diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..906cce1 --- /dev/null +++ b/Makefile @@ -0,0 +1,4 @@ +test: + docker-compose exec dev python -m pytest -s -v + +.PHONY: test \ No newline at end of file diff --git a/docs/configuration.rst b/docs/configuration.rst new file mode 100644 index 0000000..c012cb1 --- /dev/null +++ b/docs/configuration.rst @@ -0,0 +1,244 @@ +Configuring a simulation +------------------------ + +There are two ways to configure a simulation: programmatically and with a configuration file. +In both cases, the parameters used are the same. +The advantage of a configuration file is that it is a clean declarative description, and it makes it easier to reproduce. + +Simulation configuration files can be formatted in ``json`` or ``yaml`` and they define all the parameters of a simulation. +Here's an example (``example.yml``). + +.. code:: yaml + + --- + name: MyExampleSimulation + max_time: 50 + num_trials: 3 + interval: 2 + network_params: + generator: barabasi_albert_graph + n: 100 + m: 2 + network_agents: + - agent_type: SISaModel + weight: 1 + state: + id: content + - agent_type: SISaModel + weight: 1 + state: + id: discontent + - agent_type: SISaModel + weight: 8 + state: + id: neutral + environment_params: + prob_infect: 0.075 + + +This example configuration will run three trials (``num_trials``) of a simulation containing a randomly generated network (``network_params``). +The 100 nodes in the network will be SISaModel agents (``network_agents.agent_type``), which is an agent behavior that is included in Soil. +10% of the agents (``weight=1``) will start in the content state, 10% in the discontent state, and the remaining 80% (``weight=8``) in the neutral state. +All agents will have access to the environment (``environment_params``), which only contains one variable, ``prob_infected``. +The state of the agents will be updated every 2 seconds (``interval``). + +Now run the simulation with the command line tool: + +.. code:: bash + + soil example.yml + +Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``. +Three types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a ``sqlite`` file with the content of the state of every network node and the environment parameters at every step of the simulation. + + +.. code:: + + soil_output + └── MyExampleSimulation + ├── MyExampleSimulation.dumped.yml + ├── MyExampleSimulation.simulation.pickle + ├── MyExampleSimulation_trial_0.db.sqlite + ├── MyExampleSimulation_trial_1.db.sqlite + └── MyExampleSimulation_trial_2.db.sqlite + + +You may also ask soil to export the states in a ``csv`` file, and the network in gephi format (``gexf``). + +Network +======= + +The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx. + +Loading a network +################# + +To load an existing network, specify its path in the configuration: + +.. code:: yaml + + --- + network_params: + path: /tmp/mynetwork.gexf + +Soil will try to guess what networkx method to use to read the file based on its extension. +However, we only test using ``gexf`` files. + +For simple networks, you may also include them in the configuration itself using , using the ``topology`` parameter like so: + +.. code:: yaml + + --- + topology: + nodes: + - id: First + - id: Second + links: + - source: First + target: Second + + +Generating a random network +########################### + +To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm `_ and other parameters. +For example, the following configuration is equivalent to :code:`nx.complete_graph(n=100)`: + +.. code:: yaml + + network_params: + generator: complete_graph + n: 100 + +Environment +============ +The environment is the place where the shared state of the simulation is stored. +For instance, the probability of disease outbreak. +The configuration file may specify the initial value of the environment parameters: + +.. code:: yaml + + environment_params: + daily_probability_of_earthquake: 0.001 + number_of_earthquakes: 0 + +All agents have access to the environment parameters. + +In some scenarios, it is useful to have a custom environment, to provide additional methods or to control the way agents update environment state. +For example, if our agents play the lottery, the environment could provide a method to decide whether the agent wins, instead of leaving it to the agent. + + +Agents +====== +Agents are a way of modelling behavior. +Agents can be characterized with two variables: agent type (``agent_type``) and state. +Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters. +Through the environment, it can access the network topology and the state of other agents. + +There are three three types of agents according to how they are added to the simulation: network agents and environment agent. + +Network Agents +############## +Network agents are attached to a node in the topology. +The configuration file allows you to specify how agents will be mapped to topology nodes. + +The simplest way is to specify a single type of agent. +Hence, every node in the network will be associated to an agent of that type. + +.. code:: yaml + + agent_type: SISaModel + +It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property). +For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type. + +.. code:: yaml + + network_agents: + - agent_type: SISaModel + weight: 1 + - agent_type: CounterModel + weight: 5 + +The third option is to specify the type of agent on the node itself, e.g.: + + +.. code:: yaml + + topology: + nodes: + - id: first + agent_type: BaseAgent + states: + first: + agent_type: SISaModel + + +This would also work with a randomly generated network: + + +.. code:: yaml + + network: + generator: complete + n: 5 + agent_type: BaseAgent + states: + - agent_type: SISaModel + + + +In addition to agent type, you may add a custom initial state to the distribution. +This is very useful to add the same agent type with different states. +e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state: + +.. code:: yaml + + network_agents: + - agent_type: SISaModel + weight: 9 + state: + id: neutral + - agent_type: SISaModel + weight: 1 + state: + id: discontent + +Lastly, the configuration may include initial state for one or more nodes. +For instance, to add a state for the two nodes in this configuration: + +.. code:: yaml + + agent_type: SISaModel + network: + generator: complete_graph + n: 2 + states: + - id: content + - id: discontent + + +Or to add state only to specific nodes (by ``id``). +For example, to apply special skills to Linux Torvalds in a simulation: + +.. literalinclude:: ../examples/torvalds.yml + :language: yaml + + +Environment Agents +################## +In addition to network agents, more agents can be added to the simulation. +These agents are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes. + + +.. code:: + + environment_agents: + - agent_type: MyAgent + state: + mood: happy + - agent_type: DummyAgent + + +You may use environment agents to model events that a normal agent cannot control, such as natural disasters or chance. +They are also useful to add behavior that has little to do with the network and the interactions within that network. diff --git a/docs/index.rst b/docs/index.rst index 5822204..92896ce 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -6,7 +6,7 @@ Welcome to Soil's documentation! ================================ -Soil is an Agent-based Social Simulator in Python for modelling and simulation of Social Networks. +Soil is an Agent-based Social Simulator in Python focused on Social Networks. If you use Soil in your research, do not forget to cite this paper: @@ -39,6 +39,7 @@ If you use Soil in your research, do not forget to cite this paper: installation quickstart + configuration Tutorial .. diff --git a/docs/quickstart.rst b/docs/quickstart.rst index 6539ed3..b29534d 100644 --- a/docs/quickstart.rst +++ b/docs/quickstart.rst @@ -1,197 +1,71 @@ Quickstart ---------- -This section shows how to run simulations from simulation configuration files. -First of all, you need to install the package (See :doc:`installation`) - -Simulation configuration files are ``json`` or ``yaml`` files that define all the parameters of a simulation. -Here's an example (``example.yml``). - -.. code:: yaml - - --- - name: MyExampleSimulation - max_time: 50 - num_trials: 3 - interval: 2 - network_params: - network_type: barabasi_albert_graph - n: 100 - m: 2 - agent_distribution: - - agent_type: SISaModel - weight: 1 - state: - id: content - - agent_type: SISaModel - weight: 1 - state: - id: discontent - - agent_type: SISaModel - weight: 8 - state: - id: neutral - environment_params: - prob_infect: 0.075 - - -This example configuration will run three trials of a simulation containing a randomly generated network. -The 100 nodes in the network will be SISaModel agents, 10% of them will start in the content state, 10% in the discontent state, and the remaining 80% in the neutral state. -All agents will have access to the environment, which only contains one variable, ``prob_infected``. -The state of the agents will be updated every 2 seconds (``interval``). - -Now run the simulation with the command line tool: - -.. code:: bash - - soil example.yml - -Once the simulation finishes, its results will be stored in a folder named ``MyExampleSimulation``. -Four types of objects are saved by default: a pickle of the simulation; a ``YAML`` representation of the simulation (which can be used to re-launch it); and for every trial, a csv file with the content of the state of every network node and the environment parameters at every step of the simulation, as well as the network in gephi format (``gexf``). +This section shows how to run your first simulation with Soil. +For installation instructions, see :doc:`installation`. +There are mainly two parts in a simulation: agent classes and simulation configuration. +An agent class defines how the agent will behave throughout the simulation. +The configuration includes things such as number of agents to use and their type, network topology to use, etc. -.. code:: - - soil_output - ├── Sim_prob_0 - │   ├── Sim_prob_0.dumped.yml - │   ├── Sim_prob_0.simulation.pickle - │   ├── Sim_prob_0_trial_0.environment.csv - │   └── Sim_prob_0_trial_0.gexf - - -Network -======= - -The network topology for the simulation can be loaded from an existing network file or generated with one of the random network generation methods from networkx. - -Loading a network -################# - -To load an existing network, specify its path in the configuration: - -.. code:: yaml - - --- - network_params: - path: /tmp/mynetwork.gexf - -Soil will try to guess what networkx method to use to read the file based on its extension. -However, we only test using ``gexf`` files. - -Generating a random network -########################### - -To generate a random network using one of networkx's built-in methods, specify the `graph generation algorithm `_ and other parameters. -For example, the following configuration is equivalent to :code:`nx.complete_graph(100)`: - -.. code:: yaml - - network_params: - network_type: complete_graph - n: 100 - -Environment -============ -The environment is the place where the shared state of