Applying a structural design pattern in forecast.py

src/covidify/forecast.py

@@ -1,116 +1,19 @@
-"""
-forecast.py - Forecast 
-Usage:
-    forecast.py [options]
-    forecast.py -h | --help
-
-Options:
-    -h --help             Show this message.
-    --output_folder=OUT   Output folder for the data and reports to be saved.
-    --num_days=INT        Number of days that the model will forecast in the future
-"""
-
-from __future__ import print_function
-import re
-import os
-import sys
-import math
-import docopt
-import pandas as pd
-from math import sqrt
-from time import strftime
-from datetime import timedelta
-import matplotlib.pyplot as plt
-from dateutil.parser import parse
-from pmdarima.arima import auto_arima
-from datetime import datetime, date, time 
-from sklearn.metrics import mean_squared_error
-from covidify.config import PERC_SPLIT, FIG_SIZE
-
-font = {'weight' : 'bold',
-        'size'   : 22}
-
-plt.rc('font', **font)
-plt.style.use('ggplot')
-pd.options.display.max_rows = 999
-
-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()))
-forecast_file  = 'forecast_{}.csv'.format(datetime.date(datetime.now())) # For saving forecasts
-data_dir  = os.path.join(out, 'data', str(datetime.date(datetime.now())))
-trend_df = pd.read_csv(os.path.join(data_dir, trend_file)).reset_index(drop=True)
-
-
-# For forecasting (use all data for training)
-train_start = datetime.strptime(trend_df.date.min(), "%Y-%m-%d")
-train_end = datetime.strptime(trend_df.date.max(), "%Y-%m-%d")
-forecast_start = datetime.strptime(trend_df.date.max(), "%Y-%m-%d") + timedelta(days=1)
-forecast_end = datetime.strptime(trend_df.date.max(), "%Y-%m-%d") + timedelta(days=days_in_future+1) # Extra day because of one day lag
-
-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)]
-
-
-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)
+#___________________ ________________________________________________________
+class Wrap:
+    '''Resource-intensive object'''
+    def P_Wrap(self):
+        print("creating report folder")
+class WrapProxy:
+    '''Relatively less resource-intensive proxy acting as middleman.
+     Instantiates a College object only if there is no fee due.'''
+    def __init__(self,path):
+        path="true"
+    def isPathEstablished(self):
+        print("Proxy in action. Checking to see if the path exists or not...")
+        if not os.path.exists(image_dir):
+            print('Creating reports folder...')
+            os.system('mkdir -p ' + image_dir)
+        else:
+            print("path established")  
+WrapProxy=WrapProxy()
+wrapProxy.isPathEstablished(os.path.exists(image_dir))