craft-ai
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    craft ai isomorphic javascript client

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    craft ai's Explainable AI API enables product & operational teams to quickly deploy and run explainable AIs. craft ai decodes your data streams to deliver self-learning services.

    Get Started!

    1 - Create a project

    Once your account is setup, let's create your first project! Go in the 'Projects' tab in the craft ai control center at https://beta.craft.ai/inspector, and press Create a project.

    Once it's done, you can click on your newly created project to retrieve its tokens. There are two types of tokens: read and write. You'll need the write token to create, update and delete your agent.

    2 - Setup

    Install

    Node.js / Webpack / Browserify

    Let's first install the package from npm.

    npm install craft-ai --save

    Then import it in your code

    const craftai = require('craft-ai').createClient;

    or using es2015 syntax

    import craftai from 'craft-ai';
    Plain Old Javascript

    Thanks to npmcdn, you can include the pre-generated bundle in your html file, for the latest version use

    <script type="text/javascript" src="https://npmcdn.com/craft-ai/dist/craft-ai.min.js"></script>

    to include a specific version specify it in the url like

    <script type="text/javascript" src="https://npmcdn.com/craft-ai@0.1.13/dist/craft-ai.min.js"></script>

    Initialize

    // The token you retrieved for a given project
    const client = craftai('{token}');

    3 - Create an agent

    craft ai is based on the concept of agents. In most use cases, one agent is created per user or per device.

    An agent is an independent data set that stores the history of the context of its user or device's context, and learns which prediction to make based on the evolution of this context.

    In this example, we will create an agent that learns the predictive model of a light bulb based on the time of the day and the number of people in the room. This dataset is treated as continuous context updates. If your data is more like events than context changes, please refer to the Advanced Configuration section to know how to configure operations_as_events for your agent. Here, the agent's context has 4 properties or features:

    • peopleCount which is a continuous property,
    • timeOfDay which is a time_of_day property,
    • timezone, a property of type timezone needed to generate proper values for timeOfDay (cf. the context properties type section for further information),
    • and finally lightbulbState which is an enum property that is also the output.

    ℹ️ timeOfDay is auto-generated, you will find more information below.

    const AGENT_ID = 'my_first_agent';
    
    client.createAgent(
      {
        context: {
          peopleCount: {
            type: 'continuous'
          },
          timeOfDay: {
            type: 'time_of_day'
          },
          timezone: {
            type: 'timezone'
          },
          lightbulbState: {
            type: 'enum'
          }
        },
        model_type: 'decisionTree',
        output: [ 'lightbulbState' ]
      },
      AGENT_ID
    )
    .then(function(agent) {
      console.log('Agent ' + agent.id + ' successfully created!');
    })
    .catch(function(error) {
      console.error('Error!', error);
    });

    Pretty straightforward to test! Open https://beta.craft.ai/inspector, select you project and your agent is now listed.

    Now, if you run that a second time, you'll get an error: the agent 'my_first_agent' was already created. Let's see how we can delete it before recreating it.

    const AGENT_ID = 'my_first_agent';
    
    client.deleteAgent(AGENT_ID)
    .then(function() {
      console.log('Agent ' + AGENT_ID + ' no longer exists.');
      return client.createAgent(/*...*/);
    })
    .then(function(agent) {
      console.log('Agent ' + agent.id + ' successfully created!');
    })
    .catch(function(error) {
      console.error('Error!', error);
    });

    For further information, check the 'create agent' reference documentation.

    4 - Add context operations

    We have now created our first agent but it is not able to do much, yet. To learn a model it needs to be provided with data, in craft ai these are called context operations.

    In the following we add 8 operations:

    1. The initial one sets the initial state of the agent, on July 25 2016 at 5:30, in Paris, nobody is there and the light is off;
    2. At 7:02, someone enters the room the light is turned on;
    3. At 7:15, someone else enters the room;
    4. At 7:31, the light is turned off;
    5. At 8:12, everyone leaves the room;
    6. At 19:23, 2 persons enter the room;
    7. At 22:35, the light is turned on;
    8. At 23:06, everyone leaves the room and the light is turned off.
    const AGENT_ID = 'my_first_agent';
    
    // for the purpose of this test, we delete and recreate the agent
    client.deleteAgent(AGENT_ID)
    .then(function() {
      console.log('Agent ' + AGENT_ID + ' no longer exists.');
      return client.createAgent(/*...*/);
    })
    .then(function(agent) {
      console.log('Agent ' + agent.id + ' successfully created!');
      return client.addAgentContextOperations(
        AGENT_ID,
        [
          {
            timestamp: 1469410200,
            context: {
              timezone: '+02:00',
              peopleCount: 0,
              lightbulbState: 'OFF'
            }
          },
          {
            timestamp: 1469415720,
            context: {
              timezone: '+02:00',
              peopleCount: 1,
              lightbulbState: 'ON'
            }
          },
          {
            timestamp: 1469416500,
            context: {
              timezone: '+02:00',
              peopleCount: 2,
              lightbulbState: 'ON'
            }
          },
          {
            timestamp: 1469417460,
            context: {
              timezone: '+02:00',
              peopleCount: 2,
              lightbulbState: 'OFF'
            }
          },
          {
            timestamp: 1469419920,
            context: {
              timezone: '+02:00',
              peopleCount: 0,
              lightbulbState: 'OFF'
            }
          },
          {
            timestamp: 1469460180,
            context: {
              timezone: '+02:00',
              peopleCount: 2,
              lightbulbState: 'OFF'
            }
          },
          {
            timestamp: 1469471700,
            context: {
              timezone: '+02:00',
              peopleCount: 2,
              lightbulbState: 'ON'
            }
          },
          {
            timestamp: 1469473560,
            context: {
              timezone: '+02:00',
              peopleCount: 0,
              lightbulbState: 'ON'
            }
          }
        ]
      )
      .then(function() {
        return agent;
      });
    })
    .then(function(agent) {
      console.log('Successfully added initial operations to agent ' + agent.id + '.');
    })
    .catch(function(error) {
      console.error('Error!', error);
    });

    In real-world applications you will probably do the same kind of thing when the agent is created, and then regularly throughout the lifetime of the agent with newer data.

    For further information, check the 'add context operations' reference documentation.

    5 - Compute the decision tree

    The agent has acquired a context history, we can now compute a model (in this case a decision tree) from it! A decision tree models the output, allowing us to estimate what the output would be in a given context.

    The decision tree is computed at a given timestamp, which means it will consider the data from the creation of this agent up to this moment. Let's first try to compute the decision tree at midnight on July 26, 2016.

    const AGENT_ID = 'my_first_agent';
    
    // for the purpose of this test, we delete and recreate the agent
    client.deleteAgent(AGENT_ID)
    .then(function() {
      console.log('Agent ' + AGENT_ID + ' no longer exists.');
      return client.createAgent(/*...*/);
    })
    .then(function(agent) {
      console.log('Agent ' + agent.id + ' successfully created!');
      return client.addAgentContextOperations(AGENT_ID, /*...*/)
      .then(function() {
        return agent;
      });
    })
    .then(function(agent) {
      console.log('Successfully added initial operations to agent ' + agent.id + '.');
      return client.getAgentDecisionTree(AGENT_ID, 1469476800);
    })
    .then(function(tree) {
      console.log('Decision tree retrieved!', tree);
    })
    .catch(function(error) {
      console.error('Error!', error);
    });

    Try to retrieve the tree at different timestamps to see how it gradually learns from the new operations. To visualize the trees, use the inspector!

    For further information, check the 'compute decision tree' reference documentation.

    6 - Make a decision

    Once the decision tree is computed it can be used to make a decision or prediction. In our case it is basically answering this type of question: "What is the anticipated state of the lightbulb at 7:15 if there are 2 persons in the room ?".

    const AGENT_ID = 'my_first_agent';
    
    // for the purpose of this test, we delete and recreate the agent
    client.deleteAgent(AGENT_ID)
    .then(function() {
      console.log('Agent ' + AGENT_ID + ' no longer exists.');
      return client.createAgent(/*...*/);
    })
    .then(function(agent) {
      console.log('Agent ' + agent.id + ' successfully created!');
      return client.addAgentContextOperations(AGENT_ID, /*...*/)
      .then(function() {
        return agent;
      });
    })
    .then(function(agent) {
      console.log('Successfully added initial operations to agent ' + agent.id + '.');
      return client.getAgentDecisionTree(AGENT_ID, 1469476800);
    })
    .then(function(tree) {
      console.log('Decision tree retrieved!', tree);
      const res = craftai.interpreter.decide(tree, {
        timezone: '+02:00',
        timeOfDay: 7.25,
        peopleCount: 2
      });
      console.log('The anticipated lightbulb state is "' + res.output.lightbulbState.predicted_value + '".');
    })
    .catch(function(error) {
      console.error('Error!', error);
    });

    For further information, check the 'make decision' reference documentation.

