] + bb1[2] < bb2[0] or bb2[0] + bb2[2] < bb1[0]:
return False
if bb1[1] + bb1[3] < bb2[1] or bb2[1] + bb2[3] < bb1[1]:
return False
return True | python | def do_intersect(bb1, bb2):
"""
Helper function that returns True if two bounding boxes overlap.
"""
if bb1[0] + bb1[2] < bb2[0] or bb2[0] + bb2[2] < bb1[0]:
return False
if bb1[1] + bb1[3] < bb2[1] or bb2[1] + bb2[3] < bb1[1]:
return False
return True | [
"def",
"do_intersect",
"(",
"bb1",
",",
"bb2",
")",
":",
"if",
"bb1",
"[",
"0",
"]",
"+",
"bb1",
"[",
"2",
"]",
"<",
"bb2",
"[",
"0",
"]",
"or",
"bb2",
"[",
"0",
"]",
"+",
"bb2",
"[",
"2",
"]",
"<",
"bb1",
"[",
"0",
"]",
":",
"return",
... | Helper function that returns True if two bounding boxes overlap. | [
"Helper",
"function",
"that",
"returns",
"True",
"if",
"two",
"bounding",
"boxes",
"overlap",
"."
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/visual/visual_utils.py#L63-L71 |
HazyResearch/pdftotree | pdftotree/visual/visual_utils.py | get_bboxes | def get_bboxes(
img,
mask,
nb_boxes=100,
score_thresh=0.5,
iou_thresh=0.2,
prop_size=0.09,
prop_scale=1.2,
):
"""
Uses selective search to generate candidate bounding boxes and keeps the
ones that have the largest iou with the predicted mask.
:param img: original image
:... | python | def get_bboxes(
img,
mask,
nb_boxes=100,
score_thresh=0.5,
iou_thresh=0.2,
prop_size=0.09,
prop_scale=1.2,
):
"""
Uses selective search to generate candidate bounding boxes and keeps the
ones that have the largest iou with the predicted mask.
:param img: original image
:... | [
"def",
"get_bboxes",
"(",
"img",
",",
"mask",
",",
"nb_boxes",
"=",
"100",
",",
"score_thresh",
"=",
"0.5",
",",
"iou_thresh",
"=",
"0.2",
",",
"prop_size",
"=",
"0.09",
",",
"prop_scale",
"=",
"1.2",
",",
")",
":",
"min_size",
"=",
"int",
"(",
"img"... | Uses selective search to generate candidate bounding boxes and keeps the
ones that have the largest iou with the predicted mask.
:param img: original image
:param mask: predicted mask
:param nb_boxes: max number of candidate bounding boxes
:param score_thresh: scre threshold to consider prediction ... | [
"Uses",
"selective",
"search",
"to",
"generate",
"candidate",
"bounding",
"boxes",
"and",
"keeps",
"the",
"ones",
"that",
"have",
"the",
"largest",
"iou",
"with",
"the",
"predicted",
"mask",
"."
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/visual/visual_utils.py#L74-L141 |
HazyResearch/pdftotree | pdftotree/utils/pdf/pdf_utils.py | analyze_pages | def analyze_pages(file_name, char_margin=1.0):
"""
Input: the file path to the PDF file
Output: yields the layout object for each page in the PDF
"""
log = logging.getLogger(__name__)
# Open a PDF file.
with open(os.path.realpath(file_name), "rb") as fp:
# Create a PDF parser object ... | python | def analyze_pages(file_name, char_margin=1.0):
"""
Input: the file path to the PDF file
Output: yields the layout object for each page in the PDF
"""
log = logging.getLogger(__name__)
# Open a PDF file.
with open(os.path.realpath(file_name), "rb") as fp:
# Create a PDF parser object ... | [
"def",
"analyze_pages",
"(",
"file_name",
",",
"char_margin",
"=",
"1.0",
")",
":",
"log",
"=",
"logging",
".",
"getLogger",
"(",
"__name__",
")",
"# Open a PDF file.",
"with",
"open",
"(",
"os",
".",
"path",
".",
"realpath",
"(",
"file_name",
")",
",",
... | Input: the file path to the PDF file
Output: yields the layout object for each page in the PDF | [
"Input",
":",
"the",
"file",
"path",
"to",
"the",
"PDF",
"file",
"Output",
":",
"yields",
"the",
"layout",
"object",
"for",
"each",
"page",
"in",
"the",
"PDF"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/pdf_utils.py#L110-L143 |
HazyResearch/pdftotree | pdftotree/utils/pdf/pdf_utils.py | normalize_pdf | def normalize_pdf(layout, scaler):
"""
Normalizes pdf object coordinates (bot left) to image
conventions (top left origin).
Returns the list of chars and average char size
"""
chars = []
mentions = []
height = scaler * layout.height
font_size_counter = collections.Counter()
# Lin... | python | def normalize_pdf(layout, scaler):
"""
Normalizes pdf object coordinates (bot left) to image
conventions (top left origin).
Returns the list of chars and average char size
"""
chars = []
mentions = []
height = scaler * layout.height
font_size_counter = collections.Counter()
# Lin... | [
"def",
"normalize_pdf",
"(",
"layout",
",",
"scaler",
")",
":",
"chars",
"=",
"[",
"]",
"mentions",
"=",
"[",
"]",
"height",
"=",
"scaler",
"*",
"layout",
".",
"height",
"font_size_counter",
"=",
"collections",
".",
"Counter",
"(",
")",
"# Lines longer tha... | Normalizes pdf object coordinates (bot left) to image
conventions (top left origin).
Returns the list of chars and average char size | [
"Normalizes",
"pdf",
"object",
"coordinates",
"(",
"bot",
"left",
")",
"to",
"image",
"conventions",
"(",
"top",
"left",
"origin",
")",
".",
"Returns",
"the",
"list",
"of",
"chars",
"and",
"average",
"char",
"size"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/pdf_utils.py#L146-L218 |
HazyResearch/pdftotree | pdftotree/utils/pdf/pdf_utils.py | _print_dict | def _print_dict(elem_dict):
"""
Print a dict in a readable way
"""
for key, value in sorted(elem_dict.iteritems()):
if isinstance(value, collections.Iterable):
print(key, len(value))
else:
print(key, value) | python | def _print_dict(elem_dict):
"""
Print a dict in a readable way
"""
for key, value in sorted(elem_dict.iteritems()):
if isinstance(value, collections.Iterable):
print(key, len(value))
else:
print(key, value) | [
"def",
"_print_dict",
"(",
"elem_dict",
")",
":",
"for",
"key",
",",
"value",
"in",
"sorted",
"(",
"elem_dict",
".",
"iteritems",
"(",
")",
")",
":",
"if",
"isinstance",
"(",
"value",
",",
"collections",
".",
"Iterable",
")",
":",
"print",
"(",
"key",
... | Print a dict in a readable way | [
"Print",
"a",
"dict",
"in",
"a",
"readable",
"way"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/pdf_utils.py#L221-L229 |
HazyResearch/pdftotree | pdftotree/utils/pdf/pdf_utils.py | _font_of_mention | def _font_of_mention(m):
"""
Returns the font type and size of the first alphanumeric
char in the text or None if there isn't any.
"""
for ch in m:
if isinstance(ch, LTChar) and ch.get_text().isalnum():
return (ch.fontname, _font_size_of(ch))
return (None, 0) | python | def _font_of_mention(m):
"""
Returns the font type and size of the first alphanumeric
char in the text or None if there isn't any.
"""
for ch in m:
if isinstance(ch, LTChar) and ch.get_text().isalnum():
return (ch.fontname, _font_size_of(ch))
return (None, 0) | [
"def",
"_font_of_mention",
"(",
"m",
")",
":",
"for",
"ch",
"in",
"m",
":",
"if",
"isinstance",
"(",
"ch",
",",
"LTChar",
")",
"and",
"ch",
".",
"get_text",
"(",
")",
".",
"isalnum",
"(",
")",
":",
"return",
"(",
"ch",
".",
"fontname",
",",
"_fon... | Returns the font type and size of the first alphanumeric
char in the text or None if there isn't any. | [
"Returns",
"the",
"font",
"type",
"and",
"size",
"of",
"the",
"first",
"alphanumeric",
"char",
"in",
"the",
"text",
"or",
"None",
"if",
"there",
"isn",
"t",
"any",
"."
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/pdf_utils.py#L238-L246 |
HazyResearch/pdftotree | pdftotree/utils/pdf/pdf_utils.py | _allowed_char | def _allowed_char(c):
"""
Returns whether the given unicode char is allowed in output
"""
c = ord(c)
if c < 0:
return False
if c < 128:
return _ascii_allowed[c]
# Genereally allow unicodes, TODO: check for unicode control characters
# characters
return True | python | def _allowed_char(c):
"""
Returns whether the given unicode char is allowed in output
"""
c = ord(c)
if c < 0:
return False
if c < 128:
return _ascii_allowed[c]
# Genereally allow unicodes, TODO: check for unicode control characters
# characters
return True | [
"def",
"_allowed_char",
"(",
"c",
")",
":",
"c",
"=",
"ord",
"(",
"c",
")",
"if",
"c",
"<",
"0",
":",
"return",
"False",
"if",
"c",
"<",
"128",
":",
"return",
"_ascii_allowed",
"[",
"c",
"]",
"# Genereally allow unicodes, TODO: check for unicode control char... | Returns whether the given unicode char is allowed in output | [
"Returns",
"whether",
"the",
"given",
"unicode",
"char",
"is",
"allowed",
"in",
"output"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/pdf_utils.py#L258-L269 |
HazyResearch/pdftotree | pdftotree/utils/pdf/pdf_utils.py | keep_allowed_chars | def keep_allowed_chars(text):
"""
Cleans the text for output
"""
# print ','.join(str(ord(c)) for c in text)
return "".join(" " if c == "\n" else c for c in text.strip() if _allowed_char(c)) | python | def keep_allowed_chars(text):
"""
Cleans the text for output
"""
# print ','.join(str(ord(c)) for c in text)
return "".join(" " if c == "\n" else c for c in text.strip() if _allowed_char(c)) | [
"def",
"keep_allowed_chars",
"(",
"text",
")",
":",
"# print ','.join(str(ord(c)) for c in text)",
"return",
"\"\"",
".",
"join",
"(",
"\" \"",
"if",
"c",
"==",
"\"\\n\"",
"else",
"c",
"for",
"c",
"in",
"text",
".",
"strip",
"(",
")",
"if",
"_allowed_char"... | Cleans the text for output | [
"Cleans",
"the",
"text",
"for",
"output"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/pdf_utils.py#L272-L277 |
HazyResearch/pdftotree | pdftotree/utils/pdf/pdf_utils.py | CustomPDFPageAggregator.paint_path | def paint_path(self, gstate, stroke, fill, evenodd, path):
"""
Converting long paths to small segments each time we m=Move
or h=ClosePath for polygon
"""
shape = "".join(x[0] for x in path)
prev_split = 0
for i in range(len(shape)):
if shape[i] == "m" ... | python | def paint_path(self, gstate, stroke, fill, evenodd, path):
"""
Converting long paths to small segments each time we m=Move
or h=ClosePath for polygon
"""
shape = "".join(x[0] for x in path)
prev_split = 0
for i in range(len(shape)):
if shape[i] == "m" ... | [
"def",
"paint_path",
"(",
"self",
",",
"gstate",
",",
"stroke",
",",
"fill",
",",
"evenodd",
",",
"path",
")",
":",
"shape",
"=",
"\"\"",
".",
"join",
"(",
"x",
"[",
"0",
"]",
"for",
"x",
"in",
"path",
")",
"prev_split",
"=",
"0",
"for",
"i",
"... | Converting long paths to small segments each time we m=Move
or h=ClosePath for polygon | [
"Converting",
"long",
"paths",
"to",
"small",
"segments",
"each",
"time",
"we",
"m",
"=",
"Move",
"or",
"h",
"=",
"ClosePath",
"for",
"polygon"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/pdf_utils.py#L53-L74 |
HazyResearch/pdftotree | pdftotree/utils/pdf/pdf_utils.py | CustomPDFPageAggregator.paint_single_path | def paint_single_path(self, gstate, stroke, fill, evenodd, path):
"""
Converting a single path draw command into lines and curves objects
"""
if len(path) < 2:
return
shape = "".join(x[0] for x in path)
pts = []
for p in path:
for i in ran... | python | def paint_single_path(self, gstate, stroke, fill, evenodd, path):
"""
Converting a single path draw command into lines and curves objects
"""
if len(path) < 2:
return
shape = "".join(x[0] for x in path)
pts = []
for p in path:
for i in ran... | [
"def",
"paint_single_path",
"(",
"self",
",",
"gstate",
",",
"stroke",
",",
"fill",
",",
"evenodd",
",",
"path",
")",
":",
"if",
"len",
"(",
"path",
")",
"<",
"2",
":",
"return",
"shape",
"=",
"\"\"",
".",
"join",
"(",
"x",
"[",
"0",
"]",
"for",
... | Converting a single path draw command into lines and curves objects | [
"Converting",
"a",
"single",
"path",
"draw",
"command",
"into",
"lines",
"and",
"curves",
"objects"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/pdf_utils.py#L76-L107 |
HazyResearch/pdftotree | pdftotree/utils/pdf/layout_utils.py | traverse_layout | def traverse_layout(root, callback):
"""
Tree walker and invokes the callback as it
traverse pdf object tree
"""
callback(root)
if isinstance(root, collections.Iterable):
for child in root:
traverse_layout(child, callback) | python | def traverse_layout(root, callback):
"""
Tree walker and invokes the callback as it
traverse pdf object tree
"""
callback(root)
if isinstance(root, collections.Iterable):
for child in root:
traverse_layout(child, callback) | [
"def",
"traverse_layout",
"(",
"root",
",",
"callback",
")",
":",
"callback",
"(",
"root",
")",
"if",
"isinstance",
"(",
"root",
",",
"collections",
".",
"Iterable",
")",
":",
"for",
"child",
"in",
"root",
":",
"traverse_layout",
"(",
"child",
",",
"call... | Tree walker and invokes the callback as it
traverse pdf object tree | [
"Tree",
"walker",
"and",
"invokes",
"the",
"callback",
"as",
"it",
"traverse",
"pdf",
"object",
"tree"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/layout_utils.py#L17-L25 |
HazyResearch/pdftotree | pdftotree/utils/pdf/layout_utils.py | get_near_items | def get_near_items(tree, tree_key):
"""
Check both possible neighbors for key
in a binary tree
"""
try:
yield tree.floor_item(tree_key)
except KeyError:
pass
try:
yield tree.ceiling_item(tree_key)
except KeyError:
pass | python | def get_near_items(tree, tree_key):
"""
Check both possible neighbors for key
in a binary tree
"""
try:
yield tree.floor_item(tree_key)
except KeyError:
pass
try:
yield tree.ceiling_item(tree_key)
except KeyError:
pass | [
"def",
"get_near_items",
"(",
"tree",
",",
"tree_key",
")",
":",
"try",
":",
"yield",
"tree",
".",
"floor_item",
"(",
"tree_key",
")",
"except",
"KeyError",
":",
"pass",
"try",
":",
"yield",
"tree",
".",
"ceiling_item",
"(",
"tree_key",
")",
"except",
"K... | Check both possible neighbors for key
in a binary tree | [
"Check",
"both",
"possible",
"neighbors",
"for",
"key",
"in",
"a",
"binary",
"tree"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/layout_utils.py#L28-L40 |
HazyResearch/pdftotree | pdftotree/utils/pdf/layout_utils.py | align_add | def align_add(tree, key, item, align_thres=2.0):
"""
Adding the item object to a binary tree with the given
key while allow for small key differences
close_enough_func that checks if two keys are
within threshold
"""
for near_key, near_list in get_near_items(tree, key):
if abs(key - ... | python | def align_add(tree, key, item, align_thres=2.0):
"""
Adding the item object to a binary tree with the given
key while allow for small key differences
close_enough_func that checks if two keys are
within threshold
"""
for near_key, near_list in get_near_items(tree, key):
if abs(key - ... | [
"def",
"align_add",
"(",
"tree",
",",
"key",
",",
"item",
",",
"align_thres",
"=",
"2.0",
")",
":",
"for",
"near_key",
",",
"near_list",
"in",
"get_near_items",
"(",
"tree",
",",
"key",
")",
":",
"if",
"abs",
"(",
"key",
"-",
"near_key",
")",
"<",
... | Adding the item object to a binary tree with the given
key while allow for small key differences
close_enough_func that checks if two keys are
within threshold | [
"Adding",
"the",
"item",
"object",
"to",
"a",
"binary",
"tree",
"with",
"the",
"given",
"key",
"while",
"allow",
"for",
"small",
"key",
"differences",
"close_enough_func",
"that",
"checks",
"if",
"two",
"keys",
"are",
"within",
"threshold"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/layout_utils.py#L43-L55 |
HazyResearch/pdftotree | pdftotree/utils/pdf/layout_utils.py | collect_table_content | def collect_table_content(table_bboxes, elems):
"""
Returns a list of elements that are contained inside
the corresponding supplied bbox.
