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Faker is a Python package that generates fake data for you.
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Faker is a Python package that generates fake data for you. Whether you need to bootstrap your database, create good-looking XML documents, fill-in your persistence to stress test it, or anonymize data taken from a production service, Faker is for you.
Faker is heavily inspired by PHP Faker, Perl Faker, and by Ruby Faker_.
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Starting from version 4.0.0, Faker dropped support for Python 2 and from version 5.0.0
only supports Python 3.8 and above. If you still need Python 2 compatibility, please install version 3.0.1 in the
meantime, and please consider updating your codebase to support Python 3 so you can enjoy the
latest features Faker has to offer. Please see the extended docs_ for more details, especially
if you are upgrading from version 2.0.4 and below as there might be breaking changes.
This package was also previously called fake-factory which was already deprecated by the end
of 2016, and much has changed since then, so please ensure that your project and its dependencies
do not depend on the old package.
Install with pip:
.. code:: bash
pip install Faker
Use faker.Faker() to create and initialize a faker
generator, which can generate data by accessing properties named after
the type of data you want.
.. code:: python
from faker import Faker
fake = Faker()
fake.name()
# 'Lucy Cechtelar'
fake.address()
# '426 Jordy Lodge
# Cartwrightshire, SC 88120-6700'
fake.text()
# 'Sint velit eveniet. Rerum atque repellat voluptatem quia rerum. Numquam excepturi
# beatae sint laudantium consequatur. Magni occaecati itaque sint et sit tempore. Nesciunt
# amet quidem. Iusto deleniti cum autem ad quia aperiam.
# A consectetur quos aliquam. In iste aliquid et aut similique suscipit. Consequatur qui
# quaerat iste minus hic expedita. Consequuntur error magni et laboriosam. Aut aspernatur
# voluptatem sit aliquam. Dolores voluptatum est.
# Aut molestias et maxime. Fugit autem facilis quos vero. Eius quibusdam possimus est.
# Ea quaerat et quisquam. Deleniti sunt quam. Adipisci consequatur id in occaecati.
# Et sint et. Ut ducimus quod nemo ab voluptatum.'
Each call to method fake.name() yields a different (random) result.
This is because faker forwards faker.Generator.method_name() calls
to faker.Generator.format(method_name).
.. code:: python
for _ in range(10):
print(fake.name())
# 'Adaline Reichel'
# 'Dr. Santa Prosacco DVM'
# 'Noemy Vandervort V'
# 'Lexi O'Conner'
# 'Gracie Weber'
# 'Roscoe Johns'
# 'Emmett Lebsack'
# 'Keegan Thiel'
# 'Wellington Koelpin II'
# 'Ms. Karley Kiehn V'
Faker also has its own pytest plugin which provides a faker fixture you can use in your
tests. Please check out the pytest fixture docs to learn more.
Each of the generator properties (like name, address, and
lorem) are called "fake". A faker generator has many of them,
packaged in "providers".
.. code:: python
from faker import Faker
from faker.providers import internet
fake = Faker()
fake.add_provider(internet)
print(fake.ipv4_private())
Check the extended docs_ for a list of bundled providers_ and a list of
community providers_.
faker.Faker can take a locale as an argument, to return localized
data. If no localized provider is found, the factory falls back to the
default LCID string for US english, ie: en_US.
.. code:: python
from faker import Faker
fake = Faker('it_IT')
for _ in range(10):
print(fake.name())
# 'Elda Palumbo'
# 'Pacifico Giordano'
# 'Sig. Avide Guerra'
# 'Yago Amato'
# 'Eustachio Messina'
# 'Dott. Violante Lombardo'
# 'Sig. Alighieri Monti'
# 'Costanzo Costa'
# 'Nazzareno Barbieri'
# 'Max Coppola'
faker.Faker also supports multiple locales. New in v3.0.0.
