diff --git a/examples/README.md b/examples/README.md index 52c873f7..595998fe 100644 --- a/examples/README.md +++ b/examples/README.md @@ -1,10 +1,10 @@ # Open Bandit Pipeline Examples -This page contains a list of example codes written with the Open Bandit Pipeline. +This page contains a list of examples written with Open Bandit Pipeline. - [`obd/`](./obd/): example implementations for evaluating standard off-policy estimators with the small sample Open Bandit Dataset. - [`synthetic/`](./synthetic/): example implementations for evaluating several off-policy estimators with synthetic bandit datasets. - [`multiclass/`](./multiclass/): example implementations for evaluating several off-policy estimators with multi-class classification datasets. - [`online/`](./online/): example implementations for evaluating Replay Method with online bandit algorithms. - [`opl/`](./opl/): example implementations for comparing the performance of several off-policy learners with synthetic bandit datasets. -- [`quickstart/`](./quickstart/): some quickstart notebooks to guide the usage of the Open Bandit Pipeline. +- [`quickstart/`](./quickstart/): some quickstart notebooks to guide the usage of Open Bandit Pipeline. diff --git a/examples/multiclass/README.md b/examples/multiclass/README.md index d2dcbd4c..965b0387 100644 --- a/examples/multiclass/README.md +++ b/examples/multiclass/README.md @@ -1,14 +1,14 @@ -# Example with Multi-class Classification Data +# Example Experiment with Multi-class Classification Data ## Description -Here, we use multi-class classification datasets to evaluate OPE estimators. -Specifically, we evaluate the estimation performances of well-known off-policy estimators using the ground-truth policy value of an evaluation policy calculable with multi-class classification data. +We use multi-class classification datasets to evaluate OPE estimators. Specifically, we evaluate the estimation performance of some well-known OPE estimators using the ground-truth policy value of an evaluation policy calculable with multi-class classification data. ## Evaluating Off-Policy Estimators -In the following, we evaluate the estimation performances of +In the following, we evaluate the estimation performance of + - Direct Method (DM) - Inverse Probability Weighting (IPW) - Self-Normalized Inverse Probability Weighting (SNIPW) @@ -17,12 +17,12 @@ In the following, we evaluate the estimation performances of - Switch Doubly Robust (Switch-DR) - Doubly Robust with Optimistic Shrinkage (DRos) -For Switch-DR and DRos, we try some different values of hyperparameters. +For Switch-DR and DRos, we tune the built-in hyperparameters using SLOPE (Su et al., 2020; Tucker et al., 2021), a data-driven hyperparameter tuning method for OPE estimators. See [our documentation](https://zr-obp.readthedocs.io/en/latest/estimators.html) for the details about these estimators. ### Files - [`./evaluate_off_policy_estimators.py`](./evaluate_off_policy_estimators.py) implements the evaluation of OPE estimators using multi-class classification data. -- [`./conf/hyperparams.yaml`](./conf/hyperparams.yaml) defines hyperparameters of some machine learning methods used to define regression model. +- [`./conf/hyperparams.yaml`](./conf/hyperparams.yaml) defines hyperparameters of some ML methods used to define regression model. ### Scripts @@ -50,38 +50,46 @@ python evaluate_off_policy_estimators.py\ - `$base_model_for_reg_model` specifies the base ML model for defining regression model and should be one of "logistic_regression", "random_forest", or "lightgbm". - `$n_jobs` is the maximum number of concurrently running jobs. -For example, the following command compares the estimation performances (relative estimation error; relative-ee) of the OPE estimators using the digits dataset. +For example, the following command compares the estimation performance (relative estimation error; relative-ee) of the OPE estimators using the digits dataset. ```bash python evaluate_off_policy_estimators.py\ - --n_runs 20\ + --n_runs 30\ --dataset_name digits\ --eval_size 0.7\ --base_model_for_behavior_policy logistic_regression\ - --alpha_b 0.8\ - --base_model_for_evaluation_policy logistic_regression\ + --alpha_b 0.4\ + --base_model_for_evaluation_policy random_forest\ --alpha_e 0.9\ - --base_model_for_reg_model logistic_regression\ + --base_model_for_reg_model lightgbm\ --n_jobs -1\ --random_state 12345 # relative-ee of OPE estimators and their standard deviations (lower is better). -# It appears that the performances of some OPE estimators depend on the choice of their hyperparameters. # ============================================= # random_state=12345 # --------------------------------------------- -# mean std -# dm 0.093439 0.015391 -# ipw 0.013286 0.008496 -# snipw 0.006797 0.004094 -# dr 0.007780 0.004492 -# sndr 0.007210 0.004089 -# switch-dr (lambda=1) 0.173282 0.020025 -# switch-dr (lambda=100) 0.007780 0.004492 -# dr-os (lambda=1) 0.079629 0.014008 -# dr-os (lambda=100) 0.008031 0.004634 +# mean std +# dm 0.436541 0.017629 +# ipw 0.030288 0.024506 +# snipw 0.022764 0.017917 +# dr 0.016156 0.012679 +# sndr 0.022082 0.016865 +# switch-dr 0.034657 0.018575 +# dr-os 0.015868 0.012537 # ============================================= ``` -The above result can change with different situations. -You can try the evaluation of OPE with other experimental settings easily. +The above result can change with different situations. You can try the evaluation of OPE with other experimental settings easily. + + +## References + +- Yi Su, Pavithra Srinath, Akshay Krishnamurthy. [Adaptive Estimator Selection for Off-Policy Evaluation](https://arxiv.org/abs/2002.07729), ICML2020. +- Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík. [Doubly Robust Off-policy Evaluation with Shrinkage](https://arxiv.org/abs/1907.09623), ICML2020. +- George Tucker and Jonathan Lee. [Improved Estimator Selection for Off-Policy Evaluation](https://lyang36.github.io/icml2021_rltheory/camera_ready/79.pdf), Workshop on Reinforcement Learning +Theory at ICML2021. +- Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudik. [Optimal and Adaptive Off-policy Evaluation in Contextual Bandits](https://arxiv.org/abs/1612.01205), ICML2017. +- Miroslav Dudik, John Langford, Lihong Li. [Doubly Robust Policy Evaluation and Learning](https://arxiv.org/abs/1103.4601). ICML2011. +- Yuta Saito, Shunsuke Aihara, Megumi Matsutani, Yusuke Narita. [Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation](https://arxiv.org/abs/2008.07146). NeurIPS2021 Track on Datasets and Benchmarks. + diff --git a/examples/multiclass/evaluate_off_policy_estimators.py b/examples/multiclass/evaluate_off_policy_estimators.py index e39de4e4..5d8b6de0 100644 --- a/examples/multiclass/evaluate_off_policy_estimators.py +++ b/examples/multiclass/evaluate_off_policy_estimators.py @@ -17,13 +17,13 @@ from obp.dataset import MultiClassToBanditReduction from obp.ope import DirectMethod from obp.ope import DoublyRobust -from obp.ope import DoublyRobustWithShrinkage +from obp.ope import DoublyRobustWithShrinkageTuning from obp.ope import InverseProbabilityWeighting from obp.ope import OffPolicyEvaluation from obp.ope import RegressionModel from obp.ope import SelfNormalizedDoublyRobust from obp.ope import SelfNormalizedInverseProbabilityWeighting -from obp.ope import SwitchDoublyRobust +from obp.ope import SwitchDoublyRobustTuning # hyperparameters of the regression model used in model dependent OPE estimators @@ -50,10 +50,10 @@ SelfNormalizedInverseProbabilityWeighting(), DoublyRobust(), SelfNormalizedDoublyRobust(), - SwitchDoublyRobust(lambda_=1.0, estimator_name="switch-dr (lambda=1)"), - SwitchDoublyRobust(lambda_=100.0, estimator_name="switch-dr (lambda=100)"), - DoublyRobustWithShrinkage(lambda_=1.0, estimator_name="dr-os (lambda=1)"), - DoublyRobustWithShrinkage(lambda_=100.0, estimator_name="dr-os (lambda=100)"), + SwitchDoublyRobustTuning(lambdas=[10, 50, 100, 500, 1000, 5000, 10000, np.inf]), + DoublyRobustWithShrinkageTuning( + lambdas=[10, 50, 100, 500, 1000, 5000, 10000, np.inf] + ), ] if __name__ == "__main__": @@ -161,7 +161,7 @@ def process(i: int): ground_truth_policy_value = dataset.calc_ground_truth_policy_value( action_dist=action_dist ) - # estimate the mean reward function of the evaluation set of multi-class classification data with ML model + # estimate the reward function of the evaluation set of multi-class classification data with ML model regression_model = RegressionModel( n_actions=dataset.n_actions, base_model=base_model_dict[base_model_for_reg_model]( @@ -180,34 +180,35 @@ def process(i: int): bandit_feedback=bandit_feedback, ope_estimators=ope_estimators, ) - relative_ee_i = ope.evaluate_performance_of_estimators( + metric_i = ope.evaluate_performance_of_estimators( ground_truth_policy_value=ground_truth_policy_value, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, + metric="relative-ee", ) - return relative_ee_i + return metric_i processed = Parallel( n_jobs=n_jobs, verbose=50, )([delayed(process)(i) for i in np.arange(n_runs)]) - relative_ee_dict = {est.estimator_name: dict() for est in ope_estimators} - for i, relative_ee_i in enumerate(processed): + metric_dict = {est.estimator_name: dict() for est in ope_estimators} + for i, metric_i in enumerate(processed): for ( estimator_name, relative_ee_, - ) in relative_ee_i.items(): - relative_ee_dict[estimator_name][i] = relative_ee_ - relative_ee_df = DataFrame(relative_ee_dict).describe().T.round(6) + ) in metric_i.items(): + metric_dict[estimator_name][i] = relative_ee_ + result_df = DataFrame(metric_dict).describe().T.round(6) print("=" * 45) print(f"random_state={random_state}") print("-" * 45) - print(relative_ee_df[["mean", "std"]]) + print(result_df[["mean", "std"]]) print("=" * 45) # save results of the evaluation of off-policy estimators in './logs' directory. log_path = Path(f"./logs/{dataset_name}") log_path.mkdir(exist_ok=True, parents=True) - relative_ee_df.to_csv(log_path / "relative_ee_of_ope_estimators.csv") + result_df.to_csv(log_path / "evaluation_of_ope_results.csv") diff --git a/examples/obd/README.md b/examples/obd/README.md index fd75affa..cd5ea619 100644 --- a/examples/obd/README.md +++ b/examples/obd/README.md @@ -1,16 +1,27 @@ -# Example with the Open Bandit Dataset (OBD) +# Example Experiment with Open Bandit Dataset ## Description -Here, we use the open bandit dataset and pipeline to implement and evaluate OPE. Specifically, we evaluate the estimation performances of well-known off-policy estimators using the ground-truth policy value of an evaluation policy, which is calculable with our data using on-policy estimation. +We use Open Bandit Dataset to implement the evaluation of OPE. Specifically, we evaluate the estimation performance of some well-known OPE estimators using the on-policy policy value of an evaluation policy, which is calculable with the dataset. ## Evaluating Off-Policy Estimators -We evaluate the estimation performances of off-policy estimators, including Direct Method (DM), Inverse Probability Weighting (IPW), and Doubly Robust (DR). +In the following, we evaluate the estimation performance of + +- Direct Method (DM) +- Inverse Probability Weighting (IPW) +- Self-Normalized Inverse Probability Weighting (SNIPW) +- Doubly Robust (DR) +- Self-Normalized Doubly Robust (SNDR) +- Switch Doubly Robust (Switch-DR) +- Doubly Robust with Optimistic Shrinkage (DRos) + +For Switch-DR and DRos, we tune the built-in hyperparameters using SLOPE, a data-driven hyperparameter tuning method for OPE estimators. +See [our documentation](https://zr-obp.readthedocs.io/en/latest/estimators.html) for the details about these estimators. ### Files -- [`./evaluate_off_policy_estimators.py`](./evaluate_off_policy_estimators.py) implements the evaluation of OPE estimators. -- [`.conf/hyperparams.yaml`](./conf/hyperparams.yaml) defines hyperparameters of some machine learning models used as the regression model in model dependent estimators (such as DM and DR). +- [`./evaluate_off_policy_estimators.py`](./evaluate_off_policy_estimators.py) implements the evaluation of OPE estimators using Open Bandit Dataset. +- [`.conf/hyperparams.yaml`](./conf/hyperparams.yaml) defines hyperparameters of some ML models used as the regression model in model dependent estimators (such as DM and DR). ### Scripts @@ -34,11 +45,11 @@ They should be either 'bts' or 'random'. - `$n_sim_to_compute_action_dist` is the number of monte carlo simulation to compute the action distribution of a given evaluation policy. - `$n_jobs` is the maximum number of concurrently running jobs. -For example, the following command compares the estimation performances of the three OPE estimators by using Bernoulli TS as evaluation policy and Random as behavior policy in "All" campaign. +For example, the following command compares the estimation performance of the three OPE estimators by using Bernoulli TS as evaluation policy and Random as behavior policy in "All" campaign. ```bash python evaluate_off_policy_estimators.py\ - --n_runs 20\ + --n_runs 30\ --base_model logistic_regression\ --evaluation_policy bts\ --behavior_policy random\ @@ -46,16 +57,31 @@ python evaluate_off_policy_estimators.py\ --n_jobs -1 # relative estimation errors of OPE estimators and their standard deviations. -# our evaluation of OPE procedure suggests that DM performs best among the three OPE estimators, because it has low variance property. -# (Note that this result is with the small sample data, and please use the full size data for a more reasonable experiment) # ============================== # random_state=12345 # ------------------------------ -# mean std -# dm 0.180269 0.114716 -# ipw 0.333113 0.350425 -# dr 0.304422 0.347866 +# mean std +# dm 0.156876 0.109898 +# ipw 0.311082 0.311170 +# snipw 0.311795 0.334736 +# dr 0.292464 0.315485 +# sndr 0.302407 0.328434 +# switch-dr 0.258410 0.160598 +# dr-os 0.159520 0.109660 # ============================== ``` -Please refer to [this page](https://zr-obp.readthedocs.io/en/latest/evaluation_ope.html) for the evaluation of OPE protocol using our real-world data. Please visit [synthetic](../synthetic/) to try the evaluation of OPE estimators with synthetic bandit datasets. Moreover, in [benchmark/ope](https://github.com/st-tech/zr-obp/tree/master/benchmark/ope), we performed the benchmark experiments on several OPE estimators using the full size Open Bandit Dataset. +Please refer to [this page](https://zr-obp.readthedocs.io/en/latest/evaluation_ope.html) for the evaluation of OPE protocol using our real-world data. Please visit [synthetic](../synthetic/) to try the evaluation of OPE estimators with synthetic bandit data. Moreover, in [benchmark/ope](https://github.com/st-tech/zr-obp/tree/master/benchmark/ope), we performed the benchmark experiments on several OPE estimators using the full size Open Bandit Dataset. + + + +## References + +- Yi Su, Pavithra Srinath, Akshay Krishnamurthy. [Adaptive Estimator Selection for Off-Policy Evaluation](https://arxiv.org/abs/2002.07729), ICML2020. +- Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík. [Doubly Robust Off-policy Evaluation with Shrinkage](https://arxiv.org/abs/1907.09623), ICML2020. +- George Tucker and Jonathan Lee. [Improved Estimator Selection for Off-Policy Evaluation](https://lyang36.github.io/icml2021_rltheory/camera_ready/79.pdf), Workshop on Reinforcement Learning +Theory at ICML2021. +- Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudik. [Optimal and Adaptive Off-policy Evaluation in Contextual Bandits](https://arxiv.org/abs/1612.01205), ICML2017. +- Miroslav Dudik, John Langford, Lihong Li. [Doubly Robust Policy Evaluation and Learning](https://arxiv.org/abs/1103.4601). ICML2011. +- Yuta Saito, Shunsuke Aihara, Megumi Matsutani, Yusuke Narita. [Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation](https://arxiv.org/abs/2008.07146). NeurIPS2021 Track on Datasets and Benchmarks. + diff --git a/examples/obd/evaluate_off_policy_estimators.py b/examples/obd/evaluate_off_policy_estimators.py index 6e1f3ca5..e0949320 100644 --- a/examples/obd/evaluate_off_policy_estimators.py +++ b/examples/obd/evaluate_off_policy_estimators.py @@ -13,9 +13,13 @@ from obp.dataset import OpenBanditDataset from obp.ope import DirectMethod from obp.ope import DoublyRobust +from obp.ope import DoublyRobustWithShrinkageTuning from obp.ope import InverseProbabilityWeighting from obp.ope import OffPolicyEvaluation from obp.ope import RegressionModel +from obp.ope import SelfNormalizedDoublyRobust +from obp.ope import SelfNormalizedInverseProbabilityWeighting +from obp.ope import SwitchDoublyRobustTuning from obp.policy import BernoulliTS from obp.policy import Random @@ -32,8 +36,19 @@ random_forest=RandomForestClassifier, ) -# OPE estimators compared -ope_estimators = [DirectMethod(), InverseProbabilityWeighting(), DoublyRobust()] +# compared OPE estimators +ope_estimators = [ + DirectMethod(), + InverseProbabilityWeighting(), + SelfNormalizedInverseProbabilityWeighting(), + DoublyRobust(), + SelfNormalizedDoublyRobust(), + SwitchDoublyRobustTuning(lambdas=[10, 50, 100, 500, 1000, 5000, 10000, np.inf]), + DoublyRobustWithShrinkageTuning( + lambdas=[10, 50, 100, 500, 1000, 5000, 10000, np.inf] + ), +] + if __name__ == "__main__": parser = argparse.ArgumentParser(description="evaluate off-policy estimators.") @@ -123,7 +138,7 @@ def process(b: int): # sample bootstrap from batch logged bandit feedback bandit_feedback = obd.sample_bootstrap_bandit_feedback(random_state=b) - # estimate the mean reward function with an ML model + # estimate the reward function with an ML model regression_model = RegressionModel( n_actions=obd.n_actions, len_list=obd.len_list, @@ -151,6 +166,7 @@ def process(b: int): ground_truth_policy_value=ground_truth_policy_value, action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, + metric="relative-ee", ) return relative_ee_b @@ -159,22 +175,22 @@ def process(b: int): n_jobs=n_jobs, verbose=50, )([delayed(process)(i) for i in np.arange(n_runs)]) - relative_ee_dict = {est.estimator_name: dict() for est in ope_estimators} + metric_dict = {est.estimator_name: dict() for est in ope_estimators} for b, relative_ee_b in enumerate(processed): for ( estimator_name, relative_ee_, ) in relative_ee_b.items(): - relative_ee_dict[estimator_name][b] = relative_ee_ - relative_ee_df = DataFrame(relative_ee_dict).describe().T.round(6) + metric_dict[estimator_name][b] = relative_ee_ + results_df = DataFrame(metric_dict).describe().T.round(6) print("=" * 30) print(f"random_state={random_state}") print("-" * 30) - print(relative_ee_df[["mean", "std"]]) + print(results_df[["mean", "std"]]) print("=" * 30) # save results of the evaluation of off-policy estimators in './logs' directory. log_path = Path("./logs") / behavior_policy / campaign log_path.mkdir(exist_ok=True, parents=True) - relative_ee_df.to_csv(log_path / "relative_ee_of_ope_estimators.csv") + results_df.to_csv(log_path / "evaluation_of_ope_results.csv") diff --git a/examples/online/README.md b/examples/online/README.md index fce59873..2fab3a71 100644 --- a/examples/online/README.md +++ b/examples/online/README.md @@ -3,13 +3,13 @@ ## Description -Here, we use synthetic bandit datasets to evaluate OPE of online bandit algorithms. -Specifically, we evaluate the estimation performances of well-known off-policy estimators using the ground-truth policy value of an evaluation policy calculable with synthetic data. +We use synthetic bandit datasets to evaluate OPE of online bandit algorithms. +Specifically, we evaluate the estimation performance of some well-known OPE estimators using the ground-truth policy value of an evaluation policy calculable with synthetic data. ## Evaluating Off-Policy Estimators -In the following, we evaluate the estimation performances of Replay Method (RM). +In the following, we evaluate the estimation performance of Replay Method (RM). RM uses a subset of the logged bandit feedback data where actions selected by the behavior policy are the same as that of the evaluation policy. Theoretically, RM is unbiased when the behavior policy is uniformly random and the evaluation policy is fixed. However, empirically, RM works well when evaluation policies are learning algorithms. @@ -17,7 +17,7 @@ Please refer to https://arxiv.org/abs/1003.5956 about the details of RM. ### Files -- [`./evaluate_off_policy_estimators.py`](./evaluate_off_policy_estimators.py) implements the evaluation of OPE estimators by RM using synthetic bandit feedback data. +- [`./evaluate_off_policy_estimators.py`](./evaluate_off_policy_estimators.py) implements the evaluation of OPE estimators by RM using synthetic bandit data. ### Scripts @@ -33,13 +33,13 @@ python evaluate_off_policy_estimators.py\ --random_state $random_state ``` - `$n_runs` specifies the number of simulation runs in the experiment to estimate standard deviations of the performance of OPE estimators. -- `$n_rounds` and `$n_actions` specify the number of rounds (or samples) and the number of actions of the synthetic bandit data. +- `$n_rounds` and `$n_actions` specify the sample size and the number of actions of the synthetic bandit data. - `$dim_context` specifies the dimension of context vectors. - `$n_sim` specifeis the simulations in the Monte Carlo simulation to compute the ground-truth policy value. - `$evaluation_policy_name` specifeis the evaluation policy and should be one of "bernoulli_ts", "epsilon_greedy", "lin_epsilon_greedy", "lin_ts, lin_ucb", "logistic_epsilon_greedy", "logistic_ts", or "logistic_ucb". - `$n_jobs` is the maximum number of concurrently running jobs. -For example, the following command compares the estimation performances (relative estimation error; relative-ee) of the OPE estimators using the synthetic bandit feedback data with 100,000 rounds, 30 actions, five dimensional context vectors. +For example, the following command compares the estimation performance (relative estimation error; relative-ee) of the OPE estimators using synthetic bandit data with 100,000 rounds, 30 actions, five dimensional context vectors. ```bash python evaluate_off_policy_estimators.py\ diff --git a/examples/online/evaluate_off_policy_estimators.py b/examples/online/evaluate_off_policy_estimators.py index 7ba1c1f9..80c72005 100644 --- a/examples/online/evaluate_off_policy_estimators.py +++ b/examples/online/evaluate_off_policy_estimators.py @@ -35,19 +35,19 @@ "--n_rounds", type=int, default=10000, - help="number of rounds for synthetic bandit feedback.", + help="sample size of logged bandit data.", ) parser.add_argument( "--n_actions", type=int, default=10, - help="number of actions for synthetic bandit feedback.", + help="number of actions.", ) parser.add_argument( "--dim_context", type=int, default=5, - help="dimensions of context vectors characterizing each round.", + help="dimensions of context vectors.", ) parser.add_argument( "--n_sim", @@ -143,33 +143,33 @@ def process(i: int): bandit_feedback=bandit_feedback, ope_estimators=ope_estimators, ) - relative_ee_i = ope.evaluate_performance_of_estimators( + metric_i = ope.evaluate_performance_of_estimators( ground_truth_policy_value=ground_truth_policy_value, action_dist=action_dist, ) - return relative_ee_i + return metric_i processed = Parallel( n_jobs=n_jobs, verbose=50, )([delayed(process)(i) for i in np.arange(n_runs)]) - relative_ee_dict = {est.estimator_name: dict() for est in ope_estimators} - for i, relative_ee_i in enumerate(processed): + metric_dict = {est.estimator_name: dict() for est in ope_estimators} + for i, metric_i in enumerate(processed): for ( estimator_name, relative_ee_, - ) in relative_ee_i.items(): - relative_ee_dict[estimator_name][i] = relative_ee_ - relative_ee_df = DataFrame(relative_ee_dict).describe().T.round(6) + ) in metric_i.items(): + metric_dict[estimator_name][i] = relative_ee_ + se_df = DataFrame(metric_dict).describe().T.round(6) print("=" * 45) print(f"random_state={random_state}") print("-" * 45) - print(relative_ee_df[["mean", "std"]]) + print(se_df[["mean", "std"]]) print("=" * 45) # save results of the evaluation of off-policy estimators in './logs' directory. log_path = Path("./logs") log_path.mkdir(exist_ok=True, parents=True) - relative_ee_df.to_csv(log_path / "relative_ee_of_ope_estimators.csv") + se_df.to_csv(log_path / "relative_ee_of_ope_estimators.csv") diff --git a/examples/opl/README.md b/examples/opl/README.md index a4c46d0e..a38ff962 100644 --- a/examples/opl/README.md +++ b/examples/opl/README.md @@ -3,19 +3,19 @@ ## Description -Here, we use synthetic bandit datasets to evaluate off-policy learners. -Specifically, we evaluate the performances of off-policy learners using the ground-truth policy value of an evaluation policy calculable with synthetic data. +We use synthetic bandit data to evaluate some off-policy learners using their ground-truth policy value calculable with synthetic data. ## Evaluating Off-Policy Learners In the following, we evaluate the performances of -- Random Policy (Random) -- Inverse Probability Weighting Policy Learner (IPWLearner) -- Policy Learner using Neural Networks (NNPolicyLearner) -See [our documentation](https://zr-obp.readthedocs.io/en/latest/_autosummary/obp.policy.offline.html) for the details about IPWLearner and NNPolicyLearner. +- Uniform Random Policy (`Random`) +- Inverse Probability Weighting Policy Learner (`IPWLearner`) +- Policy Learner using Neural Networks (`NNPolicyLearner`) -NNPolicyLearner can use the following OPE estimators as the objective function: +See [our documentation](https://zr-obp.readthedocs.io/en/latest/_autosummary/obp.policy.offline.html) for the details about `IPWLearner` and `NNPolicyLearner`. + +`NNPolicyLearner` can use the following OPE estimators as the objective function: - Direct Method (DM) - Inverse Probability Weighting (IPW) - Doubly Robust (DR) @@ -23,8 +23,8 @@ NNPolicyLearner can use the following OPE estimators as the objective function: See [our documentation](https://zr-obp.readthedocs.io/en/latest/estimators.html) for the details about these estimators. ### Files -- [`./evaluate_off_policy_learners.py`](./evaluate_off_policy_learners.py) implements the evaluation of off-policy learners using synthetic bandit feedback data. -- [`./conf/hyperparams.yaml`](./conf/hyperparams.yaml) defines hyperparameters of some machine learning methods used to define regression model and IPWLearner. +- [`./evaluate_off_policy_learners.py`](./evaluate_off_policy_learners.py) implements the evaluation of off-policy learners using synthetic bandit data. +- [`./conf/hyperparams.yaml`](./conf/hyperparams.yaml) defines hyperparameters of some ML methods used to define regression model and IPWLearner. ### Scripts @@ -34,6 +34,7 @@ python evaluate_off_policy_learners.py\ --n_rounds $n_rounds\ --n_actions $n_actions\ --dim_context $dim_context\ + --beta $beta\ --base_model_for_evaluation_policy $base_model_for_evaluation_policy\ --base_model_for_reg_model $base_model_for_reg_model\ --off_policy_objective $off_policy_objective\ @@ -45,8 +46,9 @@ python evaluate_off_policy_learners.py\ --early_stopping\ --random_state $random_state ``` -- `$n_rounds` and `$n_actions` specify the number of rounds (or samples) and the number of actions of the synthetic bandit data. +- `$n_rounds` and `$n_actions` specify the sample size and the number of actions of the synthetic bandit data, respectively. - `$dim_context` specifies the dimension of context vectors. +- `$beta` specifies the inverse temperature parameter to control the behavior policy. - `$base_model_for_ipw_learner` specifies the base ML model for defining evaluation policy and should be one of "logistic_regression", "random_forest", or "lightgbm". - `$off_policy_objective` specifies the OPE estimator for NNPolicyLearner and should be one of "dm", "ipw", or "dr". - `$n_hidden` specifies the size of hidden layers in NNPolicyLearner. @@ -56,7 +58,7 @@ python evaluate_off_policy_learners.py\ - `$batch_size` specifies the batch size for NNPolicyLearner. - `$early_stopping` enables early stopping of training of NNPolicyLearner. -For example, the following command compares the performances of the off-policy learners using the synthetic bandit feedback data with 100,00 rounds, 10 actions, five dimensional context vectors. +For example, the following command compares the performance of the off-policy learners using synthetic bandit data with 100,00 rounds, 10 actions, five dimensional context vectors. ```bash python evaluate_off_policy_learners.py\ @@ -77,12 +79,11 @@ python evaluate_off_policy_learners.py\ # random_state=12345 # --------------------------------------------- # policy value -# random_policy 0.605604 -# ipw_learner 0.753016 -# nn_policy_learner (with ipw) 0.759228 +# random_policy 0.499925 +# ipw_learner 0.782430 +# nn_policy_learner (with ipw) 0.735947 # ============================================= ``` -The above result can change with different situations. -You can try the evaluation with other experimental settings easily. +The above result can change with different situations. You can try the evaluation with other experimental settings easily. diff --git a/examples/opl/evaluate_off_policy_learners.py b/examples/opl/evaluate_off_policy_learners.py index 5d84b26b..8000bee3 100644 --- a/examples/opl/evaluate_off_policy_learners.py +++ b/examples/opl/evaluate_off_policy_learners.py @@ -7,7 +7,6 @@ from sklearn.linear_model import LogisticRegression import yaml -from obp.dataset import linear_behavior_policy from obp.dataset import logistic_reward_function from obp.dataset import SyntheticBanditDataset from obp.policy import IPWLearner @@ -33,19 +32,25 @@ "--n_rounds", type=int, default=10000, - help="number of rounds for synthetic bandit feedback.", + help="sample size of logged bandit data.", ) parser.add_argument( "--n_actions", type=int, default=10, - help="number of actions for synthetic bandit feedback.", + help="number of actions.", ) parser.add_argument( "--dim_context", type=int, default=5, - help="dimensions of context vectors characterizing each round.", + help="dimensions of context vectors.", + ) + parser.add_argument( + "--beta", + type=float, + default=-3, + help="inverse temperature parameter to control the behavior policy.", ) parser.add_argument( "--base_model_for_ipw_learner", @@ -106,6 +111,7 @@ n_rounds = args.n_rounds n_actions = args.n_actions dim_context = args.dim_context + beta = args.beta base_model_for_ipw_learner = args.base_model_for_ipw_learner off_policy_objective = args.off_policy_objective n_hidden = args.n_hidden @@ -121,10 +127,10 @@ n_actions=n_actions, dim_context=dim_context, reward_function=logistic_reward_function, - behavior_policy_function=linear_behavior_policy, + beta=beta, random_state=random_state, ) - # sample new training and test sets of synthetic logged bandit feedback + # sample new training and test sets of synthetic logged bandit data bandit_feedback_train = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds) bandit_feedback_test = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds) @@ -149,7 +155,7 @@ early_stopping=early_stopping, random_state=random_state, ) - # train the evaluation policy on the training set of the synthetic logged bandit feedback + # train the evaluation policy on the training set of the synthetic logged bandit data ipw_learner.fit( context=bandit_feedback_train["context"], action=bandit_feedback_train["action"], @@ -162,7 +168,7 @@ reward=bandit_feedback_train["reward"], pscore=bandit_feedback_train["pscore"], ) - # predict the action decisions for the test set of the synthetic logged bandit feedback + # predict the action decisions for the test set of the synthetic logged bandit data random_action_dist = random_policy.compute_batch_action_dist(n_rounds=n_rounds) ipw_learner_action_dist = ipw_learner.predict( context=bandit_feedback_test["context"], diff --git a/examples/quickstart/README.md b/examples/quickstart/README.md index d31c7dc3..6b45f932 100644 --- a/examples/quickstart/README.md +++ b/examples/quickstart/README.md @@ -1,10 +1,10 @@ # Open Bandit Pipeline Quickstart Notebooks -This page contains a list of quickstart notebooks written with the Open Bandit Pipeline. +This page contains a list of quickstart notebooks written with Open Bandit Pipeline. -- [`obd.ipynb`](./obd.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/obd.ipynb): a quickstart guide of the Open Bandit Dataset and Pipeline. -- [`synthetic.ipynb`](./synthetic.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/synthetic.ipynb): a quickstart guide to implement the standard off-policy learning, off-policy evaluation (OPE), and the evaluation of OPE procedures with the Open Bandit Pipeline. -- [`multiclass.ipynb`](./multiclass.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/multiclass.ipynb): a quickstart guide to handle multi-class classification data as logged bandit feedback data for the standard off-policy learning, off-policy evaluation (OPE), and the evaluation of OPE procedures with the Open Bandit Pipeline. -- [`online.ipynb`](./online.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/online.ipynb): a quickstart guide to implement off-policy evaluation (OPE) and the evaluation of OPE procedures for online bandit algorithms with the Open Bandit Pipeline. -- [`opl.ipynb`](./opl.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/opl.ipynb): a quickstart guide to implement off-policy learners and the evaluation of off-policy learners with the Open Bandit Pipeline. -- [`synthetic_slate.ipynb`](./synthetic_slate.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/synthetic_slate.ipynb): a quickstart guide to implement off-policy evaluation (OPE) and the evaluation of OPE procedures for the slate recommendation setting with the Open Bandit Pipeline. +- [`obd.ipynb`](./obd.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/obd.ipynb): a quickstart guide of using Open Bandit Dataset and Pipeline to conduct some OPE experiments. +- [`synthetic.ipynb`](./synthetic.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/synthetic.ipynb): a quickstart guide to implement the standard off-policy learning, OPE, and the evaluation of OPE on synthetic bandit data with Open Bandit Pipeline. +- [`multiclass.ipynb`](./multiclass.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/multiclass.ipynb): a quickstart guide to handle multi-class classification data as logged bandit data for the standard off-policy learning, OPE, and the evaluation of OPE with Open Bandit Pipeline. +- [`online.ipynb`](./online.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/online.ipynb): a quickstart guide to implement OPE and the evaluation of OPE for online bandit algorithms with Open Bandit Pipeline. +- [`opl.ipynb`](./opl.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/opl.ipynb): a quickstart guide to implement off-policy learners and the evaluation of off-policy learners with Open Bandit Pipeline. +- [`synthetic_slate.ipynb`](./synthetic_slate.ipynb) [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/st-tech/zr-obp/blob/master/examples/quickstart/synthetic_slate.ipynb): a quickstart guide to implement OPE and the evaluation of OPE for the slate recommendation setting with Open Bandit Pipeline. diff --git a/examples/quickstart/balanced-ope-deterministic-evaluation-policy.ipynb b/examples/quickstart/balanced-ope-deterministic-evaluation-policy.ipynb deleted file mode 100644 index 8c173d7f..00000000 --- a/examples/quickstart/balanced-ope-deterministic-evaluation-policy.ipynb +++ /dev/null @@ -1,1256 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "8bf89cee", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import yaml\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.neural_network import MLPClassifier as MLP\n", - "from sklearn.svm import SVC" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "09ea0e58", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "56290a90", - "metadata": {}, - "outputs": [], - "source": [ - "from obp.dataset import (\n", - " SyntheticBanditDataset,\n", - " logistic_reward_function,\n", - " linear_behavior_policy,\n", - ")\n", - "\n", - "from obp.policy import IPWLearner\n", - "from obp.ope import (\n", - " OffPolicyEvaluation,\n", - " RegressionModel,\n", - " InverseProbabilityWeighting as IPS,\n", - " SelfNormalizedInverseProbabilityWeighting as SNIPS,\n", - " DirectMethod as DM,\n", - " DoublyRobust as DR,\n", - " DoublyRobustWithShrinkage as DRos,\n", - " BalancedInverseProbabilityWeighting as BIPW,\n", - " ImportanceWeightEstimator,\n", - " PropensityScoreEstimator\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b6c2fcfe", - "metadata": {}, - "outputs": [], - "source": [ - "with open (\"../../obp/dataset/hyperparams.yaml\", \"rb\") as f:\n", - " hyperparams = yaml.safe_load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ca19277c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'lightgbm': {'n_estimators': 100,\n", - " 'learning_rate': 0.01,\n", - " 'max_depth': 5,\n", - " 'min_samples_leaf': 10,\n", - " 'random_state': 12345},\n", - " 'random_forest': {'n_estimators': 100,\n", - " 'max_depth': 5,\n", - " 'min_samples_leaf': 10,\n", - " 'random_state': 12345},\n", - " 'ridge': {'alpha': 0.2, 'random_state': 12345},\n", - " 'svc': {'gamma': 2, 'C': 1, 'probability': True, 'random_state': 12345}}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hyperparams" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f573a4da", - "metadata": {}, - "outputs": [], - "source": [ - "from warnings import simplefilter\n", - "from sklearn.exceptions import ConvergenceWarning\n", - "simplefilter(\"ignore\", category=ConvergenceWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "789d8aaa", - "metadata": {}, - "outputs": [], - "source": [ - "from tqdm import tqdm_notebook as tqdm" - ] - }, - { - "cell_type": "markdown", - "id": "c2373011", - "metadata": {}, - "source": [ - "## (1) Generate synthetic data" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f15ba763", - "metadata": {}, - "outputs": [], - "source": [ - "# define a dataset class\n", - "n_actions = 10\n", - "dim_context = 8\n", - "len_list = 1\n", - "random_state = 12345\n", - "dataset = SyntheticBanditDataset(\n", - " n_actions=n_actions,\n", - " dim_context=dim_context,\n", - " beta=0.2,\n", - " reward_function=logistic_reward_function,\n", - " behavior_policy_function=linear_behavior_policy,\n", - " random_state=random_state,\n", - ")\n", - "\n", - "# training data is used to train an evaluation policy\n", - "train_bandit_data = dataset.obtain_batch_bandit_feedback(n_rounds=5000)\n", - "\n", - "# test bandit data is used to approximate the ground-truth policy value\n", - "test_bandit_data = dataset.obtain_batch_bandit_feedback(n_rounds=100000)" - ] - }, - { - "cell_type": "markdown", - "id": "9c6bbb96", - "metadata": {}, - "source": [ - "## (2) Off-Policy Learning (OPL)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ce6362b2", - "metadata": {}, - "outputs": [], - "source": [ - "# evaluation policy training\n", - "ipw_learner = IPWLearner(\n", - " n_actions=dataset.n_actions,\n", - " base_classifier=RandomForestClassifier(**hyperparams[\"random_forest\"]),\n", - ")\n", - "ipw_learner.fit(\n", - " context=train_bandit_data[\"context\"],\n", - " action=train_bandit_data[\"action\"],\n", - " reward=train_bandit_data[\"reward\"],\n", - " pscore=train_bandit_data[\"pscore\"],\n", - ")\n", - "\n", - "\n", - "action_dist_ipw_train = ipw_learner.predict(\n", - " context=train_bandit_data[\"context\"]\n", - ")\n", - "action_dist_ipw_test = ipw_learner.predict(\n", - " context=test_bandit_data[\"context\"]\n", - ")\n", - "policy_value_of_ipw = dataset.calc_ground_truth_policy_value(\n", - " expected_reward=test_bandit_data[\"expected_reward\"],\n", - " action_dist=action_dist_ipw_test,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "695bd1d1", - "metadata": {}, - "outputs": [], - "source": [ - "num_data = 3000\n", - "\n", - "validation_bandit_data = dataset.obtain_batch_bandit_feedback(n_rounds=num_data)\n", - "\n", - "# make decisions on validation data\n", - "action_dist_ipw_val = ipw_learner.predict(\n", - " context=validation_bandit_data[\"context\"]\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "1801b32d", - "metadata": {}, - "source": [ - "## (3) Off-Policy Evaluation (OPE)" - ] - }, - { - "cell_type": "markdown", - "id": "24bd1203", - "metadata": {}, - "source": [ - "### (3-1) Obtaining a reward estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "395fdc4a", - "metadata": {}, - "outputs": [], - "source": [ - "# OPE using validation data\n", - "regression_model = RegressionModel(\n", - " n_actions=dataset.n_actions,\n", - " base_model=RandomForestClassifier(**hyperparams[\"random_forest\"]),\n", - ")\n", - "estimated_rewards = regression_model.fit_predict(\n", - " context=validation_bandit_data[\"context\"], # context; x\n", - " action=validation_bandit_data[\"action\"], # action; a\n", - " reward=validation_bandit_data[\"reward\"], # reward; r\n", - " n_folds=2, # 2-fold cross fitting\n", - " random_state=12345,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "92d1f1da", - "metadata": {}, - "source": [ - "### (3-2) Evaluation by existing OPE estimators" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ec34a26f", - "metadata": {}, - "outputs": [], - "source": [ - "classification_model_action = PropensityScoreEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " base_model=RandomForestClassifier(**hyperparams[\"random_forest\"]),\n", - " calibration_cv=2\n", - ")\n", - "\n", - "estimated_pscore = classification_model_action.fit_predict(\n", - " action=validation_bandit_data[\"action\"],\n", - " position=validation_bandit_data[\"position\"],\n", - " context=validation_bandit_data[\"context\"],\n", - " n_folds=2,\n", - " evaluate_model_performance=True,\n", - " random_state=random_state,\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "de671cea", - "metadata": {}, - "outputs": [], - "source": [ - "ope = OffPolicyEvaluation(\n", - " bandit_feedback=validation_bandit_data,\n", - " ope_estimators=[\n", - " IPS(estimator_name=\"IPS\"),\n", - " DM(estimator_name=\"DM\"),\n", - " IPS(lambda_=100, estimator_name=\"CIPS\"),\n", - " SNIPS(estimator_name=\"SNIPS\"),\n", - " DR(estimator_name=\"DR\"),\n", - " DRos(lambda_=500, estimator_name=\"DRos\"),\n", - " IPS(\n", - " lambda_=100,\n", - " estimator_name=\"CIPS_Estimated_Pscore\",\n", - " use_estimated_pscore=True,\n", - " ),\n", - " SNIPS(estimator_name=\"SNIPS_Estimated_Pscore\", use_estimated_pscore=True),\n", - " DR(estimator_name=\"DR_Estimated_Pscore\", use_estimated_pscore=True),\n", - " DRos(\n", - " lambda_=500,\n", - " estimator_name=\"DRos_Estimated_Pscore\",\n", - " use_estimated_pscore=True,\n", - " ),\n", - " ],\n", - ")\n", - "\n", - "\n", - "squared_errors = ope.evaluate_performance_of_estimators(\n", - " ground_truth_policy_value=policy_value_of_ipw, # V(\\pi_e)\n", - " action_dist=action_dist_ipw_val, # \\pi_e(a|x)\n", - " estimated_rewards_by_reg_model=estimated_rewards, # \\hat{q}(x,a)\n", - " estimated_pscore=estimated_pscore,\n", - " metric=\"se\", # squared error\n", - ")\n", - "\n", - "ope_result = ope.summarize_off_policy_estimates(\n", - " action_dist=action_dist_ipw_val, # \\pi_e(a|x)\n", - " estimated_rewards_by_reg_model=estimated_rewards, # \\hat{q}(x,a)\n", - " estimated_pscore=estimated_pscore,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "059fc7d0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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" - ], - "text/plain": [ - " estimated_policy_value \\\n", - "IPS 0.700004 \n", - "DM 0.522973 \n", - "CIPS 0.700004 \n", - "SNIPS 0.697155 \n", - "DR 0.703660 \n", - "DRos 0.674096 \n", - "CIPS_Estimated_Pscore 0.705910 \n", - "SNIPS_Estimated_Pscore 0.700261 \n", - "DR_Estimated_Pscore 0.707573 \n", - "DRos_Estimated_Pscore 0.676160 \n", - "\n", - " relative_estimated_policy_value \n", - "IPS 1.418927 \n", - "DM 1.060081 \n", - "CIPS 1.418927 \n", - "SNIPS 1.413152 \n", - "DR 1.426337 \n", - "DRos 1.366411 \n", - "CIPS_Estimated_Pscore 1.430899 \n", - "SNIPS_Estimated_Pscore 1.419449 \n", - "DR_Estimated_Pscore 1.434269 \n", - "DRos_Estimated_Pscore 1.370595 " - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ope_result[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "b80307a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean95.0% CI (lower)95.0% CI (upper)
IPS0.6950020.5945270.770564
DM0.5227550.5199710.525753
CIPS0.6973290.6107560.791626
SNIPS0.6975810.5860640.785578
DR0.7036850.6518620.758535
DRos0.6761150.6300610.725272
CIPS_Estimated_Pscore0.7035270.6213950.793518
SNIPS_Estimated_Pscore0.6986890.5972910.791538
DR_Estimated_Pscore0.7100520.6515770.770919
DRos_Estimated_Pscore0.6755860.6386820.733095
\n", - "
