diff --git a/.github/workflows/tests.yml b/.github/workflows/tests.yml new file mode 100644 index 0000000..ac896fd --- /dev/null +++ b/.github/workflows/tests.yml @@ -0,0 +1,48 @@ +name: decomposition_pipeline_test + +on: # workflow_dispatch + push: + branches: [ "main", "**" ] + pull_request: + branches: [ "main" ] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.9", "3.10", "3.11"] + + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v3 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install poetry pipreqs black addheader pytest-cov pip-licenses types-pkg-resources==0.1.3 + poetry install + # - name: black check + # run: | + # black --check . + - name: Run tests and coverage + run: | + poetry run pytest + #- name: 'Dependency Review' + # uses: actions/dependency-review-action@v4 + # # Commonly enabled options, see https://github.com/actions/dependency-review-action#configuration-options for all available options. + # with: + # comment-summary-in-pr: always + + - name: License check + run: | + pip-licenses --format=markdown --order=license + + - name: SPDX check + run: | + addheader -t header.txt . + grep -L -f header.txt */*.py diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..40d8fc0 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,81 @@ +# Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to make participation in our project and our +community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, sex characteristics, gender identity and +expression, level of experience, education, socio-economic status, nationality, +personal appearance, race, religion, or sexual identity and orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +- Using welcoming and inclusive language +- Being respectful of differing viewpoints and experiences +- Gracefully accepting constructive criticism +- Focusing on what is best for the community +- Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +- The use of sexualized language or imagery and unwelcome sexual attention or + advances +- Trolling, insulting/derogatory comments, and personal or political attacks +- Public or private harassment +- Publishing others' private information, such as a physical or electronic + address, without explicit permission +- Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or reject +comments, commits, code, wiki edits, issues, and other contributions that are +not aligned to this Code of Conduct, or to ban temporarily or permanently any +contributor for other behaviors that they deem inappropriate, threatening, +offensive, or harmful. + +## Scope + +This Code of Conduct applies within all project spaces, and it also applies when +an individual is representing the project or its community in public spaces. +Examples of representing a project or community include using an official +project e-mail address, posting via an official social media account, or acting +as an appointed representative at an online or offline event. 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Industrially relevant constrained optimization problems, such as portfolio optimization and portfolio rebalancing, are often intractable or difficult to solve exactly. In this work, we propose and benchmark a decomposition pipeline targeting portfolio optimization and rebalancing problems with constraints. The pipeline decomposes the optimization problem into constrained subproblems, which are then solved separately and aggregated to give a final result. Our pipeline includes three main components: preprocessing of correlation matrices based on random matrix theory, modified spectral clustering based on Newman's algorithm, and risk rebalancing. Our empirical results show that our pipeline consistently decomposes real-world portfolio optimization problems into subproblems with a size reduction of approximately 80\%. Since subproblems are then solved independently, our pipeline drastically reduces the total computation time for state-of-the-art solvers. Moreover, by decomposing large problems into several smaller subproblems, the pipeline enables the use of near-term quantum devices as solvers, providing a path toward practical utility of quantum computers in portfolio optimization. + +The main blocks of this algorithm as located in the folder "dcmppln". We test some of these components in the folder "tests". We include a simple notebook that shows how to run this decomposition pipeline in the folder "examples". Note that we include some data to perform these executions in tests/data. + +SPDX-License-Identifier: Apache-2.0 +@ Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC \ No newline at end of file diff --git a/dcmppln/.ipynb_checkpoints/CONSTANT-checkpoint.py b/dcmppln/.ipynb_checkpoints/CONSTANT-checkpoint.py new file mode 100644 index 0000000..41cd7ae --- /dev/null +++ b/dcmppln/.ipynb_checkpoints/CONSTANT-checkpoint.py @@ -0,0 +1,9 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +TIMEOUT = 300 +LOG_FOLDER = "/opt/omniai/work/instance1/jupyter/atithi_acharya_summer_2023_internship_project/log" +SAVE_LP_FILE = False +DEFAULT_MIP_GAP = 1e-4 diff --git a/dcmppln/.ipynb_checkpoints/README-checkpoint.md b/dcmppln/.ipynb_checkpoints/README-checkpoint.md new file mode 100644 index 0000000..20addb6 --- /dev/null +++ b/dcmppln/.ipynb_checkpoints/README-checkpoint.md @@ -0,0 +1,65 @@ + + + +# Hybrid classical-quantum portfolio optimization using random matrix theory-based subproblem identification + +The repository has three different modules. 1) Cleaning of correlation matrices using random matrix theory 2) Decomposition pipeline using community detection and = other clustering 3) Portfolio optimization objectives (V0, V1,V1.5, V2) +When we find communities, we have two options. One with the restriction on the number of elements in a cluster, the other without any such restrictions. + + +## Directory Structure + +- **main.py** : Contains the Pipeline object and components of the pipline: Denoiser, Clustering, Filtering. +- **optimizer.py** : Contains the Optimizer objects + +- **comparing_optimization_times**: Contains files related to comparing optimization times of running CPLEX optimization routines on full problem against the decomposition pipeline. +- **create_portfolio_instance**: Scripts for creating portfolio instances. This includes generating returns (w or without log) and correlation and covariance matrices +- **modularity_maximization**: Resources for maximizing modularity. It includes many community detection algorithms , including modified versions, key ones: louvain_generalized, modularity_spectral_optimization +- **notebooks**: Jupyter notebooks or other interactive notebooks related to the project. (3 separate subfolders-- a) comparing objectives: Contains the jaccard similarity, difference and ratio in objectives,
b) thresholding and c) processing_time_comparision ) +- **out_of_sample**: Data and scripts related to out-of-sample testing. Only in the continuous setting but this has end-end sharpe ratio computations on present -- future splits of time series data. +- **random_matrix_theory**: Resources and files related to random matrix theory tools such as marchenko pastur fits, splitting correlatoins into C1, C2 and C3. +- **run_scripts** : Contains script example to run the pipeline +- **results** : Contains notebooks and results csv to visualize the results +- **thresholding**: Scripts and data for thresholding techniques. Also, includes comparision of thresholded objectives (it is in utils/utils.py) +- **utils**: Utility scripts and helper functions for the project. (Need to add a separate readme file) +- **others**: Miscellaneous files and resources not categorized elsewhere. This includes integrality gap experiments + +- **data**: This is an empty folder as a placeholder for the data files, data files should be as follows + - self.path+"_correlation.npy" + - self.path+"_covariance.npy" + - self.path+"_returns.npy" + + +## Getting Started + +1. **Setup environement with cplex** +``` +cd .. +git clone git clone --depth=1 --no-single-branch https://bitbucketdc.jpmchase.net/scm/quantumalgorithms/environment.git +environment/scripts/cplex_install.sh +source py38_cplex/bin/activate +cd atithi_acharya_summer_2023_internship_project/ +pip install -r requirement_pierre.txt +``` + +2. **Usage**: Briefly explain how to use the project or run the scripts.
+On a new omni-ai instance +If you do not want to create an environment for docplex +Follow the instructions to setup packages in 1
+a) git clone https://bitbucketdc.jpmchase.net/scm/quantumalgorithms/atithi_acharya_summer_2023_internship_project.git +b) The data folder is empty, when the user runs create_portfolio_instance (including notebook) , they have to save the files there in order to run. +c) get data and results stored on aws s3 +``` +aws s3 cp --recursive s3://app-id-108383-dep-id-105006-uu-id-ahc5pxjsqas8/decomposition_pipeline/ . +``` + +## Contributing + +Provide guidelines on how others can contribute to your project. + +## License + +Add license under (ask) + +## Acknowledgements diff --git a/dcmppln/.ipynb_checkpoints/clustering-checkpoint.py b/dcmppln/.ipynb_checkpoints/clustering-checkpoint.py new file mode 100644 index 0000000..4b8094c --- /dev/null +++ b/dcmppln/.ipynb_checkpoints/clustering-checkpoint.py @@ -0,0 +1,96 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from dcmppln.utils.utils import get_instance_non_private_attributes + +from dcmppln.modularity_maximization.modularity_spectral_optimization import ( + modularity_spectral_optimization, + modularity_spectral_threshold, +) +from dcmppln.utils.k_means import k_means_laplacian_threshold +from dcmppln.utils.utils import timeit +import numpy as np + +clustering_methods = ("louvain", "kmeans") +default_clustering_method = "louvain" + + +class Clustering: + + def __init__( + self, + max_community_size=0, + active=True, + clustering_method="louvain", + take_absolute_value=True, + cluster_on_correlation=True, + ): + """ + Supported clustering methods: louvain, kmeans, + take_absolute_value: flag to take absolute value of the matrix before clustering + cluster_on_correlation: if True, cluster on (denoised) correlation else cluster on covariance + + :param max_community_size: threshold for the biggest community size, 0 drops any constraint + :param active: bool to activate or deactivate the component, if deactivated other parameters are meaningless + :param clustering_method: select method from ["louvain", "kmeans"], default to louvain + :param take_absolute_value: bool to apply abs to matrix before sending it to the clustering method + :param cluster_on_correlation: bool to cluster on correlation otherwise cluster on covariance + """ + if clustering_method not in clustering_methods: + clustering_method = "louvain" + + self.method = clustering_method + self.active = active + self.take_absolute_value = take_absolute_value + self.max_community_size = max_community_size + self.cluster_on_correlation = cluster_on_correlation + + self.params = get_instance_non_private_attributes(self) + + def _call_louvain(self, matrix): + # Requires matrix with only non-negative terms + if self.max_community_size == 0: + partitions = modularity_spectral_optimization(matrix) + else: + partitions = modularity_spectral_threshold( + matrix, + threshold=self.max_community_size, + return_communities=False, + ) + return partitions + + def _call_kmeans(self, matrix): + # Unrestricted cluster size default, dirty trick by using huge upper bound + threshold_cluster_size = 1e9 + if self.max_community_size > 0: + threshold_cluster_size = self.max_community_size + return k_means_laplacian_threshold( + matrix, + k_values=None, + low_threshold=2, + up_threshold=threshold_cluster_size, + num_eigenvectors=80, + )[1] + + @timeit + def cluster(self, matrix): + if not self.active: + return np.zeros(len(matrix)) + if self.take_absolute_value: + matrix = np.abs(matrix) + if self.method == "louvain": + partitions = self._call_louvain(matrix) + elif self.method == "kmeans": + partitions = self._call_kmeans(matrix) + else: + raise NotImplemented + print( + "Max commmunity size:", np.max(np.unique(partitions, return_counts=True)[1]) + ) + return partitions + + # use this structure to ease profile readability + def __call__(self, matrix): + return self.cluster(matrix) diff --git a/dcmppln/.ipynb_checkpoints/denoiser-checkpoint.py b/dcmppln/.ipynb_checkpoints/denoiser-checkpoint.py new file mode 100644 index 0000000..68f37f4 --- /dev/null +++ b/dcmppln/.ipynb_checkpoints/denoiser-checkpoint.py @@ -0,0 +1,52 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np +from dcmppln.utils.utils import get_instance_non_private_attributes +from dcmppln.utils.correlation_rmt import split_covariance_matrices +from dcmppln.utils.utils import timeit + + +class Denoiser: + """ + Class to apply noise reduction before clustering + """ + + def __init__(self, active: bool = True, q: float = 0.5, q_fit: bool = True): + """ + Parameters + ---------- + active: bool to activate or deactivate the component, if deactivated other parameters are meaningless + q: ratio of number of variables to number of observations. e.g. N_stocks/N_days + q_fit: finds the best q + """ + + self.active = active + self.q = q + self.q_fit = q_fit + + self.params = get_instance_non_private_attributes(self) + + # use this structure to ease profile readability + def __call__(self, C): + return self.denoise(C) + + # Check with q=0.5, q_fit = False, q=1.5 + @timeit + def denoise(self, C: np.array) -> np.array: + """ + function to calculate denoise, if the component is deactivated return the + C else return + + Parameters + ---------- + C: np.array input correlation matrix + """ + + if not self.active: + return C + + C_1, C_2, C_3 = split_covariance_matrices(C, q=self.q, q_fit=self.q_fit) + return C_2 diff --git a/dcmppln/.ipynb_checkpoints/filtering-checkpoint.py b/dcmppln/.ipynb_checkpoints/filtering-checkpoint.py new file mode 100644 index 0000000..303096b --- /dev/null +++ b/dcmppln/.ipynb_checkpoints/filtering-checkpoint.py @@ -0,0 +1,67 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np +from dcmppln.utils.utils import get_instance_non_private_attributes +from dcmppln.utils.utils import threshold_matrix, thresholding_not_touching_diag, sparsification + + +class Filtering: + def __init__( + self, + threshold_flag=False, + take_average_threshold=True, + allow_to_modify_diag=False, + remove_diag_from_average=True, + threshold_value=0.0, + active=True, + ): + """ + Be careful, threshold_flag set to true will change the matrix to a binary matrix what ever the threshold value is + :param threshold_flag: + :param take_average_threshold: bool to use the threshold value against the average + :param allow_to_modify_diag: bool to allow to modify the diagonal + :param remove_diag_from_average: bool to remove the diagonal value when estimating the average + :param threshold_value: float, value to use as a threshold + :param active: bool to activate or deactivate the component, if deactivated other parameters are meaningless + """ + self.active = active + self.take_average_threshold = take_average_threshold + self.allow_to_modify_diag = allow_to_modify_diag + self.remove_diag_from_mean = remove_diag_from_average + self.threshold_flag = threshold_flag + self.threshold_value = threshold_value + + self.params = get_instance_non_private_attributes(self) + + def filtering(self, matrix): + if not self.active: + return matrix + if self.take_average_threshold: + if self.remove_diag_from_mean: + diag_matrix = np.diag(np.diag(matrix)) # diagonal matrix only + mean_value = np.mean(matrix - diag_matrix) + else: + mean_value = np.mean(matrix) + self.threshold_value *= mean_value + + if not self.threshold_flag: + return_matrix = sparsification( + matrix, + threshold=self.threshold_value, + allow_to_modify_diag=self.allow_to_modify_diag, + ) + else: + if not self.allow_to_modify_diag: + return_matrix = thresholding_not_touching_diag( + matrix, threshold=self.threshold_value + ) + else: + return_matrix = threshold_matrix(matrix, threshold=self.threshold_value) + return return_matrix + + # use this structure to ease profile readability + def __call__(self, matrix): + return self.filtering(matrix) diff --git a/dcmppln/.ipynb_checkpoints/optimizer-checkpoint.py b/dcmppln/.ipynb_checkpoints/optimizer-checkpoint.py new file mode 100644 index 0000000..c253684 --- /dev/null +++ b/dcmppln/.ipynb_checkpoints/optimizer-checkpoint.py @@ -0,0 +1,253 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from abc import ABC, abstractmethod + +from dcmppln.optimization.cplex_utils import append_to_log +from dcmppln.optimization.portfolio_optimization_runtime import ( + add_logger, + optimize_and_measure_time, +) +from dcmppln.utils.utils import get_instance_non_private_attributes + +from dcmppln.CONSTANT import LOG_FOLDER, TIMEOUT, DEFAULT_MIP_GAP + +from memory_profiler import profile +import numpy as np + +#from dwave.samplers import SimulatedAnnealingSampler + +# Gurobi imports +from gurobipy import GRB, Env, read +import os +import time + + +class Optimizer(ABC): + """ + Abstract class of optimizer component + Must overwrite and match the call function signature + """ + + def __init__(self): + self.name = self.__class__.__name__ + + @abstractmethod + def __call__( + self, + model, + weights, + unique_identifier=None, + log_best_feasible=False, + ): + """ + Example signature and return overwriting methods needs to follow + :param model: docplex model + :param weights: docplex variable + :param unique_identifier: hex id of the optimization + :param log_best_feasible: log best feasible solutions + :return: + """ + optimal_weights = np.zeros(10) + execution_time = 1 + processing_time = 1 + return optimal_weights, execution_time, processing_time + + +class CplexOptimizer(Optimizer): + """ + Warning mip_margin=0 actually means default = 1-e4 + """ + + def __init__( + self, + mip_margin=DEFAULT_MIP_GAP, + continuous_variables_flag=False, + timeout=TIMEOUT, + additional_listeners=None, + qtolin=-1, + ): + super().__init__() + if additional_listeners is None: + additional_listeners = list() + self.mip_margin = mip_margin + self.continuous_variables_flag = continuous_variables_flag + self.timeout = timeout + self.qtolin = qtolin + self._additional_listeners = additional_listeners + self.additional_listeners_name = [ + callback.__class__.__name__ for callback in self._additional_listeners + ] + self._cplex_parameters = { + "preprocessing.qtolin": qtolin, + "mip.tolerances.mipgap": mip_margin, + "timelimit": TIMEOUT, + } + self.params = get_instance_non_private_attributes(self) + + def __call__(self, model, weights, unique_identifier=None, log_best_feasible=False): + return self.optimize( + model, + weights, + unique_identifier=unique_identifier, + log_best_feasible=log_best_feasible, + ) + + def optimize( + self, + model, + weights, + unique_identifier=None, + log_best_feasible=False, + ): + for key, val in self._cplex_parameters.items(): + # set timeout, mipgap, qtolin + model.parameters.set_from_qualified_name(key, val) + + if log_best_feasible: + log_path = add_logger(model, unique_identifier=unique_identifier) + + for listerner in self._additional_listeners: + model.add_progress_listener(listerner) + + # Solve the model + solution, execution_time, processing_time = optimize_and_measure_time(model) + + if (unique_identifier is not None) and log_best_feasible: + msg = f"{solution.solve_details.time},{solution.objective_value},{solution.solve_details.best_bound}\n" + append_to_log(log_path, msg) + + # Retrieve the optimal weights + optimal_weights = [solution.get_value(w) for w in weights] + # Retrieve the optimal weights + + return optimal_weights, execution_time, processing_time + + +class SimulatedAnnealingDwave(Optimizer): + def __init__(self, **kwargs): + """ + :param kwargs: hyperparamters to pass to SimulatedAnnealingSampler at sample time + """ + super().__init__() + self.optimizer_parameters = kwargs + self.params = get_instance_non_private_attributes(self) + + def __call__( + self, model, weights=None, unique_identifier=None, log_best_feasible=False + ): + """ + :param model: docplex model + :param weights: docplex variable + :param unique_identifier: hex id of the optimization + :param log_best_feasible: bool log best feasible solutions + :return: + """ + # convert to QUBO with penalty + cqm = dimod.lp.loads(model.lp_string) + bqm, invert = dimod.cqm_to_bqm(cqm) + sampler = SimulatedAnnealingSampler() + + start_time = time.perf_counter() + tp_start = time.process_time() + + # Solve the model + sampleset = sampler.sample(bqm, **self.optimizer_parameters) + + ## End time + tp_stop = time.process_time() + end_time = time.perf_counter() + execution_time = end_time - start_time + processing_time = tp_stop - tp_start + + values_dict = sampleset.first.sample + if cqm.check_feasible(sampleset.first.sample): + best_sample_weights = [ + values_dict[f"weights_{i}"] for i in range(len(values_dict)) + ] + else: + # Attempted default value + best_sample_weights = [0 for i in range(len(values_dict))] + return best_sample_weights, execution_time, processing_time + + +class GurobiOptimizer(Optimizer): + def __init__(self, **kwargs): + """ + :param kwargs: hyperparamters to pass to model see https://www.gurobi.com/documentation/10.0/refman/parameters.html#sec:Parameters + """ + super().__init__() + self.optimizer_parameters = { + "TimeLimit": TIMEOUT, + "MIPGap": DEFAULT_MIP_GAP, + "Threads": 1, + } + self.name = self.__class__.__name__ + self.params = get_instance_non_private_attributes(self) + + def __call__( + self, model, weights=None, unique_identifier=None, log_best_feasible=False + ): + """ + :param model: docplex model + :param weights: docplex variable + :param unique_identifier: hex id of the optimization + :param log_best_feasible: bool log best feasible solutions + :return: + """ + with open("temp.lp", "w") as f: + f.write(model.lp_string.replace("*", " * ")) + + m = read("temp.lp") + for param, values in self.optimizer_parameters.items(): + m.setParam(param, values) + + start_time = time.perf_counter() + tp_start = time.process_time() + + # Solve the model + m.optimize() + + ## End time + tp_stop = time.process_time() + end_time = time.perf_counter() + execution_time = end_time - start_time + processing_time = tp_stop - tp_start + + solution = np.array( + [m.getVarByName(f"weights_{i}").X for i in range(m.NumVars)] + ) + solution = solution + 0.0 # trick to avoid -0.0 values + return solution, execution_time, processing_time + + +class DummyOptimizer(Optimizer): + """ + Dummy optimizer to test other component, return only zeros + """ + + def __init__(self): + super().