    Node.JS starter kit

    If you prefer to get started from an existing code base, the official Node.JS starter kit can get you there! Retrieve the sources locally and follow the "readme" to get a fully working SmartHome app using real-world data.

    📦 Get the craft ai Node.JS Starter Kit

    API

    Project

    craft ai agents belong to projects. In the current version, each identified users defines a owner and can create projects for themselves, in the future we will introduce shared projects.

    Configuration

    Each agent has a configuration defining:

    • the context schema, i.e. the list of property keys and their type (as defined in the following section),
    • the output properties, i.e. the list of property keys on which the agent makes decisions,
    • the model type, either decision tree or gradient boosting.

    Context properties types

    Base types: enum, continuous and boolean

    enum, continuous and boolean are the three base craft ai types:

    • an enum property is a string;
    • a continuous property is a real number.
    • a boolean property is a boolean value: true or false

    ⚠️ the absolute value of a continuous property must be less than 1020.

    Here is a simple example of configuration for decision tree:

    {
      "context": {
        "timezone": {
          "type": "enum"
        },
        "temperature": {
          "type": "continuous"
        },
        "lightbulbState": {
          "type": "enum"
        }
      },
      "model_type": "decisionTree",
      "output": ["lightbulbState"],
      "time_quantum": 100,
      "learning_period": 108000
    }

    And another simple example of configuration for gradient boosting:

    {
      "context": {
        "timezone": {
          "type": "enum"
        },
        "temperature": {
          "type": "continuous"
        },
        "lightbulbState": {
          "type": "enum"
        }
      },
      "model_type": "boosting",
      "output": ["lightbulbState"],
      "learning_rate": 1,
      "num_iterations": 50,
      "time_quantum": 100,
      "learning_period": 108000
    }
    Time types: timezone, time_of_day, day_of_week, day_of_month and month_of_year

    craft ai defines the following types related to time:

    • a time_of_day property is a real number belonging to [0.0; 24.0[, each value represents the number of hours in the day since midnight (e.g. 13.5 means 13:30),
    • a day_of_week property is an integer belonging to [0, 6], each value represents a day of the week starting from Monday (0 is Monday, 6 is Sunday).
    • a day_of_month property is an integer belonging to [1, 31], each value represents a day of the month.
    • a month_of_year property is an integer belonging to [1, 12], each value represents a month of the year.
    • a timezone property can be:
      • a string value representing the timezone as an offset from UTC, supported formats are:

        • ±[hh]:[mm],
        • ±[hh][mm],
        • ±[hh],

        where hh represent the hour and mm the minutes from UTC (eg. +01:30)), between -12:00 and +14:00.

      • an integer belonging to [-720, 840] which represents the timezone as an offset from UTC:

        • in hours if the integer belongs to [-15, 15]
        • in minutes otherwise
      • an abbreviation among the following:

        • UTC or Z Universal Time Coordinated,
        • GMT Greenwich Mean Time, as UTC,
        • BST British Summer Time, as UTC+1 hour,
        • IST Irish Summer Time, as UTC+1,
        • WET Western Europe Time, as UTC,
        • WEST Western Europe Summer Time, as UTC+1,
        • CET Central Europe Time, as UTC+1,
        • CEST Central Europe Summer Time, as UTC+2,
        • EET Eastern Europe Time, as UTC+2,
        • EEST Eastern Europe Summer Time, as UTC+3,
        • MSK Moscow Time, as UTC+3,
        • MSD Moscow Summer Time, as UTC+4,
        • AST Atlantic Standard Time, as UTC-4,
        • ADT Atlantic Daylight Time, as UTC-3,
        • EST Eastern Standard Time, as UTC-5,
        • EDT Eastern Daylight Saving Time, as UTC-4,
        • CST Central Standard Time, as UTC-6,
        • CDT Central Daylight Saving Time, as UTC-5,
        • MST Mountain Standard Time, as UTC-7,
        • MDT Mountain Daylight Saving Time, as UTC-6,
        • PST Pacific Standard Time, as UTC-8,
        • PDT Pacific Daylight Saving Time, as UTC-7,
        • HST Hawaiian Standard Time, as UTC-10,
        • AKST Alaska Standard Time, as UTC-9,
        • AKDT Alaska Standard Daylight Saving Time, as UTC-8,
        • AEST Australian Eastern Standard Time, as UTC+10,
        • AEDT Australian Eastern Daylight Time, as UTC+11,
        • ACST Australian Central Standard Time, as UTC+9.5,
        • ACDT Australian Central Daylight Time, as UTC+10.5,
        • AWST Australian Western Standard Time, as UTC+8.

    ℹ️ By default, the values of the time_of_day and day_of_week properties are generated from the timestamp of an agent's state and the agent's current timezone. Therefore, whenever you use generated time_of_day and/or day_of_week in your configuration, you must provide a timezone value in the context. There can only be one timezone property.

    If you wish to provide their values manually, add is_generated: false to the time types properties in your configuration. In this case, since you provide the values, the timezone property is not required, and you must update the context whenever one of these time values changes in a way that is significant for your system.

    Examples

    Let's take a look at the following configuration. It is designed to model the color of a lightbulb (the lightbulbColor property, defined as an output) depending on the outside light intensity (the lightIntensity property), the TV status (the TVactivated property) the time of the day (the time property) and the day of the week (the day property).

    day and time values will be generated automatically, hence the need for timezone, the current Time Zone, to compute their value from given timestamps.

    The time_quantum is set to 100 seconds, which means that if the lightbulb color is changed from red to blue then from blue to purple in less that 1 minutes and 40 seconds, only the change from red to purple will be taken into account.

    The learning_period is set to 108 000 seconds (one month) , which means that the state of the lightbulb from more than a month ago can be ignored when learning the decision model.

    {
      "context": {
        "lightIntensity": {
          "type": "continuous"
        },
        "TVactivated": {
          "type": "boolean"
        },
        "time": {
          "type": "time_of_day"
        },
        "day": {
          "type": "day_of_week"
        },
        "timezone": {
          "type": "timezone"
        },
        "lightbulbColor": {
          "type": "enum"
        }
      },
      "model_type": "decisionTree",
      "output": ["lightbulbColor"],
      "time_quantum": 100,
      "learning_period": 108000
    }

    In this second example, the time property is not generated, no property of type timezone is therefore needed. However values of time must be manually provided continuously.

    {
      "context": {
        "time": {
          "type": "time_of_day",
          "is_generated": false
        },
        "lightIntensity": {
          "type": "continuous"
        },
          "TVactivated": {
          "type": "boolean"
        },
        "lightbulbColor": {
          "type": "enum"
        }
      },
      "model_type": "decisionTree",
      "output": ["lightbulbColor"],
      "time_quantum": 100,
      "learning_period": 108000
    }

    Timestamp

    craft ai API heavily relies on timestamps. A timestamp is an instant represented as a Unix time, that is to say the amount of seconds elapsed since Thursday, 1 January 1970 at midnight UTC. Note that some programming languages use timestamps in milliseconds, but here we only refer to timestamps in seconds. In most programming languages this representation is easy to retrieve, you can refer to this page to find out how.

    craftai.Time

    The craftai.Time class facilitates the handling of time types in craft ai. It is able to extract the different craft ai formats from various datetime representations, thanks to Moment.js.

    From a unix timestamp and an explicit UTC offset:

    const t1 = new craftai.Time(1465496929, '+10:00');
    
    // t1 === {
    //   utc: '2016-06-09T18:28:49.000Z',
    //   timestamp: 1465496929,
    //   day_of_week: 4,
    //   day_of_month: 10,
    //   month_of_year: 6,
    //   time_of_day: 4.480277777777778,
    //   timezone: '+10:00'
    // }

    From a unix timestamp and using the local UTC offset:

    const t2 = new craftai.Time(1465496929);
    
    // Value are valid if in Paris !
    // t2 === {
    //   utc: '2016-06-09T18:28:49.000Z',
    //   timestamp: 1465496929,
    //   day_of_week: 3,
    //   day_of_month: 9,
    //   month_of_year: 6,
    //   time_of_day: 20.480277777777776,
    //   timezone: '+02:00'
    // }

    From a ISO 8601 string:

    const t3 = new craftai.Time('1977-04-22T01:00:00-05:00');
    
    // t3 === {
    //   utc: '1977-04-22T06:00:00.000Z',
    //   timestamp: 230536800,
    //   day_of_week: 4,
    //   time_of_day: 1,
    //   day_of_month: 22,
    //   month_of_year: 4,
    //   timezone: '-05:00'
    // }

    From a moment (or moment timezone) instance:

    const t4 = new craftai.Time(moment.tz('2017-05-31 12:45:00', 'Asia/Dubai'), '-08:00'));
    
    // t4 === {
    //   utc: '2017-05-31T08:45:00.000Z',
    //   timestamp: 1496220300,
    //   day_of_week: 2,
    //   day_of_month: 31,
    //   month_of_year: 5,
    //   time_of_day: 0.75,
    //   timezone: '-08:00'
    // }

    Retrieve the current time with the local UTC offset:

    const now = new craftai.Time();

    Retrieve the current time with a given UTC offset:

    const nowP5 = new craftai.Time(undefined, '+05:00');

    Configuration parameters

    The following configuration parameters can be set in specific cases.