" - ], - "text/plain": [ - " mean 95.0% CI (lower) 95.0% CI (upper)\n", - "IPS 0.695002 0.594527 0.770564\n", - "DM 0.522755 0.519971 0.525753\n", - "CIPS 0.697329 0.610756 0.791626\n", - "SNIPS 0.697581 0.586064 0.785578\n", - "DR 0.703685 0.651862 0.758535\n", - "DRos 0.676115 0.630061 0.725272\n", - "CIPS_Estimated_Pscore 0.703527 0.621395 0.793518\n", - "SNIPS_Estimated_Pscore 0.698689 0.597291 0.791538\n", - "DR_Estimated_Pscore 0.710052 0.651577 0.770919\n", - "DRos_Estimated_Pscore 0.675586 0.638682 0.733095" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ope_result[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "25478c4c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6998367440243847" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# true policy value\n", - "policy_value_of_ipw" - ] - }, - { - "cell_type": "markdown", - "id": "4d0c3e64", - "metadata": {}, - "source": [ - "### (3-3) Balanced-OPE" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "c4ed747e", - "metadata": {}, - "outputs": [], - "source": [ - "bipw = BIPW(\n", - " estimator_name=\"BIPW\", lambda_=np.inf\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "e530d74f", - "metadata": {}, - "outputs": [], - "source": [ - "clf_models = {\n", - " \"random_forest_default_raw\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"raw\",\n", - " base_model=RandomForestClassifier(random_state=random_state),\n", - " ),\n", - " \"random_forest_raw\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"raw\",\n", - " base_model=RandomForestClassifier(**hyperparams[\"random_forest\"]),\n", - " ),\n", - " \"random_forest_sample\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"sample\",\n", - " base_model=RandomForestClassifier(**hyperparams[\"random_forest\"]),\n", - " ),\n", - " \"svc_raw\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"raw\",\n", - " base_model=SVC(**hyperparams[\"svc\"]),\n", - " ),\n", - " \"svc_sample\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"sample\",\n", - " base_model=SVC(**hyperparams[\"svc\"]),\n", - " ),\n", - " \"MLP_raw\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"raw\",\n", - " base_model=MLP(random_state=random_state),\n", - " ),\n", - " \"MLP_sample\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"sample\",\n", - " base_model=MLP(random_state=random_state),\n", - " ),\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "4ada448c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":2: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n", - "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n", - " for clf_name, clf in tqdm(clf_models.items()):\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "1b89cd8089754e7faf65b33e052e803e", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/7 [00:00" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(14, 10))\n", - "(ope_expected_values - policy_value_of_ipw).plot.bar()\n", - "plt.hlines(y=0., xmin=-1, xmax=len(ope_expected_values), color=\"black\", linestyles=\"--\")" - ] - }, - { - "cell_type": "markdown", - "id": "257e6ba0", - "metadata": {}, - "source": [ - "### (4-2) Summarize" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "314d7187", - "metadata": {}, - "outputs": [], - "source": [ - "ope_add_bipw = OffPolicyEvaluation(\n", - " bandit_feedback=validation_bandit_data,\n", - " ope_estimators=[\n", - " IPS(estimator_name=\"IPS\"),\n", - " DM(estimator_name=\"DM\"),\n", - " IPS(lambda_=100, estimator_name=\"CIPS\"),\n", - " SNIPS(estimator_name=\"SNIPS\"),\n", - " DR(estimator_name=\"DR\"),\n", - " DRos(lambda_=500, estimator_name=\"DRos\"),\n", - " IPS(\n", - " lambda_=100,\n", - " estimator_name=\"CIPS_Estimated_Pscore\",\n", - " use_estimated_pscore=True,\n", - " ),\n", - " SNIPS(estimator_name=\"SNIPS_Estimated_Pscore\", use_estimated_pscore=True),\n", - " DR(estimator_name=\"DR_Estimated_Pscore\", use_estimated_pscore=True),\n", - " DRos(\n", - " lambda_=500,\n", - " estimator_name=\"DRos_Estimated_Pscore\",\n", - " use_estimated_pscore=True,\n", - " ),\n", - " BIPW(estimator_name=\"BIPW_rf_raw\", lambda_=np.inf),\n", - " BIPW(estimator_name=\"BIPW_rf_sample\", lambda_=np.inf),\n", - " BIPW(estimator_name=\"BIPW_rf_sample_clip\", lambda_=10.0),\n", - " ],\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "6e02a842", - "metadata": {}, - "outputs": [], - "source": [ - "bipw_value = {\n", - " \"BIPW_rf_raw\": balancing_weight_dict[\"random_forest_raw\"],\n", - " \"BIPW_rf_sample\": balancing_weight_dict[\"random_forest_sample\"],\n", - " \"BIPW_rf_sample_clip\": balancing_weight_dict[\"random_forest_sample\"],\n", - "}\n", - "\n", - "squared_errors = ope_add_bipw.evaluate_performance_of_estimators(\n", - " ground_truth_policy_value=policy_value_of_ipw, # V(\\pi_e)\n", - " action_dist=action_dist_ipw_val, # \\pi_e(a|x)\n", - " estimated_rewards_by_reg_model=estimated_rewards, # \\hat{q}(x,a)\n", - " estimated_pscore=estimated_pscore,\n", - " estimated_importance_weights=bipw_value,\n", - " metric=\"se\", # squared error\n", - ")\n", - "\n", - "ope_add_bipw_res = ope_add_bipw.summarize_off_policy_estimates(\n", - " action_dist=action_dist_ipw_val, # \\pi_e(a|x)\n", - " estimated_rewards_by_reg_model=estimated_rewards, # \\hat{q}(x,a)\n", - " estimated_pscore=estimated_pscore,\n", - " estimated_importance_weights=bipw_value,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "1d3bfd80", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(14, 10))\n", - "(ope_add_bipw_res[0][\"estimated_policy_value\"] - policy_value_of_ipw).plot.bar()\n", - "plt.hlines(y=0., xmin=-1, xmax=len(ope_add_bipw_res[0][\"estimated_policy_value\"]), color=\"black\", linestyles=\"--\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "10496dff", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "34a450f3", - "metadata": {}, - "source": [ - "### (4-3) Classification model visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 31, - "id": "4019b5b9", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.calibration import calibration_curve\n", - "from sklearn.metrics import roc_auc_score, roc_curve" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "id": "e74979af", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_calibration_curve(y_test, y_pred, name):\n", - " \"\"\"Plot calibration curve for est w/o and with calibration. \"\"\"\n", - " fig = plt.figure(figsize=(10, 10))\n", - " ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)\n", - " ax2 = plt.subplot2grid((3, 1), (2, 0))\n", - "\n", - " ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n", - " fraction_of_positives, mean_predicted_value = \\\n", - " calibration_curve(y_test, y_pred, n_bins=10)\n", - " ax1.plot(mean_predicted_value, fraction_of_positives, \"s-\")\n", - "\n", - " ax2.hist(y_pred, range=(0, 1), bins=10,\n", - " histtype=\"step\", lw=2)\n", - "\n", - " ax1.set_ylabel(\"Fraction of positives\")\n", - " ax1.set_ylim([-0.05, 1.05])\n", - " ax1.set_title(f'Calibration plots (reliability curve): {name}')\n", - "\n", - " ax2.set_xlabel(\"Mean predicted value\")\n", - " ax2.set_ylabel(\"Count\")\n", - "\n", - " plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "id": "4e246127", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_roc_auc_curve(y_test, y_pred, name):\n", - " fig = plt.figure(figsize=(10, 5))\n", - " fpr, tpr, _ = roc_curve(y_test, y_pred)\n", - " auc = roc_auc_score(y_test, y_pred)\n", - " plt.plot(fpr,tpr,label=\"data 1, auc=%1.3f\" %auc)\n", - " plt.legend(loc=4)\n", - " plt.title(f\"ROC AUC curve: {name}\")\n", - " plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "id": "b09e72ec", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "random_forest_default_raw\n", - "random_forest_raw\n", - "random_forest_sample\n", - "svc_raw\n", - "svc_sample\n", - "MLP_raw\n", - "MLP_sample\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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DTZIkSWqmMg/IKaVngM+WscoBwC0p5yWgc0T0rp/qJEmSVJemTp1KVVVV1mV8ReYBuRb6ABMLpifl531FRBwfEcMjYvj06dPrrThJkiStmNmzZ/Pcc88B0KtXLyoqKjKu6KsaQ0CulZTStSml8pRSeY8ePbIuR5IkSUtx4oknst9++zF79mwA2rVrl3FFX9UYAvJkYI2C6b75eZIkSWokPvvsM2bOnAnAr371Kx566CE6dOiQcVVL1hgC8lDgh/nRLLYCZqaUPsm6KEmSJNXO3Llz2XjjjTnjjDMAWGuttdh6660zrmrpWmRdQETcAewEdI+IScD5QEuAlNLfgIeAvYHxwDzgqGwqlSRJ0oqYP38+rVu3pl27dpx77rkNOhQXipRS1jUUXXl5eRo+fHjWZUiSJDVbzz33HAcffDDDhg1jk002ybqcJYqIESml8sXnN4YuFpIkSWpkBg8ezNZbb02bNm2yLmWFGZAlSZJUFDfeeCMHH3wwKSW6du3Kfffdx6BBg7Iua4UZkCVJklQUX375JTNnzlw0fFtjZR9kSZIkrZSqqiouv/xyNtpoI/bYYw9qamqICCIi69JqxT7IkiRJKqrq6mpuuukmhg4dCkBJSUmjCcfLYkCWJElSrVVWVvLnP/+ZyspKWrVqxbPPPsuVV16ZdVlFZUCWJElSrT399NP89Kc/5YEHHgCga9euTaLVuJABWZIkScs0b948XnzxRQB22203RowYwbe//e2Mq6o7BmRJkiQt00knncRee+3FzJkzAdh8880zrqhuGZAlSZL0NbNmzVoUiM877zzuu+8+OnXqlHFV9cOALEmSpK/48ssv2WSTTTj99NMBWGuttdh5550zrqr+tMi6AEmSJDUMlZWVlJWV0aZNG04//XS23HLLrEvKhC3IkiRJ4uWXX2bAgAGMHDkSyPU7HjJkSMZVZcOALEmSJAYOHMimm25KWVlZ1qVkzoAsSZLUTN1+++1897vfJaVE165defDBB9lggw2yLitzBmRJkqRmaubMmXzyySeLRqtQTqSUsq6h6MrLy9Pw4cOzLkOSJKlBqamp4aqrrmLgwIHsueee1NTUAFBS0jzbTCNiREqpfPH5zfPRkCRJaoYWLFjA3/72N+655x4gF4ybazheFh8RSZKkJqyqqoprrrmGyspKWrVqxVNPPcX111+fdVkNmgFZkiSpCXvmmWc44YQT+Ne//gVAjx49iIiMq2rYDMiSJElNTEVFBa+88goA3/zmN3nppZc49NBDM66q8TAgS5IkNTGnnHIKu+66K5999hlAs73gx8ryUtOSJElNwNy5c6murqZjx46cffbZHHjggXTt2jXrsholW5AlSZIauYqKCjbffHN+9rOfATBgwAD22muvjKtqvGxBliRJaqQWLFhAy5YtadWqFSeeeCKbbrpp1iU1CbYgS5IkNUIjRoxg4MCBvPbaawD89Kc/Zccdd8y4qqbBgCxJktQIrbXWWgwaNMgLfdQBH1FJkqRG4p///CeHHXYYKSW6dOnCI488YreKOmBAliRJaiSmT5/OhAkT+Pzzz7MupUmLlFLWNRRdeXl5Gj58eNZlSJIkrZKUEjfeeCN9+vRhzz33pKamhpqaGlq0cJyFYoiIESml8sXn24IsSZLUQC1YsIDLLruMf/zjHwCUlJQYjuuBAVmSJKkBqa6u5qabbqKiooKysjIef/zxRQFZ9cOALEmS1IA8//zzHH300dx1110A9OrVy5Eq6pmPtiRJUsYWLFjAiBEjANhhhx145pln+MEPfpBxVc2XAVmSJCljp512GjvttBMzZswAYPvttyciMq6q+bKXtyRJUgbmz59PZWUlHTt25LTTTmPnnXeme/fuWZclDMiSJEn1rrKyki233JLy8nJuuukmBgwYwIABA7IuS3kGZEmSpHpSXV1NaWkpZWVlHHPMMQwePDjrkrQEBmRJkqR68MYbb3DQQQdx1113sfnmm3Pqqaeu0vbKL3yUGXMqvza/e/syhp+32yptu7nzJD1JkqR6sMYaa9C3b1+qq6uLsr0lheNlzVftGZAlSZLqyAMPPMAPfvADUkp06dKFJ598ki233DLrsrQcBmRJkqQ6MmnSJN54441Fw7epcTAgS5IkFUlKidtvv53//ve/ABx//PEMHz6cHj16ZFyZVoQBWZIkqUiqqqq4+OKLuf766wEoKSmhZcuWRd/P7PkLir5N/Y8BWZIkaRXU1NRw2223UVFRQcuWLfnvf//LXXfdVWf7mz1/AUfe9OpSl3dvX1Zn+24uHOZNkiRpFbz44oscfvjhzJ8/n2OOOYbVV1+9zva1MBy/PvELrv7+5uy1Ue8621dzZguyJEnSCqqurmbUqFEAbLvttjz++OMcffTRdbrPwnB85WGbGY7rkAFZkiRpBZ155plst912TJ06FYBvfvObRESd7W9ORZXhuB7ZxUKSJKkWKisrqaiooEOHDpx88smUl5fTs2fPOt/vnIoqjrjxFcNxPTIgS5IkLceCBQvYeuut2Wijjbj55pvp378//fv3r/P9FobjvxiO640BWZIkaSlqamoWDdX2/e9/n3XWWafe9r14ON7bcFxv7IMsSZK0BG+//TYbbbQRI0aMAOC0005j//33r5d9z6mo4kjDcWYMyJIkSUuw+uqr06VLF+bPn1+v+10YjkcajjNjQJYkScp79NFHOeKII0gp0blzZ5577jm23Xbbett/YTj+86GG46wYkCVJkvLef/99XnnllUXDt9WnORVVHHXT/8LxPhsbjrMSKaWsayi68vLyNHz48KzLkCRJjcC//vUv2rVrxx577EFNTQ0LFiygVatW9VrDwnD82keG4/oUESNSSuWLz3cUC0mS1GxVVVVxwQUXsOaaa7LHHntQUlJiOJZdLCRJUvOSUuLee++loqKCFi1a8NBDD/Gvf/0rk1oKw/EVh25qOG4gDMiSJKlZeeWVV/jOd77DTTfdBEDfvn1p2bJlvdcxd7FwvO/Gq9d7DVoyA7IkSWryampqGD16NABDhgxh2LBhHH/88ZnVM7eiiiMNxw2WAVmSJDV55557LkOGDOHjjz8GYPfdd6ekJJsYZDhu+DxJT5IkNUlVVVXMnz+f9u3bc8IJJzBw4EB69862j2+uW8WrvPbRF1z+XcNxQ2ULsiRJanKqq6vZcccdOemkkwBYc801Ofroo4mIzGpaGI5HfPQ5l393U/bbxHDcUNmCLEmSmoyUEhFBaWkp3/72t+nbt2/WJQGG48bGFmRJktQkjBs3js0224yFFws7/fTT+e53v5txVYbjxsiALEmSmoSePXvSqlUr5syZk3Upi8ytqOKom19l+IefGY4bEQOyJElqtJ555hmOOeYYUkp07tyZl156iZ122inrsoCCcPzBZ1xx6GaG40bEgCxJkhqtsWPH8vTTTy8avi3Lk/AKGY4bt8wDckTsGRFjI2J8RJy9hOX9IuLJiBgZEW9ExN5Z1ClJkhqGhx56iGHDhgFw7LHH8uabb9KnT5+Mq/qfeZX/C8eXG44bpUxHsYiIUuAqYDdgEvBqRAxNKb1dsNp5wN0ppasjYjDwENC/3ouVJEmZq66u5uc//zk9e/Zkjz32ICJo06ZN1mUtMq+yiiNv+l843t9w3Chl3YL8DWB8Sum9lFIlcCdwwGLrJKBj/nYn4ON6rE+SJGUspcR//vMf5s+fT2lpKUOHDuU///lP1mV9jeG46cg6IPcBJhZMT8rPK3QBcHhETCLXenzykjYUEcdHxPCIGD59+vS6qFWSJGVg5MiR7L///lx33XUA9OvXj1atWmVc1VfNq8wN5WY4bhoaw4VCDgNuTildGhFbA/+IiA1TSjWFK6WUrgWuBSgvL08Z1ClJkookpcTYsWNZb7312HzzzXnggQfYY489si5rkfILH2XGnMqvze/QuoXhuAnIugV5MrBGwXTf/LxCxwB3A6SUXgRaA93rpTpJkpSJCy64gPLyciZNmgTAPvvsQ4sWDaddb0nhGGD2/Kp6rkR1Ietn2qvAwIgYQC4YHwp8b7F1PgJ2AW6OiPXJBWT7UEiS1MTU1NTw5Zdf0q5dO4455hh69erF6qs3nNbYBdU1fPjpXN6d2nAuRKK6kWlATilVRcRJwDCgFLgxpTQ6In4NDE8pDQVOB66LiJ+RO2HvyJSSXSgkSWpCampq2HXXXenbty+33HIL/fr148QTT8ykloqqaj6YMY93p83m3alzGD9tDu9Om837M+ayoNoI0hxk3YJMSukhciffFc77ZcHtt4Ft67suSZJU91JKRAQlJSXstdde9OzZs972PX9BNe9Nn7soCL87bTbvTpvDh5/Oo7omF4QjYM2ubVmnZwd2Wb8XA3u2Z2DPDux35XP1VqfqX+YBWZIkNU/vvfcehx12GFdeeSVbbrklZ5xxRp3sZ15lFROmzV0UgHOtwrP56LN55HMwpSXBmt3aMqhnB/bZqDfr5IPwWj3a0bplaZ3UpYbLgCxJkjLRrVs3qqqq+Oyzz4qyvTkVVbnuEFNn57tFzGHc1NlM+vzLReu0LA0GdG/HBqt34oBN+zCwVy4I9+/ellYtah+Eu7cvW+KJet3blxXlb1G2oil25y0vL0/Dhw/PugxJkrSYl19+mRtvvJGrr76akpKSRV0sVsTMLxcwflquFXjc1FwQHj91Nh/PnL9onbLSEtbq0Y6BvTowsGd7BvVqzzo9O7Bmt7a0LM16EC81FBExIqVUvvh8W5AlSVK9efPNN3n44YeZNGkS/fr1W2Y4/nxuZa5LxGIny02dVbFondYtS1inZ3uGrNUt3y2iPQN7dWCNLm1oYRDWSrIFWZIk1aknnniCBQsWsMcee5BSYu7cubRv3x7InaT36dzKRf2CF/YRfnfa7K90YWhbVsrAnrlW4Fy3iFzXiL5d2lBSsmIt0NJCtiBLkqR6V1NTw+mnn06Hjh3ZdKsdGD99Lu9Onc24ae8zPh+EP5+3YNH6HVq1YJ1e7fnmej0Z1KtDrlW4Vwd6d2xtEFa9sQVZkiTVytIur9y9fRnDz9sNyLUIfzJzPnc9/DRtVluLDz6bz1sfzeCjmQuYVXCVuU5tWi7qF5zrFpFrEe7VsdUK90mWVpYtyJIkaZUs7fLKM+ZUcsY9o3Iny02bw5yKfBAe9S7d2pWxTs/27D+gBwMXheEOdG9fZhBWg2VAliRJq+ypcdPp26GUg7foyzo92zN13Ot8f79dWK1L+6xLk1aYAVmSJC3XvMqqZS7/VrzCRedcxBVjxtCvXz/Yas16qkwqPgOyJElaquqaxD9fm8Slj4xd5nqHH344rVu3pk+fPvVUmVR3DMiSJGmJnnt3Bhc9NIYxn8xi0zU6f2X84cX169eP0047rR6rk+qOI2hLkqSvGDtlNkfe9AqH3/Ays+cv4C+HbcZ9J26z1Msoe3llNTW2IEuSJACmzZ7PZY+O465XJ9K+VQvO3Xt9frjNmrRqUQrAv364LocffjiXXnopW265ZcbVSnXHgCxJUjM3r7KK6555n2uemcCC6hqO3GYAJ39zHbq0+2rLcKdOnZg1axZTp07NqFKpfhiQJUlqpgpPwJs6q4K9NlyNs/Zcj/7d2y1aZ+TIkVx33XVceeWVdOrUiddee42SEntoqmnzGS5JUjP07LvT2efPz3LmvW/Qu1Mb7j1ha64+fIuvhGOAESNGcN999/Hhhx8CGI7VLHipaUmSmpGxU2bz24fG8PS46azRtQ1n7bke+2zU+ytXtXvhhReYO3cuu+22GyklZs2aRadOnTKsWqobXmpakqRmbNqs+Vz22NJPwFsopcTJJ59MWVkZu+66KxFhOFazY0CWJKkJq+0JeE899RRbbbUVrVu35p577qFnz55faVWWmhMDsiRJTVB1TeKfIybxx0fGMm12BXtvtBpn7rHe1/oYA7z99tvsvPPO/P73v+fMM89krbXWyqBiqeEwIEuS1MQ8++50LnpwDO9Mmc1m/Tpz9eGbs8WaXb+23ocffsiaa67J4MGDueeee9hnn30yqFZqeDwVVZKkJmLslNkcceMr/OCGV5hbWcWV39uMf/14myWG40svvZTBgwfzwQcfAHDwwQfTpk2beq5YaphsQZYkqZGbNms+f3p0HHcPz52Ad94+6/ODrZd8Al5FRQWtW7fmkEMOobKykj59+mRUtdRwOcybJEmN1LzKKq595j2ufeY9FlTX8MOt+3PyN9ehc9uyr62bUlrUSnzrrbdmUK3U8DjMmyRJTcSSTsA7a8/1WLPb10/AWygi+MY3vkFZWRkpJUeokJbBgCxJUiPyzLjp/Pah5Z+AB/Dxxx9zxBFHcOGFFzJkyBDOOuuseq5WapwMyJIkNQLvTJnFbx96h2fyV8C76nubs/dGqy2zJbh9+/ZMmTKFSZMmMWTIkHqsVmrcDMiSJDVg02bN59JHxnHPiIl0aN1yqSfgLTR69GiuueYaLr/8cjp27MioUaMoKXHQKmlFGJAlSWqAFp6Ad83T71FVU8NR2w5Y6gl4hV5++WXuuOMOTj75ZAYOHGg4llaCo1hIktSAVNck7h0xkUsfGce02RXss1Fvztxz3WWegDd8+HC++OILdt11V1JKfP7553TtuuR+yZL+x1EsJElq4J4eN53f5U/A23w5J+AtlFLixBNPpLq6muHDhxMRhmNpFRmQJUnKWOEJeP26tq3VCXgvvvgim222Ga1bt+aOO+6gW7duDt0mFYkBWZKkjEydNZ8/rcAJeAuNGzeObbfdlosuuohzzjmHtddeu54qlpoHA7IkSfVsbsX/roBXVVPD0dsO4KRanIA3adIk+vbty6BBg7j99tvZb7/96qliqXkxIEuSVE9W5gS8hf7yl79w9tln89ZbbzFgwAAOPfTQeqhYap4MyJIk1YOnx03ntw+OYezUhSfgbcEWa3ZZ7v0qKyspKyvjwAMPZMaMGfTu3bseqpWaN4d5kySpDo35ZBa/fWgMz747g35d23L2Xuux14bLPgEPcqNTHH744QDcdttt9VGq1Ow4zJskSfVo6qz5XPrIWO4ZMYmOrVvyi30Hc/hW/ZZ7At5CEcFGG21ESomUkiNUSPXIgCxJUhEtfgLeMdsO4ORvDqRT25bLve/UqVM55phjOO+889hqq604++yz66FiSYszIEuStBLKL3yUGXMqvzY/AlKCfTbuzVl7rEe/bm1rvc02bdrw3nvv8f7777PVVlsVs1xJK8CALEnSSlhSOIZcOP7nj7ep1Ql4AO+++y5XX301f/zjH+nYsSNvvPEGLVr48SxlyVegJEnLkFLi83kLmDprPlNmzWfarPlMmVmxzPvUNhwDPPfcc9x4440cd9xxrL/++oZjqQHwVShJarbmVVYxZeZ8ps6qYOqs+fmf/93OBeIKKqtrirrfN998k2nTprHLLrtw5JFHsu+++9KjR4+i7kPSyjMgS5KanAXVNUyf/dXQO2XR7XwInjmf2RVVX7tv27JSVuvYml4dW1O+Zhd6dWpNrw6tWa1Ta3p1bEWvjq3p0aEV657335WqLaXEcccdx7x58xg1ahQRYTiWGhgDsiSp0Vhad4eps+czdeZ8ps7OTX86t4LFh/lvURL06tianh1bMbBne7Zbpzs9O7ZaFIZzP63o0Hr5o02sjBEjRjB48GDatGnDP/7xD7p06eLQbVIDZUCWJBXF0kZ16N6+jOHn7bbc+69Kd4du7cro2bE1q3VsxYard1oUeFfr1Iqe+dbfrm3LKCkpXiDt3r5sqX/v4iZMmMCQIUM4//zz+cUvfsHAgQOLVoek4jMgS5KKYmmjOsyYU8nHX3xZp90danvxjWKqTeifMmUKq622GmuvvTY333wz++23Xz1UJmlVGZAlSXVum4uf+Mr0kro7LOzisFrH1rnW4E6tad+q8X5MXXPNNZx++umMGjWKtddee9FloyU1fI33nUeS1Gj89lsb1Wl3h4akqqqKFi1asM8++/D+++/Tu3fvrEuStIIMyJKkOve9If2yLqHOLRydYu7cudxxxx307duXiy++OOuyJK0EA7IkaZU9PmZq1iVkLiJYe+21+fLLL6mpqaGkpCTrkiStJF+9kqRV8ujbUznh1hG0WEqXiSWN6tBUzJgxg4MPPpgXX3wRgHPOOYdf//rXhmOpkbMFWZK00oaNnsJJt7/G4NU7ccvR36BTm7oZQ7ihKisr480332Ts2LFsvfXWWZcjqUj8F1eStFL++9YUfnLba2yweif+cUzzCccffPABZ511FjU1NXTs2JG33nqLI488MuuyJBWRAVmStMIefvMTTrr9NTbumwvHHevo6nMN0dNPP81f//pXRo8eDUDLls3nb5eaCwOyJGmFPPjGJ5x0x0g2XaMztxwzpM4uzdyQjB07lieeyI3l/MMf/pBx48ax0UYbZVyVpLpiH2RJUq39Z9THnHrX62zerzM3HfWNRn0hjxVxzDHH8OmnnzJ69GhKSkoc21hq4prHO5skaZXd//pkfnbX65Sv2ZWbjtqSdk08HL/55puss846tGnThhtvvJEOHTo4OoXUTPhKlyQt18JwvGX/5hGOP/zwQ7bYYotFF/oYNGiQrcZSM9K03+EkSavsvpGTOP3uUQwZ0I0bjiynbVnT/eiYMWMG3bt3Z8011+Taa69lv/32y7okSRmwBVmStFT3jpjEaXePYqu1unHjkVs26XB88803M2DAACZMmADAkUceSbdu3TKuSlIWihaQI2LbiGiXv314RPwpItYs1vYlSfXrnuETOePeUWy7dnduOGJL2pSVZl1SnaiurgZgt91249hjj6VHjx4ZVyQpa8VsQb4amBcRmwCnAxOAW4q4fUlSPbn71Ymc+c832G6d7lx/RHmTDcennHIKhx9+OAB9+vThsssuo2PHjhlXJSlrxQzIVSmlBBwAXJlSugroUMTtS5LqwZ2vfMSZ/3yDHQb24LofltO6ZdMMxwC9e/emb9++i1qRJQmKe5Le7Ig4B/gBsH1ElABNf/R4SWpCbn/5I35+35vstG4P/nb4Fk0uHH/++eecfPLJ/OQnP2HrrbfmnHPOybokSQ1QMVuQvwtUAEenlKYAfYFLirh9SVIduvWlD/n5fW/yzfV6cs0Pml44BmjRogWvvPIKb775ZtalSGrAihaQ86H4n0Cr/KwZwH3F2r4kqe7848UPOO/fb7Hr+j25+vDNadWi6YTjyZMn8/Of/5yamho6dOjAm2++yfHHH591WZIasGKOYnEccC9wTX5WH+DftbjfnhExNiLGR8TZS1nnkIh4OyJGR8TtxapZkgR/f+EDfnH/aHYb3Iu/fn+LJhWOAR5//HEuu+wyRo0aBUCrVq2Wcw9JzV0xu1j8BNgWmAWQUnoX6LmsO0REKXAVsBcwGDgsIgYvts5A4Bxg25TSBsCpRaxZkpq1G597n/OHjmb3wb246nubU9aiaQyP//777/PUU08B8IMf/IBx48ax2WabZVuUpEajmCfpVaSUKiMCgIhoAaTl3OcbwPiU0nv5+9xJbhSMtwvWOQ64KqX0OUBKaVoRa5akZuv6Z9/jwgfHsOcGq/GX721Gy9KmEY4BjjrqKCZPnsw777xDaWkpa6yxRtYlSWpEivlu+HRE/BxoExG7AfcA/1nOffoAEwumJ+XnFRoEDIqI5yPipYjYc0kbiojjI2J4RAyfPn36Sv4JktQ8LAzHe2/UdMLxO++8w7x58wC49tpreeKJJygtbVrdRSTVj2K+I54NTAfeBH4EPAScV4TttgAGAjsBhwHXRUTnxVdKKV2bUipPKZV7FSRJWrprnp7AhQ+OYZ+Ne3PFoU0jHE+aNIlNN92U3/72twAMGjTIVmNJK62YXSwOBG5JKV23AveZDBS+g/XNzys0CXg5pbQAeD8ixpELzK+uQq2S1Cxd/dQEfv/fd9hvk9W57JBNaNHIw/Hnn39Oly5d6Nu3L1deeSX77bdf1iVJagKK+c64HzAuIv4REfvm+yAvz6vAwIgYEBFlwKHA0MXW+Te51mMioju5LhfvFa1qSWomrnpyPL//7zvs30TC8e23386aa67J+PHjATj22GPp1atXxlVJagqKOQ7yUcA65PoeHwZMiIjrl3OfKuAkYBgwBrg7pTQ6In4dEfvnVxsGfBoRbwNPAmeklD4tVt2S1Bxc+cS7XDJsLAduujp/auThuKamBoAdd9yR73//+3Tr1i3jiiQ1NZHS8gaaWMENRrQE9gSOAnZIKXUv6g5qoby8PA0fPry+dytJDdIVj73LZY+N49ub9eGS72xCaUlkXdJKO/PMM5k4cSJ33HFH1qVIagIiYkRKqXzx+cW8UMheEXEz8C5wEHA9sFqxti9JWnGXPTqOyx4bx0Gb92304RigS5cudO/enaqqqqxLkdSEFfMkvR8CdwE/SilVFHG7kqQVlFLissfe5c+Pv8t3tujLxQdt3CjD8axZszjttNM46qij2HbbbTnnnHOyLklSM1DMPsiHpZT+bTiWpGyllPjTo+P48+PvcuiWa/D7RhqOAUpKSnjqqad47bXXsi5FUjOyyi3IEfFcSmm7iJjNV6+cF0BKKXVc1X1IkmonpcQlw8by16cmcNg31uCiAzeipJGF42nTpvHXv/6VX/ziF7Rv354333yTNm3aZF2WpGZklVuQU0rb5X93SCl1LPjpYDiWpPqTUuL3/82F4+8N6dcowzHAo48+yu9+97tFrcaGY0n1rZgn6f2jNvMkScWXUuLih9/hb09P4PCt+nHhARs2qnA8adIknnnmGQC+973v8c4777DllltmXJWk5qqYJ+ltUDiRv1DIFkXcviRpCVJK/PahMVz37PscsfWaXLD/BkQ0nnAMcOSRRzJhwgTeffddWrRowYABA7IuSVIzVow+yOcAPwfaRMSshbOBSuDaVd2+JGnpUkr85oEx3Pj8+xy5TX/O329wownH7733Hqutthpt27blyiuvpKysjBYtitluI0krpxh9kH+XUuoAXLJY/+NuKSXH45GkOpJS4lf/eZsbn3+fo7ZtXOH4k08+YaONNuI3v/kNAOuttx5rrbVWxlVJUk4xWpDXSym9A9wTEZsvvjyl5Ng8klRkKSUuGDqav7/4IcduN4Bz91m/UYTjWbNm0bFjR3r37s2ll17Kvvvum3VJkvQ1xfgu6zTgeODSJSxLwDeLsA9JUl5KiV/eP5p/vPQhx++wFufstV6jCMf33nsvxx57LK+++ioDBw7khBNOyLokSVqiVQ7IKaXj8793XvVyJEnLUlOT+MX9b3Hbyx/xox3X4uw9G344TikREWyzzTYcdNBBdO7cOeuSJGmZijnM23ciokP+9nkR8a+I2KxY25ek5q6mJnHuv3Ph+Mc7rd0owvH555/P9773PQBWX311brjhBnr06JFxVZK0bEULyMAvUkqzI2I7YFfgBuBvRdy+JDVbNTWJn9/3Jne88hEn7bwOZ+6xboMPxwCtW7emXbt2LFiwIOtSJKnWihmQq/O/9wGuTSk9CJQVcfuS1CzV1CTO/tcb3PnqRE755jqcvvugBhuO586dy0knncRzzz0HwNlnn831119Py5YtM65MkmqvmAF5ckRcA3wXeCgiWhV5+5LU7FTXJM785xvcPXwSP91lIKft3rBbjlNKPPzww7z44osADbpWSVqaYgbYQ4BhwB4ppS+ArsAZRdy+JDUr1TWJM+4dxb0jJnHqrgP52W6Dsi5piT777DMuuugiqqurad++PW+88QZnnOHbv6TGq2gBOaU0D5gA7BERJwE9U0qPFGv7ktScVNck/u+eUfzrtcmcvtsgTt21YYZjgGHDhnH++efz8ssvA9CuXbuMK5KkVVPMUSx+CtwG9Mz/3BoRJxdr+5LUXFRV13Da3a9z38jJnLHHupy8y8CsS/qaKVOmLOpnfOihhzJmzBi22WabjKuSpOIo5kXvjwGGpJTmAkTE74EXgb8UcR+S1KRVVdfws7tH8Z9RH3Pmnuty4k7rZF3SEh155JG8/fbbTJgwgZYtWzJwYMML8ZK0sooZkIP/jWRB/rZnZ0hSLVVV1/DTu17nwTc+4Zy91uNHO66ddUlfMXHiRLp160bbtm25/PLLARydQlKTVMyT9G4CXo6ICyLiV8BL5MZCliQtx4LqGn56Zy4cn7v3+g0uHE+bNo0NN9yQX/3qVwCst956rLfeehlXJUl1o2gtyCmlP0XEU8B2QAKOSimNLNb2JampWlBdwyl3jOTht6Zw3j7rc+z2a2Vd0iJz5syhffv29OzZk9/97nfstddeWZckSXWuLsYpjsV+S5KWorKqhpNuf42H35rCL/cd3KDC8f3330+/fv0YN24cACeeeCIDBgzIuCpJqnvFHMXil8DfgS5Ad+CmiDivWNuXpKamsqqGn9z+GsNGT+WC/QZz9HYNI3ymlADYcsst2XvvvenQoUPGFUlS/YqFb4SrvKGIscAmKaX5+ek2wOsppXWLsoMVUF5enoYPH17fu5WkWquoquYnt73GY2Om8esDNuCHW/fPuiQAfve73/HWW29x2223ZV2KJNW5iBiRUipffH4xR7H4GGgNzM9PtwImF3H7ktRolV/4KDPmVH5tfvtWpQ0mHC8UEVRWVlJWVpZ1KZKUiWIG5JnA6Ih4lNxJersBr0TEnwFSSqcUcV+S1KgsKRwDzKmoXuL8+vLll1/yi1/8ggMOOIDtt9+es88+mwhPIZHUvBUzIN+X/1noqSJuW5JUB6qrq/nXv/5F586d2X777Q3HkkRxh3n7e7G2JUmqO7NmzeLqq6/m//7v/2jfvj2jRo3yRDxJKlAXw7xJkhqw//73v/z85z/n+eefBzAcS9JiDMiS1Ax8+umnvPDCCwB85zvf4a233mKHHXbIuCpJaphWOSBHxD/yv3+66uVIUtPUusWS3267t6+fkSKOPPJIDj74YCoqKogI1l9//XrZryQ1RsXog7xFRKwOHB0Rt7DYFfRSSp8VYR+S1Gh9+OlcqmoSR2y9Jr86YMN62+8nn3xCx44dadeuHZdccgkVFRW0atWq3vYvSY1VMQLy34DHgbWAEXw1IKf8fElqtq547F1alAY/2Xmdetvnp59+yoYbbshRRx3FH//4R9Zbb71627ckNXarHJBTSn8G/hwRV6eUflyEmiSpyXh36mzue30yx2+/Fj07tq7z/X355Ze0adOGbt26cf7557PnnnvW+T4lqakp2kl6KaUfR8QmEXFS/mfjYm1bkhqrPz06jnZlLThhx7XrfF8PP/ww/fr1Y9y4cQCccsopDBo0qM73K0lNTdECckScAtwG9Mz/3BYRJxdr+5LU2Lw1eSYPvzWFo7cbQJd2dXcyXkoJgE033ZSdd96Z1q3rvqVakpqyYl5J71hgSEppLkBE/B54EfhLEfchSY3GpY+MpVOblhy7/YA628dll13GiBEjuPXWW+nduzd33313ne1LkpqLYo6DHEB1wXQ1i41oIUnNxYgPP+PJsdM5Yce16di6ZZ3tp6Kigi+//JL58+fX2T4kqbkpZgvyTcDLEXFffvpA4IYibl+SGo0/DhtH9/atOGKbNYu63YqKCi688EJ23313tt9+e84880xKSrzmkyQVU9ECckrpTxHxFLBdftZRKaWRxdq+JDUWz4+fwYvvfcr5+w2mbVkx2yGgqqqK2267DYDtt9/ecCxJdaCo79wppdeA14q5TUlqTFJK/PGRsfTu1JrDvtGvKNucO3cu1157Laeccgrt2rXjtddeo3PnzkXZtiTp62x6kKQieuKdaYz86AtO2WUgrVuWFmWbDz/8MKeddhpPPfUUgOFYkuqYAVmSiqSmJnHpI+NYs1tbDt6i7ypta+bMmbz00ksAHHTQQYwaNYpddtmlGGVKkpbDgCxJRfLwW1N4+5NZnLrrQFqWrtrb61FHHcWBBx7I/PnziQg23thrL0lSfSlaH+SI+Dbwe3IXCYn8T0opdSzWPiSpoaquSfzp0bEM7Nme/Tfps1LbmDFjBm3atKFdu3ZcdNFFzJkzx4t+SFIGitmC/Adg/5RSp5RSx5RSB8OxpObi3yMnM2H6XE7bbRClJSs+BPwXX3zBBhtswC9+8QsA1l9/fbbccstilylJqoVijmIxNaU0pojbk6RGobKqhssfH8cGq3dkzw1XW6H7zp8/n9atW9O5c2fOOecc+xlLUgNQzBbk4RFxV0QcFhHfXvhTxO1LUoN0z4iJTPzsS/5v93WJqH3r8eOPP07//v0ZO3YsAKeeeiobbbRRXZUpSaqlYrYgdwTmAbsXzEvAv4q4D0lqUOYvqOYvj49nizW7sNO6PVbovhtssAFbbbUVLVvW3aWoJUkrrphX0juqWNuSpMbi1pc+ZMqs+Vz23U1r1Xr8t7/9jRdeeIG///3vrLbaavz73/+u+yIlSSukaF0sIqJvRNwXEdPyP/+MiFUbCFSSGrC5FVVc/dQEtl2nG1uv3a1W95k5cyYzZsxg/vz5dVydJGllFbMP8k3AUGD1/M9/8vMkqUm6+YUP+HRuJafvvu5S11mwYAG//e1veeaZZwA444wzePDBB2nTpk19lSlJWkHFDMg9Uko3pZSq8j83AyvWIU+SGomZXy7gmqcnsMt6Pdm8X5elrldZWckNN9zAf/7zHwBKSkpW6EQ+SVL9K2ZA/jQiDo+I0vzP4cCnRdy+JDUY1z/7HrPmV3Ha7oO+tmz+/PlceeWVVFdX065dO1555RUuueSSDKqUJK2MYgbko4FDgCnAJ8DBgCfuSWpyPp1TwY3Pvc8+G/Vmg9U7fW35ww8/zMknn8wjjzwCQLduteufLElqGIo5isWHwP7F2p4kNVR/e3oCXy6o5me7/a/1eM6cOYwZM4Ytt9ySAw88kFdffZXy8vIMq5QkraxVDsgRcWZK6Q8R8Rdy4x5/RUrplFXdhyQ1FFNnzeeWFz/kW5v1ZZ2e7RfNP+aYY3jyySf54IMPaNu2reFYkhqxYrQgL7y89PAibEuSGrQrnxhPdU3ip7sM5IsvvqBly5a0a9eOCy64gJNPPpm2bdtmXaIkaRWtckBOKf0nf3NeSumewmUR8Z1V3b4kNRQTP5vHna9+xHe3XIPOLasYPHhDDjroIK644grWX3/9rMuTJBVJMU/SO6eW8ySpUbri8XeJCE765jp07NiRn/3sZ/zwhz/MuixJUpEVow/yXsDeQJ+I+HPBoo5A1apuX5IaggnT5/DPEZOoHvMYs6asRe9O63L66adnXZYkqQ4Uow/yx+T6H+8PjCiYPxv4WRG2L0mZu+zRcbRuWcIaNR9lXYokqY4Vow/yKGBURNwHzE0pVQNERCnQalW3L0lZuummm3jw+dcZ3n13frLz2pzxm39mXZIkqY4Vsw/yI0Cbguk2wGNF3L4k1bvp06fzduladGhVyvHbr511OZKkelDMgNw6pTRn4UT+tuMdSWpUqqurueyyy3j66acB2OWQY5jXZR2O32FtOrVtmXF1kqT6UMyAPDciNl84ERFbAF8WcfuSVOcqKiq48sorueee3KiVlz8+nq7tyjhquwEZVyZJqi/FDMinAvdExLMR8RxwF3DS8u4UEXtGxNiIGB8RZy9jvYMiIkWEl6eSVFSVlZVce+21VFVV0bZtW1588UX+8pe/8NJ7n/LsuzP48Y5r075VMc5pliQ1BkV7x08pvRoR6wHr5meNTSktWNZ98ifyXQXsBkwCXo2IoSmltxdbrwPwU+DlYtUrSQsNGzaMH/3oR/Tu3Zv99tuPnj17klLi0kfG0qtjK36w9ZpZlyhJqkfFbEGGXDgeDGwOHBYRyxtB/xvA+JTSeymlSuBO4IAlrPcb4PfA/GIWK6n5+vLLLxkxIjcy5b777ssLL7zAfvvtt2j50+Om8+oHn3PSNwfSumVpVmVKkjJQtIAcEecDf8n/7Az8gdzYyMvSB5hYMD0pP69wu5sDa6SUHixWrZJ03HHHscceezB79mwigq233nrRslzr8Tj6dmnDd8vXyLBKSVIWitmCfDCwCzAlpXQUsAnQaVU2GBElwJ+A5V6uKiKOj4jhETF8+vTpq7JbSU3U7NmzmTt3LgDnnnsud955Jx06dPjaesNGT+XNyTP56S4DKWtR7C/aJEkNXTHf+b9MKdUAVRHREZgGLK/pZfJi6/TNz1uoA7Ah8FREfABsBQxd0ol6KaVrU0rlKaXyHj16rMKfIakpmjNnDptssgnnnHMOAOuvvz677rrr19arrkn86dGxrNWjHd/arM/XlkuSmr5inpY9PCI6A9eRu+T0HODF5dznVWBgRAwgF4wPBb63cGFKaSbQfeF0RDwF/F9KaXgR65bUhFVVVdGiRQvat2/Pj3/8Y7bddttlrv/AGx8zbuoc/nLYZrQotfVYkpqjorz7R0QAv0spfZFS+hu5USmOyHe1WKqUUhW5oeCGAWOAu1NKoyPi1xGxvP7LkrRML774IgMHDmTMmDEAnHHGGWyzzTZLXX9BdQ2XPTqO9VbrwD4b9a6vMiVJDUxRWpBTSikiHgI2yk9/sAL3fQh4aLF5v1zKujutfJWSmpu11lqLtddem5qamlqt/6/XJvHBp/O47ofllJREHVcnSWqoivn94WsRsWURtydJK+yOO+7g6KOPJqVEr169eOyxx9hggw2We7+Kqmr+/Ph4NlmjM7uu37MeKpUkNVTFDMhDgJciYkJEvBERb0bEG0XcviQt16RJkxg7dixz5sxZofvd8fJHTP7iS/5v90Hkeo1JkpqrSCmt2gYi+qWUPoqIJV5qKqX04SrtYCWUl5en4cM9j09qDmpqarjmmmsYPHgwO+64I9XV1UQEJSW1////y8pqtv/Dk6zdox13Hr+VAVmSmomIGJFS+troaMVoQf43LArCf0opfVj4U4TtS9JSVVRUcOmll3LrrbcCUFpaukLhGODvL37AjDkV/N8e6xqOJUlFCciFnyZrFWF7krRMVVVV3HTTTVRVVdGmTRueffZZrr322pXa1uz5C/jb0xPYcVAPtuzftciVSpIao2IE5LSU25JUJx599FGOPvpo7r//fgB69+690i2/Nzz3Pl/MW8D/7b5uMUuUJDVixRjmbZOImEWuJblN/jb56ZRS6liEfUhq5iorK3n77bfZdNNN2XPPPXnqqafYYYcdVmmbn8+t5Ppn32ePDXqxUd9ORapUktTYrXILckqpNKXUMaXUIaXUIn974bThWFJRnHDCCeyyyy7MnDmTiGDHHXdc5f7C1zzzHnMrqzhtN1uPJUn/U8xLTUtSUc2bN4+UEu3atePMM8/k29/+Np06Faeld9rs+dz8wvvsv8nqrLtah6JsU5LUNBRzHGRJKpp58+ax2WabcfbZZwOw3nrrse+++xZt+399cgILqhM/23VQ0bYpSWoabEGW1KBUV1dTWlpK27ZtOeqooxgyZEjR9zH5iy+5/eWP+M4WfenfvV3Rty9JatxsQZbUYAwfPpz11luPMWPGAHD22Wez8847F30/f3n8XQBO3mVg0bctSWr8DMiSGox+/fqx+uqrU1FRUWf7+GDGXO4ZMYnvDelHn85t6mw/kqTGy4AsKVP33Xcfxx13HCklevbsydNPP82mm25aZ/u7/LFxtCwNTtx57TrbhySpcTMgS8rUe++9x8iRI5k5c2ad72vc1NncP+pjjtimPz07tK7z/UmSGicDsqR6lVLi5ptv5qmnngLg1FNP5aWXXqJz5851vu8/PTKOdmUtOGEHW48lSUtnQJZUryoqKrjooou48cYbASgtLaVFi7ofUOfNSTP57+gpHLPdALq0K6vz/UmSGi8DsqQ6V1NTw2233UZVVRWtW7fmySef5Oabb67XGi59dCyd27bkmO0H1Ot+JUmNjwFZUp177LHHOPzww7nnnnsA6Nu3LyUl9ff2M/yDz3hq7HR+tMPadGzdst72K0lqnAzIkupEVVUVb775JgC77bYbjz76KIceemi915FS4pJhY+nevhVHbLNmve9fktT4GJAl1YmTTjqJHXfckc8//5yIYNdddyUi6r2O58d/ysvvf8ZPdl6btmVePFSStHx+WkgqmoqKCqqrq2nbti0/+9nP2HXXXenSpUtm9aSUuOSRsazeqTXfG9IvszokSY2LLciSimL+/PmUl5dz5plnArDuuuty8MEHZ1rT42OmMWriF5yyy0BatSjNtBZJUuNhC7KkVVJTU0NJSQmtW7fmsMMOY7PNNsu6JABqahJ/fGQsa3Zry0Fb9M26HElSI2ILsqSVNmrUKDbccEPefvttAH7+85+z1157ZVxVzkNvfcI7U2bzs10H0bLUtzpJUu35qSFppa2++up07tyZuXPnZl3KV1RV1/CnR8cxsGd79ttk9azLkSQ1MgZkSSvkoYce4kc/+hEpJXr06MHzzz/PlltumXVZX3HfyMm8N30up+8+iNKS+h85Q5LUuBmQJa2QMWPG8MILL/D5558DZDJ027JUVtVwxePvsmGfjuyxwWpZlyNJaoQMyJKWKaXEnXfeydNPPw3AqaeeyogRI+jatWvGlS3ZXcMnMunzLzl993UbXHiXJDUOBmRJy1RZWckvf/lL/vrXvwJQWlpKWVlZxlUt2fwF1Vz5xLuUr9mFnQb1yLocSVIjZUCW9DUpJe655x6qqqpo1aoVjz32GLfffnvWZS3XrS99yNRZFbYeS5JWiQFZ0tc8+eSTHHLIIYtCcb9+/SgtbdgX2phTUcVfn5rAdut0Z+u1u2VdjiSpETMgSwKgurqaMWPGALDzzjvz0EMPcfjhh2dcVe3d/Pz7fDa3ktN3H5R1KZKkRs6ALAmA0047jW233ZZPP/2UiGCvvfaipKRxvEXMnLeAa555j13X78lm/bpkXY4kqZHzUtNSM7ZgwQIWLFhA27ZtOfHEEykvL2+wo1Msy3XPvsfs+VWcttu6WZciSWoCDMhSM1VZWck222zDkCFDuOqqq1h33XVZd93GFzBnzKngxuffZ5+NezN49Y5ZlyNJagIMyFIzk1IiIigrK+PAAw9kgw02yLqkVXL1UxOYv6Can+1q32NJUnE0jg6Gkopi9OjRbLrppowePRqA8847j29961sZV7Xypsyczz9e+pBvbdaXdXq2z7ocSVITYUCWmpGePXvSsmVLvvjii6xLKYq/PPEuKSVO3XVg1qVIkpoQA7LUxD3++OOceOKJpJTo0aMHr776Kttuu23WZa2yiZ/N465XJ/LdLddgja5tsy5HktSEGJClJu6NN97g8ccfZ8aMGQBN5gpzlz/2LqUlwUk723osSSouA7LUBN1///08/fTTAJxyyim8/vrr9OjRI+Oqimf8tDncN3ISP9hqTVbr1DrrciRJTYyjWEhNzIIFCzjjjDMYPHgwO+64I6WlpbRp0ybrsorqssfG0bplKSfstHbWpUiSmiBbkKUmIKXE0KFDqaqqomXLlgwbNoy7774767LqxOiPZ/LgG59w9LYD6N6+VdblSJKaIAOy1AQ899xzHHDAAdxyyy0ADBgwgLKysoyrqhuXPTqODq1bcNz2a2VdiiSpiTIgS41USolx48YBsP3223P//fdzxBFHZFxV3Xrto895bMw0frTDWnRq2zLrciRJTZQBWWqkzjjjDIYMGcK0adMA2H///SktLc24qrr1p0fG0bVdGUdtOyDrUiRJTZgn6UmNSHV1NZWVlbRp04bjjz+eddddt0mNTrEsL074lOfGz+C8fdanXSvfuiRJdcdPGamRWLBgATvttBObbLIJf/3rXxk0aBCDBg3Kuqx6kVLij4+MpVfHVhy+1ZpZlyNJauIMyFIDl1IiImjZsiV77LEHAwc2vwtjPDVuOiM+/JzfHLghrVs27W4kkqTs2QdZasDGjh3LkCFDeOuttwD45S9/yWGHHZZxVfUrpcSlj4ylb5c2fLd8jazLkSQ1AwZkqQHr2rUrlZWViy4T3RwNGz2FtybP4tRdB1HWwrcsSVLd89NGamCee+45Tj75ZFJK9OjRg5EjR7LTTjtlXVYmqmsSlz4yjrV6tOPATVfPuhxJUjNhQJYamOHDh/PAAw8wdepUACIi44qyM3TUZN6dNofTdhtEi1LfriRJ9cNPHKkBeOSRR3j66acBOPnkk3nrrbdYbbXVMq4qWwuqa7j8sXdZv3dH9t6wd9blSJKaEUexkDJWVVXFKaecwoABA9hxxx0pLS2lXbt2WZeVifILH2XGnMqvzf/Gbx9j+Hm7ZVCRJKk5sgVZysiwYcNYsGABLVq04MEHH+S+++7LuqTMLSkcL2u+JEl1wYAsZeCll15izz335KabbgJg7bXXpnXr1hlXJUmSwIAs1ZuUEu+99x4AW221Fffeey9HHXVUxlVJkqTFGZClenLeeeex+eabM2XKFAAOOuggWrZsmXFVkiRpcZ6kJ9WhmpoaKisrad26NUceeSS9evWiR48eWZclSZKWwYAs1ZGqqir22GMPBg0axNVXX83AgQMZOHBg1mU1WM+PX/rVAru3L6vHSiRJzZ0BWSqylBIRQYsWLdhhhx3o169f1iU1eJ/NreS0u19n7R7t+M/J29G2zLcmSVJ27IMsFdGECRPYbrvtePPNNwE4//zzPRFvOVJKnHnvG3w+dwFXHLqZ4ViSlDkDslREnTp1YubMmXzyySdZl9Jo3PryRzw2Zipn7rkuG/bplHU5kiQZkKVV9eqrr/Kzn/2MlBLdu3fnjTfeYPfdd8+6rEZh3NTZXPjA2+w4qAdHbzsg63IkSQIMyNIqe+GFF7jnnnv4+OOPASgp8WVVG/MXVHPKHSPp0LoFf/zOJpSURNYlSZIEGJCllfL000/z9NNPA3DSSSfx9ttv06dPn4yralwufvgd3pkym0sO3oQeHVplXY4kSYt4Noy0gqqrq/nxj3/M6quvzo477khpaSkdO3bMuqxG5Yl3pnLzCx9w1Lb92Xm9nlmXI0nSV2TeghwRe0bE2IgYHxFnL2H5aRHxdkS8ERGPR8SaWdQpPfnkkyxYsIDS0lLuv/9+7r///qxLapSmzZrP/93zBuv37shZe66XdTmSJH1NpgE5IkqBq4C9gMHAYRExeLHVRgLlKaWNgXuBP9RvlRIMHz6cb37zm1x77bUADBw4kHbt2mVcVeNTU5M4/Z5RzKus4s+HbkrrlqVZlyRJ0tdk3YL8DWB8Sum9lFIlcCdwQOEKKaUnU0rz8pMvAX3ruUY1Yx999BEA5eXl3HHHHRx77LEZV9S43fj8+zz77gx+se9gBvbqkHU5kiQtUdYBuQ8wsWB6Un7e0hwDPLykBRFxfEQMj4jh06dPL2KJaq5+85vfsPHGGy8a0/jQQw+lVStPJltZb02eye//+w67D+7F977h1QUlSQ1XozlJLyIOB8qBHZe0PKV0LXAtQHl5earH0tSEpJSorKykVatWHHbYYbRq1YoePXpkXVajN6+yilPuHEnXdmX8/qCNiXBIN0lSw5V1C/JkYI2C6b75eV8REbsC5wL7p5Qq6qk2NTPV1dXss88+nHLKKQCss846nHnmmbRo0Wj+j2ywfv2ft3l/xlwuO2RTurQry7ocSZKWKetP/leBgRExgFwwPhT4XuEKEbEZcA2wZ0ppWv2XqOaitLSULbfckp49HXasmB568xPufHUiP95pbbZZp3vW5UiStFyZtiCnlKqAk4BhwBjg7pTS6Ij4dUTsn1/tEqA9cE9EvB4RQzMqV03QRx99xC677MIbb7wBwK9+9St+8pOfZFxV0/HxF19y9j/fYJO+nThtt0FZlyNJUq1k3YJMSukh4KHF5v2y4Pau9V6Umo22bdsyefJkPvroIzbeeOOsy2lSqmsSp971OtU1iSsO3YyWpVn36JIkqXb8xFKzM2rUKE4//XRSSnTv3p3Ro0ez7777Zl1Wk3P1U+N55f3P+PUBG9K/u2NGS5IaDwOymp2nn36aW2+9lYkTcyMMlpZ6sYpiG/Hh51z22Lvsv8nqfHvzZY3cKElSwxMpNb0R0crLy9Pw4cOzLkMNyMsvv0xFRQU77LADNTU1fPHFF3Tt2jXrspqkWfMXsPcVzwLw0E+3p2PrlhlXJEnSkkXEiJRS+eLzM++DLNW1mpoajjnmGLp06cKzzz5LSUmJ4bgO/fLfb/HJzPnc/aOtDceSpEbJLhZqsp5//nkWLFhASUkJ//znP3nwwQezLqnJu2/kJP79+sf8dJeBbLFml6zLkSRppRiQ1SS9/vrrbLfddvz1r38FYN1116Vjx44ZV9W0ffjpXH7x79F8o39XfrLzOlmXI0nSSjMgq0n5+OOPAdh000255ZZbOO644zKuqHlYUF3DT+98nQi47NBNKS3xUtKSpMbLgKwm4/e//z2DBw9eFJJ/8IMf0LZt24yrah4uf2wcr0/8gou/vTF9OrfJuhxJklaJJ+mpUUspsWDBAsrKyjjooIOoqKige3cvZ1yfXpzwKX99agLfLV+DfTbunXU5kiStMod5U6NVU1PDd77zHbp168a1116bdTnN0hfzKtnz8mdpW1bKf07ejnat/J9bktR4OMybmpySkhI22GADOnXqREqJCPu91qeUEmf/800+nVvBdT/c1nAsSWoy7IOsRmXy5MnsvffejBo1CoBf//rXnH766YbjDNz56kT+O3oKZ+yxLhv17ZR1OZIkFY0BWY1K69ateffdd5kwYULWpTRr46fN5lf/Gc1263Tn2O3WyrocSZKKyoCsBm/MmDGcffbZpJTo1q0bY8aM4dvf/nbWZTVbFVXVnHzH67Qta8GfDtmEEod0kyQ1MQZkNXiPPfYY1113HR988AEALVrY1zVLf/jvWMZ8MotLDt6Ynh1bZ12OJElFZ0BWgzRy5EieffZZAH7yk5/wzjvvMGDAgIyr0lNjp3HDc+9zxNZrssv6vbIuR5KkOmFTnBqclBJHHHEEbdu25cUXX6SkpIQePXpkXVazN312Bf93zyjW7dWBc/ZeP+tyJEmqMwZkNRivvvoqm2yyCWVlZdx1112sttpqjk7RQKSUOOPeUcyaX8Vtx25F65alWZckSVKdsYuFGoTRo0czZMgQrrzySgDWX399unTpknFVWuim5z/gqbHTOW+f9Vl3tQ5ZlyNJUp0yICtTU6dOBWCDDTbghhtu4Ljjjsu4Ii3u7Y9ncfHD77Dr+j35wVZrZl2OJEl1zoCszFx++eWsu+66TJ48GYCjjjqKDh1snWxIvqys5pQ7R9K5bUv+cPAmdnmRJDUL9kFWvauqqqJFixbst99+TJ8+na5du2ZdkpbiwgffZvy0Odx6zBC6tivLuhxJkuqFAVn1JqXED37wA9q2bcu1117L2muvzUUXXZR1WVqKYaOncNvLH/GjHdZiu4Hdsy5HkqR6Y0BWvYkIBgwYQOvWrUkp+XV9A/bJzC85659vsFGfTpy++7pZlyNJUr2yD7Lq1NSpU/nWt77FqFGjAPjNb37DueeeazhuwKprEqfdNYrKqhquOHRTylr4NiFJal785FOdatGiBaNGjWLMmDFZl6JauuaZCbz43qdcsP8GrNWjfdblSJJU7wzIKroJEyZw7rnnklKiW7dujB07lkMPPTTrslQLr0/8gj89Mo59Nu7Nd7bom3U5kiRlwoCsohs2bBhXXnkl48ePB6Bly5YZV6TamFNRxU/vHEmvjq357YEb2Q1GktRsGZBVFKNHj+a5554D4IQTTuCdd95h4MCBGVelFXH+/aOZ+Nk8Lj90Uzq19Z8aSVLz5SgWWmUpJQ4//HAighEjRlBSUkLv3r2zLksr4P7XJ/PP1ybx010GsmV/x6WWJDVvBmSttFGjRrH++utTVlbGbbfdRvfu3f1avhGa+Nk8zrvvLbZYswsnf3OdrMuRJClzdrHQShk7dixbbLEFV1xxBQCDBw+mZ8+eGVelFVVVXcNP7xwJwOXf3ZQWpb4lSJLkp6FWyIwZMwBYd911ufrqqznuuOMyrkir4s9PjOe1j77gom9vxBpd22ZdjiRJDYIBWbV29dVXs8466zBp0iQAjjvuODp37pxtUVppr7z/GVc+8S4Hbd6X/TdZPetyJElqMOyDrOWqrq6mtLSU3XffnfHjxxuKm4CZ8xZw6p0j6de1Lb86YIOsy5EkqUExIGupUkocf/zxAFx33XWsvfbaXHrppRlXpVWVUuKc+95g2uwK/vnjbWjfyrcBSZIK+cmopYoIevXqRUqJlJIjVDQR9wyfxENvTuGsPddjkzU6Z12OJEkNjn2Q9RUzZszgsMMO4/XXXwfgwgsv5KKLLjIcNxETps/h/KGj2Wbtbvxoh7WyLkeSpAbJgKyvKCkp4cUXX2TUqFFZl6Iiq6zKDenWqmUJfzpkU0pK/KdHkqQlMSCLiRMncsEFF5BSomvXrowdO5Yjjjgi67JUZJc+Mpa3Js/iDwdtzGqdWmddjiRJDZYBWTzwwANccsklvPPOOwC0atUq44pUbM++O51rnnmPw7fqx+4brJZ1OZIkNWgG5Gbq3Xff5YUXXgDgRz/6Ee+88w7rr79+xlWpLnw6p4LT7h7FwJ7tOXfvwVmXI0lSg+coFs1QSonvfe97VFRUMGrUKEpKSlhjjTWyLkt1IKXEmfe+wcwvF3DL0d+gTVlp1iVJktTgGZCbkbfffpt11lmHsrIybr75Zrp06eLoFE3cP176kMffmcb5+w1m/d4dsy5HkqRGwS4WzcSECRPYdNNNF13oY4MNNmD11b28cFM2dspsLnxwDDuv24Mjt+mfdTmSJDUaBuQm7osvvgBg7bXX5oorruC4447LtiDVi/kLqjn5jtfo2Loll3xnE78pkCRpBRiQm7Abb7yRtdZai0mTJgHw4x//mO7du2dclerDbx8aw7ipc7j0kE3o3t5RSSRJWhEG5CaopqYGgJ133pnDDjuMDh06ZFyR6tNjb0/llhc/5NjtBrDjoB5ZlyNJUqPjSXpNSEqJU045hfnz53PdddcxYMAArrrqqqzLUj2aOms+Z9w7isG9O3LGnutmXY4kSY2SAbkJiQg6duxIWVkZNTU1lJT4BUFzUlOTOP3uUXy5oJo/H7YZrVo4pJskSSvDBNXIff755xx55JGMHDkSgAsvvJBLL73UcNwMXf/cezw3fgbn77cB6/Rsn3U5kiQ1WqaoJuCJJ55g+PDhAI5W0Ey9OWkmlwwby14brsahW3rRF0mSVoUBuRH65JNPuPDCC0kp0aVLF8aOHevwbc3Y3IoqTrlzJN3bt+J3397If5IkSVpFBuRGaOjQoVx44YW89dZbALRp0ybjipSlX/1nNB98OpfLvrspnduWZV2OJEmNXqSUsq6h6MrLy9PCLgdNxYcffsjHH3/M1ltvTU1NDR9++CEDBgzIuixloPzCR5kxp/Jr87u3L2P4ebtlUJEkSY1TRIxIKZUvPt9RLBqBlBKHHnooM2fO5K233qKkpMRw3IwtKRwva74kSVoxBuQGbPz48fTr14+ysjKuvfZaOnbs6OgUkiRJdcy01UB98MEHbLTRRvzhD38AYKONNmLNNdfMuCplJaXEW5Nn8psH3s66FEmSmjxbkBuYWbNm0bFjR/r3788f/vAHDj744KxLUoYmfjaPoaM+5r6Rkxk/bQ4tSx2hQpKkumYLcgNy6623MmDAACZOnAjAySefTO/evTOuSvXti3mV3PbyhxzytxfZ/g9PcsmwsXRtW8Zvv7URr567a9blSZLU5NmC3ACklIgItt12W771rW/Rrl27rEtSPZu/oJon35nGfSMn8+TYaSyoTqzdox1n7LEu+2+yOmt0bbto3e7ty5Y6ioUkSVp1DvOWsbPOOovPPvuM6667LutSVM9qahKvfPAZ/x45mQff/ITZ86vo0aEV+2+yOt/arA8brN7Ri35IklSHHOatgWrRogUtW7akpqbGESqaiXFTZ3PfyMncP3IyH8+cT9uyUvbccDUO3LQP26zdjRalPg8kScqSLcj1bNasWZx55pkcf/zxbL755qSU2PKix7zwQxM3ddZ8hr6eO9nu7U9mUVoS7DCwOwdu1ofdBveibZn/q0qSVN9sQW4gqqureeCBB9hwww3ZfPPNiQgv/NBEzZ6/gGGjp/LvkZN5fsIMUoJN1ujMBfsNZt9NVqd7+1ZZlyhJkpbAgFwPZsyYwfXXX89ZZ51Fly5deOedd2jfvn2t7vvye5/SrlUL2paVLvrdtqwFpSX2TW2IFlTX8My46fz79Y959O0pzF9QQ7+ubTn5mwM5cNPVWatH7Y67JEnKjgG5Hvz73//ml7/8JXvssQebbbZZrcMxwHevfWmJ89u0LKVdq1xYLgzP7Vu1oG1Zi0XL2pWV0rZVC9ovnF40vwVtW5XSrmCeoXvlpJQYOfEL/j1yMg+88Qmfza2kS9uWfGeLNThwsz5s3q+zJ9tJktSIGJDryMcff8zEiRMZMmQIRx99NDvssAODBg1a4e3cfuwQ5lZWM6+yirkVud9zKqqYV1nN3ILfcyurmD2/iqmz5i9ab25lNZVVNbXeV+uWJYsF53z4LpjXtlUp7cta0LbV/8J3u7L/he92rf4Xvtu2LG3SJ5y9P2Mu/x45mX+/PpkPP51HqxYl7Dq4F9/atA87DOpBWYum+7dLktSUZR6QI2JP4AqgFLg+pXTxYstbAbcAWwCfAt9NKX1Q33WuqEMPPZQpU6YwZswYSktLVyocA2yzTvdVqmNBdQ3zKqqZW1m1KGTPLQjbi/9ePHzPqahi2qyK/PwVD92tWpTkQvPCgF3Q2r0ohC9jWbuvtXzXT+guv/DRJfYB79aujFN2Gch9Iyfz+sQviICt1+rGT3Zehz03XI2OrVvWeW2SJKluZRqQI6IUuArYDZgEvBoRQ1NKbxesdgzweUppnYg4FPg98N36r3b5PvjgA3r37k2rVq248soradOmDaWlpcu9X11e+KFlaQmd2pbQqW3xgtuC6hrmLWrV/l/o/l8Qr140Pxeq/7dsbsX/QnfhuhUrEbq/1rq9sIvJoumvBuwldStZOK/lYqF7aSdIfjq3kvOHjma91Tpwzl7rsf+mq9O7U5tVejwlSVLDknUL8jeA8Sml9wAi4k7gAKAwIB8AXJC/fS9wZUREamDj002cOJENNtiAs846i1/+8pdsvPHGtb5vYxvKrWVpCZ3alNCpTV2F7v/9Xth95CtdSvLBfPFAPmNOxVdawFckdJe1KPlKV5Fl+e+p27Peah1X9U+WJEkNVNYBuQ8wsWB6EjBkaeuklKoiYibQDZhRuFJEHA8cD9CvX7+6qnep1lhjDS666CIOOuiget93U1AXobuquoZ5C6qZl2+1/kr4rqxmXkVBl5LCFvCKasZNnbPU7RqOJUlq2rIOyEWTUroWuBZyFwrJooZTTz01i91qKVqUltCxtGSl+gX3P/vBOqhIkiQ1BlmfZj8ZWKNgum9+3hLXiYgWQCdyJ+tJkiRJRZd1QH4VGBgRAyKiDDgUGLrYOkOBI/K3DwaeaGj9j9X0LO0EyWKcOClJkhq2TLtY5PsUnwQMIzfM240ppdER8WtgeEppKHAD8I+IGA98Ri5ES3WqsZ04KUmSiifzPsgppYeAhxab98uC2/OB79R3XZIkSWqesu5iIUmSJDUoBmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCkVLKuoaii4jpwIcZ7Lo7MCOD/arueWybJo9r0+WxbZo8rk1XVsd2zZRSj8VnNsmAnJWIGJ5SKs+6DhWfx7Zp8rg2XR7bpsnj2nQ1tGNrFwtJkiSpgAFZkiRJKmBALq5rsy5AdcZj2zR5XJsuj23T5HFtuhrUsbUPsiRJklTAFmRJkiSpgAFZkiRJKmBAXgkRsWdEjI2I8RFx9hKWt4qIu/LLX46I/hmUqRVUi+N6WkS8HRFvRMTjEbFmFnVqxS3v2Basd1BEpIhoMEMNaelqc1wj4pD863Z0RNxe3zVq5dTi/bhfRDwZESPz78l7Z1GnVkxE3BgR0yLiraUsj4j4c/64vxERm9d3jQsZkFdQRJQCVwF7AYOBwyJi8GKrHQN8nlJaB7gM+H39VqkVVcvjOhIoTyltDNwL/KF+q9TKqOWxJSI6AD8FXq7fCrUyanNcI2IgcA6wbUppA+DU+q5TK66Wr9nzgLtTSpsBhwJ/rd8qtZJuBvZcxvK9gIH5n+OBq+uhpiUyIK+4bwDjU0rvpZQqgTuBAxZb5wDg7/nb9wK7RETUY41accs9rimlJ1NK8/KTLwF967lGrZzavGYBfkPun9n59VmcVlptjutxwFUppc8BUkrT6rlGrZzaHNsEdMzf7gR8XI/1aSWllJ4BPlvGKgcAt6Scl4DOEdG7fqr7KgPyiusDTCyYnpSft8R1UkpVwEygW71Up5VVm+Na6Bjg4TqtSMWy3GOb/xpvjZTSg/VZmFZJbV6zg4BBEfF8RLwUEctquVLDUZtjewFweERMAh4CTq6f0lTHVvSzuM60yGKnUmMWEYcD5cCOWdeiVRcRJcCfgCMzLkXF14LcV7U7kfvG55mI2Cil9EWWRakoDgNuTildGhFbA/+IiA1TSjVZF6amwRbkFTcZWKNgum9+3hLXiYgW5L7++bReqtPKqs1xJSJ2Bc4F9k8pVdRTbVo1yzu2HYANgaci4gNgK2CoJ+o1eLV5zU4ChqaUFqSU3gfGkQvMathqc2yPAe4GSCm9CLQGutdLdapLtfosrg8G5BX3KjAwIgZERBm5kwOGLrbOUOCI/O2DgSeSV2Rp6JZ7XCNiM+AacuHYvoyNxzKPbUppZkqpe0qpf0qpP7n+5funlIZnU65qqTbvxf8m13pMRHQn1+XivXqsUSunNsf2I2AXgIhYn1xAnl6vVaouDAV+mB/NYitgZkrpkywKsYvFCkopVUXEScAwoBS4MaU0OiJ+DQxPKQ0FbiD3dc94cp3RD82uYtVGLY/rJUB74J78OZcfpZT2z6xo1Uotj60amVoe12HA7hHxNlANnJFS8tu8Bq6Wx/Z04LqI+Bm5E/aOtCGq4YuIO8j909o933/8fKAlQErpb+T6k+8NjAfmAUdlU6mXmpYkSZK+wi4WkiRJUgEDsiRJklTAgCxJkiQVMCBLkiRJBQzIkiRJUgEDsiRJklTAgCxJkiQVMCBLkiRJBQzIkiRJUgEDsiRJklTAgCxJkiQVaJF1AXWhe/fuqX///lmXIUmSpAZsxIgRM1JKPRaf3yQDcv/+/Rk+fHjWZUiSJKkBi4gPlzTfLhaSJElSAQOyJEmSVMCALEmSJBUwIEuSJEkFDMiSJElSAQOyJEmSVKDOAnJE3BgR0yLirYJ5l0TEOxHxRkTcFxGdC5adExHjI2JsROxRMH/P/LzxEXF2XdUrSZIkQd2Og3wzcCVwS8G8R4FzUkpVEfF74BzgrIgYDBwKbACsDjwWEYPy97kK2A2YBLwaEUNTSm/XYd0rpf/ZD2ZdQr374OJ9si5BkiSp6OqsBTml9Azw2WLzHkkpVeUnXwL65m8fANyZUqpIKb0PjAe+kf8Zn1J6L6VUCdyZX1eSJEmqE1leSe9o4K787T7kAvNCk/LzACYuNn/IkjYWEccDxwP069evqIWuiObQqtocW8slSVLzkclJehFxLlAF3FasbaaUrk0plaeUynv0+NoltSVJkqRaqfcW5Ig4EtgX2CWllPKzJwNrFKzWNz+PZcyXJEmSiq5eW5AjYk/gTGD/lNK8gkVDgUMjolVEDAAGAq8ArwIDI2JARJSRO5FvaH3WLEmSpOalzlqQI+IOYCege0RMAs4nN2pFK+DRiAB4KaV0QkppdETcDbxNruvFT1JK1fntnAQMA0qBG1NKo+uqZkmSJKnOAnJK6bAlzL5hGetfBFy0hPkPAQ8VsTRJkiRpqbySniRJklTAgCxJkiQVMCBLkiRJBQzIkiRJUgEDsiRJklTAgCxJkiQVMCBLkiRJBQzIkiRJUgEDsiRJklTAgCxJkiQVMCBLkiRJBQzIkiRJUgEDsiRJklTAgCxJkiQVMCBLkiRJBQzIkiRJUgEDsiRJklTAgCxJkiQVMCBLkiRJBQzIkiRJUgEDsiRJklTAgCxJkiQVqLOAHBE3RsS0iHirYF7XiHg0It7N/+6Snx8R8eeIGB8Rb0TE5gX3OSK//rsRcURd1StJkiQBtKjDbd8MXAncUjDvbODxlNLFEXF2fvosYC9gYP5nCHA1MCQiugLnA+VAAkZExNCU0ud1WLekZqD/2Q9mXUImPrh4n6xLkKQGr85akFNKzwCfLTb7AODv+dt/Bw4smH9LynkJ6BwRvYE9gEdTSp/lQ/GjwJ51VbMkSZJUly3IS9IrpfRJ/vYUoFf+dh9gYsF6k/Lzljb/ayLieOB4gH79+hWxZElNWXNpUW2uLeaStDIyO0kvpZTIdZso1vauTSmVp5TKe/ToUazNSpIkqZmp74A8Nd91gvzvafn5k4E1Ctbrm5+3tPmSJElSnajvgDwUWDgSxRHA/QXzf5gfzWIrYGa+K8YwYPeI6JIf8WL3/DxJkiSpTtRZH+SIuAPYCegeEZPIjUZxMXB3RBwDfAgckl/9IWBvYDwwDzgKIKX0WUT8Bng1v96vU0qLn/gnSZIkFU2dBeSU0mFLWbTLEtZNwE+Wsp0bgRuLWJokSZK0VF5JT5IkSSpgQJYkSZIKGJAlSZKkAgZkSZIkqYABWZIkSSpgQJYkSZIKGJAlSZKkAgZkSZIkqYABWZIkSSpQZ1fSkyQ1PP3PfjDrEurNBxfvk3UJkhopW5AlSZKkArYgS1Iz0JxaU5tTK7mkumELsiRJklTAgCxJkiQVMCBLkiRJBQzIkiRJUgEDsiRJklTAgCxJkiQVMCBLkiRJBQzIkiRJUgEDsiRJklTAgCxJkiQVyCQgR8TPImJ0RLwVEXdEROuIGBARL0fE+Ii4KyLK8uu2yk+Pzy/vn0XNkiRJah7qPSBHRB/gFKA8pbQhUAocCvweuCyltA7wOXBM/i7HAJ/n51+WX0+SJEmqE1l1sWgBtImIFkBb4BPgm8C9+eV/Bw7M3z4gP01++S4REfVXqiRJkpqTeg/IKaXJwB+Bj8gF45nACOCLlFJVfrVJQJ/87T7AxPx9q/Lrd6vPmiVJktR8ZNHFogu5VuEBwOpAO2DPImz3+IgYHhHDp0+fvqqbkyRJUjOVRReLXYH3U0rTU0oLgH8B2wKd810uAPoCk/O3JwNrAOSXdwI+XXyjKaVrU0rlKaXyHj161PXfIEmSpCYqi4D8EbBVRLTN9yXeBXgbeBI4OL/OEcD9+dtD89Pklz+RUkr1WK8kSZKakSz6IL9M7mS714A38zVcC5wFnBYR48n1Mb4hf5cbgG75+acBZ9d3zZIkSWo+Wix/leJLKZ0PnL/Y7PeAbyxh3fnAd+qjLkmSJMkr6UmSJEkFDMiSJElSAQOyJEmSVMCALEmSJBUwIEuSJEkFDMiSJElSAQOyJEmSVMCALEmSJBWoVUCOiG1rM0+SJElq7GrbgvyXWs6TJEmSGrVlXmo6IrYGtgF6RMRpBYs6AqV1WZgkSZKUhWUGZKAMaJ9fr0PB/FnAwXVVlCRJkpSVZQbklNLTwNMRcXNK6cN6qkmSJEnKzPJakBdqFRHXAv0L75NS+mZdFCVJkiRlpbYB+R7gb8D1QHXdlSNJkiRlq7YBuSqldHWdViJJkiQ1ALUd5u0/EXFiRPSOiK4Lf+q0MkmSJCkDtW1BPiL/+4yCeQlYq7jlSJIkqf/ZD2ZdQr374OJ9si5hkVoF5JTSgLouRJIkSWoIahWQI+KHS5qfUrqluOVIkiRpoYbUqlpXGmJreW27WGxZcLs1sAvwGmBAliRJUpNS2y4WJxdOR0Rn4M66KEiSJEnKUm1HsVjcXMB+yZIkSWpyatsH+T/kRq0AKAXWB+5e2Z3mW6CvBzbMb/doYCxwF7mr9X0AHJJS+jwiArgC2BuYBxyZUnptZfctSZIkLUtt+yD/seB2FfBhSmnSKuz3CuC/KaWDI6IMaAv8HHg8pXRxRJwNnA2cBewFDMz/DAGuzv+WJEmSiq5WXSxSSk8D7wAdgC5A5cruMCI6ATsAN+S3XZlS+gI4APh7frW/Awfmbx8A3JJyXgI6R0Tvld2/JEmStCy1CsgRcQjwCvAd4BDg5Yg4eCX3OQCYDtwUESMj4vqIaAf0Sil9kl9nCtArf7sPMLHg/pPy8xav8fiIGB4Rw6dPn76SpUmSJKm5q+1JeucCW6aUjkgp/RD4BvCLldxnC2Bz4OqU0mbkTvg7u3CFlFLif32eayWldG1KqTylVN6jR4+VLE2SJEnNXW0DcklKaVrB9KcrcN/FTQImpZRezk/fSy4wT13YdSL/e+H+JgNrFNy/b36eJEmSVHS1PUnvvxExDLgjP/1d4KGV2WFKaUpETIyIdVNKY8lddOTt/M8RwMX53/fn7zIUOCki7iR3ct7Mgq4YkiSpGWiIV1tT07XMgBwR65DrG3xGRHwb2C6/6EXgtlXY78nAbfkRLN4DjiLXIn13RBwDfEiurzPkgvjewHhyw7wdtQr7lSRJkpZpeS3IlwPnAKSU/gX8CyAiNsov229ldppSeh0oX8KiXZawbgJ+sjL7kSSpKWuOraofXLxP1iWoGVheP+JeKaU3F5+Zn9e/TiqSJEmSMrS8FuTOy1jWpoh1SJKklWSrqlRcy2tBHh4Rxy0+MyKOBUbUTUmSJElSdpbXgnwqcF9EfJ//BeJyoAz4Vh3WJUmSJGVimQE5pTQV2CYidgY2zM9+MKX0RJ1XJkmSJGWgVuMgp5SeBJ6s41okSZKkzK3s1fAkSZKkJqm2V9KT1Aw0xzFVJUlanC3IkiRJUgFbkCV9jWOqSpKaM1uQJUmSpAIGZEmSJKmAAVmSJEkqYECWJEmSChiQJUmSpAIGZEmSJKmAAVmSJEkq4DjIkqQmyStDSlpZtiBLkiRJBWxBliQ1KV4JUtKqsgVZkiRJKmBAliRJkgpkFpAjojQiRkbEA/npARHxckSMj4i7IqIsP79Vfnp8fnn/rGqWJElS05dlC/JPgTEF078HLksprQN8DhyTn38M8Hl+/mX59SRJkqQ6kUlAjoi+wD7A9fnpAL4J3Jtf5e/AgfnbB+SnyS/fJb++JEmSVHRZtSBfDpwJ1OSnuwFfpJSq8tOTgD75232AiQD55TPz639FRBwfEcMjYvj06dPrsHRJkiQ1ZfUekCNiX2BaSmlEMbebUro2pVSeUirv0aNHMTctSZKkZiSLcZC3BfaPiL2B1kBH4Aqgc0S0yLcS9wUm59efDKwBTIqIFkAn4NP6L1uSJEnNQb23IKeUzkkp9U0p9QcOBZ5IKX0feBI4OL/aEcD9+dtD89Pklz+RUkr1WLIkSZKakYY0DvJZwGkRMZ5cH+Mb8vNvALrl558GnJ1RfZIkSWoGMr3UdErpKeCp/O33gG8sYZ35wHfqtTBJkiQ1Ww2pBVmSJEnKnAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJkiSpgAFZkiRJKlDvATki1oiIJyPi7YgYHRE/zc/vGhGPRsS7+d9d8vMjIv4cEeMj4o2I2Ly+a5YkSVLzkUULchVwekppMLAV8JOIGAycDTyeUhoIPJ6fBtgLGJj/OR64uv5LliRJUnNR7wE5pfRJSum1/O3ZwBigD3AA8Pf8an8HDszfPgC4JeW8BHSOiN71W7UkSZKai0z7IEdEf2Az4GWgV0rpk/yiKUCv/O0+wMSCu03Kz1t8W8dHxPCIGD59+vS6K1qSJElNWmYBOSLaA/8ETk0pzSpcllJKQFqR7aWUrk0plaeUynv06FHESiVJktScZBKQI6IluXB8W0rpX/nZUxd2ncj/npafPxlYo+DuffPzJEmSpKLLYhSLAG4AxqSU/lSwaChwRP72EcD9BfN/mB/NYitgZkFXDEmSJKmoWmSwz22BHwBvRsTr+Xk/By4G7o6IY4APgUPyyx4C9gbGA/OAo+q1WkmSJDUr9R6QU0rPAbGUxbssYf0E/KROi5IkSZLyvJKeJEmSVMCALEmSJBUwIEuSJEkFsjhJT2pU+p/9YNYlSJKkemQLsiRJklTAFmSplj64eJ+sS5AkSfXAFmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJkiSpQKMJyBGxZ0SMjYjxEXF21vVIkiSpaWoUATkiSoGrgL2AwcBhETE426okSZLUFLXIuoBa+gYwPqX0HkBE3AkcALydaVXNXP+zH8y6BEmSpKJrLAG5DzCxYHoSMKRwhYg4Hjg+PzknIsbWU22FusfvmZHBflX3PLZNU3fwuDZRHtumyePadGX1ObvmkmY2loC8XCmla4Frs6whIoanlMqzrEF1w2PbNHlcmy6PbdPkcW26GtqxbRR9kIHJwBoF033z8yRJkqSiaiwB+VVgYEQMiIgy4FBgaMY1SZIkqQlqFF0sUkpVEXESMAwoBW5MKY3OuKwlybSLh+qUx7Zp8rg2XR7bpsnj2nQ1qGMbKaWsa5AkSZIajMbSxUKSJEmqFwZkSZIkqYABeSUs77LXEdEqIu7KL385IvpnUKZWUC2O62kR8XZEvBERj0fEEsdOVMNT20vVR8RBEZEiosEMNaSlq81xjYhD8q/b0RFxe33XqJVTi/fjfhHxZESMzL8n751FnVoxEXFjREyLiLeWsjwi4s/54/5GRGxe3zUuZEBeQbW87PUxwOcppXWAy4Df12+VWlG1PK4jgfKU0sbAvcAf6rdKrYzaXqo+IjoAPwVert8KtTJqc1wjYiBwDrBtSmkD4NT6rlMrrpav2fOAu1NKm5Eb2eqv9VulVtLNwJ7LWL4XMDD/czxwdT3UtEQG5BW36LLXKaVKYOFlrwsdAPw9f/teYJeIiHqsUStuucc1pfRkSmlefvIlcuNxq+GrzWsW4Dfk/pmdX5/FaaXV5rgeB1yVUvocIKU0rZ5r1MqpzbFNQMf87U7Ax/VYn1ZSSukZ4LNlrHIAcEvKeQnoHBG966e6rzIgr7glXfa6z9LWSSlVATOBbvVSnVZWbY5roWOAh+u0IhXLco9t/mu8NVJKD9ZnYVoltXnNDgIGRcTzEfFSRCyr5UoNR22O7QXA4RExCXgIOLl+SlMdW9HP4jrTKMZBlhqSiDgcKAd2zLoWrbqIKAH+BByZcSkqvhbkvqrdidw3Ps9ExEYppS+yLEpFcRhwc0rp0ojYGvhHRGyYUqrJujA1DbYgr7jaXPZ60ToR0YLc1z+f1kt1Wlm1upx5ROwKnAvsn1KqqKfatGqWd2w7ABsCT0XEB8BWwFBP1GvwavOanQQMTSktSCm9D4wjF5jVsNXm2B4D3A2QUnoRaA10r5fqVJdq9VlcHwzIK642l70eChyRv30w8ETyiiwN3XKPa0RsBlxDLhzbl7HxWOaxTSnNTCl1Tyn1Tyn1J9e/fP+U0vBsylUt1ea9+N/kWo+JiO7kuly8V481auXU5th+BOwCEBHrkwvI0+u1StWFocAP86NZbAXMTCl9kkUhdrFYQUu77HVE/BoYnlIaCtxA7uue8eQ6ox+aXcWqjVoe10uA9sA9+XMuP0op7Z9Z0aqVWh5bNTK1PK7DgN0j4m2gGjgjpeS3eQ1cLY/t6cB1EfEzcifsHWlDVMMXEXeQ+6e1e77/+PlAS4CU0t/I9SffGxgPzAOOyqZSLzUtSZIkfYVdLCRJkqQCBmRJkiSpgAFZkiRJKmBAliRJkgoYkCVJkqQCBmRJWkxEpIi4tWC6RURMj4gHsqxrRUXEB/nxf4mIF5az7pERsfoKbr9/RLy1KjUWczuSVCwGZEn6urnAhhHRJj+9GxldzWlx+atzrrCU0jbLWeVIYIUCsiQ1VQZkSVqyh4B98rcPA+5YuCAi2kXEjRHxSkSMjIgD8vP7R8SzEfFa/meb/PydIuKpiLg3It6JiNsif7WZQvl1roiI1yPirYj4Rn7+BRHxj4h4ntxFiHpExD8j4tX8z7b59bpFxCMRMToirgeiYNtzCm6fFRFvRsSoiLg4Ig4GyoHb8vtuExFbRMTTETEiIoZFRO/8fbfI328U8JMlPXARcWdE7FMwfXNEHLy0x2ex+x4ZEVcWTD8QETvlb+8eES/m73tPRLRf1gGUpJVlQJakJbsTODQiWgMbAy8XLDuX3CXkvwHsDFwSEe2AacBuKaXNge8Cfy64z2bAqcBgYC1g26Xst21KaVPgRODGgvmDgV1TSocBVwCXpZS2BA4Crs+vcz7wXEppg/9v735CrCrjMI5/H0WTIp0Sl4WLKKlA+0MplbWQajYFJgVJxNCmzQSFSxeJtCjchBauMmhhQhAIljktYkiKGEomFFwpIUkEpUg5hfa0OL8Lx8s54x0hXPR8Vuf8zvvvvIvLe3/nvfcAnwK3DzcuaRx4FnjY9lrgHdufADPA1ur7ErAb2GL7gRrHW9XEPmCy6vY5ADxf/S2leSXwoavMz7xqq8j2moP7a7xvjFo/ImIh8qrpiIgOtmclrabJHn82dPlJ4BlJ2+p8Gc1i9Gdgj6R1NK82vrNV5zvbZwAkHQNWA193dL2/+p+WtFzSWMUP2r5Yx5uAu1tJ6OWVTd0IbK76hyT93tH+JmCf7T+r3G8dZe4C7gWmqo/FwNkay5jt6Sr3ETDeUf9z4F1JNwBPA9O2L0paQf/8XM16mi8JR2tMS4FvFlA/ImJkWSBHRPQ7COwCngBWtuICnrN9sl1Y0pvAL8Bamid0c63Lf7WOL9P/+eue8z9asUXAetvt9unYtXGtBBy3vWGo/bFRKtuek/QV8BRNpvjjuvQ6/fMzcIkrn24ua41pqjLoERH/qWyxiIjo9wGww/aPQ/EvgMnBPmJJ91V8BXDW9j/ASzSZ14V6odp8FDhv+3xHmSPA5OCkMrIA08CLFRsHbumoOwVMSLqxyt1a8QvAzXV8ElglaUOVWSLpHtvngHM1NoCt89zHAWACeAw4XLFR5uc0sE7SIkm3AQ9V/FvgEUl31JhukrSQDHRExMiyQI6I6GH7jO2ufbI7gSXArKTjdQ7wPvBy/YBtDVdmfUc1J+kHYC/wSk+Z14AHJc1KOgG8WvEdwMYa02bgp457OkyTGZ+prR6DbSIfAnsrthjYArxd93IMGPygbgJ4r8rNl7I+AjwOfGn774qNMj9HgVPACZo9yt/XuH+l+aeN/ZJmabZXrJmn/4iIayZ7+GleRERcD7UtYZvtmes9loiI/7NkkCMiIiIiWpJBjoiIiIhoSQY5IiIiIqIlC+SIiIiIiJYskCMiIiIiWrJAjoiIiIhoyQI5IiIiIqLlX/bSlLoUgwpZAAAAAElFTkSuQmCC\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n_cv = 2\n", - "for