From 5cc7366f41c702921a67158695dfdef889070d14 Mon Sep 17 00:00:00 2001 From: mollyk Date: Tue, 19 Sep 2023 13:04:17 +0300 Subject: [PATCH] Dev/mip 802/svm impl scikit (#442) * Adds test and expected for SVM * Adds SVM algorithm and specs * Changes name from mipengine to exareme2 in dependencies in svm algo * Adds svm tests to prod env --- exareme2/algorithms/specifications.py | 1 + exareme2/algorithms/svm_scikit.json | 61 +++ exareme2/algorithms/svm_scikit.py | 140 +++++ .../expected/svm_scikit_expected.json | 486 ++++++++++++++++++ .../test_svm_scikit.py | 28 + .../expected/svm_scikit_expected.json | 486 ++++++++++++++++++ tests/prod_env_tests/test_svm_scikit.py | 28 + 7 files changed, 1230 insertions(+) create mode 100644 exareme2/algorithms/svm_scikit.json create mode 100644 exareme2/algorithms/svm_scikit.py create mode 100644 tests/algorithm_validation_tests/expected/svm_scikit_expected.json create mode 100644 tests/algorithm_validation_tests/test_svm_scikit.py create mode 100644 tests/prod_env_tests/expected/svm_scikit_expected.json create mode 100644 tests/prod_env_tests/test_svm_scikit.py diff --git a/exareme2/algorithms/specifications.py b/exareme2/algorithms/specifications.py index 9e71fd2e1..9190c697a 100644 --- a/exareme2/algorithms/specifications.py +++ b/exareme2/algorithms/specifications.py @@ -63,6 +63,7 @@ class AlgorithmName(str, Enum): NAIVE_BAYES_GAUSSIAN_CV = "naive_bayes_gaussian_cv" PCA = "pca" PEARSON_CORRELATION = "pearson_correlation" + SVM_SCIKIT = "svm_scikit" TTEST_INDEPENDENT = "ttest_independent" TTEST_ONESAMPLE = "ttest_onesample" TTEST_PAIRED = "ttest_paired" diff --git a/exareme2/algorithms/svm_scikit.json b/exareme2/algorithms/svm_scikit.json new file mode 100644 index 000000000..28e8a248a --- /dev/null +++ b/exareme2/algorithms/svm_scikit.json @@ -0,0 +1,61 @@ +{ + "name": "svm_scikit", + "desc": "Divide datasets into classes to find a maximum marginal hyperplane.", + "label": "SVM", + "enabled": true, + "inputdata": { + "y": { + "label": "Classes", + "desc": "Classes of x.", + "types": [ + "text", + "int", + "real" + ], + "stattypes": [ + "nominal" + ], + "notblank": true, + "multiple": true + }, + "x": { + "label": "Data points", + "desc": "Data points (support vectors) to be divided into classes.", + "types": [ + "real", + "int" + ], + "stattypes": [ + "numerical" + ], + "notblank": true, + "multiple": true + } + }, + "parameters": { + "gamma": { + "label": "Gamma", + "desc": "Gamma parameter of RBF controls the distance of the influence of a single training point.", + "types": [ + "real" + ], + "notblank": true, + "multiple": false, + "default": 0.1, + "min": 0.0, + "max": 1.0 + }, + "C": { + "label": "C", + "desc": "C regularization parameter used to set the tolerance of the model to allow the misclassification of data points in order to achieve lower generalization error. The C value controls the penalty of misclassification.", + "types": [ + "real" + ], + "notblank": true, + "multiple": false, + "default": 1.0, + "min": 0.0, + "max": 1.0 + } + } +} diff --git a/exareme2/algorithms/svm_scikit.py b/exareme2/algorithms/svm_scikit.py new file mode 100644 index 000000000..b09bbd629 --- /dev/null +++ b/exareme2/algorithms/svm_scikit.py @@ -0,0 +1,140 @@ +from typing import List +from typing import TypeVar + +from pydantic import BaseModel + +from exareme2.algorithms.algorithm import Algorithm +from exareme2.algorithms.algorithm import AlgorithmDataLoader +from exareme2.algorithms.fedaverage import fed_average +from exareme2.algorithms.helpers import get_transfer_data +from exareme2.algorithms.helpers import sum_secure_transfers +from exareme2.exceptions