Using multiple sequences of observation

[poypoyan]

Using multiple sequences of observation

[poypoyan]

Apologies for late reply. Will look into this. Again, thanks!

[poypoyan]

Good day,

The work-around for the error is to set the type of np.empty in all_obs like this: np.empty((...), dtype=np.int64).

Now to perform multiple observation, "insert" first all observations into one big ndarray, and then specify the "boundaries" between observations in a list of lengths. This is the same to how it's being done for hmmlearn (at least at the time I implemented edhsmm).

Here is the modified code that works.

import sys

import numpy as np
from edhsmm.hsmm_multinom import MultinomialHSMM

# modified initial parameters for MultinomialHSMM
def init_true_model(hsmm_class):
    hsmm_class.pi = np.array([2 / 3, 1 / 3, 0 / 3])
    hsmm_class.dur = np.array([
        [0.1, 0.005, 0.005, 0.89],
        [0.1, 0.005, 0.89, 0.005],
        [0.1, 0.89, 0.005, 0.005]
    ])
    hsmm_class.tmat = np.array([
        [0.0, 0.5, 0.5],
        [0.3, 0.0, 0.7],
        [0.4, 0.6, 0.0]
    ])
    hsmm_class.emit = np.array([
        [0.8, 0.1, 0.1],
        [0.1, 0.8, 0.1],
        [0.1, 0.1, 0.8]
    ])   # shape should be (n_states, n_symbols)

# modified initial parameters for MultinomialHSMM
def init_middle_model(hsmm_class):
    hsmm_class.pi = np.array([1/3, 1/3, 1/3])
    hsmm_class.dur = np.array([
        [0.25, 0.25, 0.25, 0.25],
        [0.25, 0.25, 0.25, 0.25],
        [0.25, 0.25, 0.25, 0.25]
    ])
    hsmm_class.tmat = np.array([
        [0.0, 0.5, 0.5],
        [0.5, 0.0, 0.5],
        [0.5, 0.5, 0.0]
    ])
    hsmm_class.emit = np.array([
        [1/3, 1/3, 1/3],
        [1/3, 1/3, 1/3],
        [1/3, 1/3, 1/3]
    ])   # shape should be (n_states, n_symbols)


# initialize HSMM
squirrel_true_model = MultinomialHSMM(n_states = 3, n_durations = 4)
init_true_model(squirrel_true_model)

rng_seed = 12345
n_samples = 100
n_obs = 10
n_var = 1

# to use multiple observers: "insert" all observations to `all_obs`
# then specify the "boundaries" between observations in `all_len`
all_obs = np.empty((n_samples * n_obs, n_var), dtype=np.int64)   # work-around
all_len = []
ctr_len = 0
for i in range(n_obs):
    sample_len, sample, _ = squirrel_true_model.sample(n_samples=n_samples, random_state=rng_seed)
    all_obs[ctr_len: ctr_len + sample_len, :] = sample
    all_len += [sample_len]
    ctr_len += sample_len
    rng_seed += 1

squirrel_middle_model = MultinomialHSMM(n_states = 3, n_durations = 4)
init_middle_model(squirrel_true_model)
squirrel_middle_model.fit(all_obs)

# display learned parameters for T
print("Start Probabilities: [T]\n", squirrel_middle_model.pi, "\n")
print("Transition Matrix: [T]\n", squirrel_middle_model.tmat, "\n")
print("Durations: [T]\n", squirrel_middle_model.dur, "\n")
print("Emission Probabilities: [T]\n", squirrel_middle_model.emit, "\n")

Thanks!
poypoyan

[rtrepos]

Hi poypoyan,
Thank you for your proposal and your availability ! I will try it very soon.
Best
Ronan

[rtrepos]

Hi poypoyan,
Finally, i found time to test it and It works! So thank you again for your help.
I am nevertheless surprised that initial probabilities are quite difficult to estimate.
For example in this setup with many samples (500) where all is well estimated except initial probabilities. If you have any idea, I would be pleased to hear that. Otherwise you can close this thread.
Best
Ronan