the simulation is stored. -For instance, the probability of disease outbreak. -The configuration file may specify the initial value of the environment parameters: - -.. code:: yaml - environment_params: - daily_probability_of_earthquake: 0.001 - number_of_earthquakes: 0 +.. image:: soil.png + :width: 80% + :align: center -Any agent has unrestricted access to the environment. -However, for the sake of simplicity, we recommend limiting environment updates to environment agents. -Agents -====== -Agents are a way of modelling behavior. -Agents can be characterized with two variables: an agent type (``agent_type``) and its state. -Only one agent is executed at a time (generally, every ``interval`` seconds), and it has access to its state and the environment parameters. -Through the environment, it can access the network topology and the state of other agents. +Soil includes several agent classes in the ``soil.agents`` module, and we will use them in this quickstart. +If you are interested in developing your own agents classes, see :doc:`soil_tutorial`. +The configuration is the following: -There are three three types of agents according to how they are added to the simulation: network agents and environment agent. - -Network Agents -############## -Network agents are attached to a node in the topology. -The configuration file allows you to specify how agents will be mapped to topology nodes. - -The simplest way is to specify a single type of agent. -Hence, every node in the network will be associated to an agent of that type. - -.. code:: yaml - - agent_type: SISaModel - -It is also possible to add more than one type of agent to the simulation, and to control the ratio of each type (using the ``weight`` property). -For instance, with following configuration, it is five times more likely for a node to be assigned a CounterModel type than a SISaModel type. - -.. code:: yaml - - agent_distribution: - - agent_type: SISaModel - weight: 1 - - agent_type: CounterModel - weight: 5 - -In addition to agent type, you may also add a custom initial state to the distribution. -This is very useful to add the same agent type with different states. -e.g., to populate the network with SISaModel, roughly 10% of them with a discontent state: - -.. code:: yaml - - agent_distribution: - - agent_type: SISaModel - weight: 9 - state: - id: neutral - - agent_type: SISaModel - weight: 1 - state: - id: discontent - -Lastly, the configuration may include initial state for one or more nodes. -For instance, to add a state for the two nodes in this configuration: +.. literalinclude:: quickstart.yml + :language: yaml -.. code:: yaml - agent_type: SISaModel - network: - network_type: complete_graph - n: 2 - states: - - id: content - - id: discontent +You may :download:`download the file ` directly. +The agent type used, SISa, is a very simple model. +It only has three states (neutral, content and discontent), +Its parameters are the probabilities to change from one state to another, either spontaneously or because of contagion from neighboring agents. -Or to add state only to specific nodes (by ``id``). -For example, to apply special skills to Linux Torvalds in a simulation: +To see the simulation in action, simply point soil to the configuration, and tell it to store the graph and the history of agent states and environment parameters at every point. -.. literalinclude:: ../examples/torvalds.yml - :language: yaml +.. code:: + ❯ soil --graph --csv quickstart.yml [13:35:29] + INFO:soil:Using config(s): quickstart + INFO:soil:Dumping results to soil_output/quickstart : ['csv', 'gexf'] + INFO:soil:Starting simulation quickstart at 13:35:30. + INFO:soil:Starting Simulation quickstart trial 0 at 13:35:30. + INFO:soil:Finished Simulation quickstart trial 0 at 13:35:49 in 19.43677067756653 seconds + INFO:soil:Starting Dumping simulation quickstart trial 0 at 13:35:49. + INFO:soil:Finished Dumping simulation quickstart trial 0 at 13:35:51 in 1.7733407020568848 seconds + INFO:soil:Dumping results to soil_output/quickstart + INFO:soil:Finished simulation quickstart at 13:35:51 in 21.29862952232361 seconds -Environment Agents -################## -In addition to network agents, more agents can be added to the simulation. -These agens are programmed in much the same way as network agents, the only difference is that they will not be assigned to network nodes. +The ``CSV`` file should look like this: .. code:: - environment_agents: - - agent_type: MyAgent - state: - mood: happy - - agent_type: DummyAgent + agent_id,t_step,key,value + env,0,neutral_discontent_spon_prob,0.05 + env,0,neutral_discontent_infected_prob,0.1 + env,0,neutral_content_spon_prob,0.2 + env,0,neutral_content_infected_prob,0.4 + env,0,discontent_neutral,0.2 + env,0,discontent_content,0.05 + env,0,content_discontent,0.05 + env,0,variance_d_c,0.05 + env,0,variance_c_d,0.1 -Visualizing the results -======================= +The environment variables are marked as ``agent_id`` env. +Th exported values are only stored when they change. +To find out how to get every key and value at every point in the simulation, check out the :doc:`soil_tutorial`. -The simulation will return a dynamic graph .gexf file which could be visualized with +The dynamic graph is exported as a .gexf file which could be visualized with `Gephi `__. +Now it is your turn to experiment with the simulation. +Change some of the parameters, such as the number of agents, the probability of becoming content, or the type of network, and see how the results change. + diff --git a/docs/quickstart.yml b/docs/quickstart.yml new file mode 100644 index 0000000..76ed3e2 --- /dev/null +++ b/docs/quickstart.yml @@ -0,0 +1,30 @@ +--- +name: quickstart +num_trials: 1 +max_time: 1000 +network_agents: + - agent_type: SISaModel + state: + id: neutral + weight: 1 + - agent_type: SISaModel + state: + id: content + weight: 2 +network_params: + n: 100 + k: 5 + p: 0.2 + generator: newman_watts_strogatz_graph +environment_params: + neutral_discontent_spon_prob: 0.05 + neutral_discontent_infected_prob: 0.1 + neutral_content_spon_prob: 0.2 + neutral_content_infected_prob: 0.4 + discontent_neutral: 0.2 + discontent_content: 0.05 + content_discontent: 0.05 + variance_d_c: 0.05 + variance_c_d: 0.1 + content_neutral: 0.1 + standard_variance: 0.1 diff --git a/docs/soil.png b/docs/soil.png new file mode 100644 index 0000000..b91da6c Binary files /dev/null and b/docs/soil.png differ diff --git a/docs/soil_tutorial.rst b/docs/soil_tutorial.rst index bf85758..08e1c3e 100644 --- a/docs/soil_tutorial.rst +++ b/docs/soil_tutorial.rst @@ -214,7 +214,7 @@ nodes in that network. Notice how node 0 is the only one with a TV. MAX_TIME = 100 EVENT_TIME = 10 - sim = soil.simulation.SoilSimulation(topology=G, + sim = soil.Simulation(topology=G, num_trials=1, max_time=MAX_TIME, environment_agents=[{'agent_type': NewsEnvironmentAgent, diff --git a/examples/NewsSpread.ipynb b/examples/NewsSpread.ipynb index 29cab68..d081818 100644 --- a/examples/NewsSpread.ipynb +++ b/examples/NewsSpread.ipynb @@ -2,14 +2,22 @@ "cells": [ { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { "ExecuteTime": { "start_time": "2017-11-02T09:48:41.843Z" }, "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], "source": [ "import soil\n", "import networkx as nx\n", @@ -39,26 +47,216 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "total 288K\r\n", + "drwxr-xr-x 7 j users 4.0K May 23 12:48 .\r\n", + "drwxr-xr-x 15 j users 20K May 7 18:59 ..\r\n", + "-rw-r--r-- 1 j users 451 Oct 17 2017 complete.yml\r\n", + "drwxr-xr-x 2 j users 4.0K Feb 18 11:22 .ipynb_checkpoints\r\n", + "drwxr-xr-x 2 j users 4.0K Oct 17 2017 long_running\r\n", + "-rw-r--r-- 1 j users 1.2K May 23 12:49 .nbgrader.log\r\n", + "drwxr-xr-x 4 j users 4.0K May 4 11:23 newsspread\r\n", + "-rw-r--r-- 1 j users 225K May 4 11:23 NewsSpread.ipynb\r\n", + "drwxr-xr-x 4 j users 4.0K May 4 11:21 rabbits\r\n", + "-rw-r--r-- 1 j users 42 Jul 3 2017 torvalds.edgelist\r\n", + "-rw-r--r-- 1 j users 245 Oct 13 2017 torvalds.yml\r\n", + "drwxr-xr-x 4 j users 4.0K May 4 11:23 tutorial\r\n" + ] + } + ], + "source": [ + "!ls " + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "ExecuteTime": { "start_time": "2017-11-02T09:48:43.440Z" } }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "---\r\n", + "default_state: {}\r\n", + "load_module: newsspread\r\n", + "environment_agents: []\r\n", + "environment_params:\r\n", + " prob_neighbor_spread: 0.0\r\n", + " prob_tv_spread: 0.01\r\n", + "interval: 1\r\n", + "max_time: 30\r\n", + "name: Sim_all_dumb\r\n", + "network_agents:\r\n", + "- agent_type: DumbViewer\r\n", + " state:\r\n", + " has_tv: false\r\n", + " weight: 1\r\n", + "- agent_type: DumbViewer\r\n", + " state:\r\n", + " has_tv: true\r\n", + " weight: 1\r\n", + "network_params:\r\n", + " generator: barabasi_albert_graph\r\n", + " n: 500\r\n", + " m: 5\r\n", + "num_trials: 50\r\n", + "---\r\n", + "default_state: {}\r\n", + "load_module: newsspread\r\n", + "environment_agents: []\r\n", + "environment_params:\r\n", + " prob_neighbor_spread: 0.0\r\n", + " prob_tv_spread: 0.01\r\n", + "interval: 1\r\n", + "max_time: 30\r\n", + "name: Sim_half_herd\r\n", + "network_agents:\r\n", + "- agent_type: DumbViewer\r\n", + " state:\r\n", + " has_tv: false\r\n", + " weight: 1\r\n", + "- agent_type: DumbViewer\r\n", + " state:\r\n", + " has_tv: true\r\n", + " weight: 1\r\n", + "- agent_type: HerdViewer\r\n", + " state:\r\n", + " has_tv: false\r\n", + " weight: 1\r\n", + "- agent_type: HerdViewer\r\n", + " state:\r\n", + " has_tv: true\r\n", + " weight: 1\r\n", + "network_params:\r\n", + " generator: barabasi_albert_graph\r\n", + " n: 500\r\n", + " m: 5\r\n", + "num_trials: 50\r\n", + "---\r\n", + "default_state: {}\r\n", + "load_module: newsspread\r\n", + "environment_agents: []\r\n", + "environment_params:\r\n", + " prob_neighbor_spread: 0.0\r\n", + " prob_tv_spread: 0.01\r\n", + "interval: 1\r\n", + "max_time: 30\r\n", + "name: Sim_all_herd\r\n", + "network_agents:\r\n", + "- agent_type: HerdViewer\r\n", + " state:\r\n", + " has_tv: true\r\n", + " id: neutral\r\n", + " weight: 1\r\n", + "- agent_type: HerdViewer\r\n", + " state:\r\n", + " has_tv: true\r\n", + " id: neutral\r\n", + " weight: 1\r\n", + "network_params:\r\n", + " generator: barabasi_albert_graph\r\n", + " n: 500\r\n", + " m: 5\r\n", + "num_trials: 50\r\n", + "---\r\n", + "default_state: {}\r\n", + "load_module: newsspread\r\n", + "environment_agents: []\r\n", + "environment_params:\r\n", + " prob_neighbor_spread: 0.0\r\n", + " prob_tv_spread: 0.01\r\n", + " prob_neighbor_cure: 0.1\r\n", + "interval: 1\r\n", + "max_time: 30\r\n", + "name: Sim_wise_herd\r\n", + "network_agents:\r\n", + "- agent_type: HerdViewer\r\n", + " state:\r\n", + " has_tv: true\r\n", + " id: neutral\r\n", + " weight: 1\r\n", + "- agent_type: WiseViewer\r\n", + " state:\r\n", + " has_tv: true\r\n", + " weight: 1\r\n", + "network_params:\r\n", + " generator: barabasi_albert_graph\r\n", + " n: 500\r\n", + " m: 5\r\n", + "num_trials: 50\r\n", + "---\r\n", + "default_state: {}\r\n", + "load_module: newsspread\r\n", + "environment_agents: []\r\n", + "environment_params:\r\n", + " prob_neighbor_spread: 0.0\r\n", + " prob_tv_spread: 