From 930fa8dd8405efc8377a56cd636a79f6d2cb312a Mon Sep 17 00:00:00 2001 From: BeGeiger Date: Tue, 31 Oct 2023 02:28:30 -0400 Subject: [PATCH 1/5] add weights and sparsity In KL-NMF, it is now possible to weight the samples in the KL-divergence loss and to add a sparsity penalty on the exposures. --- poetry.lock | 18 +-- src/salamander/nmf_framework/_utils_klnmf.py | 158 +++++++++++++++---- src/salamander/nmf_framework/corrnmf_det.py | 5 +- src/salamander/nmf_framework/klnmf.py | 83 +++++++++- tests/test_utils_klnmf.py | 73 +++++++++ 5 files changed, 287 insertions(+), 50 deletions(-) diff --git a/poetry.lock b/poetry.lock index d2931af..7859485 100644 --- a/poetry.lock +++ b/poetry.lock @@ -217,19 +217,19 @@ test = ["scipy (>=1.6.3)"] [[package]] name = "filelock" -version = "3.12.4" +version = "3.13.1" description = "A platform independent file lock." optional = false python-versions = ">=3.8" files = [ - {file = "filelock-3.12.4-py3-none-any.whl", hash = "sha256:08c21d87ded6e2b9da6728c3dff51baf1dcecf973b768ef35bcbc3447edb9ad4"}, - {file = "filelock-3.12.4.tar.gz", hash = "sha256:2e6f249f1f3654291606e046b09f1fd5eac39b360664c27f5aad072012f8bcbd"}, + {file = "filelock-3.13.1-py3-none-any.whl", hash = "sha256:57dbda9b35157b05fb3e58ee91448612eb674172fab98ee235ccb0b5bee19a1c"}, + {file = "filelock-3.13.1.tar.gz", hash = "sha256:521f5f56c50f8426f5e03ad3b281b490a87ef15bc6c526f168290f0c7148d44e"}, ] [package.extras] -docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1.24)"] -testing = ["covdefaults (>=2.3)", "coverage (>=7.3)", "diff-cover (>=7.7)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)", "pytest-timeout (>=2.1)"] -typing = ["typing-extensions (>=4.7.1)"] +docs = ["furo (>=2023.9.10)", "sphinx (>=7.2.6)", "sphinx-autodoc-typehints (>=1.24)"] +testing = ["covdefaults (>=2.3)", "coverage (>=7.3.2)", "diff-cover (>=8)", "pytest (>=7.4.3)", "pytest-cov (>=4.1)", "pytest-mock (>=3.12)", "pytest-timeout (>=2.2)"] +typing = ["typing-extensions (>=4.8)"] [[package]] name = "fonttools" @@ -298,13 +298,13 @@ woff = ["brotli (>=1.0.1)", "brotlicffi (>=0.8.0)", "zopfli (>=0.1.4)"] [[package]] name = "identify" -version = "2.5.30" +version = "2.5.31" description = "File identification library for Python" optional = false python-versions = ">=3.8" files = [ - {file = "identify-2.5.30-py2.py3-none-any.whl", hash = "sha256:afe67f26ae29bab007ec21b03d4114f41316ab9dd15aa8736a167481e108da54"}, - {file = "identify-2.5.30.tar.gz", hash = "sha256:f302a4256a15c849b91cfcdcec052a8ce914634b2f77ae87dad29cd749f2d88d"}, + {file = "identify-2.5.31-py2.py3-none-any.whl", hash = "sha256:90199cb9e7bd3c5407a9b7e81b4abec4bb9d249991c79439ec8af740afc6293d"}, + {file = "identify-2.5.31.tar.gz", hash = "sha256:7736b3c7a28233637e3c36550646fc6389bedd74ae84cb788200cc8e2dd60b75"}, ] [package.extras] diff --git a/src/salamander/nmf_framework/_utils_klnmf.py b/src/salamander/nmf_framework/_utils_klnmf.py index 70fe9aa..97d20cb 100644 --- a/src/salamander/nmf_framework/_utils_klnmf.py +++ b/src/salamander/nmf_framework/_utils_klnmf.py @@ -6,7 +6,7 @@ @njit(fastmath=True) -def kl_divergence(X: np.ndarray, W: np.ndarray, H: np.ndarray) -> float: +def kl_divergence(X: np.ndarray, W: np.ndarray, H: np.ndarray, weights=None) -> float: r""" The generalized Kullback-Leibler divergence D_KL(X || WH) = \sum_vd X_vd * ln(X_vd / (WH)_vd) - \sum_vd X_vd + \sum_vd (WH)_vd. @@ -22,6 +22,9 @@ def kl_divergence(X: np.ndarray, W: np.ndarray, H: np.ndarray) -> float: H : np.ndarray of shape (n_signatures, n_samples) exposure matrix + weights : np.ndarray of shape (n_samples,) + per sample weights + Returns ------- result : float @@ -30,19 +33,28 @@ def kl_divergence(X: np.ndarray, W: np.ndarray, H: np.ndarray) -> float: WH = W @ H result = 0.0 - for v in range(V): - for d in range(D): + for d in range(D): + summand_sample = 0.0 + + for v in range(V): if X[v, d] != 0: - result += X[v, d] * np.log(X[v, d] / WH[v, d]) - result -= X[v, d] - result += WH[v, d] + summand_sample += X[v, d] * np.log(X[v, d] / WH[v, d]) + summand_sample -= X[v, d] + summand_sample += WH[v, d] + + if weights is not None: + summand_sample *= weights[d] + + result += summand_sample return result -def samplewise_kl_divergence(X, W, H): +def samplewise_kl_divergence( + X: np.ndarray, W: np.ndarray, H: np.ndarray, weights=None +) -> np.ndarray: """ - Per sample generalized Kullback-Leibler divergence D_KL(x || Wh). + Per sample (weighted) generalized Kullback-Leibler divergence D_KL(x || Wh). Parameters ---------- @@ -55,6 +67,9 @@ def samplewise_kl_divergence(X, W, H): H : np.ndarray of shape (n_signatures, n_samples) exposure matrix + weights : np.ndarray of shape (n_samples,) + per sample weights + Returns ------- errors : np.ndarray of shape (n_samples,) @@ -71,6 +86,9 @@ def samplewise_kl_divergence(X, W, H): errors = s1 + s2 + s3 + if weights is not None: + errors *= weights + return errors @@ -140,7 +158,11 @@ def poisson_llh(X: np.ndarray, W: np.ndarray, H: np.ndarray) -> float: @njit def update_W( - X: np.ndarray, W: np.ndarray, H: np.ndarray, n_given_signatures: int = 0 + X: np.ndarray, + W: np.ndarray, + H: np.ndarray, + weights_kl=None, + n_given_signatures: int = 0, ) -> np.ndarray: """ The multiplicative update rule of the signature matrix W @@ -161,15 +183,28 @@ def update_W( H : np.ndarray of shape (n_signatures, n_samples) exposure matrix + weights_kl : np.ndarray of shape (n_samples,) + per sample weights in the KL-divergence loss + n_given_signatures : int - The number of known signatures, which will not be updated. + The number of known signatures which will not be updated. Returns ------- W : np.ndarray of shape (n_features, n_signatures) updated signature matrix """ - W_updated = W * ((X / (W @ H)) @ H.T) + n_signatures = W.shape[1] + + if n_given_signatures == n_signatures: + return W + + aux = X / (W @ H) + + if weights_kl is not None: + aux *= weights_kl + + W_updated = W * (aux @ H.T) W_updated /= W_updated.sum(axis=0) W_updated[:, :n_given_signatures] = W[:, :n_given_signatures].copy() W_updated[:, n_given_signatures:] = W_updated[:, n_given_signatures:].clip(EPSILON) @@ -178,7 +213,9 @@ def update_W( @njit -def update_H(X: np.ndarray, W: np.ndarray, H: np.ndarray) -> np.ndarray: +def update_H( + X: np.ndarray, W: np.ndarray, H: np.ndarray, weights_kl=None, weights_l_half=None +) -> np.ndarray: """ The multiplicative update rule of the exposure matrix H under the constraint of normalized