Skip to main content

learningHouse - Teach your smart home everything

Project description

learningHouse Service

License Release Build Status PyPI version Discord Chat

learningHouse Logo


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.

Contact and Feedback.

If you have any questions please get in contact with us on discord.

Discord Banner

Please share your ideas what you want to teach your home, suggestions or problems by opening an issue. Really looking forward for your feedback.

Installation and configuration

Install and update using pip.

pip install -U learninghouse

Install and update using docker

docker pull

Prepare configuration directory

mkdir -p brains

The brains directory holds the model configuration as json-file. The models are the brains of your learning house.

There will be one subdirectory per brain, where all files relevant for a brain will be stored. The brain subdirectory needs a config.json holding the basic configuration. The service will store a training_data.csv holding all data of your sensors and an object dump of the trained model to a file called trained.pkl.

Service configuration

The service is configured by environment variables. Following options can be set:

Environment Variable default (production/development) description
LEARNINGHOUSE_ENVIRONMENT production Choose environment default settings production or development.
LEARNINGHOUSE_HOST Set address the service should bind. (use for all available)
LEARNINGHOUSE_PORT 5000 Set the port the service should listen.
LEARNINGHOUSE_BASE_URL Not set Set base url for external access
LEARNINGHOUSE_CONFIG_DIRECTORY ./brains Define directory where all configuration data goes
LEARNINGHOUSE_OPENAPI_FILE /learninghouse_api.json File url path to OpenAPI json file
LEARNINGHOUSE_DOCS_URL /docs Define url path for interactive API documentation. If you set to empty the documentation will be disabled.
LEARNINGHOUSE_JWT_SECRET Generated on startup For administration authentication there is a JWT generated after login. This is signed with this secret. By default it is generated on startup this will invalid existing JWTs on each restart.
LEARNINGHOUSE_JWT_EXPIRE_MINUTES 10 JWTs refresh token will expire after given amount of minutes
LEARNINGHOUSE_DEBUG (False/True) Debugger will be automatically activated in development environment. For security reasons it is recommended not to activate in production.
LEARNINGHOUSE_RELOAD (False/True) Reload of source will be automatically activated in development environment. For security reasons it is recommended not to activate in production.

Example configuration

You can download .env.example and rename it to .env. Inside you can modify default configuration values to your needs in this file.

Run service

In console

Copy the .env.example to .env and modify it to your needs.

Then just run learninghouse to run the service. By default the service will listen to http://localhost:5000/

With docker:

docker run --name learninghouse --rm -v brains:/learninghouse/brains -p 5000:5000 learninghouseservice/learninghouse:latest


The service is protected by different authentication and authorization mechanisms. For administration you have to generated a JWT by using POST request to /api/auth/token endpoint and use the resulting access_token as Authorization: Bearer **access_token** header to each requests. Access token will expire after 1 minute and can be refreshed by sending a PUT request to /api/auth/token using the request_token of the POST result within the expire time configured above as LEARNINGHOUSE_JWT_EXPIRE_MINUTES.

Fallback password

On first run the service is set to use the fallback password learninghouse. Until this is not changed all other endpoints will be deactivated.

Do following procedure to unlock service for usage:

Security notice: Unless you use a proxy setup for SSL security of your connection, only use a seperate password for your learninghouse.

  1. Login using fallback password:
# URL is http://<host>:5000/api/auth/token
curl --location --request POST 'http://localhost:5000/api/auth/login' \
    --header 'Content-Type: application/json' \
    --data-raw '{
        "password": "learninghouse"
  1. Change password Take the returned access_token value for next call to change the fallback password to your own.
# URL is http://<host>:5000/api/auth/password
curl --location --request PUT 'http://localhost:5000/api/auth/password' \
    --header 'Content-Type: application/json' \
    --header 'Authorization: Bearer <access_token>' \
    --data-raw '{
        "old_password": "learninghouse",
        "new_password": "YOURPASSWORD"


You can use the described JWT mechanism to use training and prediction endpoints as well, but for application access you can use an API key mechanism as well. There are two roles for API key authorization user for prediction endpoint and trainer for training and prediction endpoints.