.. code:: python
from faker import Faker
fake = Faker(['it_IT', 'en_US', 'ja_JP'])
for _ in range(10):
print(fake.name())
# 鈴木 陽一
# Leslie Moreno
# Emma Williams
# 渡辺 裕美子
# Marcantonio Galuppi
# Martha Davis
# Kristen Turner
# 中津川 春香
# Ashley Castillo
# 山田 桃子
You can check available Faker locales in the source code, under the providers package. The localization of Faker is an ongoing process, for which we need your help. Please don't hesitate to create a localized provider for your own locale and submit a Pull Request (PR).
The Faker constructor takes a performance-related argument called
use_weighting. It specifies whether to attempt to have the frequency
of values match real-world frequencies (e.g. the English name Gary would
be much more frequent than the name Lorimer). If use_weighting is False,
then all items have an equal chance of being selected, and the selection
process is much faster. The default is True.
When installed, you can invoke faker from the command-line:
.. code:: console
faker [-h] [--version] [-o output]
[-l {bg_BG,cs_CZ,...,zh_CN,zh_TW}]
[-r REPEAT] [-s SEP]
[-i package.containing.custom_provider]
[fake] [fake argument [fake argument ...]]
Where:
faker: is the script when installed in your environment, in
development you could use python -m faker instead
-h, --help: shows a help message
--version: shows the program's version number
-o FILENAME: redirects the output to the specified filename
-l {bg_BG,cs_CZ,...,zh_CN,zh_TW}: allows use of a localized
provider
-r REPEAT: will generate a specified number of outputs
-s SEP: will generate the specified separator after each
generated output
-i package.containing.custom_provider additional custom provider to use. Note this
is the import path of the package containing your Provider class, not the
custom Provider class itself. Can be repeated to add multiple providers.
fake: is the name of the fake to generate an output for, such as
name, address, or text
[fake argument ...]: optional arguments to pass to the fake (e.g. the
profile fake takes an optional list of comma separated field names as the
first argument)
Examples:
.. code:: console
$ faker address
968 Bahringer Garden Apt. 722
Kristinaland, NJ 09890
$ faker -l de_DE address
Samira-Niemeier-Allee 56
94812 Biedenkopf
$ faker profile ssn,birthdate
{'ssn': '628-10-1085', 'birthdate': '2008-03-29'}
$ faker -r=3 -s=";" name
Willam Kertzmann;
Josiah Maggio;
Gayla Schmitt;
$ faker -i faker_credit_score credit_score_full
Experian/Fair Isaac Risk Model V2SM
Experian
801
.. code:: python
from faker import Faker
fake = Faker()
# first, import a similar Provider or use the default one
from faker.providers import BaseProvider
# create new provider class
class MyProvider(BaseProvider):
def foo(self) -> str:
return 'bar'
# then add new provider to faker instance
fake.add_provider(MyProvider)
# now you can use:
fake.foo()
# 'bar'
Dynamic providers can read elements from an external source.
.. code:: python
from faker import Faker
from faker.providers import DynamicProvider
medical_professions_provider = DynamicProvider(
provider_name="medical_profession",
elements=["dr.", "doctor", "nurse", "surgeon", "clerk"],
)
fake = Faker()
# then add new provider to faker instance
fake.add_provider(medical_professions_provider)
# now you can use:
fake.medical_profession()
# 'dr.'
You can provide your own sets of words if you don't want to use the
default lorem ipsum one. The following example shows how to do it with a list of words picked from cakeipsum <http://www.cupcakeipsum.com/>__ :
.. code:: python
from faker import Faker
fake = Faker()
my_word_list = [
'danish','cheesecake','sugar',
'Lollipop','wafer','Gummies',
'sesame','Jelly','beans',
'pie','bar','Ice','oat' ]
fake.sentence()
# 'Expedita at beatae voluptatibus nulla omnis.'
fake.sentence(ext_word_list=my_word_list)
# 'Oat beans oat Lollipop bar cheesecake.'
Factory Boy already ships with integration with Faker. Simply use the
factory.Faker method of factory_boy:
.. code:: python
import factory
from myapp.models import Book
class BookFactory(factory.Factory):
class Meta:
model = Book
title = factory.Faker('sentence', nb_words=4)
author_name = factory.Faker('name')
random instanceThe .random property on the generator returns the instance of
random.Random used to generate the values:
.. code:: python
from faker import Faker
fake = Faker()
fake.random
fake.random.getstate()
By default all generators share the same instance of random.Random, which
can be accessed with from faker.generator import random. Using this may
be useful for plugins that want to affect all faker instances.
