From 85c15d939afbe28da9754f7b790cc945ba4027e7 Mon Sep 17 00:00:00 2001 From: amyheather Date: Tue, 11 Feb 2025 14:32:53 +0000 Subject: [PATCH 01/28] fix(model): add error handling for results processing when there are no arrivals --- simulation/model.py | 79 +++++++++++++++++++++++++++++++-------------- 1 file changed, 55 insertions(+), 24 deletions(-) diff --git a/simulation/model.py b/simulation/model.py index fd1ca89..d462f5d 100644 --- a/simulation/model.py +++ b/simulation/model.py @@ -422,7 +422,8 @@ def valid_inputs(self): # Doesn't include number_of_nurses as this is tested by simpy.Resource validation_rules = { 'positive': ['patient_inter', 'mean_n_consult_time', - 'number_of_runs', 'audit_interval'], + 'number_of_runs', 'audit_interval', + 'number_of_nurses'], 'non_negative': ['warm_up_period', 'data_collection_period'] } # Iterate over the validation rules @@ -676,37 +677,67 @@ def run_single(self, run): model = Model(param=self.param, run_number=run) model.run() + # PATIENT RESULTS # Convert patient-level results to a dataframe and add column with run patient_results = pd.DataFrame(model.results_list) patient_results['run'] = run + # If there was at least one patient... + if len(patient_results) > 0: + # Add a column with the wait time of patients who remained unseen + # at the end of the simulation + patient_results['q_time_unseen'] = np.where( + patient_results['time_with_nurse'].isna(), + model.env.now - patient_results['arrival_time'], np.nan + ) + else: + # Set to NaN if no patients + patient_results['q_time_unseen'] = np.nan - # Add a column with the wait time of patients who remained unseen - # at the end of the simulation - patient_results['q_time_unseen'] = np.where( - patient_results['time_with_nurse'].isna(), - model.env.now - patient_results['arrival_time'], np.nan - ) - - # Create dictionary recording the run results - # Currently has two alternative methods of measuring utilisation + # RUN RESULTS + # The run, scenario and arrivals are handled the same regardless of + # whether there were any patients run_results = { 'run_number': run, 'scenario': self.param.scenario_name, - 'arrivals': len(patient_results), - 'mean_q_time_nurse': patient_results['q_time_nurse'].mean(), - 'mean_time_with_nurse': patient_results['time_with_nurse'].mean(), - 'mean_nurse_utilisation': (model.nurse_time_used / - (self.param.number_of_nurses * - self.param.data_collection_period)), - 'mean_nurse_utilisation_tw': (sum(model.nurse.area_resource_busy) / - (self.param.number_of_nurses * - self.param.data_collection_period)), - 'mean_nurse_q_length': (sum(model.nurse.area_n_in_queue) / - self.param.data_collection_period), - 'count_unseen': patient_results['time_with_nurse'].isna().sum(), - 'mean_q_time_unseen': patient_results['q_time_unseen'].mean() + 'arrivals': len(patient_results) } - + # If there was at least one patient... + if len(patient_results) > 0: + # Create dictionary recording the run results + # Currently has two alternative methods of measuring utilisation + run_results = { + **run_results, + 'mean_q_time_nurse': patient_results['q_time_nurse'].mean(), + 'mean_time_with_nurse': ( + patient_results['time_with_nurse'].mean()), + 'mean_nurse_utilisation': ( + model.nurse_time_used / ( + self.param.number_of_nurses * + self.param.data_collection_period)), + 'mean_nurse_utilisation_tw': ( + sum(model.nurse.area_resource_busy) / ( + self.param.number_of_nurses * + self.param.data_collection_period)), + 'mean_nurse_q_length': (sum(model.nurse.area_n_in_queue) / + self.param.data_collection_period), + 'count_unseen': ( + patient_results['time_with_nurse'].isna().sum()), + 'mean_q_time_unseen': patient_results['q_time_unseen'].mean() + } + else: + # Set results to NaN if no patients + run_results = { + **run_results, + 'mean_q_time_nurse': np.nan, + 'mean_time_with_nurse': np.nan, + 'mean_nurse_utilisation': np.nan, + 'mean_nurse_utilisation_tw': np.nan, + 'mean_nurse_q_length': np.nan, + 'count_unseen': np.nan, + 'mean_q_time_unseen': np.nan + } + + # INTERVAL AUDIT RESULTS # Convert interval audit results to a dataframe and add run column interval_audit_df = pd.DataFrame(model.audit_list) interval_audit_df['run'] = run From 3526aa1fc1686069f58d19c158a2230573d6f3be Mon Sep 17 00:00:00 2001 From: amyheather Date: Tue, 11 Feb 2025 15:02:48 +0000 Subject: [PATCH 02/28] tests(functional): add tests for extreme IAT, NaN, and sampled times --- tests/test_functionaltest.py | 82 +++++++++++++++++++++++++++++++++++- 1 file changed, 81 insertions(+), 1 deletion(-) diff --git a/tests/test_functionaltest.py b/tests/test_functionaltest.py index bf2a826..7e29cf7 100644 --- a/tests/test_functionaltest.py +++ b/tests/test_functionaltest.py @@ -248,7 +248,7 @@ def test_waiting_time_utilisation(param_name, initial_value, adjusted_value): def helper_param(param_name, value): """ Helper function to set a specific parameter value, run the model, - and return the waiting time. + and return the results from a single run. Arguments: param_name (string): @@ -459,3 +459,83 @@ def process_task(env, resource, duration): f'Expected queue time {expected_busy_time} but ' + f'observed {resource.area_resource_busy}.' ) + + +def test_extreme_interarrival(): + """ + Check that extremely high interarrival time results in no arrivals, and + that the model can cope with having no arrivals. + """ + param = Param(patient_inter=99999999, + warm_up_period=0, + data_collection_period=10000) + experiment = Runner(param) + results = experiment.run_single(run=0) + + assert results['run']['arrivals'] < 1, ( + "Expect no arrivals due to extreme interarrival time." + ) + + +def test_no_missing_values(): + """ + Some columns are expected to have some NaN - but for those that don't, + check that no missing values exist in the final output. + """ + param = Param() + experiment = Runner(param) + experiment.run_reps() + + # Define required columns we expect to have no missing values + req_patient = ['patient_id', 'arrival_time', 'run'] + req_run = ['run_number', 'scenario', 'arrivals', 'mean_q_time_nurse', + 'mean_time_with_nurse', 'mean_nurse_utilisation', + 'mean_nurse_utilisation_tw', 'mean_nurse_q_length', + 'count_unseen'] + + # Check for missing values + res_patient = experiment.patient_results_df[req_patient].isnull().any() + assert not res_patient.any(), { + 'Found missing values in patient results in columns that we expect ' + + f'to have none - in the columns marked True: {res_patient}' + } + res_run = experiment.run_results_df[req_run].isnull().any() + assert not res_run.any(), { + 'Found missing values in run results in columns that we expect ' + + f'to have none - in the columns marked True: {res_run}' + } + res_interval = experiment.interval_audit_df.isnull().any() + assert not res_interval.any(), { + 'Found missing values in interval results - in the columns marked ' + + f'True: {res_interval}' + } + res_overall = experiment.overall_results_df.isnull().any() + assert not res_overall.any(), { + 'Found missing values in overall results - in the columns marked ' + + f'True: {res_overall}' + } + + +def test_sampled_times(): + """ + Ensure that the mean of inter-arrival and consultation times are close to + expected value. + """ + param = Param(patient_inter=5, mean_n_consult_time=8) + experiment = Runner(param) + results = experiment.run_single(run=0) + + # Calculate the inter-arrival times between patients (from arrival times) + actual_interarrival = np.diff(sorted(results['patient']['arrival_time'])) + + # Check that the mean inter-arrival time is close to 5 + observed_mean_iat = np.mean(actual_interarrival) + assert np.isclose(observed_mean_iat, 5, atol=0.5), ( + f'Expected mean interarrival time ≈ 5, but got {observed_mean_iat}.' + ) + + # Check that the mean nurse consultation time is close to 8 + observed_mean_nur = results['patient']['time_with_nurse'].mean() + assert np.isclose(observed_mean_nur, 8, atol=0.5), ( + f"Expected mean consultation time ≈ 8, but got {observed_mean_nur}." + ) From d40ead265d534259fc7be8ab43132eaffbc1489d Mon Sep 17 00:00:00 2001 From: amyheather Date: Tue, 11 Feb 2025 15:08:04 +0000 Subject: [PATCH 03/28] fix(exponential): add error handling for invalid mean --- simulation/model.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/simulation/model.py b/simulation/model.py index d462f5d..e5d7b47 100644 --- a/simulation/model.py +++ b/simulation/model.py @@ -309,6 +309,9 @@ def __init__(self, mean, random_seed): random_seed (int|None): Random seed to reproduce samples. """ + if mean <= 0: + raise ValueError('Exponential mean must be greater than 0.') + self.mean = mean self.rand = np.random.default_rng(random_seed) From 903be4adec7baba25e34b7ed14ccdef02ea54bc4 Mon Sep 17 00:00:00 2001 From: amyheather Date: Tue, 11 Feb 2025 15:08:20 +0000 Subject: [PATCH 04/28] tests(unit): add test for invalid exponential inputs --- tests/test_unittest.py | 18 ++++++++++++++++++ 1 file changed, 18 insertions(+) diff --git a/tests/test_unittest.py b/tests/test_unittest.py index 9b0080f..b717e2f 100644 --- a/tests/test_unittest.py +++ b/tests/test_unittest.py @@ -4,6 +4,10 @@ components (e.g. methods, classes) and tests them in isolation to ensure they work as intended. +SimPy itself has lots of tests of the SimPy components themselves, as can view: +https://gitlab.com/team-simpy/simpy/-/tree/master/tests?ref_type=heads. +Hence, our focus here is testing components we have written ourselves. + Licence: This project is licensed under the MIT Licence. See the LICENSE file for more details. @@ -43,6 +47,7 @@ def test_new_attribute(): ('mean_n_consult_time', 0, 'positive'), ('number_of_runs', 0, 'positive'), ('audit_interval', 0, 'positive'), + ('number_of_nurses', 0, 'positive'), ('warm_up_period', -1, 'non_negative'), ('data_collection_period', -1, 'non_negative') ]) @@ -112,6 +117,19 @@ def test_exponentional(): ) +def test_invalid_exponential(): + """ + Ensure that Exponential distribution cannot be created with a negative + or zero mean. + """ + # Negative mean + with pytest.raises(ValueError): + Exponential(mean=-5, random_seed=42) + # Zero mean + with pytest.raises(ValueError): + Exponential(mean=0, random_seed=42) + + def test_log_to_console(): """ Confirm that logger.log() prints the provided message to the console. From 143d73ccdc6ec02b648ec41be9b06f8dc4025c43 Mon Sep 17 00:00:00 2001 From: amyheather Date: Wed, 12 Feb 2025 09:36:29 +0000 Subject: [PATCH 05/28] refactor(choosing): split choosing parameters notebooks into three seperate --- notebooks/choosing_cores.ipynb | 245 ++++++++++++ ...ters.ipynb => choosing_replications.ipynb} | 301 +------------- notebooks/choosing_warmup.ipynb | 369 ++++++++++++++++++ outputs/choose_param_cores.png | Bin 37422 -> 42221 bytes 4 files changed, 630 insertions(+), 285 deletions(-) create mode 100644 notebooks/choosing_cores.ipynb rename notebooks/{choosing_parameters.ipynb => choosing_replications.ipynb} (56%) create mode 100644 notebooks/choosing_warmup.ipynb diff --git a/notebooks/choosing_cores.ipynb b/notebooks/choosing_cores.ipynb new file mode 100644 index 0000000..b92a5fe --- /dev/null +++ b/notebooks/choosing_cores.ipynb @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Choosing the number of cores\n", + "\n", + "If choosing to run your simulation in parallel, the maximum number of cores may not be the most efficient. This notebook examines the run time for the model with varying numbers of cores.\n", + "\n", + "The run time is provided at the end of the notebook.\n", + "\n", + "Credit:\n", + "\n", + "* Code for running the model with a varying number of CPU cores was adapted from Sammi Rosser and Dan Chalk (2024) [HSMA - the little book of DES](https://github.com/hsma-programme/hsma6_des_book) (MIT Licence).\n", + "\n", + "Licence:\n", + "\n", + "* This project is licensed under the MIT Licence. See the `LICENSE` file for more details." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set-up" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load required packages." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# pylint: disable=missing-module-docstring\n", + "# To ensure any updates to `simulation/` are fetched without needing to restart\n", + "# the notebook environment, reload `simulation/` before execution of each cell\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "%aimport simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# pylint: disable=wrong-import-position\n", + "import os\n", + "import time\n", + "\n", + "import pandas as pd\n", + "import plotly.express as px\n", + "import plotly.io as pio\n", + "\n", + "from simulation.model import Param, Runner" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display plotly express figures as non-interactive figures. This means they will be visible when browsing the notebooks on GitHub. To switch these back to interactive figures, simply remove this line." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "pio.renderers.default = 'svg'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Start timer." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "notebook_start_time = time.time()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define path to outputs folder" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# pylint: disable=duplicate-code\n", + "OUTPUT_DIR = '../outputs/'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run time with varying number of CPU cores\n", + "\n", + "These are rounded to the nearest .5 seconds, as we will see minor differences in run time between re-runs of this notebook on the same machine, so rounding prevents the figure from changing with every run!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Running with cores: 1.\n", + "Running with cores: 2.\n", + "Running with cores: 3.\n", + "Running with cores: 4.\n", + "Running with cores: 5.\n", + "Running with cores: 6.\n", + "Running with cores: 7.\n", + "Running with cores: 8.\n", + " cores run_time\n", + "0 1 5.0\n", + "1 2 3.5\n", + "2 3 2.5\n", + "3 4 2.5\n", + "4 5 2.0\n", + "5 6 2.0\n", + "6 7 2.0\n", + "7 8 1.0\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "1234567811.522.533.544.55Number of coresRun time (rounded to nearest .5 seconds)" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Run with 1 to 8 cores\n", + "speed = []\n", + "for i in range(1, 9):\n", + " print(f'Running with cores: {i}.')\n", + " start_time = time.time()\n", + "\n", + " run_param = Param(cores=i)\n", + " experiment = Runner(run_param)\n", + " experiment.run_reps()\n", + "\n", + " # Round time to nearest .5 seconds\n", + " run_time = round((time.time() - start_time)*2)/2\n", + " speed.append({'cores': i, 'run_time': run_time})\n", + "\n", + "# Plot time by number of cores\n", + "timing_results = pd.DataFrame(speed)\n", + "print(timing_results)\n", + "cores_fig = px.line(timing_results, x='cores', y='run_time')\n", + "cores_fig.update_layout(\n", + " xaxis_title='Number of cores',\n", + " yaxis_title='Run time (rounded to nearest .5 seconds)',\n", + " template='plotly_white'\n", + ")\n", + "\n", + "# Display and save figure\n", + "cores_fig.show()\n", + "cores_fig.write_image(os.path.join(OUTPUT_DIR, 'choose_param_cores.png'),\n", + " width=800, height=600)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run time" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notebook run time: 0m 21s\n" + ] + } + ], + "source": [ + "# Get run time in seconds\n", + "notebook_end_time = time.time()\n", + "runtime = round(notebook_end_time - notebook_start_time)\n", + "\n", + "# Display converted to minutes and seconds\n", + "print(f'Notebook run time: {runtime // 60}m {runtime % 60}s')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "template-des", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.13.1" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/choosing_parameters.ipynb b/notebooks/choosing_replications.ipynb similarity index 56% rename from notebooks/choosing_parameters.ipynb rename to notebooks/choosing_replications.ipynb index a76218c..63bd6df 100644 --- a/notebooks/choosing_parameters.ipynb +++ b/notebooks/choosing_replications.ipynb @@ -4,23 +4,19 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Choosing parameters\n", + "# Choosing the number of replications\n", "\n", - "This notebook documents the choice of simulation parameters including:\n", + "The **confidence interval method** can be used to select the number of replications to run. The more replications you run, the narrower your confidence interval becomes, leading to a more precise estimate of the model's mean performance.\n", "\n", - "* Length of warm-up period\n", - "* Number of replications\n", - "* Number of CPU cores\n", + "First, you select a desired confidence interval - for example, 95%. Then, run the model with an increasing number of replications, and identify the number required to achieve that precision in the estimate of a given metric - and also, to maintain that precision (as the intervals may converge or expand again later on).\n", "\n", - "The run time is provided at the end of the notebook.\n", + "This method is less useful for values very close to zero - so, for example, when using utilisation (which ranges from 0 to 1) it is recommended to multiple values by 100.\n", "\n", - "\n", + "When selecting the number of replications you should repeat the analysis for all performance measures and select the highest value as your number of replications.\n", "\n", "Credit:\n", "\n", - "* Code for choice of warm-up period and replication number was adapted from Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence).\n", - "* Code for running the model with a varying number of CPU cores was adapted from Sammi Rosser and Dan Chalk (2024) [HSMA - the\n", - " little book of DES](https://github.com/hsma-programme/hsma6_des_book) (MIT Licence).\n", + "* Code for choice of replication number was adapted from Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence).\n", "\n", "Licence:\n", "\n", @@ -70,8 +66,6 @@ "import pandas as pd\n", "import plotly.express as px\n", "import plotly.io as pio\n", - "import plotly.graph_objects as go\n", - "import plotly.subplots as sp\n", "\n", "from simulation.model import Param, Runner\n", "from simulation.helper import summary_stats" @@ -160,13 +154,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Choosing length of warm-up period\n", - "\n", - "A suitable length for the warm-up period can be determined using the **time series inspection approach**. This involves looking at performance measures over time to identify when the system is exhibiting **steady state behaviour** (even though the system will never truly reach a \"steady state\").\n", - "\n", - "If we simply plot the mean result at regular intervals, this would vary too much. Therefore, we plot the **cumulative mean** of the performance measure, and look for the point at which this **smoothes out and stabilises**. This indicates the point for the warm-up period to end.\n", + "## Manual inspection\n", "\n", - "This should be assessed when running the model using the base case parameters. If these change, you should reassess the appropriate warm-up period." + "First, we demonstrate how to do this manually, by inspecting the output table and figures to determine an appropriate number of replications." ] }, { @@ -174,187 +164,6 @@ "execution_count": 7, "metadata": {}, "outputs": [], - "source": [ - "def time_series_inspection(\n", - " file, data_collection_period, warm_up=None, path=OUTPUT_DIR, labels=None\n", - "):\n", - " \"\"\"\n", - " Time series inspection method for determining length of warm-up.\n", - "\n", - " Arguments:\n", - " file (str):\n", - " Filename to save figure to.\n", - " data_collection_period (float):\n", - " Length of time to run the simulation for.\n", - " warm_up (float, optional):\n", - " Location on X axis to plot vertical red line indicating the chosen\n", - " warm-up period. Defaults to None, which will not plot a line.\n", - " path (str):\n", - " Path to save file to (exc. filename)\n", - " labels (dict):\n", - " Contains mappings from variable names to full labels. If none\n", - " provided, will default to using variable names.\n", - " \"\"\"\n", - " # Use default parameters, but with no warm-up and specified run length,\n", - " # and with no replications\n", - " param = Param(warm_up_period=0,\n", - " data_collection_period=data_collection_period,\n", - " number_of_runs=1)\n", - " # display(param.__dict__)\n", - "\n", - " # Run model\n", - " choose_warmup = Runner(param)\n", - " choose_warmup.run_reps()\n", - "\n", - " # Filter to nurse results\n", - " nurse = choose_warmup.interval_audit_df[\n", - " choose_warmup.interval_audit_df['resource_name'] == 'nurse']\n", - "\n", - " # Define columns to analyse\n", - " plot = ['utilisation', 'running_mean_wait_time']\n", - "\n", - " # Create 1x2 subplot\n", - " full_figure = sp.make_subplots(rows=2, cols=1, shared_xaxes=True)\n", - "\n", - " counter = 1\n", - " for var in plot:\n", - " # Reformat so index is simulation time and columns are each run\n", - " reformat = (\n", - " nurse[['simulation_time', var, 'run']]\n", - " .pivot(index='simulation_time',\n", - " columns='run',\n", - " values=var))\n", - "\n", - " # For utilisation, calculate cumulative mean (not necessary\n", - " # for wait time as that is already a running mean)\n", - " if var == 'utilisation':\n", - " cumulative = reformat.expanding().mean()\n", - " elif var == 'running_mean_wait_time':\n", - " cumulative = reformat.copy()\n", - " else:\n", - " print('Expected var to be utilisation or running_mean_wait_time.')\n", - " break\n", - "\n", - " # Create plot (using go.Scatter instead of px.express, for sub-plots)\n", - " full_figure.add_trace(\n", - " go.Scatter(\n", - " x=cumulative.index,\n", - " y=cumulative[0],\n", - " mode='lines',\n", - " line={'color': 'royalblue'}\n", - " ),\n", - " row=counter, col=1\n", - " )\n", - "\n", - " # Add y axis label\n", - " full_figure.update_yaxes(title_text=labels.get(var, var),\n", - " row=counter, col=1)\n", - "\n", - " # Add vertical line for warm-up period specified\n", - " if warm_up is not None:\n", - " full_figure.add_vline(\n", - " x=warm_up, line_color='red', line_dash='dash',\n", - " annotation_text='Suggested warm-up length',\n", - " annotation_position='top left',\n", - " annotation_font_color='red')\n", - " counter += 1\n", - "\n", - " # Add x axis title\n", - " full_figure.update_xaxes(title_text='Run time (minutes)')\n", - "\n", - " # Set figure dimensions and hide legend\n", - " full_figure.update_layout(\n", - " width=1400,\n", - " height=800,\n", - " showlegend=False,\n", - " template='plotly_white'\n", - " )\n", - "\n", - " # Show figure\n", - " full_figure.show()\n", - "\n", - " # Save figure\n", - " full_figure.write_image(os.path.join(path, file))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Having run the model for three days, it appears to reach a steady state at around 2500 minutes." - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "00.10.20.30.40500100015002000250030003500400000.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "time_series_inspection(\n", - " file='choose_param_time_series_1.png',\n", - " data_collection_period=1440*3, warm_up=2520,\n", - " labels=LABELS)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "However, it is important to look far ahead - so we run it for more days, and find actually a later warm-up is more appropriate." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "data": { - "image/svg+xml": [ - "00.10.20.30.40.5010k20k30k40k50k00.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "time_series_inspection(\n", - " file='choose_param_time_series_2.png',\n", - " data_collection_period=1440*40, warm_up=1440*13,\n", - " labels=LABELS)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Choosing the number of replications\n", - "\n", - "The **confidence interval method** can be used to select the number of replications to run. The more replications you run, the narrower your confidence interval becomes, leading to a more precise estimate of the model's mean performance.\n", - "\n", - "First, you select a desired confidence interval - for example, 95%. Then, run the model with an increasing number of replications, and identify the number required to achieve that precision in the estimate of a given metric - and also, to maintain that precision (as the intervals may converge or expand again later on).\n", - "\n", - "This method is less useful for values very close to zero - so, for example, when using utilisation (which ranges from 0 to 1) it is recommended to multiple values by 100.\n", - "\n", - "When selecting the number of replications you should repeat the analysis for all performance measures and select the highest value as your number of replications." - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], "source": [ "# pylint: disable=too-many-arguments,too-many-positional-arguments\n", "def confidence_interval_method(\n", @@ -440,7 +249,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -716,7 +525,7 @@ { "data": { "image/svg+xml": [ - "0510158.599.51010.511variablecumulative_meanlower_ciupper_ciNumber of replicationsMean consultation time with nurse (minutes)" + "0510158.599.51010.511variablecumulative_meanlower_ciupper_ciNumber of replicationsMean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -743,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -1349,7 +1158,7 @@ { "data": { "image/svg+xml": [ - "01020304045505560variablecumulative_meanlower_ciupper_ciNumber of replicationsMean wait time for nurse (*100) (minutes)" + "01020304045505560variablecumulative_meanlower_ciupper_ciNumber of replicationsMean wait time for nurse (*100) (minutes)" ] }, "metadata": {}, @@ -1369,7 +1178,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -1645,7 +1454,7 @@ { "data": { "image/svg+xml": [ - "05101548.54949.55050.55151.5variablecumulative_meanlower_ciupper_ciNumber of replicationsMean nurse utilisation (*100 - %)" + "05101548.54949.55050.55151.5variablecumulative_meanlower_ciupper_ciNumber of replicationsMean nurse utilisation (*100 - %)" ] }, "metadata": {}, @@ -1663,84 +1472,6 @@ ")" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Run time with varying number of CPU cores\n", - "\n", - "These are rounded to the nearest .5 seconds, as we will see minor differences in run time between re-runs of this notebook on the same machine, so rounding prevents the figure from changing with every run!" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Running with cores: 1.\n", - "Running with cores: 2.\n", - "Running with cores: 3.\n", - "Running with cores: 4.\n", - "Running with cores: 5.\n", - "Running with cores: 6.\n", - "Running with cores: 7.\n", - "Running with cores: 8.\n", - " cores run_time\n", - "0 1 5.5\n", - "1 2 4.0\n", - "2 3 3.0\n", - "3 4 2.5\n", - "4 5 2.0\n", - "5 6 2.0\n", - "6 7 2.0\n", - "7 8 1.0\n" - ] - }, - { - "data": { - "image/svg+xml": [ - "1234567812345Number of coresRun time (rounded to nearest .5 seconds)" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Run with 1 to 8 cores\n", - "speed = []\n", - "for i in range(1, 9):\n", - " print(f'Running with cores: {i}.')\n", - " start_time = time.time()\n", - "\n", - " run_param = Param(cores=i)\n", - " experiment = Runner(run_param)\n", - " experiment.run_reps()\n", - "\n", - " # Round time to nearest .5 seconds\n", - " run_time = round((time.time() - start_time)*2)/2\n", - " speed.append({'cores': i, 'run_time': run_time})\n", - "\n", - "# Plot time by number of cores\n", - "timing_results = pd.DataFrame(speed)\n", - "print(timing_results)\n", - "cores_fig = px.line(timing_results, x='cores', y='run_time')\n", - "cores_fig.update_layout(\n", - " xaxis_title='Number of cores',\n", - " yaxis_title='Run time (rounded to nearest .5 seconds)',\n", - " template='plotly_white'\n", - ")\n", - "\n", - "# Display and save figure\n", - "cores_fig.show()\n", - "cores_fig.write_image(os.path.join(OUTPUT_DIR, 'choose_param_cores.png'),\n", - " width=800, height=600)" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1750,14 +1481,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 11, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 27s\n" + "Notebook run time: 0m 4s\n" ] } ], diff --git a/notebooks/choosing_warmup.ipynb b/notebooks/choosing_warmup.ipynb new file mode 100644 index 0000000..87f79de --- /dev/null +++ b/notebooks/choosing_warmup.ipynb @@ -0,0 +1,369 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Choosing warm-up length\n", + "\n", + "A suitable length for the warm-up period can be determined using the **time series inspection approach**. This involves looking at performance measures over time to identify when the system is exhibiting **steady state behaviour** (even though the system will never truly reach a \"steady state\").\n", + "\n", + "If we simply plot the mean result at regular intervals, this would vary too much. Therefore, we plot the **cumulative mean** of the performance measure, and look for the point at which this **smoothes out and stabilises**. This indicates the point for the warm-up period to end.\n", + "\n", + "This should be assessed when running the model using the base case parameters. If these change, you should reassess the appropriate warm-up period.\n", + "\n", + "The run time is provided at the end of the notebook.\n", + "\n", + "Credit:\n", + "\n", + "* Code for choice of warm-up period was adapted from Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence).\n", + "\n", + "Licence:\n", + "\n", + "* This project is licensed under the MIT Licence. See the `LICENSE` file for more details." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Set-up\n", + "\n", + "Load required packages." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# pylint: disable=missing-module-docstring\n", + "# To ensure any updates to `simulation/` are fetched without needing to restart\n", + "# the notebook environment, reload `simulation/` before execution of each cell\n", + "%load_ext autoreload\n", + "%autoreload 1\n", + "%aimport simulation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# pylint: disable=wrong-import-position\n", + "import os\n", + "import time\n", + "\n", + "import plotly.io as pio\n", + "import plotly.graph_objects as go\n", + "import plotly.subplots as sp\n", + "\n", + "from simulation.model import Param, Runner" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Display plotly express figures as non-interactive figures. This means they will be visible when browsing the notebooks on GitHub. To switch these back to interactive figures, simply remove this line." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "pio.renderers.default = 'svg'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Start timer." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "notebook_start_time = time.time()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define path to outputs folder" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "# pylint: disable=duplicate-code\n", + "OUTPUT_DIR = '../outputs/'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Define labels for variables in the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "LABELS = {\n", + " 'arrivals': 'Patient arrivals (n)',\n", + " 'mean_q_time_nurse': 'Mean wait time for nurse (minutes)',\n", + " 'mean_n_consult_time': 'Mean consultation time with nurse (minutes)',\n", + " 'mean_time_with_nurse': 'Mean consultation time with nurse (minutes)',\n", + " 'mean_nurse_utilisation': 'Mean nurse utilisation',\n", + " 'adj_mean_nurse_utilisation': 'Mean nurse utilisation (*100 - %)',\n", + " 'adj_mean_q_time_nurse': 'Mean wait time for nurse (*100) (minutes)',\n", + " 'mean_nurse_utilisation_tw': 'Time-weighted mean nurse utilisation',\n", + " 'mean_nurse_q_length': 'Time-weighted mean queue length for nurse (n)',\n", + " 'patient_inter': 'Patient inter-arrival time',\n", + " 'number_of_nurses': 'Number of nurses',\n", + " 'utilisation': 'Utilisation',\n", + " 'running_mean_wait_time': 'Running mean nurse wait time (minutes)'\n", + "}" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Determining appropriate warm-up length" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def time_series_inspection(\n", + " file, data_collection_period, warm_up=None, path=OUTPUT_DIR, labels=None\n", + "):\n", + " \"\"\"\n", + " Time series inspection method for determining length of warm-up.\n", + "\n", + " Arguments:\n", + " file (str):\n", + " Filename to save figure to.\n", + " data_collection_period (float):\n", + " Length of time to run the simulation for.\n", + " warm_up (float, optional):\n", + " Location on X axis to plot vertical red line indicating the chosen\n", + " warm-up period. Defaults to None, which will not plot a line.\n", + " path (str):\n", + " Path to save file to (exc. filename)\n", + " labels (dict):\n", + " Contains mappings from variable names to full labels. If none\n", + " provided, will default to using variable names.\n", + " \"\"\"\n", + " # Use default parameters, but with no warm-up and specified run length,\n", + " # and with no replications\n", + " param = Param(warm_up_period=0,\n", + " data_collection_period=data_collection_period,\n", + " number_of_runs=1)\n", + " # display(param.__dict__)\n", + "\n", + " # Run model\n", + " choose_warmup = Runner(param)\n", + " choose_warmup.run_reps()\n", + "\n", + " # Filter to nurse results\n", + " nurse = choose_warmup.interval_audit_df[\n", + " choose_warmup.interval_audit_df['resource_name'] == 'nurse']\n", + "\n", + " # Define columns to analyse\n", + " plot = ['utilisation', 'running_mean_wait_time']\n", + "\n", + " # Create 1x2 subplot\n", + " full_figure = sp.make_subplots(rows=2, cols=1, shared_xaxes=True)\n", + "\n", + " counter = 1\n", + " for var in plot:\n", + " # Reformat so index is simulation time and columns are each run\n", + " reformat = (\n", + " nurse[['simulation_time', var, 'run']]\n", + " .pivot(index='simulation_time',\n", + " columns='run',\n", + " values=var))\n", + "\n", + " # For utilisation, calculate cumulative mean (not necessary\n", + " # for wait time as that is already a running mean)\n", + " if var == 'utilisation':\n", + " cumulative = reformat.expanding().mean()\n", + " elif var == 'running_mean_wait_time':\n", + " cumulative = reformat.copy()\n", + " else:\n", + " print('Expected var to be utilisation or running_mean_wait_time.')\n", + " break\n", + "\n", + " # Create plot (using go.Scatter instead of px.express, for sub-plots)\n", + " full_figure.add_trace(\n", + " go.Scatter(\n", + " x=cumulative.index,\n", + " y=cumulative[0],\n", + " mode='lines',\n", + " line={'color': 'royalblue'}\n", + " ),\n", + " row=counter, col=1\n", + " )\n", + "\n", + " # Add y axis label\n", + " full_figure.update_yaxes(title_text=labels.get(var, var),\n", + " row=counter, col=1)\n", + "\n", + " # Add vertical line for warm-up period specified\n", + " if warm_up is not None:\n", + " full_figure.add_vline(\n", + " x=warm_up, line_color='red', line_dash='dash',\n", + " annotation_text='Suggested warm-up length',\n", + " annotation_position='top left',\n", + " annotation_font_color='red')\n", + " counter += 1\n", + "\n", + " # Add x axis title\n", + " full_figure.update_xaxes(title_text='Run time (minutes)')\n", + "\n", + " # Set figure dimensions and hide legend\n", + " full_figure.update_layout(\n", + " width=1400,\n", + " height=800,\n", + " showlegend=False,\n", + " template='plotly_white'\n", + " )\n", + "\n", + " # Show figure\n", + " full_figure.show()\n", + "\n", + " # Save figure\n", + " full_figure.write_image(os.path.join(path, file))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Having run the model for three days, it appears to reach a steady state at around 2500 minutes." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "00.10.20.30.40500100015002000250030003500400000.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_series_inspection(\n", + " file='choose_param_time_series_1.png',\n", + " data_collection_period=1440*3, warm_up=2520,\n", + " labels=LABELS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "However, it is important to look far ahead - so we run it for more days, and find actually a later warm-up is more appropriate." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "00.10.20.30.40.5010k20k30k40k50k00.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "time_series_inspection(\n", + " file='choose_param_time_series_2.png',\n", + " data_collection_period=1440*40, warm_up=1440*13,\n", + " labels=LABELS)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run time" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notebook run time: 0m 2s\n" + ] + } + ], + "source": [ + "# Get run time in seconds\n", + "notebook_end_time = time.time()\n", + "runtime = round(notebook_end_time - notebook_start_time)\n", + "\n", + "# Display converted to minutes and seconds\n", + "print(f'Notebook run time: {runtime // 60}m {runtime % 60}s')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "template-des", + "language": "python", + "name": "python3" + }, + "language_info": { + 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From 1ee39a25cb11546aa633a6f0ae03a24bcb98c436 Mon Sep 17 00:00:00 2001 From: amyheather Date: Wed, 12 Feb 2025 16:33:24 +0000 Subject: [PATCH 06/28] feat(replications): add automated method for rep number from sim-tools copied methods - just minor tweaks for simplicity and compatibility, and set algorithm to aim for 5% (rather than 10%) --- notebooks/choosing_replications.ipynb | 886 +++++++++++++++++++++++++- simulation/replications.py | 381 +++++++++++ 2 files changed, 1256 insertions(+), 11 deletions(-) create mode 100644 simulation/replications.py diff --git a/notebooks/choosing_replications.ipynb b/notebooks/choosing_replications.ipynb index 63bd6df..9af273a 100644 --- a/notebooks/choosing_replications.ipynb +++ b/notebooks/choosing_replications.ipynb @@ -14,13 +14,12 @@ "\n", "When selecting the number of replications you should repeat the analysis for all performance measures and select the highest value as your number of replications.\n", "\n", - "Credit:\n", - "\n", - "* Code for choice of replication number was adapted from Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence).\n", + "This notebook shares two ways of implementing this: **manually** and **automated**.\n", "\n", - "Licence:\n", + "Credit:\n", "\n", - "* This project is licensed under the MIT Licence. See the `LICENSE` file for more details." + "* Code for manual choice of replication number was adapted from Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence).\n", + "* Code for automated detection of replication number was adapted from Tom Monks (2025) [sim-tools: tools to support the Discrete-Event Simulation process in python](https://github.com/TomMonks/sim-tools) (MIT Licence)." ] }, { @@ -66,9 +65,12 @@ "import pandas as pd\n", "import plotly.express as px\n", "import plotly.io as pio\n", + "import pandas as pd\n", + "import warnings\n", "\n", "from simulation.model import Param, Runner\n", - "from simulation.helper import summary_stats" + "from simulation.helper import summary_stats\n", + "from simulation.replications import ReplicationTabulizer, ReplicationsAlgorithm, plotly_confidence_interval_method" ] }, { @@ -525,7 +527,7 @@ { "data": { "image/svg+xml": [ - "0510158.599.51010.511variablecumulative_meanlower_ciupper_ciNumber of replicationsMean consultation time with nurse (minutes)" + "0510158.599.51010.511variablecumulative_meanlower_ciupper_ciNumber of replicationsMean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -1158,7 +1160,7 @@ { "data": { "image/svg+xml": [ - "01020304045505560variablecumulative_meanlower_ciupper_ciNumber of replicationsMean wait time for nurse (*100) (minutes)" + "01020304045505560variablecumulative_meanlower_ciupper_ciNumber of replicationsMean wait time for nurse (*100) (minutes)" ] }, "metadata": {}, @@ -1454,7 +1456,7 @@ { "data": { "image/svg+xml": [ - "05101548.54949.55050.55151.5variablecumulative_meanlower_ciupper_ciNumber of replicationsMean nurse utilisation (*100 - %)" + "05101548.54949.55050.55151.5variablecumulative_meanlower_ciupper_ciNumber of replicationsMean nurse utilisation (*100 - %)" ] }, "metadata": {}, @@ -1476,19 +1478,881 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run time" + "## Automated detection of appropriate number of replications" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, + "outputs": [], + "source": [ + "# Set up ReplicationTabulizer and ReplicationsAlgorithm\n", + "observer = ReplicationTabulizer()\n", + "analyser = ReplicationsAlgorithm(verbose=False, observer=observer)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "this looks identical to manual - only difference is this has deviation as a decimal rather than as percentage\n", + "\n", + "perhaps remove calculation/plot from 2 replications from manual method if would normally say that isn't valid" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Run the algorithm (which will run model with increasing reps) for a few different metrics." