rtbgym.envs.simulator.function.ClickThroughRate#

class rtbgym.envs.simulator.function.ClickThroughRate(n_ads, n_users, ad_feature_dim, user_feature_dim, step_per_episode, random_state=None)[source]#

Class to calculate ground-truth CTR (i.e., click per impression).

Imported as: rtbgym.envs.simulator.ClickThroughRate

Note

We define two coefficient, context coefficient (coef) and time coefficient (time_coef). First, the value is calculated linearly from context vector and coef by inner product. Then, we multiply the value with time_coef and gain (ground-truth) CTR.

In short, CTR is calculated as follows.

CTR = (context @ coef) * time_coef, where @ denotes inner product.

Tip

Use BaseClickAndConversionRate to define a custom ClickThroughRate.

Parameters:

  • n_ads (int (> 0)) – Number of ads. (This is for API consistency)

  • n_users (int (> 0)) – Number of users. (This is for API consistency)

  • ad_feature_dim (int (> 0)) – Dimension of the ad feature vectors.

  • user_feature_dim (int (> 0)) – Dimension of the user feature vectors.

  • step_per_episode (int (> 0)) – Length of the CTR trend cycle.

  • random_state (int, default=None (>= 0)) – Random state.

Attributes:
random_state

Methods

calc_prob(ad_ids, user_ids, ...)

Calculate CTR (i.e., click per impression).

sample_outcome(ad_ids, user_ids, ...)

Stochastically determine whether click occurs in impression=True case.
calc_prob(ad_ids, user_ids, ad_feature_vector, user_feature_vector, timestep)[source]#

Calculate CTR (i.e., click per impression).

Note

CTR is calculated using both context coefficient (coef) and time coefficient (time_coef).

CTR = (context @ coef) * time_coef, where @ denotes inner product.

Parameters:

  • ad_ids (array-like of shape (search_volume/n_samples, )) – Ad ids used for each auction. (not used, but for API consistency)

  • user_ids (array-like of shape (search_volume/n_samples, )) – User ids used for each auction. (not used, but for API consistency)

  • ad_feature_vector (array-like of shape (search_volume/n_samples, ad_feature_dim)) – Ad feature vector for each auction.

  • user_feature_vector (array-like of shape (search_volume/n_samples, user_feature_dim)) – User feature vector for each auction.

  • timestep ({int, array-like of shape (n_samples, )}) – Timestep in the RL environment.

Returns:

ctrs – Ground-truth CTR (i.e., click per impression) for each auction.

Return type:

ndarray of shape (search_volume/n_samples, )

sample_outcome(ad_ids, user_ids, ad_feature_vector, user_feature_vector, timestep)[source]#

Stochastically determine whether click occurs in impression=True case.

Parameters:

  • ad_ids (array-like of shape (search_volume/n_samples, )) – Ad ids used for each auction. (not used, but for API consistency)

  • user_ids (array-like of shape (search_volume/n_samples, )) – User ids used for each auction. (not used, but for API consistency)

  • ad_feature_vector (array-like of shape (search_volume/n_samples, ad_feature_dim)) – Ad feature vector for each auction.

  • user_feature_vector (array-like of shape (search_volume/n_samples, user_feature_dim)) – User feature vector for each auction.

  • timestep ({int, array-like of shape (n_samples, )}) – Timestep in the RL environment.

Returns:

clicks – Whether click occurs when impression=True.

Return type:

array-like of shape (search_volume/n_samples, )

Methods