Through use of the .unique property on the generator, you can guarantee
that any generated values are unique for this specific instance.
.. code:: python
from faker import Faker fake = Faker() names = [fake.unique.first_name() for i in range(500)] assert len(set(names)) == len(names)
On Faker instances with multiple locales, you can specify the locale to use
for the unique values by using the subscript notation:
.. code:: python
from faker import Faker fake = Faker(['en_US', 'fr_FR']) names = [fake.unique["en_US"].first_name() for i in range(500)] assert len(set(names)) == len(names)
Calling fake.unique.clear() clears the already seen values.
Note, to avoid infinite loops, after a number of attempts to find a unique
value, Faker will throw a UniquenessException. Beware of the birthday paradox <https://en.wikipedia.org/wiki/Birthday_problem>_, collisions
are more likely than you'd think.
.. code:: python
from faker import Faker
fake = Faker() for i in range(3): # Raises a UniquenessException fake.unique.boolean()
In addition, only hashable arguments and return values can be used
with .unique.
When using Faker for unit testing, you will often want to generate the same
data set. For convenience, the generator also provides a seed() method,
which seeds the shared random number generator. A Seed produces the same result
when the same methods with the same version of faker are called.
.. code:: python
from faker import Faker
fake = Faker()
Faker.seed(4321)
print(fake.name())
# 'Margaret Boehm'
Each generator can also be switched to use its own instance of random.Random,
separated from the shared one, by using the seed_instance() method, which acts
the same way. For example:
.. code:: python
from faker import Faker
fake = Faker()
fake.seed_instance(4321)
print(fake.name())
# 'Margaret Boehm'
Please note that as w
Selected from shared topics, language and repository description—not editorial ratings.
Aiven-Labs /
The Python fake data producer for Apache Kafka® is a complete demo app allowing you to quickly produce JSON fake streaming datasets and push it to an Apache Kafka topic.
72/100 healthFake News Detection using Machine Learning is a comprehensive project that utilizes machine learning and natural language processing techniques to identify and classify fake news articles. The project includes data analysis, model training, and a real-time web application for detecting fake news.
67/100 healthtaeefnajib /
Ficto is a Python package that allows you to effortlessly generate realistic dummy data in CSV or JSON format.
63/100 healthananya2001gupta /
Identify the software project, create business case, arrive at a problem statement. REQUIREMENT: Window XP, Internet, MS Office, etc. Problem Description: - 1. Introduction of AI and Machine Learning: - Artificial Intelligence applies machine learning, deep learning and other techniques to solve actual problems. Artificial intelligence (AI) brings the genuine human-to-machine interaction. Simply, Machine Learning is the algorithm that give computers the ability to learn from data and then make decisions and predictions, AI refers to idea where machines can execute tasks smartly. It is a faster process in learning the risk factors, and profitable opportunities. They have a feature of learning from their mistakes and experiences. When Machine learning is combined with Artificial Intelligence, it can be a large field to gather an immense amount of information and then rectify the errors and learn from further experiences, developing in a smarter, faster and accuracy handling technique. The main difference between Machine Learning and Artificial Intelligence is , If it is written in python then it is probably machine learning, If it is written in power point then it is artificial intelligence. As there are many existing projects that are implemented using AI and Machine Learning , And one of the project i.e., Bitcoin Price Prediction :- Bitcoin (₿ ) (founder - Satoshi Nakamoto , Ledger start: 3 January 2009 ) is a digital currency, a type of electronic money. It is decentralized advanced cash without a national bank or single chairman that