Added singleton pattern to forecast.py

src/covidify/data_visualization.py

@@ -23,11 +23,13 @@
 import matplotlib.pyplot as plt
 from covidify.utils.utils import replace_arg_score
 
+
 # plt settings
 font = {'weight' : 'bold',
         'size'   : 22}
 plt.rc('font', **font)
 plt.style.use('ggplot')
+
 
 args = docopt.docopt(__doc__)
 out = args['--output_folder']

src/covidify/forecast.py

@@ -23,7 +23,7 @@
 import matplotlib.pyplot as plt
 from dateutil.parser import parse
 from pmdarima.arima import auto_arima
-from datetime import datetime, date, time 
+from datetime import datetime, date, time
 from sklearn.metrics import mean_squared_error
 from covidify.config import PERC_SPLIT, FIG_SIZE
 
@@ -37,7 +37,7 @@
 args = docopt.docopt(__doc__)
 out = args['--output_folder']
 days_in_future = int(args['--num_days'])
-           
+
 # file paths
 image_dir =  os.path.join(out,'reports', 'images')
 trend_file  = 'trend_{}.csv'.format(datetime.date(datetime.now()))
@@ -55,62 +55,70 @@
 train_period = [d.strftime('%Y-%m-%d') for d in pd.date_range(train_start, train_end)]
 forecast_period = [d.strftime('%Y-%m-%d') for d in pd.date_range(forecast_start, forecast_end)]
 
+class SingletonMeta(type):
+    _instances = {}
+
+    def __call__(abc, *args, **kwargs):
+        if abc not in abc._instances:
+            instance = super().__call__(*args, **kwargs)
+            abc._instances[abc] = instance
+        return abc._instances[abc]
+class Singleton(metaclass=SingletonMeta):
+    if not os.path.exists(image_dir):
+        print('Creating reports folder...')
+        os.system('mkdir -p ' + image_dir)
+
+
+    def plot_forecast(tmp_df, train, index_forecast, forecast, confint):
+        '''
+        Plot the values of train and test, the predictions from ARIMA and the shadowing
+        for the confidence interval.
+
+        '''
+
+        # For shadowing
+        lower_series = pd.Series(confint[:, 0], index=index_forecast)
+        upper_series = pd.Series(confint[:, 1], index=index_forecast)
+
+        print('... saving graph')
+        fig, ax = plt.subplots(figsize=FIG_SIZE)
+        plt.title('ARIMA - Prediction for cumalitive case counts {} days in the future'.format(days_in_future))
+        plt.plot(tmp_df.cumulative_cases, label='Train',marker='o')
+        plt.plot(tmp_df.pred, label='Forecast', marker='o')
+        tmp_df.groupby('date')[['']].sum().plot(ax=ax)
+        plt.fill_between(index_forecast,
+                         upper_series,
+                         lower_series,
+                         color='k', alpha=.1)
+        plt.ylabel('Infections')
+        plt.xlabel('Date')
+        fig.legend().set_visible(True)
+        fig = ax.get_figure()
+        fig.savefig(os.path.join(image_dir, 'cumulative_forecasts.png'))
+
+
+    def forecast(tmp_df, train, index_forecast, days_in_future):
+
+        # Fit model with training data
+        model = auto_arima(train, trace=False, error_action='ignore', suppress_warnings=True)
+        model_fit = model.fit(train)
+
+        forecast, confint = model_fit.predict(n_periods=len(index_forecast), return_conf_int=True)
+
+        forecast_df = pd.concat([tmp_df, pd.DataFrame(forecast, index = index_forecast, columns=['pred'])], axis=1, sort=False)
+        date_range = [d.strftime('%Y-%m-%d') for d in pd.date_range(train_start, forecast_end)]
+        forecast_df['date'] = pd.Series(date_range).astype(str)
+        forecast_df[''] = None # Dates get messed up, so need to use pandas plotting
+
+        # Save Model and file
+        print('... saving file:', forecast_file)
+        forecast_df.to_csv(os.path.join(data_dir, forecast_file))
+
+        Singleton.plot_forecast(forecast_df, train, index_forecast, forecast, confint)
 
-if not os.path.exists(image_dir):
-    print('Creating reports folder...')
-    os.system('mkdir -p ' + image_dir)
-
-
-def plot_forecast(tmp_df, train, index_forecast, forecast, confint):
-    '''
-    Plot the values of train and test, the predictions from ARIMA and the shadowing
-    for the confidence interval.
-
-    '''
-
-    # For shadowing
-    lower_series = pd.Series(confint[:, 0], index=index_forecast)
-    upper_series = pd.Series(confint[:, 1], index=index_forecast)
-
-    print('... saving graph')
-    fig, ax = plt.subplots(figsize=FIG_SIZE)
-    plt.title('ARIMA - Prediction for cumalitive case counts {} days in the future'.format(days_in_future))    
-    plt.plot(tmp_df.cumulative_cases, label='Train',marker='o')
-    plt.plot(tmp_df.pred, label='Forecast', marker='o')
-    tmp_df.groupby('date')[['']].sum().plot(ax=ax)
-    plt.fill_between(index_forecast, 
-                     upper_series, 
-                     lower_series, 
-                     color='k', alpha=.1)
-    plt.ylabel('Infections')
-    plt.xlabel('Date')
-    fig.legend().set_visible(True)
-    fig = ax.get_figure()
-    fig.savefig(os.path.join(image_dir, 'cumulative_forecasts.png'))
-
-
-def forecast(tmp_df, train, index_forecast, days_in_future):
-
-    # Fit model with training data
-    model = auto_arima(train, trace=False, error_action='ignore', suppress_warnings=True)
-    model_fit = model.fit(train)
-
-    forecast, confint = model_fit.predict(n_periods=len(index_forecast), return_conf_int=True)
-
-    forecast_df = pd.concat([tmp_df, pd.DataFrame(forecast, index = index_forecast, columns=['pred'])], axis=1, sort=False)
-    date_range = [d.strftime('%Y-%m-%d') for d in pd.date_range(train_start, forecast_end)]
-    forecast_df['date'] = pd.Series(date_range).astype(str)
-    forecast_df[''] = None # Dates get messed up, so need to use pandas plotting
-
-    # Save Model and file
-    print('... saving file:', forecast_file)
-    forecast_df.to_csv(os.path.join(data_dir, forecast_file))
-
-    plot_forecast(forecast_df, train, index_forecast, forecast, confint)
-
 if __name__ == '__main__':
     print('Training forecasting model...')
 
     train = trend_df[trend_df.date.isin(train_period)].cumulative_cases
     index_forecast = [x for x in range(train.index[-1]+1, train.index[-1] + days_in_future+1)]
-    forecast(trend_df, train, index_forecast, days_in_future)
+    Singleton.forecast(trend_df, train, index_forecast, days_in_future)