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yogabonito/demo_AZP-BasicTabu.ipynb

Created July 30, 2017 22:00
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"Improved" Basic and Reactive Tabu variants of AZP
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demo_AZP-BasicTabu.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# AZP-BasicTabu demo"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The basic tabu version of the AZP as described on p. 432 in Openshaw & Rao (1995) is lacking a stopping condition. In [commit 8e56e23](https://github.com/yogabonito/region/commit/8e56e23e2c669b2b676fd8db97044c63cdb7d84d) we have added a termination condition: If a clustering has been visited a certain number of times (defined by the `repetitions_before_termination` argument), a termination flag is set allowing only improving moves from that point on. When no improving move is left, the result is returned.\n",
"\n",
"With this modification we get the optimal solutions in all of our three examples below."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from region.azp import *"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"from matplotlib import pyplot as plt\n",
"import pandas as pd\n",
"import geopandas as gpd\n",
"from shapely.geometry import Polygon"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example 1: 3x3 lattice"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inputs"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f72464575f8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"geometry = [\n",
" Polygon([(x, y),\n",
" (x, y+1),\n",
" (x+1, y+1),\n",
" (x+1, y)]) for y in range(3) for x in range(3)\n",
"]\n",
"\n",
"areas_gdf = gpd.GeoDataFrame(\n",
" {\"values\": [726.7, 623.6, 487.3,\n",
" 200.4, 245.0, 481.0,\n",
" 170.9, 225.9, 226.9]},\n",
" geometry=geometry)\n",
"areas_gdf.plot(column=\"values\")\n",
"plt.gca().invert_yaxis()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### &nbsp;&nbsp;&nbsp;&nbsp;without initial solution"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"generate_initial_sol got a <class 'networkx.classes.graph.Graph'>\n",
"step 1\n",
"distribute_regions_among_components got a <class 'networkx.classes.graph.Graph'>\n",
"{<networkx.classes.graph.Graph object at 0x7f7246413630>: 2}\n",
"Init with: [{0, 1, 3, 4, 5, 6, 7, 8}, {2}]\n",
"visited []\n",
"=============================================\n",
"obj_value: 6790.0\n",
"[{0, 1, 3, 4, 5, 6, 7, 8}, {2}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=1, from_idx=0, to_idx=1) objval_diff: -2158.3\n",
"step 2\n",
"[{0, 1, 3, 4, 5, 6, 7, 8}, {2}]\n",
"IMPROVING MOVE\n",
" move 1 from {0, 1, 3, 4, 5, 6, 7, 8} to {2}\n",
"visited [{frozenset({2}), frozenset({0, 1, 3, 4, 5, 6, 7, 8})}]\n",
"=============================================\n",
"obj_value: 4631.7\n",
"[{0, 3, 4, 5, 6, 7, 8}, {1, 2}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=0, from_idx=0, to_idx=1) objval_diff: -2467.6\n",
"step 2\n",
"[{0, 3, 4, 5, 6, 7, 8}, {1, 2}]\n",
"IMPROVING MOVE\n",
" move 0 from {0, 3, 4, 5, 6, 7, 8} to {1, 2}\n",
"visited [{frozenset({2}), frozenset({0, 1, 3, 4, 5, 6, 7, 8})}, {frozenset({1, 2}), frozenset({0, 3, 4, 5, 6, 7, 8})}]\n",
"=============================================\n",
"obj_value: 2164.1\n",
