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4,000 | HazyResearch/pdftotree | pdftotree/utils/pdf/grid.py | _retain_centroids | def _retain_centroids(numbers, thres):
"""Only keep one number for each cluster within thres of each other"""
numbers.sort()
prev = -1
ret = []
for n in numbers:
if prev < 0 or n - prev > thres:
ret.append(n)
prev = n
return ret | python | def _retain_centroids(numbers, thres):
"""Only keep one number for each cluster within thres of each other"""
numbers.sort()
prev = -1
ret = []
for n in numbers:
if prev < 0 or n - prev > thres:
ret.append(n)
prev = n
return ret | [
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4,001 | HazyResearch/pdftotree | pdftotree/utils/pdf/grid.py | _split_vlines_hlines | def _split_vlines_hlines(lines):
"""Separates lines into horizontal and vertical ones"""
vlines, hlines = [], []
for line in lines:
(vlines if line.x1 - line.x0 < 0.1 else hlines).append(line)
return vlines, hlines | python | def _split_vlines_hlines(lines):
"""Separates lines into horizontal and vertical ones"""
vlines, hlines = [], []
for line in lines:
(vlines if line.x1 - line.x0 < 0.1 else hlines).append(line)
return vlines, hlines | [
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4,002 | HazyResearch/pdftotree | pdftotree/utils/pdf/grid.py | _npiter | def _npiter(arr):
"""Wrapper for iterating numpy array"""
for a in np.nditer(arr, flags=["refs_ok"]):
c = a.item()
if c is not None:
yield c | python | def _npiter(arr):
"""Wrapper for iterating numpy array"""
for a in np.nditer(arr, flags=["refs_ok"]):
c = a.item()
if c is not None:
yield c | [
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4,003 | HazyResearch/pdftotree | pdftotree/utils/pdf/grid.py | Grid.get_normalized_grid | def get_normalized_grid(self):
"""
Analyzes subcell structure
"""
log = logging.getLogger(__name__)
# Resolve multirow mentions, TODO: validate against all PDFs
# subcol_count = 0
mega_rows = []
for row_id, row in enumerate(self._grid):
# maps... | python | def get_normalized_grid(self):
"""
Analyzes subcell structure
"""
log = logging.getLogger(__name__)
# Resolve multirow mentions, TODO: validate against all PDFs
# subcol_count = 0
mega_rows = []
for row_id, row in enumerate(self._grid):
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4,004 | HazyResearch/pdftotree | pdftotree/utils/pdf/grid.py | Grid._mark_grid_bounds | def _mark_grid_bounds(self, plane, region_bbox):
"""
Assume all lines define a complete grid over the region_bbox.
Detect which lines are missing so that we can recover merged
cells.
"""
# Grid boundaries
vbars = np.zeros([self.num_rows, self.num_cols + 1], dtype=... | python | def _mark_grid_bounds(self, plane, region_bbox):
"""
Assume all lines define a complete grid over the region_bbox.
Detect which lines are missing so that we can recover merged
cells.
"""
# Grid boundaries
vbars = np.zeros([self.num_rows, self.num_cols + 1], dtype=... | [
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4,005 | HazyResearch/pdftotree | pdftotree/utils/pdf/vector_utils.py | vectorize | def vectorize(e, tolerance=0.1):
"""
vectorizes the pdf object's bounding box
min_width is the width under which we consider it a line
instead of a big rectangle
"""
tolerance = max(tolerance, e.linewidth)
is_high = e.height > tolerance
is_wide = e.width > tolerance
# if skewed towar... | python | def vectorize(e, tolerance=0.1):
"""
vectorizes the pdf object's bounding box
min_width is the width under which we consider it a line
instead of a big rectangle
"""
tolerance = max(tolerance, e.linewidth)
is_high = e.height > tolerance
is_wide = e.width > tolerance
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4,006 | HazyResearch/pdftotree | pdftotree/utils/pdf/vector_utils.py | aligned | def aligned(e1, e2):
"""
alignment is determined by two boxes having one exactly the same
attribute, which could mean parallel, perpendicularly forming a
corner etc.
"""
return (
any(close(c1, c2) for c1, c2 in zip(e1.bbox, e2.bbox))
or x_center_aligned(e1, e2)
or y_cente... | python | def aligned(e1, e2):
"""
alignment is determined by two boxes having one exactly the same
attribute, which could mean parallel, perpendicularly forming a
corner etc.
