Python Measures Examples

Example #1

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utility_term_factory = utility_term_estimation.UtilityAggregatorFactory()

# # Evaluation

# In[21]:

train_measures = evaluation.Measures(
    x,
    loss,
    u,
    sample_predictive_y,
    optimal_h_bayes_estimator=optimal_h_bayes_estimator,
    y_mask=training_mask,
    GAIN_OPTIMAL_H_NUMERICALLY=GAIN_OPTIMAL_H_NUMERICALLY,
    RISK_OPTIMAL_H_NUMERICALLY=RISK_OPTIMAL_H_NUMERICALLY,
    EVAL_NSAMPLES_UTILITY_TERM_THETA=EVAL_NSAMPLES_UTILITY_TERM_THETA,
    EVAL_NSAMPLES_UTILITY_TERM_Y=EVAL_NSAMPLES_UTILITY_TERM_Y,
    EVAL_MAX_NITER=EVAL_MAX_NITER,
    EVAL_SGD_PREC=EVAL_SGD_PREC)

test_measures = evaluation.Measures(
    x,
    loss,
    u,
    sample_predictive_y,
    optimal_h_bayes_estimator=optimal_h_bayes_estimator,
    y_mask=testing_mask,
    GAIN_OPTIMAL_H_NUMERICALLY=GAIN_OPTIMAL_H_NUMERICALLY,

Example #2

# In[182]:

utility_term_factory = utility_term_estimation.UtilityAggregatorFactory()

# # Evaluation

# In[183]:

measures = evaluation.Measures(
    torch.tensor(schools_dat["y"], dtype=torch.float32),
    loss,
    u,
    sample_predictive_y,
    optimal_h_bayes_estimator=optimal_h_bayes_estimator,
    y_mask=utility_term_mask,  # all data points
    GAIN_OPTIMAL_H_NUMERICALLY=GAIN_OPTIMAL_H_NUMERICALLY,
    RISK_OPTIMAL_H_NUMERICALLY=RISK_OPTIMAL_H_NUMERICALLY,
    EVAL_NSAMPLES_UTILITY_TERM_THETA=EVAL_NSAMPLES_UTILITY_TERM_THETA,
    EVAL_NSAMPLES_UTILITY_TERM_Y=EVAL_NSAMPLES_UTILITY_TERM_Y,
    EVAL_MAX_NITER=EVAL_MAX_NITER,
    EVAL_SGD_PREC=EVAL_SGD_PREC)

# # VI

# In[136]:

# intialization
torch.manual_seed(SEED)
np.random.seed(SEED)