    Common parameters

    • model_type, i.e. the selected model. Values can be decisionTree or boosting. If not set, the default value is decisionTree.
    • time_quantum, i.e. the minimum amount of time, in seconds, that is meaningful for an agent; context updates occurring faster than this quantum won't be taken into account. As a rule of thumb, you should always choose the largest value that seems right and reduce it, if necessary, after some tests. Default value is 600. This parameter is ignored if operations_as_events is set to true.
    • operations_as_events is a boolean, either true or false. The default value is false. If you are not sure what to do, set it to true. If it is set to false, context operations are treated as state changes, and models are based on the resulting continuous state including between data points, using time_quantum as the sampling step. If it is set to true, context operations are treated as observations or events, and models are based on these data points directly, as in most machine learning libraries. If operations_as_events is true, max_training_samples and learning_period for decision trees must be set, and time_quantum is ignored because events have no duration.
    • max_training_samples is a positive integer. It can and must be set only if operations_as_events is true. It defines the maximum number of events on which a model can be based. It is complementary to learning_period for decision trees, which limits the maximum age of data on which a model is based.
    • min_samples_per_leaf is a positive integer. It defines the minimum number of samples in a tree leaf. It is complementary to tree_max_depth in preventing the tree from overgrowing, hence limiting overfitting. By default, min_samples_per_leaf is set to 4.
    • tree_max_depth is a positive integer. It defines the maximum depth of decision trees, which is the maximum distance between the root node and a leaf (terminal) node. A depth of 0 means that the tree is made of a single root node. By default, tree_max_depth is set to 6 if the output is categorical (e.g. enum), or to 4 if the output is numerical (e.g. continuous) or if it's a boosting configuration.

    Decision tree parameters

    • learning_period, i.e. the maximum amount of time, in seconds, that matters for an agent; the agent's decision model can ignore context that is older than this duration. You should generally choose the smallest value that fits this description. Default value is 15000 time quantums and the maximum learning_period value is 55000 * time_quantum.

    Boosting parameters

    • learning_rate is a positive float. It defines the step size shrinkage used between tree updates to prevent overfitting. Its value must be in ]0;1].
    • num_iterations is a positive integer. It describes the number of trees that would be created for the forest.

    Agent

    Create

    Create a new agent, and define its configuration.

    The agent's identifier is a case sensitive string between 1 and 36 characters long. It only accepts letters, digits, hyphen-minuses and underscores (i.e. the regular expression /[a-zA-Z0-9_-]{1,36}/).

    client.createAgent(
      { // The configuration
        context: {
          peopleCount: {
            type: 'continuous'
          },
          timeOfDay: {
            type: 'time_of_day'
          },
          timezone: {
            type: 'timezone'
          },
          lightbulbState: {
            type: 'enum'
          }
        },
        model_type: 'decisionTree',
        output: [ 'lightbulbState' ],
        time_quantum: 100,
        learning_period: 108000
      },
      'impervious_kraken' // id for the agent, if undefined a random id is generated
    )
    .then(function(agent) {
      // Work on the agent here
      // agent = {
      //   _version: <version>
      //   id: <agent_id>,
      //   configuration: {
      //     context: {
      //       peopleCount: {
      //         type: 'continuous'
      //       },
      //       timeOfDay: {
      //         type: 'time_of_day'
      //       },
      //       timezone: {
      //         type: 'timezone'
      //       },
      //       lightbulbState: {
      //         type: 'enum'
      //       }
      //     },
      //     output: [ 'lightbulbState' ],
      //     time_quantum: 100,
      //     learning_period: 108000
      //   }
      // }
    })
    .catch(function(error) {
      // Catch errors here
    })

    Delete

    client.deleteAgent(
      'impervious_kraken' // The agent id
    )
    .then(function() {
      // The agent was successfully deleted
    })
    .catch(function(error) {
      // Catch errors here
    })

    Retrieve

    client.getAgent(
      'impervious_kraken' // The agent id
    )
    .then(function(agent) {
      // Agent details
    })
    .catch(function(error) {
      // Catch errors here
    })

    List

    client.listAgents()
    .then(function(agentIds) {
      // list of agent ids, eg. ['impervious_kraken', 'impervious_kraken']
    })
    .catch(function(error) {
      // Catch errors here
    })

    Create and retrieve shared url

    Create and get a shareable url to view an agent tree. Only one url can be created at a time.

    client.getSharedAgentInspectorUrl(
      'impervious_kraken', // The agent id.
      1464600256 // optional, the timestamp for which you want to inspect the tree.
    )
    .then(function(url) {
      // Url to the agent's inspector
    })
    .catch(function(error) {
      // Catch errors here
    })

    Delete shared url

    Delete a shareable url. The previous url cannot access the agent tree anymore.

    client.deleteSharedAgentInspectorUrl(
      'impervious_kraken' // The agent id.
    )
    .then(function() {
      // return nothing
    })
    .catch(function(error) {
      // Catch errors here
    })

    Generator

    The craft ai API lets you generate models built on data from one or several agents by creating a generator. It is useful to:

    • test several hyper-parameters and features sets without reloading all the data for each try
    • gather data from different agents to make new models based on several data sources, enhancing the possible data combinations and allowing you to inspect the global behavior across your agents

    The data stream(s) used by a generator are defined by specifying a list of agents as a filter in its configuration. Other than the filter, the configuration of a generator is similar to an agent's configuration. But it has to verify some additional properties:

    • Every feature defined in the context configuration of the generator must be present in all the agent that match the filter, with the same context types.
    • The parameter operations_as_events must be set to true.
    • It follows that the parameters max_training_samples, and learning_period in the case of decision trees, must be set.
    • The agent names provided in the list must be valid agent identifiers.

    Create

    Create a new generator, and define its configuration.

    The generator's identifier is a case sensitive string between 1 and 36 characters long. It only accepts letters, digits, hyphen-minuses and underscores (i.e. the regular expression /[a-zA-Z0-9_-]{1,36}/).

    const GENERATOR_FILTER = ['smarthome'];
    const GENERATOR_NAME = 'smarthome_gen';
    
    const GENERATOR_CONFIGURATION = {
      context: {
          light: {
              type: 'enum'
          },
          tz: {
              type: 'timezone'
          },
          movement: {
              type: 'continuous'
          },
          time: {
              type: 'time_of_day',
              is_generated: true
          }
      },
      modelType: 'decisionTree',
      output: [
          'light'
      ],
      learning_period: 1500000,
      max_training_samples: 15000,
      operations_as_events: true,
      filter: GENERATOR_FILTER
    };
    
    client.createGenerator(GENERATOR_CONFIGURATION, GENERATOR_NAME)
      .then(function(generator) {
        console.log('Generator ' + generator.id + ' successfully created!');
      })
      .catch(function(error) {
        console.error('Error!', error);
      });

    Delete

    const GENERATOR_NAME = 'smarthome_gen'
    
    client.deleteGenerator(GENERATOR_NAME)
      .then(function() {
      // The generator was successfully deleted
      })
      .catch(function(error) {
        // Catch errors here
      })

    Retrieve

    const GENERATOR_NAME = 'smarthome_gen'
    
    client.getGenerator(GENERATOR_NAME)
      .then(function(generator) {
        // Generator's details
      })
      .catch(function(error) {
        // Catch errors here
      })

    Retrieve generators list

    const GENERATOR_NAME = 'smarthome_gen'
    
    client.listGenerators()
      .then(function(generatorsList) {
        // The list of generators in the project
      })
      .catch(function(error) {
        // Catch errors here
      })

    List operations in the generator

    Retrieve the context operations of agents matching the generator's filter. Each operation also contains the identifier of the agent for which it was added, in the agent_id property.

    client.getGeneratorContextOperations(
      'smarthome_gen', // The generator id
      1478894153, // Optional, the **start** timestamp from which the
                  // operations are retrieved (inclusive bound)
      1478895266, // Optional, the **end** timestamp up to which the
                  /// operations are retrieved (inclusive bound)
    )
    .then(function(operations) {
      // Work on operations
    })
    .catch(function(error) {
      // Catch errors here
    })

