Forced decision methods for the Python SDK

These methods will help test and debug various flows of your client applications by forcing users into a specific variation.

The Optimizely Feature Experimentation Python SDK will check forced decisions before making any decisions. If a matching item is found for the requested flag, the Python SDK will return the forced decision immediately (audience conditions and traffic allocations are ignored) before making normal decisions:

The following describes specific scenarios the Python SDK will follow:

• Flag-to-Decision – The Python SDK will look up at the beginning of any decide call for the given flag. If a matching Flag-to-Decision forced decision is found for the flag, it returns the decision.
• Experiment-Rule-to-Decision – The Python SDK will look up at the beginning of the decision for the given experiment rule (of the flag key). If a matching Experiment-Rule-to-Decision forced decision is found for the flag, it returns the decision.
• Delivery-Rule-to-Decision – The Python SDK will look up at the beginning of the decision for the given delivery rule (of the flag key). If a matching Delivery-Rule-to-Decision forced decision is found, it returns the decision.

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Warning

You must associate your variation(s) to a flag rule before calling any forced decision methods.

On forced decisions, SDK will fire impression events and notifications just like other normal decisions (unless disabled by the DISABLE_DECISION_EVENT option).

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Note

These forced decisions are not persistent and will be cleared when the OptimizelyUserContext is re-initialized.

For more information about each method, click on the method name below:

• set_forced_decision
• get_forced_decision
• remove_forced_decision
• remove_all_forced_decisions

OptimizelyDecisionContext

class OptimizelyDecisionContext(object):
  def __init__(self, flag_key, rule_key):

OptimizelyForcedDecision

class OptimizelyForcedDecision(object):
  def __init__(self, variation_key):

Set forced decision method - set_forced_decision()

Version

4.0.0

Description

Sets a forced decision (variation_key) for a given OptimizelyDecisionContext.

Parameters

This table lists the required and optional parameters for the Python SDK.

Returns

A boolean value that indicates if setting the forced decision (variation_key) was completed successfully.

Example

See the full Python SDK example here.

Get forced decision method - get_forced_decision()

Version

4.0.0

Description

Returns the forced decision (variation_key) for a given OptimizelyDecisionContext. Returns the null if there is no matching item.

Parameters

This table lists the required and optional parameters for the Python SDK.

Returns

A forced decision OptimizelyForcedDecision instance for the context or nil if there is no matching item.

Example

See the full Python SDK example here.

Remove forced decision method - remove_forced_decision()

Version

4.0.0

Description

Removes the forced decision (variation_key) for a given OptimizelyDecisionContext.

Parameters

This table lists the required and optional parameters for the Python SDK.

Returns

A success/failure boolean status if the forced decision (variation_key) was removed.

Example

See the full Python SDK example here.

Remove all forced decisions method - remove_all_forced_decisions()

Version

4.0.0

Description

Removes all forced decisions (variation_key) for the user context.

Parameters

This table lists the required and optional parameters for the Python SDK.

Returns

A success/failure boolean status.

Example

See the full Python SDK example here.

Full code example

from optimizely import optimizely, optimizely_user_context

optimizely_client = optimizely.Optimizely(sdk_key="<YOUR_SDK_KEY>")

user = optimizely_client.create_user_context("test_user", attributes)

flag_context = optimizely_user_context.OptimizelyUserContext.OptimizelyDecisionContext("flag-1",None)
flag_and_ab_test_context = optimizely_user_context.OptimizelyUserContext.OptimizelyDecisionContext("flag-1","ab-test-1")
flag_and_delivery_rule_context = optimizely_user_context.OptimizelyUserContext.OptimizelyDecisionContext("flag-1","delivery-1")
variation_a_forced_decision = optimizely_user_context.OptimizelyUserContext.OptimizelyForcedDecision("variation-a")
variation_b_forced_decision = optimizely_user_context.OptimizelyUserContext.OptimizelyForcedDecision("variation-b")
variation_on_forced_decision = optimizely_user_context.OptimizelyUserContext.OptimizelyForcedDecision("on")

# set a forced decision for a flag
success = user.set_forced_decision(flag_context, variation_a_forced_decision)
decision = user.decide("flag-1")

# set a forced decision for an ab-test rule
success = user.set_forced_decision(flag_and_ab_test_context, variation_b_forced_decision)
decision = user.decide("flag-1")

# set a forced variation for a delivery rule
success = user.set_forced_decision(flag_and_delivery_rule_context, variation_on_forced_decision)
decision = user.decide("flag-1")

# get forced variations
forced_decision = user.get_forced_decision(flag_context)
print(f"[ForcedDecision] variation_key = {forced_decision}")

# remove forced variations
success = user.remove_forced_decision(flag_and_ab_test_context)
success = user.remove_all_forced_decision()

See also

OptimizelyUserContext

Source files

The source files are available on GitHub.