Coverage for .tox/p312/lib/python3.10/site-packages/scicom/historicalletters/model.py: 96%
115 statements
« prev ^ index » next coverage.py v7.4.4, created at 2024-05-28 12:02 +0200
« prev ^ index » next coverage.py v7.4.4, created at 2024-05-28 12:02 +0200
1"""The model class for HistoricalLetters."""
2import random
3from pathlib import Path
5import mesa
6import mesa_geo as mg
7import networkx as nx
8import pandas as pd
9from numpy import mean
10from shapely import contains
11from tqdm import tqdm
13from scicom.historicalletters.agents import RegionAgent, SenderAgent
14from scicom.historicalletters.space import Nuts2Eu
15from scicom.historicalletters.utils import createData
16from scicom.utilities.statistics import prune
19def getPrunedLedger(model: mesa.Model) -> pd.DataFrame:
20 """Model reporter for simulation of archiving.
22 Returns statistics of ledger network of model run
23 and various iterations of statistics of pruned networks.
25 The routine assumes that the network contains fields of sender,
26 receiver and step information.
27 """
28 if model.runPruning is True:
29 ledgerColumns = ["sender", "receiver", "sender_location", "receiver_location", "topic", "step"]
30 modelparams = {
31 "population": model.population,
32 "moveRange": model.moveRange,
33 "letterRange": model.letterRange,
34 "useActivation": model.useActivation,
35 "useSocialNetwork": model.useSocialNetwork,
36 "similarityThreshold": model.similarityThreshold,
37 "longRangeNetworkFactor": model.longRangeNetworkFactor,
38 "shortRangeNetworkFactor": model.shortRangeNetworkFactor,
39 }
40 result = prune(
41 modelparameters=modelparams,
42 network=model.letterLedger,
43 columns=ledgerColumns,
44 iterations=3,
45 delAmounts=(0.1, 0.25, 0.5, 0.75, 0.9),
46 delTypes=("unif", "exp", "beta", "log_normal1", "log_normal2", "log_normal3"),
47 delMethod=("agents", "regions", "time"),
48 rankedVals=(True, False),
49 )
50 else:
51 result = model.letterLedger
52 return result
55def getComponents(model: mesa.Model) -> int:
56 """Model reporter to get number of components.
58 The MultiDiGraph is converted to undirected,
59 considering only edges that are reciprocal, ie.
60 edges are established if sender and receiver have
61 exchanged at least a letter in each direction.
62 """
63 newg = model.socialNetwork.to_undirected(reciprocal=True)
64 return nx.number_connected_components(newg)
67def getScaledLetters(model: mesa.Model) -> float:
68 """Return relative number of send letters."""
69 return len(model.letterLedger)/model.schedule.time
72def getScaledMovements(model: mesa.Model) -> float:
73 """Return relative number of movements."""
74 return model.movements/model.schedule.time
77class HistoricalLetters(mesa.Model):
78 """A letter sending model with historical informed initital positions.
80 Each agent has an initial topic vector, expressed as a RGB value. The
81 initial positions of the agents is based on a weighted random draw
82 based on data from [1].
84 Each step, agents generate two neighbourhoods for sending letters and
85 potential targets to move towards. The probability to send letters is
86 a self-reinforcing process. During each sending the internal topic of
87 the sender is updated as a random rotation towards the receivers topic.
89 [1] J. Lobo et al, Population-Area Relationship for Medieval European Cities,
90 PLoS ONE 11(10): e0162678.
91 """
93 def __init__(
94 self,
95 population: int = 100,
96 moveRange: float = 0.05,
97 letterRange: float = 0.2,
98 similarityThreshold: float = 0.2,
99 longRangeNetworkFactor: float = 0.3,
100 shortRangeNetworkFactor: float = 0.4,
101 regionData: str = Path(Path(__file__).parent.parent.resolve(), "data/NUTS_RG_60M_2021_3857_LEVL_2.geojson"),
102 populationDistributionData: str = Path(Path(__file__).parent.parent.resolve(), "data/pone.0162678.s003.csv"),
103 *,
104 useActivation: bool = False,
105 useSocialNetwork: bool = False,
106 runPruning: bool = False,
107 debug: bool = False,
108 ) -> None:
109 """Initialize a HistoricalLetters model."""
110 super().__init__()
112 # Parameters for agents
113 self.population = population
114 self.moveRange = moveRange
115 self.letterRange = letterRange
116 # Parameters for model
117 self.runPruning = runPruning
118 self.useActivation = useActivation
119 self.similarityThreshold = similarityThreshold
120 self.useSocialNetwork = useSocialNetwork
121 self.longRangeNetworkFactor = longRangeNetworkFactor
122 self.shortRangeNetworkFactor = shortRangeNetworkFactor
123 # Initialize social network
124 self.socialNetwork = nx.MultiDiGraph()
125 # Output variables
126 self.letterLedger = []
127 self.movements = 0
128 # Internal variables
129 self.schedule = mesa.time.RandomActivation(self)
130 self.scaleSendInput = {}
131 self.updatedTopicsDict = {}
132 self.updatedPositionDict = {}
133 self.space = Nuts2Eu()
134 self.debug = debug
136 #######
137 # Initialize region agents
138 #######
140 # Set up the grid with patches for every NUTS region
141 # Create region agents
142 ac = mg.AgentCreator(RegionAgent, model=self)
143 self.regions = ac.from_file(
144 regionData,
145 unique_id="NUTS_ID",
146 )
147 # Add regions to Nuts2Eu geospace
148 self.space.add_regions(self.regions)
150 #######
151 # Initialize sender agents
152 #######
154 # Draw initial geographic positions of agents
155 initSenderGeoDf = createData(
156 population,
157 populationDistribution=populationDistributionData,
158 )
160 # Calculate mean of mean distances for each agent.
