Forced decision methods for the Python SDK
Describes the Forced Decision methods, which you can use to force users into a specific variation in Optimizely Feature Experimentation.
These methods help test and debug various flows of your client applications by forcing users into a specific variation.
The Optimizely Feature Experimentation Python SDK checks forced decisions before making any decisions. If a matching item is found for the requested flag, the Python SDK returns the forced decision immediately (audience conditions and traffic allocations are ignored) before making normal decisions.
The Python SDK checks for forced decisions at the start of each decision process. If a matching forced decision is found, it returns the decision immediately.
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Flag-to-Decision – The SDK checks at the start of any decide call for the given flag.
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Experiment-Rule-to-Decision – The SDK checks at the start of the decision for the given experiment rule of the flag key.
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Delivery-Rule-to-Decision – The SDK checks at the start of the decision for the given delivery rule of the flag key.
WarningYou must associate your variations to a flag rule before calling any forced decision methods.
On forced decisions, the SDK fires impression events and notifications just like other normal decisions (unless disabled by the DISABLE_DECISION_EVENT option).
NoteThese forced decisions are not persistent and are cleared when the OptimizelyUserContext is re-initialized.
For information about each method, click on the method name.
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.
Parameter | Type | Description |
|---|---|---|
context required | Class | An instance of |
decision | Class | An instance of |
Returns
A boolean value that indicates if setting the forced decision (variation_key) was completed.
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 null if there is no matching item.
Parameters
This table lists the required and optional parameters for the Python SDK.
Parameter | Type | Description |
|---|---|---|
context required | Class | An instance of |
Returns
A forced decision OptimizelyForcedDecision instance for the context or None 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.
Parameters | Type | Description |
|---|---|---|
context required | Class | An instance of |
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.
| Parameters | Type | Description |
|---|---|---|
| None | N/A | N/A |
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
Source files
The source files are available on GitHub.
Updated 4 days ago