From 30d632a73a68c7a97739eb49887b4df5439a8edb Mon Sep 17 00:00:00 2001
From: SebastianBruijns <>
Date: Thu, 13 Jul 2023 11:56:38 +0200
Subject: [PATCH] commenting work
---
pybasicbayes/distributions/dynamic_glm.py | 95 +++++++++++++++--------
1 file changed, 64 insertions(+), 31 deletions(-)
diff --git a/pybasicbayes/distributions/dynamic_glm.py b/pybasicbayes/distributions/dynamic_glm.py
index 17b79a5..94a11c7 100644
--- a/pybasicbayes/distributions/dynamic_glm.py
+++ b/pybasicbayes/distributions/dynamic_glm.py
@@ -9,7 +9,7 @@ __all__ = ['Dynamic_GLM']
def local_multivariate_normal_draw(x, sigma, normal):
"""
- Function to combine pre-drawn Normals (normal) with the desired mean x and variance sigma
+ Function to combine pre-drawn Normals (normal) with the desired mean x and variance sigma (cause calls to multivariate normal are too costly)
Cholesky doesn't like 0 cov matrix, but we want it.
This might need changing if in practice we see plently of 0 matrices
@@ -23,7 +23,7 @@ def local_multivariate_normal_draw(x, sigma, normal):
print("Weird covariance matrix")
quit()
-
+# fix seed of PyPolyaGamma, otherwise results aren't reproducible
ppgsseed = 4
if ppgsseed == 4:
print("Using default seed")
@@ -52,7 +52,7 @@ class Dynamic_GLM(GibbsSampling):
self.T = T
self.jumplimit = jumplimit
self.x_0 = prior_mean
- self.P_0, self.Q = P_0, Q
+ self.P_0, self.Q = P_0, Q # initial variance of regressors P_0, and variance for timesteps Q (can have different variances, we have it fixed)
self.psi_diff_saves = [] # this can be used to resample the variance, but is currently unused
self.noise_mean = np.zeros(self.n_regressors) # save this, so as to not keep creating it
self.identity = np.eye(self.n_regressors) # not really needed, but kinda useful for state sampling
@@ -82,7 +82,7 @@ class Dynamic_GLM(GibbsSampling):
def log_likelihood(self, input, timepoint):
"""
- Given input from a single session (a matrix containing regressors and responses) and the timepoint information from that session, return log-likelihoods for observations.
+ Given input from a single session (a matrix containing features and responses) and the timepoint information from that session, return log-likelihoods for observations.
"""
predictors, responses = input[:, :-1], input[:, -1]
nans = np.isnan(responses) # can come from cross-validation
@@ -104,18 +104,22 @@ class Dynamic_GLM(GibbsSampling):
Resampling of dynamic logistic random variables.
We follow the resampling scheme of Windle: "Efficient Data Augmentation in Dynamic Models for Binary and Count Data".
This makes use of the forward filter backwards sample algorithm. Which uses Kalman filtering, for which we use Anderson & Moore 1979
+
+ Usually you just get one type of observation per timestep (one set of features), but we have a number of different feature sets per timestep. Thus,
+ we invent more timesteps, but we don't allow the regressors to change from these steps to steps, effectively making the observations occur at the same
+ time.
"""
# TODO: Clean up this mess, I always have to call delete_obs_data because of all the saved shit!
self.psi_diff_saves = []
summary_statistics, all_times = self._get_statistics(data)
- types, pseudo_counts, counts = summary_statistics
+ types, pseudo_counts, counts = summary_statistics # pseudo_counts are kappa_t
# if state is never active, resample from prior, but without dynamic change
if len(counts) == 0:
self.weights = np.tile(np.random.multivariate_normal(mean=self.x_0, cov=self.P_0), (self.T, 1))
return
- """compute Kalman filter parameters, also sort out which weight vector goes to which timepoint"""
+ # Sort out which weight vector goes to which timepoint
timepoint_map = {}
total_types = 0
actual_obs_count = 0
@@ -136,33 +140,42 @@ class Dynamic_GLM(GibbsSampling):
timepoint_map[t] = total_types - 1
prev_t = t
+ # We introduce pseudo-variance steps: we don't want change for the fake times, so only specific pseudo-Q's will be non-zero
self.pseudo_Q = np.zeros((total_types, self.n_regressors, self.n_regressors))
