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diff --git a/python-m3inference.spec b/python-m3inference.spec new file mode 100644 index 0000000..9f0c450 --- /dev/null +++ b/python-m3inference.spec @@ -0,0 +1,690 @@ +%global _empty_manifest_terminate_build 0 +Name: python-m3inference +Version: 1.1.5 +Release: 1 +Summary: M3 Inference +License: GNU Affero General Public License v3.0 +URL: https://github.com/euagendas/m3inference +Source0: https://mirrors.aliyun.com/pypi/web/packages/5a/d4/0a4a1947d84a8f4774b4fa163def170583f63e1088784680ef9cf045a66f/m3inference-1.1.5.tar.gz +BuildArch: noarch + +Requires: python3-torch +Requires: python3-numpy +Requires: python3-tqdm +Requires: python3-Pillow +Requires: python3-torchvision +Requires: python3-pycld2 +Requires: python3-requests +Requires: python3-pandas +Requires: python3-rauth + +%description +# M3-Inference +This is a PyTorch implementation of the M3 (Multimodal, Multilingual, and Multi-attribute) system described in the WebConf (WWW) 2019 paper [Demographic Inference and Representative Population Estimates from Multilingual Social Media Data](https://doi.org/10.1145/3308558.3313684). + +## Quick Links + +- [About](#about) +- [Install](#install) +- [FAQs](#faqs) +- [Citation](#citation) +- [Contact](#more-questions) +- [License](#license) + +## About +M3 is a deep learning system for demographic inference that was trained on a massive Twitter dataset. It features three major attributes: + +* Multimodal + - M3 takes both vision and text inputs. Particularly, the input may contain a profile image, a name (e.g., in the form of a natural language first and last name), a user name (e.g., the Twitter screen_name), and a short self-descriptive text (e.g., a Twitter biography). + +* Multilingual + - M3 operates in 32 major languages spoken in Europe, but note that these are not all "European" languages (e.g., Arabic is supported). They are `['en', 'cs', 'fr', 'nl', 'ar', 'ro', 'bs', 'da', 'it', 'pt', 'no', 'es', 'hr', 'tr', 'de', 'fi', 'el', 'ru', 'bg', 'hu', 'sk', 'et', 'pl', 'lv', 'sl', 'lt', 'ga', 'eu', 'mt', 'cy', 'rm', 'is', 'un']` in [ISO 639-1 two-letter codes](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) (`un` stands for languages that are not in the list). A [list with the full names of the languages is on the wiki](https://github.com/euagendas/m3inference/wiki/Languages). + +* Multi-attribute + - Thanks to multi-task learning, the model can predict three demographic attributes (gender, age, and human-vs-organization status) at the same time. + +## Install +### TL;DR +`pip install m3inference` + +* If there is an error with the installation of `torch`, you may install it with `conda` (see [here](https://pytorch.org/)). Alternatively, you could create a conda environment - see instructions below. +* Please ensure you have Python 3.6.6 or higher installed. + +### Manually Install + + +#### With pip +You must have `Python>=3.6.6` and `pip` ready to use. Then you can: +1. Install dependency packages: `pip install -r requirements.txt` +2. Install the package `python setup.py install` + +#### As a conda environment +1. Simply run `conda-env create -f env_conda.yml`, you should then have a "m3env" environment available which you can enter with `conda activate m3env`. Run everything else from within there. +2. Install the package `python setup.py install` + + +### How to use +#### With M3 +M3 takes an input of a `jsonl` file that contains `a list of json(dict) objects` (or a python object containing the data itself) and outputs the predictions for the three attributes. + +Demo with `test` dir: + +1. Clone this package (`git clone https://github.com/zijwang/m3inference.git`) and follow [Manually Install](#manually-install) to install the package. + +2. `Preprocess` the image to get them resized to the correct shape. To do this, at the same (root) dir, run + ``` + python scripts/preprocess.py --source_dir test/pic/ --output_dir test/pic_resized/ --jsonl_path test/data.jsonl --jsonl_outpath test/data_resized.jsonl --verbose + ``` + + You may also run `python scripts/preprocess.py --help` to see detailed usages. Further, see [FAQs](#faqs) for more information on images. + +3. In Python, run: + +``` +from m3inference import M3Inference +import pprint +m3 = M3Inference() # see docstring for details +pred = m3.infer('./test/data_resized.jsonl') # also see docstring for details +pprint.pprint(pred) +``` + +You should see results like the following: + + +``` +OrderedDict([('720389270335135745', + {'age': {'19-29': 0.1546, + '30-39': 0.114, + '<=18': 0.0481, + '>=40': 0.6833}, + 'gender': {'female': 0.0066, 'male': 0.9934}, + 'org': {'is-org': 0.7508, 'non-org': 0.2492}}), + ('21447363', + {'age': {'19-29': 0.0157, + '30-39': 0.9837, + '<=18': 0.0004, + '>=40': 0.0002}, + 'gender': {'female': 0.9866, 'male': 0.0134}, + 'org': {'is-org': 0.0002, 'non-org': 0.9998}}), + ... + ... +``` + + +Each entry of the input file (`./test/data.jsonl`) should have the following keys: `id`, `name`, `screen_name`, `description`, `lang`, `img_path`. +* The first four keys could be extracted directly from the Twitter JSON entry. +* For `lang`, even if the official Twitter JSON entry contains this field, we recommend to try to use our [cld2](https://github.com/CLD2Owners/cld2) wrapper method (`from m3inference import get_lang`) to get the language from either user's biography/description or the user's tweets. You could also hard-code the language if you know the ground truth from other sources. +* Images should be downloaded from Twitter as 400x400 pixel images and resized to 224x224 pixels using the preprocess code above. + + +The output file is a dict in which the `id`s are the keys and the predictions are the nested values. The values represents the probability of that category (`[0, 1]`). + + +For other model settings (e.g., output format, GPU setting, batch_size, etc.), please use the file `test/data.jsonl` as a sample input file and see the docstrings fo `M3Inference` initialization and infer method for detailed utilization. + + +#### With M3 Twitter Wrapper + +##### Existing JSON Twitter data +If you have a Twitter JSON object representing a user but do *not* have images ready, you can use our `M3Twitter` class to: +* Download and resize the images +* Add a detected language using CLD2 over the biography text +* Transform the JSON into the input structure required for M3. + +``` +from m3inference import M3Twitter +import pprint + +m3twitter=M3Twitter(cache_dir="twitter_cache") #Change the cache_dir parameter to control where profile images are downloaded +m3twitter.transform_jsonl(input_file="test/twitter_cache/example_tweets.jsonl",output_file="test/twitter_cache/m3_input.jsonl") + +pprint.pprint(m3twitter.infer("test/twitter_cache/m3_input.jsonl")) #Same method as M3Inference.infer(...) +``` + +If you already have images locally, please include the ``image_path_key`` parameter and set it to the key in your JSON object containing the path to the image locally. Similarly, if you have detected languages, you can use the ``lang_key`` parameter. An example is given in `test/test_transform_jsonl.py` + +##### Nothing but a screen_name or numeric id +You can also run the Twitter wrapper directly for a Twitter screen_name or numeric id. + +* Please download the "scripts" folders from this repository. +* To run these examples, you need Twitter API credentials. Please create a Twitter app at https://developer.twitter.com/en/apps . Once you have an app, copy `scripts/auth_example.txt` to `auth.txt` and insert the API key, API secret, access token, and access token secret into this file. + +Then you can run the following commands: + +``` +#If you have a screen_name, use +$ python m3twitter.py --screen-name=computermacgyve --auth auth.txt --skip-cache + +#If you have a numeric id, use +$python m3twitter.py --id=19854920 --auth auth.txt --skip-cache +``` + +The `--skip-cache` option ensures fresh results are retrieved rather than served from the cache. This is great for debugging but not in a real-world setting; so, remove as needed. + +## FAQs +### What if I just have a Twitter screen name or id? + +You can use the M3Twitter class to get all the needed profile information (and image) from the Twitter website. Please note this function should only be used for a small number of screen_names or numeric ids. If you have a large list, please use the Twitter API to get the required information (apart from the profile photo, which can be downloaded separately using the `.transform_jsonl(...)