clf_name, clf in clf_models.items():\n", - " if \"weighted_loss\" in clf_name:\n", - " continue\n", - " tmp_y = []\n", - " tmp_pred = []\n", - " for cv in range(n_cv):\n", - " tmp_y.append(clf.eval_result[\"y\"][cv])\n", - " tmp_pred.append(clf.eval_result[\"proba\"][cv])\n", - " print(clf_name)\n", - " y_test = np.array(tmp_y).flatten()\n", - " y_pred = np.array(tmp_pred).flatten()\n", - " plot_roc_auc_curve(y_test, y_pred, clf_name)\n", - " plot_calibration_curve(y_test, y_pred, clf_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c71fc7e2", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "cec92dcb", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/quickstart/balanced-ope-stochastic-evaluation-policy.ipynb b/examples/quickstart/balanced-ope-stochastic-evaluation-policy.ipynb deleted file mode 100644 index 335a5c47..00000000 --- a/examples/quickstart/balanced-ope-stochastic-evaluation-policy.ipynb +++ /dev/null @@ -1,1258 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "id": "8bf89cee", - "metadata": {}, - "outputs": [], - "source": [ - "from pathlib import Path\n", - "import yaml\n", - "\n", - "import numpy as np\n", - "import pandas as pd\n", - "from sklearn.linear_model import LogisticRegression\n", - "from sklearn.ensemble import RandomForestClassifier\n", - "from sklearn.neural_network import MLPClassifier as MLP\n", - "from sklearn.svm import SVC" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "id": "09ea0e58", - "metadata": {}, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt\n", - "import seaborn as sns" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "id": "56290a90", - "metadata": {}, - "outputs": [], - "source": [ - "from obp.dataset import (\n", - " SyntheticBanditDataset,\n", - " logistic_reward_function,\n", - " linear_behavior_policy,\n", - ")\n", - "\n", - "from obp.policy import IPWLearner\n", - "from obp.ope import (\n", - " OffPolicyEvaluation,\n", - " RegressionModel,\n", - " InverseProbabilityWeighting as IPS,\n", - " SelfNormalizedInverseProbabilityWeighting as SNIPS,\n", - " DirectMethod as DM,\n", - " DoublyRobust as DR,\n", - " DoublyRobustWithShrinkage as DRos,\n", - " BalancedInverseProbabilityWeighting as BIPW,\n", - " ImportanceWeightEstimator,\n", - " PropensityScoreEstimator\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "id": "b6c2fcfe", - "metadata": {}, - "outputs": [], - "source": [ - "with open (\"../../obp/dataset/hyperparams.yaml\", \"rb\") as f:\n", - " hyperparams = yaml.safe_load(f)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "ca19277c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'lightgbm': {'n_estimators': 100,\n", - " 'learning_rate': 0.01,\n", - " 'max_depth': 5,\n", - " 'min_samples_leaf': 10,\n", - " 'random_state': 12345},\n", - " 'random_forest': {'n_estimators': 100,\n", - " 'max_depth': 5,\n", - " 'min_samples_leaf': 10,\n", - " 'random_state': 12345},\n", - " 'ridge': {'alpha': 0.2, 'random_state': 12345},\n", - " 'svc': {'gamma': 2, 'C': 1, 'probability': True, 'random_state': 12345}}" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "hyperparams" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "id": "f573a4da", - "metadata": {}, - "outputs": [], - "source": [ - "from warnings import simplefilter\n", - "from sklearn.exceptions import ConvergenceWarning\n", - "simplefilter(\"ignore\", category=ConvergenceWarning)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "id": "789d8aaa", - "metadata": {}, - "outputs": [], - "source": [ - "from tqdm import tqdm_notebook as tqdm" - ] - }, - { - "cell_type": "markdown", - "id": "c2373011", - "metadata": {}, - "source": [ - "## (1) Generate synthetic data" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "f15ba763", - "metadata": {}, - "outputs": [], - "source": [ - "# define a dataset class\n", - "n_actions = 10\n", - "dim_context = 8\n", - "len_list = 1\n", - "random_state = 12345\n", - "dataset = SyntheticBanditDataset(\n", - " n_actions=n_actions,\n", - " dim_context=dim_context,\n", - " beta=0.2,\n", - " reward_function=logistic_reward_function,\n", - " behavior_policy_function=linear_behavior_policy,\n", - " random_state=random_state,\n", - ")\n", - "\n", - "# training data is used to train an evaluation policy\n", - "train_bandit_data = dataset.obtain_batch_bandit_feedback(n_rounds=5000)\n", - "\n", - "# test bandit data is used to approximate the ground-truth policy value\n", - "test_bandit_data = dataset.obtain_batch_bandit_feedback(n_rounds=100000)" - ] - }, - { - "cell_type": "markdown", - "id": "9c6bbb96", - "metadata": {}, - "source": [ - "## (2) Off-Policy Learning (OPL)" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "id": "ce6362b2", - "metadata": {}, - "outputs": [], - "source": [ - "# evaluation policy training\n", - "ipw_learner = IPWLearner(\n", - " n_actions=dataset.n_actions,\n", - " base_classifier=RandomForestClassifier(**hyperparams[\"random_forest\"]),\n", - ")\n", - "ipw_learner.fit(\n", - " context=train_bandit_data[\"context\"],\n", - " action=train_bandit_data[\"action\"],\n", - " reward=train_bandit_data[\"reward\"],\n", - " pscore=train_bandit_data[\"pscore\"],\n", - ")\n", - "\n", - "\n", - "tau = 0.01\n", - "\n", - "action_dist_ipw_train = ipw_learner.predict_proba(\n", - " context=train_bandit_data[\"context\"], tau=tau\n", - ")\n", - "action_dist_ipw_test = ipw_learner.predict_proba(\n", - " context=test_bandit_data[\"context\"], tau=tau\n", - ")\n", - "policy_value_of_ipw = dataset.calc_ground_truth_policy_value(\n", - " expected_reward=test_bandit_data[\"expected_reward\"],\n", - " action_dist=action_dist_ipw_test,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "695bd1d1", - "metadata": {}, - "outputs": [], - "source": [ - "num_data = 3000\n", - "\n", - "validation_bandit_data = dataset.obtain_batch_bandit_feedback(n_rounds=num_data)\n", - "\n", - "# make decisions on validation data\n", - "action_dist_ipw_val = ipw_learner.predict_proba(\n", - " context=validation_bandit_data[\"context\"], tau=tau\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "1801b32d", - "metadata": {}, - "source": [ - "## (3) Off-Policy Evaluation (OPE)" - ] - }, - { - "cell_type": "markdown", - "id": "24bd1203", - "metadata": {}, - "source": [ - "### (3-1) Obtaining a reward estimator" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "395fdc4a", - "metadata": {}, - "outputs": [], - "source": [ - "# OPE using validation data\n", - "regression_model = RegressionModel(\n", - " n_actions=dataset.n_actions,\n", - " base_model=RandomForestClassifier(**hyperparams[\"random_forest\"]),\n", - ")\n", - "estimated_rewards = regression_model.fit_predict(\n", - " context=validation_bandit_data[\"context\"], # context; x\n", - " action=validation_bandit_data[\"action\"], # action; a\n", - " reward=validation_bandit_data[\"reward\"], # reward; r\n", - " n_folds=2, # 2-fold cross fitting\n", - " random_state=12345,\n", - ")" - ] - }, - { - "cell_type": "markdown", - "id": "92d1f1da", - "metadata": {}, - "source": [ - "### (3-2) Evaluation by existing OPE estimators" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "c42e64d2", - "metadata": {}, - "outputs": [], - "source": [ - "classification_model_action = PropensityScoreEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " base_model=RandomForestClassifier(**hyperparams[\"random_forest\"]),\n", - " calibration_cv=2\n", - ")\n", - "\n", - "estimated_pscore = classification_model_action.fit_predict(\n", - " action=validation_bandit_data[\"action\"],\n", - " position=validation_bandit_data[\"position\"],\n", - " context=validation_bandit_data[\"context\"],\n", - " n_folds=2,\n", - " evaluate_model_performance=True,\n", - " random_state=random_state,\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "de671cea", - "metadata": {}, - "outputs": [], - "source": [ - "ope = OffPolicyEvaluation(\n", - " bandit_feedback=validation_bandit_data,\n", - " ope_estimators=[\n", - " IPS(estimator_name=\"IPS\"),\n", - " DM(estimator_name=\"DM\"),\n", - " IPS(lambda_=100, estimator_name=\"CIPS\"),\n", - " SNIPS(estimator_name=\"SNIPS\"),\n", - " DR(estimator_name=\"DR\"),\n", - " DRos(lambda_=500, estimator_name=\"DRos\"),\n", - " IPS(\n", - " lambda_=100,\n", - " estimator_name=\"CIPS_Estimated_Pscore\",\n", - " use_estimated_pscore=True,\n", - " ),\n", - " SNIPS(estimator_name=\"SNIPS_Estimated_Pscore\", use_estimated_pscore=True),\n", - " DR(estimator_name=\"DR_Estimated_Pscore\", use_estimated_pscore=True),\n", - " DRos(\n", - " lambda_=500,\n", - " estimator_name=\"DRos_Estimated_Pscore\",\n", - " use_estimated_pscore=True,\n", - " ),\n", - " ],\n", - ")\n", - "\n", - "\n", - "squared_errors = ope.evaluate_performance_of_estimators(\n", - " ground_truth_policy_value=policy_value_of_ipw, # V(\\pi_e)\n", - " action_dist=action_dist_ipw_val, # \\pi_e(a|x)\n", - " estimated_rewards_by_reg_model=estimated_rewards, # \\hat{q}(x,a)\n", - " estimated_pscore=estimated_pscore,\n", - " metric=\"se\", # squared error\n", - ")\n", - "\n", - "ope_result = ope.summarize_off_policy_estimates(\n", - " action_dist=action_dist_ipw_val, # \\pi_e(a|x)\n", - " estimated_rewards_by_reg_model=estimated_rewards, # \\hat{q}(x,a)\n", - " estimated_pscore=estimated_pscore,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "059fc7d0", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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estimated_policy_valuerelative_estimated_policy_value
IPS0.6548831.327466
DM0.5203651.054794
CIPS0.6548831.327466
SNIPS0.6624891.342884
DR0.6652881.348557
DRos0.6538601.325393
CIPS_Estimated_Pscore0.6605361.338924
SNIPS_Estimated_Pscore0.6645961.347154
DR_Estimated_Pscore0.6685051.355077
DRos_Estimated_Pscore0.6559271.329582
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" - ], - "text/plain": [ - " estimated_policy_value \\\n", - "IPS 0.654883 \n", - "DM 0.520365 \n", - "CIPS 0.654883 \n", - "SNIPS 0.662489 \n", - "DR 0.665288 \n", - "DRos 0.653860 \n", - "CIPS_Estimated_Pscore 0.660536 \n", - "SNIPS_Estimated_Pscore 0.664596 \n", - "DR_Estimated_Pscore 0.668505 \n", - "DRos_Estimated_Pscore 0.655927 \n", - "\n", - " relative_estimated_policy_value \n", - "IPS 1.327466 \n", - "DM 1.054794 \n", - "CIPS 1.327466 \n", - "SNIPS 1.342884 \n", - "DR 1.348557 \n", - "DRos 1.325393 \n", - "CIPS_Estimated_Pscore 1.338924 \n", - "SNIPS_Estimated_Pscore 1.347154 \n", - "DR_Estimated_Pscore 1.355077 \n", - "DRos_Estimated_Pscore 1.329582 " - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ope_result[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "b80307a6", - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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mean95.0% CI (lower)95.0% CI (upper)
IPS0.6573990.5970840.717763
DM0.5204450.5180910.523028
CIPS0.6565860.5951230.703077
SNIPS0.6655410.5945690.730511
DR0.6598280.6263130.698387
DRos0.6539900.6241780.687925
CIPS_Estimated_Pscore0.6625790.5863840.726416
SNIPS_Estimated_Pscore0.6674510.6121320.722384
DR_Estimated_Pscore0.6710460.6387010.707440
DRos_Estimated_Pscore0.6584550.6225660.687160
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" - ], - "text/plain": [ - " mean 95.0% CI (lower) 95.0% CI (upper)\n", - "IPS 0.657399 0.597084 0.717763\n", - "DM 0.520445 0.518091 0.523028\n", - "CIPS 0.656586 0.595123 0.703077\n", - "SNIPS 0.665541 0.594569 0.730511\n", - "DR 0.659828 0.626313 0.698387\n", - "DRos 0.653990 0.624178 0.687925\n", - "CIPS_Estimated_Pscore 0.662579 0.586384 0.726416\n", - "SNIPS_Estimated_Pscore 0.667451 0.612132 0.722384\n", - "DR_Estimated_Pscore 0.671046 0.638701 0.707440\n", - "DRos_Estimated_Pscore 0.658455 0.622566 0.687160" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "ope_result[1]" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "25478c4c", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.6764851625734692" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# true policy value\n", - "policy_value_of_ipw" - ] - }, - { - "cell_type": "markdown", - "id": "4d0c3e64", - "metadata": {}, - "source": [ - "### (3-3) Balanced-OPE" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "c4ed747e", - "metadata": {}, - "outputs": [], - "source": [ - "bipw = BIPW(\n", - " estimator_name=\"BIPW\", lambda_=np.inf\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "e530d74f", - "metadata": {}, - "outputs": [], - "source": [ - "clf_models = {\n", - " \"random_forest_default_raw\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"raw\",\n", - " base_model=RandomForestClassifier(random_state=random_state),\n", - " ),\n", - " \"random_forest_raw\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"raw\",\n", - " base_model=RandomForestClassifier(**hyperparams[\"random_forest\"]),\n", - " ),\n", - " \"random_forest_sample\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"sample\",\n", - " base_model=RandomForestClassifier(**hyperparams[\"random_forest\"]),\n", - " ),\n", - " \"svc_raw\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"raw\",\n", - " base_model=SVC(**hyperparams[\"svc\"]),\n", - " ),\n", - " \"svc_sample\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"sample\",\n", - " base_model=SVC(**hyperparams[\"svc\"]),\n", - " ),\n", - " \"MLP_raw\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"raw\",\n", - " base_model=MLP(random_state=random_state),\n", - " ),\n", - " \"MLP_sample\": ImportanceWeightEstimator(\n", - " len_list=len_list,\n", - " n_actions=n_actions,\n", - " fitting_method=\"sample\",\n", - " base_model=MLP(random_state=random_state),\n", - " ),\n", - "}" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "4ada448c", - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - ":2: TqdmDeprecationWarning: This function will be removed in tqdm==5.0.0\n", - "Please use `tqdm.notebook.tqdm` instead of `tqdm.tqdm_notebook`\n", - " for clf_name, clf in tqdm(clf_models.items()):\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5dbf273cc30b4737a69e65b75346376b", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - " 0%| | 0/7 [00:00" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(14, 10))\n", - "(ope_expected_values - policy_value_of_ipw).plot.bar()\n", - "plt.hlines(y=0., xmin=-1, xmax=len(ope_expected_values), color=\"black\", linestyles=\"--\")" - ] - }, - { - "cell_type": "markdown", - "id": "91eae584", - "metadata": {}, - "source": [ - "### (4-2) Summarize" - ] - }, - { - "cell_type": "code", - "execution_count": 24, - "id": "759272be", - "metadata": {}, - "outputs": [], - "source": [ - "ope_add_bipw = OffPolicyEvaluation(\n", - " bandit_feedback=validation_bandit_data,\n", - " ope_estimators=[\n", - " IPS(estimator_name=\"IPS\"),\n", - " DM(estimator_name=\"DM\"),\n", - " IPS(lambda_=100, estimator_name=\"CIPS\"),\n", - " SNIPS(estimator_name=\"SNIPS\"),\n", - " DR(estimator_name=\"DR\"),\n", - " DRos(lambda_=500, estimator_name=\"DRos\"),\n", - " IPS(\n", - " lambda_=100,\n", - " estimator_name=\"CIPS_Estimated_Pscore\",\n", - " use_estimated_pscore=True,\n", - " ),\n", - " SNIPS(estimator_name=\"SNIPS_Estimated_Pscore\", use_estimated_pscore=True),\n", - " DR(estimator_name=\"DR_Estimated_Pscore\", use_estimated_pscore=True),\n", - " DRos(\n", - " lambda_=500,\n", - " estimator_name=\"DRos_Estimated_Pscore\",\n", - " use_estimated_pscore=True,\n", - " ),\n", - " BIPW(estimator_name=\"BIPW_rf_raw\", lambda_=np.inf),\n", - " BIPW(estimator_name=\"BIPW_rf_sample\", lambda_=np.inf),\n", - " BIPW(estimator_name=\"BIPW_rf_sample_clip\", lambda_=10.0),\n", - " ],\n", - ")\n" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "id": "ff6982b4", - "metadata": {}, - "outputs": [], - "source": [ - "bipw_value = {\n", - " \"BIPW_rf_raw\": balancing_weight_dict[\"random_forest_raw\"],\n", - " \"BIPW_rf_sample\": balancing_weight_dict[\"random_forest_sample\"],\n", - " \"BIPW_rf_sample_clip\": balancing_weight_dict[\"random_forest_sample\"],\n", - "}\n", - "\n", - "squared_errors = ope_add_bipw.evaluate_performance_of_estimators(\n", - " ground_truth_policy_value=policy_value_of_ipw, # V(\\pi_e)\n", - " action_dist=action_dist_ipw_val, # \\pi_e(a|x)\n", - " estimated_rewards_by_reg_model=estimated_rewards, # \\hat{q}(x,a)\n", - " estimated_pscore=estimated_pscore,\n", - " estimated_importance_weights=bipw_value,\n", - " metric=\"se\", # squared error\n", - ")\n", - "\n", - "ope_add_bipw_res = ope_add_bipw.summarize_off_policy_estimates(\n", - " action_dist=action_dist_ipw_val, # \\pi_e(a|x)\n", - " estimated_rewards_by_reg_model=estimated_rewards, # \\hat{q}(x,a)\n", - " estimated_pscore=estimated_pscore,\n", - " estimated_importance_weights=bipw_value,\n", - ")" - ] - }, - { - "cell_type": "code", - "execution_count": 26, - "id": "9690370a", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "fig = plt.figure(figsize=(14, 10))\n", - "(ope_add_bipw_res[0][\"estimated_policy_value\"] - policy_value_of_ipw).plot.bar()\n", - "plt.hlines(y=0., xmin=-1, xmax=len(ope_add_bipw_res[0][\"estimated_policy_value\"]), color=\"black\", linestyles=\"--\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fc74cf7b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "34a450f3", - "metadata": {}, - "source": [ - "### (4-3) Classification model visualization" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "id": "4019b5b9", - "metadata": {}, - "outputs": [], - "source": [ - "from sklearn.calibration import calibration_curve\n", - "from sklearn.metrics import roc_auc_score, roc_curve" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "id": "40656837", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_calibration_curve(y_test, y_pred, name):\n", - " \"\"\"Plot calibration curve for est w/o and with calibration. \"\"\"\n", - " fig = plt.figure(figsize=(10, 10))\n", - " ax1 = plt.subplot2grid((3, 1), (0, 0), rowspan=2)\n", - " ax2 = plt.subplot2grid((3, 1), (2, 0))\n", - "\n", - " ax1.plot([0, 1], [0, 1], \"k:\", label=\"Perfectly calibrated\")\n", - " fraction_of_positives, mean_predicted_value = \\\n", - " calibration_curve(y_test, y_pred, n_bins=10)\n", - " ax1.plot(mean_predicted_value, fraction_of_positives, \"s-\")\n", - "\n", - " ax2.hist(y_pred, range=(0, 1), bins=10,\n", - " histtype=\"step\", lw=2)\n", - "\n", - " ax1.set_ylabel(\"Fraction of positives\")\n", - " ax1.set_ylim([-0.05, 1.05])\n", - " ax1.set_title(f'Calibration plots (reliability curve): {name}')\n", - "\n", - " ax2.set_xlabel(\"Mean predicted value\")\n", - " ax2.set_ylabel(\"Count\")\n", - "\n", - " plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 29, - "id": "198d71e7", - "metadata": {}, - "outputs": [], - "source": [ - "def plot_roc_auc_curve(y_test, y_pred, name):\n", - " fig = plt.figure(figsize=(10, 5))\n", - " fpr, tpr, _ = roc_curve(y_test, y_pred)\n", - " auc = roc_auc_score(y_test, y_pred)\n", - " plt.plot(fpr,tpr,label=\"data 1, auc=%1.3f\" %auc)\n", - " plt.legend(loc=4)\n", - " plt.title(f\"ROC AUC curve: {name}\")\n", - " plt.tight_layout()" - ] - }, - { - "cell_type": "code", - "execution_count": 30, - "id": "b09e72ec", - "metadata": { - "scrolled": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "random_forest_default_raw\n", - "random_forest_raw\n", - "random_forest_sample\n", - "svc_raw\n", - "svc_sample\n", - "MLP_raw\n", - "MLP_sample\n" - ] - }, - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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Aro+IAzs3yswZmdmeme2DBg2q06ElSZKk+qklIK8EhhTLbdV1pQ5gTma+kZlPA7+gEpglSZKk3UotAXk+MDIihkfEPsBkYE6nNrOpjB4TEQOpTLlYWr8yJUmSpMboNiBn5kZgCnAP8DhwW2Y+FhGXR8TEarN7gDURsRi4D7gwM9fsqqIlSZKkXaXby7wBZOZcYG6ndZcWzxP46+pDkiRJ2m215J30JEmSpG0xIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVKgpIEfEKRHxREQsiYip22n3xxGREdFevxIlSZKkxuk2IEdEH+Aa4FRgFHBGRIzqol1/4PPAvHoXKUmSJDVKLSPIxwBLMnNpZr4O3AJM6qLdl4B/ADbUsT5JkiSpoWoJyIOBFcVyR3XdFhFxNDAkM+/e3o4i4tyIWBARC1atWtXjYiVJkqRdbadP0ouIvYCrgPO7a5uZMzKzPTPbBw0atLOHliRJkuquloC8EhhSLLdV123WHzgSuD8ilgHvA+Z4op4kSZJ2R7UE5PnAyIgYHhH7AJOBOZs3ZubLmTkwM4dl5jDgYWBiZi7YJRVLkiRJu1C3ATkzNwJTgHuAx4HbMvOxiLg8Iibu6gIlSZKkRupbS6PMnAvM7bTu0m20PWHny5IkSZKawzvpSZIkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEkFA7IkSZJUMCBLkiRJhZYLyLPmLWfe02ubXYYkSZJ6qZYLyHcuWgnApLGDm1yJJEmSeqOWC8gA44YP4OPjhja7DEmSJPVCLRmQJUmSpG0xIEuSJEkFA7IkSZJUMCBLkiRJBQOyJEmSVDAgS5IkSQUDsiRJklQwIEuSJEmFmgJyRJwSEU9ExJKImNrF9r+OiMUR8UhE3BsRv1f/UiVJkqRdr9uAHBF9gGuAU4FRwBkRMapTs58C7Zk5Grgd+Fq9C5UkSZIaoZYR5GOAJZm5NDNfB24BJpUNMvO+zHy1uvgw0FbfMiVJkqTGqCUgDwZWFMsd1XXb8ing+11tiIhzI2JBRCxYtWpV7VVKkiRJDVLXk/Qi4hNAO3BlV9szc0Zmtmdm+6BBg+p5aEmSJKku+tbQZiUwpFhuq67bSkScCHwB+EBm/ro+5UmSJEmNVcsI8nxgZEQMj4h9gMnAnLJBRBwFXAdMzMwX6l+mJEmS1BjdBuTM3AhMAe4BHgduy8zHIuLyiJhYbXYlsB/w7YhYFBFztrE7SZIkqVerZYoFmTkXmNtp3aXF8xPrXJckSZLUFN5JT5IkSSoYkCVJkqSCAVmSJEkqGJAlSZKkggFZkiRJKhiQJUmSpIIBWZIkSSoYkCVJkqSCAVmSJEkqGJAlSZKkggFZkiRJKhiQJUmSpIIBWZIkSSoYkCVJkqSCAVmSJEkqGJAlSZKkggFZkiRJKhiQJUmSpIIBWZIkSSoYkCVJkqSCAVmSJEkqGJAlSZKkggFZkiRJKhiQJUmSpIIBWZIkSSoYkCVJkqSCAVmSJEkqGJAlSZKkggFZkiRJKhiQJUmSpIIBWZIkSSoYkCVJkqSCAVmSJEkqGJAlSZKkggFZkiRJKhiQJUmSpIIBWZIkSSoYkCVJkqSCAVmSJEkqGJAlSZKkggFZkiRJKvRtdgGSJEm70htvvEFHRwcbNmxodilqkn79+tHW1sbee+9dU3sDsiRJ2qN1dHTQv39/hg0bRkQ0uxw1WGayZs0aOjo6GD58eE2vcYqFJEnao23YsIGDDjrIcNyiIoKDDjqoR98gGJAlSdIez3Dc2nra/wZkSZIkqWBAliRJaqDLLruMadOmbbfN7NmzWbx4cY/2+/Of/5z3v//97Lvvvt3uv9Eyk8997nOMGDGC0aNH81//9V9dtrv11lsZPXo0RxxxBBdddNGW9c888wzjx49n9OjRnHDCCXR0dGz1uldeeYW2tjamTJlSl3prCsgRcUpEPBERSyJiahfb942IW6vb50XEsLpUJ0mS1IJ2JCAPGDCAr3/961xwwQW7qKod9/3vf58nn3ySJ598khkzZvAXf/EXb2mzZs0aLrzwQu69914ee+wxnnvuOe69914ALrjgAs4880weeeQRLr30Ui6++OKtXvu3f/u3HH/88XWrt9urWEREH+Aa4CSgA5gfEXMys+y1TwEvZuaIiJgM/ANwet2qlCRJqoO/u+sxFv/ylbruc9Sh+/PF047Ybpsvf/nLfOtb3+Lggw9myJAhvOc97wHg+uuvZ8aMGbz++uuMGDGCm266iUWLFjFnzhweeOABrrjiCu644w5+/OMfv6Xd2972tq2OcfDBB3PwwQdz991311z75Zdfzl133cVrr73Gsccey3XXXUdEcMIJJzBt2jTa29tZvXo17e3tLFu2jN/85jdcdNFF/OAHP2CvvfbinHPO4bOf/Wy3x7nzzjs588wziQje97738dJLL/Hss89yyCGHbGmzdOlSRo4cyaBBgwA48cQTueOOOxg/fjyLFy/mqquuAuCDH/wgH/rQh7a8buHChTz//POccsopLFiwoOb3vj21jCAfAyzJzKWZ+TpwCzCpU5tJwLeqz28Hxoez4SVJkli4cCG33HILixYtYu7cucyfP3/Lto985CPMnz+fn/3sZxx++OF885vf5Nhjj2XixIlceeWVLFq0iHe+851dtquHKVOmMH/+fB599FFee+01vve97223/YwZM1i2bBmLFi3ikUce4U//9E8BOO+88xg7duxbHl/96lcBWLlyJUOGDNmyn7a2NlauXLnVvkeMGMETTzzBsmXL2LhxI7Nnz2bFihUAjBkzhu985zsAfPe732XdunWsWbOGTZs2cf7559d9Skkt10EeDKwoljuAcdtqk5kbI+Jl4CBgddkoIs4FzgUYOnToDpa8c0Ydun9TjitJkpqvu5HeXeHBBx/kwx/+8JYR34kTJ27Z9uijj3LJJZfw0ksvsX79ek4++eQu91Fru5667777+NrXvsarr77K2rVrOeKIIzjttNO22f5HP/oRn/nMZ+jbtxIhBwwYAMDVV1+907W8/e1v59prr+X0009nr7324thjj+Wpp54CYNq0aUyZMoWZM2dy/PHHM3jwYPr06cP06dOZMGECbW1tO338UkNvFJKZM4AZAO3t7dnIY2/WjF8MSZKkrpx99tnMnj2bMWPGMHPmTO6///6datcTGzZs4C//8i9ZsGABQ4YM4bLLLttyreC+ffuyadOmLe26c95553Hfffe9Zf3kyZOZOnUqgwcP3jIaDJWbtwwePPgt7U877bQtAX3GjBn06dMHgEMPPXTLCPL69eu54447OPDAA/nJT37Cgw8+yPTp01m/fj2vv/46++2335aR6x1VyxSLlcCQYrmtuq7LNhHRFzgAWLNTlUmSJO0Bjj/+eGbPns1rr73GunXruOuuu7ZsW7duHYcccghvvPEGN99885b1/fv3Z926dd22q9X48ePfMqVhc/AdOHAg69ev5/bbb9+ybdiwYSxcuBBgq/UnnXQS1113HRs3bgRg7dq1QGUEedGiRW95TJ1aubbDxIkTufHGG8lMHn74YQ444ICt5h9v9sILLwDw4osvMn36dD796U8DsHr16i2B/Stf+Qqf/OQnAbj55ptZvnw5y5YtY9q0aZx55pk7HY6htoA8HxgZEcMjYh9gMjCnU5s5wFnV5x8FfpyZTRkhliRJ6k2OPvpoTj/9dMaMGcOpp57Ke9/73i3bvvSlLzFu3DiOO+443v3ud29ZP3nyZK688kqOOuoonnrqqW22Kz333HO0tbVx1VVXccUVV9DW1sYrr7zCpk2bWLJkyZbpEJsdeOCBnHPOORx55JGcfPLJW9V1wQUXcO2113LUUUexevWbM2Y//elPM3ToUEaPHs2YMWOYNWtWTT+DCRMm8I53vIMRI0ZwzjnnMH369C3bxo4du+X55z//eUaNGsVxxx3H1KlTOeywwwC4//77ede73sVhhx3G888/zxe+8IWajrujopYcGxETgH8C+gA3ZOaXI+JyYEFmzomIfsBNwFHAWmByZi7d3j7b29uzXmcaSpIkbcvjjz/O4Ycf3uwymubRRx/lhhtu2HIViFbV1b+DiFiYme2d29Y0Bzkz5wJzO627tHi+AfiTHapWkiRJu8yRRx7Z8uG4p7yTniRJklQwIEuSpD2ep0a1tp72vwFZkiTt0fr168eaNWsMyS0qM1mzZg39+vWr+TUNvQ6yJElSo7W1tdHR0cGqVauaXYqapF+/fj26mYgBWZIk7dH23ntvhg8f3uwytBtxioUkSZJUMCBLkiRJBQOyJEmSVKjpTnq75MARq4BnmnJwGAis7raV9gT2dWuwn1uHfd067OvW0cy+/r3MHNR5ZdMCcjNFxIKubiuoPY993Rrs59ZhX7cO+7p19Ma+doqFJEmSVDAgS5IkSYVWDcgzml2AGsa+bg32c+uwr1uHfd06el1ft+QcZEmSJGlbWnUEWZIkSeqSAVmSJEkq7NEBOSJOiYgnImJJREztYvu+EXFrdfu8iBjWhDK1k2ro57+OiMUR8UhE3BsRv9eMOrXzuuvrot0fR0RGRK+6bJBqV0tfR8THqr/bj0XErEbXqPqo4W/40Ii4LyJ+Wv07PqEZdWrnRMQNEfFCRDy6je0REV+v/jt4JCKObnSNpT02IEdEH+Aa4FRgFHBGRIzq1OxTwIuZOQK4GviHxlapnVVjP/8UaM/M0cDtwNcaW6Xqoca+JiL6A58H5jW2QtVLLX0dESOBi4HjMvMI4K8aXad2Xo2/15cAt2XmUcBkYHpjq1SdzARO2c72U4GR1ce5wLUNqGmb9tiADBwDLMnMpZn5OnALMKlTm0nAt6rPbwfGR0Q0sEbtvG77OTPvy8xXq4sPA20NrlH1UcvvNMCXqHzY3dDI4lRXtfT1OcA1mfkiQGa+0OAaVR+19HUC+1efHwD8soH1qU4y89+BtdtpMgm4MSseBg6MiEMaU91b7ckBeTCwoljuqK7rsk1mbgReBg5qSHWql1r6ufQp4Pu7tCLtKt32dfUruSGZeXcjC1Pd1fJ7fRhwWEQ8FBEPR8T2RqbUe9XS15cBn4iIDmAu8NnGlKYG6+n/57tU32YdWGq0iPgE0A58oNm1qP4iYi/gKuDsJpeixuhL5avYE6h8K/TvEfH7mflSM4vSLnEGMDMz/zEi3g/cFBFHZuamZhemPdeePIK8EhhSLLdV13XZJiL6UvnqZk1DqlO91NLPRMSJwBeAiZn56wbVpvrqrq/7A0cC90fEMuB9wBxP1Nst1fJ73QHMycw3MvNp4BdUArN2L7X09aeA2wAy8ydAP2BgQ6pTI9X0/3mj7MkBeT4wMiKGR8Q+VCb2z+nUZg5wVvX5R4Efp3dO2d10288RcRRwHZVw7DzF3dd2+zozX87MgZk5LDOHUZlvPjEzFzSnXO2EWv5+z6YyekxEDKQy5WJpA2tUfdTS18uB8QARcTiVgLyqoVWqEeYAZ1avZvE+4OXMfLZZxeyxUywyc2NETAHuAfoAN2TmYxFxObAgM+cA36TyVc0SKhPHJzevYu2IGvv5SmA/4NvVczCXZ+bEphWtHVJjX2sPUGNf3wP8j4hYDPwGuDAz/QZwN1NjX58PXB8R51E5Ye9sB7N2PxHxr1Q+1A6szif/IrA3QGZ+g8r88gnAEuBV4M+bU2mFt5qWJEmSCnvyFAtJkiSpxwzIkiRJUsGALEmSJBUMyJIkSVLBgCxJkiQVDMiSJElSwYAsSZIkFf4/wEV3ceyDEKQAAAAASUVORK5CYII=\n", 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b23Vrw4NnDa434RgMyJIkSQJGjBhBnz59eOedd3ji7Wk8N78zhb3ac/8Zg2ndtFG+y6tWDrGQJEkSffv2ZdCgQbz0STF/GjGa3bfowB2nFtK8cf2Li/YgS5Ik1VMPPfQQ3/nOd0gp0b59e46/8hZuGj6Xvft24q7TdqmX4RjsQZYkSaq3Fi5cyMyZM1m4cCGPj5nHtc9O4IBtN+OWk3akScMG+S4vb+xBliRJqifKysr405/+xD//+U8Azj77bF5++WUeencO1z47gcMGbs6fT9qpXodjMCBLkiTVGytXruQvf/kLjz32GAARwU3/mcRv/zmRowZ15abjd6RxQ+Oh74AkSVIdVlJSwl//+ldWrFhBkyZNePnll7njjjtIKfG75yfyh39/yLE7d+eG4wbRsIHREAzIkiRJddqrr77KOeecwxNPPAFAp06dALj22Qn8+eXJnDi4J789ZnsaFEQ+y6xRDMiSJEl1THFxMW+99RYAX//61xk+fDjHH388AGVliZ89PY47X5vCaV/rzXVHb0eB4fhLDMiSJEl1zIUXXsgBBxzAvHnzABg8eDBQHo4vf3IM9w//hKF7b8FVR/QnwnC8Jqd5kyRJqgOWLFlCaWkprVu35tJLL+Xoo4+mffv2q/eXlJbx48ff44l3pnPB17fiogP7GY7XwYAsSZJUyxUXF7PTTjux5557cuedd9KnTx/69Omzev/K0jJ++Mi7PPPeTC4+sB8X7N83j9XWfAZkSZKkWmrlypU0atSIJk2acN555zFo0KCvHLOipIwLHn6b58d9zmWHbsPZ+2xZ/YXWMo5BliRJqoVGjRpF3759efvttwH4/ve/zz777POlY5avLOWcB0bx/LjPueqI/objCrIHWZIkqRbaYost6NevHwUFa+/vXLailKH3F/HfD+dw3Te346TBvaq5wtrLHmRJkqRa4u9//zsnnHACKSXatWvHv/71r7UOq1hSXMLp94zktUlz+O2x2xuOK8mALEmSVEvMnj2byZMnM3/+/HUe88XylZx611uMmDKXG48bxHGFPaqxwrohUkr5riHnCgsLU1FRUb7LkCRJ2iQpJe666y66devGIYccQllZGWVlZTRsuPZRsguXruS7d7/FuOkL+ePxO/KN7btUc8W1S0SMSikVrrndHmRJkqQaauXKldx4443cf//9ABQUFKwzHM9fsoIT7xjO+BkL+fNJOxmON4EBWZIkqQYpLS3l7rvvpri4mMaNG/Piiy+uDsjrMmdxMSfcPpwPZy3mtu8WctCAzaup2rrJgCxJklSDvP7665x++uk88sgjAGy22WbrnKkCYNai5Rx/23A+nruEu0/bhf227lxdpdZZBmRJkqQ8W7lyJaNGjQJg77335tVXX+WUU07Z4OtmLFjGcX99k5kLlnHvkF3ZY6uOVV1qvWBAliRJyrOLLrqIfffdlzlz5gCw1157ERHrfc3UeUv5zm1vMnfxCu47YzCDt+hQHaXWCy4UIkmSlAfLly9nxYoVtG7dmosuuoj99tuPjh0r1gP88ZwlnHj7cJasKOXBswazffe2VVtsPWNAliRJqmYrVqxgl112obCwkLvvvps+ffrQp0+fCr120qzFnHj7cErKEg+dNZgBXdtUcbX1jwFZkiSpmpSWltKgQQMaN27MGWecQf/+/Sv1+omffcFJdwwHgofP2o2tN29VNYXWc45BliRJqgbvvfce22yzDW+//TYAP/jBDzjooIMq/Pqx0xdy/G1v0qAgeORsw3FVMiBLkiRVgx49etC9e3dKS0sr/drRUxdw4u3DadaoAY8M3Z0tO7Wsggq1igFZkiSpijzzzDOccsoppJRo164dL730Ervsskul2hj1yTxOumMEbZo34pGzd6d3xxZVVK1WMSBLkiRVkWnTpvHee++tnr6tsoZ/NJdT7nyLTq2a8OjZu9OjffMcV6i1iZRSvmvIucLCwlRUVJTvMiRJUj2TUuLhhx+mffv2HHLIIZSVlVFaWkqjRo0q3dZrH87hzPtG0r1dcx46czCdWzetgorrt4gYlVIqXHO7PciSJEk5UlJSwq9//WvuuOMOAAoKCjYqHL/0/ixOv3ckvTu04G9DdzMcVzMDsiRJ0iYoKyvjwQcfpLi4mEaNGvHPf/6TRx55ZKPbe37cZwy9v4h+m7Xk4bN2o2PLJjmsVhVhQJYkSdoEb775JieffDIPPPAAAF27dqVBgwYb1dYz783gew++zYCubXjwzN1o16JxLktVBRmQJUmSKqm0tJTRo0cDsMcee/Diiy9y+umnb1KbT74zjQsffocde7bl/jN2pU2zyg/NUG4YkCVJkirpxz/+MXvuuSeff/45AF//+teJiI1u79GRU7no0dEM7tOBe0/flVZNDcf55FLTkiRJFbBixQqKi4tp1aoVF1xwAYWFhXTu3HmT271/+Cf89Kmx7N2vE7edsjNNG23c8AzljgFZkiRpA1auXMnuu+/OwIEDueeee+jduze9e/fe5HbvfG0K1zwznv236cwtJ+1kOK4hDMiSJEnrUFZWtnqqtpNOOomtttoqZ23f+vJkfvPP9zlkwObcdMKONG7oyNeawishSZK0FuPHj2fgwIGMGjUKgIsuuogjjzwyJ23f9OKH/Oaf73PEDl25+UTDcU1jD7IkSdJadO3alXbt2rF8+fKctZlS4vp/fcDNL03imJ2689tjt6dBwcbf3Keq4a8rkiRJGS+88AKnnnoqKSXatm3La6+9xh577JGTtlNK/PK5Cdz80iRO2LUHvzMc11gGZEmSpIwpU6bw1ltvrZ6+LVfKyhJXDxvH7f+dwqm79+K6owdSYDiusSKllO8acq6wsDAVFRXluwxJklQLPPHEE7Ro0YKDDz6YsrIyVq5cSZMmuVveuawsccVTY3j4ramctVcfLj9s202aM1m5ExGjUkqFa253DLIkSaq3SkpKuPrqq+nVqxcHH3wwBQUFOQ3HpWWJHz/+Hn9/exrf229LfnTQ1objWsAhFpIkqV5JKfH4449TXFxMw4YNee6553jiiSdyfp6S0jJ++Mi7/P3taVx0YD8uOXgbw3EtYUCWJEn1yltvvcW3v/1t7r77bgC6d+9Oo0a5Xdp5RUkZFzz8DsNGz+Anh2zDhfv3zWn7qloOsZAkSXVeWVkZEyZMYMCAAQwePJjnn3+eAw44oErOVVxSyvcefJt/T5jFTw/vzxl79qmS86jq2IMsSZLqvCuuuILBgwczY8YMAA466CAKCnIfg5avLOWs+0bx7wmzuObo7QzHtZQ9yJIkqU4qKSlh+fLltGzZknPOOYe+ffvSpUuXKjvf0hUlnHFPEcOnzOU3xwzkO7v0rLJzqWrZgyxJkuqc0tJS9tlnH84//3wAevXqxemnn15lN8l9sXwlp971FiOmzOWG43YwHNdy9iBLkqQ6I6VERNCgQQO+9a1v0b179yo/58Jl5eF4zPSF3HTCjhy+fdcqP6eqlj3IkiSpTvjggw/YcccdWbVY2MUXX8x3vvOdKj3n/CUrOOmO4YybsZA/n7ST4biOsAdZkiTVCZ07d6ZJkyYsXry4StovvPYF5ixesdZ9d51WyNe32axKzqvqZw+yJEmqtV599VXOOOMMUkq0bduW4cOHs++++1bJudYVjgHDcR1jQJYkSbXWxIkTeeWVV1ZP3+ZKdcqFvA+xiIhDgD8CDYA7Ukq/XmN/T+BeoG3mmEtTSs9Vd52SJKlmeO6552jQoAEHH3wwZ555JieffDLNmjXbpDbLyhJzlhTz2cLlzFy4nJkLljFz0fLVzz9buDxH1as2yGtAjogGwC3AgcA0YGREDEspjc867Erg0ZTSrRHRH3gO6F3txUqSpLwrLS3l8ssvp3Pnzhx88MFExAbDcWlZYvYXxcxcuOx/AXjhstXBd+bC5Xy+aDklZelLr2vcoIDN2zRl8zZN2bFnWz6dt7QqvzTVIPnuQd4VmJRS+gggIv4GHAVkB+QEtM48bgPMqNYKJUlSXqWUeOaZZzjwwANp2rQpw4YNY7PNysf8riwtY9YXxXy2RuDNDsCzviimdI3w26RhAV3aNKVLm2YM7tOezds0pUubpmzeplnmc1PaN29MQcH/hmw8/a4RpL7Id0DuBkzNej4NGLzGMVcD/4qIC4AWwFoXTo+IocBQgJ49nZxbkqTarriklFmLinll5Huc/ZPfc8yY+Wyx3c6ZEPwpMxcuZ/biYtKXsy/NGjWgS9vywPu1LTvStW3T/wXg1uUBuG3zRpUer9yxZeO13qjXsWXjTfkyVQNFWvNfVXWePOJY4JCU0pmZ56cAg1NK52cdcxHldV4fEbsDdwLbpZTK1tVuYWFhWjUHoiRJyq11TXfWsWVjiq48sEJtLF9Z+r/xvYu+3Pv72cLlzFi4jLlrOUerJg1XD3vI7vFd1Ru8eZumtG7a0Jv1VCERMSqlVLjm9nz3IE8HemQ9757Zlu0M4BCAlNKbEdEU6AjMqpYKJUnSl6xrurNV25euKFkj8K4RgBctZ96Sr7bRumlDurYtD7nLZ37IpyNe5prLLqJ/766rhz20atqoSr82CfIfkEcCfSOiD+XB+HjgxDWO+RTYH7gnIrYFmgKzq7VKSZJUITv8/F8sXLbyK9vbNW+0urd3x55tvzLed/PWTWnWqIBly5bRokULPv10c57pvoyhBwykoMBZaVW98hqQU0olEXE+8DzlU7jdlVIaFxG/AIpSSsOAi4HbI+KHlN+wd1rK57gQSZLqmZQSn85byoiP5jFiyrz1HnvkDl3ZvE3T8nG/rf8XgJs2arDe15WVlXHAAQfQvXt37rvvPnr27Ml5552Xyy9DqrB89yCTmdP4uTW2/Szr8Xhgj+quS5Kk+iqlxOTZixkxZR4jPprHW1Pm8dmi8nmA27dY/w1p1xy9XaXPFREUFBRw6KGH0rlz542uW8qVvAdkSZKUX2Vlifc/+4K3psxlxJTyQDw3M0a4c6smDN6iA7v2ac9ufdqzVeeW9LksN+t1ffTRR5xwwgncfPPN7LLLLlxyySU5aVfaVAZkSZLqmZLSMsbNWMRbU+YxYspc3poyj0XLSwDo1rYZ+2zdicF92jO4Twd6dWj+lRkhcjXdWYcOHSgpKWHevPUP25CqW16neasqTvMmSdL/rCgpY8z0BQzPDJcY9cl8FheXB+I+HVuwa+/2DN6iPbv2aU/3ds2rtJYRI0Zw1113ceutt1JQULB6iIWUDzV1mjdJkpRjy1eW8s6nC1b3Dr/96XyWryxfPqDfZi05eseuDO5TPmxis9ZNq7W2MWPG8I9//INp06bRs2dPw7FqJHuQJUmq5ZYUlzDqk/mrh0yMnrqQFaVlRED/Lq3ZNTNcYpfe7ejQskm11/ef//yHlStXcvDBB5NSYsmSJbRs2bLa65DWZA+yJEl1xMJlKyn6uHy4xPAp8xg7fSGlZYkGBcF23dowZI/eDN6iPTv3ak+bZvldWKOsrIyLL76Y1q1bc9BBBxERhmPVeAZkSZJquHlLVqyeYWLER/OY8NkiUoLGDQrYoUcbzt1nS3bt056de7WjRZOa8V/7v/71L/bee2+aNm3Kk08+SefOnR1OoVqjZnwXSZKk1WYtWl4ehqfMZcRH8/hw1mIAmjYqYKee7fjB/v3YtU97duzZdoMLcOTDe++9x8EHH8wNN9zAD3/4Q3r37p3vkqRKMSBLkpRn0+YvLR8//NE83vp4HlPmLAGgReMGFPZuzzd36sbgPu0Z2K0tjRvWzGWXU0pMnjyZrbbaiu23354nn3ySww47LN9lSRvFgCxJUjVKKfHJ3KXlvcOZUDx9wTIA2jRrxC6923Pirj0ZvEV7+ndpTcMGNTMQr+lXv/oV1113HRMmTKBnz54cffTR+S5J2mgGZEmSNkHhtS+sc9GMoisPJKXEpFmLGZ5ZoW7ER3OZ9UUxAB1aNGbwFu05a68+DN6iA1tv1oqCgtozTjelxPLly2nWrBknn3wyTZs2pVu3bvkuS9pkBmRJkjbB2sLxqu3n3D+Ktz6ex7zMss2bt27K7lt2WD3t2padWtTaG9fKyso44ogjaN++Pffffz89e/bkoosuyndZUk4YkCVJqiLjZi5kv607M3iL9uzWpwM92jertYF4lVUr3xUUFLDPPvvQunVrV8NTnWNAliSpivz3x1/Pdwk59emnn3LyySdz/fXXs8suu/DjH/843yVJVaJ2jPyXJEl516ZNGxYtWsTnn3+e71KkKmVAliRpI6WU8l1ClXvnnXc477zzKCsro02bNrz99tscfvjh+S5LqlIGZEmSNtJjo6atc1/Hlo2rsZKqM2rUKJ588kk++eQTAAoKjA6q+6Iu/vZbWFiYioqK8l2GJKkOm7O4mP2vf4WtN2vF34buVqumZ9uQN954gyVLlnDggeXT1C1atIg2bdrkuywp5yJiVEqpcM3t3qQnSdJG+MX/jWfZilJ++a2BdSocp5S44IILaNy4MQcccAARYThWvePfSSRJqqSXJs5i2OgZfG+/rdiqc8t8l5MTL7/8MsuXLycieOyxx3jhhRecuk31lgFZkqRKWFJcwpVPjqVv55acu++W+S4nJ8aPH89+++3HTTfdBMAWW2xBy5Z1I/hLG8MhFpIkVcINL3zA9AXLePyc3WncsHb3M33yySf06tWL/v3789hjj/GNb3wj3yVJNULt/s6WJKkajZ66gLtfn8LJu/WksHf7fJezSa6//nr69+/Pxx9/DMCxxx5Ls2bN8luUVEPYgyxJUgWsLC3j0ifG0KlVE358yDb5LmejpJQoLi6madOmHHfccaxYsYJu3brluyypxjEgS5JUAXe+NoUJMxfxl5N3pnXTRvkup9JSSqt7iR944AF69OjBZZddlu+ypBrJgCxJ0gZ8MncJN77wAQcP2IxDtts83+VslIhg1113pXHjxqSUnKFCWg/HIEuStB4pJS5/cgyNGxTwi6O2y3c5lTJjxgwOPPBARowYAcBPfvITfvjDHxqOpQ0wIEuStB5/f3s6r0+ay08O3YbNWjfNdzmV0rJlSz777DOmTVv3ktiSvsqALEnSOsxdXMy1z46nsFc7Tty1Z77LqZBx48Zx4YUXUlZWRuvWrRk9ejTHHHNMvsuSahUDsiRJ63DNM+NZUlzCr2rRctIjRozg4YcfZvLkyQAUFPhfvVRZftdIkrQWr3wwm6fencF5+25F381a5buc9SoqKuLf//43AEOGDGHixIn07ds3z1VJtZezWEiStIalK0q44skxbNmpBeftV7OXk04pcd5551FaWkpRURERQfv2tXsREynfDMiSJK3hxhc+YNr8ZTx2zu40adgg3+Ws1ZtvvsmOO+5I06ZNefjhh+nQoYOzU0g54hALSZKyjJm2kDtfm8KJg3uySw1dTvqDDz5gjz324MYbbwRgyy23pG3btvktSqpD7EGWJCmjpLSMS594j44tm/CTGric9LRp0+jevTv9+vXjoYce4ogjjsh3SVKdZA+yJEkZd742hXEzFvHzIwfQplnNWk76T3/6E1tvvTVTpkwB4Pjjj6dFixZ5rkqqm+xBliQJ+HTuUm789wcc2L9mLSe9YsUKGjduzNFHH82cOXPo0qVLvkuS6jwDsiSp3kspccVTY2hYUMA1R21XI252Sylx8sknA/Dggw/So0cPfv7zn+e5Kql+MCBLkuq9J9+Zzn8/nMM1Rw1g8zY1YznpiGDgwIGklEgp1YjQLtUXjkGWJNVr85as4JpnxrNTz7acNLhXXmv5/PPPOfzwwxk+fDgAl156KZdddpnhWKpmBmRJUr127TPjWVxcwq+P2T7vy0k3a9aMjz76aPWNeJLyw4AsSaq3Xv1gNk+8M51z99mSfnlaTvrDDz/koosuoqysjNatW/Pee+9xwgkn5KUWSeUMyJKkemnpihKueGoMW3RqwXn7bZW3Ol577TXuuusuJk6cCEDDht4eJOWbAVmSVC/98d8fMnXeMn71zYE0bVS9y0mPGTOGF198EYDTTjuNDz/8kG233bZaa5C0bv6aKkmqd8ZOX8gdr03hhF17MHiLDtV67pQSZ511FkuXLmX06NFEBJ06darWGiStnwFZklSvrFpOun2Lxlx6aPX12o4aNYr+/fvTrFkz7r//ftq1a+fsFFIN5RALSVK9cvfrHzN2+iKuPqL6lpOePHkygwcP5ve//z0Affv2pWPHjtVybkmVZw+yJKnemDpvKTe88AEHbNuZwwZW/XLSn332GZtvvjlbbrkl99xzD0cccUSVn1PSprMHWZJUL5QvJz2WgoBfVMNy0n/961/ZaqutmDx5MgAnn3wybdq0qdJzSsoNe5AlSfXC0+/O4NUPZvPzIwfQtW2zKjtPSUkJDRs25Bvf+AZTpkyhS5cuVXYuSVXDgCxJqvPmL1nBL54Zz6AebTl5t6pZTnrV7BRLlizh4Ycfpnv37vz617+uknNJqloGZElSnXftsxNYtGwlvz5mIA2qaDnpiGDLLbdk2bJllJWVUVDgKEaptvK7V5JUp7324Rz+/vY0ztlnS7bZvHVO254zZw7HHnssb775JgCXXXYZv/jFLwzHUi3nd7Akqc5atqKUy58cQ5+OLTj/67lfTrpx48aMGTNm9TLRkuoGA7Ikqc7644sf8um8pfwyh8tJf/zxx/zkJz+hrKyM1q1bM3bsWE477bSctC2pZjAgS5LqpHEzFnL7fz/iO4U92H3L3C0n/corr/DnP/+ZcePGAdCoUfUsNiKp+hiQJUl1TmlZ4rInxtCueWMuP2zTl5OeOHEi//nPfwD47ne/ywcffMDAgQM3uV1JNZOzWEiS6py7X5/Ce9MW8qcTdqRN803v4T3jjDOYO3cu48aNo6CgwLmNpTrOgCxJqlOmzlvK9f/6gK9v05nDt9/4IDtmzBi22mormjVrxl133UWrVq2cnUKqJ/xOlyTVGSklfvp0+XLS1xy98ctJf/LJJ+y8886rF/ro16+fvcZSPWIPsiSpzhg2egYvT5zNVUf0p9tGLCc9Z84cOnbsSK9evbjttts44ogjqqBKSTWdPciSpDphwdIV/OL/xrNDj7Z8d/felX79PffcQ58+fZg8eTIAp512Gh065G72C0m1R84CckTsEREtMo9PjogbIqJqFryXJGkN1z07gYXLVvLrb1VuOenS0lIADjzwQM4880w6depUVSVKqiVy2YN8K7A0InYALgYmA/flsH1JktbqjUlzeGzUNIbuvQXbdqn4ctIXXnghJ598MgDdunXjxhtvpHXr3C5HLan2yWVALkkpJeAo4OaU0i1Aqxy2L0nSVyxfWcplT46hd4fmXLh/30q9tkuXLnTv3n11L7IkQW5v0vsiIi4DTgH2iogCwOWFJElV6qYXP+STuUt56MzBG1xOev78+VxwwQV873vfY/fdd+eyyy6rpiol1Sa57EH+DlAMnJ5S+gzoDvwuh+1LkvQlE2Yu4rZXP+LbO3fna1t13ODxDRs25K233mLMmDHVUJ2k2ipnATkTiv8ONMlsmgM8mav2JUnKVlqWuPTv79GmWSOu+Ma6l5OePn06l19+OWVlZbRq1YoxY8YwdOjQaqxUUm2Ty1kszgIeB/6a2dQNeKoCrzskIiZGxKSIuHQdxxwXEeMjYlxEPJSrmiVJtde9b3zM6GkL+dkR/WnbvPE6j3vxxRe58cYbGT16NABNmjRZ57GSBLkdYvE9YA9gEUBK6UOg8/peEBENgFuAQ4H+wAkR0X+NY/oClwF7pJQGAD/IYc2SpFpo+oJl/P5fE9l3604cuUPXr+yfMmUKL7/8MgCnnHIKH3zwATvuuGM1VymptsrlTXrFKaUVq5b1jIiGQNrAa3YFJqWUPsq85m+Uz4IxPuuYs4BbUkrzAVJKs3JYsySplkkp8dOnxgJw7TqWkx4yZAjTp0/n/fffp0GDBvTo0aO6y5RUi+WyB/mViLgcaBYRBwKPAf+3gdd0A6ZmPZ+W2ZatH9AvIl6PiOERccjaGoqIoRFRFBFFs2fP3sgvQZJU0z3z3kz+8/4sLj5oa7q3a756+/vvv8/SpUsBuO222/jPf/5Dgwbrn9VCktYmlwH5UmA2MAY4G3gOuDIH7TYE+gL7AicAt0dE2zUPSindllIqTCkVugqSJNVNC5eu5Of/N47tu7fhtK/1Xr192rRpDBo0iF/+8pcA9OvXz15jSRstl0MsjgbuSyndXonXTAeyf4J1z2zLNg0YkVJaCUyJiA8oD8wjN6FWSVIt9MvnJjB/6UruO30wDQqC+fPn065dO7p3787NN9/MEUccke8SJdUBuexBPgL4ICLuj4jDM2OQN2Qk0Dci+kREY+B4YNgaxzxFee8xEdGR8iEXH+WsaklSrfDm5Lk8UjSVs/bagv5dW/PQQw/Rq1cvJk2aBMCZZ57JZpttlucqJdUFuZwHeQiwFeVjj08AJkfEHRt4TQlwPvA8MAF4NKU0LiJ+ERFHZg57HpgbEeOBl4BLUkpzc1W3JKnmW76ylMufHEOvDs258OtbArDPPvtw0kkn0aFDhzxXJ6muiZQ2NNFEJRuMaAQcAgwB9k4pbXhpoxwrLCxMRUVF1X1aSVIV+f3zE7n5pUnsUzaaFVPH8PDDD+e7JEl1QESMSikVrrk9lwuFHBoR9wAfAscAdwCb56p9SVL99P5ni/jLK5M5ZqfubNMu6NixIyUlJfkuS1Idlsub9L4LPAKcnVIqzmG7kqR6av6ChRz3u2E0a9GBK7+xLe1a7JDvkiTVAzkLyCmlE3LVliRJAI++8xmLGrXnsBaf067FupeTlqRc2uQhFhHxWubzFxGxKOvji4hYtOklSpLqk1mzZnH11Vczde5ibnppCntu2Z5bfnxavsuSVI9scg9ySmnPzOdWm16OJKm+e+GFF/jVr37FmHZ7UJbgV8fssNblpCWpquTyJr37K7JNkqQ1TZs2jVdffRWAE088kb88+xajZq7g4oP60aN98w28WpJyK5c36Q3IfpJZKGTnHLYvSaqjTjvtNCZPnsyHH37IkhWJW4bPZmC3Ly8nLUnVZZMDckRcBlwONMsacxzACuC2TW1fklQ3ffTRR2y++eY0b96cm2++mcaNG9OwYUN+Pew95i9dwT1DdqFhg1wu+CpJFbPJP3lSSr/KjD/+XUqpdeajVUqpQ0rpshzUKEmqY2bOnMnAgQO55pprANhmm23YYostGP7RXB5+aypn7tmH7bq1yXOVkuqrXPQgb5NSeh94LCJ2WnN/SuntTT2HJKluWLRoEa1bt6ZLly5cf/31HH744av3LV9ZyuVPjKFH+2b84IB+eaxSUn2XizHIFwFDgevXsi8BX8/BOSRJtdzjjz/OmWeeyciRI+nbty/nnHPOl/b/+aVJfDRnCfefsSvNGjfIU5WSlJtp3oZmPu+36eVIkuqalBIRwde+9jWOOeYY2rZt+5VjPvj8C259ZTLf2rEbe/XtVP1FSlKWXE7z9u2IaJV5fGVEPBERO+aqfUlS7XPVVVdx4oknAtC1a1fuvPNOOnX6cgAuK0tc+vf3aNmkIVce3j8fZUrSl+Ty9uCfppS+iIg9gQOAO4G/5LB9SVIt07RpU1q0aMHKlSvXecwDIz7h7U8X8NPD+9Pe5aQl1QC5DMilmc/fAG5LKT0L+JNOkuqRJUuWcP755/Paa68BcOmll3LHHXfQqFGjtR4/c+EyfvvPiezVtyPf3LFbdZYqSeuUy4A8PSL+CnwHeC4imuS4fUlSDZdS4h//+AdvvvkmwHqXiE4p8bOnx1FSVsZ1Rw90OWlJNUYuA+xxwPPAwSmlBUB74JIcti9JqoHmzZvHddddR2lpKS1btuS9997jkks2/OP/n2M/44Xxn3PRgf3o2cHlpCXVHDkLyCmlpcBk4OCIOB/onFL6V67alyTVTM8//zxXXXUVI0aMAKBFixYbfM3CZSu5atg4BnRtzel79KnqEiWpUnI5i8X3gQeBzpmPByLigly1L0mqOT777LPV44yPP/54JkyYwNe+9rUKv/43/3yfOYuL+fW3tnc5aUk1Ti4WClnlDGBwSmkJQET8BngT+FMOzyFJqgFOO+00xo8fz+TJk2nUqBF9+/at8GvfmjKPh0Z8yll79WFgd5eTllTz5DIgB/+byYLMY++4kKQ6YurUqXTo0IHmzZvzhz/8AWCds1OsS3FJKZc98R7d2zXjhwe6nLSkmimXf9e6GxgREVdHxM+B4ZTPhSxJquVmzZrFdtttx89//nMAttlmG7bZZptKt/PnlyYzefYSrvvmQJo3zmUfjSTlTs5+OqWUboiIl4E9gQQMSSm9k6v2JUnVb/HixbRs2ZLOnTvzq1/9ikMPPXSj2/rw8y/488uTOHpQV/bp53LSkmquqrgzItb4LEmqhZ5++ml69uzJBx98AMB5551Hnz4bN+NEWVni0ifG0KJJQ37qctKSarhczmLxM+BeoB3QEbg7Iq7MVfuSpOqRUgJgl1124bDDDqNVq1ab3OaDb33KqE/mc+U3+tOhZZNNbk+SqlKs+kG4yQ1FTAR2SCktzzxvBrybUto6JyeohMLCwlRUVFTdp5WkWu9Xv/oVY8eO5cEHH8xZm58tXM6BN7zCDj3acv8Zu7pinqQaIyJGpZQK19yeyyEWM4CmWc+bANNz2L4kqRpEBCtWrMhZe1cNG8vKsjKu++Z2hmNJtUIubyFeCIyLiBcov0nvQOCtiLgJIKV0YQ7PJUnKgWXLlvHTn/6Uo446ir322otLL700pyH2n2M/4/lxn3PpodvQq8OGV9iTpJoglwH5yczHKi/nsG1JUhUoLS3liSeeoG3btuy11145DceLlq/kqmFj2bZLa87Y0+WkJdUeuZzm7d5ctSVJqjqLFi3i1ltv5Uc/+hEtW7Zk9OjRObkRb02//ef7zP6imNu/W0gjl5OWVIv4E0uS6pl//vOfXH755bz++usAVRKOiz6exwPDP2XIHn3YvnvbnLcvSVXJgCxJ9cDcuXN54403APj2t7/N2LFj2XvvvavkXMUlpVz6xBi6tW3GRS4nLakW2uSAHBH3Zz5/f9PLkSRVhdNOO41jjz2W4uJiIoJtt922ys71l5c/YtKsxVz7ze1o0cTlpCXVPrn4ybVzRHQFTo+I+1hjBb2U0rwcnEOSVEkzZ86kdevWtGjRgt/97ncUFxfTpEnVLtIxadYX3PLSJI7coSv7bd25Ss8lSVUlFwH5L8CLwBbAKL4ckFNmuySpGs2dO5ftttuOIUOG8Pvf/55tttmmSs5TeO0LzFn81TmTX5s0p0rOJ0nVYZMDckrpJuCmiLg1pXRuDmqSJG2kZcuW0axZMzp06MBVV13FIYccUqXnW1s4Bpi3JHcLjUhSdcvZTXoppXMjYoeIOD/zsX2u2pYkbdg//vEPevbsyQcffADAhRdeSL9+3iQnSZWVs4AcERcCDwKdMx8PRsQFuWpfkrR2KSUABg0axH777UfTpk3zXJEk1W65vL34TGBwSmkJQET8BngT+FMOzyFJynLjjTcyatQoHnjgAbp06cKjjz5abeeeNOuLajuXJFWnXAbkAEqznpeyxowWkqTcKi4uZtmyZSxfvrzaeo6Xryzl5v9M4q+vTq6W80lSdctlQL4bGBERT2aeHw3cmcP2JaneKy4u5tprr+Wggw5ir7324sc//jEFBdW35tN/P5zNlU+N5ZO5S/nWjt14+YPZa70hr2PLxtVWkyTlWs4Cckrphoh4Gdgzs2lISumdXLUvSYKSkhIefPBBAPbaa69qC8ezvyjm2mfH8/S7M+jTsQUPnjmYPbbqWC3nlqTqltMljlJKbwNv57JNSarvlixZwm233caFF15IixYtePvtt2nbtm21nLusLPHwyE/5zT/eZ/nKMr6/f1/O3XdLmjZqUC3nl6R8cA1QSarh/vGPf3DRRRex/fbbs//++1dbOH7/s0Vc/sQY3v50Abtt0Z7rvjmQLTu1rJZzS1I+GZAlqQZauHAhEyZMYLfdduOYY45h9OjRbL999Uwvv3RFCX988UPu/O8UWjdrxPXf3oFv7dSNCO+7llQ/GJAlqQYaMmQIb7zxBh9//DFNmzattnD80vuz+OnTY5k2fxnHFXbnskO3pV0Lb7iTVL/kLCBHxLeA31C+SEhkPlJKqXWuziFJddmcOXNo1qwZLVq04LrrrmPx4sXVNnXb54uW8/P/G8dzYz5jq84teWTobgzeokO1nFuSappc9iD/FjgipTQhh21KUr2wYMECBgwYwEknncQNN9zAtttuWy3nLS1LPDD8E373/ERWlJbxo4P6MXTvLWncsPqmjpOkmiaXAflzw7EkVc6qBT7atm3LZZddxv77719t5x47fSFXPDmG0dMWslffjlxz1Hb07tii2s4vSTVVLgNyUUQ8AjwFFK/amFJ6IofnkKQ648UXX+Skk07ilVdeYeutt+YHP/hBtZx3SXEJN7zwAXe/PoX2LZrwx+MHceQOXb0JT5IychmQWwNLgYOytiXAgCxJazFgwAB22203GjVqVG3n/Ne4z7h62DhmLFzOiYN78pODt6FN8+o7vyTVBrlcSW9IrtqSpLrqL3/5C2+88Qb33nsvm2++OU899VS1nHfGgmVcNWwcL4z/nG02b8WfTtyJnXu1q5ZzS1Jtk8tZLLoDfwL2yGz6L/D9lNK0XJ1Dkmq7hQsXMmfOHJYvX06zZs2q/HwlpWXc88bH3PDCB5SlxGWHbsPpe/ahUQNvwpOkdYmUUm4aingBeAi4P7PpZOCklNKBOTlBJRQWFqaioqLqPq0kfcXKlSv53e9+x5577snee+9NWVkZEVEt431HT13A5U+OYdyMRey3dSd+cdR29GjfvMrPK0m1RUSMSikVrrk9l2OQO6WU7s56fk9E/CCH7UtSrbNixQruvPNO5s+fz957701BQdX33C5avpLrn5/IfcM/oVPLJvz5pJ04dLvNvQlPkioolwF5bkScDDyceX4CMDeH7UtSrbB8+XLuuOMOzj33XFq0aMFbb71Fhw5Vv+hGSol/jC2/CW/24mK+u1svLj54a1o39SY8SaqMXAbk0ykfg3wj5bNXvAF4456keucf//gHF1xwAVtuuSWHHnpotYTjqfOW8rOnx/LSxNkM6Nqa279byA492lb5eSWpLsrlLBafAEfmqj1Jqk0WL17MhAkT2GWXXTj66KMZOXIkhYVfGdaWcytLy7jztSn84d8fUBDBld/YltO+1puG3oQnSRttkwNyRPw4pfTbiPgT5T3HX5JSunBTzyFJNd0ZZ5zBSy+9xMcff0zz5s2rJRyP+mQ+Vzw5hvc/+4KD+m/G1UcOoGvbqp8ZQ5Lqulz0IK9aXtppIyTVKwsWLKBRo0a0aNGCq6++mgsuuIDmzat+loiFS1fym+ff56ERn9K1TVNuO2VnDhqweZWfV5Lqi00OyCml/8s8XJpSeix7X0R8e1Pbl6SaaNGiRWy33XYcc8wx/PGPf2Tbbbet8nOmlBg2egbXPDOeeUtWcOaeffjhgf1o0SSXt5NIknL5U/Uy4LEKbJOkWmvFihU0btyY1q1b88Mf/pB99923Ws77ydwlXPnUWP774Rx26N6Ge4bsynbd2lTLuSWpvsnFGORDgcOAbhFxU9au1kDJprYvSTXFq6++yoknnsiLL77I1ltvzcUXX1zl51xRUsZtr07mT/+ZRKMGBfziqAGcNLgXDQqc01iSqkouepBnUD7++EhgVNb2L4Af5qB9SaoR+vXrx/bbb19t5xvx0VyueGosk2Yt5hsDu/CzI/qzWeum1XZ+SaqvcrnUdGtgSUqpNPO8AdAkpbQ0JyeoBJealpQrd999N6+88gp33313ta1EN3/JCn71jwk8WjSN7u2acc1R27HfNp2r5dySVJ9Ux1LT/wIOABZnnjfLbPtaDs8hSdVq9uzZTJ06laVLl9KiRYsqPVdKiSfens51z01g0bKVnLPPlnx//740a9ygSs8rSfqyXPYgv5tSGrShbdXBHmRJG6u0tJSbbrqJnXbaiX322YfS0lIKCgqqvPd48uzFXPnkWN78aC479WzLL781kG02b12l55Sk+q46epCXRMROKaW3MyfcGViWw/YlqcoVFxdz8803c+ihh7LPPvvQoEFue28Lr32BOYtXrHVf66YN+eU3B3L8Lj0o8CY8ScqbXAbkHwCPRcQMIIDNge9s6EURcQjwR6ABcEdK6dfrOO4Y4HFgl5SS3cOScmbFihXcc889nH766TRv3pw333yTTp06Vcm51hWOAV68eF86tWpSJeeVJFVczgJySmlkRGwDbJ3ZNDGltHJ9r8ncyHcLcCAwDRgZEcNSSuPXOK4V8H1gRK7qlaRVnn/+ec4++2y6dOnCEUccQefOub8hrqS0jMmzl6z3GMOxJNUMuV5+aWugP9AU2CkiSCndt57jdwUmpZQ+AoiIvwFHAePXOO4a4DfAJTmuV1I9tWzZMsaPH8/OO+/M4YcfzhtvvMHuu++ek7ZXlJTxwedfMG7GQsZMX8jY6YuYMHMRxSVlOWlfklS1chaQI+IqYF/KA/JzwKHAa8D6AnI3YGrW82nA4DXa3QnokVJ6NiIMyJJy4qyzzuKf//wnU6ZMoVWrVhsdjpevLOX9z75gzPSFjJu+kLEzFjLxsy9YWVp+A3SrJg3p37U1J+/Wi4Hd2vCDR97N4VchSaoKuexBPhbYAXgnpTQkIjYDHtiUBiOiALgBOK0Cxw4FhgL07NlzU04rqY764osvKCgooEWLFlxxxRWcdtpptGrVqsKvX1JcwviZixib6RUeN2MhH85aTGlZeRhu27wRA7u14Yw9t2C7bq3ZrmsberZv/qUb7gzIklTz5TIgL0splUVESWbRkFlAjw28Zvoax3TPbFulFbAd8HJmiqXNgWERceSaN+qllG4DboPyad426SuRVOcsXryYHXbYgcMPP5ybbrqJbbfdlm233Xadxy9ctpJxMxYybvoixs5YyNjpC/lozhJWzYzZsWUTBnZrzYH9N2NA1zZs16013do22+B0cB1bNl7rjXodWzbepK9PkpQ7uQzIRRHRFrid8iWnFwNvbuA1I4G+EdGH8mB8PHDiqp0ppYVAx1XPI+Jl4EfOYiGpokpKSmjYsCEtW7bk3HPPZY899vjKMfOWrCjvFc4E4bHTF/HpvP8tAtq1TVMGdGvDkTt0K+8Z7tZmo5d8LrrywI3+WiRJ1SMnATnKu0x+lVJaAPwlIv4JtE4pvbe+16WUSiLifOB5yqd5uyulNC4ifgEUpZSG5aI+SfXTm2++yYknnshzzz3HtttuyyWXXMKsRcv5z/ufM2Zaec/wuOkLmbFw+erX9GzfnO26teY7u/RgYLc2DOjamg4tnV1CkuqTnATklFKKiOeAgZnnH1fitc9RflNf9rafrePYfTe+Skn1SUqJ5h27sdnOB3Hv23P5fPhbjJ2xiNlfFAMQAX06tqCwd/vyINytNQO6tKFN80Z5rlySlG+5HGLxdkTsklIamcM2JWmDUkp8MncpY2cs5LEXhvPe9AXQtjvzl66ErY7k0bEL6du5FXv37bR6iMS2XVrTskmuZ7qUJNUFufzfYTBwckR8DCyhfDW9lFLaPofnkFRHrWsJ5o4tG39p3G5pWWLKnMWMnb5o9bjhcTMW8cXyEgAKaEyD4jKO7NeBQb07sl3X1myzeWuaNc7tktGSpLprkwNyRPRMKX0KHJyDeiTVU+tagnnO4hU8VjSVcTMWMWb6QsbPWMSylaUANGlYwLZdWrFVw/kM2q4jx+y3C1t0bE7TRg0oKCiozvIlSXVILnqQnwJ2Sil9EhF/Tykdk4M2JWm1Sx5/j+aNGzCga/nNc9t1a8PAbm3YslMLVq4oZuDA0+i4335sd/IB+S5VklQH5CIgZ0/6uUUO2pOkL3nx4n3o3aEFDTILbpSUlHD//fez5Smn0KxZM/773/+y+eab57lKSVJdkYu/QaZ1PJaknNiyU8vV4RjghRde4PTTT+fpp58GoEuXLhtcoEOSpIrKRQ/yDhGxiPKe5GaZx/C/m/Ra5+Ackuqw9z9btMFjVqxYwfjx4xk0aBCHHHIIL7/8MnvvvXc1VCdJqm82uQc5pdQgpdQ6pdQqpdQw83jVc8OxpPUaO30hx982nIJ1dACvWoL5nHPOYf/992fhwoVEBPvss4+9xpKkKuEkoJLy5p1P5/Pdu96iddNGPP29PejVocWX9i9dupSUykdu/fjHP+Zb3/oWbdq0yUepkqR6xHmQJOXFW1PmcfIdI2jfojGPnL3bWsPxjjvuyKWXXgrANttsw+GHH56PUiVJ9Yw9yJKq3euT5nDmvUV0bduUB8/cjc3bNF29r7S0lAYNGtC8eXOGDBnC4MGD81ipJKk+sgdZUrV66f1ZDLlnJL06NOdvQ3f/UjguKipim222YcKECQBceuml7LfffvkqVZJUT9mDLKnaPD/uM85/6G223rwV958+mHYtGn9pf8+ePenatSvFxcV5qlCSJHuQJVWT/xs9g/MefJsBXdvw4Jm7rQ7HTz75JGeddRYpJTp37swrr7zCoEGD8lusJKleMyBLqnKPj5rG9//2Djv3bMcDZw6mTbNGq/d99NFHvPPOOyxcuDCPFUqS9D+xagqluqSwsDAVFRXluwxJwEMjPuWKp8awx5Ydue27O9OsUQPuvfdeevfuzb777ktpaSkpJRo2dMSXJKl6RcSolFLhmtvtQZZUZe55fQqXPzmGfft14o5TC2neuCHFxcVcd9113HXXXQA0aNDAcCxJqlEMyJKqxF9emczV/zeeg/pvxp9P2pG/P/o3SkpKaNq0KS+99BL33HNPvkuUJGmtDMiSciqlxB///SG//sf7HLFDV245aSf++/JLnHzyyTz22GMAdO/enYICf/xIkmom/64pKWdSSvzu+Yn8+eXJfGvHrnx3mwY0alDAgQceyAsvvMD++++f7xIlSdogu3Ak5URKiWuemcCfX57MCbv2ZMlLf+Xr++3L/PnziQgOOOAAIiLfZUqStEH2IEvaZGVliZ8+PZYHR3zKybt255pvbscHA37IgQccQLt27fJdniRJlWJAlrRJSssSl/79PR4bNY3Gk15m/uJlxLduZuutt2brrbfOd3mSJFWaAVnSRispLeOiR99l2OiZfH//vixtNZqddvp6vsuSJGmTGJAlbZQVJWWc+peXeXPaMk7bsR0/PLAfHHh5vsuSJGmTeZOepEpbvrKUcx4YxZvTltF68r/4xhaNNvwiSZJqCXuQJVXKU//3LNe8PIu5jTpz7dHbcdLgw5ydQpJUpxiQJVXY4uISfvfWUuY27MjVh27Jybv1yndJkiTlnAFZ0nqllHjkkUdo03Fzbv+gEZ+VtuCGbw/gW4WGY0lS3WRAlrReK1as4KfX/prGB/6QFc07ccuJO3LIdl3yXZYkSVXGm/QkfUVKiccee4ySkhK+WAk9T72elS0689dTdjYcS5LqPHuQJX3FSy+9xHHHHcef7riPpxb2ZPqiFdx5aiF79e2U79IkSapy9iBLAqC0tJQJEyYAsN9++/HAE8/y2LzuzFywjHuG7Go4liTVGwZkSQBcdNFF7LHHHsydO5ep85bx54lNmbd4BfedMZjdtuiQ7/IkSao2DrGQ6rGVK1eycuVKmjdvznnnnUdhYSELShtz0h1vsryklIfO2o2B3dvku0xJkqqVAVmqp1asWMHXvvY1Bg8ezC233MLWW29NtO3Kd24bQUqJh8/ajW27tM53mZIkVTsDslTPpJSICBo3bszRRx/NgAEDABg3YyGn3PkWDQuCh4buxladW+W5UkmS8sMxyFI9Mm7cOAYNGsS4ceMAuPLKK/nmN7/Ju1MXcMJtw2nasIBHz97dcCxJqtfsQZbqkc6dO9OoUSMWLFiwelvRx/M47e6RtG/RmAfPHEyP9s3zV6AkSTWAPchSHffiiy9y3nnnkVKiU6dOjBw5kj322AOANybP4bt3vUXnVk145OzdDMeSJGFAluq89957jxdffJE5c+YAEBEAvDxxFkPuHkn3ds3429m70aVNs3yWKUlSjREppXzXkHOFhYWpqKgo32VIefP000/Ttm1b9tlnH0pLS1mxYgXNmv0vAL8w/nO+9+DbbNW5JQ+cOZj2LRrnsVpJkvIjIkallArX3O4YZKmOWblyJZdccgn9+/dnn332oUGDBl8Kx8++N5Pv/+0dBnRtzX2nD6ZN80Z5rFaSpJrHIRZSHZBSYtiwYZSUlNCoUSOef/55Hn300a8c9+Q707jg4bcZ1KMtD5xpOJYkaW0MyFId8Nprr3HUUUdx3333AdCnTx8aN/7ysIm/vfUpFz06msF9OnDv6bvSqqnhWJKktTEgS7VUSokPPvgAgL322ounn36aU089da3H3vfmx1z6xBj27tuJu4fsQosmjq6SJGldDMhSLXXJJZcwePBgZs2aBcCRRx5JgwYNvnLc7a9+xM+eHscB227Gbd/dmaaNvnqMJEn6H7uRpFoke0aKoUOHsvXWW9OpU6d1Hv+nFz/k+hc+4BsDu/CH4wfRqIG/E0uStCEGZKmWWLlyJfvuuy877LADf/7zn+nXrx/9+vVb67EpJa7/1wfc/NIkvrVjN3577PY0NBxLklQhBmSphkspERE0atSIgw8+mL59+27w+F8+N4Hb/zuF43fpwS+/OZCCgqimaiVJqv3sUpJqsIkTJzJ48GDGjh0LwM9+9jNOOOGEdR5fVpa4atg4bv/vFE7dvZfhWJKkjWAPslSDtW/fnhUrVqxeJnp9SssSlz8xhkeKpjJ07y247NBtVi8rLUmSKs4eZKmGee2117jgggtIKdGpUyfeeecd9t133/W+pqS0jIsffZdHiqZy4de3MhxLkrQJDMhSDVNUVMQzzzzD559/DrDBoLuytIwL//YOT707g0sO3pqLDtracCxJ0iaIlFK+a8i5wsLCVFRUlO8ypAr717/+RZMmTdhnn30oLS1l+fLltGjRYoOvKy4p5XsPvsO/J3zOld/YljP32qIaqpUkqW6IiFEppcI1tzsGWcqzkpISLrzwQvr06cM+++xDgwYNKhSOl60o5ewHRvHqB7O55qgBnLJ776ovVpKkesCALOXJ888/z9e//nUaNWrEs88+S7du3Sr82iXFJZx5bxHDp8zlt8dsz3G79KjCSiVJql8cgyzlwfDhwznkkEO4++67Adhyyy1p2rRphV67aPlKTr3rLd76eB43HjfIcCxJUo4ZkKVqklLio48+AmC33Xbj8ccfZ8iQIZVqY8HSFZxyxwjenbqAP52wI0fvWPFeZ0mSVDEGZKmaXHnlley000589tlnABxzzDE0atSowq+fu7iYE24fwYSZX/CXk3fmsIFdqqpUSZLqNccgS1WorKyMFStW0LRpU0477TQ222wzOnXqVOl2Zn2xnJNuH8Gn85Zy+6mF7NOv8m1IkqSKMSBLVaSkpISDDz6Yfv36ceutt9K3b1/69u1b6XZmLlzGSbeP4LNFy7lnyK7svmWHKqhWkiStYkCWciylRETQsGFD9t57b3r27Fmp1xde+wJzFq/4yvY2zRoZjiVJqgaOQZZyaPLkyey5556MGTMGgKuuuqrSN+KtLRwDLFy2cpPrkyRJG2ZAlnKoTZs2LFy4kJkzZ1b6tUuKS3hj8pwqqEqSJFWGQyykTTRy5EgeeughbrjhBjp27Mh7771HQcH6f/dMKTF9wTJGfTKftz+Zz6hP5zNh5heUltW9pd8lSaptDMjSJnrjjTd47LHH+NGPfkS3bt3WGo5XlJQxbsbC8kD86XxGfTKfzxcVA9C8cQMG9WjLeftuyU692jHk7pHV/SVIkqQsBmRpI7zyyisA7LPPPpx//vkMGTKE1q1br94/d3Exb3+6YHUP8ehpCyguKQOgW9tm7LZFB3bu1Y6derZjm81b0bCBo50kSaopDMhSJZWWlnLuuefStWtX9tlnHyIK+GxZ8MyET1f3EE+ZswSARg2CAV3bcPJuvVYH4s3brH9J6Y4tG6/1Rr2OLRtXydcjSZK+LFLK75jHiDgE+CPQALgjpfTrNfZfBJwJlACzgdNTSp+sr83CwsJUVFRURRWrvnrppZfYc889KS4Lnh0+jqlLGzJm5hLe/nQ+XywvAaB9i8bs1LMdhb3bsXOvdgzs1oamjRrkuXJJkrQ2ETEqpVS45va89iBHRAPgFuBAYBowMiKGpZTGZx32DlCYUloaEecCvwW+U/3Vqj5KKTFt/jIee3kU1/3lUbYcPJ85JU0oSxAB/Tq34vDtu7Jzr/JA3LtDcyIi32VLkqRNkO8hFrsCk1JKHwFExN+Ao4DVATml9FLW8cOBk6u1QtUrxSWljJuxqHxmiU/m89ZHc5i7tLx3uEPhN9iyRweO792enXu3Z1CPtrRp1ijPFUuSpFzLd0DuBkzNej4NGLye488A/rG2HRExFBgKVHrlMtVfcxYX/2+qtU/m8970hazI3EzXKoqZO3Eklw89gf2278XWm3kznSRJ9UG+A3KFRcTJQCGwz9r2p5RuA26D8jHI1ViaaonSssSHs75gVCYMv/3JfD6euxQov5luu25t+O7uvdihaysGb9mJRbOm8cQTkzjngP40bFhrvlUkSdImyvf/+tOBHlnPu2e2fUlEHABcAeyTUiquptpUy32xfCXvTl2wOhC/++kCvijODJdo0Zide7XjhF17snOvdmzXrQ2NCuCII45gao8eHPHXv9K59Vb8+Mc/zvNXIUmSqlu+A/JIoG9E9KE8GB8PnJh9QETsCPwVOCSlNKv6S1RtkFJi6rxljPp0XiYQL2DiZ4tW30y39WatOGJQV3bOzDDRs/3ab6bbZZdd6Ny5cx6+AkmSVFPUhGneDgP+QPk0b3ellK6LiF8ARSmlYRHxb2AgMDPzkk9TSkeur02neasbCq99YZ3zAb9+6dcZO33h6t7hUZ8sYM7i8j8utGzSkB17tmWnnuUzSwzq2ZbWTdd+M92nn37KkCFDuPHGG9l+++2r9OuRJEk1S42c5g0gpfQc8Nwa236W9fiAai9KNcLawvGq7QOv+hcrSstvpuvZvjl79e3ITr3aUdirHf02a0WDgopNtda8eXOmT5/Op59+akCWJElADQjI0sY4bY/e7NSzHTv1akvnVutfmW5No0eP5r777uP3v/89HTt2ZNy4cTRo4GIekiSpnHNWqVa6/LBtOWS7zSsdjgFeeeUVHnjgAaZOLZ9h0HAsSZKyGZBVIz06cuqGD6qEESNG8OqrrwJw/vnnM2HCBOfLliRJa+UQC9UopWWJXz03gTtem5KzNsvKyjjjjDNo164d//3vfykoKKB9+/Y5a1+SJNUt9iCrxli0fCWn3zOSO16bwmlf603Hlo3Xety6tq/p9ddfZ+XKlRQUFPD3v/+dZ599NpflSpKkOsoeZNUIH89Zwhn3juSTuUu57pvbcdLgXlx95ICNbu/dd99lzz335A9/+APf//732XrrrXNYrSRJqssMyMq7NybN4dwH3yYC7j9jMLtv2WGj25oxYwZdu3Zl0KBB3HfffRxzzDE5rFSSJNUHDrFQXt0//BNOuestOrdqwtPf22OTwvFvfvMb+vfvz4wZMwA45ZRTaN68ea5KlSRJ9YQ9yMqLlaVl/OL/xnP/8E/Yb+tO3HTCjrRax2p365NSYuXKlTRu3JhjjjmG4uJiOnbsWAUVS5Kk+iLvS01XBZeartkWLF3BeQ++zRuT5zJ07y34ySHbVHjlu2xlZWV8+9vfpkOHDtx2221VUKkkSarLauxS06pfJs36gjPvLWLGguX87tjt+XZhj41uq6CggAEDBtCmTRtSSkRUPmRLkiStyTHIqjYvT5zFN295g8XFJTx01uCNCsfTp0/nsMMOY/To0QD84he/4OKLLzYcS5KknDEgq8qllLjztSmcfs9IurdvzlPf24PC3hu3UEfTpk358MMPmTx5co6rlCRJKucQC1WpFSVl/PSpsTxSNJWDB2zGDccNokWTyv2zmzBhAvfeey+/+tWv6NChAxMmTKBhQ//pSpKkqmEPsqrM3MXFnHzHCB4pmsoFX9+KW0/audLhGODf//43t99+Ox9//DGA4ViSJFUpZ7FQlXj/s0WccU8RcxYX89tjt+eoQd0q9fp33nmHxYsXs9dee1FWVsbcuXPp1KlTFVUrSZLqI2exULV5Yfzn/OBv79CiSUMePXt3dujRtlKvTylx6qmn0rx5c958800KCgoMx5IkqdoYkJUzKSVufWUyv3t+IgO7teG2UwrZvE3TCr9+5MiR7LDDDjRu3JhHHnmEzTff3NkpJElStXMMsnJi+cpSLnp0NL/950S+MbALjwzdvVLheNy4cQwePJibb74ZgG233ZZ27dpVVbmSJEnrZA+yNtmsL5Yz9L5RvDt1ARcf2I/zv75VhXt+P//8czbbbDMGDBjAnXfeybHHHlvF1UqSJK2fPcjaJGOnL+Som19n4mdf8JeTd+KC/ftWOBz/4Q9/YOutt2b69OkADBkyhFatWlVluZIkSRtkD7I22nNjZnLRo+/SvnljHj93dwZ0bVOh15WUlNCwYUOOOOIIZs+eTfv2G7doiCRJUlVwmjdVWkqJm16cxI3//oCderblr6cU0qlVkwq97pRTTqF58+bcdttt1VCpJEnSujnNm3Ji2YpSfvT4aJ59bybf2qkbv/rWQJo0bFCh10YEffr0oWnTpqSUnKFCkiTVSAZkVdjMhcs4674ixs1YxOWHbcNZe22xwZD7+eefc84553D11Vezww47cM0111RTtZIkSRvHm/RUIe98Op8jb36dj+cs5Y7vFjJ07y0r1APcsGFDRo8ezYQJE6qhSkmSpE1nQNYGPfXOdL5z23CaNirgifO+xv7bbrbe4ydPnswVV1xBSokOHTowceJEjj/++GqqVpIkadMYkLVOZWWJ3/7zfX7wyLvs2KMtT39vT/pttuFp2J5//nluvvlmJk2aBECjRo2qulRJkqSccRYLrdXi4hJ++Mi7vDD+c07YtQc/P3I7Gjdc9+9T48aNY/78+ey5556UlZXx+eef06VLl2qsWJIkqXKcxUIVNnXeUs66r4gPPv+Cq4/oz6lf673e8cYpJU4++WQiglGjRlFQUGA4liRJtZYBWV8y8uN5nH3/KFaWlnHPkF3Zu1+ndR47evRott12Wxo3bsyDDz5Ix44dnbpNkiTVeo5B1mqPjpzKibcPp22zRjz1vT3WG44nTpzIzjvvzB//+EcA+vfvT+fOnaurVEmSpCpjD7IoLUv86rkJ3PHaFPbq25GbT9iJNs3XfmPdnDlz6NixI1tvvTW33nor3/72t6u5WkmSpKplD3I9t2j5Sk6/ZyR3vDaF077Wm7tP22Wd4fjWW29lq622Ytq0aQCcddZZtG3bthqrlSRJqnr2INdjH89Zwhn3juSTuUv55TcHcuLgnms9rrS0lAYNGnDQQQcxadIkQ7EkSarTnOatnnpj0hzOffBtIuDWk3Zm9y07fOWYlBJDhw4F4Pbbb6/uEiVJkqqU07xptfuHf8LVw8axRccW3HnqLvTs0Hytx0UEm222GSklUkrOUCFJkuoFA3I9srK0jF/833juH/4JX9+mM388fhCtmn55vPGcOXO44IIL+MlPfsKgQYO49tpr81StJElSfniTXj2xYOkKTr3rLe4f/gln770Ft3+38CvhGKCgoIA333yT0aNH56FKSZKk/LMHuR6YNOsLzry3iBkLlvP7b+/AsTt3/9L+qVOncuedd3LVVVfRvn17Jk6cSJMmTfJUrSRJUn7Zg1zHvTxxFt+85Q0WF5fw8NDBXwnHAM888wy/+93veP/99wEMx5IkqV4zINdRKSXufG0Kp98zku7tm/P0+Xuyc6/2q/d/+OGHvPHGGwCcffbZvP/++2y77bb5KleSJKnGcIhFHbSipIyfPjWWR4qmcvCAzbjhuEG0aPK/S51S4sQTT6S4uJjRo0dTUFBAjx498lixJElSzWFArmPmLi7m3Afe5q2P53Hh17fiBwf0o6CgfHq28ePHs9VWW9G4cWPuuece2rVr59RtkiRJa3CIRR3y/meLOPLm1xk9bQE3nbAjFx209epwPHnyZAYNGsT1118PwIABA+jatWs+y5UkSaqR7EGuI14Y/zk/+Ns7tGjSkEfP3p0derQFYMGCBbRt25Ytt9ySP/7xj3z729/Ob6GSJEk1nD3ItVxKiT+/PImh9xexZeeWDDt/z9Xh+K677mKLLbZg2rRpAJx77rl07Ngxj9VKkiTVfPYg12LLV5Zy6d/f46l3Z3DEDl353bHb07RRA8rKyigoKGC//fbjhBNOoFWrVvkuVZIkqdYwINdSsxYtZ+j9o3h36gJ+dFA/vrffVgBccMEFLF++nNtvv50+ffpwyy235LlSSZKk2sWAXAuNnb6Qs+4rYsHSlfzl5J05ZLvNV+9r3bo1jRs3Xt2LLEmSpMoxINcyz743k4sfe5f2zRvz+Lm707VZGaeddhrf//732XHHHbn22muduk2SJGkT2MVYS5SVJf7w7w/43kNvM6BrG54+f08GdG0DwH/+8x+KiooADMeSJEmbyB7kWmDZilJ+9Nhonh0zk2N26s6Fu3fkr3/8HVdccQXt2rVj4sSJNGvWLN9lSpIk1Qn2INdwMxcu49t/fYPnxs7k8sO24fff3p5/PvcM1157LWPHjgUwHEuSJOWQAbkGe+fT+Rx58+t8PGcpvzq0FwMbzSIiOOuss5gwYQIDBw7Md4mSJEl1jkMsaqgn35nGT/4+hs1aN+GBM3bltG8ezMKFCxk7diwFBQX06dMn3yVKkiTVSQbkGqasLPG7f03k1pcns/3mzbjt1F3ZvF1LbrvtNlq3bu3UbZIkSVXMtFWDLC4uYej9o7j15ckcvm07XrjiKO645Q8ADBw4kF69euW3QEmSpHrAHuQaYuq8pZx1XxEffP4FVx/Rn1O/1putF/2SY489Nt+lSZIk1Sv2INcAb02Zx1G3vM7Hsxay+Nnfsn/PhkQEF1xwAV26dMl3eZIkSfWKATnPHhn5KSfdMZy2zRrx12/35chd+9KiRYt8lyVJklRvOcQiT0pKy/jVP97nztem0H7FLJ684mTaNG/EPnfcke/SJEmS6jUDch4sWr6SCx56h1c+mM3WMYM+y9+lVdNT812WJEmSMCBXuzEff87xt7zE8oat+OU3B3LCrocREfkuS5IkSRmOQa5Gr0+aw0n3vMvikuCYDjM5cXBPw7EkSVINYw9yNZgzZw4/uOUJ3izuzhYdW/DIGV9n2x6d8l2WJEmS1sKAXAUKr32BOYtXrLG1Gw0LEk+c9zVaNW2Ul7okSZK0YQ6xqAJfDcflSsowHEuSJNVweQ/IEXFIREyMiEkRcela9jeJiEcy+0dERO88lClJkqR6Iq8BOSIaALcAhwL9gRMiov8ah50BzE8pbQXcCPymequsuI8//pji4uJ8lyFJkqRNkO8e5F2BSSmlj1JKK4C/AUetccxRwL2Zx48D+0cNnPph6tSpDBgwgN/8psbmd0mSJFVAvgNyN2Bq1vNpmW1rPSalVAIsBDqs2VBEDI2Ioogomj17dhWVu249evTguuuuY8iQIdV+bkmSJOVOvgNyzqSUbkspFaaUCjt1ys8Uaj/4wQ/o0aMHHVs2Xuv+dW2XJElSzZHvad6mAz2ynnfPbFvbMdMioiHQBphbPeVtnKIrD8x3CZIkSdpI+e5BHgn0jYg+EdEYOB4YtsYxw4BTM4+PBf6TUkrVWKMkSZLqkbz2IKeUSiLifOB5oAFwV0ppXET8AihKKQ0D7gTuj4hJwDzKQ7QkSZJUJfI9xIKU0nPAc2ts+1nW4+XAt6u7LkmSJNVP+R5iIUmSJNUoBmRJkiQpiwFZkiRJymJAliRJkrIYkCVJkqQsBmRJkiQpiwFZkiRJymJAliRJkrIYkCVJkqQsBmRJkiQpiwFZkiRJymJAliRJkrIYkCVJkqQskVLKdw05FxGzgU/ycOqOwJw8nFdVz2tbN3ld6y6vbd3kda278nVte6WUOq25sU4G5HyJiKKUUmG+61DueW3rJq9r3eW1rZu8rnVXTbu2DrGQJEmSshiQJUmSpCwG5Ny6Ld8FqMp4besmr2vd5bWtm7yudVeNuraOQZYkSZKy2IMsSZIkZTEgS5IkSVkMyBshIg6JiIkRMSkiLl3L/iYR8Uhm/4iI6J2HMlVJFbiuF0XE+Ih4LyJejIhe+ahTlbeha5t13DERkSKixkw1pHWryHWNiOMy37fjIuKh6q5RG6cCP497RsRLEfFO5mfyYfmoU5UTEXdFxKyIGLuO/RERN2Wu+3sRsVN117iKAbmSIqIBcAtwKNAfOCEi+q9x2BnA/JTSVsCNwG+qt0pVVgWv6ztAYUppe+Bx4LfVW6U2RgWvLRHRCvg+MKJ6K9TGqMh1jYi+wGXAHimlAcAPqrtOVV4Fv2evBB5NKe0IHA/8uXqr1Ea6BzhkPfsPBfpmPoYCt1ZDTWtlQK68XYFJKaWPUkorgL8BR61xzFHAvZnHjwP7R0RUY42qvA1e15TSSymlpZmnw4Hu1VyjNk5FvmcBrqH8l9nl1VmcNlpFrutZwC0ppfkAKaVZ1VyjNk5Frm0CWmcetwFmVGN92kgppVeBees55CjgvlRuONA2IrpUT3VfZkCuvG7A1Kzn0zLb1npMSqkEWAh0qJbqtLEqcl2znQH8o0orUq5s8Npm/ozXI6X0bHUWpk1Ske/ZfkC/iHg9IoZHxPp6rlRzVOTaXg2cHBHTgOeAC6qnNFWxyv5fXGUa5uOkUm0WEScDhcA++a5Fmy4iCoAbgNPyXIpyryHlf6rdl/K/+LwaEQNTSgvyWZRy4gTgnpTS9RGxO3B/RGyXUirLd2GqG+xBrrzpQI+s590z29Z6TEQ0pPzPP3OrpTptrIpcVyLiAOAK4MiUUnE11aZNs6Fr2wrYDng5Ij4GdgOGeaNejVeR79lpwLCU0sqU0hTgA8oDs2q2ilzbM4BHAVJKbwJNgY7VUp2qUoX+L64OBuTKGwn0jYg+EdGY8psDhq1xzDDg1MzjY4H/JFdkqek2eF0jYkfgr5SHY8cy1h7rvbYppYUppY4ppd4ppd6Ujy8/MqVUlJ9yVUEV+Vn8FOW9x0RER8qHXHxUjTVq41Tk2n4K7A8QEdtSHpBnV2uVqgrDgO9mZrPYDViYUpqZj0IcYlFJKaWSiDgfeB5oANyVUhoXEb8AilJKw4A7Kf9zzyTKB6Mfn7+KVREVvK6/A1oCj2Xuufw0pXRk3opWhVTw2qqWqeB1fR44KCLGA6XAJSkl/5pXw1Xw2l4M3B4RP6T8hr3T7Iiq+SLiYcp/ae2YGT9+FdAIIKX0F8rHkx8GTAKWAkPyU6lLTUuSJElf4hALSZIkKYsBWZIkScpiQJYkSZKyGJAlSZKkLAZkSZIkKYsBWZIkScpiQJYkSZKyGJAlSZKkLAZkSZIkKYsBWZIkScpiQJYkSZKyNMx3AVWhY8eOqXfv3vkuQ5IkSTXYqFGj5qSUOq25vU4G5N69e1NUVJTvMiRJklSDRcQna9vuEAtJkiQpiwFZkiRJymJAliRJkrIYkCVJkqQsBmRJkiQpiwFZkiRJymJAliRJkrLUyXmQ86H3pc/mu4Rq9/Gvv5HvEiRJqpPMFfllD7IkSZKUxR7kHKtJv/1Ulfr4W60kSflgrsgPe5AlSZKkLAZkSZIkKYsBWZIkScpiQJYkSZKyGJAlSZKkLAZkSZIkKYsBWZIkScpiQJYkSZKyGJAlSZKkLAZkSZIkKYsBWZIkScpiQJYkSZKyGJAlSZKkLFUWkCOiR0S8FBHjI2JcRHw/s/3qiJgeEe9mPg7Les1lETEpIiZGxMFZ2w/JbJsUEZdWVc2SJElSwypsuwS4OKX0dkS0AkZFxAuZfTemlH6ffXBE9AeOBwYAXYF/R0S/zO5bgAOBacDIiBiWUhpfhbVLkiSpnqqygJxSmgnMzDz+IiImAN3W85KjgL+llIqBKRExCdg1s29SSukjgIj4W+ZYA7IkSZJyrlrGIEdEb2BHYERm0/kR8V5E3BUR7TLbugFTs142LbNtXdvXPMfQiCiKiKLZs2fn+kuQJElSPVHlATkiWgJ/B36QUloE3ApsCQyivIf5+lycJ6V0W0qpMKVU2KlTp1w0KUmSpHqoKscgExGNKA/HD6aUngBIKX2etf924JnM0+lAj6yXd89sYz3bJUmSpJyqylksArgTmJBSuiFre5esw74JjM08HgYcHxFNIqIP0Bd4CxgJ9I2IPhHRmPIb+YZVVd2SJEmq36qyB3kP4BRgTES8m9l2OXBCRAwCEvAxcDZASmlcRDxK+c13JcD3UkqlABFxPvA80AC4K6U0rgrrliRJUj1WlbNYvAbEWnY9t57XXAdct5btz63vdZIkSVKuuJKeJEmSlMWALEmSJGUxIEuSJElZDMiSJElSFgOyJEmSlMWALEmSJGUxIEuSJElZDMiSJElSFgOyJEmSlMWALEmSJGUxIEuSJElZDMiSJElSFgOyJEmSlMWALEmSJGUxIEuSJElZDMiSJElSFgOyJEmSlMWALEmSJGUxIEuSJElZDMiSJElSFgOyJEmSlMWALEmSJGUxIEuSJElZDMiSJElSFgOyJEmSlKXKAnJE9IiIlyJifESMi4jvZ7a3j4gXIuLDzOd2me0RETdFxKSIeC8idspq69TM8R9GxKlVVbMkSZJUlT3IJcDFKaX+wG7A9yKiP3Ap8GJKqS/wYuY5wKFA38zHUOBWKA/UwFXAYGBX4KpVoVqSJEnKtSoLyCmlmSmltzOPvwAmAN2Ao4B7M4fdCxydeXwUcF8qNxxoGxFdgIOBF1JK81JK84EXgEOqqm5JkiTVb9UyBjkiegM7AiOAzVJKMzO7PgM2yzzuBkzNetm0zLZ1bV/zHEMjoigiimbPnp3bL0CSJEn1RpUH5IhoCfwd+EFKaVH2vpRSAlIuzpNSui2lVJhSKuzUqVMumpQkSVI9VKUBOSIaUR6OH0wpPZHZ/Hlm6ASZz7My26cDPbJe3j2zbV3bJUmSpJyrylksArgTmJBSuiFr1zBg1UwUpwJPZ23/bmY2i92AhZmhGM8DB0VEu8zNeQdltkmSJEk517AK294DOAUYExHvZrZdDvwaeDQizgA+AY7L7HsOOAyYBCwFhgCklOZFxDXAyMxxv0gpzavCuiVJklSPVVlATim9BsQ6du+/luMT8L11tHUXcFfuqpMkSZLWzpX0JEmSpCwGZEmSJCmLAVmSJEnKYkCWJEmSshiQJUmSpCwGZEmSJCmLAVmSJEnKYkCWJEmSshiQJUmSpCwGZEmSJCmLAVmSJEnKYkCWJEmSshiQJUmSpCwGZEmSJCmLAVmSJEnKYkCWJEmSshiQJUmSpCwGZEmSJCmLAVmSJEnKUqGAHBF7VGSbJEmSVNtVtAf5TxXcJkmSJNVqDde3MyJ2B74GdIqIi7J2tQYaVGVhkiRJUj6sNyADjYGWmeNaZW1fBBxbVUVJkiRJ+bLegJxSegV4JSLuSSl9Uk01SZIkSXmzoR7kVZpExG1A7+zXpJS+XhVFSZIkSflS0Zv0HgPeAa4ELsn6WKeIuCsiZkXE2KxtV0fE9Ih4N/NxWNa+yyJiUkRMjIiDs7Yfktk2KSIurcwXJ0mSJFVWRXuQS1JKt1ay7XuAm4H71th+Y0rp99kbIqI/cDwwAOgK/Dsi+mV23wIcCEwDRkbEsJTS+ErWIkmSJFVIRXuQ/y8izouILhHRftXH+l6QUnoVmFfB9o8C/pZSKk4pTQEmAbtmPiallD5KKa0A/pY5VpIkSaoSFe1BPjXzOXtYRQK22Ihznh8R3wWKgItTSvOBbsDwrGOmZbYBTF1j++C1NRoRQ4GhAD179tyIsiRJkqQK9iCnlPqs5WNjwvGtwJbAIGAmcP1GtLGuGm9LKRWmlAo7deqUq2YlSZJUz1SoBznT4/sVKaU1xxevV0rp86w2bweeyTydDvTIOrR7Zhvr2S5JkiTlXEWHWOyS9bgpsD/wNl+9AW+9IqJLSmlm5uk3gVUzXAwDHoqIGyi/Sa8v8BYQQN+I6EN5MD4eOLEy55QkSZIqo0IBOaV0QfbziGhL+Q1z6xQRDwP7Ah0jYhpwFbBvRAyifPzyx8DZmfbHRcSjwHigBPheSqk00875wPOUL219V0ppXMW+NEmSJKnyKtqDvKYlQJ/1HZBSOmEtm+9cz/HXAdetZftzwHOVLVCSJEnaGBUdg/x/lPf6QnlP7rbAo1VVlCRJkpQvFe1Bzl7YowT4JKU0rQrqkSRJkvKqotO8vQK8D7QC2gErqrIoSZIkKV8qFJAj4jjKZ5X4NnAcMCIijq3KwiRJkqR8qOgQiyuAXVJKswAiohPwb+DxqipMkiRJyocK9SADBavCccbcSrxWkiRJqjUq2oP8z4h4Hng48/w7OPWaJEmS6qD1BuSI2ArYLKV0SUR8C9gzs+tN4MGqLk6SJEmqbhvqQf4DcBlASukJ4AmAiBiY2XdEFdYmSZIkVbsNjSPeLKU0Zs2NmW29q6QiSZIkKY82FJDbrmdfsxzWIUmSJNUIGwrIRRFx1pobI+JMYFTVlCRJkiTlz4bGIP8AeDIiTuJ/gbgQaAx8swrrkiRJkvJivQE5pfQ58LWI2A/YLrP52ZTSf6q8MkmSJCkPKjQPckrpJeClKq5FkiRJyjtXw5MkSZKyGJAlSZKkLAZkSZIkKYsBWZIkScpiQJYkSZKyGJAlSZKkLAZkSZIkKYsBWZIkScpiQJYkSZKyGJAlSZKkLFUWkCPiroiYFRFjs7a1j4gXIuLDzOd2me0RETdFxKSIeC8idsp6zamZ4z+MiFOrql5JkiQJqrYH+R7gkDW2XQq8mFLqC7yYeQ5wKNA38zEUuBXKAzVwFTAY2BW4alWoliRJkqpClQXklNKrwLw1Nh8F3Jt5fC9wdNb2+1K54UDbiOgCHAy8kFKal1KaD7zAV0O3JEmSlDPVPQZ5s5TSzMzjz4DNMo+7AVOzjpuW2bau7V8REUMjoigiimbPnp3bqiVJklRv5O0mvZRSAlIO27stpVSYUirs1KlTrpqVJElSPVPdAfnzzNAJMp9nZbZPB3pkHdc9s21d2yVJkqQqUd0BeRiwaiaKU4Gns7Z/NzObxW7AwsxQjOeBgyKiXebmvIMy2yRJkqQq0bCqGo6Ih4F9gY4RMY3y2Sh+DTwaEWcAnwDHZQ5/DjgMmAQsBYYApJTmRcQ1wMjMcb9IKa15458kSZKUM1UWkFNKJ6xj1/5rOTYB31tHO3cBd+WwNEmSJGmdXElPkiRJymJAliRJkrIYkCVJkqQsBmRJkiQpiwFZkiRJymJAliRJkrIYkCVJkqQsBmRJkiQpiwFZkiRJymJAliRJkrIYkCVJkqQsBmRJkiQpiwFZkiRJymJAliRJkrIYkCVJkqQsBmRJkiQpiwFZkiRJymJAliRJkrIYkCVJkqQsBmRJkiQpiwFZkiRJymJAliRJkrIYkCVJkqQsBmRJkiQpiwFZkiRJypKXgBwRH0fEmIh4NyKKMtvaR8QLEfFh5nO7zPaIiJsiYlJEvBcRO+WjZkmSJNUP+exB3i+lNCilVJh5finwYkqpL/Bi5jnAoUDfzMdQ4NZqr1SSJEn1Rk0aYnEUcG/m8b3A0Vnb70vlhgNtI6JLHuqTJElSPZCvgJyAf0XEqIgYmtm2WUppZubxZ8BmmcfdgKlZr52W2SZJkiTlXMM8nXfPlNL0iOgMvBAR72fvTCmliEiVaTATtIcC9OzZM3eVSpIkqV7JSw9ySml65vMs4ElgV+DzVUMnMp9nZQ6fDvTIenn3zLY127wtpVSYUirs1KlTVZYvSZKkOqzaA3JEtIiIVqseAwcBY4FhwKmZw04Fns48HgZ8NzObxW7AwqyhGJIkSVJO5WOIxWbAkxGx6vwPpZT+GREjgUcj4gzgE+C4zPHPAYcBk4ClwJDqL1mSJEn1RbUH5JTSR8AOa9k+F9h/LdsT8L1qKE2SJEmqUdO8SZIkSXlnQJYkSZKyGJAlSZKkLAZkSZIkKYsBWZIkScpiQJYkSZKyGJAlSZKkLAZkSZIkKYsBWZIkScpiQJYkSZKyGJAlSZKkLAZkSZIkKYsBWZIkScpiQJYkSZKyGJAlSZKkLAZkSZIkKYsBWZIkScpiQJYkSZKyGJAlSZKkLA3zXYAkSdKG9L702XyXoHrEHmRJkiQpiz3IkiSp1vj419/IdwmqB+xBliRJkrLYgyxJUi3luFypahiQtdH8wVx3+SdMSVJ9ZkCWVC/5C57qEn+plXKr1gTkiDgE+CPQALgjpfTrPJdUb/mDuO5aFRoNj5Kk+qxWBOSIaADcAhwITANGRsSwlNL4/FYmqbbzFz5J0ppqRUAGdgUmpZQ+AoiIvwFHAQZkKYcMi5Ik1Z6A3A2YmvV8GjA4+4CIGAoMzTxdHBETq6m2bB3jN8zJw3lV9TqC17YO8rrWXV7busnrWnflK0P1WtvG2hKQNyildBtwWz5riIiilFJhPmtQ1fDa1k1e17rLa1s3eV3rrpp2bWvLQiHTgR5Zz7tntkmSJEk5VVsC8kigb0T0iYjGwPHAsDzXJEmSpDqoVgyxSCmVRMT5wPOUT/N2V0ppXJ7LWpu8DvFQlfLa1k1e17rLa1s3eV3rrhp1bSOllO8aJEmSpBqjtgyxkCRJkqqFAVmSJEnKYkDeCBFxSERMjIhJEXHpWvY3iYhHMvtHRETvPJSpSqrAdb0oIsZHxHsR8WJErHXuRNU8G7q2WccdExEpImrMVENat4pc14g4LvN9Oy4iHqruGrVxKvDzuGdEvBQR72R+Jh+WjzpVORFxV0TMioix69gfEXFT5rq/FxE7VXeNqxiQKylr2etDgf7ACRHRf43DzgDmp5S2Am4EflO9VaqyKnhd3wEKU0rbA48Dv63eKrUxKnhtiYhWwPeBEdVboTZGRa5rRPQFLgP2SCkNAH5Q3XWq8ir4PXsl8GhKaUfKZ7b6c/VWqY10D3DIevYfCvTNfAwFbq2GmtbKgFx5q5e9TimtAFYte53tKODezOPHgf0jIqqxRlXeBq9rSumllNLSzNPhlM/HrZqvIt+zANdQ/svs8uosThutItf1LOCWlNJ8gJTSrGquURunItc2Aa0zj9sAM6qxPm2klNKrwLz1HHIUcF8qNxxoGxFdqqe6LzMgV97alr3utq5jUkolwEKgQ7VUp41Vkeua7QzgH1VakXJlg9c282e8HimlZ6uzMG2SinzP9gP6RcTrETE8ItbXc6WaoyLX9mrg5IiYBjwHXFA9pamKVfb/4ipTK+ZBlmqSiDgZKAT2yXct2nQRUQDcAJyW51KUew0p/1PtvpT/xefViBiYUlqQz6KUEycA96SUro+I3YH7I2K7lFJZvgtT3WAPcuVVZNnr1cdEREPK//wzt1qq08aq0HLmEXEAcAVwZEqpuJpq06bZ0LVtBWwHvBwRHwO7AcO8Ua/Gq8j37DRgWEppZUppCvAB5YFZNVtFru0ZwKMAKaU3gaZAx2qpTlWpQv8XVwcDcuVVZNnrYcCpmcfHAv9JrshS023wukbEjsBfKQ/HjmWsPdZ7bVNKC1NKHVNKvVNKvSkfX35kSqkoP+Wqgirys/gpynuPiYiOlA+5+Kgaa9TGqci1/RTYHyAitqU8IM+u1ipVFYYB383MZrEbsDClNDMfhTjEopLWtex1RPwCKEopDQPupPzPPZMoH4x+fP4qVkVU8Lr+DmgJPJa55/LTlNKReStaFVLBa6tapoLX9XngoIgYD5QCl6SU/GteDVfBa3sxcHtE/JDyG/ZOsyOq5ouIhyn/pbVjZvz4VUAjgJTSXygfT34YMAlYCgzJT6UuNS1JkiR9iUMsJEmSpCwGZEmSJCmLAVmSJEnKYkCWJEmSshiQJUmSpCwGZElaQ0SkiHgg63nDiJgdEc/ks67/b+/eQqyq4jiOf3+K5qXUEh8KiiGiRCWvmZcyA7VESNAhM4mUXqIwKoQeClIi6AZhWfggKoioZASS5qUHmzTLzMt4IZ+SkKSCUsq0sP497P/A9rTPzDlWCPX7wIG9//u/LmcNDGvWWWdWsySdyP//i6RPusidL+m6JutvkXTk7/Txn6zHzOyf4gmymdlfnQWGSeqd91O5TKc51crTOZsWERO6SJkPNDVBNjP7r/IE2cys2hZgRl7PBdZ1PJDUV9JKSXslHZA0M+Mtkj6WtD9fEzI+WdJOSRslfSlprfK0mbLMWSrpoKQjksZmfLGkNZJ2UxxCNEjSu5I+z9fEzBsoabuko5JWACrV/XPp+hlJhyUdkvSSpFZgDLA22+4tabSkjyR9IWmbpGuz7Ogsdwh4vGrgJK2XNKN0v1pSa73xqSk7X9Ky0v37kibn9TRJe7LsO5Ku7OwHaGZ2qTxBNjOrth54QFIv4Fbgs9KzZymOkB8L3A28Kqkv8B0wNSJGAXOAN0plRgJPAkOAG4GJddrtExEjgMeAlaX4EGBKRMwFlgKvR8RtwGxgReY8D+yKiKHAe8ANtZVLmg7MBG6PiOHAKxGxEdgHzMu2LwBvAq0RMTr78WJWsQpYmGXr2QDcn+31pDgSeHMX49Op3CryXI7BqOzv042WNzNrho+aNjOrEBHtklooVo+31DyeBtwnaVHe96KYjH4DLJM0guJo45tLZfZGxEkASQeBFmBXRdPrsv02Sf0kDcj4pog4l9dTgCGlReh+uZo6CZiV5TdL+rGi/inAqoj4JfN+qMi5BRgG7Mg2ugOnsi8DIqIt89YA0yvKfwAslXQFcC/QFhHnJPWn/vh0ZRzFHwm7s089gT1NlDcza5gnyGZm9W0CXgMmAwNLcQGzI+J4OVnSYuBbYDjFJ3TnS49/LV3/Tv3fv1Hn/mwp1g0YFxHl+qnYtXGpBByNiPE19Q9opHBEnJe0E7iHYqV4fT56ivrj0+ECF3+62avUpx25gm5m9q/yFgszs/pWAksi4nBNfBuwsGMfsaSRGe8PnIqIP4CHKFZemzUn67wDOBMRZypytgMLO25yRRagDXgwY9OBqyvK7gAWSOqTeddk/Cfgqrw+DgySND5zekgaGhGngdPZN4B5nbyPDcAC4E5ga8YaGZ8TwAhJ3SRdD4zN+KfAREk3ZZ/6SmpmBdrMrGGeIJuZ1RERJyOiap/sC0APoF3S0bwHeBt4OL/ANpiLV30bdV7SAWA58EidnCeAMZLaJR0DHs34EmBS9mkW8HXFe9pKsTK+L7d6dGwTWQ0sz1h3oBV4Od/LQaDjC3ULgLcyr7Ml6+3AXcCHEfFbxhoZn93AV8Axij3K+7Pf31P8p411ktoptlcM7qR9M7NLpojaT/PMzOxyyG0JiyJi3+Xui5nZ/5lXkM3MzMzMSryCbGZmZmZW4hVkMzMzM7MST5DNzMzMzEo8QTYzMzMzK/EE2czMzMysxBNkMzMzM7OSPwFyo/GW0h+0MgAAAABJRU5ErkJggg==\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], - "source": [ - "n_cv = 2\n", - "for clf_name, clf in clf_models.items():\n", - " if \"weighted_loss\" in clf_name:\n", - " continue\n", - " tmp_y = []\n", - " tmp_pred = []\n", - " for cv in range(n_cv):\n", - " tmp_y.append(clf.eval_result[\"y\"][cv])\n", - " tmp_pred.append(clf.eval_result[\"proba\"][cv])\n", - " print(clf_name)\n", - " y_test = np.array(tmp_y).flatten()\n", - " y_pred = np.array(tmp_pred).flatten()\n", - " plot_roc_auc_curve(y_test, y_pred, clf_name)\n", - " plot_calibration_curve(y_test, y_pred, clf_name)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "975b27b1", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6704dfde", - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.1" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/examples/quickstart/multiclass.ipynb b/examples/quickstart/multiclass.ipynb index f888ed42..21594496 100644 --- a/examples/quickstart/multiclass.ipynb +++ b/examples/quickstart/multiclass.ipynb @@ -8,13 +8,13 @@ "---\n", "This notebook provides an example of conducting OPE of an evaluate policy using multi-class classification data as logged bandit data.\n", "\n", - "The example consists of the follwoing four major steps:\n", + "The example consists of the following four major steps:\n", "- (1) Bandit Reduction\n", "- (2) Off-Policy Learning\n", "- (3) Off-Policy Evaluation\n", "- (4) Evaluation of OPE Estimators\n", "\n", - "Please see [../examples/multiclass](../examples/multiclass) for a more sophisticated example of the evaluation of OPE with multi-class classification datasets." + "Please see [../examples/multiclass](../multiclass) for a more sophisticated example of the evaluation of OPE with multi-class classification datasets." ] }, { @@ -29,7 +29,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -51,14 +51,14 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0.5.0\n" + "0.5.2\n" ] } ], @@ -92,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "tags": [] }, @@ -105,14 +105,14 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "# convert the raw classification data into a logged bandit dataset\n", - "# we construct a behavior policy using Logistic Regression and parameter alpha_b\n", + "# we construct a behavior policy using Logistic Regression and parameter `alpha_b`\n", "# given a pair of a feature vector and a label (x, c), create a pair of a context vector and reward (x, r)\n", - "# where r = 1 if the output of the behavior policy is equal to c and r = 0 otherwise\n", + "# where r = 1 if the output of the behavior policy is equal to c, and r = 0 otherwise\n", "dataset = MultiClassToBanditReduction(\n", " X=X,\n", " y=y,\n", @@ -131,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -146,14 +146,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "the logged bandit data is collected by the behavior policy as follows.\n", + "the logged bandit data is generated by the behavior policy as follows.\n", "\n", - "$ \\mathcal{D}_b := \\{(x_i,a_i,r_i)\\}_{i=1}^n$ where $(x,a,r) \\sim p(x)\\pi_b(a \\mid x)p(r \\mid x,a) $" + "$ \\mathcal{D}_b := \\{(x_i,a_i,r_i)\\}_{i=1}^n$ where $(x,a,r) \\sim p(x)\\pi_b(a|x)p(r|x,a) $" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -171,10 +171,59 @@ " 'action': array([6, 8, 5, ..., 2, 5, 9]),\n", " 'reward': array([1., 1., 1., ..., 1., 1., 1.]),\n", " 'position': None,\n", + " 'pi_b': array([[[0.02],\n", + " [0.02],\n", + " [0.02],\n", + " ...,\n", + " [0.02],\n", + " [0.02],\n", + " [0.02]],\n", + " \n", + " [[0.02],\n", + " [0.02],\n", + " [0.02],\n", + " ...,\n", + " [0.02],\n", + " [0.82],\n", + " [0.02]],\n", + " \n", + " [[0.02],\n", + " [0.02],\n", + " [0.02],\n", + " ...,\n", + " [0.02],\n", + " [0.02],\n", + " [0.02]],\n", + " \n", + " ...,\n", + " \n", + " [[0.02],\n", + " [0.02],\n", + " [0.82],\n", + " ...,\n", + " [0.02],\n", + " [0.02],\n", + " [0.02]],\n", + " \n", + " [[0.02],\n", + " [0.02],\n", + " [0.02],\n", + " ...,\n", + " [0.02],\n", + " [0.02],\n", + " [0.02]],\n", + " \n", + " [[0.02],\n", + " [0.02],\n", + " [0.02],\n", + " ...,\n", + " [0.02],\n", + " [0.02],\n", + " [0.82]]]),\n", " 'pscore': array([0.82, 0.82, 0.82, ..., 0.82, 0.82, 0.82])}" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -201,12 +250,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [], "source": [ "# obtain action choice probabilities of an evaluation policy\n", - "# we construct an evaluation policy using Random Forest and parameter alpha_e\n", + "# we construct an evaluation policy using Random Forest and parameter `alpha_e`\n", "action_dist = dataset.obtain_action_dist_by_eval_policy(\n", " base_classifier_e=RandomForest(random_state=12345),\n", " alpha_e=0.9,\n", @@ -215,7 +264,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -230,7 +279,7 @@ " [0.01, 0.01, 0.01, ..., 0.01, 0.01, 0.91]])" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -252,7 +301,7 @@ "metadata": {}, "source": [ "## (3) Off-Policy Evaluation (OPE)\n", - "Our next step is OPE, which attempts to estimate the performance of evaluation policies using the logged bandit feedback and OPE estimators.\n", + "Our next step is OPE, which aims to estimate the performance of evaluation policies using logged bandit data and OPE estimators.\n", "\n", "Here, we use \n", "- `obp.ope.InverseProbabilityWeighting` (IPW)\n", @@ -274,11 +323,11 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ - "# estimate the expected reward by using an ML model (Logistic Regression here)\n", + "# estimate the expected rewards by using an ML model (Logistic Regression here)\n", "# the estimated rewards are used by model-dependent estimators such as DM and DR\n", "regression_model = RegressionModel(\n", " n_actions=dataset.n_actions,\n", @@ -288,7 +337,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [], "source": [ @@ -318,7 +367,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": { "tags": [] }, @@ -334,7 +383,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": { "tags": [] }, @@ -344,9 +393,9 @@ "output_type": "stream", "text": [ " mean 95.0% CI (lower) 95.0% CI (upper)\n", - "ipw 0.886560 0.827663 0.971508\n", - "dm 0.787145 0.780549 0.794879\n", - "dr 0.878479 0.813677 0.940087 \n", + "ipw 0.886795 0.833408 0.948891\n", + "dm 0.787927 0.780838 0.795019\n", + "dr 0.882948 0.813345 0.937172 \n", "\n" ] } @@ -362,12 +411,12 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -377,11 +426,11 @@ } ], "source": [ - "# visualize policy values of the evaluation policy estimated by the three OPE estimators\n", + "# visualize the policy values of the evaluation policy\n", "ope.visualize_off_policy_estimates(\n", " action_dist=action_dist,\n", " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", - " n_bootstrap_samples=10000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=10000, # number of resampling performed in bootstrap sampling\n", " random_state=12345,\n", ")" ] @@ -413,7 +462,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -437,7 +486,7 @@ "source": [ "### (4-2) Evaluation of OPE\n", "\n", - "We can then evaluate the estimation performance of OPE estimators by comparing the estimated policy values of the evaluation with its ground-truth as follows.\n", + "We can then evaluate the estimation performance of OPE estimators by comparing the estimated policy values with the ground-truth as follows.\n", "\n", "- $\\textit{relative-ee} (\\hat{V}; \\mathcal{D}_b) := \\left| \\frac{V(\\pi_e) - \\hat{V} (\\pi_e; \\mathcal{D}_b)}{V(\\pi_e)} \\right|$ (relative estimation error; relative-ee)\n", "- $\\textit{SE} (\\hat{V}; \\mathcal{D}_b) := \\left( V(\\pi_e) - \\hat{V} (\\pi_e; \\mathcal{D}_b) \\right)^2$ (squared error; se)" @@ -445,7 +494,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -475,15 +524,15 @@ " \n", " \n", " ipw\n", - " 0.014024\n", + " 0.016035\n", " \n", " \n", " dm\n", - " 0.102949\n", + " 0.101955\n", " \n", " \n", " dr\n", - " 0.002347\n", + " 0.003644\n", " \n", " \n", "\n", @@ -491,18 +540,18 @@ ], "text/plain": [ " relative-ee\n", - "ipw 0.014024\n", - "dm 0.102949\n", - "dr 0.002347" + "ipw 0.016035\n", + "dm 0.101955\n", + "dr 0.003644" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "# evaluate the estimation performance of OPE estimators \n", + "# evaluate the estimation performance of the OPE estimators \n", "# `evaluate_performance_of_estimators` returns a dictionary containing estimation performances of given estimators \n", "relative_ee = ope.summarize_estimators_comparison(\n", " ground_truth_policy_value=ground_truth,\n", @@ -519,9 +568,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can iterate the above process several times to get more relibale results.\n", + "We can iterate the above process several times to get more reliable results.\n", "\n", - "Please see [../examples/multiclass](../examples/multiclass) for a more sophisticated example of the evaluation of OPE with multi-class classification data." + "Please see [/examples/multiclass](../multiclass) for a more sophisticated example of the evaluation of OPE with multi-class classification data." ] }, { diff --git a/examples/quickstart/obd.ipynb b/examples/quickstart/obd.ipynb index 47c19963..a5bdb839 100644 --- a/examples/quickstart/obd.ipynb +++ b/examples/quickstart/obd.ipynb @@ -2,32 +2,34 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "# Quickstart Example with Open Bandit Dataset\n", "---\n", - "This notebook demonstrates an example of conducting OPE of Bernoulli Thompson Sampling (BernoulliTS) as an evaluation policy. We use some OPE estimators and logged bandit feedback generated by running the Random policy (behavior policy) on the ZOZOTOWN platform. We also evaluate and compare the OPE performance (accuracy) of several estimators.\n", + "This notebook demonstrates an example of conducting OPE of Bernoulli Thompson Sampling (BernoulliTS) as an evaluation policy. We use some OPE estimators and logged bandit data generated by running the Random policy (behavior policy) on the ZOZOTOWN platform. We also evaluate and compare the OPE performance (accuracy) of several estimators.\n", "\n", - "The example consists of the follwoing four major steps:\n", + "The example consists of the following four major steps:\n", "- (1) Data Loading and Preprocessing\n", "- (2) Replicating Production Policy\n", "- (3) Off-Policy Evaluation (OPE)\n", "- (4) Evaluation of OPE" - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 1, + "metadata": {}, + "outputs": [], "source": [ "# needed when using Google Colab\n", "# !pip install obp" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, + "metadata": {}, + "outputs": [], "source": [ "from sklearn.linear_model import LogisticRegression\n", "\n", @@ -42,222 +44,221 @@ " InverseProbabilityWeighting,\n", " DoublyRobust\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 2, - "source": [ - "# obp version\n", - "print(obp.__version__)" - ], + "execution_count": 3, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "0.5.0\n" + "0.5.2\n" ] } ], - "metadata": {} + "source": [ + "# obp version\n", + "print(obp.__version__)" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (1) Data Loading and Preprocessing\n", "\n", "`obp.dataset.OpenBanditDataset` is an easy-to-use data loader for Open Bandit Dataset. \n", "\n", "It takes behavior policy ('bts' or 'random') and campaign ('all', 'men', or 'women') as inputs and provides dataset preprocessing." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 3, - "source": [ - "# load and preprocess raw data in \"All\" campaign collected by the Random policy (behavior policy here)\n", - "# When `data_path` is not given, this class downloads the small-sized version of the Open Bandit Dataset.\n", - "dataset = OpenBanditDataset(behavior_policy='random', campaign='all')\n", - "\n", - "# obtain logged bandit feedback generated by behavior policy\n", - "bandit_feedback = dataset.obtain_batch_bandit_feedback()" - ], + "execution_count": 4, + "metadata": { + "tags": [] + }, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ - "INFO:obp.dataset.real:When `data_path` is not given, this class downloads the example small-sized version of the Open Bandit Dataset.\n" + "INFO:obp.dataset.real:When `data_path` is not given, this class downloads the small-sized version of Open Bandit Dataset.\n" ] } ], - "metadata": { - "tags": [] - } + "source": [ + "# load and preprocess raw data in \"All\" campaign collected by the Random policy (behavior policy here)\n", + "# When `data_path` is not given, this class downloads the small-sized version of the Open Bandit Dataset.\n", + "dataset = OpenBanditDataset(behavior_policy='random', campaign='all')\n", + "\n", + "# obtain logged bandit feedback generated by behavior policy\n", + "bandit_feedback = dataset.obtain_batch_bandit_feedback()" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "the logged bandit feedback is collected by the behavior policy as follows.\n", + "the logged bandit dataset is collected by the behavior policy as follows.\n", "\n", - "$ \\mathcal{D}_b := \\{(x_i,a_i,r_i)\\}$ where $(x,a,r) \\sim p(x)\\pi_b(a \\mid x)p(r \\mid x,a) $" - ], - "metadata": {} + "$ \\mathcal{D}_b := \\{(x_i,a_i,r_i)\\}$ where $(x,a,r) \\sim p(x)\\pi_b(a | x)p(r | x,a) $" + ] }, { "cell_type": "code", - "execution_count": 4, - "source": [ - "# `bandit_feedback` is a dictionary storing logged bandit feedback\n", - "bandit_feedback.keys()" - ], + "execution_count": 5, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "dict_keys(['n_rounds', 'n_actions', 'action', 'position', 'reward', 'pscore', 'context', 'action_context'])" ] }, + "execution_count": 5, "metadata": {}, - "execution_count": 4 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# `bandit_feedback` is a dictionary storing logged bandit feedback\n", + "bandit_feedback.keys()" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### let's see some properties of the dataset class" - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 5, - "source": [ - "# name of the dataset is 'obd' (open bandit dataset)\n", - "dataset.dataset_name" - ], + "execution_count": 6, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "'obd'" ] }, + "execution_count": 6, "metadata": {}, - "execution_count": 5 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# name of the dataset is 'obd' (open bandit dataset)\n", + "dataset.dataset_name" + ] }, { "cell_type": "code", - "execution_count": 6, - "source": [ - "# number of actions of the \"All\" campaign is 80\n", - "dataset.n_actions" - ], + "execution_count": 7, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "80" ] }, + "execution_count": 7, "metadata": {}, - "execution_count": 6 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# number of actions of the \"All\" campaign is 80\n", + "dataset.n_actions" + ] }, { "cell_type": "code", - "execution_count": 7, - "source": [ - "# small sample example data has 10,000 samples (or rounds)\n", - "dataset.n_rounds" - ], + "execution_count": 8, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "10000" ] }, + "execution_count": 8, "metadata": {}, - "execution_count": 7 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# small sample example data has 10,000 samples (or rounds)\n", + "dataset.n_rounds" + ] }, { "cell_type": "code", - "execution_count": 8, - "source": [ - "# default context (feature) engineering creates context vector with 20 dimensions\n", - "dataset.dim_context" - ], + "execution_count": 9, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "20" ] }, + "execution_count": 9, "metadata": {}, - "execution_count": 8 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# default context (feature) engineering creates context vector with 20 dimensions\n", + "dataset.dim_context" + ] }, { "cell_type": "code", - "execution_count": 9, - "source": [ - "# ZOZOTOWN recommendation interface has three positions\n", - "# (please see https://github.com/st-tech/zr-obp/blob/master/images/recommended_fashion_items.png)\n", - "dataset.len_list" - ], + "execution_count": 10, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "3" ] }, + "execution_count": 10, "metadata": {}, - "execution_count": 9 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# ZOZOTOWN recommendation interface has three positions\n", + "# (please see https://github.com/st-tech/zr-obp/blob/master/images/recommended_fashion_items.png)\n", + "dataset.len_list" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (2) Replicating Production Policy\n", "\n", @@ -267,12 +268,15 @@ "By activating its `is_zozotown_prior` argument, we can replicate (the policy parameters of) BernoulliTS used in the ZOZOTOWN production.\n", "\n", "(When `is_zozotown_prior=False`, non-informative prior distribution is used.)" - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# define BernoulliTS as an evaluation policy\n", "evaluation_policy = BernoulliTS(\n", @@ -283,27 +287,18 @@ " random_state=12345,\n", ")\n", "\n", - "# compute the action choice probabilities of the evaluation policy using Monte Carlo simulation\n", + "# compute the action choice probabilities of the evaluation policy via Monte Carlo simulation\n", "action_dist = evaluation_policy.compute_batch_action_dist(\n", " n_sim=100000, n_rounds=bandit_feedback[\"n_rounds\"],\n", ")" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 11, - "source": [ - "# action_dist is an array of shape (n_rounds, n_actions, len_list) \n", - "# representing the distribution over actions by the evaluation policy\n", - "action_dist" - ], + "execution_count": 12, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "array([[[0.01078, 0.00931, 0.00917],\n", @@ -357,24 +352,30 @@ " [0.0582 , 0.07603, 0.07998]]])" ] }, + "execution_count": 12, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# `action_dist` is an array of shape (n_rounds, n_actions, len_list) \n", + "# representing the distribution over actions by the evaluation policy\n", + "action_dist" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (3) Off-Policy Evaluation (OPE)\n", - "Our next step is OPE, which attempts to estimate the performance of evaluation policies using the logged bandit feedback and OPE estimators.\n", + "Our next step is OPE, which aims to estimate the performance of evaluation policies using logged bandit data and OPE estimators.\n", "\n", "Here, we use \n", "- `obp.ope.InverseProbabilityWeighting` (IPW)\n", @@ -382,24 +383,25 @@ "- `obp.ope.DoublyRobust` (DR)\n", "\n", "as estimators and visualize the OPE results." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### (3-1) Obtaining a reward estimator\n", "A reward estimator $\\hat{q}(x,a)$ is needed for model dependent estimators such as DM or DR.\n", "\n", "$\\hat{q}(x,a) \\approx \\mathbb{E} [r \\mid x,a]$" - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, + "metadata": {}, + "outputs": [], "source": [ - "# estimate the expected reward by using an ML model (Logistic Regression here)\n", + "# estimate the expected rewards by using an ML model (Logistic Regression here)\n", "# the estimated rewards are used by model-dependent estimators such as DM and DR\n", "regression_model = RegressionModel(\n", " n_actions=dataset.n_actions,\n", @@ -407,13 +409,13 @@ " action_context=dataset.action_context,\n", " base_model=LogisticRegression(max_iter=1000, random_state=12345),\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, + "metadata": {}, + "outputs": [], "source": [ "estimated_rewards_by_reg_model = regression_model.fit_predict(\n", " context=bandit_feedback[\"context\"],\n", @@ -424,28 +426,30 @@ " n_folds=3, # use 3-fold cross-fitting\n", " random_state=12345,\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "please refer to https://arxiv.org/abs/2002.08536 about the details of the cross-fitting procedure." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### (3-2) Off-Policy Evaluation\n", "$V(\\pi_e) \\approx \\hat{V} (\\pi_e; \\mathcal{D}_b, \\theta)$ using DM, IPW, and DR" - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# estimate the policy value of BernoulliTS based on its action choice probabilities\n", "# it is possible to set multiple OPE estimators to the `ope_estimators` argument\n", @@ -458,27 +462,17 @@ "estimated_policy_value, estimated_interval = ope.summarize_off_policy_estimates(\n", " action_dist=action_dist, \n", " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", - " n_bootstrap_samples=10000, # number of resampling performed in the bootstrap procedure.\n", + " n_bootstrap_samples=10000, # number of resampling performed in bootstrap sampling.\n", " random_state=12345,\n", ")" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 15, - "source": [ - "# the estimated policy value of the evaluation policy (the BernoulliTS policy)\n", - "# relative_estimated_policy_value is the policy value of the evaluation policy \n", - "# relative to the ground-truth policy value of the behavior policy (the Random policy here)\n", - "estimated_policy_value" - ], + "execution_count": 16, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -511,13 +505,13 @@ " \n", " \n", " dm\n", - " 0.003385\n", - " 0.890715\n", + " 0.003545\n", + " 0.932797\n", " \n", " \n", " dr\n", - " 0.004648\n", - " 1.223175\n", + " 0.004727\n", + " 1.244027\n", " \n", " \n", "\n", @@ -526,28 +520,28 @@ "text/plain": [ " estimated_policy_value relative_estimated_policy_value\n", "ipw 0.004553 1.198126\n", - "dm 0.003385 0.890715\n", - "dr 0.004648 1.223175" + "dm 0.003545 0.932797\n", + "dr 0.004727 1.244027" ] }, + "execution_count": 16, "metadata": {}, - "execution_count": 15 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# the estimated policy value of the evaluation policy (the BernoulliTS policy)\n", + "# relative_estimated_policy_value is the policy value of the evaluation policy \n", + "# relative to the ground-truth policy value of the behavior policy (the Random policy here)\n", + "estimated_policy_value" + ] }, { "cell_type": "code", - "execution_count": 16, - "source": [ - "# confidence intervals of policy value of BernoulliTS estimated by OPE estimators\n", - "# (`mean` values in this dataframe is also estimated via the non-parametric bootstrap procedure \n", - "# and is a bit different from the above values in `estimated_policy_value`)\n", - "estimated_interval" - ], + "execution_count": 17, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -582,15 +576,15 @@ " \n", " \n", " dm\n", - " 0.003385\n", - " 0.003337\n", - " 0.003432\n", + " 0.003545\n", + " 0.003501\n", + " 0.003589\n", " \n", " \n", " dr\n", - " 0.004639\n", - " 0.001625\n", - " 0.009323\n", + " 0.004717\n", + " 0.001702\n", + " 0.009416\n", " \n", " \n", "\n", @@ -599,46 +593,64 @@ "text/plain": [ " mean 95.0% CI (lower) 95.0% CI (upper)\n", "ipw 0.004544 0.001531 0.009254\n", - "dm 0.003385 0.003337 0.003432\n", - "dr 0.004639 0.001625 0.009323" + "dm 0.003545 0.003501 0.003589\n", + "dr 0.004717 0.001702 0.009416" ] }, + "execution_count": 17, "metadata": {}, - "execution_count": 16 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# confidence intervals of policy value of BernoulliTS estimated by OPE estimators\n", + "# (`mean` values in this dataframe is also estimated via the non-parametric bootstrap procedure \n", + "# and is a bit different from the above values in `estimated_policy_value`)\n", + "estimated_interval" + ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# visualize the policy values of BernoulliTS estimated by the three OPE estimators\n", - "# and their 95% confidence intervals (estimated by nonparametric bootstrap method)\n", + "# visualize the estimated policy values of BernoulliTS and their 95% confidence intervals (estimated by bootstrap)\n", "ope.visualize_off_policy_estimates(\n", " action_dist=action_dist,\n", " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", - " n_bootstrap_samples=10000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=10000, # number of resampling performed in bootstrap sampling\n", " random_state=12345,\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, "outputs": [ { - "output_type": "display_data", "data": { - "image/png": 