    Context

    Add operations

    client.addAgentContextOperations(
      'impervious_kraken', // The agent id
      [ // The list of operations
        {
          timestamp: 1469410200,
          context: {
            timezone: '+02:00',
            peopleCount: 0,
            lightbulbState: 'OFF'
          }
        },
        {
          timestamp: 1469415720,
          context: {
            timezone: '+02:00',
            peopleCount: 1,
            lightbulbState: 'ON'
          }
        },
        {
          timestamp: 1469416500,
          context: {
            timezone: '+02:00',
            peopleCount: 2,
            lightbulbState: 'ON'
          }
        },
        {
          timestamp: 1469417460,
          context: {
            timezone: '+02:00',
            peopleCount: 2,
            lightbulbState: 'OFF'
          }
        },
        {
          timestamp: 1469419920,
          context: {
            timezone: '+02:00',
            peopleCount: 0,
            lightbulbState: 'OFF'
          }
        },
        {
          timestamp: 1469460180,
          context: {
            timezone: '+02:00',
            peopleCount: 2,
            lightbulbState: 'OFF'
          }
        },
        {
          timestamp: 1469471700,
          context: {
            timezone: '+02:00',
            peopleCount: 2,
            lightbulbState: 'ON'
          }
        },
        {
          timestamp: 1469473560,
          context: {
            timezone: '+02:00',
            peopleCount: 0,
            lightbulbState: 'ON'
          }
        }
      ]
    )
    .then(function() {
      // The operations where successfully added to agent context on the server side
    })
    .catch(function(error) {
      // Catch errors here
    })
    Missing Values

    If the value of a base type property is missing, you can send a null value. craft ai will take into account as much information as possible from this incomplete context.

    A context operation with a missing value looks like:

    [
      {
        "timestamp": 1469415720,
        "context": {
          "peopleCount": "OFF",
          "lightbulbState": null
        }
      },
      ...
    ]
    Optional Values

    If the value of an optional property is not filled at some point—as should be expected from an optional value—send the empty JSON Object {} to craft ai:

    A context with an optional value looks like:

    [
      {
        "timestamp": 1469415720,
        "context": {
          "timezone": "+02:00",
          "temperature": {},
          "lightbulbState": "OFF"
        }
      },
      ...
    ]

    List operations

    client.getAgentContextOperations(
      'impervious_kraken', // The agent id
      1478894153, // Optional, the **start** timestamp from which the
                  // operations are retrieved (inclusive bound)
      1478895266, // Optional, the **end** timestamp up to which the
                  /// operations are retrieved (inclusive bound)
    )
    .then(function(operations) {
      // Work on operations
    })
    .catch(function(error) {
      // Catch errors here
    })

    This call can generate multiple requests to the craft ai API as results are paginated.

    Retrieve state

    client.getAgentContext(
      'impervious_kraken', // The agent id
      1469473600 // The timestamp at which the context state is retrieved
    )
    .then(function(context) {
      // Work on context
    })
    .catch(function(error) {
      // Catch errors here
    })

    Retrieve state history

    client.getAgentStateHistory(
      'impervious_kraken', // The agent id
      1478894153, // Optional, the **start** timestamp from which the
                  // operations are retrieved (inclusive bound)
      1478895266, // Optional, the **end** timestamp up to which the
                  /// operations are retrieved (inclusive bound)
    )
    .then(function(stateHistory) {
      // Work on states history
    })
    .catch(function(error) {
      // Catch errors here
    })

    Gradient boosting

    Models can be generated with gradient boosting by setting the configuration parameter model_type to boosting. Models are based on training data within a provided timestamp window among data that was added. You can only query predictions directly for gradient boosting models.

    The implementation is based on LightGBM, but there are some parameters that differ from the ones used by default by LightGBM.

    For classification:

    For regression:

    See the configuration section for parameters that you can set.

    Get decision using boosting for agent

    const FROM_TIMESTAMP = 1469473600;
    const TO_TIMESTAMP = 1529473600;
    const CONTEXT_OPS = {
      tz: '+02:00',
      movement: 2,
      time: 7.5
    };
    
    client.computeAgentBoostingDecision(
      'impervious_kraken', // The generator id
      FROM_TIMESTAMP, //
      TO_TIMESTAMP,
      CONTEXT_OPS
    )
      .then((decision) => {
        console.log(decision); // The decision made by the boosting
        /*
          {
            context: {
              tz: '+02:00',
              movement: 2,
              time: 7.5
            },
            output: {
              predicted_value: 'OFF'
            }
          }
        */
      })

    Get decision using boosting for generator

    const FROM_TIMESTAMP = 1469473600;
    const TO_TIMESTAMP = 1529473600;
    const CONTEXT_OPS = {
      tz: '+02:00',
      movement: 2,
      time: 7.5
    };
    
    client.computeGeneratorBoostingDecision(
      'impervious_kraken', // The generator id
      FROM_TIMESTAMP, //
      TO_TIMESTAMP,
      CONTEXT_OPS
    )
      .then((decision) => {
        console.log(decision); // The decision made by the boosting
        /*
          {
            context: {
              tz: '+02:00',
              movement: 2,
              time: 7.5
            },
            output: {
              predicted_value: 'OFF'
            }
          }
        */
      })

    Decision tree

    Models can be generated as single decision trees by setting the configuration parameter model_type to decisionTree. Decision trees are computed based on data up to a specific timestamp and dating back to the learning_period configuration parameter among data that was added.

    When you compute a decision tree, craft ai returns an object containing:

    • the version of the model's format

    • the agent's configuration as specified during the agent's creation

    • the tree itself as a JSON object:

      • Internal nodes are represented by a "decision_rule" object and a "children" array. The first one, contains the "property, and the "property"'s value, to decide which child matches a context.
      • Leaves have a "predicted_value", "confidence" and "decision_rule" object for this value, instead of a "children" array. "predicted_value" is an estimation of the output in the contexts matching the node. "confidence" is a number between 0 and 1 that indicates how confident craft ai is that the output is a reliable prediction. When the output is a numerical type, leaves also have a "standard_deviation" that indicates a margin of error around the "predicted_value".
      • The root only contains a "children" array.

    Get decision tree for an agent

    client.getAgentDecisionTree(
      'impervious_kraken', // The agent id
      1469473600 // The timestamp at which the decision tree is retrieved
    )
    .then(function(tree) {
      // Works with the given tree
      console.log(tree);
      /* Outputted tree is the following
      {
        _version:'2.0.0',
        trees:{
          lightbulbState:{
            output_values:['OFF', 'ON'],
            children:[
              {
                children:[
                  {
                    prediction:{
                      confidence:0.6774609088897705,
                      distribution:[0.8, 0.2],
                      value:'OFF',
                      nb_samples: 5
                    },
                    decision_rule:{
                      operand:0.5,
                      operator:'<',
                      property:'peopleCount'
                    }
                  },
                  {
                    prediction:{
                      confidence:0.8630361557006836,
                      distribution:[0.1, 0.9],
                      value:'ON',
                      nb_samples: 10
                    },
                    decision_rule:{
                      operand:0.5,
                      operator:'>=',
                      property:'peopleCount'
                    }
                  }
                ],
                decision_rule:{
                  operand:[
                    5,
                    5.6666665
                  ],
                  operator:'[in[',
                  property:'timeOfDay'
                }
              },
              {
                children:[
                  {
                    prediction:{
                      confidence:0.9947378635406494,
                      distribution:[1.0, 0.0],
                      value:'ON',
                      nb_samples: 10
                    },
                    decision_rule:{
                      operand:[
                        5.6666665,
                        20.666666
                      ],
                      operator:'[in[',
                      property:'timeOfDay'
                    }
                  },
                  {
                    children:[
                      {
                        prediction:{
                          confidence:0.969236433506012,
                          distribution:[0.95, 0.05],
                          value:'OFF',
                          nb_samples: 10
                        },
                        decision_rule:{
                          operand:1,
                          operator:'<',
                          property:'peopleCount'
                        }
                      },
                      {
                        prediction:{
                          confidence:0.8630361557006836,
                          distribution:[0.2, 0.8],
                          value:'ON',
                          nb_samples: 15
                        },
                        decision_rule:{
                          operand:1,
                          operator:'>=',
                          property:'peopleCount'
                        }
                      }
                    ],
                    decision_rule:{
                      operand:[
                        20.666666,
                        5
                      ],
                      operator:'[in[',
                      property:'timeOfDay'
                    }
                  }
                ],
                decision_rule:{
                  operand:[
                    5.6666665,
                    5
                  ],
                  operator:'[in[',
                  property:'timeOfDay'
                }
              }
            ]
          }
        },
        configuration:{
          time_quantum:600,
          learning_period:9000000,
          context:{
            peopleCount:{
              type:'continuous'
            },
            timeOfDay:{
              type:'time_of_day',
              is_generated:true
            },
            timezone:{
              type:'timezone'
            },
            lightbulbState:{
              type:'enum'
            }
          },
          output:[
            'lightbulbState'
          ]
        }
      }
      */
    })
    .catch(function(error) {
      if (error instanceof craftai.errors.CraftAiLongRequestTimeOutError) {
        // Handle timeout errors here
      }
      else {
        // Handle other errors here
      }
    })

    Get decision using a decision tree for an agent

    ℹ️ To make a decision (prediction) with decision tree, first compute the decision tree then use the offline interpreter.