0.01\r\n", + " prob_neighbor_cure: 0.1\r\n", + "interval: 1\r\n", + "max_time: 30\r\n", + "name: Sim_all_wise\r\n", + "network_agents:\r\n", + "- agent_type: WiseViewer\r\n", + " state:\r\n", + " has_tv: true\r\n", + " id: neutral\r\n", + " weight: 1\r\n", + "- agent_type: WiseViewer\r\n", + " state:\r\n", + " has_tv: true\r\n", + " weight: 1\r\n", + "network_params:\r\n", + " generator: barabasi_albert_graph\r\n", + " n: 500\r\n", + " m: 5\r\n", + "network_params:\r\n", + " generator: barabasi_albert_graph\r\n", + " n: 500\r\n", + " m: 5\r\n", + "num_trials: 50\r\n" + ] + } + ], "source": [ - "!cat NewsSpread.yml" + "!cat newsspread/NewsSpread.yml" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "ExecuteTime": { "start_time": "2017-11-02T09:48:43.879Z" } }, - "outputs": [], + "outputs": [ + { + "ename": "ValueError", + "evalue": "No objects to concatenate", + "output_type": "error", + "traceback": [ + "\u001b[0;31m----------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mevodumb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'soil_output/Sim_all_dumb/'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprocess\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0manalysis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_count\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'id'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mread_data\u001b[0;34m(group, *args, **kwargs)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0miterable\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_read_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mgroup\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mgroup_trials\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 14\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 15\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterable\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/git/lab.gsi/soil/soil/soil/analysis.py\u001b[0m in \u001b[0;36mgroup_trials\u001b[0;34m(trials, aggfunc)\u001b[0m\n\u001b[1;32m 159\u001b[0m \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 160\u001b[0m \u001b[0mtrials\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;32mlambda\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtuple\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 161\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconcat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrials\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0maggfunc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreorder_levels\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m,\u001b[0m\u001b[0maxis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 163\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36mconcat\u001b[0;34m(objs, axis, join, join_axes, ignore_index, keys, levels, names, verify_integrity, copy)\u001b[0m\n\u001b[1;32m 210\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mkeys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlevels\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mlevels\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnames\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 211\u001b[0m \u001b[0mverify_integrity\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mverify_integrity\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 212\u001b[0;31m copy=copy)\n\u001b[0m\u001b[1;32m 213\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_result\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 214\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/.local/lib/python3.6/site-packages/pandas/core/reshape/concat.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, objs, axis, join, join_axes, keys, levels, names, ignore_index, verify_integrity, copy)\u001b[0m\n\u001b[1;32m 243\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 244\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mobjs\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 245\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'No objects to concatenate'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 246\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 247\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mkeys\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mValueError\u001b[0m: No objects to concatenate" + ] + } + ], "source": [ "evodumb = analysis.read_data('soil_output/Sim_all_dumb/', group=True, process=analysis.get_count, keys=['id']);" ] @@ -302,7 +500,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.2" + "version": "3.6.5" }, "toc": { "colors": { diff --git a/examples/Untitled.ipynb b/examples/Untitled.ipynb new file mode 100644 index 0000000..7d44f98 --- /dev/null +++ b/examples/Untitled.ipynb @@ -0,0 +1,80808 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The autoreload extension is already loaded. To reload it, use:\n", + " %reload_ext autoreload\n", + "Populating the interactive namespace from numpy and matplotlib\n" + ] + } + ], + "source": [ + "import soil\n", + "import networkx as nx\n", + " \n", + "%load_ext autoreload\n", + "%autoreload 2\n", + "\n", + "# To display plots in the notebook\n", + "%pylab inline\n", + "\n", + "from soil import *" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "evodumb = analysis.read_data('newsspread/soil_output/Sim_all_dumb/')" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[('newsspread/soil_output/Sim_all_dumb/Sim_all_dumb.dumped.yml',\n", + " key SEED has_tv \\\n", + " agent_id env 0 1 10 100 101 102 103 \n", + " t_step \n", + " 0 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 1 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 2 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 3 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 4 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 6 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 7 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 8 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 10 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 11 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 12 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 14 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 15 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 16 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 18 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 19 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 20 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 22 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 23 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 24 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 25 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 26 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 27 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " 29 NoneSim_all_dumb_trial_0 True True True True True True True \n", + " \n", + " key ... prob_tv_spread \\\n", + " agent_id 104 105 ... 91 92 93 94 95 96 97 98 \n", + " t_step ... \n", + " 0 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 1 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 2 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 3 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 4 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 6 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 7 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 8 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 10 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 11 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 12 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 14 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 15 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 16 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 18 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 19 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 20 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 22 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 23 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 24 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 25 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 26 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 27 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 29 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " \n", + " key \n", + " agent_id 99 env \n", + " t_step \n", + " 0 0.1 0.01 \n", + " 1 0.1 0.01 \n", + " 2 0.1 0.01 \n", + " 3 0.1 0.01 \n", + " 4 0.1 0.01 \n", + " 6 0.1 0.01 \n", + " 7 0.1 0.01 \n", + " 8 0.1 0.01 \n", + " 10 0.1 0.01 \n", + " 11 0.1 0.01 \n", + " 12 0.1 0.01 \n", + " 14 0.1 0.01 \n", + " 15 0.1 0.01 \n", + " 16 0.1 0.01 \n", + " 18 0.1 0.01 \n", + " 19 0.1 0.01 \n", + " 20 0.1 0.01 \n", + " 22 0.1 0.01 \n", + " 23 0.1 0.01 \n", + " 24 0.1 0.01 \n", + " 25 0.1 0.01 \n", + " 26 0.1 0.01 \n", + " 27 0.1 0.01 \n", + " 29 0.1 0.01 \n", + " \n", + " [24 rows x 2003 columns],\n", + " {'default_state': {},\n", + " 'dir_path': 'soil_output/Sim_all_dumb',\n", + " 'dry_run': False,\n", + " 'dump': [],\n", + " 'environment_agents': [],\n", + " 'environment_params': {'prob_neighbor_spread': 0.0, 'prob_tv_spread': 0.01},\n", + " 'interval': 1,\n", + " 'load_module': 'newsspread',\n", + " 'max_time': 30,\n", + " 'name': 'Sim_all_dumb',\n", + " 'network_agents': [{'agent_type': 'DumbViewer',\n", + " 'state': {'has_tv': False},\n", + " 'weight': 1},\n", + " {'agent_type': 'DumbViewer', 'state': {'has_tv': True}, 'weight': 1}],\n", + " 'network_params': {'generator': 'barabasi_albert_graph', 'm': 5, 'n': 500},\n", + " 'num_trials': 50,\n", + " 'seed': 'None',\n", + " 'states': [],\n", + " 'topology': {'directed': False,\n", + " 'graph': {},\n", + " 'links': [{'source': 0, 'target': 5},\n", + " {'source': 0, 'target': 22},\n", + " {'source': 0, 'target': 39},\n", + " {'source': 0, 'target': 64},\n", + " {'source': 0, 'target': 103},\n", + " {'source': 1, 'target': 5},\n", + " {'source': 1, 'target': 6},\n", + " {'source': 1, 'target': 7},\n", + " {'source': 1, 'target': 10},\n", + " {'source': 1, 'target': 11},\n", + " {'source': 1, 'target': 12},\n", + " {'source': 1, 'target': 13},\n", + " {'source': 1, 'target': 14},\n", + " {'source': 1, 'target': 17},\n", + " {'source': 1, 'target': 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True True True True True True \n", + " 11 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 12 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 13 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 14 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 15 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 16 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 17 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 18 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 19 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 20 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 21 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 22 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 23 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 25 NoneSim_all_dumb_trial_15 True True True True True True \n", + " 26 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True