signatures. @@ -196,26 +233,50 @@ def update_H(X: np.ndarray, W: np.ndarray, H: np.ndarray) -> np.ndarray: H : np.ndarray of shape (n_signatures, n_samples) exposure matrix + weights_kl : np.ndarray of shape (n_samples,) + per sample weights in the KL-divergence loss + + weights_l_half : np.ndarray of shape (n_samples,) + per sample l_half penalty weights. They can be used to induce + sparse exposures. + Returns ------- - H : np.ndarray of shape (n_signatures, n_samples) - updated exposure matrix - - Reference - --------- - D. Lee, H. Seung: Algorithms for Non-negative Matrix Factorization - - Advances in neural information processing systems, 2000 - https://proceedings.neurips.cc/paper_files/paper/2000/file/f9d1152547c0bde01830b7e8bd60024c-Paper.pdf + H_updated : np.ndarray of shape (n_signatures, n_samples) + The updated exposure matrix. If possible, the update is performed + in-place. """ - H *= W.T @ (X / (W @ H)) - H = H.clip(EPSILON) + aux = X / (W @ H) + + if weights_l_half is None: + # in-place + H *= W.T @ aux + H = H.clip(EPSILON) + return H + + intermediate = 4.0 * H * (W.T @ aux) + + if weights_kl is not None: + intermediate *= weights_kl**2 - return H + discriminant = 0.25 * weights_l_half**2 + intermediate + H_updated = 0.25 * (weights_l_half / 2 - np.sqrt(discriminant)) ** 2 + + if weights_kl is not None: + H_updated /= weights_kl**2 + + H_updated = H_updated.clip(EPSILON) + return H_updated @njit def update_WH( - X: np.ndarray, W: np.ndarray, H: np.ndarray, n_given_signatures: int = 0 + X: np.ndarray, + W: np.ndarray, + H: np.ndarray, + weights_kl=None, + weights_l_half=None, + n_given_signatures: int = 0, ) -> np.ndarray: """ A joint update rule for the signature matrix W and @@ -235,30 +296,57 @@ def update_WH( H : np.ndarray of shape (n_signatures, n_samples) exposure matrix + weights_kl : np.ndarray of shape (n_samples,) + per sample weights in the KL-divergence loss + + weights_l_half : np.ndarray of shape (n_samples,) + per sample l_half penalty weights. They can be used to induce + sparse exposures. + n_given_signatures : int - The number of known signatures, which will not be updated. + The number of known signatures which will not be updated. Returns ------- - W : np.ndarray of shape (n_features, n_signatures) + W_updated : np.ndarray of shape (n_features, n_signatures) updated signature matrix - H : np.ndarray of shape (n_signatures, n_samples) - updated exposure matrix + H_updated : np.ndarray of shape (n_signatures, n_samples) + The updated exposure matrix. If possible, the update is performed + in-place. """ n_signatures = W.shape[1] aux = X / (W @ H) - if n_given_signatures < n_signatures: + if n_given_signatures == n_signatures: + W_updated = W + else: + if weights_kl is None: + scaled_aux = aux + else: + scaled_aux = weights_kl * aux # the old signatures are needed for updating H - W_updated = W * (aux @ H.T) + W_updated = W * (scaled_aux @ H.T) W_updated /= np.sum(W_updated, axis=0) W_updated[:, :n_given_signatures] = W[:, :n_given_signatures].copy() W_updated = W_updated.clip(EPSILON) - else: - W_updated = W - H *= W.T @ aux - H = H.clip(EPSILON) + if weights_l_half is None: + # in-place + H *= W.T @ aux + H = H.clip(EPSILON) + return W_updated, H + + intermediate = 4.0 * H * (W.T @ aux) + + if weights_kl is not None: + intermediate *= weights_kl**2 + + discriminant = 0.25 * weights_l_half**2 + intermediate + H_updated = 0.25 * (weights_l_half / 2 - np.sqrt(discriminant)) ** 2 + + if weights_kl is not None: + H_updated /= weights_kl**2 - return W_updated, H + H_updated = H_updated.clip(EPSILON) + return W_updated, H_updated diff --git a/src/salamander/nmf_framework/corrnmf_det.py b/src/salamander/nmf_framework/corrnmf_det.py index 64723da..38ea9fc 100644 --- a/src/salamander/nmf_framework/corrnmf_det.py +++ b/src/salamander/nmf_framework/corrnmf_det.py @@ -64,7 +64,10 @@ def _update_sigma_sq(self): def _update_W(self): self.W = update_W( - self.X, self.W, self.exposures.values, self.n_given_signatures + self.X, + self.W, + self.exposures.values, + n_given_signatures=self.n_given_signatures, ) def _update_p(self): diff --git a/src/salamander/nmf_framework/klnmf.py b/src/salamander/nmf_framework/klnmf.py index 7f419dc..5f8f0ee 100644 --- a/src/salamander/nmf_framework/klnmf.py +++ b/src/salamander/nmf_framework/klnmf.py @@ -1,7 +1,7 @@ import numpy as np import pandas as pd -from ..utils import value_checker +from ..utils import shape_checker, type_checker, value_checker from . import _utils_klnmf from .nmf import NMF @@ -59,17 +59,34 @@ def __init__( super().__init__(n_signatures, init_method, min_iterations, max_iterations, tol) value_checker("update method", update_method, ["mu-standard", "mu-joint"]) self.update_method = update_method + self.weights_kl = None + self.weights_l_half = None @property def reconstruction_error(self) -> float: + """ + The unweighted Kullback-Leibler divergence. + """ return _utils_klnmf.kl_divergence(self.X, self.W, self.H) @property def samplewise_reconstruction_error(self) -> np.ndarray: + """ + The unweighted samplewise Kullback-Leibler divergence. + """ return _utils_klnmf.samplewise_kl_divergence(self.X, self.W, self.H) def objective_function(self) -> float: - return self.reconstruction_error + """ + The sum of the (weighted) Kullback-Leibler divergence and the sparsity + penalty. + """ + of_value = _utils_klnmf.kl_divergence(self.X, self.W, self.H, self.weights_kl) + + if self.weights_l_half is not None: + of_value += np.dot(self.weights_l_half, np.sum(np.sqrt(self.H), axis=0)) + + return of_value @property def objective(self) -> str: @@ -79,10 +96,14 @@ def loglikelihood(self) -> float: return _utils_klnmf.poisson_llh(self.X, self.W, self.H) def _update_W(self): - self.W = _utils_klnmf.update_W(self.X, self.W, self.H, self.n_given_signatures) + self.W = _utils_klnmf.update_W( + self.X, self.W, self.H, self.weights_kl, self.n_given_signatures + ) def _update_H(self): - self.H = _utils_klnmf.update_H(self.X, self.W, self.H) + self.H = _utils_klnmf.update_H( + self.X, self.W, self.H, self.weights_kl, self.weights_l_half + ) def _update_WH(self): if self.update_method == "mu-standard": @@ -91,13 +112,57 @@ def _update_WH(self): self._update_W() else: self.W, self.H = _utils_klnmf.update_WH( - self.X, self.W, self.H, self.n_given_signatures + self.X, + self.W, + self.H, + self.weights_kl, + self.weights_l_half, + self.n_given_signatures, + ) + + def _check_weights(self, weights: np.ndarray, name: str = "weights"): + """ + Check if the given sample-specific loss function or l-1/2 penalty weights + are compatible with the input data. + + weights : np.ndarray of shape (n_samples,) + Sample-specific KL-divergence or sparsity penalty loss weights + + name : str, default='weights' + Name to be displayed in a potential error message + """ + type_checker(name, weights, np.ndarray) + shape_checker(name, weights, (self.n_samples,)) + + if not all(weights >= 0): + raise ValueError( + "Only non-negative KL-divergence and sparsity penalty weights " + "are allowed." ) + def _setup_weight_parameters( + self, weights_kl: np.ndarray, weights_l_half: np.ndarray + ): + for weights, name in zip( + [weights_kl, weights_l_half], ["weights_kl", "weights_l_half"] + ): + if type(weights) in [float, int]: + weights *= np.ones(self.n_samples) + + if type(weights) is list: + weights = np.array(weights) + + if weights is not None: + self._check_weights(weights, name) + + setattr(self, name, weights) + def fit( self, data: pd.DataFrame, given_signatures=None, + weights_kl=None, + weights_l_half=None, init_kwargs=None, history=False, verbose=0, @@ -117,6 +182,13 @@ def fit( The number of known signatures can be less or equal to the number of signatures specified in the algorithm instance. + weights_kl : np.ndarray, default=None + Per sample KL-divergence loss weights of shape (n_samples,). + + weights_l_half : np.ndarray, default=None + Per sample l_half penalty weights of shape (n_samples,). + They can be used to induce sparse exposures. + init_kwargs : dict, default=None Any further keyword arguments to be passed to the initialization method. This includes, for example, a possible 'seed' keyword argument @@ -135,6 +207,7 @@ def fit( Returns the instance itself. """ self._setup_data_parameters(data) + self._setup_weight_parameters(weights_kl, weights_l_half) self._initialize(given_signatures, init_kwargs) of_values = [self.objective_function()] n_iteration = 0 diff --git a/tests/test_utils_klnmf.py b/tests/test_utils_klnmf.py index 0590766..5b15444 100644 --- a/tests/test_utils_klnmf.py +++ b/tests/test_utils_klnmf.py @@ -26,6 +26,20 @@ def matrices_input(counts, n_signatures): return (counts.values, W, H) +@pytest.fixture +def weights_kl(counts): + n_samples = counts.shape[1] + weights = 2 * np.ones(n_samples) + return weights + + +@pytest.fixture +def weights_l_half(counts): + n_samples = counts.shape[1] + weights = 2 * np.zeros(n_samples) + return weights + + @pytest.fixture def kl_divergence_output(n_signatures): path = f"{PATH_TEST_DATA_UTILS_KLNMF}/kl_divergence_nsigs{n_signatures}.npy" @@ -38,6 +52,13 @@ def test_kl_divergence(matrices_input, kl_divergence_output): ) +def test_kl_divergence_weights(matrices_input, weights_kl, kl_divergence_output): + assert np.allclose( + _utils_klnmf.kl_divergence(*matrices_input, weights_kl), + 2 * kl_divergence_output, + ) + + @pytest.fixture def samplewise_kl_divergence_output(n_signatures): path = ( @@ -54,6 +75,22 @@ def test_samplewise_kl_divergence(matrices_input, samplewise_kl_divergence_outpu ) +def test_samplewise_kl_divergence_weights( + matrices_input, weights_kl, samplewise_kl_divergence_output +): + weights_kl[0] = 3 + samplewise_kl_divergence = _utils_klnmf.samplewise_kl_divergence( + *matrices_input, weights_kl + ) + assert np.allclose( + samplewise_kl_divergence[0], 3 * samplewise_kl_divergence_output[0] + ) + assert np.allclose( + samplewise_kl_divergence[1:], + 2 * samplewise_kl_divergence_output[1:], + ) + + @pytest.fixture def poisson_llh_output(n_signatures): path = f"{PATH_TEST_DATA_UTILS_KLNMF}/poisson_llh_nsigs{n_signatures}.npy" @@ -75,6 +112,12 @@ def test_update_W(matrices_input, W_updated): assert np.allclose(W_updated_utils, W_updated) +def test_update_W_weights_kl(matrices_input, weights_kl, W_updated): + # constant loss function weights do not change the updated signatures + W_updated_utils = _utils_klnmf.update_W(*matrices_input, weights_kl) + assert np.allclose(W_updated_utils, W_updated) + + def test_given_signatures_update_W(matrices_input): X, W, H = matrices_input n_signatures = W.shape[1] @@ -99,6 +142,13 @@ def test_update_H(matrices_input, H_updated): assert np.allclose(H_updated_utils, H_updated) +def test_update_H_weights_l_half(matrices_input, weights_kl, weights_l_half, H_updated): + # no l_half penalty -> identical exposure updates, + # independent of the loss function weights + H_updated_utils = _utils_klnmf.update_H(*matrices_input, weights_kl, weights_l_half) + assert np.allclose(H_updated_utils, H_updated) + + @pytest.fixture def WH_updated(n_signatures): suffix = f"_updated_mu-standard_nsigs{n_signatures}.npy" @@ -114,6 +164,29 @@ def test_update_WH(matrices_input, WH_updated): assert np.allclose(H_updated_utils, H_updated) +def test_update_WH_weights_kl(matrices_input, WH_updated, weights_kl): + # constant loss function weights do not change the updated matrices + W_updated, H_updated = WH_updated + W_updated_utils, H_updated_utils = _utils_klnmf.update_WH( + *matrices_input, weights_kl + ) + assert np.allclose(W_updated_utils, W_updated) + assert np.allclose(H_updated_utils, H_updated) + + +def test_update_WH_weights_l_half( + matrices_input, WH_updated, weights_kl, weights_l_half +): + # no l_half penalty -> identical exposure updates, + # independent of the loss function weights + W_updated, H_updated = WH_updated + W_updated_utils, H_updated_utils = _utils_klnmf.update_WH( + *matrices_input, weights_kl, weights_l_half + ) + assert np.allclose(W_updated_utils, W_updated) + assert np.allclose(H_updated_utils, H_updated) + + def test_given_signatures_update_WH(matrices_input): X, W, H = matrices_input n_signatures = W.shape[1] From ec6bca1004119d74d8e2fd9256b66b6939ca25b1 Mon Sep 17 00:00:00 2001 From: BeGeiger Date: Tue, 31 Oct 2023 02:29:00 -0400 Subject: [PATCH 2/5] improve verbosity --- src/salamander/nmf_framework/klnmf.py | 2 +- src/salamander/nmf_framework/mvnmf.