datacollector: Allow collecting data from Agent (sub)classes (#2300)

Enhanced Mesa's DataCollector to allow collecting data from Agent (sub)classes, providing more flexible and granular data collection capabilities.

## Motive
To enable more comprehensive data collection in multi-agent simulations, allowing researchers to track attributes and behaviors specific to different agent types, including custom Agent subclasses.

## Implementation
- Modified `DataCollector` class to accept `agenttype_reporters` parameter
- Added `_new_agenttype_reporter` method for handling agent-type-specific reporters
- Updated `collect` method to handle agent-type-specific data collection
- Added `get_agenttype_vars_dataframe` method for retrieving agent-type-specific data
- Updated `Model` class to support `agenttype_reporters` in `initialize_data_collector`
- Added support for collecting data from all Agent subclasses, not just predefined agent types
- Updated docstrings and module-level documentation
- Added comprehensive unit tests for the new functionality

## Usage Examples
```python
class MyModel(Model):
    def __init__(self):
        super().__init__()
        self.datacollector = DataCollector(
            agent_reporters={"life_span": "life_span"},
            # The new agenttype_reporters argument
            agenttype_reporters={
                Wolf: {"sheep_eaten": "sheep_eaten"},
                Sheep: {"wool": "wool_amount"},
                Animal: {"energy": "energy"}  # Collects from all animals
            }
        )

# Retrieve data for a specific agent type
wolf_data = model.datacollector.get_agenttype_vars_dataframe(Wolf)
# Retrieve data for all Animal subclasses, which are in this case all Wolf and Sheep
animal_data = model.datacollector.get_agenttype_vars_dataframe(Animal)
```

## Additional Notes
- Backward compatible with existing DataCollector usage
- Supports collecting data from custom Agent subclasses and superclasses

mesa/datacollection.py

@@ -1,18 +1,19 @@
 """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.
+generated by a Mesa model. It collects four types of data: model-level data,
+agent-level data, agent-type-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.
+A DataCollector is instantiated with three dictionaries of reporter names and
+associated variable names or functions for each, one for model-level data,
+one for agent-level data, and one for agent-type-level data; a fourth 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.
+variable. Then the agent-level functions are called on each agent, and the
+agent-type-level functions are called on each agent of the specified type.
 
 Additionally, other objects can write directly to tables by passing in an
 appropriate dictionary object for a table row.
@@ -21,19 +22,18 @@
     * 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
+    * _agent_records maps each model step to a list of each agent's id
       and its values.
+    * _agenttype_records maps each model step to a dictionary of agent types,
+      each containing a list of each agent's 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
+import warnings
 from copy import deepcopy
 from functools import partial
 
@@ -44,24 +44,25 @@
 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.
+    A DataCollector is instantiated with dictionaries of names of model-,
+    agent-, and agent-type-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,
+        agenttype_reporters=None,
         tables=None,
     ):
-        """Instantiate a DataCollector with lists of model and agent reporters.
+        """Instantiate a DataCollector with lists of model, agent, and agent-type 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.
+        Both model_reporters, agent_reporters, and agenttype_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:
@@ -85,6 +86,10 @@ def __init__(
         4. Functions with parameters placed in a list:
            {"Agent_Function": [function, [param_1, param_2]]}
 
+        Agenttype reporters take a dictionary mapping agent types to dictionaries
+        of reporter names and attributes/funcs/methods, similar to agent_reporters:
+           {Wolf: {"energy": lambda a: a.energy}}
+
         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
@@ -94,6 +99,8 @@ def __init__(
         Args:
             model_reporters: Dictionary of reporter names and attributes/funcs/methods.
             agent_reporters: Dictionary of reporter names and attributes/funcs/methods.
+            agenttype_reporters: Dictionary of agent types to dictionaries of
+                                 reporter names and attributes/funcs/methods.
             tables: Dictionary of table names to lists of column names.
 
         Notes:
@@ -103,9 +110,11 @@ def __init__(
         """
         self.model_reporters = {}
         self.agent_reporters = {}
+        self.agenttype_reporters = {}
 
         self.model_vars = {}
         self._agent_records = {}
+        self._agenttype_records = {}
         self.tables = {}
 
         if model_reporters is not None:
@@ -116,6 +125,11 @@ def __init__(
             for name, reporter in agent_reporters.items():
                 self._new_agent_reporter(name, reporter)
 