True True \n", + " 8 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 10 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 11 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 12 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 13 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 14 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 15 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 17 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 18 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 19 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 20 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 21 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 22 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 23 NoneSim_all_dumb_trial_24 True True True True True True \n", + " 24 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NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 9 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 12 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 13 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 14 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 15 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 16 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 17 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 18 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 19 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 21 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 22 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 23 NoneSim_all_dumb_trial_4 True True True True True True True \n", + " 25 NoneSim_all_dumb_trial_4 True True True True True True True \n", 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NoneSim_all_dumb_trial_48 True True True True True True \n", + " 1 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 2 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 3 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 5 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 6 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 7 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 8 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 9 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 10 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 12 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 13 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 14 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 15 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 16 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 17 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 18 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 19 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 22 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 23 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 24 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 25 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 26 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 27 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 28 NoneSim_all_dumb_trial_48 True True True True True True \n", + " 29 NoneSim_all_dumb_trial_48 True True True True True True \n", + " \n", + " key ... prob_tv_spread \\\n", + " agent_id 103 104 105 ... 91 92 93 94 95 96 97 \n", + " t_step ... \n", + " 0 True True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 1 True True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 2 True 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"execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "evodumb" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "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": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/complete.yml b/examples/complete.yml index 60b6639..a3f88e1 100644 --- a/examples/complete.yml +++ b/examples/complete.yml @@ -2,6 +2,7 @@ name: simple dir_path: "/tmp/" num_trials: 3 +dry_run: True max_time: 100 interval: 1 seed: "CompleteSeed!" @@ -17,6 +18,7 @@ network_agents: - agent_type: AggregatedCounter weight: 0.2 environment_agents: [] +environment_class: Environment environment_params: am_i_complete: true default_state: diff --git a/examples/pubcrawl/README.md b/examples/pubcrawl/README.md new file mode 100644 index 0000000..bf410f9 --- /dev/null +++ b/examples/pubcrawl/README.md @@ -0,0 +1,10 @@ +Simulation of pubs and drinking pals that go from pub to pub. + +Th custom environment includes a list of pubs and methods to allow agents to discover and enter pubs. +There are two types of agents: + +* Patron. A patron will do three things, in this order: + * Look for other patrons to drink with + * Look for a pub where the agent and other agents in the same group can get in. + * While in the pub, patrons only drink, until they get drunk and taken home. +* Police. There is only one police agent that will take any drunk patrons home (kick them out of the pub). diff --git a/examples/pubcrawl/pubcrawl.py b/examples/pubcrawl/pubcrawl.py new file mode 100644 index 0000000..27b1eaf --- /dev/null +++ b/examples/pubcrawl/pubcrawl.py @@ -0,0 +1,174 @@ +from soil.agents import FSM, state, default_state +from soil import Environment +from random import random, shuffle +from itertools import islice +import logging + + +class CityPubs(Environment): + '''Environment with Pubs''' + level = logging.INFO + + def __init__(self, *args, number_of_pubs=3, pub_capacity=10, **kwargs): + super(CityPubs, self).__init__(*args, **kwargs) + pubs = {} + for i in range(number_of_pubs): + newpub = { + 'name': 'The awesome pub #{}'.format(i), + 'open': True, + 'capacity': pub_capacity, + 'occupancy': 0, + } + pubs[newpub['name']] = newpub + self['pubs'] = pubs + + def enter(self, pub_id, *nodes): + '''Agents will try to enter. The pub checks if it is possible''' + try: + pub = self['pubs'][pub_id] + except KeyError: + raise ValueError('Pub {} is not available'.format(pub_id)) + if not pub['open'] or (pub['capacity'] < (len(nodes) + pub['occupancy'])): + return False + pub['occupancy'] += len(nodes) + for node in nodes: + node['pub'] = pub_id + return True + + def available_pubs(self): + for pub in self['pubs'].values(): + if pub['open'] and (pub['occupancy'] < pub['capacity']): + yield pub['name'] + + def exit(self, pub_id, *node_ids): + '''Agents will notify the pub they want to leave''' + try: + pub = self['pubs'][pub_id] + except KeyError: + raise ValueError('Pub {} is not available'.format(pub_id)) + for node_id in node_ids: + node = self.get_agent(node_id) + if pub_id == node['pub']: + del node['pub'] + pub['occupancy'] -= 1 + + +class Patron(FSM): + '''Agent that looks for friends to drink with. It will do three things: + 1) Look for other patrons to drink with + 2) Look for a bar where the agent and other agents in the same group can get in. + 3) While in the bar, patrons only drink, until they get drunk and taken home. + ''' + level = logging.INFO + + defaults = { + 'pub': None, + 'drunk': False, + 'pints': 0, + 'max_pints': 3, + } + + @default_state + @state + def looking_for_friends(self): + '''Look for friends to drink with''' + self.info('I am looking for friends') + available_friends = list(self.get_agents(drunk=False, + pub=None, + state_id=self.looking_for_friends.id)) + if not available_friends: + self.info('Life sucks and I\'m alone!') + return self.at_home + befriended = self.try_friends(available_friends) + if befriended: + return self.looking_for_pub + + @state + def looking_for_pub(self): + '''Look for a pub that accepts me and my friends''' + if self['pub'] != None: + return self.sober_in_pub + self.debug('I am looking for a pub') + group = list(self.get_neighboring_agents()) + for pub in self.env.available_pubs(): + self.debug('We\'re trying to get into {}: total: {}'.format(pub, len(group))) + if self.env.enter(pub, self, *group): + self.info('We\'re all {} getting in {}!'.format(len(group), pub)) + return self.sober_in_pub + + @state + def sober_in_pub(self): + '''Drink up.''' + self.drink() + if self['pints'] > self['max_pints']: + return self.drunk_in_pub + + @state + def drunk_in_pub(self): + '''I'm out. Take me home!''' + self.info('I\'m so drunk. Take me home!') + self['drunk'] = True + pass # out drunk + + @state + def at_home(self): + '''The end''' + self.debug('Life sucks. I\'m home!') + + def drink(self): + self['pints'] += 1 + self.debug('Cheers to that') + + def kick_out(self): + self.set_state(self.at_home) + + def befriend(self, other_agent, force=False): + ''' + Try to become friends with another agent. The chances of + success depend on both agents' openness. + ''' + if force or self['openness'] > random(): + self.env.add_edge(self, other_agent) + self.info('Made some friend {}'.format(other_agent)) + return True + return False + + def try_friends(self, others): + ''' Look for random agents around me and try to befriend them''' + befriended = False + k = int(10*self['openness']) + shuffle(others) + for friend in islice(others, k): # random.choice >= 3.7 + if friend == self: + continue + if friend.befriend(self): + self.befriend(friend, force=True) + self.debug('Hooray! new friend: {}'.format(friend.id)) + befriended = True + else: + self.debug('{} does not want to be friends'.format(friend.id)) + return befriended + + +class Police(FSM): + '''Simple agent to take drunk people out of pubs.''' + level = logging.INFO + + @default_state + @state + def patrol(self): + drunksters = list(self.get_agents(drunk=True, + state_id=Patron.drunk_in_pub.id)) + for drunk in drunksters: + self.info('Kicking out the trash: {}'.format(drunk.id)) + drunk.kick_out() + else: + self.info('No trash to take out. Too bad.') + + +if __name__ == '__main__': + from soil import simulation + simulation.run_from_config('pubcrawl.yml', + dry_run=True, + dump=None, + parallel=False) diff --git a/examples/pubcrawl/pubcrawl.yml b/examples/pubcrawl/pubcrawl.yml new file mode 100644 index 0000000..7a464a6 --- /dev/null +++ b/examples/pubcrawl/pubcrawl.yml @@ -0,0 +1,26 @@ +--- +name: pubcrawl +num_trials: 3 +max_time: 10 +dump: false +network_params: + # Generate 100 empty nodes. They will be assigned a network agent + generator: empty_graph + n: 30 +network_agents: + - agent_type: pubcrawl.Patron + description: Extroverted patron + state: + openness: 1.0 + weight: 9 + - agent_type: pubcrawl.Patron + description: Introverted patron + state: + openness: 0.1 + weight: 1 +environment_agents: + - agent_type: pubcrawl.Police +environment_class: pubcrawl.CityPubs +environment_params: + altercations: 0 + number_of_pubs: 3 diff --git a/examples/rabbits/rabbits.yml b/examples/rabbits/rabbits.yml index af22f78..25275f3 100644 --- a/examples/rabbits/rabbits.yml +++ b/examples/rabbits/rabbits.yml @@ -1,7 +1,7 @@ --- load_module: rabbit_agents name: rabbits_example -max_time: 1200 +max_time: 500 interval: 1 seed: MySeed agent_type: RabbitModel diff --git a/examples/tutorial/soil_tutorial.html b/examples/tutorial/soil_tutorial.html index aad7f09..f93ca03 100644 --- a/examples/tutorial/soil_tutorial.html +++ b/examples/tutorial/soil_tutorial.html @@ -12327,7 +12327,7 @@ Notice how node 0 is the only one with a TV.

