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test(scenario): add back test for scenario analysis (#30)
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‎notebooks/generate_exp_results.ipynb

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@@ -56,7 +56,7 @@
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"import time\n",
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"from IPython.display import display\n",
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"\n",
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"from simulation.model import Param, Runner\n",
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"from simulation.model import Param, Runner, run_scenarios\n",
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"from simulation.replications import confidence_interval_method"
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]
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},
@@ -743,6 +743,203 @@
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" os.path.join(TESTS, 'overall.csv'), index=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Scenario analysis"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"There are 4 scenarios. Running:\n",
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"{'patient_inter': 3, 'number_of_nurses': 6}\n",
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"{'patient_inter': 3, 'number_of_nurses': 7}\n",
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"{'patient_inter': 4, 'number_of_nurses': 6}\n",
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"{'patient_inter': 4, 'number_of_nurses': 7}\n"
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]
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},
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>run_number</th>\n",
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" <th>scenario</th>\n",
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" <th>arrivals</th>\n",
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" <th>mean_q_time_nurse</th>\n",
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" <th>mean_time_with_nurse</th>\n",
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" <th>mean_nurse_utilisation</th>\n",
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" <th>mean_nurse_utilisation_tw</th>\n",
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" <th>mean_nurse_q_length</th>\n",
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" <th>count_nurse_unseen</th>\n",
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" <th>mean_q_time_nurse_unseen</th>\n",
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" <th>patient_inter</th>\n",
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" <th>number_of_nurses</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>0</td>\n",
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" <td>2004</td>\n",
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" <td>0.486680</td>\n",
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" <td>9.925934</td>\n",
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" <td>0.553955</td>\n",
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" <td>0.553955</td>\n",
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" <td>0.162551</td>\n",
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" <td>0</td>\n",
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" <td>NaN</td>\n",
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" <td>3</td>\n",
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" <td>6</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>0</td>\n",
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" <td>1993</td>\n",
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" <td>0.746175</td>\n",
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" <td>10.377197</td>\n",
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" <td>0.573874</td>\n",
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" <td>0.573874</td>\n",
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" <td>0.247854</td>\n",
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" <td>0</td>\n",
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" <td>NaN</td>\n",
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" <td>3</td>\n",
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" <td>6</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>2</td>\n",
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" <td>0</td>\n",
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" <td>2017</td>\n",
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" <td>0.386324</td>\n",
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" <td>9.856724</td>\n",
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" <td>0.553419</td>\n",
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" <td>0.553419</td>\n",
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" <td>0.129869</td>\n",
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" <td>0</td>\n",
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" <td>NaN</td>\n",
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" <td>3</td>\n",
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" <td>6</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>0</td>\n",
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" <td>1</td>\n",
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" <td>2004</td>\n",
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" <td>0.121476</td>\n",
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" <td>9.925934</td>\n",
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" <td>0.474819</td>\n",
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" <td>0.474819</td>\n",
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" <td>0.040573</td>\n",
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" <td>0</td>\n",
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" <td>NaN</td>\n",
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" <td>3</td>\n",
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" <td>7</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>1</td>\n",
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" <td>1</td>\n",
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" <td>1993</td>\n",
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" <td>0.226682</td>\n",
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" <td>10.377197</td>\n",
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" <td>0.491892</td>\n",
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" <td>0.491892</td>\n",
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" <td>0.075296</td>\n",
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" <td>0</td>\n",
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" <td>NaN</td>\n",
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" <td>3</td>\n",
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" <td>7</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" run_number scenario arrivals mean_q_time_nurse mean_time_with_nurse \\\n",
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"0 0 0 2004 0.486680 9.925934 \n",
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"1 1 0 1993 0.746175 10.377197 \n",
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"2 2 0 2017 0.386324 9.856724 \n",
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"0 0 1 2004 0.121476 9.925934 \n",
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"1 1 1 1993 0.226682 10.377197 \n",
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"\n",
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" mean_nurse_utilisation mean_nurse_utilisation_tw mean_nurse_q_length \\\n",
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"0 0.553955 0.553955 0.162551 \n",
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"1 0.573874 0.573874 0.247854 \n",
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"2 0.553419 0.553419 0.129869 \n",
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"0 0.474819 0.474819 0.040573 \n",
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"1 0.491892 0.491892 0.075296 \n",
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"\n",
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" count_nurse_unseen mean_q_time_nurse_unseen patient_inter \\\n",
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"0 0 NaN 3 \n",
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"1 0 NaN 3 \n",
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"2 0 NaN 3 \n",
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"0 0 NaN 3 \n",
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"1 0 NaN 3 \n",
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"\n",
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" number_of_nurses \n",
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"0 6 \n",
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"1 6 \n",
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"2 6 \n",
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"0 7 \n",
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"1 7 "
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"# Run scenarios\n",
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"param = Param(\n",
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" patient_inter=4,\n",
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" mean_n_consult_time=10,\n",
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" number_of_nurses=5,\n",
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" warm_up_period=2000,\n",
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" data_collection_period=6000,\n",
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" number_of_runs=3,\n",
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" audit_interval=120,\n",
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" cores=1\n",
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")\n",
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"scenario_results = run_scenarios(\n",
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" scenarios={'patient_inter': [3, 4],\n",
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" 'number_of_nurses': [6, 7]},\n",
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" param=param\n",
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")\n",
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"\n",
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"# Preview\n",
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"display(scenario_results.head())\n",
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"\n",
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"# Save to csv\n",
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"scenario_results.to_csv(os.path.join(TESTS, 'scenario.csv'), index=True)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"execution_count": 11,
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"metadata": {},
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"outputs": [
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{
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},
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{
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"cell_type": "code",
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"execution_count": 11,
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"execution_count": 12,
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"metadata": {},
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"outputs": [
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{