can be sent from client to client on the shared Bitcoin arrange without middle people's requirement. Machine learning models can likely give us the insight we need to learn about the future of Cryptocurrency. It will not tell us the future but it might tell us the general trend and direction to expect the prices to move. These machine learning models predict the future of Bitcoin by coding them out in Python. Machine learning and AI-assisted trading have attracted growing interest for the past few years. this approach is to test the hypothesis that the inefficiency of the cryptocurrency market can be exploited to generate abnormal profits. the application of machine learning algorithms to the cryptocurrency market has been limited so far to the analysis of Bitcoin prices, using random forests , Bayesian neural network , long short-term memory neural network , and other algorithms. 2. Applications/Scope of AI and Machine Learning :- a) Sentiment Analysis :- It is the classification of subjective opinions or emotions (positive, negative, and neutral) within text data using natural language processing. b) It is Characterized as a use of computerized reasoning where accessible data is utilized through calculations to process or help the handling of factual information. BITCOIN PRICE PREDICTION USING AI AND MACHINE LEARNING: - The main aim of this is to find the actual Bitcoin price in US dollars can be predicted. The chance to make a model equipped for anticipating digital currencies fundamentally Bitcoin. # It works the prediction by taking the coinMarkup cap. # CoinMarketCap provides with historical data for Bitcoin price changes, keep a record of all the transactions by recording the amount of coins in circulation and the volume of coins traded in the last 24-hours. # Quandl is used to filter the dataset by using the MAT Lab properties. 3. Problem statement: - Some AI and Machine Learning problem statements are: - a) Data Privacy and Security: Once a company has dug up the data, privacy and security is eye-catching aspect that needs to be taken care of. b) Data Scarcity: The data is a very important aspect of AI, and labeled data is used to train machines to learn and make predictions. c) Data acquisition: In the process of machine learning, a large amount of data is used in the process of training and learning. d) High error susceptibility: In the process of artificial intelligence and machine learning, the high amount of data is used. Some problem statements of Bitcoin Price Prediction using AI and Machine Learning: - a) Experimental Phase Risk: It is less experimental than other counterparts. In addition, relative to traditional assets, its level can be assessed as high because this asset is not intended for conservative investors. b) Technology Risks: There is a technological risk to other cryptocurrencies in the form of the potential appearance of a more advanced cryptocurrency. Investors may simply not notice the moment when their virtual assets lose their real value. c) Price Variability: The variability of the value of cryptocurrency are the large volumes of exchange trading, the integration of Bitcoin with various companies, legislative initiatives of regulatory bodies and many other, sometimes disregarded phenomena. d) Consumer Protection: The property of the irreversibility of transactions in itself has little effect on the risks of investing in Bitcoin as an asset. e) Price Fluctuation Prediction: Since many investors care more about whether the sudden rise or fall is worth following. Bitcoin price often fluctuates by more than 10% (or even more than 30%) at some times. f) Lacks Government Regulation: Regulators in traditional financial markets are basically missing in the field of cryptocurrencies. For instance, fake news frequently affects the decisions of individual investors. g) It is difficult to use large interval data (e.g., day-level, and month-level data) . h) The change time of mining difficulties is much longer. Moreover, do not consider the news information since it is hard to determine the authenticity of a news or predict the occurrence of emergencies.
