Runner

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import pandas as pd
from sklearn.linear_model import LinearRegression

import pandas as pd from sklearn.linear_model import LinearRegression

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from autora.experimentalist.grid import grid_pool
from autora.state import StandardState, estimator_on_state, on_state
from autora.variable import Variable, VariableCollection

from autora.experimentalist.grid import grid_pool from autora.state import StandardState, estimator_on_state, on_state from autora.variable import Variable, VariableCollection

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def initial_state(_):
    state = StandardState(
        variables=VariableCollection(
            independent_variables=[Variable(name="x", allowed_values=range(100))],
            dependent_variables=[Variable(name="y")],
            covariates=[],
        ),
        conditions=None,
        experiment_data=pd.DataFrame({"x": [], "y": []}),
        models=[],
    )
    return state

def initial_state(_): state = StandardState( variables=VariableCollection( independent_variables=[Variable(name="x", allowed_values=range(100))], dependent_variables=[Variable(name="y")], covariates=[], ), conditions=None, experiment_data=pd.DataFrame({"x": [], "y": []}), models=[], ) return state

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experimentalist = on_state(grid_pool, output=["conditions"])

experimentalist = on_state(grid_pool, output=["conditions"])

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experiment_runner = on_state(
    lambda conditions: conditions.assign(y=2 * conditions["x"] + 0.5),
    output=["experiment_data"],
)

experiment_runner = on_state( lambda conditions: conditions.assign(y=2 * conditions["x"] + 0.5), output=["experiment_data"], )

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theorist = estimator_on_state(LinearRegression(fit_intercept=True))

theorist = estimator_on_state(LinearRegression(fit_intercept=True))