def transform(name=None, input_file=None):
"""
function is responsible for transforming raw datasets into Collections
"""
if not name: # user has not provided a scraper name to get collections with
logger.error('Scraper/Office name not provided. Cannot generate collections')
sys.exit(1)
try:
# load the Graph representing the deduplicated scraped datasets
GraphWrapper.load_graph(file_dir_path=Path(OUTPUT_DIR, 'graphs', name),
file_stem_name=f'{name}.deduplicate')
except:
# load the Graph representing the scraped datasets
GraphWrapper.load_graph(file_dir_path=Path(OUTPUT_DIR, 'graphs', name),
file_stem_name=name)
# get the loaded graph
graph = GraphWrapper.get_graph()
# identify collections within the graph
identify_collections_within_graph(graph)
# link dataset vertices to their appropriate collection(s) within the graph
link_datasets_to_collections_in_graph(graph)
# write the identified collections to the raw dataset files
add_collections_to_raw_datasets(graph=graph,
output_dir=OUTPUT_DIR)
# write the graph to files
# this method is explicitly thread/proccess safe, so no need for lock
GraphWrapper.write_graph(file_dir_path=Path(os.getenv('ED_OUTPUT_PATH'),
"graphs", f"{name}"),
file_stem_name=f'{name}.collections')
# create the page legend file for this graph
GraphWrapper.create_graph_page_legend(file_dir_path=Path(os.getenv('ED_OUTPUT_PATH'),
"graphs", f"{name}"),
file_stem_name=f'{name}.collections')
# create the collections.json file
collections_list = [] # holds the list of collections acquired from graph
with graph.graph_lock:
for collection in graph.vs.select(is_collection_eq=True, name_ne='base_vertex'):
collections_list.append({'collection_id': collection['collection_id'],
'collection_title': collection['title'],
'collection_url': collection['name']})
# get a list of non-duplicate collections
collections_list = get_distinct_collections_from(collections_list,
min_occurence_counter=1)
# get the path were the gotten Collections will be saved to on local disk
file_output_path = f'{CURRENT_TRANSFORMER_OUTPUT_DIR}/{(name or "all")}.collections.json'
# write to file the collections gotten from 'name' scraped output
h.write_file(file_output_path, collections_list)
# write file the collections gotten from 'name' scraped out to S3 bucket
h.upload_to_s3_if_configured(file_output_path,
f'{(name or "all")}.collections.json')
def transform(name=None, input_file=None):
"""
function is responsible for transofrming raw datasets into Sources
"""
if input_file is None: # no input file specified
file_list = h.traverse_output(
name) # run through all the files in 'name' directory
else:
try:
with open(input_file, 'r') as fp:
file_list = [line.rstrip() for line in fp]
except:
logger.warning(
f'Cannot read from list of output files at {input_file}, falling back to all collected data!'
)
file_list = h.traverse_output(name)
sources_list = [
] # holds the list of sources acquired from 'name' scraper directory
# loop through filepath in file list
for file_path in file_list:
# read the json data in each filepath
data = h.read_file(file_path)
if not data: # if data is None
continue
# retrieve source from dataset
source = extract_source_from(dataset=data, use_key='collection')
if not source: # source could not be retrieved
continue
# add source to list
sources_list.append(source)
# get a list of non-duplicate Sources
sources_list = get_distinct_sources_from(sources_list,
min_occurence_counter=2)
# get the path were the gotten Sources will be saved to on local disk
file_output_path = f'{CURRENT_TRANSFORMER_OUTPUT_DIR}/{(name or "all")}.sources.json'
# write to file the Sources gotten from 'name' scraped output
h.write_file(file_output_path, sources_list)
# write file the Sources gotten from 'name' scraped out to S3 bucket
h.upload_to_s3_if_configured(file_output_path,
f'{(name or "all")}.sources.json')
def transform(name, input_file=None):
if input_file is None:
file_list = traverse_output(name)
else:
try:
with open(input_file, 'r') as fp:
file_list = [line.rstrip() for line in fp]
except:
logger.warn(
f'Cannot read from list of output files at {input_file}, falling back to all collected data!'
