Python script demonstrates the implementation of Gaussian Mixture model

hmm_gaussian_mixture_model.py

train_df = data_df.loc[: '2019-01-01'].dropna()
test_df = data_df.loc['2019-01-01': ].dropna()
X_train = train_df[FT_COLS].values
X_test = test_df[FT_COLS].values

model = mix.GaussianMixture(n_components=N_COMPONENTS, 
                            covariance_type="full", 
                            n_init=100, 
                            random_state=SEED).fit(X_train)

# Predict the optimal sequence of internal hidden state
hidden_states = model.predict(X_train)

print("Means and vars of each hidden state")
for i in range(model.n_components):
    print(f'{i}th hidden state')
    print('mean: ', (model.means_[i]))
    print('var: ', np.diag(model.covariances_[i]))
    print()

sns.set(font_scale=1.25)
style_kwds = {'xtick.major.size': 3, 'ytick.major.size': 3, 'legend.frameon': True}
sns.set_style('white', style_kwds)

fig, axs = plt.subplots(model.n_components, sharex=True, sharey=True, figsize=(12,9))
colors = cm.rainbow(np.linspace(0, 1, model.n_components))

for i, (ax, color) in enumerate(zip(axs, colors)):
    # Use fancy indexing to plot data in each state.
    mask = hidden_states == i
    ax.plot_date(train_df.index.values[mask],
                 train_df[COL_].values[mask],
                 ".-", c=color)
    ax.set_title("{0}th hidden state".format(i), fontsize=16, fontweight='demi')

    # Format the ticks.
    ax.xaxis.set_major_locator(YearLocator())
    ax.xaxis.set_minor_locator(MonthLocator())
    sns.despine(offset=10)

plt.tight_layout()

So many variables are not defined. FT_COLS, N_COMPONENTS, COL_