Welcome to piedomains’s documentation!
Contents:
piedomains: predict the kind of content hosted by a domain based on domain name and content
The package infers the kind of content hosted by a domain using the domain name, the textual content, and the screenshot of the homepage.
We use domain category labels from Shallalist and build our own training dataset by scraping and taking screenshots of the homepage. The final dataset used to train the model is posted on the Harvard Dataverse. Python notebooks used to build the models can be found here and the model files can be found here
Installation
We strongly recommend installing piedomains inside a Python virtual environment (see venv documentation)
pip install piedomains
General API
domain.pred_shalla_cat_with_text(input)
What it does:
Predicts the kind of content hosted by a domain based on the domain name and the HTML of the homepage.
The function can use locally stored HTML files or fetch fresh HTML files.
If you specify a local folder, the function will look for HTML files corresponding to the domain.
The HTML files must be stored as domainname.html.
The function returns a pandas dataframe with predicted labels and corresponding probabilities.
Inputs:
input: list of domains. Either input or html_path must be specified.
html_path: path to the folder where the HTMLs are stored. Either input or html_path must be specified.
latest: use the latest model. The default is True.
Note: The function will by default look for a html folder on the same level as model files.
Output:
Returns a pandas dataframe with the predicted labels and probabilities
Sample usage:
from piedomains import domain domains = [ "forbes.com", "xvideos.com", "last.fm", "facebook.com", "bellesa.co", "marketwatch.com" ] # with only domains result = domain.pred_shalla_cat_with_text(domains) # with html path where htmls are stored (offline mode) result = domain.pred_shalla_cat_with_text(html_path="path/to/htmls") # with domains and html path, html_path will be used to store htmls result = domain.pred_shalla_cat_with_text(domains, html_path="path/to/htmls") print(result)Sample output:
domain text_label text_prob \ 0 xvideos.com porn 0.918919 1 marketwatch.com finance 0.627119 2 forbes.com news 0.575000 3 bellesa.co porn 0.962932 4 facebook.com recreation 0.200815 5 last.fm music 0.229545 text_domain_probs used_domain_text \ 0 {'adv': 0.001249639527059502, 'aggressive': 9.... True 1 {'adv': 0.001249639527059502, 'aggressive': 9.... True 2 {'adv': 0.010590500641848523, 'aggressive': 0.... True 3 {'adv': 0.00021545223423966907, 'aggressive': ... True 4 {'adv': 0.006381039197812215, 'aggressive': 0.... True 5 {'adv': 0.002181818181818182, 'aggressive': 0.... True extracted_text 0 xvideos furry ass history mature rough redhead... 1 marketwatch gold stocks video chrome economy v... 2 forbes featured leadership watch money breakin... 3 bellesa audio vixen sensual passionate orgy ki... 4 facebook watch messenger portal bulletin oculus 5 last twitter music reset company back merchand...
domain.pred_shalla_cat_with_images(input)
What it does:
Predicts the kind of content hosted by a domain based on screenshot of the homepage.
The function can use locally stored screenshots files or fetch fresh screenshots of the homepage.
If you specify a local folder, the function will look for jpegs corresponding to the domain.
The screenshots must be stored as domainname.jpg.
The function returns a pandas dataframe with label and corresponding probabilities.
Inputs:
input: list of domains. Either input or image_path must be specified.
image_path: path to the folder where the screenshots are stored. Either input or image_path must be specified.
latest: use the latest model. Default is True.
Note: The function will by default look for a images` folder on the same level as model files.
Output:
Returns panda dataframe with label and probabilities
Sample usage:
from piedomains import domain domains = [ "forbes.com", "xvideos.com", "last.fm", "facebook.com", "bellesa.co", "marketwatch.com" ] # with only domains result = domain.pred_shalla_cat_with_images(domains) # with image path where images are stored (offline mode) result = domain.pred_shalla_cat_with_images(image_path="path/to/images") # with domains and image path, image_path will be used to store images result = domain.pred_shalla_cat_with_images(domains, image_path="path/to/images") print(result)Sample output:
domain image_label image_prob \ 0 bellesa.co shopping 0.366663 1 facebook.com porn 0.284601 2 marketwatch.com recreation 0.367953 3 xvideos.com porn 0.916550 4 forbes.com recreation 0.415165 5 last.fm shopping 0.303097 image_domain_probs used_domain_screenshot 0 {'adv': 0.0009261096129193902, 'aggressive': 3... True 1 {'adv': 0.030470917001366615, 'aggressive': 0.... True 2 {'adv': 0.006861348636448383, 'aggressive': 0.... True 3 {'adv': 0.0004964823601767421, 'aggressive': 0... True 4 {'adv': 0.0016061498317867517, 'aggressive': 8... True 5 {'adv': 0.007956285960972309, 'aggressive': 0.... True
domain.pred_shalla_cat(input)
What it does:
Predicts the kind of content hosted by a domain based on a screenshot of the homepage.