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Mean Cumulative Mean Standard Deviation Lower Interval \\\n", + "replications \n", + "1 9.842380 9.842380 NaN NaN \n", + "2 10.060424 9.951402 NaN NaN \n", + "3 9.925090 9.942631 0.110075 9.669189 \n", + "4 9.938504 9.941599 0.089900 9.798549 \n", + "5 10.016611 9.956602 0.084775 9.851339 \n", + "6 9.883801 9.944468 0.081442 9.859000 \n", + "7 10.040555 9.958195 0.082742 9.881671 \n", + "8 10.086612 9.974247 0.089048 9.899801 \n", + "\n", + " Upper Interval % deviation \n", + "replications \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 10.216073 0.027502 \n", + "4 10.084650 0.014389 \n", + "5 10.061864 0.010572 \n", + "6 10.029936 0.008595 \n", + "7 10.034719 0.007685 \n", + "8 10.048693 0.007464 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "123456789.79.89.91010.110.2Lower IntervalUpper IntervalCumulative MeanCumulative Mean: mean_time_with_nurse" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n_reps = analyser.select(runner=Runner(Param()),\n", + " metric='mean_time_with_nurse')\n", + "print(n_reps)\n", + "display(observer.summary_table())\n", + "plotly_confidence_interval_method(n_reps, observer.summary_table(), metric_name='mean_time_with_nurse')" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "31\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Mean Cumulative Mean Standard Deviation Lower Interval \\\n", + "replications \n", + "1 0.501873 0.501873 NaN NaN \n", + "2 0.516264 0.509069 NaN NaN \n", + "3 0.531280 0.516472 0.014704 0.479945 \n", + "4 0.491104 0.510130 0.017465 0.482339 \n", + "5 0.463252 0.500755 0.025851 0.468656 \n", + "6 0.390495 0.482378 0.050605 0.429272 \n", + "7 0.473277 0.481078 0.046323 0.438236 \n", + "8 0.611502 0.497381 0.062973 0.444734 \n", + "9 0.478926 0.495330 0.059226 0.449805 \n", + "10 0.564747 0.502272 0.059999 0.459351 \n", + "11 0.571089 0.508528 0.060584 0.467827 \n", + "12 0.393387 0.498933 0.066645 0.456589 \n", + "13 0.444403 0.494738 0.065575 0.455112 \n", + "14 0.447649 0.491375 0.064247 0.454280 \n", + "15 0.614848 0.499606 0.069637 0.461043 \n", + "16 0.461512 0.497225 0.067946 0.461020 \n", + "17 0.425752 0.493021 0.068034 0.458041 \n", + "18 0.481117 0.492360 0.066062 0.459508 \n", + "19 0.376149 0.486243 0.069517 0.452738 \n", + "20 0.429078 0.483385 0.068859 0.451158 \n", + "21 0.624518 0.490106 0.073845 0.456492 \n", + "22 0.515244 0.491248 0.072264 0.459208 \n", + "23 0.525237 0.492726 0.070957 0.462042 \n", + "24 0.520427 0.493880 0.069628 0.464479 \n", + "25 0.519650 0.494911 0.068356 0.466695 \n", + "26 0.638633 0.500439 0.072664 0.471089 \n", + "27 0.514672 0.500966 0.071306 0.472758 \n", + "28 0.503287 0.501049 0.069974 0.473916 \n", + "29 0.461636 0.499690 0.069102 0.473405 \n", + "30 0.551549 0.501419 0.068557 0.475819 \n", + "31 0.509837 0.501690 0.067422 0.476960 \n", + "32 0.490932 0.501354 0.066353 0.477431 \n", + "33 0.558335 0.503081 0.066057 0.479658 \n", + "34 0.548240 0.504409 0.065508 0.481552 \n", + "35 0.457886 0.503080 0.065014 0.480746 \n", + "36 0.464776 0.502016 0.064396 0.480227 \n", + "\n", + " Upper Interval % deviation \n", + "replications \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 0.553000 0.070725 \n", + "4 0.537921 0.054479 \n", + "5 0.532853 0.064100 \n", + "6 0.535484 0.110092 \n", + "7 0.523920 0.089054 \n", + "8 0.550028 0.105848 \n", + "9 0.540856 0.091909 \n", + "10 0.545193 0.085453 \n", + "11 0.549229 0.080036 \n", + "12 0.541277 0.084869 \n", + "13 0.534365 0.080096 \n", + "14 0.528470 0.075493 \n", + "15 0.538170 0.077188 \n", + "16 0.533431 0.072816 \n", + "17 0.528001 0.070950 \n", + "18 0.525212 0.066724 \n", + "19 0.519749 0.068908 \n", + "20 0.515612 0.066670 \n", + "21 0.523719 0.068584 \n", + "22 0.523288 0.065222 \n", + "23 0.523410 0.062274 \n", + "24 0.523282 0.059531 \n", + "25 0.523127 0.057012 \n", + "26 0.529789 0.058648 \n", + "27 0.529174 0.056307 \n", + "28 0.528182 0.054153 \n", + "29 0.525975 0.052603 \n", + "30 0.527018 0.051055 \n", + "31 0.526421 0.049294 \n", + "32 0.525277 0.047716 \n", + "33 0.526503 0.046559 \n", + "34 0.527266 0.045314 \n", + "35 0.525413 0.044393 \n", + "36 0.523804 0.043402 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "51015202530350.450.50.55Lower IntervalUpper IntervalCumulative MeanCumulative Mean: mean_q_time_nurse" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "observer = ReplicationTabulizer()\n", + "analyser = ReplicationsAlgorithm(verbose=False, observer=observer)\n", + "n_reps = analyser.select(runner=Runner(Param()),\n", + " metric='mean_q_time_nurse')\n", + "print(n_reps)\n", + "display(observer.summary_table())\n", + "plotly_confidence_interval_method(n_reps, observer.summary_table(), metric_name='mean_q_time_nurse')" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Mean Cumulative Mean Standard Deviation Lower Interval \\\n", + "replications \n", + "1 0.499589 0.499589 NaN NaN \n", + "2 0.501977 0.500783 NaN NaN \n", + "3 0.498094 0.499887 0.001959 0.495021 \n", + "4 0.498249 0.499477 0.001797 0.496618 \n", + "5 0.496934 0.498969 0.001928 0.496575 \n", + "6 0.492854 0.497949 0.003034 0.494766 \n", + "7 0.503386 0.498726 0.003448 0.495537 \n", + "8 0.503411 0.499312 0.003597 0.496305 \n", + "\n", + " Upper Interval % deviation \n", + "replications \n", + "1 NaN NaN \n", + "2 NaN NaN \n", + "3 0.504753 0.009734 \n", + "4 0.502336 0.005725 \n", + "5 0.501362 0.004797 \n", + "6 0.501133 0.006394 \n", + "7 0.501915 0.006395 \n", + "8 0.502319 0.006022 " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "123456780.4940.4960.4980.50.5020.504Lower IntervalUpper IntervalCumulative MeanCumulative Mean: mean_nurse_utilisation" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "observer = ReplicationTabulizer()\n", + "analyser = ReplicationsAlgorithm(verbose=False, observer=observer)\n", + "n_reps = analyser.select(runner=Runner(Param()),\n", + " metric='mean_nurse_utilisation')\n", + "print(n_reps)\n", + "display(observer.summary_table())\n", + "plotly_confidence_interval_method(n_reps, observer.summary_table(), metric_name='mean_nurse_utilisation')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run time" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 4s\n" + "Notebook run time: 0m 13s\n" ] } ], diff --git a/simulation/replications.py b/simulation/replications.py new file mode 100644 index 0000000..2fd1f58 --- /dev/null +++ b/simulation/replications.py @@ -0,0 +1,381 @@ +"""Selecting the number of replications. + +Credit: + > These functions are adapted from Tom Monks (2021) sim-tools: + fundamental tools to support the simulation process in python + (https://github.com/TomMonks/sim-tools) (MIT Licence). + > In sim-tools, they cite that their implementation is of the "replications + algorithm" from: Hoad, Robinson, & Davies (2010). Automated selection of + the number of replications for a discrete-event simulation. Journal of the + Operational Research Society. https://www.jstor.org/stable/40926090. + +Licence: + This project is licensed under the MIT Licence. See the LICENSE file for + more details. +""" + +import plotly.graph_objects as go +import numpy as np +import pandas as pd +from scipy.stats import t +import warnings + + +class OnlineStatistics: + """ + """ + def __init__(self, data=None, alpha=0.1, observer=None): + """ + """ + self.n = 0 + self.x_i = None + self.mean = None + self._sq = None + self.alpha = alpha + self._observers = [] + if observer is not None: + self.register_observer(observer) + + if isinstance(data, np.ndarray): + for x in data: + self.update(x) + + def register_observer(self, observer): + """ + """ + self._observers.append(observer) + + @property + def variance(self): + """ + """ + return self._sq / (self.n - 1) + + @property + def std(self): + """ + """ + if self.n > 2: + return np.sqrt(self.variance) + else: + return np.nan + + @property + def std_error(self): + """ + """ + return self.std / np.sqrt(self.n) + + @property + def half_width(self): + """ + """ + dof = self.n - 1 + t_value = t.ppf(1 - (self.alpha / 2), dof) + return t_value * self.std_error + + @property + def lci(self): + """ + """ + if self.n > 2: + return self.mean - self.half_width + else: + return np.nan + + @property + def uci(self): + """ + Lower confidence interval bound + """ + if self.n > 2: + return self.mean + self.half_width + else: + return np.nan + + @property + def deviation(self): + """ + """ + if self.n > 2: + return self.half_width / self.mean + else: + return np.nan + + def update(self, x): + """ + """ + self.n += 1 + self.x_i = x + + if self.n == 1: + self.mean = x + self._sq = 0 + else: + updated_mean = self.mean + ((x - self.mean) / self.n) + self._sq += (x - self.mean) * (x - updated_mean) + self.mean = updated_mean + + self.notify() + + def notify(self): + """ + """ + for observer in self._observers: + observer.update(self) + + +class ReplicationTabulizer: + """ + """ + + def __init__(self): + self.stdev = [] + self.lower = [] + self.upper = [] + self.dev = [] + self.cumulative_mean = [] + self.x_i = [] + self.n = 0 + + def update(self, results): + """ + """ + self.x_i.append(results.x_i) + self.cumulative_mean.append(results.mean) + self.stdev.append(results.std) + self.lower.append(results.lci) + self.upper.append(results.uci) + self.dev.append(results.deviation) + self.n += 1 + + def summary_table(self): + """ + """ + results = pd.DataFrame( + [ + self.x_i, + self.cumulative_mean, + self.stdev, + self.lower, + self.upper, + self.dev, + ] + ).T + results.columns = [ + "Mean", + "Cumulative Mean", + "Standard Deviation", + "Lower Interval", + "Upper Interval", + "% deviation", + ] + results.index = np.arange(1, self.n + 1) + results.index.name = "replications" + + return results + + +class ReplicationsAlgorithm: + """ + """ + def __init__( + self, + alpha=0.05, + half_width_precision=0.05, + initial_replications=3, + look_ahead=5, + replication_budget=1000, + verbose=False, + observer=None, + ): + """ + """ + self.alpha = alpha + self.half_width_precision = half_width_precision + self.initial_replications = initial_replications + + self.look_ahead = look_ahead + + self.replication_budget = replication_budget + + self.verbose = verbose + + self.n = self.initial_replications + + self._n_solution = self.replication_budget + + self.observer = observer + + def _klimit(self) -> int: + """ + Get the current look ahead. This is lookahead if n<=100, or lookahead + as a fraction of n if n>100. + """ + return int((self.look_ahead / 100) * max(self.n, 100)) + + def select(self, runner, metric): + + converged = False + + # Run initial replications of the model and get the run results for + # the specified metric + x_i = [runner.run_single(rep)['run'][metric] + for rep in range(self.initial_replications)] + + # Using those results, initialise running mean and std dev + self.stats = OnlineStatistics( + data=np.array(x_i), alpha=self.alpha, observer=self.observer + ) + + while not converged and self.n <= self.replication_budget: + + # If after initial replications, update running mean and std dev + if self.n > self.initial_replications: + self.stats.update(x_i) + + # If precision has been achieved... + if self.stats.deviation <= self.half_width_precision: + + # Store current solution + self._n_solution = self.n + converged = True + + # If the lookahead period is greater than 0 + if self._klimit() > 0: + + # Start a new counter + k = 1 + + # Whilst the precision is achieved and k is less than + # lookahead... + while converged and k <= self._klimit(): + if self.verbose: + print(f'{self.n+k}', end=', ') + + # Run another replication and update the statistics + x_i = runner.run_single(run=self.n+k-1)['run'][metric] + self.stats.update(x_i) + + # If precision lost, set converged to false and update + # the main counter (n) + if self.stats.deviation > self.half_width_precision: + converged = False + self.n += k + # If precision is maintained, update k. + else: + k += 1 + + # If past lookahead and still converged, return _n_solution. + if converged: + return self._n_solution + + # Precision not achieved/maintained, so run another replication + self.n += 1 + if self.verbose: + print(f'{self.n}', end=', ') + x_i = runner.run_single(run=self.n-1)['run'][metric] + + # If not converged and pass replication_budget, end with warning + warnings.warn( + f'''Algorithm did not converge for metric '{metric}'. ''' + + 'Returning replication budget as solution' + ) + return self._n_solution + + +def confidence_interval_method( + replications, + alpha=0.05, + desired_precision=0.05, + min_rep=5, + decimal_places=2, +): + """ + """ + observer = ReplicationTabulizer() + stats = OnlineStatistics( + alpha=alpha, data=replications[:2], observer=observer) + + for i in range(2, len(replications)): + stats.update(replications[i]) + + results = observer.summary_table() + + try: + n_reps = ( + results.iloc[min_rep:] + .loc[results["% deviation"] <= desired_precision] + .iloc[0] + .name + ) + except IndexError: + message = "WARNING: the replications do not reach desired precision" + warnings.warn(message) + n_reps = -1 + + return n_reps, results.round(decimal_places) + + +def plotly_confidence_interval_method( + n_reps, conf_ints, metric_name, figsize=(1200, 400) +): + """ + """ + fig = go.Figure() + + # Calculate relative deviations [1][4] + deviation_pct = ( + (conf_ints["Upper Interval"] - conf_ints["Cumulative Mean"]) + / conf_ints["Cumulative Mean"] + * 100 + ).round(2) + + # Confidence interval bands with hover info + for col, color, dash in zip( + ["Lower Interval", "Upper Interval"], + ["lightblue", "lightblue"], ["dot", "dot"] + ): + fig.add_trace( + go.Scatter( + x=conf_ints.index, + y=conf_ints[col], + line=dict(color=color, dash=dash), + name=col, + text=[f"Deviation: {d}%" for d in deviation_pct], + hoverinfo="x+y+name+text", + ) + ) + + # Cumulative mean line with enhanced hover + fig.add_trace( + go.Scatter( + x=conf_ints.index, + y=conf_ints["Cumulative Mean"], + line=dict(color="blue", width=2), + name="Cumulative Mean", + hoverinfo="x+y+name", + ) + ) + + # Vertical threshold line + fig.add_shape( + type="line", + x0=n_reps, + x1=n_reps, + y0=0, + y1=1, + yref="paper", + line=dict(color="red", dash="dash"), + ) + + # Configure layout + fig.update_layout( + width=figsize[0], + height=figsize[1], + yaxis_title=f"Cumulative Mean: {metric_name}", + hovermode="x unified", + showlegend=True, + ) + + return fig \ No newline at end of file From ce122bca8ae6fe7335292de4d7b1296dd16162a6 Mon Sep 17 00:00:00 2001 From: amyheather Date: Thu, 13 Feb 2025 10:44:39 +0000 Subject: [PATCH 07/28] style(replications): docstrings and linting --- notebooks/choosing_replications.ipynb | 305 +++++++++++----------- simulation/replications.py | 354 +++++++++++++++++++------- 2 files changed, 422 insertions(+), 237 deletions(-) diff --git a/notebooks/choosing_replications.ipynb b/notebooks/choosing_replications.ipynb index 9af273a..7f84c1d 100644 --- a/notebooks/choosing_replications.ipynb +++ b/notebooks/choosing_replications.ipynb @@ -65,12 +65,12 @@ "import pandas as pd\n", "import plotly.express as px\n", "import plotly.io as pio\n", - "import pandas as pd\n", - "import warnings\n", "\n", "from simulation.model import Param, Runner\n", "from simulation.helper import summary_stats\n", - "from simulation.replications import ReplicationTabulizer, ReplicationsAlgorithm, plotly_confidence_interval_method" + "from simulation.replications import (ReplicationTabulizer,\n", + " ReplicationsAlgorithm,\n", + " plotly_confidence_interval_method)" ] }, { @@ -222,10 +222,10 @@ "\n", " # Get minimum number of replications where deviation is less than target\n", " try:\n", - " n_reps = cumulative[cumulative['perc_deviation']\n", - " <= desired_precision*100].iloc[0].name + 1\n", - " print(f'Reached desired precision ({desired_precision}) in {n_reps} ' +\n", - " 'replications.')\n", + " min_reps = cumulative[cumulative['perc_deviation']\n", + " <= desired_precision*100].iloc[0].name + 1\n", + " print(f'Reached desired precision ({desired_precision}) in ' +\n", + " f'{min_reps} replications.')\n", " except IndexError:\n", " warnings.warn(f'Running {replications} replications did not reach' +\n", " f'desired precision ({desired_precision}).')\n", @@ -527,7 +527,7 @@ { "data": { "image/svg+xml": [ - "0510158.599.51010.511variablecumulative_meanlower_ciupper_ciNumber of replicationsMean consultation time with nurse (minutes)" + "0510158.599.51010.511variablecumulative_meanlower_ciupper_ciNumber of replicationsMean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -1160,7 +1160,7 @@ { "data": { "image/svg+xml": [ - "01020304045505560variablecumulative_meanlower_ciupper_ciNumber of replicationsMean wait time for nurse (*100) (minutes)" + "01020304045505560variablecumulative_meanlower_ciupper_ciNumber of replicationsMean wait time for nurse (*100) (minutes)" ] }, "metadata": {}, @@ -1456,7 +1456,7 @@ { "data": { "image/svg+xml": [ - "05101548.54949.55050.55151.5variablecumulative_meanlower_ciupper_ciNumber of replicationsMean nurse utilisation (*100 - %)" + "05101548.54949.55050.55151.5variablecumulative_meanlower_ciupper_ciNumber of replicationsMean nurse utilisation (*100 - %)" ] }, "metadata": {}, @@ -1541,12 +1541,12 @@ " \n", " \n", " \n", - " Mean\n", - " Cumulative Mean\n", - " Standard Deviation\n", - " Lower Interval\n", - " Upper Interval\n", - " % deviation\n", + " data\n", + " cumulative_mean\n", + " stdev\n", + " lower_ci\n", + " upper_ci\n", + " deviation\n", " \n", " \n", " replications\n", @@ -1636,27 +1636,27 @@ "" ], "text/plain": [ - " Mean Cumulative Mean Standard Deviation Lower Interval \\\n", - "replications \n", - "1 9.842380 9.842380 NaN NaN \n", - "2 10.060424 9.951402 NaN NaN \n", - "3 9.925090 9.942631 0.110075 9.669189 \n", - "4 9.938504 9.941599 0.089900 9.798549 \n", - "5 10.016611 9.956602 0.084775 9.851339 \n", - "6 9.883801 9.944468 0.081442 9.859000 \n", - "7 10.040555 9.958195 0.082742 9.881671 \n", - "8 10.086612 9.974247 0.089048 9.899801 \n", + " data cumulative_mean stdev lower_ci upper_ci \\\n", + "replications \n", + "1 9.842380 9.842380 NaN NaN NaN \n", + "2 10.060424 9.951402 NaN NaN NaN \n", + "3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", + "4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", + "5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", + "6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", + "7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", + "8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", "\n", - " Upper Interval % deviation \n", - "replications \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 10.216073 0.027502 \n", - "4 10.084650 0.014389 \n", - "5 10.061864 0.010572 \n", - "6 10.029936 0.008595 \n", - "7 10.034719 0.007685 \n", - "8 10.048693 0.007464 " + " deviation \n", + "replications \n", + "1 NaN \n", + "2 NaN \n", + "3 0.027502 \n", + "4 0.014389 \n", + "5 0.010572 \n", + "6 0.008595 \n", + "7 0.007685 \n", + "8 0.007464 " ] }, "metadata": {}, @@ -1665,7 +1665,7 @@ { "data": { "image/svg+xml": [ - "123456789.79.89.91010.110.2Lower IntervalUpper IntervalCumulative MeanCumulative Mean: mean_time_with_nurse" + "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: mean_time_with_nurse" ] }, "metadata": {}, @@ -1677,7 +1677,8 @@ " metric='mean_time_with_nurse')\n", "print(n_reps)\n", "display(observer.summary_table())\n", - "plotly_confidence_interval_method(n_reps, observer.summary_table(), metric_name='mean_time_with_nurse')" + "plotly_confidence_interval_method(\n", + " n_reps, observer.summary_table(), metric_name='mean_time_with_nurse')" ] }, { @@ -1713,12 +1714,12 @@ " \n", " \n", " \n", - " Mean\n", - " Cumulative Mean\n", - " Standard Deviation\n", - " Lower Interval\n", - " Upper Interval\n", - " % deviation\n", + " data\n", + " cumulative_mean\n", + " stdev\n", + " lower_ci\n", + " upper_ci\n", + " deviation\n", " \n", " \n", " replications\n", @@ -2060,83 +2061,83 @@ "" ], "text/plain": [ - " Mean Cumulative Mean Standard Deviation Lower Interval \\\n", - "replications \n", - "1 0.501873 0.501873 NaN NaN \n", - "2 0.516264 0.509069 NaN NaN \n", - "3 0.531280 0.516472 0.014704 0.479945 \n", - "4 0.491104 0.510130 0.017465 0.482339 \n", - "5 0.463252 0.500755 0.025851 0.468656 \n", - "6 0.390495 0.482378 0.050605 0.429272 \n", - "7 0.473277 0.481078 0.046323 0.438236 \n", - "8 0.611502 0.497381 0.062973 0.444734 \n", - "9 0.478926 0.495330 0.059226 0.449805 \n", - "10 0.564747 0.502272 0.059999 0.459351 \n", - "11 0.571089 0.508528 0.060584 0.467827 \n", - "12 0.393387 0.498933 0.066645 0.456589 \n", - "13 0.444403 0.494738 0.065575 0.455112 \n", - "14 0.447649 0.491375 0.064247 0.454280 \n", - "15 0.614848 0.499606 0.069637 0.461043 \n", - "16 0.461512 0.497225 0.067946 0.461020 \n", - "17 0.425752 0.493021 0.068034 0.458041 \n", - "18 0.481117 0.492360 0.066062 0.459508 \n", - "19 0.376149 0.486243 0.069517 0.452738 \n", - "20 0.429078 0.483385 0.068859 0.451158 \n", - "21 0.624518 0.490106 0.073845 0.456492 \n", - "22 0.515244 0.491248 0.072264 0.459208 \n", - "23 0.525237 0.492726 0.070957 0.462042 \n", - "24 0.520427 0.493880 0.069628 0.464479 \n", - "25 0.519650 0.494911 0.068356 0.466695 \n", - "26 0.638633 0.500439 0.072664 0.471089 \n", - "27 0.514672 0.500966 0.071306 0.472758 \n", - "28 0.503287 0.501049 0.069974 0.473916 \n", - "29 0.461636 0.499690 0.069102 0.473405 \n", - "30 0.551549 0.501419 0.068557 0.475819 \n", - "31 0.509837 0.501690 0.067422 0.476960 \n", - "32 0.490932 0.501354 0.066353 0.477431 \n", - "33 0.558335 0.503081 0.066057 0.479658 \n", - "34 0.548240 0.504409 0.065508 0.481552 \n", - "35 0.457886 0.503080 0.065014 0.480746 \n", - "36 0.464776 0.502016 0.064396 0.480227 \n", + " data cumulative_mean stdev lower_ci upper_ci \\\n", + "replications \n", + "1 0.501873 0.501873 NaN NaN NaN \n", + "2 0.516264 0.509069 NaN NaN NaN \n", + "3 0.531280 0.516472 0.014704 0.479945 0.553000 \n", + "4 0.491104 0.510130 0.017465 0.482339 0.537921 \n", + "5 0.463252 0.500755 0.025851 0.468656 0.532853 \n", + "6 0.390495 0.482378 0.050605 0.429272 0.535484 \n", + "7 0.473277 0.481078 0.046323 0.438236 0.523920 \n", + "8 0.611502 0.497381 0.062973 0.444734 0.550028 \n", + "9 0.478926 0.495330 0.059226 0.449805 0.540856 \n", + "10 0.564747 0.502272 0.059999 0.459351 0.545193 \n", + "11 0.571089 0.508528 0.060584 0.467827 0.549229 \n", + "12 0.393387 0.498933 0.066645 0.456589 0.541277 \n", + "13 0.444403 0.494738 0.065575 0.455112 0.534365 \n", + "14 0.447649 0.491375 0.064247 0.454280 0.528470 \n", + "15 0.614848 0.499606 0.069637 0.461043 0.538170 \n", + "16 0.461512 0.497225 0.067946 0.461020 0.533431 \n", + "17 0.425752 0.493021 0.068034 0.458041 0.528001 \n", + "18 0.481117 0.492360 0.066062 0.459508 0.525212 \n", + "19 0.376149 0.486243 0.069517 0.452738 0.519749 \n", + "20 0.429078 0.483385 0.068859 0.451158 0.515612 \n", + "21 0.624518 0.490106 0.073845 0.456492 0.523719 \n", + "22 0.515244 0.491248 0.072264 0.459208 0.523288 \n", + "23 0.525237 0.492726 0.070957 0.462042 0.523410 \n", + "24 0.520427 0.493880 0.069628 0.464479 0.523282 \n", + "25 0.519650 0.494911 0.068356 0.466695 0.523127 \n", + "26 0.638633 0.500439 0.072664 0.471089 0.529789 \n", + "27 0.514672 0.500966 0.071306 0.472758 0.529174 \n", + "28 0.503287 0.501049 0.069974 0.473916 0.528182 \n", + "29 0.461636 0.499690 0.069102 0.473405 0.525975 \n", + "30 0.551549 0.501419 0.068557 0.475819 0.527018 \n", + "31 0.509837 0.501690 0.067422 0.476960 0.526421 \n", + "32 0.490932 0.501354 0.066353 0.477431 0.525277 \n", + "33 0.558335 0.503081 0.066057 0.479658 0.526503 \n", + "34 0.548240 0.504409 0.065508 0.481552 0.527266 \n", + "35 0.457886 0.503080 0.065014 0.480746 0.525413 \n", + "36 0.464776 0.502016 0.064396 0.480227 0.523804 \n", "\n", - " Upper Interval % deviation \n", - "replications \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 0.553000 0.070725 \n", - "4 0.537921 0.054479 \n", - "5 0.532853 0.064100 \n", - "6 0.535484 0.110092 \n", - "7 0.523920 0.089054 \n", - "8 0.550028 0.105848 \n", - "9 0.540856 0.091909 \n", - "10 0.545193 0.085453 \n", - "11 0.549229 0.080036 \n", - "12 0.541277 0.084869 \n", - "13 0.534365 0.080096 \n", - "14 0.528470 0.075493 \n", - "15 0.538170 0.077188 \n", - "16 0.533431 0.072816 \n", - "17 0.528001 0.070950 \n", - "18 0.525212 0.066724 \n", - "19 0.519749 0.068908 \n", - "20 0.515612 0.066670 \n", - "21 0.523719 0.068584 \n", - "22 0.523288 0.065222 \n", - "23 0.523410 0.062274 \n", - "24 0.523282 0.059531 \n", - "25 0.523127 0.057012 \n", - "26 0.529789 0.058648 \n", - "27 0.529174 0.056307 \n", - "28 0.528182 0.054153 \n", - "29 0.525975 0.052603 \n", - "30 0.527018 0.051055 \n", - "31 0.526421 0.049294 \n", - "32 0.525277 0.047716 \n", - "33 0.526503 0.046559 \n", - "34 0.527266 0.045314 \n", - "35 0.525413 0.044393 \n", - "36 0.523804 0.043402 " + " deviation \n", + "replications \n", + "1 NaN \n", + "2 NaN \n", + "3 0.070725 \n", + "4 0.054479 \n", + "5 0.064100 \n", + "6 0.110092 \n", + "7 0.089054 \n", + "8 0.105848 \n", + "9 0.091909 \n", + "10 0.085453 \n", + "11 0.080036 \n", + "12 0.084869 \n", + "13 0.080096 \n", + "14 0.075493 \n", + "15 0.077188 \n", + "16 0.072816 \n", + "17 0.070950 \n", + "18 0.066724 \n", + "19 0.068908 \n", + "20 0.066670 \n", + "21 0.068584 \n", + "22 0.065222 \n", + "23 0.062274 \n", + "24 0.059531 \n", + "25 0.057012 \n", + "26 0.058648 \n", + "27 0.056307 \n", + "28 0.054153 \n", + "29 0.052603 \n", + "30 0.051055 \n", + "31 0.049294 \n", + "32 0.047716 \n", + "33 0.046559 \n", + "34 0.045314 \n", + "35 0.044393 \n", + "36 0.043402 " ] }, "metadata": {}, @@ -2145,7 +2146,7 @@ { "data": { "image/svg+xml": [ - "51015202530350.450.50.55Lower IntervalUpper IntervalCumulative MeanCumulative Mean: mean_q_time_nurse" + "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: mean_q_time_nurse" ] }, "metadata": {}, @@ -2159,7 +2160,8 @@ " metric='mean_q_time_nurse')\n", "print(n_reps)\n", "display(observer.summary_table())\n", - "plotly_confidence_interval_method(n_reps, observer.summary_table(), metric_name='mean_q_time_nurse')" + "plotly_confidence_interval_method(\n", + " n_reps, observer.summary_table(), metric_name='mean_q_time_nurse')" ] }, { @@ -2195,12 +2197,12 @@ " \n", " \n", " \n", - " Mean\n", - " Cumulative Mean\n", - " Standard Deviation\n", - " Lower Interval\n", - " Upper Interval\n", - " % deviation\n", + " data\n", + " cumulative_mean\n", + " stdev\n", + " lower_ci\n", + " upper_ci\n", + " deviation\n", " \n", " \n", " replications\n", @@ -2290,27 +2292,27 @@ "" ], "text/plain": [ - " Mean Cumulative Mean Standard Deviation Lower Interval \\\n", - "replications \n", - "1 0.499589 0.499589 NaN NaN \n", - "2 0.501977 0.500783 NaN NaN \n", - "3 0.498094 0.499887 0.001959 0.495021 \n", - "4 0.498249 0.499477 0.001797 0.496618 \n", - "5 0.496934 0.498969 0.001928 0.496575 \n", - "6 0.492854 0.497949 0.003034 0.494766 \n", - "7 0.503386 0.498726 0.003448 0.495537 \n", - "8 0.503411 0.499312 0.003597 0.496305 \n", + " data cumulative_mean stdev lower_ci upper_ci \\\n", + "replications \n", + "1 0.499589 0.499589 NaN NaN NaN \n", + "2 0.501977 0.500783 NaN NaN NaN \n", + "3 0.498094 0.499887 0.001959 0.495021 0.504753 \n", + "4 0.498249 0.499477 0.001797 0.496618 0.502336 \n", + "5 0.496934 0.498969 0.001928 0.496575 0.501362 \n", + "6 0.492854 0.497949 0.003034 0.494766 0.501133 \n", + "7 0.503386 0.498726 0.003448 0.495537 0.501915 \n", + "8 0.503411 0.499312 0.003597 0.496305 0.502319 \n", "\n", - " Upper Interval % deviation \n", - "replications \n", - "1 NaN NaN \n", - "2 NaN NaN \n", - "3 0.504753 0.009734 \n", - "4 0.502336 0.005725 \n", - "5 0.501362 0.004797 \n", - "6 0.501133 0.006394 \n", - "7 0.501915 0.006395 \n", - "8 0.502319 0.006022 " + " deviation \n", + "replications \n", + "1 NaN \n", + "2 NaN \n", + "3 0.009734 \n", + "4 0.005725 \n", + "5 0.004797 \n", + "6 0.006394 \n", + "7 0.006395 \n", + "8 0.006022 " ] }, "metadata": {}, @@ -2319,7 +2321,7 @@ { "data": { "image/svg+xml": [ - "123456780.4940.4960.4980.50.5020.504Lower IntervalUpper IntervalCumulative MeanCumulative Mean: mean_nurse_utilisation" + "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: mean_nurse_utilisation" ] }, "metadata": {}, @@ -2333,7 +2335,8 @@ " metric='mean_nurse_utilisation')\n", "print(n_reps)\n", "display(observer.summary_table())\n", - "plotly_confidence_interval_method(n_reps, observer.summary_table(), metric_name='mean_nurse_utilisation')" + "plotly_confidence_interval_method(\n", + " n_reps, observer.summary_table(), metric_name='mean_nurse_utilisation')" ] }, { @@ -2352,7 +2355,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 13s\n" + "Notebook run time: 0m 14s\n" ] } ], diff --git a/simulation/replications.py b/simulation/replications.py index 2fd1f58..d4633a0 100644 --- a/simulation/replications.py +++ b/simulation/replications.py @@ -14,18 +14,48 @@ more details. """ +import warnings + import plotly.graph_objects as go import numpy as np import pandas as pd from scipy.stats import t -import warnings class OnlineStatistics: """ + Computes running sample mean and variance (using Welford's algorithm), + which then allows computation of confidence intervals (CIs). + + The statistics are referred to as "online" as they are computed via updates + to it's value, rather than storing lots of data and repeatedly taking the + mean after new values have been added. + + Attributes: + n (int): + Number of data points processed. + x_i (float): + Most recent data point. + mean (float): + Running mean. + _sq (float): + Sum of squared differences from the mean. + alpha (float): + Significance level for confidence interval calculations. + _observers (list): + List of observer objects. """ def __init__(self, data=None, alpha=0.1, observer=None): """ + Initialises the OnlineStatistics instance. + + Arguments: + data (np.ndarray, optional): + Array containing an initial data sample. + alpha (float, optional): + Significance level for confidence interval calculations. + observer (object, optional): + Observer to notify on updates. """ self.n = 0 self.x_i = None @@ -33,42 +63,85 @@ def __init__(self, data=None, alpha=0.1, observer=None): self._sq = None self.alpha = alpha self._observers = [] + + # If an observer is supplied, then add it to the observer list if observer is not None: self.register_observer(observer) + # If an array of initial values are supplied, then run update() if isinstance(data, np.ndarray): for x in data: self.update(x) def register_observer(self, observer): """ + Registers an observer to be notified of updates. + + Arguments: + observer (object): + Observer to notify on updates. """ self._observers.append(observer) + def update(self, x): + """ + Running update of mean and variance implemented using Welford's + algorithm (1962). + + See Knuth. D `The Art of Computer Programming` Vol 2. 2nd ed. Page 216. + + Arguments: + x (float): + A new data point. + """ + self.n += 1 + self.x_i = x + + if self.n == 1: + self.mean = x + self._sq = 0 + else: + updated_mean = self.mean + ((x - self.mean) / self.n) + self._sq += (x - self.mean) * (x - updated_mean) + self.mean = updated_mean + + self.notify() + + def notify(self): + """ + Notify all registered observers about an update. + """ + for observer in self._observers: + observer.update(self) + @property def variance(self): """ + Computes and returns the variance of the data points. """ + # Sum of squares of differences from the current mean divided by n - 1 return self._sq / (self.n - 1) @property def std(self): """ + Computes and returns the standard deviation, or NaN if not enough data. """ if self.n > 2: return np.sqrt(self.variance) - else: - return np.nan + return np.nan @property def std_error(self): """ + Computes and returns the standard error of the mean. """ return self.std / np.sqrt(self.n) @property def half_width(self): """ + Computes and returns the half-width of the confidence interval. """ dof = self.n - 1 t_value = t.ppf(1 - (self.alpha / 2), dof) @@ -77,69 +150,77 @@ def half_width(self): @property def lci(self): """ + Computes and returns the lower confidence interval bound, or NaN if + not enough data. """ if self.n > 2: return self.mean - self.half_width - else: - return np.nan + return np.nan @property def uci(self): """ - Lower confidence interval bound + Computes and returns the upper confidence interval bound, or NaN if + not enough data. """ if self.n > 2: return self.mean + self.half_width - else: - return np.nan + return np.nan @property def deviation(self): """ + Computes and returns the precision of the confidence interval + expressed as the percentage deviation of the half width from the mean. """ if self.n > 2: return self.half_width / self.mean - else: - return np.nan - - def update(self, x): - """ - """ - self.n += 1 - self.x_i = x - - if self.n == 1: - self.mean = x - self._sq = 0 - else: - updated_mean = self.mean + ((x - self.mean) / self.n) - self._sq += (x - self.mean) * (x - updated_mean) - self.mean = updated_mean - - self.notify() - - def notify(self): - """ - """ - for observer in self._observers: - observer.update(self) + return np.nan class ReplicationTabulizer: """ + Observes and records results from OnlineStatistics, updating each time new + data is processed. + + Attributes: + n (int): + Number of data points processed. + x_i (list): + List containing each data point. + cumulative_mean (list): + List of the running mean. + stdev (list): + List of the standard deviation. + lower (list): + List of the lower confidence interval bound. + upper (list): + List of the upper confidence interval bound. + dev (list): + List of the percentage deviation of the confidence interval + half width from the mean. """ - def __init__(self): + """ + Initialises empty lists for storing statistics, and n is set to zero. + """ + self.n = 0 + self.x_i = [] + self.cumulative_mean = [] self.stdev = [] self.lower = [] self.upper = [] self.dev = [] - self.cumulative_mean = [] - self.x_i = [] - self.n = 0 def update(self, results): """ + Add new results from OnlineStatistics to the appropriate lists. + + Arguments: + results (OnlineStatistics): + An instance of OnlineStatistics containing updated statistical + measures like the mean, standard deviation and confidence + intervals. """ self.x_i.append(results.x_i) self.cumulative_mean.append(results.mean) @@ -151,34 +232,70 @@ def update(self, results): def summary_table(self): """ + Create a results table from the stored lists. + + Returns: + results (pd.DataFrame): + Dataframe summarising the replication statistics. """ results = pd.DataFrame( - [ - self.x_i, - self.cumulative_mean, - self.stdev, - self.lower, - self.upper, - self.dev, - ] - ).T - results.columns = [ - "Mean", - "Cumulative Mean", - "Standard Deviation", - "Lower Interval", - "Upper Interval", - "% deviation", - ] + { + 'data': self.x_i, + 'cumulative_mean': self.cumulative_mean, + 'stdev': self.stdev, + 'lower_ci': self.lower, + 'upper_ci': self.upper, + 'deviation': self.dev + } + ) results.index = np.arange(1, self.n + 1) - results.index.name = "replications" - + results.index.name = 'replications' return results +# pylint: disable=too-many-instance-attributes,too-few-public-methods class ReplicationsAlgorithm: """ + Implements an adaptive replication algorithm for selecting the + appropriate number of simulation replications based on statistical + precision. + + Uses the "Replications Algorithm" from Hoad, Robinson, & Davies (2010). + Given a model's performance measure and a user-set confidence interval + half width prevision, automatically select the number of replications. + Combines the "confidence intervals" method with a sequential look-ahead + procedure to determine if a desired precision in the confidence interval + is maintained. + + Attributes: + alpha (float): + Significance level for confidence interval calculations. + half_width_precision (float): + The target half width precision for the algorithm (i.e. percentage + deviation of the confidence interval from the mean). + initial_replications (int): + Number of initial replications to perform. + look_ahead (int): + Additional replications to look ahead to assess stability of + precision. When the number of replications is <= 100, the value of + look_ahead is used. When they are > 100, then + look_ahead / 100 * max(n, 100) is used. + replication_budget (int): + Maximum allowed replications. Use for larger models where + replication runtime is a constraint. + verbose (bool): + Whether to print progress information. + n (int): + Current number of replications performed. + _n_solution (int): + Final determined number of replications required for maintained + precision. + observer (object): + Observer to notify on updates. + stats (OnlineStatistics): + Instance of OnlineStatistics, set in the select() method. """ + # pylint: disable=too-many-arguments,too-many-positional-arguments def __init__( self, alpha=0.05, @@ -190,36 +307,52 @@ def __init__( observer=None, ): """ + Initialise an instance of the ReplicationsAlgorithm. + + Arguments are described in the class docstring. """ self.alpha = alpha self.half_width_precision = half_width_precision self.initial_replications = initial_replications - self.look_ahead = look_ahead - self.replication_budget = replication_budget - self.verbose = verbose - self.n = self.initial_replications - self._n_solution = self.replication_budget - self.observer = observer + self.stats = None - def _klimit(self) -> int: + def _klimit(self): """ - Get the current look ahead. This is lookahead if n<=100, or lookahead - as a fraction of n if n>100. + Determines the number of additional replications to check after + precision is reached, scaling with total replications if they are + greater than 100. + + Returns: + int: + Number of additional replications to verify stability. """ return int((self.look_ahead / 100) * max(self.n, 100)) def select(self, runner, metric): + """ + Executes the replication algorithm, determining the necessary number + of replications to achieve and maintain the desired precision. + + Arguments: + runner (Runner): + An instance of Runner which executes the model replications. + metric (str): + Name of the performance measure being tracked. + + Returns: + int: + The determined number of replications needed for precision. + """ converged = False - # Run initial replications of the model and get the run results for - # the specified metric + # Run initial replications and collect results for the specified metric x_i = [runner.run_single(rep)['run'][metric] for rep in range(self.initial_replications)] @@ -292,25 +425,60 @@ def confidence_interval_method( decimal_places=2, ): """ + The confidence interval method for selecting the number of replications. + + This method accepts the results from having already run a model for a + number of replications. It then calculates the cumulative mean and + confidence intervals with each of those replications. It then checks the + results to find when the precision is first achieved. It does not check if + this precision is maintained. + + Arguments: + replications (np.ndarray): + Array with results from replications of a performance metric. + alpha (float, optional): + Significance level for confidence interval calculations. + desired_precision (float, optional): + The target half width precision for the algorithm (i.e. percentage + deviation of the confidence interval from the mean). + min_rep (int, optional): + Minimum number of replications before checking precision. Useful + when the number of replications returned does not provide a stable + precision below target. + decimal_places (int, optional): + Number of decimal places to round the results table to. + + Returns: + tuple[int, pd.DataFrame]: + - The minimum number of replications required to meet the + precision. Returns -1 if precision is not achieved. + - DataFrame containing cumulative statistics for each replication. + + Warnings: + Issues a warning if the desired precision is not met within the + provided replications. """ + # Set up method for calculating statistics and saving them as a table observer = ReplicationTabulizer() stats = OnlineStatistics( alpha=alpha, data=replications[:2], observer=observer) + # Calculate statistics with each replication, and get summary table for i in range(2, len(replications)): stats.update(replications[i]) - results = observer.summary_table() + # Find the smallest number of replications where deviation meets target try: n_reps = ( results.iloc[min_rep:] - .loc[results["% deviation"] <= desired_precision] + .loc[results['deviation'] <= desired_precision] .iloc[0] .name ) + # Return warning if there are no replications with the desired precision except IndexError: - message = "WARNING: the replications do not reach desired precision" + message = 'WARNING: the replications do not reach desired precision' warnings.warn(message) n_reps = -1 @@ -321,29 +489,43 @@ def plotly_confidence_interval_method( n_reps, conf_ints, metric_name, figsize=(1200, 400) ): """ + Generates an interactive Plotly visualisation of confidence intervals + with increasing simulation replications. + + Arguments: + n_reps (int): + The number of replications required to meet the desired precision. + conf_ints (pd.DataFrame): + A DataFrame containing confidence interval statistics, including + cumulative mean, upper/lower bounds, and deviations. As returned + by ReplicationTabulizer summary_table() method. + metric_name (str): + Name of metric being analysed. + figsize (tuple, optional): + Plot dimensions in pixels (width, height). """ fig = go.Figure() # Calculate relative deviations [1][4] deviation_pct = ( - (conf_ints["Upper Interval"] - conf_ints["Cumulative Mean"]) - / conf_ints["Cumulative Mean"] + (conf_ints['upper_ci'] - conf_ints['cumulative_mean']) + / conf_ints['cumulative_mean'] * 100 ).round(2) # Confidence interval bands with hover info for col, color, dash in zip( - ["Lower Interval", "Upper Interval"], - ["lightblue", "lightblue"], ["dot", "dot"] + ['lower_ci', 'upper_ci'], + ['lightblue', 'lightblue'], ['dot', 'dot'] ): fig.add_trace( go.Scatter( x=conf_ints.index, y=conf_ints[col], - line=dict(color=color, dash=dash), + line={'color': color, 'dash': dash}, name=col, - text=[f"Deviation: {d}%" for d in deviation_pct], - hoverinfo="x+y+name+text", + text=['Deviation: {d}%' for d in deviation_pct], + hoverinfo='x+y+name+text', ) ) @@ -351,31 +533,31 @@ def plotly_confidence_interval_method( fig.add_trace( go.Scatter( x=conf_ints.index, - y=conf_ints["Cumulative Mean"], - line=dict(color="blue", width=2), - name="Cumulative Mean", - hoverinfo="x+y+name", + y=conf_ints['cumulative_mean'], + line={'color': 'blue', 'width': 2}, + name='Cumulative Mean', + hoverinfo='x+y+name', ) ) # Vertical threshold line fig.add_shape( - type="line", + type='line', x0=n_reps, x1=n_reps, y0=0, y1=1, - yref="paper", - line=dict(color="red", dash="dash"), + yref='paper', + line={'color': 'red', 'dash': 'dash'}, ) # Configure layout fig.update_layout( width=figsize[0], height=figsize[1], - yaxis_title=f"Cumulative Mean: {metric_name}", - hovermode="x unified", + yaxis_title=f'Cumulative Mean: {metric_name}', + hovermode='x unified', showlegend=True, ) - return fig \ No newline at end of file + return fig From 0a6b02204fc65a6381d96be214ce472259bdaf4b Mon Sep 17 00:00:00 2001 From: amyheather Date: Thu, 13 Feb 2025 11:18:03 +0000 Subject: [PATCH 08/28] fix(summary_stats): no stdev/ci for n<3 --- simulation/helper.