"""
return (
any(close(c1, c2) for c1, c2 in zip(e1.bbox, e2.bbox))
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4,007 | HazyResearch/pdftotree | pdftotree/utils/pdf/vector_utils.py | bound_bboxes | def bound_bboxes(bboxes):
"""
Finds the minimal bbox that contains all given bboxes
"""
group_x0 = min(map(lambda l: l[x0], bboxes))
group_y0 = min(map(lambda l: l[y0], bboxes))
group_x1 = max(map(lambda l: l[x1], bboxes))
group_y1 = max(map(lambda l: l[y1], bboxes))
return (group_x0, gr... | python | def bound_bboxes(bboxes):
"""
Finds the minimal bbox that contains all given bboxes
"""
group_x0 = min(map(lambda l: l[x0], bboxes))
group_y0 = min(map(lambda l: l[y0], bboxes))
group_x1 = max(map(lambda l: l[x1], bboxes))
group_y1 = max(map(lambda l: l[y1], bboxes))
return (group_x0, gr... | [
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4,008 | HazyResearch/pdftotree | pdftotree/utils/pdf/vector_utils.py | bound_elems | def bound_elems(elems):
"""
Finds the minimal bbox that contains all given elems
"""
group_x0 = min(map(lambda l: l.x0, elems))
group_y0 = min(map(lambda l: l.y0, elems))
group_x1 = max(map(lambda l: l.x1, elems))
group_y1 = max(map(lambda l: l.y1, elems))
return (group_x0, group_y0, gro... | python | def bound_elems(elems):
"""
Finds the minimal bbox that contains all given elems
"""
group_x0 = min(map(lambda l: l.x0, elems))
group_y0 = min(map(lambda l: l.y0, elems))
group_x1 = max(map(lambda l: l.x1, elems))
group_y1 = max(map(lambda l: l.y1, elems))
return (group_x0, group_y0, gro... | [
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4,009 | HazyResearch/pdftotree | pdftotree/utils/pdf/vector_utils.py | intersect | def intersect(a, b):
"""
Check if two rectangles intersect
"""
if a[x0] == a[x1] or a[y0] == a[y1]:
return False
if b[x0] == b[x1] or b[y0] == b[y1]:
return False
return a[x0] <= b[x1] and b[x0] <= a[x1] and a[y0] <= b[y1] and b[y0] <= a[y1] | python | def intersect(a, b):
"""
Check if two rectangles intersect
"""
if a[x0] == a[x1] or a[y0] == a[y1]:
return False
if b[x0] == b[x1] or b[y0] == b[y1]:
return False
return a[x0] <= b[x1] and b[x0] <= a[x1] and a[y0] <= b[y1] and b[y0] <= a[y1] | [
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4,010 | HazyResearch/pdftotree | pdftotree/utils/pdf/vector_utils.py | reading_order | def reading_order(e1, e2):
"""
A comparator to sort bboxes from top to bottom, left to right
"""
b1 = e1.bbox
b2 = e2.bbox
if round(b1[y0]) == round(b2[y0]) or round(b1[y1]) == round(b2[y1]):
return float_cmp(b1[x0], b2[x0])
return float_cmp(b1[y0], b2[y0]) | python | def reading_order(e1, e2):
"""
A comparator to sort bboxes from top to bottom, left to right
"""
b1 = e1.bbox
b2 = e2.bbox
if round(b1[y0]) == round(b2[y0]) or round(b1[y1]) == round(b2[y1]):
return float_cmp(b1[x0], b2[x0])
return float_cmp(b1[y0], b2[y0]) | [
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4,011 | HazyResearch/pdftotree | pdftotree/utils/pdf/vector_utils.py | xy_reading_order | def xy_reading_order(e1, e2):
"""
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"""
b1 = e1.bbox
b2 = e2.bbox
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return float_cmp(b1[x0], b2[x0]) | python | def xy_reading_order(e1, e2):
"""
A comparator to sort bboxes from left to right, top to bottom
"""
b1 = e1.bbox
b2 = e2.bbox
if round(b1[x0]) == round(b2[x0]):
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4,012 | HazyResearch/pdftotree | pdftotree/utils/pdf/vector_utils.py | column_order | def column_order(b1, b2):
"""
A comparator that sorts bboxes first by "columns", where a column is made
up of all bboxes that overlap, then by vertical position in each column.
