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feat/Quantum art visualization (#183)
* add n-trials to get-qiskit-dataset * add example * spiral implementation * add visualization package * modify get_linearly_separable_dataset * improve example * display the number of parameters inside the circuit * comment code * just adapts parameters * [pre-commit.ci] auto fixes from pre-commit.com hooks * fix dockerfile * update version of google_cloud_firestore * fix dockerfile --------- Co-authored-by: Gregoire Cattan <gregoire.cattan@ibm.com> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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| """ | ||
| ==================================================================== | ||
| Art visualization of Variational Quantum Classifier. | ||
| ==================================================================== | ||
| Display the variability of the weights inside the variational quantum | ||
| classifier. | ||
| """ | ||
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| # Author: Gregoire Cattan | ||
| # License: BSD (3-clause) | ||
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| from pyriemann_qiskit.utils.hyper_params_factory import gen_two_local | ||
| import matplotlib.pyplot as plt | ||
| from pyriemann_qiskit.datasets import get_linearly_separable_dataset | ||
| from pyriemann_qiskit.classification import QuanticVQC | ||
| from pyriemann_qiskit.visualization import weights_spiral | ||
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| print(__doc__) | ||
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| ############################################################################### | ||
| # In this example we will display weights variability of the parameter inside | ||
| # the variational quantum circuit which is used by VQC. | ||
| # | ||
| # The idea is simple : | ||
| # - We initialize a VQC with different number of parameters and number of samples | ||
| # - We train the VQC a couple of time and we store the fitted weights. | ||
| # - We compute variability of the weight and display it in a fashion way. | ||
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| # Let's start by defining some plot area. | ||
| fig, axes = plt.subplots(2, 2) | ||
| fig.suptitle("VQC weights variability") | ||
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| # We will compute weight variability for different number of samples | ||
| for i, n_samples in enumerate([2, 20]): | ||
| # ... and for differente number of parameters. | ||
| # (n_reps controls the number of parameters inside the circuit) | ||
| for j, n_reps in enumerate([1, 3]): | ||
| # instanciate VQC. | ||
| vqc = QuanticVQC(gen_var_form=gen_two_local(reps=n_reps)) | ||
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| # Get data. We will use a toy dataset here. | ||
| X, y = get_linearly_separable_dataset(n_samples=n_samples) | ||
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| # Compute and display weight variability after training | ||
| axe = axes[i, j] | ||
| # ... This is all done in this method | ||
| # It displays a spiral. Each "branch of the spiral" is a parameter inside VQC. | ||
| # The larger is the branch, the higher is the parameter variability. | ||
| weights_spiral(axe, vqc, X, y, n_trainings=5) | ||
| n_params = vqc.parameter_count | ||
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| # Just improve display of the graphics. | ||
| if j == 0: | ||
| axe.set_ylabel(f"n_samples: {n_samples}") | ||
| if i == 0: | ||
| axe.set_xlabel(f"n_params: {n_params}") | ||
| axe.xaxis.set_label_position("top") | ||
| axe.set_xticks(()) | ||
| axe.set_yticks(()) | ||
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| plt.tight_layout() | ||
| plt.show() |
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| from .art import weights_spiral | ||
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| __all__ = ["weights_spiral"] |
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| import pandas as pd | ||
| import numpy as np | ||
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| def weights_spiral(axe, vqc, X, y, n_trainings=5): | ||
| """Artistic representation of vqc training. | ||
| Display a spiral. Each "branch" of the spiral corresponds to a parameter inside VQC. | ||
| When the branch is "large" it means that the weight of the parameter varies a lot | ||
| between different trainings. | ||
| Notes | ||
| ----- | ||
| .. versionadded:: 0.1.0 | ||
| Parameters | ||
| ---------- | ||
| axe: Axe | ||
| Pointer to the matplotlib plot or subplot. | ||
| vqc: QuanticVQC | ||
| The instance of VQC to evaluate. | ||
| X: ndarray, shape (n_samples, n_features) | ||
| Input vector, where `n_samples` is the number of samples and | ||
| `n_features` is the number of features. | ||
| y: ndarray, shape (n_samples,) | ||
| Predicted target vector relative to X. | ||
| n_trainings: int (default: 5) | ||
| Number of trainings to run, in order to evaluate the variability of the | ||
| parameters' weights. | ||
| Returns | ||
| ------- | ||
| X: ndarray, shape (n_samples, n_features) | ||
| Input vector, where `n_samples` is the number of samples and | ||
| `n_features` is the number of features. | ||
| y: ndarray, shape (n_samples,) | ||
| Predicted target vector relative to X. | ||
| """ | ||
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| weights = [] | ||
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| for i in range(5): | ||
| vqc.fit(X, y) | ||
| train_weights = vqc._classifier.weights | ||
| weights.append(train_weights) | ||
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| df = pd.DataFrame(weights) | ||
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| theta = np.arange(0, 8 * np.pi, 0.1) | ||
| a = 1 | ||
| b = 0.2 | ||
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| n_params = len(df.columns) | ||
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| max_var = df.var().max() | ||
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| # https://matplotlib.org/3.1.1/gallery/misc/fill_spiral.html | ||
| for i in range(n_params): | ||
| dt = 2 * np.pi / n_params * i | ||
| x = a * np.cos(theta + dt) * np.exp(b * theta) | ||
| y = a * np.sin(theta + dt) * np.exp(b * theta) | ||
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| var = df[i].var() | ||
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| dt = dt + (var / max_var) * np.pi / 4.0 | ||
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| x2 = a * np.cos(theta + dt) * np.exp(b * theta) | ||
| y2 = a * np.sin(theta + dt) * np.exp(b * theta) | ||
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| xf = np.concatenate((x, x2[::-1])) | ||
| yf = np.concatenate((y, y2[::-1])) | ||
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| axe.fill(xf, yf) |