    Get decision tree for a generator

    const DECISION_TREE_TIMESTAMP = 1469473600;
    const GENERATOR_NAME = 'smarthome_gen';
    client.getGeneratorDecisionTree(
      GENERATOR_NAME, // The generator id
      DECISION_TREE_TIMESTAMP // The timestamp at which the decision tree is retrieved
    )
      .then(function(tree) {
        // Works with the given tree
        console.log(tree);
        /* Outputted tree is the following
         {
        _version: '2.0.0',
        trees: {
            light: {
                children: [
                    {
                        predicted_value: 'OFF',
                        confidence: 0.9966583847999572,
                        decision_rule: {
                            operand: [
                                7.25,
                                22.65
                            ],
                            operator: '[in[',
                            property: 'time'
                        }
                    },
                    {
                        predicted_value: 'ON',
                        confidence: 0.9266583847999572,
                        decision_rule: {
                            operand: [
                                22.65,
                                7.25
                            ],
                            operator: '[in[',
                            property: 'time'
                        }
                    }
                ]
            }
        },
        configuration: {
            operations_as_events: true,
            learning_period: 1500000,
            max_training_samples: 15000,
            context: {
                light: {
                    type: 'enum'
                },
                tz: {
                    type: 'timezone'
                },
                movement: {
                    type: 'continuous'
                },
                time: {
                    type: 'time_of_day',
                    is_generated: true
                }
            },
            output: [
                'light'
            ],
            filter: [
                'smarthome'
            ]
        }
      }
        */
      })
      .catch(function(error) {
        if (error instanceof craftai.errors.CraftAiLongRequestTimeOutError) {
         // Handle timeout errors here
       }
        else {
          // Handle other errors here
        }
      })

    Get decision using a decision tree for a generator

    const CONTEXT_OPS = {
      tz: '+02:00',
      movement: 2,
      time: 7.5
    };
    const DECISION_TREE_TIMESTAMP = 1469473600;
    const GENERATOR_NAME = 'smarthome_gen';
    
    client.computeGeneratorDecision(
      GENERATOR_NAME, // The name of the generator
      DECISION_TREE_TIMESTAMP, //The timestamp at which the decision tree is retrieved
      CONTEXT_OPS // A valid context operation according to the generator configuration
    )
      .then(function(decision) => {
        console.log(decision); // The decision made by the decision tree
        /*
          {
          _version: '2.0.0',
          context: {
              tz: '+02:00',
              movement: 2,
              time: 7.5
          },
          output: {
              light: {
                  predicted_value: 'OFF',
                  confidence: 0.8386044502258301,
                  decision_rules: [
                      {
                          operand: [
                              2.1166666,
                              10.333333
                          ],
                          operator: '[in[',
                          property: 'time'
                      },
                      {
                          operand: [
                              2.1166666,
                              9.3
                          ],
                          operator: '[in[',
                          property: 'time'
                      },
                      {
                          operand: [
                              2.1166666,
                              8.883333
                          ],
                          operator: '[in[',
                          property: 'time'
                      },
                      {
                          operand: [
                              3.5333333,
                              8.883333
                          ],
                          operator: '[in[',
                          property: 'time'
                      }
                  ],
                  nb_samples: 442,
                  decision_path: '0-0-0-0-1',
                  distribution: [
                      0.85067874,
                      0.14932127
                  ]
              }
            }
          }
        */
      })

    Bulk

    The craft ai API includes a bulk route which provides a programmatic option to perform multiple operations at once.

    ⚠️ the bulk API comes on top of the basic routes described above, and requires an understanding of what they do. For more information, please refer to the basic routes that do the same operations one at a time.

    Bulk - Create agents

    To create several agents at once, use the method createAgentBulk as the following:

    const agent_ID_1 = 'my_first_agent';
    const agent_ID_2 = 'my_second_agent';
    
    const configuration_1 = {
        context: {
          peopleCount: {
            type: 'continuous'
          },
          timeOfDay: {
            type: 'time_of_day'
          },
          timezone: {
            type: 'timezone'
          },
          lightbulbState: {
            type: 'enum'
          }
        },
        output: [ 'lightbulbState' ]
      };
    const configuration_2 = { /* ... */ };
    
    const createBulkPayload = [
      {id: agent_ID_1, configuration: configuration_1},
      {id: agent_ID_2, configuration: configuration_2}
    ];
    
    client.createAgentBulk(createBulkPayload)
      .then(function(agents) {
        console.log(agents);
      })
      .catch(function(error) {
        console.error('Error!', error);
      })

    The variable agents is an array of responses. If an agent has been successfully created, the corresponding response is an object similar to the classic createAgent() response. When there are mixed results, agents should looks like:

    [
      { id: 'my_first_agent',   // creation failed
        status: 400,
        error: 'errorId',
        message: 'error-message' },
      { configuration:          // creation succeed
        { time_quantum: 100,
          learning_period: 1500000,
          context: [Object],
          output: [Object] },
        id: 'my_second_agent',
        _version: '2.0.0' }
    ]

    Bulk - Delete agents

    const agent_ID_1 = 'my_first_agent';
    const agent_ID_2 = 'my_second_agent';
    
    const deleteBulkPayload = [
      { id: agent_ID_1 },
      { id: agent_ID_2 }
    ];
    
    client.deleteAgentBulk(deleteBulkPayload)
    .then(function(deletedAgents) {
      console.log(agents);
    })
    .catch(function(error) {
      console.error('Error!', error);
    });

    The variable deletedAgents is an array of responses. If an agent has been successfully deleted, the corresponding response is an object similar to the classic deleteAgent() response. When there are mixed results, deletedAgents should looks like:

    [
      { id: 'my_first_agent',       // deletion succeed
        configuration:
         { time_quantum: 100,
           learning_period: 1500000,
           context: [Object],
           output: [Object] },
        creationDate: 1557492944277,
        lastContextUpdate: 1557492944277,
        lastTreeUpdate: 1557492944277,
        _version: '2.0.0' },
      { id: 'my_unknown_agent' },   // deletion succeed
      { id: 'my_second_agent',      // deletion failed
        status: 400,
        error: 'errorId',
        message: 'error-message' }
    ]

    Bulk - Add context operations

    const agent_ID_1 = 'my_first_agent';
    const agent_ID_2 = 'my_second_agent';
    
    const operations_agent_1 = [{
            timestamp: 1469410200,
            context: {
              timezone: '+02:00',
              peopleCount: 0,
              lightbulbState: 'OFF'
            }
          },
          {
            timestamp: 1469415720,
            context: {
              timezone: '+02:00',
              peopleCount: 1,
              lightbulbState: 'ON'
            }
          },
          {
            timestamp: 1469416500,
            context: {
              timezone: '+02:00',
              peopleCount: 2,
              lightbulbState: 'ON'
            }
          },
          {
            timestamp: 1469417460,
            context: {
              timezone: '+02:00',
              peopleCount: 2,
              lightbulbState: 'OFF'
            }
          }];
    const operations_agent_2 = [ /* ... */ ];
    
    const contextOperationBulkPayload = [
      { id: agent_ID_1, operations: operations_agent_1},
      { id: agent_ID_2, operations: operations_agent_2}
    ];
    
    client.addAgentContextOperationsBulk(contextOperationBulkPayload)
    .then(function(agents) {
      console.log(agents);
    })
    .catch(function(error) {
      console.error('Error!', error);
    });

    The variable agents is an array of responses. If an agent has successfully received operations, the corresponding response is an object similar to the classic addAgentContextOperations() response. When there are mixed results, agents should looks like:

    [
      { id: 'my_first_agent',     // add operations failed
        status: 500,
        error: 'errorId',
        message: 'error-message' },
      { id: 'my_second_agent',      // add operations succeed
        status: 201,
        message: 'Successfully added XX operation(s) to the agent "{owner}/{project}/my_second_agent" context.',
        nbOperationsAdded: XX }
    ]

    Bulk - Compute decision trees for agents

    const agent_ID_1 = 'my_first_agent';
    const agent_ID_2 = 'my_second_agent';
    
    const decisionTreePayload =  [
      { id: agent_ID_1 },
      { id: agent_ID_2 }
    ];
    
    client.getAgentDecisionTreeBulk(decisionTreePayload)
    .then(function(trees) {
      console.log(trees);
    })
    .catch(function(error) {
      console.error('Error!', error);
    });

