diff --git a/.dockerignore b/.dockerignore new file mode 100644 index 0000000..a4a9eef --- /dev/null +++ b/.dockerignore @@ -0,0 +1,2 @@ +**/soil_output +.* diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml new file mode 100644 index 0000000..067838b --- /dev/null +++ b/.gitlab-ci.yml @@ -0,0 +1,21 @@ +image: python:3.7 + +steps: + - build + - test + +build: + stage: build + image: + name: gcr.io/kaniko-project/executor:debug + entrypoint: [""] + script: + - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json + - /kaniko/executor --context $CI_PROJECT_DIR --dockerfile $CI_PROJECT_DIR/Dockerfile --destination $CI_REGISTRY_IMAGE:$CI_COMMIT_TAG + only: + - tags + + +test: + script: + python setup.py test \ No newline at end of file diff --git a/Dockerfile b/Dockerfile index 2feb5a3..3b2d8f8 100644 --- a/Dockerfile +++ b/Dockerfile @@ -1,4 +1,11 @@ -FROM python:3.4-onbuild +FROM python:3.7 + +WORKDIR /usr/src/app + +COPY test-requirements.txt requirements.txt /usr/src/app/ +RUN pip install --no-cache-dir -r test-requirements.txt -r requirements.txt + +COPY ./ /usr/src/app RUN pip install '.[web]' 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/docker-compose.yml b/docker-compose.yml index dfb338e..86da6d3 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -2,6 +2,8 @@ version: '3' services: dev: build: . + environment: + PYTHONDONTWRITEBYTECODE: 1 volumes: - .:/usr/src/app tty: true 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..701131c 100644 --- a/docs/quickstart.rst +++ b/docs/quickstart.rst @@ -1,197 +1,94 @@ Quickstart ---------- -This section shows how to run simulations from simulation configuration files. -First of all, you need to install the package (See :doc:`installation`) +This section shows how to run your first simulation with Soil. +For installation instructions, 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 +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. -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``). +.. image:: soil.png + :width: 80% + :align: center -.. code:: +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: - 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 - -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. - -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: - -.. code:: yaml - - agent_type: SISaModel - network: - network_type: 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 +.. literalinclude:: quickstart.yml :language: yaml +Configuration +============= -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. +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. +Running the 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. .. code:: - environment_agents: - - agent_type: MyAgent - state: - mood: happy - - agent_type: DummyAgent + ❯ 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 -Visualizing the results -======================= +The ``CSV`` file should look like this: -The simulation will return a dynamic graph .gexf file which could be visualized with +.. code:: + + 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 + +Results and visualization +========================= + +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 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. + + +Soil also includes a web server that allows you to upload your simulations, change parameters, and visualize the results, including a timeline of the network. +To make it work, you have to install soil like this: + +``` +pip install soil[web] +``` + +Once installed, the soil web UI can be run in two ways: + +``` +soil-web + +OR + +python -m soil.web +``` \ No newline at end of file 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 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True \n", + " 9 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 10 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 11 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 12 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 13 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 14 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 15 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 17 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 18 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 19 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 21 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 22 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 23 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 25 NoneSim_all_dumb_trial_16 True True True True True True \n", + " 26 NoneSim_all_dumb_trial_16 True 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True True True True \n", + " 13 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 14 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 16 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 18 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 19 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 20 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 21 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 22 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 23 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 24 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 25 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 26 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 27 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 