)
file_list = traverse_output(name)
logger.debug(f'{len(file_list)} files to transform.')
catalog = Catalog()
catalog.catalog_id = "datopian_data_json_" + name
datasets_number = 0
resources_number = 0
for file_path in file_list:
data = read_file(file_path)
if not data:
continue
dataset = _transform_scraped_dataset(data, name)
catalog.datasets.append(dataset)
datasets_number += 1
resources_number += len(dataset.distribution)
logger.debug('{} datasets transformed.'.format(datasets_number))
logger.debug('{} resources transformed.'.format(resources_number))
output_path = h.get_output_path('datajson')
file_path = os.path.join(output_path, f'{name}.data.json')
with open(file_path, 'w') as output:
output.write(catalog.dump())
logger.debug(f'Output file: {file_path}')
h.upload_to_s3_if_configured(file_path, f'{name}.data.json')
def transform(name=None,
input_file=None,
use_raw_datasets=False) -> pd.DataFrame:
""" function transforms the datajson/datasets into
a dataframe/csv containig data to be used for RAG analyses on
the efficacy of the scraping toolkit to get viable/usable structured data from
the unstructured data source.
The function by default operates on/utilises datajson i.e.
the json that is ready to be ingested by the ckan harvester;
However, setting 'use_raw_datasets' to True means the function will
operate on the raw, parsed data which was scraped from the data source.
PARAMETERS
- name: if provided must correspond to the name of a scraper.
if 'use_raw_datasets' is False, file with the format '<name>.data.json'
will be located in the datajson subdirectory of 'ED_OUTPUT_PATH/transformers'
and read.
if 'use_raw_datasets' is True, dataset files contained in the 'name'
scrapers subdirectory of the 'ED_OUTPUT_PATH/scrapers' will be read
input_file: if provided mut be a file with list of datajson or dataset files
to read.
If no parameters are provided, which is the default behaviour;
then all datajson files contained in datajson subdirectory of
'ED_OUTPUT_PATH/transformers' will be read.
function returns the DataFrame containing the transformed datajson/dataset files
"""
file_list = [] # holds the list of files which contain datajson/dataset
datasets_list = [] # holds the data jsons gotten from files
if use_raw_datasets == True: # work on raw datasets
if not input_file: # no input file provided
# loop over directory structure
if name:
# loop over <name> scraper output e.g nces
file_list = h.traverse_output(name)
# datasets = list of all <name> files
else:
# loop over everything
file_list = h.traverse_output(None)
# datasets = list of all JSON files
else: # input file provided
# read input_file, which is a list of files
with open(input_file, 'r') as fp:
try:
file_list = [line.rstrip() for line in fp]
except Exception:
logger.warning(
f'Cannot read from list of output files at {input_file}, falling back to all collected data!'
)
file_list = h.traverse_output(None)
else: # work with processed/transformed datajson
if not input_file: # no input file provided
if name: # name of processed datajson is provided so get the file path
file_list.append(
Path(h.get_output_path('datajson'), f'{name}.data.json'))
else: # name of processed datajson not provided
file_list.extend(
Path(h.get_output_path('datajson')).glob('*.json'))
else: # input file provided
# read input_file, which is a list of files
with open(input_file, 'r') as fp:
try:
file_list = [line.rstrip() for line in fp]
except Exception:
logger.warning(
f'Cannot read from list of output files at {input_file}, falling back to all collected data!'
