learningHouse - Teach your smart home everything
Project description
learningHouse Service
Introduction
learningHouse Service provides machine learning algorithms based on scikit-learn python library as a RESTful API, with the purpose to give smart home fans an easy possibility to teach their homes.
Add the moment this project is in a very early state. Please share your ideas what you want to teach your home by opening an issue. Really looking forward for your feedback.
Installation
Install and update using pip.
pip install -U learninghouse
Install and update using docker
docker pull learninghouseservice/learninghouse:latest
Prepare configuration directory
mkdir -p models/config
mkdir -p models/training
mkdir -p models/compiled
The config
directory holds the model configuration as json-file.
The directories training
and compiled
are used by service to store data.
Training data is stored as csv file, trained models are stored as object dump.
Configuration of models
Configuration is stored in json format.
General configuration
In general send data of all sensors to learningHouse Service especially when training your models. The service will save all data fields even if they are not used in current model configuration as a feature
. This will give you the possibility to choose different features later on to improve your model after some training (There will be some features later on to support you with this improvment).
In general there are two different data types your sensor data can be divided in. Numerical data
can be processed directly by your models. Categorical data
has to be preproccesed by the service to be used as a feature
. Categorical data
can be identified by a simple rule:
Non numerical values or if you can give each numerical value a term to describe it.
Some examples for categorical data:
- pressure_trend: Values of 'falling', 'rising', 'consistent'
- month_of_year: 1 ('January'), 2 ('Februrary'), ...
To use the data of your sensors as features
in your models you have to give the service information about the data type. For this put a sensors.json to the directory model/config. List all your sensors and their data type.
Example content of sensors.json:
{
"azimuth": "numerical",
"elevation": "numerical",
"rain_gauge": "numerical",
"pressure": "numerical",
"pressure_trend_1h": "categorical",
"temperature_outside": "numerical",
"temperature_trend_1h": "categorical",
"light_state": "categorical"
}
Example model
The model decides whether it is so dark that the light has to be switched on. It uses the sun azimuth and sun elevation, the rain gauge and the one hour trend of air pressure. The data of the other senors (pressure, temperature_outside, light_state) isn't used in this example. It use a machine learning algorithm called RandomForestClassifier.
Store a darkness.json in models/config directory with following content:
{
"estimator": {
"class": "RandomForestClassifier",
"options": {
"n_estimators": 100,
"random_state": 0
}
},
"features": ["azimuth", "elevation", "rain_gauge", "pressure_trend_1h_falling"],
"dependent": "darkness",
"dependent_encode": true,
"test_size": 0.2
}
Configuration parameter
Estimator
First of all we choose an estimator
with one of scikit-learn estimator classes and configure it with the options you can find at API description.
At the moment learningHouse service supports the following estimators from scikit-learn:
Estimator class | API Reference for options |
---|---|
DecisionTreeClassifier | https://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html#sklearn.tree.DecisionTreeClassifier |
RandomForestClassifier | https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier |
Features
The list of features
is required and holds the names of the sensor data the model uses to take a decision. Categorical data
as mentioned above will be preprocessed by the service and is divided to one column by each known value. You can use each column as a seperate feature
. There will be a feature in this service later on to help you to choose the set of best features. Meanwhile use the ones you think they maybe have influence on the decision as a first try.
Dependent variable
The dependent
variable is the one that have to be in the training data and which is predicted by the trained model.
The dependent
variable has to be a number. If it is not a number, but a string or boolean (true/false) like in the example. For this set dependent_encode
to true.
Test size
LearningHouse service only uses a part of your training data to train the model. The other part specified by test_size
will be used to score the accuracy of your model.
Give a percentage by using floating point numbers between 0.01 and 0.99 or a absolute number of data points by using integer numbers.
For the beginning a test_size
of 20 % (0.2) like the example should be fine.
The accuracy between 80 % and 90 % between is a good score to gain. Below your model is kind of underfitted and above overfitted, which both make it not working well for new data points to be predicted. You can try to change the set of used features
to gain a better accuracy.
Training of the model will start, when there are at least 10 data points.
Run service
In the console type learninghouse
to start the service in development mode. By default the service will run on port 5000. In development mode you will see some log information.
To start service in production mode and specify listen address and port use following command:
learninghouse --production --host 127.0.0.1 --port 5001
Run with docker:
docker run --name learninghouse --rm -v models:/learninghouse/models -p 5000:5000 learninghouseservice/learninghouse:latest
Train model
For training send a PUT request to the service:
# URL is http://host:5000/training/:modelname
curl --location --request PUT 'http://localhost:5000/training/darkness' \
--header 'Content-Type: application/json' \
--data-raw '{
"azimuth": 321.4441223144531,
"elevation": -19.691608428955078,
"rain_gauge": 0.0,
"pressure": 971.0,
"pressure_trend_1h": "falling",
"temperature_outside": 23.0,
"temperature_trend_1h": "rising",
"light_state": false,
"darkness": true
}'
You can send either a field timestamp
with your dataset containing a UNIX-Timestamp or the service will add this information with its current time. The service generate some further time relevant fields inside the training dataset you can although use as features
. These are month_of_year
, day_of_month
, day_of_week
, hour_of_day
and minute_of_hour
If one of your sensors is not working at the moment and for this not sending a value the service will add a value by using the following rules. For categorical data
all categorical columns will be set to zero. For numerical data
the mean of all known training set values (see Test size) for this feature
will be assumed.
As mentioned above there will be a service feature which helps you to choose the features
used in your model from time to time to improve the model. For this always send the data of every sensor when train your model. The service will store this values for possible future use.
To train the model with existing data for example after a service update use a POST request without data:
# URL is http://host:5000/training/:modelname
curl --location --request POST 'http://localhost:5000/training/darkness'
To get the information about a trained model use a GET request:
# URL is http://host:5000/info/:modelname
curl --location --request GET 'http://localhost:5000/info/darkness'
Prediction
To predict a new data set with your model send a POST request:
# URL is http://host:5000/info/:modelname
curl --location --request POST 'http://localhost:5000/prediction/darkness' \
--header 'Content-Type: application/json' \
--data-raw '{
"azimuth": 321.4441223144531,
"elevation": -19.691608428955078,
"rain_gauge": 0.0,
"pressure_trend_1h": "falling"
}'
If one of your sensors used as feature
in the model is not working at the moment and for this not sending a value the service will add this by using following rules. For categorical data
all categorical columns will be set to zero. For numerical data
the mean of all known training set values (see Test size) for this feature
will be assumed.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for learninghouse-0.9.2-py3-none-any.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | dcc8a8f5f74137b61d9af807e44ab1cdc0e1df01b515a45197c6394f0bc8f6df |
|
MD5 | 906f5311f1d00074cac376eeffe65982 |
|
BLAKE2b-256 | a68070ece92dec8d178b14b8a4e4a923195a192e7d38c8714aacbdac609a4825 |