` method described above). + +``` +import pprint +from m3inference import M3Twitter +m3twitter=M3Twitter() + +# initialize twitter api +m3twitter.twitter_init(api_key=...,api_secret=...,access_token=...,access_secret=...) +# alternatively, you may do +m3twitter.twitter_init_from_file('auth.txt') + +pprint.pprint(m3twitter.infer_id("2631881902")) +``` + +The `.infer_screen_name(...)` method does the same for a Twitter screen name. All results are stored/cached in "~/m3/cache/". This directory can be changed in the M3Twitter constructor and you can skip/update the cache for a single request by setting `skip_cache=True` on the `.infer_id(...)` or `.infer_screen_name(...)` method. + +You can also run these examples directly from the terminal to try things out: +``` +python scripts/m3twitter.py --screen-name=barackobama --auth auth.txt +``` + +### How should I get the images? + +If you have nothing that a screen name or numeric id, you can use the `M3Twitter.infer_screen_name(...)` or `M3Twitter.infer_id(...)` methods. Please note, however, that these methods directly access the Twitter website, not the API and therefore are suitable only to small lists. With a large list of screen_names/ids, please use the Twitter API to get user information. + +Once you have Twitter JSON, you can use the `M3Twitter.transform_jsonl(...)` to download images, run language detection, and transform the data to the M3 input format. + +### What if I cannot have image data? +In the package, we do provide the standalone `text-based model`. You could set `use_full_model=False` when initializing `M3Inference` object (i.e., `m3=M3Inference(use_full_model=False)`). You then do not need to provide `img_path` field in the input json file. + +*Warning*: as M3 model is optimized with the best performance when both image and text inputs are available. You may experience lower performance when using the `text-based model`. We recommend using image data whenever possible to get the most accurate predictions. + + + +## Citation +Please cite our WWW 2019 paper if you use this package in your project. + +``` +@inproceedings{wang2019demographic, + title={Demographic inference and representative population estimates from multilingual social media data}, + author={Wang, Zijian and Hale, Scott and Adelani, David Ifeoluwa and Grabowicz, Przemyslaw and Hartman, Timo and Fl{\"o}ck, Fabian and Jurgens, David}, + booktitle={The World Wide Web Conference}, + pages={2056--2067}, + year={2019}, + organization={ACM} +} +``` + +## More Questions + +We use issues on this GitHub for all questions or suggestions. For specific inqueries, please contact us at `m3@euagendas.org`. Please note that we are unable to release or provide training data for this model due to existing terms of service. + +## License + +This source code is licensed under the GNU Affero General Public License, which allows for non-commercial re-use of this software. For commercial inqueries, please contact us directly. Please see the LICENSE file in the root directory of this source tree for details. + + + + +%package -n python3-m3inference +Summary: M3 Inference +Provides: python-m3inference +BuildRequires: python3-devel +BuildRequires: python3-setuptools +BuildRequires: python3-pip +%description -n python3-m3inference +# M3-Inference +This is a PyTorch implementation of the M3 (Multimodal, Multilingual, and Multi-attribute) system described in the WebConf (WWW) 2019 paper [Demographic Inference and Representative Population Estimates from Multilingual Social Media Data](https://doi.org/10.1145/3308558.3313684). + +## Quick Links + +- [About](#about) +- [Install](#install) +- [FAQs](#faqs) +- [Citation](#citation) +- [Contact](#more-questions) +- [License](#license) + +## About +M3 is a deep learning system for demographic inference that was trained on a massive Twitter dataset. It features three major attributes: + +* Multimodal + - M3 takes both vision and text inputs. Particularly, the input may contain a profile image, a name (e.g., in the form of a natural language first and last name), a user name (e.g., the Twitter screen_name), and a short self-descriptive text (e.g., a Twitter biography). + +* Multilingual + - M3 operates in 32 major languages spoken in Europe, but note that these are not all "European" languages (e.g., Arabic is supported). They are `['en', 'cs', 'fr', 'nl', 'ar', 'ro', 'bs', 'da', 'it', 'pt', 'no', 'es', 'hr', 'tr', 