Added basic tests

tests/README.md

@@ -1,7 +1,5 @@
 # edbo
 
-Experimental Design via Bayesian Optimization
-
 ## Testing
 
 This directory contains a number of tests of the basic functionality of *edbo*. Some of the functions test *edbo*'s plotting functions so you will need to close the plot windows as they pop up for tests to proceed.

tests/acquisition_functions_gp.py

@@ -0,0 +1,163 @@
+############################################################################## Setup
+"""
+Acquisition Functions Test Parameters:
+(0) Gaussian Process model.
+(1) 1D objective.
+(2) Initialize with random data.
+(3) Test predictions, variance estimation, and sampling.
+(4) Run single iteration of each acquisition function.
+(5) Test gpytroch fast computation features.
+"""
+
+# Imports
+
+import numpy as np
+import pandas as pd
+from gpytorch.priors import GammaPrior
+from edbo.bro import BO
+from edbo.pd_utils import to_torch
+import random
+
+############################################################################## Test Functions
+
+# Objective
+
+def f(x):
+    """Noise free objective."""
+
+    return np.sin(10 * x) * x * 100
+
+# Test a precomputed objective
+
+def BO_pred(acq_func, plot=False, return_='pred', append=False, init='external', fast_comp=True):
+
+    # Experiment index
+    X = np.linspace(0,1,1000)
+    exindex = pd.DataFrame([[x, f(x)] for x in X], columns=['x', 'f(x)'])
+    training_points = [50, 300, 500, 900]
+
+    # Instatiate BO class
+    bo = BO(exindex=exindex,
+            domain=exindex.drop('f(x)', axis=1),
+            results=exindex.iloc[training_points],
+            acquisition_function=acq_func,
+            init_method=init,
+            lengthscale_prior=[GammaPrior(1.2,1.1), 0.2],
+            noise_prior=None,
+            batch_size=random.sample([1,2,3,4,5,6,7,8,9,10],1)[0],
+            fast_comp=fast_comp)
+
+    bo.run(append=append)
+
+    # Check prediction
+    if return_ == 'pred':
+
+        try:
+            bo.model.predict(to_torch(bo.obj.domain))                          # torch.tensor
+            bo.model.predict(bo.obj.domain.values)                             # numpy.array
+            bo.model.predict(list(bo.obj.domain.values))                       # list
+            bo.model.predict(exindex.drop('f(x)', axis=1))                     # pandas.DataFrame
+        except:
+            return False
+
+        pred = bo.model.predict(bo.obj.domain.iloc[[32]])
+        pred = bo.obj.scaler.unstandardize(pred)
+        return (pred[0] - 1.33) < 0.1
+
+    # Check predictive postrior variance
+    elif return_ == 'var':
+
+        try:
+            bo.model.predict(to_torch(bo.obj.domain))                          # torch.tensor
+            bo.model.predict(bo.obj.domain.values)                             # numpy.array
+            bo.model.predict(list(bo.obj.domain.values))                       # list
+            bo.model.predict(exindex.drop('f(x)', axis=1))                     # pandas.DataFrame
+        except:
+            return False
+
+        var = bo.model.variance(bo.obj.domain.iloc[[32]])
+        return (var[0] - 0.04) < 0.1
+
+    # Make sure sampling works with tensors, arrays, lists, and DataFrames
+    elif return_ == 'sample':
+        try:
+            bo.model.sample_posterior(to_torch(bo.obj.domain))                 # torch.tensor
+            bo.model.sample_posterior(bo.obj.domain.values)                    # numpy.array
+            bo.model.sample_posterior(list(bo.obj.domain.values))              # list
+            bo.model.sample_posterior(exindex.drop('f(x)', axis=1))            # pandas.DataFrame
+            return True
+        except:
+            return False
+
+    elif return_ == 'simulate':
+
+        if init != 'external':
+            bo.init_seq.batch_size = random.sample([2,3,4,5,6,7,8,9,10],1)[0]
+
+        bo.simulate(iterations=5)
+        bo.plot_convergence()
+        bo.model.regression()
+
+        return True
+
+    elif return_ == 'none':
+        return True
+
+############################################################################## Tests
+
+# Test predicted mean, variance, and sampling
+
+def test_BO_pred_mean():
+    assert BO_pred('TS', return_='pred')
+
+def test_BO_pred_var():
+    assert BO_pred('TS', return_='var')
+
+def test_BO_sample():
+    assert BO_pred('TS', return_='sample')
+
+# Test different acquisition functions
+
+def test_BO_EI():
+    assert BO_pred('EI', return_='none')
+
+def test_BO_PI():
+    assert BO_pred('PI', return_='none')
+
+def test_BO_UCB():
+    assert BO_pred('UCB', return_='none')
+
+def test_BO_rand():
+    assert BO_pred('rand', return_='none')
+
+def test_BO_MeanMax():
+    assert BO_pred('MeanMax', return_='none')
+
+def test_BO_VarMax():
+    assert BO_pred('VarMax', return_='none')
+
+def test_BO_EI_TS():
+    assert BO_pred('EI-TS', return_='none')
+
+def test_BO_PI_TS():
+    assert BO_pred('PI-TS', return_='none')
+
+def test_BO_UCB_TS():
+    assert BO_pred('UCB-TS', return_='none')
+
+def test_BO_rand_TS():
+    assert BO_pred('rand-TS', return_='none')
+
+def test_BO_MeanMax_TS():
+    assert BO_pred('MeanMax-TS', return_='none')
+
+def test_BO_VarMax_TS():
+    assert BO_pred('VarMax-TS', return_='none')
+
+def test_BO_eps_greedy():
+    assert BO_pred('eps-greedy', return_='none')
+
+# Turn fast computation off
+
+def test_fast_comp_off():
+    assert BO_pred('TS', fast_comp=False)