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ZuLu7w8/PD5s2bcILL7wAiUSCXbt2wcPDQ1ijAgBmzJiBlStXYvv27Rg+fDgUCgUUCgWWLVsmtBk/fjyOHTuGtWvXYuLEidBoNDh06BCKi4u15k0QERGRbhKNiMUYzp49i7Nnz+LOnTvo0aMHAgICEBgY2OJS2mJkZ2dj69atWst0z549W2vsBQsWwMvLCwsWLBC2lZeXY8eOHTh79iw0Gg0CAgIQHh7e6I6Ns2fPIioqCr///ruwTHdQUJBWm99++w179+7FjRs3AAADBgzA7Nmz4e3tLeoYdN3+SsA777yD3Nxc9OnTp9Gqp0REXQG/T4mna3qCqFBBujFU6MZ/rETU1fH7lHhtvvuDiIiISCyGCiIiItILhgoiIiLSC4YKIiIi0guGCiIiItILhgoiIiLSC4YKIiIi0guGCiIiItKLVocKpVKJvXv3tnofERERPdxaHSpSU1MRHR3d6n1ERET0cOPlDyIiItILhgoiIiLSC4YKIiIi0gsTMY0KCwuFv5eXlzfaBgAODg56LIuIiIgMjahQsWDBAp3bJBIJdu/erb+qiIiIyOCIChUzZsyARCIBcPe2UaVSiZkzZ3ZoYURERGRYRIWK2bNnC3+Pjo6GUqnErFmzOqwoIiIiMjycqElERER6wVBBREREesFQQURERHrR6lCh0WjatI+IiIgebqImat5r9uzZWhM3xe4jIiKihxsvfxAREZFeMFQQERGRXjQbKmpqato9uD7GICIiIsPQbKhYsGAB/vvf/6K2trbVg2ZmZuLjjz/G/v3721UcERERGY5mJ2r6+flhx44diI6ORmBgIEaNGgV3d3dIpdIm2+fl5eHChQuIi4vDlStX4ODggKlTp3ZY4URERNS1NBsq3nzzTUyaNAm7d+9GbGwsYmNjYWRkBCcnJ9ja2kImk6G2thZlZWVQqVQoKSkBAFhbW+PZZ5/FE088AVNT0047ECIiInqwdN5S6urqir/+9a/4/fffcezYMVy8eBGZmZm4ceOGVjtra2uMHDlS+M/EpNV3qhIREZGBE/XTv2/fvvjjH/8IAKiursbt27dRWloKqVQKGxsb2NnZdWiRRERE1PW1+pSCmZkZ+vbti759+3ZEPURERGSguE4FERER6QVDBREREekFQwURERHpBUMFERER6QVDBREREekFQwURERHpBUMFERER6UWr16moq6vDxYsXkZ2djaqqKsycORPA3SeSVlZWokePHjAyYlYhIiLqbloVKpKTkxEZGYni4mJhW0OoyMzMxN/+9jcsXLgQo0eP1muRRERE1PWJPqVw9epVrF27FhKJBC+99BKCgoK09ru7u0Mul+Ps2bN6L5KIiIi6PtGhIiYmBlKpFGvWrMHkyZObXKbbxcUFWVlZei2QiIiIDIPoUHHp0iUMHz4ctra2zbZxcHDQujRCRERE3YfoUFFVVQVra2udbaqrq1FfX9/uooiIiMjwiA4V9vb2uHnzps42mZmZ6N27d7uLIiIiIsMjOlT4+/vjwoULSE9Pb3K/QqFARkYGAgIC9FYcERERGQ7Rt5Q+9dRTiI+Px4cffohJkyahoKAAAHD+/HkolUocOnQItra2mDJlSocVS0RERF2X6FBhb2+P5cuXY/369Thw4ICwPSIiAgDQu3dvLFmypMV5F0RERPRwatXiV4MGDcKGDRtw/vx5ZGRkoLS0FJaWlnBzc8Pw4cNhbGzcUXUSERFRF9fqZbqNjIwwbNgwDBs2rCPqISIiIgPFh3QQERGRXog+UxEXFyd60HHjxrWpGCIiIjJcokPF559/LnrQ1oSK7OxsbNu2DRkZGZDJZAgJCcGsWbNafNJpRUUFtm/fjsTERNTX12Po0KEIDw9Hjx49tNolJiZi9+7dyM3NhVwux6xZsxAYGNhovISEBHz//fe4ceMGzMzM4OLigsWLF8Pc3Fz0sRAREXVnokPF/Pnzm9xeUVGBK1euID4+HiNGjGjVOhVlZWVYvXo1nJycsHTpUuTm5uKrr76CRqPBM888o7Pv+vXroVKpMG/ePBgZGWHXrl1Yu3YtPvjgA6FNeno61q1bh7CwMISHh0OhUGDDhg2QyWTw8/MT2h09ehTbtm3D1KlT8fzzz6O8vBwXL17k6qBEREStIDpUBAcH69z/+OOPCw8bE+vIkSOoqanB4sWLYWlpCV9fX1RWViI6OhpTp06FpaVlk/0yMjJw4cIFrFy5El5eXgDu3vK6bNkypKSkwNfXF8Ddh6B5enpi7ty5AAAfHx9kZ2dj7969QqgoKSnBjh07EB4ejtDQUOE9RowYIfo4iIiISI8TNR999FH4+fkhKipKdJ/k5GT4+flphYegoCDU1NRAqVQ220+hUMDGxkYIFADg6uoKuVyO5ORkAEBtbS0uXryIUaNGafUNDAxERkYGKioqAAC//vorgJZDExEREenW6ltKdenXrx+OHDkiun1OTg68vb21tjk4OMDMzAwqlUpnP0dHx0bbHR0dkZOTAwDIy8uDWq1u1M7R0REajQYqlQqurq64fPky+vXrh2PHjmHfvn24c+cOBg4ciJdeegmDBw8WfSxERETdnV5DRXZ2dqval5eXQyaTNdouk8lQVlams19Tl0ZkMhny8/MBQOh///hWVlbCGABw584dqFQqxMTE4Pnnn0ePHj3www8/4B//+Ac2bNjQ5KPeY2NjERsbCwBYs2YNHBwcRBxt99WwKJqxsTG/VkTUJfH7lH60O1TU19fj1q1bOHr0KBQKBYYMGaKPujqNRqNBVVUVFi1aBH9/fwCAu7s7FixYgJ9//rnJCaOhoaFa8y8KCws7q1yDpFarhT/5tSKirojfp8Tr169fs/tEh4o5c+a02MbKygrPP/+82CEhk8mEuQ33Ki8vF84oNNevtLS0yX4NZyYa+t8//v1nMGQyGSQSidb8DEtLSwwaNKjVZ16IiIi6M9GhwtPTExKJpNF2iUQCmUwGV1dXPP744616oNi9cyAaFBYWorq6WmcScnR0xNGjRxttV6lUGD58OIC7DzgzNjZGTk6OVmBQqVSQSCTC+A1zLO6n0WhaXCuDiIiI/kd0qFi5cqXe39zf3x/79+9HZWUlLCwsAADx8fGQSqVaQeB+Q4YMQUxMDNLT0+Hh4QEAuHr1KvLy8oRLGKampvDx8cGZM2cwYcIEoW98fDzc3d2FORlDhw7F3r17cfHiRWGNjYqKCly7dg1PPvmk3o+ZiIjoYfVAfxWfMGECTE1N8cknnyAlJQWxsbGIjo7GlClTtCZiLly4EJGRkcJrd3d3+Pn5YdOmTUhISMDZs2fxz3/+Ex4eHsIaFQAwY8YMpKamYvv27UhNTcXXX38NhUKBmTNnCm1cXFwwbNgwbNmyBSdOnMD58+cREREBY2NjTJw4sXO+EERERA8Bvd790VpWVlZ4//33sXXrVkREREAmk+GJJ57A7NmztdrV19c3Wt3y7bffxo4dOxAZGQmNRoOAgACEh4drtfHw8MCiRYsQFRWFw4cPQy6X46233tJaTRMA3nrrLXz11VfYuXMnqqur4eHhgRUrVuic10FERETaJJqmJhSgdc/60BpQIml2Se+Hla41NQh45513kJubiz59+mDt2rUPuhwiokb4fUq8Nt390Zqnkt6vu4UKIiIi0hEqNm3a1Jl1EBERkYFrNlT06tWrM+sgIiIiA8eFGIiIiEgv2nT3R319PUpKSlBXV9fkfq6bTkRE1P20KlTcuHEDu3btQmpqKmpra5tsI5FIsHv3br0UR0RERIZDdKjIzs7GX//6VwCAr68vzp07h0ceeQQ2Nja4fv06SktL4e3tzbMURERE3ZToULFv3z6o1Wp89NFHGDBgAObMmYMRI0Zg5syZqKqqwn/+8x8oFAq88cYbHVkvERERdVGiJ2qmpqYiICAAAwYMELY1rJtlbm6OP/3pT5DJZIiKitJ/lURERNTliQ4VpaWl6Nu37/86GhmhurpaeG1sbAxvb2+kpKTot0IiIiIyCKJDhZWVFaqqqoTX1tbWKCws1GpjYmKCiooK/VVHREREBkN0qOjduzfy8/OF1wMHDsRvv/2GO3fuAACqqqqQlJQEuVyu/yqJiIioyxM9UdPPzw8//PADqqqqYG5ujrCwMCgUCixduhSDBw/GtWvXUFBQgBdffLEj6yUiIqIuSnSoGD9+PPr164eamhqYm5sjICAAL730EqKjo5GQkACpVIpp06bhD3/4Q0fWS0RERF2UzlCxdOlShIaGYsyYMbCzs0NgYKDW/smTJ2PSpEkoKSmBjY0NJBJJhxZLREREXZfOORVZWVnYunUr5s2bhy1btuDy5cuNBzAygq2tLQMFERFRN6fzTMXq1asRGxuLM2fO4Pjx4zh+/DgGDBiA8ePHY+zYsbC0tOysOomIiKiL0xkq3N3d4e7ujvDwcJw8eRLHjh3D9evX8Z///Ae7du3CY489hvHjx8PDw6Oz6iUiIqIuStRETQsLC4SFhSEsLAyZmZmIjY3F6dOn8csvv+CXX36Bk5OTcPbCysqqo2smIiKiLkj0OhUNnJ2d8eqrr+Jf//oX3njjDQwePBjZ2dnYsWMHXn/9dWzcuLEj6iQiIqIurtWhooFUKsW4cePwwQcfYP369fDw8EBtbS1OnTqlz/qIiIjIQIhep6IpZWVliIuLw7Fjx5CdnQ0AnLxJRETUTbUpVFy8eBGxsbFITExEXV0dAMDNzQ2hoaGN1rIgIiKi7kF0qCguLsbx48dx7Ngx4RkgMpkMoaGhCA0NRf/+/TusSCIiIur6dIYKjUaD8+fP4+jRo1AoFKivrwcAeHh4YPz48XjssccglUo7pVAiIiLq2nSGijfeeAO3b98GcPfR52PHjkVoaCgcHR07pTgiIiIyHDpDxe3bt+Hl5SWclTAxade8TiIiInqI6UwJn332Gfr27dtZtRAREZEB07lOBQMFERERidXmxa+IiIiI7sVQQURERHrBUEFERER6wVBBREREesFQQURERHohOlQkJCQIK2oSERER3U/0alaffvop7Ozs8Pjjj2P8+PFwcHDoyLqIiIjIwIg+UzFx4kRUV1dj3759WLhwIdasWYNz585Bo9F0ZH1ERERkIESfqZg7dy6ef/55xMfH48iRI1AoFFAoFLC3t8f48eMREhICe3v7jqyViIg62aH9vz/oEjpFeVmd8Gd3OeaJU/W/wGWrHuYhlUoRHByM4OBg3LhxA7GxsTh58iSio6MRExODgIAATJgwAf7+/novlIiIiLq2Nj8hbMCAAVpnL6KiopCUlISkpCQ4ODhg4sSJCAsLg7m5uT7rJSIioi6qXY8draqqwqlTpxAbGys8It3Z2Rm5ubnYtWsXfvrpJ7z77rtwdnbWR60Pnd/fefVBl9Ap6gpq/v+fed3mmPuu/feDLoGIqNO1KVRcv34dR44cwenTp1FVVQWpVIqQkBBMnDgRzs7OqKqqwqFDh7Bnzx785z//wapVq/RdNxEREXUxokNFdXU1Tp8+jSNHjuDatWsAAEdHR0yYMAHjxo2DpaWl0Nbc3BzTpk3DrVu3cOzYMf1XTURERF2O6FAxb948VFZWwsjICCNHjsTEiRPh7e2ts4+9vT1qa2vbXSQRERF1faJDhYWFBaZMmYLQ0FDY2tqK6hMWFoagoKC21kZEREQGRHSo2Lx5M4yMWveoEEtLS63LIkRERPTwEp0SWhsoiIiIqHsRnRRiYmLw7LPPCreO3u/27dt49tln8f333+urNiIiIjIgokPFuXPn4OXl1exS3Pb29vDx8UFiYqLeiiMiIiLDITpU5ObmwsnJSWcbR0dH5ObmtrsoIiIiMjyiQ0VNTQ3MzMx0tpFKpaiqqmp3UURERGR4RIeKnj174vLlyzrbXL58mU8qJSIi6qZEhwo/Pz8olUrEx8c3uf/06dNQKpV8QikREVE3JXqdiunTp+PUqVPYsGED4uPj4e/vD3t7e9y+fRsKhQJJSUmwsrLC9OnTW1VAdnY2tm3bhoyMDMhkMoSEhGDWrFkt3sJaUVGB7du3IzExEfX19Rg6dCjCw8PRo0cPrXaJiYnYvXs3cnNzIZfLMWvWLAQGBjY5Zn19PZYtW4Zr167h3XffxdChQ1t1LERERN2Z6FBhb2+P5cuX49NPP0ViYmKjuzx69eqFRYsWoWfPnqLfvKysDKtXr4aTkxOWLl2K3NxcfPXVV9BoNHjmmWd09l2/fj1UKhXmzZsHIyMj7Nq1C2vXrsUHH3wgtElPT8e6desQFhaG8PBwKBQKbNiwATKZDH5+fo3GPHbsGG7duiW6fiIiIvqfVj2l1MXFBRs2bMC5c+dw+fJllJeXQyaTwc3NDUOHDoWJSeseenrkyBHU1NRg8eLFsLS0hK+vLyorKxEdHY2pU6c2uxpnRkYGLly4gJUrV8LLywvA3dCzbNkypKSkwNfXF8DdtTU8PT0xd+5cAICPjw+ys7Oxd+/eRqGirKwM3377Lf74xz9iy5YtrToOIiIiasWcigYmJiYYOXIknn/+ecybNw/PP/88Ro4c2epAAQDJycnw8/PTCg9BQUGoqamBUqlstp9CoYCNjY0QKADA1dUVcrkcycnJAIDa2lpcvHgRo0aN0uobGBiIjIwMVFRUaG2PiorC4MGD4ePj0+rjICIiojaECn3KyclBv379tLY5ODjAzMwMKpVKZz9HR8dG2x0dHZGTkwMAyMvLg1qtbtTO0dERGo1Ga/ysrCwcP34cL774YnsOh4iIqFtr9vRCXFwcAGDEiBGwsLAQXosxbtw4Ue0aLp/cTyaToaysTGe/pi6NyGQy5OfnA4DQ//7xrayshDEabNu2DZMmTUKfPn2E/rrExsYiNjYWALBmzRo4ODi02Kcpv7epFxmCtn4miLoefqd6WHXE96lmQ8Xnn38OAHBzc4OFhYXwWgyxoaIrOH36NFQqFd59913RfUJDQxEaGiq8Liws7IjSyIDxM0FEXV1bv0/df4XhXs2Givnz5wMA7OzstF7rk0wmazS3Abh7FqHhjEJz/UpLS5vs13BmoqH//ePfewajrq4OX3/9NaZNmwaNRoPy8nJUVlYCAKqrq1FZWQkLC4u2HRwREVE302yoCA4O1vlaH+6dA9GgsLAQ1dXVOpOQo6Mjjh492mi7SqXC8OHDAQC9e/eGsbExcnJytCZ0qlQqSCQS9OvXD9XV1bh16xZ27tyJnTt3ao312WefoXfv3ti4cWN7DpGIiKjbaP0tG3rk7++P/fv3a50RiI+Ph1Qq1QoC9xsyZAhiYmKQnp4ODw8PAMDVq1eRl5cnrOhpamoKHx8fnDlzBhMmTBD6xsfHw93dHZaWllCr1VixYoXW2MXFxdiwYQOeffZZ3glCRETUCg/07o8JEybA1NQUn3zyCVJSUhAbG4vo6GhMmTJFayLmwoULERkZKbx2d3eHn58fNm3ahISEBJw9exb//Oc/4eHhIaxRAQAzZsxAamoqtm/fjtTUVHz99ddQKBSYOXMmAMDY2Bje3t5a/7m5uQEABgwYIPydiIiIWtbsmYo333yzTQNKJBLRlwysrKzw/vvvY+vWrYiIiIBMJsMTTzyB2bNna7Wrr69HfX291ra3334bO3bsQGRkJDQaDQICAhAeHq7VxsPDA4sWLUJUVBQOHz4MuVyOt956q8nVNImIiKh9mg0VGo2mTQO2tp+Tk1OjSxD327x5c6NtMpkMb7zxBt544w2dfUeMGIERI0aIrkcul2PPnj2i2xMREdFdzYaKpn6QExERETXngc6pICIioodHm0NFZWUlCgsLm1xngoiIiLqfVt1SqlarceDAARw9elRrOWu5XI7x48fjySefhLGxsd6LJCIioq5PdKioq6vD3//+dyiVSkgkEjg4OMDW1hbFxcUoKCjAt99+i+TkZPz1r39t0xNLiYiIyLCJ/ul/8OBBKJVKBAQE4MUXX0Tfvn2Ffbm5udi5cyfOnTuHgwcPYvr06R1RKxEREXVhoudUnDp1Cv3798c777yjFSgAoE+fPliyZAn69++PkydP6r1IIiIi6vpEh4rc3Fz4+/vDyKjpLkZGRvD390deXp7eiiMiIiLDITpUmJiYoKqqSmeb6upqTtQkIiLqpkSHikceeQQJCQkoKSlpcn9JSQnOnDkDZ2dnfdVGREREBkR0qJg4cSJKSkrw3nvv4dixY8jLy0NNTQ3y8/Nx/PhxLF++HCUlJZg4cWJH1ktEhIiICLzzzjuIiIh40KUQ0T1E3/0RGBiIzMxM/PDDD/jXv/7VZJupU6ciMDBQb8URETWlsLAQubm5D7oMIrpPqxaUeO655zBs2DAcO3YMmZmZqKiogKWlJZydnRESEgJ3d/eOqpOIRHh5x68PuoROoS6pBADkllR2m2Pe/tKoB10CUYtEh4rS0lJIJBK4u7szPBAREVEjLYaKxMRE7Ny5U1iWu0+fPnjhhRcwbNiwDi+OiIiIDIfOiZoZGRlYt26d1nM+cnNzsW7dOmRkZHR4cURETTLvAVja3P2TiLoMnWcqDh48CI1GgxkzZmDSpEnQaDT4+eefsW/fPhw8eBCLFi3qrDqJiATGAVMfdAlE1ASdoeLy5cvw8PDA7NmzhW1z5syBUqnkmQoiIiLSovPyx507d+Dm5tZou5ubW7OLYBEREVH3pDNUqNVqmJubN9puZmYGtVrdYUURERGR4RG9oiYRERGRLi3eUnrixAmkpqZqbSsoKAAArFq1qlF7iUSC999/X0/lERERkaFoMVQUFBQIIeJ+SqVS7wURERGRYdIZKlasWNFZdRAREZGB0xkqvLy8OqsOIiIiMnCcqElERER6wVBBREREesFQQURERHrBUEFERER6wVBBREREesFQQURERHrBUEFERER6wVBBREREetHs4ld79+5t86AzZ85sc18iIiIyTM2Giujo6DYPylBBRETU/TQbKpp67sfBgwehUCgwZswYeHl5wdbWFsXFxUhNTcWpU6cQEBCAJ554okMLJiIioq6p2VBx/3M/4uLi8Ntvv+Hvf/87Bg0apLUvODgYkyZNwooVKzBy5MiOqZSIiIi6NNETNX/88UeMGjWqUaBo4OLiglGjRuHHH3/UW3FERERkOESHCpVKBTs7O51t7OzsoFKp2l0UERERGR7RocLCwgKXLl3S2ebSpUswNzdvd1FERERkeESHioCAAKSlpWHnzp2orKzU2ldZWYmdO3ciPT0dQ4cO1XuRRERE1PU1O1Hzfs899xyUSiV+/PFHHDt2DM7OzrCxscGdO3eQmZmJyspKyOVyPPvssx1ZLxEREXVRokOFjY0N/vGPf+Cbb77BqVOnkJaWJuyTSqUYP348nn32WfTo0aNDCiUiIqKuTXSoAIAePXpg3rx5ePXVV5GTk4OKigpYWlrC0dERxsbGHVUjERERGYBWhYoGxsbGGDBggL5rISIiIgPW6lBRV1eHixcvIjs7G1VVVcKS3DU1NaisrESPHj1gZMTnlBEREXU3rQoVycnJiIyMRHFxsbCtIVRkZmbib3/7GxYuXIjRo0frtUgiIiLq+kSfUrh69SrWrl0LiUSCl156CUFBQVr73d3dIZfLcfbsWb0XSURERF2f6FARExMDqVSKNWvWYPLkyejbt2+jNi4uLsjKytJrgURERGQYRIeKS5cuYfjw4bC1tW22jYODg9alESIiIuo+RIeKqqoqWFtb62xTXV2N+vr6dhdFREREhkd0qLC3t8fNmzd1tsnMzETv3r3bXRQREREZHtF3f/j7++PIkSNIT0+Hh4dHo/0KhQIZGRmYNm1aqwrIzs7Gtm3bkJGRAZlMhpCQEMyaNavF21IrKiqwfft2JCYmor6+HkOHDkV4eHijFT0TExOxe/du5ObmQi6XY9asWQgMDBT2X7lyBYcPH0ZaWhqKiorQs2dPjB49GtOmTYNUKm3VsRAREXVnokPFU089hfj4eHz44YeYNGkSCgoKAADnz5+HUqnEoUOHYGtriylTpoh+87KyMqxevRpOTk5YunQpcnNz8dVXX0Gj0eCZZ57R2Xf9+vVQqVSYN28ejIyMsGvXLqxduxYffPCB0CY9PR3r1q1DWFgYwsPDoVAosGHDBshkMvj5+QEA4uPjkZeXh2nTpqFv377IyspCVFQUsrKysGTJEtHHQkRE1N2JDhX29vZYvnw51q9fjwMHDgjbIyIiAAC9e/fGkiVLWpx3ca8jR46gpqYGixcvhqWlJXx9fVFZWYno6GhMnToVlpaWTfbLyMjAhQsXsHLlSnh5eQn1LVu2DCkpKfD19QVw944VT09PzJ07FwDg4+OD7Oxs7N27VwgV06dP16rZ29sbUqkUX3zxBQoKCtCrVy/Rx0NERNSdtWrxq0GDBmHDhg04f/48MjIyUFpaCktLS7i5uWH48OGtfv5HcnIy/Pz8tMJDUFAQdu3aBaVSiWHDhjXZT6FQwMbGRggUAODq6gq5XI7k5GT4+vqitrYWFy9eRHh4uFbfwMBAfP7558JzS5oKQc7OzgCAoqIihgoiIiKRWr1Mt5GREYYNG9bsD/zWyMnJgbe3t9Y2BwcHmJmZQaVS6ezn6OjYaLujoyNycnIAAHl5eVCr1Y3aOTo6QqPRQKVSwdXVtcnxMzIyIJFIOOmUiIioFUSHilWrViE4OBjjxo1rts0vv/yC48ePY8WKFaLGLC8vh0wma7RdJpOhrKxMZ7+mLo3IZDLk5+cDgND//vGtrKyEMZpSXFyMffv2YezYsbCxsWmyTWxsLGJjYwEAa9asgYODQ7O16vJ7m3qRIWjrZ4KoOQ/uM8XvVA+rjvhMiQ4VSqWy0VmF+xUWFkKpVLa7qAelrq4O69evh7m5OV566aVm24WGhiI0NFR4XVhY2BnlkQHhZ4L0jZ8p0re2fqb69evX7D69Pk60pqamVfMqZDIZKioqGm0vLy8Xzig016+ysrLJfg1nJhr63z9+c2cwNBoNNm3ahJs3b+K9997T+f5ERETUmF5ChUajQUFBARQKBXr27Cm6371zIBoUFhaiurpaZxJqqh8AqFQqYQ5F7969YWxs3KidSqWCRCJpNH7DmhdLly5tcr4GERER6abz8secOXO0XkdHRyM6OlrngE899ZToN/f398f+/ftRWVkJCwsLAHfXjZBKpVp3dtxvyJAhiImJ0VqI6+rVq8jLy4O/vz8AwNTUFD4+Pjhz5gwmTJgg9I2Pj4e7u7vWnIzvvvsOP//8M/785z83ubAXERERtUxnqPD09IREIgFwd06Fg4MD5HJ5o3ZGRkawsrLCo48+ipCQENFvPmHCBPz000/45JNPMG3aNOTn5yM6OhpTpkzR+qG/cOFCeHl5Yf78+QDuPmbdz88PmzZtwgsvvACJRIJdu3bBw8NDWKMCAGbMmIGVK1di+/btGD58OBQKBRQKBZYtWya0OXXqFL799lsEBwfD3t4eGRkZwr4+ffq0at0NIiKi7kxnqFi5cqXw9zlz5uDxxx/HzJkz9fbmVlZWeP/997F161ZERERAJpPhiSeewOzZs7Xa1dfXN3pQ2dtvv40dO3YgMjISGo0GAQEBjdak8PDwwKJFixAVFYXDhw9DLpfjrbfeEha+AoALFy4AAE6cOIETJ05o9X/jjTcQHByst+MlIiJ6mEk0Go1GTMOCggLIZLJmV7nsznStqaHL7++8qudKuqaPC2pQqNbAwViCpb26x/NU+q799wN535d3/PpA3pc63vaXRj2Q9z20v3vcUvrDTx+jtKwQPawcMO0PSx90OZ1i4tS+beqna86j6FtKubIkERER6dLqFTWLiorw22+/4fbt26irq2uyjT4vkRAREZFhaFWo2LNnD77//nuo1Wqd7RgqiIiIuh/RoeLkyZOIiYmBj48PJk6ciHXr1mHcuHHw8/NDamoqjh8/jscee0zr9k0iIiLqPkSHisOHDwuPF29YNVMulyMoKAhBQUEYMWIE1qxZg6CgoA4rloiIqCNYyey0/qS2ER0qbty4gaCgIK1luO+9zdPf3x9+fn44cOCAXp5gSkRE1FnGj33tQZfwUBC9TLdarUaPHj2E11KptNFzNfr374/MzEy9FUdERESGQ/SZCjs7OxQVFQmvHRwckJWVpdWmqKioVQ8Uo+7BzhgAJP//TyIieliJDhXOzs64efOm8Nrb2xtHjx7FL7/8ghEjRkCpVOLMmTN8dgY18pp991jwioiouxN9+WPo0KG4efMm8vPzAQDTp0+HpaUlNm/ejJdeegkREREAGj+EjIiIiLoH0WcqgoODtZ6D4eDggI8++ggHDhxAXl4eevXqhYkTJ2LAgAEdUScRERF1ca1eUfNecrkcr7zyir5qISIiIgMm+vIHERERkS6tPlNRX1+P27dv63z2h5eXV7sLIyIiIsPSqlCxf/9+HDhwACUlJTrbRUVFtasoIiIiMjyiQ8WePXsQExMDKysrjBs3Dvb29lyTgoiIiASiQ8Xx48chl8sREREBS0vLjqyJiIiIDJDoiZqlpaUYNmwYAwURERE1SXSo6NOnD8rLyzuyFiIiIjJgokNFWFgYzp07h+Li4g4sh4iIiAyV6DkVYWFh+P333/G3v/0NM2bMwKBBg5q9FOLg4KC3AomIiMgwtOqW0kceeQQnTpxAZGRks20kEgl2797d7sKIiIjIsIgOFUePHsUXX3wBY2NjeHt7w87OjreUEhERkUB0qDhw4ABsbGzw4YcfQi6Xd2RNREREZIBET9QsKCjAY489xkBBRERETRIdKuzt7Zt91gcRERGR6FAxbtw4KBQKVFZWdmQ9REREZKBEh4qnnnoKrq6uWL16NVJTUxkuiIiISIvoiZrPPfec8PcPPvig2Xa8pZSIiKh7Eh0qPD09IZFIOrIWIiIiMmCiQ8XKlSs7sAwiIiIydKLnVBARERHpwlBBREREetHs5Y+9e/cCACZNmgQrKyvhtRgzZ85sf2VERERkUJoNFdHR0QCAwMBAWFlZCa/FYKggIiLqfpoNFStWrADwv8eYN7wmIiIiakqzocLLy0vnayIiIqJ7iZ6oGRcXh6ysLJ1tbty4gbi4uHYXRURERIZHdKj4/PPPkZiYqLNNUlISPv/883YXRURERIZHr7eU1tfXc9VNIiKibkqvoUKlUkEmk+lzSCIiIjIQOpfpvv9SRmJiIvLz8xu1q6+vx61bt5CWloaAgAD9VkhEREQGQWeouH/SZWZmJjIzM5tt7+bmhpdeekkvhREREZFh0RkqNm3aBADQaDRYuHAhJk+ejMmTJzdqZ2RkBJlMBnNz846pkoiIiLo8naGiV69ewt9nzpwJb29vrW1EREREDUQ/+nzWrFkdWQcREREZONGh4vr168jIyMCYMWNgaWkJAKiqqsK///1vJCUlwczMDNOmTWvy8ggRERE9/ETfUvrDDz9g3759QqAAgG+++QYnT56ERqNBaWkpduzYgQsXLnRIoURERNS1iQ4VV69ehbe3t/C6rq4OcXFxcHV1xZdffolNmzbB2toaP/30U4cUSkRERF2b6FBRUlKCnj17Cq+vXbuGqqoqhIaGQiqVwt7eHsOGDWvx+SBERET0cGrVippqtVr4e3p6OgDtp5daW1ujpKRET6URERGRIREdKhwcHHD58mXhdWJiInr27InevXsL24qKimBlZaXfComIiMggiL77Y9SoUYiOjsa6detgamqKjIwMPPHEE1ptcnJytEIGERERdR+iQ8WUKVNw4cIFnD17FgDg7OyMmTNnCvvz8/Nx5coVPPXUU60qIDs7G9u2bUNGRgZkMhlCQkIwa9YsGBnpPolSUVGB7du3IzExEfX19Rg6dCjCw8PRo0cPrXaJiYnYvXs3cnNzIZfLMWvWLAQGBrZpLCIiImqe6FBhbm6O1atX48aNGwAAJyenRj/4lyxZAhcXF9FvXlZWhtWrV8PJyQlLly5Fbm4uvvrqK2g0GjzzzDM6+65fvx4qlQrz5s2DkZERdu3ahbVr1+KDDz4Q2qSnp2PdunUICwtDeHg4FAoFNmzYAJlMBj8/v1aNRURERLqJDhUNBgwY0OR2uVwOuVzeqrGOHDmCmpoaLF68GJaWlvD19UVlZSWio6MxdepUrTUx7pWRkYELFy5g5cqVwkRRe3t7LFu2DCkpKfD19QUAxMTEwNPTE3PnzgUA+Pj4IDs7G3v37hVChdixiIiISDed1xiUSiUKCwtFD5aVldXoyaa6JCcnw8/PTys8BAUFoaamBkqlstl+CoUCNjY2WneeuLq6Qi6XIzk5GQBQW1uLixcvYtSoUVp9AwMDkZGRgYqKCtFjERERUct0hopVq1bhxIkTWtu+//574Tf/+509exaff/656DfPyclBv379tLY5ODjAzMwMKpVKZz9HR8dG2x0dHZGTkwMAyMvLg1qtbtTO0dERGo1GGF/MWERERNSyVl/+qK2tRXl5uV7evLy8HDKZrNF2mUyGsrIynf2aujQik8mQn58PAEL/+8dvuOW14RjEjHW/2NhYxMbGAgDWrFnTKBiJ1W/Xf9vUj6g5h9+b8aBLoIdM+Ott+/5G3VOrFr+iu0JDQ7FmzRqsWbPmQZdiMP7yl7886BLoIcPPFOkbP1Pt90BDhUwmE+Y23Ku8vFznIloymQyVlZVN9ms4M9HQ//7x7z+DIWYsIiIiatkDDRVNzVsoLCxEdXW1zksKzc13UKlUwvyI3r17w9jYuFE7lUoFiUQijC9mLCIiImrZAw0V/v7+uHDhgtaZgvj4eEilUq27Me43ZMgQFBcXC88fAe4+RTUvLw/+/v4AAFNTU/j4+ODMmTNafePj4+Hu7i7MoxAzFrVfaGjogy6BHjL8TJG+8TPVfg80VEyYMAGmpqb45JNPkJKSgtjYWERHR2PKlClakycXLlyIyMhI4bW7uzv8/PywadMmJCQk4OzZs/jnP/8JDw8PrXUlZsyYgdTUVGzfvh2pqan4+uuvoVAotFYCFTsWtQ//sZK+8TNF+sbPVPtJNBqNprmdc+bMadOgUVFRottmZ2dj69atWst0z549W2u1zgULFsDLywsLFiwQtpWXl2PHjh04e/YsNBoNAgICEB4eDmtra63xz549i6ioKPz+++/CMt1BQUFabcSORURERM174KGCiIiIHg46QwURERGRWK1e/Iq6p/z8fLz55psYOXIkFi9eDADYvHmz1rLsEokE5ubmGDBgAIKDgxESEgKJRILU1FSsWrUKgYGBePvttxuNvXz5cly+fBmTJk1qcrXWt956C/n5+di2bVuzz4Mhw9bw+bqXmZkZrKys0L9/f/j4+CA4OLjRJck9e/Zg7969AIDnnnsO06dPb3L8hs8YAHzyySfNPsOIHi76+Fzd269v37547LHHMGXKFEil0g6v3xAxVFC7hYWFwdraGvX19SgoKEBCQgIuXbqE69ev49VXX4WbmxtMTU2RlpbWqG9VVRWuXbsGiUTS5P7bt28jNzcXgwYNYqDoBhwdHYXn9dTU1KCoqAjp6elITk5GTEwMXn31VYwZM6ZRP2NjY8TFxTUZKrKzs3H58mUYGxtDrVZ39CFQF9TWz1VQUBD69u0LACgqKkJiYiJ2796N1NRU/O1vf+vUYzAUDBXUbmFhYVq/+U2fPh3vvfcejhw5gieffBK9e/eGq6sr0tLSkJubiz59+ghtMzIyoFarMXz4cCQlJaGsrExr4bOGB8vpusWYHh5OTk6YPXu21jaNRoNTp07hyy+/xKZNmyCTyRAQEKDVxs/PD+fPn8eVK1fg6uqqte/EiRMwNjbGo48+yocEdlNt/VyNHj0aQ4cOFV7/8Y9/xJIlS/Dbb7/h4sWL8PHx6ZT6DQmX6Sa969+/P7y9vaHRaHDt2jUAgLe3NwA0evqsUqmEqakppk6dCo1G0+hsRUP7hv7U/UgkEowZMwavvfYaNBoNvvrqK9w/FSwwMBCmpqaNHoBYX1+PkydPws/PDzY2Np1YNXV1Yj5X97OyssKwYcMAQPjeRtoYKqhDSSQSAP8709BUqHB1dYWbmxssLCwa7U9LS4NEIoGnp2fnFExd1ujRoyGXy5GTk4OsrCytfTKZDMOGDUN8fDzq6uqE7RcuXEBRURGCg4M7uVoyFLo+V7oYGxt3YFWGi6GC9C47OxtKpRISiQSDBg0CcHeRMVNTU63QUFNTgytXrsDT0xNGRkYYPHiw1v7i4mLk5OTA2dmZ8ykIEokEHh4eAJr+LTE4OBhlZWVISkoStp04cULrt0ui+7X0ubrXvZ8vd3f3Dq/NEHFOBbXb4cOHYW1tDY1GI0zUrK6uxqRJkyCXywEAUqlUmFeRn58PuVyOjIwM1NXVCWchPDw8EBUVhYqKClhaWgqXQnjpgxrY2dkBAEpLSxvt8/Pzg52dHeLi4vDYY4+hvLwcSUlJCAkJgYkJv9VR85r7XJ06dQpXr14F8L+JmiUlJZgwYQLc3Nw6vU5DwH9p1G6HDx8G8L9bSp2dnfH444/j8ccf12rn5eWFtLQ0KJVKyOVyKJVKGBsbY/DgwcJ+jUaD9PR0BAQEcJImtYqRkRHGjBmDH3/8EXfu3EFCQgJqa2t56YPa7PTp0422hYaG4rXXXnsA1RgGhgpqN7H3/Xt5eSEmJgZKpRLBwcFIS0vDwIEDYW5uDgBwcXERLpE0hArOp6B7FRUVAUCzS+gHBwdj//79OHnyJOLj49G/f3+4uLh0ZolkgJr7XL377rsYOnQo6urqcPPmTWzbtg2xsbF45JFHMHHixAdRapfHORXUaQYPHgwTExOkpaWhtrYWGRkZWoHB1NQUrq6uUCqVKC0tRXZ2NpydnSGTyR5g1dRVNJzFAiDM1bmfk5MTXFxccODAAVy5cgXjxo3rzBLJAIn5XJmYmGDgwIH4y1/+AhsbG+zcuRO3bt3qzDINBkMFdZqGeRV5eXnCqen7z0J4enri+vXrUCgU0Gg0vPRBgtOnTyM/Px+Ojo46z4wFBwejqKgIRkZGGDt2bCdWSIZI7OcKuHuX0axZs1BbW4uYmJhOqtCwMFRQp2oICd99953WrOsGnp6eUKvV+OGHH7TaU/fVsEjRF198AYlEghdffFG4VbkpY8eOxZIlS7B8+XLY2tp2XqFkUFr7uWoQEhKCnj174vjx4ygsLOyESg0L51RQp/Ly8sK+fftw8+ZNPPLII1qrZwJ3L5EYGRnh5s2bnE/RDWVnZ2PPnj0AgNraWhQVFSEtLQ0FBQWwsLDAm2++iSFDhugcw8LCAiNGjOiMcslA6ONz1cDExATTp0/H1q1bsW/fPvzpT3/qyNINDkMFdaqGeRX33kp6L3NzcwwcOBBXr15tMnTQwy0nJ0d4kNO9D36aOHFikw9+IhJD35+rkJAQfPfddzhx4gSefvppODg4dETZBomPPiciIiK94JwKIiIi0guGCiIiItILhgoiIiLSC4YKIiIi0guGCiIiItILhgoiIiLSC4YKIiIi0guGCiIyeKmpqZg9ezZmz579oEsh6ta4oiaRHtXU1CAuLg7nzp1DVlYWSkpKYGJiAnt7e3h4eCAoKAg+Pj46x1iwYAEKCgoabTc3N0evXr3g6emJSZMmwcnJqVGblStXQqlUiqrVy8sLK1euFNW2pdqaMm7cOCxYsKBV49+vvLwcP/74IwDgiSeeeCifWHvixAnk5+fD29sb3t7eD7oconZhqCDSk5SUFERGRmo9EtnCwgJ1dXXIyclBTk4Ojh49iiFDhuDNN99Ejx49dI5namoKS0tLAHcfflRaWoqbN2/i5s2bOHr0KF577TWEhIQ02dfY2LjFJc7bswT6vbU1p6X9YpSXlwvLKwcHBzcbKszMzNCvX792v9+DcOLECSEIMlSQoWOoINKD+Ph4bNy4EWq1Gvb29pg9ezZGjBgh/ODOycnBkSNHcOjQISgUCixfvhyrV6+GjY1Ns2MGBgZq/aZfU1ODc+fOYdu2bbhz5w6++OILuLi44JFHHmnUd/Dgwa0+C9Ea99f2oLm6uuKzzz570GUQdXucU0HUTtnZ2YiMjIRarcaAAQPw8ccfIyQkROtMgKOjI15++WW88847MDExQW5uLv75z3+26n2kUilGjRqFhQsXAgDq6+tx+PBhvR4LEVF78EwFUTvt3r0b1dXVMDU1xaJFi3Q+8TAgIABPP/009uzZg99++w3nz59HQEBAq97P19cXdnZ2KCoqwtWrV9tbfqe6desWDhw4gJSUFBQUFECtVqNHjx6wtbWFp6cnRo8eDVdXVwCN54e8+eabWmPdOyckNTUVq1atAgDhEdcNTpw4gc8//xy9evXC5s2bkZaWhh9++AFXrlxBdXU1+vbti0mTJmldSjp//jx+/PFHZGZmorq6Gv3798eTTz6JwMDAJo8rPz8f8fHxSE1NRX5+Pm7fvg0AcHBwgJ+fH6ZMmdLoSZYNdTXYu3evcKmnwaZNmyCXy4XX9fX1OHHiBE6ePIkbN26gsrISPXr0wODBgzFx4sRmL580fC1nzpyJp59+Gj/99BNOnz6N3NxcVFRUYMWKFULfnJwcHDx4EEqlErdu3YJGo4G1tTXs7e3h7e2NcePGwdHRscn3IWKoIGqHoqIiJCYmAgCCgoJEXdefMmUKDhw4gMrKShw6dKjVoQIA7O3tUVRUhMrKylb3fVAyMzOxatUqlJeXAwCMjIxgYWGB4uJiFBUV4fr16ygvLxdChZWVFXr06IHS0lIAQI8ePWBk9L+Tq22ZE3L06FF88cUXAO7Od6murkZmZia2bNmC3NxcPPfcc9izZw/27t0LiUQCCwsL1NTU4OrVq/jss89QVlaGsLCwRuN+/vnnQgAyMTGBhYUFysrKhLk0J06cwF/+8hd4eHgIfaRSKWxsbFBWVga1Wg0zMzOYm5trjXvv8VZUVGDt2rVITU1t9PU7c+YMzpw5gyeffBIvvPBCs8dfW1uLVatW4dKlSzA2Noa5uTkkEomwPyUlBREREaitrQUAoc2tW7dw69YtXL58GSYmJrzLhprFUEHUDqmpqdBoNACAkSNHiupjbm4OX19fJCQkIC0tDWq1GsbGxq1634Y7MNoz2bKzffXVVygvL8fAgQPxyiuvwM3NDRKJBHV1dSgoKEBSUpLwtQSAJUuWID8/XzhD8dFHH2n91t5aJSUl2Lp1KyZNmoQZM2bA2toaZWVl2LFjB+Li4vDDDz9AJpNh3759eOaZZzBp0iRYWlqiqKgIkZGRSE5OxldffYXRo0c3moTq7OyMUaNGwdfXF71794aRkRHUajWuX7+OPXv2IDk5GevXr8fGjRshlUoB3J2XEhgYKJxFePLJJ3X+sI6MjERqaipMTEzwwgsvICQkBGZmZiguLsa3336L48eP48CBA+jdu3eTwQcADh06BAB44403EBgYCKlUitLSUiFYfPnll6itrYWfnx9eeOEFDBgwAMDd+Tx5eXlISEhodMaF6F4MFUTtkJ2dLfx94MCBovs5OzsjISEBVVVVKCgoQJ8+fUT3PXPmDEpKSgAAbm5uTba5dOkSXnvtNZ3jhIeHN3s6vyXx8fFITk7W2WbJkiUYPHiwVk0A8Morr8Dd3V3YbmJigr59++LJJ59sUy1iVVdXIyQkBOHh4cI2KysrzJ8/H2lpacjPz8euXbvwzDPP4Omnnxba2NnZ4e2338a8efNQXV2NpKQkjB07Vmvsl19+udH7GRsbw9XVFX/5y1/w7rvvIisrC2fOnGnUV4zLly8jISEBADB37lyEhoYK+2xtbTF//nxUVFQgISEBUVFRCA4OFsLLvaqqqrB06VIMGzZM2NZwF9KdO3eQl5cH4G7osLOzE9pIpVL0798f/fv3b3Xt1L1woiZROzScmgdad9bg3ttJy8rKWmyv0WhQUFCAn376CZGRkQDu/jCeOHFik+3VajXu3Lmj87+amhrR9d6vtra2xfHr6uq0+jTcDlpUVNTm922v6dOnN9pmZGQkrB1iamqKyZMnN2pjaWkpBKEbN2606j2NjIzg5+cHAEhPT29lxXfFx8cDAHr27NnsbcRz5swBcPczmZKS0mSb/v37awWKe1lYWAhnLB7k/yMybDxTQdRFxcXFIS4ursl95ubmWLBgAfr27dvk/rYsbNUabVnYKiAgAEePHsXmzZtx6dIlDBs2DC4uLjAzM+ugKrVZWVk1e0bI1tYWAODk5NRoXkODhtt/mwuBaWlpOHbsGC5fvoxbt26hurq6UZuGCZytde3aNQB317G4d57FvZycnGBvb4/bt2/j2rVrTYaHe88c3U8qleLRRx9FSkoK/vGPf2DChAkICAjAwIEDYWLCHxUkDj8pRO1w/xkHe3t7Uf3EnOG4d4EpiUQCMzMzODg4wNPTE+PHj0fPnj3bUXnne/7555Gbm4vU1FQcPHgQBw8ehJGREZydnREQEIDQ0FDRX7+2sLCwaHZfww9qXW0a5r2o1epG+77++mvs379fazyZTCb8MK6qqkJ1dXWTQUOMO3fuAECLX5+ePXvi9u3bQvv76bozCQBef/11REREICsrCzExMYiJiYGJiQlcXFwwfPjwRrdKE92PoYKoHe5dKvvatWuifyhev34dwP+W3m5KV1tgqr1kMhlWrFiB9PR0JCUl4dKlS7h27Zrw3/79+/H6669j9OjRD7rUVklJSRECRVhYGMLCwuDk5KR1RmH37t3Yt2+f1kTUB6G5sxwNHBwcEBERgZSUFCgUCly6dAlZWVm4dOkSLl26hO+++w6LFy9ucal56r4YKojawdvbGxKJBBqNBgkJCc1er75XVVUVfvvtNwCAp6dnq+/8MHQeHh7CrZU1NTVISUnB7t27cePGDURGRsLHx0e4HGEITp8+DQDw8/PDq6++2mSb4uLidr2HjY0NVCqV1hLwTWnYr2ul1pYYGRnB398f/v7+AIDKykqcO3cO33zzDQoLC7FhwwZERkbykgg1iRM1idrBzs4Ow4cPB3B3Mp1KpWqxz8GDB4X1JZq79a+7kEqlGDZsGJYsWQLg7gTQeycztvSbdVfQ8IO8ubt/NBqNsLZEU+5dJ6I5gwYNAnD3Fub6+vom2+Tk5AhzNlxcXFocUywLCwuMHj0ar7/+OoC7l2JaO1mVuo+u/y+WqIubM2cOpFIpamtr8emnnwq3ezZFoVBg3759AO6e5WjLwleGSK1WN/vDEIDW7Y/3Bol75zg0LJrV1TTMe8nKympy/5EjR4RbNZvScIy6ji8oKAjA3Ymex44da7JNVFQUgLvzfB599NGWC7/P/Xfr3O/e/0dighB1TwwVRO3Uv39/vP766zAyMsKNGzfw7rvv4tixY1o/JFQqFXbs2IGPP/4YdXV16N27N/7v//6v23xzvnXrFv7v//4PMTExuH79utZkx6ysLGzcuBHA3aeNenl5CftkMpkwT+X48eNNTpJ80BouEygUCuzduxdVVVUA7oaEffv2Ydu2bTqfSNuwwJRCoWj27hBXV1dhcbVt27bh559/FiZ9FhcXY8uWLThz5gyA/4Xc1rp06RKWLFmCgwcPIjs7WwiBGo0Gly5dwr///W8AdyeDNvUQOyKAcyqI9GL06NGwsrISHn2+ZcsWbNmyBZaWlqitrRWWPQbuXntfuHBhizPx20PM4lfA3RUU20LM4lcODg746KOPhNd5eXmIiopCVFQUjIyMYGlpiaqqKuE3ZBMTEyxYsKDR3QUTJkxAVFQUfv75Zxw9ehTW1tYwMjKCm5sb3n777TbVr09jx45FXFwc0tLSsGfPHkRHR8PS0hIVFRXQaDQICAiAs7OzcIbqfuPGjcOBAweQm5uL+fPnw9raWggFH3zwgXCXz/z581FaWgqlUolt27Zhx44dMDc3F94HAJ588sl2XVK7ceMGdu7ciZ07d8LY2Fg4joYwZ2FhgbfeessgLkvRg8FQQaQn/v7+2LhxI06cOIFz584hKysLpaWlMDExEW4FDQoKatOp6dZqWPyqozQsfqXLvb8t29vbY+nSpUhNTUVGRoZw26OxsTH69OkDb29vTJ48ucl1N5566ilYWFjg5MmTwrwBjUbT7F0znc3ExATLly/H999/j9OnTwtLqLu6umLcuHEIDQ1t9KCwe/Xt2xcrVqzA999/j8uXLwvPAgG0b1+1tLTE+++/LzxQLDMzE1VVVbC1tYW7uzsmTZrU7APFxHBxccGf//xnpKam4sqVKygqKkJJSQlMTU3Rv39/+Pr6YvLkyR162y8ZPonmQd/jRERERA8FnsMiIiIivWCoICIiIr1gqCAiIiK9YKggIiIivWCoICIiIr1gqCAiIiK9YKggIiIivWCoICIiIr1gqCAiIiK9YKggIiIivfh/fBMYEm2/fUwAAAAASUVORK5CYII=", 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", "text/plain": [ "
" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 18, "source": [ "# by activating the `is_relative` option\n", "# we can visualize the estimated policy value of the evaluation policy\n", @@ -646,93 +658,81 @@ "ope.visualize_off_policy_estimates(\n", " action_dist=action_dist,\n", " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", - " n_bootstrap_samples=10000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=10000, # number of resampling performed in bootstrap sampling\n", " is_relative=True,\n", " random_state=12345,\n", ")" - ], - "outputs": [ - { - "output_type": "display_data", - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {} - } - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Note that the OPE demonstration here is with the small size example version of our dataset. \n", "\n", "Please use its full size version (https://research.zozo.com/data.html) to produce more reasonable results." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (4) Evaluation of OPE\n", "\n", "Our final step is the **evaluation of OPE**, which evaluates the estimation accuracy of OPE estimators.\n", "\n", - "Specifically, we asses the accuracy of the estimator such as DM, IPW, and DR by comparing its estimation with the ground-truth policy value estimated via the on-policy estimation from the Open Bandit Dataset.\n", + "Specifically, we asses the accuracy of DM, IPW, and DR by comparing their estimations with the ground-truth policy value estimated via the on-policy estimation from Open Bandit Dataset.\n", "\n", - "This type of evaluation of OPE is possible, because Open Bandit Dataset contains a set of *multiple* different logged bandit feedback datasets collected by running different policies on the same platform at the same time.\n", + "This type of evaluation of OPE is possible, because Open Bandit Dataset contains a set of *multiple* different logged bandit datasets collected by running different policies on the same platform at the same time.\n", "\n", "Please refer to [the documentation](https://zr-obp.readthedocs.io/en/latest/evaluation_ope.html) for the details about the evaluation of OPE protocol." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### (4-1) Approximate the Ground-truth Policy Value \n", "With Open Bandit Dataset, we can estimate the ground-truth policy value of the evaluation policy in an on-policy manner as follows.\n", "\n", "$V(\\pi_e) \\approx \\frac{1}{|\\mathcal{D}_{e}|} \\sum_{i=1}^{|\\mathcal{D}_{e}|} \\mathbb{E}_{n} [r_i]$\n", "\n", - "$ \\mathcal{D}_e := \\{(x_i,a_i,r_i)\\} $ ($(x,a,r) \\sim p(x)\\pi_e(a \\mid x)p(r \\mid x,a) $) is the log data collected by the evaluation policy (, which is used only for approximating the ground-truth policy value).\n", + "$ \\mathcal{D}_e := \\{(x_i,a_i,r_i)\\} $ ($(x,a,r) \\sim p(x)\\pi_e(a | x)p(r | x,a) $) is the log data collected by the evaluation policy (, which is used only for approximating the ground-truth policy value).\n", "\n", "We can compare the policy values estimated by OPE estimators with this on-policy estimate to evaluate the accuracy of OPE." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 19, - "source": [ - "# we first calculate the ground-truth policy value of the evaluation policy\n", - "# , which is estimated by averaging the factual (observed) rewards contained in the dataset (on-policy estimation)\n", - "policy_value_bts = OpenBanditDataset.calc_on_policy_policy_value_estimate(\n", - " behavior_policy='bts', campaign='all'\n", - ")" - ], + "execution_count": 20, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ - "INFO:obp.dataset.real:When `data_path` is not given, this class downloads the example small-sized version of the Open Bandit Dataset.\n" + "INFO:obp.dataset.real:When `data_path` is not given, this class downloads the small-sized version of Open Bandit Dataset.\n" ] } ], - "metadata": {} + "source": [ + "# we first calculate the ground-truth policy value of the evaluation policy\n", + "# , which is estimated by averaging the factual (observed) rewards contained in the dataset (on-policy estimation)\n", + "policy_value_bts = OpenBanditDataset.calc_on_policy_policy_value_estimate(\n", + " behavior_policy='bts', campaign='all'\n", + ")" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### (4-2) Evaluation of OPE\n", "\n", @@ -740,28 +740,14 @@ "\n", "- $\\textit{relative-ee} (\\hat{V}; \\mathcal{D}_b) := \\left| \\frac{V(\\pi_e) - \\hat{V} (\\pi_e; \\mathcal{D}_b)}{V(\\pi_e)} \\right|$ (relative estimation error; relative-ee)\n", "- $\\textit{SE} (\\hat{V}; \\mathcal{D}_b) := \\left( V(\\pi_e) - \\hat{V} (\\pi_e; \\mathcal{D}_b) \\right)^2$ (squared error; se)" - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 20, - "source": [ - "# evaluate the estimation performance of OPE estimators \n", - "# `evaluate_performance_of_estimators` returns a dictionary containing estimation performance of given estimators \n", - "relative_ee = ope.summarize_estimators_comparison(\n", - " ground_truth_policy_value=policy_value_bts,\n", - " action_dist=action_dist,\n", - " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", - " metric=\"relative-ee\", # \"relative-ee\" (relative estimation error) or \"se\" (squared error)\n", - ")\n", - "\n", - "# estimation performances of the three estimators (lower means accurate)\n", - "relative_ee" - ], + "execution_count": 21, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -792,11 +778,11 @@ " \n", " \n", " dm\n", - " 0.194115\n", + " 0.156041\n", " \n", " \n", " dr\n", - " 0.106682\n", + " 0.125548\n", " \n", " \n", "\n", @@ -805,34 +791,54 @@ "text/plain": [ " relative-ee\n", "ipw 0.084019\n", - "dm 0.194115\n", - "dr 0.106682" + "dm 0.156041\n", + "dr 0.125548" ] }, + "execution_count": 21, "metadata": {}, - "execution_count": 20 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# evaluate the estimation performance of the OPE estimators \n", + "# `evaluate_performance_of_estimators` returns a dictionary containing estimation performance of given estimators \n", + "relative_ee = ope.summarize_estimators_comparison(\n", + " ground_truth_policy_value=policy_value_bts,\n", + " action_dist=action_dist,\n", + " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", + " metric=\"relative-ee\", # \"relative-ee\" (relative estimation error) or \"se\" (squared error)\n", + ")\n", + "\n", + "# estimation performances of the three estimators (lower means accurate)\n", + "relative_ee" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "We can iterate the above process several times to get more relibale results.\n", + "We can iterate the above process several times to get more reliable results.\n", "\n", "Please see [examples/obd](../obd) for a more sophisticated example of the evaluation of OPE with the Open Bandit Dataset." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] } ], "metadata": { + "interpreter": { + "hash": "2ff39f3b22306140fd87fd114528320b56c4f8c8e196b421a3ea939a2b6b4692" + }, + "kernelspec": { + "display_name": "Python 3.9.5 64-bit ('3.9.5': pyenv)", + "name": "python3" + }, "language_info": { "codemirror_mode": { "name": "ipython", @@ -845,15 +851,8 @@ "pygments_lexer": "ipython3", "version": "3.9.5" }, - "orig_nbformat": 2, - "kernelspec": { - "name": "python3", - "display_name": "Python 3.9.5 64-bit ('3.9.5': pyenv)" - }, - "interpreter": { - "hash": "2ff39f3b22306140fd87fd114528320b56c4f8c8e196b421a3ea939a2b6b4692" - } + "orig_nbformat": 2 }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/examples/quickstart/online.ipynb b/examples/quickstart/online.ipynb index 70a7a7bb..6830a1ad 100644 --- a/examples/quickstart/online.ipynb +++ b/examples/quickstart/online.ipynb @@ -2,6 +2,7 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "# Quickstart Example of Off-Policy Evaluation of Online Bandit Algorithms\n", "---\n", @@ -11,28 +12,29 @@ "However, empirically, RM works well when evaluation policies are learning algorithms.\n", "Please refer to https://arxiv.org/abs/1003.5956 about the details of RM.\n", "\n", - "Our example with online bandit algorithms contains the follwoing three major steps:\n", + "Our example with online bandit algorithms contains the following three major steps:\n", "- (1) Synthetic Data Generation\n", "- (2) Off-Policy Evaluation (OPE)\n", "- (3) Evaluation of OPE\n", "\n", "Please see [../examples/online](../online) for a more sophisticated example of the evaluation of OPE of online bandit algorithms." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [], "source": [ "# needed when using Google Colab\n", "# !pip install obp" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, + "metadata": {}, + "outputs": [], "source": [ "# import open bandit pipeline (obp)\n", "import obp\n", @@ -49,71 +51,51 @@ " calc_ground_truth_policy_value,\n", " run_bandit_simulation\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 2, - "source": [ - "# obp version\n", - "print(obp.__version__)" - ], + "execution_count": 3, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "0.4.0\n" + "0.5.2\n" ] } ], - "metadata": {} + "source": [ + "# obp version\n", + "print(obp.__version__)" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (1) Synthetic Data Generation\n", "We prepare easy-to-use synthetic data generator: `SyntheticBanditDataset` class in the dataset module.\n", "\n", "It takes number of actions (`n_actions`), dimension of context vectors (`dim_context`), reward function (`reward_function`), and behavior policy (`behavior_policy_function`) as inputs and generates a synthetic bandit dataset that can be used to evaluate the performance of decision making policies (obtained by `off-policy learning`) and OPE estimators." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 3, - "source": [ - "# generate a synthetic bandit dataset with 10 actions\n", - "# we use `logistic function` as the reward function\n", - "# we use the uniformly random behavior policy because it is desriable for RM\n", - "# one can define their own reward function and behavior policy such as nonlinear ones. \n", - "dataset = SyntheticBanditDataset(\n", - " n_actions=10,\n", - " dim_context=5,\n", - " reward_type=\"binary\", # \"binary\" or \"continuous\"\n", - " reward_function=logistic_reward_function,\n", - " behavior_policy_function=None, # uniformly random\n", - " random_state=12345,\n", - ")\n", - "# obtain a set of synthetic logged bandit feedback\n", - "n_rounds = 10000\n", - "bandit_feedback = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds)\n", - "\n", - "# `bandit_feedback` is a dictionary storing synthetic logged bandit feedback\n", - "bandit_feedback" - ], + "execution_count": 4, + "metadata": { + "tags": [] + }, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "{'n_rounds': 10000,\n", @@ -135,53 +117,133 @@ " [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]),\n", - " 'action': array([6, 4, 2, ..., 9, 4, 7]),\n", + " 'action': array([9, 3, 2, ..., 0, 2, 7]),\n", " 'position': None,\n", - " 'reward': array([1, 1, 1, ..., 0, 1, 0]),\n", - " 'expected_reward': array([[0.80210203, 0.73828559, 0.83199558, ..., 0.81190503, 0.70617705,\n", - " 0.68985306],\n", - " [0.94119582, 0.93473317, 0.91345213, ..., 0.94140688, 0.93152449,\n", - " 0.90132868],\n", - " [0.87248862, 0.67974991, 0.66965669, ..., 0.79229752, 0.82712978,\n", - " 0.74923536],\n", + " 'reward': array([1, 1, 0, ..., 0, 0, 1]),\n", + " 'expected_reward': array([[0.81612381, 0.62585527, 0.3867853 , ..., 0.62527072, 0.58635322,\n", + " 0.38638404],\n", + " [0.52901819, 0.30298844, 0.47277431, ..., 0.67711224, 0.55584904,\n", + " 0.60472268],\n", + " [0.47070198, 0.44459997, 0.40016028, ..., 0.71193979, 0.49769816,\n", + " 0.71876507],\n", + " ...,\n", + " [0.85229627, 0.60343336, 0.18287765, ..., 0.54555271, 0.77112271,\n", + " 0.18843358],\n", + " [0.78101646, 0.68586084, 0.40700551, ..., 0.45177062, 0.63841605,\n", + " 0.48128186],\n", + " [0.88757249, 0.75954519, 0.82721872, ..., 0.3422384 , 0.33609074,\n", + " 0.84539856]]),\n", + " 'pi_b': array([[[0.13284158],\n", + " [0.10982506],\n", + " [0.08647184],\n", + " ...,\n", + " [0.10976088],\n", + " [0.10557132],\n", + " [0.08643715]],\n", + " \n", + " [[0.10165762],\n", + " [0.08109171],\n", + " [0.09609782],\n", + " ...,\n", + " [0.11788441],\n", + " [0.1044221 ],\n", + " [0.10965236]],\n", + " \n", + " [[0.09128276],\n", + " [0.08893092],\n", + " [0.08506539],\n", + " ...,\n", + " [0.11618686],\n", + " [0.09378061],\n", + " [0.11698258]],\n", + " \n", " ...,\n", - " [0.66717573, 0.81583571, 0.77012708, ..., 0.87757008, 0.57652468,\n", - " 0.80629132],\n", - " [0.52526986, 0.39952563, 0.61892038, ..., 0.53610389, 0.49392728,\n", - " 0.58408936],\n", - " [0.55375831, 0.11662199, 0.807396 , ..., 0.22532856, 0.42629292,\n", - " 0.24120499]]),\n", - " 'pscore': array([0.1, 0.1, 0.1, ..., 0.1, 0.1, 0.1])}" + " \n", + " [[0.13979975],\n", + " [0.10900003],\n", + " [0.07157834],\n", + " ...,\n", + " [0.10287015],\n", + " [0.12890008],\n", + " [0.07197713]],\n", + " \n", + " [[0.12794035],\n", + " [0.11632739],\n", + " [0.08801904],\n", + " ...,\n", + " [0.09204875],\n", + " [0.11093714],\n", + " [0.0948057 ]],\n", + " \n", + " [[0.1309115 ],\n", + " [0.11517978],\n", + " [0.1232442 ],\n", + " ...,\n", + " [0.0758826 ],\n", + " [0.07541753],\n", + " [0.12550525]]]),\n", + " 'pscore': array([0.08643715, 0.11568983, 0.08506539, ..., 0.13979975, 0.08801904,\n", + " 0.0758826 ])}" ] }, + "execution_count": 4, "metadata": {}, - "execution_count": 3 + "output_type": "execute_result" } ], - "metadata": { - "tags": [] - } + "source": [ + "# generate a synthetic bandit dataset with 10 actions\n", + "# we use `logistic function` as the reward function\n", + "# we use the uniformly random behavior policy because it is desriable for RM\n", + "# one can define their own reward function and behavior policy such as nonlinear ones. \n", + "dataset = SyntheticBanditDataset(\n", + " n_actions=10,\n", + " dim_context=5,\n", + " reward_type=\"binary\", # \"binary\" or \"continuous\"\n", + " reward_function=logistic_reward_function,\n", + " behavior_policy_function=None, # uniformly random\n", + " random_state=12345,\n", + ")\n", + "# obtain a set of synthetic logged bandit feedback\n", + "n_rounds = 10000\n", + "bandit_feedback = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds)\n", + "\n", + "# `bandit_feedback` is a dictionary storing synthetic logged bandit data\n", + "bandit_feedback" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (2) Off-Policy Evaluation (OPE)\n", - "Our next step is OPE which attempts to estimate the performance of online bandit algorithms using the logged bandit feedback and RM.\n", + "Our next step is OPE which aims to estimate the performance of online bandit algorithms using logged bandit data and RM.\n", "\n", "Here, we visualize the OPE results." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 10000/10000 [00:00<00:00, 72377.23it/s]\n", + "100%|██████████| 10000/10000 [00:10<00:00, 924.59it/s]\n", + "100%|██████████| 10000/10000 [00:00<00:00, 10100.23it/s]\n" + ] + } + ], "source": [ "# simulations of online bandit algorithms\n", "# obtain a deterministic action distribution representing which action is selected at each round in the simulation\n", @@ -215,38 +277,50 @@ " bandit_feedback=bandit_feedback,\n", " policy=lin_ucb\n", ")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 10000/10000 [00:00<00:00, 77404.82it/s]\n", - "100%|██████████| 10000/10000 [00:09<00:00, 1039.71it/s]\n", - "100%|██████████| 10000/10000 [00:00<00:00, 12626.66it/s]\n" - ] - } - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# estimate the policy value of the online bandit algorithms using RM\n", "ope = OffPolicyEvaluation(\n", " bandit_feedback=bandit_feedback,\n", " ope_estimators=[ReplayMethod()]\n", ")" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " 95.0% CI (lower) 95.0% CI (upper) mean\n", + "rm 0.548577 0.633734 0.59423 \n", + "\n" + ] + }, + { + "data": { + "image/png": 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1Hu/l5YVevXq1U7VEREQPH737CGRmZmLHjh1679Pk7NmzCAkJUbvhR0REQC6XIzMzU9/SiIiISE96B4GMjAzEx8frvU+TwsLCZtMeSqVS2NjYCJoCeO3atZgxYwZefvllbNq0SeuSyURERKSZSV8NVFdXQyKRNNsukUiaLX18L2tra4wdOxYhISGws7NDRkYGdu/ejRs3bmDRokUaj0lMTERiYiIAYPny5ZBKpYb5JdrJdVMXQEREKh3tHqKLyUcNtIaLiwtefPFF1eegoCA4Oztjw4YNyMvLg6+vb7NjoqOjER0drfpcVlbWHqUSEdFDqKPdQ3QtOmTSeQQkEglqamqaba+uroaDg4Ne53r00UcBALm5uQapjYiIyBwIeiJwb/Kprq5utg1o3WMSb29vFBYWNruWTCbTmV50EYlErTqOiIjIHAkKAvPnz9e5TSQSYevWrXpfPDQ0FHv27EFtbS3s7OwAACkpKRCLxQgMDNTrXMePHwcA9OzZU+86iIiIzJWgIDBlyhTVN+3MzExkZmZi6tSpbb746NGj8dNPP+GTTz7BpEmTUFJSgvj4eEyYMEFtSGFsbCwCAwMxd+5cAMD27dtRV1eH3r17w87ODllZWdizZw8GDx6M7t27t7kuIiIicyEoCEyfPl31c3x8PDIzMzFt2rQ2X9zBwQGLFy/Gxo0bsWLFCkgkEsTExKhdDwAUCgUUCoXqs7e3N/bu3YsDBw5ALpdDKpVi4sSJePLJJ9tcExERkTkx+agBHx8fLFmyRGebNWvWqH2OiIhARESEMcsiIiIyC1x9kIiIyIwxCBAREZkxvYOAUqls1T4iIiJ68IiUZnr3FrKWwYPk+psvmboEIiL6nefKDaYuQS8P7MyCREREZFoMAkRERGZMaxAwxJK+XBaYiIjowaY1CMyfPx///e9/UV9fr/dJ8/Ly8PHHH2PPnj1tKo6IiIiMS+uEQiEhIdi0aRPi4+MRHh6OoUOHolevXhCLxRrb37hxA+fOnUNycjIuXbqkmu2PiIiIHlw6Rw1cunQJW7duxW+//QYAsLCwgI+PD5ydnSGRSFBfX4+qqioUFRWhoqICAODo6IiYmBjExMTA2tq6fX6LVuCoASIiaq2HadSAzimG/f398be//Q3Xr19HUlISzp8/j7y8PFy9elWtnaOjI4YMGaL6y8rK5DMXExERkQCC7tienp744x//CACQyWS4desWKisrIRaL4eTkBBcXF6MWSURERMah91d3GxsbeHp6wtPT0xj1EBERUTviPAJERERmjEGAiIjIjDEIEBERmTEGASIiIjPGIEBERGTGGASIiIjMGIMAERGRGdN7HoGGhgacP38eBQUFqKurw9SpUwHcXWmwtrYWnTp1goUF8wUREVFHoFcQOHv2LNatW4fy8nLVtqYgkJeXh7///e+IjY3FsGHDDFokERERGYfgr+6XL1/GypUrIRKJ8NxzzyEiIkJtf69eveDu7o4TJ04YvEgiIiIyDsFBYOfOnRCLxVi+fDnGjx+vcYphPz8/5OfnG7RAIiIiMh7BQSA7OxuDBg2Cs7Oz1jZSqVTttQERERE92AQHgbq6Ojg6OupsI5PJoFAo2lwUERERtQ/BQcDV1RXXrl3T2SYvLw8eHh5tLoqIiIjah+AgEBoainPnzuHChQsa96elpSEnJwdhYWEGK46IiIiMS/DwwSeeeAIpKSl4//33MW7cOJSWlgIAzpw5g8zMTOzfvx/Ozs6YMGGC0YolIiIiwxIplUql0Ma5ubn4/PPPUVJS0myfh4cHFi5ciG7duhm0QGMpKioydQl6uf7mS6YugYiIfue5coOpS9CLl5eX1n16TSjUs2dPrFq1CmfOnEFOTg4qKythb2+PgIAADBo0CJaWlm0uloiIiNqP3lMMW1hYYODAgRg4cKAx6iEiIqJ2xEUBiIiIzJjgJwLJycmCTxoZGdmqYoiIiKh9CQ4Ca9euFXxSBgEiIqKOQXAQmDt3rsbtNTU1uHTpElJSUjB48GDOI0BERNSBCA4CI0aM0Ll/5MiRqgWJ9FFQUIC4uDjk5ORAIpEgKioK06ZNg4WFsO4LCoUCb7/9NnJzc/HWW29hwIABel2fiIjInBmss2D//v0REhKCbdu2CT6mqqoKy5Ytg0gkwqJFizBlyhTs27cP27dvF3yOpKQk3Lx5szUlExERmT2Djhrw8vJCbm6u4PYJCQmQy+VYsGABgoODMWbMGEydOhX79u1DTU1Ni8dXVVXhu+++w6xZs9pSNhERkdkyaBAoKCjQq/3Zs2cREhICe3t71baIiAjI5XJkZma2ePy2bdvQu3dv9OvXT+9aiYiIyABBQKFQoLS0FFu3bkVaWhr69u0r+NjCwsJm0x5KpVLY2Ni0OAVwfn4+Dh48iGeffbZVdRMREZEenQVnzJjRYhsHBwc8/fTTgi9eXV0NiUTSbLtEIkFVVZXOY+Pi4jBu3Dh06dJF49oH90tMTERiYiIAYPny5ZBKpYLrfBBcN3UBRESk0tHuIboIDgJ9+/aFSCRqtl0kEkEikcDf3x8jR46Eo6OjQQvU5NixYygqKsJbb70l+Jjo6GhER0erPpeVlRmjNCIiMgMd7R5ikEWH3n33XUPUokYikWjsFFhdXQ0HBweNxzQ0NOA///kPJk2aBKVSierqatTW1gIAZDIZamtrYWdnZ/BaiYiIHkZ6LzpkSN7e3igsLFTbVlZWBplMpjW9yGQy3Lx5E5s3b8bmzZvV9n3xxRfw8PDA6tWrjVYzERHRw8SkQSA0NBR79uxR+xafkpICsViMwMBAjcfY2tpiyZIlatvKy8uxatUqzJo1iyMIiIiI9KA1COiztsC9RCKR1umI7zd69Gj89NNP+OSTTzBp0iSUlJQgPj4eEyZMUBtSGBsbi8DAQMydOxeWlpYICgpSO09TZ8Fu3bohICCgVXUTERGZI61BQJ/VBu8nNAg4ODhg8eLF2LhxI1asWAGJRIKYmBhMnz5drZ1CoYBCoWh1PURERKSZSKlUKjXtKC0tbfVJO3fu3Opj20tL8xQ8aK6/+ZKpSyAiot95rtxg6hL00qpRAx3hZk5ERERtY9AphomIiKhjadWoAYVCgYqKCjQ0NGjc/zDNuERERPQw0ysIXL16Fd988w0yMjJQX1+vsY1IJMLWrVsNUhwREREZl+AgUFBQgL/97W8AgODgYJw+fRrdu3eHk5MTrly5gsrKSgQFBfFpABERUQciOAjs2rULjY2N+Oijj9CtWzfMmDEDgwcPxtSpU1FXV4d//etfSEtLw7x584xZLxERERmQ4M6CGRkZCAsLQ7du3VTbmkYe2tra4uWXX4ZEIsG2bdsMXyUREREZheAgUFlZCU9Pz/8daGEBmUym+tw04196erphKyQiIiKjERwEHBwcUFdXp/rs6OjYbBlGKysrjasJEhER0YNJcBDw8PBQzekPAD169MBvv/2GO3fuAADq6upw6tQpuLu7G75KIiIiMgrBnQVDQkKwe/du1NXVwdbWFmPGjEFaWhoWLVqE3r17Izc3F6WlpXj22WeNWS8REREZkOAgMGrUKHh5eUEul8PW1hZhYWF47rnnEB8fj9TUVIjFYkyaNAl/+MMfjFkvERERGZDOILBo0SJER0fjscceg4uLC8LDw9X2jx8/HuPGjUNFRQWcnJwgEomMWiwREREZls4+Avn5+di4cSNeeeUVrF+/HhcvXmx+AgsLODs7MwQQERF1QDqfCCxbtgyJiYk4fvw4Dh48iIMHD6Jbt24YNWoUhg8fDnt7+/aqk4iIiIxApGyaFUiH2tpaHDlyBElJSbhy5QoAQCwW49FHH8WoUaPQp08foxdqaEVFRaYuQS/X33zJ1CUQEdHvPFduMHUJevHy8tK6T1AQuFdeXh4SExNx7Ngx1ZwBPj4+qqcEDg4Obau2nTAIEBFRa5l1EGgil8vx66+/4sCBA8jOzgYAWFtbY8iQIYiNjW1dpe2IQYCIiFrrYQoCgicUup9YLEZkZCTee+89fP755+jTpw/q6+tx9OjR1p6SiIiI2pngeQQ0qaqqQnJyMpKSklBQUAAA7EBIRETUgbQqCJw/fx6JiYk4efIkGhoaAAABAQGIjo5uNtcAERERPbgEB4Hy8nIcPHgQSUlJqjUHJBIJoqOjER0dja5duxqtSCIiIjIOnUFAqVTizJkzOHDgANLS0qBQKAAAffr0wahRo/Doo49CLBa3S6FERERkeDqDwLx583Dr1i0Ad5chHj58OKKjo+Ht7d0uxREREZFx6QwCt27dQmBgoOrbv5VVm/oWEhER0QNG5539iy++gKenZ3vVQkRERO1M5zwCDAFEREQPt1ZPKEREREQdH4MAERGRGWMQICIiMmMMAkRERGaMQYCIiMiMCQ4CqampqpkFiYiI6OEgeIagzz77DC4uLhg5ciRGjRoFqVRqzLqIiIioHQh+IjB27FjIZDLs2rULsbGxWL58OU6fPg2lUmnM+oiIiMiIREo97uRyuRwpKSlISEjApUuXAACurq4YNWoUoqKi4OrqqncBBQUFiIuLQ05ODiQSCaKiojBt2jRYWGjPKNeuXcPmzZtx9epVVFZWwsnJCSEhIZgxYwZcXFwEXbeoqEjvWk3p+psvmboEIiL6nefKDaYuQS9eXl5a9+kVBO519epVJCYm4siRI6ipqYGFhQXCwsIwevRohIaGCjpHVVUVFixYAB8fH0yaNAnFxcXYsmULYmJiMHPmTK3HZWdn4/Dhw+jbty9cXFxQUlKCHTt2QCKR4KOPPoKlpWWL12YQICKi1nqYgkCrVxHq1q0bZs+ejaeffhopKSnYtm0bTp06hVOnTkEqlWLs2LEYM2YMbG1ttZ4jISEBcrkcCxYsgL29PYKDg1FbW4v4+HhMnDgR9vb2Go/r3bs3evfurfocFBQENzc3vP/++8jPz0fPnj1b+2sRERGZlTYNH6yrq8Phw4fx888/q5Yr9vX1RVVVFb755hv83//9H/Ly8rQef/bsWYSEhKjd8CMiIiCXy5GZmalXLQ4ODgCAhoYG/X8RIiIiM9WqJwJXrlxBQkICjh07hrq6OojFYkRFRWHs2LHw9fVFXV0d9u/fj+3bt+Nf//oXli5dqvE8hYWFCAoKUtsmlUphY2Mj6NG9QqGAQqFASUkJvv32W/j5+cHf3781vxIREZFZEhwEZDIZjh07hoSEBOTm5gIAvL29MXr0aERGRqp9q7e1tcWkSZNw8+ZNJCUlaT1ndXU1JBJJs+0SiQRVVVUt1vTRRx/h3LlzAICePXvir3/9q9ZOhomJiUhMTAQALF++vMMNf7xu6gKIiEilo91DdBEcBF555RXU1tbCwsICQ4YMwdixY5t9m7+fq6sr6uvr21ykNrNnz0ZVVRWuX7+OXbt24cMPP8SyZcsgFoubtY2OjkZ0dLTqc1lZmdHqIiKih1tHu4cYpLOgnZ0dJkyYgOjoaDg7Ows6ZsyYMYiIiNC6XyKRoKamptn26upq1Tt/XTw9PQEAAQEB6Nu3L1599VUcPXoUUVFRguojIiIyd4KDwJo1a3SO7dfE3t5ea89/4O6rhcLCQrVtZWVlkMlkOtOLJp07d4aDgwNKSkr0Oo6IiMicCb6z6xsChAgNDcW5c+dQW1ur2paSkgKxWIzAwEC9zlVUVITKykq4u7sbukwiIqKHluC7+86dOzFr1izVMMH73bp1C7NmzcIPP/wg+OKjR4+GtbU1PvnkE6SnpyMxMRHx8fGYMGGC2pOE2NhYrFu3TvV58+bN+Oabb3DixAmcP38e+/fvxwcffAAPDw+Eh4cLvj4REZG5E/xq4PTp0wgMDNQ6jbCrqyv69euHkydPYvLkyYLO6eDggMWLF2Pjxo1YsWIFJBIJYmJiMH36dLV2TcMEm/j5+eHnn39GYmIi6uvrIZVKMWTIEEyePFnnBEZERESkTnAQKC4uxmOPPaazjbe3N44cOaJXAT4+PliyZInONmvWrFH7HBERobMTIhEREQkj+NWAXC6HjY2NzjZisRh1dXVtLoqIiIjah+Ag4ObmhosXL+psc/HixVatQEhERESmITgIhISEIDMzEykpKRr3Hzt2DJmZmYJXHiQiIiLTE9xHYPLkyTh69ChWrVqFlJQUhIaGwtXVFbdu3UJaWhpOnToFBwcHwR0FiYiIyPQEBwFXV1e88847+Oyzz3Dy5EmcPHlSbX/nzp3xxhtvwM3NzeBFEhERkXHotfqgn58fVq1ahdOnT+PixYuqRYMCAgIwYMAAWFm1ajFDIiIiMhG979xWVlYYMmQIhgwZYox6iIiIqB0Zft5gIiIi6jC0PhFITk4GAAwePBh2dnaqz0JERka2vTIiIiIyOq1BYO3atQDuLvFrZ2en+iwEgwAREVHHoDUIzJ07FwDg4uKi9pmIiIgeHlqDwIgRI3R+JiIioo6PnQWJiIjMGIMAERGRGdP6auDVV19t1QlFIhFWr17d6oKIiIio/WgNAkqlslUnbO1xRERE1P60BoE1a9a0Zx1ERERkAuwjQEREZMZaHQRqa2tRVlaGmpoaQ9ZDRERE7UivRYcaGxuxd+9eHDhwACUlJart7u7uGDVqFB5//HFYWloavEgiIiIyDsFBoKGhAR988AEyMzMhEokglUrh7OyM8vJylJaW4rvvvsPZs2fxt7/9jcsRExERdRCC79j79u1DZmYmwsLC8Oyzz8LT01O1r7i4GJs3b8bp06exb98+TJ482Ri1EhERkYEJ7iNw9OhRdO3aFW+++aZaCACALl26YOHChejatSuOHDli8CKJiIjIOAQHgeLiYoSGhsLCQvMhFhYWCA0NxY0bNwxWHBERERmX4CBgZWWFuro6nW1kMhk7CxIREXUggoNA9+7dkZqaioqKCo37KyoqcPz4cfj6+hqqNiIiIjIywUFg7NixqKiowF//+lckJSXhxo0bkMvlKCkpwcGDB/HOO++goqICY8eONWa9REREZECCRw2Eh4cjLy8Pu3fvxj//+U+NbSZOnIjw8HCDFUdERETGpdeA/6eeegoDBw5EUlIS8vLyUFNTA3t7e/j6+iIqKgq9evUyVp1ERERkBIKDQGVlJUQiEXr16sUbPhER0UOixSBw8uRJbN68WTWlcJcuXfDMM89g4MCBRi+OiIiIjEtnZ8GcnBx8+umnausKFBcX49NPP0VOTo7RiyMiIiLj0hkE9u3bB6VSiSlTpuDrr7/GV199hSeffBIKhQL79u1rrxqJiIjISHS+Grh48SL69OmD6dOnq7bNmDEDmZmZfCJARET0END5RODOnTsICAhotj0gIEDrxEJERETUcegMAo2NjbC1tW223cbGBo2NjUYrioiIiNqHXvMIGENBQQHi4uKQk5MDiUSCqKgoTJs2TeviRgBw6dIl/PLLL8jKysLt27fh5uaGYcOGYdKkSRCLxe1YPRERUcfWYhA4dOgQMjIy1LaVlpYCAJYuXdqsvUgkwuLFiwVdvKqqCsuWLYOPjw8WLVqE4uJibNmyBUqlEjNnztR6XEpKCm7cuIFJkybB09MT+fn52LZtG/Lz87Fw4UJB1yYiIiIBQaC0tFR1479fZmZmmy6ekJAAuVyOBQsWwN7eHsHBwaitrUV8fDwmTpwIe3t7jcdNnjwZjo6Oqs9BQUEQi8X46quvUFpais6dO7epLiIiInOhMwgsWbLEqBc/e/YsQkJC1G74ERER+Oabb5CZmal10qJ7Q0CTplUPb9++zSBAREQkkM4gEBgYaNSLFxYWIigoSG2bVCqFjY0NioqK9DpXTk4ORCIRPDw8DFkiERHRQ82knQWrq6shkUiabZdIJKiqqhJ8nvLycuzatQvDhw+Hk5OTxjaJiYlITEwEACxfvhxSqbR1RZvIdVMXQEREKh3tHqKLyUcNtFVDQwM+//xz2Nra4rnnntPaLjo6GtHR0arPZWVl7VEeERE9hDraPcTLy0vrPp3zCBibRCJBTU1Ns+3V1dVwcHBo8XilUokvv/wS165dw1//+ldBxxAREdH/mDQIeHt7o7CwUG1bWVkZZDKZzvTS5N///jdOnjyJRYsWwdvb21hlEhERPbRMGgRCQ0Nx7tw51NbWqralpKRALBa32FHx+++/x88//4zY2Fj06dPH2KUSERE9lEwaBEaPHg1ra2t88sknSE9PR2JiIuLj4zFhwgS1IYWxsbFYt26d6vPRo0fx3XffITIyEq6ursjJyVH9xTUQiIiIhDNpZ0EHBwcsXrwYGzduxIoVKyCRSBATE6O22iEAKBQKKBQK1edz584BuDvr4aFDh9Tazps3DyNGjDB26URERA8FkVKpVJq6CFPQd54CU7v+5kumLoGIiH7nuXKDqUvQi65+d1qfCOzYsaPVF5w6dWqrjyUiIqL2ozUIxMfHt/qkDAJEREQdg9YgoGmdgX379iEtLQ2PPfYYAgMD4ezsjPLycmRkZODo0aMICwtDTEyMUQsmIiIiw9EaBO4fvpecnIzffvsNH3zwAXr27Km2b8SIERg3bhyWLFmCIUOGGKdSIiIiMjjBwwd//PFHDB06tFkIaOLn54ehQ4fixx9/NFhxREREZFyCg0BRURFcXFx0tnFxcelwvfGJiIjMmeAgYGdnh+zsbJ1tsrOzYWtr2+aiiIiIqH0IDgJhYWHIysrC5s2b1aYEBoDa2lps3rwZFy5cwIABAwxeJBERERmH4JkFn3rqKWRmZuLHH39EUlISfH194eTkhDt37iAvLw+1tbVwd3fHrFmzjFkvERERGZDgIODk5IQPP/wQ3377LY4ePYqsrCzVPrFYjFGjRmHWrFno1KmTUQolIiIiw9NrrYFOnTrhlVdewUsvvYTCwkLU1NTA3t4e3t7esLS0NFaNREREZCStWnTI0tIS3bp1M3QtRERE1M70DgINDQ04f/48CgoKUFdXp5pOWC6Xo7a2Fp06dYKFhUlXNyYiIiKB9AoCZ8+exbp161BeXq7a1hQE8vLy8Pe//x2xsbEYNmyYQYskIiIi4xD81f3y5ctYuXIlRCIRnnvuOURERKjt79WrF9zd3XHixAmDF0lERETGITgI7Ny5E2KxGMuXL8f48ePh6enZrI2fnx/y8/MNWiAREREZj+AgkJ2djUGDBsHZ2VlrG6lUqvbagIiIiB5sgoNAXV0dHB0ddbaRyWRQKBRtLoqIiIjah+Ag4OrqimvXrulsk5eXBw8PjzYXRURERO1DcBAIDQ3FuXPncOHCBY3709LSkJOTg7CwMIMVR0RERMYlePjgE088gZSUFLz//vsYN24cSktLAQBnzpxBZmYm9u/fD2dnZ0yYMMFoxRIREZFhiZRKpVJo49zcXHz++ecoKSlpts/DwwMLFy7sMDMOFhUVmboEvVx/8yVTl0BERL/zXLnB1CXoxcvLS+s+vSYU6tmzJ1atWoUzZ84gJycHlZWVsLe3R0BAAAYNGsT1BoiIiDoYvacYtrCwwMCBAzFw4EBj1ENERETtSHBnwaVLlyI5OVlnm8OHD2Pp0qVtLoqIiIjah+AgkJmZqeogqE1ZWRkyMzPbXBQRERG1D4MuEyiXy9lPgIiIqAPRu4+AJkqlEmVlZUhLS4Obm5shTklERETtQGcQmDFjhtrn+Ph4xMfH6zzhE0880faqiIiIqF3oDAJ9+/aFSCQCcLePgFQqhbu7e7N2FhYWcHBwQP/+/REVFWWcSomIiMjgdAaBd999V/XzjBkzMHLkSEydOtXYNREREVE7EdxH4Msvv4REIjFmLURERNTOBAeBzp07G7MOIiIiMgG9Rw3cvn0bv/32G27duoWGhgaNbfj6gIiIqGPQKwhs374dP/zwAxobG3W2YxAgIiLqGAQHgSNHjmDnzp3o168fxo4di08//RSRkZEICQlBRkYGDh48iEcffRSjR482Zr1ERERkQIKDwC+//AJXV1e8/fbbqtkD3d3dERERgYiICAwePBjLly9HRESEXgUUFBQgLi4OOTk5kEgkiIqKwrRp02BhoX3Sw4aGBnz33Xe4ePEiLl++jPr6emzfvl2v6xIREZEeUwxfvXoVjzzyiNoUwgqFQvVzaGgoQkJCsHfvXsEXr6qqwrJlyyASibBo0SJMmTIF+/bta/GmLpPJkJSUBBsbG/Tu3Vvw9YiIiEid4CcCjY2N6NSpk+qzWCxGTU2NWpuuXbsiISFB8MUTEhIgl8uxYMEC2NvbIzg4GLW1tYiPj8fEiRNhb2+v8TiJRIK4uDiIRCL8/PPPOH/+vOBrEhER0f8IfiLg4uKC27dvqz5LpVLk5+ertbl9+7Zeiw6dPXsWISEhajf8iIgIyOXyFlcxbJrxkIiIiFpPcBDw9fXFtWvXVJ+DgoJw4cIFHD58GHV1dThz5gyOHz+OHj16CL54YWEhvLy81LZJpVLY2NigqKhI8HmIiIiodQS/GhgwYAA2bNiAkpISuLu7Y/Lkyfj111+xZs0arFmz5u7JrKyaLVSkS3V1tcbZCiUSCaqqqgSfR4jExEQkJiYCAJYvXw6pVGrQ8xvbdVMXQEREKh3tHqKL4CAwYsQIjBgxQvVZKpXio48+wt69e3Hjxg107twZY8eORbdu3YxRZ5tFR0cjOjpa9bmsrMyE1RARUUfW0e4h9z99v5feMwvey93dHS+++GKrj5dIJM06HAJ3nxQ4ODi0pTQiIiISQHAfAWPw9vZGYWGh2raysjLIZDKd6YWIiIgMQ+8nAgqFArdu3dK51kBgYKCgc4WGhmLPnj2ora2FnZ0dACAlJQVisVjwOYiIiKj19AoCe/bswd69e1FRUaGz3bZt2wSdb/To0fjpp5/wySefYNKkSSgpKUF8fDwmTJigNqQwNjYWgYGBmDt3rmpbWloaZDIZ8vLyAADHjx8HAPj5+XGlRCIiIoEEB4Ht27dj586dcHBwQGRkJFxdXfWaM0ATBwcHLF68GBs3bsSKFSsgkUgQExOD6dOnq7VTKBRqsxgCwIYNG1BaWqr6/NlnnwEA5s2bp9apkYiIiLQTKZVKpZCGc+fOhZWVFVasWKF1xr+OpKPNU3D9zZdMXQIREf3Oc+UGU5egF1397gR3FqysrMTAgQMfihBAREREdwkOAl26dEF1dbUxayEiIqJ2JjgIjBkzBqdPn0Z5ebkRyyEiIqL2JLiz4JgxY3D9+nX8/e9/x5QpU9CzZ0+trwkepqkXiYiIHmZ6DR/s3r07Dh06hHXr1mltIxKJsHXr1jYXRkRERMYnOAgcOHAAX331FSwtLREUFAQXF5c2Dx8kIiIi0xIcBPbu3QsnJye8//77cHd3N2ZNRERE1E4EdxYsLS3Fo48+yhBARET0EBEcBFxdXbWuLUBEREQdk+AgEBkZibS0NNTW1hqzHiIiImpHgoPAE088AX9/fyxbtgwZGRkMBERERA8BwZ0Fn3rqKdXP7733ntZ2HD5IRETUcQgOAn379oVIJDJmLURERNTOBAeBd99914hlEBERkSkI7iNAREREDx8GASIiIjOm9dXAjh07AADjxo2Dg4OD6rMQU6dObXtlREREZHRag0B8fDwAIDw8HA4ODqrPQjAIEBERdQxag8CSJUsA/G9J4abPRERE9PDQGgQCAwN1fiYiIqKOT3BnweTkZOTn5+tsc/XqVSQnJ7e5KCIiImofgoPA2rVrcfLkSZ1tTp06hbVr17a5KCIiImofBh0+qFAoOPsgERFRB2LQIFBUVASJRGLIUxIREZER6Zxi+P7H/CdPnkRJSUmzdgqFAjdv3kRWVhbCwsIMWyEREREZjc4gcH/Hv7y8POTl5WltHxAQgOeee84ghREREZHx6QwCX375JQBAqVQiNjYW48ePx/jx45u1s7CwgEQiga2trXGqJCIiIqPQGQQ6d+6s+nnq1KkICgpS20ZEREQdm+BliKdNm2bMOoiIiMgEBAeBK1euICcnB4899hjs7e0BAHV1ddiwYQNOnToFGxsbTJo0SeOrAyIiInowCR4+uHv3buzatUsVAgDg22+/xZEjR6BUKlFZWYlNmzbh3LlzRimUiIiIDE9wELh8+TKCgoJUnxsaGpCcnAx/f398/fXX+PLLL+Ho6IiffvrJKIUSERGR4QkOAhUVFXBzc1N9zs3NRV1dHaKjoyEWi+Hq6oqBAwe2uB4BERERPTj0mlmwsbFR9fOFCxcAqK9K6OjoiIqKCgOVRkRERMYmOAhIpVJcvHhR9fnkyZNwc3ODh4eHatvt27fh4OBg2AqJiIjIaASPGhg6dCji4+Px6aefwtraGjk5OYiJiVFrU1hYqBYMiIiI6MEmOAhMmDAB586dw4kTJwAAvr6+mDp1qmp/SUkJLl26hCeeeEKvAgoKChAXF4ecnBxIJBJERUVh2rRpsLDQ/bCipqYG//73v3Hy5EkoFAoMGDAAL7zwAjp16qTX9YmIiMyZSKlUKvU54OrVqwAAHx8ftZt1SUkJ8vPz4efnB1dXV0HnqqqqwoIFC+Dj44NJkyahuLgYW7ZsQUxMDGbOnKnz2A8++ABFRUV45plnYGFhgW+++QZOTk547733BF27qKhIULsHxfU3XzJ1CURE9DvPlRtMXYJevLy8tO4T/ESgSbdu3TRud3d3h7u7u17nSkhIgFwux4IFC2Bvb4/g4GDU1tYiPj4eEydOVJuz4F45OTk4d+4c3n33XVVnRVdXV7z99ttIT09HcHCwfr8UERGRmdL5/D0zMxNlZWWCT5afn99sxUJdzp49i5CQELUbfkREBORyOTIzM7Uel5aWBicnJ7URC/7+/nB3d8fZs2cFX5+IiMjc6QwCS5cuxaFDh9S2/fDDD5g9e7bG9idOnMDatWsFX7ywsLDZ4wqpVAobGxudj+4LCwvh7e3dbLu3tzcKCwsFX5+IiMjc6f1qoL6+HtXV1Qa5eHV1NSQSSbPtEokEVVVVOo/T9NpAIpGgpKRE4zGJiYlITEwEACxfvlzn+5IHkdc3/zV1CURE9BDSOwh0VNHR0YiOjjZ1GURm7S9/+QuWL19u6jKI6B56zSxoaBKJBDU1Nc22V1dX65yYSCKRoLa2VuNxmp4wEBERkWYmDQKa3umXlZVBJpPpfHSvrS9AUVGRxr4DREREpJlJg0BoaCjOnTun9u0+JSUFYrFYbUTA/R555BGUl5er1jsA7q6OeOPGDYSGhhqzZCJqA76eI3rwmDQIjB49GtbW1vjkk0+Qnp6OxMRExMfHY8KECWqdAWNjY7Fu3TrV5169eiEkJARffvklUlNTceLECfzjH/9Anz59OIcA0QOMQYDowaNzZsEZM2a06qTbtm0T3LagoAAbN25Um2J4+vTparMWzp8/H4GBgZg/f75qW3V1NTZt2oQTJ05AqVQiLCwML7zwAhwdHVtVMxERkTkyeRAgIiIi09F7rQEiIiJ6eJjNPAJEZBjvvvtusynALSwsYGdnBx8fHwwaNAhjx46FjY2NzmP9/Pzw0Ucfab2OTCbDyy+/rOpMPH78eDz//POG+0WICACDABG1kpubG6RSKQCgoaEBpaWlyM7ORnZ2Ng4ePIilS5fq7LNz+fJlFBQUwMfHR+P+EydOaJwvhIgMi0GAiFpl5MiRmD59utq2X3/9FatXr0ZhYSH+85//YN68eRqPbZoLJDk5GX/84x81tmla54RriBAZl0mHDxLRw2Xo0KGIiYkBAKSmpkKhUGhtZ21tjaNHj2psc/PmTZw/fx5eXl7w9/c3as1E5o5BgIgMKiAgAABQW1uLiooKjW0kEgkGDBiguuHf7/Dhw1AqlYiMjDRqrUTEIEBEBiaXy1U/a+ow2GTEiBEA0GypcwBITk6GSCTC8OHDDV0eEd2HQYCIDOrUqVMAAA8PD9jZ2WltFxISAicnJ5w8eVKtU2BOTg6KiorQr18/uLm5Gb1eInPHIEBEbdbQ0IDCwkLExcUhJSUFADBlyhSdx1haWuKxxx6DTCbD8ePHVduTk5MBgK8FiNoJRw0QUavs2LEDO3bsaLbd1dUV06ZNUz361yUyMhL79u1DcnIyRo4cifr6eqSkpMDOzg5DhgwxQtVEdD8GASJqlXvnEaipqUFxcTHq6+vh4OCAvn37CjpH9+7d4evri6ysLJSWluLixYuorq7GiBEjdPYvICLDYRAgola5fx6BiooKfP3110hNTcX777+PlStXQiKRtHieESNG4N///jeSk5Nx8eJF1TYiah/sI0BEBuHo6IjXXnsN3t7eKCsrw7fffivouGHDhsHS0hKJiYk4d+4c3N3dBT9RIKK2YxAgIoOxtrbG008/DQBISkrC9evXWzzG0dERjzzyCG7dugWFQoHhw4dDJBIZu1Qi+h2DABEZ1IABA9CzZ080NjZi165dgo4ZN24c+vfvj/79+3O0AFE7Yx8BIjK4KVOmYOXKlTh69CimTp0KDw8Pne2Dg4MRHBzcTtUR0b34RICIDG7gwIHo3r27Xk8FiMg0GASIyOBEIpFqQqHDhw+jpKTExBURkTYipVKpNHURREREZBp8IkBERGTGGASIiIjMGIMAERGRGWMQICIiMmMMAkRERGaMQYCIiMiMMQgQERGZMQYBIiIiM8YgQEQmkZGRgenTp2P69OmmLoXIrHHRITJ7crkcycnJOH36NPLz81FRUQErKyu4urqiT58+iIiIQL9+/XSeY/78+SgtLW223dbWFp07d0bfvn0xbtw4+Pj4NGvz7rvvIjMzU1CtgYGBePfddwW1bak2TSIjIzF//ny9zn+/6upq/PjjjwCAmJgYSCSSNp3vQXTo0CGUlJQgKCgIQUFBpi6HqE0YBMispaenY926dbh586Zqm52dHRoaGlBYWIjCwkIcOHAAjzzyCF599VV06tRJ5/msra1hb28PAFAqlaisrMS1a9dw7do1HDhwAH/6058QFRWl8VhLS0s4ODjoPH9L+4XWpk1L+4Worq7Gjh07AAAjRozQGgRsbGzg5eXV5uuZwqFDh1ThjUGAOjoGATJbKSkpWL16NRobG+Hq6orp06dj8ODBqpttYWEhEhISsH//fqSlpeGdd97BsmXL4OTkpPWc4eHhat+o5XI5Tp8+jbi4ONy5cwdfffUV/Pz80L1792bH9u7dW+9v+/q4vzZT8/f3xxdffGHqMojMHvsIkFkqKCjAunXr0NjYiG7duuHjjz9GVFSU2jdub29vPP/883jzzTdhZWWF4uJi/OMf/9DrOmKxGEOHDkVsbCwAQKFQ4JdffjHo70JE1BZ8IkBmaevWrZDJZLC2tsYbb7wBR0dHrW3DwsLw5JNPYvv27fjtt99w5swZhIWF6XW94OBguLi44Pbt27h8+XJby29XN2/exN69e5Geno7S0lI0NjaiU6dOcHZ2Rt++fTFs2DD4+/sDaN7f4dVXX1U71719HDIyMrB06VIAwPbt29XaHTp0CGvXrkXnzp2xZs0aZGVlYffu3bh06RJkMhk8PT0xbtw4tdcsZ86cwY8//oi8vDzIZDJ07doVjz/+OMLDwzX+XiUlJUhJSUFGRgZKSkpw69YtAIBUKkVISAgmTJgAqVSqsa4mO3bsUL0GafLll1/C3d1d9VmhUODQoUM4cuQIrl69itraWnTq1Am9e/fG2LFjtb5aaPqznDp1Kp588kn89NNPOHbsGIqLi1FTU4MlS5aoji0sLMS+ffuQmZmJmzdvQqlUwtHREa6urggKCkJkZCS8vb01XoeIQYDMzu3bt3Hy5EkAQEREhKD31BMmTMDevXtRW1uL/fv36x0EAMDV1RW3b99GbW2t3seaSl5eHpYuXYrq6moAgIWFBezs7FBeXo7bt2/jypUrqK6uVgUBBwcHdOrUCZWVlQCATp06wcLifw8eW9PH4cCBA/jqq68A3O2/IZPJkJeXh/Xr16O4uBhPPfUUtm/fjh07dkAkEsHOzg5yuRyXL1/GF198gaqqKowZM6bZedeuXasKLVZWVrCzs0NVVZWqb8ihQ4fwl7/8BX369FEdIxaL4eTkhKqqKjQ2NsLGxga2trZq5733962pqcHKlSuRkZHR7M/v+PHjOH78OB5//HE888wzWn//+vp6LF26FNnZ2bC0tIStrS1EIpFqf3p6OlasWIH6+noAULW5efMmbt68iYsXL8LKyoqjM0grBgEyOxkZGVAqlQCAIUOGCDrG1tYWwcHBSE1NRVZWFhobG2FpaanXdZt67relw19727JlC6qrq9GjRw+8+OKLCAgIgEgkQkNDA0pLS3Hq1CnVnyUALFy4ECUlJaonAR999JHat2N9VVRUYOPGjRg3bhymTJkCR0dHVFVVYdOmTUhOTsbu3bshkUiwa9cuzJw5E+PGjYO9vT1u376NdevW4ezZs9iyZQuGDRvWrCOkr68vhg4diuDgYHh4eMDCwgKNjY24cuUKtm/fjrNnz+Lzzz/H6tWrIRaLAdztZxEeHq76tv7444/rvMGuW7cOGRkZsLKywjPPPIOoqCjY2NigvLwc3333HQ4ePIi9e/fCw8NDY1gBgP379wMA5s2bh/DwcIjFYlRWVqrCwNdff436+nqEhITgmWeeQbdu3QDc7Z9y48YNpKamNnuyQXQvBgEyOwUFBaqfe/ToIfg4X19fpKamoq6uDqWlpejSpYvgY48fP46KigoAQEBAgMY22dnZ+NOf/qTzPC+88ILWR90tSUlJwdmzZ3W2WbhwIXr37q1WEwC8+OKL6NWrl2q7lZUVPD098fjjj7eqFqFkMhmioqLwwgsvqLY5ODhg7ty5yMrKQklJCb755hvMnDkTTz75pKqNi4sLXn/9dbzyyiuQyWQ4deoUhg8frnbu559/vtn1LC0t4e/vj7/85S946623kJ+fj+PHjzc7VoiLFy8iNTUVADB79mxER0er9jk7O2Pu3LmoqalBamoqtm3bhhEjRqgCx73q6uqwaNEiDBw4ULWtafTKnTt3cOPGDQB3g4KLi4uqjVgsRteuXdG1a1e9ayfzws6CZHaaHlsD+n07v3foYFVVVYvtlUolSktL8dNPP2HdunUA7t5Ax44dq7F9Y2Mj7ty5o/MvuVwuuN771dfXt3j+hoYGtWOahv7dvn271ddtq8mTJzfbZmFhoZrbwdraGuPHj2/Wxt7eXhVerl69qtc1LSwsEBISAgC4cOGCnhXflZKSAgBwc3PTOmR0xowZAO7+O5menq6xTdeuXdVCwL3s7OxUTwZM+c+IOjY+ESAyoOTkZCQnJ2vcZ2tri/nz58PT01Pj/tZMFqSP1kwWFBYWhgMHDmDNmjXIzs7GwIED4efnBxsbGyNVqc7BwUHrkxdnZ2cAgI+PT7P39E2ahnpqC25ZWVlISkrCxYsXcfPmTchksmZtmjoR6is3NxfA3XkG7u03cC8fHx+4urri1q1byM3N1XjDv/cJzf3EYjH69++P9PR0fPjhhxg9ejTCwsLQo0cPWFnxf+8kDP9NIbNz/zd7V1dXQccJeZJw76Q9IpEINjY2kEql6Nu3L0aNGgU3N7c2VN7+nn76aRQXFyMjIwP79u3Dvn37YGFhAV9fX4SFhSE6Olrwn19r2NnZad3XdHPV1aapH0djY2Ozff/5z3+wZ88etfNJJBLVDbSurg4ymUxjOBDizp07ANDin4+bmxtu3bqlan8/XSNaAGDOnDlYsWIF8vPzsXPnTuzcuRNWVlbw8/PDoEGDmg2LJbofgwCZnXun+c3NzRV8I7ty5QqA/00brMmDNmlPW0kkEixZsgQXLlzAqVOnkJ2djdzcXNVfe/bswZw5czBs2DBTl6qX9PR0VQgYM2YMxowZAx8fH7Vv7lu3bsWuXbvUOkOagranCU2kUilWrFiB9PR0pKWlITs7G/n5+cjOzkZ2dja+//57LFiwoMVpssl8MQiQ2QkKCoJIJIJSqURqaqrW96/3qqurw2+//QYA6Nu3r94jBjq6Pn36qIbRyeVypKenY+vWrbh69SrWrVuHfv36qR7VdwTHjh0DAISEhOCll17S2Ka8vLxN13ByckJRUZHa9NWaNO3XNWNlSywsLBAaGorQ0FAAQG1tLU6fPo1vv/0WZWVlWLVqFdatW8fXBaQROwuS2XFxccGgQYMA3O3QVVRU1OIx+/btU43/1zbMy1yIxWIMHDgQCxcuBHC3E+K9Hepa+gb7IGi6+WobNaJUKlVj/zW5dxy/Nj179gRwd7iqQqHQ2KawsFDVB8HPz6/FcwplZ2eHYcOGYc6cOQDuvqbQt8MkmY8H/79YIiOYMWMGxGIx6uvr8dlnn6mG9mmSlpaGXbt2Abj7NKE1kwl1RI2NjVpvYADUhrrde/O/951900RED5qmfhz5+fka9yckJKiG5WnS9Dvq+v0iIiIA3O1smJSUpLHNtm3bANztt9K/f/+WC7/P/aM87nfvPyMh4YXME4MAmaWuXbtizpw5sLCwwNWrV/HWW28hKSlJ7X/sRUVF2LRpEz7++GM0NDTAw8MDf/7zn83mf6g3b97En//8Z+zcuRNXrlxR63CXn5+P1atXA7i7imBgYKBqn0QiUfW7OHjwoMaOeqbW9Ag9LS0NO3bsQF1dHYC7N/Zdu3YhLi5O50qTTZP2pKWlaR1V4O/vr5qwKi4uDj///LOq42F5eTnWr1+P48ePA/hfMNVXdnY2Fi5ciH379qGgoEAV3JRKJbKzs7FhwwYAdzskalroighgHwEyY8OGDYODg4NqGeL169dj/fr1sLe3R319vWrKVuDuu+TY2NgWe3C3hZAJhYC7M8m1hpAJhaRSKT766CPV5xs3bmDbtm3Ytm0bLCwsYG9vj7q6OtU3USsrK8yfP79Zr/TRo0dj27Zt+Pnnn3HgwAE4OjrCwsICAQEBeP3111tVvyENHz4cycnJyMrKwvbt2xEfHw97e3vU1NRAqVQiLCwMvr6+qidB94uMjMTevXtRXFyMuXPnwtHRUXUjf++991SjQ+bOnYvKykpkZmYiLi4OmzZtgq2treo6APD444+36XXT1atXsXnzZmzevBmWlpaq36MpgNnZ2eG1117rEK9syDQYBMishYaGYvXq1Th06BBOnz6N/Px8VFZWwsrKSjXsLyIiolWPbfXVNKGQsTRNKKTLvd9KXV1dsWjRImRkZCAnJ0c1xM3S0hJdunRBUFAQxo8fr3FehCeeeAJ2dnY4cuSI6j24UqnUOtqivVlZWeGdd97BDz/8gGPHjqmmf/b390dkZCSio6ObLSZ0L09PTyxZsgQ//PADLl68qFp7AFAfqmhvb4/FixerFh3Ky8tDXV0dnJ2d0atXL4wbN07rokNC+Pn54f/+7/+QkZGBS5cu4fbt26ioqIC1tTW6du2K4OBgjB8/3qhDPKnjEylNPTaGiIiITIbPioiIiMwYgwAREZEZYxAgIiIyYwwCREREZoxBgIiIyIwxCBAREZkxBgEiIiIzxiBARERkxhgEiIiIzBiDABERkRn7f0qftVhq3ChvAAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# estimate the policy value of EpsilonGreedy\n", "estimated_policy_value_epsilon_greedy, estimated_interval_epsilon_greedy = ope.summarize_off_policy_estimates(\n", @@ -258,42 +332,36 @@ "# and their 95% confidence intervals (estimated by nonparametric bootstrap method)\n", "ope.visualize_off_policy_estimates(\n", " action_dist=action_dist_epsilon_greedy,\n", - " n_bootstrap_samples=10000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=10000, # number of resampling performed in bootstrap sampling\n", " random_state=12345,\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, "outputs": [ { + "name": "stdout", "output_type": "stream", - "name": "stderr", "text": [ - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n", - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n", - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n", " 95.0% CI (lower) 95.0% CI (upper) mean\n", - "rm 0.5419 0.63103 0.584203 \n", + "rm 0.615827 0.712571 0.662281 \n", "\n" ] }, { - "output_type": "display_data", "data": { - "image/png": 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", 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", "text/plain": [ "
" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], - "metadata": { - "tags": [] - } - }, - { - "cell_type": "code", - "execution_count": 7, "source": [ "# estimate the policy value of LinTS\n", "estimated_policy_value_lin_ts, estimated_interval_lin_ts = ope.summarize_off_policy_estimates(\n", @@ -305,40 +373,36 @@ "# and their 95% confidence intervals (estimated by nonparametric bootstrap method)\n", "ope.visualize_off_policy_estimates(\n", " action_dist=action_dist_lin_ts,\n", - " n_bootstrap_samples=10000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=10000, # number of resampling performed in bootstrap sampling\n", " random_state=12345,\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, "outputs": [ { + "name": "stdout", "output_type": "stream", - "name": "stderr", "text": [ - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n", - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n", - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n", " 95.0% CI (lower) 95.0% CI (upper) mean\n", - "rm 0.612249 0.695407 0.659076 \n", + "rm 0.581798 0.674513 0.626882 \n", "\n" ] }, { - "output_type": "display_data", "data": { - "image/png": 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", "text/plain": [ "
" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 8, "source": [ "# estimate the policy value of LinUCB\n", "estimated_policy_value_lin_ucb, estimated_interval_lin_ucb = ope.summarize_off_policy_estimates(\n", @@ -350,65 +414,69 @@ "# and their 95% confidence intervals (estimated by nonparametric bootstrap method)\n", "ope.visualize_off_policy_estimates(\n", " action_dist=action_dist_lin_ucb,\n", - " n_bootstrap_samples=10000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=10000, # number of resampling performed in bootstrap sampling\n", " random_state=12345,\n", ")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n", - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n", - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n", - " 95.0% CI (lower) 95.0% CI (upper) mean\n", - "rm 0.598893 0.678814 0.637894 \n", - "\n" - ] - }, - { - "output_type": "display_data", - "data": { - "image/png": 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", - "image/svg+xml": "\n\n\n\n \n \n \n \n 2021-03-20T21:47:03.869917\n image/svg+xml\n \n \n Matplotlib v3.3.4, https://matplotlib.org/\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n 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" - ] - }, - "metadata": {} - } - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "RM estimates that LinTS is the best policy." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (3) Evaluation of OPE\n", "Our final step is **the evaluation of OPE**, which evaluates and compares the estimation accuracy of OPE estimators.\n", "\n", "With synthetic data, we can calculate the policy value of the evaluation policies. \n", "Therefore, we can compare the policy values estimated by RM with the ground-turths to evaluate the accuracy of OPE." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████| 3/3 [00:18<00:00, 6.20s/it]\n", + "100%|██████████| 3/3 [00:51<00:00, 17.10s/it]\n", + "100%|██████████| 3/3 [00:21<00:00, 7.24s/it]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "policy value of EpsilonGreedy: 0.6056001659264234\n", + "policy value of LinTS: 0.7515744188226375\n", + "policy value of LinUCB: 0.6689063585401301\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "# we first calculate the policy values of the three evaluation policies\n", "# in synthetic data, we know p(r|x,a), the reward distribution, so we can perform simulations\n", @@ -436,60 +504,23 @@ "print(f'policy value of EpsilonGreedy: {policy_value_epsilon_greedy}')\n", "print(f'policy value of LinTS: {policy_value_lin_ts}')\n", "print(f'policy value of LinUCB: {policy_value_lin_ucb}')" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 3/3 [00:09<00:00, 3.03s/it]\n", - "100%|██████████| 3/3 [00:39<00:00, 13.08s/it]\n", - "100%|██████████| 3/3 [00:12<00:00, 4.04s/it]policy value of EpsilonGreedy: 0.6078535517662295\n", - "policy value of LinTS: 0.7323584842875861\n", - "policy value of LinUCB: 0.7098538447648373\n", - "\n" - ] - } - ], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "In fact, LinTS reveals the best performance among the three evaluation policies.\n", "\n", "Using the above policy values, we evaluate the estimation accuracy of the OPE estimators." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 10, - "source": [ - "# evaluate the estimation performances of OPE estimators \n", - "# by comparing the estimated policy values of EpsilonGreedy and its ground-truth.\n", - "# `summarize_estimators_comparison` returns a pandas dataframe containing estimation performances of given estimators \n", - "relative_ee_epsilon_greedy = ope.summarize_estimators_comparison(\n", - " ground_truth_policy_value=policy_value_epsilon_greedy,\n", - " action_dist=action_dist_epsilon_greedy,\n", - " metric=\"relative-ee\", # \"relative-ee\" (relative estimation error) or \"se\" (squared error)\n", - ")\n", - "\n", - "# estimation performances of the three estimators (lower means accurate)\n", - "relative_ee_epsilon_greedy" - ], + "execution_count": 11, + "metadata": {}, "outputs": [ { - "output_type": "stream", - "name": "stderr", - "text": [ - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n" - ] - }, - { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -516,7 +547,7 @@ " \n", " \n", " rm\n", - " 0.034378\n", + " 0.02448\n", " \n", " \n", "\n", @@ -524,41 +555,34 @@ ], "text/plain": [ " relative-ee\n", - "rm 0.034378" + "rm 0.02448" ] }, + "execution_count": 11, "metadata": {}, - "execution_count": 10 + "output_type": "execute_result" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 11, "source": [ - "# evaluate the estimation performance of OPE estimators \n", - "# by comparing the estimated policy values of LinTS t and its ground-truth.\n", + "# evaluate the estimation performances of OPE estimators \n", + "# by comparing the estimated policy values of EpsilonGreedy and its ground-truth.\n", "# `summarize_estimators_comparison` returns a pandas dataframe containing estimation performances of given estimators \n", - "relative_ee_lin_ts = ope.summarize_estimators_comparison(\n", - " ground_truth_policy_value=policy_value_lin_ts,\n", - " action_dist=action_dist_lin_ts,\n", + "relative_ee_epsilon_greedy = ope.summarize_estimators_comparison(\n", + " ground_truth_policy_value=policy_value_epsilon_greedy,\n", + " action_dist=action_dist_epsilon_greedy,\n", " metric=\"relative-ee\", # \"relative-ee\" (relative estimation error) or \"se\" (squared error)\n", ")\n", "\n", "# estimation performances of the three estimators (lower means accurate)\n", - "relative_ee_lin_ts" - ], + "relative_ee_epsilon_greedy" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, "outputs": [ { - "output_type": "stream", - "name": "stderr", - "text": [ - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n" - ] - }, - { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -585,7 +609,7 @@ " \n", " \n", " rm\n", - " 0.096554\n", + " 0.118454\n", " \n", " \n", "\n", @@ -593,41 +617,34 @@ ], "text/plain": [ " relative-ee\n", - "rm 0.096554" + "rm 0.118454" ] }, + "execution_count": 12, "metadata": {}, - "execution_count": 11 + "output_type": "execute_result" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 12, "source": [ "# evaluate the estimation performance of OPE estimators \n", - "# by comparing the estimated policy values of LinUCB and its ground-truth.\n", + "# by comparing the estimated policy values of LinTS t and its ground-truth.\n", "# `summarize_estimators_comparison` returns a pandas dataframe containing estimation performances of given estimators \n", - "relative_ee_lin_ucb = ope.summarize_estimators_comparison(\n", - " ground_truth_policy_value=policy_value_lin_ucb,\n", - " action_dist=action_dist_lin_ucb,\n", + "relative_ee_lin_ts = ope.summarize_estimators_comparison(\n", + " ground_truth_policy_value=policy_value_lin_ts,\n", + " action_dist=action_dist_lin_ts,\n", " metric=\"relative-ee\", # \"relative-ee\" (relative estimation error) or \"se\" (squared error)\n", ")\n", "\n", "# estimation performances of the three estimators (lower means accurate)\n", - "relative_ee_lin_ucb" - ], + "relative_ee_lin_ts" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, "outputs": [ { - "output_type": "stream", - "name": "stderr", - "text": [ - "WARNING:obp.ope.meta:`estimated_rewards_by_reg_model` is not given; model dependent estimators such as DM or DR cannot be used.\n" - ] - }, - { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -654,7 +671,7 @@ " \n", " \n", " rm\n", - " 0.097352\n", + " 0.058522\n", " \n", " \n", "\n", @@ -662,39 +679,52 @@ ], "text/plain": [ " relative-ee\n", - "rm 0.097352" + "rm 0.058522" ] }, + "execution_count": 13, "metadata": {}, - "execution_count": 12 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# evaluate the estimation performance of OPE estimators \n", + "# by comparing the estimated policy values of LinUCB and its ground-truth.\n", + "# `summarize_estimators_comparison` returns a pandas dataframe containing estimation performances of given estimators \n", + "relative_ee_lin_ucb = ope.summarize_estimators_comparison(\n", + " ground_truth_policy_value=policy_value_lin_ucb,\n", + " action_dist=action_dist_lin_ucb,\n", + " metric=\"relative-ee\", # \"relative-ee\" (relative estimation error) or \"se\" (squared error)\n", + ")\n", + "\n", + "# estimation performances of the three estimators (lower means accurate)\n", + "relative_ee_lin_ucb" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "Please see [../examples/online](../online) for a more sophisticated example of the evaluation of OPE with online bandit algorithms." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] } ], "metadata": { "kernelspec": { - "name": "python3", "display_name": "Python 3.8.2 64-bit ('3.8.2')", "metadata": { "interpreter": { "hash": "a588998c237fcc28dc215a10a422972d26151263dec0bff02e1a95f6e2b22b77" } - } + }, + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -706,9 +736,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.2-final" + "version": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/examples/quickstart/opl.ipynb b/examples/quickstart/opl.ipynb index 0f52fb7b..401f1537 100644 --- a/examples/quickstart/opl.ipynb +++ b/examples/quickstart/opl.ipynb @@ -2,33 +2,35 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "# Quickstart Example with Off-Policy Learners\n", "---\n", "This notebook provides an example of implementing several off-policy learning methods with synthetic logged bandit data.\n", "\n", - "The example consists of the follwoing four major steps:\n", + "The example consists of the following four major steps:\n", "- (1) Generating Synthetic Data\n", "- (2) Off-Policy Learning\n", "- (3) Evaluation of Off-Policy Learners\n", "\n", "Please see [../examples/opl](../opl) for a more sophisticated example of the evaluation of off-policy learners with synthetic bandit data." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 8, + "metadata": {}, + "outputs": [], "source": [ "# needed when using Google Colab\n", "# !pip install obp" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 9, + "metadata": {}, + "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier as RandomForest\n", "from sklearn.linear_model import LogisticRegression\n", @@ -38,45 +40,44 @@ "from obp.dataset import (\n", " SyntheticBanditDataset,\n", " logistic_reward_function,\n", - " linear_reward_function,\n", - " linear_behavior_policy\n", + " linear_reward_function\n", ")\n", "from obp.policy import (\n", " IPWLearner, \n", + " QLearner,\n", " NNPolicyLearner, \n", " Random\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 2, - "source": [ - "# obp version\n", - "print(obp.__version__)" - ], + "execution_count": 10, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "0.5.0\n" + "0.5.2\n" ] } ], - "metadata": {} + "source": [ + "# obp version\n", + "print(obp.__version__)" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (1) Generating Synthetic Data\n", "`obp.dataset.SyntheticBanditDataset` is an easy-to-use synthetic data generator.\n", @@ -84,66 +85,59 @@ "It takes \n", "- number of actions (`n_actions`, $|\\mathcal{A}|$)\n", "- dimension of context vectors (`dim_context`, $d$)\n", - "- reward function (`reward_function`, $q(x,a)=\\mathbb{E}[r \\mid x,a]$)\n", - "- behavior policy (`behavior_policy_function`, $\\pi_b(a|x)$) \n", + "- reward function (`reward_function`, $q(x,a)=\\mathbb{E}[r|x,a]$)\n", "\n", - "as inputs and generates a synthetic logged bandit data that can be used to evaluate the performance of decision making policies (obtained by `off-policy learning`)." - ], - "metadata": {} + "as inputs and generates synthetic logged bandit data that can be used to evaluate the performance of decision making policies (obtained by `off-policy learning`)." + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ - "# generate a synthetic bandit dataset with 10 actions\n", - "# we use `logistic function` as the reward function and `linear_behavior_policy` as the behavior policy.\n", - "# one can define their own reward function and behavior policy such as nonlinear ones. \n", + "# generate synthetic logged bandit data with 10 actions\n", + "# we use `logistic function` as the reward function and control the behavior policy with `beta`\n", + "# one can define their own reward function and behavior policy function such as nonlinear ones. \n", "dataset = SyntheticBanditDataset(\n", " n_actions=10,\n", " dim_context=5,\n", - " tau=0.2, # temperature hyperparameter to control the entropy of the behavior policy\n", + " beta=-2, # inverse temperature parameter to control the optimality and entropy of the behavior policy\n", " reward_type=\"binary\", # \"binary\" or \"continuous\"\n", " reward_function=logistic_reward_function,\n", - " behavior_policy_function=linear_behavior_policy,\n", " random_state=12345,\n", ")" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 12, + "metadata": {}, + "outputs": [], "source": [ "# obtain training and test sets of synthetic logged bandit data\n", "n_rounds_train, n_rounds_test = 10000, 10000\n", "bandit_feedback_train = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds_train)\n", "bandit_feedback_test = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds_test)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "the logged bandit data is collected by the behavior policy as follows.\n", + "the logged bandit dataset is collected by the behavior policy as follows.\n", "\n", - "$ \\mathcal{D}_b := \\{(x_i,a_i,r_i)\\}_{i=1}^n$ where $(x,a,r) \\sim p(x)\\pi_b(a \\mid x)p(r \\mid x,a) $" - ], - "metadata": {} + "$ \\mathcal{D}_b := \\{(x_i,a_i,r_i)\\}_{i=1}^n$ where $(x,a,r) \\sim p(x)\\pi_b(a | x)p(r | x,a) $" + ] }, { "cell_type": "code", - "execution_count": 5, - "source": [ - "# `bandit_feedback` is a dictionary storing synthetic logged bandit feedback\n", - "bandit_feedback_train" - ], + "execution_count": 13, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "{'n_rounds': 10000,\n", @@ -165,75 +159,139 @@ " [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]),\n", - " 'action': array([6, 1, 1, ..., 0, 1, 6]),\n", + " 'action': array([9, 2, 1, ..., 0, 3, 7]),\n", " 'position': None,\n", - " 'reward': array([1, 1, 1, ..., 0, 0, 1]),\n", - " 'expected_reward': array([[0.80210203, 0.73828559, 0.83199558, ..., 0.81190503, 0.70617705,\n", - " 0.68985306],\n", - " [0.94119582, 0.93473317, 0.91345213, ..., 0.94140688, 0.93152449,\n", - " 0.90132868],\n", - " [0.87248862, 0.67974991, 0.66965669, ..., 0.79229752, 0.82712978,\n", - " 0.74923536],\n", + " 'reward': array([1, 0, 0, ..., 0, 0, 1]),\n", + " 'expected_reward': array([[0.81612381, 0.62585527, 0.3867853 , ..., 0.62527072, 0.58635322,\n", + " 0.38638404],\n", + " [0.52901819, 0.30298844, 0.47277431, ..., 0.67711224, 0.55584904,\n", + " 0.60472268],\n", + " [0.47070198, 0.44459997, 0.40016028, ..., 0.71193979, 0.49769816,\n", + " 0.71876507],\n", " ...,\n", - " [0.66717573, 0.81583571, 0.77012708, ..., 0.87757008, 0.57652468,\n", - " 0.80629132],\n", - " [0.52526986, 0.39952563, 0.61892038, ..., 0.53610389, 0.49392728,\n", - " 0.58408936],\n", - " [0.55375831, 0.11662199, 0.807396 , ..., 0.22532856, 0.42629292,\n", - " 0.24120499]]),\n", - " 'pscore': array([0.29815101, 0.30297159, 0.30297159, ..., 0.04788441, 0.30297159,\n", - " 0.29815101])}" + " [0.85229627, 0.60343336, 0.18287765, ..., 0.54555271, 0.77112271,\n", + " 0.18843358],\n", + " [0.78101646, 0.68586084, 0.40700551, ..., 0.45177062, 0.63841605,\n", + " 0.48128186],\n", + " [0.88757249, 0.75954519, 0.82721872, ..., 0.3422384 , 0.33609074,\n", + " 0.84539856]]),\n", + " 'pi_b': array([[[0.05132742],\n", + " [0.07509562],\n", + " [0.12113457],\n", + " ...,\n", + " [0.07518346],\n", + " [0.08126913],\n", + " [0.12123183]],\n", + " \n", + " [[0.0913545 ],\n", + " [0.14356775],\n", + " [0.10223103],\n", + " ...,\n", + " [0.06793555],\n", + " [0.08658147],\n", + " [0.07851884]],\n", + " \n", + " [[0.11315543],\n", + " [0.1192195 ],\n", + " [0.13030082],\n", + " ...,\n", + " [0.06984557],\n", + " [0.1072079 ],\n", + " [0.06889862]],\n", + " \n", + " ...,\n", + " \n", + " [[0.04138881],\n", + " [0.0680836 ],\n", + " [0.15788198],\n", + " ...,\n", + " [0.07643935],\n", + " [0.04868435],\n", + " [0.15613733]],\n", + " \n", + " [[0.05611272],\n", + " [0.06787541],\n", + " [0.11855589],\n", + " ...,\n", + " [0.10840284],\n", + " [0.07463155],\n", + " [0.10218979]],\n", + " \n", + " [[0.04997525],\n", + " [0.06455919],\n", + " [0.05638682],\n", + " ...,\n", + " [0.14873944],\n", + " [0.15057953],\n", + " [0.05437344]]]),\n", + " 'pscore': array([0.12123183, 0.10223103, 0.1192195 , ..., 0.04138881, 0.11885694,\n", + " 0.14873944])}" ] }, + "execution_count": 13, "metadata": {}, - "execution_count": 5 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# `bandit_feedback` is a dictionary storing synthetic logged bandit data\n", + "bandit_feedback_train" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (2) Off-Policy Learning\n", "After generating synthetic data, we now train some decision making policies.\n", "\n", - "To train policies, we use\n", + "To train policies on logged bandit data, we use\n", "\n", "- `obp.policy.NNPolicyLearner` (Neural Network Policy Learner)\n", "- `obp.policy.IPWLearner`\n", "\n", - "For NN Learner, we use \n", + "For `NN Learner`, we use \n", "- Direct Method (\"dm\")\n", "- InverseProbabilityWeighting (\"ipw\")\n", "- DoublyRobust (\"dr\") \n", "\n", "as its objective functions (`off_policy_objective`). \n", "\n", - "For IPW Learner, we use *RandomForestClassifier* and *LogisticRegression* implemented in scikit-learn for base machine learning methods." - ], - "metadata": {} + "For `IPW Learner`, we use `RandomForestClassifier` and *LogisticRegression* implemented in scikit-learn for base ML methods." + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "A policy is trained by maximizing an OPE estimator as an objective function as follows.\n", "\n", "$$ \\hat{\\pi} \\in \\arg \\max_{\\pi \\in \\Pi} \\hat{V} (\\pi; \\mathcal{D}_{tr}) - \\lambda \\cdot \\Omega (\\pi) $$\n", "\n", "where $\\hat{V}(\\cdot; \\mathcal{D})$ is an off-policy objective and $\\mathcal{D}_{tr}$ is a training bandit dataset. $\\Omega (\\cdot)$ is a regularization term." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "q-func learning: 100%|██████████| 200/200 [00:17<00:00, 11.33it/s]\n", + "policy learning: 100%|██████████| 200/200 [00:47<00:00, 4.18it/s]\n" + ] + } + ], "source": [ "# define NNPolicyLearner with DM as its objective function\n", "nn_dm = NNPolicyLearner(\n", @@ -255,22 +313,21 @@ "action_dist_nn_dm = nn_dm.predict_proba(\n", " context=bandit_feedback_test[\"context\"]\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ - "q-func learning: 100%|██████████| 200/200 [00:16<00:00, 11.99it/s]\n", - "policy learning: 100%|██████████| 200/200 [00:40<00:00, 4.93it/s]\n" + "policy learning: 100%|██████████| 200/200 [00:41<00:00, 4.80it/s]\n" ] } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 7, "source": [ "# define NNPolicyLearner with IPW as its objective function\n", "nn_ipw = NNPolicyLearner(\n", @@ -293,21 +350,22 @@ "action_dist_nn_ipw = nn_ipw.predict_proba(\n", " context=bandit_feedback_test[\"context\"]\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ - "policy learning: 100%|██████████| 200/200 [00:36<00:00, 5.50it/s]\n" + "q-func learning: 100%|██████████| 200/200 [00:18<00:00, 11.03it/s]\n", + "policy learning: 100%|██████████| 200/200 [00:56<00:00, 3.54it/s]\n" ] } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 8, "source": [ "# define NNPolicyLearner with DR as its objective function\n", "nn_dr = NNPolicyLearner(\n", @@ -330,22 +388,15 @@ "action_dist_nn_dr = nn_dr.predict_proba(\n", " context=bandit_feedback_test[\"context\"]\n", ")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "q-func learning: 100%|██████████| 200/200 [00:15<00:00, 12.70it/s]\n", - "policy learning: 100%|██████████| 200/200 [00:51<00:00, 3.85it/s]\n" - ] - } - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# define IPWLearner with Logistic Regression as its base ML model\n", "ipw_lr = IPWLearner(\n", @@ -365,15 +416,15 @@ "action_dist_ipw_lr = ipw_lr.predict(\n", " context=bandit_feedback_test[\"context\"]\n", ")" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# define IPWLearner with Random Forest as its base ML model\n", "ipw_rf = IPWLearner(\n", @@ -395,15 +446,13 @@ "action_dist_ipw_rf = ipw_rf.predict(\n", " context=bandit_feedback_test[\"context\"]\n", ")" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 19, + "metadata": {}, + "outputs": [], "source": [ "# define Uniform Random Policy as a baseline evaluation policy\n", "random = Random(n_actions=dataset.n_actions,)\n", @@ -412,61 +461,76 @@ "action_dist_random = random.compute_batch_action_dist(\n", " n_rounds=bandit_feedback_test[\"n_rounds\"]\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 12, - "source": [ - "# action_dist is a probability distribution over actions (can be deterministic)\n", - "action_dist_ipw_lr[:, :, 0]" - ], + "execution_count": 20, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ - "array([[0., 0., 0., ..., 1., 0., 0.],\n", + "array([[1., 0., 0., ..., 0., 0., 0.],\n", " [0., 0., 1., ..., 0., 0., 0.],\n", " [0., 0., 1., ..., 0., 0., 0.],\n", " ...,\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 1., 0.],\n", - " [0., 0., 0., ..., 0., 0., 0.]])" + " [0., 0., 1., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 0., 1.],\n", + " [0., 0., 0., ..., 0., 1., 0.]])" ] }, + "execution_count": 20, "metadata": {}, - "execution_count": 12 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# action_dist is a probability distribution over actions (can be deterministic)\n", + "action_dist_ipw_lr[:, :, 0]" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (3) Evaluation of Off-Policy Learners\n", - "Our final step is the evaluation and comparison of the off-policy learnres.\n", + "Our final step is the evaluation and comparison of the off-policy learners.\n", "\n", "With synthetic data, we can calculate the policy value of the off-policy learners as follows. \n", "\n", "$$V(\\pi_e) \\approx \\frac{1}{|\\mathcal{D}_{te}|} \\sum_{i=1}^{|\\mathcal{D}_{te}|} \\mathbb{E}_{a \\sim \\pi_e(a|x_i)} [q(x_i, a)], \\; \\, where \\; \\, q(x,a) := \\mathbb{E}_{r \\sim p(r|x,a)} [r]$$\n", "\n", "where $\\mathcal{D}_{te}$ is the test set of logged bandit data." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 21, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "policy value of NN Policy Learner with DM: 0.7802291611331453\n", + "policy value of NN Policy Learner with IPW: 0.7606159767153489\n", + "policy value of NN Policy Learner with DR: 0.7639272034893267\n", + "policy value of IPW Learner with Logistic Regression: 0.7933299733929567\n", + "policy value of IPW Learner with Random Forest: 0.7050722711915117\n", + "policy value of Unifrom Random: 0.49992528545607745\n" + ] + } + ], "source": [ "# we calculate the policy values of the trained policies based on the expected rewards of the test data\n", "policy_names = [\n", @@ -492,53 +556,39 @@ " action_dist=action_dist,\n", " )\n", " print(f'policy value of {name}: {true_policy_value}')" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "policy value of NN Policy Learner with DM: 0.7401610285643739\n", - "policy value of NN Policy Learner with IPW: 0.7219954182377301\n", - "policy value of NN Policy Learner with DR: 0.7239531174451277\n", - "policy value of IPW Learner with Logistic Regression: 0.7225216225722526\n", - "policy value of IPW Learner with Random Forest: 0.6826465969408197\n", - "policy value of Unifrom Random: 0.6056038101021686\n" - ] - } - ], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "In fact, NNPolicyLearner maximizing the DM estimator seems the best in this simple setting." - ], - "metadata": {} + "In fact, `IPWLearner` with `LogisticRegression` seems to be the best in this simple setting." + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "We can iterate the above process several times to get more relibale results.\n", + "We can iterate the above process several times to get more reliable results.\n", "\n", "Please see [../examples/opl](../opl) for a more sophisticated example of the evaluation of off-policy learners with synthetic bandit data." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] } ], "metadata": { + "interpreter": { + "hash": "2983b6b3c1063922151fa571d104b6ca2cb14ad83ab0b1242d2b4dea4ead8697" + }, "kernelspec": { - "name": "python3", - "display_name": "Python 3.9.5 64-bit ('3.9.5': pyenv)" + "display_name": "Python 3.9.5 64-bit ('zr-obp': pyenv)", + "name": "python3" }, "language_info": { "codemirror_mode": { @@ -551,11 +601,8 @@ "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.5" - }, - "interpreter": { - "hash": "2ff39f3b22306140fd87fd114528320b56c4f8c8e196b421a3ea939a2b6b4692" } }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/examples/quickstart/synthetic.ipynb b/examples/quickstart/synthetic.ipynb index 764b6835..5dc3e4b3 100644 --- a/examples/quickstart/synthetic.ipynb +++ b/examples/quickstart/synthetic.ipynb @@ -1,59 +1,37 @@ { - "metadata": { - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.5" - }, - "orig_nbformat": 2, - "kernelspec": { - "name": "python3", - "display_name": "Python 3.9.5 64-bit ('3.9.5': pyenv)" - }, - "interpreter": { - "hash": "2ff39f3b22306140fd87fd114528320b56c4f8c8e196b421a3ea939a2b6b4692" - } - }, - "nbformat": 4, - "nbformat_minor": 2, "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "# Quickstart Example with Synthetic Bandit Data\n", "---\n", "This notebook provides an example of conducting OPE of several different evaluation policies with synthetic logged bandit data.\n", "\n", - "The example consists of the follwoing four major steps:\n", + "The example consists of the following four major steps:\n", "- (1) Generating Synthetic Data\n", "- (2) Off-Policy Learning\n", "- (3) Off-Policy Evaluation\n", "- (4) Evaluation of OPE Estimators\n", "\n", "Please see [../examples/synthetic](../synthetic) for a more sophisticated example of the evaluation of OPE with synthetic bandit data." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": 1, + "metadata": {}, + "outputs": [], "source": [ "# needed when using Google Colab\n", "# !pip install obp" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, + "metadata": {}, + "outputs": [], "source": [ "from sklearn.ensemble import RandomForestClassifier as RandomForest\n", "from sklearn.linear_model import LogisticRegression\n", @@ -63,8 +41,7 @@ "from obp.dataset import (\n", " SyntheticBanditDataset,\n", " logistic_reward_function,\n", - " linear_reward_function,\n", - " linear_behavior_policy\n", + " linear_reward_function\n", ")\n", "from obp.policy import IPWLearner, Random\n", "from obp.ope import (\n", @@ -74,37 +51,36 @@ " DirectMethod,\n", " DoublyRobust\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 2, - "source": [ - "# obp version\n", - "print(obp.__version__)" - ], + "execution_count": 3, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "0.5.0\n" + "0.5.2\n" ] } ], - "metadata": {} + "source": [ + "# obp version\n", + "print(obp.__version__)" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (1) Generating Synthetic Data\n", "`obp.dataset.SyntheticBanditDataset` is an easy-to-use synthetic data generator.\n", @@ -112,66 +88,59 @@ "It takes \n", "- number of actions (`n_actions`, $|\\mathcal{A}|$)\n", "- dimension of context vectors (`dim_context`, $d$)\n", - "- reward function (`reward_function`, $q(x,a)=\\mathbb{E}[r \\mid x,a]$)\n", - "- behavior policy (`behavior_policy_function`, $\\pi_b(a|x)$) \n", + "- reward function (`reward_function`, $q(x,a)=\\mathbb{E}[r|x,a]$)\n", "\n", "as inputs and generates synthetic logged bandit data that can be used to evaluate the performance of decision making policies (obtained by `off-policy learning`) and OPE estimators." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# generate synthetic logged bandit data with 10 actions\n", - "# we use `logistic function` as the reward function and `linear_behavior_policy` as the behavior policy.\n", - "# one can define their own reward function and behavior policy such as nonlinear ones. \n", + "# we use `logistic function` as the reward function and control the behavior policy with `beta`\n", + "# one can define their own reward function and behavior policy function such as nonlinear ones. \n", "dataset = SyntheticBanditDataset(\n", " n_actions=10,\n", " dim_context=5,\n", - " tau=1.0, # temperature hyperparameter to control the entropy of the behavior policy\n", + " beta=1.0, # inverse temperature parameter to control the optimality and entropy of the behavior policy\n", " reward_type=\"binary\", # \"binary\" or \"continuous\"\n", " reward_function=logistic_reward_function,\n", - " behavior_policy_function=linear_behavior_policy,\n", " random_state=12345,\n", ")" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 5, + "metadata": {}, + "outputs": [], "source": [ - "# obtain training and test sets of synthetic logged bandit feedback\n", + "# obtain training and test sets of synthetic logged bandit data\n", "n_rounds_train, n_rounds_test = 100000, 100000\n", "bandit_feedback_train = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds_train)\n", "bandit_feedback_test = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds_test)" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "the logged bandit feedback is collected by the behavior policy as follows.\n", + "Note that a logged bandit dataset is collected by the behavior policy as follows.\n", "\n", "$ \\mathcal{D}_b := \\{(x_i,a_i,r_i)\\}$ where $(x,a,r) \\sim p(x)\\pi_b(a \\mid x)p(r \\mid x,a) $" - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 5, - "source": [ - "# `bandit_feedback` is a dictionary storing synthetic logged bandit data\n", - "bandit_feedback_train" - ], + "execution_count": 6, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "{'n_rounds': 100000,\n", @@ -193,53 +162,110 @@ " [0, 0, 0, 0, 0, 0, 0, 1, 0, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 1, 0],\n", " [0, 0, 0, 0, 0, 0, 0, 0, 0, 1]]),\n", - " 'action': array([9, 2, 1, ..., 9, 1, 5]),\n", + " 'action': array([9, 3, 2, ..., 9, 1, 6]),\n", " 'position': None,\n", - " 'reward': array([0, 1, 1, ..., 0, 0, 0]),\n", - " 'expected_reward': array([[0.80210203, 0.73828559, 0.83199558, ..., 0.81190503, 0.70617705,\n", - " 0.68985306],\n", - " [0.94119582, 0.93473317, 0.91345213, ..., 0.94140688, 0.93152449,\n", - " 0.90132868],\n", - " [0.87248862, 0.67974991, 0.66965669, ..., 0.79229752, 0.82712978,\n", - " 0.74923536],\n", + " 'reward': array([1, 1, 0, ..., 1, 0, 1]),\n", + " 'expected_reward': array([[0.816903 , 0.62620894, 0.38626891, ..., 0.62562239, 0.58656774,\n", + " 0.38586634],\n", + " [0.52901931, 0.30223176, 0.47256314, ..., 0.6776292 , 0.5559511 ,\n", + " 0.60500302],\n", + " [0.47048308, 0.4442848 , 0.39968853, ..., 0.71255194, 0.49758072,\n", + " 0.71939402],\n", + " ...,\n", + " [0.59380127, 0.47008488, 0.86169364, ..., 0.24696277, 0.46450629,\n", + " 0.88492985],\n", + " [0.52537153, 0.60558918, 0.52818568, ..., 0.51244723, 0.69592556,\n", + " 0.29777665],\n", + " [0.69925393, 0.6911979 , 0.15701101, ..., 0.55612729, 0.70225288,\n", + " 0.47162585]]),\n", + " 'pi_b': array([[[0.13293841],\n", + " [0.10985836],\n", + " [0.08642283],\n", + " ...,\n", + " [0.10979394],\n", + " [0.10558863],\n", + " [0.08638804]],\n", + " \n", + " [[0.10165887],\n", + " [0.08103128],\n", + " [0.0960786 ],\n", + " ...,\n", + " [0.11794668],\n", + " [0.10443392],\n", + " [0.10968433]],\n", + " \n", + " [[0.09125127],\n", + " [0.08889169],\n", + " [0.08501455],\n", + " ...,\n", + " [0.11624334],\n", + " [0.09375777],\n", + " [0.11704142]],\n", + " \n", " ...,\n", - " [0.64856003, 0.38145901, 0.84476094, ..., 0.40962057, 0.77114661,\n", - " 0.65752798],\n", - " [0.73208527, 0.82012699, 0.78161352, ..., 0.72361416, 0.8652249 ,\n", - " 0.82571751],\n", - " [0.40348366, 0.24485417, 0.24037926, ..., 0.49613133, 0.30714854,\n", - " 0.5527749 ]]),\n", - " 'pscore': array([0.07250876, 0.10335615, 0.14110696, ..., 0.07250876, 0.14110696,\n", - " 0.11360397])}" + " \n", + " [[0.10443557],\n", + " [0.09228244],\n", + " [0.13651885],\n", + " ...,\n", + " [0.07382754],\n", + " [0.09176907],\n", + " [0.13972817]],\n", + " \n", + " [[0.10905662],\n", + " [0.11816534],\n", + " [0.10936396],\n", + " ...,\n", + " [0.10765621],\n", + " [0.12933698],\n", + " [0.0868578 ]],\n", + " \n", + " [[0.11408153],\n", + " [0.11316618],\n", + " [0.06633187],\n", + " ...,\n", + " [0.09886811],\n", + " [0.11442417],\n", + " [0.09085686]]]),\n", + " 'pscore': array([0.08638804, 0.11574433, 0.08501455, ..., 0.13972817, 0.11816534,\n", + " 0.09218379])}" ] }, + "execution_count": 6, "metadata": {}, - "execution_count": 5 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# `bandit_feedback` is a dictionary storing synthetic logged bandit data\n", + "bandit_feedback_train" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (2) Off-Policy Learning\n", "After generating synthetic data, we now train some candidate evaluation policies using the training bandit dataset.