+        if agenttype_reporters is not None:
+            for agent_type, reporters in agenttype_reporters.items():
+                for name, reporter in reporters.items():
+                    self._new_agenttype_reporter(agent_type, name, reporter)
+
         if tables is not None:
             for name, columns in tables.items():
                 self._new_table(name, columns)
@@ -163,6 +177,38 @@ def func_with_params(agent):
 
         self.agent_reporters[name] = reporter
 
+    def _new_agenttype_reporter(self, agent_type, name, reporter):
+        """Add a new agent-type-level reporter to collect.
+
+        Args:
+            agent_type: The type of agent to collect data for.
+            name: Name of the agent-type-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.
+        """
+        if agent_type not in self.agenttype_reporters:
+            self.agenttype_reporters[agent_type] = {}
+
+        # Use the same logic as _new_agent_reporter
+        if isinstance(reporter, str):
+            attribute_name = reporter
+
+            def attr_reporter(agent):
+                return getattr(agent, attribute_name, None)
+
+            reporter = attr_reporter
+
+        elif isinstance(reporter, list):
+            func, params = reporter[0], reporter[1]
+
+            def func_with_params(agent):
+                return func(agent, *params)
+
+            reporter = func_with_params
+
+        self.agenttype_reporters[agent_type][name] = reporter
+
     def _new_table(self, table_name, table_columns):
         """Add a new table that objects can write to.
 
@@ -190,6 +236,34 @@ def get_reports(agent):
         )
         return agent_records
 
+    def _record_agenttype(self, model, agent_type):
+        """Record agent-type data in a mapping of functions and agents."""
+        rep_funcs = self.agenttype_reporters[agent_type].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_types = model.agent_types
+        if agent_type in agent_types:
+            agents = model.agents_by_type[agent_type]
+        else:
+            from mesa import Agent
+
+            if issubclass(agent_type, Agent):
+                agents = [
+                    agent for agent in model.agents if isinstance(agent, agent_type)
+                ]
+            else:
+                # Raise error if agent_type is not in model.agent_types
+                raise ValueError(
+                    f"Agent type {agent_type} is not recognized as an Agent type in the model or Agent subclass. Use an Agent (sub)class, like {agent_types}."
+                )
+
+        agenttype_records = map(get_reports, agents)
+        return agenttype_records
+
     def collect(self, model):
         """Collect all the data for the given model object."""
         if self.model_reporters:
@@ -208,14 +282,21 @@ def collect(self, model):
                 elif isinstance(reporter, list):
                     self.model_vars[var].append(deepcopy(reporter[0](*reporter[1])))
                 # Assume it's a callable otherwise (e.g., method)
-                # TODO: Check if method of a class explicitly
                 else:
                     self.model_vars[var].append(deepcopy(reporter()))
 
         if self.agent_reporters:
             agent_records = self._record_agents(model)
             self._agent_records[model.steps] = list(agent_records)
 
+        if self.agenttype_reporters:
+            self._agenttype_records[model.steps] = {}
+            for agent_type in self.agenttype_reporters:
+                agenttype_records = self._record_agenttype(model, agent_type)
+                self._agenttype_records[model.steps][agent_type] = list(
+                    agenttype_records
+                )
+
     def add_table_row(self, table_name, row, ignore_missing=False):
         """Add a row dictionary to a specific table.
 
@@ -272,6 +353,38 @@ def get_agent_vars_dataframe(self):
         )
         return df
 
+    def get_agenttype_vars_dataframe(self, agent_type):
+        """Create a pandas DataFrame from the agent-type variables for a specific agent type.
+
+        The DataFrame has one column for each variable, with two additional
+        columns for tick and agent_id.
+
+        Args:
+            agent_type: The type of agent to get the data for.
+        """
+        # Check if self.agenttype_reporters dictionary is empty for this agent type, if so return empty DataFrame
+        if agent_type not in self.agenttype_reporters:
+            warnings.warn(
+                f"No agent-type reporters have been defined for {agent_type} in the DataCollector, returning empty DataFrame.",
+                UserWarning,
+                stacklevel=2,
+            )
+            return pd.DataFrame()
+
+        all_records = itertools.chain.from_iterable(
+            records[agent_type]
+            for records in self._agenttype_records.values()
+            if agent_type in records
+        )
+        rep_names = list(self.agenttype_reporters[agent_type])
+
+        df = pd.DataFrame.from_records(
+            data=all_records,
+            columns=["Step", "AgentID", *rep_names],
+            index=["Step", "AgentID"],
+        )
+        return df
+
     def get_table_dataframe(self, table_name):
         """Create a pandas DataFrame from a particular table.
 

mesa/model.py

@@ -198,13 +198,15 @@ def initialize_data_collector(
         self,
         model_reporters=None,
         agent_reporters=None,
+        agenttype_reporters=None,
         tables=None,
     ) -> None:
         """Initialize the data collector for the model.
 
         Args:
             model_reporters: model reporters to collect
             agent_reporters: agent reporters to collect
+            agenttype_reporters: agent type reporters to collect
             tables: tables to collect
 
         """
@@ -219,6 +221,7 @@ def initialize_data_collector(
         self.datacollector = DataCollector(
             model_reporters=model_reporters,
             agent_reporters=agent_reporters,
+            agenttype_reporters=agenttype_reporters,
             tables=tables,
         )
         # Collect data for the first time during initialization.