params.append(curr_params)
# fit model
currtime = time.time()
model = SparseComponentAnalysis(
n_components=n_components,
max_iter=max_iter,
gamma=gamma,
verbose=10,
tol=tol,
)
S_train = model.fit_transform(X_train)
train_time = time.time() - currtime
print(f"{train_time:.3f} elapsed to train model.")
S_test = model.transform(X_test)
# save model fit
models_by_params[curr_params] = model
S_train_by_params[curr_params] = S_train
S_test_by_params[curr_params] = S_test
# save metrics
metrics = compute_metrics(model, X_train, X_test)
metrics["sparsity_param"] = gamma
metrics["sparsity_level"] = gamma
metrics["n_components"] = n_components
metrics["train_time"] = train_time
metrics["method"] = method
metric_rows.append(metrics)
print(f"Component L0 ratio: {metrics['p_nonzero']}")
np.random.seed(seed)
currtime = time.time()
pca = PCA(n_components=n_components)
X_train_pca = pca.fit_transform(X_train)
print(f"{time.time() - currtime:.3f} elapsed to fit PCA model.")
X_test_pca = pca.transform(X_test)
#%%
np.random.seed(seed + 2)
currtime = time.time()
sca = SparseComponentAnalysis(n_components=n_components,
max_iter=30,
gamma=50,
verbose=10)
X_train_sca = sca.fit_transform(X_train)
print(f"{time.time() - currtime:.3f} elapsed for SCA.")
X_test_sca = sca.transform(X_test)
#%%
neuron_types = sequencing_df.index.get_level_values("Neuron_type").values
neuron_type_palette = dict(zip(np.unique(neuron_types), cc.glasbey_light))
y_train = index_train.get_level_values(level="Neuron_type").values
y_test = index_test.get_level_values(level="Neuron_type").values
pg = pairplot(X_train_sca[:, :4],
labels=y_train,
palette=neuron_type_palette,
diag_kind=None)
pg._legend.remove()
pg = pairplot(X_test_sca[:, :4],
curr_params = (n_components, gamma)
params.append(curr_params)
# fit model
currtime = time.time()
sca = SparseComponentAnalysis(
n_components=n_components,
max_iter=max_iter,
gamma=gamma,
verbose=10,
tol=tol,
)
S_train = sca.fit_transform(X_train)
print(f"{time.time() - currtime:.3f} elapsed to train SCA model.")
S_test = sca.transform(X_test)
# save model fit
models_by_params[curr_params] = sca
S_train_by_params[curr_params] = S_train
# save metrics
metrics = compute_metrics(sca)
metrics["gamma"] = gamma
metrics["n_components"] = n_components
metric_rows.append(metrics)
print("\n\n\n")
#%%
n_subsamples = 5