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/src/salamander/nmf_framework/klnmf.py b/src/salamander/nmf_framework/klnmf.py index 5f8f0ee..17fe292 100644 --- a/src/salamander/nmf_framework/klnmf.py +++ b/src/salamander/nmf_framework/klnmf.py @@ -216,7 +216,7 @@ def fit( while not converged: n_iteration += 1 - if verbose and n_iteration % 100 == 0: + if verbose and n_iteration % 1000 == 0: print(f"iteration: {n_iteration}; objective: {of_values[-1]:.2f}") self._update_WH() diff --git a/src/salamander/nmf_framework/mvnmf.py b/src/salamander/nmf_framework/mvnmf.py index 988b0de..fb2d43a 100644 --- a/src/salamander/nmf_framework/mvnmf.py +++ b/src/salamander/nmf_framework/mvnmf.py @@ -226,7 +226,7 @@ def fit( while not converged: n_iteration += 1 - if verbose and n_iteration % 100 == 0: + if verbose and n_iteration % 1000 == 0: print(f"iteration: {n_iteration}; objective: {of_values[-1]:.2f}") self._update_H() From b11550daaaf8691232b98aeda47af558e045409c Mon Sep 17 00:00:00 2001 From: BeGeiger Date: Tue, 31 Oct 2023 13:39:56 -0400 Subject: [PATCH 3/5] add given biases CorrNMF and MultimodalCorrNMF now allow to fix the signature or sample biases during model training. Also fixed the COSMIC catalog format from a previous commit. --- data/COSMIC_v3.3.1_SBS_GRCh38.csv | 97 +++++++++++++++ data/COSMIC_v3.3.1_SBS_GRCh38.txt | 97 --------------- src/salamander/nmf_framework/corrnmf.py | 117 ++++++++++++------ src/salamander/nmf_framework/corrnmf_det.py | 40 ++++-- .../nmf_framework/multimodal_corrnmf.py | 41 ++++-- tests/test_corrnmf.py | 74 +++++------ tests/test_multimodal_corrnmf.py | 84 +++++++------ 7 files changed, 326 insertions(+), 224 deletions(-) create mode 100644 data/COSMIC_v3.3.1_SBS_GRCh38.csv delete mode 100644 data/COSMIC_v3.3.1_SBS_GRCh38.txt diff --git a/data/COSMIC_v3.3.1_SBS_GRCh38.csv b/data/COSMIC_v3.3.1_SBS_GRCh38.csv new file mode 100644 index 0000000..3243763 --- /dev/null +++ b/data/COSMIC_v3.3.1_SBS_GRCh38.csv @@ -0,0 +1,97 @@ +Type,SBS1,SBS2,SBS3,SBS4,SBS5,SBS6,SBS7a,SBS7b,SBS7c,SBS7d,SBS8,SBS9,SBS10a,SBS10b,SBS10c,SBS10d,SBS11,SBS12,SBS13,SBS14,SBS15,SBS16,SBS17a,SBS17b,SBS18,SBS19,SBS20,SBS21,SBS22,SBS23,SBS24,SBS25,SBS26,SBS27,SBS28,SBS29,SBS30,SBS31,SBS32,SBS33,SBS34,SBS35,SBS36,SBS37,SBS38,SBS39,SBS40,SBS41,SBS42,SBS43,SBS44,SBS45,SBS46,SBS47,SBS48,SBS49,SBS50,SBS51,SBS52,SBS53,SBS54,SBS55,SBS56,SBS57,SBS58,SBS59,SBS60,SBS84,SBS85,SBS86,SBS87,SBS88,SBS89,SBS90,SBS91,SBS92,SBS93,SBS94,SBS95 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diff --git a/data/COSMIC_v3.3.1_SBS_GRCh38.txt b/data/COSMIC_v3.3.1_SBS_GRCh38.txt deleted file mode 100644 index 488f27b..0000000 --- a/data/COSMIC_v3.3.1_SBS_GRCh38.txt +++ /dev/null @@ -1,97 +0,0 @@ -Type SBS1 SBS2 SBS3 SBS4 SBS5 SBS6 SBS7a SBS7b SBS7c SBS7d SBS8 SBS9 SBS10a SBS10b SBS10c SBS10d SBS11 SBS12 SBS13 SBS14 SBS15 SBS16 SBS17a SBS17b SBS18 SBS19 SBS20 SBS21 SBS22 SBS23 SBS24 SBS25 SBS26 SBS27 SBS28 SBS29 SBS30 SBS31 SBS32 SBS33 SBS34 SBS35 SBS36 SBS37 SBS38 SBS39 SBS40 SBS41 SBS42 SBS43 SBS44 SBS45 SBS46 SBS47 SBS48 SBS49 SBS50 SBS51 SBS52 SBS53 SBS54 SBS55 SBS56 SBS57 SBS58 SBS59 SBS60 SBS84 SBS85 SBS86 SBS87 SBS88 SBS89 SBS90 SBS91 SBS92 SBS93 SBS94 SBS95 -A[C>A]A 0.000876022860356064 5.79005909129116e-07 0.0209197492105927 0.0424507363741909 0.0120520481367051 0.000425145613703031 6.71694562134893e-05 0.0023442806064472 0.0048409876668343 4.04303518755573e-05 0.0443106405724723 0.000560638966969298 0.00218526226624343 0.000180914642923439 0.00434263942606707 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0.00711424626391767 0.000676979479671814 0.00675575747023961 7.66252339497848e-05 0.0302313717024711 7.39116809280727e-18 0.00219983763776419 0.0145284853310333 0.00131365071990518 0.00193969342719315 0.00572276630184896 0.00120366828009243 0.00070393073433441 0.00892963381103051 0.000712343133593355 0.00272025456203673 0.0463441801276758 0.00544318821790856 0.002701624413367 0.00195339332066482 0.00251585019182256 0.0107261698271933 0.0210570955526898 0.0536632724546234 0.00354655022032023 8.78061896167819e-18 0.0139587706493175 0.00139803449848971 0.0034037595985473 0.00436589668498368 0.000421050076141754 0.000332031003160595 0.00295371983080816 0.00638930068595206 0.00133676649193844 0.00510418221159321 7.47220446660139e-18 0.00210333355056677 0.000743129899623465 0.0289208044713902 0.0145920557801525 0.00300730001692063 0.000530260204027339 0.00710568645938643 0.0629797270112199 0.00237249522360714 0.00523662460793772 0.0746321604361317 0.00636532191775049 0.163909572466399 0.000505797622185026 0.00606953144806548 0.0310814535437801 0.0174021363151163 0.00443008909277541 -T[T>C]A 0.000247614153404384 2.18631851368541e-16 0.0150137885023436 0.00238091223445601 0.017754387057637 0.00145399882308692 0.00211684498681263 0.00592868656713521 0.0297267448359541 0.0798060382790564 0.00205251500535226 0.0138143572676553 0.000715378492006843 0.00864947245846227 0.0167375253645118 0.00555310262293367 0.00011199337601623 0.0849768460335281 2.16670856992161e-05 0.00619123363798551 0.00336598850967147 0.0128425419024765 0.00205535449459803 0.0010406249388246 0.00177423547683216 0.00122701630485444 0.0136678065866573 0.0830727819725698 0.00457446760710334 7.71775921775898e-18 0.00144267191498561 0.0142918532221755 0.041727002510966 0.00170048357000674 0.00128928665111947 0.00255560389858499 0.000537376929112546 0.004540478017889 0.000884057052041119 0.0108218506624607 0.00138750199288905 0.000364226980557953 0.00292338399821308 0.047004742096373 0.00208879313696167 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0.000381563430299087 0.000204602017605231 0.0040473151357953 0.00169944968465966 0.321212610051355 0.00111232582827389 0.000442315503506058 0.00138593307467525 0.0100544206674269 0.00193931323751673 0.00501062843482408 0.0060486103723113 0.0206385507069883 0.00344841713346458 2.79365195217853e-05 0.0192516387329408 0.00125177633819444 0.0238138861354298 0.00159163415095686 0.000294633798718878 6.35304552928001e-05 0.00102506031282744 0.00248274185698094 0.00107136263262825 0.00475999416911969 0.0889911264782502 0.00418200117010177 0.00185466507100262 0.00297307730889571 0.00216680256603369 0.00136282879654247 0.000109456517817891 2.2628409469668e-05 0.00384875317052543 0.00185835743481057 0.00170973091970746 1.00632167719781e-18 0.00874296358777756 0.000140132553011992 0.000150066532568895 0.0130375376388545 0.0110296892435734 0.00211769591848729 0.00671743578080491 -T[T>C]T 0.00427420130684977 3.56798802313596e-05 0.0139570391889557 0.000638726965858598 0.0185496918205062 0.00173773432530434 0.000812715266231062 0.00298329006529316 0.0881552360508237 0.0430614634178574 0.00458427854415 0.0385181716462065 0.00323763712713336 2.4909293601962e-05 0.019939416555606 0.00755050142446645 0.000106795154959736 0.0598967943179672 0.000529217918591825 0.00293105721302489 0.00135067880042621 0.00842561075585042 0.0209938233021443 