"""
# list of table content chars
table_contents = [[] for _ in range(len(table_bboxes))]
prev_content = None
prev_bbox = None
for cid, c in enume... | python | def collect_table_content(table_bboxes, elems):
"""
Returns a list of elements that are contained inside
the corresponding supplied bbox.
"""
# list of table content chars
table_contents = [[] for _ in range(len(table_bboxes))]
prev_content = None
prev_bbox = None
for cid, c in enume... | [
"def",
"collect_table_content",
"(",
"table_bboxes",
",",
"elems",
")",
":",
"# list of table content chars",
"table_contents",
"=",
"[",
"[",
"]",
"for",
"_",
"in",
"range",
"(",
"len",
"(",
"table_bboxes",
")",
")",
"]",
"prev_content",
"=",
"None",
"prev_bb... | Returns a list of elements that are contained inside
the corresponding supplied bbox. | [
"Returns",
"a",
"list",
"of",
"elements",
"that",
"are",
"contained",
"inside",
"the",
"corresponding",
"supplied",
"bbox",
"."
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/layout_utils.py#L120-L150 |
HazyResearch/pdftotree | pdftotree/utils/pdf/layout_utils.py | _gaps_from | def _gaps_from(intervals):
"""
From a list of intervals extract
a list of sorted gaps in the form of [(g,i)]
where g is the size of the ith gap.
"""
sliding_window = zip(intervals, intervals[1:])
gaps = [b[0] - a[1] for a, b in sliding_window]
return gaps | python | def _gaps_from(intervals):
"""
From a list of intervals extract
a list of sorted gaps in the form of [(g,i)]
where g is the size of the ith gap.
"""
sliding_window = zip(intervals, intervals[1:])
gaps = [b[0] - a[1] for a, b in sliding_window]
return gaps | [
"def",
"_gaps_from",
"(",
"intervals",
")",
":",
"sliding_window",
"=",
"zip",
"(",
"intervals",
",",
"intervals",
"[",
"1",
":",
"]",
")",
"gaps",
"=",
"[",
"b",
"[",
"0",
"]",
"-",
"a",
"[",
"1",
"]",
"for",
"a",
",",
"b",
"in",
"sliding_window... | From a list of intervals extract
a list of sorted gaps in the form of [(g,i)]
where g is the size of the ith gap. | [
"From",
"a",
"list",
"of",
"intervals",
"extract",
"a",
"list",
"of",
"sorted",
"gaps",
"in",
"the",
"form",
"of",
"[",
"(",
"g",
"i",
")",
"]",
"where",
"g",
"is",
"the",
"size",
"of",
"the",
"ith",
"gap",
"."
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/layout_utils.py#L157-L165 |
HazyResearch/pdftotree | pdftotree/utils/pdf/layout_utils.py | project_onto | def project_onto(objs, axis, min_gap_size=4.0):
"""
Projects object bboxes onto the axis and return the
unioned intervals and groups of objects in intervals.
"""
if axis == "x":
axis = 0
if axis == "y":
axis = 1
axis_end = axis + 2
if axis == 0: # if projecting onto X ax... | python | def project_onto(objs, axis, min_gap_size=4.0):
"""
Projects object bboxes onto the axis and return the
unioned intervals and groups of objects in intervals.
"""
if axis == "x":
axis = 0
if axis == "y":
axis = 1
axis_end = axis + 2
if axis == 0: # if projecting onto X ax... | [
"def",
"project_onto",
"(",
"objs",
",",
"axis",
",",
"min_gap_size",
"=",
"4.0",
")",
":",
"if",
"axis",
"==",
"\"x\"",
":",
"axis",
"=",
"0",
"if",
"axis",
"==",
"\"y\"",
":",
"axis",
"=",
"1",
"axis_end",
"=",
"axis",
"+",
"2",
"if",
"axis",
"... | Projects object bboxes onto the axis and return the
unioned intervals and groups of objects in intervals. | [
"Projects",
"object",
"bboxes",
"onto",
"the",
"axis",
"and",
"return",
"the",
"unioned",
"intervals",
"and",
"groups",
"of",
"objects",
"in",
"intervals",
"."
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/layout_utils.py#L168-L214 |
HazyResearch/pdftotree | pdftotree/utils/pdf/layout_utils.py | recursive_xy_divide | def recursive_xy_divide(elems, avg_font_size):
"""
Recursively group/divide the document by white stripes
by projecting elements onto alternating axes as intervals.
avg_font_size: the minimum gap size between elements below
which we consider interval continuous.
"""
log = logging.getLogger(... | python | def recursive_xy_divide(elems, avg_font_size):
"""
Recursively group/divide the document by white stripes
by projecting elements onto alternating axes as intervals.
avg_font_size: the minimum gap size between elements below
which we consider interval continuous.
"""
log = logging.getLogger(... | [
"def",
"recursive_xy_divide",
"(",
"elems",
",",
"avg_font_size",
")",
":",
"log",
"=",
"logging",
".",
"getLogger",
"(",
"__name__",
")",
"log",
".",
"info",
"(",
"avg_font_size",
")",
"objects",
"=",
"list",
"(",
"elems",
".",
"mentions",
")",
"objects",... | Recursively group/divide the document by white stripes
by projecting elements onto alternating axes as intervals.
avg_font_size: the minimum gap size between elements below
which we consider interval continuous. | [
"Recursively",
"group",
"/",
"divide",
"the",
"document",
"by",
"white",
"stripes",
"by",
"projecting",
"elements",
"onto",
"alternating",
"axes",
"as",
"intervals",
"."
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/layout_utils.py#L217-L273 |
HazyResearch/pdftotree | pdftotree/utils/bbox_utils.py | doOverlap | def doOverlap(bbox1, bbox2):
"""
:param bbox1: bounding box of the first rectangle
:param bbox2: bounding box of the second rectangle
:return: 1 if the two rectangles overlap
"""
if bbox1[2] < bbox2[0] or bbox2[2] < bbox1[0]:
return False
if bbox1[3] < bbox2[1] or bbox2[3] < bbox1[1]... | python | def doOverlap(bbox1, bbox2):
"""
:param bbox1: bounding box of the first rectangle
:param bbox2: bounding box of the second rectangle
:return: 1 if the two rectangles overlap
"""
if bbox1[2] < bbox2[0] or bbox2[2] < bbox1[0]:
return False
if bbox1[3] < bbox2[1] or bbox2[3] < bbox1[1]... | [
"def",
"doOverlap",
"(",
"bbox1",
",",
"bbox2",
")",
":",
"if",
"bbox1",
"[",
"2",
"]",
"<",
"bbox2",
"[",
"0",
"]",
"or",
"bbox2",
"[",
"2",
"]",
"<",
"bbox1",
"[",
"0",
"]",
":",
"return",
"False",
"if",
"bbox1",
"[",
"3",
"]",
"<",
"bbox2"... | :param bbox1: bounding box of the first rectangle
:param bbox2: bounding box of the second rectangle
:return: 1 if the two rectangles overlap | [
":",
"param",
"bbox1",
":",
"bounding",
"box",
"of",
"the",
"first",
"rectangle",
":",
"param",
"bbox2",
":",
"bounding",
"box",
"of",
"the",
"second",
"rectangle",
":",
"return",
":",
"1",
"if",
"the",
"two",
"rectangles",
"overlap"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/bbox_utils.py#L4-L14 |
HazyResearch/pdftotree | pdftotree/utils/bbox_utils.py | isContained | def isContained(bbox1, bbox2, tol=TOLERANCE):
"""
:param bbox1: bounding box of the first rectangle
:param bbox2: bounding box of the second rectangle
:return: True if bbox1 is contaned in bbox2
"""
if bbox1[0] > bbox2[0] - tol and bbox1[1] > bbox2[1] - tol:
if bbox1[2] < bbox2[2] + tol ... | python | def isContained(bbox1, bbox2, tol=TOLERANCE):
"""
:param bbox1: bounding box of the first rectangle
:param bbox2: bounding box of the second rectangle
:return: True if bbox1 is contaned in bbox2
"""
if bbox1[0] > bbox2[0] - tol and bbox1[1] > bbox2[1] - tol:
if bbox1[2] < bbox2[2] + tol ... | [
"def",
"isContained",
"(",
"bbox1",
",",
"bbox2",
",",
"tol",
"=",
"TOLERANCE",
")",
":",
"if",
"bbox1",
"[",
"0",
"]",
">",
"bbox2",
"[",
"0",
"]",
"-",
"tol",
"and",
"bbox1",
"[",
"1",
"]",
">",
"bbox2",
"[",
"1",
"]",
"-",
"tol",
":",
"if"... | :param bbox1: bounding box of the first rectangle
:param bbox2: bounding box of the second rectangle
:return: True if bbox1 is contaned in bbox2 | [
":",
"param",
"bbox1",
":",
"bounding",
"box",
"of",
"the",
"first",
"rectangle",
":",
"param",
"bbox2",
":",
"bounding",
"box",
"of",
"the",
"second",
"rectangle",
":",
"return",
":",
"True",
"if",
"bbox1",
"is",
"contaned",
"in",
"bbox2"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/bbox_utils.py#L17-L26 |
HazyResearch/pdftotree | pdftotree/utils/bbox_utils.py | mergeBboxes | def mergeBboxes(bbox1, bbox2):
"""
:param bbox1: (top, left, bottom, right)
:param bbox2: (top, left, bottom, right)
:return: Merge bounding boxes
"""
if isContained(bbox1, bbox2):
return bbox2
elif isContained(bbox2, bbox1):
return bbox1
else:
return (
... | python | def mergeBboxes(bbox1, bbox2):
"""
:param bbox1: (top, left, bottom, right)
:param bbox2: (top, left, bottom, right)
:return: Merge bounding boxes
"""
if isContained(bbox1, bbox2):
return bbox2
elif isContained(bbox2, bbox1):
return bbox1
else:
return (
... | [
"def",
"mergeBboxes",
"(",
"bbox1",
",",
"bbox2",
")",
":",
"if",
"isContained",
"(",
"bbox1",
",",
"bbox2",
")",
":",
"return",
"bbox2",
"elif",
"isContained",
"(",
"bbox2",
",",
"bbox1",
")",
":",
"return",
"bbox1",
"else",
":",
"return",
"(",
"min",... | :param bbox1: (top, left, bottom, right)
:param bbox2: (top, left, bottom, right)
:return: Merge bounding boxes | [
":",
"param",
"bbox1",
":",
"(",
"top",
"left",
"bottom",
"right",
")",
":",
"param",
"bbox2",
":",
"(",
"top",
"left",
"bottom",
"right",
")",
":",
"return",
":",
"Merge",
"bounding",
"boxes"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/bbox_utils.py#L29-L45 |
HazyResearch/pdftotree | pdftotree/utils/bbox_utils.py | get_rectangles | def get_rectangles(vertical_lines, horizontal_lines):
"""
:param vertical_lines: list of vertical lines coordinates
:param horizontal_lines: list of horizontal lines coordinates
:return: List of bounding boxes for tables
"""
rectangles = []
i = 0
j = 0
while i < len(horizontal_lines)... | python | def get_rectangles(vertical_lines, horizontal_lines):
"""
:param vertical_lines: list of vertical lines coordinates
:param horizontal_lines: list of horizontal lines coordinates
:return: List of bounding boxes for tables
"""
rectangles = []
i = 0
j = 0
while i < len(horizontal_lines)... | [
"def",
"get_rectangles",
"(",
"vertical_lines",
",",
"horizontal_lines",
")",
":",
"rectangles",
"=",
"[",
"]",
"i",
"=",
"0",
"j",
"=",
"0",
"while",
"i",
"<",
"len",
"(",
"horizontal_lines",
")",
"and",
"j",
"<",
"len",
"(",
"vertical_lines",
")",
":... | :param vertical_lines: list of vertical lines coordinates
:param horizontal_lines: list of horizontal lines coordinates
:return: List of bounding boxes for tables | [
":",
"param",
"vertical_lines",
":",
"list",
"of",
"vertical",
"lines",
"coordinates",
":",
"param",
"horizontal_lines",
":",
"list",
"of",
"horizontal",
"lines",
"coordinates",
":",
"return",
":",
"List",
"of",
"bounding",
"boxes",
"for",
"tables"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/bbox_utils.py#L48-L78 |
HazyResearch/pdftotree | pdftotree/utils/bbox_utils.py | get_outer_bounding_boxes | def get_outer_bounding_boxes(rectangles):
"""
:param rectangles: list of bounding boxes (top, left, bottom, right)
:return: outer bounding boxes (only the largest bbox when bboxes intersect)
"""
if len(rectangles) == 0:
return []
outer_bboxes = [rectangles[0]]
for bbox2 in rectangles... | python | def get_outer_bounding_boxes(rectangles):
"""
:param rectangles: list of bounding boxes (top, left, bottom, right)
:return: outer bounding boxes (only the largest bbox when bboxes intersect)
"""
if len(rectangles) == 0:
return []
outer_bboxes = [rectangles[0]]
for bbox2 in rectangles... | [
"def",
"get_outer_bounding_boxes",
"(",
"rectangles",
")",
":",
"if",
"len",
"(",
"rectangles",
")",
"==",
"0",
":",
"return",
"[",
"]",
"outer_bboxes",
"=",
"[",
"rectangles",
"[",
"0",
"]",
"]",
"for",
"bbox2",
"in",
"rectangles",
"[",
"1",
":",
"]",... | :param rectangles: list of bounding boxes (top, left, bottom, right)
:return: outer bounding boxes (only the largest bbox when bboxes intersect) | [
":",
"param",
"rectangles",
":",
"list",
"of",
"bounding",
"boxes",
"(",
"top",
"left",
"bottom",
"right",
")",
":",
"return",
":",
"outer",
"bounding",
"boxes",
"(",
"only",
"the",
"largest",
"bbox",
"when",
"bboxes",
"intersect",
")"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/bbox_utils.py#L81-L99 |
HazyResearch/pdftotree | pdftotree/utils/bbox_utils.py | get_intersection | def get_intersection(bbox1, bbox2):
"""
:param bbox1: (page, width, height, top, left, bottom, right)
:param bbox2: (page, width, height, top, left, bottom, right)
:return: intersection if bboxes are in the same page and intersect
"""
intersection = []
page_1, page_width, page_height, top_1,... | python | def get_intersection(bbox1, bbox2):
"""
:param bbox1: (page, width, height, top, left, bottom, right)
:param bbox2: (page, width, height, top, left, bottom, right)
:return: intersection if bboxes are in the same page and intersect
"""
intersection = []
page_1, page_width, page_height, top_1,... | [
"def",
"get_intersection",
"(",
"bbox1",
",",
"bbox2",
")",
":",
"intersection",
"=",
"[",
"]",
"page_1",
",",
"page_width",
",",
"page_height",
",",
"top_1",
",",
"left_1",
",",
"bottom_1",
",",
"right_1",
"=",
"bbox1",
"page_2",
",",
"_",
",",
"_",
"... | :param bbox1: (page, width, height, top, left, bottom, right)
:param bbox2: (page, width, height, top, left, bottom, right)
:return: intersection if bboxes are in the same page and intersect | [
":",
"param",
"bbox1",
":",
"(",
"page",
"width",
"height",
"top",
"left",
"bottom",
"right",
")",
":",
"param",
"bbox2",
":",
"(",
"page",
"width",
"height",
"top",
"left",
"bottom",
"right",
")",
":",
"return",
":",
"intersection",
"if",
"bboxes",
"a... | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/bbox_utils.py#L102-L126 |
HazyResearch/pdftotree | pdftotree/utils/bbox_utils.py | compute_iou | def compute_iou(bbox1, bbox2):
"""
:param bbox1: (page, width, height, top, left, bottom, right)
:param bbox2: (page, width, height, top, left, bottom, right)
:return: intersection over union if bboxes are in the same page and intersect
"""
top_1, left_1, bottom_1, right_1 = bbox1
top_2, lef... | python | def compute_iou(bbox1, bbox2):
"""
:param bbox1: (page, width, height, top, left, bottom, right)
:param bbox2: (page, width, height, top, left, bottom, right)
:return: intersection over union if bboxes are in the same page and intersect
"""
top_1, left_1, bottom_1, right_1 = bbox1
top_2, lef... | [
"def",
"compute_iou",
"(",
"bbox1",
",",
"bbox2",
")",
":",
"top_1",
",",
"left_1",
",",
"bottom_1",
",",
"right_1",
"=",
"bbox1",
"top_2",
",",
"left_2",
",",
"bottom_2",
",",
"right_2",
"=",
"bbox2",
"if",
"doOverlap",
"(",
"(",
"top_1",
",",
"left_1... | :param bbox1: (page, width, height, top, left, bottom, right)
:param bbox2: (page, width, height, top, left, bottom, right)
:return: intersection over union if bboxes are in the same page and intersect | [
":",
"param",
"bbox1",
":",
"(",
"page",
"width",
"height",
"top",
"left",
"bottom",
"right",
")",
":",
"param",
"bbox2",
":",
"(",
"page",
"width",
"height",
"top",
"left",
"bottom",
"right",
")",
":",
"return",
":",
"intersection",
"over",
"union",
"... | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/bbox_utils.py#L129-L149 |
HazyResearch/pdftotree | pdftotree/utils/display_utils.py | display_bounding_boxes | def display_bounding_boxes(img, blocks, alternatecolors=False, color=Color("blue")):