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/simulation/helper.py b/simulation/helper.py index 058fce5..472019f 100644 --- a/simulation/helper.py +++ b/simulation/helper.py @@ -34,8 +34,8 @@ def summary_stats(data): # If there are no observations, then set all to NaN if count == 0: mean, std_dev, ci_lower, ci_upper = np.nan, np.nan, np.nan, np.nan - # If there is only one observation, can do mean but not others - elif count == 1: + # If there is only one or two observations, can do mean but not others + elif count < 3: mean = data.mean() std_dev, ci_lower, ci_upper = np.nan, np.nan, np.nan # With more than one observation, can calculate all... From 91fa43915534b75f18bf7b2f9507831bd2788018 Mon Sep 17 00:00:00 2001 From: amyheather Date: Thu, 13 Feb 2025 11:18:37 +0000 Subject: [PATCH 09/28] feat(replications): streamline confidence_interval_methods so complementary to each other --- notebooks/choosing_replications.ipynb | 1564 ++++++------------------- outputs/choose_param_conf_int_1.png | Bin 40999 -> 45637 bytes outputs/choose_param_conf_int_2.png | Bin 53221 -> 51901 bytes outputs/choose_param_conf_int_3.png | Bin 45944 -> 53399 bytes outputs/choose_param_cores.png | Bin 42221 -> 41980 bytes simulation/replications.py | 133 ++- 6 files changed, 445 insertions(+), 1252 deletions(-) diff --git a/notebooks/choosing_replications.ipynb b/notebooks/choosing_replications.ipynb index 7f84c1d..9cb98e4 100644 --- a/notebooks/choosing_replications.ipynb +++ b/notebooks/choosing_replications.ipynb @@ -68,9 +68,10 @@ "\n", "from simulation.model import Param, Runner\n", "from simulation.helper import summary_stats\n", - "from simulation.replications import (ReplicationTabulizer,\n", - " ReplicationsAlgorithm,\n", - " plotly_confidence_interval_method)" + "from simulation.replications import (\n", + " ReplicationTabulizer, ReplicationsAlgorithm,\n", + " confidence_interval_method_simple, confidence_interval_method,\n", + " plotly_confidence_interval_method)" ] }, { @@ -158,95 +159,75 @@ "source": [ "## Manual inspection\n", "\n", - "First, we demonstrate how to do this manually, by inspecting the output table and figures to determine an appropriate number of replications." + "First, we demonstrate how to do this manually, by inspecting the output table and figures to determine an appropriate number of replications.\n", + "\n", + "We provide two functions to do this - `confidence_interval_method_simple` and `confidence_interval_method` - both will provide the same results, they just use different methods for calculating the statistics." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### `confidence_interval_method_simple`" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reached desired precision (0.05) in 6 replications.\n", + " replications cumulative_mean stdev lower_ci upper_ci deviation\n", + "0 0 9.842380 NaN NaN NaN NaN\n", + "1 1 9.951402 NaN NaN NaN NaN\n", + "2 2 9.942631 0.110075 9.669189 10.216073 2.750194\n", + "3 3 9.941599 0.089900 9.798549 10.084650 1.438907\n", + "4 4 9.956602 0.084775 9.851339 10.061864 1.057210\n", + "5 5 9.944468 0.081442 9.859000 10.029936 0.859452\n", + "6 6 9.958195 0.082742 9.881671 10.034719 0.768450\n", + "7 7 9.974247 0.089048 9.899801 10.048693 0.746385\n", + "8 8 9.999643 0.112884 9.912872 10.086413 0.867734\n", + "9 9 10.009002 0.110467 9.929979 10.088025 0.789522\n", + "10 10 10.015701 0.107128 9.943732 10.087671 0.718565\n", + "11 11 10.005693 0.107865 9.937159 10.074228 0.684953\n", + "12 12 9.990554 0.116811 9.919966 10.061143 0.706549\n", + "13 13 9.986430 0.113284 9.921022 10.051839 0.654973\n", + "14 14 9.998395 0.118591 9.932721 10.064069 0.656843\n", + "15 15 9.996950 0.114716 9.935822 10.058077 0.611465\n", + "16 16 9.985912 0.120035 9.924196 10.047628 0.618032\n", + "17 17 9.986032 0.116452 9.928122 10.043942 0.579912\n", + "18 18 9.976331 0.120813 9.918102 10.034561 0.583679\n", + "19 19 9.973684 0.118185 9.918372 10.028996 0.554581\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "0246810121416189.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ - "# pylint: disable=too-many-arguments,too-many-positional-arguments\n", - "def confidence_interval_method(\n", - " file, replications, metric, desired_precision,\n", - " min_rep=None, path=OUTPUT_DIR, labels=None\n", - "):\n", - " \"\"\"\n", - " Use the confidence interval method to select the number of replications.\n", - "\n", - " Arguments:\n", - " file (str):\n", - " Filename to save figure to.\n", - " replications (int):\n", - " Number of times to run the model.\n", - " metric (string):\n", - " Name of performance metric to assess.\n", - " desired_precision (float):\n", - " Desired mean deviation from confidence interval.\n", - " min_rep (int):\n", - " A suggested minimum number of replications, using to draw vertical\n", - " line on plot. If none provided, will not add a line.\n", - " path (str):\n", - " Path to save file to (exc. filename).\n", - " labels (dict):\n", - " Contains mappings from variable names to full labels. If none\n", - " provided, will default to using variable names.\n", - " \"\"\"\n", - " param = Param(number_of_runs=replications)\n", - " choose_rep = Runner(param)\n", - " choose_rep.run_reps()\n", - "\n", - " # If mean of metric is less than 1, multiply by 100\n", - " df = choose_rep.run_results_df\n", - " if df[metric].mean() < 1:\n", - " df[f'adj_{metric}'] = df[metric] * 100\n", - " metric = f'adj_{metric}'\n", - "\n", - " # Compute cumulative statistics\n", - " cumulative = pd.DataFrame([\n", - " {\n", - " 'replications': i,\n", - " 'cumulative_mean': stats[0],\n", - " 'cumulative_std': stats[1],\n", - " 'lower_ci': stats[2],\n", - " 'upper_ci': stats[3],\n", - " 'perc_deviation': ((stats[3] - stats[0]) / stats[0]) * 100\n", - " }\n", - " for i, stats in enumerate(\n", - " (summary_stats(df[metric].iloc[:i])\n", - " for i in range(1, replications + 1))\n", - " )\n", - " ])\n", - " display(cumulative)\n", - "\n", - " # Get minimum number of replications where deviation is less than target\n", - " try:\n", - " min_reps = cumulative[cumulative['perc_deviation']\n", - " <= desired_precision*100].iloc[0].name + 1\n", - " print(f'Reached desired precision ({desired_precision}) in ' +\n", - " f'{min_reps} replications.')\n", - " except IndexError:\n", - " warnings.warn(f'Running {replications} replications did not reach' +\n", - " f'desired precision ({desired_precision}).')\n", - "\n", - " # Plot the cumulative mean and confidence interval\n", - " figure = px.line(cumulative,\n", - " x='replications',\n", - " y=['cumulative_mean', 'lower_ci', 'upper_ci'])\n", - " figure.update_layout(\n", - " xaxis_title='Number of replications',\n", - " yaxis_title=labels.get(metric, metric),\n", - " template='plotly_white'\n", - " )\n", - " if min_rep is not None:\n", - " figure.add_vline(x=min_rep, line_color='red', line_dash='dash')\n", + "n_reps, cumulative_df = confidence_interval_method_simple(\n", + " replications=20,\n", + " metric='mean_time_with_nurse',\n", + " desired_precision=0.05\n", + ")\n", "\n", - " # Show figure\n", - " figure.show()\n", + "print(cumulative_df)\n", "\n", - " # Save figure\n", - " figure.write_image(os.path.join(path, file))" + "plotly_confidence_interval_method(\n", + " n_reps=n_reps,\n", + " conf_ints=cumulative_df,\n", + " metric_name='Mean time with nurse',\n", + " file_path=os.path.join(OUTPUT_DIR, 'choose_param_conf_int_1.png'))" ] }, { @@ -254,280 +235,117 @@ "execution_count": 8, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reached desired precision (0.05) in 31 replications.\n", + " replications cumulative_mean stdev lower_ci upper_ci deviation\n", + "0 0 50.187329 NaN NaN NaN NaN\n", + "1 1 50.906857 NaN NaN NaN NaN\n", + "2 2 51.647229 1.470432 47.994472 55.299986 7.072512\n", + "3 3 51.013012 1.746532 48.233890 53.792134 5.447869\n", + "4 4 50.075454 2.585121 46.865602 53.285306 6.410031\n", + "5 5 48.237798 5.060452 42.927179 53.548416 11.009247\n", + "6 6 48.107789 4.632328 43.823600 52.391977 8.905395\n", + "7 7 49.738086 6.297295 44.473416 55.002756 10.584787\n", + "8 8 49.533032 5.922614 44.980508 54.085556 9.190885\n", + "9 9 50.227203 5.999883 45.935145 54.519261 8.545285\n", + "10 10 50.852808 6.058378 46.782735 54.922882 8.003636\n", + "11 11 49.893302 6.664462 45.658905 54.127699 8.486905\n", + "12 12 49.473836 6.557527 45.511162 53.436511 8.009636\n", + "13 13 49.137487 6.424735 45.427956 52.847019 7.549290\n", + "14 14 49.960640 6.963659 46.104294 53.816985 7.718767\n", + "15 15 49.722548 6.794609 46.101956 53.343139 7.281589\n", + "16 16 49.302117 6.803399 45.804132 52.800102 7.095000\n", + "17 17 49.235981 6.606228 45.950781 52.521181 6.672356\n", + "18 18 48.624347 6.951654 45.273757 51.974937 6.890766\n", + "19 19 48.338519 6.885928 45.115806 51.561233 6.666968\n", + "20 20 49.010582 7.384458 45.649217 52.371946 6.858447\n", + "21 21 49.124845 7.226394 45.920841 52.328848 6.522166\n", + "22 22 49.272621 7.095729 46.204197 52.341044 6.227441\n", + "23 23 49.388041 6.962757 46.447926 52.328155 5.953090\n", + "24 24 49.491120 6.835615 46.669517 52.312723 5.701231\n", + "25 25 50.043896 7.266443 47.108917 52.978876 5.864810\n", + "26 26 50.096611 7.130596 47.275841 52.917381 5.630660\n", + "27 27 50.104902 6.997440 47.391578 52.818226 5.415286\n", + "28 28 49.968994 6.910217 47.340488 52.597500 5.260274\n", + "29 29 50.141858 6.855726 47.581888 52.701829 5.105455\n", + "30 30 50.169013 6.742191 47.695958 52.642069 4.929448\n", + "31 31 50.135393 6.635281 47.743119 52.527666 4.771627\n", + "32 32 50.308064 6.605680 47.965791 52.650336 4.655859\n", + "33 33 50.440885 6.550767 48.155214 52.726555 4.531385\n", + "34 34 50.307962 6.501446 48.074637 52.541287 4.439307\n", + "35 35 50.201564 6.439617 48.022711 52.380417 4.340209\n", + "36 36 50.588822 6.772415 48.330786 52.846858 4.463507\n", + "37 37 50.913387 6.973451 48.621271 53.205503 4.501991\n", + "38 38 51.016379 6.911078 48.776067 53.256691 4.391359\n", + "39 39 51.340667 7.123540 49.062449 53.618886 4.437454\n" + ] + }, { "data": { - 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" - ], - "text/plain": [ - " replications cumulative_mean cumulative_std lower_ci upper_ci \\\n", - "0 0 9.842380 NaN NaN NaN \n", - "1 1 9.951402 0.154180 8.566149 11.336655 \n", - "2 2 9.942631 0.110075 9.669189 10.216073 \n", - "3 3 9.941599 0.089900 9.798549 10.084650 \n", - "4 4 9.956602 0.084775 9.851339 10.061864 \n", - "5 5 9.944468 0.081442 9.859000 10.029936 \n", - "6 6 9.958195 0.082742 9.881671 10.034719 \n", - "7 7 9.974247 0.089048 9.899801 10.048693 \n", - "8 8 9.999643 0.112884 9.912872 10.086413 \n", - "9 9 10.009002 0.110467 9.929979 10.088025 \n", - "10 10 10.015701 0.107128 9.943732 10.087671 \n", - "11 11 10.005693 0.107865 9.937159 10.074228 \n", - "12 12 9.990554 0.116811 9.919966 10.061143 \n", - "13 13 9.986430 0.113284 9.921022 10.051839 \n", - "14 14 9.998395 0.118591 9.932721 10.064069 \n", - "15 15 9.996950 0.114716 9.935822 10.058077 \n", - "16 16 9.985912 0.120035 9.924196 10.047628 \n", - "17 17 9.986032 0.116452 9.928122 10.043942 \n", - "18 18 9.976331 0.120813 9.918102 10.034561 \n", - "19 19 9.973684 0.118185 9.918372 10.028996 \n", - "\n", - " perc_deviation \n", - "0 NaN \n", - "1 13.920181 \n", - "2 2.750194 \n", - "3 1.438907 \n", - "4 1.057210 \n", - "5 0.859452 \n", - "6 0.768450 \n", - "7 0.746385 \n", - "8 0.867734 \n", - "9 0.789522 \n", - "10 0.718565 \n", - "11 0.684953 \n", - "12 0.706549 \n", - "13 0.654973 \n", - "14 0.656843 \n", - "15 0.611465 \n", - "16 0.618032 \n", - "17 0.579912 \n", - "18 0.583679 \n", - "19 0.554581 " + "image/svg+xml": [ + "05101520253035455055lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "n_reps, cumulative_df = confidence_interval_method_simple(\n", + " replications=40,\n", + " metric='mean_q_time_nurse',\n", + " desired_precision=0.05\n", + ")\n", + "\n", + "print(cumulative_df)\n", + "\n", + "plotly_confidence_interval_method(\n", + " n_reps=n_reps,\n", + " conf_ints=cumulative_df,\n", + " metric_name='Mean wait time for nurse',\n", + " file_path=os.path.join(OUTPUT_DIR, 'choose_param_conf_int_2.png'))" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Reached desired precision (0.05) in 3 replications.\n" + "Reached desired precision (0.05) in 6 replications.\n", + " replications cumulative_mean stdev lower_ci upper_ci deviation\n", + "0 0 49.958879 NaN NaN NaN NaN\n", + "1 1 50.078314 NaN NaN NaN NaN\n", + "2 2 49.988671 0.195888 49.502058 50.475284 0.973446\n", + "3 3 49.947723 0.179690 49.661797 50.233649 0.572451\n", + "4 4 49.896857 0.192752 49.657523 50.136190 0.479656\n", + "5 5 49.794946 0.303376 49.476572 50.113320 0.639370\n", + "6 6 49.872608 0.344844 49.553681 50.191536 0.639485\n", + "7 7 49.931174 0.359679 49.630475 50.231873 0.602227\n", + "8 8 50.036796 0.462171 49.681540 50.392052 0.709990\n", + "9 9 50.032553 0.435945 49.720697 50.344410 0.623307\n", + "10 10 50.118632 0.502542 49.781020 50.456245 0.673627\n", + "11 11 50.022268 0.583972 49.651230 50.393306 0.741746\n", + "12 12 50.046693 0.566003 49.704660 50.388725 0.683426\n", + "13 13 49.966383 0.621298 49.607656 50.325110 0.717936\n", + "14 14 50.055989 0.692010 49.672767 50.439212 0.765588\n", + "15 15 50.058413 0.668616 49.702132 50.414693 0.711729\n", + "16 16 49.976690 0.729824 49.601449 50.351931 0.750831\n", + "17 17 49.952212 0.715609 49.596348 50.308076 0.712409\n", + "18 18 49.846465 0.834334 49.444329 50.248601 0.806749\n", + "19 19 49.812628 0.826059 49.426020 50.199235 0.776124\n" ] }, { "data": { "image/svg+xml": [ - "0510158.599.51010.511variablecumulative_meanlower_ciupper_ciNumber of replicationsMean consultation time with nurse (minutes)" + "02468101214161849.449.649.85050.250.4lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -535,632 +353,141 @@ } ], "source": [ - "confidence_interval_method(\n", - " file='choose_param_conf_int_1.png',\n", + "n_reps, cumulative_df = confidence_interval_method_simple(\n", " replications=20,\n", - " metric='mean_time_with_nurse',\n", - " desired_precision=0.05,\n", - " min_rep=3,\n", - " labels=LABELS\n", - ")" + " metric='mean_nurse_utilisation',\n", + " desired_precision=0.05\n", + ")\n", + "\n", + "print(cumulative_df)\n", + "\n", + "plotly_confidence_interval_method(\n", + " n_reps=n_reps,\n", + " conf_ints=cumulative_df,\n", + " metric_name='Mean nurse utilisation',\n", + " file_path=os.path.join(OUTPUT_DIR, 'choose_param_conf_int_3.png'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "It is important to check ahead 10-20 replications, to check that the 5% precision is maintained. For this example, 3 replications is < 5, but then it quickly goes back up, and is not stable until 31 replications." + "### `confidence_interval_method`" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reached desired precision (0.05) in 6 replications.\n", + " data cumulative_mean stdev lower_ci upper_ci deviation\n", + "replications \n", + "1 9.93 9.93 NaN NaN NaN NaN\n", + "2 9.94 9.93 NaN NaN NaN NaN\n", + "3 10.02 9.96 0.05 9.84 10.08 0.01\n", + "4 9.88 9.94 0.06 9.85 10.03 0.01\n", + "5 10.04 9.96 0.07 9.88 10.04 0.01\n", + "6 10.09 9.98 0.08 9.90 10.06 0.01\n", + "7 10.20 10.01 0.11 9.91 10.11 0.01\n", + "8 10.09 10.02 0.11 9.94 10.11 0.01\n", + "9 10.08 10.03 0.10 9.95 10.11 0.01\n", + "10 9.90 10.02 0.10 9.94 10.09 0.01\n", + "11 9.81 10.00 0.12 9.92 10.08 0.01\n", + "12 9.93 9.99 0.11 9.92 10.06 0.01\n", + "13 10.17 10.01 0.12 9.93 10.08 0.01\n", + "14 9.98 10.00 0.11 9.94 10.07 0.01\n", + "15 9.81 9.99 0.12 9.92 10.06 0.01\n", + "16 9.99 9.99 0.12 9.93 10.05 0.01\n", + "17 9.80 9.98 0.12 9.92 10.04 0.01\n", + "18 9.92 9.98 0.12 9.92 10.04 0.01\n" + ] + }, { "data": { - "text/html": [ - "
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replicationscumulative_meancumulative_stdlower_ciupper_ciperc_deviation
0050.187329NaNNaNNaNNaN
1150.9068571.01756741.76437960.04933517.959227
2251.6472291.47043247.99447255.2999867.072512
3351.0130121.74653248.23389053.7921345.447869
4450.0754542.58512146.86560253.2853066.410031
5548.2377985.06045242.92717953.54841611.009247
6648.1077894.63232843.82360052.3919778.905395
7749.7380866.29729544.47341655.00275610.584787
8849.5330325.92261444.98050854.0855569.190885
9950.2272035.99988345.93514554.5192618.545285
101050.8528086.05837846.78273554.9228828.003636
111149.8933026.66446245.65890554.1276998.486905
121249.4738366.55752745.51116253.4365118.009636
131349.1374876.42473545.42795652.8470197.549290
141449.9606406.96365946.10429453.8169857.718767
151549.7225486.79460946.10195653.3431397.281589
161649.3021176.80339945.80413252.8001027.095000
171749.2359816.60622845.95078152.5211816.672356
181848.6243476.95165445.27375751.9749376.890766
191948.3385196.88592845.11580651.5612336.666968
202049.0105827.38445845.64921752.3719466.858447
212149.1248457.22639445.92084152.3288486.522166
222249.2726217.09572946.20419752.3410446.227441
232349.3880416.96275746.44792652.3281555.953090
242449.4911206.83561546.66951752.3127235.701231
252550.0438967.26644347.10891752.9788765.864810
262650.0966117.13059647.27584152.9173815.630660
272750.1049026.99744047.39157852.8182265.415286
282849.9689946.91021747.34048852.5975005.260274
292950.1418586.85572647.58188852.7018295.105455
303050.1690136.74219147.69595852.6420694.929448
313150.1353936.63528147.74311952.5276664.771627
323250.3080646.60568047.96579152.6503364.655859
333350.4408856.55076748.15521452.7265554.531385
343450.3079626.50144648.07463752.5412874.439307
353550.2015646.43961748.02271152.3804174.340209
363650.5888226.77241548.33078652.8468584.463507
373750.9133876.97345148.62127153.2055034.501991
383851.0163796.91107848.77606753.2566914.391359
393951.3406677.12354049.06244953.6188864.437454
404051.5178207.12480949.26895353.7666874.365222
414151.4322757.05918949.23247753.6320734.277077
424251.5442887.01321549.38593953.7026374.187368
434351.6015366.94158149.49110253.7119694.089866
444451.9483167.24582449.77142954.1252034.190485
454552.3286007.61495850.06723654.5899634.321468
464652.1760537.60399249.94343754.4086694.279005
474752.2195217.52869050.03341854.4056254.186372
484852.1637827.46006450.02100154.3065624.107794
494952.1382287.38575950.03921854.2372374.025855
\n", - "
" - ], - "text/plain": [ - " replications cumulative_mean cumulative_std lower_ci upper_ci \\\n", - "0 0 50.187329 NaN NaN NaN \n", - "1 1 50.906857 1.017567 41.764379 60.049335 \n", - "2 2 51.647229 1.470432 47.994472 55.299986 \n", - "3 3 51.013012 1.746532 48.233890 53.792134 \n", - "4 4 50.075454 2.585121 46.865602 53.285306 \n", - "5 5 48.237798 5.060452 42.927179 53.548416 \n", - "6 6 48.107789 4.632328 43.823600 52.391977 \n", - "7 7 49.738086 6.297295 44.473416 55.002756 \n", - "8 8 49.533032 5.922614 44.980508 54.085556 \n", - "9 9 50.227203 5.999883 45.935145 54.519261 \n", - "10 10 50.852808 6.058378 46.782735 54.922882 \n", - "11 11 49.893302 6.664462 45.658905 54.127699 \n", - "12 12 49.473836 6.557527 45.511162 53.436511 \n", - "13 13 49.137487 6.424735 45.427956 52.847019 \n", - "14 14 49.960640 6.963659 46.104294 53.816985 \n", - "15 15 49.722548 6.794609 46.101956 53.343139 \n", - "16 16 49.302117 6.803399 45.804132 52.800102 \n", - "17 17 49.235981 6.606228 45.950781 52.521181 \n", - "18 18 48.624347 6.951654 45.273757 51.974937 \n", - "19 19 48.338519 6.885928 45.115806 51.561233 \n", - "20 20 49.010582 7.384458 45.649217 52.371946 \n", - "21 21 49.124845 7.226394 45.920841 52.328848 \n", - "22 22 49.272621 7.095729 46.204197 52.341044 \n", - "23 23 49.388041 6.962757 46.447926 52.328155 \n", - "24 24 49.491120 6.835615 46.669517 52.312723 \n", - "25 25 50.043896 7.266443 47.108917 52.978876 \n", - "26 26 50.096611 7.130596 47.275841 52.917381 \n", - "27 27 50.104902 6.997440 47.391578 52.818226 \n", - "28 28 49.968994 6.910217 47.340488 52.597500 \n", - "29 29 50.141858 6.855726 47.581888 52.701829 \n", - "30 30 50.169013 6.742191 47.695958 52.642069 \n", - "31 31 50.135393 6.635281 47.743119 52.527666 \n", - "32 32 50.308064 6.605680 47.965791 52.650336 \n", - "33 33 50.440885 6.550767 48.155214 52.726555 \n", - "34 34 50.307962 6.501446 48.074637 52.541287 \n", - "35 35 50.201564 6.439617 48.022711 52.380417 \n", - "36 36 50.588822 6.772415 48.330786 52.846858 \n", - "37 37 50.913387 6.973451 48.621271 53.205503 \n", - "38 38 51.016379 6.911078 48.776067 53.256691 \n", - "39 39 51.340667 7.123540 49.062449 53.618886 \n", - "40 40 51.517820 7.124809 49.268953 53.766687 \n", - "41 41 51.432275 7.059189 49.232477 53.632073 \n", - "42 42 51.544288 7.013215 49.385939 53.702637 \n", - "43 43 51.601536 6.941581 49.491102 53.711969 \n", - "44 44 51.948316 7.245824 49.771429 54.125203 \n", - "45 45 52.328600 7.614958 50.067236 54.589963 \n", - "46 46 52.176053 7.603992 49.943437 54.408669 \n", - "47 47 52.219521 7.528690 50.033418 54.405625 \n", - "48 48 52.163782 7.460064 50.021001 54.306562 \n", - "49 49 52.138228 7.385759 50.039218 54.237237 \n", - "\n", - " perc_deviation \n", - "0 NaN \n", - "1 17.959227 \n", - "2 7.072512 \n", - "3 5.447869 \n", - "4 6.410031 \n", - "5 11.009247 \n", - "6 8.905395 \n", - "7 10.584787 \n", - "8 9.190885 \n", - "9 8.545285 \n", - "10 8.003636 \n", - "11 8.486905 \n", - "12 8.009636 \n", - "13 7.549290 \n", - "14 7.718767 \n", - "15 7.281589 \n", - "16 7.095000 \n", - "17 6.672356 \n", - "18 6.890766 \n", - "19 6.666968 \n", - "20 6.858447 \n", - "21 6.522166 \n", - "22 6.227441 \n", - "23 5.953090 \n", - "24 5.701231 \n", - "25 5.864810 \n", - "26 5.630660 \n", - "27 5.415286 \n", - "28 5.260274 \n", - "29 5.105455 \n", - "30 4.929448 \n", - "31 4.771627 \n", - "32 4.655859 \n", - "33 4.531385 \n", - "34 4.439307 \n", - "35 4.340209 \n", - "36 4.463507 \n", - "37 4.501991 \n", - "38 4.391359 \n", - "39 4.437454 \n", - "40 4.365222 \n", - "41 4.277077 \n", - "42 4.187368 \n", - "43 4.089866 \n", - "44 4.190485 \n", - "45 4.321468 \n", - "46 4.279005 \n", - "47 4.186372 \n", - "48 4.107794 \n", - "49 4.025855 " + "image/svg+xml": [ + "0246810121416189.859.99.951010.0510.1lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" ] }, "metadata": {}, "output_type": "display_data" - }, + } + ], + "source": [ + "n_reps, cumulative_df = confidence_interval_method(\n", + " replications=20,\n", + " metric='mean_time_with_nurse',\n", + " desired_precision=0.05\n", + ")\n", + "\n", + "print(cumulative_df)\n", + "\n", + "plotly_confidence_interval_method(\n", + " n_reps=n_reps,\n", + " conf_ints=cumulative_df,\n", + " metric_name='Mean time with nurse')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Reached desired precision (0.05) in 31 replications.\n" + "Reached desired precision (0.05) in 31 replications.\n", + " data cumulative_mean stdev lower_ci upper_ci deviation\n", + "replications \n", + "1 0.53 0.53 NaN NaN NaN NaN\n", + "2 0.49 0.51 NaN NaN NaN NaN\n", + "3 0.46 0.50 0.03 0.41 0.58 0.17\n", + "4 0.39 0.47 0.06 0.37 0.56 0.20\n", + "5 0.47 0.47 0.05 0.41 0.53 0.14\n", + "6 0.61 0.49 0.07 0.42 0.57 0.16\n", + "7 0.48 0.49 0.07 0.43 0.55 0.13\n", + "8 0.56 0.50 0.07 0.44 0.56 0.11\n", + "9 0.57 0.51 0.07 0.46 0.56 0.10\n", + "10 0.39 0.50 0.07 0.44 0.55 0.11\n", + "11 0.44 0.49 0.07 0.44 0.54 0.10\n", + "12 0.45 0.49 0.07 0.44 0.53 0.09\n", + "13 0.61 0.50 0.08 0.45 0.54 0.09\n", + "14 0.46 0.50 0.07 0.45 0.54 0.08\n", + "15 0.43 0.49 0.07 0.45 0.53 0.08\n", + "16 0.48 0.49 0.07 0.45 0.53 0.08\n", + "17 0.38 0.48 0.07 0.45 0.52 0.08\n", + "18 0.43 0.48 0.07 0.44 0.52 0.07\n", + "19 0.62 0.49 0.08 0.45 0.53 0.08\n", + "20 0.52 0.49 0.08 0.45 0.52 0.07\n", + "21 0.53 0.49 0.07 0.46 0.52 0.07\n", + "22 0.52 0.49 0.07 0.46 0.52 0.07\n", + "23 0.52 0.49 0.07 0.46 0.52 0.06\n", + "24 0.64 0.50 0.08 0.47 0.53 0.06\n", + "25 0.51 0.50 0.07 0.47 0.53 0.06\n", + "26 0.50 0.50 0.07 0.47 0.53 0.06\n", + "27 0.46 0.50 0.07 0.47 0.53 0.06\n", + "28 0.55 0.50 0.07 0.47 0.53 0.05\n", + "29 0.51 0.50 0.07 0.47 0.53 0.05\n", + "30 0.49 0.50 0.07 0.48 0.53 0.05\n", + "31 0.56 0.50 0.07 0.48 0.53 0.05\n", + "32 0.55 0.50 0.07 0.48 0.53 0.05\n", + "33 0.46 0.50 0.07 0.48 0.53 0.05\n", + "34 0.46 0.50 0.07 0.48 0.52 0.05\n", + "35 0.65 0.51 0.07 0.48 0.53 0.05\n", + "36 0.63 0.51 0.07 0.48 0.53 0.05\n", + "37 0.55 0.51 0.07 0.49 0.53 0.05\n", + "38 0.64 0.51 0.07 0.49 0.54 0.05\n" ] }, { "data": { "image/svg+xml": [ - "01020304045505560variablecumulative_meanlower_ciupper_ciNumber of replicationsMean wait time for nurse (*100) (minutes)" + "51015202530350.40.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" ] }, "metadata": {}, @@ -1168,295 +495,63 @@ } ], "source": [ - "confidence_interval_method(\n", - " file='choose_param_conf_int_2.png',\n", - " replications=50,\n", + "n_reps, cumulative_df = confidence_interval_method(\n", + " replications=40,\n", " metric='mean_q_time_nurse',\n", - " desired_precision=0.05,\n", - " min_rep=31,\n", - " labels=LABELS\n", - ")" + " desired_precision=0.05\n", + ")\n", + "\n", + "print(cumulative_df)\n", + "\n", + "plotly_confidence_interval_method(\n", + " n_reps=n_reps,\n", + " conf_ints=cumulative_df,\n", + " metric_name='Mean wait time for nurse')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Rounding is causing this... perhaps remove the rounding..." ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - "
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replicationscumulative_meancumulative_stdlower_ciupper_ciperc_deviation
0049.958879NaNNaNNaNNaN
1150.0783140.16890748.56074751.5958813.030387
2249.9886710.19588849.50205850.4752840.973446
3349.9477230.17969049.66179750.2336490.572451
4449.8968570.19275249.65752350.1361900.479656
5549.7949460.30337649.47657250.1133200.639370
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" - ], - "text/plain": [ - " replications cumulative_mean cumulative_std lower_ci upper_ci \\\n", - "0 0 49.958879 NaN NaN NaN \n", - "1 1 50.078314 0.168907 48.560747 51.595881 \n", - "2 2 49.988671 0.195888 49.502058 50.475284 \n", - "3 3 49.947723 0.179690 49.661797 50.233649 \n", - "4 4 49.896857 0.192752 49.657523 50.136190 \n", - "5 5 49.794946 0.303376 49.476572 50.113320 \n", - "6 6 49.872608 0.344844 49.553681 50.191536 \n", - "7 7 49.931174 0.359679 49.630475 50.231873 \n", - "8 8 50.036796 0.462171 49.681540 50.392052 \n", - "9 9 50.032553 0.435945 49.720697 50.344410 \n", - "10 10 50.118632 0.502542 49.781020 50.456245 \n", - "11 11 50.022268 0.583972 49.651230 50.393306 \n", - "12 12 50.046693 0.566003 49.704660 50.388725 \n", - "13 13 49.966383 0.621298 49.607656 50.325110 \n", - "14 14 50.055989 0.692010 49.672767 50.439212 \n", - "15 15 50.058413 0.668616 49.702132 50.414693 \n", - "16 16 49.976690 0.729824 49.601449 50.351931 \n", - "17 17 49.952212 0.715609 49.596348 50.308076 \n", - "18 18 49.846465 0.834334 49.444329 50.248601 \n", - "19 19 49.812628 0.826059 49.426020 50.199235 \n", - "\n", - " perc_deviation \n", - "0 NaN \n", - "1 3.030387 \n", - "2 0.973446 \n", - "3 0.572451 \n", - "4 0.479656 \n", - "5 0.639370 \n", - "6 0.639485 \n", - "7 0.602227 \n", - "8 0.709990 \n", - "9 0.623307 \n", - "10 0.673627 \n", - "11 0.741746 \n", - "12 0.683426 \n", - "13 0.717936 \n", - "14 0.765588 \n", - "15 0.711729 \n", - "16 0.750831 \n", - "17 0.712409 \n", - "18 0.806749 \n", - "19 0.776124 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "name": "stdout", "output_type": "stream", "text": [ - "Reached desired precision (0.05) in 2 replications.\n" + "Reached desired precision (0.05) in 6 replications.\n", + " data cumulative_mean stdev lower_ci upper_ci deviation\n", + "replications \n", + "1 0.50 0.5 NaN NaN NaN NaN\n", + "2 0.50 0.5 NaN NaN NaN NaN\n", + "3 0.50 0.5 0.00 0.50 0.50 0.00\n", + "4 0.49 0.5 0.00 0.49 0.50 0.01\n", + "5 0.50 0.5 0.00 0.49 0.50 0.01\n", + "6 0.50 0.5 0.00 0.49 0.50 0.01\n", + "7 0.51 0.5 0.01 0.50 0.51 0.01\n", + "8 0.50 0.5 0.00 0.50 0.50 0.01\n", + "9 0.51 0.5 0.01 0.50 0.51 0.01\n", + "10 0.49 0.5 0.01 0.50 0.50 0.01\n", + "11 0.50 0.5 0.01 0.50 0.50 0.01\n", + "12 0.49 0.5 0.01 0.50 0.50 0.01\n", + "13 0.51 0.5 0.01 0.50 0.51 0.01\n", + "14 0.50 0.5 0.01 0.50 0.50 0.01\n", + "15 0.49 0.5 0.01 0.50 0.50 0.01\n", + "16 0.50 0.5 0.01 0.50 0.50 0.01\n", + "17 0.48 0.5 0.01 0.49 0.50 0.01\n", + "18 0.49 0.5 0.01 0.49 0.50 0.01\n" ] }, { "data": { "image/svg+xml": [ - "05101548.54949.55050.55151.5variablecumulative_meanlower_ciupper_ciNumber of replicationsMean nurse utilisation (*100 - %)" + "0246810121416180.490.4950.50.5050.51lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -1464,14 +559,18 @@ } ], "source": [ - "confidence_interval_method(\n", - " file='choose_param_conf_int_3.png',\n", + "n_reps, cumulative_df = confidence_interval_method(\n", " replications=20,\n", " metric='mean_nurse_utilisation',\n", - " desired_precision=0.05,\n", - " min_rep=3,\n", - " labels=LABELS\n", - ")" + " desired_precision=0.05\n", + ")\n", + "\n", + "print(cumulative_df)\n", + "\n", + "plotly_confidence_interval_method(\n", + " n_reps=n_reps,\n", + " conf_ints=cumulative_df,\n", + " metric_name='Mean nurse utilisation')" ] }, { @@ -1483,7 +582,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -1492,15 +591,6 @@ "analyser = ReplicationsAlgorithm(verbose=False, observer=observer)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "this looks identical to manual - only difference is this has deviation as a decimal rather than as percentage\n", - "\n", - "perhaps remove calculation/plot from 2 replications from manual method if would normally say that isn't valid" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1510,7 +600,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1665,7 +755,7 @@ { "data": { "image/svg+xml": [ - "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: mean_time_with_nurse" + "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: mean_time_with_nurse" ] }, "metadata": {}, @@ -1683,7 +773,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -2146,7 +1236,7 @@ { "data": { "image/svg+xml": [ - "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: mean_q_time_nurse" + "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: mean_q_time_nurse" ] }, "metadata": {}, @@ -2166,7 +1256,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -2321,7 +1411,7 @@ { "data": { "image/svg+xml": [ - "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: mean_nurse_utilisation" + "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: mean_nurse_utilisation" ] }, "metadata": {}, @@ -2348,14 +1438,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 14s\n" + "Notebook run time: 0m 15s\n" ] } ], diff --git a/outputs/choose_param_conf_int_1.png b/outputs/choose_param_conf_int_1.png index 97fd28a1fcc9df101c4120a9fbe8bff4689ccd62..2b8924bafaab322181ef82e0f59302f259cdf561 100644 GIT binary patch literal 45637 zcmeFZWmJ@F+Xf6ngMf5*2r`0#NC^lENQ&gpqlARCw8Ri1-Q6NRz|bf)bcfO{Asv#^ zeD}Eb6Z?I(&-?xO*82XuYmKZm19M+}oyT#U$7ztNiX0(6Ej}6=8ln8tCu(SDmP@H>l@sVIc~VCSwT__ zTh8gbN{MbO(;i|V*dw%uPkk`|{`E*5M1&7kW@~*Bg@zaTcP}mwIi#NC@4xMXS;_eb zmgD3qEKJIb{H_>~-SyZ5yKMVc7p7zsoYd`8IDq0~yuBr)*cn>@@~x_faE 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| Bin 53399 -> 53890 bytes simulation/replications.py | 49 +- tests/test_functionaltest_replications.py | 110 +++ tests/test_unittest_replications.py | 72 ++ 7 files changed, 772 insertions(+), 394 deletions(-) create mode 100644 tests/test_functionaltest_replications.py create mode 100644 tests/test_unittest_replications.py diff --git a/notebooks/choosing_replications.ipynb b/notebooks/choosing_replications.ipynb index 9cb98e4..fc24233 100644 --- a/notebooks/choosing_replications.ipynb +++ b/notebooks/choosing_replications.ipynb @@ -59,15 +59,11 @@ "# pylint: disable=wrong-import-position\n", "import os\n", "import time\n", - "import warnings\n", "\n", "from IPython.display import display\n", - "import pandas as pd\n", - "import plotly.express as px\n", "import plotly.io as pio\n", "\n", "from simulation.model import Param, Runner\n", - "from simulation.helper import summary_stats\n", "from simulation.replications import (\n", " ReplicationTabulizer, ReplicationsAlgorithm,\n", " confidence_interval_method_simple, confidence_interval_method,\n", @@ -168,7 +164,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### `confidence_interval_method_simple`" + "### `confidence_interval_method_simple`\n", + "\n", + "plots below have dots but these don't" ] }, { @@ -181,33 +179,55 @@ "output_type": "stream", "text": [ "Reached desired precision (0.05) in 6 replications.\n", - " replications cumulative_mean stdev lower_ci upper_ci deviation\n", - "0 0 9.842380 NaN NaN NaN NaN\n", - "1 1 9.951402 NaN NaN NaN NaN\n", - "2 2 9.942631 0.110075 9.669189 10.216073 2.750194\n", - "3 3 9.941599 0.089900 9.798549 10.084650 1.438907\n", - "4 4 9.956602 0.084775 9.851339 10.061864 1.057210\n", - "5 5 9.944468 0.081442 9.859000 10.029936 0.859452\n", - "6 6 9.958195 0.082742 9.881671 10.034719 0.768450\n", - "7 7 9.974247 0.089048 9.899801 10.048693 0.746385\n", - "8 8 9.999643 0.112884 9.912872 10.086413 0.867734\n", - "9 9 10.009002 0.110467 9.929979 10.088025 0.789522\n", - "10 10 10.015701 0.107128 9.943732 10.087671 0.718565\n", - "11 11 10.005693 0.107865 9.937159 10.074228 0.684953\n", - "12 12 9.990554 0.116811 9.919966 10.061143 0.706549\n", - "13 13 9.986430 0.113284 9.921022 10.051839 0.654973\n", - "14 14 9.998395 0.118591 9.932721 10.064069 0.656843\n", - "15 15 9.996950 0.114716 9.935822 10.058077 0.611465\n", - "16 16 9.985912 0.120035 9.924196 10.047628 0.618032\n", - "17 17 9.986032 0.116452 9.928122 10.043942 0.579912\n", - "18 18 9.976331 0.120813 9.918102 10.034561 0.583679\n", - "19 19 9.973684 0.118185 9.918372 10.028996 0.554581\n" + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 9.842380 9.842380 NaN NaN NaN \n", + "1 2 10.060424 9.951402 NaN NaN NaN \n", + "2 3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", + "3 4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", + "4 5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", + "5 6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", + "6 7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", + "7 8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", + "8 9 10.202806 9.999643 0.112884 9.912872 10.086413 \n", + "9 10 10.093238 10.009002 0.110467 9.929979 10.088025 \n", + "10 11 10.082694 10.015701 0.107128 9.943732 10.087671 \n", + "11 12 9.895607 10.005693 0.107865 9.937159 10.074228 \n", + "12 13 9.808886 9.990554 0.116811 9.919966 10.061143 \n", + "13 14 9.932816 9.986430 0.113284 9.921022 10.051839 \n", + "14 15 10.165899 9.998395 0.118591 9.932721 10.064069 \n", + "15 16 9.975272 9.996950 0.114716 9.935822 10.058077 \n", + "16 17 9.809312 9.985912 0.120035 9.924196 10.047628 \n", + "17 18 9.988074 9.986032 0.116452 9.928122 10.043942 \n", + "18 19 9.801716 9.976331 0.120813 9.918102 10.034561 \n", + "19 20 9.923390 9.973684 0.118185 9.918372 10.028996 \n", + "\n", + " deviation \n", + "0 NaN \n", + "1 NaN \n", + "2 2.750194 \n", + "3 1.438907 \n", + "4 1.057210 \n", + "5 0.859452 \n", + "6 0.768450 \n", + "7 0.746385 \n", + "8 0.867734 \n", + "9 0.789522 \n", + "10 0.718565 \n", + "11 0.684953 \n", + "12 0.706549 \n", + "13 0.654973 \n", + "14 0.656843 \n", + "15 0.611465 \n", + "16 0.618032 \n", + "17 0.579912 \n", + "18 0.583679 \n", + "19 0.554581 \n" ] }, { "data": { "image/svg+xml": [ - "0246810121416189.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" + "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" ] }, "metadata": {}, @@ -240,53 +260,95 @@ "output_type": "stream", "text": [ "Reached desired precision (0.05) in 31 replications.\n", - " replications cumulative_mean stdev lower_ci upper_ci deviation\n", - "0 0 50.187329 NaN NaN NaN NaN\n", - "1 1 50.906857 NaN NaN NaN NaN\n", - "2 2 51.647229 1.470432 47.994472 55.299986 7.072512\n", - "3 3 51.013012 1.746532 48.233890 53.792134 5.447869\n", - "4 4 50.075454 2.585121 46.865602 53.285306 6.410031\n", - "5 5 48.237798 5.060452 42.927179 53.548416 11.009247\n", - "6 6 48.107789 4.632328 43.823600 52.391977 8.905395\n", - "7 7 49.738086 6.297295 44.473416 55.002756 10.584787\n", - "8 8 49.533032 5.922614 44.980508 54.085556 9.190885\n", - "9 9 50.227203 5.999883 45.935145 54.519261 8.545285\n", - "10 10 50.852808 6.058378 46.782735 54.922882 8.003636\n", - "11 11 49.893302 6.664462 45.658905 54.127699 8.486905\n", - "12 12 49.473836 6.557527 45.511162 53.436511 8.009636\n", - "13 13 49.137487 6.424735 45.427956 52.847019 7.549290\n", - "14 14 49.960640 6.963659 46.104294 53.816985 7.718767\n", - "15 15 49.722548 6.794609 46.101956 53.343139 7.281589\n", - "16 16 49.302117 6.803399 45.804132 52.800102 7.095000\n", - "17 17 49.235981 6.606228 45.950781 52.521181 6.672356\n", - "18 18 48.624347 6.951654 45.273757 51.974937 6.890766\n", - "19 19 48.338519 6.885928 45.115806 51.561233 6.666968\n", - "20 20 49.010582 7.384458 45.649217 52.371946 6.858447\n", - "21 21 49.124845 7.226394 45.920841 52.328848 6.522166\n", - "22 22 49.272621 7.095729 46.204197 52.341044 6.227441\n", - "23 23 49.388041 6.962757 46.447926 52.328155 5.953090\n", - "24 24 49.491120 6.835615 46.669517 52.312723 5.701231\n", - "25 25 50.043896 7.266443 47.108917 52.978876 5.864810\n", - "26 26 50.096611 7.130596 47.275841 52.917381 5.630660\n", - "27 27 50.104902 6.997440 47.391578 52.818226 5.415286\n", - "28 28 49.968994 6.910217 47.340488 52.597500 5.260274\n", - "29 29 50.141858 6.855726 47.581888 52.701829 5.105455\n", - "30 30 50.169013 6.742191 47.695958 52.642069 4.929448\n", - "31 31 50.135393 6.635281 47.743119 52.527666 4.771627\n", - "32 32 50.308064 6.605680 47.965791 52.650336 4.655859\n", - "33 33 50.440885 6.550767 48.155214 52.726555 4.531385\n", - "34 34 50.307962 6.501446 48.074637 52.541287 4.439307\n", - "35 35 50.201564 6.439617 48.022711 52.380417 4.340209\n", - "36 36 50.588822 6.772415 48.330786 52.846858 4.463507\n", - "37 37 50.913387 6.973451 48.621271 53.205503 4.501991\n", - "38 38 51.016379 6.911078 48.776067 53.256691 4.391359\n", - "39 39 51.340667 7.123540 49.062449 53.618886 4.437454\n" + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 50.187329 50.187329 NaN NaN NaN \n", + "1 2 51.626386 50.906857 NaN NaN NaN \n", + "2 3 53.127972 51.647229 1.470432 47.994472 55.299986 \n", + "3 4 49.110360 51.013012 1.746532 48.233890 53.792134 \n", + "4 5 46.325224 50.075454 2.585121 46.865602 53.285306 \n", + "5 6 39.049515 48.237798 5.060452 42.927179 53.548416 \n", + "6 7 47.327735 48.107789 4.632328 43.823600 52.391977 \n", + "7 8 61.150168 49.738086 6.297295 44.473416 55.002756 \n", + "8 9 47.892602 49.533032 5.922614 44.980508 54.085556 \n", + "9 10 56.474736 50.227203 5.999883 45.935145 54.519261 \n", + "10 11 57.108864 50.852808 6.058378 46.782735 54.922882 \n", + "11 12 39.338731 49.893302 6.664462 45.658905 54.127699 \n", + "12 13 44.440250 49.473836 6.557527 45.511162 53.436511 \n", + "13 14 44.764948 49.137487 6.424735 45.427956 52.847019 \n", + "14 15 61.484773 49.960640 6.963659 46.104294 53.816985 \n", + "15 16 46.151166 49.722548 6.794609 46.101956 53.343139 \n", + "16 17 42.575227 49.302117 6.803399 45.804132 52.800102 \n", + "17 18 48.111666 49.235981 6.606228 45.950781 52.521181 \n", + "18 19 37.614948 48.624347 6.951654 45.273757 51.974937 \n", + "19 20 42.907789 48.338519 6.885928 45.115806 51.561233 \n", + "20 21 62.451823 49.010582 7.384458 45.649217 52.371946 \n", + "21 22 51.524367 49.124845 7.226394 45.920841 52.328848 \n", + "22 23 52.523697 49.272621 7.095729 46.204197 52.341044 \n", + "23 24 52.042697 49.388041 6.962757 46.447926 52.328155 \n", + "24 25 51.965038 49.491120 6.835615 46.669517 52.312723 \n", + "25 26 63.863289 50.043896 7.266443 47.108917 52.978876 \n", + "26 27 51.467205 50.096611 7.130596 47.275841 52.917381 \n", + "27 28 50.328744 50.104902 6.997440 47.391578 52.818226 \n", + "28 29 46.163567 49.968994 6.910217 47.340488 52.597500 \n", + "29 30 55.154934 50.141858 6.855726 47.581888 52.701829 \n", + "30 31 50.983655 50.169013 6.742191 47.695958 52.642069 \n", + "31 32 49.093156 50.135393 6.635281 47.743119 52.527666 \n", + "32 33 55.833541 50.308064 6.605680 47.965791 52.650336 \n", + "33 34 54.823971 50.440885 6.550767 48.155214 52.726555 \n", + "34 35 45.788609 50.307962 6.501446 48.074637 52.541287 \n", + "35 36 46.477607 50.201564 6.439617 48.022711 52.380417 \n", + "36 37 64.530116 50.588822 6.772415 48.330786 52.846858 \n", + "37 38 62.922305 50.913387 6.973451 48.621271 53.205503 \n", + "38 39 54.930072 51.016379 6.911078 48.776067 53.256691 \n", + "39 40 63.987910 51.340667 7.123540 49.062449 53.618886 \n", + "\n", + " deviation \n", + "0 NaN \n", + "1 NaN \n", + "2 7.072512 \n", + "3 5.447869 \n", + "4 6.410031 \n", + "5 11.009247 \n", + "6 8.905395 \n", + "7 10.584787 \n", + "8 9.190885 \n", + "9 8.545285 \n", + "10 8.003636 \n", + "11 8.486905 \n", + "12 8.009636 \n", + "13 7.549290 \n", + "14 7.718767 \n", + "15 7.281589 \n", + "16 7.095000 \n", + "17 6.672356 \n", + "18 6.890766 \n", + "19 6.666968 \n", + "20 6.858447 \n", + "21 6.522166 \n", + "22 6.227441 \n", + "23 5.953090 \n", + "24 5.701231 \n", + "25 5.864810 \n", + "26 5.630660 \n", + "27 5.415286 \n", + "28 5.260274 \n", + "29 5.105455 \n", + "30 4.929448 \n", + "31 4.771627 \n", + "32 4.655859 \n", + "33 4.531385 \n", + "34 4.439307 \n", + "35 4.340209 \n", + "36 4.463507 \n", + "37 4.501991 \n", + "38 4.391359 \n", + "39 4.437454 \n" ] }, { "data": { "image/svg+xml": [ - "05101520253035455055lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" + "510152025303540455055lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" ] }, "metadata": {}, @@ -319,33 +381,55 @@ "output_type": "stream", "text": [ "Reached desired precision (0.05) in 6 replications.