__init__() + self.params = get_instance_non_private_attributes(self) + + def __call__( + self, + model, + weights, + unique_identifier=None, + log_best_feasible=False, + ): + """ + Example signature and return overwriting methods needs to follow + :param model: docplex model + :param weights: docplex variable + :param unique_identifier: hex id of the optimization + :param log_best_feasible: log best feasible solutions + :return: + """ + optimal_weights = np.zeros(len(weights)) + execution_time = 0 + processing_time = 0 + return optimal_weights, execution_time, processing_time diff --git a/dcmppln/.ipynb_checkpoints/pipeline-checkpoint.py b/dcmppln/.ipynb_checkpoints/pipeline-checkpoint.py new file mode 100644 index 0000000..86e98d3 --- /dev/null +++ b/dcmppln/.ipynb_checkpoints/pipeline-checkpoint.py @@ -0,0 +1,205 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np +import json +import time +import os +from uuid import uuid4 + +from dcmppln.optimizer import Optimizer, DummyOptimizer +from dcmppln.utils.risk_rebalance import rebalancing_risk_factor +from dcmppln.utils.objective_after_clustering import optimize_and_score + +from dcmppln.denoiser import Denoiser +from dcmppln.clustering import Clustering +from dcmppln.filtering import Filtering + +from dcmppln.CONSTANT import LOG_FOLDER, TIMEOUT + + +class Pipeline: + """ + Structure to efficiently run a grid search on a list of Denoiser, Clustering, Filtering, Optimizer + main method is run which is 4 for loops + """ + + def __init__( + self, + correlation_martix: np.array, + covariance_matrix: np.array, + returns: float, + denoiser: Denoiser = Denoiser(active=False), + cluster: Clustering = Clustering(active=False), + filter_func: Filtering = Filtering(active=False), + optimize_func: Optimizer = DummyOptimizer(), + ): + self.correlation_matrix = correlation_martix + self.covariance_matrix = covariance_matrix + self.returns = returns + self.denoiser_func = denoiser + self.cluster_func = cluster + self.filter_func = filter_func + self.optimize_func = optimize_func + self.__state__ = dict() + + def run( + self, + risk_rebalancing=False, + cluster_on_correlation=True, + optimize_on_correlation=False, + log_best_feasible=False, + input_risk_factor=1, + run_optimizer=False, + ): + """ + Method to run the grid search over the components (one inner for loop per component) and get score metrics + + :param risk_rebalancing: bool to use risk rebalancing technique + :param cluster_on_correlation: bool to cluster on correlation else cluster on covariance + :param optimize_on_correlation: bool to optimize on correlation else cluster on covariance + :param log_best_feasible: bool to log best feasible solution (only available on CPLEX optimizer for now) + :param cardinality_divider: set cardinality constraint to len(returns)//cardinality_divider + :param input_risk_factor: risk factor in the markovitz optimization + :return: dict with key being input and component parameters and values the score, run times etc... + """ + + matrix_to_cluster = ( + self.correlation_matrix + if cluster_on_correlation + else self.covariance_matrix + ) + matrix_to_optimize = ( + self.correlation_matrix + if optimize_on_correlation + else self.covariance_matrix + ) + + C_2, denoise_process_time, denoise_time = self.denoiser_func(matrix_to_cluster) + partitions, clustering_process_time, clustering_time = self.cluster_func(C_2) + + + # ----------------------------------------------------------------------------------------- + + unique_identifier_base = uuid4().hex + if log_best_feasible: + log_sub_dir = os.path.join(LOG_FOLDER, unique_identifier_base) + os.mkdir(log_sub_dir) + risk_factor = input_risk_factor + if run_optimizer: + return self.optimize_and_score( + matrix_to_cluster, + matrix_to_optimize, + partitions, + risk_factor, + unique_identifier_base, + log_best_feasible, + cluster_on_correlation, + optimize_on_correlation, + denoise_process_time, + clustering_process_time, + denoise_time, + clustering_time, + risk_rebalancing=False, + cardinality_divider=2, + ) + return C_2, partitions + + def optimize_and_score( + self, + matrix_to_cluster, + matrix_to_optimize, + partitions, + risk_factor, + unique_identifier_base, + log_best_feasible, + cluster_on_correlation, + optimize_on_correlation, + denoise_process_time, + clustering_process_time, + denoise_time, + clustering_time, + risk_rebalancing=False, + cardinality_divider=2, + ): + ( + obj_after_clustering, + full_recombined_soln, + opt_and_filter_process_time_dict, + opt_and_filter_time_dict, + ) = optimize_and_score( + self.returns, + matrix_to_optimize, + partitions, + card_=len(self.returns) // cardinality_divider, + input_risk_factor=risk_factor, + return_time=True, + filtering_method=self.filter_func, + optimizer=self.optimize_func, + rebalanced_risk_factor=( + rebalancing_risk_factor( + risk_factor, matrix_to_optimize, self.returns, partitions + ) + if risk_rebalancing + else None + ), + unique_identifier_base=unique_identifier_base, + log_best_feasible=log_best_feasible, + ) + opt_and_filter_process_time = sum(opt_and_filter_process_time_dict.values()) + opt_and_filter_time = sum(opt_and_filter_time_dict.values()) + + key_parameters = { + "denoiser": self.denoiser_func.params, + "clustering": self.cluster_func.params, + "filtering": self.filter_func.params, + "optimizer": self.optimize_func.params, + "problem_parameters": { + "problem_size": len(self.returns), + "cardinality_divider": cardinality_divider, + "input_risk_factor": risk_factor, + "cluster_on_correlation": cluster_on_correlation, + "optimize_on_correlation": optimize_on_correlation, + "risk_rebalancing": risk_rebalancing, + }, + } + self.__state__["key_parameters"] = key_parameters + run_time = { + "process_time": denoise_process_time + + clustering_process_time + + opt_and_filter_process_time, + "clock_time": denoise_time + clustering_time + opt_and_filter_time, + "process_times": [ + denoise_process_time, + clustering_process_time, + opt_and_filter_process_time, + ], + "clock_times": [ + denoise_time, + clustering_time, + opt_and_filter_time, + ], + "opt_process_times": opt_and_filter_process_time_dict, + "opt_clock_times": opt_and_filter_time_dict, + } + self.__state__["run_time"] = run_time + result = { + "score": obj_after_clustering, + "recombined_solution": full_recombined_soln, + "run_time": run_time, + "partitions": partitions, + "max_community_size_found": np.max( + np.unique(partitions, return_counts=True)[1] + ), + "size_of_partitions": np.unique(partitions, return_counts=True)[1], + "unique_identifier_base": unique_identifier_base, + "total_input_size": len(self.returns), + } + self.__state__["result"] = result + + return { + "score": obj_after_clustering, + "recombined_solution": full_recombined_soln, + } diff --git a/dcmppln/CONSTANT.py b/dcmppln/CONSTANT.py new file mode 100644 index 0000000..41cd7ae --- /dev/null +++ b/dcmppln/CONSTANT.py @@ -0,0 +1,9 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +TIMEOUT = 300 +LOG_FOLDER = "/opt/omniai/work/instance1/jupyter/atithi_acharya_summer_2023_internship_project/log" +SAVE_LP_FILE = False +DEFAULT_MIP_GAP = 1e-4 diff --git a/dcmppln/README.md b/dcmppln/README.md new file mode 100644 index 0000000..20addb6 --- /dev/null +++ b/dcmppln/README.md @@ -0,0 +1,65 @@ + + + +# Hybrid classical-quantum portfolio optimization using random matrix theory-based subproblem identification + +The repository has three different modules. 1) Cleaning of correlation matrices using random matrix theory 2) Decomposition pipeline using community detection and = other clustering 3) Portfolio optimization objectives (V0, V1,V1.5, V2) +When we find communities, we have two options. One with the restriction on the number of elements in a cluster, the other without any such restrictions. + + +## Directory Structure + +- **main.py** : Contains the Pipeline object and components of the pipline: Denoiser, Clustering, Filtering. +- **optimizer.py** : Contains the Optimizer objects + +- **comparing_optimization_times**: Contains files related to comparing optimization times of running CPLEX optimization routines on full problem against the decomposition pipeline. +- **create_portfolio_instance**: Scripts for creating portfolio instances. This includes generating returns (w or without log) and correlation and covariance matrices +- **modularity_maximization**: Resources for maximizing modularity. It includes many community detection algorithms , including modified versions, key ones: louvain_generalized, modularity_spectral_optimization +- **notebooks**: Jupyter notebooks or other interactive notebooks related to the project. (3 separate subfolders-- a) comparing objectives: Contains the jaccard similarity, difference and ratio in objectives,
b) thresholding and c) processing_time_comparision ) +- **out_of_sample**: Data and scripts related to out-of-sample testing. Only in the continuous setting but this has end-end sharpe ratio computations on present -- future splits of time series data. +- **random_matrix_theory**: Resources and files related to random matrix theory tools such as marchenko pastur fits, splitting correlatoins into C1, C2 and C3. +- **run_scripts** : Contains script example to run the pipeline +- **results** : Contains notebooks and results csv to visualize the results +- **thresholding**: Scripts and data for thresholding techniques. Also, includes comparision of thresholded objectives (it is in utils/utils.py) +- **utils**: Utility scripts and helper functions for the project. (Need to add a separate readme file) +- **others**: Miscellaneous files and resources not categorized elsewhere. This includes integrality gap experiments + +- **data**: This is an empty folder as a placeholder for the data files, data files should be as follows + - self.path+"_correlation.npy" + - self.path+"_covariance.npy" + - self.path+"_returns.npy" + + +## Getting Started + +1. **Setup environement with cplex** +``` +cd .. +git clone git clone --depth=1 --no-single-branch https://bitbucketdc.jpmchase.net/scm/quantumalgorithms/environment.git +environment/scripts/cplex_install.sh +source py38_cplex/bin/activate +cd atithi_acharya_summer_2023_internship_project/ +pip install -r requirement_pierre.txt +``` + +2. **Usage**: Briefly explain how to use the project or run the scripts.
+On a new omni-ai instance +If you do not want to create an environment for docplex +Follow the instructions to setup packages in 1
+a) git clone https://bitbucketdc.jpmchase.net/scm/quantumalgorithms/atithi_acharya_summer_2023_internship_project.git +b) The data folder is empty, when the user runs create_portfolio_instance (including notebook) , they have to save the files there in order to run. +c) get data and results stored on aws s3 +``` +aws s3 cp --recursive s3://app-id-108383-dep-id-105006-uu-id-ahc5pxjsqas8/decomposition_pipeline/ . +``` + +## Contributing + +Provide guidelines on how others can contribute to your project. + +## License + +Add license under (ask) + +## Acknowledgements diff --git a/dcmppln/__pycache__/CONSTANT.cpython-311.pyc b/dcmppln/__pycache__/CONSTANT.cpython-311.pyc new file mode 100644 index 0000000..e730990 Binary files /dev/null and b/dcmppln/__pycache__/CONSTANT.cpython-311.pyc differ diff --git a/dcmppln/__pycache__/CONSTANT.cpython-39.pyc b/dcmppln/__pycache__/CONSTANT.cpython-39.pyc new file mode 100644 index 0000000..969378a Binary files /dev/null and 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b/dcmppln/__pycache__/pipeline.cpython-311.pyc new file mode 100644 index 0000000..97752fe Binary files /dev/null and b/dcmppln/__pycache__/pipeline.cpython-311.pyc differ diff --git a/dcmppln/__pycache__/pipeline.cpython-39.pyc b/dcmppln/__pycache__/pipeline.cpython-39.pyc new file mode 100644 index 0000000..af665bb Binary files /dev/null and b/dcmppln/__pycache__/pipeline.cpython-39.pyc differ diff --git a/dcmppln/clustering.py b/dcmppln/clustering.py new file mode 100644 index 0000000..4b8094c --- /dev/null +++ b/dcmppln/clustering.py @@ -0,0 +1,96 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from dcmppln.utils.utils import get_instance_non_private_attributes + +from dcmppln.modularity_maximization.modularity_spectral_optimization import ( + modularity_spectral_optimization, + modularity_spectral_threshold, +) +from dcmppln.utils.k_means import k_means_laplacian_threshold +from dcmppln.utils.utils import timeit +import numpy as np + +clustering_methods = ("louvain", "kmeans") +default_clustering_method = "louvain" + + +class Clustering: + + def __init__( + self, + max_community_size=0, + active=True, + clustering_method="louvain", + take_absolute_value=True, + cluster_on_correlation=True, + ): + """ + Supported clustering methods: louvain, kmeans, + take_absolute_value: flag to take absolute value of the matrix before clustering + cluster_on_correlation: if True, cluster on (denoised) correlation else cluster on covariance + + :param max_community_size: threshold for the biggest community size, 0 drops any constraint + :param active: bool to activate or deactivate the component, if deactivated other parameters are meaningless + :param clustering_method: select method from ["louvain", "kmeans"], default to louvain + :param take_absolute_value: bool to apply abs to matrix before sending it to the clustering method + :param cluster_on_correlation: bool to cluster on correlation otherwise cluster on covariance + """ + if clustering_method not in clustering_methods: + clustering_method = "louvain" + + self.method = clustering_method + self.active = active + self.take_absolute_value = take_absolute_value + self.max_community_size = max_community_size + self.cluster_on_correlation = cluster_on_correlation + + self.params = get_instance_non_private_attributes(self) + + def _call_louvain(self, matrix): + # Requires matrix with only non-negative terms + if self.max_community_size == 0: + partitions = modularity_spectral_optimization(matrix) + else: + partitions = modularity_spectral_threshold( + matrix, + threshold=self.max_community_size, + return_communities=False, + ) + return partitions + + def _call_kmeans(self, matrix): + # Unrestricted cluster size default, dirty trick by using huge upper bound + threshold_cluster_size = 1e9 + if self.max_community_size > 0: + threshold_cluster_size = self.max_community_size + return k_means_laplacian_threshold( + matrix, + k_values=None, + low_threshold=2, + up_threshold=threshold_cluster_size, + num_eigenvectors=80, + )[1] + + @timeit + def cluster(self, matrix): + if not self.active: + return np.zeros(len(matrix)) + if self.take_absolute_value: + matrix = np.abs(matrix) + if self.method == "louvain": + partitions = self._call_louvain(matrix) + elif self.method == "kmeans": + partitions = self._call_kmeans(matrix) + else: + raise NotImplemented + print( + "Max commmunity size:", np.max(np.unique(partitions, return_counts=True)[1]) + ) + return partitions + + # use this structure to ease profile readability + def __call__(self, matrix): + return self.cluster(matrix) diff --git a/dcmppln/denoiser.py b/dcmppln/denoiser.py new file mode 100644 index 0000000..68f37f4 --- /dev/null +++ b/dcmppln/denoiser.py @@ -0,0 +1,52 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np +from dcmppln.utils.utils import get_instance_non_private_attributes +from dcmppln.utils.correlation_rmt import split_covariance_matrices +from dcmppln.utils.utils import timeit + + +class Denoiser: + """ + Class to apply noise reduction before clustering + """ + + def __init__(self, active: bool = True, q: float = 0.5, q_fit: bool = True): + """ + Parameters + ---------- + active: bool to activate or deactivate the component, if deactivated other parameters are meaningless + q: ratio of number of variables to number of observations. e.g. N_stocks/N_days + q_fit: finds the best q + """ + + self.active = active + self.q = q + self.q_fit = q_fit + + self.params = get_instance_non_private_attributes(self) + + # use this structure to ease profile readability + def __call__(self, C): + return self.denoise(C) + + # Check with q=0.5, q_fit = False, q=1.5 + @timeit + def denoise(self, C: np.array) -> np.array: + """ + function to calculate denoise, if the component is deactivated return the + C else return + + Parameters + ---------- + C: np.array input correlation matrix + """ + + if not self.active: + return C + + C_1, C_2, C_3 = split_covariance_matrices(C, q=self.q, q_fit=self.q_fit) + return C_2 diff --git a/dcmppln/filtering.py b/dcmppln/filtering.py new file mode 100644 index 0000000..303096b --- /dev/null +++ b/dcmppln/filtering.py @@ -0,0 +1,67 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np +from dcmppln.utils.utils import get_instance_non_private_attributes +from dcmppln.utils.utils import threshold_matrix, thresholding_not_touching_diag, sparsification + + +class Filtering: + def __init__( + self, + threshold_flag=False, + take_average_threshold=True, + allow_to_modify_diag=False, + remove_diag_from_average=True, + threshold_value=0.0, + active=True, + ): + """ + Be careful, threshold_flag set to true will change the matrix to a binary matrix what ever the threshold value is + :param threshold_flag: + :param take_average_threshold: bool to use the threshold value against the average + :param allow_to_modify_diag: bool to allow to modify the diagonal + :param remove_diag_from_average: bool to remove the diagonal value when estimating the average + :param threshold_value: float, value to use as a threshold + :param active: bool to activate or deactivate the component, if deactivated other parameters are meaningless + """ + self.active = active + self.take_average_threshold = take_average_threshold + self.allow_to_modify_diag = allow_to_modify_diag + self.remove_diag_from_mean = remove_diag_from_average + self.threshold_flag = threshold_flag + self.threshold_value = threshold_value + + self.params = get_instance_non_private_attributes(self) + + def filtering(self, matrix): + if not self.active: + return matrix + if self.take_average_threshold: + if self.remove_diag_from_mean: + diag_matrix = np.diag(np.diag(matrix)) # diagonal matrix only + mean_value = np.mean(matrix - diag_matrix) + else: + mean_value = np.mean(matrix) + self.threshold_value *= mean_value + + if not self.threshold_flag: + return_matrix = sparsification( + matrix, + threshold=self.threshold_value, + allow_to_modify_diag=self.allow_to_modify_diag, + ) + else: + if not self.allow_to_modify_diag: + return_matrix = thresholding_not_touching_diag( + matrix, threshold=self.threshold_value + ) + else: + return_matrix = threshold_matrix(matrix, threshold=self.threshold_value) + return return_matrix + + # use this structure to ease profile readability + def __call__(self, matrix): + return self.filtering(matrix) diff --git a/dcmppln/modularity_maximization/.ipynb_checkpoints/modularity_spectral_optimization-checkpoint.py b/dcmppln/modularity_maximization/.ipynb_checkpoints/modularity_spectral_optimization-checkpoint.py new file mode 100644 index 0000000..7d524cc --- /dev/null +++ b/dcmppln/modularity_maximization/.ipynb_checkpoints/modularity_spectral_optimization-checkpoint.py @@ -0,0 +1,154 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np + +def modularity_matrix(adjacency_matrix, degree_vector, total_degree): + g_size = adjacency_matrix.shape[0] + inv_total_degree = 1 / total_degree + + # Pre-calculate the term for subtraction which is used for both diagonal and off-diagonal elements + subtraction_term = inv_total_degree * np.outer(degree_vector, degree_vector) + + B_hat_g = adjacency_matrix - subtraction_term + + # Adjust diagonal elements + B_g_rowsum = adjacency_matrix.sum(axis=1) + np.fill_diagonal(B_hat_g, B_hat_g.diagonal() - inv_total_degree * (B_g_rowsum * degree_vector)) + + return B_hat_g + + +def modularity_spectral_optimization(adjacency_matrix, return_communities=False): + n = len(adjacency_matrix) + total_degree = np.sum(adjacency_matrix) + degree_vector = np.sum(adjacency_matrix, axis=1) + subgraphs = [np.arange(n)] + + Q_g_dict = {} + partition = np.zeros(n, dtype=int) + + i = 0 + while subgraphs: + current_subgraph = subgraphs.pop(0) + B = modularity_matrix( + adjacency_matrix[current_subgraph][:, current_subgraph], + degree_vector[current_subgraph], + total_degree, + ) + if np.isinf(B).any() or np.isnan(B).any(): + print("Nan or inf detected in B") + B = np.nan_to_num(B, copy=True, nan=0.0, posinf=n, neginf=-n) + + eigenvalues, eigenvectors = np.linalg.eigh(B) + leading_eigenvector = eigenvectors[:, np.argmax(eigenvalues)] + + positive_indices = np.where(leading_eigenvector >= 0)[0] + negative_indices = np.where(leading_eigenvector < 0)[0] + + if len(positive_indices) == 0 or len(negative_indices) == 0: + continue + + partition[positive_indices] = 2**i + partition[negative_indices] = (2**i) + 1 + positive_subgraph = current_subgraph[positive_indices] + negative_subgraph = current_subgraph[negative_indices] + + subgraphs.append(positive_subgraph) + subgraphs.append(negative_subgraph) + + B_pos = modularity_matrix( + adjacency_matrix[positive_indices][:, positive_indices], + degree_vector[positive_indices], + total_degree, + ) + B_neg = modularity_matrix( + adjacency_matrix[negative_indices][:, negative_indices], + degree_vector[negative_indices], + total_degree, + ) + + Qg = (np.sum(B_pos) + np.sum(B_neg)) / total_degree + + Q_g_dict[(i, Qg)] = (positive_subgraph, negative_subgraph) + i += 1 + + if Qg <= 0: + # Create a new list without the positive and negative subgraphs + subgraphs = [ + graph + for graph in subgraphs + if not np.array_equal(graph, positive_subgraph) + and not np.array_equal(graph, negative_subgraph) + ] + + partition = np.unique(partition, return_inverse=True)[1] + + if return_communities: + communities = [] + for el in np.unique(partition): + communities.append(list(np.where(partition == el)[0])) + + return communities, partition + + return partition + + +########################################################################## + +def modularity_spectral_threshold( + adjacency_matrix, threshold=30, return_communities=True +): + partition = modularity_spectral_optimization(adjacency_matrix) + + def iterative_community_detection(initial_indices): + community_list = [initial_indices] + + while community_list: + subgraph_indices = community_list.pop() + + if len(subgraph_indices) <= threshold: + yield subgraph_indices + continue + + sub_adj_matrix = adjacency_matrix[ + np.ix_(subgraph_indices, subgraph_indices) + ] + sub_degrees = np.sum(sub_adj_matrix, axis=1) + total_sub_degree = np.sum(sub_degrees) + + B_hat_g = modularity_matrix(sub_adj_matrix, sub_degrees, total_sub_degree) + + + eigenvalues, eigenvectors = np.linalg.eigh(B_hat_g) + leading_eigenvector = eigenvectors[:, np.argmax(eigenvalues)] + + positive_indices = np.where(leading_eigenvector >= 0)[0] + negative_indices = np.where(leading_eigenvector < 0)[0] + + if len(positive_indices) == 0 or len(negative_indices) == 0: + yield subgraph_indices + else: + positive_subgraph = [subgraph_indices[idx] for idx in positive_indices] + negative_subgraph = [subgraph_indices[idx] for idx in negative_indices] + community_list.append(positive_subgraph) + community_list.append(negative_subgraph) + + communities = list( + iterative_community_detection(list(range(len(adjacency_matrix)))) + ) + + # Create a partition map with community labels + partition_map = {} + for i, comm in enumerate(communities): + for node in comm: + partition_map[node] = i + + partition = np.array([partition_map[node] for node in range(len(adjacency_matrix))]) + + if return_communities: + return communities, partition + else: + return partition diff --git a/dcmppln/modularity_maximization/.ipynb_checkpoints/optimize_modularity-checkpoint.py b/dcmppln/modularity_maximization/.ipynb_checkpoints/optimize_modularity-checkpoint.py new file mode 100644 index 0000000..59043d8 --- /dev/null +++ b/dcmppln/modularity_maximization/.ipynb_checkpoints/optimize_modularity-checkpoint.py @@ -0,0 +1,83 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +""" +Note: This has to be run outside of py39 since CPLEX installation needs it +""" + +from docplex.mp.model import Model + +from docplex.cp.model import CpoModel + +import time +import numpy as np + +import networkx as nx + + +def maximize_modularity(adjacency_matrix): + """Doctest + >>> G = nx.barbell_graph(3, 0) + >>> adjacency_matrix = nx.to_numpy_array(G) + >>> expected_modularity = 0.357142 + >>> best_partition, best_modularity = maximize_modularity(adjacency_matrix) + >>> assert