piedomains 0.0.11

Predict categories based domain names and it's content

The package infers the kind of content hosted by domain using the domain name, and the content, and screenshot from 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

1. domain.pred_shalla_cat_with_text(input)

• What it does: - Predicts the kind of content hosted by a domain based on domain name and 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 label 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. The function will by default look for a html folder on the same level as model files.

  • latest: use the latest model. Default is True.

• Output: - Returns a pandas dataframe with label and probabilities

Example -

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)

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...

2. 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. The function will by default look for a images` folder on the same level as model files.

  • latest: use the latest model. Default is True.

• Output - Returns panda dataframe with label and probabilities

Example -

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)

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

3. domain.pred_shalla_cat(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 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 function returns a pandas dataframe with label 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. The function will by default look for a html folder on the same level as model files. - image_path: path to the folder where the screenshots are stored. Either input, image_path, or html_path must be specified. The function will by default look for a images folder on the same level as model files. - latest: use the latest model. Default is True.

• Output - Returns panda dataframe with label and probabilities

Example -

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)

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....

Authors

Rajashekar Chintalapati and Gaurav Sood

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.