"""
Mesa Data Collection Module
===========================
DataCollector is meant to provide a simple, standard way to collect data
generated by a Mesa model. It collects three types of data: model-level data,
agent-level data, and tables.
A DataCollector is instantiated with two dictionaries of reporter names and
associated variable names or functions for each, one for model-level data and
one for agent-level data; a third dictionary provides table names and columns.
Variable names are converted into functions which retrieve attributes of that
name.
When the collect() method is called, each model-level function is called, with
the model as the argument, and the results associated with the relevant
variable. Then the agent-level functions are called on each agent.
Additionally, other objects can write directly to tables by passing in an
appropriate dictionary object for a table row.
The DataCollector then stores the data it collects in dictionaries:
* model_vars maps each reporter to a list of its values
* tables maps each table to a dictionary, with each column as a key with a
list as its value.
* _agent_records maps each model step to a list of each agents id
and its values.
Finally, DataCollector can create a pandas DataFrame from each collection.
The default DataCollector here makes several assumptions:
* The model has an agent list called agents
* For collecting agent-level variables, agents must have a unique_id
"""
import contextlib
import itertools
import types
from functools import partial
with contextlib.suppress(ImportError):
import pandas as pd
[docs]
class DataCollector:
"""Class for collecting data generated by a Mesa model.
A DataCollector is instantiated with dictionaries of names of model- and
agent-level variables to collect, associated with attribute names or
functions which actually collect them. When the collect(...) method is
called, it collects these attributes and executes these functions one by
one and stores the results.
"""
def __init__(
self,
model_reporters=None,
agent_reporters=None,
tables=None,
):
"""
Instantiate a DataCollector with lists of model and agent reporters.
Both model_reporters and agent_reporters accept a dictionary mapping a
variable name to either an attribute name, a function, a method of a class/instance,
or a function with parameters placed in a list.
Model reporters can take four types of arguments:
1. Lambda function:
{"agent_count": lambda m: len(m.agents)}
2. Method of a class/instance:
{"agent_count": self.get_agent_count} # self here is a class instance
{"agent_count": Model.get_agent_count} # Model here is a class
3. Class attributes of a model:
{"model_attribute": "model_attribute"}
4. Functions with parameters that have been placed in a list:
{"Model_Function": [function, [param_1, param_2]]}
Agent reporters can similarly take:
1. Attribute name (string) referring to agent's attribute:
{"energy": "energy"}
2. Lambda function:
{"energy": lambda a: a.energy}
3. Method of an agent class/instance:
{"agent_action": self.do_action} # self here is an agent class instance
{"agent_action": Agent.do_action} # Agent here is a class
4. Functions with parameters placed in a list:
{"Agent_Function": [function, [param_1, param_2]]}
The tables arg accepts a dictionary mapping names of tables to lists of
columns. For example, if we want to allow agents to write their age
when they are destroyed (to keep track of lifespans), it might look
like:
{"Lifespan": ["unique_id", "age"]}
Args:
model_reporters: Dictionary of reporter names and attributes/funcs/methods.
agent_reporters: Dictionary of reporter names and attributes/funcs/methods.
tables: Dictionary of table names to lists of column names.
Notes:
- If you want to pickle your model you must not use lambda functions.
- If your model includes a large number of agents, it is recommended to
use attribute names for the agent reporter, as it will be faster.
"""
self.model_reporters = {}
self.agent_reporters = {}
self.model_vars = {}
self._agent_records = {}
self.tables = {}
if model_reporters is not None:
for name, reporter in model_reporters.items():
self._new_model_reporter(name, reporter)
if agent_reporters is not None:
for name, reporter in agent_reporters.items():
self._new_agent_reporter(name, reporter)
if tables is not None:
for name, columns in tables.items():
self._new_table(name, columns)
def _new_model_reporter(self, name, reporter):
"""Add a new model-level reporter to collect.
Args:
name: Name of the model-level variable to collect.
reporter: Attribute string, or function object that returns the
variable when given a model instance.
"""
self.model_reporters[name] = reporter
self.model_vars[name] = []
def _new_agent_reporter(self, name, reporter):
"""Add a new agent-level reporter to collect.