'de', 'fi', 'el', 'ru', 'bg', 'hu', 'sk', 'et', 'pl', 'lv', 'sl', 'lt', 'ga', 'eu', 'mt', 'cy', 'rm', 'is', 'un']` in [ISO 639-1 two-letter codes](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) (`un` stands for languages that are not in the list). A [list with the full names of the languages is on the wiki](https://github.com/euagendas/m3inference/wiki/Languages). + +* Multi-attribute + - Thanks to multi-task learning, the model can predict three demographic attributes (gender, age, and human-vs-organization status) at the same time. + +## Install +### TL;DR +`pip install m3inference` + +* If there is an error with the installation of `torch`, you may install it with `conda` (see [here](https://pytorch.org/)). Alternatively, you could create a conda environment - see instructions below. +* Please ensure you have Python 3.6.6 or higher installed. + +### Manually Install + + +#### With pip +You must have `Python>=3.6.6` and `pip` ready to use. Then you can: +1. Install dependency packages: `pip install -r requirements.txt` +2. Install the package `python setup.py install` + +#### As a conda environment +1. Simply run `conda-env create -f env_conda.yml`, you should then have a "m3env" environment available which you can enter with `conda activate m3env`. Run everything else from within there. +2. Install the package `python setup.py install` + + +### How to use +#### With M3 +M3 takes an input of a `jsonl` file that contains `a list of json(dict) objects` (or a python object containing the data itself) and outputs the predictions for the three attributes. + +Demo with `test` dir: + +1. Clone this package (`git clone https://github.com/zijwang/m3inference.git`) and follow [Manually Install](#manually-install) to install the package. + +2. `Preprocess` the image to get them resized to the correct shape. To do this, at the same (root) dir, run + ``` + python scripts/preprocess.py --source_dir test/pic/ --output_dir test/pic_resized/ --jsonl_path test/data.jsonl --jsonl_outpath test/data_resized.jsonl --verbose + ``` + + You may also run `python scripts/preprocess.py --help` to see detailed usages. Further, see [FAQs](#faqs) for more information on images. + +3. In Python, run: + +``` +from m3inference import M3Inference +import pprint +m3 = M3Inference() # see docstring for details +pred = m3.infer('./test/data_resized.jsonl') # also see docstring for details +pprint.pprint(pred) +``` + +You should see results like the following: + + +``` +OrderedDict([('720389270335135745', + {'age': {'19-29': 0.1546, + '30-39': 0.114, + '<=18': 0.0481, + '>=40': 0.6833}, + 'gender': {'female': 0.0066, 'male': 0.9934}, + 'org': {'is-org': 0.7508, 'non-org': 0.2492}}), + ('21447363', + {'age': {'19-29': 0.0157, + '30-39': 0.9837, + '<=18': 0.0004, + '>=40': 0.0002}, + 'gender': {'female': 0.9866, 'male': 0.0134}, + 'org': {'is-org': 0.0002, 'non-org': 0.9998}}), + ... + ... +``` + + +Each entry of the input file (`./test/data.jsonl`) should have the following keys: `id`, `name`, `screen_name`, `description`, `lang`, `img_path`. +* The first four keys could be extracted directly from the Twitter JSON entry. +* For `lang`, even if the official Twitter JSON entry contains this field, we recommend to try to use our [cld2](https://github.com/CLD2Owners/cld2) wrapper method (`from m3inference import get_lang`) to get the language from either user's biography/description or the user's tweets. You could also hard-code the language if you know the ground truth from other sources. +* Images should be downloaded from Twitter as 400x400 pixel images and resized to 224x224 pixels using the preprocess code above. + + +The output file is a dict in which the `id`s are the keys and the predictions are the nested values. The values represents the probability of that category (`[0, 1]`). + + +For other model settings (e.g., output format, GPU setting, batch_size, etc.), please use the file `test/data.jsonl` as a sample input file and see the docstrings fo `M3Inference` initialization and infer method for detailed utilization. + + +#### With M3 Twitter Wrapper + +##### Existing JSON Twitter data +If you have a Twitter JSON object representing a user but do *not* have images ready, you can use our `M3Twitter` class to: +* Download and resize the images +* Add a detected language using CLD2 