move test functions (#345)

Summary:
Pull Request resolved: #345

I don't know why, but sometimes unit tests would sometimes complain that test functions couldn't be found while testing locally, so I'm moving all the test functions into the benchmark folder. This makes sense since these functions can be used as part of benchmarks, anyway.

Reviewed By: tymmsc

Differential Revision: D54454214

fbshipit-source-id: 5aaafbfb8c1c94471a9c1d7045479eb6ec1a838c

aepsych/benchmark/test_functions.py

@@ -220,3 +220,172 @@ def modified_hartmann6(X):
             t += a_t[i, j] * ((X[j] - p_t[i, j]) ** 2)
         y += C_i * np.exp(-t)
     return -10 * (float(y) - 0.1)
+
+
+def f_1d(x, mu=0):
+    """
+    latent is just a gaussian bump at mu
+    """
+    return np.exp(-((x - mu) ** 2))
+
+
+def f_2d(x):
+    """
+    a gaussian bump at 0 , 0
+    """
+    return np.exp(-np.linalg.norm(x, axis=-1))
+
+
+def new_novel_det_params(freq, scale_factor=1.0):
+    """Get the loc and scale params for 2D synthetic novel_det(frequency) function
+        Keyword arguments:
+    freq -- 1D array of frequencies whose thresholds to return
+    scale factor -- scale for the novel_det function, where higher is steeper/lower SD
+    target -- target threshold
+    """
+    locs = 0.66 * np.power(0.8 * freq * (0.2 * freq - 1), 2) + 0.05
+    scale = 2 * locs / (3 * scale_factor)
+    loc = -1 + 2 * locs
+    return loc, scale
+
+
+def target_new_novel_det(freq, scale_factor=1.0, target=0.75):
+    """Get the target (i.e. threshold) for 2D synthetic novel_det(frequency) function
+        Keyword arguments:
+    freq -- 1D array of frequencies whose thresholds to return
+    scale factor -- scale for the novel_det function, where higher is steeper/lower SD
+    target -- target threshold
+    """
+    locs, scale = new_novel_det_params(freq, scale_factor)
+    return norm.ppf(target, loc=locs, scale=scale)
+
+
+def new_novel_det(x, scale_factor=1.0):
+    """Get the cdf for 2D synthetic novel_det(frequency) function
+        Keyword arguments:
+    x -- array of shape (n,2) of locations to sample;
+         x[...,0] is frequency from -1 to 1; x[...,1] is intensity from -1 to 1
+    scale factor -- scale for the novel_det function, where higher is steeper/lower SD
+    """
+    freq = x[..., 0]
+    locs, scale = new_novel_det_params(freq, scale_factor)
+    return (x[..., 1] - locs) / scale
+
+
+def cdf_new_novel_det(x, scale_factor=1.0):
+    """Get the cdf for 2D synthetic novel_det(frequency) function
+        Keyword arguments:
+    x -- array of shape (n,2) of locations to sample;
+         x[...,0] is frequency from -1 to 1; x[...,1] is intensity from -1 to 1
+    scale factor -- scale for the novel_det function, where higher is steeper/lower SD
+    """
+    return norm.cdf(new_novel_det(x, scale_factor))
+
+
+def new_novel_det_channels_params(channel, scale_factor=1.0, wave_freq=1, target=0.75):
+    """Get the target parameters for 2D synthetic novel_det(channel) function
+        Keyword arguments:
+    channel -- 1D array of channel locations whose thresholds to return
+    scale factor -- scale for the novel_det function, where higher is steeper/lower SD
+    wave_freq -- frequency of location waveform on [-1,1]
+    target -- target threshold
+    """
+    locs = -0.3 * np.sin(5 * wave_freq * (channel - 1 / 6) / np.pi) ** 2 - 0.5
+    scale = (
+        1 / (10 * scale_factor) * (0.75 + 0.25 * np.cos(10 * (0.3 + channel) / np.pi))
+    )
+    return locs, scale
+
+
+def target_new_novel_det_channels(channel, scale_factor=1.0, wave_freq=1, target=0.75):
+    """Get the target (i.e. threshold) for 2D synthetic novel_det(channel) function
+        Keyword arguments:
+    channel -- 1D array of channel locations whose thresholds to return
+    scale factor -- scale for the novel_det function, where higher is steeper/lower SD
+    wave_freq -- frequency of location waveform on [-1,1]
+    target -- target threshold
+    """
+    locs, scale = new_novel_det_channels_params(
+        channel, scale_factor, wave_freq, target
+    )
+    return norm.ppf(target, loc=locs, scale=scale)
+
+
+def new_novel_det_channels(x, channel, scale_factor=1.0, wave_freq=1, target=0.75):
+    """Get the 2D synthetic novel_det(channel) function
+        Keyword arguments:
+    x -- array of shape (n,2) of locations to sample;
+         x[...,0] is channel from -1 to 1; x[...,1] is intensity from -1 to 1
+    scale factor -- scale for the novel_det function, where higher is steeper/lower SD
+    wave_freq -- frequency of location waveform on [-1,1]
+    """
+    locs, scale = new_novel_det_channels_params(
+        channel, scale_factor, wave_freq, target
+    )
+    return (x - locs) / scale
+
+
+def cdf_new_novel_det_channels(channel, scale_factor=1.0, wave_freq=1, target=0.75):
+    """Get the cdf for 2D synthetic novel_det(channel) function
+        Keyword arguments:
+    x -- array of shape (n,2) of locations to sample;
+         x[...,0] is channel from -1 to 1; x[...,1] is intensity from -1 to 1
+    scale factor -- scale for the novel_det function, where higher is steeper/lower SD
+    wave_freq -- frequency of location waveform on [-1,1]
+    """
+    return norm.cdf(new_novel_det_channels(channel, scale_factor, wave_freq, target))
+
+
+def new_novel_det_3D_params(x, scale_factor=1.0):
+    freq = x[..., 0]
+    chan = x[..., 1]
+    locs_freq = -0.32 + 2 * (0.66 * np.power(0.8 * freq * (0.2 * freq - 1), 2) + 0.05)
+    locs = (
+        0.7 * ((-0.35 * np.sin(5 * (chan - 1 / 6) / np.pi) ** 2) - 0.5)
+        + 0.9 * locs_freq
+    )
+    scale = 0.3 * locs / (3 * scale_factor) * 1 / (10 * scale_factor) + 0.15 * (
+        0.75 + 0.25 * np.cos(10 * (0.6 + chan) / np.pi)
+    )
+    return locs, scale
+
+
+def new_novel_det_3D(x, scale_factor=1.0):
+    """
+    Get the synthetic 3D novel_det
+    function over freqs,channels and amplitudes
+
+    """
+    locs, scale = new_novel_det_3D_params(x, scale_factor)
+    return (x[..., 2] - locs) / scale
+
+
+def cdf_new_novel_det_3D(x, scale_factor=1.0):
+    """
+    Get the cdf for 3D synthetic novel_det function
+
+    x -- array of shape (n,3) of locations to sample
+         x[...,0] is frequency, x[...,1] is channel, x[...,2] is intensity
+
+    scale factor -- scale for the novel_det function, where higher is steeper/lower SD
+    """
+    return norm.cdf(new_novel_det_3D(x, scale_factor))
+
+
+def target_new_novel_det_3D(x, scale_factor=1.0, target=0.75):
+    """
+    Get target for 3D synthetic novel_det function at location x
+
+    x -- array of shape (n,2) of locations to sample
+         x[...,0] is frequency, x[...,1] is channel,
+
+    scale factor -- scale for the novel_det function, where higher is steeper/lower SD
+    target -- target threshold
+
+    """
+    locs, scale = new_novel_det_3D_params(x, scale_factor)
+    return norm.ppf(target, loc=locs, scale=scale)
+
+
+def f_pairwise(f, x, noise_scale=1):
+    return norm.cdf((f(x[..., 1]) - f(x[..., 0])) / (noise_scale * np.sqrt(2)))