)
file_list.extend(
Path(h.get_output_path('datajson')).glob('*.json'))
if use_raw_datasets == True: # work on raw datasets
# read the contents in file_list
for file_path in file_list:
# read json from the file using helper
data = h.read_file(file_path)
# compute the weight score of the dataset
compute_score(data, append_score=True, use_raw_datasets=True)
datasets_list.append(data)
else: # work with processed json data
# read the contents in the file_list
for file_path in file_list:
# read json from file using helper function
data = h.read_file(file_path)
for dataset_dict in data.get(
'dataset',
[]): # loop through the datasets contained in data
# compute the weighted score of the dataset
compute_score(dataset_dict,
append_score=True,
use_raw_datasets=False)
datasets_list.append(dataset_dict)
if use_raw_datasets == True: # work on raw datasets
# map the datasets to pandas format
dataset_rows_list = map(lambda dataset: [dataset.get('publisher'),\
dataset.get('source_url'), \
dataset.get('_weighted_score'), \
dataset.get('_weighted_score_ratio')],
datasets_list)
else: # work on processed datajson
# map the dataset to pandas format
dataset_rows_list = map(lambda dataset: [dataset.get('publisher')['name'],\
dataset.get('scraped_from'), \
dataset.get('_weighted_score'), \
dataset.get('_weighted_score_ratio')],
datasets_list)
# create the pandas df
weighted_datasets_scores_df = pd.DataFrame(dataset_rows_list,
columns=[
'publisher', 'source url',
'weighted score',
'weighted score ratio'
])
# create a df that incorporates domain info
weighted_datasets_scores_df2 = pd.DataFrame(columns=['domain'])
weighted_datasets_scores_df2['domain'] = weighted_datasets_scores_df.\
apply(lambda row: urllib.parse.\
urlparse(row['source url']).hostname.\
replace('www2.', 'www.').replace('www.', ''), axis=1)
weighted_datasets_scores_df2['publisher'] = weighted_datasets_scores_df[
'publisher']
weighted_datasets_scores_df2['source url'] = weighted_datasets_scores_df[
'source url']
weighted_datasets_scores_df2[
'weighted score'] = weighted_datasets_scores_df['weighted score']
weighted_datasets_scores_df2[
'weighted score ratio'] = weighted_datasets_scores_df[
'weighted score ratio']
# create the output csv file name
output_dated_dir = os.path.join(
OUTPUT_DIR, f'{dt.now().year}-{dt.now().month}-{dt.now().day}')
Path(output_dated_dir).mkdir(parents=True, exist_ok=True)
if use_raw_datasets == True: # use raw datasets
output_filename = "datasets_weighted_scores_{}_raw.csv".format(
name or "all")
else: # use processed datajson
output_filename = "datasets_weighted_scores_{}.csv".format(name
or "all")
# create the fullpath weer file will be written
fullpath = os.path.join(OUTPUT_DIR, output_filename)
# write the dataframe to csv
weighted_datasets_scores_df2.to_csv(fullpath, index=False)
weighted_datasets_scores_df2.to_csv(os.path.join(output_dated_dir,
output_filename),
index=False)
# write the csv to S3 bucket
h.upload_to_s3_if_configured(fullpath, f'{output_filename}')
return weighted_datasets_scores_df2
def transform(name, input_file=None):
if input_file is None:
file_list = traverse_output(name)
else:
try:
with open(input_file, 'r') as fp:
file_list = [line.rstrip() for line in fp]
except:
logger.warning(
f'Cannot read from list of output files at {input_file}, falling back to all collected data!'
)
file_list = traverse_output(name)
logger.debug(f'{len(file_list)} files to transform.')
catalog = Catalog()
catalog.catalog_id = "datopian_data_json_" + (name or 'all')
# keep track/stata for item transformed
datasets_number = 0
resources_number = 0
sources_number = 0
collections_number = 0
# loop through the list of filepaths to be transformed
for file_path in file_list:
data = read_file(file_path)
if not data:
continue
dataset = _transform_scraped_dataset(data, name)
if not dataset: # no dataset was returned (i.e. dataset probably marked for removal)
continue
catalog.datasets.append(dataset)
datasets_number += 1
resources_number += len(dataset.distribution)
# TODO WORK FROM BELOW HERE
# get the list of Sources for this catalog
catalog_sources = list()
try:
# read the list of preprocessed (but still 'raw') Sources from file
catalog_sources = read_file(
f"{h.get_output_path('sources')}/{(name or 'all')}.sources.json")
# transform the list of preprocessed Sources to a list of Source objects acceptable for the catalog object
catalog_sources = _transform_preprocessed_sources(catalog_sources)
except:
logger.warning(