\n", "\n", "We use `obp.ope.IPWLearner` to train evaluation policies. \n", - "We also use *RandomForestClassifier* and *LogisticRegression* implemented in scikit-learn for base machine learning methods." - ], - "metadata": {} + "We also use `RandomForestClassifier` and `LogisticRegression` implemented in scikit-learn for base ML methods." + ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# define IPWLearner with Logistic Regression as its base ML model\n", "ipw_lr = IPWLearner(\n", @@ -257,15 +283,15 @@ "\n", "# obtains action choice probabilities for the test set\n", "action_dist_ipw_lr = ipw_lr.predict(context=bandit_feedback_test[\"context\"])" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# define IPWLearner with Random Forest as its base ML model\n", "ipw_rf = IPWLearner(\n", @@ -283,15 +309,13 @@ "\n", "# obtains action choice probabilities for the test set\n", "action_dist_ipw_rf = ipw_rf.predict(context=bandit_feedback_test[\"context\"])" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, + "metadata": {}, + "outputs": [], "source": [ "# define Uniform Random Policy as a baseline evaluation policy\n", "random = Random(n_actions=dataset.n_actions,)\n", @@ -300,49 +324,48 @@ "action_dist_random = random.compute_batch_action_dist(\n", " n_rounds=bandit_feedback_test[\"n_rounds\"]\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 9, - "source": [ - "# action_dist is a probability distribution over actions (can be deterministic)\n", - "action_dist_ipw_lr[:, :, 0]" - ], + "execution_count": 10, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ "array([[0., 0., 1., ..., 0., 0., 0.],\n", - " [0., 0., 0., ..., 0., 0., 1.],\n", - " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 1., 0.],\n", + " [1., 0., 0., ..., 0., 0., 0.],\n", " ...,\n", - " [0., 0., 0., ..., 0., 0., 1.],\n", - " [0., 0., 0., ..., 0., 0., 1.],\n", - " [0., 0., 0., ..., 0., 0., 1.]])" + " [0., 0., 0., ..., 0., 1., 0.],\n", + " [0., 0., 0., ..., 0., 0., 0.],\n", + " [0., 0., 0., ..., 0., 1., 0.]])" ] }, + "execution_count": 10, "metadata": {}, - "execution_count": 9 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# action_dist is a probability distribution over actions (can be deterministic)\n", + "action_dist_ipw_lr[:, :, 0]" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (3) Off-Policy Evaluation (OPE)\n", - "Our next step is OPE, which attempts to estimate the performance of evaluation policies using the logged bandit feedback and OPE estimators.\n", + "Our next step is OPE, which aims to estimate the performance of evaluation policies using logged bandit data and OPE estimators.\n", "\n", "Here, we use \n", "- `obp.ope.InverseProbabilityWeighting` (IPW)\n", @@ -350,37 +373,38 @@ "- `obp.ope.DoublyRobust` (DR)\n", "\n", "as OPE estimators and visualize the OPE results." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### (3-1) Obtaining a reward estimator\n", "A reward estimator $\\hat{q}(x,a)$ is needed for model dependent estimators such as DM or DR.\n", "\n", "$\\hat{q}(x,a) \\approx \\mathbb{E} [r \\mid x,a]$" - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, + "metadata": {}, + "outputs": [], "source": [ - "# estimate the expected reward by using an ML model (Logistic Regression here)\n", + "# estimate the expected rewards by using an ML model (Logistic Regression here)\n", "# the estimated rewards are used by model-dependent estimators such as DM and DR\n", "regression_model = RegressionModel(\n", " n_actions=dataset.n_actions,\n", " action_context=dataset.action_context,\n", " base_model=LogisticRegression(random_state=12345),\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, + "metadata": {}, + "outputs": [], "source": [ "estimated_rewards_by_reg_model = regression_model.fit_predict(\n", " context=bandit_feedback_test[\"context\"],\n", @@ -389,28 +413,30 @@ " n_folds=3, # use 3-fold cross-fitting\n", " random_state=12345,\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "please refer to https://arxiv.org/abs/2002.08536 about the details of the cross-fitting procedure." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### (3-2) Off-Policy Evaluation\n", "$V(\\pi_e) \\approx \\hat{V} (\\pi_e; \\mathcal{D}_b, \\theta)$ using DM, IPW, and DR" - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# estimate the policy value of the evaluation policies based on their action choice probabilities\n", "# it is possible to set multiple OPE estimators to the `ope_estimators` argument\n", @@ -418,15 +444,37 @@ " bandit_feedback=bandit_feedback_test,\n", " ope_estimators=[InverseProbabilityWeighting(), DirectMethod(), DoublyRobust()]\n", ")" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mean 95.0% CI (lower) 95.0% CI (upper)\n", + "ipw 0.794666 0.784136 0.810203\n", + "dm 0.598203 0.597871 0.598573\n", + "dr 0.794610 0.784466 0.802190 \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# estimate the policy value of IPWLearner with Logistic Regression\n", "estimated_policy_value_a, estimated_interval_a = ope.summarize_off_policy_estimates(\n", @@ -435,44 +483,44 @@ ")\n", "print(estimated_interval_a, '\\n')\n", "\n", - "# visualize policy values of IPWLearner with Logistic Regression estimated by the three OPE estimators\n", + "# visualize the estimated policy values of IPWLearner with Logistic Regression\n", "ope.visualize_off_policy_estimates(\n", " action_dist=action_dist_ipw_lr,\n", " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", - " n_bootstrap_samples=1000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=1000, # number of resampling performed in bootstrap sampling\n", " random_state=12345,\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "tags": [] + }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ " mean 95.0% CI (lower) 95.0% CI (upper)\n", - "ipw 0.784805 0.767594 0.803541\n", - "dm 0.648009 0.646855 0.649021\n", - "dr 0.770691 0.760420 0.778522 \n", + "ipw 0.731147 0.716350 0.743979\n", + "dm 0.592805 0.592405 0.593211\n", + "dr 0.728655 0.721025 0.736589 \n", "\n" ] }, { - "output_type": "display_data", "data": { - "image/png": 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", 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GBnl5eTq3y8jIQI8ePXD48GHs2bMHd+/eRZ8+ffDss8/Cx8fH1GUTERE9NNo1CFRUVEAqlTZaLpVKG01i1NDdu3eRl5eH3bt346mnnkKnTp2wd+9efPjhh/jyyy+bfBxyfHw84uPjAQCrVq2CTCZrUc2/t2gr6ghaek4QPXj4SfWwMsXnVLsGgZZSq9Worq7GW2+9haCgIABAv379MH/+fPz0009NdjSMiIjQ6k9QVFTUVuVSB8FzgogedC39nOrRo4fOde3aR0AqlaKysrLR8oqKCtjb2+vdTiQSwc/PT7NMIpGgb9++yMnJMUmtRERED6N2vSLg5uaG3NxcrWVFRUVQKBR604ubmxvUanWj5Wq1WtD8A0QPkuc2/9reJZCJ/OfZEe1dAlGzDP7WlMvl2LVrl8HrmhIUFIQLFy6gqqpKsywpKQlisVjr1/79Bg0aBAC4dOmSZlllZSUyMzPRu3dvwccnIiIydwYHgdTUVMTGxhq8rimRkZGwtrbG6tWrcfHiRcTHxyM2NhZRUVFaQwoXLlyIDRs2aF57enpi8ODB2LhxI44ePYpz584hOjoalpaWmDBhgqFviYiIyGy1660Be3t7LFu2DJs2bUJ0dDSkUikmT56MmTNnarVTqVSNZgx8/fXXsXXrVmzZsgUKhQK+vr5Yvny53r4FREREpK3dRw24u7tj+fLletusW7eu0TJbW1u8/PLLePnll01VGhER0UOPPeuIiIjMmKArAg3HLVZUVDRaBnAyFiIioo5IUBCYP3++3mUikQjbt283XlVERETUJgQFgSeeeAIikQjAvSGCcrkc06dPN2lhREREZHqCgkDDXvyxsbGQy+WYMWOGyYoiIiKitsHOgkRERGaMQYCIiMiMMQgQERGZMYODQFMP+xGyjoiIiB48Bs8sOHPmzEZTAAtZR0RERA8e3hogIiIyYwwCREREZkxnEFAqla3euTH2QURERKajMwjMnz8f//vf/1BTU2PwTrOysvDxxx9j3759rSqOiIiITEtnZ8HAwEBs3rwZsbGxCAkJwYgRI9CvXz+IxeIm29+6dQsXLlxAQkICrl69CplMhilTppiscCIiImo9nUFgwYIFmDhxIrZv3474+HjEx8fDwsIC7u7ucHR0hFQqRU1NDcrLy5GXl4fS0lIAgIODA+bMmYPJkyfD2tq6zd4IERERGU7v8EEvLy/8/e9/x++//47Dhw/j0qVLyMrKwo0bN7TaOTg4YNiwYZp/rKwMHpVIRERE7UDQN7arqyv+/Oc/AwAUCgXu3LmDsrIyiMVidO7cGU5OTiYtkoiIiEzD4J/uNjY2cHV1haurqynqISIiojbEeQSIiIjMGIMAERGRGWMQICIiMmMMAkRERGaMQYCIiMiMMQgQERGZMQYBIiIiM2bwPAK1tbW4dOkScnJyUF1djenTpwO496TBqqoqdOrUCRYWzBdEREQdgUFB4Pz589iwYQNKSko0y+qDQFZWFt59910sXLgQI0eONGqRREREZBqCg8C1a9fwySefoFOnTnj22Wdx9epVJCYmatb369cPLi4uOHXqlEFBICcnBzExMUhPT4dUKkV4eDhmzJih96pCQUEBFixY0Gh5SEgI3njjDcHHJiIiMneCg8Du3bshFouxatUqODo6IjY2tlEbT09PXL9+XfDBy8vLsXLlSri7u2PJkiXIz8/H1q1boVarMXv27Ga3f/rpp+Hj46N57eDgIPjYREREZEAQuHLlCoYMGQJHR0edbWQyGVJSUgQfPC4uDkqlEosWLYJEIkFAQACqqqoQGxuLKVOmQCKR6N2+R48e6Nevn+DjERERkTbBvfqqq6ub/cWtUCigUqkEH/z8+fMIDAzU+sIPDQ2FUqmEXC4XvB8iIiJqGcFXBJydnXHz5k29bbKystCtWzfBB8/NzYW/v7/WMplMBhsbG+Tl5TW7/fr161FeXo7OnTsjNDQUc+bMgVgsFnx8IiIicyc4CAQFBSEuLg6XL1+Gr69vo/UpKSlIT0/H1KlTBR+8oqICUqm00XKpVIry8nKd21lbW2PChAkIDAyEnZ0dUlNTsXfvXty6dQtLlixpcpv4+HjEx8cDAFatWgWZTCa4zoZ+b9FW1BG09Jwg0qX9zil+Uj2sTHFOCQ4Cjz32GJKSkvDBBx9g4sSJKCwsBACcO3cOcrkcP//8MxwdHREVFWX0Iu/n5OSEF198UfPa398fjo6O+Oabb5CVlQUPD49G20RERCAiIkLzuqioyOR1UsfCc4KMjecUGVtLz6kePXroXCe4j4CzszOWLl0KJycn7N+/HydPngQAREdHY//+/XBycsLSpUsN6rkvlUpRWVnZaHlFRQXs7e0F7wcAhg8fDgDIzMw0aDsiIiJzZtCEQn379sWXX36Jc+fOIT09HWVlZZBIJPD29saQIUNgaWlp0MHd3NyQm5urtayoqAgKhUJvetFHJBK1aDsiIiJzZPAUwxYWFhg8eDAGDx7c6oMHBQVh3759qKqqgp2dHQAgKSkJYrEYfn5+Bu2r/gpF3759W10XERGRuTA4CBhTZGQkDh48iNWrV2Pq1KkoKChAbGwsoqKitIYULly4EH5+fpg3bx4AYOfOnaiuroaPjw/s7OyQlpaGffv2YejQoejdu3d7vR0iIqIOR3AQSEhIELzTMWPGCGpnb2+PZcuWYdOmTYiOjoZUKsXkyZMxc+ZMrXYqlUprfgI3Nzfs378fhw4dglKphEwmw5QpU/D4448LrpGIiIgMCALr168XvFOhQQAA3N3dsXz5cr1t1q1bp/U6NDQUoaGhgo9BRERETRMcBOovy9+vsrISV69eRVJSEoYOHYrg4GCjFUdERESmJTgIhIWF6V0/duxYrFq1CpMmTWptTURERNRGBM8j0JyBAwciMDAQO3bsMNYuiYiIyMSMFgSAezMXcUIfIiKijsOoQSAnJ8eYuyMiIiITa/U8AiqVCrdv38ahQ4eQkpKCRx55xBh1ERERURsQHARmzZrVbBt7e3s89dRTrSqIiIiI2o7gINC/f/8m5/EXiUSQSqXw8vLC2LFjDXroEBEREbUvwUHgvffeM2EZRERE1B6M2lmQiIiIOhYGASIiIjOm89aAIc8WaEgkEumcjpiIiIgeLDqDgCFPG7wfgwAREVHHoDMIrF27ti3rICIionagMwh07dq1LesgIiKidsDOgkRERGasRVMMq1QqlJaWora2tsn1MpmsVUURERFR2zAoCNy4cQPffvstUlNTUVNT02QbkUiE7du3G6U4IiIiMi3BQSAnJwd///vfAQABAQE4e/Ysevfujc6dO+P69esoKyuDv78/rwYQERF1IIKDwJ49e1BXV4ePPvoIvXr1wqxZszB06FBMnz4d1dXV+Pe//42UlBS89tprpqyXiIiIjEhwZ8HU1FQEBwejV69emmVqtRoAYGtri1deeQVSqRQ7duwwfpVERERkEoKDQFlZGVxdXf/Y0MICCoVC89rS0hL+/v64ePGicSskIiIikxEcBOzt7VFdXa157eDggKKiIq02VlZWqKysNF51REREZFKCg0C3bt1QUFCged2nTx/89ttvuHv3LgCguroaZ86cgYuLi/GrJCIiIpMQ3FkwMDAQe/fuRXV1NWxtbTF+/HikpKRgyZIl8PHxQWZmJgoLC/HMM8+Ysl4iIiIyIsFBYNy4cejRoweUSiVsbW0RHByMZ599FrGxsUhOToZYLMbUqVPxpz/9yZT1EhERkRHpDQJLlixBREQERo0aBScnJ4SEhGitnzRpEiZOnIjS0lJ07twZIpHIpMUSERGRcentI5CdnY1Nmzbh1VdfxcaNG5GRkdF4BxYWcHR0ZAggIiLqgPReEVi5ciXi4+Nx8uRJHDlyBEeOHEGvXr0wbtw4jB49GhKJpK3qJCIiIhPQGwT69euHfv364fnnn8fx48dx+PBhXL9+Hf/+97/x7bffYvjw4Rg3bhx8fX1bXEBOTg5iYmKQnp4OqVSK8PBwzJgxAxYWwgY0qFQq/O1vf0NmZibefvttDBo0qMW1EBERmRtBnQXt7Owwfvx4jB8/HllZWYiPj0diYiKOHTuGY8eOwd3dXXOVwN7eXvDBy8vLsXLlSri7u2PJkiXIz8/H1q1boVarMXv2bEH7OHz4MG7fvi34mERERPQHwfMI1PPw8MBLL72Ef/3rX3jttdfg4+ODnJwcbN68GXPnzsWaNWsE7ysuLg5KpRKLFi1CQEAAxo8fj+nTp+PAgQOCJiYqLy/Hd999hzlz5hj6NoiIiAgtCAL1xGIxxowZg/fffx+ff/45fH19UVNTgxMnTgjex/nz5xEYGKjV1yA0NBRKpRJyubzZ7Xfs2AEfHx8MGDCgRe+BiIjI3AmeR6Ap5eXlSEhIwOHDh5GTkwMABnUgzM3Nhb+/v9YymUwGGxsb5OXl6d02OzsbR44cwerVqw0vnIiIiAC0MAhcunQJ8fHxOH36NGprawEA3t7eiIiIaDTXgD4VFRWQSqWNlkulUpSXl+vdNiYmBhMnTkT37t21pj7WJT4+HvHx8QCAVatWQSaTCa6zod9btBV1BC09J4h0ab9zip9UDytTnFOCg0BJSQmOHDmCw4cPa754pVIpIiIiEBERgZ49exq9OF0SExORl5eHt99+W/A29XXWu/+BSUQ8J8jYeE6RsbX0nOrRo4fOdXqDgFqtxrlz53Do0CGkpKRApVIBAHx9fTFu3DgMHz4cYrG4RUUB94JEU50CKyoqdI4+qK2txX//+19MnToVarUaFRUVqKqqAgAoFApUVVXBzs6uxTURERGZE71B4LXXXsOdO3cA3HsM8ejRoxEREQE3NzejHNzNzQ25ublay4qKiqBQKHSmF4VCgdu3b2PLli3YsmWL1rovvvgC3bp1M2jkAhERkTnTGwTu3LkDPz8/za9/K6tW9S1sJCgoCPv27dP6FZ+UlASxWAw/P78mt7G1tcXy5cu1lpWUlODLL7/EnDlzOIKAiIjIAHq/2b/44gu4urqa7OCRkZE4ePAgVq9ejalTp6KgoACxsbGIiorSGn2wcOFC+Pn5Yd68ebC0tGw00qC+z0KvXr3g7e1tsnqJiIgeNnrnETBlCADu3W5YtmwZVCoVoqOjsXPnTkyePBkzZ87UaqdSqTT9E4iIiMh4jHutvwXc3d0bXeq/37p16/Sud3Fxwc6dO41ZFhERkVlo8cyCRERE1PExCBAREZkxBgEiIiIzxiBARERkxgQHgeTkZPbcJyIiesgIHjXw2WefwcnJCWPHjsW4ceP4gBYiIqKHgOArAhMmTIBCocCePXuwcOFCrFq1CmfPnoVarTZlfURERGRCgq8IvPDCC3jqqaeQlJSEuLg4pKSkICUlBc7Ozhg3bhzCw8Ph7OxsylqJiIjIyAyaUEgsFiMsLAxhYWG4ceMG4uPjcfz4ccTGxmL37t0IDg5GZGQkgoKCTFQuERERGVOLZxbs1auX1lWCHTt24MyZMzhz5gxkMhkmTJiA8ePHw9bW1pj1EhERkRG1avhgdXU1jh07hp9++knzuGIPDw+Ul5fj22+/xZtvvomsrCxj1ElEREQm0KIrAtevX0dcXBwSExNRXV0NsViM8PBwTJgwAR4eHqiursbPP/+MnTt34t///jdWrFhh7LqJiIjICAQHAYVCgcTERMTFxSEzMxMA4ObmhsjISIwZM0brscG2traYOnUqbt++jcOHDxu/aiIiIjIKwUHg1VdfRVVVFSwsLDBs2DBMmDAB/v7+erdxdnZGTU1Nq4skIiIi0xAcBOzs7BAVFYWIiAg4OjoK2mb8+PEIDQ1taW1ERERkYoKDwLp162BhYVjfQolEonXLgIiIiB4sgr/ZDQ0BRERE9OAT/O2+e/duzJkzRzNM8H537tzBnDlz8MMPPxirNiIiIjIxwUHg7Nmz8PPz0zmNsLOzMwYMGIDTp08brTgiIiIyLcFBID8/H+7u7nrbuLm5IT8/v9VFERERUdsQHASUSiVsbGz0thGLxaiurm51UURERNQ2BAeBLl26ICMjQ2+bjIwMPoGQiIioAxEcBAIDAyGXy5GUlNTk+sTERMjlcj55kIiIqAMRPI/AtGnTcOLECXz55ZdISkpCUFAQnJ2dcefOHaSkpODMmTOwt7fHtGnTTFguERERGZPgIODs7IylS5fis88+w+nTpxuNDujatSveeustdOnSxehFEhERkWkY9PRBT09PfPnllzh79iwyMjJQUVEBqVQKb29vDBo0CFZWLXqYIREREbUTg7+5raysMGzYMAwbNswU9RAREVEb4rzBREREZkznFYGEhAQAwNChQ2FnZ6d5LcSYMWNaXxkRERGZnM4gsH79egCAt7c37OzsNK+FMCQI5OTkICYmBunp6ZBKpQgPD8eMGTP0PuTo5s2b2LJlC27cuIGysjJ07twZgYGBmDVrFpycnAQfm4iIyNzpDALz5s0DAM0Xa/1rYyovL8fKlSvh7u6OJUuWID8/H1u3boVarcbs2bN1bldZWQkXFxeMGTMGTk5OKCgowK5du5CZmYmPPvoIlpaWRq+ViIjoYaQzCISFhel9bQxxcXFQKpVYtGgRJBIJAgICUFVVhdjYWEyZMgUSiaTJ7Xx8fODj46N57e/vjy5duuCDDz5AdnY2+vbta/RaiYiIHkbt2lnw/PnzCAwM1PrCDw0NhVKphFwuN2hf9vb2AIDa2lqj1khERPQwa9cgkJubix49emgtk8lksLGxQV5eXrPbq1Qq1NbWIi8vD9u2bYOnpye8vLxMVS4REdFDR+etgQULFrRohyKRCGvWrBHUtn5CovtJpVKUl5c3u/1HH32ECxcuAAD69u2Lv/71rzo7GcbHxyM+Ph4AsGrVKshkMkE13u/3Fm1FHUFLzwkiXdrvnOIn1cPKFOeUziCgVqtbtMOWbtcSL7zwAsrLy/H7779jz549+PDDD7Fy5UqIxeJGbSMiIhAREaF5XVRU1GZ1UsfAc4KMjecUGVtLz6n7r743pDMIrFu3rkUHM4RUKkVlZWWj5RUVFZp7/vq4uroCuDfEsX///liwYAFOnDiB8PBwo9dKRET0MGrXPgJubm7Izc3VWlZUVASFQqE3vTSla9eusLe3R0FBgTFLJCIieqi1OAhUVVWhqKioyV/0QgUFBeHChQuoqqrSLEtKSoJYLIafn59B+8rLy0NZWRlcXFxaXA8REZG5MeihQ3V1ddi/fz8OHTqk9cvbxcUF48aNw6OPPmrQZD6RkZE4ePAgVq9ejalTp6KgoACxsbGIiorSGlK4cOFC+Pn5aSY12rJlCywtLeHt7Q2JRILc3Fzs27cP3bp1Q0hIiCFviYiIyKwJDgK1tbX4xz/+AblcDpFIBJlMBkdHR5SUlKCwsBDfffcdzp8/j7///e+CH0dsb2+PZcuWYdOmTYiOjoZUKsXkyZMxc+ZMrXYqlQoqlUrz2tPTEz/99BPi4+NRU1MDmUyGYcOGYdq0abC1tRX6loiIiMye4CBw4MAByOVyBAcH45lnntF01AOA/Px8bNmyBWfPnsWBAwcwbdo0wQW4u7tj+fLletvc33ExNDQUoaGhgo9BRERETRPcR+DEiRPo2bMn/vKXv2iFAADo3r07Fi9ejJ49e+L48eNGL5KIiIhMQ3AQyM/PR1BQkM4JeywsLBAUFIRbt24ZrTgiIiIyLcFBwMrKCtXV1XrbKBQKPvmPiIioAxEcBHr37o3k5GSUlpY2ub60tBQnT56Eh4eHsWojIiIiExMcBCZMmIDS0lL89a9/xeHDh3Hr1i0olUoUFBTgyJEjWLp0KUpLSzFhwgRT1ktERERGJHjUQEhICLKysrB3717861//arLNlClTOI6fiIioAzFoQqEnn3wSgwcPxuHDh5GVlYXKykpIJBJ4eHggPDwc/fr1M1WdREREZAKCg0BZWRlEIhH69evHL3wiIqKHRLNB4PTp09iyZYtmSuHu3bvj6aefxuDBg01eHBEREZmW3s6C6enp+PTTT7WeK5Cfn49PP/0U6enpJi+OiIiITEtvEDhw4ADUajWeeOIJfP311/jqq6/w+OOPQ6VS4cCBA21VIxEREZmI3lsDGRkZ8PX11XoI0KxZsyCXy3lFgIiI6CGg94rA3bt34e3t3Wi5t7e3zomFiIiIqOPQGwTq6uqafKyvjY0N6urqTFYUERERtQ3BMwsSERHRw6fZ4YNHjx5Famqq1rLCwkIAwIoVKxq1F4lEWLZsmZHKIyIiIlNqNggUFhZqvvjvJ5fLjV4QERERtR29QWD58uVtVQcRERG1A71BwM/Pr63qICIionbAzoJERERmjEGAiIjIjDEIEBERmTEGASIiIjPGIEBERGTGGASIiIjMGIMAERGRGWMQICIiMmM6JxTatWtXi3c6ffr0Fm9LREREbUdnEIiNjW3xTg0JAjk5OYiJiUF6ejqkUinCw8MxY8YMWFjovlhx9epV/PLLL0hLS0NxcTG6dOmCkSNHYurUqRCLxS2um4iIyNzoDAJNPWfgwIEDSElJwahRo+Dn5wdHR0eUlJQgNTUVJ06cQHBwMCZPniz44OXl5Vi5ciXc3d2xZMkS5OfnY+vWrVCr1Zg9e7bO7ZKSknDr1i1MnToVrq6uyM7Oxo4dO5CdnY3FixcLPj4REZG50xkE7n/OQEJCAn777Tf84x//QN++fbXWhYWFYeLEiVi+fDmGDRsm+OBxcXFQKpVYtGgRJBIJAgICUFVVhdjYWEyZMgUSiaTJ7aZNmwYHBwfNa39/f4jFYnz11VcoLCxE165dBddARERkzgR3Fvzxxx8xYsSIRiGgnqenJ0aMGIEff/xR8MHPnz+PwMBArS/80NBQKJVKvY84bhgC6nl4eAAAiouLBR+fiIjI3AkOAnl5eXByctLbxsnJCXl5eYIPnpubix49emgtk8lksLGxMWg/AJCeng6RSIRu3boZtB0REZE50/sY4obs7Oxw5coVvW2uXLkCW1tbwQevqKiAVCpttFwqlaK8vFzwfkpKSrBnzx6MHj0anTt3brJNfHw84uPjAQCrVq2CTCYTvP+Gfm/RVtQRtPScINKl/c4pflI9rExxTgkOAsHBwTh69Ci2bNmCGTNmwM7OTrOu/r7+5cuXMXbsWKMXqU9tbS0+//xz2Nra4tlnn9XZLiIiAhEREZrXRUVFbVEedSA8J8jYeE6RsbX0nLr/6ntDgoPAk08+Cblcjh9//BGHDx+Gh4cHOnfujLt37yIrKwtVVVVwcXHBnDlzBBcmlUpRWVnZaHlFRQXs7e2b3V6tVmPt2rW4efMmVq5cKWgbIiIi+oPgINC5c2d8+OGH2LZtG06cOIG0tDTNOrFYjHHjxmHOnDno1KmT4IO7ubkhNzdXa1lRUREUCoXe9FLvP//5D06fPo13330Xbm5ugo9LRERE9wgOAgDQqVMnvPrqq3jppZeQm5uLyspKSCQSuLm5wdLS0uCDBwUFYd++faiqqtLcakhKSoJYLG40fPF+33//PX766Se8+eab8PX1NfjYREREZGAQqGdpaYlevXq1+uCRkZE4ePAgVq9ejalTp6KgoACxsbGIiorSGlK4cOFC+Pn5Yd68eQCAEydO4LvvvkNYWBicnZ2Rnp6uadu9e/cmhxcSERFRYwYHgdraWly6dAk5OTmorq7WTCesVCpRVVWFTp066Z0euCF7e3ssW7YMmzZtQnR0NKRSKSZPnoyZM2dqtVOpVFCpVJrXFy5cAAAcPXoUR48e1Wr72muvISwszNC3RUREZJYMCgLnz5/Hhg0bUFJSollWHwSysrLw7rvvYuHChRg5cqTgfbq7uzc5nXFD69at03o9f/58zJ8/X3jhRERE1CTBEwpdu3YNn3zyCUQiEZ599lmEhoZqre/Xrx9cXFxw6tQpoxdJREREpiE4COzevRtisRirVq3CpEmT4Orq2qiNp6cnsrOzjVogERERmY7gIHDlyhUMGTIEjo6OOtvIZDKt2wZERET0YBMcBKqrq5vtja9QKLQ69REREdGDTXAQcHZ2xs2bN/W2ycrK4kN/iIiIOhDBQSAoKAgXLlzA5cuXm1yfkpKC9PR0BAcHG604IiIiMi3Bwwcfe+wxJCUl4YMPPsDEiRNRWFgIADh37hzkcjl+/vlnODo6IioqymTFEhERkXEJDgLOzs5YunQpPv/8c+zfv1+zPDo6GgDQrVs3LF68mLP6ERERdSAGTSjUt29ffPnllzh37hzS09NRVlYGiUQCb29vDBkypEXPGyAiIqL2Y/AUwxYWFhg8eDAGDx5sinqIiIioDQnuLLhixQokJCTobXPs2DGsWLGi1UURERFR2xAcBORyuaaDoC5FRUWQy+WtLoqIiIjahuAgIIRSqWQ/ASIiog7E4D4CTVGr1SgqKkJKSgq6dOlijF0SERFRG9AbBGbNmqX1OjY2FrGxsXp3+Nhjj7W+KiIiImoTeoNA//79IRKJANzrIyCTyeDi4tKonYWFBezt7TFw4ECEh4ebplIiIiIyOr1B4L333tP8edasWRg7diymT59u6pqIiIiojQjuI7B27VpIpVJT1kJERERtTHAQ6Nq1qynrICIionZg8KiB4uJi/Pbbb7hz5w5qa2ubbMPbB0RERB2DQUFg586d+OGHH1BXV6e3HYMAERFRxyA4CBw/fhy7d+/GgAEDMGHCBHz66acYM2YMAgMDkZqaiiNHjmD48OGIjIw0Zb1ERERkRIKDwC+//AJnZ2f87W9/08we6OLigtDQUISGhmLo0KFYtWoVQkNDTVYsERERGZfgKYZv3LiBRx55RGsKYZVKpflzUFAQAgMDsX//fuNWSERERCYjOAjU1dWhU6dOmtdisRiVlZVabXr27ImsrCyjFUdERESmJTgIODk5obi4WPNaJpMhOztbq01xcTEfOkRERNSBCA4CHh4euHnzpua1v78/Ll++jGPHjqG6uhrnzp3DyZMn0adPH5MUSkRERMYnOAgMGjQIN2/eREFBAQBg2rRpkEgkWLduHZ599llER0cDaPygIiIiInpwCR41EBYWhrCwMM1rmUyGjz76CPv378etW7fQtWtXTJgwAb169TJFnURERGQCBs8s2JCLiwtefPHFVhWQk5ODmJgYpKenQyqVIjw8HDNmzICFhe6LFbW1tfjuu++QkZGBa9euoaamBjt37mxVHUREROZI8K0BUygvL8fKlSshEomwZMkSPPHEEzhw4ECzX+oKhQKHDx+GjY0NfHx82qhaIiKih4/BVwRUKhXu3Lmj91kDfn5+gvYVFxcHpVKJRYsWQSKRICAgAFVVVYiNjcWUKVMgkUia3E4qlSImJgYikQg//fQTLl26ZOjbICIiIhgYBPbt24f9+/ejtLRUb7sdO3YI2t/58+cRGBio9YUfGhqKb7/9FnK5HIMHD9a5rUgkElY0ERER6SQ4COzcuRO7d++Gvb09xowZA2dn51bPGZCbmwt/f3+tZTKZDDY2NsjLy2vVvomIiKh5goPAkSNH4OLigujoaJ2X7A1VUVEBqVTaaLlUKkV5eblRjlEvPj4e8fHxAIBVq1ZBJpO1aD+/G7MoeqC09Jwg0qX9zil+Uj2sTHFOCQ4CZWVliIyMNFoIaGsRERGIiIjQvC4qKmrHauhBxHOCjI3nFBlbS8+pHj166FwneNRA9+7dUVFR0aICdJFKpY2eVwDcu1Jgb29v1GMRERFRY4KDwPjx43H27FmUlJQY7eBubm7Izc3VWlZUVASFQqE3vRAREZFxCL41MH78ePz+++9499138cQTT6Bv3746bxMIvYcRFBSEffv2oaqqCnZ2dgCApKQkiMViwUMQiYiIqOUMGj7Yu3dvHD16FBs2bNDZRiQSYfv27YL2FxkZiYMHD2L16tWYOnUqCgoKEBsbi6ioKK2QsXDhQvj5+WHevHmaZSkpKVAoFJrHHp88eRIA4Onpia5duxrytoiIiMyW4CBw6NAhfPXVV7C0tIS/vz+cnJxaPXzQ3t4ey5Ytw6ZNmxAdHQ2pVIrJkydj5syZWu1UKhVUKpXWsm+++QaFhYWa15999hkA4LXXXtN6JgIRERHpJjgI7N+/H507d8YHH3wAFxcXoxXg7u6O5cuX622zbt06QcuIiIjIMII7CxYWFmL48OFGDQFERETUvgQHAWdnZ53PFiAiIqKOSXAQGDNmDFJSUlBVVWXKeoiIiKgNCQ4Cjz32GLy8vLBy5UqkpqYyEBARET0EBHcWfPLJJzV/fv/993W2M2T4IBEREbUvwUGgf//+fPQvERHRQ0ZwEHjvvfdMWAYRERG1B8F9BIiIiOjhwyBARERkxnTeGti1axcAYOLEibC3t9e8FmL69Omtr4yIiIhMTmcQiI2NBQCEhITA3t5e81oIBgEiIqKOQWcQqJ//v/6Rws09D4CIiIg6Hp1BwM/PT+9rIiIi6vgEdxZMSEhAdna23jY3btxAQkJCq4siIiKitiE4CKxfvx6nT5/W2+bMmTNYv359q4siIiKitmHU4YMqlYqzDxIREXUgRg0CeXl5kEqlxtwlERERmZDeKYbvv8x/+vRpFBQUNGqnUqlw+/ZtpKWlITg42LgVEhERkcnoDQL3d/zLyspCVlaWzvbe3t549tlnjVIYERERmZ7eILB27VoAgFqtxsKFCzFp0iRMmjSpUTsLCwtIpVLY2tqapkoiIiIyCb1BoGvXrpo/T58+Hf7+/lrLiIiIqGMT/BjiGTNmmLIOIiIiageCg8D169eRnp6OUaNGQSKRAACqq6vxzTff4MyZM7CxscHUqVObvHVAREREDybBwwf37t2LPXv2aEIAAGzbtg3Hjx+HWq1GWVkZNm/ejAsXLpikUCIiIjI+wUHg2rVr8Pf317yura1FQkICvLy88PXXX2Pt2rVwcHDAwYMHTVIoERERGZ/gIFBaWoouXbpoXmdmZqK6uhoREREQi8VwdnbG4MGDm30eARERET04DJpZsK6uTvPny5cvA9B+KqGDgwNKS0uNVBoRERGZmuAgIJPJkJGRoXl9+vRpdOnSBd26ddMsKy4uhr29vXErJCIiIpMRPGpgxIgRiI2Nxaeffgpra2ukp6dj8uTJWm1yc3O1ggERERE92AQHgaioKFy4cAGnTp0CAHh4eGD69Oma9QUFBbh69Soee+wxgwrIyclBTEwM0tPTIZVKER4ejhkzZsDCQv/FisrKSvznP//B6dOnoVKpMGjQIDz//PPo1KmTQccnIiIyZ4KDgK2tLVauXIkbN24AANzd3Rt9WS9evBienp6CD15eXo6VK1fC3d0dS5YsQX5+PrZu3Qq1Wo3Zs2fr3fbzzz9HXl4eXn31VVhYWODbb7/FJ598gvfff1/w8YmIiMyd4CBQr1evXk0ud3FxgYuLi0H7iouLg1KpxKJFiyCRSBAQEICqqirExsZiypQpWnMWNJSeno4LFy7gvffe03RWdHZ2xt/+9jdcvHgRAQEBhr0pIiIiM6X3+rtcLkdRUZHgnWVnZzd6YqE+58+fR2BgoNYXfmhoKJRKJeRyuc7tUlJS0LlzZ60RC15eXnBxccH58+cFH5+IiMjc6Q0CK1aswNGjR7WW/fDDD3jhhReabH/q1CmsX79e8MFzc3PRo0cPrWUymQw2NjbIy8vTu52bm1uj5W5ubsjNzRV8fCIiInNn8K2BmpoaVFRUGOXgFRUVkEqljZZLpVKUl5fr3a6p2wZSqRQFBQVNbhMfH4/4+HgAwKpVqxoFEKF6fPu/Fm1HpMsvf32ivUugh8zzc1v2+UbmyaAJhTqyiIgIrFq1CqtWrWrvUjqMd955p71LoIcMzykyNp5TrdeuQUAqlaKysrLR8oqKCr0TE0mlUlRVVTW5XVNXGIiIiKhp7RoEmrqnX1RUBIVCoffSva6+AHl5eU32HSAiIqKmtWsQCAoKwoULF7R+3SclJUEsFmuNCLjfI488gpKSEs3zDoB7T0e8desWgoKCTFmyWYmIiGjvEughw3OKjI3nVOu1axCIjIyEtbU1Vq9ejYsXLyI+Ph6xsbGIiorS6gy4cOFCbNiwQfO6X79+CAwMxNq1a5GcnIxTp07hn//8J3x9fTmHgBHxfzAyNp5TZGw8p1pPpFar1bpWzpo1q0U73bFjh+C2OTk52LRpk9YUwzNnztSatXD+/Pnw8/PD/PnzNcsqKiqwefNmnDp1Cmq1GsHBwXj++efh4ODQopqJiIjMUbsHASIiImo/eoMAERERPdwMnlCIOo6CggIsWLAAw4YNw6JFiwAA69at05oGWiQSwdbWFr169UJYWBjCw8MhEomQmpqKFStWICQkBG+88UajfS9duhQZGRmYOHFikzNNvv766ygoKEBMTIzOZ0ZQx1Z/fjVkY2MDe3t79OzZEwMGDEBYWFij23U7d+7Erl27AABPPvkkpk2b1uT+688xAFi9erXO55zQw8UY51XD7VxdXTF8+HBERUVBLBabvP6OiEHATI0fPx4ODg5QqVQoLCxEcnIyrly5guvXr+Oll16Ct7c3rK2tkZaW1mjb6upqZGZmQiQSNbn+zp07yM/PR9++fRkCzICbmxtGjBgBAFAqlSguLsbly5dx/vx57N69Gy+99BJGjRrVaDtLS0skJCQ0GQRycnKQkZEBS0tL1NXVmfot0AOopedVaGgoXF1dAQDFxcU4ffo0tm/fjtTUVLz77rtt+h46CgYBMzV+/HitX1jTpk3DX//6V8TFxeHRRx9Ft27d4OXlhbS0NOTn56N79+6atunp6airq8OQIUNw5swZlJeXa00AVf/AKH1DQOnh4e7ujpkzZ2otU6vVOHHiBL7++musXbsWUqkUwcHBWm0CAwNx7tw5XL16FV5eXlrrjh49CktLSwwcOJAPEjNTLT2vRo4ciUGDBmle//nPf8bixYvx22+/4dKlSxgwYECb1N+RmM0Uw6Rfz5494e/vD7VajczMTACAv78/ADR6EqRcLoe1tTWmTJkCtVrd6KpAffv67cn8iEQijBo1Ci+//DLUajW2bt2K+7sjhYSEwNrautGDzVQqFY4fP47AwEB07ty5DaumB52Q8+p+9vb2GDx4MABoPttIG4MANSISiQD88Yu+qSDg5eUFb29v2NnZNVqflpYGkUiE/v37t03B9MAaOXIkXFxckJubi+zsbK11UqkUgwcPRlJSEmprazXLL1y4gOLiYoSFhbVxtdRR6Duv9LG0tDRhVR0XgwABuHdPVi6XQyQSoW/fvgDuTdxkbW2t9UWvVCpx9epV9O/fHxYWFvDx8dFaX1JSgtzcXHh4eLB/AEEkEsHX1xdA07/GwsLCUF5ejjNnzmiWHT16VOtXHNH9mjuvGmp4fvXr18/ktXVE7CNgpn755Rc4ODhArVZrOgsqFApMnDgRLi4uAACxWKzpJ1BQUAAXFxekp6ejtrZW82vf19cXO3bsQGVlJSQSieY2AW8LUD0nJycAQFlZWaN1gYGBcHJyQkJCAoYPH46KigqcOXMG4eHhsLLixxPppuu8OnHiBK5duwbgj86CpaWliIyMhLe3d5vX2RHw/zQz9csvvwD4Y/igh4cHxo4di7Fjx2q18/PzQ1paGuRyOVxcXCCXy2FpaQkfHx/NerVajcuXLyM4OJgdBckgFhYWGDVqFH788UfcvXsXycnJqKmp4W0BarHExMRGyyIiIvDyyy+3QzUdA4OAmRI6LtvPzw+7d++GXC5HWFgY0tLS0KdPH9ja2gIAPD09NbcP6oMA+wdQQ8XFxQCgc/rvsLAw7Nu3D8ePH0dSUhJ69uwJT0/PtiyROiBd59Xbb7+NQYMGoba2Fjdv3kRMTAzi4+PRu3dvTJgwoT1KfeCxjwDp5ePjAysrK6SlpaGmpgbp6elaX/LW1tbw8vKCXC5HWVkZcnJy4OHhAalU2o5V04Oi/moRAE3fk/u5u7vD09MT+/fvx9WrVzFmzJi2LJE6ICHnlZWVFfr06YN33nkHnTt3xpYtW3D79u22LLPDYBAgver7Cdy6dUtz2fb+X/v9+/fH9evXkZKSArVazdsCpJGYmIiCggK4ubnpvQIVFhaG4uJiWFhYYPTo0W1YIXVEQs8r4N7olBkzZqCmpga7d+9uowo7FgYBalb9F/v333+v1Vu3Xv/+/VFXV4e9e/dqtSfzVT/xy1dffQWRSIRnnnlGMyy1KaNHj8bixYuxdOlSODo6tl2h1KEYel7VCw8PR5cuXXDkyBEUFRW1QaUdC/sIULP8/PywZ88e3Lx5E71799aaRRC4d/vAwsICN2/eZP8AM5STk4OdO3cCAGpqalBcXIy0tDQUFhbCzs4OCxYswCOPPKJ3H3Z2dhg6dGhblEsdhDHOq3pWVlaYNm0aNm3ahD179uCVV14xZekdDoMANau+n0DDYYMN2draok+fPrh27VqTQYEebrm5uZqHvTR8OMyECROafDgMkRDGPq/Cw8Px/fff4+jRo3j88cchk8lMUXaHxMcQExERmTH2ESAiIjJjDAJERERmjEGAiIjIjDEIEBERmTEGASIiIjPGIEBERGTGGASIiIjMGIMAEbWL1NRUzJw5EzNnzmzvUojMGmcWJLOnVCqRkJCAs2fPIjs7G6WlpbCysoKzszN8fX0RGhqKAQMG6N3H/PnzUVhY2Gi5ra0tunbtiv79+2PixIlwd3dv1Oa9996DXC4XVKufnx/ee+89QW2bq60pY8aMwfz58w3a//0qKirw448/AgAmT578UD6J8ujRoygoKIC/vz/8/f3buxyiVmEQILN28eJFbNiwQevxpHZ2dqitrUVubi5yc3Nx6NAhPPLII1iwYAE6deqkd3/W1taQSCQA7j0gpaysDDdv3sTNmzdx6NAhvPzyywgPD29yW0tLy2anZ27N9M0Na9OlufVCVFRUaKaGDQsL0xkEbGxs0KNHj1Yfrz0cPXpUE94YBKijYxAgs5WUlIQ1a9agrq4Ozs7OmDlzJoYOHar5ss3NzUVcXBx+/vlnpKSkYOnSpVi5ciU6d+6sc58hISFav6iVSiXOnj2LmJgY3L17F1999RU8PT3Ru3fvRtv6+PgY/GvfEPfX1t68vLzwxRdftHcZRGaPfQTILOXk5GDDhg2oq6tDr1698PHHHyM8PFzrF7ebmxuee+45/OUvf4GVlRXy8/Pxz3/+06DjiMVijBgxAgsXLgQAqFQq/PLLL0Z9L0RErcErAmSWtm/fDoVCAWtra7z11lt6n2QWHByMxx9/HDt37sRvv/2Gc+fOITg42KDjBQQEwMnJCcXFxbh27Vpry29Tt2/fxv79+3Hx4kUUFhairq4OnTp1gqOjI/r374+RI0fCy8sLQOP+DgsWLNDaV8M+DqmpqVixYgUAaB43W+/o0aNYv349unbtinXr1iEtLQ179+7F1atXoVAo4OrqiokTJ2rdZjl37hx+/PFHZGVlQaFQoGfPnnj00UcREhLS5PsqKChAUlISUlNTUVBQgDt37gAAZDIZAgMDERUV1egJdfV11du1a5fmNki9tWvXwsXFRfNapVLh6NGjOH78OG7cuIGqqip06tQJPj4+mDBhgs5bC/V/l9OnT8fjjz+OgwcPIjExEfn5+aisrMTy5cs12+bm5uLAgQOQy+W4ffs21Go1HBwc4OzsDH9/f4wZMwZubm5NHoeIQYDMTnFxMU6fPg0ACA0NFXSfOioqCvv370dVVRV+/vlng4MAADg7O6O4uBhVVVUGb9tesrKysGLFClRUVAAALCwsYGdnh5KSEhQXF+P69euoqKjQBAF7e3t06tQJZWVlAIBOnTrBwuKPC48t6eNw6NAhfPXVVwDu9d9QKBTIysrCxo0bkZ+fjyeffBI7d+7Erl27IBKJYGdnB6VSiWvXruGLL75AeXk5xo8f32i/69ev14QWKysr2NnZoby8XNM35OjRo3jnnXfg6+ur2UYsFqNz584oLy9HXV0dbGxsYGtrq7Xfhu+3srISn3zyCVJTUxv9/Z08eRInT57Eo48+iqefflrn+6+pqcGKFStw5coVWFpawtbWFiKRSLP+4sWLiI6ORk1NDQBo2ty+fRu3b99GRkYGrKysODqDdGIQILOTmpqK+qdvDxs2TNA2tra2CAgIQHJyMtLS0lBXVwdLS0uDjlvfc781Hf7a2tatW1FRUYE+ffrgxRdfhLe3N0QiEWpra1FYWIgzZ86g4ZPMFy9ejIKCAs2VgI8++kjr17GhSktLsWnTJkycOBFPPPEEHBwcUF5ejs2bNyMhIQF79+6FVCrFnj17MHv2bEycOBESiQTFxcXYsGEDzp8/j61bt2LkyJGNOkJ6eHhgxIgRCAgIQLdu3WBhYYG6ujpcv34dO3fuxPnz5/H5559jzZo1EIvFAO71swgJCdH8Wn/00Uf1fsFu2LABqampsLKywtNPP43w8HDY2NigpKQE3333HY4cOYL9+/ejW7duTYYVAPj5558BAK+99hpCQkIgFotRVlamCQNff/01ampqEBgYiKeffhq9evUCcK9/yq1bt5CcnNzoygZRQwwCZHZycnI0f+7Tp4/g7Tw8PJCcnIzq6moUFhaie/fugrc9efIkSktLAQDe3t5Ntrly5Qpefvllvft5/vnndV7qbk5SUhLOnz+vt83ixYvh4+OjVRMAvPjii+jXr59muZWVFVxdXfHoo4+2qBahFAoFwsPD8fzzz2uW2dvbY968eUhLS0NBQQG+/fZbzJ49G48//rimjZOTE9544w28+uqrUCgUOHPmDEaPHq217+eee67R8SwtLeHl5YV33nkHb7/9NrKzs3Hy5MlG2wqRkZGB5ORkAMALL7yAiIgIzTpHR0fMmzcPlZWVSE5Oxo4dOxAWFqYJHA1VV1djyZIlGDx4sGZZ/eiVu3fv4tatWwDuBQUnJydNG7FYjJ49e6Jnz54G107mhZ0FyezUX7YGDPt13nDoYHl5ebPt1Wo1CgsLcfDgQWzYsAHAvS/QCRMmNNm+rq4Od+/e1fuPUqkUXO/9ampqmt1/bW2t1jb1Q/+Ki4tbfNzWmjZtWqNlFhYWmrkdrK2tMWnSpEZtJBKJJrzcuHHDoGNaWFggMDAQAHD58mUDK74nKSkJANClSxedQ0ZnzZoF4N45efHixSbb9OzZUysENGRnZ6e5MtCe/42oY+MVASIjSkhIQEJCQpPrbG1tMX/+fLi6uja5viWTBRmiJZMFBQcH49ChQ1i3bh2uXLmCwYMHw9PTEzY2NiaqUpu9vb3OKy+Ojo4AAHd390b36evVD/XUFdzS0tJw+PBhZGRk4Pbt21AoFI3a1HciNFRmZiaAe/MMNOw30JC7uzucnZ1x584dZGZmNvmF3/AKzf3EYjEGDhyIixcv4sMPP0RkZCSCg4PRp08fWFnx452E4ZlCZuf+X/bOzs6CthNyJaHhpD0ikQg2NjaQyWTo378/xo0bhy5durSi8rb31FNPIT8/H6mpqThw4AAOHDgACwsLeHh4IDg4GBEREYL//lrCzs5O57r6L1d9ber7cdTV1TVa99///hf79u3T2p9UKtV8gVZXV0OhUDQZDoS4e/cuADT799OlSxfcuXNH0/5++ka0AMDcuXMRHR2N7Oxs7N69G7t374aVlRU8PT0xZMiQRsNiie7HIEBmp+E0v5mZmYK/yK5fvw7gj2mDm/KgTdrTWlKpFMuXL8fly5dx5swZXLlyBZmZmZp/9u3bh7lz52LkyJHtXapBLl68qAkB48ePx/jx4+Hu7q71y3379u3Ys2ePVmfI9qDrakI9mUyG6OhoXLx4ESkpKbhy5Qqys7Nx5coVXLlyBd9//z0WLVrU7DTZZL4YBMjs+Pv7QyQSQa1WIzk5Wef914aqq6vx22+/AQD69+9v8IiBjs7X11czjE6pVOLixYvYvn07bty4gQ0bNmDAgAGaS/UdQWJiIgAgMDAQL730UpNtSkpKWnWMzp07Iy8vT2v66qbUr9c3Y2VzLCwsEBQUhKCgIABAVVUVzp49i23btqGoqAhffvklNmzYwNsF1CR2FiSz4+TkhCFDhgC416ErLy+v2W0OHDigGf+va5iXuRCLxRg8eDAWL14M4F4nxIYd6pr7BfsgqP/y1TVqRK1Wa8b+N6XhOH5d+vbtC+DecFWVStVkm9zcXE0fBE9Pz2b3KZSdnR1GjhyJuXPnArh3m8LQDpNkPh78/2OJTGDWrFkQi8WoqanBZ599phna15SUlBTs2bMHwL2rCS2ZTKgjqqur0/kFBkBrqFvDL/+G9+zrJyJ60NT348jOzm5yfVxcnGZYXlPq36O+9xcaGgrgXmfDw4cPN9lmx44dAO71Wxk4cGDzhd/n/lEe92v430hIeCHzxCBAZqlnz56YO3cuLCwscOPGDbz99ts4fPiw1gd7Xl4eNm/ejI8//hi1tbXo1q0b/u///s9sPlBv376N//u//8Pu3btx/fp1rQ532dnZWLNmDYB7TxH08/PTrJNKpZp+F0eOHGmyo157q7+EnpKSgl27dqG6uhrAvS/2PXv2ICYmRu+TJusn7UlJSdE5qsDLy0szYVVMTAx++uknTcfDkpISbNy4ESdPngTwRzA11JUrV7B48WIcOHAAOTk5muCmVqtx5coVfPPNNwDudUhs6kFXRAD7CJAZGzlyJOzt7TWPId64cSM2btwIiUSCmpoazZStwL17yQsXLmy2B3drCJlQCLg3k1xLCJlQSCaT4aOPPtK8vnXrFnbs2IEdO3bAwsICEokE1dXVml+iVlZWmD9/fqNe6ZGRkdixYwd++uknHDp0CA4ODrCwsIC3tzfeeOONFtVvTKNHj0ZCQgLS0tKwc+dOxMbGQiKRoLKyEmq1GsHBwfDw8NBcCbrfmDFjsH//fuTn52PevHlwcHDQfJG///77mtEh8+bNQ1lZGeRyOWJiYrB582bY2tpqjgMAjz76aKtuN924cQNbtmzBli1bYGlpqXkf9QHMzs4Or7/+eoe4ZUPtg0GAzFpQUBDWrFmDo0eP4uzZs8jOzkZZWRmsrKw0w/5CQ0NbdNnWUPUTCplK/YRC+jT8Vers7IwlS5YgNTUV6enpmiFulpaW6N69O/z9/TFp0qQm50V47LHHYGdnh+PHj2vug6vVap2jLdqalZUVli5dih9++AGJiYma6Z+9vLwwZswYRERENHqYUEOurq5Yvnw5fvjhB2RkZGiePQBoD1WUSCRYtmyZ5qFDWVlZqK6uhqOjI/r164eJEyfqfOiQEJ6ennjzzTeRmpqKq1evori4GKWlpbC2tkbPnj0REBCASZMmmXSIJ3V8InV7j40hIiKidsNrRURERGaMQYCIiMiMMQgQERGZMQYBIiIiM8YgQEREZMYYBIiIiMwYgwAREZEZYxAgIiIyYwwCREREZoxBgIiIyIz9P43nAEPOcDS0AAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], - "metadata": { - "tags": [] - } - }, - { - "cell_type": "code", - "execution_count": 14, "source": [ "# estimate the policy value of IPWLearner with Random Forest\n", "estimated_policy_value_b, estimated_interval_b = ope.summarize_off_policy_estimates(\n", @@ -481,44 +529,44 @@ ")\n", "print(estimated_interval_b, '\\n')\n", "\n", - "# visualize policy values of IPWLearner with Random Forest estimated by the three OPE estimators\n", + "# visualize the estimated policy values of IPWLearner with Random Forest\n", "ope.visualize_off_policy_estimates(\n", " action_dist=action_dist_ipw_rf,\n", " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", - " n_bootstrap_samples=1000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=1000, # number of resampling performed in bootstrap sampling\n", " random_state=12345,\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "tags": [] + }, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ " mean 95.0% CI (lower) 95.0% CI (upper)\n", - "ipw 0.707511 0.691352 0.725633\n", - "dm 0.627372 0.626142 0.628639\n", - "dr 0.703989 0.695135 0.712832 \n", + "ipw 0.500488 0.497832 0.503318\n", + "dm 0.527341 0.526954 0.527765\n", + "dr 0.500927 0.498359 0.504220 \n", "\n" ] }, { - "output_type": "display_data", "data": { - "image/png": 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", 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", 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" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], - "metadata": { - "tags": [] - } - }, - { - "cell_type": "code", - "execution_count": 15, "source": [ "# estimate the policy value of Uniform Random\n", "estimated_policy_value_c, estimated_interval_c = ope.summarize_off_policy_estimates(\n", @@ -527,80 +575,68 @@ ")\n", "print(estimated_interval_c, '\\n')\n", "\n", - "# visualize policy values of Uniform Random estimated by the three OPE estimators\n", + "# visualize the estimated policy values of Uniform Random\n", "ope.visualize_off_policy_estimates(\n", " action_dist=action_dist_random,\n", " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", - " n_bootstrap_samples=1000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=1000, # number of resampling performed in bootstrap sampling\n", " random_state=12345,\n", ")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " mean 95.0% CI (lower) 95.0% CI (upper)\n", - "ipw 0.605677 0.602447 0.608713\n", - "dm 0.605383 0.604123 0.606918\n", - "dr 0.605225 0.602239 0.608934 \n", - "\n" - ] - }, - { - "output_type": "display_data", - "data": { - "image/png": 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", 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" - ] - }, - "metadata": {} - } - ], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "OPE procedure estimates that IPWLearners largely outperform the Uniform Random policy.\n", + "OPE procedure estimates that IPWLearners outperform the Uniform Random policy by a large margin.\n", "\n", "Moreover, IPWLearner with Logistic Regression seems to be the best one." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (4) Evaluation of OPE estimators\n", "Our final step is **the evaluation of OPE**, which evaluates and compares the estimation accuracy of OPE estimators." - ], - "metadata": {} + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### (4-1) Approximate the Ground-truth Policy Value \n", "With synthetic data, we can calculate the ground-truth policy value of the evaluation policies as follows.\n", "\n", "$$V(\\pi_e) \\approx \\frac{1}{|\\mathcal{D}_{te}|} \\sum_{i=1}^{|\\mathcal{D}_{te}|} \\mathbb{E}_{a \\sim \\pi_e(a|x_i)} [q(x_i, a)], \\; \\, where \\; \\, q(x,a) := \\mathbb{E}_{r \\sim p(r|x,a)} [r]$$" - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "policy value of IPW Learner with Logistic Regression: 0.7976472840228284\n", + "policy value of IPWLearner with Random Forest: 0.7246527951245796\n", + "policy value of Unifrom Random: 0.4999098979105803\n" + ] + } + ], "source": [ - "# we first calculate the policy values of the three evaluation policies based on the expected rewards of the test data\n", + "# we first calculate the true policy values of the three evaluation policies based on the expected rewards of the test data\n", "policy_names = [\"IPW Learner with Logistic Regression\", \"IPWLearner with Random Forest\", \"Unifrom Random\"]\n", "for name, action_dist in zip(policy_names, [action_dist_ipw_lr, action_dist_ipw_rf, action_dist_random]):\n", " true_policy_value = dataset.calc_ground_truth_policy_value(\n", @@ -608,33 +644,20 @@ " action_dist=action_dist,\n", " )\n", " print(f'policy value of {name}: {true_policy_value}')" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "policy value of IPW Learner with Logistic Regression: 0.7745466707388633\n", - "policy value of IPWLearner with Random Forest: 0.7083979540442642\n", - "policy value of Unifrom Random: 0.6061787431111193\n" - ] - } - ], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "In fact, IPWLearner with Logistic Regression reveals the best performance among the three evaluation policies.\n", "\n", - "Using the above policy values, we evaluate the estimation accuracy of the OPE estimators." - ], - "metadata": {} + "Using the true policy values, we evaluate the estimation accuracy of the OPE estimators." + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "### (4-2) Evaluation of OPE\n", "\n", @@ -642,31 +665,14 @@ "\n", "- $\\textit{relative-ee} (\\hat{V}; \\mathcal{D}_b) := \\left| \\frac{V(\\pi_e) - \\hat{V} (\\pi_e; \\mathcal{D}_b)}{V(\\pi_e)} \\right|$ (relative estimation error; relative-ee)\n", "- $\\textit{SE} (\\hat{V}; \\mathcal{D}_b) := \\left( V(\\pi_e) - \\hat{V} (\\pi_e; \\mathcal{D}_b) \\right)^2$ (squared error; se)" - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 17, - "source": [ - "# evaluate the estimation performances of OPE estimators for IPWLearner with Logistic Regression\n", - "# `summarize_estimators_comparison` returns a pandas dataframe containing estimation performances of given estimators \n", - "relative_ee_for_ipw_lr = ope.summarize_estimators_comparison(\n", - " ground_truth_policy_value=dataset.calc_ground_truth_policy_value(\n", - " expected_reward=bandit_feedback_test[\"expected_reward\"],\n", - " action_dist=action_dist_ipw_lr,\n", - " ),\n", - " action_dist=action_dist_ipw_lr,\n", - " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", - " metric=\"relative-ee\", # \"relative-ee\" (relative estimation error) or \"se\" (squared error)\n", - ")\n", - "\n", - "# estimation performance of the estimators (lower means accurate)\n", - "relative_ee_for_ipw_lr" - ], + "execution_count": 18, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -693,15 +699,15 @@ " \n", " \n", " ipw\n", - " 0.013322\n", + " 0.005126\n", " \n", " \n", " dm\n", - " 0.163305\n", + " 0.250047\n", " \n", " \n", " dr\n", - " 0.005725\n", + " 0.004002\n", " \n", " \n", "\n", @@ -709,38 +715,39 @@ ], "text/plain": [ " relative-ee\n", - "ipw 0.013322\n", - "dm 0.163305\n", - "dr 0.005725" + "ipw 0.005126\n", + "dm 0.250047\n", + "dr 0.004002" ] }, + "execution_count": 18, "metadata": {}, - "execution_count": 17 + "output_type": "execute_result" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 18, "source": [ - "# evaluate the estimation performance of OPE estimators for IPW with Random Forest\n", - "relative_ee_for_ipw_rf = ope.summarize_estimators_comparison(\n", + "# evaluate the estimation performances of OPE estimators for IPWLearner with Logistic Regression\n", + "# `summarize_estimators_comparison` returns a pandas dataframe containing estimation performances of given estimators \n", + "relative_ee_for_ipw_lr = ope.summarize_estimators_comparison(\n", " ground_truth_policy_value=dataset.calc_ground_truth_policy_value(\n", " expected_reward=bandit_feedback_test[\"expected_reward\"],\n", - " action_dist=action_dist_ipw_rf,\n", + " action_dist=action_dist_ipw_lr,\n", " ),\n", - " action_dist=action_dist_ipw_rf,\n", + " action_dist=action_dist_ipw_lr,\n", " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", " metric=\"relative-ee\", # \"relative-ee\" (relative estimation error) or \"se\" (squared error)\n", ")\n", "\n", "# estimation performance of the estimators (lower means accurate)\n", - "relative_ee_for_ipw_rf" - ], + "relative_ee_for_ipw_lr" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -767,15 +774,15 @@ " \n", " \n", " ipw\n", - " 0.000467\n", + " 0.010095\n", " \n", " \n", " dm\n", - " 0.114327\n", + " 0.181911\n", " \n", " \n", " dr\n", - " 0.006050\n", + " 0.005120\n", " \n", " \n", "\n", @@ -783,38 +790,38 @@ ], "text/plain": [ " relative-ee\n", - "ipw 0.000467\n", - "dm 0.114327\n", - "dr 0.006050" + "ipw 0.010095\n", + "dm 0.181911\n", + "dr 0.005120" ] }, + "execution_count": 19, "metadata": {}, - "execution_count": 18 + "output_type": "execute_result" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 19, "source": [ - "# evaluate the estimation performance of OPE estimators for Uniform Random\n", - "relative_ee_for_random = ope.summarize_estimators_comparison(\n", + "# evaluate the estimation performance of OPE estimators for IPW with Random Forest\n", + "relative_ee_for_ipw_rf = ope.summarize_estimators_comparison(\n", " ground_truth_policy_value=dataset.calc_ground_truth_policy_value(\n", " expected_reward=bandit_feedback_test[\"expected_reward\"],\n", - " action_dist=action_dist_random,\n", + " action_dist=action_dist_ipw_rf,\n", " ),\n", - " action_dist=action_dist_random,\n", + " action_dist=action_dist_ipw_rf,\n", " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", " metric=\"relative-ee\", # \"relative-ee\" (relative estimation error) or \"se\" (squared error)\n", ")\n", "\n", "# estimation performance of the estimators (lower means accurate)\n", - "relative_ee_for_random" - ], + "relative_ee_for_ipw_rf" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -841,15 +848,15 @@ " \n", " \n", " ipw\n", - " 0.001566\n", + " 0.001533\n", " \n", " \n", " dm\n", - " 0.001440\n", + " 0.054871\n", " \n", " \n", " dr\n", - " 0.001492\n", + " 0.002189\n", " \n", " \n", "\n", @@ -857,32 +864,71 @@ ], "text/plain": [ " relative-ee\n", - "ipw 0.001566\n", - "dm 0.001440\n", - "dr 0.001492" + "ipw 0.001533\n", + "dm 0.054871\n", + "dr 0.002189" ] }, + "execution_count": 20, "metadata": {}, - "execution_count": 19 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# evaluate the estimation performance of OPE estimators for Uniform Random\n", + "relative_ee_for_random = ope.summarize_estimators_comparison(\n", + " ground_truth_policy_value=dataset.calc_ground_truth_policy_value(\n", + " expected_reward=bandit_feedback_test[\"expected_reward\"],\n", + " action_dist=action_dist_random,\n", + " ),\n", + " action_dist=action_dist_random,\n", + " estimated_rewards_by_reg_model=estimated_rewards_by_reg_model,\n", + " metric=\"relative-ee\", # \"relative-ee\" (relative estimation error) or \"se\" (squared error)\n", + ")\n", + "\n", + "# estimation performance of the estimators (lower means accurate)\n", + "relative_ee_for_random" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ - "We can iterate the above process several times to get more relibale results.\n", + "We can iterate the above process several times to get more reliable results.\n", "\n", "Please see [../examples/synthetic](../synthetic) for a more sophisticated example of the evaluation of OPE with synthetic bandit data." - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] } - ] -} \ No newline at end of file + ], + "metadata": { + "interpreter": { + "hash": "2ff39f3b22306140fd87fd114528320b56c4f8c8e196b421a3ea939a2b6b4692" + }, + "kernelspec": { + "display_name": "Python 3.9.5 64-bit ('3.9.5': pyenv)", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.5" + }, + "orig_nbformat": 2 + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/quickstart/synthetic_slate.ipynb b/examples/quickstart/synthetic_slate.ipynb index 7b28d5e4..f1398b2c 100644 --- a/examples/quickstart/synthetic_slate.ipynb +++ b/examples/quickstart/synthetic_slate.ipynb @@ -2,36 +2,36 @@ "cells": [ { "cell_type": "markdown", + "metadata": {}, "source": [ "# Quickstart Example with Synthetic Slate Bandit Data\n", "---\n", - "This notebook provides an example of conducting OPE of several different evaluation policies with synthetic slate bandit feedback data.\n", + "This notebook provides an example of conducting OPE of several different evaluation policies with synthetic slate bandit data.\n", "\n", - "Our example with synthetic bandit data contains the follwoing four major steps:\n", + "Our example with synthetic bandit data contains the following four major steps:\n", "- (1) Synthetic Slate Data Generation\n", - "- (2) Defining Evaluation Policy\n", + "- (2) Defining Evaluation Policies\n", "- (3) Off-Policy Evaluation\n", "- (4) Evaluation of OPE Estimators\n", "\n", - "The second step could be replaced by some Off-Policy Learning (OPL) step, but obp still does not implement any OPL module for slate bandit data. Implementing OPL for slate bandit data is our future work.\n", - "\n", - "" - ], - "metadata": {} + "The second step could be replaced by some Off-Policy Learning (OPL) method, but obp still does not implement any OPL module for slate bandit data. Implementing OPL for slate bandits is our future work." + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": {}, + "outputs": [], "source": [ "# needed when using Google Colab\n", "# !pip install obp" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, + "metadata": {}, + "outputs": [], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -43,67 +43,66 @@ " logistic_reward_function,\n", " SyntheticSlateBanditDataset,\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, + "metadata": {}, + "outputs": [], "source": [ "from itertools import product\n", "from copy import deepcopy" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, + "metadata": {}, + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import seaborn as sns" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 4, - "source": [ - "# obp version\n", - "print(obp.__version__)" - ], + "execution_count": 5, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - "0.4.0\n" + "0.5.2\n" ] } ], - "metadata": {} + "source": [ + "# obp version\n", + "print(obp.__version__)" + ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, + "metadata": {}, + "outputs": [], "source": [ "import warnings\n", "warnings.filterwarnings('ignore')" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (1) Synthetic Slate Data Generation\n", "We prepare easy-to-use synthetic slate data generator: `SyntheticSlateBanditDataset` class in the dataset module.\n", @@ -119,12 +118,15 @@ "- behavior policy (`behavior_policy_function`)\n", "\n", "We use a uniform random policy as a behavior policy here." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# generate a synthetic bandit dataset with 10 actions\n", "# we use `logistic_reward_function` as the reward function and `linear_behavior_policy_logit` as the behavior policy.\n", @@ -139,17 +141,37 @@ "random_state=12345\n", "base_reward_function=logistic_reward_function\n", "\n", - "# obtain test sets of synthetic logged bandit feedback\n", + "# obtain test sets of synthetic logged bandit data\n", "n_rounds_test = 10000" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[sample_action_and_obtain_pscore]: 100%|██████████| 10000/10000 [00:03<00:00, 2821.57it/s]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.6461\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "# define Uniform Random Policy as a baseline behavior policy\n", "dataset_with_random_behavior = SyntheticSlateBanditDataset(\n", @@ -164,7 +186,7 @@ " base_reward_function=base_reward_function,\n", ")\n", "\n", - "# compute the factual action choice probabililties for the test set of the synthetic logged bandit feedback\n", + "# compute the factual action choice probabililties for the test set of the synthetic logged bandit data\n", "bandit_feedback_with_random_behavior = dataset_with_random_behavior.obtain_batch_bandit_feedback(\n", " n_rounds=n_rounds_test,\n", " return_pscore_item_position=True,\n", @@ -176,164 +198,144 @@ " slate_id=bandit_feedback_with_random_behavior[\"slate_id\"],\n", ")\n", "print(random_policy_value)" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[sample_action_and_obtain_pscore]: 100%|██████████| 10000/10000 [00:01<00:00, 6149.51it/s]" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "1.8366\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "\n" - ] - } - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (2) Evaluation Policy Definition (Off-Policy Learning)\n", - " After generating synthetic data, we now define the evaluation policy as follows:\n", - " \n", - "1. Generate logit values of three valuation policies (`random`, `optimal`, and `anti-optimal`).\n", - " - A `optimal` policy is defined by a policy that samples actions using`3 * base_expected_reward`.\n", - " - An `anti-optimal` policy is defined by a policy that samples actions using the sign inversion of `-3 * base_expected_reward`.\n", - "2. Obtain pscores of the evaluation policies by `obtain_pscore_given_evaluation_policy_logit` method." - ], - "metadata": {} + "After generating synthetic slate bandit data, we now define some evaluation policies in the following." + ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, + "metadata": {}, + "outputs": [], "source": [ "random_policy_logit_ = np.zeros((n_rounds_test, n_unique_action))" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, + "metadata": {}, + "outputs": [], "source": [ "base_expected_reward = dataset_with_random_behavior.base_reward_function(\n", " context=bandit_feedback_with_random_behavior[\"context\"],\n", " action_context=dataset_with_random_behavior.action_context,\n", " random_state=dataset_with_random_behavior.random_state,\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, + "metadata": {}, + "outputs": [], "source": [ "optimal_policy_logit_ = base_expected_reward * 3\n", "anti_optimal_policy_logit_ = -3 * base_expected_reward" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[obtain_pscore_given_evaluation_policy_logit]: 100%|██████████| 10000/10000 [00:12<00:00, 782.75it/s]\n" + ] + } + ], "source": [ "random_policy_pscores = dataset_with_random_behavior.obtain_pscore_given_evaluation_policy_logit(\n", " action=bandit_feedback_with_random_behavior[\"action\"],\n", " evaluation_policy_logit_=random_policy_logit_\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ - "[obtain_pscore_given_evaluation_policy_logit]: 100%|██████████| 10000/10000 [00:09<00:00, 1037.09it/s]\n" + "[obtain_pscore_given_evaluation_policy_logit]: 100%|██████████| 10000/10000 [00:14<00:00, 707.16it/s]\n" ] } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 12, "source": [ "optimal_policy_pscores = dataset_with_random_behavior.obtain_pscore_given_evaluation_policy_logit(\n", " action=bandit_feedback_with_random_behavior[\"action\"],\n", " evaluation_policy_logit_=optimal_policy_logit_\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ - "[obtain_pscore_given_evaluation_policy_logit]: 100%|██████████| 10000/10000 [00:10<00:00, 995.15it/s]\n" + "[obtain_pscore_given_evaluation_policy_logit]: 100%|██████████| 10000/10000 [00:14<00:00, 706.80it/s]\n" ] } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 13, "source": [ "anti_optimal_policy_pscores = dataset_with_random_behavior.obtain_pscore_given_evaluation_policy_logit(\n", " action=bandit_feedback_with_random_behavior[\"action\"],\n", " evaluation_policy_logit_=anti_optimal_policy_logit_\n", ")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "[obtain_pscore_given_evaluation_policy_logit]: 100%|██████████| 10000/10000 [00:10<00:00, 996.97it/s]\n" - ] - } - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (3) Off-Policy Evaluation (OPE)\n", - "Our next step is OPE which attempts to estimate the performance of evaluation policies using the logged bandit feedback and OPE estimators.\n", + "Our next step is OPE, which aims to estimate the performance of evaluation policies using logged bandit data and OPE estimators.\n", "\n", - "Here, we use the **SlateStandardIPS (SIPS)**, **SlateIndependentIPS (IIPS)**, and **SlateRewardInteractionIPS (RIPS)** estimators and visualize the OPE results." - ], - "metadata": {} + "Here, we use \n", + "- `SlateStandardIPS` (SIPS)\n", + "- `SlateIndependentIPS` (IIPS)\n", + "- `SlateRewardInteractionIPS` (RIPS)\n", + "\n", + "and visualize the OPE results." + ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, + "metadata": { + "tags": [] + }, + "outputs": [], "source": [ "# estimate the policy value of the evaluation policies based on their action choice probabilities\n", "# it is possible to set multiple OPE estimators to the `ope_estimators` argument\n", @@ -346,15 +348,35 @@ " bandit_feedback=bandit_feedback_with_random_behavior,\n", " ope_estimators=[sips, iips, rips]\n", ")" - ], - "outputs": [], - "metadata": { - "tags": [] - } + ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " mean 95.0% CI (lower) 95.0% CI (upper) policy_name\n", + "sips 1.646317 1.631293 1.662105 random\n", + "iips 1.646317 1.631293 1.662105 random\n", + "rips 1.646317 1.631293 1.662105 random \n", + "\n" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "_, estimated_interval_random = ope.summarize_off_policy_estimates(\n", " evaluation_policy_pscore=random_policy_pscores[0],\n", @@ -374,38 +396,38 @@ " evaluation_policy_pscore_item_position=random_policy_pscores[1],\n", " evaluation_policy_pscore_cascade=random_policy_pscores[2],\n", " alpha=0.05,\n", - " n_bootstrap_samples=1000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=1000, # number of resampling performed in bootstrap sampling\n", " random_state=dataset_with_random_behavior.random_state,\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ " mean 95.0% CI (lower) 95.0% CI (upper) policy_name\n", - "sips 1.836816 1.8205 1.852505 random\n", - "iips 1.836816 1.8205 1.852505 random\n", - "rips 1.836816 1.8205 1.852505 random \n", + "sips 1.629474 1.585960 1.675414 optimal\n", + "iips 1.674750 1.655507 1.692978 optimal\n", + "rips 1.626834 1.594925 1.658154 optimal \n", "\n" ] }, { - "output_type": "display_data", "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 16, "source": [ "_, estimated_interval_optimal = ope.summarize_off_policy_estimates(\n", " evaluation_policy_pscore=optimal_policy_pscores[0],\n", @@ -426,38 +448,38 @@ " evaluation_policy_pscore_item_position=optimal_policy_pscores[1],\n", " evaluation_policy_pscore_cascade=optimal_policy_pscores[2],\n", " alpha=0.05,\n", - " n_bootstrap_samples=1000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=1000, # number of resampling performed in bootstrap sampling\n", " random_state=dataset_with_random_behavior.random_state,\n", ")" - ], + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stdout", + "output_type": "stream", "text": [ - " mean 95.0% CI (lower) 95.0% CI (upper) policy_name\n", - "sips 1.830555 1.803695 1.860548 optimal\n", - "iips 1.843117 1.825576 1.859695 optimal\n", - "rips 1.838866 1.815574 1.862451 optimal \n", + " mean 95.0% CI (lower) 95.0% CI (upper) policy_name\n", + "sips 1.691671 1.647720 1.737568 anti-optimal\n", + "iips 1.602862 1.584707 1.623184 anti-optimal\n", + "rips 1.687415 1.654940 1.722200 anti-optimal \n", "\n" ] }, { - "output_type": "display_data", "data": { - "image/png": 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", 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", "text/plain": [ "
" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 17, "source": [ "_, estimated_interval_anti_optimal = ope.summarize_off_policy_estimates(\n", " evaluation_policy_pscore=anti_optimal_policy_pscores[0],\n", @@ -477,152 +499,130 @@ " evaluation_policy_pscore_item_position=anti_optimal_policy_pscores[1],\n", " evaluation_policy_pscore_cascade=anti_optimal_policy_pscores[2],\n", " alpha=0.05,\n", - " n_bootstrap_samples=1000, # number of resampling performed in the bootstrap procedure\n", + " n_bootstrap_samples=1000, # number of resampling performed in bootstrap sampling\n", " random_state=dataset_with_random_behavior.random_state,\n", ")" - ], - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - " mean 95.0% CI (lower) 95.0% CI (upper) policy_name\n", - "sips 1.854516 1.829643 1.877320 anti-optimal\n", - "iips 1.832793 1.815842 1.848599 anti-optimal\n", - "rips 1.844397 1.824965 1.864795 anti-optimal \n", - "\n" - ] - }, - { - "output_type": "display_data", - "data": { - "image/png": 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", - "text/plain": [ - "
" - ] - }, - "metadata": {} - } - ], - "metadata": {} + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "## (4) Evaluation of OPE estimators\n", "Our final step is **the evaluation of OPE**, which evaluates and compares the estimation accuracy of OPE estimators.\n", "\n", - "With synthetic slate data, we can calculate the policy value of the evaluation policies. \n", + "With synthetic slate bandit data, we can calculate the policy value of the evaluation policies. \n", "Therefore, we can compare the policy values estimated by OPE estimators with the ground-turths to evaluate the accuracy of OPE." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 18, - "source": [ - "ground_truth_policy_value_random = dataset_with_random_behavior.calc_ground_truth_policy_value(\n", - " context=bandit_feedback_with_random_behavior[\"context\"],\n", - " evaluation_policy_logit_=random_policy_logit_\n", - ")\n", - "ground_truth_policy_value_random" - ], + "execution_count": 19, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ - "[calc_ground_truth_policy_value (pscore)]: 100%|██████████| 10000/10000 [00:04<00:00, 2268.03it/s]\n", - "[calc_ground_truth_policy_value (expected reward), batch_size=3334]: 100%|██████████| 3/3 [00:01<00:00, 1.64it/s]\n" + "[calc_ground_truth_policy_value (pscore)]: 100%|██████████| 10000/10000 [00:05<00:00, 1703.90it/s]\n", + "[calc_ground_truth_policy_value (expected reward), batch_size=3334]: 100%|██████████| 3/3 [00:02<00:00, 1.13it/s]\n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ - "1.837144428308276" + "1.644826072014703" ] }, + "execution_count": 19, "metadata": {}, - "execution_count": 18 + "output_type": "execute_result" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 19, "source": [ - "ground_truth_policy_value_optimal = dataset_with_random_behavior.calc_ground_truth_policy_value(\n", + "ground_truth_policy_value_random = dataset_with_random_behavior.calc_ground_truth_policy_value(\n", " context=bandit_feedback_with_random_behavior[\"context\"],\n", - " evaluation_policy_logit_=optimal_policy_logit_\n", + " evaluation_policy_logit_=random_policy_logit_\n", ")\n", - "ground_truth_policy_value_optimal" - ], + "ground_truth_policy_value_random" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ - "[calc_ground_truth_policy_value (pscore)]: 100%|██████████| 10000/10000 [00:04<00:00, 2177.17it/s]\n", - "[calc_ground_truth_policy_value (expected reward), batch_size=3334]: 100%|██████████| 3/3 [00:01<00:00, 1.71it/s]\n" + "[calc_ground_truth_policy_value (pscore)]: 100%|██████████| 10000/10000 [00:06<00:00, 1639.37it/s]\n", + "[calc_ground_truth_policy_value (expected reward), batch_size=3334]: 100%|██████████| 3/3 [00:02<00:00, 1.16it/s]\n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ - "1.8474242800908984" + "1.6125410474183628" ] }, + "execution_count": 20, "metadata": {}, - "execution_count": 19 + "output_type": "execute_result" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 20, "source": [ - "ground_truth_policy_value_anti_optimal = dataset_with_random_behavior.calc_ground_truth_policy_value(\n", + "ground_truth_policy_value_optimal = dataset_with_random_behavior.calc_ground_truth_policy_value(\n", " context=bandit_feedback_with_random_behavior[\"context\"],\n", - " evaluation_policy_logit_=anti_optimal_policy_logit_\n", + " evaluation_policy_logit_=optimal_policy_logit_\n", ")\n", - "ground_truth_policy_value_anti_optimal" - ], + "ground_truth_policy_value_optimal" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, "outputs": [ { - "output_type": "stream", "name": "stderr", + "output_type": "stream", "text": [ - "[calc_ground_truth_policy_value (pscore)]: 100%|██████████| 10000/10000 [00:04<00:00, 2176.73it/s]\n", - "[calc_ground_truth_policy_value (expected reward), batch_size=3334]: 100%|██████████| 3/3 [00:01<00:00, 1.71it/s]\n" + "[calc_ground_truth_policy_value (pscore)]: 100%|██████████| 10000/10000 [00:06<00:00, 1637.62it/s]\n", + "[calc_ground_truth_policy_value (expected reward), batch_size=3334]: 100%|██████████| 3/3 [00:02<00:00, 1.16it/s]\n" ] }, { - "output_type": "execute_result", "data": { "text/plain": [ - "1.8352871486686428" + "1.6893330966600806" ] }, + "execution_count": 21, "metadata": {}, - "execution_count": 20 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "ground_truth_policy_value_anti_optimal = dataset_with_random_behavior.calc_ground_truth_policy_value(\n", + " context=bandit_feedback_with_random_behavior[\"context\"],\n", + " evaluation_policy_logit_=anti_optimal_policy_logit_\n", + ")\n", + "ground_truth_policy_value_anti_optimal" + ] }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, + "metadata": {}, + "outputs": [], "source": [ "estimated_interval_random[\"ground_truth\"] = ground_truth_policy_value_random\n", "estimated_interval_optimal[\"ground_truth\"] = ground_truth_policy_value_optimal\n", @@ -635,19 +635,14 @@ " estimated_interval_anti_optimal\n", " ]\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 22, - "source": [ - "estimated_intervals" - ], + "execution_count": 23, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -678,75 +673,75 @@ " \n", " \n", " sips\n", - " 1.836816\n", - " 1.820500\n", - " 1.852505\n", + " 1.646317\n", + " 1.631293\n", + " 1.662105\n", " random\n", - " 1.837144\n", + " 1.644826\n", " \n", " \n", " iips\n", - " 1.836816\n", - " 1.820500\n", - " 1.852505\n", + " 1.646317\n", + " 1.631293\n", + " 1.662105\n", " random\n", - " 1.837144\n", + " 1.644826\n", " \n", " \n", " rips\n", - " 1.836816\n", - " 1.820500\n", - " 1.852505\n", + " 1.646317\n", + " 1.631293\n", + " 1.662105\n", " random\n", - " 1.837144\n", + " 1.644826\n", " \n", " \n", " sips\n", - " 1.830555\n", - " 1.803695\n", - " 1.860548\n", + " 1.629474\n", + " 1.585960\n", + " 1.675414\n", " optimal\n", - " 1.847424\n", + " 1.612541\n", " \n", " \n", " iips\n", - " 1.843117\n", - " 1.825576\n", - " 1.859695\n", + " 1.674750\n", + " 1.655507\n", + " 1.692978\n", " optimal\n", - " 1.847424\n", + " 1.612541\n", " \n", " \n", " rips\n", - " 1.838866\n", - " 1.815574\n", - " 1.862451\n", + " 1.626834\n", + " 1.594925\n", + " 1.658154\n", " optimal\n", - " 1.847424\n", + " 1.612541\n", " \n", " \n", " sips\n", - " 1.854516\n", - " 1.829643\n", - " 1.877320\n", + " 1.691671\n", + " 1.647720\n", + " 1.737568\n", " anti-optimal\n", - " 1.835287\n", + " 1.689333\n", " \n", " \n", " iips\n", - " 1.832793\n", - " 1.815842\n", - " 1.848599\n", + " 1.602862\n", + " 1.584707\n", + " 1.623184\n", " anti-optimal\n", - " 1.835287\n", + " 1.689333\n", " \n", " \n", " rips\n", - " 1.844397\n", - " 1.824965\n", - " 1.864795\n", + " 1.687415\n", + " 1.654940\n", + " 1.722200\n", " anti-optimal\n", - " 1.835287\n", + " 1.689333\n", " \n", " \n", "\n", @@ -754,51 +749,41 @@ ], "text/plain": [ " mean 95.0% CI (lower) 95.0% CI (upper) policy_name ground_truth\n", - "sips 1.836816 1.820500 1.852505 random 1.837144\n", - "iips 1.836816 1.820500 1.852505 random 1.837144\n", - "rips 1.836816 1.820500 1.852505 random 1.837144\n", - "sips 1.830555 1.803695 1.860548 optimal 1.847424\n", - "iips 1.843117 1.825576 1.859695 optimal 1.847424\n", - "rips 1.838866 1.815574 1.862451 optimal 1.847424\n", - "sips 1.854516 1.829643 1.877320 anti-optimal 1.835287\n", - "iips 1.832793 1.815842 1.848599 anti-optimal 1.835287\n", - "rips 1.844397 1.824965 1.864795 anti-optimal 1.835287" + "sips 1.646317 1.631293 1.662105 random 1.644826\n", + "iips 1.646317 1.631293 1.662105 random 1.644826\n", + "rips 1.646317 1.631293 1.662105 random 1.644826\n", + "sips 1.629474 1.585960 1.675414 optimal 1.612541\n", + "iips 1.674750 1.655507 1.692978 optimal 1.612541\n", + "rips 1.626834 1.594925 1.658154 optimal 1.612541\n", + "sips 1.691671 1.647720 1.737568 anti-optimal 1.689333\n", + "iips 1.602862 1.584707 1.623184 anti-optimal 1.689333\n", + "rips 1.687415 1.654940 1.722200 anti-optimal 1.689333" ] }, + "execution_count": 23, "metadata": {}, - "execution_count": 22 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "estimated_intervals" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "We can confirm that the three OPE estimators return the same results when the behavior policy and the evaluation policy is the same, and the estimates are quite similar to the `random_policy_value` calcurated above.\n", "\n", "We can also observe that the performance of OPE estimators are as follows in this simulation: `IIPS > RIPS > SIPS`." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 23, - "source": [ - "# evaluate the estimation performances of OPE estimators \n", - "# by comparing the estimated policy values and its ground-truth.\n", - "# `summarize_estimators_comparison` returns a pandas dataframe containing estimation performances of given estimators \n", - "\n", - "relative_ee_for_random_evaluation_policy = ope.summarize_estimators_comparison(\n", - " ground_truth_policy_value=ground_truth_policy_value_random,\n", - " evaluation_policy_pscore=random_policy_pscores[0],\n", - " evaluation_policy_pscore_item_position=random_policy_pscores[1],\n", - " evaluation_policy_pscore_cascade=random_policy_pscores[2],\n", - ")\n", - "relative_ee_for_random_evaluation_policy" - ], + "execution_count": 24, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -819,58 +804,58 @@ " \n", " \n", " \n", - " relative-ee\n", + " se\n", " \n", " \n", " \n", " \n", " sips\n", - " 0.000296\n", + " 0.000002\n", " \n", " \n", " iips\n", - " 0.000296\n", + " 0.000002\n", " \n", " \n", " rips\n", - " 0.000296\n", + " 0.000002\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " relative-ee\n", - "sips 0.000296\n", - "iips 0.000296\n", - "rips 0.000296" + " se\n", + "sips 0.000002\n", + "iips 0.000002\n", + "rips 0.000002" ] }, + "execution_count": 24, "metadata": {}, - "execution_count": 23 + "output_type": "execute_result" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 24, "source": [ "# evaluate the estimation performances of OPE estimators \n", "# by comparing the estimated policy values and its ground-truth.\n", "# `summarize_estimators_comparison` returns a pandas dataframe containing estimation performances of given estimators \n", "\n", - "relative_ee_for_optimal_evaluation_policy = ope.summarize_estimators_comparison(\n", - " ground_truth_policy_value=ground_truth_policy_value_optimal,\n", - " evaluation_policy_pscore=optimal_policy_pscores[0],\n", - " evaluation_policy_pscore_item_position=optimal_policy_pscores[1],\n", - " evaluation_policy_pscore_cascade=optimal_policy_pscores[2],\n", + "relative_ee_for_random_evaluation_policy = ope.summarize_estimators_comparison(\n", + " ground_truth_policy_value=ground_truth_policy_value_random,\n", + " evaluation_policy_pscore=random_policy_pscores[0],\n", + " evaluation_policy_pscore_item_position=random_policy_pscores[1],\n", + " evaluation_policy_pscore_cascade=random_policy_pscores[2],\n", ")\n", - "relative_ee_for_optimal_evaluation_policy" - ], + "relative_ee_for_random_evaluation_policy" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -891,58 +876,58 @@ " \n", " \n", " \n", - " relative-ee\n", + " se\n", " \n", " \n", " \n", " \n", " sips\n", - " 0.009303\n", + " 0.000283\n", " \n", " \n", " iips\n", - " 0.002470\n", + " 0.003849\n", " \n", " \n", " rips\n", - " 0.004732\n", + " 0.000201\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " relative-ee\n", - "sips 0.009303\n", - "iips 0.002470\n", - "rips 0.004732" + " se\n", + "sips 0.000283\n", + "iips 0.003849\n", + "rips 0.000201" ] }, + "execution_count": 25, "metadata": {}, - "execution_count": 24 + "output_type": "execute_result" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": 25, "source": [ "# evaluate the estimation performances of OPE estimators \n", "# by comparing the estimated policy values and its ground-truth.\n", "# `summarize_estimators_comparison` returns a pandas dataframe containing estimation performances of given estimators \n", "\n", - "relative_ee_for_anti_optimal_evaluation_policy = ope.summarize_estimators_comparison(\n", - " ground_truth_policy_value=ground_truth_policy_value_anti_optimal,\n", - " evaluation_policy_pscore=anti_optimal_policy_pscores[0],\n", - " evaluation_policy_pscore_item_position=anti_optimal_policy_pscores[1],\n", - " evaluation_policy_pscore_cascade=anti_optimal_policy_pscores[2],\n", + "relative_ee_for_optimal_evaluation_policy = ope.summarize_estimators_comparison(\n", + " ground_truth_policy_value=ground_truth_policy_value_optimal,\n", + " evaluation_policy_pscore=optimal_policy_pscores[0],\n", + " evaluation_policy_pscore_item_position=optimal_policy_pscores[1],\n", + " evaluation_policy_pscore_cascade=optimal_policy_pscores[2],\n", ")\n", - "relative_ee_for_anti_optimal_evaluation_policy" - ], + "relative_ee_for_optimal_evaluation_policy" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/html": [ "
