Build in models

PCA

from BI import bi, jnp

m=bi()
m.data('iris.csv', sep=',') # Data is already scaled
m.data_on_model = dict(
    X=jnp.array(m.df.iloc[:,0:-2].values)
)
m.fit(m.models.pca(type="classic"), progress_bar=False) # or robust, sparse, classic, sparse_robust_ard

m.models.pca.plot(
    X=m.df.iloc[:,0:-2].values,
    y=m.df.iloc[:,-2].values, 
    feature_names=m.df.columns[0:-2], 
    target_names=m.df.iloc[:,-1].unique(),
    color_var=m.df.iloc[:,0].values,
    shape_var=m.df.iloc[:,-2].values
)

jax.local_device_count 16

Survival analysis

from BI import bi, jnp

m = bi()
m.data('mastectomy.csv', sep=',').head()
m.df.metastasized = (m.df.metastasized.values == "yes").astype(jnp.int64)

# Import time-steps and events
m.models.survival.import_time_even(
    m.df.time.values, 
    m.df.event.values, interval_length=3
)

# To import time-fixed covariates
m.models.survival.import_covF(
    m.df.metastasized.values, ['metastasized']
) 

# To import time-varying covariates ⚠️ Experimental feature
# m.models.survival.import_covV 

m.fit(m.models.survival.model, num_samples=500) 

m.summary()

m.models.survival.plot_surv( beta = 'Hazard_rate_metastasized')

jax.local_device_count 16
------------------------------------------------------------------------------
Survival concern 44 individuals in 76 intervals.
18.0 individuals experienced the event.
------------------------------------------------------------------------------
Covariates imported: ['metastasized']
Surv object now has 1 covariates: ['metastasized']
⚠️This function is still in development. Use it with caution. ⚠️
⚠️This function is still in development. Use it with caution. ⚠️

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⚠️This function is still in development. Use it with caution. ⚠️

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size 7.57e-03. acc. prob=0.85]sample:  82%|████████▎ | 825/1000 [00:21<00:03, 50.93it/s, 511 steps of size 7.57e-03. acc. prob=0.85]sample:  83%|████████▎ | 831/1000 [00:21<00:03, 49.96it/s, 511 steps of size 7.57e-03. acc. prob=0.85]sample:  84%|████████▎ | 837/1000 [00:21<00:03, 50.93it/s, 454 steps of size 7.57e-03. acc. prob=0.85]sample:  84%|████████▍ | 843/1000 [00:21<00:03, 51.33it/s, 255 steps of size 7.57e-03. acc. prob=0.85]sample:  85%|████████▍ | 849/1000 [00:21<00:02, 51.76it/s, 511 steps of size 7.57e-03. acc. prob=0.85]sample:  86%|████████▌ | 855/1000 [00:22<00:02, 50.46it/s, 511 steps of size 7.57e-03. acc. prob=0.85]sample:  86%|████████▌ | 861/1000 [00:22<00:02, 49.54it/s, 511 steps of size 7.57e-03. acc. prob=0.85]sample:  87%|████████▋ | 867/1000 [00:22<00:02, 50.04it/s, 511 steps of size 7.57e-03. acc. prob=0.86]sample:  87%|████████▋ | 873/1000 [00:22<00:02, 49.36it/s, 511 steps of size 7.57e-03. acc. prob=0.86]sample:  88%|████████▊ | 878/1000 [00:22<00:02, 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prob=0.86]sample:  99%|█████████▉| 993/1000 [00:24<00:00, 52.11it/s, 511 steps of size 7.57e-03. acc. prob=0.86]sample: 100%|█████████▉| 999/1000 [00:24<00:00, 51.21it/s, 511 steps of size 7.57e-03. acc. prob=0.86]sample: 100%|██████████| 1000/1000 [00:24<00:00, 40.03it/s, 511 steps of size 7.57e-03. acc. prob=0.86]

⚠️This function is still in development. Use it with caution. ⚠️
⚠️This function is still in development. Use it with caution. ⚠️
⚠️This function is still in development. Use it with caution. ⚠️

arviz - WARNING - Shape validation failed: input_shape: (1, 500), minimum_shape: (chains=2, draws=4)
/home/sosa/work/.venv/lib/python3.12/site-packages/arviz/plots/hdiplot.py:166: FutureWarning:

hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions

/home/sosa/work/.venv/lib/python3.12/site-packages/arviz/plots/hdiplot.py:166: FutureWarning:

hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions

/home/sosa/work/.venv/lib/python3.12/site-packages/arviz/plots/hdiplot.py:166: FutureWarning:

hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions

/home/sosa/work/.venv/lib/python3.12/site-packages/arviz/plots/hdiplot.py:166: FutureWarning:

hdi currently interprets 2d data as (draw, shape) but this will change in a future release to (chain, draw) for coherence with other functions

Gaussian Mixture Models

from BI import bi
from sklearn.datasets import make_blobs
m = bi()