over the biography text +* Transform the JSON into the input structure required for M3. + +``` +from m3inference import M3Twitter +import pprint + +m3twitter=M3Twitter(cache_dir="twitter_cache") #Change the cache_dir parameter to control where profile images are downloaded +m3twitter.transform_jsonl(input_file="test/twitter_cache/example_tweets.jsonl",output_file="test/twitter_cache/m3_input.jsonl") + +pprint.pprint(m3twitter.infer("test/twitter_cache/m3_input.jsonl")) #Same method as M3Inference.infer(...) +``` + +If you already have images locally, please include the ``image_path_key`` parameter and set it to the key in your JSON object containing the path to the image locally. Similarly, if you have detected languages, you can use the ``lang_key`` parameter. An example is given in `test/test_transform_jsonl.py` + +##### Nothing but a screen_name or numeric id +You can also run the Twitter wrapper directly for a Twitter screen_name or numeric id. + +* Please download the "scripts" folders from this repository. +* To run these examples, you need Twitter API credentials. Please create a Twitter app at https://developer.twitter.com/en/apps . Once you have an app, copy `scripts/auth_example.txt` to `auth.txt` and insert the API key, API secret, access token, and access token secret into this file. + +Then you can run the following commands: + +``` +#If you have a screen_name, use +$ python m3twitter.py --screen-name=computermacgyve --auth auth.txt --skip-cache + +#If you have a numeric id, use +$python m3twitter.py --id=19854920 --auth auth.txt --skip-cache +``` + +The `--skip-cache` option ensures fresh results are retrieved rather than served from the cache. This is great for debugging but not in a real-world setting; so, remove as needed. + +## FAQs +### What if I just have a Twitter screen name or id? + +You can use the M3Twitter class to get all the needed profile information (and image) from the Twitter website. Please note this function should only be used for a small number of screen_names or numeric ids. If you have a large list, please use the Twitter API to get the required information (apart from the profile photo, which can be downloaded separately using the `.transform_jsonl(...)` method described above). + +``` +import pprint +from m3inference import M3Twitter +m3twitter=M3Twitter() + +# initialize twitter api +m3twitter.twitter_init(api_key=...,api_secret=...,access_token=...,access_secret=...) +# alternatively, you may do +m3twitter.twitter_init_from_file('auth.txt') + +pprint.pprint(m3twitter.infer_id("2631881902")) +``` + +The `.infer_screen_name(...)` method does the same for a Twitter screen name. All results are stored/cached in "~/m3/cache/". This directory can be changed in the M3Twitter constructor and you can skip/update the cache for a single request by setting `skip_cache=True` on the `.infer_id(...)` or `.infer_screen_name(...)` method. + +You can also run these examples directly from the terminal to try things out: +``` +python scripts/m3twitter.py --screen-name=barackobama --auth auth.txt +``` + +### How should I get the images? + +If you have nothing that a screen name or numeric id, you can use the `M3Twitter.infer_screen_name(...)` or `M3Twitter.infer_id(...)` methods. Please note, however, that these methods directly access the Twitter website, not the API and therefore are suitable only to small lists. With a large list of screen_names/ids, please use the Twitter API to get user information. + +Once you have Twitter JSON, you can use the `M3Twitter.transform_jsonl(...)` to download images, run language detection, and transform the data to the M3 input format. + +### What if I cannot have image data? +In the package, we do provide the standalone `text-based model`. You could set `use_full_model=False` when initializing `M3Inference` object (i.e., `m3=M3Inference(use_full_model=False)`). You then do not need to provide `img_path` field in the input json file. + +*Warning*: as M3 model is optimized with the best performance when both image and text inputs are available. You may experience lower performance when using the `text-based model`. We recommend using image data whenever possible to get the most accurate predictions. + + + +## Citation +Please cite our WWW 2019 paper if you use this package in your