0.000997976135774103 0.00337676195907078 0.0014786921769949 0.0194316123957711 0.0408199871957622 2.22968920862079e-16 7.39116809280727e-18 0.00368648590438109 0.0170333965950045 0.0575599628416469 0.00153768442673861 0.00206099625556099 0.000824512771863318 0.000446860451895595 0.0121676316569222 0.000685292128520189 0.00912085353153491 0.00713297381095531 0.00106650268225195 0.00280168457682504 0.0615068460968306 0.00307940063479081 0.00950318597773759 0.0102277321255922 0.0462406889749185 0.00237780071589652 0.00184222792354479 0.0372568626683224 0.000548150217033733 0.0365403603961695 0.0115420257189224 0.00052581840494698 0.000208647881140192 0.00511644350353549 0.0213310508794324 0.00199517386856482 0.00220042500854117 0.0125372558164453 0.000888408028263203 0.00028466669542028 0.195140376191041 0.0319825880112931 0.00262510135256283 0.00132064805531338 0.00988437743459446 0.0943261342137136 0.00344976232298314 0.00655752653440012 0.0966168280845929 0.00680553584923923 0.00403044424363779 0.00126728263738267 0.0143468343994853 0.0141231105401887 0.0168427697231051 0.00571949677402028 -T[T>G]A 2.16541004741874e-16 1.63728785554019e-05 0.00716118201592046 0.000372449756178388 0.00514877224671474 0.000103155321908193 0.000126026120145124 0.000943649278602353 0.0189975554746329 0.000207377759567499 2.19066505378943e-16 0.0648288051774966 0.00263612074451836 0.011031494219032 0.00859659068036419 0.0195621623423897 8.4341914150276e-05 0.00160873703872998 0.000180395645640532 9.79538081161343e-05 0.00022735185547781 0.00966154306201693 2.18319951100843e-16 1.19770040128868e-05 0.000686037717708436 0.00164261860165997 2.19873410307095e-16 0.000458774864812886 0.00499931017104599 0.000184749448456007 0.000121540168180294 0.00205014170359484 0.00141062916290027 0.00117649735366745 0.041631164383476 0.000460798077466869 0.00011656337800603 0.00222572451857304 2.22739011996983e-05 0.000460420555457419 2.20018173158121e-16 0.00424601561574354 0.000775631848684142 0.0180332658357154 0.00115824074419202 0.00827353407866637 0.00834483481469867 0.041343344094102 0.00113980222115137 0.000200920344135446 7.58685527203991e-18 0.000281978604064094 0.000689360206646428 0.00237941090703131 0.000243070563555427 0.000148016493270465 0.000322079618832065 0.00251212379207952 0.00140331445255744 0.00128491387188189 3.19672805730103e-05 0.00597076099302563 0.00929503745922545 0.00316983463449279 0.000784575468467777 0.00158304681806403 0.00158455474803141 0.00185709948363716 0.00701086419122068 0.00704063343239573 0.00803378080430973 0.0301634056325708 0.00676939937617531 0.018419132786527 0.00582790190338827 0.00218157236075335 0.0529606566036755 0.00402383671831976 3.70922534544785e-06 -T[T>G]C 5.52133366518628e-05 7.12259644056158e-05 0.00640123040419171 0.000177391457584975 0.00667719604829963 0.000290965398680233 0.000117859798877126 0.00158050563535294 0.0176744850949071 0.000116636552142844 0.00116087368615123 0.00877742290417431 2.25514770286608e-16 0.00559121240768633 0.000967472446635159 0.000749058448780639 6.88313150731349e-05 0.00115088786225254 2.24604426821595e-16 0.00122277910353731 0.000298600797512851 0.00561715422170478 0.00011772143570411 0.00886448536337728 0.00213606841536688 0.00206270503126902 2.2689755830151e-16 0.000385871748383677 0.000766715981851926 5.40179292099938e-18 0.00269200435569581 0.000588919885094795 0.00175090417126447 0.000285666382009104 0.0362579726004918 0.000101111402915784 9.82684851439715e-05 0.00242953387500041 0.000227820593004193 0.000166498302183214 2.27046945793998e-16 0.00198145007688457 0.000755717469204061 0.0198296485161894 0.000922481864136688 0.00516951644721396 0.0116040433465528 0.0157826667703646 0.000280245941100622 0.00838503354396975 7.8292274359527e-18 0.000137835845490044 0.00101626104805252 2.2924086230381e-05 0.00022274216856116 0.000140525470521206 0.000665754178284685 0.00352766585030334 0.00113421162186437 0.0044470730614856 0.000718823875309714 0.00574463738782201 1.01395134998696e-06 0.00402284250687827 0.00161319202971474 0.00237895817888486 0.0107657514303432 0.000753940163468971 0.00652019303583308 0.00241166967228635 0.00252345570499119 0.0211428792213598 0.00298027112627399 0.000188973253628445 0.000144874062526328 0.000306410169150267 0.0135181712277083 0.00125988308582045 0.000429361367150199 -T[T>G]G 0.000577613846957071 9.54312693870027e-05 0.00811290410758782 0.00233855767709101 0.00698435660833494 0.00032477388947405 2.24021250029031e-16 0.0013610521495079 0.00767304115456111 0.000125349755892465 0.00311110910523333 0.0109739481133841 2.15973529904078e-05 0.00274915190118586 0.00111349547850193 0.00315775722245881 0.000102133690144334 0.00088112853544792 3.67120003031808e-05 0.000114854564755359 0.000148621621436395 0.00528168726709669 0.000923128235665582 0.00478795453285506 0.00145827863382612 0.00208993486307971 0.00281940650848766 0.000131871733538925 0.00561498392643583 0.000184448594791724 0.00306494059755911 0.00948344204428778 0.00285843892502071 0.00237053085306105 0.0140585322916688 0.000181217460300358 0.000819415962133265 0.00539978453279423 0.000822573871036448 0.000962634040747653 0.00153703205361482 0.0048674902440621 0.000905877995186288 0.0303643504156291 0.00191911205795925 0.00732477409749385 0.00871563491251512 0.0195311162788085 0.00296300944312353 0.0229119656509606 0.000924280247021296 0.000714723909227149 0.000904324789953031 0.0015513396154896 0.000347211547311808 0.000112764041090232 0.00876326071358354 0.00277423786448073 0.00148188476288767 0.00620211104662841 0.00774142265404687 0.0799204550536275 0.000101254687660143 0.00542385725271513 0.006761226525494 0.00197862551201721 0.0240000988609871 1.01019692708923e-18 0.00343249069551912 0.00202426962141699 0.00199416988005381 1.00632167719781e-18 0.0050676413188152 0.000295834603098359 0.000627359679752215 0.00175661772288664 0.0127911359675955 0.00307375166944344 0.000467825206835238 -T[T>G]T 2.20126302870092e-16 2.22251767921597e-16 0.0105430871571248 0.000570435403471201 0.0135362615987478 0.001008684866987 8.29730241016687e-05 0.00177792034194239 0.0217136052474787 0.000165851576040935 0.000999111910278643 0.0640966450050156 0.0188281051393293 0.0902093541202528 0.0251381696414515 0.0145197508759015 5.5916331134579e-05 0.00221728501579075 1.88365699837768e-05 0.000607290963657555 0.00599301186263181 0.0158670619716849 0.00457865289161051 0.121753088564441 0.00517035362279086 0.00425501218278124 