b1 = [b1.type, b1.top, b1.left, b1.bottom, b1.right]
b2 = [b2.type, b2.top, b2.left, b2.bottom, b2.right]
"""
(top... | python | def column_order(b1, b2):
"""
A comparator that sorts bboxes first by "columns", where a column is made
up of all bboxes that overlap, then by vertical position in each column.
b1 = [b1.type, b1.top, b1.left, b1.bottom, b1.right]
b2 = [b2.type, b2.top, b2.left, b2.bottom, b2.right]
"""
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4,013 | HazyResearch/pdftotree | pdftotree/utils/pdf/vector_utils.py | merge_intervals | def merge_intervals(elems, overlap_thres=2.0):
"""
Project in x axis
Sort by start
Go through segments and keep max x1
Return a list of non-overlapping intervals
"""
overlap_thres = max(0.0, overlap_thres)
ordered = sorted(elems, key=lambda e: e.x0)
intervals = []
cur = [-overl... | python | def merge_intervals(elems, overlap_thres=2.0):
"""
Project in x axis
Sort by start
Go through segments and keep max x1
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overlap_thres = max(0.0, overlap_thres)
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4,014 | HazyResearch/pdftotree | pdftotree/visual/visual_utils.py | predict_heatmap | def predict_heatmap(pdf_path, page_num, model, img_dim=448, img_dir="tmp/img"):
"""
Return an image corresponding to the page of the pdf
documents saved at pdf_path. If the image is not found in img_dir this
function creates it and saves it in img_dir.
:param pdf_path: path to the pdf document.
... | python | def predict_heatmap(pdf_path, page_num, model, img_dim=448, img_dir="tmp/img"):
"""
Return an image corresponding to the page of the pdf
documents saved at pdf_path. If the image is not found in img_dir this
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:param pdf_path: path to the pdf document.
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4,015 | 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
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4,016 | 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
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:param img: original image
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4,017 | 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) | [
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4,018 | 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) | [
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4,019 | 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 | [
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4,020 | 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)) | [
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4,021 | 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
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"""
shape = "".join(x[0] for x in path)
prev_split = 0
for i in range(len(shape)):
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4,022 | 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 = []
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4,023 | 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) | [
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4,024 | 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:
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4,025 | 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):
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4,026 | 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
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4,027 | 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... | [
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4,028 | 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... | [
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4,029 | 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... | [
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4,030 | 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 = []
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4,031 | 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
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4,032 | 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) | [
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4,033 | 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:
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4,034 | 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))
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4,035 | 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... | [
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4,036 | 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
"""
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4,037 | 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
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4,038 | 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... | [
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4,039 | 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)
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4,040 | 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... | [
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4,041 | 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 | [
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4,042 | 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_,
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4,043 | albahnsen/CostSensitiveClassification | costcla/models/bagging.py | BaseBagging._evaluate_oob_savings | def _evaluate_oob_savings(self, X, y, cost_mat):
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estimators_weight = []
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4,044 | 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.
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4,045 | albahnsen/CostSensitiveClassification | costcla/models/bagging.py | BaggingClassifier.predict_proba | def predict_proba(self, X):
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4,046 | 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
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X : array-like of shape = [n_samples, n_features]
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4,047 | 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]
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income : array of shape = [n_samples]
Monthly income of each example
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4,048 | albahnsen/CostSensitiveClassification | costcla/probcal/probcal.py | ROCConvexHull.predict_proba | def predict_proba(self, p):
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----------
y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities to be calibrated using calibration map
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4,049 | 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
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4,050 | albahnsen/CostSensitiveClassification | costcla/utils/cross_validation.py | _safe_split | def _safe_split(estimator, X, y, indices, train_indices=None):
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4,051 | albahnsen/CostSensitiveClassification | costcla/utils/cross_validation.py | _score | def _score(estimator, X_test, y_test, scorer):
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score = scorer(estimator, X_test)
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4,052 | albahnsen/CostSensitiveClassification | costcla/utils/cross_validation.py | _shuffle | def _shuffle(y, labels, random_state):
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ind = random_state.permutation(len(y))
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4,053 | albahnsen/CostSensitiveClassification | costcla/utils/cross_validation.py | check_cv | def check_cv(cv, X=None, y=None, classifier=False):
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4,054 | albahnsen/CostSensitiveClassification | costcla/sampling/_smote.py | _borderlineSMOTE | def _borderlineSMOTE(X, y, minority_target, N, k):
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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
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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
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4,055 | albahnsen/CostSensitiveClassification | costcla/models/directcost.py | BayesMinimumRiskClassifier.fit | def fit(self,y_true_cal=None, y_prob_cal=None):
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Parameters
----------
y_true_cal : array-like of shape = [n_samples], optional default = None
True class to be used for calibrating the probabilities
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y_true_cal : array-like of shape = [n_samples], optional default = None
True class to be used for calibrating the probabilities
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4,056 | 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]
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4,057 | albahnsen/CostSensitiveClassification | costcla/models/directcost.py | ThresholdingOptimization.predict | def predict(self, y_prob):
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Parameters
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y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
Returns
-------
y_pred : array-like of shape = [n_samples]
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""" Calculate the prediction using the ThresholdingOptimization.
