learninghouse 1.0.1

learningHouse - Teach your smart home everything

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.

At this moment the project is in an 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 brains/config
mkdir -p brains/training
mkdir -p brains/compiled

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

The directories training and compiled are used by service to store data.

Training data is stored as csv file, trained brains are stored as object dump.

Service configuration

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

Configuration of brains

Configuration is stored in json format.

General configuration

In general 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'), ...

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 brain/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 brain

The brain 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 brains/config directory with following content:

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

Configuration parameter

Estimator

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
RandomForestRegressor	https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestRegressor.html#sklearn.ensemble.RandomForestRegressor
RandomForestClassifier	https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier

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 (see above) to gain a better score

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

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

API Documentation

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

Train brain

For training send a PUT request to the service:

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

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

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

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

Prediction

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

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