"""
Displays each of the bounding boxes passed in 'boxes' on an image of the pdf
pointed to by pdf_file
boxes is a list of 5-tuples (page, top, left, bottom, right)
"""
draw = Drawing()
draw.fill_color = Col... | python | def display_bounding_boxes(img, blocks, alternatecolors=False, color=Color("blue")):
"""
Displays each of the bounding boxes passed in 'boxes' on an image of the pdf
pointed to by pdf_file
boxes is a list of 5-tuples (page, top, left, bottom, right)
"""
draw = Drawing()
draw.fill_color = Col... | [
"def",
"display_bounding_boxes",
"(",
"img",
",",
"blocks",
",",
"alternatecolors",
"=",
"False",
",",
"color",
"=",
"Color",
"(",
"\"blue\"",
")",
")",
":",
"draw",
"=",
"Drawing",
"(",
")",
"draw",
".",
"fill_color",
"=",
"Color",
"(",
"\"rgba(0, 0, 0, 0... | Displays each of the bounding boxes passed in 'boxes' on an image of the pdf
pointed to by pdf_file
boxes is a list of 5-tuples (page, top, left, bottom, right) | [
"Displays",
"each",
"of",
"the",
"bounding",
"boxes",
"passed",
"in",
"boxes",
"on",
"an",
"image",
"of",
"the",
"pdf",
"pointed",
"to",
"by",
"pdf_file",
"boxes",
"is",
"a",
"list",
"of",
"5",
"-",
"tuples",
"(",
"page",
"top",
"left",
"bottom",
"righ... | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/display_utils.py#L8-L31 |
HazyResearch/pdftotree | pdftotree/utils/display_utils.py | display_bounding_boxes_within_notebook | def display_bounding_boxes_within_notebook(
page_num, extractor, blocks, alternatecolors=False, color=Color("blue")
):
"""
Displays each of the bounding boxes passed in 'boxes' on an image of the pdf
pointed to by pdf_file
boxes is a list of 5-tuples (page, top, left, bottom, right)
"""
elem... | python | def display_bounding_boxes_within_notebook(
page_num, extractor, blocks, alternatecolors=False, color=Color("blue")
):
"""
Displays each of the bounding boxes passed in 'boxes' on an image of the pdf
pointed to by pdf_file
boxes is a list of 5-tuples (page, top, left, bottom, right)
"""
elem... | [
"def",
"display_bounding_boxes_within_notebook",
"(",
"page_num",
",",
"extractor",
",",
"blocks",
",",
"alternatecolors",
"=",
"False",
",",
"color",
"=",
"Color",
"(",
"\"blue\"",
")",
")",
":",
"elems",
"=",
"extractor",
".",
"elems",
"[",
"page_num",
"]",
... | Displays each of the bounding boxes passed in 'boxes' on an image of the pdf
pointed to by pdf_file
boxes is a list of 5-tuples (page, top, left, bottom, right) | [
"Displays",
"each",
"of",
"the",
"bounding",
"boxes",
"passed",
"in",
"boxes",
"on",
"an",
"image",
"of",
"the",
"pdf",
"pointed",
"to",
"by",
"pdf_file",
"boxes",
"is",
"a",
"list",
"of",
"5",
"-",
"tuples",
"(",
"page",
"top",
"left",
"bottom",
"righ... | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/display_utils.py#L34-L62 |
HazyResearch/pdftotree | pdftotree/utils/display_utils.py | pdf_to_img | def pdf_to_img(pdf_file, page_num, page_width, page_height):
"""
Converts pdf file into image
:param pdf_file: path to the pdf file
:param page_num: page number to convert (index starting at 1)
:return: wand image object
"""
img = Image(filename="{}[{}]".format(pdf_file, page_num - 1))
i... | python | def pdf_to_img(pdf_file, page_num, page_width, page_height):
"""
Converts pdf file into image
:param pdf_file: path to the pdf file
:param page_num: page number to convert (index starting at 1)
:return: wand image object
"""
img = Image(filename="{}[{}]".format(pdf_file, page_num - 1))
i... | [
"def",
"pdf_to_img",
"(",
"pdf_file",
",",
"page_num",
",",
"page_width",
",",
"page_height",
")",
":",
"img",
"=",
"Image",
"(",
"filename",
"=",
"\"{}[{}]\"",
".",
"format",
"(",
"pdf_file",
",",
"page_num",
"-",
"1",
")",
")",
"img",
".",
"resize",
... | Converts pdf file into image
:param pdf_file: path to the pdf file
:param page_num: page number to convert (index starting at 1)
:return: wand image object | [
"Converts",
"pdf",
"file",
"into",
"image",
":",
"param",
"pdf_file",
":",
"path",
"to",
"the",
"pdf",
"file",
":",
"param",
"page_num",
":",
"page",
"number",
"to",
"convert",
"(",
"index",
"starting",
"at",
"1",
")",
":",
"return",
":",
"wand",
"imag... | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/display_utils.py#L65-L74 |
HazyResearch/pdftotree | pdftotree/utils/pdf/render.py | Renderer.draw_rect | def draw_rect(self, bbox, cell_val):
"""
Fills the bbox with the content values
Float bbox values are normalized to have non-zero area
"""
new_x0 = int(bbox[x0])
new_y0 = int(bbox[y0])
new_x1 = max(new_x0 + 1, int(bbox[x1]))
new_y1 = max(new_y0 + 1, int(bb... | python | def draw_rect(self, bbox, cell_val):
"""
Fills the bbox with the content values
Float bbox values are normalized to have non-zero area
"""
new_x0 = int(bbox[x0])
new_y0 = int(bbox[y0])
new_x1 = max(new_x0 + 1, int(bbox[x1]))
new_y1 = max(new_y0 + 1, int(bb... | [
"def",
"draw_rect",
"(",
"self",
",",
"bbox",
",",
"cell_val",
")",
":",
"new_x0",
"=",
"int",
"(",
"bbox",
"[",
"x0",
"]",
")",
"new_y0",
"=",
"int",
"(",
"bbox",
"[",
"y0",
"]",
")",
"new_x1",
"=",
"max",
"(",
"new_x0",
"+",
"1",
",",
"int",
... | Fills the bbox with the content values
Float bbox values are normalized to have non-zero area | [
"Fills",
"the",
"bbox",
"with",
"the",
"content",
"values",
"Float",
"bbox",
"values",
"are",
"normalized",
"to",
"have",
"non",
"-",
"zero",
"area"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/render.py#L57-L67 |
HazyResearch/pdftotree | pdftotree/TreeVisualizer.py | TreeVisualizer.display_boxes | def display_boxes(self, tree, html_path, filename_prefix, alternate_colors=False):
"""
Displays each of the bounding boxes passed in 'boxes' on images of the pdf
pointed to by pdf_file
boxes is a list of 5-tuples (page, top, left, bottom, right)
"""
imgs = []
colo... | python | def display_boxes(self, tree, html_path, filename_prefix, alternate_colors=False):
"""
Displays each of the bounding boxes passed in 'boxes' on images of the pdf
pointed to by pdf_file
boxes is a list of 5-tuples (page, top, left, bottom, right)
"""
imgs = []
colo... | [
"def",
"display_boxes",
"(",
"self",
",",
"tree",
",",
"html_path",
",",
"filename_prefix",
",",
"alternate_colors",
"=",
"False",
")",
":",
"imgs",
"=",
"[",
"]",
"colors",
"=",
"{",
"\"section_header\"",
":",
"Color",
"(",
"\"blue\"",
")",
",",
"\"figure... | Displays each of the bounding boxes passed in 'boxes' on images of the pdf
pointed to by pdf_file
boxes is a list of 5-tuples (page, top, left, bottom, right) | [
"Displays",
"each",
"of",
"the",
"bounding",
"boxes",
"passed",
"in",
"boxes",
"on",
"images",
"of",
"the",
"pdf",
"pointed",
"to",
"by",
"pdf_file",
"boxes",
"is",
"a",
"list",
"of",
"5",
"-",
"tuples",
"(",
"page",
"top",
"left",
"bottom",
"right",
"... | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/TreeVisualizer.py#L22-L59 |
HazyResearch/pdftotree | pdftotree/TreeVisualizer.py | TreeVisualizer.display_candidates | def display_candidates(self, tree, html_path, filename_prefix):
"""
Displays the bounding boxes corresponding to candidates on an image of the pdf
boxes is a list of 5-tuples (page, top, left, bottom, right)
"""
imgs = self.display_boxes(
tree, html_path, filename_pre... | python | def display_candidates(self, tree, html_path, filename_prefix):
"""
Displays the bounding boxes corresponding to candidates on an image of the pdf
boxes is a list of 5-tuples (page, top, left, bottom, right)
"""
imgs = self.display_boxes(
tree, html_path, filename_pre... | [
"def",
"display_candidates",
"(",
"self",
",",
"tree",
",",
"html_path",
",",
"filename_prefix",
")",
":",
"imgs",
"=",
"self",
".",
"display_boxes",
"(",
"tree",
",",
"html_path",
",",
"filename_prefix",
",",
"alternate_colors",
"=",
"True",
")",
"return",
... | Displays the bounding boxes corresponding to candidates on an image of the pdf
boxes is a list of 5-tuples (page, top, left, bottom, right) | [
"Displays",
"the",
"bounding",
"boxes",
"corresponding",
"to",
"candidates",
"on",
"an",
"image",
"of",
"the",
"pdf",
"boxes",
"is",
"a",
"list",
"of",
"5",
"-",
"tuples",
"(",
"page",
"top",
"left",
"bottom",
"right",
")"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/TreeVisualizer.py#L61-L69 |
HazyResearch/pdftotree | pdftotree/TreeVisualizer.py | TreeVisualizer.pdf_to_img | def pdf_to_img(self, page_num, pdf_dim=None):
"""
Converts pdf file into image
:param pdf_file: path to the pdf file
:param page_num: page number to convert (index starting at 1)
:return: wand image object
"""
if not pdf_dim:
pdf_dim = get_pdf_dim(self... | python | def pdf_to_img(self, page_num, pdf_dim=None):
"""
Converts pdf file into image
:param pdf_file: path to the pdf file
:param page_num: page number to convert (index starting at 1)
:return: wand image object
"""
if not pdf_dim:
pdf_dim = get_pdf_dim(self... | [
"def",
"pdf_to_img",
"(",
"self",
",",
"page_num",
",",
"pdf_dim",
"=",
"None",
")",
":",
"if",
"not",
"pdf_dim",
":",
"pdf_dim",
"=",
"get_pdf_dim",
"(",
"self",
".",
"pdf_file",
")",
"page_width",
",",
"page_height",
"=",
"pdf_dim",
"img",
"=",
"Image"... | Converts pdf file into image
:param pdf_file: path to the pdf file
:param page_num: page number to convert (index starting at 1)
:return: wand image object | [
"Converts",
"pdf",
"file",
"into",
"image",
":",
"param",
"pdf_file",
":",
"path",
"to",
"the",
"pdf",
"file",
":",
"param",
"page_num",
":",
"page",
"number",
"to",
"convert",
"(",
"index",
"starting",
"at",
"1",
")",
":",
"return",
":",
"wand",
"imag... | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/TreeVisualizer.py#L71-L83 |
HazyResearch/pdftotree | pdftotree/utils/pdf/pdf_parsers.py | parse_layout | def parse_layout(elems, font_stat, combine=False):
"""
Parses pdf texts into a hypergraph grouped into rows
and columns and then output
"""
boxes_segments = elems.segments
boxes_curves = elems.curves
boxes_figures = elems.figures
page_width = elems.layout.width
# page_height = elems... | python | def parse_layout(elems, font_stat, combine=False):
"""
Parses pdf texts into a hypergraph grouped into rows
and columns and then output
"""
boxes_segments = elems.segments
boxes_curves = elems.curves
boxes_figures = elems.figures
page_width = elems.layout.width
# page_height = elems... | [
"def",
"parse_layout",
"(",
"elems",
",",
"font_stat",
",",
"combine",
"=",
"False",
")",
":",
"boxes_segments",
"=",
"elems",
".",
"segments",
"boxes_curves",
"=",
"elems",
".",
"curves",
"boxes_figures",
"=",
"elems",
".",
"figures",
"page_width",
"=",
"el... | Parses pdf texts into a hypergraph grouped into rows
and columns and then output | [
"Parses",
"pdf",
"texts",
"into",
"a",
"hypergraph",
"grouped",
"into",
"rows",
"and",
"columns",
"and",
"then",
"output"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/pdf_parsers.py#L23-L63 |
HazyResearch/pdftotree | pdftotree/utils/pdf/pdf_parsers.py | merge_nodes | def merge_nodes(nodes, plane, page_stat, merge_indices):
"""
Merges overlapping nodes
"""
# Merge inner boxes to the best outer box
# nodes.sort(key=Node.area)
to_be_removed = set()
for inner_idx in range(len(nodes)):
inner = nodes[inner_idx]
outers = []
outers_indice... | python | def merge_nodes(nodes, plane, page_stat, merge_indices):
"""
Merges overlapping nodes
"""
# Merge inner boxes to the best outer box
# nodes.sort(key=Node.area)
to_be_removed = set()
for inner_idx in range(len(nodes)):
inner = nodes[inner_idx]
outers = []
outers_indice... | [
"def",
"merge_nodes",
"(",
"nodes",
",",
"plane",
",",
"page_stat",
",",
"merge_indices",
")",
":",
"# Merge inner boxes to the best outer box",
"# nodes.sort(key=Node.area)",
"to_be_removed",
"=",
"set",
"(",
")",
"for",
"inner_idx",
"in",
"range",
"(",
"len",
"(",... | Merges overlapping nodes | [
"Merges",
"overlapping",
"nodes"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/pdf_parsers.py#L1266-L1296 |
HazyResearch/pdftotree | pdftotree/utils/pdf/pdf_parsers.py | get_most_common_font_pts | def get_most_common_font_pts(mentions, font_stat):
"""
font_stat: Counter object of font sizes
"""
log = logging.getLogger(__name__)
try:
# default min font size of 1 pt in case no font present
most_common_font_size = font_stat.most_common(1)[0][0]
count = 0.01 # avoid divi... | python | def get_most_common_font_pts(mentions, font_stat):
"""
font_stat: Counter object of font sizes
"""
log = logging.getLogger(__name__)
try:
# default min font size of 1 pt in case no font present
most_common_font_size = font_stat.most_common(1)[0][0]
count = 0.01 # avoid divi... | [
"def",
"get_most_common_font_pts",
"(",
"mentions",
",",
"font_stat",
")",
":",
"log",
"=",
"logging",
".",
"getLogger",
"(",
"__name__",
")",
"try",
":",
"# default min font size of 1 pt in case no font present",
"most_common_font_size",
"=",
"font_stat",
".",
"most_co... | font_stat: Counter object of font sizes | [
"font_stat",
":",
"Counter",
"object",
"of",
"font",
"sizes"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/pdf_parsers.py#L1299-L1318 |
HazyResearch/pdftotree | pdftotree/utils/pdf/node.py | _get_cols | def _get_cols(row_content):
"""
Counting the number columns based on the content of this row
"""
cols = []
subcell_col = []
prev_bar = None
for _coord, item in row_content:
if isinstance(item, LTTextLine):
subcell_col.append(item)
else: # bar, add column content
... | python | def _get_cols(row_content):
"""
Counting the number columns based on the content of this row
"""
cols = []
subcell_col = []
prev_bar = None
for _coord, item in row_content:
if isinstance(item, LTTextLine):
subcell_col.append(item)
else: # bar, add column content
... | [
"def",
"_get_cols",
"(",
"row_content",
")",
":",
"cols",
"=",
"[",
"]",
"subcell_col",
"=",
"[",
"]",
"prev_bar",
"=",
"None",
"for",
"_coord",
",",
"item",
"in",
"row_content",
":",
"if",
"isinstance",
"(",
"item",
",",
"LTTextLine",
")",
":",
"subce... | Counting the number columns based on the content of this row | [
"Counting",
"the",
"number",
"columns",
"based",
"on",
"the",
"content",
"of",
"this",
"row"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/node.py#L187-L206 |
HazyResearch/pdftotree | pdftotree/utils/pdf/node.py | _one_contains_other | def _one_contains_other(s1, s2):
"""
Whether one set contains the other
"""
return min(len(s1), len(s2)) == len(s1 & s2) | python | def _one_contains_other(s1, s2):
"""
Whether one set contains the other
"""
return min(len(s1), len(s2)) == len(s1 & s2) | [
"def",
"_one_contains_other",
"(",
"s1",
",",
"s2",
")",
":",
"return",
"min",
"(",
"len",
"(",
"s1",
")",
",",
"len",
"(",
"s2",
")",
")",
"==",
"len",
"(",
"s1",
"&",
"s2",
")"
] | Whether one set contains the other | [
"Whether",
"one",
"set",
"contains",
"the",
"other"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/node.py#L241-L245 |
HazyResearch/pdftotree | pdftotree/utils/pdf/node.py | Node.is_table | def is_table(self):
"""
Count the node's number of mention al ignment in both axes to determine
if the node is a table.