tests/acquisition/test_mi.py

@@ -14,6 +14,7 @@
     MonotonicBernoulliMCMutualInformation,
 )
 from aepsych.acquisition.objective import ProbitObjective
+from aepsych.benchmark.test_functions import f_1d
 from aepsych.generators import (
     MonotonicRejectionGenerator,
     OptimizeAcqfGenerator,
@@ -25,8 +26,6 @@
 from gpytorch.means import ConstantMean
 from scipy.stats import bernoulli, multivariate_normal, norm, pearsonr
 
-from ..common import f_1d
-
 
 class SingleProbitMI(unittest.TestCase):
     def test_1d_monotonic_single_probit(self):

tests/models/test_pairwise_probit.py

@@ -14,6 +14,7 @@
 import torch
 from aepsych import server, utils_logging
 from aepsych.acquisition.objective import ProbitObjective
+from aepsych.benchmark.test_functions import f_1d, f_2d, f_pairwise, new_novel_det
 from aepsych.config import Config
 from aepsych.generators import OptimizeAcqfGenerator, SobolGenerator
 from aepsych.models import PairwiseProbitModel
@@ -25,8 +26,6 @@
 from botorch.acquisition.active_learning import PairwiseMCPosteriorVariance
 from scipy.stats import bernoulli, norm, pearsonr
 
-from ..common import f_1d, f_2d, f_pairwise, new_novel_det
-
 
 class PairwiseProbitModelStrategyTest(unittest.TestCase):
     def test_pairs_to_comparisons(self):