From bddfea1ae2bc96dc03733653971d81ba3eb09c01 Mon Sep 17 00:00:00 2001 From: mollyk Date: Wed, 10 Jul 2024 13:00:10 +0300 Subject: [PATCH] Dev/mip 897/add log transformation (#487) * Adds tests and generator to PCA POC * Separates PCA from PCA with transformations completely * Fixes pca transformations poc * Attemt to fix black error * Removes erroneous test cases * Fix complexity for codeclimate * Fixes comments from pr * Fixes errors in testcase_generator * Minor fix in test case generator --------- Co-authored-by: K.Filippopolitis <56073635+KFilippopolitis@users.noreply.github.com> --- .../exareme2/pca_with_tranformations.py | 283 + .../exareme2/pca_with_transformations.json | 37 + exareme2/algorithms/specifications.py | 1 + .../pca_with_transformation_expected.json | 36026 ++++++++++++++++ ...test_pca_with_transformation_validation.py | 37 + ...with_transformations_testcase_generator.py | 75 + .../testcase_generators/testcase_generator.py | 64 +- 7 files changed, 36520 insertions(+), 3 deletions(-) create mode 100644 exareme2/algorithms/exareme2/pca_with_tranformations.py create mode 100644 exareme2/algorithms/exareme2/pca_with_transformations.json create mode 100644 tests/algorithm_validation_tests/exareme2/expected/pca_with_transformation_expected.json create mode 100644 tests/algorithm_validation_tests/exareme2/test_pca_with_transformation_validation.py create mode 100644 tests/testcase_generators/pca_with_transformations_testcase_generator.py diff --git a/exareme2/algorithms/exareme2/pca_with_tranformations.py b/exareme2/algorithms/exareme2/pca_with_tranformations.py new file mode 100644 index 000000000..fca473fb4 --- /dev/null +++ b/exareme2/algorithms/exareme2/pca_with_tranformations.py @@ -0,0 +1,283 @@ +from typing import List +from typing import TypeVar + +from pydantic import BaseModel + +from exareme2 import DType +from exareme2.algorithms.exareme2.algorithm import Algorithm +from exareme2.algorithms.exareme2.algorithm import AlgorithmDataLoader +from exareme2.algorithms.exareme2.helpers import get_transfer_data +from exareme2.algorithms.exareme2.pca import PCAResult +from exareme2.algorithms.exareme2.pca import global1 +from exareme2.algorithms.exareme2.pca import global2 +from exareme2.algorithms.exareme2.pca import local1 +from exareme2.algorithms.exareme2.pca import local2 +from exareme2.algorithms.exareme2.udfgen import DEFERRED +from exareme2.algorithms.exareme2.udfgen import literal +from exareme2.algorithms.exareme2.udfgen import merge_transfer +from exareme2.algorithms.exareme2.udfgen import relation +from exareme2.algorithms.exareme2.udfgen import secure_transfer +from exareme2.algorithms.exareme2.udfgen import state +from exareme2.algorithms.exareme2.udfgen import transfer +from exareme2.algorithms.exareme2.udfgen import udf +from exareme2.worker_communication import BadUserInput + +ALGORITHM_NAME = "pca_with_transformation" + + +class PCADataLoader(AlgorithmDataLoader, algname=ALGORITHM_NAME): + def get_variable_groups(self): + return [self._variables.y] + + +class PCAAlgorithm(Algorithm, algname=ALGORITHM_NAME): + def run(self, data, metadata): + [X_relation] = data + + if "data_transformation" in self.algorithm_parameters: + if any( + trans in self.algorithm_parameters["data_transformation"] + for trans in ["log", "exp"] + ): + X_relation = self.handle_data_transformation(X_relation) + + if any( + trans in self.algorithm_parameters["data_transformation"] + for trans in ["standardize", "center"] + ): + X_relation = self.handle_standardize_and_center(X_relation) + + return self.perform_pca(X_relation) + + def handle_data_transformation(self, X_relation): + local_run = self.engine.run_udf_on_local_workers + + data_transformation = self.algorithm_parameters["data_transformation"] + output_schema = [("row_id", DType.INT)] + output_schema += [(colname, DType.FLOAT) for colname in X_relation.columns] + + try: + X_relation = local_run( + func=local_data_processing, + keyword_args={ + "data": X_relation, + "data_transformation_dict": { + k: v + for k, v in data_transformation.items() + if k in ["log", "exp"] + }, + }, + output_schema=output_schema, + share_to_global=[False], + ) + except Exception as ex: + self.handle_data_transformation_exceptions(ex) + + return X_relation + + def handle_data_transformation_exceptions(self, ex): + if ( + "Log transformation cannot be applied to non-positive values in column." + in str(ex) + or "Unknown transformation" in str(ex) + ): + raise BadUserInput(str(ex)) + raise ex + + def handle_standardize_and_center(self, X_relation): + local_run = self.engine.run_udf_on_local_workers + global_run = self.engine.run_udf_on_global_worker + + data_transformation = self.algorithm_parameters.get("data_transformation", {}) + + try: + local_transfers_stats = local_run( + func=local_stats, + keyword_args={ + "x": X_relation, + }, + share_to_global=[True], + ) + _, global_stats_transfer = global_run( + func=global_stats, + keyword_args={"local_transfers": local_transfers_stats}, + share_to_locals=[True, True], + ) + X_relation = local_run( + func=local_transform, + keyword_args={ + "x": X_relation, + "global_transfer": global_stats_transfer, + "data_transformation_dict": { + k: v + for k, v in data_transformation.items() + if k in ["center", "standardize"] + }, + }, + output_schema=[("row_id", DType.INT)] + + [(colname, DType.FLOAT) for colname in X_relation.columns], + share_to_global=[False], + ) + except Exception as ex: + self.handle_standardize_and_center_exceptions(ex) + + return X_relation + + def handle_standardize_and_center_exceptions(self, ex): + if "Unknown transformation" in str( + ex + ) or "Standardization cannot be applied to column" in str(ex): + raise BadUserInput(str(ex)) + raise ex + + def perform_pca(self, X_relation): + local_run = self.engine.run_udf_on_local_workers + global_run = self.engine.run_udf_on_global_worker + + local_transfers = local_run( + func=local1, + keyword_args={"x": X_relation}, + share_to_global=[True], + ) + global_state, global_transfer = global_run( + func=global1, + keyword_args=dict(local_transfers=local_transfers), + share_to_locals=[False, True], + ) + local_transfers = local_run( + func=local2, + keyword_args=dict(x=X_relation, global_transfer=global_transfer), + share_to_global=[True], + ) + result = global_run( + func=global2, + keyword_args=dict(local_transfers=local_transfers, prev_state=global_state), + ) + result = get_transfer_data(result) + n_obs = result["n_obs"] + eigenvalues = result["eigenvalues"] + eigenvectors = result["eigenvectors"] + + return PCAResult( + title="Eigenvalues and Eigenvectors", + n_obs=n_obs, + eigenvalues=eigenvalues, + eigenvectors=eigenvectors, + ) + + +S = TypeVar("S") + + +@udf( + data=relation(schema=S), + data_transformation_dict=literal(), + return_type=relation(schema=DEFERRED), +) +def local_data_processing(data, data_transformation_dict): + """ + Function to normalize a skewed distribution. + + :param data: the actual data passed to the algorithm + :param data_transformation_dict: the dict passed to the algorithm indicating which variables need to change with which method + :return: data columns transformed with an error message column if applicable + """ + import numpy as np + import pandas as pd + + def apply_log_transformation(data, variables): + for variable in variables: + if (data[variable] <= 0).any(): + raise ValueError( + f"Log transformation cannot be applied to non-positive values in column '{variable}'." + ) + data[variable] = np.log(data[variable]) + return data + + def apply_exp_transformation(data, variables): + for variable in variables: + data[variable] = np.exp(data[variable]) + return data + + transformation_functions = { + "log": apply_log_transformation, + "exp": apply_exp_transformation, + } + + for transformation, variables in data_transformation_dict.items(): + if transformation not in transformation_functions: + raise ValueError(f"Unknown transformation: {transformation}") + data = transformation_functions[transformation](data, variables) + + result = pd.DataFrame(data=data, index=data.index, columns=data.columns) + return result + + +@udf(x=relation(schema=S), return_type=[secure_transfer(sum_op=True)]) +def local_stats(x): + n_obs = len(x) + sx = numpy.einsum("ij->j", x) + sxx = numpy.einsum("ij,ij->j", x, x) + + transfer_ = {} + transfer_["n_obs"] = {"data": n_obs, "operation": "sum", "type": "int"} + transfer_["sx"] = {"data": sx.tolist(), "operation": "sum", "type": "float"} + transfer_["sxx"] = {"data": sxx.tolist(), "operation": "sum", "type": "float"} + return transfer_ + + +@udf(local_transfers=secure_transfer(sum_op=True), return_type=[state(), transfer()]) +def global_stats(local_transfers): + import numpy as np + + n_obs = local_transfers["n_obs"] + sx = numpy.array(local_transfers["sx"]) + sxx = numpy.array(local_transfers["sxx"]) + + means = sx / n_obs + sigmas = ((sxx - n_obs * means**2) / (n_obs - 1)) ** 0.5 + + state_ = dict(n_obs=n_obs) + transfer_ = dict(means=means.tolist(), sigmas=sigmas.tolist()) + return state_, transfer_ + + +@udf( + x=relation(schema=S), + global_transfer=transfer(), + data_transformation_dict=literal(), + return_type=relation(schema=DEFERRED), +) +def local_transform(x, global_transfer, data_transformation_dict): + import numpy as np + import pandas as pd + + # Extract means and sigmas from global_transfer + means = np.array(global_transfer.get("means", [])) + sigmas = np.array(global_transfer.get("sigmas", [])) + + # Convert input relation x to a Pandas DataFrame if it's not already + if not isinstance(x, pd.DataFrame): + x = pd.DataFrame(x) + + # Helper function to apply centering + def apply_centering(x, col_name, means): + x[col_name] -= means[x.columns.get_loc(col_name)] + + # Helper function to apply standardization + def apply_standardization(x, col_name, means, sigmas): + x[col_name] = (x[col_name] - means[x.columns.get_loc(col_name)]) / sigmas[ + x.columns.get_loc(col_name) + ] + + # Apply centering and standardization + center_cols = data_transformation_dict.get("center", []) + standardize_cols = data_transformation_dict.get("standardize", []) + + for col_name in x.columns: + if col_name in center_cols: + apply_centering(x, col_name, means) + if col_name in standardize_cols: + apply_standardization(x, col_name, means, sigmas) + + return x diff --git a/exareme2/algorithms/exareme2/pca_with_transformations.json b/exareme2/algorithms/exareme2/pca_with_transformations.json new file mode 100644 index 000000000..78729d33e --- /dev/null +++ b/exareme2/algorithms/exareme2/pca_with_transformations.json @@ -0,0 +1,37 @@ +{ + "name": "pca_with_transformation", + "desc": "Computes the principal components of a set of correlated variables. The principal components can then be used to represent the original data with reduced dimensions.", + "label": "Principal Component Analysis (PCA)", + "enabled": true, + "type": "exareme2", + "inputdata": { + "y": { + "label": "Variables", + "desc": "A list of numerical variables.", + "types": [ + "real", + "int" + ], + "stattypes": [ + "numerical" + ], + "notblank": true, + "multiple": true + } + }, + "parameters": { + "data_transformation": { + "label": "Data Transformation", + "desc": "Transform a column with on of the given methods (log, exp, center, standardize) to reduce skewness of a distribution towards