The function can use locally stored screenshots and HTMLs or fetch fresh data.
If you specify local folders, the function will look for jpegs corresponding to the domain.
The screenshots must be stored as domainname.jpg.
The HTML files must be stored as domainname.html.
The function returns a pandas dataframe with the predicted labels and corresponding probabilities.
Inputs:
input: list of domains. Either input or html_path must be specified.
html_path: path to the folder where the screenshots are stored. Either input, image_path, or html_path must be specified.
image_path: path to the folder where the screenshots are stored. Either input, image_path, or html_path must be specified.
latest: use the latest model. Default is True.
Note: The function will by default look for a html folder on the same level as model files.
Note: The function will by default look for a images folder on the same level as model files.
Output
Returns panda dataframe with label and probabilities
Sample usage:
from piedomains import domain domains = [ "forbes.com", "xvideos.com", "last.fm", "facebook.com", "bellesa.co", "marketwatch.com" ] # with only domains result = domain.pred_shalla_cat(domains) # with html path where htmls are stored (offline mode) result = domain.pred_shalla_cat(html_path="path/to/htmls") # with image path where images are stored (offline mode) result = domain.pred_shalla_cat(image_path="path/to/images") print(result)Sample output:
domain text_label text_prob \ 0 xvideos.com porn 0.918919 1 marketwatch.com finance 0.627119 2 forbes.com news 0.575000 3 bellesa.co porn 0.962932 4 facebook.com recreation 0.200815 5 last.fm music 0.229545 text_domain_probs used_domain_text \ 0 {'adv': 0.001249639527059502, 'aggressive': 9.... True 1 {'adv': 0.001249639527059502, 'aggressive': 9.... True 2 {'adv': 0.010590500641848523, 'aggressive': 0.... True 3 {'adv': 0.00021545223423966907, 'aggressive': ... True 4 {'adv': 0.006381039197812215, 'aggressive': 0.... True 5 {'adv': 0.002181818181818182, 'aggressive': 0.... True extracted_text image_label image_prob \ 0 xvideos furry ass history mature rough redhead... porn 0.916550 1 marketwatch gold stocks video chrome economy v... recreation 0.370665 2 forbes featured leadership watch money breakin... recreation 0.422517 3 bellesa audio vixen sensual passionate orgy ki... porn 0.409875 4 facebook watch messenger portal bulletin oculus porn 0.284601 5 last twitter music reset company back merchand... shopping 0.420788 image_domain_probs used_domain_screenshot \ 0 {'adv': 0.0004964823601767421, 'aggressive': 0... True 1 {'adv': 0.007065971381962299, 'aggressive': 0.... True 2 {'adv': 0.0016623957781121135, 'aggressive': 7... True 3 {'adv': 0.0008810096187517047, 'aggressive': 0... True 4 {'adv': 0.030470917001366615, 'aggressive': 0.... True 5 {'adv': 0.01235155574977398, 'aggressive': 0.0... True label label_prob combined_domain_probs 0 porn 0.917735 {'adv': 0.0008730609436181221, 'aggressive': 0... 1 finance 0.315346 {'adv': 0.004157805454510901, 'aggressive': 0.... 2 news 0.367533 {'adv': 0.006126448209980318, 'aggressive': 0.... 3 porn 0.686404 {'adv': 0.0005482309264956868, 'aggressive': 0... 4 porn 0.223327 {'adv': 0.018425978099589416, 'aggressive': 0.... 5 shopping 0.232422 {'adv': 0.007266686965796081, 'aggressive': 0....
Contributor Code of Conduct
The project welcomes contributions from everyone! In fact, it depends on it. To maintain this welcoming atmosphere, and to collaborate in a fun and productive way, we expect contributors to the project to abide by the Contributor Code of Conduct.
License
The package is released under the MIT License.