Upgrade to v2.0.0

Author: RobertWezemanTNO

CHANGELOG.md

@@ -0,0 +1,15 @@
+# 2.0.0 (2025 - 05 - 13)
+
+### Features
+
+* **Use tno.quantum.optimization.qubo:** Allow easier usage of different qubo solvers.
+
+
+### BREAKING CHANGES
+
+* **PortfolioOptimizer input:** Input PortFolioOptimizer now takes a solver instead of a sampler.deserialization.
+
+
+# 1.0.0 (2024 - 05 - 01)
+
+* **Initial release**: Initial release of PortfolioOptimization.

CITATION.cff

@@ -18,5 +18,5 @@ contact:
 repository-code: https://github.com/TNO-Quantum/problems.portfolio_optimization
 repository-artifact: https://pypi.org/project/tno.quantum.problems.portfolio_optimization
 title: TNO Quantum - Problems - Portfolio Optimization
-version: v1.0.0
+version: v2.0.0
 date-released: 2024-05-01

LICENSE

@@ -186,7 +186,7 @@
       same "printed page" as the copyright notice for easier
       identification within third-party archives.
 
-   Copyright 2024 The Netherlands Organisation for Applied Scientific Research (TNO)
+   Copyright 2025 The Netherlands Organisation for Applied Scientific Research (TNO)
 
    Licensed under the Apache License, Version 2.0 (the "License");
    you may not use this file except in compliance with the License.

README.md

@@ -15,7 +15,7 @@ The following objectives can be considered
 
 Additionally, we allow for a capital growth factor and arbitrary emission reduction constraints to be considered.
 
-The `Pareto front`, the set of solutions where one objective can't be improved without worsening the other objective,
+The Pareto front, the set of solutions where one objective can't be improved without worsening the other objective,
 can be computed for the objectives return on capital and diversification. 
 
 The codebase is based on the following paper:
@@ -25,11 +25,10 @@ The codebase is based on the following paper:
 **Funding:** This research was funded by Rabobank and Stichting TKI High Tech Systems
 and Materials, under a program by Brightland's Techruption.
 
-*Limitations in (end-)use: the content of this software package may solely be used for applications that comply with international export control laws.*
 
 ## Documentation
 
-Documentation of the `tno.quantum.problems.portfolio_optimization` package can be found [here](https://tno-quantum.github.io/problems.portfolio_optimization/).
+Documentation of the `tno.quantum.problems.portfolio_optimization` package can be found [here](https://tno-quantum.github.io/documentation/).
 