f'"sources transformer" output file ({(name or "all")}.sources.json) not found. This datajson output will have no "source" field'
)
# add the list of Source objects to the catalog
catalog.sources = catalog_sources or []
# update the number fo transformed Sources
sources_number = len(catalog_sources or [])
# get the list of Collections for this catalog
catalog_collections = list()
try:
# read the list of preprocessed (but still 'raw') Collections from file
catalog_collections = read_file(
f"{h.get_output_path('collections')}/{(name or 'all')}.collections.json"
)
# transform the list of preprocessed Collections to a list of Collection objects acceptable for the catalog object
catalog_collections = _transform_preprocessed_collections(
catalog_collections)
except:
logger.warning(
f'"sources transformer" output file ({(name or "all")}.collections.json) not found. This datajson output will have no "collection" field'
)
# add the list of Collection objects to the catalog
catalog.collections = catalog_collections or []
# update the number fo transformed Collections
collections_number = len(catalog_collections or [])
# validate the catalog object
if not catalog.validate_catalog(pls_fix=True):
logger.error(f"catalog validation Failed! Ending transform process")
return
logger.debug('{} Sources transformed.'.format(sources_number))
logger.debug('{} Collections transformed.'.format(collections_number))
logger.debug('{} datasets transformed.'.format(datasets_number))
logger.debug('{} resources transformed.'.format(resources_number))
output_path = h.get_output_path('datajson')
file_path = os.path.join(output_path, f'{(name or "all")}.data.json')
with open(file_path, 'w') as output:
output.write(catalog.dump())
logger.debug(f'Output file: {file_path}')
h.upload_to_s3_if_configured(file_path, f'{(name or "all")}.data.json')
def compare():
air_csv = './tools/data/AIR.csv'
summary = Summary()
if path.exists('./output/deduplicated_all.lst'):
output_list_file = './output/deduplicated_all.lst'
else:
output_list_file = None
try:
print("Generating AIR data frame... ", end = '', flush=True)
summary.air_df = pd.read_csv(air_csv)
print('done.')
print("Generating Datopian data frame... ", end = '', flush=True)
try:
summary.out_df = summary.generate_output_df(use_dump=True, output_list_file=output_list_file)
except:
summary.out_df = summary.generate_output_df(use_dump=False, output_list_file=output_list_file)
print('done.')
except Exception as e:
print(e)
summary.calculate_totals()
# if name == 'nt':
# _ = system('cls')
# else:
# _ = system('clear')
statistics = {
'total': {
'datopian': {
'datasets': int(summary.total['out_datasets']),
'resources': int(summary.total['out_resources']),
'pages': summary.get_resources_dict('out', 'source_url'),
'datasets_by_office': summary.get_datasets_dict(),
'resources_by_office': summary.get_resources_dict('out', 'url')
},
'air': {
'datasets': 0,
'resources': int(summary.total['air_resources']),
'pages': summary.get_resources_dict('air', 'source_url'),
'resources_by_office': summary.get_resources_dict('air', 'url')
}
},
'intersections': {
'datopian_only': {
'pages': int(summary.get_values_only_in('out', 'source_url')),
'resources': int(summary.get_values_only_in('out', 'url'))
},
'air_only': {
'pages': int(summary.get_values_only_in('air', 'source_url')),
'resources': int(summary.get_values_only_in('air', 'url'))
}
}
}
print(
f"Total number of raw datasets: {summary.total['out_datasets']}\n"
f"\n---\n\n"
f"Total number of raw datasets per scraper: \n\n{summary.get_datasets_table()}\n"
f"\n---\n\n"
f"Total number of resources:\n"
f" AIR: {summary.total['air_resources']}\n"
f"Datopian: {summary.total['out_resources']}\n"
f"\n---\n\n"
f"Total number of resources by office: \n{summary.get_resources_table(column='url')}\n"
f"\n---\n\n"
f"Total number of pages by office: \n{summary.get_resources_table(column='source_url')}\n"
f"\n---\n\n"
f"Pages scraped by AIR only: {summary.get_values_only_in('air', 'source_url')}\n"
f"Pages scraped by Datopian only: {summary.get_values_only_in('out', 'source_url')}\n"
f"\n---\n"
f"Resources collected by AIR only: {summary.get_values_only_in('air', 'url')}\n"
f"Resources collected by Datopian only: {summary.get_values_only_in('out', 'url')}\n"
f"\n---\n\n"
f"CSV file with all the resources was dumped in {summary.dump('./output/datopian.csv')}"
)
with open('./output/statistics.json', 'w') as stats_file:
json.dump(statistics, stats_file)
upload_to_s3_if_configured('./output/statistics.json', 'compare-statistics.json')