project. + +``` +@inproceedings{wang2019demographic, + title={Demographic inference and representative population estimates from multilingual social media data}, + author={Wang, Zijian and Hale, Scott and Adelani, David Ifeoluwa and Grabowicz, Przemyslaw and Hartman, Timo and Fl{\"o}ck, Fabian and Jurgens, David}, + booktitle={The World Wide Web Conference}, + pages={2056--2067}, + year={2019}, + organization={ACM} +} +``` + +## More Questions + +We use issues on this GitHub for all questions or suggestions. For specific inqueries, please contact us at `m3@euagendas.org`. Please note that we are unable to release or provide training data for this model due to existing terms of service. + +## License + +This source code is licensed under the GNU Affero General Public License, which allows for non-commercial re-use of this software. For commercial inqueries, please contact us directly. Please see the LICENSE file in the root directory of this source tree for details. + + + + +%package help +Summary: Development documents and examples for m3inference +Provides: python3-m3inference-doc +%description help +# M3-Inference +This is a PyTorch implementation of the M3 (Multimodal, Multilingual, and Multi-attribute) system described in the WebConf (WWW) 2019 paper [Demographic Inference and Representative Population Estimates from Multilingual Social Media Data](https://doi.org/10.1145/3308558.3313684). + +## Quick Links + +- [About](#about) +- [Install](#install) +- [FAQs](#faqs) +- [Citation](#citation) +- [Contact](#more-questions) +- [License](#license) + +## About +M3 is a deep learning system for demographic inference that was trained on a massive Twitter dataset. It features three major attributes: + +* Multimodal + - M3 takes both vision and text inputs. Particularly, the input may contain a profile image, a name (e.g., in the form of a natural language first and last name), a user name (e.g., the Twitter screen_name), and a short self-descriptive text (e.g., a Twitter biography). + +* Multilingual + - M3 operates in 32 major languages spoken in Europe, but note that these are not all "European" languages (e.g., Arabic is supported). They are `['en', 'cs', 'fr', 'nl', 'ar', 'ro', 'bs', 'da', 'it', 'pt', 'no', 'es', 'hr', 'tr', 'de', 'fi', 'el', 'ru', 'bg', 'hu', 'sk', 'et', 'pl', 'lv', 'sl', 'lt', 'ga', 'eu', 'mt', 'cy', 'rm', 'is', 'un']` in [ISO 639-1 two-letter codes](https://en.wikipedia.org/wiki/List_of_ISO_639-1_codes) (`un` stands for languages that are not in the list). A [list with the full names of the languages is on the wiki](https://github.com/euagendas/m3inference/wiki/Languages). + +* Multi-attribute + - Thanks to multi-task learning, the model can predict three demographic attributes (gender, age, and human-vs-organization status) at the same time. + +## Install +### TL;DR +`pip install m3inference` + +* If there is an error with the installation of `torch`, you may install it with `conda` (see [here](https://pytorch.org/)). Alternatively, you could create a conda environment - see instructions below. +* Please ensure you have Python 3.6.6 or higher installed. + +### Manually Install + + +#### With pip +You must have `Python>=3.6.6` and `pip` ready to use. Then you can: +1. Install dependency packages: `pip install -r requirements.txt` +2. Install the package `python setup.py install` + +#### As a conda environment +1. Simply run `conda-env create -f env_conda.yml`, you should then have a "m3env" environment available which you can enter with `conda activate m3env`. Run everything else from within there. +2. Install the package `python setup.py install` + + +### How to use +#### With M3 +M3 takes an input of a `jsonl` file that contains `a list of json(dict) objects` (or a python object containing the data itself) and outputs the predictions for the three attributes. + +Demo with `test` dir: + +1. Clone this package (`git clone https://github.com/zijwang/m3inference.git`) and follow [Manually Install](#manually-install) to install the package. + +2. `Preprocess` the image to get them resized to the correct shape. To do this, at the same (root) dir, run + ``` + python scripts/preprocess.py --source_dir test/pic/ --output_dir test/pic_resized/ --jsonl_path test/data.jsonl --jsonl_outpath test/data_resized.jsonl --verbose + ``` + + You may also run `python