np.isclose(best_modularity, expected_modularity), f"Modularity test failed. Expected: {expected_modularity}, Actual: {best_modularity}" + >>> assert (np.allclose(best_partition, np.array([0, 0,0, 1,1, 1])) or np.allclose(best_partition, np.array([1,1,1, 0,0,0]) ) ) + """ + + """ + Find the best binary string that maximizes the modularity score. + + Parameters: + adjacency_matrix (numpy.ndarray): The adjacency matrix representing the network. + + Returns: + best_partition (list): The best partition that maximizes the modularity score. + best_modularity (float): The modularity score corresponding to the best partition. + """ + + n = len(adjacency_matrix) + m = np.sum(adjacency_matrix) / 2.0 + + # Create the docplex model + model = Model(name="ModularityMaximization") + + # Create binary variables for each node in the partition + partition_vars = model.binary_var_list(n, name="partition") + + # Calculate the modularity score + modularity_score = model.sum( + ( + adjacency_matrix[i][j] + - (np.sum(adjacency_matrix[i]) * np.sum(adjacency_matrix[j])) / (2.0 * m) + ) + * (partition_vars[i] * partition_vars[j]) + for i in range(n) + for j in range(n) + ) / (m) + + # Maximize the modularity score + model.maximize(modularity_score) + + # Solve the model + if model.solve(): + # Retrieve the best partition and modularity score + best_partition = np.array( + [int(partition.solution_value) for partition in partition_vars] + ) + best_modularity = model.objective_value + return best_partition, best_modularity + else: + print("No feasible solution found.") + return None + + +if __name__ == "__main__": + import doctest + + doctest.testmod() + print("Passed the test") + + diff --git a/dcmppln/modularity_maximization/__pycache__/modularity_spectral_optimization.cpython-311.pyc b/dcmppln/modularity_maximization/__pycache__/modularity_spectral_optimization.cpython-311.pyc new file mode 100644 index 0000000..39d5387 Binary files /dev/null and b/dcmppln/modularity_maximization/__pycache__/modularity_spectral_optimization.cpython-311.pyc differ diff --git a/dcmppln/modularity_maximization/__pycache__/modularity_spectral_optimization.cpython-39.pyc b/dcmppln/modularity_maximization/__pycache__/modularity_spectral_optimization.cpython-39.pyc new file mode 100644 index 0000000..4f3128c Binary files /dev/null and b/dcmppln/modularity_maximization/__pycache__/modularity_spectral_optimization.cpython-39.pyc differ diff --git a/dcmppln/modularity_maximization/modularity_spectral_optimization.py b/dcmppln/modularity_maximization/modularity_spectral_optimization.py new file mode 100644 index 0000000..7d524cc --- /dev/null +++ b/dcmppln/modularity_maximization/modularity_spectral_optimization.py @@ -0,0 +1,154 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np + +def modularity_matrix(adjacency_matrix, degree_vector, total_degree): + g_size = adjacency_matrix.shape[0] + inv_total_degree = 1 / total_degree + + # Pre-calculate the term for subtraction which is used for both diagonal and off-diagonal elements + subtraction_term = inv_total_degree * np.outer(degree_vector, degree_vector) + + B_hat_g = adjacency_matrix - subtraction_term + + # Adjust diagonal elements + B_g_rowsum = adjacency_matrix.sum(axis=1) + np.fill_diagonal(B_hat_g, B_hat_g.diagonal() - inv_total_degree * (B_g_rowsum * degree_vector)) + + return B_hat_g + + +def modularity_spectral_optimization(adjacency_matrix, return_communities=False): + n = len(adjacency_matrix) + total_degree = np.sum(adjacency_matrix) + degree_vector = np.sum(adjacency_matrix, axis=1) + subgraphs = [np.arange(n)] + + Q_g_dict = {} + partition = np.zeros(n, dtype=int) + + i = 0 + while subgraphs: + current_subgraph = subgraphs.pop(0) + B = modularity_matrix( + adjacency_matrix[current_subgraph][:, current_subgraph], + degree_vector[current_subgraph], + total_degree, + ) + if np.isinf(B).any() or np.isnan(B).any(): + print("Nan or inf detected in B") + B = np.nan_to_num(B, copy=True, nan=0.0, posinf=n, neginf=-n) + + eigenvalues, eigenvectors = np.linalg.eigh(B) + leading_eigenvector = eigenvectors[:, np.argmax(eigenvalues)] + + positive_indices = np.where(leading_eigenvector >= 0)[0] + negative_indices = np.where(leading_eigenvector < 0)[0] + + if len(positive_indices) == 0 or len(negative_indices) == 0: + continue + + partition[positive_indices] = 2**i + partition[negative_indices] = (2**i) + 1 + positive_subgraph = current_subgraph[positive_indices] + negative_subgraph = current_subgraph[negative_indices] + + subgraphs.append(positive_subgraph) + subgraphs.append(negative_subgraph) + + B_pos = modularity_matrix( + adjacency_matrix[positive_indices][:, positive_indices], + degree_vector[positive_indices], + total_degree, + ) + B_neg = modularity_matrix( + adjacency_matrix[negative_indices][:, negative_indices], + degree_vector[negative_indices], + total_degree, + ) + + Qg = (np.sum(B_pos) + np.sum(B_neg)) / total_degree + + Q_g_dict[(i, Qg)] = (positive_subgraph, negative_subgraph) + i += 1 + + if Qg <= 0: + # Create a new list without the positive and negative subgraphs + subgraphs = [ + graph + for graph in subgraphs + if not np.array_equal(graph, positive_subgraph) + and not np.array_equal(graph, negative_subgraph) + ] + + partition = np.unique(partition, return_inverse=True)[1] + + if return_communities: + communities = [] + for el in np.unique(partition): + communities.append(list(np.where(partition == el)[0])) + + return communities, partition + + return partition + + +########################################################################## + +def modularity_spectral_threshold( + adjacency_matrix, threshold=30, return_communities=True +): + partition = modularity_spectral_optimization(adjacency_matrix) + + def iterative_community_detection(initial_indices): + community_list = [initial_indices] + + while community_list: + subgraph_indices = community_list.pop() + + if len(subgraph_indices) <= threshold: + yield subgraph_indices + continue + + sub_adj_matrix = adjacency_matrix[ + np.ix_(subgraph_indices, subgraph_indices) + ] + sub_degrees = np.sum(sub_adj_matrix, axis=1) + total_sub_degree = np.sum(sub_degrees) + + B_hat_g = modularity_matrix(sub_adj_matrix, sub_degrees, total_sub_degree) + + + eigenvalues, eigenvectors = np.linalg.eigh(B_hat_g) + leading_eigenvector = eigenvectors[:, np.argmax(eigenvalues)] + + positive_indices = np.where(leading_eigenvector >= 0)[0] + negative_indices = np.where(leading_eigenvector < 0)[0] + + if len(positive_indices) == 0 or len(negative_indices) == 0: + yield subgraph_indices + else: + positive_subgraph = [subgraph_indices[idx] for idx in positive_indices] + negative_subgraph = [subgraph_indices[idx] for idx in negative_indices] + community_list.append(positive_subgraph) + community_list.append(negative_subgraph) + + communities = list( + iterative_community_detection(list(range(len(adjacency_matrix)))) + ) + + # Create a partition map with community labels + partition_map = {} + for i, comm in enumerate(communities): + for node in comm: + partition_map[node] = i + + partition = np.array([partition_map[node] for node in range(len(adjacency_matrix))]) + + if return_communities: + return communities, partition + else: + return partition diff --git a/dcmppln/modularity_maximization/optimize_modularity.py b/dcmppln/modularity_maximization/optimize_modularity.py new file mode 100644 index 0000000..59043d8 --- /dev/null +++ b/dcmppln/modularity_maximization/optimize_modularity.py @@ -0,0 +1,83 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +""" +Note: This has to be run outside of py39 since CPLEX installation needs it +""" + +from docplex.mp.model import Model + +from docplex.cp.model import CpoModel + +import time +import numpy as np + +import networkx as nx + + +def maximize_modularity(adjacency_matrix): + """Doctest + >>> G = nx.barbell_graph(3, 0) + >>> adjacency_matrix = nx.to_numpy_array(G) + >>> expected_modularity = 0.357142 + >>> best_partition, best_modularity = maximize_modularity(adjacency_matrix) + >>> assert np.isclose(best_modularity, expected_modularity), f"Modularity test failed. Expected: {expected_modularity}, Actual: {best_modularity}" + >>> assert (np.allclose(best_partition, np.array([0, 0,0, 1,1, 1])) or np.allclose(best_partition, np.array([1,1,1, 0,0,0]) ) ) + """ + + """ + Find the best binary string that maximizes the modularity score. + + Parameters: + adjacency_matrix (numpy.ndarray): The adjacency matrix representing the network. + + Returns: + best_partition (list): The best partition that maximizes the modularity score. + best_modularity (float): The modularity score corresponding to the best partition. + """ + + n = len(adjacency_matrix) + m = np.sum(adjacency_matrix) / 2.0 + + # Create the docplex model + model = Model(name="ModularityMaximization") + + # Create binary variables for each node in the partition + partition_vars = model.binary_var_list(n, name="partition") + + # Calculate the modularity score + modularity_score = model.sum( + ( + adjacency_matrix[i][j] + - (np.sum(adjacency_matrix[i]) * np.sum(adjacency_matrix[j])) / (2.0 * m) + ) + * (partition_vars[i] * partition_vars[j]) + for i in range(n) + for j in range(n) + ) / (m) + + # Maximize the modularity score + model.maximize(modularity_score) + + # Solve the model + if model.solve(): + # Retrieve the best partition and modularity score + best_partition = np.array( + [int(partition.solution_value) for partition in partition_vars] + ) + best_modularity = model.objective_value + return best_partition, best_modularity + else: + print("No feasible solution found.") + return None + + +if __name__ == "__main__": + import doctest + + doctest.testmod() + print("Passed the test") + + diff --git a/dcmppln/optimization/.ipynb_checkpoints/__init__-checkpoint.py b/dcmppln/optimization/.ipynb_checkpoints/__init__-checkpoint.py new file mode 100644 index 0000000..65eb3d0 --- /dev/null +++ b/dcmppln/optimization/.ipynb_checkpoints/__init__-checkpoint.py @@ -0,0 +1,5 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### \ No newline at end of file diff --git a/dcmppln/optimization/.ipynb_checkpoints/cplex_utils-checkpoint.py b/dcmppln/optimization/.ipynb_checkpoints/cplex_utils-checkpoint.py new file mode 100644 index 0000000..5af43fa --- /dev/null +++ b/dcmppln/optimization/.ipynb_checkpoints/cplex_utils-checkpoint.py @@ -0,0 +1,144 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import os +import sys +import time +import logging +import numpy as np + +from docplex.mp.progress import ProgressListener, ProgressClock, SolutionListener + +def append_to_log(log_path, msg): + with open(log_path, "a") as f: + f.write(msg) + +def write_to_log(log_path, msg): + with open(log_path, "w") as f: + f.write(msg) + +class BestBoundAborter(ProgressListener): + """Custom aborter to stop when finding a feasible solution matching the bound. + see: https://ibmdecisionoptimization.github.io/docplex-doc/mp/_modules/docplex/mp/progress.html#ProgressClock + https://dataplatform.cloud.ibm.com/exchange/public/entry/view/6e2bffa5869dacbae6500c7037ecd36f + + usage: + from docplex.mp.model_reader import ModelReader + mdl = ModelReader.read(path, ignore_names=False) + log_path = "path_to_log_results.log" + log_file_obj = open(log_path, "w") + mdl.add_progress_listener( + BestBoundAborter(max_best_bound=stops_at_value, log_file_obj=log_file_obj) + ) + """ + + def __init__( + self, max_best_bound: float = 0, minimize: bool = False, log_file_obj=None + ): + super(BestBoundAborter, self).__init__(ProgressClock.BestBound) + self.max_best_bound = max_best_bound + self.last_obj = None + self.minimize = minimize + self.log_file_obj = log_file_obj + + def notify_start(self): + super(BestBoundAborter, self).notify_start() + self.last_obj = None + + def stopping_condition(self): + if self.minimize: + return self.last_obj <= self.max_best_bound + else: + return self.last_obj >= self.max_best_bound + + def notify_progress(self, pdata): + super(BestBoundAborter, self).notify_progress(pdata) + if pdata.has_incumbent: + self.last_obj = pdata.current_objective + if self.stopping_condition(): + msg = f"_____ FOUND Feasible solution {self.last_obj} better than stopping condition {self.max_best_bound}\n" + if self.log_file_obj: + self.log_file_obj.write(msg) + else: + print(msg) + self.abort() + + +class BestFeasibleLogger(ProgressListener): + """ + Custom logger of time and new best feasible solution in a separate file + see: https://ibmdecisionoptimization.github.io/docplex-doc/mp/_modules/docplex/mp/progress.html#ProgressClock + https://dataplatform.cloud.ibm.com/exchange/public/entry/view/6e2bffa5869dacbae6500c7037ecd36f + log format csv: time,feasible_solution,best_bound + + from docplex.mp.model_reader import ModelReader + mdl = ModelReader.read(path, ignore_names=False) + log_path = "path_to_log_results.log" + mdl.add_progress_listener( + BestFeasibleLogger(log_path=log_path) + ) + """ + + def __init__( + self, log_path=None + ): + super().__init__(ProgressClock.Gap) + self.last_obj = None + self.log_path = log_path + if self.log_path: + write_to_log(log_path, "time,feasible_solution,best_bound\n") + + def notify_start(self): + super().notify_start() + self.last_obj = None + + def is_improving(self, new_obj, eps=1e-4): + last_obj = self.last_obj + return last_obj is None or (abs(new_obj- last_obj) >= eps) + + def notify_progress(self, pdata): + super().notify_progress(pdata) + if pdata.has_incumbent and self.is_improving(pdata.current_objective): + self.last_obj = pdata.current_objective + msg = f"{pdata.time},{self.last_obj},{pdata.best_bound}\n" + if self.log_path: + append_to_log(self.log_path, msg) + else: + print(msg) + + +class FirstNodeAborter(ProgressListener): + """ + Abort at the first node after the root relaxation + see: https://ibmdecisionoptimization.github.io/docplex-doc/mp/_modules/docplex/mp/progress.html#ProgressClock + https://dataplatform.cloud.ibm.com/exchange/public/entry/view/6e2bffa5869dacbae6500c7037ecd36f + log format csv: time,feasible_solution,best_bound + + from docplex.mp.model_reader import ModelReader + mdl = ModelReader.read(path, ignore_names=False) + log_path = "path_to_log_results.log" + mdl.add_progress_listener( + BestFeasibleLogger(log_path=log_path) + ) + """ + + def __init__( + self + ): + super().__init__(ProgressClock.Gap) + self.last_obj = None + + def notify_start(self): + super().notify_start() + self.last_obj = None + + def is_improving(self, new_obj, eps=1e-4): + last_obj = self.last_obj + return last_obj is None or (abs(new_obj- last_obj) >= eps) + + def notify_progress(self, pdata): + super().notify_progress(pdata) + if pdata.has_incumbent and self.is_improving(pdata.current_objective): + self.abort() \ No newline at end of file diff --git a/dcmppln/optimization/.ipynb_checkpoints/portfolio_optimization_runtime-checkpoint.py b/dcmppln/optimization/.ipynb_checkpoints/portfolio_optimization_runtime-checkpoint.py new file mode 100644 index 0000000..e6f2101 --- /dev/null +++ b/dcmppln/optimization/.ipynb_checkpoints/portfolio_optimization_runtime-checkpoint.py @@ -0,0 +1,153 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import time +from typing import Union +import os +import numpy as np +from docplex.mp.model import Model + +from dcmppln.utils.portfolio_model import construct_markovitz_model +from dcmppln.optimization.cplex_utils import BestFeasibleLogger, append_to_log + +from dcmppln.CONSTANT import TIMEOUT,LOG_FOLDER, SAVE_LP_FILE + + +def optimize_and_measure_time(model): + start_time = time.perf_counter() + tp_start = time.process_time() + + # Solve the model + solution = model.solve() + + ## End time + tp_stop = time.process_time() + end_time = time.perf_counter() + execution_time = end_time - start_time + processing_time = tp_stop - tp_start + return solution, execution_time, processing_time + + +def add_logger(model, unique_identifier=None): + log_path = None + if unique_identifier is not None: + unique_identifier_base, partition_id = unique_identifier.split("_") + log_path = os.path.join(LOG_FOLDER, unique_identifier_base, f"best_feasible_{unique_identifier}.csv") + model.add_progress_listener(BestFeasibleLogger(log_path=log_path)) + if SAVE_LP_FILE: + lp_path = os.path.join(LOG_FOLDER, unique_identifier_base, f"{unique_identifier}.lp") + model.export_as_lp(lp_path) + return log_path + + +def base_markovitz_portfolio_optimization_time( + returns, + covariance_matrix, + target_return=None, + risk_factor=None, + budget=None, + return_sol=False, + mip_margin=None, + model_name="Markowitz Portfolio Optimization", + continuous_variables_flag=False, + timeout=TIMEOUT, + unique_identifier = None, + log_best_feasible = False, + additional_listerners = [] +): + """ + Trick if the budget is <0 we drop the constraint + unique_identifier: hex+"_{partition_number}" + """ + n = len(returns) # Number of assets + + if not budget: + budget = n // 2 + + ## q: Risk factor + if not risk_factor: + risk_factor = 0.5 + + model, weights = construct_markovitz_model( + returns, + covariance_matrix, + name=model_name, + budget=budget, + risk_factor=risk_factor, + continuous_variables=continuous_variables_flag, + ) + + if mip_margin is not None: + model.parameters.mip.tolerances.mipgap.set(mip_margin) + + model.set_time_limit(timeout) + if log_best_feasible: + log_path = add_logger(model, unique_identifier=unique_identifier) + + for listerner in additional_listerners: + model.add_progress_listener(listerner) + + # Solve the model + solution, execution_time, processing_time = optimize_and_measure_time(model) + + if (unique_identifier is not None) and log_best_feasible: + msg = f"{solution.solve_details.time},{solution.objective_value},{solution.solve_details.best_bound}\n" + append_to_log(log_path, msg) + + # Retrieve the optimal weights + optimal_weights = [solution.get_value(w) for w in weights] + + if return_sol: + return optimal_weights, execution_time, processing_time + else: + return execution_time, processing_time + + +def continuous_markowitz_portfolio_optimization_time( + returns, + covariance_matrix, + target_return=None, + risk_factor=None, + budget=None, + return_sol=False, + mip_margin=None, +): + return base_markovitz_portfolio_optimization_time( + returns, + covariance_matrix, + target_return=target_return, + risk_factor=risk_factor, + budget=budget, + return_sol=return_sol, + mip_margin=mip_margin, + model_name="continuous Markowitz Portfolio Optimization", + continuous_variables_flag=True, + ) + + +############################################################################################### + + +def Boolean_markowitz_portfolio_optimization_time( + returns, + covariance_matrix, + target_return=None, + risk_factor=None, + budget=None, + return_sol=False, + mip_margin=None, + model_name="", +): + return base_markovitz_portfolio_optimization_time( + returns, + covariance_matrix, + target_return=target_return, + risk_factor=risk_factor, + budget=budget, + return_sol=return_sol, + mip_margin=mip_margin, + model_name=model_name, + continuous_variables_flag=False, + ) diff --git a/dcmppln/optimization/__init__.py b/dcmppln/optimization/__init__.py new file mode 100644 index 0000000..65eb3d0 --- /dev/null +++ b/dcmppln/optimization/__init__.py @@ -0,0 +1,5 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### \ No newline at end of file diff --git a/dcmppln/optimization/__pycache__/__init__.cpython-311.pyc b/dcmppln/optimization/__pycache__/__init__.cpython-311.pyc new file mode 100644 index 0000000..3b80c21 Binary files /dev/null and b/dcmppln/optimization/__pycache__/__init__.cpython-311.pyc differ diff --git a/dcmppln/optimization/__pycache__/__init__.cpython-39.pyc b/dcmppln/optimization/__pycache__/__init__.cpython-39.pyc new file mode 100644 index 0000000..d94337c Binary files /dev/null and b/dcmppln/optimization/__pycache__/__init__.cpython-39.pyc differ diff --git a/dcmppln/optimization/__pycache__/cplex_utils.cpython-311.pyc b/dcmppln/optimization/__pycache__/cplex_utils.cpython-311.pyc new file mode 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b/dcmppln/optimization/__pycache__/portfolio_optimization_runtime.cpython-39.pyc differ diff --git a/dcmppln/optimization/cplex_utils.py b/dcmppln/optimization/cplex_utils.py new file mode 100644 index 0000000..5af43fa --- /dev/null +++ b/dcmppln/optimization/cplex_utils.py @@ -0,0 +1,144 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import os +import sys +import time +import logging +import numpy as np + +from docplex.mp.progress import ProgressListener, ProgressClock, SolutionListener + +def append_to_log(log_path, msg): + with open(log_path, "a") as f: + f.write(msg) + +def write_to_log(log_path, msg): + with open(log_path, "w") as f: + f.write(msg) + +class BestBoundAborter(ProgressListener): + """Custom aborter to stop when finding a feasible solution matching the bound. + see: https://ibmdecisionoptimization.github.io/docplex-doc/mp/_modules/docplex/mp/progress.html#ProgressClock + https://dataplatform.cloud.ibm.com/exchange/public/entry/view/6e2bffa5869dacbae6500c7037ecd36f + + usage: + from docplex.mp.model_reader import ModelReader + mdl = ModelReader.read(path, ignore_names=False) + log_path = "path_to_log_results.log" + log_file_obj = open(log_path, "w") + mdl.add_progress_listener( + BestBoundAborter(max_best_bound=stops_at_value, log_file_obj=log_file_obj) + ) + """ + + def __init__( + self, max_best_bound: float = 0, minimize: bool = False, log_file_obj=None + ): + super(BestBoundAborter, self).__init__(ProgressClock.BestBound) + self.max_best_bound = max_best_bound + self.last_obj = None + self.minimize = minimize + self.log_file_obj = log_file_obj + + def notify_start(self): + super(BestBoundAborter, self).notify_start() + self.last_obj = None + + def stopping_condition(self): + if self.minimize: + return self.last_obj <= self.max_best_bound + else: + return self.last_obj >= self.max_best_bound + + def notify_progress(self, pdata): + super(BestBoundAborter, self).notify_progress(pdata) + if pdata.has_incumbent: + self.last_obj = pdata.current_objective + if self.stopping_condition(): + msg = f"_____ FOUND Feasible solution {self.last_obj} better than stopping condition {self.max_best_bound}\n" + if self.log_file_obj: + self.log_file_obj.write(msg) + else: + print(msg) + self.abort() + + +class BestFeasibleLogger(ProgressListener): + """ + Custom logger of time and new best feasible solution in a separate file + see: https://ibmdecisionoptimization.github.io/docplex-doc/mp/_modules/docplex/mp/progress.html#ProgressClock + https://dataplatform.cloud.ibm.com/exchange/public/entry/view/6e2bffa5869dacbae6500c7037ecd36f + log format csv: time,feasible_solution,best_bound + + from docplex.mp.model_reader import ModelReader + mdl = ModelReader.read(path, ignore_names=False) + log_path = "path_to_log_results.log" + mdl.add_progress_listener( + BestFeasibleLogger(log_path=log_path) + ) + """ + + def __init__( + self, log_path=None + ): + super().__init__(ProgressClock.Gap) + self.last_obj = None + self.log_path = log_path + if self.log_path: + write_to_log(log_path, "time,feasible_solution,best_bound\n") + + def notify_start(self): + super().notify_start() + self.last_obj = None + + def is_improving(self, new_obj, eps=1e-4): + last_obj = self.last_obj + return last_obj is None or (abs(new_obj- last_obj) >= eps) + + def notify_progress(self, pdata): + super().notify_progress(pdata) + if pdata.has_incumbent and self.is_improving(pdata.current_objective): + self.last_obj = pdata.current_objective + msg = f"{pdata.time},{self.last_obj},{pdata.best_bound}\n" + if self.log_path: + append_to_log(self.log_path, msg) + else: + print(msg) + + +class FirstNodeAborter(ProgressListener): + """ + Abort at the first node after the root relaxation + see: https://ibmdecisionoptimization.github.io/docplex-doc/mp/_modules/docplex/mp/progress.html#ProgressClock + https://dataplatform.cloud.ibm.com/exchange/public/entry/view/6e2bffa5869dacbae6500c7037ecd36f + log format csv: time,feasible_solution,best_bound + + from docplex.mp.model_reader import ModelReader + mdl = ModelReader.read(path, ignore_names=False) + log_path = "path_to_log_results.log" + mdl.add_progress_listener( + BestFeasibleLogger(log_path=log_path) + ) + """ + + def __init__( + self + ): + super().