MAX_TIME = 100 EVENT_TIME = 10 -sim = soil.simulation.SoilSimulation(topology=G, +sim = soil.Simulation(topology=G, num_trials=1, max_time=MAX_TIME, environment_agents=[{'agent_type': NewsEnvironmentAgent, @@ -21883,7 +21883,7 @@ bgAAAABJRU5ErkJggg==
-
267M	../rabbits/soil_output/rabbits_example/
+
267M	../rabbits/soil_output/rabbits_example/
diff --git a/examples/tutorial/soil_tutorial.ipynb b/examples/tutorial/soil_tutorial.ipynb index 4753242..425131b 100644 --- a/examples/tutorial/soil_tutorial.ipynb +++ b/examples/tutorial/soil_tutorial.ipynb @@ -426,7 +426,7 @@ "MAX_TIME = 100\n", "EVENT_TIME = 10\n", "\n", - "sim = soil.simulation.SoilSimulation(topology=G,\n", + "sim = soil.Simulation(topology=G,\n", " num_trials=1,\n", " max_time=MAX_TIME,\n", " environment_agents=[{'agent_type': NewsEnvironmentAgent,\n", diff --git a/soil/__init__.py b/soil/__init__.py index 6d05256..1d2ddce 100644 --- a/soil/__init__.py +++ b/soil/__init__.py @@ -14,12 +14,11 @@ except NameError: logging.basicConfig() from . import agents -from . import simulation -from . import environment +from .simulation import * +from .environment import Environment from . import utils from . import analysis - def main(): import argparse from . import simulation @@ -46,11 +45,12 @@ def main(): args = parser.parse_args() - if args.module: + if os.getcwd() not in sys.path: sys.path.append(os.getcwd()) + if args.module: importlib.import_module(args.module) - logging.info('Loading config file: {}'.format(args.file, args.output)) + logging.info('Loading config file: {}'.format(args.file)) try: dump = [] @@ -64,7 +64,7 @@ def main(): dump=dump, parallel=(not args.synchronous and not args.pdb), results_dir=args.output) - except Exception as ex: + except Exception: if args.pdb: pdb.post_mortem() else: diff --git a/soil/agents/SISaModel.py b/soil/agents/SISaModel.py index 9aa6c2e..61bc86e 100644 --- a/soil/agents/SISaModel.py +++ b/soil/agents/SISaModel.py @@ -10,7 +10,7 @@ class SISaModel(FSM): neutral_discontent_infected_prob - neutral_content_spong_prob + neutral_content_spon_prob neutral_content_infected_prob @@ -29,27 +29,27 @@ class SISaModel(FSM): standard_variance """ - def __init__(self, environment=None, agent_id=0, state=()): + def __init__(self, environment, agent_id=0, state=()): super().__init__(environment=environment, agent_id=agent_id, state=state) - self.neutral_discontent_spon_prob = np.random.normal(environment.environment_params['neutral_discontent_spon_prob'], - environment.environment_params['standard_variance']) - self.neutral_discontent_infected_prob = np.random.normal(environment.environment_params['neutral_discontent_infected_prob'], - environment.environment_params['standard_variance']) - self.neutral_content_spon_prob = np.random.normal(environment.environment_params['neutral_content_spon_prob'], - environment.environment_params['standard_variance']) - self.neutral_content_infected_prob = np.random.normal(environment.environment_params['neutral_content_infected_prob'], - environment.environment_params['standard_variance']) + self.neutral_discontent_spon_prob = np.random.normal(self.env['neutral_discontent_spon_prob'], + self.env['standard_variance']) + self.neutral_discontent_infected_prob = np.random.normal(self.env['neutral_discontent_infected_prob'], + self.env['standard_variance']) + self.neutral_content_spon_prob = np.random.normal(self.env['neutral_content_spon_prob'], + self.env['standard_variance']) + self.neutral_content_infected_prob = np.random.normal(self.env['neutral_content_infected_prob'], + self.env['standard_variance']) - self.discontent_neutral = np.random.normal(environment.environment_params['discontent_neutral'], - environment.environment_params['standard_variance']) - self.discontent_content = np.random.normal(environment.environment_params['discontent_content'], - environment.environment_params['variance_d_c']) + self.discontent_neutral = np.random.normal(self.env['discontent_neutral'], + self.env['standard_variance']) + self.discontent_content = np.random.normal(self.env['discontent_content'], + self.env['variance_d_c']) - self.content_discontent = np.random.normal(environment.environment_params['content_discontent'], - environment.environment_params['variance_c_d']) - self.content_neutral = np.random.normal(environment.environment_params['content_neutral'], - environment.environment_params['standard_variance']) + self.content_discontent = np.random.normal(self.env['content_discontent'], + self.env['variance_c_d']) + self.content_neutral = np.random.normal(self.env['content_neutral'], + self.env['standard_variance']) @state def neutral(self): diff --git a/soil/agents/__init__.py b/soil/agents/__init__.py index 29562e3..7b34eb7 100644 --- a/soil/agents/__init__.py +++ b/soil/agents/__init__.py @@ -16,23 +16,15 @@ from functools import wraps from .. import utils, history -agent_types = {} - -class MetaAgent(type): - def __init__(cls, name, bases, nmspc): - super(MetaAgent, cls).__init__(name, bases, nmspc) - agent_types[name] = cls - - -class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent): +class BaseAgent(nxsim.BaseAgent): """ A special simpy BaseAgent that keeps track of its state history. """ defaults = {} - def __init__(self, environment=None, agent_id=None, state=None, + def __init__(self, environment, agent_id=None, state=None, name='network_process', interval=None, **state_params): # Check for REQUIRED arguments assert environment is not None, TypeError('__init__ missing 1 required keyword argument: \'environment\'. ' @@ -152,14 +144,18 @@ class BaseAgent(nxsim.BaseAgent, metaclass=MetaAgent): def count_neighboring_agents(self, state_id=None): return len(super().get_agents(state_id, limit_neighbors=True)) - def get_agents(self, state_id=None, limit_neighbors=False, iterator=False, **kwargs): + def get_agents(self, state_id=None, agent_type=None, limit_neighbors=False, iterator=False, **kwargs): + agents = self.env.agents if limit_neighbors: agents = super().get_agents(state_id, limit_neighbors) - else: - agents = filter(lambda x: state_id is None or x.state.get('id', None) == state_id, - self.env.agents) def matches_all(agent): + if state_id is not None: + if agent.state.get('id', None) != state_id: + return False + if agent_type is not None: + if type(agent) != agent_type: + return False state = agent.state for k, v in kwargs.items(): if state.get(k, None) != v: @@ -219,7 +215,7 @@ def default_state(func): return func -class MetaFSM(MetaAgent): +class MetaFSM(type): def __init__(cls, name, bases, nmspc): super(MetaFSM, cls).__init__(name, bases, nmspc) states = {} @@ -328,16 +324,42 @@ def calculate_distribution(network_agents=None, return network_agents -def _serialize_distribution(network_agents): - d = _convert_agent_types(network_agents, - to_string=True) +def serialize_agent_type(agent_type): + if isinstance(agent_type, str): + return agent_type + type_name = agent_type.__name__ + if type_name not in globals(): + type_name = utils.name(agent_type) + return type_name + +def serialize_distribution(network_agents): ''' When serializing an agent distribution, remove the thresholds, in order to avoid cluttering the YAML definition file. ''' + d = deepcopy(network_agents) for v in d: if 'threshold' in v: del v['threshold'] + v['agent_type'] = serialize_agent_type(v['agent_type']) + return d + + +def deserialize_type(agent_type, known_modules=[]): + if not isinstance(agent_type, str): + return agent_type + if agent_type in globals(): + agent_type = globals()[agent_type] + else: + known = known_modules + ['soil.agents', 'soil.agents.custom' ] + agent_type = utils.deserializer(agent_type, known_modules=known) + return agent_type + + +def deserialize_distribution(ind): + d = deepcopy(ind) + for v in d: + v['agent_type'] = deserialize_type(v['agent_type']) return d @@ -354,14 +376,9 @@ def _validate_states(states, topology): def _convert_agent_types(ind, to_string=False): '''Convenience method to allow specifying agents by class or class name.''' - d = deepcopy(ind) - for v in d: - agent_type = v['agent_type'] - if to_string and not isinstance(agent_type, str): - v['agent_type'] = str(agent_type.