Pre-release version of v1.0
J. Fernando Sánchez
1 year ago
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What are the main changes between version 0.3 and 0.2?
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######################################################
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What are the main changes in version 1.0?
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#########################################
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Version 0.3 is a major rewrite of the Soil system, focused on simplifying the API, aligning it with Mesa, and making it easier to use.
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Version 1.0 is a major rewrite of the Soil system, focused on simplifying the API, aligning it with Mesa, and making it easier to use.
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Unfortunately, this comes at the cost of backwards compatibility.
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We drew several lessons from the previous version of Soil, and tried to address them in this version.
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---
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name: quickstart
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num_trials: 1
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max_time: 1000
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model_params:
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agents:
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- agent_class: SISaModel
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topology: true
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state:
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id: neutral
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weight: 1
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- agent_class: SISaModel
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topology: true
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state:
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id: content
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weight: 2
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topology:
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params:
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n: 100
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k: 5
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p: 0.2
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generator: newman_watts_strogatz_graph
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neutral_discontent_spon_prob: 0.05
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neutral_discontent_infected_prob: 0.1
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neutral_content_spon_prob: 0.2
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neutral_content_infected_prob: 0.4
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discontent_neutral: 0.2
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discontent_content: 0.05
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content_discontent: 0.05
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variance_d_c: 0.05
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variance_c_d: 0.1
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content_neutral: 0.1
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standard_variance: 0.1
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from soil import Simulation
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from social_wealth import MoneyEnv, graph_generator
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sim = Simulation(name="mesa_sim", dump=False, max_steps=10, interval=2, model=MoneyEnv, model_params=dict(generator=graph_generator, N=10, width=50, height=50))
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sim = Simulation(name="mesa_sim", dump=False, max_steps=10, interval=2, model=MoneyEnv, parameters=dict(generator=graph_generator, N=10, width=50, height=50))
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if __name__ == "__main__":
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sim.run()
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@ -1 +1 @@
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0.30.0rc4
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0.1.0rc1
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import os
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import sqlalchemy
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import pandas as pd
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from collections import namedtuple
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def plot(env, agent_df=None, model_df=None, steps=False, ignore=["agent_count", ]):
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"""Plot the model dataframe and agent dataframe together."""
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if agent_df is None:
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agent_df = env.agent_df()
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if model_df is None:
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model_df = env.model_df()
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ignore = list(ignore)
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if not steps:
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ignore.append("step")
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else:
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ignore.append("time")
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ax = model_df.drop(ignore, axis='columns').plot();
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if not agent_df.empty:
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agent_df.unstack().apply(lambda x: x.value_counts(),
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axis=1).fillna(0).plot(ax=ax, secondary_y=True);
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Results = namedtuple("Results", ["config", "parameters", "env", "agents"])
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#TODO implement reading from CSV and SQLITE
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def read_sql(fpath=None, name=None, include_agents=False):
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if not (fpath is None) ^ (name is None):
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raise ValueError("Specify either a path or a simulation name")
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if name:
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fpath = os.path.join("soil_output", name, f"{name}.sqlite")
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fpath = os.path.abspath(fpath)
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# TODO: improve url parsing. This is a hacky way to check we weren't given a URL
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if "://" not in fpath:
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fpath = f"sqlite:///{fpath}"
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engine = sqlalchemy.create_engine(fpath)
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with engine.connect() as conn:
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env = pd.read_sql_table("env", con=conn,
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index_col="step").reset_index().set_index([
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"simulation_id", "params_id",
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"iteration_id", "step"
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])
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agents = pd.read_sql_table("agents", con=conn, index_col=["simulation_id", "params_id", "iteration_id", "step", "agent_id"])
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config = pd.read_sql_table("configuration", con=conn, index_col="simulation_id")
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parameters = pd.read_sql_table("parameters", con=conn, index_col=["iteration_id", "params_id", "simulation_id"])
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try:
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parameters = parameters.pivot(columns="key", values="value")
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except Exception as e:
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print(f"warning: coult not pivot parameters: {e}")
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return Results(config, parameters, env, agents)
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