    The variable trees is an array of responses. If an agent's model has successfully been created, the corresponding response is an object similar to the classic getAgentDecisionTree() response. When there are mixed results, trees should looks like:

    [
      { id: 'my_first_agent',       // computation failed
        (...)
        status: 400,
        error: 'errorId',
        message: 'error-message' },
      { id: 'my_second_agent',        // computation succeed
        timestamp: 1464601500,
        tree: { _version: '1.1.0', trees: [Object], configuration: [Object] } }
    ]

    Bulk - Compute boosting decisions for agents

    const requestPayload = [
      {
        entityName: 'my_first_agent',
        timeWindow: [1469415600, 1679415800],
        context: {
          peopleCount: 19,
          timeOfDay: 7.5,
          timezone: '+02:00'
        }
      },
      {
        entityName: 'my_first_agent',
        timeWindow: [1469415600, 1679415800],
        context: {
          peopleCount: 21,
          timeOfDay: 5,
          timezone: '+02:00'
        }
      },
      {
        entityName: 'my_second_agent',
        timeWindow: [1469415600, 1679415800],
        context: {
          peopleCount: 33,
          timeOfDay: 8,
          timezone: '+01:00'
        }
      }
    ];
    
    client.computeAgentBoostingDecisionBulk(requestPayload)
      .then(function(boostingResults) {
        console.log(boostingResults);
      })
      .catch(function(error) {
        console.error('Error!', error);
      });

    What is in boostingResults is:

    [
      {
        entityName: 'my_first_agent',
        context: {
          peopleCount: 19,
          timeOfDay: 7.5,
          timezone: '+02:00'
        },
        timeWindow: [1469415600, 1679415800],
        output: {
          predicted_value: 'ON'
        }
      },
      {
        entityName: 'my_first_agent',
        context: {
          peopleCount: 21,
          timeOfDay: 5,
          timezone: '+02:00'
        },
        timeWindow: [1469415600, 1679415800],
        output: {
          predicted_value: 'OFF'
        }
      },
      {
        entityName: 'my_second_agent',
        context: {
          peopleCount: 33,
          timeOfDay: 8,
          timezone: '+01:00'
        },
        timeWindow: [1469415600, 1679415800],
        output: {
          predicted_value: 'ON'
        }
      }
    ]

    Bulk - Create generators

    const configuration = {
      context: {
        peopleCount: {
          type: 'continuous'
        },
        timeOfDay: {
          type: 'time_of_day'
        },
        timezone: {
          type: 'timezone'
        },
        lightbulbState: {
          type: 'enum'
        }
      },
      model_type: 'decisionTree',
      output: ['lightbulbState'],
      operations_as_events: true,
      learning_period: 1500000,
      max_training_samples: 55000,
      filter: [ 'my_agent_name' , 'my_other_agent_name' ]
    };
    const payload = [
      { id: 'my_first_generator_name', configuration },
      { id: 'my_second_generator_name', configuration }
    ];
    
    client.createGeneratorBulk(payload)
      .then(function(results) {
        console.log(results);
      })
      .catch(function(error) {
        console.error('Error!', error);
      });

    What is in results is:

    [
      {
        id: 'my_first_generator_name',
        configuration: {
          context: {
            peopleCount: {
              type: 'continuous'
            },
            timeOfDay: {
              type: 'time_of_day'
            },
            timezone: {
              type: 'timezone'
            },
            lightbulbState: {
              type: 'enum'
            }
          },
          output: ['lightbulbState'],
          operations_as_events: true,
          learning_period: 1500000,
          max_training_samples: 55000,
          filter: [ 'my_agent_name' , 'my_other_agent_name' ]
        }
      },
      {
        id: 'my_second_generator_name',
        configuration: {
          context: {
            peopleCount: {
              type: 'continuous'
            },
            timeOfDay: {
              type: 'time_of_day'
            },
            timezone: {
              type: 'timezone'
            },
            lightbulbState: {
              type: 'enum'
            }
          },
          output: ['lightbulbState'],
          operations_as_events: true,
          learning_period: 1500000,
          max_training_samples: 55000,
          filter: [ 'my_agent_name' , 'my_other_agent_name' ]
        }
      }
    ]

    Bulk - Delete generators

    client.deleteGeneratorBulk(['my_first_generator_name', 'my_second_generator_name'])
      .then(function(results) {
        console.log(results);
      })
      .catch(function(error) {
        console.error('Error!', error);
      });

    Deleted generators are returned. What is in results is:

    [
      {
        id: 'my_first_generator_name',
        configuration: {
          context: {
            peopleCount: {
              type: 'continuous'
            },
            timeOfDay: {
              type: 'time_of_day'
            },
            timezone: {
              type: 'timezone'
            },
            lightbulbState: {
              type: 'enum'
            }
          },
          output: ['lightbulbState'],
          filter: [ 'my_agent_name' , 'my_other_agent_name' ]
        }
      },
      {
        id: 'my_second_generator_name',
        configuration: {
          context: {
            peopleCount: {
              type: 'continuous'
            },
            timeOfDay: {
              type: 'time_of_day'
            },
            timezone: {
              type: 'timezone'
            },
            lightbulbState: {
              type: 'enum'
            }
          },
          output: ['lightbulbState'],
          filter: [ 'my_agent_name' , 'my_other_agent_name' ]
        }
      }
    ]

    Bulk - Compute decision trees for generators

    const payload = [
      { id: 'a_generator_name', timestamp: 1464600500 },
      { id: 'another_generator_name', timestamp: 1564600900 }
    ];
    
    client.getGeneratorDecisionTreeBulk(payload)
      .then(function(trees) {
        console.log(trees);
      })
      .catch(function(error) {
        console.error('Error!', error);
      });

    What is in results is for example:

    [
      {
        id: 'a_generator_name',
        timestamp: 1464600500,
        tree: {
          _version: '2.0.0',
          trees: {
            light: {
            children: [
              {
                predicted_value: 'OFF',
                confidence: 0.9966583847999572,
                decision_rule: {
                  operand: [
                    7.25,
                    22.65
                  ],
                  operator: '[in[',
                  property: 'time'
                }
              },
              {
                children: [
                  {
                    predicted_value: 'ON',
                      confidence: 0.9618390202522278,
                      decision_rule: {
                        operand: [
                          22.65,
                          0.06666667
                        ],
                        operator: '[in[',
                        property: 'time'
                      }
                  },
                  {
                    predicted_value: 'OFF',
                    confidence: 0.92118390202522278,
                    decision_rule: {
                      operand: [
                        0.06666667,
                        7.25
                      ],
                      operator: '[in[',
                      property: 'time'
                    }
                  }
                ],
                decision_rule: {
                  operand: [
                    22.65,
                    7.25
                  ],
                  operator: '[in[',
                  property: 'time'
                }
              }
            ]
          }
        },
        configuration: {
          operations_as_events: true,
          learning_period: 1500000,
          max_training_samples: 15000,
          context: {
            light: {
              type: 'enum'
            },
            tz: {
              type: 'timezone'
            },
            movement: {
              type: 'continuous'
            },
            time: {
              type: 'time_of_day',
              is_generated: true
            }
          },
          output: [
            'light'
          ],
          filter: [
            'smarthome'
          ]
        }
      },
      {
        id: 'another_generator_name',
        timestamp: 1564600900,
        tree: {
          _version: '2.0.0',
          trees: {
            light: {
            children: [
              {
                predicted_value: 'OFF',
                confidence: 0.9966583847999572,
                decision_rule: {
                  operand: [
                    7.25,
                    22.65
                  ],
                  operator: '[in[',
                  property: 'time'
                }
              },
              {
                children: [
                  {
                    predicted_value: 'ON',
                      confidence: 0.9618390202522278,
                      decision_rule: {
                        operand: [
                          22.65,
                          0.06666667
                        ],
                        operator: '[in[',
                        property: 'time'
                      }
                  },
                  {
                    predicted_value: 'OFF',
                    confidence: 0.92118390202522278,
                    decision_rule: {
                      operand: [
                        0.06666667,
                        7.25
                      ],
                      operator: '[in[',
                      property: 'time'
                    }
                  }
                ],
                decision_rule: {
                  operand: [
                    22.65,
                    7.25
                  ],
                  operator: '[in[',
                  property: 'time'
                }
              }
            ]
          }
        },
        configuration: {
          operations_as_events: true,
          learning_period: 1500000,
          max_training_samples: 15000,
          context: {
            light: {
              type: 'enum'
            },
            tz: {
              type: 'timezone'
            },
            movement: {
              type: 'continuous'
            },
            time: {
              type: 'time_of_day',
              is_generated: true
            }
          },
          output: [
            'light'
          ],
          filter: [
            'smarthome'
          ]
        }
      }
    ]