Parameters
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y_prob : array-like of shape = [n_samples, 2]
Predicted probabilities.
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y_pred : array-like of shape = [n_samples]
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4,058 | 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... | [
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Ground truth (correct) labels.
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4,059 | 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]
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4,060 | 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
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4,061 | 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.
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y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_samples, n_features]
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4,062 | 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
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y_true : array indicator matrix
Ground truth (correct) labels.
X : array-like of shape = [n_samples, n_features]
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4,063 | 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]
"""
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4,064 | 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)
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4,065 | albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier.predict | def predict(self, X):
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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]
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4,066 | albahnsen/CostSensitiveClassification | costcla/models/cost_tree.py | CostSensitiveDecisionTreeClassifier.predict_proba | def predict_proba(self, X):
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Parameters
----------
X : array-like of shape = [n_samples, n_features]
The input samples.
Returns
-------
prob : array of shape = [n_samples, 2]
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"""Predict class probabilities of the input samples X.
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----------
X : array-like of shape = [n_samples, n_features]
The input samples.
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prob : array of shape = [n_samples, 2]
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4,067 | 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
"""
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4,068 | 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.
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""" 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.
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4,069 | 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.
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""" 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.
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4,070 | 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
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4,071 | 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
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4,072 | 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
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4,073 | 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.
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4,074 | albahnsen/CostSensitiveClassification | costcla/models/regression.py | CostSensitiveLogisticRegression.predict | def predict(self, X, cut_point=0.5):
"""Predicted class.
Parameters
----------
X : array-like, shape = [n_samples, n_features]
Returns
-------
T : array-like, shape = [n_samples]
Returns the prediction of the sample..
"""
return np.fl... | python | def predict(self, X, cut_point=0.5):
"""Predicted class.
Parameters
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4,075 | MozillaSecurity/laniakea | laniakea/core/userdata.py | UserData.list_tags | def list_tags(userdata):
"""List all used macros within a UserData script.
:param userdata: The UserData script.
:type userdata: str
"""
macros = re.findall('@(.*?)@', userdata)
logging.info('List of available macros:')
for macro in macros:
logging.in... | python | def list_tags(userdata):
"""List all used macros within a UserData script.
:param userdata: The UserData script.
:type userdata: str
"""
macros = re.findall('@(.*?)@', userdata)
logging.info('List of available macros:')
for macro in macros:
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4,076 | MozillaSecurity/laniakea | laniakea/core/userdata.py | UserData.handle_tags | def handle_tags(userdata, macros):
"""Insert macro values or auto export variables in UserData scripts.
:param userdata: The UserData script.
:type userdata: str
:param macros: UserData macros as key value pair.
:type macros: dict
:return: UserData script with the macros... | python | def handle_tags(userdata, macros):
"""Insert macro values or auto export variables in UserData scripts.
:param userdata: The UserData script.
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4,077 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager.retry_on_ec2_error | def retry_on_ec2_error(self, func, *args, **kwargs):
"""
Call the given method with the given arguments, retrying if the call
failed due to an EC2ResponseError. This method will wait at most 30
seconds and perform up to 6 retries. If the method still fails, it will
propagate the ... | python | def retry_on_ec2_error(self, func, *args, **kwargs):
"""
Call the given method with the given arguments, retrying if the call
failed due to an EC2ResponseError. This method will wait at most 30
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4,078 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager.connect | def connect(self, region, **kw_params):
"""Connect to a EC2.