scripts/preprocess.py --help` to see detailed usages. Further, see [FAQs](#faqs) for more information on images. + +3. In Python, run: + +``` +from m3inference import M3Inference +import pprint +m3 = M3Inference() # see docstring for details +pred = m3.infer('./test/data_resized.jsonl') # also see docstring for details +pprint.pprint(pred) +``` + +You should see results like the following: + + +``` +OrderedDict([('720389270335135745', + {'age': {'19-29': 0.1546, + '30-39': 0.114, + '<=18': 0.0481, + '>=40': 0.6833}, + 'gender': {'female': 0.0066, 'male': 0.9934}, + 'org': {'is-org': 0.7508, 'non-org': 0.2492}}), + ('21447363', + {'age': {'19-29': 0.0157, + '30-39': 0.9837, + '<=18': 0.0004, + '>=40': 0.0002}, + 'gender': {'female': 0.9866, 'male': 0.0134}, + 'org': {'is-org': 0.0002, 'non-org': 0.9998}}), + ... + ... +``` + + +Each entry of the input file (`./test/data.jsonl`) should have the following keys: `id`, `name`, `screen_name`, `description`, `lang`, `img_path`. +* The first four keys could be extracted directly from the Twitter JSON entry. +* For `lang`, even if the official Twitter JSON entry contains this field, we recommend to try to use our [cld2](https://github.com/CLD2Owners/cld2) wrapper method (`from m3inference import get_lang`) to get the language from either user's biography/description or the user's tweets. You could also hard-code the language if you know the ground truth from other sources. +* Images should be downloaded from Twitter as 400x400 pixel images and resized to 224x224 pixels using the preprocess code above. + + +The output file is a dict in which the `id`s are the keys and the predictions are the nested values. The values represents the probability of that category (`[0, 1]`). + + +For other model settings (e.g., output format, GPU setting, batch_size, etc.), please use the file `test/data.jsonl` as a sample input file and see the docstrings fo `M3Inference` initialization and infer method for detailed utilization. + + +#### With M3 Twitter Wrapper + +##### Existing JSON Twitter data +If you have a Twitter JSON object representing a user but do *not* have images ready, you can use our `M3Twitter` class to: +* Download and resize the images +* Add a detected language using CLD2 over the biography text +* Transform the JSON into the input structure required for M3. + +``` +from m3inference import M3Twitter +import pprint + +m3twitter=M3Twitter(cache_dir="twitter_cache") #Change the cache_dir parameter to control where profile images are downloaded +m3twitter.transform_jsonl(input_file="test/twitter_cache/example_tweets.jsonl",output_file="test/twitter_cache/m3_input.jsonl") + +pprint.pprint(m3twitter.infer("test/twitter_cache/m3_input.jsonl")) #Same method as M3Inference.infer(...) +``` + +If you already have images locally, please include the ``image_path_key`` parameter and set it to the key in your JSON object containing the path to the image locally. Similarly, if you have detected languages, you can use the ``lang_key`` parameter. An example is given in `test/test_transform_jsonl.py` + +##### Nothing but a screen_name or numeric id +You can also run the Twitter wrapper directly for a Twitter screen_name or numeric id. + +* Please download the "scripts" folders from this repository. +* To run these examples, you need Twitter API credentials. Please create a Twitter app at https://developer.twitter.com/en/apps . Once you have an app, copy `scripts/auth_example.txt` to `auth.txt` and insert the API key, API secret, access token, and access token secret into this file. + +Then you can run the following commands: + +``` +#If you have a screen_name, use +$ python m3twitter.py --screen-name=computermacgyve --auth auth.txt --skip-cache + +#If you have a numeric id, use +$python m3twitter.py --id=19854920 --auth auth.txt --skip-cache +``` + +The `--skip-cache` option ensures fresh results are retrieved rather than served from the cache. This is great for debugging but not in a real-world setting; so, remove as needed. + +## FAQs +### What if I just have a Twitter screen name or id? + +You can use the M3Twitter class to get all the needed profile information (and image) from the Twitter website. Please note this function should only be used for a small number of screen_names or numeric ids. If you have a large list, please use the Twitter API to get the required information (apart from the profile photo, which can be downloaded separately using the `.transform_jsonl(...)