0.00152029713562769 2.22654475613249e-16 0.000597596882490708 7.39116809280727e-18 0.00397778860527223 0.00788546065898152 0.0094763353458809 0.000140703150159089 0.47923165359889 0.000930836803271484 0.00892716723000414 0.00734080256987869 0.000636199563757778 0.0022502105752142 0.000233735864991755 0.00364220727335101 0.00181108895859047 0.0291506387853057 0.00532353900589653 0.0109266589828417 0.0250679708960592 0.0881682551170355 0.00175312425663556 0.00132159829297779 0.00903805293840706 0.000728184875472334 0.00100110576427862 0.0036332289654347 3.16281747336529e-07 0.000365133791995337 0.00160201753535357 0.00291424216240133 0.00252389494373451 0.00708355995900239 0.000500487252192495 0.04126540108731 0.0289660847971512 0.11908566547043 0.0216881924951581 1.30752174648723e-17 0.0318156122416404 0.00664590542112645 0.0127454822235667 0.0049020668231568 0.00622207517854644 0.102402266411758 0.0057030189959015 0.00839340013737567 0.00100355099977588 0.00180187614429687 0.0551214850885336 0.00334864554898619 0.000288827939845249 diff --git a/src/salamander/nmf_framework/corrnmf.py b/src/salamander/nmf_framework/corrnmf.py index a99ec8a..2387cea 100644 --- a/src/salamander/nmf_framework/corrnmf.py +++ b/src/salamander/nmf_framework/corrnmf.py @@ -338,27 +338,56 @@ def _update_u(self, index, aux_col, outer_prods_L): shape: (n_signatures, dim_embeddings, dim_embeddings) """ - def _check_given_signature_embeddings(self, given_signature_embeddings: np.ndarray): - type_checker("signature embeddings", given_signature_embeddings, np.ndarray) - shape_checker( - "given_signature_embeddings", - given_signature_embeddings, - (self.dim_embeddings, self.n_signatures), - ) + def _check_given_biases(self, given_biases, expected_n_biases, name): + type_checker(name, given_biases, np.ndarray) + shape_checker(name, given_biases, (expected_n_biases,)) - def _check_given_sample_embeddings(self, given_sample_embeddings: np.ndarray): - type_checker("sample embeddings", given_sample_embeddings, np.ndarray) + def _check_given_embeddings(self, given_embeddings, expected_n_embeddings, name): + type_checker(name, given_embeddings, np.ndarray) shape_checker( - "given_sample_embeddings", - given_sample_embeddings, - (self.dim_embeddings, self.n_samples), + name, given_embeddings, (self.dim_embeddings, expected_n_embeddings) ) + def _check_given_parameters( + self, + given_signatures, + given_signature_biases, + given_signature_embeddings, + given_sample_biases, + given_sample_embeddings, + ): + if given_signatures is not None: + self._check_given_signatures(given_signatures) + + if given_signature_biases is not None: + self._check_given_biases( + given_signature_biases, self.n_signatures, "given_signature_biases" + ) + + if given_signature_embeddings is not None: + self._check_given_embeddings( + given_signature_embeddings, + self.n_signatures, + "given_signature_embeddings", + ) + + if given_sample_biases is not None: + self._check_given_biases( + given_sample_biases, self.n_samples, "given_sample_biases" + ) + + if given_sample_embeddings is not None: + self._check_given_embeddings( + given_sample_embeddings, self.n_samples, "given_sample_embeddings" + ) + def _initialize( self, given_signatures=None, + given_signature_biases=None, given_signature_embeddings=None, - given_sample_embeddings=True, + given_sample_biases=None, + given_sample_embeddings=None, init_kwargs=None, ): """ @@ -374,9 +403,17 @@ def _initialize( algorithm instance, and the mutation type names have to match the mutation types of the count data. + given_signature_biases : np.ndarray, default=None + Known signature biases of shape (n_signatures,) that will be fixed + during model fitting. + given_signature_embeddings : np.ndarray, default=None A priori known signature embeddings of shape (dim_embeddings, n_signatures). + given_sample_biases : np.ndarray, default=None + Known sample biases of shape (n_samples,) that will be fixed + during model fitting. + given_sample_embeddings : np.ndarray, default=None A priori known sample embeddings of shape (dim_embeddings, n_samples). @@ -385,18 +422,19 @@ def _initialize( This includes, for example, a possible 'seed' keyword argument for all stochastic methods. """ + self._check_given_parameters( + given_signatures, + given_signature_biases, + given_signature_embeddings, + given_sample_biases, + given_sample_embeddings, + ) + if given_signatures is not None: - self._check_given_signatures(given_signatures) self.n_given_signatures = len(given_signatures.columns) else: self.n_given_signatures = 0 - if given_signature_embeddings is not None: - self._check_given_signature_embeddings(given_signature_embeddings) - - if given_sample_embeddings is not None: - self._check_given_sample_embeddings(given_sample_embeddings) - init_kwargs = {} if init_kwargs is None else init_kwargs.copy() if self.init_method == "custom": @@ -426,7 +464,6 @@ def _initialize( self.signature_names = np.concatenate( [given_signatures_names, new_signatures_names] ) - else: self.signature_names = np.array( [f"Sig{k+1}" for k in range(self.n_signatures)], dtype="kd", self.X, p) if given_signature_embeddings is None: @@ -183,7 +189,9 @@ def fit( self, data: pd.DataFrame, given_signatures=None, + given_signature_biases=None, given_signature_embeddings=None, + given_sample_biases=None, given_sample_embeddings=None, init_kwargs=None, history=False, @@ -198,13 +206,21 @@ def fit( The mutation count data given_signatures: pd.DataFrame, default=None - Known signatures which will be fixed during model fitting. + Known signatures that will be fixed during model fitting. + + given_signature_biases : np.ndarray, default=None + Known signature biases of shape (n_signatures,) that will be fixed + during model fitting. given_signature_embeddings: np.ndarray, default=None - Known signature embeddings which will be fixed during model fitting. + Known signature embeddings that will be fixed during model fitting. + + given_sample_biases : np.ndarray, default=None + Known sample biases of shape (n_samples,) that will be fixed + during model fitting. given_sample_embeddings: np.ndarray, default=None - Known sample embeddings which will be fixed during model fitting. + Known sample embeddings that will be fixed during model fitting. init_kwargs: dict Any further keywords arguments to be passed to the initialization method. @@ -226,7 +242,9 @@ def