Create a new API key like this:

# URL is http://<host>:5000/api/auth/apikey
curl --location --request POST 'http://localhost:5000/api/auth/apikey' \
    --header 'Content-Type: application/json' \
    --data-raw '{
        "description": "my_trainer_app",
        "role": "trainer"

Your API key will only displayed in this response and can not be requested again. So save it for your usage. If you forget it you have to delete this API key and recreate.

# URL is http://<host>:5000/api/auth/apikey/{description}
curl --location --request DELETE 'http://localhost:5000/api/auth/apikey/my_trainer_app'

You have to give this API key to all requests either as query parameter ?api_key=YOURSECRETKEY or as header field X-LEARNINGHOUSE-API-KEY: YOURSECRETKEY.

Brains and sensors configuration

Sensors configuration

Send data of all sensors to learningHouse Service especially when training your brains. The service will save all data fields even if they are not used as a feature at the moment. The service will choose the best feature set each time you train a brain.

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'), ...
  • weather_condition: 'sunny', 'cloudy'
  • switch: 'ON', 'OFF'

To enable the service to use the data of your sensors as features for your brain, you have to give the service information about the data type. For this put a sensors.json to the directory brains. 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 brain

The brain decides whether it is so dark that the light has to be switched on. It uses a machine learning algorithm called RandomForestClassifier.

Store a config.json in brains/darkness subdirectory with following content:

    "estimator": {
        "typed": "classifier",
        "estimators": 100, 
        "max_depth": 5
    "dependent": "darkness",
    "dependent_encode": true,
    "test_size": 0.2

Configuration parameter


LearningHouse Service can predict values using an estimator. An estimator can be of type classifier which fits best for your needs if you have somekind of categorical output like in the example true and false. If you want to predict a numerical value for example the setpoint of an heating equipment use the type regressor instead.

For both types learningHouse Service uses a machine learning algorithm called random forest estimation. This algorithm builds a "forest" of decision trees with your features and takes the mean of the prediction of all of them to give you a best result. For more details see the API description of scikit-learn:

Estimator type API Reference

You can adjust the amount of decision trees by using estimators (default: 100) option. And the maximum depth of each tree by using max_depth (default: 5) option. Both options are optional. Try to resize this value to optimize the accuracy of your model.

Dependent variable

The dependent variable is the one that have to be in the training data and which is predicted by the trained brain.

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 brain. The other part specified by test_size will be used to score the accuracy of your brain.

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 brain 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 estimator configuration to gain a better score

Training of the brain will start, when there are at least 10 data points.

Change configuration via RESTful API

You can change the configuration of sensors and brains although via the API. Visit the interactive API documentation when the service is running.

The configuration endpoints are always protected by JWT authentication mechanism (see Security).

API Documentation

When the service is running, you can reach an interactive API documentation by calling url http://localhost:5000/docs

Train brain

For training send a PUT request to the service:

You need JWT or API key role trainer for this request (see Security)

# URL is http://<host>:5000/api/brain/:name/training
curl --location --request PUT 'http://localhost:5000/api/brain/darkness/training' \
    --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.

To train the brain with existing data for example after a service update use a POST request without data:

You need JWT or API key role trainer for this request (see Security)

# URL is http://host:5000/api/brain/:name/training
curl --location \
    --request POST 'http://localhost:5000/api/brain/darkness/training'

To get the information about a trained brain use a GET request:

You need JWT or API key role trainer or user for this request (see Security)

# URL is http://host:5000/api/brain/:name/info
curl --location \
    --request GET 'http://localhost:5000/brain/darkness/info'


To predict a new data set with your brain send a POST request:

You need JWT or API key role trainer or user for this request (see Security)

# URL is http://host:5000/api/brain/:name/prediction
curl --location --request POST 'http://localhost:5000/api/brain/darkness/prediction' \
    --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 brain 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

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

learninghouse-1.7.4.tar.gz (2.8 MB view hashes)

Uploaded source

Built Distribution

learninghouse-1.7.4-py3-none-any.whl (2.8 MB view hashes)

Uploaded py3

Supported by

AWS AWS Cloud computing Datadog Datadog Monitoring Fastly Fastly CDN Google Google Object Storage and Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page