__init__(ProgressClock.Gap) + self.last_obj = None + + def notify_start(self): + super().notify_start() + self.last_obj = None + + def is_improving(self, new_obj, eps=1e-4): + last_obj = self.last_obj + return last_obj is None or (abs(new_obj- last_obj) >= eps) + + def notify_progress(self, pdata): + super().notify_progress(pdata) + if pdata.has_incumbent and self.is_improving(pdata.current_objective): + self.abort() \ No newline at end of file diff --git a/dcmppln/optimization/portfolio_optimization_runtime.py b/dcmppln/optimization/portfolio_optimization_runtime.py new file mode 100644 index 0000000..e6f2101 --- /dev/null +++ b/dcmppln/optimization/portfolio_optimization_runtime.py @@ -0,0 +1,153 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import time +from typing import Union +import os +import numpy as np +from docplex.mp.model import Model + +from dcmppln.utils.portfolio_model import construct_markovitz_model +from dcmppln.optimization.cplex_utils import BestFeasibleLogger, append_to_log + +from dcmppln.CONSTANT import TIMEOUT,LOG_FOLDER, SAVE_LP_FILE + + +def optimize_and_measure_time(model): + start_time = time.perf_counter() + tp_start = time.process_time() + + # Solve the model + solution = model.solve() + + ## End time + tp_stop = time.process_time() + end_time = time.perf_counter() + execution_time = end_time - start_time + processing_time = tp_stop - tp_start + return solution, execution_time, processing_time + + +def add_logger(model, unique_identifier=None): + log_path = None + if unique_identifier is not None: + unique_identifier_base, partition_id = unique_identifier.split("_") + log_path = os.path.join(LOG_FOLDER, unique_identifier_base, f"best_feasible_{unique_identifier}.csv") + model.add_progress_listener(BestFeasibleLogger(log_path=log_path)) + if SAVE_LP_FILE: + lp_path = os.path.join(LOG_FOLDER, unique_identifier_base, f"{unique_identifier}.lp") + model.export_as_lp(lp_path) + return log_path + + +def base_markovitz_portfolio_optimization_time( + returns, + covariance_matrix, + target_return=None, + risk_factor=None, + budget=None, + return_sol=False, + mip_margin=None, + model_name="Markowitz Portfolio Optimization", + continuous_variables_flag=False, + timeout=TIMEOUT, + unique_identifier = None, + log_best_feasible = False, + additional_listerners = [] +): + """ + Trick if the budget is <0 we drop the constraint + unique_identifier: hex+"_{partition_number}" + """ + n = len(returns) # Number of assets + + if not budget: + budget = n // 2 + + ## q: Risk factor + if not risk_factor: + risk_factor = 0.5 + + model, weights = construct_markovitz_model( + returns, + covariance_matrix, + name=model_name, + budget=budget, + risk_factor=risk_factor, + continuous_variables=continuous_variables_flag, + ) + + if mip_margin is not None: + model.parameters.mip.tolerances.mipgap.set(mip_margin) + + model.set_time_limit(timeout) + if log_best_feasible: + log_path = add_logger(model, unique_identifier=unique_identifier) + + for listerner in additional_listerners: + model.add_progress_listener(listerner) + + # Solve the model + solution, execution_time, processing_time = optimize_and_measure_time(model) + + if (unique_identifier is not None) and log_best_feasible: + msg = f"{solution.solve_details.time},{solution.objective_value},{solution.solve_details.best_bound}\n" + append_to_log(log_path, msg) + + # Retrieve the optimal weights + optimal_weights = [solution.get_value(w) for w in weights] + + if return_sol: + return optimal_weights, execution_time, processing_time + else: + return execution_time, processing_time + + +def continuous_markowitz_portfolio_optimization_time( + returns, + covariance_matrix, + target_return=None, + risk_factor=None, + budget=None, + return_sol=False, + mip_margin=None, +): + return base_markovitz_portfolio_optimization_time( + returns, + covariance_matrix, + target_return=target_return, + risk_factor=risk_factor, + budget=budget, + return_sol=return_sol, + mip_margin=mip_margin, + model_name="continuous Markowitz Portfolio Optimization", + continuous_variables_flag=True, + ) + + +############################################################################################### + + +def Boolean_markowitz_portfolio_optimization_time( + returns, + covariance_matrix, + target_return=None, + risk_factor=None, + budget=None, + return_sol=False, + mip_margin=None, + model_name="", +): + return base_markovitz_portfolio_optimization_time( + returns, + covariance_matrix, + target_return=target_return, + risk_factor=risk_factor, + budget=budget, + return_sol=return_sol, + mip_margin=mip_margin, + model_name=model_name, + continuous_variables_flag=False, + ) diff --git a/dcmppln/optimizer.py b/dcmppln/optimizer.py new file mode 100644 index 0000000..c253684 --- /dev/null +++ b/dcmppln/optimizer.py @@ -0,0 +1,253 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from abc import ABC, abstractmethod + +from dcmppln.optimization.cplex_utils import append_to_log +from dcmppln.optimization.portfolio_optimization_runtime import ( + add_logger, + optimize_and_measure_time, +) +from dcmppln.utils.utils import get_instance_non_private_attributes + +from dcmppln.CONSTANT import LOG_FOLDER, TIMEOUT, DEFAULT_MIP_GAP + +from memory_profiler import profile +import numpy as np + +#from dwave.samplers import SimulatedAnnealingSampler + +# Gurobi imports +from gurobipy import GRB, Env, read +import os +import time + + +class Optimizer(ABC): + """ + Abstract class of optimizer component + Must overwrite and match the call function signature + """ + + def __init__(self): + self.name = self.__class__.__name__ + + @abstractmethod + def __call__( + self, + model, + weights, + unique_identifier=None, + log_best_feasible=False, + ): + """ + Example signature and return overwriting methods needs to follow + :param model: docplex model + :param weights: docplex variable + :param unique_identifier: hex id of the optimization + :param log_best_feasible: log best feasible solutions + :return: + """ + optimal_weights = np.zeros(10) + execution_time = 1 + processing_time = 1 + return optimal_weights, execution_time, processing_time + + +class CplexOptimizer(Optimizer): + """ + Warning mip_margin=0 actually means default = 1-e4 + """ + + def __init__( + self, + mip_margin=DEFAULT_MIP_GAP, + continuous_variables_flag=False, + timeout=TIMEOUT, + additional_listeners=None, + qtolin=-1, + ): + super().__init__() + if additional_listeners is None: + additional_listeners = list() + self.mip_margin = mip_margin + self.continuous_variables_flag = continuous_variables_flag + self.timeout = timeout + self.qtolin = qtolin + self._additional_listeners = additional_listeners + self.additional_listeners_name = [ + callback.__class__.__name__ for callback in self._additional_listeners + ] + self._cplex_parameters = { + "preprocessing.qtolin": qtolin, + "mip.tolerances.mipgap": mip_margin, + "timelimit": TIMEOUT, + } + self.params = get_instance_non_private_attributes(self) + + def __call__(self, model, weights, unique_identifier=None, log_best_feasible=False): + return self.optimize( + model, + weights, + unique_identifier=unique_identifier, + log_best_feasible=log_best_feasible, + ) + + def optimize( + self, + model, + weights, + unique_identifier=None, + log_best_feasible=False, + ): + for key, val in self._cplex_parameters.items(): + # set timeout, mipgap, qtolin + model.parameters.set_from_qualified_name(key, val) + + if log_best_feasible: + log_path = add_logger(model, unique_identifier=unique_identifier) + + for listerner in self._additional_listeners: + model.add_progress_listener(listerner) + + # Solve the model + solution, execution_time, processing_time = optimize_and_measure_time(model) + + if (unique_identifier is not None) and log_best_feasible: + msg = f"{solution.solve_details.time},{solution.objective_value},{solution.solve_details.best_bound}\n" + append_to_log(log_path, msg) + + # Retrieve the optimal weights + optimal_weights = [solution.get_value(w) for w in weights] + # Retrieve the optimal weights + + return optimal_weights, execution_time, processing_time + + +class SimulatedAnnealingDwave(Optimizer): + def __init__(self, **kwargs): + """ + :param kwargs: hyperparamters to pass to SimulatedAnnealingSampler at sample time + """ + super().__init__() + self.optimizer_parameters = kwargs + self.params = get_instance_non_private_attributes(self) + + def __call__( + self, model, weights=None, unique_identifier=None, log_best_feasible=False + ): + """ + :param model: docplex model + :param weights: docplex variable + :param unique_identifier: hex id of the optimization + :param log_best_feasible: bool log best feasible solutions + :return: + """ + # convert to QUBO with penalty + cqm = dimod.lp.loads(model.lp_string) + bqm, invert = dimod.cqm_to_bqm(cqm) + sampler = SimulatedAnnealingSampler() + + start_time = time.perf_counter() + tp_start = time.process_time() + + # Solve the model + sampleset = sampler.sample(bqm, **self.optimizer_parameters) + + ## End time + tp_stop = time.process_time() + end_time = time.perf_counter() + execution_time = end_time - start_time + processing_time = tp_stop - tp_start + + values_dict = sampleset.first.sample + if cqm.check_feasible(sampleset.first.sample): + best_sample_weights = [ + values_dict[f"weights_{i}"] for i in range(len(values_dict)) + ] + else: + # Attempted default value + best_sample_weights = [0 for i in range(len(values_dict))] + return best_sample_weights, execution_time, processing_time + + +class GurobiOptimizer(Optimizer): + def __init__(self, **kwargs): + """ + :param kwargs: hyperparamters to pass to model see https://www.gurobi.com/documentation/10.0/refman/parameters.html#sec:Parameters + """ + super().__init__() + self.optimizer_parameters = { + "TimeLimit": TIMEOUT, + "MIPGap": DEFAULT_MIP_GAP, + "Threads": 1, + } + self.name = self.__class__.__name__ + self.params = get_instance_non_private_attributes(self) + + def __call__( + self, model, weights=None, unique_identifier=None, log_best_feasible=False + ): + """ + :param model: docplex model + :param weights: docplex variable + :param unique_identifier: hex id of the optimization + :param log_best_feasible: bool log best feasible solutions + :return: + """ + with open("temp.lp", "w") as f: + f.write(model.lp_string.replace("*", " * ")) + + m = read("temp.lp") + for param, values in self.optimizer_parameters.items(): + m.setParam(param, values) + + start_time = time.perf_counter() + tp_start = time.process_time() + + # Solve the model + m.optimize() + + ## End time + tp_stop = time.process_time() + end_time = time.perf_counter() + execution_time = end_time - start_time + processing_time = tp_stop - tp_start + + solution = np.array( + [m.getVarByName(f"weights_{i}").X for i in range(m.NumVars)] + ) + solution = solution + 0.0 # trick to avoid -0.0 values + return solution, execution_time, processing_time + + +class DummyOptimizer(Optimizer): + """ + Dummy optimizer to test other component, return only zeros + """ + + def __init__(self): + super().__init__() + self.params = get_instance_non_private_attributes(self) + + def __call__( + self, + model, + weights, + unique_identifier=None, + log_best_feasible=False, + ): + """ + Example signature and return overwriting methods needs to follow + :param model: docplex model + :param weights: docplex variable + :param unique_identifier: hex id of the optimization + :param log_best_feasible: log best feasible solutions + :return: + """ + optimal_weights = np.zeros(len(weights)) + execution_time = 0 + processing_time = 0 + return optimal_weights, execution_time, processing_time diff --git a/dcmppln/pipeline.py b/dcmppln/pipeline.py new file mode 100644 index 0000000..86e98d3 --- /dev/null +++ b/dcmppln/pipeline.py @@ -0,0 +1,205 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np +import json +import time +import os +from uuid import uuid4 + +from dcmppln.optimizer import Optimizer, DummyOptimizer +from dcmppln.utils.risk_rebalance import rebalancing_risk_factor +from dcmppln.utils.objective_after_clustering import optimize_and_score + +from dcmppln.denoiser import Denoiser +from dcmppln.clustering import Clustering +from dcmppln.filtering import Filtering + +from dcmppln.CONSTANT import LOG_FOLDER, TIMEOUT + + +class Pipeline: + """ + Structure to efficiently run a grid search on a list of Denoiser, Clustering, Filtering, Optimizer + main method is run which is 4 for loops + """ + + def __init__( + self, + correlation_martix: np.array, + covariance_matrix: np.array, + returns: float, + denoiser: Denoiser = Denoiser(active=False), + cluster: Clustering = Clustering(active=False), + filter_func: Filtering = Filtering(active=False), + optimize_func: Optimizer = DummyOptimizer(), + ): + self.correlation_matrix = correlation_martix + self.covariance_matrix = covariance_matrix + self.returns = returns + self.denoiser_func = denoiser + self.cluster_func = cluster + self.filter_func = filter_func + self.optimize_func = optimize_func + self.__state__ = dict() + + def run( + self, + risk_rebalancing=False, + cluster_on_correlation=True, + optimize_on_correlation=False, + log_best_feasible=False, + input_risk_factor=1, + run_optimizer=False, + ): + """ + Method to run the grid search over the components (one inner for loop per component) and get score metrics + + :param risk_rebalancing: bool to use risk rebalancing technique + :param cluster_on_correlation: bool to cluster on correlation else cluster on covariance + :param optimize_on_correlation: bool to optimize on correlation else cluster on covariance + :param log_best_feasible: bool to log best feasible solution (only available on CPLEX optimizer for now) + :param cardinality_divider: set cardinality constraint to len(returns)//cardinality_divider + :param input_risk_factor: risk factor in the markovitz optimization + :return: dict with key being input and component parameters and values the score, run times etc... + """ + + matrix_to_cluster = ( + self.correlation_matrix + if cluster_on_correlation + else self.covariance_matrix + ) + matrix_to_optimize = ( + self.correlation_matrix + if optimize_on_correlation + else self.covariance_matrix + ) + + C_2, denoise_process_time, denoise_time = self.denoiser_func(matrix_to_cluster) + partitions, clustering_process_time, clustering_time = self.cluster_func(C_2) + + + # ----------------------------------------------------------------------------------------- + + unique_identifier_base = uuid4().hex + if log_best_feasible: + log_sub_dir = os.path.join(LOG_FOLDER, unique_identifier_base) + os.mkdir(log_sub_dir) + risk_factor = input_risk_factor + if run_optimizer: + return self.optimize_and_score( + matrix_to_cluster, + matrix_to_optimize, + partitions, + risk_factor, + unique_identifier_base, + log_best_feasible, + cluster_on_correlation, + optimize_on_correlation, + denoise_process_time, + clustering_process_time, + denoise_time, + clustering_time, + risk_rebalancing=False, + cardinality_divider=2, + ) + return C_2, partitions + + def optimize_and_score( + self, + matrix_to_cluster, + matrix_to_optimize, + partitions, + risk_factor, + unique_identifier_base, + log_best_feasible, + cluster_on_correlation, + optimize_on_correlation, + denoise_process_time, + clustering_process_time, + denoise_time, + clustering_time, + risk_rebalancing=False, + cardinality_divider=2, + ): + ( + obj_after_clustering, + full_recombined_soln, + opt_and_filter_process_time_dict, + opt_and_filter_time_dict, + ) = optimize_and_score( + self.returns, + matrix_to_optimize, + partitions, + card_=len(self.returns) // cardinality_divider, + input_risk_factor=risk_factor, + return_time=True, + filtering_method=self.filter_func, + optimizer=self.optimize_func, + rebalanced_risk_factor=( + rebalancing_risk_factor( + risk_factor, matrix_to_optimize, self.returns, partitions + ) + if risk_rebalancing + else None + ), + unique_identifier_base=unique_identifier_base, + log_best_feasible=log_best_feasible, + ) + opt_and_filter_process_time = sum(opt_and_filter_process_time_dict.values()) + opt_and_filter_time = sum(opt_and_filter_time_dict.values()) + + key_parameters = { + "denoiser": self.denoiser_func.params, + "clustering": self.cluster_func.params, + "filtering": self.filter_func.params, + "optimizer": self.optimize_func.params, + "problem_parameters": { + "problem_size": len(self.returns), + "cardinality_divider": cardinality_divider, + "input_risk_factor": risk_factor, + "cluster_on_correlation": cluster_on_correlation, + "optimize_on_correlation": optimize_on_correlation, + "risk_rebalancing": risk_rebalancing, + }, + } + self.__state__["key_parameters"] = key_parameters + run_time = { + "process_time": denoise_process_time + + clustering_process_time + + opt_and_filter_process_time, + "clock_time": denoise_time + clustering_time + opt_and_filter_time, + "process_times": [ + denoise_process_time, + clustering_process_time, + opt_and_filter_process_time, + ], + "clock_times": [ + denoise_time, + clustering_time, + opt_and_filter_time, + ], + "opt_process_times": opt_and_filter_process_time_dict, + "opt_clock_times": opt_and_filter_time_dict, + } + self.__state__["run_time"] = run_time + result = { + "score": obj_after_clustering, + "recombined_solution": full_recombined_soln, + "run_time": run_time, + "partitions": partitions, + "max_community_size_found": np.max( + np.unique(partitions, return_counts=True)[1] + ), + "size_of_partitions": np.unique(partitions, return_counts=True)[1], + "unique_identifier_base": unique_identifier_base, + "total_input_size": len(self.returns), + } + self.__state__["result"] = result + + return { + "score": obj_after_clustering, + "recombined_solution": full_recombined_soln, + } diff --git a/dcmppln/utils/.ipynb_checkpoints/correlation_rmt-checkpoint.py b/dcmppln/utils/.ipynb_checkpoints/correlation_rmt-checkpoint.py new file mode 100644 index 0000000..2f5d688 --- /dev/null +++ b/dcmppln/utils/.ipynb_checkpoints/correlation_rmt-checkpoint.py @@ -0,0 +1,148 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import math +import numpy as np +from dcmppln.utils.utils import timeit +from joblib import Parallel, delayed + + +def marchenko_pastur_pdf(eigenvalue:np.ndarray, beta:float,sigma:float=1.0) -> np.ndarray: + """Used to characteize the eigenvalues that are predominantly noise induced + Parameters + ---------- + eigenvalue:np.ndarray + eignvalue of the covariance matrix + beta: float + number of rows/columns -> N_stocks/N_days + sigma: float + variance of the noise (between 0.0 and 1.0?) + + Returns + ------- + pdf : np.ndarray + distribution for each Stock + """ + lambda_plus = (sigma**2)*((1 + np.sqrt(beta)) ** 2) + lambda_minus = (sigma**2)*((1 - np.sqrt(beta)) ** 2) + pdf = np.sqrt(np.maximum(lambda_plus - eigenvalue, 0) * np.maximum(eigenvalue - lambda_minus, 0)) / (2 * np.pi * beta * eigenvalue*(sigma**2)) + return pdf + +def fit_marchenko_pastur(eigenvalues:np.ndarray, beta:float, q_fit:bool=True,sigma:float=0.5,steps=1000)-> float: + """ + Fit the Marchenko-Pastur distribution to the given eigenvalues. + Parameters: + ----------- + eigenvalues: numpy.ndarray + The eigenvalues of the covariance to fit. + beta: float + The ratio of the number of variables to the number of observations. (e.g. N_stocks/N_days) + sigma: float + The variance of the noise. + q_fit: bool + True or False whether fit is required for best beta or not + steps: int + Number of steps to perform better fit + + Returns + -------- + lambda_min: float + represent minimum eigenvalue + lambda_max: float + represent maximum eigenvalue + """ + if not q_fit: + sigma = 0.5 + lambda_min = (sigma**2)*((1 - np.sqrt(beta)) ** 2) + lambda_max = (sigma**2)*((1 + np.sqrt(beta)) ** 2) + return lambda_min, lambda_max + beta_best = _find_best_beta(eigenvalues, beta, sigma) + lambda_min = (sigma**2)*((1 - np.sqrt(beta_best)) ** 2) + lambda_max = (sigma**2)*((1 + np.sqrt(beta_best)) ** 2) + return lambda_min, lambda_max + +def _find_best_beta(eigenvalues:np.ndarray, beta:float, sigma:float=0.5, steps:int=1000) -> float: + """ + Function to derive best beta value by finding minumum of the mean distribution + Parameters + ---------- + eigenvalues: numpy.ndarray + The eigenvalues of the covariance to fit. + beta: float + The ratio of the number of variables to the number of observations. (e.g. N_stocks/N_days) + sigma: float + The variance of the noise. + steps: int + Number of steps to perform better fit + + Return + ------ + beta_best:float + best bit beta value based on steps + """ + if beta < 1: + beta_values = np.linspace(beta, 1, steps) + else: + beta_values = np.linspace(1, beta, steps) + + min = math.inf + for beta_ in beta_values[1:]: + # Compute the theoretical eigenvalue density + theoretical_pdf = marchenko_pastur_pdf(eigenvalues, beta_, sigma) + # Compute the mean squared error between the theoretical and sample eigenvalue density + mse = np.mean((theoretical_pdf - eigenvalues) ** 2) + if mse < min: + min = mse + beta_best=beta_ + return beta_best + +def split_covariance_matrices(C:np.ndarray, beta:float=0.5, q_fit:bool=True): + """ + This function split the covariance matrices to C_Noise, C_Star, C_Global + + Parameters + ---------- + C:np.ndarray + Input correlation matrix + beta:float + ratio of number of variables to number of observations. e.g. N_stocks/N_days + q_fit:bool + True or False whether fit is required for best beta or not + Return + C_Noise:np.ndarray + a split of correlation matrices whose eignevalues are at most lambda_max + C_Star:np.ndarray + a split of correlation matrices whose eigenvalues between lambda_max and largest lambda + C_Global:np.ndarray + a split of correlation matrices , Lambda1 which is typically much greater than lambda+ + """ + eigenvalues, eigenvectors = np.linalg.eigh(C) + eigenvalues = eigenvalues[::-1] # Reverse order sorting + eigenvectors = eigenvectors[:, ::-1] # Reverse order sorting + + lambda_min, lambda_max = fit_marchenko_pastur(eigenvalues[1:],beta, q_fit=True) + # Split the eigenvalues and eigenvectors into three regimes. + # First regime has eigenvalues less than lambda_max. + eigenvalues_1 = eigenvalues[eigenvalues < lambda_max] + eigenvectors_1 = eigenvectors[:, eigenvalues < lambda_max] + + # Second regime has eigenvalues between lambda_max and the largest lambda. + eigenvalues_2 = eigenvalues[ + (eigenvalues >= lambda_max) & (eigenvalues < eigenvalues[0]) + ] + eigenvectors_2 = eigenvectors[ + :, (eigenvalues >= lambda_max) & (eigenvalues < eigenvalues[0]) + ] + + # Third regime is the eigenvalue greater than lambda_max. + eigenvalues_3 = eigenvalues[0] + eigenvectors_3 = eigenvectors[:, 0] + + # Compute the correlation matrices for each regime + C_Noise = eigenvectors_1 @ np.diag(eigenvalues_1) @ eigenvectors_1.T # CNoise + C_Star = eigenvectors_2 @ np.diag(eigenvalues_2) @ eigenvectors_2.T # C* + C_Global = eigenvectors_3 * eigenvalues_3 * eigenvectors_3.T #CGlobal + + return C_Noise, C_Star, C_Global diff --git a/dcmppln/utils/.ipynb_checkpoints/k_means-checkpoint.py