    Bulk - Compute boosting decisions for generators

    const requestPayload = [
      {
        entityName: 'my_first_agent',
        timeWindow: [1469415600, 1679415800],
        context: {
          peopleCount: 19,
          timeOfDay: 7.5,
          timezone: '+02:00'
        }
      },
      {
        entityName: 'my_first_agent',
        timeWindow: [1469415600, 1679415800],
        context: {
          peopleCount: 21,
          timeOfDay: 5,
          timezone: '+02:00'
        }
      },
      {
        entityName: 'my_second_agent',
        timeWindow: [1469415600, 1679415800],
        context: {
          peopleCount: 33,
          timeOfDay: 8,
          timezone: '+01:00'
        }
      }
    ];
    
    client.computeGeneratorBoostingDecisionBulk(requestPayload)
      .then(function(boostingResults) {
        console.log(boostingResults);
      })
      .catch(function(error) {
        console.error('Error!', error);
      });

    What is in boostingResults is:

    [
      {
        entityName: 'my_first_agent',
        context: {
          peopleCount: 19,
          timeOfDay: 7.5,
          timezone: '+02:00'
        },
        timeWindow: [1469415600, 1679415800],
        output: {
          predicted_value: 'ON'
        }
      },
      {
        entityName: 'my_first_agent',
        context: {
          peopleCount: 21,
          timeOfDay: 5,
          timezone: '+02:00'
        },
        timeWindow: [1469415600, 1679415800],
        output: {
          predicted_value: 'OFF'
        }
      },
      {
        entityName: 'my_second_agent',
        context: {
          peopleCount: 33,
          timeOfDay: 8,
          timezone: '+01:00'
        },
        timeWindow: [1469415600, 1679415800],
        output: {
          predicted_value: 'ON'
        }
      }
    ]

    Advanced client configuration

    The simple configuration to create the client is just the token. For special needs, additional advanced configuration can be provided.

    Amount of operations sent in one chunk

    client.addAgentContextOperations splits the provided operations into chunks in order to limit the size of the http requests to the craft ai API. In the client configuration, operationsChunksSize can be increased in order to limit the number of request, or decreased when large http requests cause errors.

    const client = craftai({
      // Mandatory, the token
      token: '{token}',
      // Optional, default value is 500
      operationsChunksSize: {max_number_of_operations_sent_at_once}
    });

    Timeout duration for decision trees retrieval

    It is possible to increase or decrease the timeout duration of client.getAgentDecisionTree, for exemple to account for especially long computations.

    const client = craftai({
      // Mandatory, the token
      token: '{token}',
      // Optional, default value is 5 minutes (300000)
      decisionTreeRetrievalTimeout: {timeout_duration_for_decision_trees_retrieval}
    });

    Proxy

    ℹ️ This setting can only be set in Node.js environements. In a browser environement the settings of the browser will be used automatically.

    It is possible to provide proxy configuration in the proxy property of the client configuration. It will be used to call the craft ai API (through HTTPS). The expected format is a host name or IP and port, optionally preceded by credentials such as http://user:pass@10.10.1.10:1080.

    const client = craftai({
      // Mandatory, the token
      token: '{token}',
      // Optional, no default value
      proxy: 'http://{user}:{password}@{host_or_ip}:{port}'
    });

    Score

    The following functions let you compute model scores.

    ⚠️ At the moment, this is only available as bulk functions, for generators set to generate decision trees.

    Score - Sliding window

    client.getSlidingWindowScoresBulk(body).

    Body

    The body should be an array of objects containing the following keys:

    • id string (required)

      The identifier of the generator whose model is evaluated.

    • test_from number

      The beginning timestamp of the first test window (inclusive). 3 parameters among test_from, test_to, step_size and nb_steps must be defined.

    • test_to number

      The end timestamp of the last test window (inclusive). 3 parameters among test_from, test_to, step_size and nb_steps must be defined.

    • step_size number

      The timestamp difference between the beginning of a test window and the next. 3 parameters among test_from, test_to, step and nb_steps must be defined.

    • nb_steps number

      The number of test windows. 3 parameters among test_from, test_to, step_size and nb_steps must be defined.

    • test_size number

      The actual size of the test set from the beginning of a test window. If the size of a window (step_size) is larger than this, only the beginning of a window will be used to compute scores. If the size of a window is smaller than this, data in a window can be used in several score computations.

    • gap_size number

      The timestamp difference between the end of a training window (data used in the model) and the beginning of a test window. The end of the training window is defined by the formula testWindowStart - gap_size - 1. By default gap_size is 0, in which case the test set starts directly after the end of the training set, i.e. the end of the training set is just before the beginning of the test window. A negative gap means that there is an overlap between the training and test data.

    • metrics array

      Array of objects containing a name property with the name of a valid metric. The metrics are used to evaluate the ML model. For classification models, the available metrics are accuracy and f1; for regression models, r2, mae and rmse. By default all available metrics are computed.

    + training data
    * test data
                       gap_size test_size
                          <-><------------>
    window 1  .+++++++++++...**************...................................
                             |
                  window 1 start (test_from)
                             <--------->
                              step_size
    window 2  ............+++++++++++...**************........................
                                        |
                                 window 2 start
                                        <--------->
                                         step_size
    window 3  .......................+++++++++++...**************.............
                                                   |
                                             window 3 start

    Example:

      const slidingWindowScoresRequestPayload = [
        {
          id: 'generator1',
          test_from: 1461132001,
          test_to: 1462106220,
          step_size: 500000,
          metrics: [{ name: 'accuracy' }, { name: 'f1' }]
        },
        {
          id: 'generator2',
          test_from: 1477000801,
          test_to: 1485385200,
          step_size: 5000000,
          metrics: [{ name: 'r2' }, { name: 'mae' }, { name: 'rmse' }]
        }
      ];
    
      client.getSlidingWindowScoresBulk(slidingWindowScoresRequestPayload)
        .then((response) => {
          console.log(response);
          /* Outputted response is the following
            [
              {
                "id": "generator1",
                "scores": [
                  {
                    "from": 1461132001,
                    "to": 1461632000,
                    "modelTimestamp": 1461132000,
                    "nbSamples": 15,
                    "type": "classification",
                    "accuracy": 0.467,
                    "f1": {
                      "class_OPEN": {
                        "nbSamples": 7,
                        "score": 0.636
                      }
                    },
                    "f1_weighted": 0.297
                  },
                  {
                    "from": 1461632001,
                    "to": 1462106220,
                    "modelTimestamp": 1461632000,
                    "nbSamples": 14,
                    "type": "classification",
                    "accuracy": 0.786,
                    "f1": {
                      "class_CLOSED": {
                        "nbSamples": 7,
                        "score": 0.8
                      },
                      "class_OPEN": {
                        "nbSamples": 7,
                        "score": 0.769
                      }
                    },
                    "f1_weighted": 0.785
                  }
                ]
              },
              {
                "id": "generator2",
                "scores": [
                  {
                    "from": 1477000801,
                    "to": 1482000800,
                    "modelTimestamp": 1477000800,
                    "nbSamples": 57,
                    "type": "regression",
                    "r2": -0.864,
                    "mae": 2.186,
                    "rmse": 2.489
                  },
                  {
                    "from": 1482000801,
                    "to": 1485385200,
                    "modelTimestamp": 1482000800,
                    "nbSamples": 39,
                    "type": "regression",
                    "r2": 0.52,
                    "mae": 0.95,
                    "rmse": 1.164
                  }
                ]
              }
            ]
          */
        })
        .catch(function(error) {
          if (error instanceof craftai.errors.CraftAiLongRequestTimeOutError) {
           // Handle timeout errors here
         }
          else {
            // Handle other errors here
          }
        });

    Score - Single window

    client.getSingleWindowScoreBulk(body)

    Body

    The body should be an array of objects containing the following keys:

    • id string (required)

      The identifier of the generator whose model is evaluated.

    • test_from number (required)

      The beginning timestamp of the test window (inclusive).

    • test_to number (required)

      The end timestamp of the test window (inclusive).

    • model_timestamp number

      The last timestamp of the training data.

    • metrics array

      Array of objects containing a name property with the name of a valid metric. The metrics are used to evaluate the ML model. For classification models, the available metrics are accuracy and f1; for regression models, r2, mae and rmse. By default all available metrics are computed.

    + training data
    * test data
                model_timestamp    test_from            test_to
    window  ++++++++++++|..............|*******************|..................

