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amyheatheramyheather
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refactor(occupancy)/feat(replications): create Runner to run multiple replications, and move creation of occupancy table to Runner
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‎notebooks/analysis.ipynb‎

Lines changed: 112 additions & 81 deletions
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@@ -17,7 +17,8 @@
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"import plotly.express as px\n",
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"\n",
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"from simulation.parameters import Param\n",
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"from simulation.model import Model"
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"from simulation.model import Model\n",
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"from simulation.runner import Runner"
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]
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},
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{
@@ -43,9 +44,8 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"# Run the model\n",
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"model = Model(param=Param(), run_number=0)\n",
48-
"model.run()"
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"# Set up runner\n",
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"runner = Runner(param=Param(cores=8))"
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]
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},
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{
@@ -55,12 +55,84 @@
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"outputs": [
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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>beds</th>\n",
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" <th>freq</th>\n",
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" <th>pct</th>\n",
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" <th>c_pct</th>\n",
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" <th>prob_delay</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>1</td>\n",
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" <td>1</td>\n",
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" <td>0.000548</td>\n",
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" <td>0.000548</td>\n",
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" <td>1.000000</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>2</td>\n",
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" <td>12</td>\n",
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" <td>0.006575</td>\n",
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" <td>0.007123</td>\n",
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" <td>0.923077</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>3</td>\n",
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" <td>39</td>\n",
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" <td>0.021370</td>\n",
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" <td>0.028493</td>\n",
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" <td>0.750000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>76</td>\n",
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" <td>0.041644</td>\n",
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" <td>0.070137</td>\n",
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" <td>0.593750</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>134</td>\n",
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" <td>0.073425</td>\n",
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" <td>0.143562</td>\n",
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" <td>0.511450</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": [
59-
"[{'time': 1095, 'asu_occupancy': 7, 'rehab_occupancy': 12},\n",
60-
" {'time': 1096, 'asu_occupancy': 5, 'rehab_occupancy': 11},\n",
61-
" {'time': 1097, 'asu_occupancy': 5, 'rehab_occupancy': 10},\n",
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" {'time': 1098, 'asu_occupancy': 4, 'rehab_occupancy': 10},\n",
63-
" {'time': 1099, 'asu_occupancy': 5, 'rehab_occupancy': 10}]"
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" beds freq pct c_pct prob_delay\n",
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"0 1 1 0.000548 0.000548 1.000000\n",
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"1 2 12 0.006575 0.007123 0.923077\n",
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"2 3 39 0.021370 0.028493 0.750000\n",
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"3 4 76 0.041644 0.070137 0.593750\n",
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"4 5 134 0.073425 0.143562 0.511450"
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]
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},
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"execution_count": 4,
@@ -69,67 +141,28 @@
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}
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],
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"source": [
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"# Preview some of the results\n",
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"model.audit_list[0:5]"
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"# Run once and preview results\n",
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"single_results = runner.run_single(run=0)\n",
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"single_results[\"asu\"].head()"
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]
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},
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{
77-
"cell_type": "markdown",
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"cell_type": "code",
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"execution_count": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"## Figure 1\n",
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"\n",
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"**Figure 1.** Simulation probability density function for occupancy of an acute stroke unit.\n",
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"\n",
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"**Relevant functions...**"
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"# Run replications\n",
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"rep_results = runner.run_reps()"
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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": 5,
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"cell_type": "markdown",
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"metadata": {},
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"outputs": [],
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"source": [
93-
"def get_occupancy_freq(unit):\n",
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" \"\"\"\n",
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" Count the frequency each occupancy level was observed at within the\n",
96-
" interval audit for the specified unit.\n",
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"\n",
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" Parameters\n",
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" ----------\n",
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" unit: str\n",
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" Name of unit to plot (\"asu\", \"rehab\").\n",
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"\n",
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" Returns\n",
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" -------\n",
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" df: pd.DataFrame\n",
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" Dataframe with two columns: the number of beds (\"beds\") and the\n",
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" frequency this was observed in the audit (\"freq\").\n",
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" \"\"\"\n",
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" # Get occupancy audit results\n",
110-
" occ_audit = pd.DataFrame(model.audit_list)\n",
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"\n",
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" # Get the frequency of each number of beds in the specified unit\n",
113-
" frequency = Counter(occ_audit[f\"{unit}_occupancy\"])\n",