"""
if self.type_counts["text"] < 6 or "figure" in self.type_counts:
return False
for e in self.elems:
# Characters written as c... | python | def is_table(self):
"""
Count the node's number of mention al ignment in both axes to determine
if the node is a table.
"""
if self.type_counts["text"] < 6 or "figure" in self.type_counts:
return False
for e in self.elems:
# Characters written as c... | [
"def",
"is_table",
"(",
"self",
")",
":",
"if",
"self",
".",
"type_counts",
"[",
"\"text\"",
"]",
"<",
"6",
"or",
"\"figure\"",
"in",
"self",
".",
"type_counts",
":",
"return",
"False",
"for",
"e",
"in",
"self",
".",
"elems",
":",
"# Characters written a... | Count the node's number of mention al ignment in both axes to determine
if the node is a table. | [
"Count",
"the",
"node",
"s",
"number",
"of",
"mention",
"al",
"ignment",
"in",
"both",
"axes",
"to",
"determine",
"if",
"the",
"node",
"is",
"a",
"table",
"."
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/node.py#L73-L115 |
HazyResearch/pdftotree | pdftotree/utils/pdf/node.py | Node.get_grid | def get_grid(self):
"""
Standardize the layout of the table into grids
"""
mentions, lines = _split_text_n_lines(self.elems)
# Sort mentions in reading order where y values are snapped to half
# height-sized grid
mentions.sort(key=lambda m: (m.yc_grid, m.xc))
... | python | def get_grid(self):
"""
Standardize the layout of the table into grids
"""
mentions, lines = _split_text_n_lines(self.elems)
# Sort mentions in reading order where y values are snapped to half
# height-sized grid
mentions.sort(key=lambda m: (m.yc_grid, m.xc))
... | [
"def",
"get_grid",
"(",
"self",
")",
":",
"mentions",
",",
"lines",
"=",
"_split_text_n_lines",
"(",
"self",
".",
"elems",
")",
"# Sort mentions in reading order where y values are snapped to half",
"# height-sized grid",
"mentions",
".",
"sort",
"(",
"key",
"=",
"lam... | Standardize the layout of the table into grids | [
"Standardize",
"the",
"layout",
"of",
"the",
"table",
"into",
"grids"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/pdf/node.py#L118-L128 |
HazyResearch/pdftotree | pdftotree/utils/lines_utils.py | reorder_lines | def reorder_lines(lines, tol=TOLERANCE):
"""
Changes the line coordinates to be given as (top, left, bottom, right)
:param lines: list of lines coordinates
:return: reordered list of lines coordinates
"""
reordered_lines = []
for line in lines:
# we divide by tol and multiply by tol ... | python | def reorder_lines(lines, tol=TOLERANCE):
"""
Changes the line coordinates to be given as (top, left, bottom, right)
:param lines: list of lines coordinates
:return: reordered list of lines coordinates
"""
reordered_lines = []
for line in lines:
# we divide by tol and multiply by tol ... | [
"def",
"reorder_lines",
"(",
"lines",
",",
"tol",
"=",
"TOLERANCE",
")",
":",
"reordered_lines",
"=",
"[",
"]",
"for",
"line",
"in",
"lines",
":",
"# we divide by tol and multiply by tol to truncate numbers, stairs function",
"reordered_lines",
"+=",
"[",
"(",
"int",
... | Changes the line coordinates to be given as (top, left, bottom, right)
:param lines: list of lines coordinates
:return: reordered list of lines coordinates | [
"Changes",
"the",
"line",
"coordinates",
"to",
"be",
"given",
"as",
"(",
"top",
"left",
"bottom",
"right",
")",
":",
"param",
"lines",
":",
"list",
"of",
"lines",
"coordinates",
":",
"return",
":",
"reordered",
"list",
"of",
"lines",
"coordinates"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/lines_utils.py#L4-L21 |
HazyResearch/pdftotree | pdftotree/utils/lines_utils.py | merge_vertical_lines | def merge_vertical_lines(lines, tol=TOLERANCE):
"""
This function merges lines segment when they are vertically aligned
:param lines: list of lines coordinates (top, left, bottom, right)
:return: list of merged lines coordinates
"""
if len(lines) == 0:
return []
merged_lines = [lines... | python | def merge_vertical_lines(lines, tol=TOLERANCE):
"""
This function merges lines segment when they are vertically aligned
:param lines: list of lines coordinates (top, left, bottom, right)
:return: list of merged lines coordinates
"""
if len(lines) == 0:
return []
merged_lines = [lines... | [
"def",
"merge_vertical_lines",
"(",
"lines",
",",
"tol",
"=",
"TOLERANCE",
")",
":",
"if",
"len",
"(",
"lines",
")",
"==",
"0",
":",
"return",
"[",
"]",
"merged_lines",
"=",
"[",
"lines",
"[",
"0",
"]",
"]",
"for",
"line",
"in",
"lines",
"[",
"1",
... | This function merges lines segment when they are vertically aligned
:param lines: list of lines coordinates (top, left, bottom, right)
:return: list of merged lines coordinates | [
"This",
"function",
"merges",
"lines",
"segment",
"when",
"they",
"are",
"vertically",
"aligned",
":",
"param",
"lines",
":",
"list",
"of",
"lines",
"coordinates",
"(",
"top",
"left",
"bottom",
"right",
")",
":",
"return",
":",
"list",
"of",
"merged",
"lin... | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/lines_utils.py#L24-L45 |
HazyResearch/pdftotree | pdftotree/utils/lines_utils.py | get_vertical_and_horizontal | def get_vertical_and_horizontal(lines):
"""
Extracts vertical and horizontal lines lists
:param lines: list of lines coordinates
:return: vertical_lines, horitontal_lines (2 lists of coordinates)
"""
# TODO: add some angle tolerance when lines are not perfectly aligned (eg:
# scanned pdf)
... | python | def get_vertical_and_horizontal(lines):
"""
Extracts vertical and horizontal lines lists
:param lines: list of lines coordinates
:return: vertical_lines, horitontal_lines (2 lists of coordinates)
"""
# TODO: add some angle tolerance when lines are not perfectly aligned (eg:
# scanned pdf)
... | [
"def",
"get_vertical_and_horizontal",
"(",
"lines",
")",
":",
"# TODO: add some angle tolerance when lines are not perfectly aligned (eg:",
"# scanned pdf)",
"vertical_lines",
"=",
"sorted",
"(",
"[",
"e",
"for",
"e",
"in",
"lines",
"if",
"e",
"[",
"1",
"]",
"==",
"e"... | Extracts vertical and horizontal lines lists
:param lines: list of lines coordinates
:return: vertical_lines, horitontal_lines (2 lists of coordinates) | [
"Extracts",
"vertical",
"and",
"horizontal",
"lines",
"lists",
":",
"param",
"lines",
":",
"list",
"of",
"lines",
"coordinates",
":",
"return",
":",
"vertical_lines",
"horitontal_lines",
"(",
"2",
"lists",
"of",
"coordinates",
")"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/lines_utils.py#L72-L88 |
HazyResearch/pdftotree | pdftotree/utils/img_utils.py | lazy_load_font | def lazy_load_font(font_size=default_font_size):
"""
Lazy loading font according to system platform
"""
if font_size not in _font_cache:
if _platform.startswith("darwin"):
font_path = "/Library/Fonts/Arial.ttf"
elif _platform.startswith("linux"):
font_path = "/usr... | python | def lazy_load_font(font_size=default_font_size):
"""
Lazy loading font according to system platform
"""
if font_size not in _font_cache:
if _platform.startswith("darwin"):
font_path = "/Library/Fonts/Arial.ttf"
elif _platform.startswith("linux"):
font_path = "/usr... | [
"def",
"lazy_load_font",
"(",
"font_size",
"=",
"default_font_size",
")",
":",
"if",
"font_size",
"not",
"in",
"_font_cache",
":",
"if",
"_platform",
".",
"startswith",
"(",
"\"darwin\"",
")",
":",
"font_path",
"=",
"\"/Library/Fonts/Arial.ttf\"",
"elif",
"_platfo... | Lazy loading font according to system platform | [
"Lazy",
"loading",
"font",
"according",
"to",
"system",
"platform"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/img_utils.py#L24-L36 |
HazyResearch/pdftotree | pdftotree/utils/img_utils.py | normalize_bbox | def normalize_bbox(coords, ymax, scaler=2):
"""
scales all coordinates and flip y axis due to different
origin coordinates (top left vs. bottom left)
"""
return [
coords[0] * scaler,
ymax - (coords[3] * scaler),
coords[2] * scaler,
ymax - (coords[1] * scaler),
] | python | def normalize_bbox(coords, ymax, scaler=2):
"""
scales all coordinates and flip y axis due to different
origin coordinates (top left vs. bottom left)
"""
return [
coords[0] * scaler,
ymax - (coords[3] * scaler),
coords[2] * scaler,
ymax - (coords[1] * scaler),
] | [
"def",
"normalize_bbox",
"(",
"coords",
",",
"ymax",
",",
"scaler",
"=",
"2",
")",
":",
"return",
"[",
"coords",
"[",
"0",
"]",
"*",
"scaler",
",",
"ymax",
"-",
"(",
"coords",
"[",
"3",
"]",
"*",
"scaler",
")",
",",
"coords",
"[",
"2",
"]",
"*"... | scales all coordinates and flip y axis due to different
origin coordinates (top left vs. bottom left) | [
"scales",
"all",
"coordinates",
"and",
"flip",
"y",
"axis",
"due",
"to",
"different",
"origin",
"coordinates",
"(",
"top",
"left",
"vs",
".",
"bottom",
"left",
")"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/img_utils.py#L39-L49 |
HazyResearch/pdftotree | pdftotree/utils/img_utils.py | normalize_pts | def normalize_pts(pts, ymax, scaler=2):
"""
scales all coordinates and flip y axis due to different
origin coordinates (top left vs. bottom left)
"""
return [(x * scaler, ymax - (y * scaler)) for x, y in pts] | python | def normalize_pts(pts, ymax, scaler=2):
"""
scales all coordinates and flip y axis due to different
origin coordinates (top left vs. bottom left)
"""
return [(x * scaler, ymax - (y * scaler)) for x, y in pts] | [
"def",
"normalize_pts",
"(",
"pts",
",",
"ymax",
",",
"scaler",
"=",
"2",
")",
":",
"return",
"[",
"(",
"x",
"*",
"scaler",
",",
"ymax",
"-",
"(",
"y",
"*",
"scaler",
")",
")",
"for",
"x",
",",
"y",
"in",
"pts",
"]"
] | scales all coordinates and flip y axis due to different
origin coordinates (top left vs. bottom left) | [
"scales",
"all",
"coordinates",
"and",
"flip",
"y",
"axis",
"due",
"to",
"different",
"origin",
"coordinates",
"(",
"top",
"left",
"vs",
".",
"bottom",
"left",
")"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/img_utils.py#L52-L57 |
HazyResearch/pdftotree | pdftotree/utils/img_utils.py | render_debug_img | def render_debug_img(
file_name,
page_num,
elems,
nodes=[],
scaler=1,
print_segments=False,
print_curves=True,
print_table_bbox=True,
print_text_as_rect=True,
):
"""
Shows an image rendering of the pdf page along with debugging
info printed
"""
# For debugging sho... | python | def render_debug_img(
file_name,
page_num,
elems,
nodes=[],
scaler=1,
print_segments=False,
print_curves=True,
print_table_bbox=True,
print_text_as_rect=True,
):
"""
Shows an image rendering of the pdf page along with debugging
info printed
"""
# For debugging sho... | [
"def",
"render_debug_img",
"(",
"file_name",
",",
"page_num",
",",
"elems",
",",
"nodes",
"=",
"[",
"]",
",",
"scaler",
"=",
"1",
",",
"print_segments",
"=",
"False",
",",
"print_curves",
"=",
"True",
",",
"print_table_bbox",
"=",
"True",
",",
"print_text_... | Shows an image rendering of the pdf page along with debugging
info printed | [
"Shows",
"an",
"image",
"rendering",
"of",
"the",
"pdf",
"page",
"along",
"with",
"debugging",
"info",
"printed"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/utils/img_utils.py#L93-L160 |
HazyResearch/pdftotree | pdftotree/ml/TableExtractML.py | TableExtractorML.get_labels | def get_labels(self, gt_tables):
"""
:param gt_tables: dict, keys are page number and values are list of
tables bbox within that page
:return:
"""
labels = np.zeros(len(self.candidates))
for i, candidate in enumerate(self.candidates):
... | python | def get_labels(self, gt_tables):
"""
:param gt_tables: dict, keys are page number and values are list of
tables bbox within that page
:return:
"""
labels = np.zeros(len(self.candidates))
for i, candidate in enumerate(self.candidates):
... | [
"def",
"get_labels",
"(",
"self",
",",
"gt_tables",
")",
":",
"labels",
"=",
"np",
".",
"zeros",
"(",
"len",
"(",
"self",
".",
"candidates",
")",
")",
"for",
"i",
",",
"candidate",
"in",
"enumerate",
"(",
"self",
".",
"candidates",
")",
":",
"page_nu... | :param gt_tables: dict, keys are page number and values are list of
tables bbox within that page
:return: | [
":",
"param",
"gt_tables",
":",
"dict",
"keys",
"are",
"page",
"number",
"and",
"values",
"are",
"list",
"of",
"tables",
"bbox",
"within",
"that",
"page",
":",
"return",
":"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/ml/TableExtractML.py#L222-L250 |
HazyResearch/pdftotree | pdftotree/ml/features.py | get_text_sparsity | def get_text_sparsity(bbox, mentions):
"""
computes text area coverage
:param mentions:
:return:
"""
b = bbox[-4:]
bbox_area = (b[2] - b[0]) * (b[3] - b[1])
text_area = 0
for m in mentions:
text_area += (m.x1 - m.x0) * (m.y1 - m.y0)
try:
return 1.0 * text_area / b... | python | def get_text_sparsity(bbox, mentions):
"""
computes text area coverage
:param mentions:
:return:
"""
b = bbox[-4:]
bbox_area = (b[2] - b[0]) * (b[3] - b[1])
text_area = 0
for m in mentions:
text_area += (m.x1 - m.x0) * (m.y1 - m.y0)
try:
return 1.0 * text_area / b... | [
"def",
"get_text_sparsity",
"(",
"bbox",
",",
"mentions",
")",
":",
"b",
"=",
"bbox",
"[",
"-",
"4",
":",
"]",
"bbox_area",
"=",
"(",
"b",
"[",
"2",
"]",
"-",
"b",
"[",
"0",
"]",
")",
"*",
"(",
"b",
"[",
"3",
"]",
"-",
"b",
"[",
"1",
"]",... | computes text area coverage
:param mentions:
:return: | [
"computes",
"text",
"area",
"coverage",
":",
"param",
"mentions",
":",
":",
"return",
":"
] | train | https://github.com/HazyResearch/pdftotree/blob/5890d668b475d5d3058d1d886aafbfd83268c440/pdftotree/ml/features.py#L53-L67 |
albahnsen/CostSensitiveClassification | costcla/models/bagging.py | _partition_estimators | def _partition_estimators(n_estimators, n_jobs):
"""Private function used to partition estimators between jobs."""