# Generate synthetic data
data, true_labels = make_blobs(
    n_samples=500, centers=8, cluster_std=0.8,
    center_box=(-10,10), random_state=101
)

m.data_on_model = {"data": data,"K": 8 }
m.fit(m.models.gmm) # Optimize model parameters through MCMC sampling
m.plot(X=data,sampler=m.sampler)  # ⚠️ Experimental feature

jax.local_device_count 16

  0%|          | 0/1000 [00:00<?, ?it/s]warmup:   0%|          | 1/1000 [00:04<1:11:59,  4.32s/it, 1 steps of size 2.34e+00. acc. prob=0.00]warmup:   3%|▎         | 29/1000 [00:04<01:45,  9.16it/s, 31 steps of size 3.59e-02. acc. prob=0.73]warmup:   5%|▍         | 46/1000 [00:04<00:59, 16.14it/s, 31 steps of size 2.97e-02. acc. prob=0.75]warmup:   8%|▊         | 79/1000 [00:04<00:26, 34.49it/s, 7 steps of size 7.94e-02. acc. prob=0.77] warmup:  10%|█         | 105/1000 [00:04<00:17, 51.60it/s, 31 steps of size 3.26e-01. acc. prob=0.77]warmup:  13%|█▎        | 132/1000 [00:04<00:11, 73.01it/s, 7 steps of size 4.17e-01. acc. prob=0.78] warmup:  16%|█▌        | 159/1000 [00:04<00:08, 97.13it/s, 7 steps of size 2.93e-01. acc. prob=0.78]warmup:  19%|█▊        | 186/1000 [00:05<00:06, 122.66it/s, 15 steps of size 4.28e-01. acc. prob=0.78]warmup:  22%|██▏       | 216/1000 [00:05<00:05, 152.82it/s, 15 steps of size 5.35e-01. acc. prob=0.78]warmup:  24%|██▍       | 244/1000 [00:05<00:04, 178.17it/s, 15 steps of size 3.82e-01. acc. prob=0.78]warmup:  27%|██▋       | 272/1000 [00:05<00:03, 193.00it/s, 7 steps of size 9.71e-01. acc. prob=0.78] warmup:  30%|██▉       | 298/1000 [00:05<00:03, 206.12it/s, 7 steps of size 9.47e-01. acc. prob=0.78]warmup:  32%|███▎      | 325/1000 [00:05<00:03, 221.45it/s, 15 steps of size 5.69e-01. acc. prob=0.78]warmup:  36%|███▌      | 357/1000 [00:05<00:02, 246.71it/s, 7 steps of size 2.49e-01. acc. prob=0.78] warmup:  39%|███▉      | 390/1000 [00:05<00:02, 268.69it/s, 7 steps of size 6.56e-01. acc. prob=0.79]warmup:  42%|████▎     | 425/1000 [00:05<00:01, 290.89it/s, 7 steps of size 7.48e-01. acc. prob=0.79]warmup:  46%|████▌     | 459/1000 [00:05<00:01, 304.07it/s, 15 steps of size 6.23e-01. acc. prob=0.79]warmup:  49%|████▉     | 491/1000 [00:06<00:01, 297.64it/s, 31 steps of size 2.14e-01. acc. prob=0.79]sample:  52%|█████▏    | 522/1000 [00:06<00:01, 288.36it/s, 15 steps of size 4.07e-01. acc. prob=0.87]sample:  55%|█████▌    | 552/1000 [00:06<00:01, 283.29it/s, 7 steps of size 4.07e-01. acc. prob=0.89] sample:  58%|█████▊    | 583/1000 [00:06<00:01, 288.64it/s, 7 steps of size 4.07e-01. acc. prob=0.89]sample:  61%|██████▏   | 613/1000 [00:06<00:01, 272.88it/s, 15 steps of size 4.07e-01. acc. prob=0.90]sample:  64%|██████▍   | 641/1000 [00:06<00:01, 274.34it/s, 15 steps of size 4.07e-01. acc. prob=0.90]sample:  67%|██████▋   | 669/1000 [00:06<00:01, 274.56it/s, 15 steps of size 4.07e-01. acc. prob=0.90]sample:  70%|██████▉   | 697/1000 [00:06<00:01, 273.02it/s, 15 steps of size 4.07e-01. acc. prob=0.90]sample:  72%|███████▎  | 725/1000 [00:06<00:01, 267.55it/s, 15 steps of size 4.07e-01. acc. prob=0.90]sample:  75%|███████▌  | 752/1000 [00:07<00:00, 267.48it/s, 7 steps of size 4.07e-01. acc. prob=0.90] sample:  78%|███████▊  | 779/1000 [00:07<00:00, 266.52it/s, 15 steps of size 4.07e-01. acc. prob=0.90]sample:  81%|████████  | 806/1000 [00:07<00:00, 264.74it/s, 7 steps of size 4.07e-01. acc. prob=0.90] sample:  83%|████████▎ | 834/1000 [00:07<00:00, 265.20it/s, 31 steps of size 4.07e-01. acc. prob=0.90]sample:  86%|████████▋ | 863/1000 [00:07<00:00, 269.96it/s, 15 steps of size 4.07e-01. acc. prob=0.89]sample:  89%|████████▉ | 891/1000 [00:07<00:00, 271.18it/s, 7 steps of size 4.07e-01. acc. prob=0.90] sample:  92%|█████████▏| 919/1000 [00:07<00:00, 273.38it/s, 23 steps of size 4.07e-01. acc. prob=0.89]sample:  95%|█████████▍| 947/1000 [00:07<00:00, 272.58it/s, 15 steps of size 4.07e-01. acc. prob=0.90]sample:  98%|█████████▊| 975/1000 [00:07<00:00, 270.71it/s, 7 steps of size 4.07e-01. acc. prob=0.90] sample: 100%|██████████| 1000/1000 [00:07<00:00, 125.19it/s, 15 steps of size 4.07e-01. acc. prob=0.90]