"[{3, 4, 5, 6, 7, 8}, {0, 1, 2}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=5, from_idx=0, to_idx=1) objval_diff: -941.3\n",
"step 2\n",
"[{3, 4, 5, 6, 7, 8}, {0, 1, 2}]\n",
"IMPROVING MOVE\n",
" move 5 from {3, 4, 5, 6, 7, 8} to {0, 1, 2}\n",
"visited [{frozenset({2}), frozenset({0, 1, 3, 4, 5, 6, 7, 8})}, {frozenset({1, 2}), frozenset({0, 3, 4, 5, 6, 7, 8})}, {frozenset({0, 1, 2}), frozenset({3, 4, 5, 6, 7, 8})}]\n",
"=============================================\n",
"obj_value: 1222.8\n",
"[{3, 4, 6, 7, 8}, {0, 1, 2, 5}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=4, from_idx=0, to_idx=1) objval_diff: 1182.7\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=1, from_idx=1, to_idx=0), move(area=0, from_idx=1, to_idx=0), move(area=5, from_idx=1, to_idx=0)], maxlen=20)\n",
"step 4\n",
"[{3, 4, 6, 7, 8}, {0, 1, 2, 5}]\n",
"No improving, no aspiration ==> do the best you can\n",
" move 4 from {3, 4, 6, 7, 8} to {0, 1, 2, 5}\n",
"visited [{frozenset({2}), frozenset({0, 1, 3, 4, 5, 6, 7, 8})}, {frozenset({1, 2}), frozenset({0, 3, 4, 5, 6, 7, 8})}, {frozenset({0, 1, 2}), frozenset({3, 4, 5, 6, 7, 8})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}]\n",
"=============================================\n",
"obj_value: 2405.5\n",
"[{3, 6, 7, 8}, {0, 1, 2, 4, 5}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=8, from_idx=0, to_idx=1) objval_diff: 1345.6\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=1, from_idx=1, to_idx=0), move(area=0, from_idx=1, to_idx=0), move(area=5, from_idx=1, to_idx=0), move(area=4, from_idx=1, to_idx=0)], maxlen=20)\n",
"[{3, 6, 7, 8}, {0, 1, 2, 4, 5}]\n",
"ASPIRATION MOVE\n",
" move 4 from {0, 1, 2, 4, 5} to {3, 6, 7, 8}\n",
"visited [{frozenset({2}), frozenset({0, 1, 3, 4, 5, 6, 7, 8})}, {frozenset({1, 2}), frozenset({0, 3, 4, 5, 6, 7, 8})}, {frozenset({0, 1, 2}), frozenset({3, 4, 5, 6, 7, 8})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}, {frozenset({0, 1, 2, 4, 5}), frozenset({8, 3, 6, 7})}]\n",
"=============================================\n",
"obj_value: 1222.8\n",
"[{3, 4, 6, 7, 8}, {0, 1, 2, 5}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=8, from_idx=0, to_idx=1) objval_diff: 1309.4\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=1, from_idx=1, to_idx=0), move(area=0, from_idx=1, to_idx=0), move(area=5, from_idx=1, to_idx=0), move(area=4, from_idx=1, to_idx=0), move(area=4, from_idx=0, to_idx=1)], maxlen=20)\n",
"step 4\n",
"[{3, 4, 6, 7, 8}, {0, 1, 2, 5}]\n",
"No improving, no aspiration ==> do the best you can\n",
" move 8 from {3, 4, 6, 7, 8} to {0, 1, 2, 5}\n",
"visited [{frozenset({2}), frozenset({0, 1, 3, 4, 5, 6, 7, 8})}, {frozenset({1, 2}), frozenset({0, 3, 4, 5, 6, 7, 8})}, {frozenset({0, 1, 2}), frozenset({3, 4, 5, 6, 7, 8})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}, {frozenset({0, 1, 2, 4, 5}), frozenset({8, 3, 6, 7})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}]\n",
"=============================================\n",
"obj_value: 2532.2\n",
"[{3, 4, 6, 7}, {0, 1, 2, 5, 8}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=7, from_idx=0, to_idx=1) objval_diff: 1316.4\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=1, from_idx=1, to_idx=0), move(area=0, from_idx=1, to_idx=0), move(area=5, from_idx=1, to_idx=0), move(area=4, from_idx=1, to_idx=0), move(area=4, from_idx=0, to_idx=1), move(area=8, from_idx=1, to_idx=0)], maxlen=20)\n",
