def __init__(self, name, spec, item_schemas, all_extractors, **kw):
super(IblSpider, self).__init__(name, **kw)
default_item = spec['scrapes']
self._default_schema = item_schemas[default_item]
if not self._default_schema:
self.log("Scraping unknown default item schema: %s" % default_item, \
log.WARNING)
self._item_template_pages = sorted((
[t.get('scrapes', default_item), dict_to_page(t, 'annotated_body'),
t.get('extractors', [])] \
for t in spec['templates'] if t.get('page_type', 'item') == 'item'
), key=lambda pair: pair[0])
# generate ibl extractor for links pages
_links_pages = [dict_to_page(t, 'annotated_body')
for t in spec['templates'] if t.get('page_type') == 'links']
_links_item_descriptor = create_slybot_item_descriptor({'id': "_links", 'properties': ()})
self._links_ibl_extractor = InstanceBasedLearningExtractor([(t, _links_item_descriptor) for t in _links_pages]) \
if _links_pages else None
self._ipages = [page for _, page, _ in self._item_template_pages]
self._fpages = [
dict_to_page(t, 'annotated_body')
for t in spec['templates'] if t.get('page_type', 'item') == 'form'
]
self.start_urls = self.start_urls or spec.get('start_urls')
if isinstance(self.start_urls, basestring):
self.start_urls = self.start_urls.splitlines()
self.link_extractor = LinkExtractor()
self.allowed_domains = self._get_allowed_domains(self._ipages)
self.build_url_filter(spec)
default_item_cls = get_iblitem_class(self._default_schema)
default_item_descriptor = create_slybot_item_descriptor(self._default_schema)
self.itemcls_info = {}
for itemclass_name, triplets in itertools.groupby(self._item_template_pages, operator.itemgetter(0)):
page_extractors_pairs = map(operator.itemgetter(1, 2), triplets)
schema = item_schemas[itemclass_name]
item_cls = get_iblitem_class(schema) if schema else default_item_cls
page_descriptor_pairs = []
for page, extractors in page_extractors_pairs:
item_descriptor = create_slybot_item_descriptor(schema) if schema else default_item_descriptor
apply_extractors(item_descriptor, extractors, all_extractors)
page_descriptor_pairs.append((page, item_descriptor))
extractor = InstanceBasedLearningExtractor(page_descriptor_pairs)
self.itemcls_info[itemclass_name] = {
'class': item_cls,
'descriptor': item_descriptor,
'extractor': extractor,
}
def __init__(self, name, spec, item_schemas, all_extractors, **kw):
super(IblSpider, self).__init__(name, **kw)
self._item_template_pages = sorted((
[t['scrapes'], dict_to_page(t, 'annotated_body'),
t.get('extractors', [])] \
for t in spec['templates'] if t.get('page_type', 'item') == 'item'
), key=lambda pair: pair[0])
# generate ibl extractor for links pages
_links_pages = [dict_to_page(t, 'annotated_body')
for t in spec['templates'] if t.get('page_type') == 'links']
_links_item_descriptor = create_slybot_item_descriptor({'fields': {}})
self._links_ibl_extractor = InstanceBasedLearningExtractor([(t, _links_item_descriptor) for t in _links_pages]) \
if _links_pages else None
self._ipages = [page for _, page, _ in self._item_template_pages]
self.start_urls = self.start_urls or spec.get('start_urls')
if isinstance(self.start_urls, basestring):
self.start_urls = self.start_urls.splitlines()
self.html_link_extractor = HtmlLinkExtractor()
self.rss_link_extractor = RssLinkExtractor()
self.allowed_domains = self._get_allowed_domains(self._ipages)
self.build_url_filter(spec)
self.itemcls_info = {}
for itemclass_name, triplets in itertools.groupby(self._item_template_pages, operator.itemgetter(0)):
page_extractors_pairs = map(operator.itemgetter(1, 2), triplets)
schema = item_schemas[itemclass_name]
item_cls = get_iblitem_class(schema)
page_descriptor_pairs = []