\n", - " replications cumulative_mean stdev lower_ci upper_ci deviation\n", - "0 0 49.958879 NaN NaN NaN NaN\n", - "1 1 50.078314 NaN NaN NaN NaN\n", - "2 2 49.988671 0.195888 49.502058 50.475284 0.973446\n", - "3 3 49.947723 0.179690 49.661797 50.233649 0.572451\n", - "4 4 49.896857 0.192752 49.657523 50.136190 0.479656\n", - "5 5 49.794946 0.303376 49.476572 50.113320 0.639370\n", - "6 6 49.872608 0.344844 49.553681 50.191536 0.639485\n", - "7 7 49.931174 0.359679 49.630475 50.231873 0.602227\n", - "8 8 50.036796 0.462171 49.681540 50.392052 0.709990\n", - "9 9 50.032553 0.435945 49.720697 50.344410 0.623307\n", - "10 10 50.118632 0.502542 49.781020 50.456245 0.673627\n", - "11 11 50.022268 0.583972 49.651230 50.393306 0.741746\n", - "12 12 50.046693 0.566003 49.704660 50.388725 0.683426\n", - "13 13 49.966383 0.621298 49.607656 50.325110 0.717936\n", - "14 14 50.055989 0.692010 49.672767 50.439212 0.765588\n", - "15 15 50.058413 0.668616 49.702132 50.414693 0.711729\n", - "16 16 49.976690 0.729824 49.601449 50.351931 0.750831\n", - "17 17 49.952212 0.715609 49.596348 50.308076 0.712409\n", - "18 18 49.846465 0.834334 49.444329 50.248601 0.806749\n", - "19 19 49.812628 0.826059 49.426020 50.199235 0.776124\n" + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 49.958879 49.958879 NaN NaN NaN \n", + "1 2 50.197749 50.078314 NaN NaN NaN \n", + "2 3 49.809386 49.988671 0.195888 49.502058 50.475284 \n", + "3 4 49.824879 49.947723 0.179690 49.661797 50.233649 \n", + "4 5 49.693390 49.896857 0.192752 49.657523 50.136190 \n", + "5 6 49.285394 49.794946 0.303376 49.476572 50.113320 \n", + "6 7 50.338583 49.872608 0.344844 49.553681 50.191536 \n", + "7 8 50.341133 49.931174 0.359679 49.630475 50.231873 \n", + "8 9 50.881773 50.036796 0.462171 49.681540 50.392052 \n", + "9 10 49.994369 50.032553 0.435945 49.720697 50.344410 \n", + "10 11 50.979421 50.118632 0.502542 49.781020 50.456245 \n", + "11 12 48.962260 50.022268 0.583972 49.651230 50.393306 \n", + "12 13 50.339787 50.046693 0.566003 49.704660 50.388725 \n", + "13 14 48.922360 49.966383 0.621298 49.607656 50.325110 \n", + "14 15 51.310478 50.055989 0.692010 49.672767 50.439212 \n", + "15 16 50.094761 50.058413 0.668616 49.702132 50.414693 \n", + "16 17 48.669128 49.976690 0.729824 49.601449 50.351931 \n", + "17 18 49.536082 49.952212 0.715609 49.596348 50.308076 \n", + "18 19 47.943016 49.846465 0.834334 49.444329 50.248601 \n", + "19 20 49.169728 49.812628 0.826059 49.426020 50.199235 \n", + "\n", + " deviation \n", + "0 NaN \n", + "1 NaN \n", + "2 0.973446 \n", + "3 0.572451 \n", + "4 0.479656 \n", + "5 0.639370 \n", + "6 0.639485 \n", + "7 0.602227 \n", + "8 0.709990 \n", + "9 0.623307 \n", + "10 0.673627 \n", + "11 0.741746 \n", + "12 0.683426 \n", + "13 0.717936 \n", + "14 0.765588 \n", + "15 0.711729 \n", + "16 0.750831 \n", + "17 0.712409 \n", + "18 0.806749 \n", + "19 0.776124 \n" ] }, { "data": { "image/svg+xml": [ - "02468101214161849.449.649.85050.250.4lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "246810121416182049.449.649.85050.250.4lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -384,33 +468,52 @@ "name": "stdout", "output_type": "stream", "text": [ - "Reached desired precision (0.05) in 6 replications.\n", - " data cumulative_mean stdev lower_ci upper_ci deviation\n", - "replications \n", - "1 9.93 9.93 NaN NaN NaN NaN\n", - "2 9.94 9.93 NaN NaN NaN NaN\n", - "3 10.02 9.96 0.05 9.84 10.08 0.01\n", - "4 9.88 9.94 0.06 9.85 10.03 0.01\n", - "5 10.04 9.96 0.07 9.88 10.04 0.01\n", - "6 10.09 9.98 0.08 9.90 10.06 0.01\n", - "7 10.20 10.01 0.11 9.91 10.11 0.01\n", - "8 10.09 10.02 0.11 9.94 10.11 0.01\n", - "9 10.08 10.03 0.10 9.95 10.11 0.01\n", - "10 9.90 10.02 0.10 9.94 10.09 0.01\n", - "11 9.81 10.00 0.12 9.92 10.08 0.01\n", - "12 9.93 9.99 0.11 9.92 10.06 0.01\n", - "13 10.17 10.01 0.12 9.93 10.08 0.01\n", - "14 9.98 10.00 0.11 9.94 10.07 0.01\n", - "15 9.81 9.99 0.12 9.92 10.06 0.01\n", - "16 9.99 9.99 0.12 9.93 10.05 0.01\n", - "17 9.80 9.98 0.12 9.92 10.04 0.01\n", - "18 9.92 9.98 0.12 9.92 10.04 0.01\n" + "Reached desired precision (0.05) in 5 replications.\n", + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 9.925090 9.925090 NaN NaN NaN \n", + "1 2 9.938504 9.931797 NaN NaN NaN \n", + "2 3 10.016611 9.960068 0.049425 9.837290 10.082846 \n", + "3 4 9.883801 9.941001 0.055522 9.852653 10.029350 \n", + "4 5 10.040555 9.960912 0.065530 9.879545 10.042279 \n", + "5 6 10.086612 9.981862 0.077903 9.900108 10.063616 \n", + "6 7 10.202806 10.013426 0.109686 9.911983 10.114869 \n", + "7 8 10.093238 10.023402 0.105397 9.935288 10.111517 \n", + "8 9 10.082694 10.029990 0.100552 9.952699 10.107281 \n", + "9 10 9.895607 10.016552 0.103890 9.942233 10.090870 \n", + "10 11 9.808886 9.997673 0.116766 9.919229 10.076118 \n", + "11 12 9.932816 9.992268 0.112895 9.920538 10.063999 \n", + "12 13 10.165899 10.005624 0.118331 9.934118 10.077131 \n", + "13 14 9.975272 10.003456 0.113978 9.937648 10.069265 \n", + "14 15 9.809312 9.990513 0.120731 9.923655 10.057372 \n", + "15 16 9.988074 9.990361 0.116638 9.928209 10.052513 \n", + "16 17 9.801716 9.979264 0.121851 9.916614 10.041914 \n", + "17 18 9.923390 9.976160 0.118944 9.917011 10.035310 \n", + "\n", + " deviation \n", + "0 NaN \n", + "1 NaN \n", + "2 0.012327 \n", + "3 0.008887 \n", + "4 0.008169 \n", + "5 0.008190 \n", + "6 0.010131 \n", + "7 0.008791 \n", + "8 0.007706 \n", + "9 0.007420 \n", + "10 0.007846 \n", + "11 0.007179 \n", + "12 0.007147 \n", + "13 0.006579 \n", + "14 0.006692 \n", + "15 0.006221 \n", + "16 0.006278 \n", + "17 0.005929 \n" ] }, { "data": { "image/svg+xml": [ - "0246810121416189.859.99.951010.0510.1lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" + "0246810121416189.859.99.951010.0510.1lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" ] }, "metadata": {}, @@ -441,53 +544,92 @@ "name": "stdout", "output_type": "stream", "text": [ - "Reached desired precision (0.05) in 31 replications.\n", - " data cumulative_mean stdev lower_ci upper_ci deviation\n", - "replications \n", - "1 0.53 0.53 NaN NaN NaN NaN\n", - "2 0.49 0.51 NaN NaN NaN NaN\n", - "3 0.46 0.50 0.03 0.41 0.58 0.17\n", - "4 0.39 0.47 0.06 0.37 0.56 0.20\n", - "5 0.47 0.47 0.05 0.41 0.53 0.14\n", - "6 0.61 0.49 0.07 0.42 0.57 0.16\n", - "7 0.48 0.49 0.07 0.43 0.55 0.13\n", - "8 0.56 0.50 0.07 0.44 0.56 0.11\n", - "9 0.57 0.51 0.07 0.46 0.56 0.10\n", - "10 0.39 0.50 0.07 0.44 0.55 0.11\n", - "11 0.44 0.49 0.07 0.44 0.54 0.10\n", - "12 0.45 0.49 0.07 0.44 0.53 0.09\n", - "13 0.61 0.50 0.08 0.45 0.54 0.09\n", - "14 0.46 0.50 0.07 0.45 0.54 0.08\n", - "15 0.43 0.49 0.07 0.45 0.53 0.08\n", - "16 0.48 0.49 0.07 0.45 0.53 0.08\n", - "17 0.38 0.48 0.07 0.45 0.52 0.08\n", - "18 0.43 0.48 0.07 0.44 0.52 0.07\n", - "19 0.62 0.49 0.08 0.45 0.53 0.08\n", - "20 0.52 0.49 0.08 0.45 0.52 0.07\n", - "21 0.53 0.49 0.07 0.46 0.52 0.07\n", - "22 0.52 0.49 0.07 0.46 0.52 0.07\n", - "23 0.52 0.49 0.07 0.46 0.52 0.06\n", - "24 0.64 0.50 0.08 0.47 0.53 0.06\n", - "25 0.51 0.50 0.07 0.47 0.53 0.06\n", - "26 0.50 0.50 0.07 0.47 0.53 0.06\n", - "27 0.46 0.50 0.07 0.47 0.53 0.06\n", - "28 0.55 0.50 0.07 0.47 0.53 0.05\n", - "29 0.51 0.50 0.07 0.47 0.53 0.05\n", - "30 0.49 0.50 0.07 0.48 0.53 0.05\n", - "31 0.56 0.50 0.07 0.48 0.53 0.05\n", - "32 0.55 0.50 0.07 0.48 0.53 0.05\n", - "33 0.46 0.50 0.07 0.48 0.53 0.05\n", - "34 0.46 0.50 0.07 0.48 0.52 0.05\n", - "35 0.65 0.51 0.07 0.48 0.53 0.05\n", - "36 0.63 0.51 0.07 0.48 0.53 0.05\n", - "37 0.55 0.51 0.07 0.49 0.53 0.05\n", - "38 0.64 0.51 0.07 0.49 0.54 0.05\n" + "Reached desired precision (0.05) in 30 replications.\n", + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 0.531280 0.531280 NaN NaN NaN \n", + "1 2 0.491104 0.511192 NaN NaN NaN \n", + "2 3 0.463252 0.495212 0.034199 0.410256 0.580168 \n", + "3 4 0.390495 0.469033 0.059339 0.374611 0.563454 \n", + "4 5 0.473277 0.469882 0.051424 0.406030 0.533733 \n", + "5 6 0.611502 0.493485 0.073880 0.415953 0.571017 \n", + "6 7 0.478926 0.491405 0.067667 0.428823 0.553987 \n", + "7 8 0.564747 0.500573 0.067802 0.443889 0.557257 \n", + "8 9 0.571089 0.508408 0.067638 0.456416 0.560399 \n", + "9 10 0.393387 0.496906 0.073414 0.444389 0.549423 \n", + "10 11 0.444403 0.492133 0.071423 0.444150 0.540116 \n", + "11 12 0.447649 0.488426 0.069299 0.444395 0.532457 \n", + "12 13 0.614848 0.498151 0.075044 0.452802 0.543499 \n", + "13 14 0.461512 0.495534 0.072762 0.453522 0.537545 \n", + "14 15 0.425752 0.490882 0.072393 0.450792 0.530971 \n", + "15 16 0.481117 0.490271 0.069981 0.452981 0.527561 \n", + "16 17 0.376149 0.483558 0.073194 0.445925 0.521191 \n", + "17 18 0.429078 0.480531 0.072160 0.444647 0.516416 \n", + "18 19 0.624518 0.488110 0.077518 0.450747 0.525472 \n", + "19 20 0.515244 0.489466 0.075694 0.454041 0.524892 \n", + "20 21 0.525237 0.491170 0.074189 0.457399 0.524940 \n", + "21 22 0.520427 0.492500 0.072669 0.460280 0.524719 \n", + "22 23 0.519650 0.493680 0.071224 0.462881 0.524480 \n", + "23 24 0.638633 0.499720 0.075682 0.467762 0.531677 \n", + "24 25 0.514672 0.500318 0.074149 0.469711 0.530925 \n", + "25 26 0.503287 0.500432 0.072653 0.471087 0.529777 \n", + "26 27 0.461636 0.498995 0.071632 0.470658 0.527332 \n", + "27 28 0.551549 0.500872 0.070991 0.473345 0.528400 \n", + "28 29 0.509837 0.501181 0.069732 0.474657 0.527706 \n", + "29 30 0.490932 0.500840 0.068545 0.475245 0.526435 \n", + "30 31 0.558335 0.502694 0.068179 0.477686 0.527703 \n", + "31 32 0.548240 0.504118 0.067552 0.479762 0.528473 \n", + "32 33 0.457886 0.502717 0.066973 0.478969 0.526464 \n", + "33 34 0.464776 0.501601 0.066271 0.478478 0.524724 \n", + "34 35 0.645301 0.505706 0.069661 0.481777 0.529636 \n", + "35 36 0.629223 0.509137 0.071679 0.484885 0.533390 \n", + "36 37 0.549301 0.510223 0.070984 0.486556 0.533890 \n", + "37 38 0.639879 0.513635 0.073109 0.489605 0.537665 \n", + "\n", + " deviation \n", + "0 NaN \n", + "1 NaN \n", + "2 0.171554 \n", + "3 0.201312 \n", + "4 0.135889 \n", + "5 0.157112 \n", + "6 0.127352 \n", + "7 0.113238 \n", + "8 0.102263 \n", + "9 0.105688 \n", + "10 0.097499 \n", + "11 0.090148 \n", + "12 0.091034 \n", + "13 0.084780 \n", + "14 0.081669 \n", + "15 0.076060 \n", + "16 0.077825 \n", + "17 0.074677 \n", + "18 0.076545 \n", + "19 0.072376 \n", + "20 0.068755 \n", + "21 0.065421 \n", + "22 0.062388 \n", + "23 0.063951 \n", + "24 0.061175 \n", + "25 0.058640 \n", + "26 0.056788 \n", + "27 0.054959 \n", + "28 0.052924 \n", + "29 0.051104 \n", + "30 0.049749 \n", + "31 0.048312 \n", + "32 0.047239 \n", + "33 0.046099 \n", + "34 0.047319 \n", + "35 0.047635 \n", + "36 0.046386 \n", + "37 0.046785 \n" ] }, { "data": { "image/svg+xml": [ - "51015202530350.40.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" + "51015202530350.40.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" ] }, "metadata": {}, @@ -525,33 +667,52 @@ "name": "stdout", "output_type": "stream", "text": [ - "Reached desired precision (0.05) in 6 replications.\n", - " data cumulative_mean stdev lower_ci upper_ci deviation\n", - "replications \n", - "1 0.50 0.5 NaN NaN NaN NaN\n", - "2 0.50 0.5 NaN NaN NaN NaN\n", - "3 0.50 0.5 0.00 0.50 0.50 0.00\n", - "4 0.49 0.5 0.00 0.49 0.50 0.01\n", - "5 0.50 0.5 0.00 0.49 0.50 0.01\n", - "6 0.50 0.5 0.00 0.49 0.50 0.01\n", - "7 0.51 0.5 0.01 0.50 0.51 0.01\n", - "8 0.50 0.5 0.00 0.50 0.50 0.01\n", - "9 0.51 0.5 0.01 0.50 0.51 0.01\n", - "10 0.49 0.5 0.01 0.50 0.50 0.01\n", - "11 0.50 0.5 0.01 0.50 0.50 0.01\n", - "12 0.49 0.5 0.01 0.50 0.50 0.01\n", - "13 0.51 0.5 0.01 0.50 0.51 0.01\n", - "14 0.50 0.5 0.01 0.50 0.50 0.01\n", - "15 0.49 0.5 0.01 0.50 0.50 0.01\n", - "16 0.50 0.5 0.01 0.50 0.50 0.01\n", - "17 0.48 0.5 0.01 0.49 0.50 0.01\n", - "18 0.49 0.5 0.01 0.49 0.50 0.01\n" + "Reached desired precision (0.05) in 5 replications.\n", + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 0.498094 0.498094 NaN NaN NaN \n", + "1 2 0.498249 0.498171 NaN NaN NaN \n", + "2 3 0.496934 0.497759 0.000719 0.495974 0.499544 \n", + "3 4 0.492854 0.496533 0.002522 0.492520 0.500545 \n", + "4 5 0.503386 0.497903 0.003763 0.493231 0.502576 \n", + "5 6 0.503411 0.498821 0.004048 0.494573 0.503069 \n", + "6 7 0.508818 0.500249 0.005285 0.495362 0.505137 \n", + "7 8 0.499944 0.500211 0.004894 0.496120 0.504303 \n", + "8 9 0.509794 0.501276 0.005582 0.496985 0.505567 \n", + "9 10 0.489623 0.500111 0.006425 0.495514 0.504707 \n", + "10 11 0.503398 0.500409 0.006175 0.496261 0.504558 \n", + "11 12 0.489224 0.499477 0.006715 0.495211 0.503744 \n", + "12 13 0.513105 0.500526 0.007458 0.496019 0.505032 \n", + "13 14 0.500948 0.500556 0.007166 0.496418 0.504693 \n", + "14 15 0.486691 0.499631 0.007778 0.495324 0.503939 \n", + "15 16 0.495361 0.499364 0.007590 0.495320 0.503409 \n", + "16 17 0.479430 0.498192 0.008797 0.493669 0.502715 \n", + "17 18 0.491697 0.497831 0.008670 0.493519 0.502143 \n", + "\n", + " deviation \n", + "0 NaN \n", + "1 NaN \n", + "2 0.003586 \n", + "3 0.008081 \n", + "4 0.009385 \n", + "5 0.008516 \n", + "6 0.009771 \n", + "7 0.008180 \n", + "8 0.008560 \n", + "9 0.009190 \n", + "10 0.008290 \n", + "11 0.008542 \n", + "12 0.009004 \n", + "13 0.008266 \n", + "14 0.008621 \n", + "15 0.008099 \n", + "16 0.009079 \n", + "17 0.008661 \n" ] }, { "data": { "image/svg+xml": [ - "0246810121416180.490.4950.50.5050.51lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "0246810121416180.4950.50.505lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -631,6 +792,7 @@ " \n", " \n", " \n", + " replications\n", " data\n", " cumulative_mean\n", " stdev\n", @@ -638,19 +800,11 @@ " upper_ci\n", " deviation\n", " \n", - " \n", - " replications\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " 1\n", + " 0\n", + " 1\n", " 9.842380\n", " 9.842380\n", " NaN\n", @@ -659,7 +813,8 @@ " NaN\n", " \n", " \n", - " 2\n", + " 1\n", + " 2\n", " 10.060424\n", " 9.951402\n", " NaN\n", @@ -668,7 +823,8 @@ " NaN\n", " \n", " \n", - " 3\n", + " 2\n", + " 3\n", " 9.925090\n", " 9.942631\n", " 0.110075\n", @@ -677,7 +833,8 @@ " 0.027502\n", " \n", " \n", - " 4\n", + " 3\n", + " 4\n", " 9.938504\n", " 9.941599\n", " 0.089900\n", @@ -686,7 +843,8 @@ " 0.014389\n", " \n", " \n", - " 5\n", + " 4\n", + " 5\n", " 10.016611\n", " 9.956602\n", " 0.084775\n", @@ -695,7 +853,8 @@ " 0.010572\n", " \n", " \n", - " 6\n", + " 5\n", + " 6\n", " 9.883801\n", " 9.944468\n", " 0.081442\n", @@ -704,7 +863,8 @@ " 0.008595\n", " \n", " \n", - " 7\n", + " 6\n", + " 7\n", " 10.040555\n", " 9.958195\n", " 0.082742\n", @@ -713,7 +873,8 @@ " 0.007685\n", " \n", " \n", - " 8\n", + " 7\n", + " 8\n", " 10.086612\n", " 9.974247\n", " 0.089048\n", @@ -726,27 +887,25 @@ "" ], "text/plain": [ - " data cumulative_mean stdev lower_ci upper_ci \\\n", - "replications \n", - "1 9.842380 9.842380 NaN NaN NaN \n", - "2 10.060424 9.951402 NaN NaN NaN \n", - "3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", - "4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", - "5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", - "6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", - "7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", - "8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 9.842380 9.842380 NaN NaN NaN \n", + "1 2 10.060424 9.951402 NaN NaN NaN \n", + "2 3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", + "3 4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", + "4 5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", + "5 6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", + "6 7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", + "7 8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", "\n", - " deviation \n", - "replications \n", - "1 NaN \n", - "2 NaN \n", - "3 0.027502 \n", - "4 0.014389 \n", - "5 0.010572 \n", - "6 0.008595 \n", - "7 0.007685 \n", - "8 0.007464 " + " deviation \n", + "0 NaN \n", + "1 NaN \n", + "2 0.027502 \n", + "3 0.014389 \n", + "4 0.010572 \n", + "5 0.008595 \n", + "6 0.007685 \n", + "7 0.007464 " ] }, "metadata": {}, @@ -755,7 +914,7 @@ { "data": { "image/svg+xml": [ - "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: mean_time_with_nurse" + "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: mean_time_with_nurse" ] }, "metadata": {}, @@ -804,6 +963,7 @@ " \n", " \n", " \n", + " replications\n", " data\n", " cumulative_mean\n", " stdev\n", @@ -811,19 +971,11 @@ " upper_ci\n", " deviation\n", " \n", - " \n", - " replications\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " 1\n", + " 0\n", + " 1\n", " 0.501873\n", " 0.501873\n", " NaN\n", @@ -832,7 +984,8 @@ " NaN\n", " \n", " \n", - " 2\n", + " 1\n", + " 2\n", " 0.516264\n", " 0.509069\n", " NaN\n", @@ -841,7 +994,8 @@ " NaN\n", " \n", " \n", - " 3\n", + " 2\n", + " 3\n", " 0.531280\n", " 0.516472\n", " 0.014704\n", @@ -850,7 +1004,8 @@ " 0.070725\n", " \n", " \n", - " 4\n", + " 3\n", + " 4\n", " 0.491104\n", " 0.510130\n", " 0.017465\n", @@ -859,7 +1014,8 @@ " 0.054479\n", " \n", " \n", - " 5\n", + " 4\n", + " 5\n", " 0.463252\n", " 0.500755\n", " 0.025851\n", @@ -868,7 +1024,8 @@ " 0.064100\n", " \n", " \n", - " 6\n", + " 5\n", + " 6\n", " 0.390495\n", " 0.482378\n", " 0.050605\n", @@ -877,7 +1034,8 @@ " 0.110092\n", " \n", " \n", - " 7\n", + " 6\n", + " 7\n", " 0.473277\n", " 0.481078\n", " 0.046323\n", @@ -886,7 +1044,8 @@ " 0.089054\n", " \n", " \n", - " 8\n", + " 7\n", + " 8\n", " 0.611502\n", " 0.497381\n", " 0.062973\n", @@ -895,7 +1054,8 @@ " 0.105848\n", " \n", " \n", - " 9\n", + " 8\n", + " 9\n", " 0.478926\n", " 0.495330\n", " 0.059226\n", @@ -904,7 +1064,8 @@ " 0.091909\n", " \n", " \n", - " 10\n", + " 9\n", + " 10\n", " 0.564747\n", " 0.502272\n", " 0.059999\n", @@ -913,7 +1074,8 @@ " 0.085453\n", " \n", " \n", - " 11\n", + " 10\n", + " 11\n", " 0.571089\n", " 0.508528\n", " 0.060584\n", @@ -922,7 +1084,8 @@ " 0.080036\n", " \n", " \n", - " 12\n", + " 11\n", + " 12\n", " 0.393387\n", " 0.498933\n", " 0.066645\n", @@ -931,7 +1094,8 @@ " 0.084869\n", " \n", " \n", - " 13\n", + " 12\n", + " 13\n", " 0.444403\n", " 0.494738\n", " 0.065575\n", @@ -940,7 +1104,8 @@ " 0.080096\n", " \n", " \n", - " 14\n", + " 13\n", + " 14\n", " 0.447649\n", " 0.491375\n", " 0.064247\n", @@ -949,7 +1114,8 @@ " 0.075493\n", " \n", " \n", - " 15\n", + " 14\n", + " 15\n", " 0.614848\n", " 0.499606\n", " 0.069637\n", @@ -958,7 +1124,8 @@ " 0.077188\n", " \n", " \n", - " 16\n", + " 15\n", + " 16\n", " 0.461512\n", " 0.497225\n", " 0.067946\n", @@ -967,7 +1134,8 @@ " 0.072816\n", " \n", " \n", - " 17\n", + " 16\n", + " 17\n", " 0.425752\n", " 0.493021\n", " 0.068034\n", @@ -976,7 +1144,8 @@ " 0.070950\n", " \n", " \n", - " 18\n", + " 17\n", + " 18\n", " 0.481117\n", " 0.492360\n", " 0.066062\n", @@ -985,7 +1154,8 @@ " 0.066724\n", " \n", " \n", - " 19\n", + " 18\n", + " 19\n", " 0.376149\n", " 0.486243\n", " 0.069517\n", @@ -994,7 +1164,8 @@ " 0.068908\n", " \n", " \n", - " 20\n", + " 19\n", + " 20\n", " 0.429078\n", " 0.483385\n", " 0.068859\n", @@ -1003,7 +1174,8 @@ " 0.066670\n", " \n", " \n", - " 21\n", + " 20\n", + " 21\n", " 0.624518\n", " 0.490106\n", " 0.073845\n", @@ -1012,7 +1184,8 @@ " 0.068584\n", " \n", " \n", - " 22\n", + " 21\n", + " 22\n", " 0.515244\n", " 0.491248\n", " 0.072264\n", @@ -1021,7 +1194,8 @@ " 0.065222\n", " \n", " \n", - " 23\n", + " 22\n", + " 23\n", " 0.525237\n", " 0.492726\n", " 0.070957\n", @@ -1030,7 +1204,8 @@ " 0.062274\n", " \n", " \n", - " 24\n", + " 23\n", + " 24\n", " 0.520427\n", " 0.493880\n", " 0.069628\n", @@ -1039,7 +1214,8 @@ " 0.059531\n", " \n", " \n", - " 25\n", + " 24\n", + " 25\n", " 0.519650\n", " 0.494911\n", " 0.068356\n", @@ -1048,7 +1224,8 @@ " 0.057012\n", " \n", " \n", - " 26\n", + " 25\n", + " 26\n", " 0.638633\n", " 0.500439\n", " 0.072664\n", @@ -1057,7 +1234,8 @@ " 0.058648\n", " \n", " \n", - " 27\n", + " 26\n", + " 27\n", " 0.514672\n", " 0.500966\n", " 0.071306\n", @@ -1066,7 +1244,8 @@ " 0.056307\n", " \n", " \n", - " 28\n", + " 27\n", + " 28\n", " 0.503287\n", " 0.501049\n", " 0.069974\n", @@ -1075,7 +1254,8 @@ " 0.054153\n", " \n", " \n", - " 29\n", + " 28\n", + " 29\n", " 0.461636\n", " 0.499690\n", " 0.069102\n", @@ -1084,7 +1264,8 @@ " 0.052603\n", " \n", " \n", - " 30\n", + " 29\n", + " 30\n", " 0.551549\n", " 0.501419\n", " 0.068557\n", @@ -1093,7 +1274,8 @@ " 0.051055\n", " \n", " \n", - " 31\n", + " 30\n", + " 31\n", " 0.509837\n", " 0.501690\n", " 0.067422\n", @@ -1102,7 +1284,8 @@ " 0.049294\n", " \n", " \n", - " 32\n", + " 31\n", + " 32\n", " 0.490932\n", " 0.501354\n", " 0.066353\n", @@ -1111,7 +1294,8 @@ " 0.047716\n", " \n", " \n", - " 33\n", + " 32\n", + " 33\n", " 0.558335\n", " 0.503081\n", " 0.066057\n", @@ -1120,7 +1304,8 @@ " 0.046559\n", " \n", " \n", - " 34\n", + " 33\n", + " 34\n", " 0.548240\n", " 0.504409\n", " 0.065508\n", @@ -1129,7 +1314,8 @@ " 0.045314\n", " \n", " \n", - " 35\n", + " 34\n", + " 35\n", " 0.457886\n", " 0.503080\n", " 0.065014\n", @@ -1138,7 +1324,8 @@ " 0.044393\n", " \n", " \n", - " 36\n", + " 35\n", + " 36\n", " 0.464776\n", " 0.502016\n", " 0.064396\n", @@ -1151,83 +1338,81 @@ "" ], "text/plain": [ - " data cumulative_mean stdev lower_ci upper_ci \\\n", - "replications \n", - "1 0.501873 0.501873 NaN NaN NaN \n", - "2 0.516264 0.509069 NaN NaN NaN \n", - "3 0.531280 0.516472 0.014704 0.479945 0.553000 \n", - "4 0.491104 0.510130 0.017465 0.482339 0.537921 \n", - "5 0.463252 0.500755 0.025851 0.468656 0.532853 \n", - "6 0.390495 0.482378 0.050605 0.429272 0.535484 \n", - "7 0.473277 0.481078 0.046323 0.438236 0.523920 \n", - "8 0.611502 0.497381 0.062973 0.444734 0.550028 \n", - "9 0.478926 0.495330 0.059226 0.449805 0.540856 \n", - "10 0.564747 0.502272 0.059999 0.459351 0.545193 \n", - "11 0.571089 0.508528 0.060584 0.467827 0.549229 \n", - "12 0.393387 0.498933 0.066645 0.456589 0.541277 \n", - "13 0.444403 0.494738 0.065575 0.455112 0.534365 \n", - "14 0.447649 0.491375 0.064247 0.454280 0.528470 \n", - "15 0.614848 0.499606 0.069637 0.461043 0.538170 \n", - "16 0.461512 0.497225 0.067946 0.461020 0.533431 \n", - "17 0.425752 0.493021 0.068034 0.458041 0.528001 \n", - "18 0.481117 0.492360 0.066062 0.459508 0.525212 \n", - "19 0.376149 0.486243 0.069517 0.452738 0.519749 \n", - "20 0.429078 0.483385 0.068859 0.451158 0.515612 \n", - "21 0.624518 0.490106 0.073845 0.456492 0.523719 \n", - "22 0.515244 0.491248 0.072264 0.459208 0.523288 \n", - "23 0.525237 0.492726 0.070957 0.462042 0.523410 \n", - "24 0.520427 0.493880 0.069628 0.464479 0.523282 \n", - "25 0.519650 0.494911 0.068356 0.466695 0.523127 \n", - "26 0.638633 0.500439 0.072664 0.471089 0.529789 \n", - "27 0.514672 0.500966 0.071306 0.472758 0.529174 \n", - "28 0.503287 0.501049 0.069974 0.473916 0.528182 \n", - "29 0.461636 0.499690 0.069102 0.473405 0.525975 \n", - "30 0.551549 0.501419 0.068557 0.475819 0.527018 \n", - "31 0.509837 0.501690 0.067422 0.476960 0.526421 \n", - "32 0.490932 0.501354 0.066353 0.477431 0.525277 \n", - "33 0.558335 0.503081 0.066057 0.479658 0.526503 \n", - "34 0.548240 0.504409 0.065508 0.481552 0.527266 \n", - "35 0.457886 0.503080 0.065014 0.480746 0.525413 \n", - "36 0.464776 0.502016 0.064396 0.480227 0.523804 \n", + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 0.501873 0.501873 NaN NaN NaN \n", + "1 2 0.516264 0.509069 NaN NaN NaN \n", + "2 3 0.531280 0.516472 0.014704 0.479945 0.553000 \n", + "3 4 0.491104 0.510130 0.017465 0.482339 0.537921 \n", + "4 5 0.463252 0.500755 0.025851 0.468656 0.532853 \n", + "5 6 0.390495 0.482378 0.050605 0.429272 0.535484 \n", + "6 7 0.473277 0.481078 0.046323 0.438236 0.523920 \n", + "7 8 0.611502 0.497381 0.062973 0.444734 0.550028 \n", + "8 9 0.478926 0.495330 0.059226 0.449805 0.540856 \n", + "9 10 0.564747 0.502272 0.059999 0.459351 0.545193 \n", + "10 11 0.571089 0.508528 0.060584 0.467827 0.549229 \n", + "11 12 0.393387 0.498933 0.066645 0.456589 0.541277 \n", + "12 13 0.444403 0.494738 0.065575 0.455112 0.534365 \n", + "13 14 0.447649 0.491375 0.064247 0.454280 0.528470 \n", + "14 15 0.614848 0.499606 0.069637 0.461043 0.538170 \n", + "15 16 0.461512 0.497225 0.067946 0.461020 0.533431 \n", + "16 17 0.425752 0.493021 0.068034 0.458041 0.528001 \n", + "17 18 0.481117 0.492360 0.066062 0.459508 0.525212 \n", + "18 19 0.376149 0.486243 0.069517 0.452738 0.519749 \n", + "19 20 0.429078 0.483385 0.068859 0.451158 0.515612 \n", + "20 21 0.624518 0.490106 0.073845 0.456492 0.523719 \n", + "21 22 0.515244 0.491248 0.072264 0.459208 0.523288 \n", + "22 23 0.525237 0.492726 0.070957 0.462042 0.523410 \n", + "23 24 0.520427 0.493880 0.069628 0.464479 0.523282 \n", + "24 25 0.519650 0.494911 0.068356 0.466695 0.523127 \n", + "25 26 0.638633 0.500439 0.072664 0.471089 0.529789 \n", + "26 27 0.514672 0.500966 0.071306 0.472758 0.529174 \n", + "27 28 0.503287 0.501049 0.069974 0.473916 0.528182 \n", + "28 29 0.461636 0.499690 0.069102 0.473405 0.525975 \n", + "29 30 0.551549 0.501419 0.068557 0.475819 0.527018 \n", + "30 31 0.509837 0.501690 0.067422 0.476960 0.526421 \n", + "31 32 0.490932 0.501354 0.066353 0.477431 0.525277 \n", + "32 33 0.558335 0.503081 0.066057 0.479658 0.526503 \n", + "33 34 0.548240 0.504409 0.065508 0.481552 0.527266 \n", + "34 35 0.457886 0.503080 0.065014 0.480746 0.525413 \n", + "35 36 0.464776 0.502016 0.064396 0.480227 0.523804 \n", "\n", - " deviation \n", - "replications \n", - "1 NaN \n", - "2 NaN \n", - "3 0.070725 \n", - "4 0.054479 \n", - "5 0.064100 \n", - "6 0.110092 \n", - "7 0.089054 \n", - "8 0.105848 \n", - "9 0.091909 \n", - "10 0.085453 \n", - "11 0.080036 \n", - "12 0.084869 \n", - "13 0.080096 \n", - "14 0.075493 \n", - "15 0.077188 \n", - "16 0.072816 \n", - "17 0.070950 \n", - "18 0.066724 \n", - "19 0.068908 \n", - "20 0.066670 \n", - "21 0.068584 \n", - "22 0.065222 \n", - "23 0.062274 \n", - "24 0.059531 \n", - "25 0.057012 \n", - "26 0.058648 \n", - "27 0.056307 \n", - "28 0.054153 \n", - "29 0.052603 \n", - "30 0.051055 \n", - "31 0.049294 \n", - "32 0.047716 \n", - "33 0.046559 \n", - "34 0.045314 \n", - "35 0.044393 \n", - "36 0.043402 " + " deviation \n", + "0 NaN \n", + "1 NaN \n", + "2 0.070725 \n", + "3 0.054479 \n", + "4 0.064100 \n", + "5 0.110092 \n", + "6 0.089054 \n", + "7 0.105848 \n", + "8 0.091909 \n", + "9 0.085453 \n", + "10 0.080036 \n", + "11 0.084869 \n", + "12 0.080096 \n", + "13 0.075493 \n", + "14 0.077188 \n", + "15 0.072816 \n", + "16 0.070950 \n", + "17 0.066724 \n", + "18 0.068908 \n", + "19 0.066670 \n", + "20 0.068584 \n", + "21 0.065222 \n", + "22 0.062274 \n", + "23 0.059531 \n", + "24 0.057012 \n", + "25 0.058648 \n", + "26 0.056307 \n", + "27 0.054153 \n", + "28 0.052603 \n", + "29 0.051055 \n", + "30 0.049294 \n", + "31 0.047716 \n", + "32 0.046559 \n", + "33 0.045314 \n", + "34 0.044393 \n", + "35 0.043402 " ] }, "metadata": {}, @@ -1236,7 +1421,7 @@ { "data": { "image/svg+xml": [ - "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: mean_q_time_nurse" + "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: mean_q_time_nurse" ] }, "metadata": {}, @@ -1287,6 +1472,7 @@ " \n", " \n", " \n", + " replications\n", " data\n", " cumulative_mean\n", " stdev\n", @@ -1294,19 +1480,11 @@ " upper_ci\n", " deviation\n", " \n", - " \n", - " replications\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", " \n", " \n", " \n", - " 1\n", + " 0\n", + " 1\n", " 0.499589\n", " 0.499589\n", " NaN\n", @@ -1315,7 +1493,8 @@ " NaN\n", " \n", " \n", - " 2\n", + " 1\n", + " 2\n", " 0.501977\n", " 0.500783\n", " NaN\n", @@ -1324,7 +1503,8 @@ " NaN\n", " \n", " \n", - " 3\n", + " 2\n", + " 3\n", " 0.498094\n", " 0.499887\n", " 0.001959\n", @@ -1333,7 +1513,8 @@ " 0.009734\n", " \n", " \n", - " 4\n", + " 3\n", + " 4\n", " 0.498249\n", " 0.499477\n", " 0.001797\n", @@ -1342,7 +1523,8 @@ " 0.005725\n", " \n", " \n", - " 5\n", + " 4\n", + " 5\n", " 0.496934\n", " 0.498969\n", " 0.001928\n", @@ -1351,7 +1533,8 @@ " 0.004797\n", " \n", " \n", - " 6\n", + " 5\n", + " 6\n", " 0.492854\n", " 0.497949\n", " 0.003034\n", @@ -1360,7 +1543,8 @@ " 0.006394\n", " \n", " \n", - " 7\n", + " 6\n", + " 7\n", " 0.503386\n", " 0.498726\n", " 0.003448\n", @@ -1369,7 +1553,8 @@ " 0.006395\n", " \n", " \n", - " 8\n", + " 7\n", + " 8\n", " 0.503411\n", " 0.499312\n", " 0.003597\n", @@ -1382,27 +1567,25 @@ "" ], "text/plain": [ - " data cumulative_mean stdev lower_ci upper_ci \\\n", - "replications \n", - "1 0.499589 0.499589 NaN NaN NaN \n", - "2 0.501977 0.500783 NaN NaN NaN \n", - "3 0.498094 0.499887 0.001959 0.495021 0.504753 \n", - "4 0.498249 0.499477 0.001797 0.496618 0.502336 \n", - "5 0.496934 0.498969 0.001928 0.496575 0.501362 \n", - "6 0.492854 0.497949 0.003034 0.494766 0.501133 \n", - "7 0.503386 0.498726 0.003448 0.495537 0.501915 \n", - "8 0.503411 0.499312 0.003597 0.496305 0.502319 \n", + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 0.499589 0.499589 NaN NaN NaN \n", + "1 2 0.501977 0.500783 NaN NaN NaN \n", + "2 3 0.498094 0.499887 0.001959 0.495021 0.504753 \n", + "3 4 0.498249 0.499477 0.001797 0.496618 0.502336 \n", + "4 5 0.496934 0.498969 0.001928 0.496575 0.501362 \n", + "5 6 0.492854 0.497949 0.003034 0.494766 0.501133 \n", + "6 7 0.503386 0.498726 0.003448 0.495537 0.501915 \n", + "7 8 0.503411 0.499312 0.003597 0.496305 0.502319 \n", "\n", - " deviation \n", - "replications \n", - "1 NaN \n", - "2 NaN \n", - "3 0.009734 \n", - "4 0.005725 \n", - "5 0.004797 \n", - "6 0.006394 \n", - "7 0.006395 \n", - "8 0.006022 " + " deviation \n", + "0 NaN \n", + "1 NaN \n", + "2 0.009734 \n", + "3 0.005725 \n", + "4 0.004797 \n", + "5 0.006394 \n", + "6 0.006395 \n", + "7 0.006022 " ] }, "metadata": {}, @@ -1411,7 +1594,7 @@ { "data": { "image/svg+xml": [ - "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: mean_nurse_utilisation" + "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: mean_nurse_utilisation" ] }, "metadata": {}, @@ -1445,7 +1628,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 15s\n" + "Notebook run time: 0m 16s\n" ] } ], diff --git a/outputs/choose_param_conf_int_1.png b/outputs/choose_param_conf_int_1.png index 2b8924bafaab322181ef82e0f59302f259cdf561..1f97f42dc8ce21381afa6a539e4f43befd0dde92 100644 GIT binary patch literal 46631 zcmeFZbySpH+cqu(BPAe6D4o)vgfu8f3rG(Q0@5MfI8xFfAq~df($Xc}-ThtT z6Yu>z&wW3?Z+&b1{(IM&r89GJ#oqhe`#jF$IQ9g-m6ycAAjY_N?;e)4)N7@C_fTN> z?jc2>-3PvDs*KmYcaQR(^y^nDF1p*vs4gn1mjT|KU!>{$-e#iUE53e+qElG#%|d6o zIH)lVp8)d(zB{rzRJ@cqcx!WNylRsz=+JqGT2<%eL$qYt%6 zeF^&0m&BtqI0wJbgRsB2{zp*oz&-ScKYe{1LScY~$m96XAmLH|*((jc$oUudrPb#G zo-1Rd?blyDHv{ba!Jpsv9{OXskD%W5<7d2oeqTyNlac?ly}+0M+vNWo>;E0)KSucf 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When the number of replications is <= 100, the value + of look_ahead is used. When they are > 100, then look_ahead / 100 * max(n, 100) is used. replication_budget (int): Maximum allowed replications. Use for larger models where @@ -325,6 +324,24 @@ def __init__( self.observer = observer self.stats = None + # Check validity of provided parameters + self.valid_inputs() + + def valid_inputs(self): + """ + Checks validity of provided parameters. + """ + for p in [self.initial_replications, self.look_ahead]: + if not isinstance(p, int) or p < 0: + raise ValueError(f'{p} must be a non-negative integer.') + + if self.half_width_precision <= 0: + raise ValueError('half_width_precision must be greater than 0.') + + if self.replication_budget < self.initial_replications: + raise ValueError( + 'replication_budget must be less than initial_replications.') + def _klimit(self): """ Determines the number of additional replications to check after @@ -425,8 +442,7 @@ def confidence_interval_method( metric, alpha=0.05, desired_precision=0.05, - min_rep=5, - decimal_places=2, + min_rep=5 ): """ The confidence interval method for selecting the number of replications. @@ -451,8 +467,6 @@ def confidence_interval_method( Minimum number of replications before checking precision. Useful when the number of replications returned does not provide a stable precision below target. - decimal_places (int, optional): - Number of decimal places to round the results table to. Returns: tuple[int, pd.DataFrame]: @@ -497,11 +511,11 @@ def confidence_interval_method( warnings.warn(message) n_reps = -1 - return n_reps, results.round(decimal_places) + return n_reps, results def confidence_interval_method_simple( - replications, metric, desired_precision=0.05, min_rep=5, decimal_places=2 + replications, metric, desired_precision=0.05, min_rep=5 ): """ Simple implementation using the confidence interval method to select the @@ -524,8 +538,6 @@ def confidence_interval_method_simple( Minimum number of replications before checking precision. Useful when the number of replications returned does not provide a stable precision below target. - decimal_places (int, optional): - Number of decimal places to round the results table to. Returns: tuple[int, pd.DataFrame]: @@ -550,12 +562,13 @@ def confidence_interval_method_simple( # Compute cumulative statistics cumulative = pd.DataFrame([ { - 'replications': i, + 'replications': i + 1, # Adjusted as counted from zero + 'data': df[metric][i], 'cumulative_mean': stats[0], 'stdev': stats[1], 'lower_ci': stats[2], 'upper_ci': stats[3], - 'deviation': ((stats[3] - stats[0]) / stats[0]) * 100 + 'deviation': (stats[3] - stats[0]) / stats[0] } for i, stats in enumerate( (summary_stats(df[metric].iloc[:i]) @@ -567,7 +580,7 @@ def confidence_interval_method_simple( try: n_reps = ( cumulative.iloc[min_rep:] - .loc[cumulative['deviation'] <= desired_precision*100] + .loc[cumulative['deviation'] <= desired_precision] .iloc[0] .name ) + 1 @@ -619,7 +632,7 @@ def plotly_confidence_interval_method( ): fig.add_trace( go.Scatter( - x=conf_ints.index, + x=conf_ints['replications'], y=conf_ints[col], line={'color': color, 'dash': dash}, name=col, @@ -631,7 +644,7 @@ def plotly_confidence_interval_method( # Cumulative mean line with enhanced hover fig.add_trace( go.Scatter( - x=conf_ints.index, + x=conf_ints['replications'], y=conf_ints['cumulative_mean'], line={'color': 'blue', 'width': 2}, name='Cumulative Mean', diff --git a/tests/test_functionaltest_replications.py b/tests/test_functionaltest_replications.py new file mode 100644 index 0000000..3421c25 --- /dev/null +++ b/tests/test_functionaltest_replications.py @@ -0,0 +1,110 @@ +"""Functional Testing for objects in replications.py + +Functional tests verify that the system or components perform their intended +functionality. + +Licence: + This project is licensed under the MIT Licence. See the LICENSE file for + more details. + +Typical usage example: + + pytest +""" + +import pandas as pd +import pytest + +from simulation.model import Param, Runner +from simulation.replications import ( + confidence_interval_method, confidence_interval_method_simple, + ReplicationTabulizer, ReplicationsAlgorithm) + + +@pytest.mark.parametrize('ci_function', [ + confidence_interval_method, + confidence_interval_method_simple +]) +def test_ci_method_output(ci_function): + """ + Check that the output from confidence_interval_method and + confidence_interval_method_simple meets our expectations. + + Arguments: + ci_function (function): + Function to run the confidence interval method. + """ + # Create empty list to store errors (else if each were an assert + # statement, it would stop after the first) + errors = [] + + # Choose a number of replications to run for + reps = 20 + + # Run the confidence interval method + min_reps, cumulative_df = ci_function( + replications=reps, metric='mean_time_with_nurse') + + # Check that the results dataframe contains the right number of rows + if not len(cumulative_df) == reps: + errors.append( + f'Ran {reps} replications but cumulative_df only has ' + + f'{len(cumulative_df)} entries.') + + # Check that the replications are appropriately numbered + if not min(cumulative_df['replications']) == 1: + errors.append( + 'Minimum replication in cumulative_df should be 1 but it is ' + + f'{min(cumulative_df['replications'])}.') + + if not max(cumulative_df['replications']) == reps: + errors.append( + f'As we ran {reps} replications, the maximum replication in ' + + f'cumulative_df should be {reps} but it is ' + + f'{max(cumulative_df['replications'])}.') + + # Check that min_reps is no more than the number run + if not min_reps <= reps: + errors.append( + 'The minimum number of replications required as returned by the ' + + 'confidence_interval_method should be less than the number we ' + + f'ran ({reps}) but it returned {min_reps}.') + + # Check if there were any errors + assert not errors, 'Errors occurred:\n{}'.format('\n'.join(errors)) + + +@pytest.mark.parametrize('ci_function', [ + confidence_interval_method, + confidence_interval_method_simple +]) +def test_consistent_outputs(ci_function): + """ + Check that the output cumulative statistics from the manual confidence + interval methods are consistent with those from the algorithm. + + Arguments: + ci_function (function): + Function to run the manual confidence interval method. + """ + # Choose a number of replications to run for + reps = 20 + + # Run the manual confidence interval method + _, man_df = ci_function( + replications=reps, metric='mean_time_with_nurse') + + # Run the algorithm + observer = ReplicationTabulizer() + analyser = ReplicationsAlgorithm( + verbose=False, + observer=observer, + initial_replications=reps, + replication_budget=reps) + _ = analyser.select(runner=Runner(Param()), metric='mean_time_with_nurse') + # Get first 20 rows (may have more if met precision and went into + # look ahead period beyond budget) + auto_df = observer.summary_table().head(20) + + # Compare the dataframes + pd.testing.assert_frame_equal(man_df, auto_df) diff --git a/tests/test_unittest_replications.py b/tests/test_unittest_replications.py new file mode 100644 index 0000000..acf358c --- /dev/null +++ b/tests/test_unittest_replications.py @@ -0,0 +1,72 @@ +"""Unit Testing for objects in replications.py + +Unit tests are a type of functional testing that focuses on individual +components (e.g. methods, classes) and tests them in isolation to ensure they +work as intended. + +Licence: + This project is licensed under the MIT Licence. See the LICENSE file for + more details. + +Typical usage example: + + pytest +""" + +import pytest + +from simulation.replications import ReplicationsAlgorithm + + +# pylint: disable=protected-access +@pytest.mark.parametrize('look_ahead, n, exp', [ + (100, 100, 100), + (100, 101, 101), + (0, 500, 0) +]) +def test_klimit(look_ahead, n, exp): + """ + Check that the _klimit() calculations are as expected. + + Arguments: + look_ahead (int): + Minimum additional replications to look ahead to assess stability + of precision. + n (int): + Number of replications that would already be completed. + exp (int): + Expected number of replications for _klimit() to return. + """ + # Calculate additional replications that would be required + calc = ReplicationsAlgorithm( + look_ahead=100, initial_replications=100)._klimit() + # Check that this meets our expected value + assert calc == 100, ( + f'With look_ahead {look_ahead} and n={n}, the additional ' + + f'replications required should be {exp} but _klimit() returned {calc}.' + ) + + +@pytest.mark.parametrize('arg, value', [ + ('initial_replications', -1), + ('initial_replications', 0.5), + ('look_ahead', -1), + ('look_ahead', 0.5), + ('half_width_precision', 0) +]) +def test_algorithm_invalid(arg, value): + """ + Ensure that ReplicationsAlgorithm responds appropriately to invalid inputs. + """ + with pytest.raises(ValueError): + ReplicationsAlgorithm(**{arg: value}) + + +def test_algorithm_invalid_budget(): + """ + Ensure that ReplicationsAlgorithm responds appropriately when + replication_budget is less than initial_replications. + """ + with pytest.raises(ValueError): + ReplicationsAlgorithm(initial_replications=10, + replication_budget=9) From ce30a4279a23b9eacef6bb072a87318836c46a12 Mon Sep 17 00:00:00 2001 From: amyheather Date: Thu, 13 Feb 2025 14:21:27 +0000 Subject: [PATCH 11/28] chore(replications): reran notebook to go with last commit --- notebooks/choosing_replications.ipynb | 168 +++++++++++++------------- 1 file changed, 84 insertions(+), 84 deletions(-) diff --git a/notebooks/choosing_replications.ipynb b/notebooks/choosing_replications.ipynb index fc24233..9cca72a 100644 --- a/notebooks/choosing_replications.ipynb +++ b/notebooks/choosing_replications.ipynb @@ -204,30 +204,30 @@ " deviation \n", "0 NaN \n", "1 NaN \n", - "2 2.750194 \n", - "3 1.438907 \n", - "4 1.057210 \n", - "5 0.859452 \n", - "6 0.768450 \n", - "7 0.746385 \n", - "8 0.867734 \n", - "9 0.789522 \n", - "10 0.718565 \n", - "11 0.684953 \n", - "12 0.706549 \n", - "13 0.654973 \n", - "14 0.656843 \n", - "15 0.611465 \n", - "16 0.618032 \n", - "17 0.579912 \n", - "18 0.583679 \n", - "19 0.554581 \n" + "2 0.027502 \n", + "3 0.014389 \n", + "4 0.010572 \n", + "5 0.008595 \n", + "6 0.007685 \n", + "7 0.007464 \n", + "8 0.008677 \n", + "9 0.007895 \n", + "10 0.007186 \n", + "11 0.006850 \n", + "12 0.007065 \n", + "13 0.006550 \n", + "14 0.006568 \n", + "15 0.006115 \n", + "16 0.006180 \n", + "17 0.005799 \n", + "18 0.005837 \n", + "19 0.005546 \n" ] }, { "data": { "image/svg+xml": [ - "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" + "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" ] }, "metadata": {}, @@ -305,50 +305,50 @@ " deviation \n", "0 NaN \n", "1 NaN \n", - "2 7.072512 \n", - "3 5.447869 \n", - "4 6.410031 \n", - "5 11.009247 \n", - "6 8.905395 \n", - "7 10.584787 \n", - "8 9.190885 \n", - "9 8.545285 \n", - "10 8.003636 \n", - "11 8.486905 \n", - "12 8.009636 \n", - "13 7.549290 \n", - "14 7.718767 \n", - "15 7.281589 \n", - "16 7.095000 \n", - "17 6.672356 \n", - "18 6.890766 \n", - "19 6.666968 \n", - "20 6.858447 \n", - "21 6.522166 \n", - "22 6.227441 \n", - "23 5.953090 \n", - "24 5.701231 \n", - "25 5.864810 \n", - "26 5.630660 \n", - "27 5.415286 \n", - "28 5.260274 \n", - "29 5.105455 \n", - "30 4.929448 \n", - "31 4.771627 \n", - "32 4.655859 \n", - "33 4.531385 \n", - "34 4.439307 \n", - "35 4.340209 \n", - "36 4.463507 \n", - "37 4.501991 \n", - "38 4.391359 \n", - "39 4.437454 \n" + "2 0.070725 \n", + "3 0.054479 \n", + "4 0.064100 \n", + "5 0.110092 \n", + "6 0.089054 \n", + "7 0.105848 \n", + "8 0.091909 \n", + "9 0.085453 \n", + "10 0.080036 \n", + "11 0.084869 \n", + "12 0.080096 \n", + "13 0.075493 \n", + "14 0.077188 \n", + "15 0.072816 \n", + "16 0.070950 \n", + "17 0.066724 \n", + "18 0.068908 \n", + "19 0.066670 \n", + "20 0.068584 \n", + "21 0.065222 \n", + "22 0.062274 \n", + "23 0.059531 \n", + "24 0.057012 \n", + "25 0.058648 \n", + "26 0.056307 \n", + "27 0.054153 \n", + "28 0.052603 \n", + "29 0.051055 \n", + "30 0.049294 \n", + "31 0.047716 \n", + "32 0.046559 \n", + "33 0.045314 \n", + "34 0.044393 \n", + "35 0.043402 \n", + "36 0.044635 \n", + "37 0.045020 \n", + "38 0.043914 \n", + "39 0.044375 \n" ] }, { "data": { "image/svg+xml": [ - "510152025303540455055lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" + "510152025303540455055lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" ] }, "metadata": {}, @@ -406,30 +406,30 @@ " deviation \n", "0 NaN \n", "1 NaN \n", - "2 0.973446 \n", - "3 0.572451 \n", - "4 0.479656 \n", - "5 0.639370 \n", - "6 0.639485 \n", - "7 0.602227 \n", - "8 0.709990 \n", - "9 0.623307 \n", - "10 0.673627 \n", - "11 0.741746 \n", - "12 0.683426 \n", - "13 0.717936 \n", - "14 0.765588 \n", - "15 0.711729 \n", - "16 0.750831 \n", - "17 0.712409 \n", - "18 0.806749 \n", - "19 0.776124 \n" + "2 0.009734 \n", + "3 0.005725 \n", + "4 0.004797 \n", + "5 0.006394 \n", + "6 0.006395 \n", + "7 0.006022 \n", + "8 0.007100 \n", + "9 0.006233 \n", + "10 0.006736 \n", + "11 0.007417 \n", + "12 0.006834 \n", + "13 0.007179 \n", + "14 0.007656 \n", + "15 0.007117 \n", + "16 0.007508 \n", + "17 0.007124 \n", + "18 0.008067 \n", + "19 0.007761 \n" ] }, { "data": { "image/svg+xml": [ - "246810121416182049.449.649.85050.250.4lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "246810121416182049.449.649.85050.250.4lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -513,7 +513,7 @@ { "data": { "image/svg+xml": [ - "0246810121416189.859.99.951010.0510.1lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" + "0246810121416189.859.99.951010.0510.1lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" ] }, "metadata": {}, @@ -629,7 +629,7 @@ { "data": { "image/svg+xml": [ - "51015202530350.40.