import BadUserInput +from exareme2.udfgen import literal +from exareme2.udfgen import relation +from exareme2.udfgen import secure_transfer +from exareme2.udfgen import udf + +ALGORITHM_NAME = "svm_scikit" + + +class SVMDataLoader(AlgorithmDataLoader, algname=ALGORITHM_NAME): + def get_variable_groups(self): + return [self._variables.x, self._variables.y] + + +class SVMResult(BaseModel): + title: str + n_obs: int + coeff: List[float] + support_vectors: List[float] + + +class SVMAlgorithm(Algorithm, algname=ALGORITHM_NAME): + def run(self, data, metadata): + X, y = data + gamma = self.algorithm_parameters["gamma"] + C = self.algorithm_parameters["C"] + + y_name = y.columns[0] + y_enums = metadata[y_name]["enumerations"].keys() + + if len(y_enums) < 2: + raise BadUserInput( + f"The variable {y_name} has less than 2 levels and SVM cannot be " + "performed. Please choose another variable." + ) + + models = SVMFedAverage(self.engine) + models.fit(X, y, gamma, C) + + result = SVMResult( + title="SVM Result", + n_obs=models.nobs_train, + coeff=models.coeff, + support_vectors=models.support_vectors, + ) + + return result + + +S = TypeVar("S") + + +class SVMFedAverage: + def __init__(self, engine): + self.num_local_nodes = engine.num_local_nodes + self.local_run = engine.run_udf_on_local_nodes + self.global_run = engine.run_udf_on_global_node + + def fit(self, x, y, gamma, C): + params_to_average, other_params = self.local_run( + func=self._fit_local, + keyword_args={"x": x, "y": y, "gamma": gamma, "C": C}, + share_to_global=[True, True], + ) + averaged_params_table = self.global_run( + func=fed_average, + keyword_args=dict( + params=params_to_average, num_local_nodes=self.num_local_nodes + ), + ) + other_params_table = self.global_run( + func=sum_secure_transfers, + keyword_args=dict(loctransf=other_params), + ) + + averaged_params = get_transfer_data(averaged_params_table) + other_params = get_transfer_data(other_params_table) + + self.coeff = averaged_params["coeff"] + self.support_vectors = averaged_params["support_vectors"] + self.nobs_train = other_params["nobs_train"] + + @staticmethod + @udf( + x=relation(schema=S), + y=relation(schema=S), + gamma=literal(), + C=literal(), + return_type=[secure_transfer(sum_op=True), secure_transfer(sum_op=True)], + ) + def _fit_local(x, y, gamma, C): + import numpy as np + from sklearn.svm import SVC + + y = y.to_numpy() + X = x.to_numpy() + + y_unq = np.unique(y) + if len(y_unq) < 2: + raise ValueError("Cannot perform SVM. Covariable has only one level.") + + model = SVC(kernel="linear", gamma=gamma, C=C) + model.fit(X, y) + + if len(model.coef_) < 2: + coeff = [model.coef_.squeeze().tolist()] + else: + coeff = model.coef_.squeeze().tolist() + + params_to_average = {} + params_to_average["coeff"] = { + "data": coeff, + "operation": "sum", + "type": "float", + } + params_to_average["support_vectors"] = { + "data": model.support_vectors_.squeeze().tolist(), + "operation": "sum", + "type": "float", + } + + other_params = { + "nobs_train": { + "data": len(y), + "operation": "sum", + "type": "int", + } + } # other quantities not meant to be averaged + + return params_to_average, other_params diff --git a/tests/algorithm_validation_tests/expected/svm_scikit_expected.json b/tests/algorithm_validation_tests/expected/svm_scikit_expected.json new file mode 100644 index 000000000..7a6d94133 --- /dev/null +++ b/tests/algorithm_validation_tests/expected/svm_scikit_expected.json @@ -0,0 +1,486 @@ +{ + "test_cases": [ + { + "input": { + "inputdata": { + "y": [ + "dataset" + ], + "x": [ + "rightgregyrusrectus" + ], + "data_model": "dementia:0.1", + "datasets": [ + "ppmi2", + "desd-synthdata6", + "ppmi6", + "desd-synthdata0", + "ppmi8", + "ppmi9", + "ppmi4", + "desd-synthdata4", + "edsd6", + "edsd2", + "edsd8", + "desd-synthdata5", + "desd-synthdata3", + "desd-synthdata2", + "edsd1", + "ppmi0", + "edsd5", + "ppmi5", + "ppmi1" + ], + "filters": null + }, + "parameters": { + "gamma": 0.2, + "C": 0.90 + }, + "test_case_num": 0 + }, + "output": { + "n_obs": 1296, + "coeff": [-8.737739953801338e-06, -8.582338750784402e-08, 1.5781284140814478e-05, -2.2240308723553426e-06, 1.0469008387303802e-05, -2.415749704625746, -1.8745188803848123e-05, -2.295600154411659e-07, -4.064924759461519e-06, 6.62449265576015e-06, -2.3468684148435273, -1.9155600000000064, 9.086167935379308e-06, -2.140709767672888e-06, 2.2978672170381742e-05, 1.1242218533880077e-06, 8.381942393498321e-07, 1.0435477250325675e-05, 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4.1976, + 3.8023, + 3.3951, + 3.6644 + ] + } + } + ] +} diff --git a/tests/algorithm_validation_tests/test_svm_scikit.py b/tests/algorithm_validation_tests/test_svm_scikit.py new file mode 100644 index 000000000..65b522da0 --- /dev/null +++ b/tests/algorithm_validation_tests/test_svm_scikit.py @@ -0,0 +1,28 @@ +from pathlib import Path + +import pytest + +from tests.algorithm_validation_tests.helpers import algorithm_request +from tests.algorithm_validation_tests.helpers import assert_allclose +from tests.algorithm_validation_tests.helpers import get_test_params +from tests.algorithm_validation_tests.helpers import parse_response + +algorithm_name = "svm_scikit" + +expected_file = Path(__file__).parent / "expected" / f"{algorithm_name}_expected.json" + + +@pytest.mark.parametrize( + "test_input, expected", + get_test_params( + expected_file, + skip_indices=[4], + skip_reason="Tests that when covariable has only one level, the algorithm raises a value error", + ), +) +def test_svm_scikit(test_input, expected, subtests): + response = algorithm_request(algorithm_name, test_input) + result = parse_response(response) + + # this test only ensures that the algorithm runs smoothly without errors + assert result diff --git a/tests/prod_env_tests/expected/svm_scikit_expected.json b/tests/prod_env_tests/expected/svm_scikit_expected.json new file mode 100644 index 000000000..7a6d94133 --- /dev/null +++ b/tests/prod_env_tests/expected/svm_scikit_expected.json @@ -0,0 +1,486 @@ +{ + "test_cases": [ + { + "input": { + "inputdata": { + "y": [ + "dataset" + ], + "x": [ + "rightgregyrusrectus" + ], + "data_model": "dementia:0.1", + "datasets": [ + "ppmi2", + "desd-synthdata6", + "ppmi6", + "desd-synthdata0", + "ppmi8", + "ppmi9", + "ppmi4", + "desd-synthdata4", + "edsd6", + "edsd2", + "edsd8", + "desd-synthdata5", + "desd-synthdata3", + "desd-synthdata2", + "edsd1", + "ppmi0", + "edsd5", + "ppmi5", + "ppmi1" + ], + "filters": null + }, + "parameters": { + "gamma": 0.2, 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tests.algorithm_validation_tests.helpers import get_test_params +from tests.algorithm_validation_tests.helpers import parse_response + +algorithm_name = "svm_scikit" + +expected_file = Path(__file__).parent / "expected" / f"{algorithm_name}_expected.json" + + +@pytest.mark.parametrize( + "test_input, expected", + get_test_params( + expected_file, + skip_indices=[4], + skip_reason="Tests that when covariable has only one level, the algorithm raises a value error", + ), +) +def test_svm_scikit(test_input, expected, subtests): + response = algorithm_request(algorithm_name, test_input) + result = parse_response(response) + + # this test only ensures that the algorithm runs smoothly without errors + assert result