# TODO: is it okay to cut off last timepoint here?
for k in range(self.T):
if k in timepoint_map:
- self.pseudo_Q[timepoint_map[k]] = self.Q[k] # for every timepoint, map it's variance onto the pseudo_Q
+ self.pseudo_Q[timepoint_map[k]] = self.Q[k] # for every real timepoint, map it's variance onto the pseudo_Q
- """Prepare for pseudo-observation sampling"""
- temp = np.empty(actual_obs_count)
+ """Prepare for pseudo-observation sampling, collect all the psi's"""
psis = np.empty(actual_obs_count)
psi_counter = 0
predictors = []
for type, time in zip(types, all_times):
for t in type:
+ # psi (for a specific observation type t) is weights at time multiplied with the predictors t, i.e. the log odds
psis[psi_counter] = np.sum(self.weights[time] * t)
predictors.append(t)
psi_counter += 1
# Here we use the pg package to draw augmenting variable from a polya-gamma distribution
- # Windle calls the counts b_t = a_t + d_t, just the total number of times a psi has been seen. temp is the array in which our draws will be saved
- ppgs.pgdrawv(np.concatenate(counts).astype(float), psis, temp)
+ # Windle calls the counts b_t = a_t + d_t, just the total number of times a psi has been seen. omega is the array in which our draws will be saved
+ omega = np.empty(actual_obs_count)
+ ppgs.pgdrawv(np.concatenate(counts).astype(float), psis, omega)
+
+ # R is the variance on the observations in Kalman filtering. According to Windle, that is 1 / omega here
self.R = np.zeros(total_types)
mask = np.ones(total_types, dtype=np.bool)
- mask[fake_times] = False
- self.R[mask] = 1 / temp
+ mask[fake_times] = False # during fake times, we make no observations, so no variance
+ self.R[mask] = 1 / omega
+
+ # Create pseudo-obs. They can be treated as if they came from N(psi, 1 / omega)
self.pseudo_obs = np.zeros(total_types)
- self.pseudo_obs[mask] = np.concatenate(pseudo_counts) / temp
+ self.pseudo_obs[mask] = np.concatenate(pseudo_counts) / omega # Windle: pseudo-data points are z_t = kappa_t / omega_t
self.pseudo_obs = self.pseudo_obs.reshape(total_types, 1)
+
+ # H is the matrix that transforms the latents into the (means of the) observations
+ # in our case, these are the features, they multiply with the regressors to give us the means that we noisily observe
self.H = np.zeros((total_types, self.n_regressors, 1))
self.H[mask] = np.array(predictors).reshape(actual_obs_count, self.n_regressors, 1)
@@ -171,38 +184,48 @@ class Dynamic_GLM(GibbsSampling):
self.compute_sigmas(total_types)
self.compute_means(total_types)
- """sample states"""
+ """Sample states
+ We follow Apendix 1 from "On Gibbs Sampling for State Space Models" by Carter & Kohn"""
self.weights.fill(0)
pseudo_weights = np.empty((total_types, self.n_regressors))
+ # the first weight can be sampled directly, from the end of the Kalman inference process
pseudo_weights[total_types - 1] = np.random.multivariate_normal(self.x_hat_k[total_types - 1], self.sigma_k[total_types - 1])
- normals = np.random.standard_normal((total_types - 1, self.n_regressors))
+ # the other weights now depend on the weight sampled just after them, we need to update the Kalman estimates
+ normals = np.random.standard_normal((total_types - 1, self.n_regressors)) # pregenerate some normals rvs to use later
for k in range(total_types - 2, -1, -1): # normally -1, but we already did first sampling
- if np.all(self.pseudo_Q[k] == 0):
+ if np.all(self.pseudo_Q[k] == 0): # if this is a fake time, no change is allowed, just copy the weight
pseudo_weights[k] = pseudo_weights[k + 1]
else:
+ # start with the Kalman filter inferences
updated_x = self.x_hat_k[k].copy() # not sure whether copy is necessary here
updated_sigma = self.sigma_k[k].copy()
+ # then update them with the subsequent weights
for m in range(self.n_regressors):
+ # these are the Carter & Kohn equations, making use of the fact that F is identity, therefore x^tilde is x
+ # and delta_i is the variance Q
epsilon = pseudo_weights[k + 1, m] - updated_x[m]
state_R = updated_sigma[m, m] + self.pseudo_Q[k, m, m]
- updated_x += updated_sigma[:, m] * epsilon / state_R # I don't think it's important, but probs we need the first column
+ updated_x += updated_sigma[:, m] * epsilon / state_R
updated_sigma -= updated_sigma.dot(np.outer(self.identity[m], self.identity[m])).dot(updated_sigma) / state_R
- pseudo_weights[k] = local_multivariate_normal_draw(updated_x, updated_sigma, normals[k])
+ pseudo_weights[k] = local_multivariate_normal_draw(updated_x, updated_sigma, normals[k]) # transform our normal according to computed mean and var
+ # put the pseudo_weights where they belong