‎tests/exp_results/scenario.csv

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,run_number,scenario,arrivals,mean_q_time_nurse,mean_time_with_nurse,mean_nurse_utilisation,mean_nurse_utilisation_tw,mean_nurse_q_length,count_nurse_unseen,mean_q_time_nurse_unseen,patient_inter,number_of_nurses
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0,0,0,2004,0.48667967755432595,9.925933909873555,0.5539549536279254,0.5539549536279247,0.16255101230314487,0,,3,6
3+
1,1,0,1993,0.7461746423993406,10.377197279111792,0.5738739636111149,0.5738739636111151,0.24785434371698098,0,,3,6
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2,2,0,2017,0.38632400402627576,9.856723933105343,0.5534193173586592,0.5534193173586589,0.12986925268683305,0,,3,6
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0,0,1,2004,0.12147605450060947,9.925933909873555,0.47481853168107885,0.47481853168107835,0.04057300220320356,0,,3,7
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1,1,1,1993,0.2266816332232325,10.377197279111792,0.491891968809527,0.4918919688095272,0.07529608250231706,0,,3,7
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2,2,1,2017,0.11960199026500552,9.856723933105343,0.47435941487885075,0.47435941487885047,0.040206202394086024,0,,3,7
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0,0,2,1492,0.08184145365169877,10.04701005331526,0.41472102750962625,0.4147210275096257,0.020351241474722427,0,,4,6
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1,1,2,1515,0.15440193319353135,10.296390547789509,0.4336195261552104,0.4336195261552114,0.03898648813136667,0,,4,6
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2,2,2,1525,0.11498914338292632,9.877249497718147,0.4180848124362831,0.41808481243628204,0.029226407276493775,0,,4,6
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0,0,3,1492,0.02076347209562216,10.04701005331526,0.35547516643682253,0.35547516643682203,0.00516318339444471,0,,4,7
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1,1,3,1515,0.03494302435135906,10.296390547789509,0.3716738795616089,0.37167387956160974,0.008823113648718163,0,,4,7
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2,2,3,1525,0.03358631740445475,9.877249497718147,0.3583584106596712,0.35835841065967033,0.008536522340298915,0,,4,7

‎tests/test_backtest.py

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from pathlib import Path
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import pandas as pd
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from simulation.model import Param, Runner
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from simulation.model import Param, Runner, run_scenarios
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def test_reproduction():
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exp_overall = pd.read_csv(
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Path(__file__).parent.joinpath('exp_results/overall.csv'), index_col=0)
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pd.testing.assert_frame_equal(experiment.overall_results_df, exp_overall)
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def test_scenarios():
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"""
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Check that results from run with scenarios are consistent with those
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previously generated.
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"""
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# Run scenarios
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param = Param(
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patient_inter=4,
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mean_n_consult_time=10,
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number_of_nurses=5,
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warm_up_period=2000,
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data_collection_period=6000,
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number_of_runs=3,
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audit_interval=120,
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cores=1
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)
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results = run_scenarios(
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scenarios={'patient_inter': [3, 4],
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'number_of_nurses': [6, 7]},
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param=param
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)
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# Compare to expected results
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exp_results = pd.read_csv(
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Path(__file__).parent.joinpath('exp_results/scenario.csv'),
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index_col=0)
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pd.testing.assert_frame_equal(results, exp_results)

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