for page, template_extractors in page_extractors_pairs:
item_descriptor = create_slybot_item_descriptor(schema)
apply_extractors(item_descriptor, template_extractors, all_extractors)
page_descriptor_pairs.append((page, item_descriptor))
extractor = InstanceBasedLearningExtractor(page_descriptor_pairs)
self.itemcls_info[itemclass_name] = {
'class': item_cls,
'descriptor': item_descriptor,
'extractor': extractor,
}
self.login_requests = []
self.form_requests = []
for rdata in spec.get("init_requests", []):
if rdata["type"] == "login":
request = Request(url=rdata.pop("loginurl"), meta=rdata,
callback=self.parse_login_page, dont_filter=True)
self.login_requests.append(request)
elif rdata["type"] == "form":
self.generic_form = GenericForm(**kw)
self.form_requests.append(self.get_generic_form_start_request(rdata))
def __init__(self, name, spec, item_schemas, all_extractors, **kw):
super(IblSpider, self).__init__(name, **kw)
default_item = spec['scrapes']
self._default_schema = item_schemas[default_item]
if not self._default_schema:
self.log("Scraping unknown default item schema: %s" % default_item, \
log.WARNING)
self._item_template_pages = sorted((
[t.get('scrapes', default_item), dict_to_page(t, 'annotated_body'),
t.get('extractors', [])] \
for t in spec['templates'] if t.get('page_type', 'item') == 'item'
), key=lambda pair: pair[0])
# generate ibl extractor for links pages
_links_pages = [
dict_to_page(t, 'annotated_body') for t in spec['templates']
if t.get('page_type') == 'links'
]
_links_item_descriptor = create_slybot_item_descriptor({
'id': "_links",
'properties': ()
})
self._links_ibl_extractor = InstanceBasedLearningExtractor([(t, _links_item_descriptor) for t in _links_pages]) \
if _links_pages else None
self._ipages = [page for _, page, _ in self._item_template_pages]
self.start_urls = self.start_urls or spec.get('start_urls')
if isinstance(self.start_urls, basestring):
self.start_urls = self.start_urls.splitlines()
self.link_extractor = LinkExtractor()
self.allowed_domains = self._get_allowed_domains(self._ipages)
self.build_url_filter(spec)
default_item_cls = get_iblitem_class(self._default_schema)
default_item_descriptor = create_slybot_item_descriptor(
self._default_schema)
self.itemcls_info = {}
for itemclass_name, triplets in itertools.groupby(
self._item_template_pages, operator.itemgetter(0)):
page_extractors_pairs = map(operator.itemgetter(1, 2), triplets)
schema = item_schemas[itemclass_name]
item_cls = get_iblitem_class(
schema) if schema else default_item_cls
page_descriptor_pairs = []
for page, extractors in page_extractors_pairs:
item_descriptor = create_slybot_item_descriptor(
schema) if schema else default_item_descriptor
apply_extractors(item_descriptor, extractors, all_extractors)
page_descriptor_pairs.append((page, item_descriptor))
extractor = InstanceBasedLearningExtractor(page_descriptor_pairs)
self.itemcls_info[itemclass_name] = {
'class': item_cls,
'descriptor': item_descriptor,
'extractor': extractor,
}
self.login_requests = []
self.form_requests = []
for rdata in spec.get("init_requests", []):
if rdata["type"] == "login":
request = Request(url=rdata.pop("loginurl"),
meta=rdata,
callback=self.parse_login_page)
self.login_requests.append(request)
elif rdata["type"] == "form":
request = Request(url=rdata.pop("form_url"),
meta=rdata,
callback=self.parse_form_page)
self.form_requests.append(request)
def __init__(self, name, spec, item_schemas, all_extractors, **kw):
super(IblSpider, self).__init__(name, **kw)
self._item_template_pages = sorted((
[t['scrapes'], dict_to_page(t, 'annotated_body'),
t.get('extractors', [])] \
for t in spec['templates'] if t.get('page_type', 'item') == 'item'