__name__) - elif not to_string and isinstance(agent_type, str): - v['agent_type'] = agent_types[agent_type] - return d + if to_string: + return serialize_distribution(ind) + return deserialize_distribution(ind) def _agent_from_distribution(distribution, value=-1): diff --git a/soil/analysis.py b/soil/analysis.py index 8719013..647f03c 100644 --- a/soil/analysis.py +++ b/soil/analysis.py @@ -56,7 +56,7 @@ def read_csv(filename, keys=None, convert_types=False, **kwargs): def convert_row(row): - row['value'] = utils.convert(row['value'], row['value_type']) + row['value'] = utils.deserialize(row['value_type'], row['value']) return row diff --git a/soil/environment.py b/soil/environment.py index 0790d9d..e0cf1e4 100644 --- a/soil/environment.py +++ b/soil/environment.py @@ -15,7 +15,7 @@ import nxsim from . import utils, agents, analysis, history -class SoilEnvironment(nxsim.NetworkEnvironment): +class Environment(nxsim.NetworkEnvironment): """ The environment is key in a simulation. It contains the network topology, a reference to network and environment agents, as well as the environment @@ -23,7 +23,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment): The environment parameters and the state of every agent can be accessed both by using the environment as a dictionary or with the environment's - :meth:`soil.environment.SoilEnvironment.get` method. + :meth:`soil.environment.Environment.get` method. """ def __init__(self, name=None, @@ -49,7 +49,8 @@ class SoilEnvironment(nxsim.NetworkEnvironment): self.dry_run = dry_run self.interval = interval self.dir_path = dir_path or tempfile.mkdtemp('soil-env') - self.get_path() + if not dry_run: + self.get_path() self._history = history.History(name=self.name if not dry_run else None, dir_path=self.dir_path) # Add environment agents first, so their events get @@ -93,17 +94,35 @@ class SoilEnvironment(nxsim.NetworkEnvironment): if not network_agents: return for ix in self.G.nodes(): - agent, state = agents._agent_from_distribution(network_agents) - self.set_agent(ix, agent_type=agent, state=state) + self.init_agent(ix, agent_distribution=network_agents) + + def init_agent(self, agent_id, agent_distribution): + node = self.G.nodes[agent_id] + init = False + state = dict(node) + + agent_type = None + if 'agent_type' in self.states.get(agent_id, {}): + agent_type = self.states[agent_id] + elif 'agent_type' in node: + agent_type = node['agent_type'] + elif 'agent_type' in self.default_state: + agent_type = self.default_state['agent_type'] + + if agent_type: + agent_type = agents.deserialize_agent_type(agent_type) + else: + agent_type, state = agents._agent_from_distribution(agent_distribution) + return self.set_agent(agent_id, agent_type, state) def set_agent(self, agent_id, agent_type, state=None): node = self.G.nodes[agent_id] - defstate = deepcopy(self.default_state) + defstate = deepcopy(self.default_state) or {} defstate.update(self.states.get(agent_id, {})) + defstate.update(node.get('state', {})) if state: defstate.update(state) state = defstate - state.update(node.get('state', {})) a = agent_type(environment=self, agent_id=agent_id, state=state) @@ -118,6 +137,10 @@ class SoilEnvironment(nxsim.NetworkEnvironment): return a def add_edge(self, agent1, agent2, attrs=None): + if hasattr(agent1, 'id'): + agent1 = agent1.id + if hasattr(agent2, 'id'): + agent2 = agent2.id return self.G.add_edge(agent1, agent2) def run(self, *args, **kwargs): @@ -202,7 +225,7 @@ class SoilEnvironment(nxsim.NetworkEnvironment): with open(csv_name, 'w') as f: cr = csv.writer(f) - cr.writerow(('agent_id', 't_step', 'key', 'value', 'value_type')) + cr.writerow(('agent_id', 't_step', 'key', 'value')) for i in self.history_to_tuples(): cr.writerow(i) @@ -302,7 +325,6 @@ class SoilEnvironment(nxsim.NetworkEnvironment): state['network_agents'] = agents._serialize_distribution(self.network_agents) state['environment_agents'] = agents._convert_agent_types(self.environment_agents, to_string=True) - del state['_queue'] return state def __setstate__(self, state): @@ -311,3 +333,6 @@ class SoilEnvironment(nxsim.NetworkEnvironment): self.network_agents = self.calculate_distribution(self._convert_agent_types(self.network_agents)) self.environment_agents = self._convert_agent_types(self.environment_agents) return state + + +SoilEnvironment = Environment diff --git a/soil/history.py b/soil/history.py index 37720a5..4482417 100644 --- a/soil/history.py +++ b/soil/history.py @@ -17,12 +17,12 @@ class History: if db_path is None and name: db_path = os.path.join(dir_path or os.getcwd(), '{}.db.sqlite'.format(name)) - if db_path is None: - db_path = ":memory:" - else: + if db_path: if backup and os.path.exists(db_path): newname = db_path + '.backup{}.sqlite'.format(time.time()) os.rename(db_path, newname) + else: + db_path = ":memory:" self.db_path = db_path self.db = db_path @@ -34,12 +34,6 @@ class History: self._dtypes = {} self._tups = [] - def conversors(self, key): - """Get the serializer and deserializer for a given key.""" - if key not in self._dtypes: - self.read_types() - return self._dtypes[key] - @property def db(self): try: @@ -58,55 +52,88 @@ class History: @property def dtypes(self): + self.read_types() return {k:v[0] for k, v in self._dtypes.items()} def save_tuples(self, tuples): + ''' + Save a series of tuples, converting them to records if necessary + ''' self.save_records(Record(*tup) for tup in tuples) def save_records(self, records): - with self.db: - for rec in records: - if not isinstance(rec, Record): - rec = Record(*rec) - if rec.key not in self._dtypes: - name = utils.name(rec.value) - serializer = utils.serializer(name) - deserializer = utils.deserializer(name) - self._dtypes[rec.key] = (name, serializer, deserializer) - self.db.execute("replace into value_types (key, value_type) values (?, ?)", (rec.key, name)) - self.db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value)) + ''' + Save a collection of records + ''' + for record in records: + if not isinstance(record, Record): + record = Record(*record) + self.save_record(*record) - def save_record(self, *args, **kwargs): - self._tups.append(Record(*args, **kwargs)) + def save_record(self, agent_id, t_step, key, value): + ''' + Save a collection of records to the database. + Database writes are cached. + ''' + value = self.convert(key, value) + self._tups.append(Record(agent_id=agent_id, + t_step=t_step, + key=key, + value=value)) if len(self._tups) > 100: self.flush_cache() + def convert(self, key, value): + """Get the serialized value for a given key.""" + if key not in self._dtypes: + self.read_types() + if key not in self._dtypes: + name = utils.name(value) + serializer = utils.serializer(name) + deserializer = utils.deserializer(name) + self._dtypes[key] = (name, serializer, deserializer) + with self.db: + self.db.execute("replace into value_types (key, value_type) values (?, ?)", (key, name)) + return self._dtypes[key][1](value) + + def recover(self, key, value): + """Get the deserialized value for a given key, and the serialized version.""" + if key not in self._dtypes: + self.read_types() + if key not in self._dtypes: + raise ValueError("Unknown datatype for {} and {}".format(key, value)) + return self._dtypes[key][2](value) + + def flush_cache(self): ''' Use a cache to save state changes to avoid opening a session for every change. The cache will be flushed at the end of the simulation, and when history is accessed. ''' - self.save_records(self._tups) + with self.db: + for rec in self._tups: + self.db.execute("replace into history(agent_id, t_step, key, value) values (?, ?, ?, ?)", (rec.agent_id, rec.t_step, rec.key, rec.value)) self._tups = list() def to_tuples(self): - self.flush_cache() - with self.db: - res = self.db.execute("select agent_id, t_step, key, value from history ").fetchall() - for r in res: - agent_id, t_step, key, value = r - _, _ , des = self.conversors(key) - yield agent_id, t_step, key, des(value) + self.flush_cache() + with self.db: + res = self.db.execute("select agent_id, t_step, key, value from history ").fetchall() + for r in res: + agent_id, t_step, key, value = r + value = self.recover(key, value) + yield agent_id, t_step, key, value def read_types(self): - with self.db: - res = self.db.execute("select key, value_type from value_types ").fetchall() - for k, v in res: - serializer = utils.serializer(v) - deserializer = utils.deserializer(v) - self._dtypes[k] = (v, serializer, deserializer) + with self.db: + res = self.db.execute("select key, value_type from value_types ").fetchall() + for k, v in res: + serializer = utils.serializer(v) + deserializer = utils.deserializer(v) + self._dtypes[k] = (v, serializer, deserializer) def __getitem__(self, key): + self.flush_cache() key = Key(*key) agent_ids = [key.agent_id] if key.agent_id is not None else [] t_steps = [key.t_step] if key.t_step is not None else [] @@ -176,7 +203,7 @@ class History: for k, v in self._dtypes.items(): if k in df_p: dtype, _, deserial = v - df_p[k] = df_p[k].fillna(method='ffill').fillna(deserial()).astype(dtype) + df_p[k] = df_p[k].fillna(method='ffill').astype(dtype) if t_steps: df_p = df_p.reindex(t_steps, method='ffill') return df_p.ffill() diff --git a/soil/simulation.py b/soil/simulation.py index f9aad8e..b519ac6 100644 --- a/soil/simulation.py +++ b/soil/simulation.py @@ -12,11 +12,12 @@ import pickle from nxsim import NetworkSimulation -from . import utils, environment, basestring, agents +from . import utils, basestring, agents +from .environment import Environment from .utils import logger -class SoilSimulation(NetworkSimulation): +class Simulation(NetworkSimulation): """ Subclass of nsim.NetworkSimulation with three main differences: 1) agent type can be specified by name or by class. @@ -43,13 +44,47 @@ class SoilSimulation(NetworkSimulation): 'agent_type_1'. 