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 andtraffic 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 anydecide 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.

❗️

Warning

You must associate your variation(s) to aflag 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 theDISABLE_DECISION_EVENT option).

📘

Note

These forced decisions are not persistent and will be cleared when theOptimizelyUserContext 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 givenOptimizelyDecisionContext.

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 examplehere.

Get forced decision method - get_forced_decision()

Version

4.0.0

Description

Returns the forced decision (variation_key) for a givenOptimizelyDecisionContext. 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 decisionOptimizelyForcedDecision instance for the context or nil if there is no matching item.

Example

See the full Python SDK examplehere.

Remove forced decision method - remove_forced_decision()

Version

4.0.0

Description

Removes the forced decision (variation_key) for a givenOptimizelyDecisionContext.

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 examplehere.

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 examplehere.

Full code example

from optimizely import optimizely, optimizely_user_contextoptimizely_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 flagsuccess = user.set_forced_decision(flag_context, variation_a_forced_decision)decision = user.decide("flag-1")# set a forced decision for an ab-test rulesuccess = 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 rulesuccess = user.set_forced_decision(flag_and_delivery_rule_context, variation_on_forced_decision)decision = user.decide("flag-1")# get forced variationsforced_decision = user.get_forced_decision(flag_context)print(f"[ForcedDecision] variation_key = {forced_decision}")# remove forced variationssuccess = 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 onGitHub.

Updated 17 days ago