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Mobility model alpha parameter regression from data #15

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merged 9 commits into from
Oct 11, 2024
Merged

Mobility model alpha parameter regression from data #15

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d3a5927
Fixed PR changes for params_regression
tanzim10 Oct 1, 2024
fabd34b
Fixed Unittest for param_regression according to PR change
tanzim10 Oct 1, 2024
71f6c96
Fixed the mobility notebook and radp_library file(PR changes done)
tanzim10 Oct 1, 2024
deb3bc3
Resolved test_param_regression unittest cases
tanzim10 Oct 2, 2024
064e285
Updated Param Regression and radp library as instructed in the PR review
tanzim10 Oct 4, 2024
f774355
Changed the scipy dependency to solve dependency error
tanzim10 Oct 4, 2024
962e4db
Changed how seed value works especially made it user friendly
tanzim10 Oct 4, 2024
625be10
Refactored functions and changed the imports
tanzim10 Oct 11, 2024
9d507a1
Resolved test_param_regression imports
tanzim10 Oct 11, 2024
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361 changes: 361 additions & 0 deletions notebooks/mobility_model.ipynb
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  1. generate alpha0 closer to cell where it is first used
  2. update params with re-computed alpha1

"cells": [
{
"cell_type": "markdown",
"id": "ec7ab4ae",
"metadata": {},
"source": [
"# Alpha Optimization in a Gauss-Markov Mobility Model\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "754a70e3",
"metadata": {},
"outputs": [],
"source": [
"import sys\n",
"from pathlib import Path\n",
"sys.path.append(f\"{Path().absolute().parent}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b3d3ca1a",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import scipy\n",
"import numpy as np\n",
"from radp_library import *\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.cm as cm"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "309ead1f",
"metadata": {},
"outputs": [],
"source": [
"params = {\n",
" \"ue_tracks_generation\": {\n",
" \"params\": {\n",
" \"simulation_duration\": 3600,\n",
" \"simulation_time_interval_seconds\": 0.01,\n",
" \"num_ticks\": 50,\n",
" \"num_batches\": 1,\n",
" \"ue_class_distribution\": {\n",
" \"stationary\": {\n",
" \"count\": 10,\n",
" \"velocity\": 0,\n",
" \"velocity_variance\": 1\n",
" },\n",
" \"pedestrian\": {\n",
" \"count\": 5,\n",
" \"velocity\": 2,\n",
" \"velocity_variance\": 1\n",
" },\n",
" \"cyclist\": {\n",
" \"count\": 5,\n",
" \"velocity\": 5,\n",
" \"velocity_variance\": 1\n",
" },\n",
" \"car\": {\n",
" \"count\": 12,\n",
" \"velocity\": 20,\n",
" \"velocity_variance\": 1\n",
" }\n",
" },\n",
" \"lat_lon_boundaries\": {\n",
" \"min_lat\": -90,\n",
" \"max_lat\": 90,\n",
" \"min_lon\": -180,\n",
" \"max_lon\": 180\n",
" },\n",
" \"gauss_markov_params\": {\n",
" \"alpha\": 0.5,\n",
" \"variance\": 0.8,\n",
" \"rng_seed\": 42,\n",
" \"lon_x_dims\": 100,\n",
" \"lon_y_dims\": 100,\n",
" \"// TODO\": \"Account for supporting the user choosing the anchor_loc and cov_around_anchor.\",\n",
" \"// Current implementation\": \"the UE Tracks generator will not be using these values.\",\n",
" \"// anchor_loc\": {},\n",
" \"// cov_around_anchor\": {}\n",
" }\n",
" }\n",
" }\n",
"}"
]
},
{
"cell_type": "markdown",
"id": "d5d3f4fb",
"metadata": {},
"source": [
"## Generate Data Set 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bf32a451",
"metadata": {},
"outputs": [],
"source": [
"data1 = get_ue_data(params)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0217fb7d",
"metadata": {},
"outputs": [],
"source": [
"data1.head(10)"
]
},
{
"cell_type": "markdown",
"id": "0f112187",
"metadata": {},
"source": [
"## Plot Dataset 1 "
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "57d3c99b",
"metadata": {},
"outputs": [],
"source": [
"plot_ue_tracks(data1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "507f5c0b",
"metadata": {},
"outputs": [],
"source": [
"velocity = preprocess_ue_data(data1)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "60b0bc3b",
"metadata": {},
"outputs": [],
"source": [
"velocity"
]
},
{
"cell_type": "markdown",
"id": "3e4713fc",
"metadata": {},
"source": [
"## Alpha Initialization"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ee3e00c1",
"metadata": {},
"outputs": [],
"source": [
"alpha0 = np.random.choice(np.arange(0, 1.1, 0.1))"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "119f1534",
"metadata": {},
"outputs": [],
"source": [
"print(\"Alpha0:\",alpha0)"
]
},
{
"cell_type": "markdown",
"id": "97a8c1ec",
"metadata": {},
"source": [
"## Regress to Find Alpha 1"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "12fdaf4b",
"metadata": {},
"outputs": [],
"source": [
"alpha1 = get_predicted_alpha(data1,alpha0,seed=42) # Adding seed for reproducibility"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "39eed46d",
"metadata": {},
"outputs": [],
"source": [
"alpha1"
]
},
{
"cell_type": "markdown",
"id": "1dc52c9d",
"metadata": {},
"source": [
"## Generating new data using alpha 1\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "46d7e685",
"metadata": {},
"outputs": [],
"source": [
"#Changing alpha value to alpha1 in the params dictionary\n",
"params['ue_tracks_generation']['params']['gauss_markov_params']['alpha'] = alpha1"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ab4eb7cd",
"metadata": {},
"outputs": [],
"source": [
"print(params['ue_tracks_generation']['params']['gauss_markov_params']['alpha'])"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3c700855",
"metadata": {},
"outputs": [],
"source": [
"data2 = get_ue_data(params)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "18a2b294",
"metadata": {},
"outputs": [],
"source": [
"data2"
]
},
{
"cell_type": "markdown",
"id": "07a4c3aa",
"metadata": {},
"source": [
"## Plot Dataset 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "df4b1b11",
"metadata": {},
"outputs": [],
"source": [
"plot_ue_tracks(data2)"
]
},
{
"cell_type": "markdown",
"id": "b727758e",
"metadata": {},
"source": [
"## Regress to Find Alpha 2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "5a268312",
"metadata": {},
"outputs": [],
"source": [
"alpha2 = get_predicted_alpha(data1,alpha1,seed=42) # Adding seed for reproducibility"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "02575be1",
"metadata": {},
"outputs": [],
"source": [
"alpha2"
]
},
{
"cell_type": "markdown",
"id": "da3b378b",
"metadata": {},
"source": [
"## Comparison Plot of Dataset 1 and Dataset2"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0798532d",
"metadata": {},
"outputs": [],
"source": [
"plot_ue_tracks_side_by_side(data1, data2)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "4c61c293",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.11.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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