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" + "51015202530350.40.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" ] }, "metadata": {}, @@ -712,7 +712,7 @@ { "data": { "image/svg+xml": [ - "0246810121416180.4950.50.505lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "0246810121416180.4950.50.505lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -914,7 +914,7 @@ { "data": { "image/svg+xml": [ - "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: mean_time_with_nurse" + "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: mean_time_with_nurse" ] }, "metadata": {}, @@ -1421,7 +1421,7 @@ { "data": { "image/svg+xml": [ - "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: mean_q_time_nurse" + "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: mean_q_time_nurse" ] }, "metadata": {}, @@ -1594,7 +1594,7 @@ { "data": { "image/svg+xml": [ - "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: mean_nurse_utilisation" + "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: mean_nurse_utilisation" ] }, "metadata": {}, @@ -1628,7 +1628,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 16s\n" + "Notebook run time: 0m 15s\n" ] } ], From 023219bdd58ca3f665004677ed16fb42dcbc3644 Mon Sep 17 00:00:00 2001 From: amyheather Date: Thu, 13 Feb 2025 14:35:16 +0000 Subject: [PATCH 12/28] fix(replications): fixed confidence_interval_method and add test Results were wrong as it was provided as series to data in OnlineStatistics. At validation to OnlineStatistics to prevent this, and test to check that OnlineStatistics fails with this input type. --- notebooks/choosing_replications.ipynb | 318 +++++++++++++------------- simulation/replications.py | 15 +- tests/test_unittest_replications.py | 11 +- 3 files changed, 185 insertions(+), 159 deletions(-) diff --git a/notebooks/choosing_replications.ipynb b/notebooks/choosing_replications.ipynb index 9cca72a..f1b4caa 100644 --- a/notebooks/choosing_replications.ipynb +++ b/notebooks/choosing_replications.ipynb @@ -164,9 +164,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "### `confidence_interval_method_simple`\n", - "\n", - "plots below have dots but these don't" + "### `confidence_interval_method_simple`" ] }, { @@ -227,7 +225,7 @@ { "data": { "image/svg+xml": [ - "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" + "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" ] }, "metadata": {}, @@ -348,7 +346,7 @@ { "data": { "image/svg+xml": [ - "510152025303540455055lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" + "510152025303540455055lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" ] }, "metadata": {}, @@ -429,7 +427,7 @@ { "data": { "image/svg+xml": [ - "246810121416182049.449.649.85050.250.4lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "246810121416182049.449.649.85050.250.4lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -470,50 +468,54 @@ "text": [ "Reached desired precision (0.05) in 5 replications.\n", " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 9.925090 9.925090 NaN NaN NaN \n", - "1 2 9.938504 9.931797 NaN NaN NaN \n", - "2 3 10.016611 9.960068 0.049425 9.837290 10.082846 \n", - "3 4 9.883801 9.941001 0.055522 9.852653 10.029350 \n", - "4 5 10.040555 9.960912 0.065530 9.879545 10.042279 \n", - "5 6 10.086612 9.981862 0.077903 9.900108 10.063616 \n", - "6 7 10.202806 10.013426 0.109686 9.911983 10.114869 \n", - "7 8 10.093238 10.023402 0.105397 9.935288 10.111517 \n", - "8 9 10.082694 10.029990 0.100552 9.952699 10.107281 \n", - "9 10 9.895607 10.016552 0.103890 9.942233 10.090870 \n", - "10 11 9.808886 9.997673 0.116766 9.919229 10.076118 \n", - "11 12 9.932816 9.992268 0.112895 9.920538 10.063999 \n", - "12 13 10.165899 10.005624 0.118331 9.934118 10.077131 \n", - "13 14 9.975272 10.003456 0.113978 9.937648 10.069265 \n", - "14 15 9.809312 9.990513 0.120731 9.923655 10.057372 \n", - "15 16 9.988074 9.990361 0.116638 9.928209 10.052513 \n", - "16 17 9.801716 9.979264 0.121851 9.916614 10.041914 \n", - "17 18 9.923390 9.976160 0.118944 9.917011 10.035310 \n", + "0 1 9.842380 9.842380 NaN NaN NaN \n", + "1 2 10.060424 9.951402 NaN NaN NaN \n", + "2 3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", + "3 4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", + "4 5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", + "5 6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", + "6 7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", + "7 8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", + "8 9 10.202806 9.999643 0.112884 9.912872 10.086413 \n", + "9 10 10.093238 10.009002 0.110467 9.929979 10.088025 \n", + "10 11 10.082694 10.015701 0.107128 9.943732 10.087671 \n", + "11 12 9.895607 10.005693 0.107865 9.937159 10.074228 \n", + "12 13 9.808886 9.990554 0.116811 9.919966 10.061143 \n", + "13 14 9.932816 9.986430 0.113284 9.921022 10.051839 \n", + "14 15 10.165899 9.998395 0.118591 9.932721 10.064069 \n", + "15 16 9.975272 9.996950 0.114716 9.935822 10.058077 \n", + "16 17 9.809312 9.985912 0.120035 9.924196 10.047628 \n", + "17 18 9.988074 9.986032 0.116452 9.928122 10.043942 \n", + "18 19 9.801716 9.976331 0.120813 9.918102 10.034561 \n", + "19 20 9.923390 9.973684 0.118185 9.918372 10.028996 \n", "\n", " deviation \n", "0 NaN \n", "1 NaN \n", - "2 0.012327 \n", - "3 0.008887 \n", - "4 0.008169 \n", - "5 0.008190 \n", - "6 0.010131 \n", - "7 0.008791 \n", - "8 0.007706 \n", - "9 0.007420 \n", - "10 0.007846 \n", - "11 0.007179 \n", - "12 0.007147 \n", - "13 0.006579 \n", - "14 0.006692 \n", - "15 0.006221 \n", - "16 0.006278 \n", - "17 0.005929 \n" + "2 0.027502 \n", + "3 0.014389 \n", + "4 0.010572 \n", + "5 0.008595 \n", + "6 0.007685 \n", + "7 0.007464 \n", + "8 0.008677 \n", + "9 0.007895 \n", + "10 0.007186 \n", + "11 0.006850 \n", + "12 0.007065 \n", + "13 0.006550 \n", + "14 0.006568 \n", + "15 0.006115 \n", + "16 0.006180 \n", + "17 0.005799 \n", + "18 0.005837 \n", + "19 0.005546 \n" ] }, { "data": { "image/svg+xml": [ - "0246810121416189.859.99.951010.0510.1lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" + "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" ] }, "metadata": {}, @@ -546,90 +548,94 @@ "text": [ "Reached desired precision (0.05) in 30 replications.\n", " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 0.531280 0.531280 NaN NaN NaN \n", - "1 2 0.491104 0.511192 NaN NaN NaN \n", - "2 3 0.463252 0.495212 0.034199 0.410256 0.580168 \n", - "3 4 0.390495 0.469033 0.059339 0.374611 0.563454 \n", - "4 5 0.473277 0.469882 0.051424 0.406030 0.533733 \n", - "5 6 0.611502 0.493485 0.073880 0.415953 0.571017 \n", - "6 7 0.478926 0.491405 0.067667 0.428823 0.553987 \n", - "7 8 0.564747 0.500573 0.067802 0.443889 0.557257 \n", - "8 9 0.571089 0.508408 0.067638 0.456416 0.560399 \n", - "9 10 0.393387 0.496906 0.073414 0.444389 0.549423 \n", - "10 11 0.444403 0.492133 0.071423 0.444150 0.540116 \n", - "11 12 0.447649 0.488426 0.069299 0.444395 0.532457 \n", - "12 13 0.614848 0.498151 0.075044 0.452802 0.543499 \n", - "13 14 0.461512 0.495534 0.072762 0.453522 0.537545 \n", - "14 15 0.425752 0.490882 0.072393 0.450792 0.530971 \n", - "15 16 0.481117 0.490271 0.069981 0.452981 0.527561 \n", - "16 17 0.376149 0.483558 0.073194 0.445925 0.521191 \n", - "17 18 0.429078 0.480531 0.072160 0.444647 0.516416 \n", - "18 19 0.624518 0.488110 0.077518 0.450747 0.525472 \n", - "19 20 0.515244 0.489466 0.075694 0.454041 0.524892 \n", - "20 21 0.525237 0.491170 0.074189 0.457399 0.524940 \n", - "21 22 0.520427 0.492500 0.072669 0.460280 0.524719 \n", - "22 23 0.519650 0.493680 0.071224 0.462881 0.524480 \n", - "23 24 0.638633 0.499720 0.075682 0.467762 0.531677 \n", - "24 25 0.514672 0.500318 0.074149 0.469711 0.530925 \n", - "25 26 0.503287 0.500432 0.072653 0.471087 0.529777 \n", - "26 27 0.461636 0.498995 0.071632 0.470658 0.527332 \n", - "27 28 0.551549 0.500872 0.070991 0.473345 0.528400 \n", - "28 29 0.509837 0.501181 0.069732 0.474657 0.527706 \n", - "29 30 0.490932 0.500840 0.068545 0.475245 0.526435 \n", - "30 31 0.558335 0.502694 0.068179 0.477686 0.527703 \n", - "31 32 0.548240 0.504118 0.067552 0.479762 0.528473 \n", - "32 33 0.457886 0.502717 0.066973 0.478969 0.526464 \n", - "33 34 0.464776 0.501601 0.066271 0.478478 0.524724 \n", - "34 35 0.645301 0.505706 0.069661 0.481777 0.529636 \n", - "35 36 0.629223 0.509137 0.071679 0.484885 0.533390 \n", - "36 37 0.549301 0.510223 0.070984 0.486556 0.533890 \n", - "37 38 0.639879 0.513635 0.073109 0.489605 0.537665 \n", + "0 1 0.501873 0.501873 NaN NaN NaN \n", + "1 2 0.516264 0.509069 NaN NaN NaN \n", + "2 3 0.531280 0.516472 0.014704 0.479945 0.553000 \n", + "3 4 0.491104 0.510130 0.017465 0.482339 0.537921 \n", + "4 5 0.463252 0.500755 0.025851 0.468656 0.532853 \n", + "5 6 0.390495 0.482378 0.050605 0.429272 0.535484 \n", + "6 7 0.473277 0.481078 0.046323 0.438236 0.523920 \n", + "7 8 0.611502 0.497381 0.062973 0.444734 0.550028 \n", + "8 9 0.478926 0.495330 0.059226 0.449805 0.540856 \n", + "9 10 0.564747 0.502272 0.059999 0.459351 0.545193 \n", + "10 11 0.571089 0.508528 0.060584 0.467827 0.549229 \n", + "11 12 0.393387 0.498933 0.066645 0.456589 0.541277 \n", + "12 13 0.444403 0.494738 0.065575 0.455112 0.534365 \n", + "13 14 0.447649 0.491375 0.064247 0.454280 0.528470 \n", + "14 15 0.614848 0.499606 0.069637 0.461043 0.538170 \n", + "15 16 0.461512 0.497225 0.067946 0.461020 0.533431 \n", + "16 17 0.425752 0.493021 0.068034 0.458041 0.528001 \n", + "17 18 0.481117 0.492360 0.066062 0.459508 0.525212 \n", + "18 19 0.376149 0.486243 0.069517 0.452738 0.519749 \n", + "19 20 0.429078 0.483385 0.068859 0.451158 0.515612 \n", + "20 21 0.624518 0.490106 0.073845 0.456492 0.523719 \n", + "21 22 0.515244 0.491248 0.072264 0.459208 0.523288 \n", + "22 23 0.525237 0.492726 0.070957 0.462042 0.523410 \n", + "23 24 0.520427 0.493880 0.069628 0.464479 0.523282 \n", + "24 25 0.519650 0.494911 0.068356 0.466695 0.523127 \n", + "25 26 0.638633 0.500439 0.072664 0.471089 0.529789 \n", + "26 27 0.514672 0.500966 0.071306 0.472758 0.529174 \n", + "27 28 0.503287 0.501049 0.069974 0.473916 0.528182 \n", + "28 29 0.461636 0.499690 0.069102 0.473405 0.525975 \n", + "29 30 0.551549 0.501419 0.068557 0.475819 0.527018 \n", + "30 31 0.509837 0.501690 0.067422 0.476960 0.526421 \n", + "31 32 0.490932 0.501354 0.066353 0.477431 0.525277 \n", + "32 33 0.558335 0.503081 0.066057 0.479658 0.526503 \n", + "33 34 0.548240 0.504409 0.065508 0.481552 0.527266 \n", + "34 35 0.457886 0.503080 0.065014 0.480746 0.525413 \n", + "35 36 0.464776 0.502016 0.064396 0.480227 0.523804 \n", + "36 37 0.645301 0.505888 0.067724 0.483308 0.528469 \n", + "37 38 0.629223 0.509134 0.069735 0.486213 0.532055 \n", + "38 39 0.549301 0.510164 0.069111 0.487761 0.532567 \n", + "39 40 0.639879 0.513407 0.071235 0.490624 0.536189 \n", "\n", " deviation \n", "0 NaN \n", "1 NaN \n", - "2 0.171554 \n", - "3 0.201312 \n", - "4 0.135889 \n", - "5 0.157112 \n", - "6 0.127352 \n", - "7 0.113238 \n", - "8 0.102263 \n", - "9 0.105688 \n", - "10 0.097499 \n", - "11 0.090148 \n", - "12 0.091034 \n", - "13 0.084780 \n", - "14 0.081669 \n", - "15 0.076060 \n", - "16 0.077825 \n", - "17 0.074677 \n", - "18 0.076545 \n", - "19 0.072376 \n", - "20 0.068755 \n", - "21 0.065421 \n", - "22 0.062388 \n", - "23 0.063951 \n", - "24 0.061175 \n", - "25 0.058640 \n", - "26 0.056788 \n", - "27 0.054959 \n", - "28 0.052924 \n", - "29 0.051104 \n", - "30 0.049749 \n", - "31 0.048312 \n", - "32 0.047239 \n", - "33 0.046099 \n", - "34 0.047319 \n", - "35 0.047635 \n", - "36 0.046386 \n", - "37 0.046785 \n" + "2 0.070725 \n", + "3 0.054479 \n", + "4 0.064100 \n", + "5 0.110092 \n", + "6 0.089054 \n", + "7 0.105848 \n", + "8 0.091909 \n", + "9 0.085453 \n", + "10 0.080036 \n", + "11 0.084869 \n", + "12 0.080096 \n", + "13 0.075493 \n", + "14 0.077188 \n", + "15 0.072816 \n", + "16 0.070950 \n", + "17 0.066724 \n", + "18 0.068908 \n", + "19 0.066670 \n", + "20 0.068584 \n", + "21 0.065222 \n", + "22 0.062274 \n", + "23 0.059531 \n", + "24 0.057012 \n", + "25 0.058648 \n", + "26 0.056307 \n", + "27 0.054153 \n", + "28 0.052603 \n", + "29 0.051055 \n", + "30 0.049294 \n", + "31 0.047716 \n", + "32 0.046559 \n", + "33 0.045314 \n", + "34 0.044393 \n", + "35 0.043402 \n", + "36 0.044635 \n", + "37 0.045020 \n", + "38 0.043914 \n", + "39 0.044375 \n" ] }, { "data": { "image/svg+xml": [ - "51015202530350.40.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" + "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" ] }, "metadata": {}, @@ -669,50 +675,54 @@ "text": [ "Reached desired precision (0.05) in 5 replications.\n", " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 0.498094 0.498094 NaN NaN NaN \n", - "1 2 0.498249 0.498171 NaN NaN NaN \n", - "2 3 0.496934 0.497759 0.000719 0.495974 0.499544 \n", - "3 4 0.492854 0.496533 0.002522 0.492520 0.500545 \n", - "4 5 0.503386 0.497903 0.003763 0.493231 0.502576 \n", - "5 6 0.503411 0.498821 0.004048 0.494573 0.503069 \n", - "6 7 0.508818 0.500249 0.005285 0.495362 0.505137 \n", - "7 8 0.499944 0.500211 0.004894 0.496120 0.504303 \n", - "8 9 0.509794 0.501276 0.005582 0.496985 0.505567 \n", - "9 10 0.489623 0.500111 0.006425 0.495514 0.504707 \n", - "10 11 0.503398 0.500409 0.006175 0.496261 0.504558 \n", - "11 12 0.489224 0.499477 0.006715 0.495211 0.503744 \n", - "12 13 0.513105 0.500526 0.007458 0.496019 0.505032 \n", - "13 14 0.500948 0.500556 0.007166 0.496418 0.504693 \n", - "14 15 0.486691 0.499631 0.007778 0.495324 0.503939 \n", - "15 16 0.495361 0.499364 0.007590 0.495320 0.503409 \n", - "16 17 0.479430 0.498192 0.008797 0.493669 0.502715 \n", - "17 18 0.491697 0.497831 0.008670 0.493519 0.502143 \n", + "0 1 0.499589 0.499589 NaN NaN NaN \n", + "1 2 0.501977 0.500783 NaN NaN NaN \n", + "2 3 0.498094 0.499887 0.001959 0.495021 0.504753 \n", + "3 4 0.498249 0.499477 0.001797 0.496618 0.502336 \n", + "4 5 0.496934 0.498969 0.001928 0.496575 0.501362 \n", + "5 6 0.492854 0.497949 0.003034 0.494766 0.501133 \n", + "6 7 0.503386 0.498726 0.003448 0.495537 0.501915 \n", + "7 8 0.503411 0.499312 0.003597 0.496305 0.502319 \n", + "8 9 0.508818 0.500368 0.004622 0.496815 0.503921 \n", + "9 10 0.499944 0.500326 0.004359 0.497207 0.503444 \n", + "10 11 0.509794 0.501186 0.005025 0.497810 0.504562 \n", + "11 12 0.489623 0.500223 0.005840 0.496512 0.503933 \n", + "12 13 0.503398 0.500467 0.005660 0.497047 0.503887 \n", + "13 14 0.489224 0.499664 0.006213 0.496077 0.503251 \n", + "14 15 0.513105 0.500560 0.006920 0.496728 0.504392 \n", + "15 16 0.500948 0.500584 0.006686 0.497021 0.504147 \n", + "16 17 0.486691 0.499767 0.007298 0.496014 0.503519 \n", + "17 18 0.495361 0.499522 0.007156 0.495963 0.503081 \n", + "18 19 0.479430 0.498465 0.008343 0.494443 0.502486 \n", + "19 20 0.491697 0.498126 0.008261 0.494260 0.501992 \n", "\n", " deviation \n", "0 NaN \n", "1 NaN \n", - "2 0.003586 \n", - "3 0.008081 \n", - "4 0.009385 \n", - "5 0.008516 \n", - "6 0.009771 \n", - "7 0.008180 \n", - "8 0.008560 \n", - "9 0.009190 \n", - "10 0.008290 \n", - "11 0.008542 \n", - "12 0.009004 \n", - "13 0.008266 \n", - "14 0.008621 \n", - "15 0.008099 \n", - "16 0.009079 \n", - "17 0.008661 \n" + "2 0.009734 \n", + "3 0.005725 \n", + "4 0.004797 \n", + "5 0.006394 \n", + "6 0.006395 \n", + "7 0.006022 \n", + "8 0.007100 \n", + "9 0.006233 \n", + "10 0.006736 \n", + "11 0.007417 \n", + "12 0.006834 \n", + "13 0.007179 \n", + "14 0.007656 \n", + "15 0.007117 \n", + "16 0.007508 \n", + "17 0.007124 \n", + "18 0.008067 \n", + "19 0.007761 \n" ] }, { "data": { "image/svg+xml": [ - "0246810121416180.4950.50.505lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "24681012141618200.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -914,7 +924,7 @@ { "data": { "image/svg+xml": [ - "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: mean_time_with_nurse" + "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: mean_time_with_nurse" ] }, "metadata": {}, @@ -1421,7 +1431,7 @@ { "data": { "image/svg+xml": [ - "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: mean_q_time_nurse" + "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: mean_q_time_nurse" ] }, "metadata": {}, @@ -1594,7 +1604,7 @@ { "data": { "image/svg+xml": [ - "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: mean_nurse_utilisation" + "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: mean_nurse_utilisation" ] }, "metadata": {}, diff --git a/simulation/replications.py b/simulation/replications.py index 3cd6b2f..4534339 100644 --- a/simulation/replications.py +++ b/simulation/replications.py @@ -72,9 +72,15 @@ def __init__(self, data=None, alpha=0.1, observer=None): self.register_observer(observer) # If an array of initial values are supplied, then run update() - if isinstance(data, np.ndarray): - for x in data: - self.update(x) + if data is not None: + if isinstance(data, np.ndarray): + for x in data: + self.update(x) + # Raise an error if in different format - else will invisibly + # proceed and won't notice it hasn't done this + else: + raise ValueError( + f'data must be np.ndarray but is type {type(data)}') def register_observer(self, observer): """ @@ -488,7 +494,8 @@ def confidence_interval_method( # Set up method for calculating statistics and saving them as a table observer = ReplicationTabulizer() - stats = OnlineStatistics(alpha=alpha, data=rep_res[:2], observer=observer) + stats = OnlineStatistics( + alpha=alpha, data=np.array(rep_res[:2]), observer=observer) # Calculate statistics with each replication, and get summary table for i in range(2, len(rep_res)): diff --git a/tests/test_unittest_replications.py b/tests/test_unittest_replications.py index acf358c..fb206f5 100644 --- a/tests/test_unittest_replications.py +++ b/tests/test_unittest_replications.py @@ -13,9 +13,10 @@ pytest """ +import pandas as pd import pytest -from simulation.replications import ReplicationsAlgorithm +from simulation.replications import ReplicationsAlgorithm, OnlineStatistics # pylint: disable=protected-access @@ -70,3 +71,11 @@ def test_algorithm_invalid_budget(): with pytest.raises(ValueError): ReplicationsAlgorithm(initial_replications=10, replication_budget=9) + + +def test_onlinestat_data(): + """ + Check that OnlineStatistics will fail if an invalid data type is provided. + """ + with pytest.raises(ValueError): + OnlineStatistics(data=pd.Series([9, 2, 3])) From 1bb6cf43ff5b2478afb3cfd7a5482a07f2eb79f1 Mon Sep 17 00:00:00 2001 From: amyheather Date: Thu, 13 Feb 2025 14:54:05 +0000 Subject: [PATCH 13/28] tests(replication): add back tests for the replications.py objects --- notebooks/generate_exp_results.ipynb | 364 ++++++++++++++++++++++++++- tests/exp_results/replications.csv | 21 ++ tests/test_backtest_replications.py | 73 ++++++ 3 files changed, 452 insertions(+), 6 deletions(-) create mode 100644 tests/exp_results/replications.csv create mode 100644 tests/test_backtest_replications.py diff --git a/notebooks/generate_exp_results.ipynb b/notebooks/generate_exp_results.ipynb index 3e8180d..dae5f8e 100644 --- a/notebooks/generate_exp_results.ipynb +++ b/notebooks/generate_exp_results.ipynb @@ -4,11 +4,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Generate expected results\n", + "## Generate expected results for back tests\n", "\n", - "This notebook is used to run a specific version of the model and save each results dataframe as a csv. These are used in `test_backtest.py` to verify that the model produces consistent results.\n", + "This notebook generates results for backtests. These include\n", "\n", - "The notebook is provided as it is possible that results may change due to alterations to the model structure and operations. Once it has been confirmed that changes are intentional and not any introduced errors, this script can be run to regenerate the `.csv` files used in the test.\n", + "* Backtests for run of simulation with default parameters (`test_backtest.py`).\n", + "* Backtests for runs of simulation where you are determining an appropriate number of replications (`test_backtest_replications.py`).\n", + "\n", + "They save the relevant results dataframe as `.csv`, and use these to verify that the model produces consistent results.\n", "\n", "The run time is provided at the end of this notebook." ] @@ -53,7 +56,10 @@ "import time\n", "from IPython.display import display\n", "\n", - "from simulation.model import Param, Runner" + "from simulation.model import Param, Runner\n", + "from simulation.replications import (\n", + " ReplicationsAlgorithm, ReplicationTabulizer, confidence_interval_method,\n", + " confidence_interval_method_simple)" ] }, { @@ -94,7 +100,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Run model and save results" + "## Run of simulation with default parameters" ] }, { @@ -739,6 +745,38 @@ " os.path.join(TESTS, 'overall.csv'), index=True)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Running the simulation when attempting to determine an appropriate number of parameters\n", + "\n", + "Each of these should return the same output dataframe:\n", + "\n", + "```\n", + "_, man_df_simple = confidence_interval_method_simple(\n", + " replications=20, metric='mean_time_with_nurse')\n", + "```\n", + "\n", + "```\n", + "_, man_df = confidence_interval_method(\n", + " replications=20, metric='mean_time_with_nurse')\n", + "```\n", + "\n", + "```\n", + "observer = ReplicationTabulizer()\n", + "analyser = ReplicationsAlgorithm(\n", + " verbose=False,\n", + " observer=observer,\n", + " initial_replications=20,\n", + " replication_budget=20)\n", + "_ = analyser.select(runner=Runner(Param()), metric='mean_time_with_nurse')\n", + "auto_df = observer.summary_table().head(20)\n", + "```\n", + "\n", + "Hence, we will just run one, but will compare all against it in backtests." + ] + }, { "cell_type": "code", "execution_count": 10, @@ -748,7 +786,321 @@ "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 0s\n" + "Reached desired precision (0.05) in 5 replications.\n" + ] + }, + { + "data": { + "text/html": [ + "

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replicationsdatacumulative_meanstdevlower_ciupper_cideviation
019.8423809.842380NaNNaNNaNNaN
1210.0604249.951402NaNNaNNaNNaN
239.9250909.9426310.1100759.66918910.2160730.027502
349.9385049.9415990.0899009.79854910.0846500.014389
4510.0166119.9566020.0847759.85133910.0618640.010572
569.8838019.9444680.0814429.85900010.0299360.008595
6710.0405559.9581950.0827429.88167110.0347190.007685
7810.0866129.9742470.0890489.89980110.0486930.007464
8910.2028069.9996430.1128849.91287210.0864130.008677
91010.09323810.0090020.1104679.92997910.0880250.007895
101110.08269410.0157010.1071289.94373210.0876710.007186
11129.89560710.0056930.1078659.93715910.0742280.006850
12139.8088869.9905540.1168119.91996610.0611430.007065
13149.9328169.9864300.1132849.92102210.0518390.006550
141510.1658999.9983950.1185919.93272110.0640690.006568
15169.9752729.9969500.1147169.93582210.0580770.006115
16179.8093129.9859120.1200359.92419610.0476280.006180
17189.9880749.9860320.1164529.92812210.0439420.005799
18199.8017169.9763310.1208139.91810210.0345610.005837
19209.9233909.9736840.1181859.91837210.0289960.005546
\n", + "
" + ], + "text/plain": [ + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 9.842380 9.842380 NaN NaN NaN \n", + "1 2 10.060424 9.951402 NaN NaN NaN \n", + "2 3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", + "3 4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", + "4 5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", + "5 6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", + "6 7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", + "7 8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", + "8 9 10.202806 9.999643 0.112884 9.912872 10.086413 \n", + "9 10 10.093238 10.009002 0.110467 9.929979 10.088025 \n", + "10 11 10.082694 10.015701 0.107128 9.943732 10.087671 \n", + "11 12 9.895607 10.005693 0.107865 9.937159 10.074228 \n", + "12 13 9.808886 9.990554 0.116811 9.919966 10.061143 \n", + "13 14 9.932816 9.986430 0.113284 9.921022 10.051839 \n", + "14 15 10.165899 9.998395 0.118591 9.932721 10.064069 \n", + "15 16 9.975272 9.996950 0.114716 9.935822 10.058077 \n", + "16 17 9.809312 9.985912 0.120035 9.924196 10.047628 \n", + "17 18 9.988074 9.986032 0.116452 9.928122 10.043942 \n", + "18 19 9.801716 9.976331 0.120813 9.918102 10.034561 \n", + "19 20 9.923390 9.973684 0.118185 9.918372 10.028996 \n", + "\n", + " deviation \n", + "0 NaN \n", + "1 NaN \n", + "2 0.027502 \n", + "3 0.014389 \n", + "4 0.010572 \n", + "5 0.008595 \n", + "6 0.007685 \n", + "7 0.007464 \n", + "8 0.008677 \n", + "9 0.007895 \n", + "10 0.007186 \n", + "11 0.006850 \n", + "12 0.007065 \n", + "13 0.006550 \n", + "14 0.006568 \n", + "15 0.006115 \n", + "16 0.006180 \n", + "17 0.005799 \n", + "18 0.005837 \n", + "19 0.005546 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "_, man_df = confidence_interval_method(\n", + " replications=20, metric='mean_time_with_nurse')\n", + "\n", + "display(man_df)\n", + "\n", + "man_df.to_csv(\n", + " os.path.join(TESTS, 'replications.csv'), index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Run time" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Notebook run time: 0m 2s\n" ] } ], diff --git a/tests/exp_results/replications.csv b/tests/exp_results/replications.csv new file mode 100644 index 0000000..9cff384 --- /dev/null +++ b/tests/exp_results/replications.csv @@ -0,0 +1,21 @@ +replications,data,cumulative_mean,stdev,lower_ci,upper_ci,deviation +1,9.842379990825465,9.842379990825465,,,, +2,10.060423560536304,9.951401775680885,,,, +3,9.925089673744292,9.942631075035354,0.11007508865543447,9.669189396185518,10.216072753885191,0.02750194357873877 +4,9.938503720153147,9.941599236314802,0.08989962287415575,9.79854887498365,10.084649597645955,0.01438906939726725 +5,10.016611116790997,9.956601612410042,0.08477507350159925,9.851339455698028,10.061863769122056,0.010572096866948431 +6,9.883800932020227,9.944468165678407,0.08144184211630967,9.85900019398277,10.029936137374044,0.00859452413861758 +7,10.040555259165185,9.958194893319375,0.08274220649011921,9.881671115440534,10.034718671198215,0.007684502934379949 +8,10.086612424440187,9.974247084709477,0.08904839448371414,9.899800763883556,10.048693405535397,0.007463853681752782 +9,10.202806309000646,9.999642554075162,0.1128839371604415,9.91287227877846,10.086412829371865,0.00867733769757002 +10,10.093238146039786,10.009002113271624,0.11046688166466083,9.929978866589812,10.088025359953436,0.007895217304133596 +11,10.082693576415949,10.015701337193835,0.1071275611735933,9.943732062327035,10.087670612060636,0.007185645063071047 +12,9.895606898184578,10.005693467276398,0.10786520560721555,9.937159185316156,10.07422774923664,0.006849528439421251 +13,9.808885513355504,9.990554393897868,0.11681103296143085,9.919966193143505,10.061142594652232,0.007065493862630651 +14,9.932816012126937,9.986430223771373,0.11328432808933316,9.921021800077227,10.05183864746552,0.0065497302067409405 +15,10.165898811805276,9.998394796306966,0.11859147043224914,9.932721028994578,10.064068563619355,0.0065684310982244845 +16,9.97527206509048,9.996949625605936,0.11471598564431212,9.935821791738446,10.058077459473425,0.006114648583495752 +17,9.80931192832356,9.98591211400109,0.12003468577232226,9.92419597341084,10.047628254591341,0.006180320824546403 +18,9.988073964553942,9.986032216809582,0.11645186497858809,9.928122066759526,10.043942366859637,0.005799115083223496 +19,9.801716381189982,9.976331383355918,0.12081251236817435,9.918101616330164,10.034561150381672,0.005836791580811218 +20,9.92338975169577,9.973684301772911,0.11818464860447063,9.918372183608344,10.028996419937478,0.0055458059921482256 diff --git a/tests/test_backtest_replications.py b/tests/test_backtest_replications.py new file mode 100644 index 0000000..37fa354 --- /dev/null +++ b/tests/test_backtest_replications.py @@ -0,0 +1,73 @@ +"""Back Testing + +Back tests check that the model code produces results consistent with those +generated historically/from prior code. + +Licence: + This project is licensed under the MIT Licence. See the LICENSE file for + more details. + +Typical usage example: + + pytest +""" + +from pathlib import Path + +import pandas as pd +import pytest + +from simulation.model import Runner, Param +from simulation.replications import ( + confidence_interval_method, confidence_interval_method_simple, + ReplicationTabulizer, ReplicationsAlgorithm) + + +@pytest.mark.parametrize('ci_function', [ + confidence_interval_method, + confidence_interval_method_simple +]) +def test_cimethods(ci_function): + """ + Check that results from the manual confidence interval methods are + consistent with those generated previously. + + Arguments: + ci_function (function): + Function to run the manual confidence interval method. + """ + # Run the confidence interval method + _, cumulative_df = ci_function( + replications=20, metric='mean_time_with_nurse') + + # Import the expected results + exp_df = pd.read_csv( + Path(__file__).parent.joinpath('exp_results/replications.csv')) + + # Compare them + pd.testing.assert_frame_equal(cumulative_df, exp_df) + + +def test_algorithm(): + """ + Check that the ReplicationsAlgorithm produces results consistent with those + previously generated. + """ + # Run the algorithm + observer = ReplicationTabulizer() + analyser = ReplicationsAlgorithm( + verbose=False, + observer=observer, + initial_replications=20, + replication_budget=20) + _ = analyser.select(runner=Runner(Param()), metric='mean_time_with_nurse') + # Get first 20 rows (may have more if met precision and went into + # look ahead period beyond budget) + cumulative_df = observer.summary_table().head(20) + + # Import the expected results + exp_df = pd.read_csv( + Path(__file__).parent.joinpath('exp_results/replications.csv')) + + # Compare them + pd.testing.assert_frame_equal(cumulative_df, exp_df) From fe42a2d6d468612f9de7bc55a8de39d9abbb1ec7 Mon Sep 17 00:00:00 2001 From: amyheather Date: Thu, 13 Feb 2025 16:03:30 +0000 Subject: [PATCH 14/28] refactor(replications): simplify OnlineStatistics Remove _observers and notify(), and instead just save observer as class attribute and run update directly within update() --- simulation/replications.py | 30 +++++------------------------- 1 file changed, 5 insertions(+), 25 deletions(-) diff --git a/simulation/replications.py b/simulation/replications.py index 4534339..f387628 100644 --- a/simulation/replications.py +++ b/simulation/replications.py @@ -45,8 +45,8 @@ class OnlineStatistics: Sum of squared differences from the mean. alpha (float): Significance level for confidence interval calculations. - _observers (list): - List of observer objects. + observer (list): + object to notify on updates. """ def __init__(self, data=None, alpha=0.1, observer=None): """ @@ -65,11 +65,7 @@ def __init__(self, data=None, alpha=0.1, observer=None): self.mean = None self._sq = None self.alpha = alpha - self._observers = [] - - # If an observer is supplied, then add it to the observer list - if observer is not None: - self.register_observer(observer) + self.observer = observer # If an array of initial values are supplied, then run update() if data is not None: @@ -82,16 +78,6 @@ def __init__(self, data=None, alpha=0.1, observer=None): raise ValueError( f'data must be np.ndarray but is type {type(data)}') - def register_observer(self, observer): - """ - Registers an observer to be notified of updates. - - Arguments: - observer (object): - Observer to notify on updates. - """ - self._observers.append(observer) - def update(self, x): """ Running update of mean and variance implemented using Welford's @@ -114,14 +100,8 @@ def update(self, x): self._sq += (x - self.mean) * (x - updated_mean) self.mean = updated_mean - self.notify() - - def notify(self): - """ - Notify all registered observers about an update. - """ - for observer in self._observers: - observer.update(self) + # Run the observer update() method + self.observer.update(self) @property def variance(self): From cf5efb406ca8f0035f0dad88a7f247e473c9b6b3 Mon Sep 17 00:00:00 2001 From: amyheather Date: Fri, 14 Feb 2025 10:06:12 +0000 Subject: [PATCH 15/28] refactor(replications): convert ReplicationsAlgorithm to check multiple metrics --- notebooks/choosing_replications.ipynb | 543 ++++++++++------------ notebooks/generate_exp_results.ipynb | 4 +- simulation/replications.py | 164 ++++--- tests/test_backtest_replications.py | 26 +- tests/test_functionaltest_replications.py | 21 +- 5 files changed, 344 insertions(+), 414 deletions(-) diff --git a/notebooks/choosing_replications.ipynb b/notebooks/choosing_replications.ipynb index f1b4caa..826802a 100644 --- a/notebooks/choosing_replications.ipynb +++ b/notebooks/choosing_replications.ipynb @@ -65,9 +65,8 @@ "\n", "from simulation.model import Param, Runner\n", "from simulation.replications import (\n", - " ReplicationTabulizer, ReplicationsAlgorithm,\n", - " confidence_interval_method_simple, confidence_interval_method,\n", - " plotly_confidence_interval_method)" + " ReplicationsAlgorithm, confidence_interval_method_simple,\n", + " confidence_interval_method, plotly_confidence_interval_method)" ] }, { @@ -225,7 +224,7 @@ { "data": { "image/svg+xml": [ - "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" + "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" ] }, "metadata": {}, @@ -346,7 +345,7 @@ { "data": { "image/svg+xml": [ - "510152025303540455055lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" + "510152025303540455055lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" ] }, "metadata": {}, @@ -427,7 +426,7 @@ { "data": { "image/svg+xml": [ - "246810121416182049.449.649.85050.250.4lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "246810121416182049.449.649.85050.250.4lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -515,7 +514,7 @@ { "data": { "image/svg+xml": [ - "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" + "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" ] }, "metadata": {}, @@ -635,7 +634,7 @@ { "data": { "image/svg+xml": [ - "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" + "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" ] }, "metadata": {}, @@ -657,13 +656,6 @@ " metric_name='Mean wait time for nurse')" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Rounding is causing this... perhaps remove the rounding..." - ] - }, { "cell_type": "code", "execution_count": 12, @@ -722,7 +714,7 @@ { "data": { "image/svg+xml": [ - "24681012141618200.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "24681012141618200.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -748,38 +740,26 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Automated detection of appropriate number of replications" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "# Set up ReplicationTabulizer and ReplicationsAlgorithm\n", - "observer = ReplicationTabulizer()\n", - "analyser = ReplicationsAlgorithm(verbose=False, observer=observer)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ + "## Automated detection of appropriate number of replications\n", + "\n", "Run the algorithm (which will run model with increasing reps) for a few different metrics." ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 13, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "3\n" - ] + "data": { + "text/plain": [ + "{'mean_time_with_nurse': 3,\n", + " 'mean_q_time_nurse': 31,\n", + " 'mean_nurse_utilisation': 3}" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { "data": { @@ -809,6 +789,7 @@ " lower_ci\n", " upper_ci\n", " deviation\n", + " metric\n", " \n", " \n", " \n", @@ -821,6 +802,7 @@ " NaN\n", " NaN\n", " NaN\n", + " mean_time_with_nurse\n", " \n", " \n", " 1\n", @@ -831,6 +813,7 @@ " NaN\n", " NaN\n", " NaN\n", + " mean_time_with_nurse\n", " \n", " \n", " 2\n", @@ -841,6 +824,7 @@ " 9.669189\n", " 10.216073\n", " 0.027502\n", + " mean_time_with_nurse\n", " \n", " \n", " 3\n", @@ -851,6 +835,7 @@ " 9.798549\n", " 10.084650\n", " 0.014389\n", + " mean_time_with_nurse\n", " \n", " \n", " 4\n", @@ -861,6 +846,7 @@ " 9.851339\n", " 10.061864\n", " 0.010572\n", + " mean_time_with_nurse\n", " \n", " \n", " 5\n", @@ -871,6 +857,7 @@ " 9.859000\n", " 10.029936\n", " 0.008595\n", + " mean_time_with_nurse\n", " \n", " \n", " 6\n", @@ -881,6 +868,7 @@ " 9.881671\n", " 10.034719\n", " 0.007685\n", + " mean_time_with_nurse\n", " \n", " \n", " 7\n", @@ -891,98 +879,8 @@ " 9.899801\n", " 10.048693\n", " 0.007464\n", + " mean_time_with_nurse\n", " \n", - " \n", - "\n", - "" - ], - "text/plain": [ - " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 9.842380 9.842380 NaN NaN NaN \n", - "1 2 10.060424 9.951402 NaN NaN NaN \n", - "2 3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", - "3 4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", - "4 5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", - "5 6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", - "6 7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", - "7 8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", - "\n", - " deviation \n", - "0 NaN \n", - "1 NaN \n", - "2 0.027502 \n", - "3 0.014389 \n", - "4 0.010572 \n", - "5 0.008595 \n", - "6 0.007685 \n", - "7 0.007464 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/svg+xml": [ - "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: mean_time_with_nurse" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n_reps = analyser.select(runner=Runner(Param()),\n", - " metric='mean_time_with_nurse')\n", - "print(n_reps)\n", - "display(observer.summary_table())\n", - "plotly_confidence_interval_method(\n", - " n_reps, observer.summary_table(), metric_name='mean_time_with_nurse')" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "31\n" - ] - }, - { - "data": { - "text/html": [ - "