    Example:

      const singleWindowScoreRequestPayload = [
        {
          id: 'generator1',
          test_from: 1461132001,
          test_to: 1461632000,
          model_timestamp: 1461132000,
          metrics: [{ name: 'accuracy' }, { name: 'f1' }]
        },
        {
          id: 'generator2',
          test_from: 1477000801,
          test_to: 1485385200,
          model_timestamp: 1477000800,
          metrics: [{ name: 'r2' }, { name: 'mae' }, { name: 'rmse' }]
        }
      ];
    
      client.getSingleWindowScoreBulk(singleWindowScoreRequestPayload)
        .then((response) => {
          console.log(response);
          /* Outputted response is the following
            [
              {
                "id": "myGeneratorClassificationValidation1",
                "score": {
                  "from": 1461132001,
                  "to": 1461632000,
                  "modelTimestamp": 1461132000,
                  "nbSamples": 15,
                  "type": "classification",
                  "accuracy": 0.467,
                  "f1": {
                    "class_OPEN": {
                      "nbSamples": 7,
                      "score": 0.636
                    }
                  },
                  "f1_weighted": 0.297
                }
              },
              {
                "id": "myGeneratorRegressionValidation",
                "score": {
                  "from": 1477000801,
                  "to": 1485385200,
                  "modelTimestamp": 1477000800,
                  "nbSamples": 96,
                  "type": "regression",
                  "r2": -0.761,
                  "mae": 2.06,
                  "rmse": 2.356
                }
              }
            ]
          */
        })
        .catch(function(error) {
          if (error instanceof craftai.errors.CraftAiLongRequestTimeOutError) {
           // Handle timeout errors here
         }
          else {
            // Handle other errors here
          }
        });

    Interpreter

    The decision tree interpreter can be used offline from decisions tree computed through the API.

    Make decision

    // `tree` is the decision tree as retrieved through the craft ai REST API
    const tree = { ... };
    // Compute the decision with specifying every context field
    const decision = craftai.interpreter.decide(
      tree,
      {
        timezone: '+02:00',
        timeOfDay: 7.5,
        peopleCount: 3
      });
    
    // Or Compute the decision on a context created from the given one and filling the
    // `day_of_week`, `time_of_day` and `timezone` properties from the given `Time`
    const decision = craftai.interpreter.decide(
      tree,
      {
        timezone: '+02:00',
        peopleCount: 3
      },
      new craftai.Time('2010-01-01T07:30:30'));

    Any number of partial contexts and/or craftai.Time instances can be provided to decide, it follows the same semantics as Object.assign(...): the later arguments overriding the properties value from the previous ones)

    A computed decision on an enum type would look like:

    {
      context: { // In which context the decision was made
        timezone: '+02:00',
        timeOfDay: 7.5,
        peopleCount: 3
      },
      output: { // The decision itself
        lightbulbState: {
          predicted_value: 'ON',
          confidence: 0.9937745256361138, // The confidence in the decision
          decision_rules: [ // The ordered list of decision_rules that were validated to reach this decision
            {
              property: 'timeOfDay',
              operator: '>=',
              operand: 6
            },
            {
              property: 'peopleCount',
              operator: '>=',
              operand: 2
            }
          ],
          nb_samples: 25,
          distribution: [0.05, 0.95],
          decision_path: '0-1-1'
        }
      }
    }

    A decision for a numerical output type would look like:

      output: {
        lightbulbState: {
          predicted_value: 'OFF',
          confidence: ...,
          distribution: [ ... ],
          nb_samples: 25,
          decision_rules: [ ... ],
          decision_path: ...
        }
      }

    A decision for a categorical output type would look like:

      output: {
        lightbulbIntensity: {
          predicted_value: 10.5,
          standard_deviation: 1.25,
          confidence: ...,
          min: 8.0,
          max: 11,
          nb_samples: 25,
          decision_rules: [ ... ],
          decision_path: ...
      }

    A decision in a case where the tree cannot make a prediction:

      decision: {
        lightbulbState: {
          predicted_value: null, // No decision
          distribution : [ ... ], // Distribution of the output classes normalized by the number of samples in the reached node.
          confidence: 0, // Zero confidence if the decision is null
          nb_samples: 25,
          decision_rules: [ ... ],
          decision_path: ...
        }
      },

    Make multiple decisions

    From the tree previously retrieved, ask for multiple decisions. The decideFromContextArray allows to pass craftai Time along with context, in this case generated features will be automatically generated.

    // `tree` is the decision tree as retrieved through the craft ai REST API
    const tree = { ... };
    // Pass an array containing each context on which you want to make a decision
    const decisions = craftai.interpreter.decideFromContextsArray(tree, [
      {
        timezone: '+02:00',
        peopleCount: 3,
        timeOfDay: 7.5
      },
      {
        timezone: '+02:00',
        peopleCount: 4,
        timeOfDay: 7.5
      },
      {
        timezone: '+02:00',
        peopleCount: 0,
        timeOfDay: 4.5
      },
      [
        {
          peopleCount: 0
        },
        new craftai.Time('2010-01-01T07:30:30Z')
        // This is equivalent to {
        //  timezone: 'UTC',
        //  peopleCount: 0,
        //  timeOfDay: 4.5
        //}
      ]
    ])

    Results for craftai.interpreter.decideFromContextsArray would look like:

    [
      {
        context: { // In which context the decision was made
          timezone: '+02:00',
          timeOfDay: 7.5,
          peopleCount: 3
        },
        output: { // The decision itself
          lightbulbState: {
            predicted_value: 'ON',
            distribution: [0.0, 1.0],
            nb_samples: 20,
            confidence: 0.9937745256361138, // The confidence in the decision
            decision_path: '0-1-1',
            decision_rules: [ // The ordered list of decision_rules that were validated to reach this decision
              {
                property: 'timeOfDay',
                operator: '>=',
                operand: 6
              },
              {
                property: 'peopleCount',
                operator: '>=',
                operand: 2
              }
            ]
          }
        }
      },
      {
        context: {
          timezone: '+02:00',
          timeOfDay: 7.5,
          peopleCount: 4
        },
        output: {
          lightbulbState: {
            predicted_value: 'ON',
            distribution: [0.0, 1.0],
            nb_samples: 20,
            confidence: 0.9937745256361138,
            decision_path: '0-1-1',
            decision_rules: [
              {
                property: 'timeOfDay',
                operator: '>=',
                operand: 6
              },
              {
                property: 'peopleCount',
                operator: '>=',
                operand: 2
              }
            ]
          }
        }
      },
      {
        context: {
          timezone: '+02:00',
          timeOfDay: 4.5,
          peopleCount: 0
        },
        output: {
          lightbulbState: {
            predicted_value: 'OFF',
            distribution: [0.95, 0.05],
            nb_samples: 12,
            confidence: 0.9545537233352661,
            decision_path: '0-0-0',
            decision_rules: [ // The ordered list of decision_rules that were validated to reach this decision
              {
                property: 'timeOfDay',
                operator: '<',
                operand: 5.666666507720947
              },
              {
                property: 'peopleCount',
                operator: '<',
                operand: 1
              }
            ]
          }
        }
      }
    ]

    Reduce decision rules

    From a list of decision rules, as retrieved when making a decision with a decision tree, compute an equivalent & minimal list of rules.

    // `decision` is the decision tree as retrieved from taking a decision
    const decision = craftai.interpreter.decide( ... );
    
    // `decisionRules` is the decision rules that led to decision for the `lightBulbState` value
    const decisionRules = decision.output.lightBulbState.decision_rules;
    
    // `minimalDecisionRules` has the mininum list of rules strictly equivalent to `decisionRules`
    const minimalDecisionRules = craftai.interpreter.reduceDecisionRules(decisionRules)

    Format decision rules

    From a list of decision rules, compute a human readable version of these rules, in english.

    // `decision` is the decision tree as retrieved from taking a decision
    const decision = craftai.interpreter.decide( ... );
    
    // `decisionRules` is the decision rules that led to decision for the `lightBulbState` value
    const decisionRules = decision.output.lightBulbState.decision_rules;
    
    // `decisionRulesStr` is a human readable string representation of the rules.
    const decisionRulesStr = craftai.interpreter.formatDecisionRules(decisionRules);

    Get decision rules properties

    Retrieve the context properties that matters to a previously computed tree.

    // `tree` is the decision tree as retrieved through the craft ai REST API
    const tree = { ... };
    
    const decisionRules = craftai.interpreter.getDecisionRulesProperties(tree)

    Results for craftai.interpreter.getDecisionRulesProperties would look like:

    [
      {
        property: 'timeOfDay',
        is_generated: true,
        type: 'time_of_day'
      }
    ]

    Logging

    The craft ai client is using visionmedia/debug under the namespace 'craft-ai:client:*', please refer to their documentation for further information.

    Keywords

    Install

    npm i craft-ai

    DownloadsWeekly Downloads

    126

    Version

    1.22.0

    License

    BSD-3-Clause

    Unpacked Size

    2.87 MB

    Total Files

    231

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    • craft-ai