28 NoneSim_all_dumb_trial_25 True True True True True True \n", + " 29 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NoneSim_all_dumb_trial_33 True True True True True True \n", + " 10 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 11 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 12 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 14 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 15 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 16 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 17 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 18 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 19 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 20 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 21 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 22 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 24 NoneSim_all_dumb_trial_33 True True True True True True \n", + " 25 NoneSim_all_dumb_trial_33 True True True True 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NoneSim_all_dumb_trial_49 True True True True True True \n", + " 4 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 5 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 6 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 7 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 8 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 9 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 11 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 12 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 13 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 14 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 15 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 16 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 17 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 19 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 20 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 21 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 22 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 23 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 25 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 26 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 27 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 28 NoneSim_all_dumb_trial_49 True True True True True True \n", + " 29 NoneSim_all_dumb_trial_49 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 True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 3 True True True ... 0.1 0.1 0.1 0.1 0.1 0.1 0.1 \n", + " 4 True True True ... 0.1 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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/setup.cfg b/setup.cfg new file mode 100644 index 0000000..970eadf --- /dev/null +++ b/setup.cfg @@ -0,0 +1,4 @@ +[aliases] +test=pytest +[tool:pytest] +addopts = --verbose \ No newline at end of file diff --git a/soil/VERSION b/soil/VERSION index d33c3a2..51de330 100644 --- a/soil/VERSION +++ b/soil/VERSION @@ -1 +1 @@ -0.12.0 \ No newline at end of file +0.13.0 \ No newline at end of file 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/SentimentCorrelationModel.py b/soil/agents/SentimentCorrelationModel.py index 6294d7d..32eb768 100644 --- a/soil/agents/SentimentCorrelationModel.py +++ b/soil/agents/SentimentCorrelationModel.py @@ -16,7 +16,7 @@ class SentimentCorrelationModel(BaseAgent): disgust_prob """ - 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.outside_effects_prob = environment.environment_params['outside_effects_prob'] self.anger_prob = environment.environment_params['anger_prob'] diff --git a/soil/agents/__init__.py b/soil/agents/__init__.py index 29562e3..9d416f8 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 +324,39 @@ 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_type(agent_type, known_modules=[], **kwargs): + if isinstance(agent_type, str): + return agent_type + known_modules += ['soil.agents'] + return utils.serialize(agent_type, known_modules=known_modules, **kwargs)[1] # Get the name of the class + + +def serialize_distribution(network_agents, known_modules=[]): ''' 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_type(v['agent_type'], + known_modules=known_modules) + return d + + +def deserialize_type(agent_type, known_modules=[]): + if not isinstance(agent_type, str): + return agent_type + known = known_modules + ['soil.agents', 'soil.agents.custom' ] + agent_type = utils.deserializer(agent_type, known_modules=known) + return agent_type + + +def deserialize_distribution(ind, **kwargs): + d = deepcopy(ind) + for v in d: + v['agent_type'] = deserialize_type(v['agent_type'], **kwargs) return d @@ -352,16 +371,11 @@ def _validate_states(states, topology): return states -def _convert_agent_types(ind, to_string=False): +def _convert_agent_types(ind, to_string=False, **kwargs): '''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, **kwargs) + return deserialize_distribution(ind, **kwargs) def _agent_from_distribution(distribution, value=-1): diff --git a/soil/analysis.py b/soil/analysis.py index 8719013..d68f0c4 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 @@ -123,7 +123,7 @@ def get_count(df, *keys): df = df[list(keys)] counts = pd.DataFrame() for key in df.columns.levels[0]: - g = df[key].apply(pd.Series.value_counts, axis=1).fillna(0) + g = df[[key]].apply(pd.Series.value_counts, axis=1).fillna(0) for