# Compute the number of jobs
if n_jobs == -1:
n_jobs = min(cpu_count(), n_estimators)
else:
n_jobs = min(n_jobs, n_estimators)
# Partition estimators between jobs
... | python | def _partition_estimators(n_estimators, n_jobs):
"""Private function used to partition estimators between jobs."""
# Compute the number of jobs
if n_jobs == -1:
n_jobs = min(cpu_count(), n_estimators)
else:
n_jobs = min(n_jobs, n_estimators)
# Partition estimators between jobs
... | [
"def",
"_partition_estimators",
"(",
"n_estimators",
",",
"n_jobs",
")",
":",
"# Compute the number of jobs",
"if",
"n_jobs",
"==",
"-",
"1",
":",
"n_jobs",
"=",
"min",
"(",
"cpu_count",
"(",
")",
",",
"n_estimators",
")",
"else",
":",
"n_jobs",
"=",
"min",
... | Private function used to partition estimators between jobs. | [
"Private",
"function",
"used",
"to",
"partition",
"estimators",
"between",
"jobs",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/bagging.py#L36-L51 |
albahnsen/CostSensitiveClassification | costcla/models/bagging.py | _parallel_build_estimators | def _parallel_build_estimators(n_estimators, ensemble, X, y, cost_mat,
seeds, verbose):
"""Private function used to build a batch of estimators within a job."""
# Retrieve settings
n_samples, n_features = X.shape
max_samples = ensemble.max_samples
max_features = ensemb... | python | def _parallel_build_estimators(n_estimators, ensemble, X, y, cost_mat,
seeds, verbose):
"""Private function used to build a batch of estimators within a job."""
# Retrieve settings
n_samples, n_features = X.shape
max_samples = ensemble.max_samples
max_features = ensemb... | [
"def",
"_parallel_build_estimators",
"(",
"n_estimators",
",",
"ensemble",
",",
"X",
",",
"y",
",",
"cost_mat",
",",
"seeds",
",",
"verbose",
")",
":",
"# Retrieve settings",
"n_samples",
",",
"n_features",
"=",
"X",
".",
"shape",
"max_samples",
"=",
"ensemble... | Private function used to build a batch of estimators within a job. | [
"Private",
"function",
"used",
"to",
"build",
"a",
"batch",
"of",
"estimators",
"within",
"a",
"job",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/bagging.py#L54-L116 |
albahnsen/CostSensitiveClassification | costcla/models/bagging.py | _parallel_predict_proba | def _parallel_predict_proba(estimators, estimators_features, X, n_classes, combination, estimators_weight):
"""Private function used to compute (proba-)predictions within a job."""
n_samples = X.shape[0]
proba = np.zeros((n_samples, n_classes))
for estimator, features, weight in zip(estimators, estimat... | python | def _parallel_predict_proba(estimators, estimators_features, X, n_classes, combination, estimators_weight):
"""Private function used to compute (proba-)predictions within a job."""
n_samples = X.shape[0]
proba = np.zeros((n_samples, n_classes))
for estimator, features, weight in zip(estimators, estimat... | [
"def",
"_parallel_predict_proba",
"(",
"estimators",
",",
"estimators_features",
",",
"X",
",",
"n_classes",
",",
"combination",
",",
"estimators_weight",
")",
":",
"n_samples",
"=",
"X",
".",
"shape",
"[",
"0",
"]",
"proba",
"=",
"np",
".",
"zeros",
"(",
... | Private function used to compute (proba-)predictions within a job. | [
"Private",
"function",
"used",
"to",
"compute",
"(",
"proba",
"-",
")",
"predictions",
"within",
"a",
"job",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/bagging.py#L119-L131 |
albahnsen/CostSensitiveClassification | costcla/models/bagging.py | _parallel_predict | def _parallel_predict(estimators, estimators_features, X, n_classes, combination, estimators_weight):
"""Private function used to compute predictions within a job."""
n_samples = X.shape[0]
pred = np.zeros((n_samples, n_classes))
n_estimators = len(estimators)
for estimator, features, weight in zip... | python | def _parallel_predict(estimators, estimators_features, X, n_classes, combination, estimators_weight):
"""Private function used to compute predictions within a job."""
n_samples = X.shape[0]
pred = np.zeros((n_samples, n_classes))
n_estimators = len(estimators)
for estimator, features, weight in zip... | [
"def",
"_parallel_predict",
"(",
"estimators",
",",
"estimators_features",
",",
"X",
",",
"n_classes",
",",
"combination",
",",
"estimators_weight",
")",
":",
"n_samples",
"=",
"X",
".",
"shape",
"[",
"0",
"]",
"pred",
"=",
"np",
".",
"zeros",
"(",
"(",
... | Private function used to compute predictions within a job. | [
"Private",
"function",
"used",
"to",
"compute",
"predictions",
"within",
"a",
"job",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/bagging.py#L134-L150 |
albahnsen/CostSensitiveClassification | costcla/models/bagging.py | _create_stacking_set | def _create_stacking_set(estimators, estimators_features, estimators_weight, X, combination):
"""Private function used to create the stacking training set."""
n_samples = X.shape[0]
valid_estimators = np.nonzero(estimators_weight)[0]
n_valid_estimators = valid_estimators.shape[0]
X_stacking = np.ze... | python | def _create_stacking_set(estimators, estimators_features, estimators_weight, X, combination):
"""Private function used to create the stacking training set."""
n_samples = X.shape[0]
valid_estimators = np.nonzero(estimators_weight)[0]
n_valid_estimators = valid_estimators.shape[0]
X_stacking = np.ze... | [
"def",
"_create_stacking_set",
"(",
"estimators",
",",
"estimators_features",
",",
"estimators_weight",
",",
"X",
",",
"combination",
")",
":",
"n_samples",
"=",
"X",
".",
"shape",
"[",
"0",
"]",
"valid_estimators",
"=",
"np",
".",
"nonzero",
"(",
"estimators_... | Private function used to create the stacking training set. | [
"Private",
"function",
"used",
"to",
"create",
"the",
"stacking",
"training",
"set",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/bagging.py#L153-L167 |
albahnsen/CostSensitiveClassification | costcla/models/bagging.py | BaseBagging.fit | def fit(self, X, y, cost_mat, sample_weight=None):
"""Build a Bagging ensemble of estimators from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrices are accepted only i... | python | def fit(self, X, y, cost_mat, sample_weight=None):
"""Build a Bagging ensemble of estimators from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrices are accepted only i... | [
"def",
"fit",
"(",
"self",
",",
"X",
",",
"y",
",",
"cost_mat",
",",
"sample_weight",
"=",
"None",
")",
":",
"random_state",
"=",
"check_random_state",
"(",
"self",
".",
"random_state",
")",
"# Convert data",
"# X, y = check_X_y(X, y, ['csr', 'csc', 'coo']) # Not i... | Build a Bagging ensemble of estimators from the training set (X, y).
Parameters
----------
X : {array-like, sparse matrix} of shape = [n_samples, n_features]
The training input samples. Sparse matrices are accepted only if
they are supported by the base estimator.
... | [
"Build",
"a",
"Bagging",
"ensemble",
"of",
"estimators",
"from",
"the",
"training",
"set",
"(",
"X",
"y",
")",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/bagging.py#L202-L293 |
albahnsen/CostSensitiveClassification | costcla/models/bagging.py | BaseBagging._fit_bmr_model | def _fit_bmr_model(self, X, y):
"""Private function used to fit the BayesMinimumRisk model."""
self.f_bmr = BayesMinimumRiskClassifier()
X_bmr = self.predict_proba(X)
self.f_bmr.fit(y, X_bmr)
return self | python | def _fit_bmr_model(self, X, y):
"""Private function used to fit the BayesMinimumRisk model."""
self.f_bmr = BayesMinimumRiskClassifier()
X_bmr = self.predict_proba(X)
self.f_bmr.fit(y, X_bmr)
return self | [
"def",
"_fit_bmr_model",
"(",
"self",
",",
"X",
",",
"y",
")",
":",
"self",
".",
"f_bmr",
"=",
"BayesMinimumRiskClassifier",
"(",
")",
"X_bmr",
"=",
"self",
".",
"predict_proba",
"(",
"X",
")",
"self",
".",
"f_bmr",
".",
"fit",
"(",
"y",
",",
"X_bmr"... | Private function used to fit the BayesMinimumRisk model. | [
"Private",
"function",
"used",
"to",
"fit",
"the",
"BayesMinimumRisk",
"model",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/bagging.py#L295-L300 |
albahnsen/CostSensitiveClassification | costcla/models/bagging.py | BaseBagging._fit_stacking_model | def _fit_stacking_model(self,X, y, cost_mat, max_iter=100):
"""Private function used to fit the stacking model."""
self.f_staking = CostSensitiveLogisticRegression(verbose=self.verbose, max_iter=max_iter)
X_stacking = _create_stacking_set(self.estimators_, self.estimators_features_,
... | python | def _fit_stacking_model(self,X, y, cost_mat, max_iter=100):
"""Private function used to fit the stacking model."""
self.f_staking = CostSensitiveLogisticRegression(verbose=self.verbose, max_iter=max_iter)
X_stacking = _create_stacking_set(self.estimators_, self.estimators_features_,
... | [
"def",
"_fit_stacking_model",
"(",
"self",
",",
"X",
",",
"y",
",",
"cost_mat",
",",
"max_iter",
"=",
"100",
")",
":",
"self",
".",
"f_staking",
"=",
"CostSensitiveLogisticRegression",
"(",
"verbose",
"=",
"self",
".",
"verbose",
",",
"max_iter",
"=",
"max... | Private function used to fit the stacking model. | [
"Private",
"function",
"used",
"to",
"fit",
"the",
"stacking",
"model",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/bagging.py#L302-L308 |
albahnsen/CostSensitiveClassification | costcla/models/bagging.py | BaseBagging._evaluate_oob_savings | def _evaluate_oob_savings(self, X, y, cost_mat):
"""Private function used to calculate the OOB Savings of each estimator."""
estimators_weight = []
for estimator, samples, features in zip(self.estimators_, self.estimators_samples_,
self.estimators_... | python | def _evaluate_oob_savings(self, X, y, cost_mat):
"""Private function used to calculate the OOB Savings of each estimator."""
estimators_weight = []
for estimator, samples, features in zip(self.estimators_, self.estimators_samples_,
self.estimators_... | [
"def",
"_evaluate_oob_savings",
"(",
"self",
",",
"X",
",",
"y",
",",
"cost_mat",
")",
":",
"estimators_weight",
"=",
"[",
"]",
"for",
"estimator",
",",
"samples",
",",
"features",
"in",
"zip",
"(",
"self",
".",
"estimators_",
",",
"self",
".",
"estimato... | Private function used to calculate the OOB Savings of each estimator. | [
"Private",
"function",
"used",
"to",
"calculate",
"the",
"OOB",
"Savings",
"of",
"each",
"estimator",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/bagging.py#L311-L334 |
albahnsen/CostSensitiveClassification | costcla/models/bagging.py | BaggingClassifier.predict | def predict(self, X, cost_mat=None):
"""Predict class for X.
The predicted class of an input sample is computed as the class with
the highest mean predicted probability. If base estimators do not
implement a ``predict_proba`` method, then it resorts to voting.
Parameters
... | python | def predict(self, X, cost_mat=None):
"""Predict class for X.
The predicted class of an input sample is computed as the class with
the highest mean predicted probability. If base estimators do not
implement a ``predict_proba`` method, then it resorts to voting.
Parameters
... | [
"def",
"predict",
"(",
"self",
",",
"X",
",",
"cost_mat",
"=",
"None",
")",
":",
"# Check data",
"# X = check_array(X, accept_sparse=['csr', 'csc', 'coo']) # Dont in version 0.15",
"if",
"self",
".",
"n_features_",
"!=",
"X",
".",
"shape",
"[",
"1",
"]",
":",
"ra... | Predict class for X.
The predicted class of an input sample is computed as the class with
the highest mean predicted probability. If base estimators do not
implement a ``predict_proba`` method, then it resorts to voting.
Parameters
----------
X : {array-like, sparse mat... | [
"Predict",
"class",
"for",
"X",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/bagging.py#L491-L554 |
albahnsen/CostSensitiveClassification | costcla/models/bagging.py | BaggingClassifier.predict_proba | def predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the mean predicted class probabilities of the base estimators in the
ensemble. If base estimators do not implement a ``predict_proba``
method, th... | python | def predict_proba(self, X):
"""Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the mean predicted class probabilities of the base estimators in the
ensemble. If base estimators do not implement a ``predict_proba``
method, th... | [
"def",
"predict_proba",
"(",
"self",
",",
"X",
")",
":",
"# Check data",
"# X = check_array(X, accept_sparse=['csr', 'csc', 'coo']) # Dont in version 0.15",
"if",
"self",
".",
"n_features_",
"!=",
"X",
".",
"shape",
"[",
"1",
"]",
":",
"raise",
"ValueError",
"(",
"... | Predict class probabilities for X.
The predicted class probabilities of an input sample is computed as
the mean predicted class probabilities of the base estimators in the
ensemble. If base estimators do not implement a ``predict_proba``
method, then it resorts to voting and the predict... | [
"Predict",
"class",
"probabilities",
"for",
"X",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/bagging.py#L556-L610 |
albahnsen/CostSensitiveClassification | costcla/sampling/cost_sampling.py | cost_sampling | def cost_sampling(X, y, cost_mat, method='RejectionSampling', oversampling_norm=0.1, max_wc=97.5):
"""Cost-proportionate sampling.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y : array-like of shape = [n_samples]
Ground... | python | def cost_sampling(X, y, cost_mat, method='RejectionSampling', oversampling_norm=0.1, max_wc=97.5):
"""Cost-proportionate sampling.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y : array-like of shape = [n_samples]
Ground... | [
"def",
"cost_sampling",
"(",
"X",
",",
"y",
",",
"cost_mat",
",",
"method",
"=",
"'RejectionSampling'",
",",
"oversampling_norm",
"=",
"0.1",
",",
"max_wc",
"=",
"97.5",
")",
":",
"#TODO: Check consistency of input",
"# The methods are construct only for the misclassifi... | Cost-proportionate sampling.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y : array-like of shape = [n_samples]
Ground truth (correct) labels.
cost_mat : array-like of shape = [n_samples, 4]
Cost matrix ... | [
"Cost",
"-",
"proportionate",
"sampling",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/sampling/cost_sampling.py#L11-L123 |
albahnsen/CostSensitiveClassification | costcla/datasets/base.py | load_churn | def load_churn():
"""Load and return the churn dataset (classification).
The bank marketing is a easily transformable example-dependent cost-sensitive classification dataset.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to le... | python | def load_churn():
"""Load and return the churn dataset (classification).
The bank marketing is a easily transformable example-dependent cost-sensitive classification dataset.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to le... | [
"def",
"load_churn",
"(",
")",
":",
"module_path",
"=",
"dirname",
"(",
"__file__",
")",
"raw_data",
"=",
"pd",
".",
"read_csv",
"(",
"join",
"(",
"module_path",
",",
"'data'",
",",
"'churn_tv_subscriptions.csv.gz'",
")",
",",
"delimiter",
"=",
"','",
",",
... | Load and return the churn dataset (classification).
The bank marketing is a easily transformable example-dependent cost-sensitive classification dataset.
Returns
-------
data : Bunch
Dictionary-like object, the interesting attributes are:
'data', the data to learn, 'target', the classi... | [
"Load",
"and",
"return",
"the",
"churn",
"dataset",
"(",
"classification",
")",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/datasets/base.py#L26-L78 |
albahnsen/CostSensitiveClassification | costcla/datasets/base.py | load_bankmarketing | def load_bankmarketing(cost_mat_parameters=None):
"""Load and return the bank marketing dataset (classification).