"[{3, 4, 6, 7}, {0, 1, 2, 5, 8}]\n",
"ASPIRATION MOVE\n",
" move 8 from {0, 1, 2, 5, 8} to {3, 4, 6, 7}\n",
"visited [{frozenset({2}), frozenset({0, 1, 3, 4, 5, 6, 7, 8})}, {frozenset({1, 2}), frozenset({0, 3, 4, 5, 6, 7, 8})}, {frozenset({0, 1, 2}), frozenset({3, 4, 5, 6, 7, 8})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}, {frozenset({0, 1, 2, 4, 5}), frozenset({8, 3, 6, 7})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}, {frozenset({0, 1, 2, 5, 8}), frozenset({3, 4, 6, 7})}]\n",
"VISITED [{3, 4, 6, 7, 8}, {0, 1, 2, 5}] FOR 2 TIMES --> TERMINATING BEFORE NEXT NON-IMPROVING MOVE\n",
"=============================================\n",
"obj_value: 1222.8\n",
"[{3, 4, 6, 7, 8}, {0, 1, 2, 5}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=3, from_idx=0, to_idx=1) objval_diff: 1390.9\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=1, from_idx=1, to_idx=0), move(area=0, from_idx=1, to_idx=0), move(area=5, from_idx=1, to_idx=0), move(area=4, from_idx=1, to_idx=0), move(area=4, from_idx=0, to_idx=1), move(area=8, from_idx=1, to_idx=0), move(area=8, from_idx=0, to_idx=1)], maxlen=20)\n",
"step 4\n",
"[{3, 4, 6, 7, 8}, {0, 1, 2, 5}]\n",
"No improving, no aspiration ==> do the best you can\n"
]
},
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAQIAAAD8CAYAAACcoKqNAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n68S9kQN3AAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f72462c84e0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"azpbt = AZPBasicTabu(n_regions=2, random_state=2,\n",
" tabu_length=20,\n",
" repetitions_before_termination=2)\n",
"regions_dict_bt_wo = azpbt.fit(areas=areas_gdf,\n",
" data=[\"values\"],\n",
" contiguity=\"rook\")\n",
"areas_gdf[\"region\"] = pd.Series(regions_dict_bt_wo)\n",
"areas_gdf.plot(column=\"region\", cmap='tab20c')\n",
"plt.gca().invert_yaxis()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example 2: Islands"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"islands_geometry = [\n",
" Polygon([(x, y),\n",
" (x, y+1),\n",
" (x+1, y+1),\n",
" (x+1, y)]) for y in range(3) for x in range(3)\n",
"] + [\n",
" Polygon([(x, y),\n",
" (x, y+1),\n",
" (x+1, y+1),\n",
" (x+1, y)]) for y in range(4,6) for x in range(5,8)\n",
"]"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"islands_gdf = gpd.GeoDataFrame(\n",
" {\"values\": [726.7, 623.6, 487.3,\n",
" 200.4, 245.0, 481.0,\n",
" 170.9, 225.9, 226.9] +\n",
" [100, 120, 190,\n",
" 175, 185, 210]},\n",
" geometry=islands_geometry)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAAUEAAAD8CAYAAADpLRYuAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f7246413f60>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"islands_gdf.plot(column=\"values\")\n",
"plt.gca().invert_yaxis()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clustering"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### &nbsp;&nbsp;&nbsp;&nbsp;without initial solution"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"generate_initial_sol got a <class 'networkx.classes.graph.Graph'>\n",
"step 1\n",
"distribute_regions_among_components got a <class 'networkx.classes.graph.Graph'>\n",
"{<networkx.classes.graph.Graph object at 0x7f7242a749b0>: 2, <networkx.classes.graph.Graph object at 0x7f7242a309e8>: 1}\n",
"Init with: [{0, 3}, {1, 2, 4, 5, 6, 7, 8}]\n",
"visited []\n",