60/100 healthajaybhatiya1234 /
 Read the technical deep dive: https://www.dessa.com/post/deepfake-detection-that-actually-works # Visual DeepFake Detection In our recent [article](https://www.dessa.com/post/deepfake-detection-that-actually-works), we make the following contributions: * We show that the model proposed in current state of the art in video manipulation (FaceForensics++) does not generalize to real-life videos randomly collected from Youtube. * We show the need for the detector to be constantly updated with real-world data, and propose an initial solution in hopes of solving deepfake video detection. Our Pytorch implementation, conducts extensive experiments to demonstrate that the datasets produced by Google and detailed in the FaceForensics++ paper are not sufficient for making neural networks generalize to detect real-life face manipulation techniques. It also provides a current solution for such behavior which relies on adding more data. Our Pytorch model is based on a pre-trained ResNet18 on Imagenet, that we finetune to solve the deepfake detection problem. We also conduct large scale experiments using Dessa's open source scheduler + experiment manger [Atlas](https://github.com/dessa-research/atlas). ## Setup ## Prerequisities To run the code, your system should meet the following requirements: RAM >= 32GB , GPUs >=1 ## Steps 0. Install [nvidia-docker](https://github.com/nvidia/nvidia-docker/wiki/Installation-(version-2.0)) 00. Install [ffmpeg](https://www.ffmpeg.org/download.html) or `sudo apt install ffmpeg` 1. Git Clone this repository. 2. If you haven't already, install [Atlas](https://github.com/dessa-research/atlas). 3. Once you've installed Atlas, activate your environment if you haven't already, and navigate to your project folder. That's it, You're ready to go! ## Datasets Half of the dataset used in this project is from the [FaceForensics](https://github.com/ondyari/FaceForensics/tree/master/dataset) deepfake detection dataset. . To download this data, please make sure to fill out the [google form](https://github.com/ondyari/FaceForensics/#access) to request access to the data. For the dataset that we collected from Youtube, it is accessible on [S3](ttps://deepfake-detection.s3.amazonaws.com/augment_deepfake.tar.gz) for download. To automatically download and restructure both datasets, please execute: ``` bash restructure_data.sh faceforensics_download.py ``` Note: You need to have received the download script from FaceForensics++ people before executing the restructure script. Note2: We created the `restructure_data.sh` to do a split that replicates our exact experiments avaiable in the UI above, please feel free to change the splits as you wish. ## Walkthrough Before starting to train/evaluate models, we should first create the docker image that we will be running our experiments with. To do so, we already prepared a dockerfile to do that inside `custom_docker_image`. To create the docker image, execute the following commands in terminal: ``` cd custom_docker_image nvidia-docker build . -t atlas_ff ``` Note: if you change the image name, please make sure you also modify line 16 of `job.config.yaml` to match the docker image name. Inside `job.config.yaml`, please modify the data path on host from `/media/biggie2/FaceForensics/datasets/` to the absolute path of your `datasets` folder. The folder containing your datasets should have the following structure: ``` datasets ├── augment_deepfake (2) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── base_deepfake (1) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── both_deepfake (3) │ ├── fake │ │ └── frames │ ├── real │ │ └── frames │ └── val │ ├── fake │ └── real ├── precomputed (4) └── T_deepfake (0) ├── manipulated_sequences │ ├── DeepFakeDetection │ ├── Deepfakes │ ├── Face2Face │ ├── FaceSwap │ └── NeuralTextures └── original_sequences ├── actors └── youtube ``` Notes: * (0) is the dataset downloaded using the FaceForensics repo scripts * (1) is a reshaped version of FaceForensics data to match the expected structure by the codebase. subfolders called `frames` contain frames collected using `ffmpeg` * (2) is the augmented dataset, collected from youtube, available on s3. * (3) is the combination of both base and augmented datasets. * (4) precomputed will be automatically created during training. It holds cashed cropped frames. Then, to run all the experiments we will show in the article to come, you can launch the script `hparams_search.py` using: ```bash python hparams_search.py ``` ## Results In the following pictures, the title for each subplot is in the form `real_prob, fake_prob | prediction | label`. #### Model trained on FaceForensics++ dataset For models trained on the paper dataset alone, we notice that the model only learns to detect the manipulation techniques mentioned in the paper and misses all the manipulations in real world data (from data)   #### Model trained on Youtube dataset Models trained on the youtube data alone learn to detect real world deepfakes, but also learn to detect easy deepfakes in the paper dataset as well. These models however fail to detect any other type of manipulation (such as NeuralTextures).   #### Model trained on Paper + Youtube dataset Finally, models trained on the combination of both datasets together, learns to detect both real world manipulation techniques as well as the other methods mentioned in FaceForensics++ paper.   for a more in depth explanation of these results, please refer to the [article](https://www.dessa.com/post/deepfake-detection-that-actually-works) we published. More results can be seen in the [interactive UI](http://deepfake-detection.dessa.com/projects) ## Help improve this technology Please feel free to fork this work and keep pushing on it. If you also want to help improving the deepfake detection datasets, please share your real/forged samples at foundations@dessa.com. ## LICENSE © 2020 Square, Inc. ATLAS, DESSA, the Dessa Logo, and others are trademarks of Square, Inc. All third party names and trademarks are properties of their respective owners and are used for identification purposes only.