Dirichlet Process Mixture Models

Python

from BI import bi
from sklearn.datasets import make_blobs
m = bi()

# Generate synthetic data
data, true_labels = make_blobs(
    n_samples=500, centers=8, cluster_std=0.8,
    center_box=(-10,10), random_state=101
)
m.data_on_model = dict(data=data,T=10)
m.fit(m.models.dpmm)
m.plot(data,m.sampler) # ⚠️ Experimental feature

jax.local_device_count 16

  0%|          | 0/1000 [00:00<?, ?it/s]warmup:   0%|          | 1/1000 [00:02<42:59,  2.58s/it, 1 steps of size 2.34e+00. acc. prob=0.00]warmup:   2%|▏         | 15/1000 [00:02<02:14,  7.30it/s, 511 steps of size 9.88e-03. acc. prob=0.65]warmup:   2%|▏         | 19/1000 [00:02<01:49,  8.92it/s, 511 steps of size 5.50e-03. acc. prob=0.67]warmup:   2%|▏         | 23/1000 [00:03<01:41,  9.59it/s, 319 steps of size 2.97e-02. acc. prob=0.71]warmup:   3%|▎         | 27/1000 [00:03<01:21, 11.99it/s, 255 steps of size 1.66e-02. acc. prob=0.72]warmup:   3%|▎         | 31/1000 [00:03<01:06, 14.52it/s, 255 steps of size 1.59e-02. acc. prob=0.72]warmup:   4%|▎         | 37/1000 [00:03<00:55, 17.44it/s, 511 steps of size 2.80e-02. acc. prob=0.74]warmup:   4%|▍         | 40/1000 [00:04<00:56, 16.92it/s, 31 steps of size 3.96e-02. acc. prob=0.75] warmup:   4%|▍         | 45/1000 [00:04<00:44, 21.64it/s, 159 steps of size 8.74e-02. acc. prob=0.76]warmup:   5%|▍         | 49/1000 [00:04<00:48, 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Network Models

Python

# Setup device------------------------------------------------
from BI import bi, jnp

# Setup device------------------------------------------------
m = bi(platform='cpu')
# Simulate data ------------------------------------------------
N = 50
individual_predictor = m.dist.normal(0,1, shape = (N,1), sample = True)

kinship = m.dist.bernoulli(0.3, shape = (N,N), sample = True)
kinship = kinship.at[jnp.diag_indices(N)].set(0)

def sim_network(kinship, individual_predictor):
  # Intercept
  alpha = m.dist.normal(0,1, sample = True)

  # SR
  sr = m.net.sender_receiver(individual_predictor, individual_predictor, s_mu = 0.4, r_mu = -0.4, sample = True)

  # D
  DR = m.net.dyadic_effect(kinship, d_sd=2.5, sample = True)

  return m.dist.bernoulli(logits = alpha + sr + DR, sample = True)


network = sim_network(m.net.mat_to_edgl(kinship), individual_predictor)

# Predictive model ------------------------------------------------

m.data_on_model = dict(
    network = network, 
    dyadic_predictors = m.net.mat_to_edgl(kinship),
    focal_individual_predictors = individual_predictor,
    target_individual_predictors = individual_predictor
)


def model(network, dyadic_predictors, focal_individual_predictors, target_individual_predictors):
    N_id = network.shape[0]

    # Block ---------------------------------------
    alpha = m.dist.normal(0,1, sample = True)

    ## SR shape =  N individuals---------------------------------------
    sr =  m.net.sender_receiver(
      focal_individual_predictors,
      target_individual_predictors,
      s_mu = 0.4, r_mu = -0.4
    )

    # Dyadic shape = N dyads--------------------------------------  
    dr = m.net.dyadic_effect(dyadic_predictors, d_sd=2.5) # Diadic effect intercept only 

    m.dist.bernoulli(logits = alpha + sr + dr, obs=network)

m.fit(model, num_samples = 500, num_warmup = 500, num_chains = 1, thinning = 1)