\n", @@ -963,123 +948,119 @@ " \n", " \n", " \n", - " relative-ee\n", + " se\n", " \n", " \n", " \n", " \n", " sips\n", - " 0.010281\n", + " 0.000006\n", " \n", " \n", " iips\n", - " 0.001506\n", + " 0.007534\n", " \n", " \n", " rips\n", - " 0.004751\n", + " 0.000005\n", " \n", " \n", "\n", "
" ], "text/plain": [ - " relative-ee\n", - "sips 0.010281\n", - "iips 0.001506\n", - "rips 0.004751" + " se\n", + "sips 0.000006\n", + "iips 0.007534\n", + "rips 0.000005" ] }, + "execution_count": 26, "metadata": {}, - "execution_count": 25 + "output_type": "execute_result" } ], - "metadata": {} + "source": [ + "# evaluate the estimation performances of OPE estimators \n", + "# by comparing the estimated policy values and its ground-truth.\n", + "# `summarize_estimators_comparison` returns a pandas dataframe containing estimation performances of given estimators \n", + "\n", + "relative_ee_for_anti_optimal_evaluation_policy = ope.summarize_estimators_comparison(\n", + " ground_truth_policy_value=ground_truth_policy_value_anti_optimal,\n", + " evaluation_policy_pscore=anti_optimal_policy_pscores[0],\n", + " evaluation_policy_pscore_item_position=anti_optimal_policy_pscores[1],\n", + " evaluation_policy_pscore_cascade=anti_optimal_policy_pscores[2],\n", + ")\n", + "relative_ee_for_anti_optimal_evaluation_policy" + ] }, { "cell_type": "markdown", + "metadata": {}, "source": [ "The variance of OPE estimators is as follows: `SIPS > RIPS > IIPS`." - ], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, + "metadata": {}, + "outputs": [], "source": [ "estimated_intervals[\"errbar_length\"] = (\n", " estimated_intervals.drop([\"mean\", \"policy_name\", \"ground_truth\"], axis=1).diff(axis=1).iloc[:, -1].abs()\n", ")" - ], - "outputs": [], - "metadata": {} + ] }, { "cell_type": "code", - "execution_count": 27, - "source": [ - "alpha = 0.05\n", - "plt.style.use(\"ggplot\")\n", - "\n", - "def errplot(x, y, yerr, **kwargs):\n", - " ax = plt.gca()\n", - " data = kwargs.pop(\"data\")\n", - " data.plot(x=x, y=y, yerr=yerr, kind=\"bar\", ax=ax, **kwargs)\n", - " ax.hlines(data[\"ground_truth\"].iloc[0], -1, len(x)+1)\n", - "# ax.set_xlabel(\"OPE estimator\")\n", - " \n", - "g = sns.FacetGrid(\n", - " estimated_intervals.reset_index().rename(columns={\"index\": \"OPE estimator\", \"mean\": \"Policy value\"}),\n", - " col=\"policy_name\"\n", - ")\n", - "g.map_dataframe(errplot, \"OPE estimator\", \"Policy value\", \"errbar_length\")\n", - "plt.ylim((1.7, 1.9))" - ], + "execution_count": 28, + "metadata": {}, "outputs": [ { - "output_type": "execute_result", "data": { "text/plain": [ - "(1.7, 1.9)" + "" ] }, + "execution_count": 28, "metadata": {}, - "execution_count": 27 + "output_type": "execute_result" }, { - "output_type": "display_data", "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ "
" ] }, - "metadata": {} + "metadata": {}, + "output_type": "display_data" } ], - "metadata": {} - }, - { - "cell_type": "code", - "execution_count": null, - "source": [], - "outputs": [], - "metadata": {} - }, - { - "cell_type": "markdown", "source": [ - "It is surprising that `RIPS` estimator does not achieve the best performance even if the reward structure is not independent. If we run a simulation where the reward of each position depends heavily on those of other positions, `RIPS`estimator could achieve the best performance.\n", - "" - ], - "metadata": {} + "alpha = 0.05\n", + "plt.style.use(\"ggplot\")\n", + "\n", + "def errplot(x, y, yerr, **kwargs):\n", + " ax = plt.gca()\n", + " data = kwargs.pop(\"data\")\n", + " data.plot(x=x, y=y, yerr=yerr, kind=\"bar\", ax=ax, **kwargs)\n", + " ax.hlines(data[\"ground_truth\"].iloc[0], -1, len(x)+1)\n", + " \n", + "g = sns.FacetGrid(\n", + " estimated_intervals.reset_index().rename(columns={\"index\": \"OPE estimator\", \"mean\": \"Policy value\"}),\n", + " col=\"policy_name\"\n", + ")\n", + "g.map_dataframe(errplot, \"OPE estimator\", \"Policy value\", \"errbar_length\")" + ] }, { "cell_type": "code", "execution_count": null, - "source": [], + "metadata": {}, "outputs": [], - "metadata": {} + "source": [] } ], "metadata": { @@ -1098,9 +1079,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.1" + "version": "3.9.5" } }, "nbformat": 4, "nbformat_minor": 2 -} \ No newline at end of file +} diff --git a/examples/synthetic/README.md b/examples/synthetic/README.md index b6a77705..9f25429b 100644 --- a/examples/synthetic/README.md +++ b/examples/synthetic/README.md @@ -1,14 +1,13 @@ -# Example with Synthetic Bandit Data - +# Example Experiment with Synthetic Bandit Data ## Description -Here, we use synthetic bandit datasets to evaluate OPE estimators. -Specifically, we evaluate the estimation performances of well-known off-policy estimators using the ground-truth policy value of an evaluation policy calculable with synthetic data. +We use synthetic bandit datasets to evaluate OPE estimators. Specifically, we evaluate the estimation performance of well-known estimators using the ground-truth policy value of an evaluation policy calculable with synthetic data. ## Evaluating Off-Policy Estimators -In the following, we evaluate the estimation performances of +In the following, we evaluate the estimation performance of + - Direct Method (DM) - Inverse Probability Weighting (IPW) - Self-Normalized Inverse Probability Weighting (SNIPW) @@ -17,12 +16,12 @@ In the following, we evaluate the estimation performances of - Switch Doubly Robust (Switch-DR) - Doubly Robust with Optimistic Shrinkage (DRos) -For Switch-IPW, Switch-DR, and DRos, we try some different values of hyperparameters. +For Switch-DR and DRos, we tune the built-in hyperparameters using SLOPE, a data-driven hyperparameter tuning method for OPE estimators. See [our documentation](https://zr-obp.readthedocs.io/en/latest/estimators.html) for the details about these estimators. ### Files -- [`./evaluate_off_policy_estimators.py`](./evaluate_off_policy_estimators.py) implements the evaluation of OPE estimators using synthetic bandit feedback data. -- [`./conf/hyperparams.yaml`](./conf/hyperparams.yaml) defines hyperparameters of some machine learning methods used to define regression model and IPWLearner. +- [`./evaluate_off_policy_estimators.py`](./evaluate_off_policy_estimators.py) implements the evaluation of OPE estimators using synthetic bandit data. +- [`./conf/hyperparams.yaml`](./conf/hyperparams.yaml) defines hyperparameters of some ML methods used to define regression model and IPWLearner. ### Scripts @@ -33,48 +32,58 @@ python evaluate_off_policy_estimators.py\ --n_rounds $n_rounds\ --n_actions $n_actions\ --dim_context $dim_context\ + --beta $beta\ --base_model_for_evaluation_policy $base_model_for_evaluation_policy\ --base_model_for_reg_model $base_model_for_reg_model\ --n_jobs $n_jobs\ --random_state $random_state ``` - `$n_runs` specifies the number of simulation runs in the experiment to estimate standard deviations of the performance of OPE estimators. -- `$n_rounds` and `$n_actions` specify the number of rounds (or samples) and the number of actions of the synthetic bandit data. +- `$n_rounds` and `$n_actions` specify the sample size and the number of actions of the synthetic bandit data, respectively. - `$dim_context` specifies the dimension of context vectors. +- `$beta` specifies the inverse temperature parameter to control the behavior policy. - `$base_model_for_evaluation_policy` specifies the base ML model for defining evaluation policy and should be one of "logistic_regression", "random_forest", or "lightgbm". - `$base_model_for_reg_model` specifies the base ML model for defining regression model and should be one of "logistic_regression", "random_forest", or "lightgbm". - `$n_jobs` is the maximum number of concurrently running jobs. -For example, the following command compares the estimation performances (relative estimation error; relative-ee) of the OPE estimators using the synthetic bandit feedback data with 100,000 rounds, 30 actions, five dimensional context vectors. +For example, the following command compares the estimation performance (relative estimation error; relative-ee) of the OPE estimators using synthetic bandit data with 10,000 samples, 30 actions, five dimensional context vectors. ```bash python evaluate_off_policy_estimators.py\ --n_runs 20\ - --n_rounds 100000\ + --n_rounds 10000\ --n_actions 30\ --dim_context 5\ + --beta -3\ --base_model_for_evaluation_policy logistic_regression\ --base_model_for_reg_model logistic_regression\ --n_jobs -1\ --random_state 12345 # relative-ee of OPE estimators and their standard deviations (lower means accurate). -# It appears that the performances of some OPE estimators depend on the choice of their hyperparameters. # ============================================= # random_state=12345 # --------------------------------------------- -# mean std -# dm 0.194715 0.011648 -# ipw 0.017928 0.013640 -# snipw 0.006098 0.004345 -# dr 0.005692 0.005207 -# sndr 0.004725 0.003328 -# switch-dr (tau=1) 0.194715 0.011648 -# switch-dr (tau=100) 0.005692 0.005207 -# dr-os (lambda=1) 0.194484 0.011651 -# dr-os (lambda=100) 0.174531 0.011997 +# mean std +# dm 0.074390 0.024525 +# ipw 0.009481 0.006899 +# snipw 0.006665 0.004541 +# dr 0.006175 0.004245 +# sndr 0.006118 0.003997 +# switch-dr 0.006175 0.004245 +# dr-os 0.021951 0.013337 # ============================================= ``` -The above result can change with different situations. -You can try the evaluation of OPE with other experimental settings easily. +The above result can change with different situations. You can try the evaluation of OPE with other experimental settings easily. + +## References + +- Yi Su, Pavithra Srinath, Akshay Krishnamurthy. [Adaptive Estimator Selection for Off-Policy Evaluation](https://arxiv.org/abs/2002.07729), ICML2020. +- Yi Su, Maria Dimakopoulou, Akshay Krishnamurthy, Miroslav Dudík. [Doubly Robust Off-policy Evaluation with Shrinkage](https://arxiv.org/abs/1907.09623), ICML2020. +- George Tucker and Jonathan Lee. [Improved Estimator Selection for Off-Policy Evaluation](https://lyang36.github.io/icml2021_rltheory/camera_ready/79.pdf), Workshop on Reinforcement Learning +Theory at ICML2021. +- Yu-Xiang Wang, Alekh Agarwal, Miroslav Dudik. [Optimal and Adaptive Off-policy Evaluation in Contextual Bandits](https://arxiv.org/abs/1612.01205), ICML2017. +- Miroslav Dudik, John Langford, Lihong Li. [Doubly Robust Policy Evaluation and Learning](https://arxiv.org/abs/1103.4601). ICML2011. +- Yuta Saito, Shunsuke Aihara, Megumi Matsutani, Yusuke Narita. [Open Bandit Dataset and Pipeline: Towards Realistic and Reproducible Off-Policy Evaluation](https://arxiv.org/abs/2008.07146). NeurIPS2021 Track on Datasets and Benchmarks. + diff --git a/examples/synthetic/evaluate_off_policy_estimators.py b/examples/synthetic/evaluate_off_policy_estimators.py index c93e6415..f429d5bc 100644 --- a/examples/synthetic/evaluate_off_policy_estimators.py +++ b/examples/synthetic/evaluate_off_policy_estimators.py @@ -10,18 +10,17 @@ from sklearn.linear_model import LogisticRegression import yaml -from obp.dataset import linear_behavior_policy from obp.dataset import logistic_reward_function from obp.dataset import SyntheticBanditDataset from obp.ope import DirectMethod from obp.ope import DoublyRobust -from obp.ope import DoublyRobustWithShrinkage +from obp.ope import DoublyRobustWithShrinkageTuning from obp.ope import InverseProbabilityWeighting from obp.ope import OffPolicyEvaluation from obp.ope import RegressionModel from obp.ope import SelfNormalizedDoublyRobust from obp.ope import SelfNormalizedInverseProbabilityWeighting -from obp.ope import SwitchDoublyRobust +from obp.ope import SwitchDoublyRobustTuning from obp.policy import IPWLearner @@ -42,15 +41,15 @@ SelfNormalizedInverseProbabilityWeighting(), DoublyRobust(), SelfNormalizedDoublyRobust(), - SwitchDoublyRobust(lambda_=1.0, estimator_name="switch-dr (lambda=1)"), - SwitchDoublyRobust(lambda_=100.0, estimator_name="switch-dr (lambda=100)"), - DoublyRobustWithShrinkage(lambda_=1.0, estimator_name="dr-os (lambda=1)"), - DoublyRobustWithShrinkage(lambda_=100.0, estimator_name="dr-os (lambda=100)"), + SwitchDoublyRobustTuning(lambdas=[10, 50, 100, 500, 1000, 5000, 10000, np.inf]), + DoublyRobustWithShrinkageTuning( + lambdas=[10, 50, 100, 500, 1000, 5000, 10000, np.inf] + ), ] if __name__ == "__main__": parser = argparse.ArgumentParser( - description="evaluate off-policy estimators with synthetic bandit data." + description="evaluate the accuracy of OPE estimators on synthetic bandit data." ) parser.add_argument( "--n_runs", type=int, default=1, help="number of simulations in the experiment." @@ -59,19 +58,25 @@ "--n_rounds", type=int, default=10000, - help="number of rounds for synthetic bandit feedback.", + help="sample size of logged bandit data.", ) parser.add_argument( "--n_actions", type=int, default=10, - help="number of actions for synthetic bandit feedback.", + help="number of actions.", ) parser.add_argument( "--dim_context", type=int, default=5, - help="dimensions of context vectors characterizing each round.", + help="dimensions of context vectors.", + ) + parser.add_argument( + "--beta", + type=float, + default=3, + help="inverse temperature parameter to control the behavior policy.", ) parser.add_argument( "--base_model_for_evaluation_policy", @@ -102,6 +107,7 @@ n_rounds = args.n_rounds n_actions = args.n_actions dim_context = args.dim_context + beta = args.beta base_model_for_evaluation_policy = args.base_model_for_evaluation_policy base_model_for_reg_model = args.base_model_for_reg_model n_jobs = args.n_jobs @@ -113,7 +119,7 @@ def process(i: int): n_actions=n_actions, dim_context=dim_context, reward_function=logistic_reward_function, - behavior_policy_function=linear_behavior_policy, + beta=beta, random_state=i, ) # define evaluation policy using IPWLearner @@ -123,21 +129,21 @@ def process(i: int): **hyperparams[base_model_for_evaluation_policy] ), ) - # sample new training and test sets of synthetic logged bandit feedback + # sample new training and test sets of synthetic logged bandit data bandit_feedback_train = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds) bandit_feedback_test = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds) - # train the evaluation policy on the training set of the synthetic logged bandit feedback + # train the evaluation policy on the training set of the synthetic logged bandit data evaluation_policy.fit( context=bandit_feedback_train["context"], action=bandit_feedback_train["action"], reward=bandit_feedback_train["reward"], pscore=bandit_feedback_train["pscore"], ) - # predict the action decisions for the test set of the synthetic logged bandit feedback - action_dist = evaluation_policy.predict( + # predict the action decisions for the test set of the synthetic logged bandit data + action_dist = evaluation_policy.predict_proba( context=bandit_feedback_test["context"], ) - # estimate the mean reward function of the test set of synthetic bandit feedback with ML model + # estimate the reward function of the test set of synthetic bandit feedback with ML model regression_model = RegressionModel( n_actions=dataset.n_actions, action_context=dataset.action_context, @@ -157,37 +163,38 @@ def process(i: int): bandit_feedback=bandit_feedback_test, ope_estimators=ope_estimators, ) - relative_ee_i = ope.evaluate_performance_of_estimators( + metric_i = ope.evaluate_performance_of_estimators( ground_truth_policy_value=dataset.calc_ground_truth_policy_value( expected_reward=bandit_feedback_test["expected_reward"], action_dist=action_dist, ), action_dist=action_dist, estimated_rewards_by_reg_model=estimated_rewards_by_reg_model, + metric="relative-ee", ) - return relative_ee_i + return metric_i processed = Parallel( n_jobs=n_jobs, verbose=50, )([delayed(process)(i) for i in np.arange(n_runs)]) - relative_ee_dict = {est.estimator_name: dict() for est in ope_estimators} - for i, relative_ee_i in enumerate(processed): + metric_dict = {est.estimator_name: dict() for est in ope_estimators} + for i, metric_i in enumerate(processed): for ( estimator_name, relative_ee_, - ) in relative_ee_i.items(): - relative_ee_dict[estimator_name][i] = relative_ee_ - relative_ee_df = DataFrame(relative_ee_dict).describe().T.round(6) + ) in metric_i.items(): + metric_dict[estimator_name][i] = relative_ee_ + results_df = DataFrame(metric_dict).describe().T.round(6) print("=" * 45) print(f"random_state={random_state}") print("-" * 45) - print(relative_ee_df[["mean", "std"]]) + print(results_df[["mean", "std"]]) print("=" * 45) - # save results of the evaluation of off-policy estimators in './logs' directory. + # save results of the evaluation of OPE in './logs' directory. log_path = Path("./logs") log_path.mkdir(exist_ok=True, parents=True) - relative_ee_df.to_csv(log_path / "relative_ee_of_ope_estimators.csv") + results_df.to_csv(log_path / "evaluation_of_ope_results.csv") diff --git a/examples/synthetic/obtain_slate_bandit_feedback.py b/examples/synthetic/obtain_slate_bandit_feedback.py deleted file mode 100644 index d13518a8..00000000 --- a/examples/synthetic/obtain_slate_bandit_feedback.py +++ /dev/null @@ -1,53 +0,0 @@ -import argparse - -from obp.dataset import linear_behavior_policy_logit -from obp.dataset import logistic_reward_function -from obp.dataset import SyntheticSlateBanditDataset - - -if __name__ == "__main__": - parser = argparse.ArgumentParser(description="run slate dataset.") - parser.add_argument( - "--n_unique_action", type=int, default=10, help="number of unique actions." - ) - parser.add_argument( - "--len_list", type=int, default=3, help="number of item positions." - ) - parser.add_argument("--n_rounds", type=int, default=100, help="number of slates.") - parser.add_argument( - "--clip_logit_value", - type=float, - default=None, - help="a float parameter to clip logit value.", - ) - parser.add_argument( - "--is_factorizable", - type=bool, - default=False, - help="a boolean parameter whether to use factorizable evaluation policy.", - ) - parser.add_argument( - "--return_pscore_item_position", - type=bool, - default=True, - help="a boolean parameter whether `pscore_item_position` is returned or not", - ) - parser.add_argument("--random_state", type=int, default=12345) - args = parser.parse_args() - dataset = SyntheticSlateBanditDataset( - n_unique_action=args.n_unique_action, - dim_context=5, - len_list=args.len_list, - base_reward_function=logistic_reward_function, - behavior_policy_function=linear_behavior_policy_logit, - reward_type="binary", - reward_structure="cascade_additive", - click_model="cascade", - random_state=12345, - is_factorizable=args.is_factorizable, - ) - bandit_feedback = dataset.obtain_batch_bandit_feedback( - n_rounds=args.n_rounds, - return_pscore_item_position=args.return_pscore_item_position, - clip_logit_value=args.clip_logit_value, - ) diff --git a/obd/README.md b/obd/README.md index 710bb6d4..0dc81351 100644 --- a/obd/README.md +++ b/obd/README.md @@ -23,7 +23,7 @@ When using this dataset, please cite the paper with following bibtex: ## Data description Open Bandit Dataset is constructed in an A/B test of two multi-armed bandit policies on a large-scale fashion e-commerce platform, [ZOZOTOWN](https://zozo.jp/). It currently consists of a total of about 26M rows, each one representing a user impression with some feature values, selected items as actions, true propensity scores, and click indicators as an outcome. -This is especially suitable for evaluating *off-policy evaluation* (OPE), which attempts to estimate the counterfactual performance of hypothetical algorithms using data generated by a different algorithm. +This is especially suitable for evaluating *off-policy evaluation* (OPE), which aims to estimate the counterfactual performance of hypothetical algorithms using data generated by a different algorithm. ## Fields diff --git a/obp/version.py b/obp/version.py index dd9b22cc..72251527 100644 --- a/obp/version.py +++ b/obp/version.py @@ -1 +1 @@ -__version__ = "0.5.1" +__version__ = "0.5.2" diff --git a/poetry.lock b/poetry.lock index f3ea071b..60c0ab8e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -293,11 +293,11 @@ six = "*" [[package]] name = "pillow" -version = "8.3.2" +version = 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">=3.7.1,<3.10" torch = "^1.9.0" -scikit-learn = "^0.24.2" +scikit-learn = "1.0.2" pandas = "^1.3.2" numpy = "^1.21.2" matplotlib = "^3.4.3" tqdm = "^4.62.2" -scipy = "^1.7.1" +scipy = "1.7.3" PyYAML = "^5.4.1" seaborn = "^0.11.2" pyieoe = "^0.1.1" pingouin = "^0.4.0" mypy-extensions = "^0.4.3" +Pillow = "9.0.0" [tool.poetry.dev-dependencies] flake8 = "^3.9.2" diff --git a/setup.py b/setup.py index 5e8177b7..2fd7bc38 100644 --- a/setup.py +++ b/setup.py @@ -25,16 +25,16 @@ long_description=long_description, long_description_content_type="text/markdown", install_requires=[ - "matplotlib>=3.2.2", + "matplotlib>=3.4.3", "mypy-extensions>=0.4.3", "numpy>=1.21.2", "pandas>=1.3.2", "pyyaml>=5.1", "seaborn>=0.10.1", - "scikit-learn>=0.24.2", - "scipy>=1.4.1", + "scikit-learn>=1.0.2", + "scipy>=1.7.3", "torch>=1.9.0", - "tqdm>=4.41.1", + "tqdm>=4.62.2", "pyieoe>=0.1.1", "pingouin>=0.4.0", ], diff --git a/tests/dataset/test_synthetic.py b/tests/dataset/test_synthetic.py index dace8db3..ff80caee 100644 --- a/tests/dataset/test_synthetic.py +++ b/tests/dataset/test_synthetic.py @@ -25,7 +25,7 @@ None, 12345, TypeError, - "`n_actions` must be an instance of , not .", + "n_actions must be an instance of , not .", ), ( 1, # @@ -37,7 +37,7 @@ None, 12345, ValueError, - "`n_actions`= 1, must be >= 2.", + "n_actions == 1, must be >= 2.", ), ( 3, @@ -49,7 +49,7 @@ None, 12345, TypeError, - "`dim_context` must be an instance of , not .", + "dim_context must be an instance of , not .", ), ( 3, @@ -61,7 +61,7 @@ None, 12345, ValueError, - "`dim_context`= 0, must be >= 1.", + "dim_context == 0, must be >= 1.", ), ( 3, @@ -85,7 +85,7 @@ None, 12345, TypeError, - r"`reward_std` must be an instance of \(, \), not .", + r"reward_std must be an instance of \(, \), not .", ), ( 3, @@ -97,7 +97,7 @@ None, 12345, ValueError, - "`reward_std`= -1.0, must be >= 0.", + "reward_std == -1.0, must be >= 0.", ), ( 3, @@ -109,7 +109,7 @@ None, 12345, TypeError, - r"`beta` must be an instance of \(, \), not .", + r"beta must be an instance of \(, \), not .", ), ( 3, @@ -121,7 +121,7 @@ None, 12345, TypeError, - "`n_deficient_actions` must be an instance of , not .", + "n_deficient_actions must be an instance of , not .", ), ( 3, @@ -133,7 +133,7 @@ None, 12345, TypeError, - "`n_deficient_actions` must be an instance of , not .", + "n_deficient_actions must be an instance of , not .", ), ( 3, @@ -145,7 +145,7 @@ None, 12345, ValueError, - "`n_deficient_actions`= 10, must be <= 2.", + "n_deficient_actions == 10, must be <= 2.", ), ( 3, diff --git a/tests/dataset/test_synthetic_continuous.py b/tests/dataset/test_synthetic_continuous.py index fe01fc8e..88384062 100644 --- a/tests/dataset/test_synthetic_continuous.py +++ b/tests/dataset/test_synthetic_continuous.py @@ -20,7 +20,7 @@ 1.0, 12345, ValueError, - "`dim_context`= 0, must be >= 1.", + "dim_context == 0, must be >= 1.", ), ( 1.0, # @@ -30,7 +30,7 @@ 1.0, 12345, TypeError, - "`dim_context` must be an instance of , not .", + "dim_context must be an instance of , not .", ), ( "3", # @@ -40,7 +40,7 @@ 1.0, 12345, TypeError, - "`dim_context` must be an instance of , not .", + "dim_context must be an instance of , not .", ), ( None, # @@ -50,7 +50,7 @@ 1.0, 12345, TypeError, - "`dim_context` must be an instance of , not .", + "dim_context must be an instance of , not .", ), ( 3, @@ -60,7 +60,7 @@ 1.0, 12345, ValueError, - "`action_noise`= -1.0, must be >= 0.", + "action_noise == -1.0, must be >= 0.", ), ( 3, @@ -70,7 +70,7 @@ 1.0, 12345, TypeError, - r"`action_noise` must be an instance of \(, \), not .", + r"action_noise must be an instance of \(, \), not .", ), ( 3, @@ -80,7 +80,7 @@ 1.0, 12345, TypeError, - r"`action_noise` must be an instance of \(, \), not .", + r"action_noise must be an instance of \(, \), not .", ), ( 3, @@ -90,7 +90,7 @@ 1.0, 12345, ValueError, - "`reward_noise`= -1.0, must be >= 0.", + "reward_noise == -1.0, must be >= 0.", ), ( 3, @@ -100,7 +100,7 @@ 1.0, 12345, TypeError, - r"`reward_noise` must be an instance of \(, \), not .", + r"reward_noise must be an instance of \(, \), not .", ), ( 3, @@ -110,7 +110,7 @@ 1.0, 12345, TypeError, - r"`reward_noise` must be an instance of \(, \), not .", + r"reward_noise must be an instance of \(, \), not .", ), ( 3, @@ -120,7 +120,7 @@ 1.0, 12345, TypeError, - r"`min_action_value` must be an instance of \(, \), not .", + r"min_action_value must be an instance of \(, \), not .", ), ( 3, @@ -130,7 +130,7 @@ 1.0, 12345, TypeError, - r"`min_action_value` must be an instance of \(, \), not .", + r"min_action_value must be an instance of \(, \), not .", ), ( 3, @@ -140,7 +140,7 @@ "3", # 12345, TypeError, - r"`max_action_value` must be an instance of \(, \), not .", + r"max_action_value must be an instance of \(, \), not .", ), ( 3, @@ -150,7 +150,7 @@ None, # 12345, TypeError, - r"`max_action_value` must be an instance of \(, \), not .", + r"max_action_value must be an instance of \(, \), not .", ), ( 3, @@ -215,22 +215,22 @@ def test_synthetic_continuous_init_using_invalid_inputs( ( 0, # ValueError, - "`n_rounds`= 0, must be >= 1.", + "n_rounds == 0, must be >= 1.", ), ( 1.0, # TypeError, - "`n_rounds` must be an instance of , not .", + "n_rounds must be an instance of , not .", ), ( "3", # TypeError, - "`n_rounds` must be an instance of , not .", + "n_rounds must be an instance of , not .", ), ( None, # TypeError, - "`n_rounds` must be an instance of , not .", + "n_rounds must be an instance of , not .", ), ] diff --git a/tests/dataset/test_synthetic_multi.py b/tests/dataset/test_synthetic_multi.py index 00f25b3e..9f507774 100644 --- a/tests/dataset/test_synthetic_multi.py +++ b/tests/dataset/test_synthetic_multi.py @@ -17,7 +17,7 @@ None, 12345, TypeError, - "`n_actions` must be an instance of , not .", + "n_actions must be an instance of , not .", ), ( 1, # @@ -30,7 +30,7 @@ None, 12345, ValueError, - "`n_actions`= 1, must be >= 2.", + "n_actions == 1, must be >= 2.", ), ( 3, @@ -43,7 +43,7 @@ None, 12345, TypeError, - "`dim_context` must be an instance of , not .", + "dim_context must be an instance of , not .", ), ( 3, @@ -56,7 +56,7 @@ None, 12345, ValueError, - "`dim_context`= 0, must be >= 1.", + "dim_context == 0, must be >= 1.", ), ( 3, @@ -82,7 +82,7 @@ None, 12345, TypeError, - r"`reward_std` must be an instance of \(, \), not .", + r"reward_std must be an instance of \(, \), not .", ), ( 3, @@ -95,7 +95,7 @@ None, 12345, ValueError, - "`reward_std`= -1.0, must be >= 0.", + "reward_std == -1.0, must be >= 0.", ), ( 3, @@ -134,7 +134,7 @@ None, 12345, TypeError, - r"`betas\[0\]` must be an instance of \(, \), not .", + r"betas\[0\] must be an instance of \(, \), not .", ), ( 3, @@ -173,7 +173,7 @@ None, 12345, TypeError, - r"`rhos\[0\]` must be an instance of \(, \), not .", + r"rhos\[0\] must be an instance of \(, \), not .", ), ( 3, @@ -186,7 +186,7 @@ None, 12345, ValueError, - r"`rhos\[2\]`= -1, must be >= 0.0.", + r"rhos\[2\] == -1, must be >= 0.0.", ), ( 3, @@ -212,7 +212,7 @@ None, 12345, TypeError, - "`n_deficient_actions` must be an instance of , not .", + "n_deficient_actions must be an instance of , not .", ), ( 3, @@ -225,7 +225,7 @@ None, 12345, TypeError, - "`n_deficient_actions` must be an instance of , not .", + "n_deficient_actions must be an instance of , not .", ), ( 3, @@ -238,7 +238,7 @@ None, 12345, ValueError, - "`n_deficient_actions`= 10, must be <= 2.", + "n_deficient_actions == 10, must be <= 2.", ), ( 3, diff --git a/tests/dataset/test_synthetic_slate.py b/tests/dataset/test_synthetic_slate.py index 8b764941..3e043340 100644 --- a/tests/dataset/test_synthetic_slate.py +++ b/tests/dataset/test_synthetic_slate.py @@ -24,7 +24,7 @@ 1.0, 1, TypeError, - "`n_unique_action` must be an instance of , not .", + "n_unique_action must be an instance of , not .", ), ( 1, @@ -37,7 +37,7 @@ 1.0, 1, ValueError, - "`n_unique_action`= 1, must be >= 2.", + "n_unique_action == 1, must be >= 2.", ), ( 5, @@ -50,7 +50,7 @@ 1.0, 1, TypeError, - "`len_list` must be an instance of , not .", + "len_list must be an instance of , not .", ), ( 5, @@ -63,7 +63,7 @@ 1.0, 1, ValueError, - "`len_list`= -1, must be >= 2.", + "len_list == -1, must be >= 2.", ), ( 5, @@ -76,7 +76,7 @@ 1.0, 1, ValueError, - "`len_list`= 10, must be <= 5.", + "len_list == 10, must be <= 5.", ), ( 5, @@ -89,7 +89,7 @@ 1.0, 1, ValueError, - "`dim_context`= 0, must be >= 1.", + "dim_context == 0, must be >= 1.", ), ( 5, @@ -102,7 +102,7 @@ 1.0, 1, TypeError, - "`dim_context` must be an instance of , not .", + "dim_context must be an instance of , not .", ), ( 5, @@ -167,7 +167,7 @@ "aaa", 1, TypeError, - "`eta` must be an instance of , not .", + "eta must be an instance of , not .", ), ( 5, @@ -180,7 +180,7 @@ -1.0, 1, ValueError, - "`eta`= -1.0, must be >= 0.0.", + "eta == -1.0, must be >= 0.0.", ), ( 5, @@ -1228,7 +1228,7 @@ def test_calc_on_policy_policy_value_using_valid_input_data( np.ones([5, 2]), np.tile(np.arange(3), 5), TypeError, - "`epsilon` must be an instance of , not .", + "epsilon must be an instance of , not .", ), ( "optimal", @@ -1236,7 +1236,7 @@ def test_calc_on_policy_policy_value_using_valid_input_data( np.ones([5, 2]), np.tile(np.arange(3), 5), ValueError, - "`epsilon`= -1.0, must be >= 0.0.", + "epsilon == -1.0, must be >= 0.0.", ), ( "optimal", @@ -1244,7 +1244,7 @@ def test_calc_on_policy_policy_value_using_valid_input_data( np.ones([5, 2]), np.tile(np.arange(3), 5), ValueError, - "`epsilon`= 2.0, must be <= 1.0.", + "epsilon == 2.0, must be <= 1.0.", ), ] diff --git a/tests/ope/test_all_estimators.py b/tests/ope/test_all_estimators.py index 762654e0..c9b99cf3 100644 --- a/tests/ope/test_all_estimators.py +++ b/tests/ope/test_all_estimators.py @@ -352,22 +352,22 @@ def test_estimation_of_all_estimators_using_valid_input_data( ValueError, "'s' cannot be used to seed a numpy.random.RandomState instance", ), - (0.05, -1, 1, ValueError, "`n_bootstrap_samples`= -1, must be >= 1"), + (0.05, -1, 1, ValueError, "n_bootstrap_samples == -1, must be >= 1"), ( 0.05, "s", 1, TypeError, - "`n_bootstrap_samples` must be an instance of , not ", + "n_bootstrap_samples must be an instance of , not ", ), - (-1.0, 1, 1, ValueError, "`alpha`= -1.0, must be >= 0.0"), - (2.0, 1, 1, ValueError, "`alpha`= 2.0, must be <= 1.0"), + (-1.0, 1, 1, ValueError, "alpha == -1.0, must be >= 0.0"), + (2.0, 1, 1, ValueError, "alpha == 2.0, must be <= 1.0"), ( "0", 1, 1, TypeError, - "`alpha` must be an instance of , not ", + "alpha must be an instance of , not ", ), ] diff --git a/tests/ope/test_bipw_estimators.py b/tests/ope/test_bipw_estimators.py index bb4d7853..0159da1f 100644 --- a/tests/ope/test_bipw_estimators.py +++ b/tests/ope/test_bipw_estimators.py @@ -13,14 +13,14 @@ ( "", TypeError, - r"`lambda_` must be an instance of \(, \), not .", + r"lambda_ must be an instance of \(, \), not .", ), ( None, TypeError, - r"`lambda_` must be an instance of \(, \), not .", + r"lambda_ must be an instance of \(, \), not .", ), - (-1.0, ValueError, "`lambda_`= -1.0, must be >= 0.0."), + (-1.0, ValueError, "lambda_ == -1.0, must be >= 0.0."), (np.nan, ValueError, "`lambda_` must not be nan"), ] diff --git a/tests/ope/test_dr_estimators.py b/tests/ope/test_dr_estimators.py index 6f3469d6..06a031d7 100644 --- a/tests/ope/test_dr_estimators.py +++ b/tests/ope/test_dr_estimators.py @@ -23,15 +23,15 @@ "", False, TypeError, - r"`lambda_` must be an instance of \(, \), not .", + r"lambda_ must be an instance of \(, \), not .", ), ( None, False, TypeError, - r"`lambda_` must be an instance of \(, \), not .", + r"lambda_ must be an instance of \(, \), not .", ), - (-1.0, False, ValueError, "`lambda_`= -1.0, must be >= 0.0."), + (-1.0, False, ValueError, "lambda_ == -1.0, must be >= 0.0."), (np.nan, False, ValueError, "`lambda_` must not be nan"), ( 1.0, @@ -93,7 +93,7 @@ def test_dr_init_using_invalid_inputs( 0.05, False, TypeError, - r"`an element of lambdas` must be an instance of \(, \), not .", + r"an element of lambdas must be an instance of \(, \), not .", ), ( [None], # @@ -102,7 +102,7 @@ def test_dr_init_using_invalid_inputs( 0.05, False, TypeError, - r"`an element of lambdas` must be an instance of \(, \), not .", + r"an element of lambdas must be an instance of \(, \), not .", ), ( [], # @@ -120,7 +120,7 @@ def test_dr_init_using_invalid_inputs( 0.05, False, ValueError, - "`an element of lambdas`= -1.0, must be >= 0.0.", + "an element of lambdas == -1.0, must be >= 0.0.", ), ( [np.nan], @@ -165,7 +165,7 @@ def test_dr_init_using_invalid_inputs( "", # False, TypeError, - "`delta` must be an instance of ", + "delta must be an instance of ", ), ( [1], @@ -174,7 +174,7 @@ def test_dr_init_using_invalid_inputs( None, # False, TypeError, - "`delta` must be an instance of ", + "delta must be an instance of ", ), ( [1], @@ -183,7 +183,7 @@ def test_dr_init_using_invalid_inputs( -1.0, # False, ValueError, - "`delta`= -1.0, must be >= 0.0.", + "delta == -1.0, must be >= 0.0.", ), ( [1], @@ -192,7 +192,7 @@ def test_dr_init_using_invalid_inputs( 1.1, # False, ValueError, - "`delta`= 1.1, must be <= 1.0.", + "delta == 1.1, must be <= 1.0.", ), ( [1], diff --git a/tests/ope/test_dr_estimators_continuous.py b/tests/ope/test_dr_estimators_continuous.py index 7bc33032..ee2adb47 100644 --- a/tests/ope/test_dr_estimators_continuous.py +++ b/tests/ope/test_dr_estimators_continuous.py @@ -231,22 +231,22 @@ def test_dr_continuous_using_valid_input_data( ValueError, "'s' cannot be used to seed a numpy.random.RandomState instance", ), - (0.05, -1, 1, ValueError, "`n_bootstrap_samples`= -1, must be >= 1"), + (0.05, -1, 1, ValueError, "n_bootstrap_samples == -1, must be >= 1"), ( 0.05, "s", 1, TypeError, - "`n_bootstrap_samples` must be an instance of , not ", + "n_bootstrap_samples must be an instance of , not ", ), - (-1.0, 1, 1, ValueError, "`alpha`= -1.0, must be >= 0.0"), - (2.0, 1, 1, ValueError, "`alpha`= 2.0, must be <= 1.0"), + (-1.0, 1, 1, ValueError, "alpha == -1.0, must be >= 0.0"), + (2.0, 1, 1, ValueError, "alpha == 2.0, must be <= 1.0"), ( "0", 1, 1, TypeError, - "`alpha` must be an instance of , not ", + "alpha must be an instance of , not ", ), ] diff --git a/tests/ope/test_dr_estimators_slate.py b/tests/ope/test_dr_estimators_slate.py index cc00b6cf..fa8db836 100644 --- a/tests/ope/test_dr_estimators_slate.py +++ b/tests/ope/test_dr_estimators_slate.py @@ -487,22 +487,22 @@ def test_cascade_dr_using_valid_input_data( ValueError, "'s' cannot be used to seed a numpy.random.RandomState instance", ), - (0.05, -1, 1, ValueError, "`n_bootstrap_samples`= -1, must be >= 1"), + (0.05, -1, 1, ValueError, "n_bootstrap_samples == -1, must be >= 1"), ( 0.05, "s", 1, TypeError, - "`n_bootstrap_samples` must be an instance of , not ", + "n_bootstrap_samples must be an instance of , not ", ), - (-1.0, 1, 1, ValueError, "`alpha`= -1.0, must be >= 0.0"), - (2.0, 1, 1, ValueError, "`alpha`= 2.0, must be <= 1.0"), + (-1.0, 1, 1, ValueError, "alpha == -1.0, must be >= 0.0"), + (2.0, 1, 1, ValueError, "alpha == 2.0, must be <= 1.0"), ( "0", 1, 1, TypeError, - "`alpha` must be an instance of , not ", + "alpha must be an instance of , not ", ), ] diff --git a/tests/ope/test_importance_weight_estimator.py b/tests/ope/test_importance_weight_estimator.py index 2476c964..761f5eae 100644 --- a/tests/ope/test_importance_weight_estimator.py +++ b/tests/ope/test_importance_weight_estimator.py @@ -43,7 +43,7 @@ RandomForestClassifier(**hyperparams["random_forest"]), 2, TypeError, - "`n_actions` must be an instance of , not .", + "n_actions must be an instance of , not .", ), ( np.random.uniform(size=(n_actions, 8)), @@ -53,7 +53,7 @@ RandomForestClassifier(**hyperparams["random_forest"]), 2, ValueError, - "`n_actions`= 1, must be >= 2", + "n_actions == 1, must be >= 2", ), ( np.random.uniform(size=(n_actions, 8)), @@ -63,7 +63,7 @@ RandomForestClassifier(**hyperparams["random_forest"]), 2, TypeError, - "`len_list` must be an instance of , not .", + "len_list must be an instance of , not .", ), ( np.random.uniform(size=(n_actions, 8)), @@ -73,7 +73,7 @@ RandomForestClassifier(**hyperparams["random_forest"]), 2, ValueError, - "`len_list`= 0, must be >= 1", + "len_list == 0, must be >= 1", ), ( np.random.uniform(size=(n_actions, 8)), @@ -113,7 +113,7 @@ RandomForestClassifier(**hyperparams["random_forest"]), 1.5, TypeError, - "`calibration_cv` must be an instance of , not .", + "calibration_cv must be an instance of , not .", ), ] @@ -470,7 +470,7 @@ None, 2, TypeError, - "`n_folds` must be an instance of , not ", + "n_folds must be an instance of , not ", ), ( np.random.uniform(size=(n_rounds, 7)), @@ -486,7 +486,7 @@ None, 2, ValueError, - "`n_folds`= 0, must be >= 1.", + "n_folds == 0, must be >= 1.", ), ( np.random.uniform(size=(n_rounds, 7)), diff --git a/tests/ope/test_ipw_estimators.py b/tests/ope/test_ipw_estimators.py index df0897a4..1c27df46 100644 --- a/tests/ope/test_ipw_estimators.py +++ b/tests/ope/test_ipw_estimators.py @@ -18,15 +18,15 @@ "", False, TypeError, - r"`lambda_` must be an instance of \(, \), not .", + r"lambda_ must be an instance of \(, \), not .", ), ( None, False, TypeError, - r"`lambda_` must be an instance of \(, \), not .", + r"lambda_ must be an instance of \(, \), not .", ), - (-1.0, False, ValueError, "`lambda_`= -1.0, must be >= 0.0."), + (-1.0, False, ValueError, "lambda_ == -1.0, must be >= 0.0."), (np.nan, False, ValueError, "`lambda_` must not be nan"), ( 1.0, @@ -80,7 +80,7 @@ def test_ipw_init_using_invalid_inputs( 0.05, False, TypeError, - r"`an element of lambdas` must be an instance of \(, \), not .", + r"an element of lambdas must be an instance of \(, \), not .", ), ( [None], # @@ -89,7 +89,7 @@ def test_ipw_init_using_invalid_inputs( 0.05, False, TypeError, - r"`an element of lambdas` must be an instance of \(, \), not .", + r"an element of lambdas must be an instance of \(, \), not .", ), ( [], # @@ -107,7 +107,7 @@ def test_ipw_init_using_invalid_inputs( 0.05, False, ValueError, - "`an element of lambdas`= -1.0, must be >= 0.0.", + "an element of lambdas == -1.0, must be >= 0.0.", ), ( [np.nan], @@ -152,7 +152,7 @@ def test_ipw_init_using_invalid_inputs( "", # False, TypeError, - "`delta` must be an instance of ", + "delta must be an instance of ", ), ( [1], @@ -161,7 +161,7 @@ def test_ipw_init_using_invalid_inputs( None, # False, TypeError, - "`delta` must be an instance of ", + "delta must be an instance of ", ), ( [1], @@ -170,7 +170,7 @@ def test_ipw_init_using_invalid_inputs( -1.0, # False, ValueError, - "`delta`= -1.0, must be >= 0.0.", + "delta == -1.0, must be >= 0.0.", ), ( [1], @@ -179,7 +179,7 @@ def test_ipw_init_using_invalid_inputs( 1.1, # False, ValueError, - "`delta`= 1.1, must be <= 1.0.", + "delta == 1.1, must be <= 1.0.", ), ( [1], diff --git a/tests/ope/test_ipw_estimators_continuous.py b/tests/ope/test_ipw_estimators_continuous.py index 93c01ca6..d7a923dc 100644 --- a/tests/ope/test_ipw_estimators_continuous.py +++ b/tests/ope/test_ipw_estimators_continuous.py @@ -246,22 +246,22 @@ def test_ipw_continuous_using_valid_input_data( ValueError, "'s' cannot be used to seed a numpy.random.RandomState instance", ), - (0.05, -1, 1, ValueError, "`n_bootstrap_samples`= -1, must be >= 1"), + (0.05, -1, 1, ValueError, "n_bootstrap_samples == -1, must be >= 1"), ( 0.05, "s", 1, TypeError, - "`n_bootstrap_samples` must be an instance of , not ", + "n_bootstrap_samples must be an instance of , not ", ), - (-1.0, 1, 1, ValueError, "`alpha`= -1.0, must be >= 0.0"), - (2.0, 1, 1, ValueError, "`alpha`= 2.0, must be <= 1.0"), + (-1.0, 1, 1, ValueError, "alpha == -1.0, must be >= 0.0"), + (2.0, 1, 1, ValueError, "alpha == 2.0, must be <= 1.0"), ( "0", 1, 1, TypeError, - "`alpha` must be an instance of , not ", + "alpha must be an instance of , not ", ), ] diff --git a/tests/ope/test_ipw_estimators_slate.py b/tests/ope/test_ipw_estimators_slate.py index 66c73e71..4bf990fb 100644 --- a/tests/ope/test_ipw_estimators_slate.py +++ b/tests/ope/test_ipw_estimators_slate.py @@ -713,22 +713,22 @@ def test_rips_using_invalid_input_data( ValueError, "'s' cannot be used to seed a numpy.random.RandomState instance", ), - (0.05, -1, 1, ValueError, "`n_bootstrap_samples`= -1, must be >= 1"), + (0.05, -1, 1, ValueError, "n_bootstrap_samples == -1, must be >= 1"), ( 0.05, "s", 1, TypeError, - "`n_bootstrap_samples` must be an instance of , not ", + "n_bootstrap_samples must be an instance of , not ", ), - (-1.0, 1, 1, ValueError, "`alpha`= -1.0, must be >= 0.0"), - (2.0, 1, 1, ValueError, "`alpha`= 2.0, must be <= 1.0"), + (-1.0, 1, 1, ValueError, "alpha == -1.0, must be >= 0.0"), + (2.0, 1, 1, ValueError, "alpha == 2.0, must be <= 1.0"), ( "0", 1, 1, TypeError, - "`alpha` must be an instance of , not ", + "alpha must be an instance of , not ", ), ] diff --git a/tests/ope/test_meta.py b/tests/ope/test_meta.py index b1611085..1df2ca25 100644 --- a/tests/ope/test_meta.py +++ b/tests/ope/test_meta.py @@ -475,22 +475,22 @@ def test_meta_estimate_policy_values_using_valid_input_data( ValueError, "'s' cannot be used to seed a numpy.random.RandomState instance", ), - (0.05, -1, 1, ValueError, "`n_bootstrap_samples`= -1, must be >= 1"), + (0.05, -1, 1, ValueError, "n_bootstrap_samples == -1, must be >= 1"), ( 0.05, "s", 1, TypeError, - "`n_bootstrap_samples` must be an instance of , not ", + "n_bootstrap_samples must be an instance of , not ", ), - (-1.0, 1, 1, ValueError, "`alpha`= -1.0, must be >= 0.0"), - (2.0, 1, 1, ValueError, "`alpha`= 2.0, must be <= 1.0"), + (-1.0, 1, 1, ValueError, "alpha == -1.0, must be >= 0.0"), + (2.0, 1, 1, ValueError, "alpha == 2.0, must be <= 1.0"), ( "0", 1, 1, TypeError, - "`alpha` must be an instance of , not ", + "alpha must be an instance of , not ", ), ] @@ -676,13 +676,13 @@ def test_meta_summarize_off_policy_estimates( "se", 1, TypeError, - "`ground_truth_policy_value` must be an instance of , not .", + "ground_truth_policy_value must be an instance of , not .", ), ( "se", "a", TypeError, - "`ground_truth_policy_value` must be an instance of , not .", + "ground_truth_policy_value must be an instance of , not .", ), ( "relative-ee", diff --git a/tests/ope/test_meta_continuous.py b/tests/ope/test_meta_continuous.py index 2270f56e..891e28b4 100644 --- a/tests/ope/test_meta_continuous.py +++ b/tests/ope/test_meta_continuous.py @@ -405,22 +405,22 @@ def test_meta_estimate_policy_values_using_valid_input_data( ValueError, "'s' cannot be used to seed a numpy.random.RandomState instance", ), - (0.05, -1, 1, ValueError, "`n_bootstrap_samples`= -1, must be >= 1"), + (0.05, -1, 1, ValueError, "n_bootstrap_samples == -1, must be >= 1"), ( 0.05, "s", 1, TypeError, - "`n_bootstrap_samples` must be an instance of , not ", + "n_bootstrap_samples must be an instance of , not ", ), - (-1.0, 1, 1, ValueError, "`alpha`= -1.0, must be >= 0.0"), - (2.0, 1, 1, ValueError, "`alpha`= 2.0, must be <= 1.0"), + (-1.0, 1, 1, ValueError, "alpha == -1.0, must be >= 0.0"), + (2.0, 1, 1, ValueError, "alpha == 2.0, must be <= 1.0"), ( "0", 1, 1, TypeError, - "`alpha` must be an instance of , not ", + "alpha must be an instance of , not ", ), ] @@ -606,13 +606,13 @@ def test_meta_summarize_off_policy_estimates( "se", 1, TypeError, - "`ground_truth_policy_value` must be an instance of , not .", + "ground_truth_policy_value must be an instance of , not .", ), ( "se", "a", TypeError, - "`ground_truth_policy_value` must be an instance of , not .", + "ground_truth_policy_value must be an instance of , not .", ), ( "relative-ee", diff --git a/tests/ope/test_meta_slate.py b/tests/ope/test_meta_slate.py index a56af1c4..bf570496 100644 --- a/tests/ope/test_meta_slate.py +++ b/tests/ope/test_meta_slate.py @@ -573,22 +573,22 @@ def test_meta_estimate_policy_values_using_various_pscores( ValueError, "'s' cannot be used to seed a numpy.random.RandomState instance", ), - (0.05, -1, 1, ValueError, "`n_bootstrap_samples`= -1, must be >= 1"), + (0.05, -1, 1, ValueError, "n_bootstrap_samples == -1, must be >= 1"), ( 0.05, "s", 1, TypeError, - "`n_bootstrap_samples` must be an instance of , not ", + "n_bootstrap_samples must be an instance of , not ", ), - (-1.0, 1, 1, ValueError, "`alpha`= -1.0, must be >= 0.0"), - (2.0, 1, 1, ValueError, "`alpha`= 2.0, must be <= 1.0"), + (-1.0, 1, 1, ValueError, "alpha == -1.0, must be >= 0.0"), + (2.0, 1, 1, ValueError, "alpha == 2.0, must be <= 1.0"), ( "0", 1, 1, TypeError, - "`alpha` must be an instance of , not ", + "alpha must be an instance of , not ", ), ] @@ -786,13 +786,13 @@ def test_meta_summarize_off_policy_estimates( "se", 1, TypeError, - "`ground_truth_policy_value` must be an instance of , not .", + "ground_truth_policy_value must be an instance of , not .", ), ( "se", "a", TypeError, - "`ground_truth_policy_value` must be an instance of , not .", + "ground_truth_policy_value must be an instance of , not .", ), ( "relative-ee", diff --git a/tests/ope/test_offline_estimation_performance.py b/tests/ope/test_offline_estimation_performance.py index 971b31b3..7dea5d51 100644 --- a/tests/ope/test_offline_estimation_performance.py +++ b/tests/ope/test_offline_estimation_performance.py @@ -260,17 +260,17 @@ def process(i: int): **hyperparams[base_model_for_iw_estimator] ), ) - # sample new training and test sets of synthetic logged bandit feedback + # sample new training and test sets of synthetic logged bandit data bandit_feedback_train = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds) bandit_feedback_test = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds) - # train the evaluation policy on the training set of the synthetic logged bandit feedback + # train the evaluation policy on the training set of the synthetic logged bandit data evaluation_policy.fit( context=bandit_feedback_train["context"], action=bandit_feedback_train["action"], reward=bandit_feedback_train["reward"], pscore=bandit_feedback_train["pscore"], ) - # predict the action decisions for the test set of the synthetic logged bandit feedback + # predict the action decisions for the test set of the synthetic logged bandit data action_dist = evaluation_policy.predict_proba( context=bandit_feedback_test["context"], ) @@ -347,14 +347,14 @@ def process(i: int): n_jobs=-1, verbose=0, )([delayed(process)(i) for i in np.arange(n_runs)]) - relative_ee_dict = {est.estimator_name: dict() for est in ope_estimators} + metric_dict = {est.estimator_name: dict() for est in ope_estimators} for i, relative_ee_i in enumerate(processed): for ( estimator_name, relative_ee_, ) in relative_ee_i.items(): - relative_ee_dict[estimator_name][i] = relative_ee_ - relative_ee_df = DataFrame(relative_ee_dict).describe().T.round(6) + metric_dict[estimator_name][i] = relative_ee_ + relative_ee_df = DataFrame(metric_dict).describe().T.round(6) relative_ee_df_mean = relative_ee_df["mean"] tested_estimators = [ diff --git a/tests/ope/test_propensity_score_estimator.py b/tests/ope/test_propensity_score_estimator.py index 84be5952..c2760d10 100644 --- a/tests/ope/test_propensity_score_estimator.py +++ b/tests/ope/test_propensity_score_estimator.py @@ -40,7 +40,7 @@ RandomForestClassifier(**hyperparams["random_forest"]), 2, TypeError, - "`n_actions` must be an instance of , not .", + "n_actions must be an instance of , not .", ), ( 1, # @@ -48,7 +48,7 @@ RandomForestClassifier(**hyperparams["random_forest"]), 2, ValueError, - "`n_actions`= 1, must be >= 2", + "n_actions == 1, must be >= 2", ), ( n_actions, @@ -56,7 +56,7 @@ RandomForestClassifier(**hyperparams["random_forest"]), 2, TypeError, - "`len_list` must be an instance of , not .", + "len_list must be an instance of , not .", ), ( n_actions, @@ -64,7 +64,7 @@ RandomForestClassifier(**hyperparams["random_forest"]), 2, ValueError, - "`len_list`= 0, must be >= 1", + "len_list == 0, must be >= 1", ), ( n_actions, @@ -80,7 +80,7 @@ RandomForestClassifier(**hyperparams["random_forest"]), 1.5, TypeError, - "`calibration_cv` must be an instance of , not .", + "calibration_cv must be an instance of , not .", ), ] @@ -306,7 +306,7 @@ None, 2, TypeError, - "`n_folds` must be an instance of , not ", + "n_folds must be an instance of , not ", ), ( np.random.uniform(size=(n_rounds, 7)), @@ -319,7 +319,7 @@ None, 2, ValueError, - "`n_folds`= 0, must be >= 1.", + "n_folds == 0, must be >= 1.", ), ( np.random.uniform(size=(n_rounds, 7)), diff --git a/tests/ope/test_regression_models.py b/tests/ope/test_regression_models.py index b93ce819..71c1df91 100644 --- a/tests/ope/test_regression_models.py +++ b/tests/ope/test_regression_models.py @@ -43,7 +43,7 @@ "normal", Ridge(**hyperparams["ridge"]), TypeError, - "`n_actions` must be an instance of , not .", + "n_actions must be an instance of , not .", ), ( np.random.uniform(size=(n_actions, 8)), @@ -52,7 +52,7 @@ "normal", Ridge(**hyperparams["ridge"]), ValueError, - "`n_actions`= 1, must be >= 2", + "n_actions == 1, must be >= 2", ), ( np.random.uniform(size=(n_actions, 8)), @@ -61,7 +61,7 @@ "normal", Ridge(**hyperparams["ridge"]), TypeError, - "`len_list` must be an instance of , not .", + "len_list must be an instance of , not .", ), ( np.random.uniform(size=(n_actions, 8)), @@ -70,7 +70,7 @@ "normal", Ridge(**hyperparams["ridge"]), ValueError, - "`len_list`= 0, must be >= 1", + "len_list == 0, must be >= 1", ), ( np.random.uniform(size=(n_actions, 8)), @@ -595,7 +595,7 @@ "a", # None, TypeError, - "`n_folds` must be an instance of , not ", + "n_folds must be an instance of , not ", ), ( np.random.uniform(size=(n_rounds, 7)), @@ -612,7 +612,7 @@ 0, # None, ValueError, - "`n_folds`= 0, must be >= 1.", + "n_folds == 0, must be >= 1.", ), ( np.random.uniform(size=(n_rounds, 7)), diff --git a/tests/ope/test_regression_models_slate.py b/tests/ope/test_regression_models_slate.py index fecd7671..2978d2ce 100644 --- a/tests/ope/test_regression_models_slate.py +++ b/tests/ope/test_regression_models_slate.py @@ -46,7 +46,7 @@ "normal", Ridge(**hyperparams["ridge"]), TypeError, - "`n_unique_action` must be an instance of , not .", + "n_unique_action must be an instance of , not .", ), ( 1, # @@ -54,7 +54,7 @@ "normal", Ridge(**hyperparams["ridge"]), ValueError, - "`n_unique_action`= 1, must be >= 2", + "n_unique_action == 1, must be >= 2", ), ( n_unique_action, @@ -62,7 +62,7 @@ "normal", Ridge(**hyperparams["ridge"]), TypeError, - "`len_list` must be an instance of , not .", + "len_list must be an instance of , not .", ), ( n_unique_action, @@ -70,7 +70,7 @@ "normal", Ridge(**hyperparams["ridge"]), ValueError, - "`len_list`= 0, must be >= 1", + "len_list == 0, must be >= 1", ), ( n_unique_action, diff --git a/tests/policy/test_offline.py b/tests/policy/test_offline.py index 02a8d0d1..a7826358 100644 --- a/tests/policy/test_offline.py +++ b/tests/policy/test_offline.py @@ -19,19 +19,19 @@ 0, # 1, base_classifier, - "`n_actions`= 0, must be >= 1", + "n_actions == 0, must be >= 1", ), ( 10, -1, # base_classifier, - "`len_list`= -1, must be >= 0", + "len_list == -1, must be >= 0", ), ( 10, 20, # base_classifier, - "`len_list`= 20, must be <= 10", + "len_list == 20, must be <= 10", ), (10, 1, base_regressor, "base_classifier must be a classifier"), ] @@ -233,21 +233,21 @@ def test_ipw_learner_sample_action(): 1, base_classifier, "normal", - "`n_actions`= 0, must be >= 1", + "n_actions == 0, must be >= 1", ), ( 10, -1, # base_classifier, "normal", - "`len_list`= -1, must be >= 0", + "len_list == -1, must be >= 0", ), ( 10, 20, # base_classifier, "normal", - "`len_list`= 20, must be <= 10", + "len_list == 20, must be <= 10", ), (10, 1, "base_regressor", "normal", "`base_model` must be BaseEstimator"), # ( @@ -464,7 +464,7 @@ def test_q_learner_sample_action(): 1e-8, 10, ValueError, - "`n_actions`= 0, must be >= 1", + "n_actions == 0, must be >= 1", ), ( 10, @@ -492,7 +492,7 @@ def test_q_learner_sample_action(): 1e-8, 10, ValueError, - "`len_list`= -1, must be >= 0", + "len_list == -1, must be >= 0", ), ( 10, @@ -520,7 +520,7 @@ def test_q_learner_sample_action(): 1e-8, 10, ValueError, - "`dim_context`= -1, must be >= 1.", + "dim_context == -1, must be >= 1.", ), ( 10, @@ -604,7 +604,7 @@ def test_q_learner_sample_action(): 1e-8, 10, TypeError, - r"`policy_reg_param` must be an instance of \(, \), not .", + r"policy_reg_param must be an instance of \(, \), not .", ), ( 10, @@ -632,7 +632,7 @@ def test_q_learner_sample_action(): 1e-8, 10, TypeError, - r"`policy_reg_param` must be an instance of \(, \), not .", + r"policy_reg_param must be an instance of \(, \), not .", ), ( 10, @@ -660,7 +660,7 @@ def test_q_learner_sample_action(): 1e-8, 10, ValueError, - r"`policy_reg_param`= -1.0, must be >= 0.0.", + r"policy_reg_param == -1.0, must be >= 0.0.", ), ( 10, @@ -688,7 +688,7 @@ def test_q_learner_sample_action(): 1e-8, 10, TypeError, - r"`var_reg_param` must be an instance of \(, \), not .", + r"var_reg_param must be an instance of \(, \), not .", ), ( 10, @@ -716,7 +716,7 @@ def test_q_learner_sample_action(): 1e-8, 10, TypeError, - r"`var_reg_param` must be an instance of \(, \), not .", + r"var_reg_param must be an instance of \(, \), not .", ), ( 10, @@ -744,7 +744,7 @@ def test_q_learner_sample_action(): 1e-8, 10, ValueError, - r"`var_reg_param`= -1.0, must be >= 0.0.", + r"var_reg_param == -1.0, must be >= 0.0.", ), ( 10, @@ -856,7 +856,7 @@ def test_q_learner_sample_action(): 1e-8, 10, ValueError, - "`alpha`= -1.0, must be >= 0.0", + "alpha == -1.0, must be >= 0.0", ), ( 10, @@ -940,7 +940,7 @@ def test_q_learner_sample_action(): 1e-8, 10, ValueError, - "`max_iter`= 0, must be >= 1", + "max_iter == 0, must be >= 1", ), ( 10, @@ -1052,7 +1052,7 @@ def test_q_learner_sample_action(): 1e-8, 10, ValueError, - "`momentum`= 2.0, must be <= 1.0", + "momentum == 2.0, must be <= 1.0", ), ( 10, @@ -1164,7 +1164,7 @@ def test_q_learner_sample_action(): 1e-8, 10, ValueError, - "`validation_fraction`= 2.0, must be <= 1.0", + "validation_fraction == 2.0, must be <= 1.0", ), ( 10, @@ -1192,7 +1192,7 @@ def test_q_learner_sample_action(): 1e-8, 10, ValueError, - "`beta_1`= 2.0, must be <= 1.0", + "beta_1 == 2.0, must be <= 1.0", ), ( 10, @@ -1220,7 +1220,7 @@ def test_q_learner_sample_action(): 1e-8, 10, ValueError, - "`beta_2`= 2.0, must be <= 1.0", + "beta_2 == 2.0, must be <= 1.0", ), ( 10, @@ -1248,7 +1248,7 @@ def test_q_learner_sample_action(): -1.0, # 10, ValueError, - "`epsilon`= -1.0, must be >= 0.0", + "epsilon == -1.0, must be >= 0.0", ), ( 10, @@ -1276,7 +1276,7 @@ def test_q_learner_sample_action(): 1e-8, 0, # ValueError, - "`n_iter_no_change`= 0, must be >= 1.", + "n_iter_no_change == 0, must be >= 1.", ), ] diff --git a/tests/policy/test_offline_continuous.py b/tests/policy/test_offline_continuous.py index 8b1205e8..7a0541d2 100644 --- a/tests/policy/test_offline_continuous.py +++ b/tests/policy/test_offline_continuous.py @@ -33,7 +33,7 @@ 1e-8, 10, None, - "`dim_context`= 0, must be >= 1", + "dim_context == 0, must be >= 1", ), ( 10, @@ -215,7 +215,7 @@ 1e-8, 10, None, - "`alpha`= -1.0, must be >= 0.0", + "alpha == -1.0, must be >= 0.0", ), ( 10, @@ -293,7 +293,7 @@ 1e-8, 10, None, - "`max_iter`= 0, must be >= 1", + "max_iter == 0, must be >= 1", ), ( 10, @@ -397,7 +397,7 @@ 1e-8, 10, None, - "`momentum`= 2.0, must be <= 1.0", + "momentum == 2.0, must be <= 1.0", ), ( 10, @@ -501,7 +501,7 @@ 1e-8, 10, None, - "`validation_fraction`= 2.0, must be <= 1.0", + "validation_fraction == 2.0, must be <= 1.0", ), ( 10, @@ -527,7 +527,7 @@ 1e-8, 10, None, - "`beta_1`= 2.0, must be <= 1.0", + "beta_1 == 2.0, must be <= 1.0", ), ( 10, @@ -553,7 +553,7 @@ 1e-8, 10, None, - "`beta_2`= 2.0, must be <= 1.0", + "beta_2 == 2.0, must be <= 1.0", ), ( 10, @@ -579,7 +579,7 @@ -1.0, # 10, None, - "`epsilon`= -1.0, must be >= 0.0", + "epsilon == -1.0, must be >= 0.0", ), ( 10, @@ -605,7 +605,7 @@ 1e-8, 0, # None, - "`n_iter_no_change`= 0, must be >= 1", + "n_iter_no_change == 0, must be >= 1", ), ( 10, diff --git a/tests/policy/test_offline_learner_continuous_performance.py b/tests/policy/test_offline_learner_continuous_performance.py index 4cfe3228..1619aa6f 100644 --- a/tests/policy/test_offline_learner_continuous_performance.py +++ b/tests/policy/test_offline_learner_continuous_performance.py @@ -96,21 +96,21 @@ def process(i: int): ) # baseline method 1. RandomPolicy random_policy = RandomPolicy(output_space=(min_action_value, max_action_value)) - # sample new training and test sets of synthetic logged bandit feedback + # sample new training and test sets of synthetic logged bandit data bandit_feedback_train = dataset.obtain_batch_bandit_feedback( n_rounds=n_rounds, ) bandit_feedback_test = dataset.obtain_batch_bandit_feedback( n_rounds=n_rounds, ) - # train the evaluation policy on the training set of the synthetic logged bandit feedback + # train the evaluation policy on the training set of the synthetic logged bandit data nn_policy.fit( context=bandit_feedback_train["context"], action=bandit_feedback_train["action"], reward=bandit_feedback_train["reward"], pscore=bandit_feedback_train["pscore"], ) - # predict the action decisions for the test set of the synthetic logged bandit feedback + # predict the action decisions for the test set of the synthetic logged bandit data actions_predicted_by_nn_policy = nn_policy.predict( context=bandit_feedback_test["context"], ) diff --git a/tests/policy/test_offline_learner_performance.py b/tests/policy/test_offline_learner_performance.py index 40bc5e43..351b0d51 100644 --- a/tests/policy/test_offline_learner_performance.py +++ b/tests/policy/test_offline_learner_performance.py @@ -192,7 +192,7 @@ def process(i: int): behavior_policy_function=linear_behavior_policy, random_state=i, ) - # sample new training and test sets of synthetic logged bandit feedback + # sample new training and test sets of synthetic logged bandit data bandit_feedback_train = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds) bandit_feedback_test = dataset.obtain_batch_bandit_feedback(n_rounds=n_rounds)