161 # This is used as a measure for the range of exchanges.
162 meandistances = []
163 for idx in initSenderGeoDf.index.to_numpy():
164 name = initSenderGeoDf.loc[idx, "unique_id"]
165 geom = initSenderGeoDf.loc[idx, "geometry"]
166 otherAgents = initSenderGeoDf.query(f"unique_id != '{name}'").copy()
167 geometries = otherAgents.geometry.to_numpy()
168 distances = [geom.distance(othergeom) for othergeom in geometries]
169 meandistances.append(mean(distances))
170 self.meandistance = mean(meandistances)
172 # Populate factors dictionary
173 self.factors = {
174 "similarityThreshold": similarityThreshold,
175 "moveRange": moveRange,
176 "letterRange": letterRange,
177 }
179 # Set up agent creator for senders
180 ac_senders = mg.AgentCreator(
181 SenderAgent,
182 model=self,
183 agent_kwargs=self.factors,
184 )
186 # Create agents based on random coordinates generated
187 # in the createData step above, see util.py file.
188 senders = ac_senders.from_GeoDataFrame(
189 initSenderGeoDf,
190 unique_id="unique_id",
191 )
193 # Create random set of initial topic vectors.
194 topics = [
195 tuple(
196 [random.random() for x in range(3)],
197 ) for x in range(self.population)
198 ]
200 # Setup senders
201 for idx, sender in enumerate(senders):
202 # Add to social network
203 self.socialNetwork.add_node(
204 sender.unique_id,
205 numLettersSend=0,
206 numLettersReceived=0,
207 )
208 # Give sender topic
209 sender.topicVec = topics[idx]
210 # Add current topic to dict
211 self.updatedTopicsDict.update(
212 {sender.unique_id: topics[idx]},
213 )
214 # Set random activation weight
215 if useActivation is True:
216 sender.activationWeight = random.random()
217 # Add sender to its region
218 regionID = [
219 x.unique_id for x in self.regions if contains(x.geometry, sender.geometry)
220 ]
221 try:
222 self.space.add_sender(sender, regionID[0])
223 except IndexError as exc:
224 text = f"Problem finding region for {sender.geometry}."
225 raise IndexError(text) from exc
226 # Prepopulate positions dict
227 self.updatedPositionDict.update(
228 {sender.unique_id: [sender.geometry, regionID[0]]},
229 )
230 # Add sender to schedule
231 self.schedule.add(sender)
233 # Create social network
234 if useSocialNetwork is True:
235 for agent in self.schedule.agents:
236 if isinstance(agent, SenderAgent):
237 self._createSocialEdges(agent, self.socialNetwork)
239 self.datacollector = mesa.DataCollector(
240 model_reporters={
241 "Ledger": getPrunedLedger,
242 "Letters": getScaledLetters ,
243 "Movements": getScaledMovements,
244 "Clusters": getComponents,
245 },
246 )
248 def _createSocialEdges(self, agent: SenderAgent, graph: nx.MultiDiGraph) -> None:
249 """Create social edges with the different wiring factors.
251 Define a close range by using the moveRange parameter. Among
252 these neighbors, create a connection with probability set by
253 the shortRangeNetworkFactor.
255 For all other agents, that are not in this closeRange group,
256 create a connection with the probability set by the longRangeNetworkFactor.
257 """
258 closerange = [x for x in self.space.get_neighbors_within_distance(
259 agent,
260 distance=self.moveRange * self.meandistance,
261 center=False,
262 ) if isinstance(x, SenderAgent)]
263 for neighbor in closerange:
264 if neighbor.unique_id != agent.unique_id:
265 connect = random.choices(
266 population=[True, False],
267 weights=[self.shortRangeNetworkFactor, 1 - self.shortRangeNetworkFactor],
268 k=1,
269 )
270 if connect[0] is True:
271 graph.add_edge(agent.unique_id, neighbor.unique_id, step=0)
272 longrange = [x for x in self.schedule.agents if x not in closerange and isinstance(x, SenderAgent)]
273 for neighbor in longrange:
274 if neighbor.unique_id != agent.unique_id:
275 connect = random.choices(
276 population=[True, False],
277 weights=[self.longRangeNetworkFactor, 1 - self.longRangeNetworkFactor],
278 k=1,
279 )
280 if connect[0] is True:
281 graph.add_edge(agent.unique_id, neighbor.unique_id, step=0)
283 def step(self) -> None:
284 """One simulation step with data collection."""
285 self.step_no_data()
286 self.datacollector.collect(self)
288 def step_no_data(self) -> None:
289 """One simulation step without data collection."""
290 self.scaleSendInput.update(
291 **{x.unique_id: x.numLettersReceived for x in self.schedule.agents},
292 )
293 # Update the currently held topicVec for each agent, based
294 # on potential previouse communication events and the position
295 # based on a previous movement.
296 for agent in self.schedule.agents:
297 agent.topicVec = self.updatedTopicsDict[agent.unique_id]
298 newGeom = self.updatedPositionDict[agent.unique_id]
299 if newGeom[0] != agent.geometry:
300 self.space.move_sender(agent, newGeom[0], newGeom[1])
301 self.schedule.step()
303 def run(self, n:int) -> None:
304 """Run the model for n steps.
306 Data collection is only run at the end of n steps.
307 This is useful for batch runs accross different
308 parameters.
309 """
310 if self.debug is True:
311 for _ in tqdm(range(n)):
312 self.step_no_data()
313 else:
314 for _ in range(n):
315 self.step_no_data()
316 self.datacollector.collect(self)