:param region: The name of the region to connect to.
:type region: str
:param kw_params:
:type kw_params: dict
"""
self.ec2 = boto.ec2.connect_to_region(region, **kw_params)
if not self.ec2:
... | python | def connect(self, region, **kw_params):
"""Connect to a EC2.
:param region: The name of the region to connect to.
:type region: str
:param kw_params:
:type kw_params: dict
"""
self.ec2 = boto.ec2.connect_to_region(region, **kw_params)
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4,079 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager.resolve_image_name | def resolve_image_name(self, image_name):
"""Look up an AMI for the connected region based on an image name.
:param image_name: The name of the image to resolve.
:type image_name: str
:return: The AMI for the given image.
:rtype: str
"""
# look at each scope in o... | python | def resolve_image_name(self, image_name):
"""Look up an AMI for the connected region based on an image name.
:param image_name: The name of the image to resolve.
:type image_name: str
:return: The AMI for the given image.
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4,080 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager.create_on_demand | def create_on_demand(self,
instance_type='default',
tags=None,
root_device_type='ebs',
size='default',
vol_type='gp2',
delete_on_termination=False):
"""Create one... | python | def create_on_demand(self,
instance_type='default',
tags=None,
root_device_type='ebs',
size='default',
vol_type='gp2',
delete_on_termination=False):
"""Create one... | [
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4,081 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager.create_spot_requests | def create_spot_requests(self,
price,
instance_type='default',
root_device_type='ebs',
size='default',
vol_type='gp2',
delete_on_termination=False... | python | def create_spot_requests(self,
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instance_type='default',
root_device_type='ebs',
size='default',
vol_type='gp2',
delete_on_termination=False... | [
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4,082 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager.check_spot_requests | def check_spot_requests(self, requests, tags=None):
"""Check status of one or more EC2 spot instance requests.
:param requests: List of EC2 spot instance request IDs.
:type requests: list
:param tags:
:type tags: dict
:return: List of boto.ec2.instance.Instance's created... | python | def check_spot_requests(self, requests, tags=None):
"""Check status of one or more EC2 spot instance requests.
:param requests: List of EC2 spot instance request IDs.
:type requests: list
:param tags:
:type tags: dict
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4,083 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager.cancel_spot_requests | def cancel_spot_requests(self, requests):
"""Cancel one or more EC2 spot instance requests.
:param requests: List of EC2 spot instance request IDs.
:type requests: list
"""
ec2_requests = self.retry_on_ec2_error(self.ec2.get_all_spot_instance_requests, request_ids=requests)
... | python | def cancel_spot_requests(self, requests):
"""Cancel one or more EC2 spot instance requests.
:param requests: List of EC2 spot instance request IDs.
:type requests: list
"""
ec2_requests = self.retry_on_ec2_error(self.ec2.get_all_spot_instance_requests, request_ids=requests)
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] | 7e80adc6ae92c6c1332d4c08473bb271fb3b6833 | https://github.com/MozillaSecurity/laniakea/blob/7e80adc6ae92c6c1332d4c08473bb271fb3b6833/laniakea/core/providers/ec2/manager.py#L223-L232 |
4,084 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager.create_spot | def create_spot(self,
price,
instance_type='default',
tags=None,
root_device_type='ebs',
size='default',
vol_type='gp2',
delete_on_termination=False,
timeout=No... | python | def create_spot(self,
price,
instance_type='default',
tags=None,
root_device_type='ebs',
size='default',
vol_type='gp2',
delete_on_termination=False,
timeout=No... | [
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:param price: Max price to pay for spot instance per hour.
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4,085 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager._scale_down | def _scale_down(self, instances, count):
"""Return a list of |count| last created instances by launch time.
:param instances: A list of instances.
:type instances: list
:param count: Number of instances to scale down.
:type count: integer
:return: List of instances to be... | python | def _scale_down(self, instances, count):
"""Return a list of |count| last created instances by launch time.
:param instances: A list of instances.
:type instances: list
:param count: Number of instances to scale down.
:type count: integer
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4,086 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager._configure_ebs_volume | def _configure_ebs_volume(self, vol_type, name, size, delete_on_termination):
"""Sets the desired root EBS size, otherwise the default EC2 value is used.
