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craft ai API client for python

Project Description

**craft ai** *AI-as-a-service* enables your services to learn every day: provide a personalized experience to each user and automate complex tasks.

Get Started!

0 - Signup

If you’re reading this you are probably already registered with craft ai, if not, head to `https://beta.craft.ai/signup <https://beta.craft.ai/signup>`__.

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/projects <https://beta.craft.ai/projects>`__, 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

PIP / PyPI

Let’s first install the package from pip.

pip install --upgrade craft-ai

Then import it in your code

import craftai

This client also provides helpers to use it in conjuction with
`pandas <#pandas-support>`__

Initialize

config = {
    "token": "{token}"
}
client = craftai.Client(config)

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 module that stores the history of the context of its user or device’s context, and learns which decision to take based on the evolution of this context in the form of a decision tree.

In this example, we will create an agent that learns the decision model of a light bulb based on the time of the day and the number of people in the room. In practice, it means the agent’s context have 4 properties:

  • 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.
agent_id = "my_first_agent"
configuration = {
    "context": {
        "peopleCount": {
            "type": "continuous"
        },
        "timeOfDay": {
            "type": "time_of_day"
        },
        "timezone": {
            "type": "timezone"
        },
        "lightbulbState": {
            "type": "enum"
        }
    },
    "output": ["lightbulbState"]
}

agent = client.create_agent(configuration, agent_id)
print("Agent", agent["id"], "has successfully been created")

Pretty straightforward to test! Open `https://beta.craft.ai/inspector <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' is already existing. Let’s see how we can delete it before recreating it.

agent_id = "my_first_agent"
client.delete_agent(agent_id)
print("Agent", agent_id, "no longer exists")

configuration = ...
agent = client.create_agent(configuration, agent_id)
print("Agent", agent["id"], "has successfully been created")

For further information, check the `’create agent’ reference documentation <#create>`__.

4 - Add context operations

We have now created our first agent but it is not able to do much, yet. To learn a decision 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.
agent_id = "my_first_agent"
client.delete_agent(agent_id)
print("Agent", agent_id, "no longer exists")

configuration = ...
agent = client.create_agent(configuration, agent_id)
print("Agent", agent["id"], "has successfully been created")

context_list = [
    {
        "timestamp": 1469410200,
        "context": {
            "timezone": "+02:00",
            "peopleCount": 0,
            "lightbulbState": "OFF"
        }
    },
    {
        "timestamp": 1469415720,
        "context": {
            "peopleCount": 1,
            "lightbulbState": "ON"
        }
    },
    {
        "timestamp": 1469416500,
        "context": {
            "peopleCount": 2
        }
    },
    {
        "timestamp": 1469417460,
        "context": {
            "lightbulbState": "OFF"
        }
    },
    {
        "timestamp": 1469419920,
        "context": {
            "peopleCount": 0
        }
    },
    {
        "timestamp": 1469460180,
        "context": {
            "peopleCount": 2
        }
    },
    {
        "timestamp": 1469471700,
        "context": {
            "lightbulbState": "ON"
        }
    },
    {
        "timestamp": 1469473560,
        "context": {
            "peopleCount": 0
        }
    }
]
client.add_operations(agent_id, context_list)
print("Successfully added initial operations to agent", agent_id, "!")

In real-world applications, you’ll probably do the same kind of things 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 <#add-operations>`__.

5 - Compute the decision tree

The agent has acquired a context history, we can now compute 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 context history 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.

agent_id = "my_first_agent"

client.delete_agent(agent_id)
print("Agent", agent_id, "no longer exists")

configuration = ...
agent = client.create_agent(configuration, agent_id)
print("Agent", agent["id"], "has successfully been created")

context_list = ...
client.add_operations(agent_id, context_list)
print("Successfully added initial operations to agent", agent_id, "!")