b/dcmppln/utils/.ipynb_checkpoints/k_means-checkpoint.py new file mode 100644 index 0000000..40f4b6c --- /dev/null +++ b/dcmppln/utils/.ipynb_checkpoints/k_means-checkpoint.py @@ -0,0 +1,207 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from scipy.linalg import eigh +from sklearn.cluster import KMeans +import numpy as np + +def silhouette_score(X, labels): + """ + X: data matrix + """ + score = 0 + for i in range(len(X)): + a = 0 + b = 0 + for j in range(len(X)): + if labels[i] == labels[j]: + a += np.linalg.norm(X[i] - X[j]) + else: + b += np.linalg.norm(X[i] - X[j]) + if a < b: + score += 1 - a / b + else: + score += b / a - 1 + + return score / len(X) + + +#### Implementation for Calinski-Harabaz Score. +def calinski_harabaz_score(X, labels): + """ + Variance ratio criterion. + Between cluster dispersion divided by within cluster dispersion. + """ + n_samples = len(X) + n_labels = len(np.unique(labels)) + if n_labels == 1: + return 1.0 + mean = np.mean(X, axis=0) + B, W = 0, 0 + for k in range(n_labels): + cluster_k = X[labels == k] + mean_k = np.mean(cluster_k, axis=0) + B += len(cluster_k) * np.sum((mean_k - mean) ** 2) + W += np.sum((cluster_k - mean_k) ** 2) + return (B / W) * (n_samples - n_labels) / (n_labels - 1) + + +def correlation_to_distance_linear(correlation_matrix): + num_rows, num_cols = correlation_matrix.shape + distance_matrix = np.empty((num_rows, num_cols)) + + for i in range(num_rows): + for j in range(num_cols): + if i == j: + distance_matrix[i, j] = 0 + else: + distance_matrix[i, j] = (3 - correlation_matrix[i, j]) / 2 + + return distance_matrix + + +def correlation_to_distance(correlation_matrix): + num_rows, num_cols = correlation_matrix.shape + distance_matrix = np.empty((num_rows, num_cols)) + + for i in range(num_rows): + for j in range(num_cols): + distance_matrix[i, j] = np.sqrt(2 * (1 - correlation_matrix[i, j])) + + return distance_matrix + + +def k_means_sweep(C, k_values=None, corr=True, distance_type="non_linear"): + if corr and distance_type == "non_linear": + D = np.sqrt(2 * (1 - C)) + elif corr and distance_type == "linear": + D = correlation_to_distance_linear(C) + + else: + D = C + + if k_values is None: # If no k values are specified, sweep over a range of k values + k_values = range(1, len(D)) + + best_score = -1 + best_k = -1 + best_clustering = None + + for k in k_values: + kmeans = KMeans(n_clusters=k, n_init=5) + clustering = kmeans.fit_predict(D) + score = calinski_harabaz_score(D, clustering) + if score > best_score: + best_score = score + best_k = k + best_clustering = clustering + return best_k, best_clustering ## + + +######### This is with threshold or contraints on cluster sizes ################ +def k_means_sweep_threshold( + C, + k_values=None, + corr=True, + low_threshold=2, + up_threshold=30, + distance_type="non_linear", +): + if corr and distance_type == "linear": + D = correlation_to_distance_linear(C) + + if corr and distance_type == "non_linear": + D = correlation_to_distance_linear(C) + + else: + D = C + + best_score = -1 + best_k = -1 + best_clustering = None + + if k_values is None: + k_values = range(1, len(D)) + + for k in k_values: + kmeans = KMeans( + n_clusters=k, n_init=5 + ) + clustering = kmeans.fit_predict(D) + + # Calculate the size of each cluster + cluster_sizes = np.bincount(clustering) + + # Check if all clusters satisfy the constraints + if np.all(cluster_sizes >= low_threshold) and np.all( + cluster_sizes <= up_threshold + ): + score = calinski_harabaz_score(D, clustering) + if score > best_score: + best_score = score + best_k = k + best_clustering = clustering + + return best_k, best_clustering + + +######### This is with threshold or contraints on cluster sizes ################ +def k_means_laplacian_threshold( + C, k_values=None, low_threshold=2, up_threshold=30, num_eigenvectors=40 +): + cov_matrix = C.copy() + # Step 2: Compute Laplacian Matrix + n = cov_matrix.shape[0] + D = np.diag(np.sum(cov_matrix, axis=1)) + laplacian_matrix = D - cov_matrix + + # Step 3: Compute Eigenvectors + # Number of smallest eigenvectors to consider + eigenvalues, eigenvectors = eigh(laplacian_matrix, D, type=1) + + sorted_indices = np.argsort(eigenvalues) + eigenvectors[:, sorted_indices] + eigenvectors = eigenvectors.copy() + + # Step 4: Perform Clustering + + best_score = -1 + best_k = -1 + best_clustering = None + + if k_values is None: # If no k values are specified, sweep over a range of k values + k_values = range(1, n) + + found_soln = False + + for k in k_values: + for num_eigenvectors in np.arange(2, n): + # print(num_eigenvectors) + D = eigenvectors[:, 1:num_eigenvectors] + + kmeans = KMeans( + n_clusters=k, n_init=5 + ) + clustering = kmeans.fit_predict(D) + + # Calculate the size of each cluster + cluster_sizes = np.bincount(clustering) + + + # Check if all clusters satisfy the constraints + if np.all(cluster_sizes <= up_threshold): + found_soln = True + best_clustering = clustering + break + + if found_soln: + break + + if found_soln: + break + + + return best_k, best_clustering + diff --git a/dcmppln/utils/.ipynb_checkpoints/objective_after_clustering-checkpoint.py b/dcmppln/utils/.ipynb_checkpoints/objective_after_clustering-checkpoint.py new file mode 100644 index 0000000..dd4d2b7 --- /dev/null +++ b/dcmppln/utils/.ipynb_checkpoints/objective_after_clustering-checkpoint.py @@ -0,0 +1,299 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from dcmppln.optimization.portfolio_optimization_runtime import * +from dcmppln.optimizer import Optimizer, CplexOptimizer +from dcmppln.utils.utils import ( + compute_objective_value, + timeit, +) +from dcmppln.utils.portfolio_model import construct_markovitz_model + + +def get_model(returns, + covariance_matrix, + target_return=None, + risk_factor=None, + budget=None, + model_name="Markowitz Portfolio Optimization", + continuous_variables_flag=False): + """ + Generator docplex model from input data + target_return is not used in this version + :returns docplex model and list of variables for each asset + """ + n = len(returns) # Number of assets + + if not budget: + budget = n // 2 + + ## q: Risk factor + if not risk_factor: + risk_factor = 0.5 + + model, weights = construct_markovitz_model( + returns, + covariance_matrix, + name=model_name, + budget=budget, + risk_factor=risk_factor, + continuous_variables=continuous_variables_flag, + ) + return model, weights + + +def split_cardinality_constraint(list_of_int, cardinality): + """ + list_of_int: list of int of type [n1,...,nk] + cardinality: int to match + return list of int such that its sum = cardinality and relative weight of each int is somewhat preserved + """ + continuous_solution = np.array(list_of_int)*cardinality/np.sum(list_of_int) + integer_solution = np.floor(continuous_solution).astype(int) + increment = cardinality - np.sum(integer_solution) + + index_order_for_increase = np.argsort(list_of_int)[::-1] + + i = 0 + while increment > 0: + integer_solution[index_order_for_increase[i]] += 1 + increment -= 1 + i += 1 + assert sum(integer_solution) == cardinality + return integer_solution + + +def optimize_and_score( + full_returns_, + correlation_, + partitions_: Union[np.ndarray, list], + card_: int = 0, + input_risk_factor=1, + return_time=False, + filtering_method=None, + optimizer: Optimizer=None, + rebalanced_risk_factor = None, + unique_identifier_base = None, + log_best_feasible = False +): + """ + Each partition will be separated, filtered, solved separately, recombined, and score the combined solution + Poor copy paste of optimize_base during transition + :param full_returns_: returns vector + :param correlation_: correlation matrix + :param partitions_: partitions detected + :param card_: cardinality constraint value + :param input_risk_factor: base risk factor + :param return_time: bool to return measured runtimes + :param filtering_method: object of class Filtering + :param unique_identifier_base: unique identifier uuid4 in hex + :return: + """ + if optimizer is None: + optimizer = CplexOptimizer() + if filtering_method is None: + filtering_method = lambda x: x + if rebalanced_risk_factor is None: + rebalanced_risk_factor = input_risk_factor + + num_communities = len(np.unique(partitions_)) + unique_labels = np.unique(partitions_) + + if unique_identifier_base is None: + unique_identifiers = [None] * len(unique_labels) + else: + unique_identifiers = [unique_identifier_base+f"_{int(i)}" for i in unique_labels] + + communities_ = [] + for i, label in enumerate(unique_labels): + communities_.append( + np.argwhere(partitions_ == label).reshape( + -1, + ) + ) + partitions_[partitions_ == label] = i + + sorted_communities = sorted(communities_, key=lambda x: x.shape[0], reverse=True) + + full_dim = len(partitions_) + if not card_: + card_ = full_dim // 2 + + card_subproblems = split_cardinality_constraint(list(map(len, sorted_communities)), card_) + + assert card_subproblems.sum() == card_, "The sum of card doesn't match" + + weights_dict = {} + process_times_dict = {} + clock_time_dict = {} + + for i, card in enumerate(card_subproblems): + comm_indices = sorted_communities[i] + returns = full_returns_[comm_indices] + + community_correlation_matrix = filtering_method( + correlation_[comm_indices][:, comm_indices] + ) + + model, weights = get_model(returns, + community_correlation_matrix, + budget=card, + risk_factor=rebalanced_risk_factor,) + ( + optimal_weights, + clock_time, + processing_time, + ) = optimizer( + model, + weights, + unique_identifier = unique_identifiers[i], + log_best_feasible = log_best_feasible, + ) + + weights_dict[i] = optimal_weights + process_times_dict[i] = processing_time + clock_time_dict[i] = clock_time + + # Recombining solution to compare the objective later + full_recombined_soln = np.zeros(full_dim, dtype=type(weights_dict[0])) + + for i, comm in enumerate(sorted_communities): + full_recombined_soln[comm] = weights_dict[i] + + obj_after_clustering = compute_objective_value( + full_returns_, correlation_, full_recombined_soln, risk_factor=input_risk_factor + ) + + print("Objective value with this comm detection algorithm", obj_after_clustering) + print("Communities found at level 1: ", num_communities) + + if return_time: + return obj_after_clustering, full_recombined_soln, process_times_dict, clock_time_dict + else: + return obj_after_clustering, full_recombined_soln + + +def objective_after_clustering( + full_returns_, + correlation_, + partitions_: Union[np.ndarray, list], + community_detection_alg: str = "", + card_: int = 0, + k_max: int = 10, + input_risk_factor=0.5, + return_time=False, + filtering_method=None, + threshold_mratio=0, +): + """ + We do no q (risk apetite) rescaling here + + Parameters: + partitions: List or array indicating labels for each node + community_detection_alg: Type of community detection algorthm + card_= Cardinality constraint for the full problem + k_max: This argument is passed only for the spectral clustering algo + + Returns: + objective value + """ + + assert community_detection_alg in ( + "mod_spectral_full", + "mod_spectral_denoised", + "louvain_full", + "louvain_denoised", + "q_louvain_full", + "q_louvain_denoised", + "nx_louvain_full", + "nx_louvain_denoised", + "spectral_clustering", + "", + ), "The algorithm type has not been found" + if filtering_method is None: + filtering_method = sparsification_covariance_no_diag_mean_ratio + num_communities = len(np.unique(partitions_)) + unique_labels = np.unique(partitions_) + + communities_ = [] + for i, label in enumerate(unique_labels): + communities_.append( + np.argwhere(partitions_ == label).reshape( + -1, + ) + ) + partitions_[partitions_ == label] = i + + sorted_communities = sorted(communities_, key=lambda x: x.shape[0], reverse=True) + weights_by_count = [ + sorted_communities[i].shape[0] for i in range(len(sorted_communities)) + ] + weights_by_count = np.array(weights_by_count) / sum(weights_by_count) + + total_assets = len(partitions_) + full_dim = total_assets + if not card_: + card_ = total_assets // 2 + + card_subproblems = np.array([int(el * card_) for el in weights_by_count]) + ## Adding the extra cardinality constraint (if any) to the largest community + card_subproblems[0] = card_ - card_subproblems[1:].sum() + + assert card_subproblems.sum() == card_, "The sum of card doesn't match" + + ### Running the cplex routine + weights_dict = {} + process_times_dict = {} + for i, card in enumerate(card_subproblems): + comm_indices = sorted_communities[i] + returns = full_returns_[comm_indices] + + community_correlation_matrix = correlation_[comm_indices][:, comm_indices] + + #### THRESHOLD + if threshold_mratio: + community_correlation_matrix = filtering_method( + community_correlation_matrix, threshold_mratio=threshold_mratio + ) + ( + optimal_weights, + execution_time, + processing_time, + ) = Boolean_markowitz_portfolio_optimization_time( + returns, + community_correlation_matrix, + budget=card, + risk_factor=input_risk_factor, + return_sol=True, + ) + + weights_dict[i] = optimal_weights + process_times_dict[i] = processing_time + + total_processing_time = sum(process_times_dict.values()) + + ### Recombining solution to compare the objective later + full_recombined_soln = np.zeros(full_dim, dtype=type(weights_dict[0])) + + for i, comm in enumerate(sorted_communities): + full_recombined_soln[comm] = weights_dict[i] + + obj_after_clustering = compute_objective_value( + full_returns_, correlation_, full_recombined_soln, risk_factor=input_risk_factor + ) + + print("Objective value with this comm detection algorithm", obj_after_clustering[0]) + print("Communities found at level 1: ", num_communities) + + if return_time == False: + return obj_after_clustering, full_recombined_soln + else: + return obj_after_clustering, full_recombined_soln, total_processing_time + + + + + + diff --git a/dcmppln/utils/.ipynb_checkpoints/portfolio_model-checkpoint.py b/dcmppln/utils/.ipynb_checkpoints/portfolio_model-checkpoint.py new file mode 100644 index 0000000..4c40164 --- /dev/null +++ b/dcmppln/utils/.ipynb_checkpoints/portfolio_model-checkpoint.py @@ -0,0 +1,40 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from docplex.mp.model import Model + +def construct_markovitz_model( + returns, + covariance_matrix, + budget=None, + risk_factor=None, + name="Markowitz Portfolio Optimization", + continuous_variables=False, +): + """ + Trick if the budget is <0 we drop the constraint + """ + n = len(returns) # Number of assets + if budget is None: + budget = n // 2 + # q: Risk factor + if risk_factor is None: + risk_factor = 0.5 + + model = Model(name) + if continuous_variables: + weights = model.continuous_var_list(n, lb=0.0, ub=1.0, name="weights") + else: + weights = model.binary_var_list(n, name="weights") + portfolio_risk = model.sum( + risk_factor * weights[i] * covariance_matrix[i, j] * weights[j] + for i in range(n) + for j in range(n) + ) - model.sum(returns[i] * weights[i] for i in range(n)) + + if budget >= 0: + model.add_constraint(model.sum(weights[i] for i in range(n)) == budget) + model.minimize(portfolio_risk) + return model, weights diff --git a/dcmppln/utils/.ipynb_checkpoints/risk_rebalance-checkpoint.py b/dcmppln/utils/.ipynb_checkpoints/risk_rebalance-checkpoint.py new file mode 100644 index 0000000..97601c0 --- /dev/null +++ b/dcmppln/utils/.ipynb_checkpoints/risk_rebalance-checkpoint.py @@ -0,0 +1,42 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np + +def rebalancing_risk_factor(risk_factor, full_covariance, full_avg_return, communities): + + #### we have the full covariance matrix and the full average return vector, and the communities + + balanced_risk_factor = risk_factor + + ### calculate norm of the covariance matrix, and the norm of the average return vector + cov_norm = np.linalg.norm(full_covariance) + avg_return_norm = np.linalg.norm(full_avg_return) + + ### calculate the number of the masked covariance that only takes blocks corresponding to the communities + num_communities = len(communities) + + if type(communities) == dict: + pass + else: + communities = {k: v for k, v in enumerate(communities)} + + community_cov_norm = 0 + community_avg_return_norm = 0 + + for community_idx, community in communities.items(): + community = np.array([community]).astype(int) + community_covariance = full_covariance[np.ix_(community, community)] + community_avg_return = full_avg_return[community] + + ### calculate the norm of the covariance matrix and the norm of the average return vector + community_cov_norm += np.linalg.norm(community_covariance) + community_avg_return_norm += np.linalg.norm(community_avg_return) + + + ### add the ratio of the norms to the balanced risk factor + balanced_risk_factor *= (cov_norm/community_cov_norm)*(community_avg_return_norm/avg_return_norm) + + return balanced_risk_factor diff --git a/dcmppln/utils/.ipynb_checkpoints/utils-checkpoint.py b/dcmppln/utils/.ipynb_checkpoints/utils-checkpoint.py new file mode 100644 index 0000000..f5f25ca --- /dev/null +++ b/dcmppln/utils/.ipynb_checkpoints/utils-checkpoint.py @@ -0,0 +1,61 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from typing import Optional, Union +import numpy as np +import time + + +def get_instance_non_private_attributes(instance): + """ + instance: instance of a class + returns: all non private attributes and values in a dictionnary + """ + return { k:v for k,v in vars(instance).items() if k[0]!="_" } + + + +def compute_objective_value(returns, covariance, weights, risk_factor=0.5): + weights = np.array(weights).reshape(-1, 1) + term1 = risk_factor * weights.T @ covariance @ weights + term2 = returns @ weights + obj_value = term1 - term2 + return obj_value[0,0] # Convert single value array to scalar + + +def threshold_matrix(correlation_matrix, threshold=0.5): + binary_matrix = (correlation_matrix > threshold).astype(float) + return binary_matrix + + +def thresholding_not_touching_diag(correlation_matrix, threshold=0.5): + binary_matrix = threshold_matrix(correlation_matrix, threshold) + np.fill_diagonal(binary_matrix, np.diagonal(correlation_matrix)) + return binary_matrix + +def sparsification(correlation_matrix, threshold=0.5, allow_to_modify_diag=False): + sparse_matrix = np.where(correlation_matrix > threshold, correlation_matrix, 0) + if not allow_to_modify_diag: + np.fill_diagonal(sparse_matrix, np.diagonal(correlation_matrix)) + return sparse_matrix + +def sparsification_covariance_no_diag_mean_ratio(correlation_matrix, threshold_mratio=0.2): + correlation_matrix_without_diagonal = np.array(correlation_matrix) + np.fill_diagonal(correlation_matrix_without_diagonal, 0) + avg_weight_without_diagonal = np.mean(correlation_matrix_without_diagonal) + threshold = threshold_mratio * avg_weight_without_diagonal + sparse_matrix = sparsification(correlation_matrix, threshold) + return sparse_matrix + + +def timeit(func): + def wrapper(*args, **kwargs)-> Union[Optional[np.array], Optional[float], Optional[float]]: + start_process_time = time.perf_counter() + start_time = time.process_time() + result = func(*args, **kwargs) + end_time = time.perf_counter() - start_process_time + process_time = time.process_time() - start_time + return result, process_time, end_time + return wrapper \ No newline at end of file diff --git a/dcmppln/utils/.ipynb_checkpoints/variation_of_information-checkpoint.py b/dcmppln/utils/.ipynb_checkpoints/variation_of_information-checkpoint.py new file mode 100644 index 0000000..c89cc87 --- /dev/null +++ b/dcmppln/utils/.ipynb_checkpoints/variation_of_information-checkpoint.py @@ -0,0 +1,84 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +""" +We quantitatively measured the performance of our +method in terms of a metric known as Variation of Information (V I) [52, 53], which measures the entropy +difference between two partitions of the same network, +providing a rigorous way for us to quantify the similarity between the ‘true’ partition and the one identified +by our method. +""" + +import numpy as np + + +def variation_of_information(true_partition, partition): + """ + partition is a list of integers, where each integer represents a community for the corresponding node + """ + unique_labels = set(true_partition) + true_partition_dict = dict(zip(unique_labels, range(len(unique_labels)))) + true_partition = [ + true_partition_dict[true_partition[i]] for i in range(len(true_partition)) + ] + + unique_labels = set(partition) + partition_dict = dict(zip(unique_labels, range(len(unique_labels)))) + partition = [partition_dict[partition[i]] for i in range(len(partition))] + + true_partition = list(true_partition) + partition = list(partition) + + n_c1 = len(set(true_partition)) + + true_probability = [0.0 for i in range(n_c1)] + for i, cluster_label in enumerate(set(true_partition)): + # count number of nodes in each cluster + true_probability[i] = true_partition.count(cluster_label) / len(true_partition) + + entropy_true = 0.0 + for i in range(len(true_probability)): + entropy_true += -1.0 * true_probability[i] * np.log2(true_probability[i]) + + n_c2 = len(set(partition)) + partition_probability = [0.0 for i in range(n_c2)] + + for i, cluster_label in enumerate(set(partition)): + partition_probability[i] = partition.count(cluster_label) / len(partition) + + # Entropy associated with partition + entropy_partition = 0.0 + for i in range(len(partition_probability)): + entropy_partition += ( + -1.0 * partition_probability[i] * np.log2(partition_probability[i]) + ) + + # Compute mutual information between two clusterings + # mutual information is a symmetric measure + mutual_information = 0.0 + for i in range(n_c1): + for j in range(n_c2): + # count number of nodes in each cluster + n_ij = 0 + for k in range(len(true_partition)): + if true_partition[k] == i and partition[k] == j: + n_ij += 1 + if n_ij > 0: + mutual_information += ( + n_ij + / len(true_partition) + * np.log2( + (n_ij / len(true_partition)) + / (true_probability[i] * partition_probability[j]) + ) + ) + + # Compute variation of information + variation_of_information = ( + entropy_true + entropy_partition - 2.0 * mutual_information + ) + + return variation_of_information + diff --git a/dcmppln/utils/__pycache__/correlation_rmt.cpython-311.pyc b/dcmppln/utils/__pycache__/correlation_rmt.cpython-311.pyc new file mode 100644 index 0000000..fe39856 Binary files /dev/null and b/dcmppln/utils/__pycache__/correlation_rmt.cpython-311.pyc differ diff --git a/dcmppln/utils/__pycache__/correlation_rmt.cpython-39.pyc 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b/dcmppln/utils/correlation_rmt.py @@ -0,0 +1,148 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import math +import numpy as np +from dcmppln.utils.utils import timeit +from joblib import Parallel, delayed + + +def marchenko_pastur_pdf(eigenvalue:np.ndarray, beta:float,sigma:float=1.0) -> np.ndarray: + """Used to characteize the eigenvalues that are predominantly noise induced + Parameters + ---------- + eigenvalue:np.ndarray + eignvalue of the covariance matrix + beta: float + number of rows/columns -> N_stocks/N_days + sigma: float + variance of the noise (between 0.0 and 1.0?) + + Returns + ------- + pdf : np.ndarray + distribution for each Stock + """ + lambda_plus = (sigma**2)*((1 + np.sqrt(beta)) ** 2) + lambda_minus = (sigma**2)*((1 - np.sqrt(beta)) ** 2) + pdf = np.sqrt(np.maximum(lambda_plus - eigenvalue, 0) * np.maximum(eigenvalue - lambda_minus, 0)) / (2 * np.pi * beta * eigenvalue*(sigma**2)) + return pdf + +def fit_marchenko_pastur(eigenvalues:np.ndarray, beta:float, q_fit:bool=True,sigma:float=0.5,steps=1000)-> float: + """ + Fit the Marchenko-Pastur distribution to the given eigenvalues. + Parameters: + ----------- + eigenvalues: numpy.ndarray + The eigenvalues of the covariance to fit. + beta: float + The ratio of the number of variables to the number of observations. (e.g. N_stocks/N_days) + sigma: float + The variance of the noise. + q_fit: bool + True or False whether fit is required for best beta or not + steps: int + Number of steps to perform better fit + + Returns + -------- + lambda_min: float + represent minimum eigenvalue + lambda_max: float + represent maximum eigenvalue + """ + if not q_fit: + sigma = 0.5 + lambda_min = (sigma**2)*((1 - np.sqrt(beta)) ** 2) + lambda_max = (sigma**2)*((1 + np.sqrt(beta)) ** 2) + return lambda_min, lambda_max + beta_best = _find_best_beta(eigenvalues, beta, sigma) + lambda_min = (sigma**2)*((1 - np.sqrt(beta_best)) ** 2) + lambda_max = (sigma**2)*((1 + np.sqrt(beta_best)) ** 2) + return lambda_min, lambda_max + +def _find_best_beta(eigenvalues:np.ndarray, beta:float, sigma:float=0.5, steps:int=1000) -> float: + """ + Function to derive best beta value by finding minumum of the mean distribution + Parameters + ---------- + eigenvalues: numpy.ndarray + The eigenvalues of the covariance to fit. + beta: float + The ratio of the number of variables to the number of observations. (e.g. N_stocks/N_days) + sigma: float + The variance of the noise. + steps: int + Number of steps to perform better fit + + Return + ------ + beta_best:float + best bit beta value based on steps + """ + if beta < 1: + beta_values = np.linspace(beta, 1, steps) + else: + beta_values = np.linspace(1, beta, steps) + + min = math.inf + for beta_ in beta_values[1:]: + # Compute the theoretical eigenvalue density + theoretical_pdf = marchenko_pastur_pdf(eigenvalues, beta_, sigma) + # Compute the mean squared error between the theoretical and sample eigenvalue density + mse = np.mean((theoretical_pdf - eigenvalues) ** 2) + if mse < min: + min = mse + beta_best=beta_ + return beta_best + +def split_covariance_matrices(C:np.ndarray, beta:float=0.5, q_fit:bool=True): + """ + This function split the covariance matrices to C_Noise, C_Star, C_Global + + Parameters + ---------- + C:np.ndarray + Input correlation matrix + beta:float + ratio of number of variables to number of observations. e.g. N_stocks/N_days + q_fit:bool + True or False whether fit is required for best beta or not + Return + C_Noise:np.ndarray + a split of correlation matrices whose eignevalues are at most lambda_max + C_Star:np.ndarray + a split of correlation matrices whose eigenvalues between lambda_max and largest lambda + C_Global:np.ndarray + a split of correlation matrices , Lambda1 which is typically much greater than lambda+ + """ + eigenvalues, eigenvectors = np.linalg.eigh(C) + eigenvalues = eigenvalues[::-1] # Reverse order sorting + eigenvectors = eigenvectors[:, ::-1] # Reverse order sorting + + lambda_min, lambda_max = fit_marchenko_pastur(eigenvalues[1:],beta, q_fit=True) + # Split the eigenvalues and eigenvectors into three regimes. + # First regime has eigenvalues less than lambda_max. + eigenvalues_1 = eigenvalues[eigenvalues < lambda_max] + eigenvectors_1 = eigenvectors[:, eigenvalues < lambda_max] + + # Second regime has eigenvalues between lambda_max and the largest lambda. + eigenvalues_2 = eigenvalues[ + (eigenvalues >= lambda_max) & (eigenvalues < eigenvalues[0]) + ] + eigenvectors_2 = eigenvectors[ + :, (eigenvalues >= lambda_max) & (eigenvalues < eigenvalues[0]) + ] + + # Third regime is the eigenvalue greater than lambda_max. + eigenvalues_3 = eigenvalues[0] + eigenvectors_3 = eigenvectors[:, 0] + + # Compute the correlation matrices for each regime + C_Noise = eigenvectors_1 @ np.diag(eigenvalues_1) @ eigenvectors_1.T # CNoise + C_Star = eigenvectors_2 @ np.diag(eigenvalues_2) @ eigenvectors_2.T # C* + C_Global = eigenvectors_3 * eigenvalues_3 * eigenvectors_3.T #CGlobal + + return C_Noise, C_Star, C_Global diff --git a/dcmppln/utils/k_means.py b/dcmppln/utils/k_means.py new file mode 100644 index 0000000..40f4b6c --- /dev/null +++ b/dcmppln/utils/k_means.py @@ -0,0 +1,207 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from scipy.linalg import eigh +from sklearn.cluster import KMeans +import numpy as np + +def silhouette_score(X, labels): + """ + X: data matrix + """ + score = 0 + for i in range(len(X)): + a = 0 + b = 0 + for j in range(len(X)): + if labels[i] == labels[j]: + a += np.linalg.norm(X[i] - X[j]) + else: + b += np.linalg.norm(X[i] - X[j]) + if a < b: + score += 1 - a / b + else: + score += b / a - 1 + + return score / len(X) + + +#### Implementation for Calinski-Harabaz Score. +def calinski_harabaz_score(X, labels): + """ + Variance ratio criterion. + Between cluster dispersion divided by within cluster dispersion. + """ + n_samples = len(X) + n_labels = len(np.unique(labels)) + if n_labels == 1: + return 1.0 + mean = np.mean(X, axis=0) + B, W = 0, 0 + for k in range(n_labels): + cluster_k = X[labels == k] + mean_k = np.mean(cluster_k, axis=0) + B += len(cluster_k) * np.sum((mean_k - mean) ** 2) + W += np.sum((cluster_k - mean_k) ** 2) + return (B / W) * (n_samples - n_labels) / (n_labels - 1) + + +def correlation_to_distance_linear(correlation_matrix): + num_rows, num_cols = correlation_matrix.shape + distance_matrix = np.empty((num_rows, num_cols)) + + for i in range(num_rows): + for j in range(num_cols): + if i == j: + distance_matrix[i, j] = 0 + else: + distance_matrix[i, j] = (3 - correlation_matrix[i, j]) / 2 + + return distance_matrix + + +def correlation_to_distance(correlation_matrix): + num_rows, num_cols = correlation_matrix.shape + distance_matrix = np.empty((num_rows, num_cols)) + + for i in range(num_rows): + for j in range(num_cols): + distance_matrix[i, j] = np.sqrt(2 * (1 - correlation_matrix[i, j])) + + return distance_matrix + + +def k_means_sweep(C, k_values=None, corr=True, distance_type="non_linear"): + if corr and distance_type == "non_linear": + D = np.sqrt(2 * (1 - C)) + elif corr and distance_type == "linear": + D = correlation_to_distance_linear(C) + + else: + D = C + + if k_values is None: # If no k values are specified, sweep over a range of k values + k_values = range(1, len(D)) + + best_score = -1 + best_k = -1 + best_clustering = None + + for k in k_values: + kmeans = KMeans(n_clusters=k, n_init=5) + clustering = kmeans.fit_predict(D) + score = calinski_harabaz_score(D, clustering) + if score > best_score: + best_score = score + best_k = k + best_clustering = clustering + return best_k, best_clustering ## + + +######### This is with threshold or contraints on cluster sizes ################ +def k_means_sweep_threshold( + C, + k_values=None, + corr=True, + low_threshold=2, + up_threshold=30, + distance_type="non_linear", +): + if corr and distance_type == "linear": + D = correlation_to_distance_linear(C) + + if corr and distance_type == "non_linear": + D = correlation_to_distance_linear(C) + + else: + D = C + + best_score = -1 + best_k = -1 + best_clustering = None + + if k_values is None: + k_values = range(1, len(D)) + + for k in k_values: + kmeans = KMeans( + n_clusters=k, n_init=5 + ) + clustering = kmeans.fit_predict(D) + + # Calculate the size of each cluster + cluster_sizes = np.bincount(clustering) + + # Check if all clusters satisfy the constraints + if np.all(cluster_sizes >= low_threshold) and np.all( + cluster_sizes <= up_threshold + ): + score = calinski_harabaz_score(D, clustering) + if score > best_score: + best_score = score + best_k = k + best_clustering = clustering + + return best_k, best_clustering + + +######### This is with threshold or contraints on cluster sizes ################ +def k_means_laplacian_threshold( + C, k_values=None, low_threshold=2, up_threshold=30, num_eigenvectors=40 +): + cov_matrix = C.copy() + # Step 2: Compute Laplacian Matrix + n = cov_matrix.shape[0] + D = np.diag(np.sum(cov_matrix, axis=1)) + laplacian_matrix = D - cov_matrix + + # Step 3: Compute Eigenvectors + # Number of smallest eigenvectors to consider + eigenvalues, eigenvectors = eigh(laplacian_matrix, D, type=1) + + sorted_indices = np.argsort(eigenvalues) + eigenvectors[:, sorted_indices] + eigenvectors = eigenvectors.copy() + + # Step 4: Perform Clustering + + best_score = -1 + best_k = -1 + best_clustering = None + + if k_values is None: # If no k values are specified, sweep over a range of k values + k_values = range(1, n) + + found_soln = False + + for k in k_values: + for num_eigenvectors in np.arange(2, n): + # print(num_eigenvectors) + D = eigenvectors[:, 1:num_eigenvectors] + + kmeans = KMeans( + n_clusters=k, n_init=5 + ) + clustering = kmeans.fit_predict(D) + + # Calculate the size of each cluster + cluster_sizes = np.bincount(clustering) + + + # Check if all clusters satisfy the constraints + if np.all(cluster_sizes <= up_threshold): + found_soln = True + best_clustering = clustering + break + + if found_soln: + break + + if found_soln: + break + + + return best_k, best_clustering + diff --git a/dcmppln/utils/objective_after_clustering.py b/dcmppln/utils/objective_after_clustering.py new file mode 100644 index 0000000..dd4d2b7 --- /dev/null +++ b/dcmppln/utils/objective_after_clustering.py @@ -0,0 +1,299 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from dcmppln.optimization.portfolio_optimization_runtime import * +from dcmppln.optimizer import Optimizer, CplexOptimizer +from dcmppln.utils.utils import ( + compute_objective_value, + timeit, +) +from dcmppln.utils.portfolio_model import construct_markovitz_model + + +def get_model(returns, + covariance_matrix, + target_return=None, + risk_factor=None, + budget=None, + model_name="Markowitz Portfolio Optimization", + continuous_variables_flag=False): + """ + Generator docplex model from input data + target_return is not used in this version + :returns docplex model and list of variables for each asset + """ + n = len(returns) # Number of assets + + if not budget: + budget = n // 2 + + ## q: Risk factor + if not risk_factor: + risk_factor = 0.5 + + model, weights = construct_markovitz_model( + returns, + covariance_matrix, + name=model_name, + budget=budget, + risk_factor=risk_factor, + continuous_variables=continuous_variables_flag, + ) + return model, weights + + +def split_cardinality_constraint(list_of_int, cardinality): + """ + list_of_int: list of int of type [n1,...,nk] + cardinality: int to match + return list of int such that its sum = cardinality and relative weight of each int is somewhat preserved + """ + continuous_solution = np.array(list_of_int)*cardinality/np.sum(list_of_int) + integer_solution = np.floor(continuous_solution).astype(int) + increment = cardinality - np.sum(integer_solution) + + index_order_for_increase = np.argsort(list_of_int)[::-1] + + i = 0 + while increment > 0: + integer_solution[index_order_for_increase[i]] += 1 + increment -= 1 + i += 1 + assert sum(integer_solution) == cardinality + return integer_solution + + +def optimize_and_score( + full_returns_, + correlation_, + partitions_: Union[np.ndarray, list], + card_: int = 0, + input_risk_factor=1, + return_time=False, + filtering_method=None, + optimizer: Optimizer=None, + rebalanced_risk_factor = None, + unique_identifier_base = None, + log_best_feasible = False +): + """ + Each partition will be separated, filtered, solved separately, recombined, and score the combined solution + Poor copy paste of optimize_base during transition + :param full_returns_: returns vector + :param correlation_: correlation matrix + :param partitions_: partitions detected + :param card_: cardinality constraint value + :param input_risk_factor: base risk factor + :param return_time: bool to return measured runtimes + :param filtering_method: object of class Filtering + :param unique_identifier_base: unique identifier uuid4 in hex + :return: + """ + if optimizer is None: + optimizer = CplexOptimizer() + if filtering_method is None: + filtering_method = lambda x: x + if rebalanced_risk_factor is None: + rebalanced_risk_factor = input_risk_factor + + num_communities = len(np.unique(partitions_)) + unique_labels = np.unique(partitions_) + + if unique_identifier_base is None: + unique_identifiers = [None] * len(unique_labels) + else: + unique_identifiers = [unique_identifier_base+f"_{int(i)}" for i in unique_labels] + + communities_ = [] + for i, label in enumerate(unique_labels): + communities_.append( + np.argwhere(partitions_ == label).reshape( + -1, + ) + ) + partitions_[partitions_ == label] = i + + sorted_communities = sorted(communities_, key=lambda x: x.shape[0], reverse=True) + + full_dim = len(partitions_) + if not card_: + card_ = full_dim // 2 + + card_subproblems = split_cardinality_constraint(list(map(len, sorted_communities)), card_) + + assert card_subproblems.sum() == card_, "The sum of card doesn't match" + + weights_dict = {} + process_times_dict = {} + clock_time_dict = {} + + for i, card in enumerate(card_subproblems): + comm_indices = sorted_communities[i] + returns = full_returns_[comm_indices] + + community_correlation_matrix = filtering_method( + correlation_[comm_indices][:, comm_indices] + ) + + model, weights = get_model(returns, + community_correlation_matrix, + budget=card, + risk_factor=rebalanced_risk_factor,) + ( + optimal_weights, + clock_time, + processing_time, + ) = optimizer( + model, + weights, + unique_identifier = unique_identifiers[i], + log_best_feasible = log_best_feasible, + ) + + weights_dict[i] = optimal_weights + process_times_dict[i] = processing_time + clock_time_dict[i] = clock_time + + # Recombining solution to compare the objective later + full_recombined_soln = np.zeros(full_dim, dtype=type(weights_dict[0])) + + for i, comm in enumerate(sorted_communities): + full_recombined_soln[comm] = weights_dict[i] + + obj_after_clustering = compute_objective_value( + full_returns_, correlation_, full_recombined_soln, risk_factor=input_risk_factor + ) + + print("Objective value with this comm detection algorithm", obj_after_clustering) + print("Communities found at level 1: ", num_communities) + + if return_time: + return obj_after_clustering, full_recombined_soln, process_times_dict, clock_time_dict + else: + return obj_after_clustering, full_recombined_soln + + +def objective_after_clustering( + full_returns_, + correlation_, + partitions_: Union[np.ndarray, list], + community_detection_alg: str = "", + card_: int = 0, + k_max: int = 10, + input_risk_factor=0.5, + return_time=False, + filtering_method=None, + threshold_mratio=0, +): + """ + We do no q (risk apetite) rescaling here + + Parameters: + partitions: List or array indicating labels for each node + community_detection_alg: Type of community detection algorthm + card_= Cardinality constraint for the full problem + k_max: This argument is passed only for the spectral clustering algo + + Returns: + objective value + """ + + assert community_detection_alg in ( + "mod_spectral_full", + "mod_spectral_denoised", + "louvain_full", + "louvain_denoised", + "q_louvain_full", + "q_louvain_denoised", + "nx_louvain_full", + "nx_louvain_denoised", + "spectral_clustering", + "", + ), "The algorithm type has not been found" + if filtering_method is None: + filtering_method = sparsification_covariance_no_diag_mean_ratio + num_communities = len(np.unique(partitions_)) + unique_labels = np.unique(partitions_) + + communities_ = [] + for i, label in enumerate(unique_labels): + communities_.append( + np.argwhere(partitions_ == label).reshape( + -1, + ) + ) + partitions_[partitions_ == label] = i + + sorted_communities = sorted(communities_, key=lambda x: x.shape[0], reverse=True) + weights_by_count = [ + sorted_communities[i].shape[0] for i in range(len(sorted_communities)) + ] + weights_by_count = np.array(weights_by_count) / sum(weights_by_count) + + total_assets = len(partitions_) + full_dim = total_assets + if not card_: + card_ = total_assets // 2 + + card_subproblems = np.array([int(el * card_) for el in weights_by_count]) + ## Adding the extra cardinality constraint (if any) to the largest community + card_subproblems[0] = card_ - card_subproblems[1:].sum() + + assert card_subproblems.sum() == card_, "The sum of card doesn't match" + + ### Running the cplex routine + weights_dict = {} + process_times_dict = {} + for i, card in enumerate(card_subproblems): + comm_indices = sorted_communities[i] + returns = full_returns_[comm_indices] + + community_correlation_matrix = correlation_[comm_indices][:, comm_indices] + + #### THRESHOLD + if threshold_mratio: + community_correlation_matrix = filtering_method( + community_correlation_matrix, threshold_mratio=threshold_mratio + ) + ( + optimal_weights, + execution_time, + processing_time, + ) = Boolean_markowitz_portfolio_optimization_time( + returns, + community_correlation_matrix, + budget=card, + risk_factor=input_risk_factor, + return_sol=True, + ) + + weights_dict[i] = optimal_weights + process_times_dict[i] = processing_time + + total_processing_time = sum(process_times_dict.values()) + + ### Recombining solution to compare the objective later + full_recombined_soln = np.zeros(full_dim, dtype=type(weights_dict[0])) + + for i, comm in enumerate(sorted_communities): + full_recombined_soln[comm] = weights_dict[i] + + obj_after_clustering = compute_objective_value( + full_returns_, correlation_, full_recombined_soln, risk_factor=input_risk_factor + ) + + print("Objective value with this comm detection algorithm", obj_after_clustering[0]) + print("Communities found at level 1: ", num_communities) + + if return_time == False: + return obj_after_clustering, full_recombined_soln + else: + return obj_after_clustering, full_recombined_soln, total_processing_time + + + + + + diff --git a/dcmppln/utils/portfolio_model.py b/dcmppln/utils/portfolio_model.py new file mode 100644 index 0000000..4c40164 --- /dev/null +++ b/dcmppln/utils/portfolio_model.py @@ -0,0 +1,40 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from docplex.mp.model import Model + +def construct_markovitz_model( + returns, + covariance_matrix, + budget=None, + risk_factor=None, + name="Markowitz Portfolio Optimization", + continuous_variables=False, +): + """ + Trick if the budget is <0 we drop the constraint + """ + n = len(returns) # Number of assets + if budget is None: + budget = n // 2 + # q: Risk factor + if risk_factor is None: + risk_factor = 0.5 + + model = Model(name) + if continuous_variables: + weights = model.continuous_var_list(n, lb=0.0, ub=1.0, name="weights") + else: + weights = model.binary_var_list(n, name="weights") + portfolio_risk = model.sum( + risk_factor * weights[i] * covariance_matrix[i, j] * weights[j] + for i in range(n) + for j in range(n) + ) - model.sum(returns[i] * weights[i] for i in range(n)) + + if budget >= 0: + model.add_constraint(model.sum(weights[i] for i in range(n)) == budget) + model.minimize(portfolio_risk) + return model, weights diff --git a/dcmppln/utils/risk_rebalance.py b/dcmppln/utils/risk_rebalance.py new file mode 100644 index 0000000..97601c0 --- /dev/null +++ b/dcmppln/utils/risk_rebalance.py @@ -0,0 +1,42 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np + +def rebalancing_risk_factor(risk_factor, full_covariance, full_avg_return, communities): + + #### we have the full covariance matrix and the full average return vector, and the communities + + balanced_risk_factor = risk_factor + + ### calculate norm of the covariance matrix, and the norm of the average return vector + cov_norm = np.linalg.norm(full_covariance) + avg_return_norm = np.linalg.norm(full_avg_return) + + ### calculate the number of the masked covariance that only takes blocks corresponding to the communities + num_communities = len(communities) + + if type(communities) == dict: + pass + else: + communities = {k: v for k, v in enumerate(communities)} + + community_cov_norm = 0 + community_avg_return_norm = 0 + + for community_idx, community in communities.items(): + community = np.array([community]).astype(int) + community_covariance = full_covariance[np.ix_(community, community)] + community_avg_return = full_avg_return[community] + + ### calculate the norm of the covariance matrix and the norm of the average return vector + community_cov_norm += np.linalg.norm(community_covariance) + community_avg_return_norm += np.linalg.norm(community_avg_return) + + + ### add the ratio of the norms to the balanced risk factor + balanced_risk_factor *= (cov_norm/community_cov_norm)*(community_avg_return_norm/avg_return_norm) + + return balanced_risk_factor diff --git a/dcmppln/utils/utils.py b/dcmppln/utils/utils.py new file mode 100644 index 0000000..f5f25ca --- /dev/null +++ b/dcmppln/utils/utils.py @@ -0,0 +1,61 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from typing import Optional, Union +import numpy as np +import time + + +def get_instance_non_private_attributes(instance): + """ + instance: instance of a class + returns: all non private attributes and values in a dictionnary + """ + return { k:v for k,v in vars(instance).items() if k[0]!="_" } + + + +def compute_objective_value(returns, covariance, weights, risk_factor=0.5): + weights = np.array(weights).reshape(-1, 1) + term1 = risk_factor * weights.T @ covariance @ weights + term2 = returns @ weights + obj_value = term1 - term2 + return obj_value[0,0] # Convert single value array to scalar + + +def threshold_matrix(correlation_matrix, threshold=0.5): + binary_matrix = (correlation_matrix > threshold).astype(float) + return binary_matrix + + +def thresholding_not_touching_diag(correlation_matrix, threshold=0.5): + binary_matrix = threshold_matrix(correlation_matrix, threshold) + np.fill_diagonal(binary_matrix, np.diagonal(correlation_matrix)) + return binary_matrix + +def sparsification(correlation_matrix, threshold=0.5, allow_to_modify_diag=False): + sparse_matrix = np.where(correlation_matrix > threshold, correlation_matrix, 0) + if not allow_to_modify_diag: + np.fill_diagonal(sparse_matrix, np.diagonal(correlation_matrix)) + return sparse_matrix + +def sparsification_covariance_no_diag_mean_ratio(correlation_matrix, threshold_mratio=0.2): + correlation_matrix_without_diagonal = np.array(correlation_matrix) + np.fill_diagonal(correlation_matrix_without_diagonal, 0) + avg_weight_without_diagonal = np.mean(correlation_matrix_without_diagonal) + threshold = threshold_mratio * avg_weight_without_diagonal + sparse_matrix = sparsification(correlation_matrix, threshold) + return sparse_matrix + + +def timeit(func): + def wrapper(*args, **kwargs)-> Union[Optional[np.array], Optional[float], Optional[float]]: + start_process_time = time.perf_counter() + start_time = time.process_time() + result = func(*args, **kwargs) + end_time = time.perf_counter() - start_process_time + process_time = time.process_time() - start_time + return result, process_time, end_time + return wrapper \ No newline at end of file diff --git a/dcmppln/utils/variation_of_information.py b/dcmppln/utils/variation_of_information.py new file mode 100644 index 0000000..c89cc87 --- /dev/null +++ b/dcmppln/utils/variation_of_information.py @@ -0,0 +1,84 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +""" +We quantitatively measured the performance of our +method in terms of a metric known as Variation of Information (V I) [52, 53], which measures the entropy +difference between two partitions of the same network, +providing a rigorous way for us to quantify the similarity between the ‘true’ partition and the one identified +by our method. +""" + +import numpy as np + + +def variation_of_information(true_partition, partition): + """ + partition is a list of integers, where each integer represents a community for the corresponding node + """ + unique_labels = set(true_partition) + true_partition_dict = dict(zip(unique_labels, range(len(unique_labels)))) + true_partition = [ + true_partition_dict[true_partition[i]] for i in range(len(true_partition)) + ] + + unique_labels = set(partition) + partition_dict = dict(zip(unique_labels, range(len(unique_labels)))) + partition = [partition_dict[partition[i]] for i in range(len(partition))] + + true_partition = list(true_partition) + partition = list(partition) + + n_c1 = len(set(true_partition)) + + true_probability = [0.0 for i in range(n_c1)] + for i, cluster_label in enumerate(set(true_partition)): + # count number of nodes in each cluster + true_probability[i] = true_partition.count(cluster_label) / len(true_partition) + + entropy_true = 0.0 + for i in range(len(true_probability)): + entropy_true += -1.0 * true_probability[i] * np.log2(true_probability[i]) + + n_c2 = len(set(partition)) + partition_probability = [0.0 for i in range(n_c2)] + + for i, cluster_label in enumerate(set(partition)): + partition_probability[i] = partition.count(cluster_label) / len(partition) + + # Entropy associated with partition + entropy_partition = 0.0 + for i in range(len(partition_probability)): + entropy_partition += ( + -1.0 * partition_probability[i] * np.log2(partition_probability[i]) + ) + + # Compute mutual information between two clusterings + # mutual information is a symmetric measure + mutual_information = 0.0 + for i in range(n_c1): + for j in range(n_c2): + # count number of nodes in each cluster + n_ij = 0 + for k in range(len(true_partition)): + if true_partition[k] == i and partition[k] == j: + n_ij += 1 + if n_ij > 0: + mutual_information += ( + n_ij + / len(true_partition) + * np.log2( + (n_ij / len(true_partition)) + / (true_probability[i] * partition_probability[j]) + ) + ) + + # Compute variation of information + variation_of_information = ( + entropy_true + entropy_partition - 2.0 * mutual_information + ) + + return variation_of_information + diff --git a/examples/.ipynb_checkpoints/main-checkpoint.ipynb b/examples/.ipynb_checkpoints/main-checkpoint.ipynb new file mode 100644 index 0000000..4b1df02 --- /dev/null +++ b/examples/.ipynb_checkpoints/main-checkpoint.