3) if no initial state is given, each node's state will be set to `{'id': 0}`. + + Parameters + --------- + name : str, optional + name of the Simulation + topology : networkx.Graph instance, optional + network_params : dict + parameters used to create a topology with networkx, if no topology is given + network_agents : dict + definition of agents to populate the topology with + agent_type : NetworkAgent subclass, optional + Default type of NetworkAgent to use for nodes not specified in network_agents + states : list, optional + List of initial states corresponding to the nodes in the topology. Basic form is a list of integers + whose value indicates the state + dir_path : str, optional + Directory path where to save pickled objects + seed : str, optional + Seed to use for the random generator + num_trials : int, optional + Number of independent simulation runs + max_time : int, optional + Time how long the simulation should run + environment_params : dict, optional + Dictionary of globally-shared environmental parameters + environment_agents: dict, optional + Similar to network_agents. Distribution of Agents that control the environment + environment_class: soil.environment.Environment subclass, optional + Class for the environment. It defailts to soil.environment.Environment + load_module : str, module name, deprecated + If specified, soil will load the content of this module under 'soil.agents.custom' + + """ def __init__(self, name=None, topology=None, network_params=None, network_agents=None, agent_type=None, states=None, default_state=None, interval=1, dump=None, dry_run=False, dir_path=None, num_trials=1, max_time=100, - agent_module=None, load_module=None, seed=None, - environment_agents=None, environment_params=None, **kwargs): + load_module=None, seed=None, + environment_agents=None, environment_params=None, + environment_class=None, **kwargs): if topology is None: topology = utils.load_network(network_params, @@ -70,11 +105,15 @@ class SoilSimulation(NetworkSimulation): self.dump = dump self.dry_run = dry_run self.environment_params = environment_params or {} + self.environment_class = utils.deserialize(environment_class, + known_modules=['soil.environment',]) or Environment + + self._loaded_module = None if load_module: path = sys.path + [self.dir_path, os.getcwd()] f, fp, desc = imp.find_module(load_module, path) - imp.load_module('soil.agents.custom', f, fp, desc) + self._loaded_module = imp.load_module('soil.agents.custom', f, fp, desc) environment_agents = environment_agents or [] self.environment_agents = agents._convert_agent_types(environment_agents) @@ -128,7 +167,7 @@ class SoilSimulation(NetworkSimulation): 'dir_path': self.dir_path, }) opts.update(kwargs) - env = environment.SoilEnvironment(**opts) + env = self.environment_class(**opts) return env def run_trial(self, trial_id=0, until=None, return_env=True, **opts): @@ -177,11 +216,18 @@ class SoilSimulation(NetworkSimulation): pickle.dump(self, f) def __getstate__(self): - state = self.__dict__.copy() + state = {} + for k, v in self.__dict__.items(): + if k[0] != '_': + state[k] = v state['topology'] = json_graph.node_link_data(self.topology) - state['network_agents'] = agents._serialize_distribution(self.network_agents) + state['network_agents'] = agents.serialize_distribution(self.network_agents) state['environment_agents'] = agents._convert_agent_types(self.environment_agents, to_string=True) + state['environment_class'] = utils.serialize(self.environment_class, + known_modules=['soil.environment', ])[1] # func, name + if state['load_module'] is None: + del state['load_module'] return state def __setstate__(self, state): @@ -189,6 +235,8 @@ class SoilSimulation(NetworkSimulation): self.topology = json_graph.node_link_graph(state['topology']) self.network_agents = agents.calculate_distribution(agents._convert_agent_types(self.network_agents)) self.environment_agents = agents._convert_agent_types(self.environment_agents) + self.environment_class = utils.deserialize(self.environment_class, + known_modules=['soil.environment', ]) # func, name return state @@ -197,11 +245,11 @@ def from_config(config): if len(config) > 1: raise AttributeError('Provide only one configuration') config = config[0][0] - sim = SoilSimulation(**config) + sim = Simulation(**config) return sim -def run_from_config(*configs, results_dir='soil_output', dry_run=False, dump=None, timestamp=False, **kwargs): +def run_from_config(*configs, results_dir='soil_output', dump=None, timestamp=False, **kwargs): for config_def in configs: # logger.info("Found {} config(s)".format(len(ls))) for config, _ in utils.load_config(config_def): @@ -214,6 +262,8 @@ def run_from_config(*configs, results_dir='soil_output', dry_run=False, dump=Non else: sim_folder = name dir_path = os.path.join(results_dir, sim_folder) - sim = SoilSimulation(dir_path=dir_path, dump=dump, **config) + if dump is not None: + config['dump'] = dump + sim = Simulation(dir_path=dir_path, **config) logger.info('Dumping results to {} : {}'.format(sim.dir_path, sim.dump)) sim.run_simulation(**kwargs) diff --git a/soil/utils.py b/soil/utils.py index c6dd75e..f072fe3 100644 --- a/soil/utils.py +++ b/soil/utils.py @@ -1,8 +1,9 @@ import os +import ast import yaml import logging import importlib -from time import time +import time from glob import glob from random import random from copy import deepcopy @@ -62,44 +63,89 @@ def load_config(config): @contextmanager def timer(name='task', pre="", function=logger.info, to_object=None): - start = time() - function('{}Starting {} at {}.'.format(pre, name, start)) + start = time.time() + function('{}Starting {} at {}.'.format(pre, name, + time.strftime("%X", time.gmtime(start)))) yield start - end = time() - function('{}Finished {} in {} seconds'.format(pre, name, str(end-start))) + end = time.time() + function('{}Finished {} at {} in {} seconds'.format(pre, name, + time.strftime("%X", time.gmtime(end)), + str(end-start))) if to_object: to_object.start = start to_object.end = end -def repr(v): - func = serializer(v) - tname = name(v) - return func(v), tname - - -def name(v): - return type(v).__name__ +builtins = importlib.import_module('builtins') + +def name(value, known_modules=[]): + '''Return a name that can be imported, to serialize/deserialize an object''' + if value is None: + return 'None' + if not isinstance(value, type): # Get the class name first + value = type(value) + tname = value.__name__ + if hasattr(builtins, tname): + return tname + modname = value.__module__ + if modname == '__main__': + return tname + if known_modules and modname in known_modules: + return tname + for mod_name in known_modules: + module = importlib.import_module(mod_name) + if hasattr(module, tname): + return tname + return '{}.