decision_tree = client.get_decision_tree(agent_id, 1469476800)
print("The full decision tree at timestamp", dt_timestamp, "is the following:")
print(decision_tree)
""" Outputed tree is the following
  {
    "_version": "1.0.0",
    "configuration": {
      "context": {
        "peopleCount": {
          "type": "continuous"
        },
        "timeOfDay": {
          "type": "time_of_day",
          "is_generated": true
        },
        "timezone": {
          "type": "timezone"
        },
        "lightbulbState": {
          "type": "enum"
        }
      },
      "output": [
        "lightbulbState"
      ],
      "time_quantum": 600,
      "learning_period": 108000
    },
    "trees": {
      "lightbulbState": {
        "children": [
          {
            "children": [
              {
                "children": [
                  {
                    "confidence": 0.9545537233352661,
                    "decision_rule": {
                      "operator": "continuous.lessthan",
                      "operand": 1,
                      "property": "peopleCount"
                    },
                    "predicted_value": "OFF"
                  },
                  {
                    "confidence": 0.8630361557006836,
                    "decision_rule": {
                      "operator": ">=",
                      "operand": 1,
                      "property": "peopleCount"
                    },
                    "predicted_value": "ON"
                  }
                ],
                "decision_rule": {
                  "operator": "<",
                  "operand": 5.666666507720947,
                  "property": "timeOfDay"
                }
              },
              {
                "confidence": 0.9947378635406494,
                "decision_rule": {
                  "operator": ">=",
                  "operand": 5.666666507720947,
                  "property": "timeOfDay"
                },
                "predicted_value": "OFF"
              }
            ],
            "decision_rule": {
              "operator": "<",
              "operand": 20.66666603088379,
              "property": "timeOfDay"
            }
          },
          {
            "confidence": 0.8630361557006836,
            "decision_rule": {
              "operator": ">=",
              "operand": 20.66666603088379,
              "property": "timeOfDay"
            },
            "predicted_value": "ON"
          }
        ],
      }
    }
  ]
  """

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 <#compute>`__.

6 - Take a decision

Once the decision tree is computed it can be used to take a decision. 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 ?”.

agent_id = "my_first_agent"

client.delete_agent(agent_id)
print("Agent", agent_id, "no longer exists")

configuration = ...
agent = client.create_agent(configuration, agent_id)
print("Agent", agent["id"], "has successfully been created")

context_list = ...
client.add_operations(agent_id, context_list)
print("Successfully added initial operations to agent", agent_id, "!")

decision_tree = client.get_decision_tree(agent_id, 1469476800)
print("The decision tree at timestamp", dt_timestamp, "is the following:")
print(decision_tree)

context = {
    "timezone": "+02:00",
    "timeOfDay": 7.25,
    "peopleCount": 2
}
resp = client.decide(decision_tree, context)
print("The anticipated lightbulb state is:", resp["output"]["lightbulbState"]["predicted_value"])

For further information, check the `’take decision’ reference documentation <#take-decision>`__.

Python starter kit

If you prefer to get started from an existing code base, the official Python starter kit can get you there! Retrieve the sources locally and follow the “readme” to get a fully working Wellness Coach example using real-world data.

📦 *Get the **craft ai** Python 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 takes decisions,

    ⚠️ In the current version, only one output property can be provided.

  • the 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.

  • the 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.

    ⚠️ if no time_quantum is specified, the default value is 600.

    ⚠️ if no learning_period is specified, the default value is 15000 time quantums.

Context properties types

Base types: enum and continuous

enum and continuous are the two base craft ai types:

  • an enum property is a string;

  • a continuous property is a real number.

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

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 is a string value representing the timezone as an offset from UTC, the expected format is ±[hh]:[mm] where hh represent the hour and mm the minutes from UTC (eg. +01:30)), between -12:00 and +14:00.

    ℹ️ By default, the values of the time_of_day and day_of_week
    properties are generated from the `timestamp <#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 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 <#timestamp>`__.
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"
      },
      "time": {
        "type": "time_of_day"
      },
      "day": {
        "type": "day_of_week"
      },
      "timezone": {
        "type": "timezone"
      },
      "lightbulbColor": {
          "type": "enum"
      }
  },
  "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"
    },
    "lightbulbColor": {
        "type": "enum"
    }
  },
  "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. 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 datetime.