fit( self._setup_data_parameters(data) self._initialize( given_signatures=given_signatures, + given_signature_biases=given_signature_biases, given_signature_embeddings=given_signature_embeddings, + given_sample_biases=given_sample_biases, given_sample_embeddings=given_sample_embeddings, init_kwargs=init_kwargs, ) @@ -240,9 +258,9 @@ def fit( if verbose and n_iteration % 100 == 0: print(f"iteration: {n_iteration}; objective: {of_values[-1]:.2f}") - self._update_alpha() + self._update_alpha(given_sample_biases) p = self._update_p() - self._update_beta(p) + self._update_beta(p, given_signature_biases) self._update_LU(p, given_signature_embeddings, given_sample_embeddings) self._update_sigma_sq() diff --git a/src/salamander/nmf_framework/multimodal_corrnmf.py b/src/salamander/nmf_framework/multimodal_corrnmf.py index e081868..8017d21 100755 --- a/src/salamander/nmf_framework/multimodal_corrnmf.py +++ b/src/salamander/nmf_framework/multimodal_corrnmf.py @@ -177,13 +177,13 @@ def _n_parameters(self) -> int: def bic(self) -> float: return self._n_parameters * np.log(self.n_samples) - 2 * self.loglikelihood() - def _update_alphas(self): - for model in self.models: - model._update_alpha() + def _update_alphas(self, given_sample_biases): + for model, given_sam_biases in zip(self.models, given_sample_biases): + model._update_alpha(given_sam_biases) - def _update_betas(self, ps): - for model, p in zip(self.models, ps): - model._update_beta(p) + def _update_betas(self, ps, given_signature_biases): + for model, p, given_sig_biases in zip(self.models, ps, given_signature_biases): + model._update_beta(p, given_sig_biases) def _update_sigma_sq(self): Ls = np.concatenate([model.L for model in self.models], axis=1) @@ -345,7 +345,9 @@ def _setup_data_parameters(self, data: list): def _initialize( self, given_signatures=None, + given_signature_biases=None, given_signature_embeddings=None, + given_sample_biases=None, given_sample_embeddings=None, init_kwargs=None, ): @@ -358,14 +360,25 @@ def _initialize( else: U = given_sample_embeddings - for model, modality_name, given_sigs, given_sig_embs in zip( + for ( + model, + modality_name, + given_sigs, + given_sig_biases, + given_sig_embs, + given_sam_biases, + ) in zip( self.models, self.modality_names, given_signatures, + given_signature_biases, given_signature_embeddings, + given_sample_biases, ): model._initialize( given_signatures=given_sigs, + given_signature_biases=given_sig_biases, + given_sample_biases=given_sam_biases, given_signature_embeddings=given_sig_embs, given_sample_embeddings=U, init_kwargs=init_kwargs, @@ -378,7 +391,9 @@ def fit( self, data: list, given_signatures=None, + given_signature_biases=None, given_signature_embeddings=None, + given_sample_biases=None, given_sample_embeddings=None, init_kwargs=None, history=False, @@ -387,13 +402,21 @@ def fit( if given_signatures is None: given_signatures = [None for _ in range(self.n_modalities)] + if given_signature_biases is None: + given_signature_biases = [None for _ in range(self.n_modalities)] + if given_signature_embeddings is None: given_signature_embeddings = [None for _ in range(self.n_modalities)] + if given_sample_biases is None: + given_sample_biases = [None for _ in range(self.n_modalities)] + self._setup_data_parameters(data) self._initialize( given_signatures=given_signatures, + given_signature_biases=given_signature_biases, given_signature_embeddings=given_signature_embeddings, + given_sample_biases=given_sample_biases, given_sample_embeddings=given_sample_embeddings, init_kwargs=init_kwargs, ) @@ -407,9 +430,9 @@ def fit( if verbose and n_iteration % 100 == 0: print(f"iteration: {n_iteration}; objective: {of_values[-1]:.2f}") - self._update_alphas() + self._update_alphas(given_sample_biases) ps = self._update_ps() - self._update_betas(ps) + self._update_betas(ps, given_signature_biases) self._update_LsU(ps, given_signature_embeddings, given_sample_embeddings) self._update_sigma_sq() self._update_Ws() diff --git a/tests/test_corrnmf.py b/tests/test_corrnmf.py index 1b03efd..2dad2f4 100644 --- a/tests/test_corrnmf.py +++ b/tests/test_corrnmf.py @@ -168,47 +168,49 @@ def test_update_sigma_sq(self, model_init, sigma_sq_updated): model_init._update_sigma_sq() assert np.allclose(model_init.sigma_sq, sigma_sq_updated) - def test_given_signatures(self, n_signatures, counts): - for n_given_signatures in range(1, n_signatures + 1): + +@pytest.mark.parametrize("n_signatures,dim_embeddings", [(1, 1), (2, 1), (2, 2)]) +class TestGivenParametersCorrNMFDet: + @pytest.fixture + def model(self, n_signatures, dim_embeddings): + return corrnmf_det.CorrNMFDet( + n_signatures=n_signatures, + dim_embeddings=dim_embeddings, + min_iterations=3, + max_iterations=3, + ) + + def test_given_signatures(self, model, counts): + for n_given_signatures in range(1, model.n_signatures + 1): given_signatures = counts.iloc[:, :n_given_signatures].astype(float).copy() given_signatures /= given_signatures.sum(axis=0) - model = corrnmf_det.CorrNMFDet( - n_signatures=n_signatures, - dim_embeddings=n_signatures, - min_iterations=3, - max_iterations=3, - ) model.fit(counts, given_signatures=given_signatures) assert np.allclose( given_signatures, model.signatures.iloc[:, :n_given_signatures] ) - def test_given_signature_embeddings(self, n_signatures, counts): - for dim_embeddings in range(1, n_signatures + 1): - given_signature_embeddings = np.random.uniform( - size=(dim_embeddings, n_signatures) - ) - model = corrnmf_det.CorrNMFDet( - n_signatures=n_signatures, - dim_embeddings=dim_embeddings, - min_iterations=3, - max_iterations=3, - ) - model.fit(counts, given_signature_embeddings=given_signature_embeddings) - assert np.allclose(given_signature_embeddings, model.L) - - def test_given_sample_embeddings(self, n_signatures, counts): + def test_given_signature_biases(self, model, counts): + given_signature_biases = np.random.uniform(size=model.n_signatures) + model.fit(counts, given_signature_biases=given_signature_biases) + assert np.allclose(given_signature_biases, model.beta) + + def test_given_signature_embeddings(self, model, counts): + given_signature_embeddings = np.random.uniform( + size=(model.dim_embeddings, model.n_signatures) + ) + model.fit(counts, given_signature_embeddings=given_signature_embeddings) + assert np.allclose(given_signature_embeddings, model.L) + + def test_given_sample_biases(self, model, counts): + n_samples = counts.shape[1] + given_sample_biases = np.random.uniform(size=n_samples) + model.fit(counts, given_sample_biases=given_sample_biases) + assert