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replicationsdatacumulative_meanstdevlower_ciupper_cideviation
01NaNNaNNaNmean_q_time_nurse
1NaNNaNNaNmean_q_time_nurse
20.4799450.5530000.070725mean_q_time_nurse
30.4823390.5379210.054479mean_q_time_nurse
40.4686560.5328530.064100mean_q_time_nurse
50.4292720.5354840.110092mean_q_time_nurse
60.4382360.5239200.089054mean_q_time_nurse
70.4447340.5500280.105848mean_q_time_nurse
80.4498050.5408560.091909mean_q_time_nurse
90.4593510.5451930.085453mean_q_time_nurse
100.4678270.5492290.080036mean_q_time_nurse
110.4565890.5412770.084869mean_q_time_nurse
120.4551120.5343650.080096mean_q_time_nurse
130.4542800.5284700.075493mean_q_time_nurse
140.4610430.5381700.077188mean_q_time_nurse
150.4610200.5334310.072816mean_q_time_nurse
160.4580410.5280010.070950mean_q_time_nurse
170.4595080.5252120.066724mean_q_time_nurse
180.4527380.5197490.068908mean_q_time_nurse
190.4511580.5156120.066670mean_q_time_nurse
200.4564920.5237190.068584mean_q_time_nurse
210.4592080.5232880.065222mean_q_time_nurse
220.4620420.5234100.062274mean_q_time_nurse
230.4644790.5232820.059531mean_q_time_nurse
240.4666950.5231270.057012mean_q_time_nurse
250.4710890.5297890.058648mean_q_time_nurse
260.4727580.5291740.056307mean_q_time_nurse
270.4739160.5281820.054153mean_q_time_nurse
280.4734050.5259750.052603mean_q_time_nurse
290.4758190.5270180.051055mean_q_time_nurse
300.4769600.5264210.049294mean_q_time_nurse
310.4774310.5252770.047716mean_q_time_nurse
320.4796580.5265030.046559mean_q_time_nurse
330.4815520.5272660.045314mean_q_time_nurse
340.4807460.5254130.044393mean_q_time_nurse
350.4802270.5238040.043402mean_q_time_nurse
\n", - "
" - ], - "text/plain": [ - " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 0.501873 0.501873 NaN NaN NaN \n", - "1 2 0.516264 0.509069 NaN NaN NaN \n", - "2 3 0.531280 0.516472 0.014704 0.479945 0.553000 \n", - "3 4 0.491104 0.510130 0.017465 0.482339 0.537921 \n", - "4 5 0.463252 0.500755 0.025851 0.468656 0.532853 \n", - "5 6 0.390495 0.482378 0.050605 0.429272 0.535484 \n", - "6 7 0.473277 0.481078 0.046323 0.438236 0.523920 \n", - "7 8 0.611502 0.497381 0.062973 0.444734 0.550028 \n", - "8 9 0.478926 0.495330 0.059226 0.449805 0.540856 \n", - "9 10 0.564747 0.502272 0.059999 0.459351 0.545193 \n", - "10 11 0.571089 0.508528 0.060584 0.467827 0.549229 \n", - "11 12 0.393387 0.498933 0.066645 0.456589 0.541277 \n", - "12 13 0.444403 0.494738 0.065575 0.455112 0.534365 \n", - "13 14 0.447649 0.491375 0.064247 0.454280 0.528470 \n", - "14 15 0.614848 0.499606 0.069637 0.461043 0.538170 \n", - "15 16 0.461512 0.497225 0.067946 0.461020 0.533431 \n", - "16 17 0.425752 0.493021 0.068034 0.458041 0.528001 \n", - "17 18 0.481117 0.492360 0.066062 0.459508 0.525212 \n", - "18 19 0.376149 0.486243 0.069517 0.452738 0.519749 \n", - "19 20 0.429078 0.483385 0.068859 0.451158 0.515612 \n", - "20 21 0.624518 0.490106 0.073845 0.456492 0.523719 \n", - "21 22 0.515244 0.491248 0.072264 0.459208 0.523288 \n", - "22 23 0.525237 0.492726 0.070957 0.462042 0.523410 \n", - "23 24 0.520427 0.493880 0.069628 0.464479 0.523282 \n", - "24 25 0.519650 0.494911 0.068356 0.466695 0.523127 \n", - "25 26 0.638633 0.500439 0.072664 0.471089 0.529789 \n", - "26 27 0.514672 0.500966 0.071306 0.472758 0.529174 \n", - "27 28 0.503287 0.501049 0.069974 0.473916 0.528182 \n", - "28 29 0.461636 0.499690 0.069102 0.473405 0.525975 \n", - "29 30 0.551549 0.501419 0.068557 0.475819 0.527018 \n", - "30 31 0.509837 0.501690 0.067422 0.476960 0.526421 \n", - "31 32 0.490932 0.501354 0.066353 0.477431 0.525277 \n", - "32 33 0.558335 0.503081 0.066057 0.479658 0.526503 \n", - "33 34 0.548240 0.504409 0.065508 0.481552 0.527266 \n", - "34 35 0.457886 0.503080 0.065014 0.480746 0.525413 \n", - "35 36 0.464776 0.502016 0.064396 0.480227 0.523804 \n", - "\n", - " deviation \n", - "0 NaN \n", - "1 NaN \n", - "2 0.070725 \n", - "3 0.054479 \n", - "4 0.064100 \n", - "5 0.110092 \n", - "6 0.089054 \n", - "7 0.105848 \n", - "8 0.091909 \n", - "9 0.085453 \n", - "10 0.080036 \n", - "11 0.084869 \n", - "12 0.080096 \n", - "13 0.075493 \n", - "14 0.077188 \n", - "15 0.072816 \n", - "16 0.070950 \n", - "17 0.066724 \n", - "18 0.068908 \n", - "19 0.066670 \n", - "20 0.068584 \n", - "21 0.065222 \n", - "22 0.062274 \n", - "23 0.059531 \n", - "24 0.057012 \n", - "25 0.058648 \n", - "26 0.056307 \n", - "27 0.054153 \n", - "28 0.052603 \n", - "29 0.051055 \n", - "30 0.049294 \n", - "31 0.047716 \n", - "32 0.046559 \n", - "33 0.045314 \n", - "34 0.044393 \n", - "35 0.043402 " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "image/svg+xml": [ - "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: mean_q_time_nurse" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "observer = ReplicationTabulizer()\n", - "analyser = ReplicationsAlgorithm(verbose=False, observer=observer)\n", - "n_reps = analyser.select(runner=Runner(Param()),\n", - " metric='mean_q_time_nurse')\n", - "print(n_reps)\n", - "display(observer.summary_table())\n", - "plotly_confidence_interval_method(\n", - " n_reps, observer.summary_table(), metric_name='mean_q_time_nurse')" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "3\n" - ] - }, - { - "data": { - "text/html": [ - "
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replicationsdatacumulative_meanstdevlower_ciupper_cideviation
01NaNNaNNaNmean_nurse_utilisation
1NaNNaNNaNmean_nurse_utilisation
20.4950210.5047530.009734mean_nurse_utilisation
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70.4963050.5023190.006022mean_nurse_utilisation
\n", "
" ], "text/plain": [ - " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 0.499589 0.499589 NaN NaN NaN \n", - "1 2 0.501977 0.500783 NaN NaN NaN \n", - "2 3 0.498094 0.499887 0.001959 0.495021 0.504753 \n", - "3 4 0.498249 0.499477 0.001797 0.496618 0.502336 \n", - "4 5 0.496934 0.498969 0.001928 0.496575 0.501362 \n", - "5 6 0.492854 0.497949 0.003034 0.494766 0.501133 \n", - "6 7 0.503386 0.498726 0.003448 0.495537 0.501915 \n", - "7 8 0.503411 0.499312 0.003597 0.496305 0.502319 \n", + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 9.842380 9.842380 NaN NaN NaN \n", + "1 2 10.060424 9.951402 NaN NaN NaN \n", + "2 3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", + "3 4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", + "4 5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", + "5 6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", + "6 7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", + "7 8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", + "0 1 0.501873 0.501873 NaN NaN NaN \n", + "1 2 0.516264 0.509069 NaN NaN NaN \n", + "2 3 0.531280 0.516472 0.014704 0.479945 0.553000 \n", + "3 4 0.491104 0.510130 0.017465 0.482339 0.537921 \n", + "4 5 0.463252 0.500755 0.025851 0.468656 0.532853 \n", + "5 6 0.390495 0.482378 0.050605 0.429272 0.535484 \n", + "6 7 0.473277 0.481078 0.046323 0.438236 0.523920 \n", + "7 8 0.611502 0.497381 0.062973 0.444734 0.550028 \n", + "8 9 0.478926 0.495330 0.059226 0.449805 0.540856 \n", + "9 10 0.564747 0.502272 0.059999 0.459351 0.545193 \n", + "10 11 0.571089 0.508528 0.060584 0.467827 0.549229 \n", + "11 12 0.393387 0.498933 0.066645 0.456589 0.541277 \n", + "12 13 0.444403 0.494738 0.065575 0.455112 0.534365 \n", + "13 14 0.447649 0.491375 0.064247 0.454280 0.528470 \n", + "14 15 0.614848 0.499606 0.069637 0.461043 0.538170 \n", + "15 16 0.461512 0.497225 0.067946 0.461020 0.533431 \n", + "16 17 0.425752 0.493021 0.068034 0.458041 0.528001 \n", + "17 18 0.481117 0.492360 0.066062 0.459508 0.525212 \n", + "18 19 0.376149 0.486243 0.069517 0.452738 0.519749 \n", + "19 20 0.429078 0.483385 0.068859 0.451158 0.515612 \n", + "20 21 0.624518 0.490106 0.073845 0.456492 0.523719 \n", + "21 22 0.515244 0.491248 0.072264 0.459208 0.523288 \n", + "22 23 0.525237 0.492726 0.070957 0.462042 0.523410 \n", + "23 24 0.520427 0.493880 0.069628 0.464479 0.523282 \n", + "24 25 0.519650 0.494911 0.068356 0.466695 0.523127 \n", + "25 26 0.638633 0.500439 0.072664 0.471089 0.529789 \n", + "26 27 0.514672 0.500966 0.071306 0.472758 0.529174 \n", + "27 28 0.503287 0.501049 0.069974 0.473916 0.528182 \n", + "28 29 0.461636 0.499690 0.069102 0.473405 0.525975 \n", + "29 30 0.551549 0.501419 0.068557 0.475819 0.527018 \n", + "30 31 0.509837 0.501690 0.067422 0.476960 0.526421 \n", + "31 32 0.490932 0.501354 0.066353 0.477431 0.525277 \n", + "32 33 0.558335 0.503081 0.066057 0.479658 0.526503 \n", + "33 34 0.548240 0.504409 0.065508 0.481552 0.527266 \n", + "34 35 0.457886 0.503080 0.065014 0.480746 0.525413 \n", + "35 36 0.464776 0.502016 0.064396 0.480227 0.523804 \n", + "0 1 0.499589 0.499589 NaN NaN NaN \n", + "1 2 0.501977 0.500783 NaN NaN NaN \n", + "2 3 0.498094 0.499887 0.001959 0.495021 0.504753 \n", + "3 4 0.498249 0.499477 0.001797 0.496618 0.502336 \n", + "4 5 0.496934 0.498969 0.001928 0.496575 0.501362 \n", + "5 6 0.492854 0.497949 0.003034 0.494766 0.501133 \n", + "6 7 0.503386 0.498726 0.003448 0.495537 0.501915 \n", + "7 8 0.503411 0.499312 0.003597 0.496305 0.502319 \n", "\n", - " deviation \n", - "0 NaN \n", - "1 NaN \n", - "2 0.009734 \n", - "3 0.005725 \n", - "4 0.004797 \n", - "5 0.006394 \n", - "6 0.006395 \n", - "7 0.006022 " + " deviation metric \n", + "0 NaN mean_time_with_nurse \n", + "1 NaN mean_time_with_nurse \n", + "2 0.027502 mean_time_with_nurse \n", + "3 0.014389 mean_time_with_nurse \n", + "4 0.010572 mean_time_with_nurse \n", + "5 0.008595 mean_time_with_nurse \n", + "6 0.007685 mean_time_with_nurse \n", + "7 0.007464 mean_time_with_nurse \n", + "0 NaN mean_q_time_nurse \n", + "1 NaN mean_q_time_nurse \n", + "2 0.070725 mean_q_time_nurse \n", + "3 0.054479 mean_q_time_nurse \n", + "4 0.064100 mean_q_time_nurse \n", + "5 0.110092 mean_q_time_nurse \n", + "6 0.089054 mean_q_time_nurse \n", + "7 0.105848 mean_q_time_nurse \n", + "8 0.091909 mean_q_time_nurse \n", + "9 0.085453 mean_q_time_nurse \n", + "10 0.080036 mean_q_time_nurse \n", + "11 0.084869 mean_q_time_nurse \n", + "12 0.080096 mean_q_time_nurse \n", + "13 0.075493 mean_q_time_nurse \n", + "14 0.077188 mean_q_time_nurse \n", + "15 0.072816 mean_q_time_nurse \n", + "16 0.070950 mean_q_time_nurse \n", + "17 0.066724 mean_q_time_nurse \n", + "18 0.068908 mean_q_time_nurse \n", + "19 0.066670 mean_q_time_nurse \n", + "20 0.068584 mean_q_time_nurse \n", + "21 0.065222 mean_q_time_nurse \n", + "22 0.062274 mean_q_time_nurse \n", + "23 0.059531 mean_q_time_nurse \n", + "24 0.057012 mean_q_time_nurse \n", + "25 0.058648 mean_q_time_nurse \n", + "26 0.056307 mean_q_time_nurse \n", + "27 0.054153 mean_q_time_nurse \n", + "28 0.052603 mean_q_time_nurse \n", + "29 0.051055 mean_q_time_nurse \n", + "30 0.049294 mean_q_time_nurse \n", + "31 0.047716 mean_q_time_nurse \n", + "32 0.046559 mean_q_time_nurse \n", + "33 0.045314 mean_q_time_nurse \n", + "34 0.044393 mean_q_time_nurse \n", + "35 0.043402 mean_q_time_nurse \n", + "0 NaN mean_nurse_utilisation \n", + "1 NaN mean_nurse_utilisation \n", + "2 0.009734 mean_nurse_utilisation \n", + "3 0.005725 mean_nurse_utilisation \n", + "4 0.004797 mean_nurse_utilisation \n", + "5 0.006394 mean_nurse_utilisation \n", + "6 0.006395 mean_nurse_utilisation \n", + "7 0.006022 mean_nurse_utilisation " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Choose metrics to assess\n", + "test_metrics = [\n", + " 'mean_time_with_nurse', 'mean_q_time_nurse', 'mean_nurse_utilisation']\n", + "\n", + "# Set up and run the algorithm\n", + "analyser = ReplicationsAlgorithm()\n", + "solutions, cumulative = analyser.select(runner=Runner(Param()),\n", + " metrics=test_metrics)\n", + "\n", + "# View results\n", + "display(solutions)\n", + "display(cumulative)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create plots" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -1604,7 +1531,7 @@ { "data": { "image/svg+xml": [ - "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: mean_nurse_utilisation" + "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -1612,14 +1539,12 @@ } ], "source": [ - "observer = ReplicationTabulizer()\n", - "analyser = ReplicationsAlgorithm(verbose=False, observer=observer)\n", - "n_reps = analyser.select(runner=Runner(Param()),\n", - " metric='mean_nurse_utilisation')\n", - "print(n_reps)\n", - "display(observer.summary_table())\n", - "plotly_confidence_interval_method(\n", - " n_reps, observer.summary_table(), metric_name='mean_nurse_utilisation')" + "for metric in test_metrics:\n", + " p = plotly_confidence_interval_method(\n", + " n_reps = solutions[metric],\n", + " conf_ints = cumulative[cumulative['metric'] == metric],\n", + " metric_name=LABELS[metric])\n", + " p.show()" ] }, { @@ -1631,14 +1556,14 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 15s\n" + "Notebook run time: 0m 14s\n" ] } ], diff --git a/notebooks/generate_exp_results.ipynb b/notebooks/generate_exp_results.ipynb index dae5f8e..a83c037 100644 --- a/notebooks/generate_exp_results.ipynb +++ b/notebooks/generate_exp_results.ipynb @@ -57,9 +57,7 @@ "from IPython.display import display\n", "\n", "from simulation.model import Param, Runner\n", - "from simulation.replications import (\n", - " ReplicationsAlgorithm, ReplicationTabulizer, confidence_interval_method,\n", - " confidence_interval_method_simple)" + "from simulation.replications import confidence_interval_method" ] }, { diff --git a/simulation/replications.py b/simulation/replications.py index f387628..76ce8cb 100644 --- a/simulation/replications.py +++ b/simulation/replications.py @@ -241,7 +241,6 @@ def summary_table(self): return results -# pylint: disable=too-many-instance-attributes,too-few-public-methods class ReplicationsAlgorithm: """ Implements an adaptive replication algorithm for selecting the @@ -271,17 +270,8 @@ class ReplicationsAlgorithm: replication_budget (int): Maximum allowed replications. Use for larger models where replication runtime is a constraint. - verbose (bool): - Whether to print progress information. n (int): Current number of replications performed. - _n_solution (int): - Final determined number of replications required for maintained - precision. - observer (object): - Observer to notify on updates. - stats (OnlineStatistics): - Instance of OnlineStatistics, set in the select() method. """ # pylint: disable=too-many-arguments,too-many-positional-arguments def __init__( @@ -290,9 +280,7 @@ def __init__( half_width_precision=0.05, initial_replications=3, look_ahead=5, - replication_budget=1000, - verbose=False, - observer=None, + replication_budget=1000 ): """ Initialise an instance of the ReplicationsAlgorithm. @@ -304,11 +292,9 @@ def __init__( self.initial_replications = initial_replications self.look_ahead = look_ahead self.replication_budget = replication_budget - self.verbose = verbose + + # Initially set n to number of initial replications self.n = self.initial_replications - self._n_solution = self.replication_budget - self.observer = observer - self.stats = None # Check validity of provided parameters self.valid_inputs() @@ -340,7 +326,7 @@ def _klimit(self): """ return int((self.look_ahead / 100) * max(self.n, 100)) - def select(self, runner, metric): + def select(self, runner, metrics): """ Executes the replication algorithm, determining the necessary number of replications to achieve and maintain the desired precision. @@ -348,79 +334,109 @@ def select(self, runner, metric): Arguments: runner (Runner): An instance of Runner which executes the model replications. - metric (str): - Name of the performance measure being tracked. + metrics (list): + List of performance measure to track (should correspond to + column names from the run results dataframe). Returns: int: The determined number of replications needed for precision. """ + # Create instances of observers and stats for each metric + observers = { + metric: ReplicationTabulizer() + for metric in metrics} + stats = { + metric: OnlineStatistics( + alpha=self.alpha, observer=observers[metric]) + for metric in metrics + } - converged = False + # Empty dictionary to store record for each metric of: + # - nreps (the solution - replications required for precision) + # - target_met (record of how many times in a row the target has + # has been met, used to check if lookahead period has been passed) + # - solved (whether it has maintained precision for lookahead) + solutions = { + metric: {'nreps': None, + 'target_met': 0, + 'solved': False} for metric in metrics} + + # Run initial replications and get results for each metric + for rep in range(self.initial_replications): + results = runner.run_single(rep)['run'] + for metric in metrics: + stats[metric].update(results[metric]) + + # If any have met precision, add solution and update count + if stats[metric].deviation <= self.half_width_precision: + solutions[metric]['nreps'] = self.n + solutions[metric]['target_met'] = 1 + + # If there is no lookahead, mark as solved + if self._klimit() == 0: + solutions[metric]['solved'] = True + + # Whilst under replication budget + lookahead, and have not yet + # got all metrics marked as solved = TRUE... + while ( + sum(1 for v in solutions.values() + if v['solved']) < len(metrics) + and self.n <= self.replication_budget + self._klimit() + ): + + # Run another replication + results = runner.run_single(self.n)['run'] + + # Increment counter + self.n += 1 - # Run initial replications and collect results for the specified metric - x_i = [runner.run_single(rep)['run'][metric] - for rep in range(self.initial_replications)] + # Loop through the metrics... + for metric in metrics: - # Using those results, initialise running mean and std dev - self.stats = OnlineStatistics( - data=np.array(x_i), alpha=self.alpha, observer=self.observer - ) + # If it is not yet solved... + if not solutions[metric]['solved']: - while not converged and self.n <= self.replication_budget: + # Update the running mean and stdev for that metric + stats[metric].update(results[metric]) - # If after initial replications, update running mean and std dev - if self.n > self.initial_replications: - self.stats.update(x_i) + # If precision has been achieved... + if stats[metric].deviation <= self.half_width_precision: - # If precision has been achieved... - if self.stats.deviation <= self.half_width_precision: + # Check if target met the time prior - if not, record + # the solution. + if solutions[metric]['target_met'] == 0: + solutions[metric]['nreps'] = self.n - # Store current solution - self._n_solution = self.n - converged = True + # Update how many times precision has been met in a row + solutions[metric]['target_met'] += 1 - # If the lookahead period is greater than 0 - if self._klimit() > 0: + # Mark as solved if have finished lookahead period + if solutions[metric]['target_met'] > self._klimit(): + solutions[metric]['solved'] = True - # Start a new counter - k = 1 + # If precision was not achieved, ensure nreps is None + # (e.g. in cases where precision is lost after a success) + else: + solutions[metric]['nreps'] = None - # Whilst the precision is achieved and k is less than - # lookahead... - while converged and k <= self._klimit(): - if self.verbose: - print(f'{self.n+k}', end=', ') + # Extract minimum replications for each metric + nreps = {metric: value['nreps'] for metric, value in solutions.items()} - # Run another replication and update the statistics - x_i = runner.run_single(run=self.n+k-1)['run'][metric] - self.stats.update(x_i) - - # If precision lost, set converged to false and update - # the main counter (n) - if self.stats.deviation > self.half_width_precision: - converged = False - self.n += k - # If precision is maintained, update k. - else: - k += 1 + # Combine observer summary frames into a single table + summary_frame = pd.concat( + [observer.summary_table().assign(metric=metric) + for metric, observer in observers.items()] + ) - # If past lookahead and still converged, return _n_solution. - if converged: - return self._n_solution + # Extract any metrics that were not solved and return warning + if None in nreps.values(): + unsolved = [k for k, v in nreps.items() if v is None] + warnings.warn( + 'WARNING: the replications did not reach the desired ' + + f'precision for the following metrics: {unsolved}.') - # Precision not achieved/maintained, so run another replication - self.n += 1 - if self.verbose: - print(f'{self.n}', end=', ') - x_i = runner.run_single(run=self.n-1)['run'][metric] - - # If not converged and pass replication_budget, end with warning - warnings.warn( - f'''Algorithm did not converge for metric '{metric}'. ''' - + 'Returning replication budget as solution' - ) - return self._n_solution + return nreps, summary_frame def confidence_interval_method( @@ -654,7 +670,7 @@ def plotly_confidence_interval_method( fig.update_layout( width=figsize[0], height=figsize[1], - yaxis_title=f'Cumulative Mean: {metric_name}', + yaxis_title=f'Cumulative Mean:\n{metric_name}', hovermode='x unified', showlegend=True, ) diff --git a/tests/test_backtest_replications.py b/tests/test_backtest_replications.py index 37fa354..609f258 100644 --- a/tests/test_backtest_replications.py +++ b/tests/test_backtest_replications.py @@ -20,7 +20,7 @@ from simulation.model import Runner, Param from simulation.replications import ( confidence_interval_method, confidence_interval_method_simple, - ReplicationTabulizer, ReplicationsAlgorithm) + ReplicationsAlgorithm) @pytest.mark.parametrize('ci_function', [ @@ -53,21 +53,17 @@ def test_algorithm(): Check that the ReplicationsAlgorithm produces results consistent with those previously generated. """ - # Run the algorithm - observer = ReplicationTabulizer() - analyser = ReplicationsAlgorithm( - verbose=False, - observer=observer, - initial_replications=20, - replication_budget=20) - _ = analyser.select(runner=Runner(Param()), metric='mean_time_with_nurse') - # Get first 20 rows (may have more if met precision and went into - # look ahead period beyond budget) - cumulative_df = observer.summary_table().head(20) - # Import the expected results exp_df = pd.read_csv( Path(__file__).parent.joinpath('exp_results/replications.csv')) - # Compare them - pd.testing.assert_frame_equal(cumulative_df, exp_df) + # Run the algorithm + analyser = ReplicationsAlgorithm(initial_replications=20, + replication_budget=20) + _, summary_table = analyser.select( + runner=Runner(Param()), metrics=['mean_time_with_nurse']) + + # Get first 20 rows (may have more if met precision and went into + # look ahead period beyond budget), and compare dataframes + pd.testing.assert_frame_equal( + summary_table.drop(['metric'], axis=1).head(20), exp_df) diff --git a/tests/test_functionaltest_replications.py b/tests/test_functionaltest_replications.py index 3421c25..f950390 100644 --- a/tests/test_functionaltest_replications.py +++ b/tests/test_functionaltest_replications.py @@ -18,7 +18,7 @@ from simulation.model import Param, Runner from simulation.replications import ( confidence_interval_method, confidence_interval_method_simple, - ReplicationTabulizer, ReplicationsAlgorithm) + ReplicationsAlgorithm) @pytest.mark.parametrize('ci_function', [ @@ -95,16 +95,11 @@ def test_consistent_outputs(ci_function): replications=reps, metric='mean_time_with_nurse') # Run the algorithm - observer = ReplicationTabulizer() - analyser = ReplicationsAlgorithm( - verbose=False, - observer=observer, - initial_replications=reps, - replication_budget=reps) - _ = analyser.select(runner=Runner(Param()), metric='mean_time_with_nurse') + analyser = ReplicationsAlgorithm(initial_replications=reps, + replication_budget=reps) + _, summary_table = analyser.select(runner=Runner(Param()), + metrics=['mean_time_with_nurse']) # Get first 20 rows (may have more if met precision and went into - # look ahead period beyond budget) - auto_df = observer.summary_table().head(20) - - # Compare the dataframes - pd.testing.assert_frame_equal(man_df, auto_df) + # look ahead period beyond budget) and compare dataframes + pd.testing.assert_frame_equal( + man_df, summary_table.drop(['metric'], axis=1).head(20)) From 79839b923a74ba75032000fca9904c3d9e0b7c14 Mon Sep 17 00:00:00 2001 From: amyheather Date: Fri, 14 Feb 2025 10:36:28 +0000 Subject: [PATCH 16/28] tests(replications): add more unit tests --- tests/test_unittest_replications.py | 103 +++++++++++++++++++++++++++- 1 file changed, 102 insertions(+), 1 deletion(-) diff --git a/tests/test_unittest_replications.py b/tests/test_unittest_replications.py index fb206f5..aead7c2 100644 --- a/tests/test_unittest_replications.py +++ b/tests/test_unittest_replications.py @@ -13,10 +13,14 @@ pytest """ +import numpy as np import pandas as pd import pytest +import scipy.stats as st +from unittest.mock import MagicMock -from simulation.replications import ReplicationsAlgorithm, OnlineStatistics +from simulation.replications import ( + ReplicationsAlgorithm, OnlineStatistics, ReplicationTabulizer) # pylint: disable=protected-access @@ -79,3 +83,100 @@ def test_onlinestat_data(): """ with pytest.raises(ValueError): OnlineStatistics(data=pd.Series([9, 2, 3])) + + +def test_onlinestat_computations(): + """ + Feed three values into OnlineStatistics and verify mean, standard + deviation, confidence intervals, and deviation calculations. + """ + values = [10, 20, 30] + + # Provide three values + stats = OnlineStatistics(data=np.array(values), alpha=0.05, observer=None) + + # Expected values + expected_mean = np.mean(values) + expected_std = np.std(values, ddof=1) + expected_lci, expected_uci = st.t.interval( + confidence=0.95, + df=len(values)-1, + loc=np.mean(values), + scale=st.sem(values) + ) + expected_dev = ((expected_uci - expected_mean) / expected_mean) + + # Assertions + assert np.isclose(stats.mean, expected_mean), ( + f'Expected mean {expected_mean}, got {stats.mean}') + assert np.isclose(stats.std, expected_std), ( + f'Expected std dev {expected_std}, got {stats.std}') + assert np.isclose(stats.lci, expected_lci), ( + f'Expected lower confidence interval {expected_lci}, got {stats.lci}') + assert np.isclose(stats.uci, expected_uci), ( + f'Expected upper confidence interval {expected_uci}, got {stats.uci}') + assert np.isclose(stats.deviation, expected_dev), ( + f'Expected deviation {expected_dev}, got {stats.deviation}') + + +def test_tabulizer_initial_state(): + """ + Test that ReplicationTabulizer initializes with empty lists and n = 0. + """ + tab = ReplicationTabulizer() + assert tab.n == 0 + assert tab.x_i == [] + assert tab.cumulative_mean == [] + assert tab.stdev == [] + assert tab.lower == [] + assert tab.upper == [] + assert tab.dev == [] + + +def test_tabulizer_update(): + """ + Test that update correctly appends new statistical data. + """ + tab = ReplicationTabulizer() + mock_results = MagicMock() + mock_results.x_i = 10 + mock_results.mean = 5.5 + mock_results.std = 1.2 + mock_results.lci = 4.8 + mock_results.uci = 6.2 + mock_results.deviation = 0.1 + + tab.update(mock_results) + + assert tab.n == 1 + assert tab.x_i == [10] + assert tab.cumulative_mean == [5.5] + assert tab.stdev == [1.2] + assert tab.lower == [4.8] + assert tab.upper == [6.2] + assert tab.dev == [0.1] + + +def test_tabulizer_summary_table(): + """ + Test that summary_table returns a properly formatted DataFrame. + """ + tab = ReplicationTabulizer() + tab.n = 3 + tab.x_i = [10, 20, 30] + tab.cumulative_mean = [5, 10, 15] + tab.stdev = [1, 2, 3] + tab.lower = [3, 8, 13] + tab.upper = [7, 12, 17] + tab.dev = [0.1, 0.2, 0.3] + + df = tab.summary_table() + + assert isinstance(df, pd.DataFrame) + assert len(df) == 3 + assert df['data'].tolist() == [10, 20, 30] + assert df['cumulative_mean'].tolist() == [5, 10, 15] + assert df['stdev'].tolist() == [1, 2, 3] + assert df['lower_ci'].tolist() == [3, 8, 13] + assert df['upper_ci'].tolist() == [7, 12, 17] + assert df['deviation'].tolist() == [0.1, 0.2, 0.3] From fd7487707f7da5a0a58778e639046d9a4582c187 Mon Sep 17 00:00:00 2001 From: amyheather Date: Fri, 14 Feb 2025 11:29:00 +0000 Subject: [PATCH 17/28] feat(replications): convert maual methods to check multiple metrics Also, renamed the output plots to more meaningful names --- notebooks/choosing_replications.ipynb | 971 +++++++++--------- outputs/choose_param_conf_int_1.png | Bin 46631 -> 0 bytes outputs/choose_param_conf_int_2.png | Bin 51979 -> 0 bytes outputs/choose_param_conf_int_3.png | Bin 53890 -> 0 bytes .../choose_reps_mean_nurse_utilisation.png | Bin 0 -> 56478 bytes outputs/choose_reps_mean_q_time_nurse.png | Bin 0 -> 53914 bytes outputs/choose_reps_mean_time_with_nurse.png | Bin 0 -> 50182 bytes simulation/replications.py | 215 ++-- tests/test_backtest_replications.py | 4 +- tests/test_functionaltest_replications.py | 10 +- 10 files changed, 629 insertions(+), 571 deletions(-) delete mode 100644 outputs/choose_param_conf_int_1.png delete mode 100644 outputs/choose_param_conf_int_2.png delete mode 100644 outputs/choose_param_conf_int_3.png create mode 100644 outputs/choose_reps_mean_nurse_utilisation.png create mode 100644 outputs/choose_reps_mean_q_time_nurse.png create mode 100644 outputs/choose_reps_mean_time_with_nurse.png diff --git a/notebooks/choosing_replications.ipynb b/notebooks/choosing_replications.ipynb index 826802a..865516b 100644 --- a/notebooks/choosing_replications.ipynb +++ b/notebooks/choosing_replications.ipynb @@ -122,7 +122,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Define labels for variables in the dataset." + "Define labels for variables in the dataset, and metrics to use in assessments." ] }, { @@ -145,7 +145,10 @@ " 'number_of_nurses': 'Number of nurses',\n", " 'utilisation': 'Utilisation',\n", " 'running_mean_wait_time': 'Running mean nurse wait time (minutes)'\n", - "}" + "}\n", + "\n", + "METRICS = [\n", + " 'mean_time_with_nurse', 'mean_q_time_nurse', 'mean_nurse_utilisation']" ] }, { @@ -175,56 +178,226 @@ "name": "stdout", "output_type": "stream", "text": [ - "Reached desired precision (0.05) in 6 replications.\n", - " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 9.842380 9.842380 NaN NaN NaN \n", - "1 2 10.060424 9.951402 NaN NaN NaN \n", - "2 3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", - "3 4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", - "4 5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", - "5 6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", - "6 7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", - "7 8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", - "8 9 10.202806 9.999643 0.112884 9.912872 10.086413 \n", - "9 10 10.093238 10.009002 0.110467 9.929979 10.088025 \n", - "10 11 10.082694 10.015701 0.107128 9.943732 10.087671 \n", - "11 12 9.895607 10.005693 0.107865 9.937159 10.074228 \n", - "12 13 9.808886 9.990554 0.116811 9.919966 10.061143 \n", - "13 14 9.932816 9.986430 0.113284 9.921022 10.051839 \n", - "14 15 10.165899 9.998395 0.118591 9.932721 10.064069 \n", - "15 16 9.975272 9.996950 0.114716 9.935822 10.058077 \n", - "16 17 9.809312 9.985912 0.120035 9.924196 10.047628 \n", - "17 18 9.988074 9.986032 0.116452 9.928122 10.043942 \n", - "18 19 9.801716 9.976331 0.120813 9.918102 10.034561 \n", - "19 20 9.923390 9.973684 0.118185 9.918372 10.028996 \n", - "\n", - " deviation \n", - "0 NaN \n", - "1 NaN \n", - "2 0.027502 \n", - "3 0.014389 \n", - "4 0.010572 \n", - "5 0.008595 \n", - "6 0.007685 \n", - "7 0.007464 \n", - "8 0.008677 \n", - "9 0.007895 \n", - "10 0.007186 \n", - "11 0.006850 \n", - "12 0.007065 \n", - "13 0.006550 \n", - "14 0.006568 \n", - "15 0.006115 \n", - "16 0.006180 \n", - "17 0.005799 \n", - "18 0.005837 \n", - "19 0.005546 \n" + "mean_time_with_nurse: Reached desired precision in 6 replications.\n", + "mean_q_time_nurse: Reached desired precision in 31 replications.\n", + "mean_nurse_utilisation: Reached desired precision in 6 replications.\n" ] }, { "data": { - "image/svg+xml": [ - "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" + "text/html": [ + "
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" + ], + "text/plain": [ + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "35 36 0.492981 0.497883 0.007143 0.495466 0.500300 \n", + "36 37 0.508118 0.498159 0.007242 0.495745 0.500574 \n", + "37 38 0.502153 0.498264 0.007172 0.495907 0.500622 \n", + "38 39 0.499857 0.498305 0.007082 0.496010 0.500601 \n", + "39 40 0.512021 0.498648 0.007319 0.496307 0.500989 \n", + "\n", + " deviation metric \n", + "35 0.004854 mean_nurse_utilisation \n", + "36 0.004847 mean_nurse_utilisation \n", + "37 0.004731 mean_nurse_utilisation \n", + "38 0.004607 mean_nurse_utilisation \n", + "39 0.004694 mean_nurse_utilisation " ] }, "metadata": {}, @@ -232,19 +405,15 @@ } ], "source": [ - "n_reps, cumulative_df = confidence_interval_method_simple(\n", - " replications=20,\n", - " metric='mean_time_with_nurse',\n", - " desired_precision=0.05\n", + "solutions, cumulative = confidence_interval_method_simple(\n", + " replications=40,\n", + " metrics=METRICS,\n", + " desired_precision=0.05,\n", + " verbose=True\n", ")\n", "\n", - "print(cumulative_df)\n", - "\n", - "plotly_confidence_interval_method(\n", - " n_reps=n_reps,\n", - " conf_ints=cumulative_df,\n", - " metric_name='Mean time with nurse',\n", - " file_path=os.path.join(OUTPUT_DIR, 'choose_param_conf_int_1.png'))" + "display(cumulative.head())\n", + "display(cumulative.tail())" ] }, { @@ -253,180 +422,27 @@ "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reached desired precision (0.05) in 31 replications.\n", - " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 50.187329 50.187329 NaN NaN NaN \n", - "1 2 51.626386 50.906857 NaN NaN NaN \n", - "2 3 53.127972 51.647229 1.470432 47.994472 55.299986 \n", - "3 4 49.110360 51.013012 1.746532 48.233890 53.792134 \n", - "4 5 46.325224 50.075454 2.585121 46.865602 53.285306 \n", - "5 6 39.049515 48.237798 5.060452 42.927179 53.548416 \n", - "6 7 47.327735 48.107789 4.632328 43.823600 52.391977 \n", - "7 8 61.150168 49.738086 6.297295 44.473416 55.002756 \n", - 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"\n", - " deviation \n", - "0 NaN \n", - "1 NaN \n", - "2 0.070725 \n", - "3 0.054479 \n", - "4 0.064100 \n", - "5 0.110092 \n", - "6 0.089054 \n", - "7 0.105848 \n", - "8 0.091909 \n", - "9 0.085453 \n", - "10 0.080036 \n", - "11 0.084869 \n", - "12 0.080096 \n", - "13 0.075493 \n", - "14 0.077188 \n", - "15 0.072816 \n", - "16 0.070950 \n", - "17 0.066724 \n", - "18 0.068908 \n", - "19 0.066670 \n", - "20 0.068584 \n", - "21 0.065222 \n", - "22 0.062274 \n", - "23 0.059531 \n", - "24 0.057012 \n", - "25 0.058648 \n", - "26 0.056307 \n", - "27 0.054153 \n", - "28 0.052603 \n", - "29 0.051055 \n", - "30 0.049294 \n", - "31 0.047716 \n", - "32 0.046559 \n", - "33 0.045314 \n", - "34 0.044393 \n", - "35 0.043402 \n", - "36 0.044635 \n", - "37 0.045020 \n", - "38 0.043914 \n", - "39 0.044375 \n" - ] + "data": { + "image/svg+xml": [ + "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { "data": { "image/svg+xml": [ - "510152025303540455055lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" + "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "n_reps, cumulative_df = confidence_interval_method_simple(\n", - " replications=40,\n", - " metric='mean_q_time_nurse',\n", - " desired_precision=0.05\n", - ")\n", - "\n", - "print(cumulative_df)\n", - "\n", - "plotly_confidence_interval_method(\n", - " n_reps=n_reps,\n", - " conf_ints=cumulative_df,\n", - " metric_name='Mean wait time for nurse',\n", - " file_path=os.path.join(OUTPUT_DIR, 'choose_param_conf_int_2.png'))" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reached desired precision (0.05) in 6 replications.\n", - " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 49.958879 49.958879 NaN NaN NaN \n", - "1 2 50.197749 50.078314 NaN NaN NaN \n", - "2 3 49.809386 49.988671 0.195888 49.502058 50.475284 \n", - "3 4 49.824879 49.947723 0.179690 49.661797 50.233649 \n", - "4 5 49.693390 49.896857 0.192752 49.657523 50.136190 \n", - "5 6 49.285394 49.794946 0.303376 49.476572 50.113320 \n", - "6 7 50.338583 49.872608 0.344844 49.553681 50.191536 \n", - "7 8 50.341133 49.931174 0.359679 49.630475 50.231873 \n", - "8 9 50.881773 50.036796 0.462171 49.681540 50.392052 \n", - "9 10 49.994369 50.032553 0.435945 49.720697 50.344410 \n", - "10 11 50.979421 50.118632 0.502542 49.781020 50.456245 \n", - "11 12 48.962260 50.022268 0.583972 49.651230 50.393306 \n", - "12 13 50.339787 50.046693 0.566003 49.704660 50.388725 \n", - "13 14 48.922360 49.966383 0.621298 49.607656 50.325110 \n", - "14 15 51.310478 50.055989 0.692010 49.672767 50.439212 \n", - "15 16 50.094761 50.058413 0.668616 49.702132 50.414693 \n", - "16 17 48.669128 49.976690 0.729824 49.601449 50.351931 \n", - "17 18 49.536082 49.952212 0.715609 49.596348 50.308076 \n", - "18 19 47.943016 49.846465 0.834334 49.444329 50.248601 \n", - "19 20 49.169728 49.812628 0.826059 49.426020 50.199235 \n", - "\n", - " deviation \n", - "0 NaN \n", - "1 NaN \n", - "2 0.009734 \n", - "3 0.005725 \n", - "4 0.004797 \n", - "5 0.006394 \n", - "6 0.006395 \n", - "7 0.006022 \n", - "8 0.007100 \n", - "9 0.006233 \n", - "10 0.006736 \n", - "11 0.007417 \n", - "12 0.006834 \n", - "13 0.007179 \n", - "14 0.007656 \n", - "15 0.007117 \n", - "16 0.007508 \n", - "17 0.007124 \n", - "18 0.008067 \n", - "19 0.007761 \n" - ] }, { "data": { "image/svg+xml": [ - "246810121416182049.449.649.85050.250.4lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -434,19 +450,13 @@ } ], "source": [ - "n_reps, cumulative_df = confidence_interval_method_simple(\n", - " replications=20,\n", - " metric='mean_nurse_utilisation',\n", - " desired_precision=0.05\n", - ")\n", - "\n", - "print(cumulative_df)\n", - "\n", - "plotly_confidence_interval_method(\n", - " n_reps=n_reps,\n", - " conf_ints=cumulative_df,\n", - " metric_name='Mean nurse utilisation',\n", - " file_path=os.path.join(OUTPUT_DIR, 'choose_param_conf_int_3.png'))" + "for metric in METRICS:\n", + " p = plotly_confidence_interval_method(\n", + " n_reps=solutions[metric],\n", + " conf_ints=cumulative[cumulative['metric'] == metric],\n", + " metric_name=LABELS[metric],\n", + " file_path=os.path.join(OUTPUT_DIR, f'choose_reps_{metric}.png'))\n", + " p.show()" ] }, { @@ -458,183 +468,233 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Reached desired precision (0.05) in 5 replications.\n", - " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 9.842380 9.842380 NaN NaN NaN \n", - "1 2 10.060424 9.951402 NaN NaN NaN \n", - "2 3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", - "3 4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", - "4 5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", - "5 6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", - "6 7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", - "7 8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", - "8 9 10.202806 9.999643 0.112884 9.912872 10.086413 \n", - "9 10 10.093238 10.009002 0.110467 9.929979 10.088025 \n", - "10 11 10.082694 10.015701 0.107128 9.943732 10.087671 \n", - "11 12 9.895607 10.005693 0.107865 9.937159 10.074228 \n", - "12 13 9.808886 9.990554 0.116811 9.919966 10.061143 \n", - "13 14 9.932816 9.986430 0.113284 9.921022 10.051839 \n", - "14 15 10.165899 9.998395 0.118591 9.932721 10.064069 \n", - "15 16 9.975272 9.996950 0.114716 9.935822 10.058077 \n", - "16 17 9.809312 9.985912 0.120035 9.924196 10.047628 \n", - "17 18 9.988074 9.986032 0.116452 9.928122 10.043942 \n", - "18 19 9.801716 9.976331 0.120813 9.918102 10.034561 \n", - "19 20 9.923390 9.973684 0.118185 9.918372 10.028996 \n", - "\n", - " deviation \n", - "0 NaN \n", - "1 NaN \n", - "2 0.027502 \n", - "3 0.014389 \n", - "4 0.010572 \n", - "5 0.008595 \n", - "6 0.007685 \n", - "7 0.007464 \n", - "8 0.008677 \n", - "9 0.007895 \n", - "10 0.007186 \n", - "11 0.006850 \n", - "12 0.007065 \n", - "13 0.006550 \n", - "14 0.006568 \n", - "15 0.006115 \n", - "16 0.006180 \n", - "17 0.005799 \n", - "18 0.005837 \n", - "19 0.005546 \n" + "mean_time_with_nurse: Reached desired precision in 5 replications.\n", + "mean_q_time_nurse: Reached desired precision in 30 replications.\n", + "mean_nurse_utilisation: Reached desired precision in 5 replications.\n" ] }, { "data": { - "image/svg+xml": [ - "24681012141618209.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean time with nurse" + "text/html": [ + "