), key=lambda pair: pair[0])
# generate ibl extractor for links pages
_links_pages = [dict_to_page(t, 'annotated_body')
for t in spec['templates'] if t.get('page_type') == 'links']
_links_item_descriptor = create_slybot_item_descriptor({'fields': {}})
self._links_ibl_extractor = InstanceBasedLearningExtractor([(t, _links_item_descriptor) for t in _links_pages]) \
if _links_pages else None
self._ipages = [page for _, page, _ in self._item_template_pages]
self.start_urls = self.start_urls or spec.get('start_urls')
if isinstance(self.start_urls, basestring):
self.start_urls = self.start_urls.splitlines()
self.html_link_extractor = HtmlLinkExtractor()
self.rss_link_extractor = RssLinkExtractor()
self.allowed_domains = spec.get('allowed_domains',
self._get_allowed_domains(self._ipages))
if not self.allowed_domains:
self.allowed_domains = None
self.build_url_filter(spec)
self.itemcls_info = {}
for itemclass_name, triplets in itertools.groupby(self._item_template_pages, operator.itemgetter(0)):
page_extractors_pairs = map(operator.itemgetter(1, 2), triplets)
schema = item_schemas[itemclass_name]
item_cls = get_iblitem_class(schema)
page_descriptor_pairs = []
for page, template_extractors in page_extractors_pairs:
item_descriptor = create_slybot_item_descriptor(schema)
apply_extractors(item_descriptor, template_extractors, all_extractors)
page_descriptor_pairs.append((page, item_descriptor))
extractor = InstanceBasedLearningExtractor(page_descriptor_pairs)
self.itemcls_info[itemclass_name] = {
'class': item_cls,
'descriptor': item_descriptor,
'extractor': extractor,
}
self.login_requests = []
self.form_requests = []
for rdata in spec.get("init_requests", []):
if rdata["type"] == "login":
request = Request(url=rdata.pop("loginurl"), meta=rdata,
callback=self.parse_login_page, dont_filter=True)
self.login_requests.append(request)
elif rdata["type"] == "form":
self.generic_form = GenericForm(**kw)
self.form_requests.append(self.get_generic_form_start_request(rdata))
def __init__(self, name, spec, item_schemas, all_extractors, **kw):
super(IblSpider, self).__init__(name, **kw)
default_item = spec["scrapes"]
self._default_schema = item_schemas[default_item]
if not self._default_schema:
self.log("Scraping unknown default item schema: %s" % default_item, log.WARNING)
self._item_template_pages = sorted(
(
[t.get("scrapes", default_item), dict_to_page(t, "annotated_body"), t.get("extractors", [])]
for t in spec["templates"]
if t.get("page_type", "item") == "item"
),
key=lambda pair: pair[0],
)
# generate ibl extractor for links pages
_links_pages = [dict_to_page(t, "annotated_body") for t in spec["templates"] if t.get("page_type") == "links"]
_links_item_descriptor = create_slybot_item_descriptor({"id": "_links", "properties": ()})
self._links_ibl_extractor = (
InstanceBasedLearningExtractor([(t, _links_item_descriptor) for t in _links_pages])
if _links_pages
else None
)
self._ipages = [page for _, page, _ in self._item_template_pages]
self._fpages = [
dict_to_page(t, "annotated_body") for t in spec["templates"] if t.get("page_type", "item") == "form"
]
self.start_urls = self.start_urls or spec.get("start_urls")
if isinstance(self.start_urls, basestring):
self.start_urls = self.start_urls.splitlines()
self.link_extractor = LinkExtractor()
self.allowed_domains = self._get_allowed_domains(self._ipages)
self.build_url_filter(spec)
default_item_cls = get_iblitem_class(self._default_schema)
default_item_descriptor = create_slybot_item_descriptor(self._default_schema)
self.itemcls_info = {}
for itemclass_name, triplets in itertools.groupby(self._item_template_pages, operator.itemgetter(0)):
