How to estimate and plot the cost values of PELT Algorithm

Question

I'm using the PELT Algorithm provided in the Ruptures Package for change point detection (i.e. mean-shifts) in a timeseries signal. https://centre-borelli.github.io/ruptures-docs/

I'm able to successfully apply it to detect the mean-shifts in my signals. But what I'm looking for is to be able to plot the cost function (values) that the algorithm determines for my signal.

On the documentation page of the ruptures package they provide an example for doing the same, using the L1 Cost function & the Window Search Method of the package, as follows. (Please refer to the detailed description of the same on the ruptures website: https://centre-borelli.github.io/ruptures-docs/examples/merging-cost-functions/)

import matplotlib.pyplot as plt
import numpy as np
import ruptures as rpt
from ruptures.base import BaseCost

WINDOW_SIZE = 200


#1. CREATING SYNTHETIC SIGNAL

def minmax_scale(array: np.ndarray) -> np.ndarray:
    """Scale each dimension to the [0, 1] range."""
    return (array - np.min(array, axis=0)) / (
        np.max(array, axis=0) - np.min(array, axis=0) + 1e-8
    )


def fig_ax(figsize=(10, 3)):
    return plt.subplots(figsize=figsize)

# fmt: off
signal_no_noise = np.array([[0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], [0.0, 0.0], 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[1.0, 1.0], [1.0, 1.0]])
# fmt: on
bkps_on_dim_0 = [329, 656, 1642, 2000]
bkps_on_dim_1 = [656, 972, 1291, 2000]
bkps_all_dims = [329, 656, 972, 1291, 1642, 2000]

signal_with_noise = signal_no_noise + np.random.normal(size=signal_no_noise.shape)

fig, axes = rpt.display(signal_no_noise, bkps_all_dims)
_ = axes[0].set_title("Toy signal (no noise)")

fig, axes = rpt.display(signal_with_noise, bkps_all_dims)
_ = axes[0].set_title("Toy signal (with noise)")

##########
#2. MEAN SHIFT DETECTION

class CostL2OnSingleDim(BaseCost):
    """This cost function detects mean-shift on a single dimension of a multivariate signal."""

    # The 2 following attributes must be specified for compatibility.
    model = "CostL2OnSingleDim"
    min_size = 1

    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def fit(self, signal):
        """Set the internal parameter."""
        self.signal = signal[:, self.dim].reshape(-1, 1)
        return self

    def error(self, start, end) -> float:
        """Return the approximation cost on the segment [start:end].

        Args:
            start (int): start of the segment
            end (int): end of the segment

        Returns:
            segment cost

        Raises:
            NotEnoughPoints: when the segment is too short (less than `min_size` samples).
        """
        if end - start < self.min_size:
            raise rpt.exceptions.NotEnoughPoints
        if end - start == 1:
            return 0.0
        return self.signal[start:end].var(axis=0).sum() * (end - start)
    
# Detect mean-shift on the first dimension
cost_function = CostL2OnSingleDim(dim=0)
algo_on_dim_0 = rpt.Window(width=WINDOW_SIZE, custom_cost=cost_function, jump=1).fit(
    signal_with_noise
)
bkps_pred = algo_on_dim_0.predict(
    n_bkps=len(bkps_on_dim_0) - 1
)  # the number of changes is known
# display signal and changes
fig, axes = rpt.display(signal_with_noise, bkps_on_dim_0, bkps_pred)
_ = axes[0].set_title(
    (
        f"""Detection of mean-shifts using only Dimension 0:\n"""
        f"""true changes: {bkps_on_dim_0[:-1]}, detected changes: {bkps_pred[:-1]}."""
    )
)

# Detect mean-shift on the second dimension
cost_function = CostL2OnSingleDim(dim=1)
algo_on_dim_1 = rpt.Window(width=WINDOW_SIZE, custom_cost=cost_function, jump=1).fit(
    signal_with_noise
)
bkps_pred = algo_on_dim_1.predict(
    n_bkps=len(bkps_on_dim_1) - 1
)  # the number of changes is known
# display signal and changes
fig, axes = rpt.display(signal_with_noise, bkps_on_dim_1, bkps_pred)
_ = axes[0].set_title(
    (
        f"""Detection of mean-shifts using only Dimension 1:\n"""
        f"""true changes: {bkps_on_dim_1[:-1]}, detected changes: {bkps_pred[:-1]}."""
    )
)

################
#3. COST FUNCTION
for dim, algo in enumerate([algo_on_dim_0, algo_on_dim_1]):
    fig, ax = fig_ax()
    ax.plot(np.r_[np.zeros(WINDOW_SIZE // 2), algo.score, np.zeros(WINDOW_SIZE // 2)])
    ax.set_xmargin(0)
    ax.set_title(f"Score for CostL2OnSingleDim(dim={dim})")