# From a unix timestamp and an explicit UTC offset
t1 = craftai.Time(1465496929, "+10:00")

# t1 == {
#   utc: "2016-06-09T18:28:49.000Z",
#   timestamp: 1465496929,
#   day_of_week: 4,
#   time_of_day: 4.480277777777778,
#   timezone: "+10:00"
# }

# From a unix timestamp and using the local UTC offset.
t2 = craftai.Time(1465496929)

# Value are valid if in Paris !
# t2 == {
#   utc: "2016-06-09T18:28:49.000Z",
#   timestamp: 1465496929,
#   day_of_week: 3,
#   time_of_day: 20.480277777777776,
#   timezone: "+02:00"
# }

# From a ISO 8601 string. Note that here it should not have any ":" in the timezone part
t3 = craftai.Time("1977-04-22T01:00:00-0500")

# t3 == {
#   utc: "1977-04-22T06:00:00.000Z",
#   timestamp: 230536800,
#   day_of_week: 4,
#   time_of_day: 1,
#   timezone: "-05:00"
# }

# Retrieve the current time with the local UTC offset
now = craftai.Time()

# Retrieve the current time with the given UTC offset
nowP5 = craftai.Time(timezone="+05:00")

Advanced configuration

The following advanced configuration parameters can be set in specific cases. They are optional. Usually you would not need them.

  • operations_as_events is a boolean, either true or false. The default value is false. If it is set to true, all context operations are treated as events, as opposed to context updates. This is appropriate if the data for an agent is made of events that have no duration, and if many events are more significant than a few. If operations_as_events is true, learning_period and the advanced parameter tree_max_operations must be set as well. In that case, time_quantum is ignored because events have no duration, as opposed to the evolution of an agent’s context over time.
  • tree_max_operations 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 single decision tree can be based. It is complementary to learning_period, which limits the maximum age of events on which a decision tree is based.
  • 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).

These advanced configuration parameters are optional, and will appear in the agent information returned by craft ai only if you set them to something other than their default value. If you intend to use them in a production environment, please get in touch with us.

Agent

Create

Create a new agent, and create its configuration.

client.create_agent(
    { # The configuration
        "context": {
          "peopleCount": {
            "type": "continuous"
          },
          "timeOfDay": {
            "type": "time_of_day"
          },
          "timezone": {
            "type": "timezone"
          },
          "lightbulbState": {
            "type": "enum"
          }
        },
        "output": [ "lightbulbState" ],
        "time_quantum": 100,
        "learning_period": 108000
    },
    "impervious_kraken", # id for the agent, if undefined a random id is generated

Delete

client.delete_agent(
    "impervious_kraken" # The agent id
)

Retrieve

client.get_agent(
    "impervious_kraken" # The agent id
)

List

client.list_agents()
# Return a list of agents' name
# Example: [ "impervious_kraken", "joyful_octopus", ... ]

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.get_shared_agent_inspector_url(
    "impervious_kraken", # The agent id.
    1464600256 # optional, the timestamp for which you want to inspect the tree.
)

Delete shared url

Delete a shareable url.
The previous url cannot access the agent tree anymore.
client.delete_shared_agent_inspector_url(
    'impervious_kraken' # The agent id.
)

Context

Add operations

client.add_operations(
    "impervious_kraken", # The agent id
    [ # The list of context operations
        {
            "timestamp": 1469410200,
            "context": {
                "timezone": "+02:00",
                "peopleCount": 0,
                "lightbulbState": "OFF"
            }
        },
        {
            "timestamp": 1469415720,
            "context": {
                "peopleCount": 1,
                "lightbulbState": "ON"
            }
        },
        {
            "timestamp": 1469416500,
            "context": {
                "peopleCount": 2
            }
        },
        {
            "timestamp": 1469417460,
            "context": {
                "lightbulbState": "OFF"
            }
        },
        {
            "timestamp": 1469419920,
            "context": {
                "peopleCount": 0
            }
        },
        {
            "timestamp": 1469460180,
            "context": {
                "peopleCount": 2
            }
        },
        {
            "timestamp": 1469471700,
            "context": {
                "lightbulbState": "ON"
            }
        },
        {
            "timestamp": 1469473560,
            "context": {
                "peopleCount": 0
            }
        }
    ]
)

List operations

client.get_operations_list(
    "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)
)

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

Retrieve state

client.get_context_state(
    "impervious_kraken", # The agent id
    1469473600 # The timestamp at which the context state is retrieved
)

Decision tree

Decision trees are computed at specific timestamps, directly by craft ai which learns from the context operations added throughout time.

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

  • the API version
  • 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.