value, series in g.iteritems(): counts[key, value] = series counts.columns = pd.MultiIndex.from_tuples(counts.columns) diff --git a/soil/environment.py b/soil/environment.py index 0790d9d..501ecc9 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_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..2f6023b 100644 --- a/soil/history.py +++ b/soil/history.py @@ -3,7 +3,7 @@ import os import pandas as pd import sqlite3 import copy -from collections import UserDict, Iterable, namedtuple +from collections import UserDict, namedtuple from . import utils @@ -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..a35d517 100644 --- a/soil/simulation.py +++ b/soil/simulation.py @@ -1,8 +1,9 @@ import os import time -import imp +import importlib import sys import yaml +import traceback import networkx as nx from networkx.readwrite import json_graph from multiprocessing import Pool @@ -12,11 +13,12 @@ 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 +45,48 @@ 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, @@ -69,19 +106,21 @@ class SoilSimulation(NetworkSimulation): self.seed = str(seed) or str(time.time()) self.dump = dump self.dry_run = dry_run - self.environment_params = environment_params or {} - 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) + sys.path += [self.dir_path, os.getcwd()] + + self.environment_params = environment_params or {} + self.environment_class = utils.deserialize(environment_class, + known_modules=['soil.environment', ]) or Environment environment_agents = environment_agents or [] - self.environment_agents = agents._convert_agent_types(environment_agents) + self.environment_agents = agents._convert_agent_types(environment_agents, + known_modules=[self.load_module]) distro = agents.calculate_distribution(network_agents, agent_type) - self.network_agents = agents._convert_agent_types(distro) + self.network_agents = agents._convert_agent_types(distro, + known_modules=[self.load_module]) self.states = agents._validate_states(states, self.topology) @@ -97,13 +136,17 @@ class SoilSimulation(NetworkSimulation): p = Pool() with utils.timer('simulation {}'.format(self.name)): if parallel: - func = partial(self.run_trial, dry_run=dry_run or self.dry_run, - return_env=not parallel, **kwargs) + func = partial(self.run_trial_exceptions, dry_run=dry_run or self.dry_run, + return_env=True, + **kwargs) for i in p.imap_unordered(func, range(self.num_trials)): + if isinstance(i, Exception): + logger.error('Trial failed:\n\t{}'.format(i.message)) + continue yield i else: for i in range(self.num_trials): - yield self.run_trial(i, dry_run=dry_run or self.dry_run, **kwargs) + yield self.run_trial(i, dry_run = dry_run or self.dry_run, **kwargs) if not (dry_run or self.dry_run): logger.info('Dumping results to {}'.format(self.dir_path)) self.dump_pickle(self.dir_path) @@ -111,9 +154,9 @@ class SoilSimulation(NetworkSimulation): else: logger.info('NOT dumping results') - def get_env(self, trial_id=0, **kwargs): - opts = self.environment_params.copy() - env_name = '{}_trial_{}'.format(self.name, trial_id) + def get_env(self, trial_id = 0, **kwargs): + opts=self.environment_params.copy() + env_name='{}_trial_{}'.format(self.name, trial_id) opts.update({ 'name': env_name, 'topology': self.topology.copy(), @@ -128,10 +171,10 @@ 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): + def run_trial(self, trial_id = 0, until = None, return_env = True, **opts): """Run a single trial of the simulation Parameters @@ -139,16 +182,27 @@ class SoilSimulation(NetworkSimulation): trial_id : int """ # Set-up trial environment and graph - until = until or self.max_time - env = self.get_env(trial_id=trial_id, **opts) + until=until or self.max_time + env=self.get_env(trial_id = trial_id, **opts) # Set up agents on nodes with utils.timer('Simulation {} trial {}'.format(self.name, trial_id)): env.run(until) if self.dump and not self.dry_run: with utils.timer('Dumping simulation {} trial {}'.format(self.name, trial_id)): - env.dump(formats=self.dump) + env.dump(formats = self.dump) if return_env: return env + def run_trial_exceptions(self, *args, **kwargs): + ''' + A wrapper for run_trial that catches exceptions and returns them. + It is meant for async simulations + ''' + try: + return self.run_trial(*args, **kwargs) + except Exception as ex: + c = ex.__cause__ + c.message = ''.join(traceback.format_tb(c.__traceback__)[3:]) + return c def to_dict(self): return self.__getstate__() @@ -156,39 +210,53 @@ class SoilSimulation(NetworkSimulation): def to_yaml(self): return yaml.dump(self.to_dict()) - def dump_yaml(self, dir_path=None, file_name=None): - dir_path = dir_path or self.dir_path + def dump_yaml(self, dir_path = None, file_name = None): + dir_path=dir_path or self.dir_path if not os.path.exists(dir_path): os.makedirs(dir_path) if not file_name: - file_name = os.path.join(dir_path, + file_name=os.path.join(dir_path, '{}.dumped.yml'.format(self.name)) with open(file_name, 'w') as f: f.write(self.to_yaml()) - def dump_pickle(self, dir_path=None, pickle_name=None): - dir_path = dir_path or self.dir_path + def dump_pickle(self, dir_path = None, pickle_name = None): + dir_path=dir_path or self.dir_path if not os.path.exists(dir_path): os.makedirs(dir_path) if not pickle_name: - pickle_name = os.path.join(dir_path, + pickle_name=os.path.join(dir_path, '{}.simulation.pickle'.format(self.name)) with open(pickle_name, 'wb') as f: pickle.dump(self, f) def __getstate__(self): - state = self.