Args:
name: Name of the agent-level variable to collect.
reporter: Attribute string, function object, method of a class/instance, or
function with parameters placed in a list that returns the
variable when given an agent instance.
"""
# Check if the reporter is an attribute string
if isinstance(reporter, str):
attribute_name = reporter
def attr_reporter(agent):
return getattr(agent, attribute_name, None)
reporter = attr_reporter
# Check if the reporter is a function with arguments placed in a list
elif isinstance(reporter, list):
func, params = reporter[0], reporter[1]
def func_with_params(agent):
return func(agent, *params)
reporter = func_with_params
# For other types (like lambda functions, method of a class/instance),
# it's already suitable to be used as a reporter directly.
self.agent_reporters[name] = reporter
def _new_table(self, table_name, table_columns):
"""Add a new table that objects can write to.
Args:
table_name: Name of the new table.
table_columns: List of columns to add to the table.
"""
new_table = {column: [] for column in table_columns}
self.tables[table_name] = new_table
def _record_agents(self, model):
"""Record agents data in a mapping of functions and agents."""
rep_funcs = self.agent_reporters.values()
def get_reports(agent):
_prefix = (agent.model._steps, agent.unique_id)
reports = tuple(rep(agent) for rep in rep_funcs)
return _prefix + reports
agent_records = map(
get_reports,
model.schedule.agents
if hasattr(model, "schedule") and model.schedule is not None
else model.agents,
)
return agent_records
[docs]
def collect(self, model):
"""Collect all the data for the given model object."""
if self.model_reporters:
for var, reporter in self.model_reporters.items():
# Check if lambda or partial function
if isinstance(reporter, (types.LambdaType, partial)):
self.model_vars[var].append(reporter(model))
# Check if model attribute
elif isinstance(reporter, str):
self.model_vars[var].append(getattr(model, reporter, None))
# Check if function with arguments
elif isinstance(reporter, list):
self.model_vars[var].append(reporter[0](*reporter[1]))
# TODO: Check if method of a class, as of now it is assumed
# implicitly if the other checks fail.
else:
self.model_vars[var].append(reporter())
if self.agent_reporters:
agent_records = self._record_agents(model)
self._agent_records[model._steps] = list(agent_records)
[docs]
def add_table_row(self, table_name, row, ignore_missing=False):
"""Add a row dictionary to a specific table.
Args:
table_name: Name of the table to append a row to.
row: A dictionary of the form {column_name: value...}
ignore_missing: If True, fill any missing columns with Nones;
if False, throw an error if any columns are missing
"""
if table_name not in self.tables:
raise Exception("Table does not exist.")
for column in self.tables[table_name]:
if column in row:
self.tables[table_name][column].append(row[column])
elif ignore_missing:
self.tables[table_name][column].append(None)
else:
raise Exception("Could not insert row with missing column")
[docs]
def get_model_vars_dataframe(self):
"""Create a pandas DataFrame from the model variables.
The DataFrame has one column for each model variable, and the index is
(implicitly) the model tick.
"""
# Check if self.model_reporters dictionary is empty, if so raise warning
if not self.model_reporters:
raise UserWarning(
"No model reporters have been defined in the DataCollector, returning empty DataFrame."
)
return pd.DataFrame(self.model_vars)
[docs]
def get_agent_vars_dataframe(self):
"""Create a pandas DataFrame from the agent variables.
The DataFrame has one column for each variable, with two additional
columns for tick and agent_id.
"""
# Check if self.agent_reporters dictionary is empty, if so raise warning
if not self.agent_reporters:
raise UserWarning(
"No agent reporters have been defined in the DataCollector, returning empty DataFrame."
)
all_records = itertools.chain.from_iterable(self._agent_records.values())
rep_names = list(self.agent_reporters)
df = pd.DataFrame.from_records(
data=all_records,
columns=["Step", "AgentID", *rep_names],
index=["Step", "AgentID"],
)
return df
[docs]
def get_table_dataframe(self, table_name):
"""Create a pandas DataFrame from a particular table.
Args:
table_name: The name of the table to convert.
"""
if table_name not in self.tables:
raise Exception("No such table.")
return pd.DataFrame(self.tables[table_name])