The bank marketing is a easily transformable example-dependent cost-sensitive classification dataset.
Parameters
----------
cost_mat_parameters : Dictionary-like object, optional (defa... | python | def load_bankmarketing(cost_mat_parameters=None):
"""Load and return the bank marketing dataset (classification).
The bank marketing is a easily transformable example-dependent cost-sensitive classification dataset.
Parameters
----------
cost_mat_parameters : Dictionary-like object, optional (defa... | [
"def",
"load_bankmarketing",
"(",
"cost_mat_parameters",
"=",
"None",
")",
":",
"module_path",
"=",
"dirname",
"(",
"__file__",
")",
"raw_data",
"=",
"pd",
".",
"read_csv",
"(",
"join",
"(",
"module_path",
",",
"'data'",
",",
"'bankmarketing.csv.gz'",
")",
","... | Load and return the bank marketing dataset (classification).
The bank marketing is a easily transformable example-dependent cost-sensitive classification dataset.
Parameters
----------
cost_mat_parameters : Dictionary-like object, optional (default=None)
If not None, must include 'per_balance'... | [
"Load",
"and",
"return",
"the",
"bank",
"marketing",
"dataset",
"(",
"classification",
")",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/datasets/base.py#L81-L180 |
albahnsen/CostSensitiveClassification | costcla/datasets/base.py | load_creditscoring1 | def load_creditscoring1(cost_mat_parameters=None):
"""Load and return the credit scoring Kaggle Credit competition dataset (classification).
The credit scoring is a easily transformable example-dependent cost-sensitive classification dataset.
Parameters
----------
cost_mat_parameters : Dictionary-... | python | def load_creditscoring1(cost_mat_parameters=None):
"""Load and return the credit scoring Kaggle Credit competition dataset (classification).
The credit scoring is a easily transformable example-dependent cost-sensitive classification dataset.
Parameters
----------
cost_mat_parameters : Dictionary-... | [
"def",
"load_creditscoring1",
"(",
"cost_mat_parameters",
"=",
"None",
")",
":",
"module_path",
"=",
"dirname",
"(",
"__file__",
")",
"raw_data",
"=",
"pd",
".",
"read_csv",
"(",
"join",
"(",
"module_path",
",",
"'data'",
",",
"'creditscoring1.csv.gz'",
")",
"... | Load and return the credit scoring Kaggle Credit competition dataset (classification).
The credit scoring is a easily transformable example-dependent cost-sensitive classification dataset.
Parameters
----------
cost_mat_parameters : Dictionary-like object, optional (default=None)
If not None, ... | [
"Load",
"and",
"return",
"the",
"credit",
"scoring",
"Kaggle",
"Credit",
"competition",
"dataset",
"(",
"classification",
")",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/datasets/base.py#L183-L249 |
albahnsen/CostSensitiveClassification | costcla/datasets/base.py | load_creditscoring2 | def load_creditscoring2(cost_mat_parameters=None):
"""Load and return the credit scoring PAKDD 2009 competition dataset (classification).
The credit scoring is a easily transformable example-dependent cost-sensitive classification dataset.
Parameters
----------
cost_mat_parameters : Dictionary-lik... | python | def load_creditscoring2(cost_mat_parameters=None):
"""Load and return the credit scoring PAKDD 2009 competition dataset (classification).
The credit scoring is a easily transformable example-dependent cost-sensitive classification dataset.
Parameters
----------
cost_mat_parameters : Dictionary-lik... | [
"def",
"load_creditscoring2",
"(",
"cost_mat_parameters",
"=",
"None",
")",
":",
"module_path",
"=",
"dirname",
"(",
"__file__",
")",
"raw_data",
"=",
"pd",
".",
"read_csv",
"(",
"join",
"(",
"module_path",
",",
"'data'",
",",
"'creditscoring2.csv.gz'",
")",
"... | Load and return the credit scoring PAKDD 2009 competition dataset (classification).
The credit scoring is a easily transformable example-dependent cost-sensitive classification dataset.
Parameters
----------
cost_mat_parameters : Dictionary-like object, optional (default=None)
If not None, mus... | [
"Load",
"and",
"return",
"the",
"credit",
"scoring",
"PAKDD",
"2009",
"competition",
"dataset",
"(",
"classification",
")",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/datasets/base.py#L252-L333 |
albahnsen/CostSensitiveClassification | costcla/datasets/base.py | _creditscoring_costmat | def _creditscoring_costmat(income, debt, pi_1, cost_mat_parameters):
""" Private function to calculate the cost matrix of credit scoring models.
Parameters
----------
income : array of shape = [n_samples]
Monthly income of each example
debt : array of shape = [n_samples]
Debt ratio... | python | def _creditscoring_costmat(income, debt, pi_1, cost_mat_parameters):
""" Private function to calculate the cost matrix of credit scoring models.
Parameters
----------
income : array of shape = [n_samples]
Monthly income of each example
debt : array of shape = [n_samples]
Debt ratio... | [
"def",
"_creditscoring_costmat",
"(",
"income",
",",
"debt",
",",
"pi_1",
",",
"cost_mat_parameters",
")",
":",
"def",
"calculate_a",
"(",
"cl_i",
",",
"int_",
",",
"n_term",
")",
":",
"\"\"\" Private function \"\"\"",
"return",
"cl_i",
"*",
"(",
"(",
"int_",
... | Private function to calculate the cost matrix of credit scoring models.
Parameters
----------
income : array of shape = [n_samples]
Monthly income of each example
debt : array of shape = [n_samples]
Debt ratio each example
pi_1 : float
Percentage of positives in the traini... | [
"Private",
"function",
"to",
"calculate",
"the",
"cost",
"matrix",
"of",
"credit",
"scoring",
"models",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/datasets/base.py#L336-L415 |
albahnsen/CostSensitiveClassification | costcla/probcal/probcal.py | _convexhull | def _convexhull(P):
""" Private function that calculate the convex hull of a set of points
The algorithm was taken from [1].
http://code.activestate.com/recipes/66527-finding-the-convex-hull-of-a-set-of-2d-points/
References
----------
.. [1] Alex Martelli, Anna Ravenscroft, David Ascher, 'Pyt... | python | def _convexhull(P):
""" Private function that calculate the convex hull of a set of points
The algorithm was taken from [1].
http://code.activestate.com/recipes/66527-finding-the-convex-hull-of-a-set-of-2d-points/
References
----------
.. [1] Alex Martelli, Anna Ravenscroft, David Ascher, 'Pyt... | [
"def",
"_convexhull",
"(",
"P",
")",
":",
"def",
"mydet",
"(",
"p",
",",
"q",
",",
"r",
")",
":",
"\"\"\"Calc. determinant of a special matrix with three 2D points.\n\n The sign, \"-\" or \"+\", determines the side, right or left,\n respectivly, on which the point r lie... | Private function that calculate the convex hull of a set of points
The algorithm was taken from [1].
http://code.activestate.com/recipes/66527-finding-the-convex-hull-of-a-set-of-2d-points/
References
----------
.. [1] Alex Martelli, Anna Ravenscroft, David Ascher, 'Python Cookbook', O'Reilly Medi... | [
"Private",
"function",
"that",
"calculate",
"the",
"convex",
"hull",
"of",
"a",
"set",
"of",
"points",
"The",
"algorithm",
"was",
"taken",
"from",
"[",
"1",
"]",
".",
"http",
":",
"//",
"code",
".",
"activestate",
".",
"com",
"/",
"recipes",
"/",
"6652... | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/probcal/probcal.py#L164-L225 |
albahnsen/CostSensitiveClassification | costcla/probcal/probcal.py | ROCConvexHull.fit | def fit(self, y, p):
""" Fit the calibration map
Parameters
----------
y_true : array-like of shape = [n_samples]
True class to be used for calibrating the probabilities
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities to be used for... | python | def fit(self, y, p):
""" Fit the calibration map
Parameters
----------
y_true : array-like of shape = [n_samples]
True class to be used for calibrating the probabilities
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities to be used for... | [
"def",
"fit",
"(",
"self",
",",
"y",
",",
"p",
")",
":",
"# TODO: Check input",
"if",
"p",
".",
"size",
"!=",
"p",
".",
"shape",
"[",
"0",
"]",
":",
"p",
"=",
"p",
"[",
":",
",",
"1",
"]",
"fpr",
",",
"tpr",
",",
"thresholds",
"=",
"roc_curve... | Fit the calibration map
Parameters
----------
y_true : array-like of shape = [n_samples]
True class to be used for calibrating the probabilities
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities to be used for calibrating the probabilities
... | [
"Fit",
"the",
"calibration",
"map"
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/probcal/probcal.py#L62-L135 |
albahnsen/CostSensitiveClassification | costcla/probcal/probcal.py | ROCConvexHull.predict_proba | def predict_proba(self, p):
""" Calculate the calibrated probabilities
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities to be calibrated using calibration map
Returns
-------
y_prob_cal : array-like of shape... | python | def predict_proba(self, p):
""" Calculate the calibrated probabilities
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities to be calibrated using calibration map
Returns
-------
y_prob_cal : array-like of shape... | [
"def",
"predict_proba",
"(",
"self",
",",
"p",
")",
":",
"# TODO: Check input",
"if",
"p",
".",
"size",
"!=",
"p",
".",
"shape",
"[",
"0",
"]",
":",
"p",
"=",
"p",
"[",
":",
",",
"1",
"]",
"calibrated_proba",
"=",
"np",
".",
"zeros",
"(",
"p",
... | Calculate the calibrated probabilities
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities to be calibrated using calibration map
Returns
-------
y_prob_cal : array-like of shape = [n_samples, 1]
Predicted ... | [
"Calculate",
"the",
"calibrated",
"probabilities"
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/probcal/probcal.py#L137-L161 |
albahnsen/CostSensitiveClassification | costcla/utils/cross_validation.py | cross_val_score | def cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1,
verbose=0, fit_params=None, pre_dispatch='2*n_jobs'):
"""Evaluate a score by cross-validation
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
... | python | def cross_val_score(estimator, X, y=None, scoring=None, cv=None, n_jobs=1,
verbose=0, fit_params=None, pre_dispatch='2*n_jobs'):
"""Evaluate a score by cross-validation
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
... | [
"def",
"cross_val_score",
"(",
"estimator",
",",
"X",
",",
"y",
"=",
"None",
",",
"scoring",
"=",
"None",
",",
"cv",
"=",
"None",
",",
"n_jobs",
"=",
"1",
",",
"verbose",
"=",
"0",
",",
"fit_params",
"=",
"None",
",",
"pre_dispatch",
"=",
"'2*n_jobs'... | Evaluate a score by cross-validation
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
X : array-like
The data to fit. Can be, for example a list, or an array at least 2d.
y : array-like, optional, default: None
The ta... | [
"Evaluate",
"a",
"score",
"by",
"cross",
"-",
"validation"
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/utils/cross_validation.py#L1080-L1151 |
albahnsen/CostSensitiveClassification | costcla/utils/cross_validation.py | _fit_and_score | def _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters,
fit_params, return_train_score=False,
return_parameters=False):
"""Fit estimator and compute scores for a given dataset split.
Parameters
----------
estimator : estimator object implement... | python | def _fit_and_score(estimator, X, y, scorer, train, test, verbose, parameters,
fit_params, return_train_score=False,
return_parameters=False):
"""Fit estimator and compute scores for a given dataset split.
Parameters
----------
estimator : estimator object implement... | [
"def",
"_fit_and_score",
"(",
"estimator",
",",
"X",
",",
"y",
",",
"scorer",
",",
"train",
",",
"test",
",",
"verbose",
",",
"parameters",
",",
"fit_params",
",",
"return_train_score",
"=",
"False",
",",
"return_parameters",
"=",
"False",
")",
":",
"if",
... | Fit estimator and compute scores for a given dataset split.
Parameters
----------
estimator : estimator object implementing 'fit'
The object to use to fit the data.
X : array-like of shape at least 2D
The data to fit.
y : array-like, optional, default: None
The target vari... | [
"Fit",
"estimator",
"and",
"compute",
"scores",
"for",
"a",
"given",
"dataset",
"split",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/utils/cross_validation.py#L1154-L1255 |
albahnsen/CostSensitiveClassification | costcla/utils/cross_validation.py | _safe_split | def _safe_split(estimator, X, y, indices, train_indices=None):
"""Create subset of dataset and properly handle kernels."""
if hasattr(estimator, 'kernel') and isinstance(estimator.kernel, collections.Callable):
# cannot compute the kernel values with custom function
raise ValueError("Cannot use ... | python | def _safe_split(estimator, X, y, indices, train_indices=None):
"""Create subset of dataset and properly handle kernels."""
if hasattr(estimator, 'kernel') and isinstance(estimator.kernel, collections.Callable):
# cannot compute the kernel values with custom function
raise ValueError("Cannot use ... | [
"def",
"_safe_split",
"(",
"estimator",
",",
"X",
",",
"y",
",",
"indices",
",",
"train_indices",
"=",
"None",
")",
":",
"if",
"hasattr",
"(",
"estimator",
",",
"'kernel'",
")",
"and",
"isinstance",
"(",
"estimator",
".",
"kernel",
",",
"collections",
".... | Create subset of dataset and properly handle kernels. | [
"Create",
"subset",
"of",
"dataset",
"and",
"properly",
"handle",
"kernels",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/utils/cross_validation.py#L1258-L1287 |
albahnsen/CostSensitiveClassification | costcla/utils/cross_validation.py | _score | def _score(estimator, X_test, y_test, scorer):
"""Compute the score of an estimator on a given test set."""
if y_test is None:
score = scorer(estimator, X_test)
else:
score = scorer(estimator, X_test, y_test)
if not isinstance(score, numbers.Number):
raise ValueError("scoring mus... | python | def _score(estimator, X_test, y_test, scorer):
"""Compute the score of an estimator on a given test set."""
if y_test is None:
score = scorer(estimator, X_test)
else:
score = scorer(estimator, X_test, y_test)
if not isinstance(score, numbers.Number):
raise ValueError("scoring mus... | [
"def",
"_score",
"(",
"estimator",
",",
"X_test",
",",
"y_test",
",",
"scorer",
")",
":",
"if",
"y_test",
"is",
"None",
":",
"score",
"=",
"scorer",
"(",
"estimator",
",",
"X_test",
")",
"else",
":",
"score",
"=",
"scorer",
"(",
"estimator",
",",
"X_... | Compute the score of an estimator on a given test set. | [
"Compute",
"the",
"score",
"of",
"an",
"estimator",
"on",
"a",
"given",
"test",
"set",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/utils/cross_validation.py#L1290-L1299 |
albahnsen/CostSensitiveClassification | costcla/utils/cross_validation.py | _shuffle | def _shuffle(y, labels, random_state):
"""Return a shuffled copy of y eventually shuffle among same labels."""
if labels is None:
ind = random_state.permutation(len(y))
else:
ind = np.arange(len(labels))
for label in np.unique(labels):
this_mask = (labels == label)
... | python | def _shuffle(y, labels, random_state):
"""Return a shuffled copy of y eventually shuffle among same labels."""
if labels is None:
ind = random_state.permutation(len(y))
else:
ind = np.arange(len(labels))
for label in np.unique(labels):
this_mask = (labels == label)
... | [
"def",
"_shuffle",
"(",
"y",
",",
"labels",
",",
"random_state",
")",
":",
"if",
"labels",
"is",
"None",
":",
"ind",
"=",
"random_state",
".",
"permutation",
"(",
"len",
"(",
"y",
")",
")",
"else",
":",
"ind",
"=",
"np",
".",
"arange",
"(",
"len",
... | Return a shuffled copy of y eventually shuffle among same labels. | [
"Return",
"a",
"shuffled",
"copy",
"of",
"y",
"eventually",
"shuffle",
"among",
"same",
"labels",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/utils/cross_validation.py#L1311-L1320 |
albahnsen/CostSensitiveClassification | costcla/utils/cross_validation.py | check_cv | def check_cv(cv, X=None, y=None, classifier=False):
"""Input checker utility for building a CV in a user friendly way.
Parameters
----------
cv : int, a cv generator instance, or None
The input specifying which cv generator to use. It can be an
integer, in which case it is the number of... | python | def check_cv(cv, X=None, y=None, classifier=False):
"""Input checker utility for building a CV in a user friendly way.