The following plots of the cost are obtained by executing the above code:

Now, when I tried doing the same thing but by using the L1 Cost function (instead of the L2 Cost function) & the PELT Algorithm (instead of the Window Search Method), I see that there's actually no attribute called 'score' in PELT's variables algo_on_dim_0 & algo_on_dim_1 as compared to that of the Window method used previously. Hence, I can't make use of ax.plot(algo.cost) to plot the cost values as was done in the previous case. Here's my code:

import matplotlib.pyplot as plt
import numpy as np
import ruptures as rpt
from ruptures.base import BaseCost



#1. CREATING SYNTHETIC SIGNAL

def minmax_scale(array: np.ndarray) -> np.ndarray:
    """Scale each dimension to the [0, 1] range."""
    return (array - np.min(array, axis=0)) / (
        np.max(array, axis=0) - np.min(array, axis=0) + 1e-8
    )


def fig_ax(figsize=(10, 3)):
    return plt.subplots(figsize=figsize)

# fmt: off
signal_no_noise = np.array([[0.0, 0.0], [0.0, 0.0],... ) #Same as previously defined.
# fmt: on
bkps_on_dim_0 = [329, 656, 1642, 2000]
bkps_on_dim_1 = [656, 972, 1291, 2000]
bkps_all_dims = [329, 656, 972, 1291, 1642, 2000]

signal_with_noise = signal_no_noise + np.random.normal(size=signal_no_noise.shape)

fig, axes = rpt.display(signal_no_noise, bkps_all_dims)
_ = axes[0].set_title("Toy signal (no noise)")

fig, axes = rpt.display(signal_with_noise, bkps_all_dims)
_ = axes[0].set_title("Toy signal (with noise)")

##########
#2. MEAN SHIFT DETECTION

class CostL1OnSingleDim(BaseCost):
    """This cost function detects mean-shift on a single dimension of a multivariate signal."""

    # The 2 following attributes must be specified for compatibility.
    model = "CostL1OnSingleDim"
    min_size = 1

    def __init__(self, dim):
        super().__init__()
        self.dim = dim

    def fit(self, signal):
        """Set the internal parameter."""
        self.signal = signal[:, self.dim].reshape(-1, 1)
        return self

    def error(self, start, end) -> float:
        """Return the approximation cost on the segment [start:end].

        Args:
            start (int): start of the segment
            end (int): end of the segment

        Returns:
            segment cost

        Raises:
            NotEnoughPoints: when the segment is too short (less than `min_size` samples).
        """
        if end - start < self.min_size:
            raise rpt.exceptions.NotEnoughPoints
        if end - start == 1:
            return 0.0
        return self.signal[start:end].var(axis=0).sum() * (end - start)
    
# Detect mean-shift on the first dimension
cost_function = CostL1OnSingleDim(dim=0)
algo_on_dim_0 = rpt.Pelt(model='l1', min_size=1, custom_cost=cost_function).fit(signal_with_noise)
bkps_pred = algo_on_dim_0.predict(pen=11)
# Perform segmentation
bkps_on_dim_0 = np.concatenate(([0], bkps_pred))
# display signal and changes
fig, axes = rpt.display(signal_with_noise, bkps_on_dim_0, bkps_pred)
_ = axes[0].set_title(
    (
        f"""Detection of mean-shifts using only Dimension 0:\n"""
        f"""true changes: {bkps_on_dim_0[:-1]}, detected changes: {bkps_pred[:-1]}."""
    )
)

# Detect mean-shift on the second dimension
cost_function = CostL1OnSingleDim(dim=1)
algo_on_dim_1 = rpt.Pelt(model='l1', min_size=1, custom_cost=cost_function).fit(signal_with_noise)
bkps_pred = algo_on_dim_1.predict(pen=11)
# Perform segmentation
bkps_on_dim_1 = np.concatenate(([0], bkps_pred))
# display signal and changes
fig, axes = rpt.display(signal_with_noise, bkps_on_dim_1, bkps_pred)
_ = axes[0].set_title(
    (
        f"""Detection of mean-shifts using only Dimension 1:\n"""
        f"""true changes: {bkps_on_dim_1[:-1]}, detected changes: {bkps_pred[:-1]}."""
    )
)

################
#3. COST FUNCTION
for dim, algo in enumerate([algo_on_dim_0, algo_on_dim_1]):
    fig, ax = fig_ax()
    ax.plot(algo.cost)  #<----NOT WORKING
    ax.set_xmargin(0)
    ax.set_title(f"Score for CostL1OnSingleDim(dim={dim})")
    

Hence, how can the code be updated to plot the cost values for the signal?