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"\n",
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" # Fill in any gaps - find the maximum bed count, then create complete\n",
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" # dictionary include bed counts which were never observed\n",
117-
" min_bed_count = min(frequency.keys())\n",
118-
" max_bed_count = max(frequency.keys())\n",
119-
" complete_frequency = {i: frequency.get(i, 0)\n",
120-
" for i in range(min_bed_count, max_bed_count + 1)}\n",
121-
"\n",
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" # Convert into a dataframe\n",
123-
" df = pd.DataFrame(\n",
124-
" complete_frequency.items(), columns=[\"beds\", \"freq\"])\n",
125-
"\n",
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" # Add column with frequencies converted to proportions\n",
127-
" df[\"pct\"] = df[\"freq\"] / df[\"freq\"].sum()\n",
128-
"\n",
129-
" # Add column with the cumulative percentage\n",
130-
" df[\"c_pct\"] = df[\"pct\"].cumsum()\n",
163+
"## Figure 1\n",
131164
"\n",
132-
" return df"
165+
"**Figure 1.** Simulation probability density function for occupancy of an acute stroke unit."
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]
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},
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{
@@ -1900,13 +1933,11 @@
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],
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"source": [
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"# Acute stroke unit\n",
1903-
"asu_occupancy = get_occupancy_freq(\"asu\")\n",
1904-
"plot_occupancy_freq(asu_occupancy, unit=\"asu\",\n",
1936+
"plot_occupancy_freq(single_results[\"asu\"], unit=\"asu\",\n",
19051937
" file=\"occupancy_freq_asu.png\")\n",
19061938
"\n",
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"# Rehabilitation unit\n",
1908-
"rehab_occupancy = get_occupancy_freq(\"rehab\")\n",
1909-
"plot_occupancy_freq(rehab_occupancy, unit=\"rehab\",\n",
1940+
"plot_occupancy_freq(single_results[\"rehab\"], unit=\"rehab\",\n",
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" file=\"occupancy_freq_rehab.png\")"
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]
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},
@@ -1918,11 +1949,9 @@
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"\n",
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"**Figure 3**. Simulated trade-off between the probability that a patient is delayed and the no. of acute beds available.\n",
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"\n",
1921-
"### Explanation of the method for calculating blocking probability\n",
1922-
"\n",
19231952
"We can use our frequency and cumulative frequency of occupied beds from the simulation to calculate blocking probability.\n",
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"\n",
1925-
"Above, we created the tables..."
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"Our model output these tables..."
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]
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},
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{
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" <th>freq</th>\n",
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" <th>pct</th>\n",
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" <th>c_pct</th>\n",
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" <th>prob_delay</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <td>1</td>\n",
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" <td>0.000548</td>\n",
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" <td>0.000548</td>\n",
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" <td>1.000000</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>2</td>\n",
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" <td>12</td>\n",
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" <td>0.006575</td>\n",
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" <td>0.007123</td>\n",
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" <td>0.923077</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>3</td>\n",
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" <td>39</td>\n",
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" <td>0.021370</td>\n",
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" <td>0.028493</td>\n",
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" <td>0.750000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>4</td>\n",
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" <td>76</td>\n",
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" <td>0.041644</td>\n",
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" <td>0.070137</td>\n",
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" <td>0.593750</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>5</td>\n",
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" <td>134</td>\n",
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" <td>0.073425</td>\n",
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" <td>0.143562</td>\n",
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" <td>0.511450</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": [
2001-
" beds freq pct c_pct\n",
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"0 1 1 0.000548 0.000548\n",
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"1 2 12 0.006575 0.007123\n",
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"2 3 39 0.021370 0.028493\n",
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"3 4 76 0.041644 0.070137\n",
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"4 5 134 0.073425 0.143562"
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" beds freq pct c_pct prob_delay\n",
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"0 1 1 0.000548 0.000548 1.000000\n",
2038+
"1 2 12 0.006575 0.007123 0.923077\n",
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"2 3 39 0.021370 0.028493 0.750000\n",
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"3 4 76 0.041644 0.070137 0.593750\n",
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"4 5 134 0.073425 0.143562 0.511450"
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]
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},
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"execution_count": 8,
@@ -2012,7 +2047,7 @@
20122047
}
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],
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"source": [
2015-
"asu_occupancy.head()"
2050+
"single_results[\"asu\"].head()"
20162051
]
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},
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{
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20452080
"* Any new patients arriving when 8 beds are occupied would experience a delay.\n",
20462081
"* So 0.6 represents the probability that, given we're at or below capacity (7 or 8 beds), we're actually at full capacity (8 beds)\n",
20472082
"\n",
2048-
"In other words, `pct/c_pct` is the probability that a new arrival will experience a delay when the system has exactly x beds occupied, given that the capacity of the system is x beds.\n",
2049-
"\n",
2050-
"### Implementing this method"
2083+
"In other words, `pct/c_pct` is the probability that a new arrival will experience a delay when the system has exactly x beds occupied, given that the capacity of the system is x beds."
20512084
]
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},
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{
@@ -2073,9 +2106,6 @@
20732106
" path: str\n",
20742107
" Path to save file to (excluding filename).\n",
20752108
" \"\"\"\n",
2076-
" # Calculate the probability of delay\n",
2077-
" df[\"prob_delay\"] = df[\"pct\"] / df[\"c_pct\"]\n",
2078-
"\n",
20792109
" # Create the step plot\n",
20802110
" fig = px.line(df, x=\"beds\", y=\"prob_delay\",\n",
20812111
" color_discrete_sequence=[\"black\"])\n",
@@ -3902,8 +3932,9 @@
39023932
}
39033933
],
39043934
"source": [
3905-
"plot_delay_prob(asu_occupancy, unit=\"asu\", file=\"delay_prob_asu.png\")\n",
3906-
"plot_delay_prob(rehab_occupancy, unit=\"rehab\", file=\"delay_prob_rehab.png\")"
3935+
"plot_delay_prob(single_results[\"asu\"], unit=\"asu\", file=\"delay_prob_asu.png\")\n",
3936+
"plot_delay_prob(single_results[\"rehab\"], unit=\"rehab\",\n",
3937+
" file=\"delay_prob_rehab.png\")"
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]
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}
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],