__dict__.copy() - state['topology'] = json_graph.node_link_data(self.topology) - state['network_agents'] = agents._serialize_distribution(self.network_agents) - state['environment_agents'] = agents._convert_agent_types(self.environment_agents, - to_string=True) + 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, + known_modules = []) + state['environment_agents']=agents.serialize_distribution(self.environment_agents, + known_modules = []) + 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): self.__dict__ = state + self.load_module = getattr(self, 'load_module', None) + if self.dir_path not in sys.path: + sys.path += [self.dir_path, os.getcwd()] 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_agents = agents._convert_agent_types(self.environment_agents, + known_modules=[self.load_module]) + self.environment_class = utils.deserialize(self.environment_class, + known_modules=[self.load_module, 'soil.environment', ]) # func, name return state @@ -197,11 +265,11 @@ 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 +282,8 @@ 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..0352e3d 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 +63,92 @@ 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 +builtins = importlib.import_module('builtins') - -def name(v): - return type(v).__name__ +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 kmod in known_modules: + if not kmod: + continue + module = importlib.import_module(kmod) + 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 +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: + if mod: + options.append((mod, type_)) + + if '.' in type_: # Fully qualified module module, type_ = type_.rsplit(".", 1) - module = importlib.import_module(module) - cls = getattr(module, type_) - return cls + options.append ((module, type_)) + + errors = [] + for modname, tname in options: + try: + module = importlib.import_module(modname) + cls = getattr(module, tname) + return getattr(cls, 'deserialize', cls) + except (ImportError, AttributeError) as ex: + errors.append((modname, tname, ex)) + raise Exception('Could not find type {}. Tried: {}'.format(type_, errors)) -def convert(value, type_): - return deserializer(type_)(value) +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/test-requirements.txt b/test-requirements.txt index e69de29..55b033e 100644 --- a/test-requirements.txt +++ b/test-requirements.txt @@ -0,0 +1 @@ +pytest \ No newline at end of file diff --git a/tests/test.gexf b/tests/test.gexf new file mode 100644 index 0000000..5606350 --- /dev/null +++ b/tests/test.gexf @@ -0,0 +1,12 @@ + + + + + + + + + + + + diff --git a/tests/test_examples.py b/tests/test_examples.py new file mode 100644 index 0000000..671b6b0 --- /dev/null +++ b/tests/test_examples.py @@ -0,0 +1,48 @@ +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) + iterations = s.max_time * s.num_trials + if iterations > 1000: + self.skipTest('This example would probably take too long') + 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..1bedccd 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 @@ -128,6 +129,28 @@ class TestHistory(TestCase): backuppaths = glob(db_path + '.backup*.sqlite') assert len(backuppaths) == 1 backuppath = backuppaths[0] - assert newhistory._db_path == h._db_path + 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..8bdc0e2 100644 --- a/tests/test_main.py +++ b/tests/test_main.py @@ -6,14 +6,18 @@ 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 CustomAgent(agents.BaseAgent): + def step(self): + self.state['neighbors'] = self.count_agents(state_id=0, + limit_neighbors=True) + class TestMain(TestCase): def test_load_graph(self): @@ -127,10 +131,6 @@ class TestMain(TestCase): def test_custom_agent(self): """Allow for search of neighbors with a certain state_id""" - class CustomAgent(agents.BaseAgent): - def step(self): - self.state['neighbors'] = self.count_agents(state_id=0, - limit_neighbors=True) config = { 'dry_run': True, 'network_params': { @@ -188,8 +188,6 @@ 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 +195,17 @@ 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 +224,7 @@ 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 +235,7 @@ 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 +243,65 @@ 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_serialize_agent_type(self): + '''A class from soil.agents should be serialized without the module part''' + ser = agents.serialize_type(CustomAgent) + assert ser == 'test_main.CustomAgent' + ser = agents.serialize_type(agents.BaseAgent) + assert ser == 'BaseAgent' + + def test_deserialize_agent_distribution(self): + agent_distro = [ + { + 'agent_type': 'CounterModel', + 'weight': 1 + }, + { + 'agent_type': 'test_main.CustomAgent', + 'weight': 2 + }, + ] + converted = agents.deserialize_distribution(agent_distro) + assert converted[0]['agent_type'] == agents.CounterModel + assert converted[1]['agent_type'] == CustomAgent + + def test_serialize_agent_distribution(self): + agent_distro = [ + { + 'agent_type': agents.CounterModel, + 'weight': 1 + }, + { + 'agent_type': CustomAgent, + 'weight': 2 + }, + ] + converted = agents.serialize_distribution(agent_distro) + assert converted[0]['agent_type'] == 'CounterModel' + assert converted[1]['agent_type'] == 'test_main.CustomAgent' + + 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"