{}'.format(modname, tname) def serializer(type_): - if type_ == 'bool': - return lambda x: "true" if x else "" + if type_ != 'str' and hasattr(builtins, type_): + return repr return lambda x: x -def deserializer(type_): - try: - # Check if it's a builtin type - module = importlib.import_module('builtins') - cls = getattr(module, type_) - except AttributeError: - # if not, separate module and class - module, type_ = type_.rsplit(".", 1) - module = importlib.import_module(module) - cls = getattr(module, type_) - return cls +def serialize(v, known_modules=[]): + '''Get a text representation of an object.''' + tname = name(v, known_modules=known_modules) + func = serializer(tname) + return func(v), tname +def deserializer(type_, known_modules=[]): + if type_ == 'str': + return lambda x='': x + if type_ == 'None': + return lambda x=None: None + if hasattr(builtins, type_): # Check if it's a builtin type + cls = getattr(builtins, type_) + return lambda x=None: ast.literal_eval(x) if x is not None else cls() + # Otherwise, see if we can find the module and the class + modules = known_modules or [] + options = [] + + for mod in modules: + options.append((mod, type_)) + + if '.' in type_: # Fully qualified module + module, type_ = type_.rsplit(".", 1) + options.append ((module, type_)) -def convert(value, type_): - return deserializer(type_)(value) + errors = [] + for module, name in options: + try: + module = importlib.import_module(module) + cls = getattr(module, name) + return getattr(cls, 'deserialize', cls) + except (ImportError, AttributeError) as ex: + errors.append((module, name, ex)) + raise Exception('Could not find module {}. Tried: {}'.format(type_, errors)) + + +def deserialize(type_, value=None, **kwargs): + '''Get an object from a text representation''' + if not isinstance(type_, str): + return type_ + des = deserializer(type_, **kwargs) + if value is None: + return des + return des(value) diff --git a/soil/web/__init__.py b/soil/web/__init__.py index 97902d9..04323bb 100644 --- a/soil/web/__init__.py +++ b/soil/web/__init__.py @@ -271,4 +271,4 @@ def main(): parser.add_argument('--verbose', '-v', help='verbose mode', action='store_true') args = parser.parse_args() - run(name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose) + run(name=args.name, port=(args.port[0] if isinstance(args.port, list) else args.port), verbose=args.verbose) \ No newline at end of file diff --git a/tests/test_examples.py b/tests/test_examples.py new file mode 100644 index 0000000..5c7e00f --- /dev/null +++ b/tests/test_examples.py @@ -0,0 +1,45 @@ +from unittest import TestCase +import os +from os.path import join + +from soil import utils, simulation + +ROOT = os.path.abspath(os.path.dirname(__file__)) +EXAMPLES = join(ROOT, '..', 'examples') + + +class TestExamples(TestCase): + pass + + +def make_example_test(path, config): + def wrapped(self): + root = os.getcwd() + os.chdir(os.path.dirname(path)) + s = simulation.from_config(config) + envs = s.run_simulation(dry_run=True) + assert envs + for env in envs: + assert env + try: + n = config['network_params']['n'] + assert len(list(env.network_agents)) == n + assert env.now > 2 # It has run + assert env.now <= config['max_time'] # But not further than allowed + except KeyError: + pass + os.chdir(root) + return wrapped + + +def add_example_tests(): + for config, path in utils.load_config(join(EXAMPLES, '**', '*.yml')): + p = make_example_test(path=path, config=config) + fname = os.path.basename(path) + p.__name__ = 'test_example_file_%s' % fname + p.__doc__ = '%s should be a valid configuration' % fname + setattr(TestExamples, p.__name__, p) + del p + + +add_example_tests() diff --git a/tests/test_history.py b/tests/test_history.py index 19d0893..6393837 100644 --- a/tests/test_history.py +++ b/tests/test_history.py @@ -116,6 +116,7 @@ class TestHistory(TestCase): db_path = os.path.join(DBROOT, 'test') h = history.History(db_path=db_path) h.save_tuples(tuples) + h.flush_cache() assert os.path.exists(db_path) # Recover the data @@ -131,3 +132,25 @@ class TestHistory(TestCase): assert newhistory._db_path == h._db_path assert os.path.exists(backuppath) assert not len(newhistory[None, None, None]) + + def test_history_tuples(self): + """ + The data recovered should be equal to the one recorded. + """ + tuples = ( + ('a_1', 0, 'id', 'v'), + ('a_1', 1, 'id', 'a'), + ('a_1', 2, 'id', 'l'), + ('a_1', 3, 'id', 'u'), + ('a_1', 4, 'id', 'e'), + ('env', 1, 'prob', 1), + ('env', 2, 'prob', 2), + ('env', 3, 'prob', 3), + ('a_2', 7, 'finished', True), + ) + h = history.History() + h.save_tuples(tuples) + recovered = list(h.to_tuples()) + assert recovered + for i in recovered: + assert i in tuples diff --git a/tests/test_main.py b/tests/test_main.py index 2b9063f..14fb261 100644 --- a/tests/test_main.py +++ b/tests/test_main.py @@ -6,14 +6,12 @@ import networkx as nx from functools import partial from os.path import join -from soil import simulation, environment, agents, utils +from soil import simulation, Environment, agents, utils, history ROOT = os.path.abspath(os.path.dirname(__file__)) - EXAMPLES = join(ROOT, '..', 'examples') - class TestMain(TestCase): def test_load_graph(self): @@ -188,8 +186,6 @@ class TestMain(TestCase): recovered = yaml.load(serial) with utils.timer('deleting'): del recovered['topology'] - del recovered['load_module'] - del recovered['dry_run'] assert config == recovered def test_configuration_changes(self): @@ -197,25 +193,17 @@ class TestMain(TestCase): The configuration should not change after running the simulation. """ - config = utils.load_file('examples/complete.yml')[0] + config = utils.load_file(join(EXAMPLES, 'complete.yml'))[0] s = simulation.from_config(config) s.dry_run = True for i in range(5): s.run_simulation(dry_run=True) nconfig = s.to_dict() del nconfig['topology'] - del nconfig['dry_run'] - del nconfig['load_module'] assert config == nconfig - def test_examples(self): - """ - Make sure all examples in the examples folder are correct - """ - pass - def test_row_conversion(self): - env = environment.SoilEnvironment(dry_run=True) + env = Environment(dry_run=True) env['test'] = 'test_value' res = list(env.history_to_tuples()) @@ -234,7 +222,7 @@ class TestMain(TestCase): from geometric models. We should work around it. """ G = nx.random_geometric_graph(20, 0.1) - env = environment.SoilEnvironment(topology=G, dry_run=True) + env = Environment(topology=G, dry_run=True) env.dump_gexf('/tmp/dump-gexf') def test_save_graph(self): @@ -245,7 +233,7 @@ class TestMain(TestCase): ''' G = nx.cycle_graph(5) distribution = agents.calculate_distribution(None, agents.BaseAgent) - env = environment.SoilEnvironment(topology=G, network_agents=distribution, dry_run=True) + env = Environment(topology=G, network_agents=distribution, dry_run=True) env[0, 0, 'testvalue'] = 'start' env[0, 10, 'testvalue'] = 'finish' nG = env.history_to_graph() @@ -253,33 +241,58 @@ class TestMain(TestCase): assert ('start', 0, 10) in values assert ('finish', 10, None) in values + def test_serialize_class(self): + ser, name = utils.serialize(agents.BaseAgent) + assert name == 'soil.agents.BaseAgent' + assert ser == agents.BaseAgent -def make_example_test(path, config): - def wrapped(self): - root = os.getcwd() - os.chdir(os.path.dirname(path)) - s = simulation.from_config(config) - envs = s.run_simulation(dry_run=True) - assert envs - for env in envs: - assert env - try: - n = config['network_params']['n'] - assert len(env.get_agents()) == n - except KeyError: - pass - os.chdir(root) - return wrapped + class CustomAgent(agents.BaseAgent): + pass + ser, name = utils.serialize(CustomAgent) + assert name == 'test_main.CustomAgent' + assert ser == CustomAgent -def add_example_tests(): - for config, path in utils.load_config(join(EXAMPLES, '*.yml')): - p = make_example_test(path=path, config=config) - fname = os.path.basename(path) - p.__name__ = 'test_example_file_%s' % fname - p.__doc__ = '%s should be a valid configuration' % fname - setattr(TestMain, p.__name__, p) - del p + def test_serialize_builtin_types(self): + for i in [1, None, True, False, {}, [], list(), dict()]: + ser, name = utils.serialize(i) + assert type(ser) == str + des = utils.deserialize(name, ser) + assert i == des -add_example_tests() + def test_deserialize_agent_distribution(self): + agent_distro = [ + { + 'agent_type': 'CounterModel', + 'weight': 1 + }, + { + 'agent_type': 'BaseAgent', + 'weight': 2 + }, + ] + converted = agents.deserialize_distribution(agent_distro) + assert converted[0]['agent_type'] == agents.CounterModel + assert converted[1]['agent_type'] == agents.BaseAgent + + def test_serialize_agent_distribution(self): + agent_distro = [ + { + 'agent_type': agents.CounterModel, + 'weight': 1 + }, + { + 'agent_type': agents.BaseAgent, + 'weight': 2 + }, + ] + converted = agents.serialize_distribution(agent_distro) + assert converted[0]['agent_type'] == 'CounterModel' + assert converted[1]['agent_type'] == 'BaseAgent' + + def test_history(self): + '''Test storing in and retrieving from history (sqlite)''' + h = history.History() + h.save_record(agent_id=0, t_step=0, key="test", value="hello") + assert h[0, 0, "test"] == "hello"