 ## Install
 
@@ -39,97 +38,12 @@ Easily install the `tno.quantum.problems.portfolio_optimization` package using p
 $ python -m pip install tno.quantum.problems.portfolio_optimization
 ```
 
-If you wish to use D-Wave quantum hardware:
-```console
-$ python -m pip install 'tno.quantum.problems.portfolio_optimization[quantum]'
-```
-
 If you wish to run the tests you can use:
 ```console
-$ python -m pip install 'tno.quantum.problems.portfolio_optimization[tests]'
+$ python -m pip install tno.quantum.problems.portfolio_optimization[tests]
 ```
 
-## Usage
-
-<details>
-  <summary>Solve portfolio optimization problem.</summary>
-
-Here's an example of how the `PortfolioOptimizer` class can be used to define an portfolio optimization problem,
-and subsequently, how the Pareto front can be computed using the simulated annealing sampler from D-Wave. 
-
-```python
-import numpy as np
-from dwave.samplers import SimulatedAnnealingSampler
-
-from tno.quantum.problems.portfolio_optimization import PortfolioOptimizer
-
-# Choose sampler for solving qubo
-sampler = SimulatedAnnealingSampler()
-sampler_kwargs = {"num_reads": 20, "num_sweeps": 200}
-
-# Set up penalty coefficients for the constraints
-lambdas1 = np.logspace(-16, 1, 25, endpoint=False, base=10.0)
-lambdas2 = np.logspace(-16, 1, 25, endpoint=False, base=10.0)
-lambdas3 = np.array([1])
-
-# Create portfolio optimization problem
-portfolio_optimizer = PortfolioOptimizer("benchmark_dataset")
-portfolio_optimizer.add_minimize_hhi(weights=lambdas1)
-portfolio_optimizer.add_maximize_roc(formulation=1, weights_roc=lambdas2)
-portfolio_optimizer.add_emission_constraint(
-   weights=lambdas3,
-   emission_now="emis_intens_now",
-   emission_future="emis_intens_future",
-   name="emission"
-)
-
-# Solve the portfolio optimization problem
-results = portfolio_optimizer.run(sampler, sampler_kwargs)
-```
-</details>
-
-<details>
-  <summary>Visualize results.</summary>
-
-The results can be inspected in more detail by looking at the Pandas results DataFrame
-`results.results_df`.
-
-Alternatively, the results can be plotted in a `(Diversification, ROC)`-graph. The
-following example first slices the results in data points that do and do not satisfy the
-constraints using the method `slice_results`. 
-
-Note that:
-
-- Individual data points can subsequently be plotted using the `plot_points` function.
-- The Pareto front can be plotted using the `plot_front` function.
-
-```python
-import matplotlib.pyplot as plt
-
-from tno.quantum.problems.portfolio_optimization import plot_front, plot_points
-
-(x1, y1), (x2, y2) = results.slice_results()
-fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(12, 5))
-
-# Plot data points
-plot_points(x2, y2, color="orange", label="QUBO constraint not met", ax=ax1)
-plot_points(x1, y1, color="green", label="QUBO constraint met", ax=ax1)
-ax1.set_title("Points")
-
-# Plot Pareto front
-plot_front(x2, y2, color="orange", label="QUBO constraint not met", ax=ax2)
-plot_front(x1, y1, color="green", label="QUBO constraint met", ax=ax2)
-ax2.set_title("Pareto Front")
-fig.tight_layout()
-plt.show()
-```
-
-</details>
-
-![(Diversification, ROC)-Graph](./images_for_docs/example.png)
-
-
-More elaborate examples can be found in our [examples repository](https://github.com/TNO-Quantum/examples ).
+Usage examples can be found in the [documentation](https://tno-quantum.github.io/documentation/).
 
 Data input
 ----------
@@ -148,28 +62,9 @@ The data needs to contain at least the following columns:
 
 If the input datafile contains all the correct information, but has different column
 names, it is possible to rename the columns without altering the input file.
-Details and examples can be found in the [documentation]((https://tno-quantum.github.io/problems.portfolio_optimization/)).
-
-The data that was used for the publication can be found in the `tno/quantum/problems/portfolio_optimization/datasets/` folder.
-
 
-Using Quantum Annealing Solvers
--------------------------------
+The data that was used for the publication can be found in the `src/tno/quantum/problems/portfolio_optimization/datasets/` folder.
 
-By default, the portfolio optimization QUBO is solved using simulated annealing.
-Any D-Wave ``Sampler`` is however supported and can be provided to the `PortfolioOptimizer.run` method.
- 
-
-Below is an example how to initialise a quantum annealing sampler that uses `100` micro seconds annealing time per sample.
-The example assumes a proper [configuration setup](https://docs.ocean.dwavesys.com/en/stable/overview/sapi.html) to the D-Wave's Solver API.
-
-```python
-from dwave.system import DWaveSampler, LazyFixedEmbeddingComposite
-
-# Define QPU D-Wave Sampler
-qpu = DWaveSampler()
-sampler = LazyFixedEmbeddingComposite(qpu)
-sampler_kwargs = {"annealing_time": 100}
-```
 
-We refer to the [D-Wave Sampler documentation](https://docs.ocean.dwavesys.com/projects/system/en/stable/reference/samplers.html) for information on usage of different samplers and their sampler arguments.
+## (End)use limitations
+The content of this software may solely be used for applications that comply with international export control laws.