"=============================================\n",
"obj_value: 4796.3\n",
"[{0, 3}, {1, 2, 4, 5, 6, 7, 8}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=1, from_idx=1, to_idx=0) objval_diff: -1378.3\n",
"step 2\n",
"[{0, 3}, {1, 2, 4, 5, 6, 7, 8}]\n",
"IMPROVING MOVE\n",
" move 1 from {1, 2, 4, 5, 6, 7, 8} to {0, 3}\n",
"visited [{frozenset({1, 2, 4, 5, 6, 7, 8}), frozenset({0, 3})}]\n",
"=============================================\n",
"obj_value: 3418.0\n",
"[{0, 1, 3}, {2, 4, 5, 6, 7, 8}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=2, from_idx=1, to_idx=0) objval_diff: -424.2\n",
"step 2\n",
"[{0, 1, 3}, {2, 4, 5, 6, 7, 8}]\n",
"IMPROVING MOVE\n",
" move 2 from {2, 4, 5, 6, 7, 8} to {0, 1, 3}\n",
"visited [{frozenset({1, 2, 4, 5, 6, 7, 8}), frozenset({0, 3})}, {frozenset({2, 4, 5, 6, 7, 8}), frozenset({0, 1, 3})}]\n",
"=============================================\n",
"obj_value: 2993.8\n",
"[{0, 1, 2, 3}, {4, 5, 6, 7, 8}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=3, from_idx=0, to_idx=1) objval_diff: -829.7\n",
"step 2\n",
"[{0, 1, 2, 3}, {4, 5, 6, 7, 8}]\n",
"IMPROVING MOVE\n",
" move 3 from {0, 1, 2, 3} to {4, 5, 6, 7, 8}\n",
"visited [{frozenset({1, 2, 4, 5, 6, 7, 8}), frozenset({0, 3})}, {frozenset({2, 4, 5, 6, 7, 8}), frozenset({0, 1, 3})}, {frozenset({0, 1, 2, 3}), frozenset({8, 4, 5, 6, 7})}]\n",
"=============================================\n",
"obj_value: 2164.1\n",
"[{0, 1, 2}, {3, 4, 5, 6, 7, 8}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=5, from_idx=1, to_idx=0) objval_diff: -941.3\n",
"step 2\n",
"[{0, 1, 2}, {3, 4, 5, 6, 7, 8}]\n",
"IMPROVING MOVE\n",
" move 5 from {3, 4, 5, 6, 7, 8} to {0, 1, 2}\n",
"visited [{frozenset({1, 2, 4, 5, 6, 7, 8}), frozenset({0, 3})}, {frozenset({2, 4, 5, 6, 7, 8}), frozenset({0, 1, 3})}, {frozenset({0, 1, 2, 3}), frozenset({8, 4, 5, 6, 7})}, {frozenset({0, 1, 2}), frozenset({3, 4, 5, 6, 7, 8})}]\n",
"=============================================\n",
"obj_value: 1222.8\n",
"[{0, 1, 2, 5}, {3, 4, 6, 7, 8}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=4, from_idx=1, to_idx=0) objval_diff: 1182.7\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=2, from_idx=0, to_idx=1), move(area=3, from_idx=1, to_idx=0), move(area=5, from_idx=0, to_idx=1)], maxlen=3)\n",
"step 4\n",
"[{0, 1, 2, 5}, {3, 4, 6, 7, 8}]\n",
"No improving, no aspiration ==> do the best you can\n",
" move 4 from {3, 4, 6, 7, 8} to {0, 1, 2, 5}\n",
"visited [{frozenset({1, 2, 4, 5, 6, 7, 8}), frozenset({0, 3})}, {frozenset({2, 4, 5, 6, 7, 8}), frozenset({0, 1, 3})}, {frozenset({0, 1, 2, 3}), frozenset({8, 4, 5, 6, 7})}, {frozenset({0, 1, 2}), frozenset({3, 4, 5, 6, 7, 8})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}]\n",
"=============================================\n",
"obj_value: 2405.5\n",
"[{0, 1, 2, 4, 5}, {3, 6, 7, 8}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=0, from_idx=0, to_idx=1) objval_diff: 1012.8\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=3, from_idx=1, to_idx=0), move(area=5, from_idx=0, to_idx=1), move(area=4, from_idx=0, to_idx=1)], maxlen=3)\n",
"[{0, 1, 2, 4, 5}, {3, 6, 7, 8}]\n",
"ASPIRATION MOVE\n",
" move 4 from {0, 1, 2, 4, 5} to {3, 6, 7, 8}\n",