:param vol_type: Type of EBS storage - gp2 (SSD), io1 or standard (magnetic)
:type vol_type: str
:param size: Desired root EBS size.
... | python | def _configure_ebs_volume(self, vol_type, name, size, delete_on_termination):
"""Sets the desired root EBS size, otherwise the default EC2 value is used.
:param vol_type: Type of EBS storage - gp2 (SSD), io1 or standard (magnetic)
:type vol_type: str
:param size: Desired root EBS size.
... | [
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4,087 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager.stop | def stop(self, instances, count=0):
"""Stop each provided running instance.
:param count:
:param instances: A list of instances.
:type instances: list
"""
if not instances:
return
if count > 0:
instances = self._scale_down(instances, count... | python | def stop(self, instances, count=0):
"""Stop each provided running instance.
:param count:
:param instances: A list of instances.
:type instances: list
"""
if not instances:
return
if count > 0:
instances = self._scale_down(instances, count... | [
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4,088 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager.terminate | def terminate(self, instances, count=0):
"""Terminate each provided running or stopped instance.
:param count:
:param instances: A list of instances.
:type instances: list
"""
if not instances:
return
if count > 0:
instances = self._scale_... | python | def terminate(self, instances, count=0):
"""Terminate each provided running or stopped instance.
:param count:
:param instances: A list of instances.
:type instances: list
"""
if not instances:
return
if count > 0:
instances = self._scale_... | [
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4,089 | MozillaSecurity/laniakea | laniakea/core/providers/ec2/manager.py | EC2Manager.find | def find(self, instance_ids=None, filters=None):
"""Flatten list of reservations to a list of instances.
:param instance_ids: A list of instance ids to filter by
:type instance_ids: list
:param filters: A dict of Filter.N values defined in http://goo.gl/jYNej9
:type filters: dic... | python | def find(self, instance_ids=None, filters=None):
"""Flatten list of reservations to a list of instances.
:param instance_ids: A list of instance ids to filter by
:type instance_ids: list
:param filters: A dict of Filter.N values defined in http://goo.gl/jYNej9
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:param filters: A dict of Filter.N values defined in http://goo.gl/jYNej9
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4,090 | MozillaSecurity/laniakea | laniakea/core/common.py | ModuleLoader.load | def load(self, root, module_path, pkg_name):
"""Load modules dynamically.
"""
root = os.path.join(root, module_path)
import_name = os.path.join(pkg_name, module_path).replace(os.sep, '.')
for (_, name, _) in pkgutil.iter_modules([root]):
self.modules[name] = import_mo... | python | def load(self, root, module_path, pkg_name):
"""Load modules dynamically.
"""
root = os.path.join(root, module_path)
import_name = os.path.join(pkg_name, module_path).replace(os.sep, '.')
for (_, name, _) in pkgutil.iter_modules([root]):
self.modules[name] = import_mo... | [
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4,091 | MozillaSecurity/laniakea | laniakea/core/common.py | ModuleLoader.command_line_interfaces | def command_line_interfaces(self):
"""Return the CommandLine classes from each provider.
"""
interfaces = []
for _, module in self.modules.items():
for entry in dir(module):
if entry.endswith('CommandLine'):
interfaces.append((module, entry... | python | def command_line_interfaces(self):
"""Return the CommandLine classes from each provider.
"""
interfaces = []
for _, module in self.modules.items():
for entry in dir(module):
if entry.endswith('CommandLine'):
interfaces.append((module, entry... | [
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4,092 | MozillaSecurity/laniakea | laniakea/core/common.py | Common.pluralize | def pluralize(item):
"""Nothing to see here.
"""
assert isinstance(item, (int, list))
if isinstance(item, int):
return 's' if item > 1 else ''
if isinstance(item, list):
return 's' if len(item) > 1 else ''
return '' | python | def pluralize(item):
"""Nothing to see here.
"""
assert isinstance(item, (int, list))
if isinstance(item, int):
return 's' if item > 1 else ''
if isinstance(item, list):
return 's' if len(item) > 1 else ''
return '' | [
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4,093 | MozillaSecurity/laniakea | laniakea/core/providers/packet/manager.py | PacketConfiguration.validate | def validate(self):
"""Perform some basic configuration validation.