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replicationsdatacumulative_meanstdevlower_ciupper_cideviationmetric
019.8423809.842380NaNNaNNaNNaNmean_time_with_nurse
1210.0604249.951402NaNNaNNaNNaNmean_time_with_nurse
239.9250909.9426310.1100759.66918910.2160730.027502mean_time_with_nurse
349.9385049.9415990.0899009.79854910.0846500.014389mean_time_with_nurse
4510.0166119.9566020.0847759.85133910.0618640.010572mean_time_with_nurse
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" + ], + "text/plain": [ + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "0 1 9.842380 9.842380 NaN NaN NaN \n", + "1 2 10.060424 9.951402 NaN NaN NaN \n", + "2 3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", + "3 4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", + "4 5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", + "\n", + " deviation metric \n", + "0 NaN mean_time_with_nurse \n", + "1 NaN mean_time_with_nurse \n", + "2 0.027502 mean_time_with_nurse \n", + "3 0.014389 mean_time_with_nurse \n", + "4 0.010572 mean_time_with_nurse " ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "n_reps, cumulative_df = confidence_interval_method(\n", - " replications=20,\n", - " metric='mean_time_with_nurse',\n", - " desired_precision=0.05\n", - ")\n", - "\n", - "print(cumulative_df)\n", - "\n", - "plotly_confidence_interval_method(\n", - " n_reps=n_reps,\n", - " conf_ints=cumulative_df,\n", - " metric_name='Mean time with nurse')" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reached desired precision (0.05) in 30 replications.\n", - " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 0.501873 0.501873 NaN NaN NaN \n", - "1 2 0.516264 0.509069 NaN NaN NaN \n", - "2 3 0.531280 0.516472 0.014704 0.479945 0.553000 \n", - "3 4 0.491104 0.510130 0.017465 0.482339 0.537921 \n", - "4 5 0.463252 0.500755 0.025851 0.468656 0.532853 \n", - "5 6 0.390495 0.482378 0.050605 0.429272 0.535484 \n", - "6 7 0.473277 0.481078 0.046323 0.438236 0.523920 \n", - "7 8 0.611502 0.497381 0.062973 0.444734 0.550028 \n", - "8 9 0.478926 0.495330 0.059226 0.449805 0.540856 \n", - "9 10 0.564747 0.502272 0.059999 0.459351 0.545193 \n", - "10 11 0.571089 0.508528 0.060584 0.467827 0.549229 \n", - "11 12 0.393387 0.498933 0.066645 0.456589 0.541277 \n", - "12 13 0.444403 0.494738 0.065575 0.455112 0.534365 \n", - "13 14 0.447649 0.491375 0.064247 0.454280 0.528470 \n", - "14 15 0.614848 0.499606 0.069637 0.461043 0.538170 \n", - "15 16 0.461512 0.497225 0.067946 0.461020 0.533431 \n", - "16 17 0.425752 0.493021 0.068034 0.458041 0.528001 \n", - "17 18 0.481117 0.492360 0.066062 0.459508 0.525212 \n", - "18 19 0.376149 0.486243 0.069517 0.452738 0.519749 \n", - "19 20 0.429078 0.483385 0.068859 0.451158 0.515612 \n", - "20 21 0.624518 0.490106 0.073845 0.456492 0.523719 \n", - "21 22 0.515244 0.491248 0.072264 0.459208 0.523288 \n", - "22 23 0.525237 0.492726 0.070957 0.462042 0.523410 \n", - "23 24 0.520427 0.493880 0.069628 0.464479 0.523282 \n", - "24 25 0.519650 0.494911 0.068356 0.466695 0.523127 \n", - "25 26 0.638633 0.500439 0.072664 0.471089 0.529789 \n", - "26 27 0.514672 0.500966 0.071306 0.472758 0.529174 \n", - "27 28 0.503287 0.501049 0.069974 0.473916 0.528182 \n", - "28 29 0.461636 0.499690 0.069102 0.473405 0.525975 \n", - "29 30 0.551549 0.501419 0.068557 0.475819 0.527018 \n", - "30 31 0.509837 0.501690 0.067422 0.476960 0.526421 \n", - "31 32 0.490932 0.501354 0.066353 0.477431 0.525277 \n", - "32 33 0.558335 0.503081 0.066057 0.479658 0.526503 \n", - "33 34 0.548240 0.504409 0.065508 0.481552 0.527266 \n", - "34 35 0.457886 0.503080 0.065014 0.480746 0.525413 \n", - "35 36 0.464776 0.502016 0.064396 0.480227 0.523804 \n", - "36 37 0.645301 0.505888 0.067724 0.483308 0.528469 \n", - "37 38 0.629223 0.509134 0.069735 0.486213 0.532055 \n", - "38 39 0.549301 0.510164 0.069111 0.487761 0.532567 \n", - "39 40 0.639879 0.513407 0.071235 0.490624 0.536189 \n", - "\n", - " deviation \n", - "0 NaN \n", - "1 NaN \n", - "2 0.070725 \n", - "3 0.054479 \n", - "4 0.064100 \n", - "5 0.110092 \n", - "6 0.089054 \n", - "7 0.105848 \n", - "8 0.091909 \n", - "9 0.085453 \n", - "10 0.080036 \n", - "11 0.084869 \n", - "12 0.080096 \n", - "13 0.075493 \n", - "14 0.077188 \n", - "15 0.072816 \n", - "16 0.070950 \n", - "17 0.066724 \n", - "18 0.068908 \n", - "19 0.066670 \n", - "20 0.068584 \n", - "21 0.065222 \n", - "22 0.062274 \n", - "23 0.059531 \n", - "24 0.057012 \n", - "25 0.058648 \n", - "26 0.056307 \n", - "27 0.054153 \n", - "28 0.052603 \n", - "29 0.051055 \n", - "30 0.049294 \n", - "31 0.047716 \n", - "32 0.046559 \n", - "33 0.045314 \n", - "34 0.044393 \n", - "35 0.043402 \n", - "36 0.044635 \n", - "37 0.045020 \n", - "38 0.043914 \n", - "39 0.044375 \n" - ] }, { "data": { - "image/svg+xml": [ - "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse" + "text/html": [ + "
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replicationsdatacumulative_meanstdevlower_ciupper_cideviationmetric
35360.4929810.4978830.0071430.4954660.5003000.004854mean_nurse_utilisation
36370.5081180.4981590.0072420.4957450.5005740.004847mean_nurse_utilisation
37380.5021530.4982640.0071720.4959070.5006220.004731mean_nurse_utilisation
38390.4998570.4983050.0070820.4960100.5006010.004607mean_nurse_utilisation
39400.5120210.4986480.0073190.4963070.5009890.004694mean_nurse_utilisation
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" + ], + "text/plain": [ + " replications data cumulative_mean stdev lower_ci upper_ci \\\n", + "35 36 0.492981 0.497883 0.007143 0.495466 0.500300 \n", + "36 37 0.508118 0.498159 0.007242 0.495745 0.500574 \n", + "37 38 0.502153 0.498264 0.007172 0.495907 0.500622 \n", + "38 39 0.499857 0.498305 0.007082 0.496010 0.500601 \n", + "39 40 0.512021 0.498648 0.007319 0.496307 0.500989 \n", + "\n", + " deviation metric \n", + "35 0.004854 mean_nurse_utilisation \n", + "36 0.004847 mean_nurse_utilisation \n", + "37 0.004731 mean_nurse_utilisation \n", + "38 0.004607 mean_nurse_utilisation \n", + "39 0.004694 mean_nurse_utilisation " ] }, "metadata": {}, @@ -642,79 +702,44 @@ } ], "source": [ - "n_reps, cumulative_df = confidence_interval_method(\n", + "solutions, cumulative = confidence_interval_method(\n", " replications=40,\n", - " metric='mean_q_time_nurse',\n", - " desired_precision=0.05\n", + " metrics=METRICS,\n", + " desired_precision=0.05,\n", + " verbose=True\n", ")\n", "\n", - "print(cumulative_df)\n", - "\n", - "plotly_confidence_interval_method(\n", - " n_reps=n_reps,\n", - " conf_ints=cumulative_df,\n", - " metric_name='Mean wait time for nurse')" + "display(cumulative.head())\n", + "display(cumulative.tail())" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 10, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reached desired precision (0.05) in 5 replications.\n", - " replications data cumulative_mean stdev lower_ci upper_ci \\\n", - "0 1 0.499589 0.499589 NaN NaN NaN \n", - "1 2 0.501977 0.500783 NaN NaN NaN \n", - "2 3 0.498094 0.499887 0.001959 0.495021 0.504753 \n", - "3 4 0.498249 0.499477 0.001797 0.496618 0.502336 \n", - "4 5 0.496934 0.498969 0.001928 0.496575 0.501362 \n", - "5 6 0.492854 0.497949 0.003034 0.494766 0.501133 \n", - "6 7 0.503386 0.498726 0.003448 0.495537 0.501915 \n", - "7 8 0.503411 0.499312 0.003597 0.496305 0.502319 \n", - "8 9 0.508818 0.500368 0.004622 0.496815 0.503921 \n", - "9 10 0.499944 0.500326 0.004359 0.497207 0.503444 \n", - "10 11 0.509794 0.501186 0.005025 0.497810 0.504562 \n", - "11 12 0.489623 0.500223 0.005840 0.496512 0.503933 \n", - "12 13 0.503398 0.500467 0.005660 0.497047 0.503887 \n", - "13 14 0.489224 0.499664 0.006213 0.496077 0.503251 \n", - "14 15 0.513105 0.500560 0.006920 0.496728 0.504392 \n", - "15 16 0.500948 0.500584 0.006686 0.497021 0.504147 \n", - "16 17 0.486691 0.499767 0.007298 0.496014 0.503519 \n", - "17 18 0.495361 0.499522 0.007156 0.495963 0.503081 \n", - "18 19 0.479430 0.498465 0.008343 0.494443 0.502486 \n", - "19 20 0.491697 0.498126 0.008261 0.494260 0.501992 \n", - "\n", - " deviation \n", - "0 NaN \n", - "1 NaN \n", - "2 0.009734 \n", - "3 0.005725 \n", - "4 0.004797 \n", - "5 0.006394 \n", - "6 0.006395 \n", - "7 0.006022 \n", - "8 0.007100 \n", - "9 0.006233 \n", - "10 0.006736 \n", - "11 0.007417 \n", - "12 0.006834 \n", - "13 0.007179 \n", - "14 0.007656 \n", - "15 0.007117 \n", - "16 0.007508 \n", - "17 0.007124 \n", - "18 0.008067 \n", - "19 0.007761 \n" - ] + "data": { + "image/svg+xml": [ + "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/svg+xml": [ + "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" + ] + }, + "metadata": {}, + "output_type": "display_data" }, { "data": { "image/svg+xml": [ - "24681012141618200.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -722,18 +747,12 @@ } ], "source": [ - "n_reps, cumulative_df = confidence_interval_method(\n", - " replications=20,\n", - " metric='mean_nurse_utilisation',\n", - " desired_precision=0.05\n", - ")\n", - "\n", - "print(cumulative_df)\n", - "\n", - "plotly_confidence_interval_method(\n", - " n_reps=n_reps,\n", - " conf_ints=cumulative_df,\n", - " metric_name='Mean nurse utilisation')" + "for metric in METRICS:\n", + " p = plotly_confidence_interval_method(\n", + " n_reps=solutions[metric],\n", + " conf_ints=cumulative[cumulative['metric'] == metric],\n", + " metric_name=LABELS[metric])\n", + " p.show()" ] }, { @@ -747,7 +766,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -1484,14 +1503,10 @@ } ], "source": [ - "# Choose metrics to assess\n", - "test_metrics = [\n", - " 'mean_time_with_nurse', 'mean_q_time_nurse', 'mean_nurse_utilisation']\n", - "\n", "# Set up and run the algorithm\n", "analyser = ReplicationsAlgorithm()\n", "solutions, cumulative = analyser.select(runner=Runner(Param()),\n", - " metrics=test_metrics)\n", + " metrics=METRICS)\n", "\n", "# View results\n", "display(solutions)\n", @@ -1507,13 +1522,13 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "image/svg+xml": [ - "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -1522,7 +1537,7 @@ { "data": { "image/svg+xml": [ - "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" + "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -1531,7 +1546,7 @@ { "data": { "image/svg+xml": [ - "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -1539,7 +1554,7 @@ } ], "source": [ - "for metric in test_metrics:\n", + "for metric in METRICS:\n", " p = plotly_confidence_interval_method(\n", " n_reps = solutions[metric],\n", " conf_ints = cumulative[cumulative['metric'] == metric],\n", @@ -1556,14 +1571,14 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 14s\n" + "Notebook run time: 0m 11s\n" ] } ], diff --git a/outputs/choose_param_conf_int_1.png b/outputs/choose_param_conf_int_1.png deleted file mode 100644 index 1f97f42dc8ce21381afa6a539e4f43befd0dde92..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 46631 zcmeFZbySpH+cqu(BPAe6D4o)vgfu8f3rG(Q0@5MfI8xFfAq~df($Xc}-ThtT z6Yu>z&wW3?Z+&b1{(IM&r89GJ#oqhe`#jF$IQ9g-m6ycAAjY_N?;e)4)N7@C_fTN> z?jc2>-3PvDs*KmYcaQR(^y^nDF1p*vs4gn1mjT|KU!>{$-e#iUE53e+qElG#%|d6o 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z2L_9Z*nAu4t8n3w^V@xRx~A#mpPi8-#7!JahdRfz>MTm`#2kTtSCrM2-dr>Z_%B%4 BCgcDB literal 0 HcmV?d00001 diff --git a/simulation/replications.py b/simulation/replications.py index 76ce8cb..84d8555 100644 --- a/simulation/replications.py +++ b/simulation/replications.py @@ -101,7 +101,8 @@ def update(self, x): self.mean = updated_mean # Run the observer update() method - self.observer.update(self) + if self.observer is not None: + self.observer.update(self) @property def variance(self): @@ -339,8 +340,15 @@ def select(self, runner, metrics): column names from the run results dataframe). Returns: - int: - The determined number of replications needed for precision. + tuple[dict, pd.DataFrame]: + - A dictionary with the minimum number of replications required + to meet the precision for each metric. + - DataFrame containing cumulative statistics for each + replication for each metric. + + Warnings: + Issues a warning if the desired precision is not met for any + metrics before the replication limit is met. """ # Create instances of observers and stats for each metric observers = { @@ -441,10 +449,11 @@ def select(self, runner, metrics): def confidence_interval_method( replications, - metric, + metrics, alpha=0.05, desired_precision=0.05, - min_rep=5 + min_rep=5, + verbose=False ): """ The confidence interval method for selecting the number of replications. @@ -458,8 +467,9 @@ def confidence_interval_method( Arguments: replications (int): Number of times to run the model. - metric (str): - Name of performance metric to assess. + metrics (list): + List of performance metrics to assess (should correspond to + column names from the run results dataframe). alpha (float, optional): Significance level for confidence interval calculations. desired_precision (float, optional): @@ -469,12 +479,15 @@ def confidence_interval_method( Minimum number of replications before checking precision. Useful when the number of replications returned does not provide a stable precision below target. + verbose (bool, optional): + Whether to print progress updates. Returns: - tuple[int, pd.DataFrame]: - - The minimum number of replications required to meet the - precision. Returns -1 if precision is not achieved. - - DataFrame containing cumulative statistics for each replication. + tuple[dict, pd.DataFrame]: + - A dictionary with the minimum number of replications required + to meet the precision for each metric. + - DataFrame containing cumulative statistics for each + replication for each metric. Warnings: Issues a warning if the desired precision is not met within the @@ -485,40 +498,55 @@ def confidence_interval_method( choose_rep = Runner(param) choose_rep.run_reps() - # Extract replication results for the specified metric - rep_res = choose_rep.run_results_df[metric] - - # Set up method for calculating statistics and saving them as a table - observer = ReplicationTabulizer() - stats = OnlineStatistics( - alpha=alpha, data=np.array(rep_res[:2]), observer=observer) - - # Calculate statistics with each replication, and get summary table - for i in range(2, len(rep_res)): - stats.update(rep_res[i]) - results = observer.summary_table() - - # Get minimum number of replications where deviation is less than target - try: - n_reps = ( - results.iloc[min_rep:] - .loc[results['deviation'] <= desired_precision] - .iloc[0] - .name - ) - print(f'Reached desired precision ({desired_precision}) in ' + - f'{n_reps} replications.') - # Return warning if there are no replications with the desired precision - except IndexError: - message = 'WARNING: the replications do not reach desired precision' - warnings.warn(message) - n_reps = -1 + nreps_dict = {} + summary_table_list = [] + + for metric in metrics: + # Extract replication results for the specified metric + rep_res = choose_rep.run_results_df[metric] + + # Set up method for calculating statistics and saving them as a table + observer = ReplicationTabulizer() + stats = OnlineStatistics( + alpha=alpha, data=np.array(rep_res[:2]), observer=observer) + + # Calculate statistics with each replication, and get summary table + for i in range(2, len(rep_res)): + stats.update(rep_res[i]) + results = observer.summary_table() + + # Get minimum number of replications where deviation is below target + try: + nreps = ( + results.iloc[min_rep:] + .loc[results['deviation'] <= desired_precision] + .iloc[0] + .name + ) + if verbose: + print(f'{metric}: Reached desired precision in {nreps} ' + + 'replications.') + # Return warning if there are no replications with desired precision + except IndexError: + message = f'WARNING: {metric} does not reach desired precision.' + warnings.warn(message) + nreps = None - return n_reps, results + # Add solution to dictionary + nreps_dict[metric] = nreps + + # Add metric name to table then append to list + results['metric'] = metric + summary_table_list.append(results) + + # Combine into a single table + summary_frame = pd.concat(summary_table_list) + + return nreps_dict, summary_frame def confidence_interval_method_simple( - replications, metric, desired_precision=0.05, min_rep=5 + replications, metrics, desired_precision=0.05, min_rep=5, verbose=False ): """ Simple implementation using the confidence interval method to select the @@ -532,8 +560,8 @@ def confidence_interval_method_simple( Arguments: replications (int): Number of times to run the model. - metric (string): - Name of performance metric to assess. + metrics (list): + List of performance metrics to assess. desired_precision (float, optional): The target half width precision (i.e. percentage deviation of the confidence interval from the mean). @@ -541,11 +569,15 @@ def confidence_interval_method_simple( Minimum number of replications before checking precision. Useful when the number of replications returned does not provide a stable precision below target. + verbose (bool, optional): + Whether to print progress updates. Returns: - tuple[int, pd.DataFrame]: - - The minimum number of replications required to meet precision. - - DataFrame containing cumulative statistics for each replication. + tuple[dict, pd.DataFrame]: + - A dictionary with the minimum number of replications required + to meet the precision for each metric. + - DataFrame containing cumulative statistics for each + replication for each metric. Warnings: Issues a warning if the desired precision is not met within the @@ -555,47 +587,58 @@ def confidence_interval_method_simple( param = Param(number_of_runs=replications) choose_rep = Runner(param) choose_rep.run_reps() - - # If mean of metric is less than 1, multiply by 100 df = choose_rep.run_results_df - if df[metric].mean() < 1: - df[f'adj_{metric}'] = df[metric] * 100 - metric = f'adj_{metric}' - - # Compute cumulative statistics - cumulative = pd.DataFrame([ - { - 'replications': i + 1, # Adjusted as counted from zero - 'data': df[metric][i], - 'cumulative_mean': stats[0], - 'stdev': stats[1], - 'lower_ci': stats[2], - 'upper_ci': stats[3], - 'deviation': (stats[3] - stats[0]) / stats[0] - } - for i, stats in enumerate( - (summary_stats(df[metric].iloc[:i]) - for i in range(1, replications + 1)) - ) - ]) - - # Get minimum number of replications where deviation is less than target - try: - n_reps = ( - cumulative.iloc[min_rep:] - .loc[cumulative['deviation'] <= desired_precision] - .iloc[0] - .name - ) + 1 - print(f'Reached desired precision ({desired_precision}) in ' + - f'{n_reps} replications.') - # Return warning if there are no replications with the desired precision - except IndexError: - warnings.warn(f'Running {replications} replications did not reach' + - f'desired precision ({desired_precision}).') - n_reps = -1 - - return n_reps, cumulative + + nreps_dict = {} + summary_table_list = [] + + for metric in metrics: + # Compute cumulative statistics + cumulative = pd.DataFrame([ + { + 'replications': i + 1, # Adjusted as counted from zero + 'data': df[metric][i], + 'cumulative_mean': stats[0], + 'stdev': stats[1], + 'lower_ci': stats[2], + 'upper_ci': stats[3], + 'deviation': (stats[3] - stats[0]) / stats[0] + } + for i, stats in enumerate( + (summary_stats(df[metric].iloc[:i]) + for i in range(1, replications + 1)) + ) + ]) + + # Get minimum number of replications where deviation is below target + try: + nreps = ( + cumulative.iloc[min_rep:] + .loc[cumulative['deviation'] <= desired_precision] + .iloc[0] + .name + ) + 1 + if verbose: + print(f'{metric}: Reached desired precision in {nreps} ' + + 'replications.') + # Return warning if there are no replications with desired precision + except IndexError: + warnings.warn(f'Running {replications} replications did not ' + + f'reach desired precision ({desired_precision})' + + f'for metric {metric}.') + nreps = None + + # Add solution to dictionary + nreps_dict[metric] = nreps + + # Add metric name to table then append to list + cumulative['metric'] = metric + summary_table_list.append(cumulative) + + # Combine into a single table + summary_frame = pd.concat(summary_table_list) + + return nreps_dict, summary_frame def plotly_confidence_interval_method( diff --git a/tests/test_backtest_replications.py b/tests/test_backtest_replications.py index 609f258..3e2960b 100644 --- a/tests/test_backtest_replications.py +++ b/tests/test_backtest_replications.py @@ -38,14 +38,14 @@ def test_cimethods(ci_function): """ # Run the confidence interval method _, cumulative_df = ci_function( - replications=20, metric='mean_time_with_nurse') + replications=20, metrics=['mean_time_with_nurse']) # Import the expected results exp_df = pd.read_csv( Path(__file__).parent.joinpath('exp_results/replications.csv')) # Compare them - pd.testing.assert_frame_equal(cumulative_df, exp_df) + pd.testing.assert_frame_equal(cumulative_df.drop(['metric'], axis=1), exp_df) def test_algorithm(): diff --git a/tests/test_functionaltest_replications.py b/tests/test_functionaltest_replications.py index f950390..be5c0d1 100644 --- a/tests/test_functionaltest_replications.py +++ b/tests/test_functionaltest_replications.py @@ -43,7 +43,7 @@ def test_ci_method_output(ci_function): # Run the confidence interval method min_reps, cumulative_df = ci_function( - replications=reps, metric='mean_time_with_nurse') + replications=reps, metrics=['mean_time_with_nurse']) # Check that the results dataframe contains the right number of rows if not len(cumulative_df) == reps: @@ -64,11 +64,11 @@ def test_ci_method_output(ci_function): f'{max(cumulative_df['replications'])}.') # Check that min_reps is no more than the number run - if not min_reps <= reps: + if not min_reps['mean_time_with_nurse'] <= reps: errors.append( 'The minimum number of replications required as returned by the ' + 'confidence_interval_method should be less than the number we ' + - f'ran ({reps}) but it returned {min_reps}.') + f'ran ({reps}) but it was {min_reps['mean_time_with_nurse']}.') # Check if there were any errors assert not errors, 'Errors occurred:\n{}'.format('\n'.join(errors)) @@ -92,7 +92,7 @@ def test_consistent_outputs(ci_function): # Run the manual confidence interval method _, man_df = ci_function( - replications=reps, metric='mean_time_with_nurse') + replications=reps, metrics=['mean_time_with_nurse']) # Run the algorithm analyser = ReplicationsAlgorithm(initial_replications=reps, @@ -102,4 +102,4 @@ def test_consistent_outputs(ci_function): # Get first 20 rows (may have more if met precision and went into # look ahead period beyond budget) and compare dataframes pd.testing.assert_frame_equal( - man_df, summary_table.drop(['metric'], axis=1).head(20)) + man_df, summary_table.head(20)) From 06a86c52d1b18607b4268bbb39c240c1e714363a Mon Sep 17 00:00:00 2001 From: amyheather Date: Fri, 14 Feb 2025 11:51:09 +0000 Subject: [PATCH 18/28] fix(replications): prevent bonus rep after limit hit --- simulation/replications.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/simulation/replications.py b/simulation/replications.py index 84d8555..6554a1c 100644 --- a/simulation/replications.py +++ b/simulation/replications.py @@ -390,7 +390,7 @@ def select(self, runner, metrics): while ( sum(1 for v in solutions.values() if v['solved']) < len(metrics) - and self.n <= self.replication_budget + self._klimit() + and self.n < self.replication_budget + self._klimit() ): # Run another replication @@ -447,6 +447,8 @@ def select(self, runner, metrics): return nreps, summary_frame +# pylint: disable=too-many-arguments,too-many-positional-arguments +# pylint: disable=too-many-locals def confidence_interval_method( replications, metrics, From 130aa77162363080b809f7acca5ee8dc764fd393 Mon Sep 17 00:00:00 2001 From: amyheather Date: Fri, 14 Feb 2025 11:51:31 +0000 Subject: [PATCH 19/28] tests(replications): update backtest data to multiple metrics --- notebooks/generate_exp_results.ipynb | 266 +++++++++------------------ tests/exp_results/replications.csv | 142 +++++++++++--- tests/test_backtest_replications.py | 25 ++- 3 files changed, 220 insertions(+), 213 deletions(-) diff --git a/notebooks/generate_exp_results.ipynb b/notebooks/generate_exp_results.ipynb index a83c037..9b7063f 100644 --- a/notebooks/generate_exp_results.ipynb +++ b/notebooks/generate_exp_results.ipynb @@ -780,13 +780,6 @@ "execution_count": 10, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Reached desired precision (0.05) in 5 replications.\n" - ] - }, { "data": { "text/html": [ @@ -815,6 +808,7 @@ " lower_ci\n", " upper_ci\n", " deviation\n", + " metric\n", " \n", " \n", " \n", @@ -827,6 +821,7 @@ " NaN\n", " NaN\n", " NaN\n", + " mean_time_with_nurse\n", " \n", " \n", " 1\n", @@ -837,6 +832,7 @@ " NaN\n", " NaN\n", " NaN\n", + " mean_time_with_nurse\n", " \n", " \n", " 2\n", @@ -847,6 +843,7 @@ " 9.669189\n", " 10.216073\n", " 0.027502\n", + " mean_time_with_nurse\n", " \n", " \n", " 3\n", @@ -857,6 +854,7 @@ " 9.798549\n", " 10.084650\n", " 0.014389\n", + " mean_time_with_nurse\n", " \n", " \n", " 4\n", @@ -867,159 +865,77 @@ " 9.851339\n", " 10.061864\n", " 0.010572\n", + " mean_time_with_nurse\n", " \n", " \n", - " 5\n", - " 6\n", - " 9.883801\n", - " 9.944468\n", - " 0.081442\n", - " 9.859000\n", - " 10.029936\n", - " 0.008595\n", - " \n", - " \n", - " 6\n", - " 7\n", - " 10.040555\n", - " 9.958195\n", - " 0.082742\n", - " 9.881671\n", - " 10.034719\n", - " 0.007685\n", - " \n", - " \n", - " 7\n", - " 8\n", - " 10.086612\n", - " 9.974247\n", - " 0.089048\n", - " 9.899801\n", - " 10.048693\n", - " 0.007464\n", - " \n", - " \n", - " 8\n", - " 9\n", - " 10.202806\n", - " 9.999643\n", - " 0.112884\n", - " 9.912872\n", - " 10.086413\n", - " 0.008677\n", - " \n", - " \n", - " 9\n", - " 10\n", - " 10.093238\n", - " 10.009002\n", - " 0.110467\n", - " 9.929979\n", - " 10.088025\n", - " 0.007895\n", - " \n", - " \n", - " 10\n", - " 11\n", - " 10.082694\n", - " 10.015701\n", - " 0.107128\n", - " 9.943732\n", - " 10.087671\n", - " 0.007186\n", - " \n", - " \n", - " 11\n", - " 12\n", - " 9.895607\n", - " 10.005693\n", - " 0.107865\n", - " 9.937159\n", - " 10.074228\n", - " 0.006850\n", - " \n", - " \n", - " 12\n", - " 13\n", - " 9.808886\n", - " 9.990554\n", - " 0.116811\n", - " 9.919966\n", - " 10.061143\n", - " 0.007065\n", - " \n", - " \n", - " 13\n", - " 14\n", - " 9.932816\n", - " 9.986430\n", - " 0.113284\n", - " 9.921022\n", - " 10.051839\n", - " 0.006550\n", - " \n", - " \n", - " 14\n", - " 15\n", - " 10.165899\n", - " 9.998395\n", - " 0.118591\n", - " 9.932721\n", - " 10.064069\n", - " 0.006568\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", + " ...\n", " \n", " \n", - " 15\n", - " 16\n", - " 9.975272\n", - " 9.996950\n", - " 0.114716\n", - " 9.935822\n", - " 10.058077\n", - " 0.006115\n", + " 35\n", + " 36\n", + " 0.492981\n", + " 0.497883\n", + " 0.007143\n", + " 0.495466\n", + " 0.500300\n", + " 0.004854\n", + " mean_nurse_utilisation\n", " \n", " \n", - " 16\n", - " 17\n", - " 9.809312\n", - " 9.985912\n", - " 0.120035\n", - " 9.924196\n", - " 10.047628\n", - " 0.006180\n", + " 36\n", + " 37\n", + " 0.508118\n", + " 0.498159\n", + " 0.007242\n", + " 0.495745\n", + " 0.500574\n", + " 0.004847\n", + " mean_nurse_utilisation\n", " \n", " \n", - " 17\n", - " 18\n", - " 9.988074\n", - " 9.986032\n", - " 0.116452\n", - " 9.928122\n", - " 10.043942\n", - " 0.005799\n", + " 37\n", + " 38\n", + " 0.502153\n", + " 0.498264\n", + " 0.007172\n", + " 0.495907\n", + " 0.500622\n", + " 0.004731\n", + " mean_nurse_utilisation\n", " \n", " \n", - " 18\n", - " 19\n", - " 9.801716\n", - " 9.976331\n", - " 0.120813\n", - " 9.918102\n", - " 10.034561\n", - " 0.005837\n", + " 38\n", + " 39\n", + " 0.499857\n", + " 0.498305\n", + " 0.007082\n", + " 0.496010\n", + " 0.500601\n", + " 0.004607\n", + " mean_nurse_utilisation\n", " \n", " \n", - " 19\n", - " 20\n", - " 9.923390\n", - " 9.973684\n", - " 0.118185\n", - " 9.918372\n", - " 10.028996\n", - " 0.005546\n", + " 39\n", + " 40\n", + " 0.512021\n", + " 0.498648\n", + " 0.007319\n", + " 0.496307\n", + " 0.500989\n", + " 0.004694\n", + " mean_nurse_utilisation\n", " \n", " \n", "\n", + "

120 rows × 8 columns

\n", "" ], "text/plain": [ @@ -1029,43 +945,27 @@ "2 3 9.925090 9.942631 0.110075 9.669189 10.216073 \n", "3 4 9.938504 9.941599 0.089900 9.798549 10.084650 \n", "4 5 10.016611 9.956602 0.084775 9.851339 10.061864 \n", - "5 6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", - "6 7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", - "7 8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", - "8 9 10.202806 9.999643 0.112884 9.912872 10.086413 \n", - "9 10 10.093238 10.009002 0.110467 9.929979 10.088025 \n", - "10 11 10.082694 10.015701 0.107128 9.943732 10.087671 \n", - "11 12 9.895607 10.005693 0.107865 9.937159 10.074228 \n", - "12 13 9.808886 9.990554 0.116811 9.919966 10.061143 \n", - "13 14 9.932816 9.986430 0.113284 9.921022 10.051839 \n", - "14 15 10.165899 9.998395 0.118591 9.932721 10.064069 \n", - "15 16 9.975272 9.996950 0.114716 9.935822 10.058077 \n", - "16 17 9.809312 9.985912 0.120035 9.924196 10.047628 \n", - "17 18 9.988074 9.986032 0.116452 9.928122 10.043942 \n", - "18 19 9.801716 9.976331 0.120813 9.918102 10.034561 \n", - "19 20 9.923390 9.973684 0.118185 9.918372 10.028996 \n", + ".. ... ... ... ... ... ... \n", + "35 36 0.492981 0.497883 0.007143 0.495466 0.500300 \n", + "36 37 0.508118 0.498159 0.007242 0.495745 0.500574 \n", + "37 38 0.502153 0.498264 0.007172 0.495907 0.500622 \n", + "38 39 0.499857 0.498305 0.007082 0.496010 0.500601 \n", + "39 40 0.512021 0.498648 0.007319 0.496307 0.500989 \n", "\n", - " deviation \n", - "0 NaN \n", - "1 NaN \n", - "2 0.027502 \n", - "3 0.014389 \n", - "4 0.010572 \n", - "5 0.008595 \n", - "6 0.007685 \n", - "7 0.007464 \n", - "8 0.008677 \n", - "9 0.007895 \n", - "10 0.007186 \n", - "11 0.006850 \n", - "12 0.007065 \n", - "13 0.006550 \n", - "14 0.006568 \n", - "15 0.006115 \n", - "16 0.006180 \n", - "17 0.005799 \n", - "18 0.005837 \n", - "19 0.005546 " + " deviation metric \n", + "0 NaN mean_time_with_nurse \n", + "1 NaN mean_time_with_nurse \n", + "2 0.027502 mean_time_with_nurse \n", + "3 0.014389 mean_time_with_nurse \n", + "4 0.010572 mean_time_with_nurse \n", + ".. ... ... \n", + "35 0.004854 mean_nurse_utilisation \n", + "36 0.004847 mean_nurse_utilisation \n", + "37 0.004731 mean_nurse_utilisation \n", + "38 0.004607 mean_nurse_utilisation \n", + "39 0.004694 mean_nurse_utilisation \n", + "\n", + "[120 rows x 8 columns]" ] }, "metadata": {}, @@ -1074,7 +974,9 @@ ], "source": [ "_, man_df = confidence_interval_method(\n", - " replications=20, metric='mean_time_with_nurse')\n", + " replications=40, metrics=['mean_time_with_nurse',\n", + " 'mean_q_time_nurse',\n", + " 'mean_nurse_utilisation'])\n", "\n", "display(man_df)\n", "\n", diff --git a/tests/exp_results/replications.csv b/tests/exp_results/replications.csv index 9cff384..e874a8e 100644 --- a/tests/exp_results/replications.csv +++ b/tests/exp_results/replications.csv @@ -1,21 +1,121 @@ -replications,data,cumulative_mean,stdev,lower_ci,upper_ci,deviation -1,9.842379990825465,9.842379990825465,,,, 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confidence interval method _, cumulative_df = ci_function( - replications=20, metrics=['mean_time_with_nurse']) + replications=40, metrics=['mean_time_with_nurse', + 'mean_q_time_nurse', + 'mean_nurse_utilisation']) # Import the expected results exp_df = pd.read_csv( Path(__file__).parent.joinpath('exp_results/replications.csv')) # Compare them - pd.testing.assert_frame_equal(cumulative_df.drop(['metric'], axis=1), exp_df) + pd.testing.assert_frame_equal(cumulative_df.reset_index(drop=True), + exp_df.reset_index(drop=True)) def test_algorithm(): @@ -57,13 +60,15 @@ def test_algorithm(): exp_df = pd.read_csv( Path(__file__).parent.joinpath('exp_results/replications.csv')) - # Run the algorithm - analyser = ReplicationsAlgorithm(initial_replications=20, - replication_budget=20) + # Run the algorithm, forcing only 40 replications + analyser = ReplicationsAlgorithm(initial_replications=40, + replication_budget=40, + look_ahead=0) _, summary_table = analyser.select( - runner=Runner(Param()), metrics=['mean_time_with_nurse']) + runner=Runner(Param()), metrics=['mean_time_with_nurse', + 'mean_q_time_nurse', + 'mean_nurse_utilisation']) - # Get first 20 rows (may have more if met precision and went into - # look ahead period beyond budget), and compare dataframes - pd.testing.assert_frame_equal( - summary_table.drop(['metric'], axis=1).head(20), exp_df) + # Compare dataframes + pd.testing.assert_frame_equal(summary_table.reset_index(drop=True), + exp_df.reset_index(drop=True)) From e6ee7523c2219af3497e12d829e09c2a0f47fcb1 Mon Sep 17 00:00:00 2001 From: amyheather Date: Fri, 14 Feb 2025 11:53:47 +0000 Subject: [PATCH 20/28] chore(general): linting, docstrings, standardisation, rerun, etc. --- README.md | 18 +++++++++--------- notebooks/choosing_cores.ipynb | 14 +++++--------- notebooks/choosing_replications.ipynb | 18 +++++++++--------- notebooks/choosing_warmup.ipynb | 10 +++------- simulation/model.py | 6 ++++-- tests/test_backtest.py | 1 - tests/test_unittest_replications.py | 18 ++++++++++-------- 7 files changed, 40 insertions(+), 45 deletions(-) diff --git a/README.md b/README.md index adc2977..46139a4 100644 --- a/README.md +++ b/README.md @@ -32,8 +32,6 @@ Table of contents: ## 📌  Introduction - - This repository provides a template for building discrete-event simulation (DES) models in Python. 😊 **Simple:** Easy-to-follow code structure using [SimPy](https://simpy.readthedocs.io/en/latest/). Implements a simple M/M/s queueing model in which patients arrive, wait to see a nurse, have a consultation with the nurse and then leave. Follows a modular structure: uses object-oriented programming, with the simulation implemented through classes. @@ -43,7 +41,7 @@ This repository provides a template for building discrete-event simulation (DES) * ["Levels of RAP" framework](https://nhsdigital.github.io/rap-community-of-practice/introduction_to_RAP/levels_of_RAP/) from the NHS RAP Community of Practice (`docs/nhs_rap.md`). * Recommendations from [Heather et al. 2025](https://doi.org/10.48550/arXiv.2501.13137) "*On the reproducibility of discrete-event simulation studies in health research: an empirical study using open models*" (`docs/heather_2025.md`). -🚀 **Extendable:** This template adapts from Sammi Rosser and Dan Chalk (2024) ["HSMA - the little book of DES"](https://github.com/hsma-programme/hsma6_des_book). The book includes additional advanced features that can be used to extend the model in this template, including: +🚀 **Extendable:** This template is partly adapted from Sammi Rosser and Dan Chalk (2024) ["HSMA - the little book of DES"](https://github.com/hsma-programme/hsma6_des_book). The book includes additional advanced features that can be used to extend the model in this template, including: * Multiple activities * Branching paths @@ -247,10 +245,12 @@ repo/ The overall run time will vary depending on how the template model is used. A few example implementations are provided in `notebooks/` and the run times for these were: * `analysis.ipynb` - 23s -* `choosing_parameters.ipynb` - 22s +* `choosing_cores.ipynb` - 21s +* `choosing_replications.ipynb` - 11s +* `choosing_warmup.ipynb` - 2s * `generate_exp_results.ipynb` - 0s - + These times were obtained on an Intel Core i7-12700H with 32GB RAM running Ubuntu 24.04.1 Linux. @@ -268,10 +268,10 @@ You can also cite the GitHub repository: Researcher details: -| Contributor | Role | ORCID | GitHub | -| --- | --- | --- | --- | -| Amy Heather | Author | [![ORCID: Heather](https://img.shields.io/badge/ORCID-0000--0002--6596--3479-brightgreen)](https://orcid.org/0000-0002-6596-3479) | https://github.com/amyheather | -| Tom Monks | Code review | [![ORCID: Monks](https://img.shields.io/badge/ORCID-0000--0003--2631--4481-brightgreen)](https://orcid.org/0000-0003-2631-4481) | https://github.com/TomMonks | +| Contributor | ORCID | GitHub | +| --- | --- | --- | +| Amy Heather | [![ORCID: Heather](https://img.shields.io/badge/ORCID-0000--0002--6596--3479-brightgreen)](https://orcid.org/0000-0002-6596-3479) | https://github.com/amyheather | +| Tom Monks | [![ORCID: Monks](https://img.shields.io/badge/ORCID-0000--0003--2631--4481-brightgreen)](https://orcid.org/0000-0003-2631-4481) | https://github.com/TomMonks |