for k in range(self.T):
if k in timepoint_map:
self.weights[k] = pseudo_weights[timepoint_map[k]]
- """Sample before and after active times too"""
+ # Sample before and after active times too
+ # fill weights up before first determined weight, at least until the jumplimit is reached (in principle this would need to be shrunk by P_0)
for k in range(all_times[0] - 1, -1, -1):
if k > all_times[0] - self.jumplimit - 1:
self.weights[k] = self.weights[k + 1] + np.random.multivariate_normal(self.noise_mean, self.Q[k])
else:
self.weights[k] = self.weights[k + 1]
+ # fill weights up after last determined weights, at least until jumplimit
for k in range(all_times[-1] + 1, self.T):
if k < min(all_times[-1] + 1 + self.jumplimit, self.T):
self.weights[k] = self.weights[k - 1] + np.random.multivariate_normal(self.noise_mean, self.Q[k])
@@ -214,7 +237,11 @@ class Dynamic_GLM(GibbsSampling):
# self.psi_diff_saves = np.concatenate(self.psi_diff_saves)
def _get_statistics(self, data):
- # TODO: improve
+ """For the data of a list of sessions, we need to know:
+ which unique features did the state encounter (types)
+ what is their corresponding pseudo-observation (pseudo_counts)
+ how often did they occur (counts)
+ """
summary_statistics = [[], [], []]
times = []
if isinstance(data, np.ndarray):
@@ -229,7 +256,8 @@ class Dynamic_GLM(GibbsSampling):
pseudo_counts = np.zeros(len(types))
for j, c in enumerate(counts):
mask = inverses == j
- pseudo_counts[j] = np.sum(responses[mask]) - c / 2
+ # find out how the animals answered on these trials, and turn it into a pseudo-observation
+ pseudo_counts[j] = np.sum(responses[mask]) - c / 2 # already turn this into the desired format: kappa_t = a_t -b_t / 2
summary_statistics[0].append(types)
summary_statistics[1].append(pseudo_counts)
summary_statistics[2].append(counts)
@@ -241,29 +269,33 @@ class Dynamic_GLM(GibbsSampling):
"""Sigmas can be precomputed (without z), we do this here."""
# We rely on the fact that H.T.dot(sigma).dot(H) is just a number, no matrix inversion needed
# furthermore we use the fact that many matrices are identities, namely F and G
- # We follow Anderson & Moore 1979, equations are listed where applicable
+ # We follow Anderson & Moore 1979, p.44
self.sigma_k = [] # we have to reset this for repeating this calculation later for the resampling (R changes)
- self.sigma_k_k_minus = [self.P_0] # (1.10)
+ self.sigma_k_k_minus = [self.P_0]
self.gain_save = []
for k in range(T):
- if self.R[k] == 0:
- self.gain_save.append(None)
+ if self.R[k] == 0: # these are fake times, no observations were made but we allow change as per the jumplimit
+ self.gain_save.append(None) # need a placeholder
self.sigma_k.append(self.sigma_k_k_minus[k])
- self.sigma_k_k_minus.append(self.sigma_k[k] + self.pseudo_Q[k])
+ self.sigma_k_k_minus.append(self.sigma_k[k] + self.pseudo_Q[k]) # the amount of allowed change just adds up if no obs
else:
sigma, H = self.sigma_k_k_minus[k], self.H[k] # we will need this a lot, so shorten it
- gain = sigma.dot(H).dot(1 / (H.T.dot(sigma).dot(H) + self.R[k]))
- self.gain_save.append(gain)
+ gain = sigma.dot(H).dot(1 / (H.T.dot(sigma).dot(H) + self.R[k])) # matrix K, elsewhere called gain
+ self.gain_save.append(gain) # the gain matrix is useful for compute_means as well, save it
+
+ # the two main equations, from p.44:
self.sigma_k.append(sigma - gain.dot(H.T).dot(sigma))
self.sigma_k_k_minus.append(self.sigma_k[k] + self.pseudo_Q[k])
def compute_means(self, T):
"""Compute the means, the estimates of the states.
- Used to also contain self.x_hat_k_k_minus, but it's not necessary for our setup"""
+ Used to also contain self.x_hat_k_k_minus, but it's not necessary for our setup
+ (Especially (if not generally), with F=1, x_hat_k_k_minus is just equal to x_hat_k, see also p.40)"""
+ # We follow Anderson & Moore 1979, p.44
self.x_hat_k = [self.x_0] # we have to reset this for repeating this calculation later for the resampling
for k in range(T): # this will leave out last state which doesn't have observation
if self.gain_save[k] is None:
- self.x_hat_k.append(self.x_hat_k[k])
+ self.x_hat_k.append(self.x_hat_k[k]) # mean stays unchanged if no observations
else:
x, H = self.x_hat_k[k], self.H[k] # we will need this a lot, so shorten it
self.x_hat_k.append(x + self.gain_save[k].dot(self.pseudo_obs[k] - H.T.dot(x)))
@@ -271,6 +303,7 @@ class Dynamic_GLM(GibbsSampling):
self.x_hat_k.pop(0) # remove initialisation element from list
def num_parameters(self):
+ # Compute WAIC in principle
return self.weights.size
### Max likelihood
--
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