normalcy.", + "types": ["dict"], + "notblank": false, + "multiple": false, + "transformation_method": { + "type": "text", "source": ["log", "exp", "center", "standardize"] + }, + "variables_to_transform": { + "type": "list", "source": ["x", "y"] + } + } + } +} diff --git a/exareme2/algorithms/specifications.py b/exareme2/algorithms/specifications.py index ffc1d6028..2480accbb 100644 --- a/exareme2/algorithms/specifications.py +++ b/exareme2/algorithms/specifications.py @@ -73,6 +73,7 @@ class AlgorithmName(str, Enum): NAIVE_BAYES_CATEGORICAL_CV = "naive_bayes_categorical_cv" NAIVE_BAYES_GAUSSIAN_CV = "naive_bayes_gaussian_cv" PCA = "pca" + PCA_WITH_TRANSFORMATION = "pca_with_transformation" PEARSON_CORRELATION = "pearson_correlation" SVM_SCIKIT = "svm_scikit" TTEST_INDEPENDENT = "ttest_independent" diff --git a/tests/algorithm_validation_tests/exareme2/expected/pca_with_transformation_expected.json b/tests/algorithm_validation_tests/exareme2/expected/pca_with_transformation_expected.json new file mode 100644 index 000000000..421ed4e3e --- /dev/null +++ b/tests/algorithm_validation_tests/exareme2/expected/pca_with_transformation_expected.json @@ -0,0 +1,36026 @@ +{ + "test_cases": [ + { + "input": { + "inputdata": { + "y": [ + "leftfugfusiformgyrus", + "rightitginferiortemporalgyrus", + "rightmpogpostcentralgyrusmedialsegment" + ], + "data_model": "dementia:0.1", + "datasets": [ + "edsd0", + "edsd7", + "edsd1", + "desd-synthdata9", + "desd-synthdata8", + "desd-synthdata0", + "ppmi0", + "edsd2", + "desd-synthdata7", + "ppmi1", + "ppmi3", + "edsd4" + ], + "filters": null + }, + "parameters": { + "data_transformation": { + "exp": [ + "leftfugfusiformgyrus", + "rightitginferiortemporalgyrus", + "rightmpogpostcentralgyrusmedialsegment" + ] + } + }, + "test_case_num": 1 + }, + "output": { + "n_obs": 670, + "eigen_vals": [ + 1.911577365557045, + 0.7674679397561303, + 0.32095469468682486 + ], + "eigen_vecs": [ + [ + 0.6441054414178599, + 0.6100064500401623, + 0.4615412346099778 + ], + [ + 0.2070440154821594, + 0.4418213023176705, + -0.8728841346200198 + ], + [ + -0.7363837016046235, + 0.6577887713603178, + 0.15828132007080442 + ] + ], + "means": [ + 1590.2922610707358, + 165778.92384841666, + 2.911571153011931 + ], + "sigmas": [ + 1532.497770595651, + 301879.5525577236, + 0.44473350255962796 + ] + } + }, + { + "input": { + "inputdata": { + "y": [ + "leftsplsuperiorparietallobule", + "rightangangulargyrus", + "rightopifgopercularpartoftheinferiorfrontalgyrus", + "leftppplanumpolare", + "rightcalccalcarinecortex", + "rightmfcmedialfrontalcortex", + "rightmogmiddleoccipitalgyrus", + "rightmsfgsuperiorfrontalgyrusmedialsegment", + "lefttmptemporalpole", + "rightcerebellumwhitematter", + "leftamygdala", + "rightpoparietaloperculum", + "righttrifgtriangularpartoftheinferiorfrontalgyrus", + "leftofugoccipitalfusiformgyrus", + "_3rdventricle", + "leftptplanumtemporale", + "rightmpogpostcentralgyrusmedialsegment", + "rightventraldc", + "leftgregyrusrectus", + "rightppplanumpolare", + "leftmcggmiddlecingulategyrus", + "leftmsfgsuperiorfrontalgyrusmedialsegment", + "subjectage", + "rightocpoccipitalpole", + "lefttrifgtriangularpartoftheinferiorfrontalgyrus", + "leftphgparahippocampalgyrus", + "cerebellarvermallobulesviiix", + "rightsfgsuperiorfrontalgyrus", + "rightitginferiortemporalgyrus", + "leftsmgsupramarginalgyrus" + ], + "data_model": "dementia:0.1", + "datasets": [ + "desd-synthdata1", + "ppmi5", + "desd-synthdata5", + "desd-synthdata4", + "edsd7", + "edsd5", + "ppmi7", + "desd-synthdata3", + "edsd2", + "ppmi6", + "desd-synthdata6", + "edsd0", + "ppmi9", + "desd-synthdata0" + ], + "filters": null + }, + "parameters": { + "data_transformation": { + "standardize": [ + "rightventraldc", + "rightitginferiortemporalgyrus", + "rightmpogpostcentralgyrusmedialsegment", + "lefttrifgtriangularpartoftheinferiorfrontalgyrus" + ], + "center": [ + "rightpoparietaloperculum", + "cerebellarvermallobulesviiix", + "rightsfgsuperiorfrontalgyrus" + ], + "exp": [ + "leftsplsuperiorparietallobule", + "rightocpoccipitalpole" + ] + } + }, + "test_case_num": 2 + }, + "output": { + "n_obs": 436, + "eigen_vals": [ + 15.566139613911583, + 2.1864609457798916, + 1.5275143287381467, + 1.0498442656555949, + 0.9709761235388811, + 0.8435476529417101, + 0.8261029198187368, + 0.7540805672372416, + 0.6615919059416718, + 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+from tests.algorithm_validation_tests.exareme2.helpers import get_test_params +from tests.algorithm_validation_tests.exareme2.helpers import parse_response + +algorithm_name = "pca_with_transformation" + +expected_file = Path(__file__).parent / "expected" / f"{algorithm_name}_expected.json" + + +@pytest.mark.parametrize("test_input, expected", get_test_params(expected_file)) +def test_pca_algorithm(test_input, expected): + response = algorithm_request(algorithm_name, test_input) + + if "errors" in expected: + assert "Log transformation cannot be applied to non-positive values." in str( + response.content + ) or "Standardization cannot be applied to column" in str(response.content) + else: + result = parse_response(response) + assert int(result["n_obs"]) == int(expected["n_obs"]) + np.testing.assert_allclose( + result["eigenvalues"], + expected["eigen_vals"], + rtol=1e-7, + atol=1e-10, + ) + + +def assert_vectors_are_collinear(u, v): + cosine_similarity = np.dot(v, u) / (np.sqrt(np.dot(v, v)) * np.sqrt(np.dot(u, u))) + np.testing.assert_allclose(abs(cosine_similarity), 1, rtol=1e-7, atol=1e-10) diff --git a/tests/testcase_generators/pca_with_transformations_testcase_generator.py b/tests/testcase_generators/pca_with_transformations_testcase_generator.py new file mode 100644 index 000000000..ba484f552 --- /dev/null +++ b/tests/testcase_generators/pca_with_transformations_testcase_generator.py @@ -0,0 +1,75 @@ +from pathlib import Path + +import numpy as np +import pandas as pd +from sklearn.decomposition import PCA + +from tests.testcase_generators.testcase_generator import TestCaseGenerator + +SPECS_PATH = Path("exareme2", "algorithms", "exareme2", "pca_with_transformations.json") +EXPECTED_PATH = Path( + "tests", + "algorithm_validation_tests", + "exareme2", + "expected", + "pca_with_transformation_expected.json", +) + + +class PCATestCaseGenerator(TestCaseGenerator): + def compute_expected_output(self, input_data, input_parameters=None, metadata=None): + X, _ = input_data + + if "data_transformation" in input_parameters: + for transformation, variables in input_parameters[ + "data_transformation" + ].items(): + for variable in variables: + try: + if transformation == "log": + if (X[variable] <= 0).any(): + raise ValueError( + f"Log transformation cannot be applied to non-positive values in column '{variable}'." + ) + X[variable] = np.log(X[variable]) + elif transformation == "exp": + X[variable] = np.exp(X[variable]) + elif transformation == "center": + mean = np.mean(X[variable]) + X[variable] = X[variable] - mean + elif transformation == "standardize": + mean = np.mean(X[variable]) + std = np.std(X[variable]) + if std == 0: + raise ValueError( + f"Standardization cannot be applied to column '{variable}' because the standard deviation is zero." + ) + X[variable] = (X[variable] - mean) / std + else: + raise ValueError( + f"Unknown transformation: {transformation}" + ) + + except Exception as e: + return {"errors": [str(e)]} + + X -= X.mean(axis=0) + X /= X.std(axis=0, ddof=1) + pca = PCA() + pca.fit(X) + + output = { + "n_obs": len(X), + "eigen_vals": pca.explained_variance_.tolist(), + "eigen_vecs": pca.components_.tolist(), + } + return output + + +if __name__ == "__main__": + with open( + "exareme2/algorithms/exareme2/pca_with_transformations.json" + ) as specs_file: + pcagen = PCATestCaseGenerator(specs_file) + with open(EXPECTED_PATH, "w") as expected_file: + pcagen.write_test_cases(expected_file) diff --git a/tests/testcase_generators/testcase_generator.py b/tests/testcase_generators/testcase_generator.py index f27542533..e9c27cdb7 100644 --- a/tests/testcase_generators/testcase_generator.py +++ b/tests/testcase_generators/testcase_generator.py @@ -13,7 +13,7 @@ TESTING_DATAMODEL = "dementia:0.1" DATAMODEL_BASEPATH = Path("tests/test_data/dementia_v_0_1/") DATAMODEL_CDESPATH = DATAMODEL_BASEPATH / "CDEsMetadata.json" - +TRANSFORMATIONS = ["log", "exp", "center", "standardize"] random.seed(0) @@ -252,6 +252,47 @@ def enums(self): def draw(self): return random.choice(self.enums) + import random + + +class DataTransformationParam(AlgorithmParameter): + db = DB() + + def __init__(self, var_names): + self.var_names = var_names + + def draw(self): + # Shuffle var_names to randomize their selection + var_names_copy = self.var_names[:] + random.shuffle(var_names_copy) + + # Select a random number of transformations to use + num_transformations = random.randint(1, len(TRANSFORMATIONS)) + chosen_transformations = random.sample(TRANSFORMATIONS, num_transformations) + + result = {} + MAX_VARS_PER_TRANSFORMATION = 4 + + for transformation in chosen_transformations: + # Ensure num_vars is not larger than the remaining items in var_names_copy + num_vars = max( + 1, + min( + random.randint(1, MAX_VARS_PER_TRANSFORMATION), len(var_names_copy) + ), + ) + selected_vars = random.sample(var_names_copy, num_vars) + result[transformation] = selected_vars + + # Remove used var_names to prevent repetition + var_names_copy = [var for var in var_names_copy if var not in selected_vars] + + # Stop if no more var_names are left + if not var_names_copy: + break + + return result + def make_parameters(properties, variable_groups): if "enums" in properties and properties["enums"]["type"] == "list": @@ -266,6 +307,11 @@ def make_parameters(properties, variable_groups): return FloatParameter(min=properties["min"], max=properties["max"]) if "int" in properties["types"]: return IntegerParameter(min=properties["min"], max=properties["max"]) + if "dict" in properties["types"] and "transformation_method" in properties: + var_names = list(variable_groups["y"]) + ( + list(variable_groups["x"] if variable_groups["x"] else []) + ) + return DataTransformationParam(var_names=var_names) raise TypeError(f"Unknown parameter type: {properties['types']}.") @@ -280,6 +326,7 @@ def __init__(self, specs): self.datasets_gen = DatasetsGenerator() self.filters_gen = FiltersGenerator() self._seen = set() + self._counter = 0 def init_variable_gens(self): numerical_vars = self.db.get_numerical_variables() @@ -287,7 +334,11 @@ def init_variable_gens(self): numerical_pool = list(random_permutation(numerical_vars)) nominal_pool = list(random_permutation(nominal_vars)) - # if specs contain only y variable, pass entire pools to variable generator + self._counter += 1 + if self._counter == 1: + if not isinstance(self.specs, dict): + with open(self.specs.name, "r") as f2: + self.specs = json.loads(f2.read()) if len(self.specs["inputdata"]) == 1: assert "y" in self.specs["inputdata"], "There should be a 'y' in inputdata" self.inputdata_gens = { @@ -363,7 +414,14 @@ def draw(self): } input_ = {"inputdata": inputdata, "parameters": parameters} - input_key = (*inputdata.values(), *parameters.values()) + data_transformation = parameters.get("data_transformation", None) + param_without_data_transformation = parameters.copy() + if "data_transformation" in param_without_data_transformation: + param_without_data_transformation.pop("data_transformation") + input_key = ( + *inputdata.values(), + *param_without_data_transformation.values(), + ) if input_key not in self._seen: self._seen.add(input_key) return input_