added plot_gp_slice.py

[Jimbo994]

Added a plot function to plot the "slice" of a GP prediction given some fixed input features, two input features that are varied between the bounds of the domain and the output feature of interest.

To run some quick tests on the ValueErrors:

x1 = ContinuousInput(key="x1", bounds=(0, 1))
x2 = ContinuousInput(key="x2", bounds=(0, 1))
x3 = ContinuousInput(key="x3", bounds=(0, 1))
x4 = ContinuousInput(key="x4", bounds=(0, 1))
x5 = ContinuousInput(key="x5", bounds=(0, 1))

y = ContinuousOutput(key="y")
y2 = ContinuousOutput(key="y2")


# Define the domain
inputs = Inputs(features=[x1, x2, x3, x4, x5])
outputs = Outputs(features=[y])
domain = Domain(inputs=inputs, outputs=outputs)

# generate some data
# Generate some synthetic data
data = pd.DataFrame({
    "x1": np.random.rand(100),
    "x2": np.random.rand(100),
    "x3": np.random.rand(100),
    "x4": np.random.rand(100),
    "x5": np.random.rand(100),
})

data2 = pd.DataFrame({
    "x1": np.random.rand(100),
    "x2": np.random.rand(100),
    "x3": np.random.rand(100),
    "x4": np.random.rand(100),
    "x5": np.random.rand(100),
})

# add a datapoint in the slice to test the plotting
new_data = pd.DataFrame([{
    "x1": 0.5,
    "x2": 0.5,
    "x3": 0.5,
    "x4": 0.5,
    "x5": 0.5,
}])

data = pd.concat([data, new_data], ignore_index=True)

data["y"] = data["x1"] + data["x2"] + np.random.normal(0, 0.1, 101)
data["valid_y"] = 1

# Define the surrogate model
surrogate_data = SingleTaskGPSurrogate(inputs=domain.inputs.get_by_keys(domain.inputs.get_keys()), outputs=domain.outputs)
surrogate_gp = surrogates.map(surrogate_data)
surrogate_gp.fit(data)

input_features = [x1, x2]
fixed_input_features = [x3, x4, x5]
fixed_values = [0.5, 0.5, 0.5]
fig, fig_sd = plot_gp_slice_plotly(surrogate_gp, fixed_input_features, fixed_values, input_features, y, observed_data=data)

assert isinstance(fig, go.Figure)
assert isinstance(fig_sd, go.Figure)