---

import sys

import numpy as np
from edhsmm.hsmm_multinom import MultinomialHSMM

# modified initial parameters for MultinomialHSMM
def init_true_model(hsmm_class):
    hsmm_class.pi = np.array([2 / 3, 1 / 3, 0 / 3])
    hsmm_class.dur = np.array([
        [0.1, 0.005, 0.005, 0.89],
        [0.1, 0.005, 0.89, 0.005],
        [0.1, 0.89, 0.005, 0.005]
    ])
    hsmm_class.tmat = np.array([
        [0.0, 0.5, 0.5],
        [0.3, 0.0, 0.7],
        [0.4, 0.6, 0.0]
    ])
    hsmm_class.emit = np.array([
        [0.8, 0.1, 0.1],
        [0.1, 0.8, 0.1],
        [0.1, 0.1, 0.8]
    ])   # shape should be (n_states, n_symbols)

# modified initial parameters for MultinomialHSMM
def init_middle_model(hsmm_class):
    hsmm_class.pi = np.array([1/3, 1/3, 1/3])
    hsmm_class.dur = np.array([
        [0.25, 0.25, 0.25, 0.25],
        [0.25, 0.25, 0.25, 0.25],
        [0.25, 0.25, 0.25, 0.25]
    ])
    hsmm_class.tmat = np.array([
        [0.0, 0.5, 0.5],
        [0.5, 0.0, 0.5],
        [0.5, 0.5, 0.0]
    ])
    hsmm_class.emit = np.array([
        [1/3, 1/3, 1/3],
        [1/3, 1/3, 1/3],
        [1/3, 1/3, 1/3]
    ])   # shape should be (n_states, n_symbols)


# initialize HSMM
squirrel_true_model = MultinomialHSMM(n_states = 3, n_durations = 4)
init_true_model(squirrel_true_model)

rng_seed = 123598
n_samples = 500
n_obs = 300
n_var = 1

# to use multiple observers: "insert" all observations to `all_obs`
# then specify the "boundaries" between observations in `all_len`
all_obs = np.empty((n_samples * n_obs, n_var), dtype=np.int64)   # work-around
all_len = []
ctr_len = 0
for i in range(n_obs):
    sample_len, sample, _ = squirrel_true_model.sample(n_samples=n_samples, random_state=rng_seed)
    all_obs[ctr_len: ctr_len + sample_len, :] = sample
    all_len += [sample_len]
    ctr_len += sample_len
    rng_seed += 1

squirrel_middle_model = MultinomialHSMM(n_states = 3, n_durations = 4)
init_middle_model(squirrel_true_model)
squirrel_middle_model.fit(all_obs)

# display learned parameters for T
print("Start Probabilities: [T]\n", squirrel_middle_model.pi, "\n")
print("Transition Matrix: [T]\n", squirrel_middle_model.tmat, "\n")
print("Durations: [T]\n", squirrel_middle_model.dur, "\n")
print("Emission Probabilities: [T]\n", squirrel_middle_model.emit, "\n")

[poypoyan]

Hello.

I forgot a few things:

1. For multiple observation, the "boundaries" (all_len) should be included in fit():

squirrel_middle_model.fit(all_obs, all_len)

2. Regarding random_state: I am thinking of just having it in the HSMM object, as in

squirrel_true_model = MultinomialHSMM(n_states = 3, n_durations = 4, random_state = 42)

hence removing it in sample(). What do you think?

I'll investigate the issue with initial probabilities. I'll make a new issue, and close this issue.

Thanks!
poypoyan

[rtrepos]

Hello,
I am sorry to have taken so much time to answer you, I was not available recently.
Thanks again for your help and to answer your questions :

• I have put all_len when calling the fit process, surprisingly, the results are not really better but that's ok.
• For the random_state, I think you use the np.random.default_rng which is a global rng (is this really the case ?) then my advice would be to not include it as an argument of the MultinomialHSMM. Because, internally you will modify this global rng and a user could be surprised to see the differences if he uses it for another purpose in the same script. Even more, I would remove it from the sample function and let the user set for example in front of his script : np.random.default_rng(random_state)

Best
Ronan

[poypoyan]

Hello.

I cannot get myself to work on this repo anymore 😓. Because of this, I am in the process of archiving this repo. I am sorry that I cannot work on resolving this issue. Thank you for your interest and understanding.

poypoyan