"""
if not self.conf.get('auth_token'):
raise PacketManagerException('The auth token for Packet is not defined but required.')
if not self.conf.get('projects'):
raise PacketManagerException('Requi... | python | def validate(self):
"""Perform some basic configuration validation.
"""
if not self.conf.get('auth_token'):
raise PacketManagerException('The auth token for Packet is not defined but required.')
if not self.conf.get('projects'):
raise PacketManagerException('Requi... | [
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4,094 | MozillaSecurity/laniakea | laniakea/core/providers/packet/manager.py | PacketManager.print_projects | def print_projects(self, projects):
"""Print method for projects.
"""
for project in projects:
print('{}: {}'.format(project.name, project.id)) | python | def print_projects(self, projects):
"""Print method for projects.
"""
for project in projects:
print('{}: {}'.format(project.name, project.id)) | [
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4,095 | MozillaSecurity/laniakea | laniakea/core/providers/packet/manager.py | PacketManager.print_operating_systems | def print_operating_systems(self, operating_systems):
"""Print method for operating systems.
"""
for _os in operating_systems:
print('{}: {}'.format(_os.name, _os.slug)) | python | def print_operating_systems(self, operating_systems):
"""Print method for operating systems.
"""
for _os in operating_systems:
print('{}: {}'.format(_os.name, _os.slug)) | [
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4,096 | MozillaSecurity/laniakea | laniakea/core/providers/packet/manager.py | PacketManager.print_plans | def print_plans(self, plans):
"""Print method for plans.
"""
for plan in plans:
print('Name: {} "{}" Price: {} USD'.format(plan.name, plan.slug, plan.pricing['hour']))
self.pprint(plan.specs)
print('\n') | python | def print_plans(self, plans):
"""Print method for plans.
"""
for plan in plans:
print('Name: {} "{}" Price: {} USD'.format(plan.name, plan.slug, plan.pricing['hour']))
self.pprint(plan.specs)
print('\n') | [
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4,097 | MozillaSecurity/laniakea | laniakea/core/providers/packet/manager.py | PacketManager.print_facilities | def print_facilities(self, facilities):
"""Print method for facilities.
"""
for facility in facilities:
print('{} - ({}): {}'.format(facility.code, facility.name, ",".join(facility.features))) | python | def print_facilities(self, facilities):
"""Print method for facilities.
"""
for facility in facilities:
print('{} - ({}): {}'.format(facility.code, facility.name, ",".join(facility.features))) | [
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4,098 | MozillaSecurity/laniakea | laniakea/core/providers/packet/manager.py | PacketManager.list_devices | def list_devices(self, project_id, conditions=None, params=None):
"""Retrieve list of devices in a project by one of more conditions.
"""
default_params = {'per_page': 1000}
if params:
default_params.update(params)
data = self.api('projects/%s/devices' % project_id, p... | python | def list_devices(self, project_id, conditions=None, params=None):
"""Retrieve list of devices in a project by one of more conditions.
"""
default_params = {'per_page': 1000}
if params:
default_params.update(params)
data = self.api('projects/%s/devices' % project_id, p... | [
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4,099 | MozillaSecurity/laniakea | laniakea/core/providers/packet/manager.py | PacketManager.print_devices | def print_devices(self, devices):
"""Print method for devices.
"""
for device in devices:
print('ID: {} OS: {} IP: {} State: {} ({}) Tags: {}'
.format(device.id,
device.operating_system.slug,
self.get_public_ip(dev... | python | def print_devices(self, devices):
"""Print method for devices.
"""
for device in devices:
print('ID: {} OS: {} IP: {} State: {} ({}) Tags: {}'
.format(device.id,
device.operating_system.slug,
self.get_public_ip(dev... | [
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"devices",
":",
"print",
"(",
"'ID: {} OS: {} IP: {} State: {} ({}) Tags: {}'",
".",
"format",
"(",
"device",
".",
"id",
",",
"device",
".",
"operating_system",
".",
"slug",
... | Print method for devices. | [
"Print",
"method",
"for",
"devices",
"."
] | 7e80adc6ae92c6c1332d4c08473bb271fb3b6833 | https://github.com/MozillaSecurity/laniakea/blob/7e80adc6ae92c6c1332d4c08473bb271fb3b6833/laniakea/core/providers/packet/manager.py#L146-L156 |
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