` method described above). + +``` +import pprint +from m3inference import M3Twitter +m3twitter=M3Twitter() + +# initialize twitter api +m3twitter.twitter_init(api_key=...,api_secret=...,access_token=...,access_secret=...) +# alternatively, you may do +m3twitter.twitter_init_from_file('auth.txt') + +pprint.pprint(m3twitter.infer_id("2631881902")) +``` + +The `.infer_screen_name(...)` method does the same for a Twitter screen name. All results are stored/cached in "~/m3/cache/". This directory can be changed in the M3Twitter constructor and you can skip/update the cache for a single request by setting `skip_cache=True` on the `.infer_id(...)` or `.infer_screen_name(...)` method. + +You can also run these examples directly from the terminal to try things out: +``` +python scripts/m3twitter.py --screen-name=barackobama --auth auth.txt +``` + +### How should I get the images? + +If you have nothing that a screen name or numeric id, you can use the `M3Twitter.infer_screen_name(...)` or `M3Twitter.infer_id(...)` methods. Please note, however, that these methods directly access the Twitter website, not the API and therefore are suitable only to small lists. With a large list of screen_names/ids, please use the Twitter API to get user information. + +Once you have Twitter JSON, you can use the `M3Twitter.transform_jsonl(...)` to download images, run language detection, and transform the data to the M3 input format. + +### What if I cannot have image data? +In the package, we do provide the standalone `text-based model`. You could set `use_full_model=False` when initializing `M3Inference` object (i.e., `m3=M3Inference(use_full_model=False)`). You then do not need to provide `img_path` field in the input json file. + +*Warning*: as M3 model is optimized with the best performance when both image and text inputs are available. You may experience lower performance when using the `text-based model`. We recommend using image data whenever possible to get the most accurate predictions. + + + +## Citation +Please cite our WWW 2019 paper if you use this package in your project. + +``` +@inproceedings{wang2019demographic, + title={Demographic inference and representative population estimates from multilingual social media data}, + author={Wang, Zijian and Hale, Scott and Adelani, David Ifeoluwa and Grabowicz, Przemyslaw and Hartman, Timo and Fl{\"o}ck, Fabian and Jurgens, David}, + booktitle={The World Wide Web Conference}, + pages={2056--2067}, + year={2019}, + organization={ACM} +} +``` + +## More Questions + +We use issues on this GitHub for all questions or suggestions. For specific inqueries, please contact us at `m3@euagendas.org`. Please note that we are unable to release or provide training data for this model due to existing terms of service. + +## License + +This source code is licensed under the GNU Affero General Public License, which allows for non-commercial re-use of this software. For commercial inqueries, please contact us directly. Please see the LICENSE file in the root directory of this source tree for details. + + + + +%prep +%autosetup -n m3inference-1.1.5 + +%build +%py3_build + +%install +%py3_install +install -d -m755 %{buildroot}/%{_pkgdocdir} +if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi +if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi +if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi +if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi +pushd %{buildroot} +if [ -d usr/lib ]; then + find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/lib64 ]; then + find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/bin ]; then + find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +if [ -d usr/sbin ]; then + find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst +fi +touch doclist.lst +if [ -d usr/share/man ]; then + find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst +fi +popd +mv %{buildroot}/filelist.lst . +mv %{buildroot}/doclist.lst . + +%files -n python3-m3inference -f filelist.lst +%dir %{python3_sitelib}/* + +%files help -f doclist.lst +%{_docdir}/* + +%changelog +* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 1.1.5-1 +- Package Spec generated |