"visited [{frozenset({1, 2, 4, 5, 6, 7, 8}), frozenset({0, 3})}, {frozenset({2, 4, 5, 6, 7, 8}), frozenset({0, 1, 3})}, {frozenset({0, 1, 2, 3}), frozenset({8, 4, 5, 6, 7})}, {frozenset({0, 1, 2}), frozenset({3, 4, 5, 6, 7, 8})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}, {frozenset({0, 1, 2, 4, 5}), frozenset({8, 3, 6, 7})}]\n",
"=============================================\n",
"obj_value: 1222.8\n",
"[{0, 1, 2, 5}, {3, 4, 6, 7, 8}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=8, from_idx=1, to_idx=0) objval_diff: 1309.4\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=5, from_idx=0, to_idx=1), move(area=4, from_idx=0, to_idx=1), move(area=4, from_idx=1, to_idx=0)], maxlen=3)\n",
"step 4\n",
"[{0, 1, 2, 5}, {3, 4, 6, 7, 8}]\n",
"No improving, no aspiration ==> do the best you can\n",
" move 8 from {3, 4, 6, 7, 8} to {0, 1, 2, 5}\n",
"visited [{frozenset({1, 2, 4, 5, 6, 7, 8}), frozenset({0, 3})}, {frozenset({2, 4, 5, 6, 7, 8}), frozenset({0, 1, 3})}, {frozenset({0, 1, 2, 3}), frozenset({8, 4, 5, 6, 7})}, {frozenset({0, 1, 2}), frozenset({3, 4, 5, 6, 7, 8})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}, {frozenset({0, 1, 2, 4, 5}), frozenset({8, 3, 6, 7})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}]\n",
"=============================================\n",
"obj_value: 2532.2\n",
"[{0, 1, 2, 5, 8}, {3, 4, 6, 7}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=0, from_idx=0, to_idx=1) objval_diff: 976.6\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=4, from_idx=0, to_idx=1), move(area=4, from_idx=1, to_idx=0), move(area=8, from_idx=0, to_idx=1)], maxlen=3)\n",
"[{0, 1, 2, 5, 8}, {3, 4, 6, 7}]\n",
"ASPIRATION MOVE\n",
" move 8 from {0, 1, 2, 5, 8} to {3, 4, 6, 7}\n",
"visited [{frozenset({1, 2, 4, 5, 6, 7, 8}), frozenset({0, 3})}, {frozenset({2, 4, 5, 6, 7, 8}), frozenset({0, 1, 3})}, {frozenset({0, 1, 2, 3}), frozenset({8, 4, 5, 6, 7})}, {frozenset({0, 1, 2}), frozenset({3, 4, 5, 6, 7, 8})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}, {frozenset({0, 1, 2, 4, 5}), frozenset({8, 3, 6, 7})}, {frozenset({8, 3, 4, 6, 7}), frozenset({0, 1, 2, 5})}, {frozenset({0, 1, 2, 5, 8}), frozenset({3, 4, 6, 7})}]\n",
"VISITED [{0, 1, 2, 5}, {3, 4, 6, 7, 8}] FOR 2 TIMES --> TERMINATING BEFORE NEXT NON-IMPROVING MOVE\n",
"=============================================\n",
"obj_value: 1222.8\n",
"[{0, 1, 2, 5}, {3, 4, 6, 7, 8}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=5, from_idx=0, to_idx=1) objval_diff: 941.3\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=4, from_idx=1, to_idx=0), move(area=8, from_idx=0, to_idx=1), move(area=8, from_idx=1, to_idx=0)], maxlen=3)\n",
"step 4\n",
"[{0, 1, 2, 5}, {3, 4, 6, 7, 8}]\n",
"No improving, no aspiration ==> do the best you can\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7242a309e8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"azpbt = AZPBasicTabu(n_regions=3, tabu_length=3, random_state=0,\n",
" repetitions_before_termination=2)\n",
"islands_dict_bt_wo = azpbt.fit(areas=islands_gdf,\n",
" data=[\"values\"],\n",
" contiguity=\"rook\")\n",
"islands_gdf[\"region\"] = pd.Series(islands_dict_bt_wo)\n",
"islands_gdf.plot(column=\"region\", cmap='tab20c')\n",
"plt.gca().invert_yaxis()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Example 3: A tricky toy example"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inputs"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7242982da0>"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f7242a6dbe0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"squares = [\n",