diff --git a/notebooks/choosing_cores.ipynb b/notebooks/choosing_cores.ipynb index b92a5fe..8a8d048 100644 --- a/notebooks/choosing_cores.ipynb +++ b/notebooks/choosing_cores.ipynb @@ -12,11 +12,7 @@ "\n", "Credit:\n", "\n", - "* Code for running the model with a varying number of CPU cores was adapted from Sammi Rosser and Dan Chalk (2024) [HSMA - the little book of DES](https://github.com/hsma-programme/hsma6_des_book) (MIT Licence).\n", - "\n", - "Licence:\n", - "\n", - "* This project is licensed under the MIT Licence. See the `LICENSE` file for more details." + "* Code for running the model with a varying number of CPU cores was adapted from Sammi Rosser and Dan Chalk (2024) [HSMA - the little book of DES](https://github.com/hsma-programme/hsma6_des_book) (MIT Licence)." ] }, { @@ -141,9 +137,9 @@ "Running with cores: 8.\n", " cores run_time\n", "0 1 5.0\n", - "1 2 3.5\n", + "1 2 3.0\n", "2 3 2.5\n", - "3 4 2.5\n", + "3 4 2.0\n", "4 5 2.0\n", "5 6 2.0\n", "6 7 2.0\n", @@ -153,7 +149,7 @@ { "data": { "image/svg+xml": [ - "1234567811.522.533.544.55Number of coresRun time (rounded to nearest .5 seconds)" + "1234567811.522.533.544.55Number of coresRun time (rounded to nearest .5 seconds)" ] }, "metadata": {}, @@ -207,7 +203,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 21s\n" + "Notebook run time: 0m 20s\n" ] } ], diff --git a/notebooks/choosing_replications.ipynb b/notebooks/choosing_replications.ipynb index 865516b..0f8eedb 100644 --- a/notebooks/choosing_replications.ipynb +++ b/notebooks/choosing_replications.ipynb @@ -424,7 +424,7 @@ { "data": { "image/svg+xml": [ - "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -433,7 +433,7 @@ { "data": { "image/svg+xml": [ - "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" + "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -442,7 +442,7 @@ { "data": { "image/svg+xml": [ - "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -721,7 +721,7 @@ { "data": { "image/svg+xml": [ - "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -730,7 +730,7 @@ { "data": { "image/svg+xml": [ - "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" + "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -739,7 +739,7 @@ { "data": { "image/svg+xml": [ - "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -1528,7 +1528,7 @@ { "data": { "image/svg+xml": [ - "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -1537,7 +1537,7 @@ { "data": { "image/svg+xml": [ - "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" + "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -1546,7 +1546,7 @@ { "data": { "image/svg+xml": [ - "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, diff --git a/notebooks/choosing_warmup.ipynb b/notebooks/choosing_warmup.ipynb index 87f79de..0f60e40 100644 --- a/notebooks/choosing_warmup.ipynb +++ b/notebooks/choosing_warmup.ipynb @@ -16,11 +16,7 @@ "\n", "Credit:\n", "\n", - "* Code for choice of warm-up period was adapted from Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence).\n", - "\n", - "Licence:\n", - "\n", - "* This project is licensed under the MIT Licence. See the `LICENSE` file for more details." + "* Code for choice of warm-up period was adapted from Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence)." ] }, { @@ -272,7 +268,7 @@ { "data": { "image/svg+xml": [ - "00.10.20.30.40500100015002000250030003500400000.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" + "00.10.20.30.40500100015002000250030003500400000.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" ] }, "metadata": {}, @@ -301,7 +297,7 @@ { "data": { "image/svg+xml": [ - "00.10.20.30.40.5010k20k30k40k50k00.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" + "00.10.20.30.40.5010k20k30k40k50k00.10.20.30.40.50.60.7Run time (minutes)Run time (minutes)UtilisationRunning mean nurse wait time (minutes)Suggested warm-up lengthSuggested warm-up lengthSuggested warm-up length" ] }, "metadata": {}, diff --git a/simulation/model.py b/simulation/model.py index e5d7b47..adaf8ed 100644 --- a/simulation/model.py +++ b/simulation/model.py @@ -231,7 +231,8 @@ def request(self, *args, **kwargs): Keyword arguments to be passed to the parent class. Returns: - simpy.events.Event: Event representing the request. + simpy.events.Event: + Event representing the request. """ # Update time-weighted statistics self.update_time_weighted_stats() @@ -250,7 +251,8 @@ def release(self, *args, **kwargs): Keyword arguments to be passed to the parent class.# Returns: - simpy.events.Event: Event representing the request. + simpy.events.Event: + Event representing the request. """ # Update time-weighted statistics self.update_time_weighted_stats() diff --git a/tests/test_backtest.py b/tests/test_backtest.py index 0930cd5..0ca87b4 100644 --- a/tests/test_backtest.py +++ b/tests/test_backtest.py @@ -10,7 +10,6 @@ Typical usage example: pytest - """ from pathlib import Path diff --git a/tests/test_unittest_replications.py b/tests/test_unittest_replications.py index aead7c2..3da0137 100644 --- a/tests/test_unittest_replications.py +++ b/tests/test_unittest_replications.py @@ -13,11 +13,12 @@ pytest """ +from unittest.mock import MagicMock + import numpy as np import pandas as pd import pytest import scipy.stats as st -from unittest.mock import MagicMock from simulation.replications import ( ReplicationsAlgorithm, OnlineStatistics, ReplicationTabulizer) @@ -104,7 +105,7 @@ def test_onlinestat_computations(): loc=np.mean(values), scale=st.sem(values) ) - expected_dev = ((expected_uci - expected_mean) / expected_mean) + expected_dev = (expected_uci - expected_mean) / expected_mean # Assertions assert np.isclose(stats.mean, expected_mean), ( @@ -125,12 +126,13 @@ def test_tabulizer_initial_state(): """ tab = ReplicationTabulizer() assert tab.n == 0 - assert tab.x_i == [] - assert tab.cumulative_mean == [] - assert tab.stdev == [] - assert tab.lower == [] - assert tab.upper == [] - assert tab.dev == [] + # Checks for empty lists (equivalent to len(tab.x_i)==0) + assert not tab.x_i + assert not tab.cumulative_mean + assert not tab.stdev + assert not tab.lower + assert not tab.upper + assert not tab.dev def test_tabulizer_update(): From 31cd66026ffe308e5d59e2e9234bf2d944388d38 Mon Sep 17 00:00:00 2001 From: amyheather Date: Fri, 14 Feb 2025 14:16:22 +0000 Subject: [PATCH 21/28] feat(replications): add explanation of OnlineStatistics and ReplicationTabulizer --- images/replications_statistics.drawio | 145 ++++++++++++++++++++++++++ images/replications_statistics.png | Bin 0 -> 420177 bytes notebooks/choosing_replications.ipynb | 122 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b/notebooks/choosing_replications.ipynb @@ -16,10 +16,13 @@ "\n", "This notebook shares two ways of implementing this: **manually** and **automated**.\n", "\n", + "At the end, there is a section that walks **step-by-step through the classes and algorithm used for the automated versions**.\n", + "\n", "Credit:\n", "\n", - "* Code for manual choice of replication number was adapted from Tom Monks (2024) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence).\n", - "* Code for automated detection of replication number was adapted from Tom Monks (2025) [sim-tools: tools to support the Discrete-Event Simulation process in python](https://github.com/TomMonks/sim-tools) (MIT Licence)." + "* Code for this notebooks was adapted from:\n", + " * Tom Monks (2025) [HPDM097 - Making a difference with health data](https://github.com/health-data-science-OR/stochastic_systems) (MIT Licence).\n", + " * Tom Monks (2025) [sim-tools: tools to support the Discrete-Event Simulation process in python](https://github.com/TomMonks/sim-tools) (MIT Licence)." ] }, { @@ -424,7 +427,7 @@ { "data": { "image/svg+xml": [ - "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -433,7 +436,7 @@ { "data": { "image/svg+xml": [ - "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" + "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -442,7 +445,7 @@ { "data": { "image/svg+xml": [ - "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -721,7 +724,7 @@ { "data": { "image/svg+xml": [ - "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -730,7 +733,7 @@ { "data": { "image/svg+xml": [ - "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" + "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -739,7 +742,7 @@ { "data": { "image/svg+xml": [ - "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -1528,7 +1531,7 @@ { "data": { "image/svg+xml": [ - "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -1537,7 +1540,7 @@ { "data": { "image/svg+xml": [ - "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" + "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -1546,7 +1549,7 @@ { "data": { "image/svg+xml": [ - "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -1562,6 +1565,101 @@ " p.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Explanation of the automated method\n", + "\n", + "This section walks through how the automation code is structured. The algorithm that determines the number of replications is `ReplicationsAlgorithm`. This depends on other classes including `OnlineStatistics` and `ReplicationTabulizer`.\n", + "\n", + "### OnlineStatistics\n", + "\n", + "`OnlineStatistics` is designed to:\n", + "\n", + "* Keep a **running mean and variance**.\n", + "* Return **other statistics** based on these (e.g. standard deviation, confidence intervals).\n", + "* **Call the `update()`** method of `ReplicationTabulizer` whenever a new data point is processed by `OnlineStatistics.\n", + "\n", + "More information...\n", + "\n", + "

\n", + " How do the running mean and variance calculations work?\n", + "\n", + "The running mean and variance are updated iteratively with each new data point provided, **without requiring the storage of all previous data points**. This approach is called \"online\" because we only need to store a small set of values (such as the current mean and variance), rather than maintaining an entire list of past values.\n", + "\n", + "For example, focusing on the mean, normally you would need to store all the data points in a list and sum them up to compute the average - for example:\n", + "\n", + "```\n", + "data_points = [1, 2, 3, 4, 5]\n", + "mean = sum(data_points) / len(data_points)\n", + "```\n", + "\n", + "This works fine for small datasets, but as the data grows, maintaining the entire list becomes impractical. Instead, we can update the mean without storing the previous data points using **Welford's algorithm**. The formula for the running mean is:\n", + "\n", + "$$\n", + "\\mu_n = \\mu_{n-1} + \\frac{x_n - \\mu_{n-1}}{n}\n", + "$$\n", + "\n", + "Where:\n", + "\n", + "- $\\mu_n$ is the running mean after the $n$-th data point.\n", + "- $x_n$ is the new data point.\n", + "- $\\mu_{n-1}$ is the running mean before the new data point.\n", + "\n", + "The key thing to notice here is that, to update the mean, **all we needed to know was the current running mean, the new data point, and the number of data points**. A similar formula exists for calculating variance.\n", + "\n", + "In our code, every time we call `update()` with a new data point, the mean and variance are adjusted, with `n` keeping track of the number of data points so far.\n", + "\n", + "```\n", + "class OnlineStatistics:\n", + " def __init__(self):\n", + " self.mean = 0\n", + " self.n = 0\n", + "\n", + " def update(self, new_data_point):\n", + " self.n += 1\n", + " self.mean += (new_data_point - self.mean) / self.n\n", + "```\n", + "
\n", + "\n", + "
\n", + "\n", + "
\n", + " What other statistics can it calculate?\n", + " \n", + "`OnlineStatistics` then has a series of methods which can return other statistics based on the current mean, variance, and count:\n", + "\n", + "* Variance\n", + "* Standard deviation\n", + "* Standard error\n", + "* Half width of the confidence interval\n", + "* Lower confidence interval bound\n", + "* Upper confidence interval bound\n", + "* Deviation of confidence interval from the mean\n", + "\n", + "These are each marked with `@property`. This is a stylist choice, that simply means, we are able to get the value by running e.g. `stats.lci`, instead of `stats.lci()`.\n", + "
\n", + "\n", + "### ReplicationTabulizer\n", + "\n", + "`ReplicationTabulizer` keeps track of our results. It:\n", + "\n", + "* Stores **lists with various statistics**, which are updated whenever `update()` is called.\n", + "* Can convert these into a **dataframe** using the `summary_frame()` method.\n", + "\n", + "![Interaction between OnlineStatistics and ReplicationTabulizer](../images/replications_statistics.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### ReplicationsAlgorithm\n", + "\n", + "TODO" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -1578,7 +1676,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Notebook run time: 0m 11s\n" + "Notebook run time: 0m 10s\n" ] } ], From 842bbe5bc4e29c7b24dd7f99f7b857c6c703cdb5 Mon Sep 17 00:00:00 2001 From: amyheather Date: Wed, 26 Feb 2025 16:32:51 +0000 Subject: [PATCH 22/28] fix(replications): ReplicationsAlgorithm now can run initial replications in parallel, and will only check precision after all have been run (and not for each) --- simulation/replications.py | 59 ++++++++++++++++++++++++-------------- 1 file changed, 37 insertions(+), 22 deletions(-) diff --git a/simulation/replications.py b/simulation/replications.py index 6554a1c..8235219 100644 --- a/simulation/replications.py +++ b/simulation/replications.py @@ -262,7 +262,11 @@ class ReplicationsAlgorithm: The target half width precision for the algorithm (i.e. percentage deviation of the confidence interval from the mean). initial_replications (int): - Number of initial replications to perform. + Number of initial replications to perform. Note that the minimum + solution will be the value of initial_replications (i.e. if require + 20 initial replications but was resolved in 3, solution output + will still be 20). Recommend this is set to at least 3, as the + statistics cannot be calculated with less than 3. look_ahead (int): Minimum additional replications to look ahead to assess stability of precision. When the number of replications is <= 100, the value @@ -350,17 +354,12 @@ def select(self, runner, metrics): Issues a warning if the desired precision is not met for any metrics before the replication limit is met. """ - # Create instances of observers and stats for each metric + # Create instances of observers for each metric observers = { metric: ReplicationTabulizer() for metric in metrics} - stats = { - metric: OnlineStatistics( - alpha=self.alpha, observer=observers[metric]) - for metric in metrics - } - # Empty dictionary to store record for each metric of: + # Create dictionary to store record for each metric of: # - nreps (the solution - replications required for precision) # - target_met (record of how many times in a row the target has # has been met, used to check if lookahead period has been passed) @@ -370,20 +369,36 @@ def select(self, runner, metrics): 'target_met': 0, 'solved': False} for metric in metrics} - # Run initial replications and get results for each metric - for rep in range(self.initial_replications): - results = runner.run_single(rep)['run'] + # If there are no initial replications, create empty instances of stats + # for each metric... + if self.initial_replications == 0: + stats = { + metric: OnlineStatistics( + alpha=self.alpha, observer=observers[metric]) + for metric in metrics + } + # If there are, run the replications, then create instances of stats + # pre-loaded with data from the initial replications... + # (we use run_reps() which allows for parallel processing if desired) + else: + stats = {} + runner.param.number_of_runs = self.initial_replications + runner.run_reps() for metric in metrics: - stats[metric].update(results[metric]) - - # If any have met precision, add solution and update count - if stats[metric].deviation <= self.half_width_precision: - solutions[metric]['nreps'] = self.n - solutions[metric]['target_met'] = 1 - - # If there is no lookahead, mark as solved - if self._klimit() == 0: - solutions[metric]['solved'] = True + stats[metric] = OnlineStatistics( + alpha=self.alpha, + observer=observers[metric], + data=np.array(runner.run_results_df[metric])) + + # After completing all replications, check if any have met precision, + # add solution and update count + for metric in metrics: + if stats[metric].deviation <= self.half_width_precision: + solutions[metric]['nreps'] = self.n + solutions[metric]['target_met'] = 1 + # If there is no lookahead, mark as solved + if self._klimit() == 0: + solutions[metric]['solved'] = True # Whilst under replication budget + lookahead, and have not yet # got all metrics marked as solved = TRUE... @@ -435,7 +450,7 @@ def select(self, runner, metrics): summary_frame = pd.concat( [observer.summary_table().assign(metric=metric) for metric, observer in observers.items()] - ) + ).reset_index(drop=True) # Extract any metrics that were not solved and return warning if None in nreps.values(): From 7e4ca18802517414effe64bf741f743e16bb66ea Mon Sep 17 00:00:00 2001 From: amyheather Date: Wed, 26 Feb 2025 16:33:27 +0000 Subject: [PATCH 23/28] tests(replication): add test related to recent fix - that algorithm only check precision after all initial replications have been run (and not for each) --- tests/test_functionaltest_replications.py | 34 +++++++++++++++++++++++ 1 file changed, 34 insertions(+) diff --git a/tests/test_functionaltest_replications.py b/tests/test_functionaltest_replications.py index be5c0d1..efcb59b 100644 --- a/tests/test_functionaltest_replications.py +++ b/tests/test_functionaltest_replications.py @@ -103,3 +103,37 @@ def test_consistent_outputs(ci_function): # look ahead period beyond budget) and compare dataframes pd.testing.assert_frame_equal( man_df, summary_table.head(20)) + + +def test_algorithm_initial(): + """ + Check that the solution from the ReplicationsAlgorithm is as expected when + there is a high number of initial replications specified. + """ + # Set parameters - inc. high number of initial replications & no look-ahead + # As model is currently designed, all would be solved before 200 reps + initial_replications = 200 + look_ahead = 0 + metrics = ['mean_time_with_nurse', + 'mean_q_time_nurse', + 'mean_nurse_utilisation'] + + # Set up algorithm class + analyser = ReplicationsAlgorithm( + initial_replications=initial_replications, + look_ahead=look_ahead) + + # Run the algorithm + nreps, summary_table = analyser.select( + runner=Runner(Param()), + metrics=metrics) + + # For each metric... + for metric in metrics: + + # Check that nrow for each metric in summary table equals initial reps + nrows = len(summary_table[summary_table['metric'] == metric]) + assert nrows == initial_replications + + # Check that solution is equal to the initial replications + assert nreps[metric] == initial_replications From bf81ec2cd8bbf9ce77113cc2d94fee3c289d8ad7 Mon Sep 17 00:00:00 2001 From: amyheather Date: Wed, 26 Feb 2025 16:46:28 +0000 Subject: [PATCH 24/28] chore(docs): minor corrections to replications.py docstring and hsma_changes.md --- docs/hsma_changes.md | 2 +- simulation/replications.py | 7 ++++--- 2 files changed, 5 insertions(+), 4 deletions(-) diff --git a/docs/hsma_changes.md b/docs/hsma_changes.md index ca31275..650d5a1 100644 --- a/docs/hsma_changes.md +++ b/docs/hsma_changes.md @@ -1,6 +1,6 @@ # Changes from the HSMA model -This template model is adapted from Sammi Rosser and Dan Chalk (2024) HSMA - the little book of DES (https://github.com/hsma-programme/hsma6_des_book) (MIT Licence). It also uses an exponentional distribution class from Monks (2021) sim-tools: fundamental tools to support the simulation process in python (https://github.com/TomMonks/sim-tools) (MIT Licence). +This template model is based on a few sources - including: Sammi Rosser and Dan Chalk (2024) HSMA - the little book of DES (https://github.com/hsma-programme/hsma6_des_book) (MIT Licence). This page explains some of the differences between the models in the HSMA book, and this template. diff --git a/simulation/replications.py b/simulation/replications.py index 8235219..6f4a9fe 100644 --- a/simulation/replications.py +++ b/simulation/replications.py @@ -264,9 +264,10 @@ class ReplicationsAlgorithm: initial_replications (int): Number of initial replications to perform. Note that the minimum solution will be the value of initial_replications (i.e. if require - 20 initial replications but was resolved in 3, solution output - will still be 20). Recommend this is set to at least 3, as the - statistics cannot be calculated with less than 3. + 20 initial replications but was resolved in 5, solution output + will still be 20). Although, if initial_replications < 3, solution + will still be at least 3, as that is the minimum required to + calculate the confidence intervals. look_ahead (int): Minimum additional replications to look ahead to assess stability of precision. When the number of replications is <= 100, the value From be7988a50d90d25006ecce447402446c8a5c027d Mon Sep 17 00:00:00 2001 From: amyheather Date: Wed, 26 Feb 2025 16:46:48 +0000 Subject: [PATCH 25/28] tests(replication): add test for ReplicationsAlgorithm when initial replications < 3 --- tests/test_functionaltest_replications.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) diff --git a/tests/test_functionaltest_replications.py b/tests/test_functionaltest_replications.py index efcb59b..7648bd9 100644 --- a/tests/test_functionaltest_replications.py +++ b/tests/test_functionaltest_replications.py @@ -137,3 +137,24 @@ def test_algorithm_initial(): # Check that solution is equal to the initial replications assert nreps[metric] == initial_replications + + +def test_algorithm_nosolution(): + """ + Check that running for less than 3 replications in total will result + in no solution, and that a warning message is then created. + """ + # Set up algorithm to run max of 2 replications + analyser = ReplicationsAlgorithm( + initial_replications=0, replication_budget=2, look_ahead=0) + + # Run algorithm, checking that it produces a warning + with pytest.warns(UserWarning): + solutions, summary_table = analyser.select( + runner=Runner(Param()), metrics=['mean_time_with_nurse']) + + # Check that there is no solution + assert solutions['mean_time_with_nurse'] is None + + # Check that the summary tables has no more than 2 rows + assert len(summary_table) < 3 From 5df7a0c741cd9f5017dd38917e26911537961aaa Mon Sep 17 00:00:00 2001 From: amyheather Date: Thu, 27 Feb 2025 10:10:58 +0000 Subject: [PATCH 26/28] docs(replications): add explanation of ReplicationsAlgorithm --- images/replications_algorithm.drawio | 411 ++++++++++++++++++++++++++ images/replications_algorithm.png | Bin 0 -> 1094804 bytes notebooks/choosing_replications.ipynb | 323 ++++++++++---------- 3 files changed, 578 insertions(+), 156 deletions(-) create mode 100644 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+725,7 @@ { "data": { "image/svg+xml": [ - "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + "5101520253035409.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -733,7 +734,7 @@ { "data": { "image/svg+xml": [ - "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" + "5101520253035400.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -742,7 +743,7 @@ { "data": { "image/svg+xml": [ - "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "5101520253035400.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -904,7 +905,7 @@ " mean_time_with_nurse\n", " \n", " \n", - " 0\n", + " 8\n", " 1\n", " 0.501873\n", " 0.501873\n", @@ -915,7 +916,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 1\n", + " 9\n", " 2\n", " 0.516264\n", " 0.509069\n", @@ -926,7 +927,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 2\n", + " 10\n", " 3\n", " 0.531280\n", " 0.516472\n", @@ -937,7 +938,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 3\n", + " 11\n", " 4\n", " 0.491104\n", " 0.510130\n", @@ -948,7 +949,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 4\n", + " 12\n", " 5\n", " 0.463252\n", " 0.500755\n", @@ -959,7 +960,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 5\n", + " 13\n", " 6\n", " 0.390495\n", " 0.482378\n", @@ -970,7 +971,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 6\n", + " 14\n", " 7\n", " 0.473277\n", " 0.481078\n", @@ -981,7 +982,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 7\n", + " 15\n", " 8\n", " 0.611502\n", " 0.497381\n", @@ -992,7 +993,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 8\n", + " 16\n", " 9\n", " 0.478926\n", " 0.495330\n", @@ -1003,7 +1004,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 9\n", + " 17\n", " 10\n", " 0.564747\n", " 0.502272\n", @@ -1014,7 +1015,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 10\n", + " 18\n", " 11\n", " 0.571089\n", " 0.508528\n", @@ -1025,7 +1026,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 11\n", + " 19\n", " 12\n", " 0.393387\n", " 0.498933\n", @@ -1036,7 +1037,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 12\n", + " 20\n", " 13\n", " 0.444403\n", " 0.494738\n", @@ -1047,7 +1048,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 13\n", + " 21\n", " 14\n", " 0.447649\n", " 0.491375\n", @@ -1058,7 +1059,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 14\n", + " 22\n", " 15\n", " 0.614848\n", " 0.499606\n", @@ -1069,7 +1070,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 15\n", + " 23\n", " 16\n", " 0.461512\n", " 0.497225\n", @@ -1080,7 +1081,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 16\n", + " 24\n", " 17\n", " 0.425752\n", " 0.493021\n", @@ -1091,7 +1092,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 17\n", + " 25\n", " 18\n", " 0.481117\n", " 0.492360\n", @@ -1102,7 +1103,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 18\n", + " 26\n", " 19\n", " 0.376149\n", " 0.486243\n", @@ -1113,7 +1114,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 19\n", + " 27\n", " 20\n", " 0.429078\n", " 0.483385\n", @@ -1124,7 +1125,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 20\n", + " 28\n", " 21\n", " 0.624518\n", " 0.490106\n", @@ -1135,7 +1136,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 21\n", + " 29\n", " 22\n", " 0.515244\n", " 0.491248\n", @@ -1146,7 +1147,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 22\n", + " 30\n", " 23\n", " 0.525237\n", " 0.492726\n", @@ -1157,7 +1158,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 23\n", + " 31\n", " 24\n", " 0.520427\n", " 0.493880\n", @@ -1168,7 +1169,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 24\n", + " 32\n", " 25\n", " 0.519650\n", " 0.494911\n", @@ -1179,7 +1180,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 25\n", + " 33\n", " 26\n", " 0.638633\n", " 0.500439\n", @@ -1190,7 +1191,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 26\n", + " 34\n", " 27\n", " 0.514672\n", " 0.500966\n", @@ -1201,7 +1202,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 27\n", + " 35\n", " 28\n", " 0.503287\n", " 0.501049\n", @@ -1212,7 +1213,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 28\n", + " 36\n", " 29\n", " 0.461636\n", " 0.499690\n", @@ -1223,7 +1224,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 29\n", + " 37\n", " 30\n", " 0.551549\n", " 0.501419\n", @@ -1234,7 +1235,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 30\n", + " 38\n", " 31\n", " 0.509837\n", " 0.501690\n", @@ -1245,7 +1246,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 31\n", + " 39\n", " 32\n", " 0.490932\n", " 0.501354\n", @@ -1256,7 +1257,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 32\n", + " 40\n", " 33\n", " 0.558335\n", " 0.503081\n", @@ -1267,7 +1268,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 33\n", + " 41\n", " 34\n", " 0.548240\n", " 0.504409\n", @@ -1278,7 +1279,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 34\n", + " 42\n", " 35\n", " 0.457886\n", " 0.503080\n", @@ -1289,7 +1290,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 35\n", + " 43\n", " 36\n", " 0.464776\n", " 0.502016\n", @@ -1300,7 +1301,7 @@ " mean_q_time_nurse\n", " \n", " \n", - " 0\n", + " 44\n", " 1\n", " 0.499589\n", " 0.499589\n", @@ -1311,7 +1312,7 @@ " mean_nurse_utilisation\n", " \n", " \n", - " 1\n", + " 45\n", " 2\n", " 0.501977\n", " 0.500783\n", @@ -1322,7 +1323,7 @@ " mean_nurse_utilisation\n", " \n", " \n", - " 2\n", + " 46\n", " 3\n", " 0.498094\n", " 0.499887\n", @@ -1333,7 +1334,7 @@ " mean_nurse_utilisation\n", " \n", " \n", - " 3\n", + " 47\n", " 4\n", " 0.498249\n", " 0.499477\n", @@ -1344,7 +1345,7 @@ " mean_nurse_utilisation\n", " \n", " \n", - " 4\n", + " 48\n", " 5\n", " 0.496934\n", " 0.498969\n", @@ -1355,7 +1356,7 @@ " mean_nurse_utilisation\n", " \n", " \n", - " 5\n", + " 49\n", " 6\n", " 0.492854\n", " 0.497949\n", @@ -1366,7 +1367,7 @@ " mean_nurse_utilisation\n", " \n", " \n", - " 6\n", + " 50\n", " 7\n", " 0.503386\n", " 0.498726\n", @@ -1377,7 +1378,7 @@ " mean_nurse_utilisation\n", " \n", " \n", - " 7\n", + " 51\n", " 8\n", " 0.503411\n", " 0.499312\n", @@ -1401,50 +1402,50 @@ "5 6 9.883801 9.944468 0.081442 9.859000 10.029936 \n", "6 7 10.040555 9.958195 0.082742 9.881671 10.034719 \n", "7 8 10.086612 9.974247 0.089048 9.899801 10.048693 \n", - "0 1 0.501873 0.501873 NaN NaN NaN \n", - "1 2 0.516264 0.509069 NaN NaN NaN \n", - "2 3 0.531280 0.516472 0.014704 0.479945 0.553000 \n", - "3 4 0.491104 0.510130 0.017465 0.482339 0.537921 \n", - "4 5 0.463252 0.500755 0.025851 0.468656 0.532853 \n", - "5 6 0.390495 0.482378 0.050605 0.429272 0.535484 \n", - "6 7 0.473277 0.481078 0.046323 0.438236 0.523920 \n", - "7 8 0.611502 0.497381 0.062973 0.444734 0.550028 \n", - "8 9 0.478926 0.495330 0.059226 0.449805 0.540856 \n", - "9 10 0.564747 0.502272 0.059999 0.459351 0.545193 \n", - "10 11 0.571089 0.508528 0.060584 0.467827 0.549229 \n", - "11 12 0.393387 0.498933 0.066645 0.456589 0.541277 \n", - "12 13 0.444403 0.494738 0.065575 0.455112 0.534365 \n", - "13 14 0.447649 0.491375 0.064247 0.454280 0.528470 \n", - "14 15 0.614848 0.499606 0.069637 0.461043 0.538170 \n", - "15 16 0.461512 0.497225 0.067946 0.461020 0.533431 \n", - "16 17 0.425752 0.493021 0.068034 0.458041 0.528001 \n", - "17 18 0.481117 0.492360 0.066062 0.459508 0.525212 \n", - "18 19 0.376149 0.486243 0.069517 0.452738 0.519749 \n", - "19 20 0.429078 0.483385 0.068859 0.451158 0.515612 \n", - "20 21 0.624518 0.490106 0.073845 0.456492 0.523719 \n", - "21 22 0.515244 0.491248 0.072264 0.459208 0.523288 \n", - "22 23 0.525237 0.492726 0.070957 0.462042 0.523410 \n", - "23 24 0.520427 0.493880 0.069628 0.464479 0.523282 \n", - "24 25 0.519650 0.494911 0.068356 0.466695 0.523127 \n", - "25 26 0.638633 0.500439 0.072664 0.471089 0.529789 \n", - "26 27 0.514672 0.500966 0.071306 0.472758 0.529174 \n", - "27 28 0.503287 0.501049 0.069974 0.473916 0.528182 \n", - "28 29 0.461636 0.499690 0.069102 0.473405 0.525975 \n", - "29 30 0.551549 0.501419 0.068557 0.475819 0.527018 \n", - "30 31 0.509837 0.501690 0.067422 0.476960 0.526421 \n", - "31 32 0.490932 0.501354 0.066353 0.477431 0.525277 \n", - "32 33 0.558335 0.503081 0.066057 0.479658 0.526503 \n", - "33 34 0.548240 0.504409 0.065508 0.481552 0.527266 \n", - "34 35 0.457886 0.503080 0.065014 0.480746 0.525413 \n", - "35 36 0.464776 0.502016 0.064396 0.480227 0.523804 \n", - "0 1 0.499589 0.499589 NaN NaN NaN \n", - "1 2 0.501977 0.500783 NaN NaN NaN \n", - "2 3 0.498094 0.499887 0.001959 0.495021 0.504753 \n", - "3 4 0.498249 0.499477 0.001797 0.496618 0.502336 \n", - "4 5 0.496934 0.498969 0.001928 0.496575 0.501362 \n", - "5 6 0.492854 0.497949 0.003034 0.494766 0.501133 \n", - "6 7 0.503386 0.498726 0.003448 0.495537 0.501915 \n", - "7 8 0.503411 0.499312 0.003597 0.496305 0.502319 \n", + "8 1 0.501873 0.501873 NaN NaN NaN \n", + "9 2 0.516264 0.509069 NaN NaN NaN \n", + "10 3 0.531280 0.516472 0.014704 0.479945 0.553000 \n", + "11 4 0.491104 0.510130 0.017465 0.482339 0.537921 \n", + "12 5 0.463252 0.500755 0.025851 0.468656 0.532853 \n", + "13 6 0.390495 0.482378 0.050605 0.429272 0.535484 \n", + "14 7 0.473277 0.481078 0.046323 0.438236 0.523920 \n", + "15 8 0.611502 0.497381 0.062973 0.444734 0.550028 \n", + "16 9 0.478926 0.495330 0.059226 0.449805 0.540856 \n", + "17 10 0.564747 0.502272 0.059999 0.459351 0.545193 \n", + "18 11 0.571089 0.508528 0.060584 0.467827 0.549229 \n", + "19 12 0.393387 0.498933 0.066645 0.456589 0.541277 \n", + "20 13 0.444403 0.494738 0.065575 0.455112 0.534365 \n", + "21 14 0.447649 0.491375 0.064247 0.454280 0.528470 \n", + "22 15 0.614848 0.499606 0.069637 0.461043 0.538170 \n", + "23 16 0.461512 0.497225 0.067946 0.461020 0.533431 \n", + "24 17 0.425752 0.493021 0.068034 0.458041 0.528001 \n", + "25 18 0.481117 0.492360 0.066062 0.459508 0.525212 \n", + "26 19 0.376149 0.486243 0.069517 0.452738 0.519749 \n", + "27 20 0.429078 0.483385 0.068859 0.451158 0.515612 \n", + "28 21 0.624518 0.490106 0.073845 0.456492 0.523719 \n", + "29 22 0.515244 0.491248 0.072264 0.459208 0.523288 \n", + "30 23 0.525237 0.492726 0.070957 0.462042 0.523410 \n", + "31 24 0.520427 0.493880 0.069628 0.464479 0.523282 \n", + "32 25 0.519650 0.494911 0.068356 0.466695 0.523127 \n", + "33 26 0.638633 0.500439 0.072664 0.471089 0.529789 \n", + "34 27 0.514672 0.500966 0.071306 0.472758 0.529174 \n", + "35 28 0.503287 0.501049 0.069974 0.473916 0.528182 \n", + "36 29 0.461636 0.499690 0.069102 0.473405 0.525975 \n", + "37 30 0.551549 0.501419 0.068557 0.475819 0.527018 \n", + "38 31 0.509837 0.501690 0.067422 0.476960 0.526421 \n", + "39 32 0.490932 0.501354 0.066353 0.477431 0.525277 \n", + "40 33 0.558335 0.503081 0.066057 0.479658 0.526503 \n", + "41 34 0.548240 0.504409 0.065508 0.481552 0.527266 \n", + "42 35 0.457886 0.503080 0.065014 0.480746 0.525413 \n", + "43 36 0.464776 0.502016 0.064396 0.480227 0.523804 \n", + "44 1 0.499589 0.499589 NaN NaN NaN \n", + "45 2 0.501977 0.500783 NaN NaN NaN \n", + "46 3 0.498094 0.499887 0.001959 0.495021 0.504753 \n", + "47 4 0.498249 0.499477 0.001797 0.496618 0.502336 \n", + "48 5 0.496934 0.498969 0.001928 0.496575 0.501362 \n", + "49 6 0.492854 0.497949 0.003034 0.494766 0.501133 \n", + "50 7 0.503386 0.498726 0.003448 0.495537 0.501915 \n", + "51 8 0.503411 0.499312 0.003597 0.496305 0.502319 \n", "\n", " deviation metric \n", "0 NaN mean_time_with_nurse \n", @@ -1455,50 +1456,50 @@ "5 0.008595 mean_time_with_nurse \n", "6 0.007685 mean_time_with_nurse \n", "7 0.007464 mean_time_with_nurse \n", - "0 NaN mean_q_time_nurse \n", - "1 NaN mean_q_time_nurse \n", - "2 0.070725 mean_q_time_nurse \n", - "3 0.054479 mean_q_time_nurse \n", - "4 0.064100 mean_q_time_nurse \n", - "5 0.110092 mean_q_time_nurse \n", - "6 0.089054 mean_q_time_nurse \n", - "7 0.105848 mean_q_time_nurse \n", - "8 0.091909 mean_q_time_nurse \n", - "9 0.085453 mean_q_time_nurse \n", - "10 0.080036 mean_q_time_nurse \n", - "11 0.084869 mean_q_time_nurse \n", - "12 0.080096 mean_q_time_nurse \n", - "13 0.075493 mean_q_time_nurse \n", - "14 0.077188 mean_q_time_nurse \n", - "15 0.072816 mean_q_time_nurse \n", - "16 0.070950 mean_q_time_nurse \n", - "17 0.066724 mean_q_time_nurse \n", - "18 0.068908 mean_q_time_nurse \n", - "19 0.066670 mean_q_time_nurse \n", - "20 0.068584 mean_q_time_nurse \n", - "21 0.065222 mean_q_time_nurse \n", - "22 0.062274 mean_q_time_nurse \n", - "23 0.059531 mean_q_time_nurse \n", - "24 0.057012 mean_q_time_nurse \n", - "25 0.058648 mean_q_time_nurse \n", - "26 0.056307 mean_q_time_nurse \n", - "27 0.054153 mean_q_time_nurse \n", - "28 0.052603 mean_q_time_nurse \n", - "29 0.051055 mean_q_time_nurse \n", - "30 0.049294 mean_q_time_nurse \n", - "31 0.047716 mean_q_time_nurse \n", - "32 0.046559 mean_q_time_nurse \n", - "33 0.045314 mean_q_time_nurse \n", - "34 0.044393 mean_q_time_nurse \n", - "35 0.043402 mean_q_time_nurse \n", - "0 NaN mean_nurse_utilisation \n", - "1 NaN mean_nurse_utilisation \n", - "2 0.009734 mean_nurse_utilisation \n", - "3 0.005725 mean_nurse_utilisation \n", - "4 0.004797 mean_nurse_utilisation \n", - "5 0.006394 mean_nurse_utilisation \n", - "6 0.006395 mean_nurse_utilisation \n", - "7 0.006022 mean_nurse_utilisation " + "8 NaN mean_q_time_nurse \n", + "9 NaN mean_q_time_nurse \n", + "10 0.070725 mean_q_time_nurse \n", + "11 0.054479 mean_q_time_nurse \n", + "12 0.064100 mean_q_time_nurse \n", + "13 0.110092 mean_q_time_nurse \n", + "14 0.089054 mean_q_time_nurse \n", + "15 0.105848 mean_q_time_nurse \n", + "16 0.091909 mean_q_time_nurse \n", + "17 0.085453 mean_q_time_nurse \n", + "18 0.080036 mean_q_time_nurse \n", + "19 0.084869 mean_q_time_nurse \n", + "20 0.080096 mean_q_time_nurse \n", + "21 0.075493 mean_q_time_nurse \n", + "22 0.077188 mean_q_time_nurse \n", + "23 0.072816 mean_q_time_nurse \n", + "24 0.070950 mean_q_time_nurse \n", + "25 0.066724 mean_q_time_nurse \n", + "26 0.068908 mean_q_time_nurse \n", + "27 0.066670 mean_q_time_nurse \n", + "28 0.068584 mean_q_time_nurse \n", + "29 0.065222 mean_q_time_nurse \n", + "30 0.062274 mean_q_time_nurse \n", + "31 0.059531 mean_q_time_nurse \n", + "32 0.057012 mean_q_time_nurse \n", + "33 0.058648 mean_q_time_nurse \n", + "34 0.056307 mean_q_time_nurse \n", + "35 0.054153 mean_q_time_nurse \n", + "36 0.052603 mean_q_time_nurse \n", + "37 0.051055 mean_q_time_nurse \n", + "38 0.049294 mean_q_time_nurse \n", + "39 0.047716 mean_q_time_nurse \n", + "40 0.046559 mean_q_time_nurse \n", + "41 0.045314 mean_q_time_nurse \n", + "42 0.044393 mean_q_time_nurse \n", + "43 0.043402 mean_q_time_nurse \n", + "44 NaN mean_nurse_utilisation \n", + "45 NaN mean_nurse_utilisation \n", + "46 0.009734 mean_nurse_utilisation \n", + "47 0.005725 mean_nurse_utilisation \n", + "48 0.004797 mean_nurse_utilisation \n", + "49 0.006394 mean_nurse_utilisation \n", + "50 0.006395 mean_nurse_utilisation \n", + "51 0.006022 mean_nurse_utilisation " ] }, "metadata": {}, @@ -1531,7 +1532,7 @@ { "data": { "image/svg+xml": [ - "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" + "123456789.79.89.91010.110.2lower_ciupper_ciCumulative MeanCumulative Mean: Mean consultation time with nurse (minutes)" ] }, "metadata": {}, @@ -1540,7 +1541,7 @@ { "data": { "image/svg+xml": [ - "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" + "51015202530350.450.50.55lower_ciupper_ciCumulative MeanCumulative Mean: Mean wait time for nurse (minutes)" ] }, "metadata": {}, @@ -1549,7 +1550,7 @@ { "data": { "image/svg+xml": [ - "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" + "123456780.4940.4960.4980.50.5020.504lower_ciupper_ciCumulative MeanCumulative Mean: Mean nurse utilisation" ] }, "metadata": {}, @@ -1581,10 +1582,7 @@ "* Return **other statistics** based on these (e.g. standard deviation, confidence intervals).\n", "* **Call the `update()`** method of `ReplicationTabulizer` whenever a new data point is processed by `OnlineStatistics.\n", "\n", - "More information...\n", - "\n", - "

\n", - " How do the running mean and variance calculations work?\n", + "#### How do the running mean and variance calculations work?\n", "\n", "The running mean and variance are updated iteratively with each new data point provided, **without requiring the storage of all previous data points**. This approach is called \"online\" because we only need to store a small set of values (such as the current mean and variance), rather than maintaining an entire list of past values.\n", "\n", @@ -1621,12 +1619,8 @@ " self.n += 1\n", " self.mean += (new_data_point - self.mean) / self.n\n", "```\n", - "
\n", "\n", - "
\n", - "\n", - "
\n", - " What other statistics can it calculate?\n", + "#### What other statistics can it calculate?\n", " \n", "`OnlineStatistics` then has a series of methods which can return other statistics based on the current mean, variance, and count:\n", "\n", @@ -1639,7 +1633,6 @@ "* Deviation of confidence interval from the mean\n", "\n", "These are each marked with `@property`. This is a stylist choice, that simply means, we are able to get the value by running e.g. `stats.lci`, instead of `stats.lci()`.\n", - "
\n", "\n", "### ReplicationTabulizer\n", "\n", @@ -1657,7 +1650,25 @@ "source": [ "### ReplicationsAlgorithm\n", "\n", - "TODO" + "The diagram below is a visual representation of the logic in the **ReplicationsAlgorithm**.\n", + "\n", + "Once set up with the relevant parameters, it will first check if there are **initial_replications** to run. These might be specified if the user knows that the model will need at least X amount of replications before any metrics start to get close to the desired precision. The benefit of specifying these is that they are run using **run_reps()** and so can be run in parallel if chosen.\n", + "\n", + "Once these are run, it checks if any metrics meet precision already. Typically more replications will be required (for the length of the lookahead period) - but if there is no lookahead, they can be marked as solved.\n", + "\n", + "> **What is the lookahead period?**\n", + ">\n", + "> We want to make sure that the desired precision is stable and maintained for several replications. Here, we refer to this as the lookahead period.\n", + ">\n", + "> The user will specify **look_ahead** - as noted in [sim-tools](https://tommonks.github.io/sim-tools/04_replications/01_automated_reps.html), this is recommended to be **5** by [Hoad et al. (2010)](https://www.jstor.org/stable/40926090).\n", + ">\n", + "> The algorithm contains a method **_k_limit()** which will scale up the lookahead if more than 100 replications have been run, to ensure a sufficient period is being checked for stability, relative to the number of replications. This is simply: `look_ahead/100 * replications`. For example, if we have run 200 replications and look_ahead is 5: `5/100 * 200 = 10`.\n", + "\n", + "After any initial replications, the algorithm enters a while loop. This continues until all metrics are solved or the number of replications surpasses the user-specified **replication_budget** - whichever comes first!\n", + "\n", + "With each loop, it runs the model for another replication, then updates the results for any unsolved metrics from this replication, and checks if precision is met. The **target_met** is a record of how many times in a row precision has been met - once this passes the lookahead period, the metric is marked as solved.\n", + "\n", + "![Visual representation of logic in ReplicationsAlgorithm](../images/replications_algorithm.png)" ] }, { From 183df48ae1f53388efbcac2422a34925e1208509 Mon Sep 17 00:00:00 2001 From: amyheather Date: Thu, 27 Feb 2025 10:39:36 +0000 Subject: [PATCH 27/28] tests(model): add extreme nurses test --- tests/test_functionaltest.py | 29 ++++++++++++++++++++++++++++- 1 file changed, 28 insertions(+), 1 deletion(-) diff --git a/tests/test_functionaltest.py b/tests/test_functionaltest.py index 7e29cf7..d62058f 100644 --- a/tests/test_functionaltest.py +++ b/tests/test_functionaltest.py @@ -473,7 +473,34 @@ def test_extreme_interarrival(): results = experiment.run_single(run=0) assert results['run']['arrivals'] < 1, ( - "Expect no arrivals due to extreme interarrival time." + 'Expect no arrivals due to extreme interarrival time.' + ) + + +def test_extreme_nurses(): + """ + Check that extremely high number of nurses results in no wait times and all + patients being seen. + """ + # Run model with extremely large number of nurses + param = Param(number_of_nurses=10_000_000) + experiment = Runner(param) + results = experiment.run_single(run=0) + + # Check that no patients wait + assert results['run']['mean_q_time_nurse'] == 0, ( + 'Expect no patients to wait but mean wait time is ' + + f'{results['run']['mean_q_time_nurse']:.2f}.' + ) + + # Check that all patients are seen + assert results['run']['count_unseen'] == 0, ( + 'Expect all patients to be seen, but ' + + f'{results['run']['count_unseen']} were not seen by a nurse.' + ) + assert np.isnan(results['run']['mean_q_time_unseen']), ( + 'Expect all patients to be seen, and so have no mean wait time for ' + + f'unseen patients, but was {results['run']['mean_q_time_unseen']:.2f}.' ) From 9dff66e5c43bc7ed8befe535ca2f3e3fe08ca6bf Mon Sep 17 00:00:00 2001 From: amyheather Date: Thu, 27 Feb 2025 10:40:43 +0000 Subject: [PATCH 28/28] chore(linting): lint replications.py --- simulation/replications.py | 1 + 1 file changed, 1 insertion(+) diff --git a/simulation/replications.py b/simulation/replications.py index 6f4a9fe..1566511 100644 --- a/simulation/replications.py +++ b/simulation/replications.py @@ -332,6 +332,7 @@ def _klimit(self): """ return int((self.look_ahead / 100) * max(self.n, 100)) + # pylint: disable=too-many-branches def select(self, runner, metrics): """ Executes the replication algorithm, determining the necessary number

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