Parameters
----------
cv : int, a cv generator instance, or None
The input specifying which cv generator to use. It can be an
integer, in which case it is the number of... | [
"def",
"check_cv",
"(",
"cv",
",",
"X",
"=",
"None",
",",
"y",
"=",
"None",
",",
"classifier",
"=",
"False",
")",
":",
"return",
"_check_cv",
"(",
"cv",
",",
"X",
"=",
"X",
",",
"y",
"=",
"y",
",",
"classifier",
"=",
"classifier",
",",
"warn_mask... | Input checker utility for building a CV in a user friendly way.
Parameters
----------
cv : int, a cv generator instance, or None
The input specifying which cv generator to use. It can be an
integer, in which case it is the number of folds in a KFold,
None, in which case 3 fold is us... | [
"Input",
"checker",
"utility",
"for",
"building",
"a",
"CV",
"in",
"a",
"user",
"friendly",
"way",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/utils/cross_validation.py#L1323-L1350 |
albahnsen/CostSensitiveClassification | costcla/sampling/_smote.py | _SMOTE | def _SMOTE(T, N, k, h = 1.0):
"""
Returns (N/100) * n_minority_samples synthetic minority samples.
Parameters
----------
T : array-like, shape = [n_minority_samples, n_features]
Holds the minority samples
N : percetange of new synthetic samples:
n_synthetic_samples = N/100 * n_m... | python | def _SMOTE(T, N, k, h = 1.0):
"""
Returns (N/100) * n_minority_samples synthetic minority samples.
Parameters
----------
T : array-like, shape = [n_minority_samples, n_features]
Holds the minority samples
N : percetange of new synthetic samples:
n_synthetic_samples = N/100 * n_m... | [
"def",
"_SMOTE",
"(",
"T",
",",
"N",
",",
"k",
",",
"h",
"=",
"1.0",
")",
":",
"n_minority_samples",
",",
"n_features",
"=",
"T",
".",
"shape",
"if",
"N",
"<",
"100",
":",
"#create synthetic samples only for a subset of T.",
"#TODO: select random minortiy sample... | Returns (N/100) * n_minority_samples synthetic minority samples.
Parameters
----------
T : array-like, shape = [n_minority_samples, n_features]
Holds the minority samples
N : percetange of new synthetic samples:
n_synthetic_samples = N/100 * n_minority_samples. Can be < 100.
k : int... | [
"Returns",
"(",
"N",
"/",
"100",
")",
"*",
"n_minority_samples",
"synthetic",
"minority",
"samples",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/sampling/_smote.py#L40-L89 |
albahnsen/CostSensitiveClassification | costcla/sampling/_smote.py | _borderlineSMOTE | def _borderlineSMOTE(X, y, minority_target, N, k):
"""
Returns synthetic minority samples.
Parameters
----------
X : array-like, shape = [n__samples, n_features]
Holds the minority and majority samples
y : array-like, shape = [n__samples]
Holds the class targets for samples
... | python | def _borderlineSMOTE(X, y, minority_target, N, k):
"""
Returns synthetic minority samples.
Parameters
----------
X : array-like, shape = [n__samples, n_features]
Holds the minority and majority samples
y : array-like, shape = [n__samples]
Holds the class targets for samples
... | [
"def",
"_borderlineSMOTE",
"(",
"X",
",",
"y",
",",
"minority_target",
",",
"N",
",",
"k",
")",
":",
"n_samples",
",",
"_",
"=",
"X",
".",
"shape",
"#Learn nearest neighbours on complete training set",
"neigh",
"=",
"NearestNeighbors",
"(",
"n_neighbors",
"=",
... | Returns synthetic minority samples.
Parameters
----------
X : array-like, shape = [n__samples, n_features]
Holds the minority and majority samples
y : array-like, shape = [n__samples]
Holds the class targets for samples
minority_target : value for minority class
N : percetange o... | [
"Returns",
"synthetic",
"minority",
"samples",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/sampling/_smote.py#L91-L147 |
albahnsen/CostSensitiveClassification | costcla/metrics/__init__.py | binary_classification_metrics | def binary_classification_metrics(y_true, y_pred, y_prob):
#TODO: update description
"""classification_metrics.
This function cal...
Parameters
----------
y_true : array-like
Ground truth (correct) labels.
y_pred : array-like
Predicted labels, as returned by a classifier.
... | python | def binary_classification_metrics(y_true, y_pred, y_prob):
#TODO: update description
"""classification_metrics.
This function cal...
Parameters
----------
y_true : array-like
Ground truth (correct) labels.
y_pred : array-like
Predicted labels, as returned by a classifier.
... | [
"def",
"binary_classification_metrics",
"(",
"y_true",
",",
"y_pred",
",",
"y_prob",
")",
":",
"#TODO: update description",
"y_true",
"=",
"column_or_1d",
"(",
"y_true",
")",
"y_pred",
"=",
"column_or_1d",
"(",
"y_pred",
")",
"y_prob",
"=",
"column_or_1d",
"(",
... | classification_metrics.
This function cal...
Parameters
----------
y_true : array-like
Ground truth (correct) labels.
y_pred : array-like
Predicted labels, as returned by a classifier.
y_prob : array-like
Predicted probabilities, as returned by a classifier.
Retu... | [
"classification_metrics",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/metrics/__init__.py#L22-L92 |
albahnsen/CostSensitiveClassification | costcla/models/directcost.py | BayesMinimumRiskClassifier.fit | def fit(self,y_true_cal=None, y_prob_cal=None):
""" If calibration, then train the calibration of probabilities
Parameters
----------
y_true_cal : array-like of shape = [n_samples], optional default = None
True class to be used for calibrating the probabilities
y_pr... | python | def fit(self,y_true_cal=None, y_prob_cal=None):
""" If calibration, then train the calibration of probabilities
Parameters
----------
y_true_cal : array-like of shape = [n_samples], optional default = None
True class to be used for calibrating the probabilities
y_pr... | [
"def",
"fit",
"(",
"self",
",",
"y_true_cal",
"=",
"None",
",",
"y_prob_cal",
"=",
"None",
")",
":",
"if",
"self",
".",
"calibration",
":",
"self",
".",
"cal",
"=",
"ROCConvexHull",
"(",
")",
"self",
".",
"cal",
".",
"fit",
"(",
"y_true_cal",
",",
... | If calibration, then train the calibration of probabilities
Parameters
----------
y_true_cal : array-like of shape = [n_samples], optional default = None
True class to be used for calibrating the probabilities
y_prob_cal : array-like of shape = [n_samples, 2], optional defa... | [
"If",
"calibration",
"then",
"train",
"the",
"calibration",
"of",
"probabilities"
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/directcost.py#L56-L74 |
albahnsen/CostSensitiveClassification | costcla/models/directcost.py | BayesMinimumRiskClassifier.predict | def predict(self, y_prob, cost_mat):
""" Calculate the prediction using the Bayes minimum risk classifier.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
cost_mat : array-like of shape = [n_samples, 4]
Cost m... | python | def predict(self, y_prob, cost_mat):
""" Calculate the prediction using the Bayes minimum risk classifier.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
cost_mat : array-like of shape = [n_samples, 4]
Cost m... | [
"def",
"predict",
"(",
"self",
",",
"y_prob",
",",
"cost_mat",
")",
":",
"if",
"self",
".",
"calibration",
":",
"y_prob",
"[",
":",
",",
"1",
"]",
"=",
"self",
".",
"cal",
".",
"predict_proba",
"(",
"y_prob",
"[",
":",
",",
"1",
"]",
")",
"y_prob... | Calculate the prediction using the Bayes minimum risk classifier.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
cost_mat : array-like of shape = [n_samples, 4]
Cost matrix of the classification problem
W... | [
"Calculate",
"the",
"prediction",
"using",
"the",
"Bayes",
"minimum",
"risk",
"classifier",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/directcost.py#L76-L105 |
albahnsen/CostSensitiveClassification | costcla/models/directcost.py | BayesMinimumRiskClassifier.fit_predict | def fit_predict(self, y_prob, cost_mat, y_true_cal=None, y_prob_cal=None):
""" Calculate the prediction using the Bayes minimum risk classifier.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
cost_mat : array-like of sha... | python | def fit_predict(self, y_prob, cost_mat, y_true_cal=None, y_prob_cal=None):
""" Calculate the prediction using the Bayes minimum risk classifier.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
cost_mat : array-like of sha... | [
"def",
"fit_predict",
"(",
"self",
",",
"y_prob",
",",
"cost_mat",
",",
"y_true_cal",
"=",
"None",
",",
"y_prob_cal",
"=",
"None",
")",
":",
"#TODO: Check input",
"if",
"self",
".",
"calibration",
":",
"self",
".",
"cal",
"=",
"ROCConvexHull",
"(",
")",
... | Calculate the prediction using the Bayes minimum risk classifier.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
cost_mat : array-like of shape = [n_samples, 4]
Cost matrix of the classification problem
W... | [
"Calculate",
"the",
"prediction",
"using",
"the",
"Bayes",
"minimum",
"risk",
"classifier",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/directcost.py#L107-L150 |
albahnsen/CostSensitiveClassification | costcla/models/directcost.py | ThresholdingOptimization.fit | def fit(self, y_prob, cost_mat, y_true):
""" Calculate the optimal threshold using the ThresholdingOptimization.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
cost_mat : array-like of shape = [n_samples, 4]
... | python | def fit(self, y_prob, cost_mat, y_true):
""" Calculate the optimal threshold using the ThresholdingOptimization.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
cost_mat : array-like of shape = [n_samples, 4]
... | [
"def",
"fit",
"(",
"self",
",",
"y_prob",
",",
"cost_mat",
",",
"y_true",
")",
":",
"#TODO: Check input",
"if",
"self",
".",
"calibration",
":",
"cal",
"=",
"ROCConvexHull",
"(",
")",
"cal",
".",
"fit",
"(",
"y_true",
",",
"y_prob",
"[",
":",
",",
"1... | Calculate the optimal threshold using the ThresholdingOptimization.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
cost_mat : array-like of shape = [n_samples, 4]
Cost matrix of the classification problem
... | [
"Calculate",
"the",
"optimal",
"threshold",
"using",
"the",
"ThresholdingOptimization",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/directcost.py#L199-L238 |
albahnsen/CostSensitiveClassification | costcla/models/directcost.py | ThresholdingOptimization.predict | def predict(self, y_prob):
""" Calculate the prediction using the ThresholdingOptimization.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
Returns
-------
y_pred : array-like of shape = [n_samples]
... | python | def predict(self, y_prob):
""" Calculate the prediction using the ThresholdingOptimization.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
Returns
-------
y_pred : array-like of shape = [n_samples]
... | [
"def",
"predict",
"(",
"self",
",",
"y_prob",
")",
":",
"y_pred",
"=",
"np",
".",
"floor",
"(",
"y_prob",
"[",
":",
",",
"1",
"]",
"+",
"(",
"1",
"-",
"self",
".",
"threshold_",
")",
")",
"return",
"y_pred"
] | Calculate the prediction using the ThresholdingOptimization.
Parameters
----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
Returns
-------
y_pred : array-like of shape = [n_samples]
Predicted class | [
"Calculate",
"the",
"prediction",
"using",
"the",
"ThresholdingOptimization",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/directcost.py#L240-L255 |
albahnsen/CostSensitiveClassification | costcla/sampling/sampling.py | undersampling | def undersampling(X, y, cost_mat=None, per=0.5):
"""Under-sampling.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y : array-like of shape = [n_samples]
Ground truth (correct) labels.
cost_mat : array-like of shap... | python | def undersampling(X, y, cost_mat=None, per=0.5):
"""Under-sampling.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y : array-like of shape = [n_samples]
Ground truth (correct) labels.
cost_mat : array-like of shap... | [
"def",
"undersampling",
"(",
"X",
",",
"y",
",",
"cost_mat",
"=",
"None",
",",
"per",
"=",
"0.5",
")",
":",
"n_samples",
"=",
"X",
".",
"shape",
"[",
"0",
"]",
"#TODO: allow y different from (0, 1)",
"num_y1",
"=",
"y",
".",
"sum",
"(",
")",
"num_y0",
... | Under-sampling.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y : array-like of shape = [n_samples]
Ground truth (correct) labels.
cost_mat : array-like of shape = [n_samples, 4], optional (default=None)
... | [
"Under",
"-",
"sampling",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/sampling/sampling.py#L11-L57 |
albahnsen/CostSensitiveClassification | costcla/sampling/sampling.py | smote | def smote(X, y, cost_mat=None, per=0.5):
"""SMOTE: synthetic minority over-sampling technique
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y : array-like of shape = [n_samples]
Ground truth (correct) labels.
cos... | python | def smote(X, y, cost_mat=None, per=0.5):
"""SMOTE: synthetic minority over-sampling technique
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y : array-like of shape = [n_samples]
Ground truth (correct) labels.
cos... | [
"def",
"smote",
"(",
"X",
",",
"y",
",",
"cost_mat",
"=",
"None",
",",
"per",
"=",
"0.5",
")",
":",
"#TODO: Add random state",
"#TODO: Check input",
"n_samples",
"=",
"X",
".",
"shape",
"[",
"0",
"]",
"#TODO: allow y different from (0, 1)",
"num_y1",
"=",
"y... | SMOTE: synthetic minority over-sampling technique
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y : array-like of shape = [n_samples]
Ground truth (correct) labels.
cost_mat : array-like of shape = [n_samples, 4], op... | [
"SMOTE",
":",
"synthetic",
"minority",
"over",
"-",
"sampling",
"technique"
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/sampling/sampling.py#L60-L128 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier._node_cost | def _node_cost(self, y_true, cost_mat):
""" Private function to calculate the cost of a node.
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
cost_mat : array-like of shape = [n_samples, 4]
Cost matrix of the classifi... | python | def _node_cost(self, y_true, cost_mat):
""" Private function to calculate the cost of a node.
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
cost_mat : array-like of shape = [n_samples, 4]
Cost matrix of the classifi... | [
"def",
"_node_cost",
"(",
"self",
",",
"y_true",
",",
"cost_mat",
")",
":",
"n_samples",
"=",
"len",
"(",
"y_true",
")",
"# Evaluates the cost by predicting the node as positive and negative",
"costs",
"=",
"np",
".",
"zeros",
"(",
"2",
")",
"costs",
"[",
"0",
... | Private function to calculate the cost of a node.
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
cost_mat : array-like of shape = [n_samples, 4]
Cost matrix of the classification problem
Where the columns represe... | [
"Private",
"function",
"to",
"calculate",
"the",
"cost",
"of",
"a",
"node",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L138-L183 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier._calculate_gain | def _calculate_gain(self, cost_base, y_true, X, cost_mat, split):
""" Private function to calculate the gain in cost of using split in the
current node.
Parameters
----------
cost_base : float
Cost of the naive prediction
y_true : array indicator matrix
... | python | def _calculate_gain(self, cost_base, y_true, X, cost_mat, split):
""" Private function to calculate the gain in cost of using split in the
current node.
Parameters
----------
cost_base : float
Cost of the naive prediction
y_true : array indicator matrix
... | [
"def",
"_calculate_gain",
"(",
"self",
",",
"cost_base",
",",
"y_true",
",",
"X",
",",
"cost_mat",
",",
"split",
")",
":",
"# Check if cost_base == 0, then no gain is possible",
"#TODO: This must be check in _best_split",
"if",
"cost_base",
"==",
"0.0",
":",
"return",
... | Private function to calculate the gain in cost of using split in the
current node.
Parameters
----------
cost_base : float
Cost of the naive prediction
y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_... | [
"Private",
"function",
"to",
"calculate",
"the",
"gain",
"in",
"cost",
"of",
"using",
"split",
"in",
"the",
"current",
"node",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L185-L242 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier._best_split | def _best_split(self, y_true, X, cost_mat):
""" Private function to calculate the split that gives the best gain.
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_samples, n_features]
The i... | python | def _best_split(self, y_true, X, cost_mat):
""" Private function to calculate the split that gives the best gain.
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_samples, n_features]
The i... | [
"def",
"_best_split",
"(",
"self",
",",
"y_true",
",",
"X",
",",
"cost_mat",
")",
":",
"n_samples",
",",
"n_features",
"=",
"X",
".",
"shape",
"num_pct",
"=",
"self",
".",
"num_pct",
"cost_base",
",",
"y_pred",
",",
"y_prob",
"=",
"self",
".",
"_node_c... | Private function to calculate the split that gives the best gain.