" Polygon([(x, 0),\n",
" (x, 1),\n",
" (x+1, 1),\n",
" (x+1, 0)]) for x in range(3)\n",
"]\n",
"values = [0, 1, 0]\n",
"squares_gdf = gpd.GeoDataFrame({\"values\": values},\n",
" geometry=squares)\n",
"squares_gdf.plot(column=\"values\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Clustering"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"generate_initial_sol got a <class 'networkx.classes.graph.Graph'>\n",
"step 1\n",
"distribute_regions_among_components got a <class 'networkx.classes.graph.Graph'>\n",
"{<networkx.classes.graph.Graph object at 0x7f72429a2438>: 2}\n",
"Init with: [{0, 1}, {2}]\n",
"visited []\n",
"=============================================\n",
"obj_value: 1.0\n",
"[{0, 1}, {2}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: move(area=1, from_idx=0, to_idx=1) objval_diff: 0.0\n",
"step 2\n",
"[{0, 1}, {2}]\n",
"IMPROVING MOVE\n",
" move 1 from {0, 1} to {2}\n",
"visited [{frozenset({2}), frozenset({0, 1})}]\n",
"=============================================\n",
"obj_value: 1.0\n",
"[{0}, {1, 2}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: None objval_diff: inf\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=1, from_idx=1, to_idx=0)], maxlen=3)\n",
"step 4\n",
"[{0}, {1, 2}]\n",
"No improving, no aspiration ==> do the best you can\n",
"visited [{frozenset({2}), frozenset({0, 1})}, {frozenset({1, 2}), frozenset({0})}]\n",
"=============================================\n",
"obj_value: 1.0\n",
"[{0}, {1, 2}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: None objval_diff: inf\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=1, from_idx=1, to_idx=0)], maxlen=3)\n",
"step 4\n",
"[{0}, {1, 2}]\n",
"No improving, no aspiration ==> do the best you can\n",
"visited [{frozenset({2}), frozenset({0, 1})}, {frozenset({1, 2}), frozenset({0})}, {frozenset({1, 2}), frozenset({0})}]\n",
"VISITED [{0}, {1, 2}] FOR 2 TIMES --> TERMINATING BEFORE NEXT NON-IMPROVING MOVE\n",
"=============================================\n",
"obj_value: 1.0\n",
"[{0}, {1, 2}]\n",
"-----------------------------------\n",
"step 1\n",
" best move: None objval_diff: inf\n",
"step 2\n",
"step 3\n",
"Tabu: deque([move(area=1, from_idx=1, to_idx=0)], maxlen=3)\n",
"step 4\n",
"[{0}, {1, 2}]\n",
"No improving, no aspiration ==> do the best you can\n"
]
},
{
"data": {
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"text/plain": [
"<matplotlib.figure.Figure at 0x7f724299e400>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"azpbt = AZPBasicTabu(n_regions=2, tabu_length=3, random_state=0,\n",
" repetitions_before_termination=2)\n",
"regions_dict_squares_bt = azpbt.fit(areas=squares_gdf,\n",
" data=[\"values\"],\n",
" contiguity=\"rook\")\n",
"squares_gdf[\"region\"] = pd.Series(regions_dict_squares_bt)\n",
"squares_gdf.plot(column=\"region\", cmap='tab20c')\n",
"plt.gca().invert_yaxis()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
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},
"file_extension": ".py",
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"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
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},
"nbformat": 4,
"nbformat_minor": 2
}
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