‎simulation/parameters.py‎

Lines changed: 11 additions & 0 deletions
Original file line numberDiff line numberDiff line change
@@ -419,7 +419,9 @@ def __init__(
419419
rehab_routing=RehabRouting(),
420420
warm_up_period=365*3, # 3 years
421421
data_collection_period=365*5, # 5 years
422+
number_of_runs=150,
422423
audit_interval=1,
424+
cores=1,
423425
log_to_console=False,
424426
log_to_file=False
425427
):
@@ -445,8 +447,15 @@ def __init__(
445447
Length of the warm-up period.
446448
data_collection_period: int
447449
Length of the data collection period.
450+
number_of_runs: int
451+
The number of runs (i.e. replications), defining how many times to
452+
re-run the simulation (with different random numbers).
448453
audit_interval: float
449454
Frequency of simulation audits in days.
455+
cores: int
456+
Number of CPU cores to use for parallel execution. Set to desired
457+
number, or to -1 to use all available cores. For sequential
458+
execution, set to 1.
450459
log_to_console: boolean
451460
Whether to print log messages to the console.
452461
log_to_file: boolean
@@ -461,7 +470,9 @@ def __init__(
461470
self.rehab_routing = rehab_routing
462471
self.warm_up_period = warm_up_period
463472
self.data_collection_period = data_collection_period
473+
self.number_of_runs = number_of_runs
464474
self.audit_interval = audit_interval
475+
self.cores = cores
465476

466477
# Set up logger
467478
self.logger = SimLogger(log_to_console=log_to_console,

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