Compute

client.get_decision_tree(
    "impervious_kraken", # The agent id
    1469473600 # The timestamp at which the decision tree is retrieved
)

Take Decision

To get a chance to store and reuse the decision tree, use get_decision_tree and use decide, a simple function evaluating a decision tree offline.

tree = { ... } # Decision tree as retrieved through the craft ai REST API

# Compute the decision on a fully described context
decision = client.decide(
    tree,
    { # The context on which the decision is taken
        "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`

decision = client.decide(
  tree,
  {
    "timezone": "+02:00",
    "peopleCount": 3
  },
  craftai.Time("2010-01-01T07:30:30")
)

A computed decision on an enum output type would look like:

{
  "context": { # In which context the decision was taken
    "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
        }
      ]
    },
  }
}

A decision for a numerical output type would look like:

"output": {
  "lightbulbIntensity": {
    "predicted_value": 10.5,
    "standard_deviation": 1.25, // For numerical types, this field is returned in decisions.
    "decision_rules": [ ... ],
    "confidence": ...
  }
}

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

"output": {
  "lightbulbState": {
    "predicted_value": None,
    "confidence": 0 // Zero confidence if the decision is null
    "decision_rules": [ ... ]
  }
}

Error Handling

When using this client, you should be careful wrapping calls to the API with try/except blocks, in accordance with the EAFP principle.

The craft ai python client has its specific exception types, all of them inheriting from the CraftAIError type.

All methods which have to send an http request (all of them except decide) may raise either of these exceptions: CraftAINotFoundError, CraftAIBadRequestError, CraftAICredentialsError or CraftAIUnknownError.

The decide method only raises CrafAIDecisionError of CraftAiNullDecisionError type of exceptions. The latter is raised when no the given context is valid but no decision can be taken.

Pandas support

The craft ai python client optionally supports pandas a very popular library used for all things data.

Basically instead of importing the default module, you can do the following

import craftai.pandas

# Most of the time you'll need the following
import numpy as np
import pandas as pd

The craft ai pandas module is derived for the vanilla one, with the following methods are overriden to support pandas’ `DataFrame <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.html>`__.

craftai.pandas.Client.get_operations_list

Retrieves the desired operations as a DataFrame where:

  • each operation is a row,
  • each context property is a column,
  • the index is *time based* matching the operations timestamps,
  • np.NaN means no value were given at this property for this timestamp.
df = client.get_operations_list("impervious_kraken")

# `df` is a pd.DataFrame looking like
#
#              peopleCount  lightbulbState   timezone
# 2013-01-01   0            OFF              +02:00
# 2013-01-02   1            ON               NaN
# 2013-01-03   2            NaN              NaN
# 2013-01-04   NaN          OFF              NaN
# 2013-01-05   0            NaN              NaN

craftai.pandas.Client.add_operations

Add a DataFrame of operations to the desired agent. The format is the same as above.

df = pd.DataFrame(
  [
    [0, "OFF", "+02:00"],
    [1, "ON", np.nan],
    [2, np.nan, np.nan],
    [np.nan, "OFF", np.nan],
    [0, np.nan, np.nan]
  ],
  columns=['peopleCount', 'lightbulbState', 'timezone'],
  index=pd.date_range('20130101', periods=5, freq='D')
)
client.add_operations("impervious_kraken", df)

Given something that is not a DataFrame this method behave like the vanilla craftai.Client.add_operations.

craftai.pandas.Client.decide_from_contexts_df

Take multiple decisions on a given DataFrame following the same format as above.

decisions_df = client.decide(tree, pd.DataFrame(
  [
    [0, "+02:00"],
    [1, np.nan],
    [2, np.nan],
    [np.nan, np.nan],
    [0, np.nan]
  ],
  columns=['peopleCount', 'timezone'],
  index=pd.date_range('20130101', periods=5, freq='D')
))
# `decisions_df` is a pd.DataFrame looking like
#
#              lightbulbState_predicted_value   lightbulbState_confidence ...
# 2013-01-01   OFF                              0.999449                  ...
# 2013-01-02   ON                               0.970325                  ...
# 2013-01-03   ON                               0.970325                  ...
# 2013-01-04   ON                               0.970325                  ...
# 2013-01-05   OFF                              0.999449                  ...

This function never raises CraftAiNullDecisionError, instead it inserts these errors in the result Dataframe in a specific error column.

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