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "### Running the base first " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# // SPDX-License-Identifier: Apache-2.0\n", + "# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC\n", + "#\n", + "###############################################################################\n", + "\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from itertools import product" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"../\")\n", + "\n", + "\n", + "from dcmppln.pipeline import Pipeline\n", + "\n", + "from dcmppln.optimizer import GurobiOptimizer\n", + "\n", + "\n", + "from tests.input_data import InputData" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "### Dummy Optimizer \n", + "\n", + "input_data = InputData()\n", + "\n", + "\n", + "correlation_matrix,covariance_matrix,full_returns = input_data.data_for_pipeline()\n", + "p = Pipeline(\n", + " correlation_matrix,\n", + " covariance_matrix,\n", + " full_returns,\n", + " )\n", + "dct = p.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Restricted license - for non-production use only - expires 2025-11-24\n", + "Read LP format model from file temp.lp\n", + "Reading time = 0.01 seconds\n", + "obj: 1 rows, 90 columns, 90 nonzeros\n", + "Set parameter TimeLimit to value 300\n", + "Set parameter Threads to value 1\n", + "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (linux64 - \"Red Hat Enterprise Linux 8.3 (Ootpa)\")\n", + "\n", + "CPU model: AMD EPYC 7R13 Processor, instruction set [SSE2|AVX|AVX2]\n", + "Thread count: 48 physical cores, 96 logical processors, using up to 1 threads\n", + "\n", + "Optimize a model with 1 rows, 90 columns and 90 nonzeros\n", + "Model fingerprint: 0x76cf1be8\n", + "Model has 4095 quadratic objective terms\n", + "Variable types: 0 continuous, 90 integer (90 binary)\n", + "Coefficient statistics:\n", + " Matrix range [1e+00, 1e+00]\n", + " Objective range [2e-05, 3e-03]\n", + " QObjective range [7e-07, 6e-03]\n", + " Bounds range [1e+00, 1e+00]\n", + " RHS range [4e+01, 4e+01]\n", + "Found heuristic solution: objective 0.2684985\n", + "Presolve time: 0.03s\n", + "Presolved: 1 rows, 90 columns, 90 nonzeros\n", + "Presolved model has 4095 quadratic objective terms\n", + "Variable types: 0 continuous, 90 integer (90 binary)\n", + "Found heuristic solution: objective 0.1743180\n", + "\n", + "Root relaxation: objective 1.320440e-01, 97 iterations, 0.00 seconds (0.00 work units)\n", + "\n", + " Nodes | Current Node | Objective Bounds | Work\n", + " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", + "\n", + " 0 0 0.13204 0 5 0.17432 0.13204 24.3% - 0s\n", + "H 0 0 0.1323776 0.13204 0.25% - 0s\n", + " 0 0 0.13204 0 5 0.13238 0.13204 0.25% - 0s\n", + " 0 0 0.13221 0 3 0.13238 0.13221 0.12% - 0s\n", + " 0 0 0.13232 0 2 0.13238 0.13232 0.04% - 0s\n", + " 0 0 0.13232 0 2 0.13238 0.13232 0.04% - 0s\n", + " 0 2 0.13235 0 2 0.13238 0.13235 0.02% - 0s\n", + "\n", + "Explored 4 nodes (124 simplex iterations) in 0.05 seconds (0.01 work units)\n", + "Thread count was 1 (of 96 available processors)\n", + "\n", + "Solution count 3: 0.132378 0.174318 0.268499 \n", + "\n", + "Optimal solution found (tolerance 1.00e-04)\n", + "Best objective 1.323775589725e-01, best bound 1.323775589725e-01, gap 0.0000%\n", + "Objective value with this comm detection algorithm 0.13237755898088116\n", + "Communities found at level 1: 1\n", + "0.13237755898088116\n", + "[1.0 0.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", + " 0.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0\n", + " 0.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0\n", + " 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0\n", + " 0.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0]\n" + ] + } + ], + "source": [ + "## If running with Gurobi , then do this\n", + "\n", + "input_data = InputData()\n", + "correlation_matrix,covariance_matrix,full_returns = input_data.data_for_pipeline()\n", + "p = Pipeline(\n", + " correlation_matrix,\n", + " covariance_matrix,\n", + " full_returns, optimize_func=GurobiOptimizer() )\n", + "\n", + "dct = p.run(run_optimizer=True, )\n", + "\n", + "\n", + "print(dct[\"score\"])\n", + "print(dct[\"recombined_solution\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "simenv", + "language": "python", + "name": "simenv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/.ipynb_checkpoints/temp-checkpoint.lp b/examples/.ipynb_checkpoints/temp-checkpoint.lp new file mode 100644 index 0000000..39a3190 --- /dev/null +++ b/examples/.ipynb_checkpoints/temp-checkpoint.lp @@ -0,0 +1,4158 @@ +\ This file has been generated by DOcplex +\ 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0.000594147892 weights_78 * weights_87 + + 0.000362597095 weights_78 * weights_88 + + 0.000603805224 weights_78 * weights_89 + 0.005008798234 weights_79^2 + + 0.000278430904 weights_79 * weights_80 + + 0.000302476239 weights_79 * weights_81 + + 0.000023497441 weights_79 * weights_82 + + 0.000505541212 weights_79 * weights_83 + + 0.000394076069 weights_79 * weights_84 + + 0.000541388427 weights_79 * weights_85 + + 0.000334482660 weights_79 * weights_86 + + 0.000476761838 weights_79 * weights_87 + + 0.000059661810 weights_79 * weights_88 + + 0.000129443796 weights_79 * weights_89 + 0.000426112048 weights_80^2 + + 0.000606128104 weights_80 * weights_81 + + 0.000062254621 weights_80 * weights_82 + + 0.000772787493 weights_80 * weights_83 + + 0.000581391194 weights_80 * weights_84 + + 0.000723659765 weights_80 * weights_85 + + 0.000277869175 weights_80 * weights_86 + + 0.000611943637 weights_80 * weights_87 + + 0.000276214508 weights_80 * weights_88 + + 0.000691319788 weights_80 * weights_89 + 0.001210123007 weights_81^2 + + 0.000226493740 weights_81 * weights_82 + + 0.001034269732 weights_81 * weights_83 + + 0.000842827141 weights_81 * weights_84 + + 0.001302763357 weights_81 * weights_85 + + 0.000436335190 weights_81 * weights_86 + + 0.000952401924 weights_81 * weights_87 + + 0.000440131226 weights_81 * weights_88 + + 0.001158025034 weights_81 * weights_89 + 0.002632263135 weights_82^2 + + 0.000153112585 weights_82 * weights_83 + + 0.000086674781 weights_82 * weights_84 + + 0.000115638372 weights_82 * weights_85 + + 0.000067133041 weights_82 * weights_86 + + 0.000197608454 weights_82 * weights_87 + + 0.000198173331 weights_82 * weights_88 + + 0.000148888946 weights_82 * weights_89 + 0.000863252122 weights_83^2 + + 0.000752645339 weights_83 * weights_84 + + 0.001183477165 weights_83 * weights_85 + + 0.000476671969 weights_83 * weights_86 + + 0.000912824101 weights_83 * weights_87 + + 0.000433572326 weights_83 * weights_88 + + 0.001056571239 weights_83 * weights_89 + 0.000983197959 weights_84^2 + + 0.000854100298 weights_84 * weights_85 + + 0.000399448604 weights_84 * weights_86 + + 0.000780949985 weights_84 * weights_87 + + 0.000284654099 weights_84 * weights_88 + + 0.000846803186 weights_84 * weights_89 + 0.001737839829 weights_85^2 + + 0.000559526355 weights_85 * weights_86 + + 0.001082421004 weights_85 * weights_87 + + 0.000502689136 weights_85 * weights_88 + + 0.001148715913 weights_85 * weights_89 + 0.000801589835 weights_86^2 + + 0.000516723011 weights_86 * weights_87 + + 0.000262864598 weights_86 * weights_88 + + 0.000417182599 weights_86 * weights_89 + 0.000819616678 weights_87^2 + + 0.000383305804 weights_87 * weights_88 + + 0.000935162538 weights_87 * weights_89 + 0.000546158616 weights_88^2 + + 0.000482055932 weights_88 * weights_89 + 0.001801062242 weights_89^2 ]/2 +Subject To + c1: weights_0 + weights_1 + weights_2 + weights_3 + weights_4 + weights_5 + + weights_6 + weights_7 + weights_8 + weights_9 + weights_10 + weights_11 + + weights_12 + weights_13 + weights_14 + weights_15 + weights_16 + + weights_17 + weights_18 + weights_19 + weights_20 + weights_21 + + weights_22 + weights_23 + weights_24 + weights_25 + weights_26 + + weights_27 + weights_28 + weights_29 + weights_30 + weights_31 + + weights_32 + weights_33 + weights_34 + weights_35 + weights_36 + + weights_37 + weights_38 + weights_39 + weights_40 + weights_41 + + weights_42 + weights_43 + weights_44 + weights_45 + weights_46 + + weights_47 + weights_48 + weights_49 + weights_50 + weights_51 + + weights_52 + weights_53 + weights_54 + weights_55 + weights_56 + + weights_57 + weights_58 + weights_59 + weights_60 + weights_61 + + weights_62 + weights_63 + weights_64 + weights_65 + weights_66 + + weights_67 + weights_68 + weights_69 + weights_70 + weights_71 + + weights_72 + weights_73 + weights_74 + weights_75 + weights_76 + + weights_77 + weights_78 + weights_79 + weights_80 + weights_81 + + weights_82 + weights_83 + weights_84 + weights_85 + weights_86 + + weights_87 + weights_88 + weights_89 = 45 + +Bounds + 0 <= weights_0 <= 1 + 0 <= weights_1 <= 1 + 0 <= weights_2 <= 1 + 0 <= weights_3 <= 1 + 0 <= weights_4 <= 1 + 0 <= weights_5 <= 1 + 0 <= weights_6 <= 1 + 0 <= weights_7 <= 1 + 0 <= weights_8 <= 1 + 0 <= weights_9 <= 1 + 0 <= weights_10 <= 1 + 0 <= weights_11 <= 1 + 0 <= weights_12 <= 1 + 0 <= weights_13 <= 1 + 0 <= weights_14 <= 1 + 0 <= weights_15 <= 1 + 0 <= weights_16 <= 1 + 0 <= weights_17 <= 1 + 0 <= weights_18 <= 1 + 0 <= weights_19 <= 1 + 0 <= weights_20 <= 1 + 0 <= weights_21 <= 1 + 0 <= weights_22 <= 1 + 0 <= weights_23 <= 1 + 0 <= weights_24 <= 1 + 0 <= weights_25 <= 1 + 0 <= weights_26 <= 1 + 0 <= weights_27 <= 1 + 0 <= weights_28 <= 1 + 0 <= weights_29 <= 1 + 0 <= weights_30 <= 1 + 0 <= weights_31 <= 1 + 0 <= weights_32 <= 1 + 0 <= weights_33 <= 1 + 0 <= weights_34 <= 1 + 0 <= weights_35 <= 1 + 0 <= weights_36 <= 1 + 0 <= weights_37 <= 1 + 0 <= weights_38 <= 1 + 0 <= weights_39 <= 1 + 0 <= weights_40 <= 1 + 0 <= weights_41 <= 1 + 0 <= weights_42 <= 1 + 0 <= weights_43 <= 1 + 0 <= weights_44 <= 1 + 0 <= weights_45 <= 1 + 0 <= weights_46 <= 1 + 0 <= weights_47 <= 1 + 0 <= weights_48 <= 1 + 0 <= weights_49 <= 1 + 0 <= weights_50 <= 1 + 0 <= weights_51 <= 1 + 0 <= weights_52 <= 1 + 0 <= weights_53 <= 1 + 0 <= weights_54 <= 1 + 0 <= weights_55 <= 1 + 0 <= weights_56 <= 1 + 0 <= weights_57 <= 1 + 0 <= weights_58 <= 1 + 0 <= weights_59 <= 1 + 0 <= weights_60 <= 1 + 0 <= weights_61 <= 1 + 0 <= weights_62 <= 1 + 0 <= weights_63 <= 1 + 0 <= weights_64 <= 1 + 0 <= weights_65 <= 1 + 0 <= weights_66 <= 1 + 0 <= weights_67 <= 1 + 0 <= weights_68 <= 1 + 0 <= weights_69 <= 1 + 0 <= weights_70 <= 1 + 0 <= weights_71 <= 1 + 0 <= weights_72 <= 1 + 0 <= weights_73 <= 1 + 0 <= weights_74 <= 1 + 0 <= weights_75 <= 1 + 0 <= weights_76 <= 1 + 0 <= weights_77 <= 1 + 0 <= weights_78 <= 1 + 0 <= weights_79 <= 1 + 0 <= weights_80 <= 1 + 0 <= weights_81 <= 1 + 0 <= weights_82 <= 1 + 0 <= weights_83 <= 1 + 0 <= weights_84 <= 1 + 0 <= weights_85 <= 1 + 0 <= weights_86 <= 1 + 0 <= weights_87 <= 1 + 0 <= weights_88 <= 1 + 0 <= weights_89 <= 1 + +Binaries + weights_0 weights_1 weights_2 weights_3 weights_4 weights_5 weights_6 weights_7 + weights_8 weights_9 weights_10 weights_11 weights_12 weights_13 weights_14 + weights_15 weights_16 weights_17 weights_18 weights_19 weights_20 weights_21 + weights_22 weights_23 weights_24 weights_25 weights_26 weights_27 weights_28 + weights_29 weights_30 weights_31 weights_32 weights_33 weights_34 weights_35 + weights_36 weights_37 weights_38 weights_39 weights_40 weights_41 weights_42 + weights_43 weights_44 weights_45 weights_46 weights_47 weights_48 weights_49 + weights_50 weights_51 weights_52 weights_53 weights_54 weights_55 weights_56 + weights_57 weights_58 weights_59 weights_60 weights_61 weights_62 weights_63 + weights_64 weights_65 weights_66 weights_67 weights_68 weights_69 weights_70 + weights_71 weights_72 weights_73 weights_74 weights_75 weights_76 weights_77 + weights_78 weights_79 weights_80 weights_81 weights_82 weights_83 weights_84 + weights_85 weights_86 weights_87 weights_88 weights_89 +End diff --git a/examples/main.ipynb b/examples/main.ipynb new file mode 100644 index 0000000..4b1df02 --- /dev/null +++ b/examples/main.ipynb @@ -0,0 +1,190 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "### Running the base first " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "###############################################################################\n", + "# // SPDX-License-Identifier: Apache-2.0\n", + "# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC\n", + "#\n", + "###############################################################################\n", + "\n", + "\n", + "import numpy as np\n", + "import pandas as pd\n", + "from itertools import product" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append(\"../\")\n", + "\n", + "\n", + "from dcmppln.pipeline import Pipeline\n", + "\n", + "from dcmppln.optimizer import GurobiOptimizer\n", + "\n", + "\n", + "from tests.input_data import InputData" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "### Dummy Optimizer \n", + "\n", + "input_data = InputData()\n", + "\n", + "\n", + "correlation_matrix,covariance_matrix,full_returns = input_data.data_for_pipeline()\n", + "p = Pipeline(\n", + " correlation_matrix,\n", + " covariance_matrix,\n", + " full_returns,\n", + " )\n", + "dct = p.run()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Restricted license - for non-production use only - expires 2025-11-24\n", + "Read LP format model from file temp.lp\n", + "Reading time = 0.01 seconds\n", + "obj: 1 rows, 90 columns, 90 nonzeros\n", + "Set parameter TimeLimit to value 300\n", + "Set parameter Threads to value 1\n", + "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (linux64 - \"Red Hat Enterprise Linux 8.3 (Ootpa)\")\n", + "\n", + "CPU model: AMD EPYC 7R13 Processor, instruction set [SSE2|AVX|AVX2]\n", + "Thread count: 48 physical cores, 96 logical processors, using up to 1 threads\n", + "\n", + "Optimize a model with 1 rows, 90 columns and 90 nonzeros\n", + "Model fingerprint: 0x76cf1be8\n", + "Model has 4095 quadratic objective terms\n", + "Variable types: 0 continuous, 90 integer (90 binary)\n", + "Coefficient statistics:\n", + " Matrix range [1e+00, 1e+00]\n", + " Objective range [2e-05, 3e-03]\n", + " QObjective range [7e-07, 6e-03]\n", + " Bounds range [1e+00, 1e+00]\n", + " RHS range [4e+01, 4e+01]\n", + "Found heuristic solution: objective 0.2684985\n", + "Presolve time: 0.03s\n", + "Presolved: 1 rows, 90 columns, 90 nonzeros\n", + "Presolved model has 4095 quadratic objective terms\n", + "Variable types: 0 continuous, 90 integer (90 binary)\n", + "Found heuristic solution: objective 0.1743180\n", + "\n", + "Root relaxation: objective 1.320440e-01, 97 iterations, 0.00 seconds (0.00 work units)\n", + "\n", + " Nodes | Current Node | Objective Bounds | Work\n", + " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", + "\n", + " 0 0 0.13204 0 5 0.17432 0.13204 24.3% - 0s\n", + "H 0 0 0.1323776 0.13204 0.25% - 0s\n", + " 0 0 0.13204 0 5 0.13238 0.13204 0.25% - 0s\n", + " 0 0 0.13221 0 3 0.13238 0.13221 0.12% - 0s\n", + " 0 0 0.13232 0 2 0.13238 0.13232 0.04% - 0s\n", + " 0 0 0.13232 0 2 0.13238 0.13232 0.04% - 0s\n", + " 0 2 0.13235 0 2 0.13238 0.13235 0.02% - 0s\n", + "\n", + "Explored 4 nodes (124 simplex iterations) in 0.05 seconds (0.01 work units)\n", + "Thread count was 1 (of 96 available processors)\n", + "\n", + "Solution count 3: 0.132378 0.174318 0.268499 \n", + "\n", + "Optimal solution found (tolerance 1.00e-04)\n", + "Best objective 1.323775589725e-01, best bound 1.323775589725e-01, gap 0.0000%\n", + "Objective value with this comm detection algorithm 0.13237755898088116\n", + "Communities found at level 1: 1\n", + "0.13237755898088116\n", + "[1.0 0.0 1.0 0.0 1.0 1.0 1.0 0.0 0.0 1.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0\n", + " 0.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0\n", + " 0.0 1.0 0.0 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0\n", + " 1.0 1.0 1.0 1.0 0.0 1.0 0.0 0.0 1.0 0.0 0.0 1.0 0.0 1.0 0.0 0.0 1.0 1.0\n", + " 0.0 1.0 0.0 1.0 1.0 0.0 0.0 1.0 1.0 0.0 1.0 0.0 0.0 0.0 1.0 0.0 1.0 0.0]\n" + ] + } + ], + "source": [ + "## If running with Gurobi , then do this\n", + "\n", + "input_data = InputData()\n", + "correlation_matrix,covariance_matrix,full_returns = input_data.data_for_pipeline()\n", + "p = Pipeline(\n", + " correlation_matrix,\n", + " covariance_matrix,\n", + " full_returns, optimize_func=GurobiOptimizer() )\n", + "\n", + "dct = p.run(run_optimizer=True, )\n", + "\n", + "\n", + "print(dct[\"score\"])\n", + "print(dct[\"recombined_solution\"])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "simenv", + "language": "python", + "name": "simenv" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.19" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/examples/temp.lp b/examples/temp.lp new file mode 100644 index 0000000..39a3190 --- /dev/null +++ b/examples/temp.lp @@ -0,0 +1,4158 @@ +\ This file has been generated by DOcplex +\ ENCODING=ISO-8859-1 +\Problem name: Markowitz Portfolio Optimization + +Minimize + obj: - 0.001414793290 weights_0 - 0.000129207028 weights_1 + - 0.001381945450 weights_2 - 0.000635912072 weights_3 + - 0.000333364142 weights_4 - 0.000812384836 weights_5 + - 0.000810281672 weights_6 - 0.000527304042 weights_7 + - 0.000926180076 weights_8 - 0.000694638686 weights_9 + - 0.000682995072 weights_10 - 0.000364942804 weights_11 + - 0.000840246670 weights_12 - 0.000403274469 weights_13 + - 0.001112305703 weights_14 - 0.000895069262 weights_15 + - 0.000324306259 weights_16 - 0.000429257981 weights_17 + - 0.000637804428 weights_18 - 0.000726517010 weights_19 + + 0.000494080696 weights_20 - 0.000728953523 weights_21 + - 0.000023757716 weights_22 - 0.001390053965 weights_23 + - 0.000580408251 weights_24 - 0.000591977860 weights_25 + - 0.000906794412 weights_26 - 0.001192140528 weights_27 + - 0.000791062424 weights_28 - 0.001107518519 weights_29 + - 0.001085171146 weights_30 - 0.000590187424 weights_31 + - 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+[build-system] +requires = ["poetry-core"] +build-backend = "poetry.core.masonry.api" + + diff --git a/tests/.ipynb_checkpoints/__init__-checkpoint.py b/tests/.ipynb_checkpoints/__init__-checkpoint.py new file mode 100644 index 0000000..65eb3d0 --- /dev/null +++ b/tests/.ipynb_checkpoints/__init__-checkpoint.py @@ -0,0 +1,5 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### \ No newline at end of file diff --git a/tests/.ipynb_checkpoints/conftest-checkpoint.py b/tests/.ipynb_checkpoints/conftest-checkpoint.py new file mode 100644 index 0000000..9bdfe08 --- /dev/null +++ b/tests/.ipynb_checkpoints/conftest-checkpoint.py @@ -0,0 +1,19 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import pytest +import numpy as np + +import os, sys + +sys.path.append(os.path.dirname(os.path.realpath(__file__))) + +from tests.input_data import InputData + + +@pytest.fixture +def sample_input_data(): + input_data = InputData() + return input_data.data_for_pipeline() diff --git a/tests/.ipynb_checkpoints/input_data-checkpoint.py b/tests/.ipynb_checkpoints/input_data-checkpoint.py new file mode 100644 index 0000000..7045014 --- /dev/null +++ b/tests/.ipynb_checkpoints/input_data-checkpoint.py @@ -0,0 +1,75 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import os, sys + +sys.path.append(os.path.dirname(os.path.realpath(__file__))) +from dcmppln.utils.utils import get_instance_non_private_attributes +import numpy as np + + +class InputData: + """ + Component to input data into the pipeline + """ + + def __init__(self, path="", abs_cov=True, abs_corr=True, subprob_idx=None): + default_paths = ["../tests/data/", "tests/data/"] + + # Use the provided path or the first existing default path + if path: + self.path = path + else: + self.path = None + for default_path in default_paths: + if self._path_exists(default_path): + self.path = default_path + self.path=self.path+"seed_1000_size_90" + break + if not self.path: + raise FileNotFoundError("None of the default paths exist.") + + self.abs_corr = abs_corr + self.abs_cov = abs_cov + self.params = get_instance_non_private_attributes(self) + + self._subprob_idx = subprob_idx + + def __call__(self): + return self.data_for_pipeline() + + def _path_exists(self, path): + return os.path.exists(path) + + def load_data(self, path): + try: + return np.load(path) + except IOError: + ## output operation error + raise IOError(f"File not found: {path}") + + def data_for_pipeline(self): + corr_path = self.path + "_correlation.npy" + cov_path = self.path + "_covariance.npy" + returns_path = self.path + "_returns.npy" + + correlation_matrix = self.load_data(corr_path) + covariance_matrix = self.load_data(cov_path) + full_returns = self.load_data(returns_path) + + if self.abs_corr: + correlation_matrix = np.abs(correlation_matrix) + + if self.abs_cov: + covaraiance_matrix = np.abs(covariance_matrix) + + if self._subprob_idx is not None: + return ( + correlation_matrix[self._subprob_idx][:, self._subprob_idx], + covaraiance_matrix[self._subprob_idx][:, self._subprob_idx], + full_returns[self._subprob_idx], + ) + + return correlation_matrix, covariance_matrix, full_returns diff --git a/tests/.ipynb_checkpoints/test_clustering-checkpoint.py b/tests/.ipynb_checkpoints/test_clustering-checkpoint.py new file mode 100644 index 0000000..779eed6 --- /dev/null +++ b/tests/.ipynb_checkpoints/test_clustering-checkpoint.py @@ -0,0 +1,57 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from dcmppln.denoiser import Denoiser +from dcmppln.clustering import Clustering + + +def test_clustering_with_louvain_false(sample_input_data): + denoiser = Denoiser(active=False) + correlation_matrix, covariance_matrix, full_returns = sample_input_data + C_2, _, _ = denoiser(correlation_matrix) + clustering_1 = Clustering(active=False) + partitions_1, _, _ = clustering_1(C_2) + clustering_2 = Clustering(active=False) + partitions_2, _, _ = clustering_2(C_2) + print(partitions_1) + assert (partitions_1 == partitions_2).all() + + +def test_clustering_with_louvain_true(sample_input_data): + denoiser = Denoiser(active=False) + correlation_matrix, covariance_matrix, full_returns = sample_input_data + C_2, _, _ = denoiser(correlation_matrix) + clustering_1 = Clustering(active=True) + partitions_1, _, _ = clustering_1(C_2) + clustering_2 = Clustering(active=True) + partitions_2, _, _ = clustering_2(C_2) + assert (partitions_1 == partitions_2).all() + + +def test_clustering_with_kmeans_false(sample_input_data): + denoiser = Denoiser(active=False) + correlation_matrix, covariance_matrix, full_returns = sample_input_data + C_2, _, _ = denoiser(correlation_matrix) + clustering_1 = Clustering(active=False, clustering_method="kmeans") + partitions_1, _, _ = clustering_1(C_2) + clustering_2 = Clustering(active=False, clustering_method="kmeans") + partitions_2, _, _ = clustering_2(C_2) + assert (partitions_1 == partitions_2).all() + + +def test_clustering_with_kmeans_true(sample_input_data): + denoiser = Denoiser(active=False) + correlation_matrix, covariance_matrix, full_returns = sample_input_data + C_2, _, _ = denoiser(correlation_matrix) + clustering_1 = Clustering(active=True, clustering_method="kmeans") + partitions_1, _, _ = clustering_1(C_2) + clustering_2 = Clustering(active=True, clustering_method="kmeans") + partitions_2, _, _ = clustering_2(C_2) + assert (partitions_1 == partitions_2).all() + + +def test_default_clustering_method(): + clustering = Clustering(active=True, clustering_method="xyz") + assert clustering.method == "louvain" diff --git a/tests/.ipynb_checkpoints/test_correlation_rmt-checkpoint.py b/tests/.ipynb_checkpoints/test_correlation_rmt-checkpoint.py new file mode 100644 index 0000000..1a11ebe --- /dev/null +++ b/tests/.ipynb_checkpoints/test_correlation_rmt-checkpoint.py @@ -0,0 +1,92 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np + +from gtar.utils.correlation_rmt import ( + marchenko_pastur_pdf, + fit_marchenko_pastur, + _find_best_beta, + split_covariance_matrices, +) + + +def test_marchenko_pastur_pdf(): + rng = np.random.default_rng(100) + a = rng.normal(size=(5, 30)) + covariance_matrix = np.matmul(a, a.transpose()) / a.shape[1] + eigenvalue, eigenvector = np.linalg.eigh(covariance_matrix) + q = 5.0 / 30.0 + pdf_1 = marchenko_pastur_pdf(eigenvalue, q) + rng = np.random.default_rng(100) + a = rng.normal(size=(5, 30)) + covariance_matrix = np.matmul(a, a.transpose()) / a.shape[1] + eigenvalue, eigenvector = np.linalg.eigh(covariance_matrix) + pdf_2 = marchenko_pastur_pdf(eigenvalue, q) + assert (pdf_1 == pdf_2).all() + + +def test_marchenko_pastur_pdf_for_zero(): + rng = np.random.default_rng(100) + a = rng.normal(size=(5, 30)) + covariance_matrix = np.matmul(a, a.transpose()) / a.shape[1] + eigenvalue, eigenvector = np.linalg.eigh(covariance_matrix) + """ + Hardcoded to test else condition that should return zero if lamda is not + within the range of lambda_plus and lambda_minus + lambda_minus <= lambda <= lambda_plus + """ + q = 0.05 + + pdf = marchenko_pastur_pdf(eigenvalue, q) + assert 0 in pdf + + +def test_fit_marchenko_pastur(sample_input_data): + correlation_matrix, covariance_matrix, full_returns = sample_input_data + eigenvalues, eigenvectors = np.linalg.eigh(covariance_matrix) + eigenvalues = eigenvalues[::-1] # Reverse order sorting + eigenvectors = eigenvectors[:, ::-1] # Reverse order sorting + rows, columns = 90.0, 1000.0 + beta = float(rows) / float(columns) + lambda_min_1, lambda_max_1 = fit_marchenko_pastur(eigenvalues[1:], beta) + lambda_min_2, lambda_max_2 = fit_marchenko_pastur(eigenvalues[1:], beta) + assert (lambda_min_1 == lambda_min_2) and (lambda_max_1 == lambda_max_2) + + +def test_fit_marchenko_paastur_q_fit_false(sample_input_data): + correlation_matrix, covariance_matrix, full_returns = sample_input_data + eigenvalues, eigenvectors = np.linalg.eigh(covariance_matrix) + eigenvalues = eigenvalues[::-1] # Reverse order sorting + eigenvectors = eigenvectors[:, ::-1] # Reverse order sorting + rows, columns = 90.0, 1000.0 + beta = float(rows) / float(columns) + lambda_min_false, lambda_max_false = fit_marchenko_pastur( + eigenvalues[1:], beta, q_fit=False + ) + lambda_min_true, lambda_max_true = fit_marchenko_pastur(eigenvalues[1:], beta) + assert lambda_max_false != lambda_max_true + + +def test_find_best_beta_sigma_1(sample_input_data): + correlation_matrix, covariance_matrix, full_returns = sample_input_data + eigenvalues, eigenvectors = np.linalg.eigh(covariance_matrix) + eigenvalues = eigenvalues[::-1] # Reverse order sorting + eigenvectors = eigenvectors[:, ::-1] # Reverse order sorting + rows, columns = 90.0, 1000.0 + beta = float(rows) / float(columns) + best_beta_1 = _find_best_beta(eigenvalues, beta, sigma=1.0) + assert beta != best_beta_1 + + +def test_split_covariance_matrices(sample_input_data): + correlation_matrix, covariance_matrix, full_returns = sample_input_data + C_Noise_1, C_Star_1, C_Global_1 = split_covariance_matrices(covariance_matrix) + C_Noise_2, C_Star_2, C_Global_2 = split_covariance_matrices(covariance_matrix) + assert ( + (C_Noise_1 == C_Noise_2).all() + and (C_Star_1 == C_Star_2).all() + and (C_Global_1 == C_Global_2).all() + ) diff --git a/tests/.ipynb_checkpoints/test_denoiser-checkpoint.py b/tests/.ipynb_checkpoints/test_denoiser-checkpoint.py new file mode 100644 index 0000000..e1767fc --- /dev/null +++ b/tests/.ipynb_checkpoints/test_denoiser-checkpoint.py @@ -0,0 +1,15 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from dcmppln.denoiser import Denoiser + + +def test_denoiser_function(sample_input_data): + denoiser = Denoiser(active=False) + correlation_matrix, covariance_matrix, full_returns = sample_input_data + correlation_matrix_1, covariance_matrix_1, full_returns_1 = sample_input_data + C_2_1, _, _ = denoiser(correlation_matrix) + C_2_2, _, _ = denoiser(correlation_matrix_1) + assert (C_2_1 == C_2_2).all() diff --git a/tests/.ipynb_checkpoints/test_pipeline-checkpoint.py b/tests/.ipynb_checkpoints/test_pipeline-checkpoint.py new file mode 100644 index 0000000..d5c86b0 --- /dev/null +++ b/tests/.ipynb_checkpoints/test_pipeline-checkpoint.py @@ -0,0 +1,22 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import os +from dcmppln.pipeline import Pipeline +from dcmppln.optimizer import GurobiOptimizer +import gurobipy as gp +from gurobipy import GRB + +def test_pipeline_with_gurobi(sample_input_data): + correlation_matrix, covariance_matrix, full_returns = sample_input_data + p = Pipeline( + correlation_matrix, + covariance_matrix, + full_returns, + optimize_func=GurobiOptimizer(), + ) + result = p.run(run_optimizer=True) + print(result) + assert result["score"] == 0.13237755898088116 diff --git a/tests/.ipynb_checkpoints/test_utils-checkpoint.py b/tests/.ipynb_checkpoints/test_utils-checkpoint.py new file mode 100644 index 0000000..0c176b6 --- /dev/null +++ b/tests/.ipynb_checkpoints/test_utils-checkpoint.py @@ -0,0 +1,73 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from dcmppln.utils.utils import ( + sparsification, + threshold_matrix, + thresholding_not_touching_diag, + sparsification_covariance_no_diag_mean_ratio, +) +import numpy as np + + +def test_threshold_matrix(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + binary_matrix = threshold_matrix(correlation_matrix) + assert np.array_equal(binary_matrix, binary_matrix.astype(bool)) + + +def test_thresholding_not_touching_diag(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + binary_matrix_diagonal_intact = thresholding_not_touching_diag(correlation_matrix) + di_1 = np.diagonal(correlation_matrix) + di_2 = np.diagonal(binary_matrix_diagonal_intact) + assert (di_1 == di_2).all() + + +def test_sparsification_for_diagonal(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + sparse_matrix = sparsification(correlation_matrix) + di_1 = np.diagonal(correlation_matrix) + di_2 = np.diagonal(sparse_matrix) + assert (di_1 == di_2).all() + + +def test_sparsification_for_threshold(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + threshold = 0.5 + sparse_matrix = sparsification(correlation_matrix, allow_to_modify_diag=True) + sparse_matrix_indices = np.argwhere(sparse_matrix == 0) + correlation_matrix_indices = np.argwhere(correlation_matrix <= threshold) + assert (sparse_matrix_indices == correlation_matrix_indices).all() + + +def test_sparsification_covariance_no_diag_mean_ratio_for_diagonal(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + di_1 = np.diag(correlation_matrix) + sparse_matrix = sparsification_covariance_no_diag_mean_ratio(correlation_matrix) + di_2 = np.diag(sparse_matrix) + assert (di_1 == di_2).all() + + +def test_sparsification_covariance_no_diag_mean_ratio_for_threshold(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + sparse_matrix = sparsification_covariance_no_diag_mean_ratio(correlation_matrix) + sparse_matrix_indices = np.argwhere(sparse_matrix == 0) + correlation_matrix_without_diagonal = np.array(correlation_matrix) + np.fill_diagonal(correlation_matrix_without_diagonal, 0) + avg_weight_without_diagonal = np.mean(correlation_matrix_without_diagonal) + np.fill_diagonal( + correlation_matrix_without_diagonal, avg_weight_without_diagonal + 1 + ) + correlation_matrix_indices = np.argwhere( + correlation_matrix_without_diagonal <= avg_weight_without_diagonal + ) + assert (sparse_matrix_indices == correlation_matrix_indices).all() diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000..65eb3d0 --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1,5 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### \ No newline at end of file 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differ diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 0000000..9bdfe08 --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,19 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import pytest +import numpy as np + +import os, sys + +sys.path.append(os.path.dirname(os.path.realpath(__file__))) + +from tests.input_data import InputData + + +@pytest.fixture +def sample_input_data(): + input_data = InputData() + return input_data.data_for_pipeline() diff --git a/tests/data/.gitkeep b/tests/data/.gitkeep new file mode 100644 index 0000000..e69de29 diff --git a/tests/data/1_2016-01-01_correlation.npy b/tests/data/1_2016-01-01_correlation.npy new file mode 100644 index 0000000..604923a Binary files /dev/null and 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a/tests/data/log_returns_20100101_russell3000.npy b/tests/data/log_returns_20100101_russell3000.npy new file mode 100644 index 0000000..279e4d6 Binary files /dev/null and b/tests/data/log_returns_20100101_russell3000.npy differ diff --git a/tests/data/log_returns_20140101_russell3000.npy b/tests/data/log_returns_20140101_russell3000.npy new file mode 100644 index 0000000..cae273d Binary files /dev/null and b/tests/data/log_returns_20140101_russell3000.npy differ diff --git a/tests/data/log_returns_20180101_russell3000_1.npy b/tests/data/log_returns_20180101_russell3000_1.npy new file mode 100644 index 0000000..06c2172 Binary files /dev/null and b/tests/data/log_returns_20180101_russell3000_1.npy differ diff --git a/tests/data/results/01082024_SA_Covariance.csv b/tests/data/results/01082024_SA_Covariance.csv new file mode 100644 index 0000000..accd265 --- /dev/null +++ b/tests/data/results/01082024_SA_Covariance.csv @@ -0,0 +1,2691 @@ 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+############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import os, sys + +sys.path.append(os.path.dirname(os.path.realpath(__file__))) +from dcmppln.utils.utils import get_instance_non_private_attributes +import numpy as np + + +class InputData: + """ + Component to input data into the pipeline + """ + + def __init__(self, path="", abs_cov=True, abs_corr=True, subprob_idx=None): + default_paths = ["../tests/data/", "tests/data/"] + + # Use the provided path or the first existing default path + if path: + self.path = path + else: + self.path = None + for default_path in default_paths: + if self._path_exists(default_path): + self.path = default_path + self.path=self.path+"seed_1000_size_90" + break + if not self.path: + raise FileNotFoundError("None of the default paths exist.") + + self.abs_corr = abs_corr + self.abs_cov = abs_cov + self.params = get_instance_non_private_attributes(self) + + self._subprob_idx = subprob_idx + + def __call__(self): + return self.data_for_pipeline() + + def _path_exists(self, path): + return os.path.exists(path) + + def load_data(self, path): + try: + return np.load(path) + except IOError: + ## output operation error + raise IOError(f"File not found: {path}") + + def data_for_pipeline(self): + corr_path = self.path + "_correlation.npy" + cov_path = self.path + "_covariance.npy" + returns_path = self.path + "_returns.npy" + + correlation_matrix = self.load_data(corr_path) + covariance_matrix = self.load_data(cov_path) + full_returns = self.load_data(returns_path) + + if self.abs_corr: + correlation_matrix = np.abs(correlation_matrix) + + if self.abs_cov: + covaraiance_matrix = np.abs(covariance_matrix) + + if self._subprob_idx is not None: + return ( + correlation_matrix[self._subprob_idx][:, self._subprob_idx], + covaraiance_matrix[self._subprob_idx][:, self._subprob_idx], + full_returns[self._subprob_idx], + ) + + return correlation_matrix, covariance_matrix, full_returns diff --git a/tests/test_clustering.py b/tests/test_clustering.py new file mode 100644 index 0000000..779eed6 --- /dev/null +++ b/tests/test_clustering.py @@ -0,0 +1,57 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from dcmppln.denoiser import Denoiser +from dcmppln.clustering import Clustering + + +def test_clustering_with_louvain_false(sample_input_data): + denoiser = Denoiser(active=False) + correlation_matrix, covariance_matrix, full_returns = sample_input_data + C_2, _, _ = denoiser(correlation_matrix) + clustering_1 = Clustering(active=False) + partitions_1, _, _ = clustering_1(C_2) + clustering_2 = Clustering(active=False) + partitions_2, _, _ = clustering_2(C_2) + print(partitions_1) + assert (partitions_1 == partitions_2).all() + + +def test_clustering_with_louvain_true(sample_input_data): + denoiser = Denoiser(active=False) + correlation_matrix, covariance_matrix, full_returns = sample_input_data + C_2, _, _ = denoiser(correlation_matrix) + clustering_1 = Clustering(active=True) + partitions_1, _, _ = clustering_1(C_2) + clustering_2 = Clustering(active=True) + partitions_2, _, _ = clustering_2(C_2) + assert (partitions_1 == partitions_2).all() + + +def test_clustering_with_kmeans_false(sample_input_data): + denoiser = Denoiser(active=False) + correlation_matrix, covariance_matrix, full_returns = sample_input_data + C_2, _, _ = denoiser(correlation_matrix) + clustering_1 = Clustering(active=False, clustering_method="kmeans") + partitions_1, _, _ = clustering_1(C_2) + clustering_2 = Clustering(active=False, clustering_method="kmeans") + partitions_2, _, _ = clustering_2(C_2) + assert (partitions_1 == partitions_2).all() + + +def test_clustering_with_kmeans_true(sample_input_data): + denoiser = Denoiser(active=False) + correlation_matrix, covariance_matrix, full_returns = sample_input_data + C_2, _, _ = denoiser(correlation_matrix) + clustering_1 = Clustering(active=True, clustering_method="kmeans") + partitions_1, _, _ = clustering_1(C_2) + clustering_2 = Clustering(active=True, clustering_method="kmeans") + partitions_2, _, _ = clustering_2(C_2) + assert (partitions_1 == partitions_2).all() + + +def test_default_clustering_method(): + clustering = Clustering(active=True, clustering_method="xyz") + assert clustering.method == "louvain" diff --git a/tests/test_correlation_rmt.py b/tests/test_correlation_rmt.py new file mode 100644 index 0000000..e576ff4 --- /dev/null +++ b/tests/test_correlation_rmt.py @@ -0,0 +1,92 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import numpy as np + +from dcmppln.utils.correlation_rmt import ( + marchenko_pastur_pdf, + fit_marchenko_pastur, + _find_best_beta, + split_covariance_matrices, +) + + +def test_marchenko_pastur_pdf(): + rng = np.random.default_rng(100) + a = rng.normal(size=(5, 30)) + covariance_matrix = np.matmul(a, a.transpose()) / a.shape[1] + eigenvalue, eigenvector = np.linalg.eigh(covariance_matrix) + q = 5.0 / 30.0 + pdf_1 = marchenko_pastur_pdf(eigenvalue, q) + rng = np.random.default_rng(100) + a = rng.normal(size=(5, 30)) + covariance_matrix = np.matmul(a, a.transpose()) / a.shape[1] + eigenvalue, eigenvector = np.linalg.eigh(covariance_matrix) + pdf_2 = marchenko_pastur_pdf(eigenvalue, q) + assert (pdf_1 == pdf_2).all() + + +def test_marchenko_pastur_pdf_for_zero(): + rng = np.random.default_rng(100) + a = rng.normal(size=(5, 30)) + covariance_matrix = np.matmul(a, a.transpose()) / a.shape[1] + eigenvalue, eigenvector = np.linalg.eigh(covariance_matrix) + """ + Hardcoded to test else condition that should return zero if lamda is not + within the range of lambda_plus and lambda_minus + lambda_minus <= lambda <= lambda_plus + """ + q = 0.05 + + pdf = marchenko_pastur_pdf(eigenvalue, q) + assert 0 in pdf + + +def test_fit_marchenko_pastur(sample_input_data): + correlation_matrix, covariance_matrix, full_returns = sample_input_data + eigenvalues, eigenvectors = np.linalg.eigh(covariance_matrix) + eigenvalues = eigenvalues[::-1] # Reverse order sorting + eigenvectors = eigenvectors[:, ::-1] # Reverse order sorting + rows, columns = 90.0, 1000.0 + beta = float(rows) / float(columns) + lambda_min_1, lambda_max_1 = fit_marchenko_pastur(eigenvalues[1:], beta) + lambda_min_2, lambda_max_2 = fit_marchenko_pastur(eigenvalues[1:], beta) + assert (lambda_min_1 == lambda_min_2) and (lambda_max_1 == lambda_max_2) + + +def test_fit_marchenko_paastur_q_fit_false(sample_input_data): + correlation_matrix, covariance_matrix, full_returns = sample_input_data + eigenvalues, eigenvectors = np.linalg.eigh(covariance_matrix) + eigenvalues = eigenvalues[::-1] # Reverse order sorting + eigenvectors = eigenvectors[:, ::-1] # Reverse order sorting + rows, columns = 90.0, 1000.0 + beta = float(rows) / float(columns) + lambda_min_false, lambda_max_false = fit_marchenko_pastur( + eigenvalues[1:], beta, q_fit=False + ) + lambda_min_true, lambda_max_true = fit_marchenko_pastur(eigenvalues[1:], beta) + assert lambda_max_false != lambda_max_true + + +def test_find_best_beta_sigma_1(sample_input_data): + correlation_matrix, covariance_matrix, full_returns = sample_input_data + eigenvalues, eigenvectors = np.linalg.eigh(covariance_matrix) + eigenvalues = eigenvalues[::-1] # Reverse order sorting + eigenvectors = eigenvectors[:, ::-1] # Reverse order sorting + rows, columns = 90.0, 1000.0 + beta = float(rows) / float(columns) + best_beta_1 = _find_best_beta(eigenvalues, beta, sigma=1.0) + assert beta != best_beta_1 + + +def test_split_covariance_matrices(sample_input_data): + correlation_matrix, covariance_matrix, full_returns = sample_input_data + C_Noise_1, C_Star_1, C_Global_1 = split_covariance_matrices(covariance_matrix) + C_Noise_2, C_Star_2, C_Global_2 = split_covariance_matrices(covariance_matrix) + assert ( + (C_Noise_1 == C_Noise_2).all() + and (C_Star_1 == C_Star_2).all() + and (C_Global_1 == C_Global_2).all() + ) diff --git a/tests/test_denoiser.py b/tests/test_denoiser.py new file mode 100644 index 0000000..e1767fc --- /dev/null +++ b/tests/test_denoiser.py @@ -0,0 +1,15 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from dcmppln.denoiser import Denoiser + + +def test_denoiser_function(sample_input_data): + denoiser = Denoiser(active=False) + correlation_matrix, covariance_matrix, full_returns = sample_input_data + correlation_matrix_1, covariance_matrix_1, full_returns_1 = sample_input_data + C_2_1, _, _ = denoiser(correlation_matrix) + C_2_2, _, _ = denoiser(correlation_matrix_1) + assert (C_2_1 == C_2_2).all() diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py new file mode 100644 index 0000000..d5c86b0 --- /dev/null +++ b/tests/test_pipeline.py @@ -0,0 +1,22 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +import os +from dcmppln.pipeline import Pipeline +from dcmppln.optimizer import GurobiOptimizer +import gurobipy as gp +from gurobipy import GRB + +def test_pipeline_with_gurobi(sample_input_data): + correlation_matrix, covariance_matrix, full_returns = sample_input_data + p = Pipeline( + correlation_matrix, + covariance_matrix, + full_returns, + optimize_func=GurobiOptimizer(), + ) + result = p.run(run_optimizer=True) + print(result) + assert result["score"] == 0.13237755898088116 diff --git a/tests/test_utils.py b/tests/test_utils.py new file mode 100644 index 0000000..bfce2ef --- /dev/null +++ b/tests/test_utils.py @@ -0,0 +1,70 @@ +############################################################################### +# // SPDX-License-Identifier: Apache-2.0 +# // Copyright 2024: Amazon Web Services, Inc. - Contributions from JPMC +# +############################################################################### +from dcmppln.utils.utils import ( + sparsification, + threshold_matrix, + thresholding_not_touching_diag, + sparsification_covariance_no_diag_mean_ratio, +) +import numpy as np + +def test_threshold_matrix(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + binary_matrix = threshold_matrix(correlation_matrix) + assert np.array_equal(binary_matrix, binary_matrix.astype(bool)) + + +def test_thresholding_not_touching_diag(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + binary_matrix_diagonal_intact = thresholding_not_touching_diag(correlation_matrix) + di_1 = np.diagonal(correlation_matrix) + di_2 = np.diagonal(binary_matrix_diagonal_intact) + assert (di_1 == di_2).all() + +def test_sparsification_for_diagonal(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + sparse_matrix = sparsification(correlation_matrix) + di_1 = np.diagonal(correlation_matrix) + di_2 = np.diagonal(sparse_matrix) + assert (di_1 == di_2).all() + + +def test_sparsification_for_threshold(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + threshold = 0.5 + sparse_matrix = sparsification(correlation_matrix, allow_to_modify_diag=True) + sparse_matrix_indices = np.argwhere(sparse_matrix == 0) + correlation_matrix_indices = np.argwhere(correlation_matrix <= threshold) + assert (sparse_matrix_indices == correlation_matrix_indices).all() + + +def test_sparsification_covariance_no_diag_mean_ratio_for_diagonal(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + di_1 = np.diag(correlation_matrix) + sparse_matrix = sparsification_covariance_no_diag_mean_ratio(correlation_matrix) + di_2 = np.diag(sparse_matrix) + assert (di_1 == di_2).all() + +def test_sparsification_covariance_no_diag_mean_ratio_for_threshold(): + np.random.seed(42) + correlation_matrix = np.random.laplace(0.0, 1.0, (3, 3)) + sparse_matrix = sparsification_covariance_no_diag_mean_ratio(correlation_matrix) + sparse_matrix_indices = np.argwhere(sparse_matrix == 0) + correlation_matrix_without_diagonal = np.array(correlation_matrix) + np.fill_diagonal(correlation_matrix_without_diagonal, 0) + avg_weight_without_diagonal = np.mean(correlation_matrix_without_diagonal) + np.fill_diagonal( + correlation_matrix_without_diagonal, avg_weight_without_diagonal + 1 + ) + correlation_matrix_indices = np.argwhere( + correlation_matrix_without_diagonal <= avg_weight_without_diagonal + ) + assert (sparse_matrix_indices == correlation_matrix_indices).all()