The pyfbad library supports anomaly detection projects. An end-to-end anomaly detection application can be written using the source codes of this library only.
Given below is a basic application. Each section has more alternatives like mysql under database, slack under notification or isolation forest under model.
Installation:
Python 2 is no longer supported. Make sure Python3+ is used as the programming language. The optimal version would be Python 3.7. It is recommended to use pip or conda for installation. Please make sure the latest version is installed, as pyfbad is updated frequently:
pip install pyfbad # normal install
pip install --upgrade pyfbad # or update if needed
Database operations:
# connet to mongodb
from pyfbad.data import database as db
database_obj = db.MongoDB('db_name', PORT, 'db_path')
database = database_obj.get_mongo_db()
# check the collections
collections = dataset_obj.get_collection_names(database)
# buil mongodb query
filter = dataset_obj.add_filter(
[],
'time',
{
"column_name": "datetime",
"date_type": "hourly",
"start_time": "2019-02-06 00:00:00",
"finish_time": "2019-10-06 00:00:00"
})
# get data from db as dataframe
data = dataset_obj.get_data_as_df(
database=database,
collection=collections[0],
filter=filter
)
Feature Operations:
from pyfbad.features import create_feature as cf
cf_obj = cf.Features()
df_model = cf_obj.get_model_data(df=df, time_column_name="_id.datetime", value_column_name="_id.count", filter=['_id.country','TR'])
Model Operations:
from pyfbad.models import models as md
models=md.Model_Prophet()
model_result = models.train_model(df_model)
anomaly_result = models.train_forecast(model_result)
Notification Operations:
from pyfbad.notification import notifications as nt
gmail_obj = nt.Email()
if 1 or -1 in anomaly_result['anomaly']:
gmail_obj.send_gmail('sample_from@gmail.com','password','sample_to@gmail.com')
Required Dependencies:
Depencies can be shown in requirements.txt file.
├── LICENSE
├── Makefile <- Makefile with commands like `make data` or `make train`
├── README.md <- The top-level README for developers using this project.
├── data
│ ├── external <- Data from third party sources.
│ ├── interim <- Intermediate data that has been transformed.
│ ├── processed <- The final, canonical data sets for modeling.
│ └── raw <- The original, immutable data dump.
│
├── docs <- A default Sphinx project; see sphinx-doc.org for details
│
├── models <- Trained and serialized models, model predictions, or model summaries
│
├── notebooks <- Jupyter notebooks. Naming convention is a number (for ordering),
│ the creator's initials, and a short `-` delimited description, e.g.
│ `1.0-jqp-initial-data-exploration`.
│
├── references <- Data dictionaries, manuals, and all other explanatory materials.
│
├── reports <- Generated analysis as HTML, PDF, LaTeX, etc.
│ └── figures <- Generated graphics and figures to be used in reporting
│
├── requirements.txt <- The requirements file for reproducing the analysis environment, e.g.
│ generated with `pip freeze > requirements.txt`
│
├── setup.py <- makes project pip installable (pip install -e .) so src can be imported
├── src <- Source code for use in this project.
│ └── pyfbad
│ ├── __init__.py <- Makes pyfbad a Python module
│ │
│ ├── data <- Scripts to read raw data
│ │ └── database.py
│ │ └── __init__.py
│ │
│ ├── features <- Scripts to turn raw data into features for modeling
│ │ └── create_feature.py
│ │ └── __init__.py
│ │
│ ├── models <- Scripts to train models and then use trained models to make
│ │ │ predictions
│ │ └── models.py
│ │ └── __init__.py
│ │
│ └── notification <- Scripts for setting up notification systems.
│ └── notification.py
│ └── __init__.py
│
└── tox.ini <- tox file with settings for running tox; see tox.readthedocs.io