52/100 healthsmith-jj /
# Employee Database: A Mystery in Two Parts  ## Background It is a beautiful spring day, and it is two weeks since you have been hired as a new data engineer at Pewlett Hackard. Your first major task is a research project on employees of the corporation from the 1980s and 1990s. All that remain of the database of employees from that period are six CSV files. In this assignment, you will design the tables to hold data in the CSVs, import the CSVs into a SQL database, and answer questions about the data. In other words, you will perform: 1. Data Modeling 2. Data Engineering 3. Data Analysis ## Instructions #### Data Modeling Inspect the CSVs and sketch out an ERD of the tables. Feel free to use a tool like [http://www.quickdatabasediagrams.com](http://www.quickdatabasediagrams.com). #### Data Engineering * Use the information you have to create a table schema for each of the six CSV files. Remember to specify data types, primary keys, foreign keys, and other constraints. * Import each CSV file into the corresponding SQL table. #### Data Analysis Once you have a complete database, do the following: 1. List the following details of each employee: employee number, last name, first name, gender, and salary. 2. List employees who were hired in 1986. 3. List the manager of each department with the following information: department number, department name, the manager's employee number, last name, first name, and start and end employment dates. 4. List the department of each employee with the following information: employee number, last name, first name, and department name. 5. List all employees whose first name is "Hercules" and last names begin with "B." 6. List all employees in the Sales department, including their employee number, last name, first name, and department name. 7. List all employees in the Sales and Development departments, including their employee number, last name, first name, and department name. 8. In descending order, list the frequency count of employee last names, i.e., how many employees share each last name. ## Bonus (Optional) As you examine the data, you are overcome with a creeping suspicion that the dataset is fake. You surmise that your boss handed you spurious data in order to test the data engineering skills of a new employee. To confirm your hunch, you decide to take the following steps to generate a visualization of the data, with which you will confront your boss: 1. Import the SQL database into Pandas. (Yes, you could read the CSVs directly in Pandas, but you are, after all, trying to prove your technical mettle.) This step may require some research. Feel free to use the code below to get started. Be sure to make any necessary modifications for your username, password, host, port, and database name: ```sql from sqlalchemy import create_engine engine = create_engine('postgresql://localhost:5432/<your_db_name>') connection = engine.connect() ``` * Consult [SQLAlchemy documentation](https://docs.sqlalchemy.org/en/latest/core/engines.html#postgresql) for more information. * If using a password, do not upload your password to your GitHub repository. See [https://www.youtube.com/watch?v=2uaTPmNvH0I](https://www.youtube.com/watch?v=2uaTPmNvH0I) and [https://martin-thoma.com/configuration-files-in-python/](https://martin-thoma.com/configuration-files-in-python/) for more information. 2. Create a bar chart of average salary by title. 3. You may also include a technical report in markdown format, in which you outline the data engineering steps taken in the homework assignment. ## Epilogue Evidence in hand, you march into your boss's office and present the visualization. With a sly grin, your boss thanks you for your work. On your way out of the office, you hear the words, "Search your ID number." You look down at your badge to see that your employee ID number is 499942. ## Submission * Create an image file of your ERD. * Create a `.sql` file of your table schemata. * Create a `.sql` file of your queries. * (Optional) Create a Jupyter Notebook of the bonus analysis. * Create and upload a repository with the above files to GitHub and post a link on BootCamp Spot.
31/100 health