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_samples, n_features]
The input samples.
cost_mat : array-like of shape = ... | [
"Private",
"function",
"to",
"calculate",
"the",
"split",
"that",
"gives",
"the",
"best",
"gain",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L244-L302 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier._tree_grow | def _tree_grow(self, y_true, X, cost_mat, level=0):
""" Private recursive function to grow the decision tree.
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_samples, n_features]
The input... | python | def _tree_grow(self, y_true, X, cost_mat, level=0):
""" Private recursive function to grow the decision tree.
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_samples, n_features]
The input... | [
"def",
"_tree_grow",
"(",
"self",
",",
"y_true",
",",
"X",
",",
"cost_mat",
",",
"level",
"=",
"0",
")",
":",
"#TODO: Find error, add min_samples_split",
"if",
"len",
"(",
"X",
".",
"shape",
")",
"==",
"1",
":",
"tree",
"=",
"dict",
"(",
"y_pred",
"=",... | Private recursive function to grow the decision tree.
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_samples, n_features]
The input samples.
cost_mat : array-like of shape = [n_samples, ... | [
"Private",
"recursive",
"function",
"to",
"grow",
"the",
"decision",
"tree",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L304-L369 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier.fit | def fit(self, X, y, cost_mat, check_input=False):
""" Build a example-dependent cost-sensitive decision tree from the training set (X, y, cost_mat)
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_samp... | python | def fit(self, X, y, cost_mat, check_input=False):
""" Build a example-dependent cost-sensitive decision tree from the training set (X, y, cost_mat)
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_samp... | [
"def",
"fit",
"(",
"self",
",",
"X",
",",
"y",
",",
"cost_mat",
",",
"check_input",
"=",
"False",
")",
":",
"#TODO: Check input",
"#TODO: Add random state",
"n_samples",
",",
"self",
".",
"n_features_",
"=",
"X",
".",
"shape",
"self",
".",
"tree_",
"=",
... | Build a example-dependent cost-sensitive decision tree from the training set (X, y, cost_mat)
Parameters
----------
y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_samples, n_features]
The input samples.
cost_... | [
"Build",
"a",
"example",
"-",
"dependent",
"cost",
"-",
"sensitive",
"decision",
"tree",
"from",
"the",
"training",
"set",
"(",
"X",
"y",
"cost_mat",
")"
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L379-L442 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier._nodes | def _nodes(self, tree):
""" Private function that find the number of nodes in a tree.
Parameters
----------
tree : object
Returns
-------
nodes : array like of shape [n_nodes]
"""
def recourse(temp_tree_, nodes):
if isinstance(temp_tr... | python | def _nodes(self, tree):
""" Private function that find the number of nodes in a tree.
Parameters
----------
tree : object
Returns
-------
nodes : array like of shape [n_nodes]
"""
def recourse(temp_tree_, nodes):
if isinstance(temp_tr... | [
"def",
"_nodes",
"(",
"self",
",",
"tree",
")",
":",
"def",
"recourse",
"(",
"temp_tree_",
",",
"nodes",
")",
":",
"if",
"isinstance",
"(",
"temp_tree_",
",",
"dict",
")",
":",
"if",
"temp_tree_",
"[",
"'split'",
"]",
"!=",
"-",
"1",
":",
"nodes",
... | Private function that find the number of nodes in a tree.
Parameters
----------
tree : object
Returns
-------
nodes : array like of shape [n_nodes] | [
"Private",
"function",
"that",
"find",
"the",
"number",
"of",
"nodes",
"in",
"a",
"tree",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L444-L466 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier._classify | def _classify(self, X, tree, proba=False):
""" Private function that classify a dataset using tree.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
tree : object
proba : bool, optional (default=False)
... | python | def _classify(self, X, tree, proba=False):
""" Private function that classify a dataset using tree.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
tree : object
proba : bool, optional (default=False)
... | [
"def",
"_classify",
"(",
"self",
",",
"X",
",",
"tree",
",",
"proba",
"=",
"False",
")",
":",
"n_samples",
",",
"n_features",
"=",
"X",
".",
"shape",
"predicted",
"=",
"np",
".",
"ones",
"(",
"n_samples",
")",
"# Check if final node",
"if",
"tree",
"["... | Private function that classify a dataset using tree.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
tree : object
proba : bool, optional (default=False)
If True then return probabilities else return class
... | [
"Private",
"function",
"that",
"classify",
"a",
"dataset",
"using",
"tree",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L468-L513 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier.predict | def predict(self, X):
""" Predict class of X.
The predicted class for each sample in X is returned.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
Returns
-------
y : array of shape = [n_samples]
... | python | def predict(self, X):
""" Predict class of X.
The predicted class for each sample in X is returned.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
Returns
-------
y : array of shape = [n_samples]
... | [
"def",
"predict",
"(",
"self",
",",
"X",
")",
":",
"#TODO: Check consistency of X",
"if",
"self",
".",
"pruned",
":",
"tree_",
"=",
"self",
".",
"tree_",
".",
"tree_pruned",
"else",
":",
"tree_",
"=",
"self",
".",
"tree_",
".",
"tree",
"return",
"self",
... | Predict class of X.
The predicted class for each sample in X is returned.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
Returns
-------
y : array of shape = [n_samples]
The predicted classes, | [
"Predict",
"class",
"of",
"X",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L515-L537 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier.predict_proba | def predict_proba(self, X):
"""Predict class probabilities of the input samples X.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
Returns
-------
prob : array of shape = [n_samples, 2]
The class... | python | def predict_proba(self, X):
"""Predict class probabilities of the input samples X.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
Returns
-------
prob : array of shape = [n_samples, 2]
The class... | [
"def",
"predict_proba",
"(",
"self",
",",
"X",
")",
":",
"#TODO: Check consistency of X",
"n_samples",
",",
"n_features",
"=",
"X",
".",
"shape",
"prob",
"=",
"np",
".",
"zeros",
"(",
"(",
"n_samples",
",",
"2",
")",
")",
"if",
"self",
".",
"pruned",
"... | Predict class probabilities of the input samples X.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
Returns
-------
prob : array of shape = [n_samples, 2]
The class probabilities of the input samples. | [
"Predict",
"class",
"probabilities",
"of",
"the",
"input",
"samples",
"X",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L539-L564 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier._delete_node | def _delete_node(self, tree, node):
""" Private function that eliminate node from tree.
Parameters
----------
tree : object
node : int
node to be eliminated from tree
Returns
-------
pruned_tree : object
"""
# Calculate gai... | python | def _delete_node(self, tree, node):
""" Private function that eliminate node from tree.
Parameters
----------
tree : object
node : int
node to be eliminated from tree
Returns
-------
pruned_tree : object
"""
# Calculate gai... | [
"def",
"_delete_node",
"(",
"self",
",",
"tree",
",",
"node",
")",
":",
"# Calculate gains",
"temp_tree",
"=",
"copy",
".",
"deepcopy",
"(",
"tree",
")",
"def",
"recourse",
"(",
"temp_tree_",
",",
"del_node",
")",
":",
"if",
"isinstance",
"(",
"temp_tree_"... | Private function that eliminate node from tree.
Parameters
----------
tree : object
node : int
node to be eliminated from tree
Returns
-------
pruned_tree : object | [
"Private",
"function",
"that",
"eliminate",
"node",
"from",
"tree",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L566-L599 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier._pruning | def _pruning(self, X, y_true, cost_mat):
""" Private function that prune the decision tree.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y_true : array indicator matrix
Ground truth (correct) labels.
... | python | def _pruning(self, X, y_true, cost_mat):
""" Private function that prune the decision tree.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y_true : array indicator matrix
Ground truth (correct) labels.
... | [
"def",
"_pruning",
"(",
"self",
",",
"X",
",",
"y_true",
",",
"cost_mat",
")",
":",
"# Calculate gains",
"nodes",
"=",
"self",
".",
"_nodes",
"(",
"self",
".",
"tree_",
".",
"tree_pruned",
")",
"n_nodes",
"=",
"len",
"(",
"nodes",
")",
"gains",
"=",
... | Private function that prune the decision tree.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y_true : array indicator matrix
Ground truth (correct) labels.
cost_mat : array-like of shape = [n_samples, 4]
... | [
"Private",
"function",
"that",
"prune",
"the",
"decision",
"tree",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L601-L652 |
albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier.pruning | def pruning(self, X, y, cost_mat):
""" Function that prune the decision tree.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y_true : array indicator matrix
Ground truth (correct) labels.
cost_mat : a... | python | def pruning(self, X, y, cost_mat):
""" Function that prune the decision tree.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y_true : array indicator matrix
Ground truth (correct) labels.
cost_mat : a... | [
"def",
"pruning",
"(",
"self",
",",
"X",
",",
"y",
",",
"cost_mat",
")",
":",
"self",
".",
"tree_",
".",
"tree_pruned",
"=",
"copy",
".",
"deepcopy",
"(",
"self",
".",
"tree_",
".",
"tree",
")",
"if",
"self",
".",
"tree_",
".",
"n_nodes",
">",
"0... | Function that prune the decision tree.
Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
y_true : array indicator matrix
Ground truth (correct) labels.
cost_mat : array-like of shape = [n_samples, 4]
... | [
"Function",
"that",
"prune",
"the",
"decision",
"tree",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/cost_tree.py#L654-L676 |
albahnsen/CostSensitiveClassification | costcla/metrics/costs.py | cost_loss | def cost_loss(y_true, y_pred, cost_mat):
#TODO: update description
"""Cost classification loss.
This function calculates the cost of using y_pred on y_true with
cost-matrix cost-mat. It differ from traditional classification evaluation
measures since measures such as accuracy asing the same cost to... | python | def cost_loss(y_true, y_pred, cost_mat):
#TODO: update description
"""Cost classification loss.
This function calculates the cost of using y_pred on y_true with
cost-matrix cost-mat. It differ from traditional classification evaluation
measures since measures such as accuracy asing the same cost to... | [
"def",
"cost_loss",
"(",
"y_true",
",",
"y_pred",
",",
"cost_mat",
")",
":",
"#TODO: update description",
"#TODO: Check consistency of cost_mat",
"y_true",
"=",
"column_or_1d",
"(",
"y_true",
")",
"y_true",
"=",
"(",
"y_true",
"==",
"1",
")",
".",
"astype",
"(",... | Cost classification loss.
This function calculates the cost of using y_pred on y_true with
cost-matrix cost-mat. It differ from traditional classification evaluation
measures since measures such as accuracy asing the same cost to different
errors, but that is not the real case in several real-world cla... | [
"Cost",
"classification",
"loss",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/metrics/costs.py#L19-L81 |
albahnsen/CostSensitiveClassification | costcla/metrics/costs.py | savings_score | def savings_score(y_true, y_pred, cost_mat):
#TODO: update description
"""Savings score.
This function calculates the savings cost of using y_pred on y_true with
cost-matrix cost-mat, as the difference of y_pred and the cost_loss of a naive
classification model.
Parameters
----------
y... | python | def savings_score(y_true, y_pred, cost_mat):
#TODO: update description
"""Savings score.
This function calculates the savings cost of using y_pred on y_true with
cost-matrix cost-mat, as the difference of y_pred and the cost_loss of a naive
classification model.
Parameters
----------
y... | [
"def",
"savings_score",
"(",
"y_true",
",",
"y_pred",
",",
"cost_mat",
")",
":",
"#TODO: update description",
"#TODO: Check consistency of cost_mat",
"y_true",
"=",
"column_or_1d",
"(",
"y_true",
")",
"y_pred",
"=",
"column_or_1d",
"(",
"y_pred",
")",
"n_samples",
"... | Savings score.
This function calculates the savings cost of using y_pred on y_true with
cost-matrix cost-mat, as the difference of y_pred and the cost_loss of a naive
classification model.
Parameters
----------
y_true : array-like or label indicator matrix
Ground truth (correct) labels... | [
"Savings",
"score",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/metrics/costs.py#L84-L143 |
albahnsen/CostSensitiveClassification | costcla/metrics/costs.py | brier_score_loss | def brier_score_loss(y_true, y_prob):
"""Compute the Brier score
The smaller the Brier score, the better, hence the naming with "loss".
Across all items in a set N predictions, the Brier score measures the
mean squared difference between (1) the predicted probability assigned
to the possible outco... | python | def brier_score_loss(y_true, y_prob):
"""Compute the Brier score
The smaller the Brier score, the better, hence the naming with "loss".
Across all items in a set N predictions, the Brier score measures the
mean squared difference between (1) the predicted probability assigned
to the possible outco... | [
"def",
"brier_score_loss",
"(",
"y_true",
",",
"y_prob",
")",
":",
"y_true",
"=",
"column_or_1d",
"(",
"y_true",
")",
"y_prob",
"=",
"column_or_1d",
"(",
"y_prob",
")",
"return",
"np",
".",
"mean",
"(",
"(",
"y_true",
"-",
"y_prob",
")",
"**",
"2",
")"... | Compute the Brier score
The smaller the Brier score, the better, hence the naming with "loss".
Across all items in a set N predictions, the Brier score measures the
mean squared difference between (1) the predicted probability assigned
to the possible outcomes for item i, and (2) the actual outcome.
... | [
"Compute",
"the",
"Brier",
"score"
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/metrics/costs.py#L149-L200 |
albahnsen/CostSensitiveClassification | costcla/models/regression.py | _intercept_dot | def _intercept_dot(w, X):
"""Computes y * np.dot(X, w).
It takes into consideration if the intercept should be fit or not.
Parameters
----------
w : ndarray, shape (n_features,) or (n_features + 1,)
Coefficient vector.
X : {array-like, sparse matrix}, shape (n_samples, n_features)
... | python | def _intercept_dot(w, X):
"""Computes y * np.dot(X, w).
It takes into consideration if the intercept should be fit or not.
Parameters
----------
w : ndarray, shape (n_features,) or (n_features + 1,)
Coefficient vector.
X : {array-like, sparse matrix}, shape (n_samples, n_features)
... | [
"def",
"_intercept_dot",
"(",
"w",
",",
"X",
")",
":",
"c",
"=",
"0.",
"if",
"w",
".",
"size",
"==",
"X",
".",
"shape",
"[",
"1",
"]",
"+",
"1",
":",
"c",
"=",
"w",
"[",
"-",
"1",
"]",
"w",
"=",
"w",
"[",
":",
"-",
"1",
"]",
"z",
"=",... | Computes y * np.dot(X, w).
It takes into consideration if the intercept should be fit or not.
Parameters
----------
w : ndarray, shape (n_features,) or (n_features + 1,)
Coefficient vector.
X : {array-like, sparse matrix}, shape (n_samples, n_features)
Training data. | [
"Computes",
"y",
"*",
"np",
".",
"dot",
"(",
"X",
"w",
")",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/regression.py#L20-L40 |
albahnsen/CostSensitiveClassification | costcla/models/regression.py | _logistic_cost_loss | def _logistic_cost_loss(w, X, y, cost_mat, alpha):
"""Computes the logistic loss.
Parameters
----------
w : array-like, shape (n_w, n_features,) or (n_w, n_features + 1,)
Coefficient vector or matrix of coefficient.
X : array-like, shape (n_samples, n_features)
Training data.
... | python | def _logistic_cost_loss(w, X, y, cost_mat, alpha):
"""Computes the logistic loss.
Parameters
----------
w : array-like, shape (n_w, n_features,) or (n_w, n_features + 1,)
Coefficient vector or matrix of coefficient.
X : array-like, shape (n_samples, n_features)
Training data.
... | [
"def",
"_logistic_cost_loss",
"(",
"w",
",",
"X",
",",
"y",
",",
"cost_mat",
",",
"alpha",
")",
":",
"if",
"w",
".",
"shape",
"[",
"0",
"]",
"==",
"w",
".",
"size",
":",
"# Only evaluating one w",
"return",
"_logistic_cost_loss_i",
"(",
"w",
",",
"X",
... | Computes the logistic loss.
Parameters
----------
w : array-like, shape (n_w, n_features,) or (n_w, n_features + 1,)
Coefficient vector or matrix of coefficient.
X : array-like, shape (n_samples, n_features)
Training data.
y : ndarray, shape (n_samples,)
Array of labels.
... | [
"Computes",
"the",
"logistic",
"loss",
"."
] | train | https://github.com/albahnsen/CostSensitiveClassification/blob/75778ae32c70671c0cdde6c4651277b6a8b58871/costcla/models/regression.py#L58-L98 |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.