page_extractors_pairs = map(operator.itemgetter(1, 2), triplets)
schema = item_schemas[itemclass_name]
item_cls = get_iblitem_class(schema) if schema else default_item_cls
page_descriptor_pairs = []
for page, extractors in page_extractors_pairs:
item_descriptor = create_slybot_item_descriptor(schema) if schema else default_item_descriptor
apply_extractors(item_descriptor, extractors, all_extractors)
page_descriptor_pairs.append((page, item_descriptor))
extractor = InstanceBasedLearningExtractor(page_descriptor_pairs)
self.itemcls_info[itemclass_name] = {
"class": item_cls,
"descriptor": item_descriptor,
"extractor": extractor,
}
self._itemversion_cache = {}
def __init__(self, name, spec, item_schemas, all_extractors, **kw):
super(IblSpider, self).__init__(name, **kw)
default_item = spec['scrapes']
self._default_schema = item_schemas[default_item]
if not self._default_schema:
self.log("Scraping unknown default item schema: %s" % default_item, \
log.WARNING)
self._item_template_pages = sorted((
[t.get('scrapes', default_item), dict_to_page(t, 'annotated_body'),
t.get('extractors', [])] \
for t in spec['templates'] if t.get('page_type', 'item') == 'item'
), key=lambda pair: pair[0])
# generate ibl extractor for links pages
_links_pages = [dict_to_page(t, 'annotated_body')
for t in spec['templates'] if t.get('page_type') == '_links']
_links_item_descriptor = create_slybot_item_descriptor({'id': "_links", 'properties': ()})
self._links_ibl_extractor = InstanceBasedLearningExtractor([(t, _links_item_descriptor) for t in _links_pages]) \
if _links_pages else None
self._ipages = [page for _, page, _ in self._item_template_pages]
self._fpages = [
dict_to_page(t, 'annotated_body')
for t in spec['templates'] if t.get('page_type', 'item') == 'form'
]
self._start_urls = spec.get('start_urls')
self.link_extractor = LinkExtractor()
self.allowed_domains = self._get_allowed_domains(self._ipages)
# make a filter for links
respect_nofollow = spec.get('respect_nofollow', True)
patterns = spec.get('follow_patterns')
if patterns:
pattern = patterns[0] if len(patterns) == 1 else "(?:%s)" % '|'.join(patterns)
follow_pattern = re.compile(pattern)
if respect_nofollow:
url_filterf = lambda x: follow_pattern.search(x.url) and not x.nofollow
else:
url_filterf = lambda x: follow_pattern.search(x.url)
elif respect_nofollow:
url_filterf = lambda x: not x.nofollow
else:
url_filterf = bool
# apply exclude patterns
exclude_patterns = spec.get('exclude_patterns')
if exclude_patterns:
pattern = exclude_patterns[0] if len(exclude_patterns) == 1 else "(?:%s)" % '|'.join(exclude_patterns)
exclude_pattern = re.compile(pattern)
self.url_filterf = lambda x: not exclude_pattern.search(x.url) and url_filterf(x)
else:
self.url_filterf = url_filterf
default_item_cls = get_iblitem_class(self._default_schema)
default_item_descriptor = create_slybot_item_descriptor(self._default_schema)
self.itemcls_info = {}
for itemclass_name, triplets in itertools.groupby(self._item_template_pages, operator.itemgetter(0)):
page_extractors_pairs = map(operator.itemgetter(1, 2), triplets)
schema = item_schemas[itemclass_name]
item_cls = get_iblitem_class(schema) if schema else default_item_cls
page_descriptor_pairs = []
for page, extractors in page_extractors_pairs:
item_descriptor = create_slybot_item_descriptor(schema) if schema else default_item_descriptor
apply_extractors(item_descriptor, extractors, all_extractors)
page_descriptor_pairs.append((page, item_descriptor))
extractor = InstanceBasedLearningExtractor(page_descriptor_pairs)
self.itemcls_info[itemclass_name] = {
'class': item_cls,
'descriptor': item_descriptor,
'extractor': extractor,
}
self._itemversion_cache = {}
