Find best parameter for sarimax model and log useful stuff into MLflow

sarimax_model_param.py

# Initial approximation of parameters
Qs = range(0, 2)
qs = range(0, 3)
Ps = range(0, 3)
ps = range(0, 3)
D=1
d=1
parameters = product(ps, qs, Ps, Qs)
parameters_list = list(parameters)
len(parameters_list)

# Model Selection
results = []
best_aic = float("inf")
warnings.filterwarnings('ignore')
for param in parameters_list:
    # start mlflow run
    with mlflow.start_run(run_name='sarimax_param'):
    
      # log parameters
      mlflow.log_param('order-Qs', param[0])
      mlflow.log_param('order-qs', param[1])
      mlflow.log_param('seasonal-order-Ps', param[2])
      mlflow.log_param('seasonal-order-ps', param[3])
      
      try:
  #        model = SARIMAX(btc_month.close_box, order=(param[0], d, param[1]), seasonal_order=(param[2], D, param[3], 12)).fit(disp=-1)
          model = SARIMAX(btc_month.close_box, order=(param[0], d, param[1]), seasonal_order=(param[2], D, param[3], 4)).fit(disp=-1)
      except ValueError:
          print('bad parameter combination:', param)
          continue
      aic = model.aic
      if aic < best_aic:
          best_model = model
          best_aic = aic
          best_param = param
      results.append([param, model.aic])
      
      # log metric
      mlflow.log_metric('aic',aic)
      mlflow.log_metric('dickey-fuller-test',adfuller(model.resid[13:])[1])
      
      # log artifact: model summary
      plt.rc('figure', figsize=(12, 7))
      plt.text(0.01, 0.05, str(model.summary()), {'fontsize': 10}, fontproperties = 'monospace') 
      plt.axis('off')
      plt.tight_layout()
      summary_fn = 'model_sarimax_summary_{}_{}_{}_{}.png'.format(param[0],param[1],param[2],param[3])
      plt.savefig(summary_fn)
      mlflow.log_artifact(summary_fn) # logging to mlflow
      plt.close()
      
      # log artifact: diagnostics plot
      model.plot_diagnostics(figsize=(15, 12))      
      fig1_fn = 'figure_diagnostics_{}_{}_{}_{}.png'.format(param[0],param[1],param[2],param[3])
      plt.savefig(fig1_fn)
      mlflow.log_artifact(fig1_fn) # logging to mlflow      
      plt.close()
      
      # log artifact: residuals and pacf plot
      plt.subplot(211)
      model.resid[13:].plot()
      plt.ylabel(u'Residuals')
      ax = plt.subplot(212)
      plot_acf(model.resid[13:].values.squeeze(), lags=12, ax=ax)
      plt.tight_layout()
      fig2_fn = 'figure_res_pacf_{}_{}_{}_{}.png'.format(param[0],param[1],param[2],param[3])
      plt.savefig(fig2_fn)
      mlflow.log_artifact(fig2_fn) # logging to mlflow      
      plt.close()

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