np.allclose(given_sample_biases, model.alpha) + + def test_given_sample_embeddings(self, model, counts): n_samples = len(counts.columns) - - for dim_embeddings in range(1, n_signatures + 1): - given_sample_embeddings = np.random.uniform( - size=(dim_embeddings, n_samples) - ) - model = corrnmf_det.CorrNMFDet( - n_signatures=n_signatures, - dim_embeddings=dim_embeddings, - min_iterations=3, - max_iterations=3, - ) - model.fit(counts, given_sample_embeddings=given_sample_embeddings) - assert np.allclose(given_sample_embeddings, model.U) + given_sample_embeddings = np.random.uniform( + size=(model.dim_embeddings, n_samples) + ) + model.fit(counts, given_sample_embeddings=given_sample_embeddings) + assert np.allclose(given_sample_embeddings, model.U) diff --git a/tests/test_multimodal_corrnmf.py b/tests/test_multimodal_corrnmf.py index 10d3a3b..63feac5 100644 --- a/tests/test_multimodal_corrnmf.py +++ b/tests/test_multimodal_corrnmf.py @@ -170,7 +170,7 @@ def sigma_sq_updated(): return np.load(f"{PATH_TEST_DATA}/sigma_sq_updated.npy") -class TestUpdatesMultimodalCorrNMFDet: +class TestUpdatesMultimodalCorrNMF: def test_update_W(self, multi_model_init, Ws_updated): multi_model_init._update_Ws() @@ -178,13 +178,17 @@ def test_update_W(self, multi_model_init, Ws_updated): assert np.allclose(model.W, W_updated) def test_update_alpha(self, multi_model_init, alphas_updated): - multi_model_init._update_alphas() + n_modalities = multi_model_init.n_modalities + given_sample_biases = [None for _ in range(n_modalities)] + multi_model_init._update_alphas(given_sample_biases) for model, alpha_updated in zip(multi_model_init.models, alphas_updated): assert np.allclose(model.alpha, alpha_updated) def test_update_beta(self, multi_model_init, _ps, betas_updated): - multi_model_init._update_betas(_ps) + n_modalities = multi_model_init.n_modalities + given_signature_biases = [None for _ in range(n_modalities)] + multi_model_init._update_betas(_ps, given_signature_biases) for model, beta_updated in zip(multi_model_init.models, betas_updated): assert np.allclose(model.beta, beta_updated) @@ -215,21 +219,30 @@ def test_update_sigma_sq(self, multi_model_init, sigma_sq_updated): for model in multi_model_init.models: assert np.allclose(model.sigma_sq, sigma_sq_updated) - @pytest.mark.parametrize("ns_signatures", [[1, 2], [2, 2]]) - def test_given_signatures(self, ns_signatures, counts): - for n_given_signatures in range(1, ns_signatures[0] + 1): + +@pytest.mark.parametrize( + "ns_signatures,dim_embeddings", [([1, 2], 1), ([2, 2], 1), ([2, 2], 2)] +) +class TestGivenParametersMultimodalCorrNMF: + @pytest.fixture() + def multi_model(self, ns_signatures, dim_embeddings): + model = multimodal_corrnmf.MultimodalCorrNMF( + n_modalities=2, + ns_signatures=ns_signatures, + dim_embeddings=dim_embeddings, + min_iterations=3, + max_iterations=3, + ) + return model + + def test_given_signatures(self, multi_model, counts): + n_signatures0 = multi_model.ns_signatures[0] + for n_given_signatures in range(1, n_signatures0 + 1): given_signatures0 = ( counts[0].iloc[:, :n_given_signatures].astype(float).copy() ) given_signatures0 /= given_signatures0.sum(axis=0) given_signatures = [given_signatures0, None] - multi_model = multimodal_corrnmf.MultimodalCorrNMF( - n_modalities=2, - ns_signatures=ns_signatures, - dim_embeddings=2, - min_iterations=3, - max_iterations=3, - ) multi_model.fit(counts, given_signatures=given_signatures) assert np.allclose( given_signatures0, @@ -240,37 +253,36 @@ def test_given_signatures(self, ns_signatures, counts): multi_model.models[1].signatures.iloc[:, :n_given_signatures], ) - @pytest.mark.parametrize( - "ns_signatures,dim_embeddings", [([1, 2], 1), ([2, 2], 1), ([2, 2], 2)] - ) - def test_given_signature_embeddings(self, ns_signatures, dim_embeddings, counts): + def test_given_signature_biases(self, multi_model, counts): + n_signatures0 = multi_model.ns_signatures[0] + given_signature_biases0 = np.random.uniform(size=n_signatures0) + given_signature_biases = [given_signature_biases0, None] + multi_model.fit(counts, given_signature_biases=given_signature_biases) + assert np.allclose(given_signature_biases0, multi_model.models[0].beta) + assert not np.allclose(given_signature_biases0, multi_model.models[1].beta) + + def test_given_signature_embeddings(self, multi_model, counts): + n_signatures0 = multi_model.ns_signatures[0] given_signature_embeddings0 = np.random.uniform( - size=(dim_embeddings, ns_signatures[0]) + size=(multi_model.dim_embeddings, n_signatures0) ) given_signature_embeddings = [given_signature_embeddings0, None] - multi_model = multimodal_corrnmf.MultimodalCorrNMF( - n_modalities=2, - ns_signatures=ns_signatures, - dim_embeddings=dim_embeddings, - min_iterations=3, - max_iterations=3, - ) multi_model.fit(counts, given_signature_embeddings=given_signature_embeddings) assert np.allclose(given_signature_embeddings0, multi_model.models[0].L) assert not np.allclose(given_signature_embeddings0, multi_model.models[1].L) - @pytest.mark.parametrize( - "ns_signatures,dim_embeddings", [([1, 2], 1), ([2, 2], 1), ([2, 2], 2)] - ) - def test_given_sample_embeddings(self, ns_signatures, dim_embeddings, counts): + def test_given_sample_biases(self, multi_model, counts): n_samples = len(counts[0].columns) - given_sample_embeddings = np.random.uniform(size=(dim_embeddings, n_samples)) - multi_model = multimodal_corrnmf.MultimodalCorrNMF( - n_modalities=2, - ns_signatures=ns_signatures, - dim_embeddings=dim_embeddings, - min_iterations=3, - max_iterations=3, + given_sample_biases0 = np.random.uniform(size=n_samples) + given_sample_biases = [given_sample_biases0, None] + multi_model.fit(counts, given_sample_biases=given_sample_biases) + assert np.allclose(given_sample_biases0, multi_model.models[0].alpha) + assert not np.allclose(given_sample_biases0, multi_model.models[1].alpha) + + def test_given_sample_embeddings(self, multi_model, counts): + n_samples = len(counts[0].columns) + given_sample_embeddings = np.random.uniform( + size=(multi_model.dim_embeddings, n_samples) ) multi_model.fit(counts, given_sample_embeddings=given_sample_embeddings) From 364e3782ea5c6043d116a5dae9b5a7dfd4265475 Mon Sep 17 00:00:00 2001 From: BeGeiger Date: Tue, 31 Oct 2023 14:54:33 -0400 Subject: [PATCH 4/5] improve initialization --- src/salamander/nmf_framework/corrnmf.py | 46 +------- .../nmf_framework/initialization.py | 101 ++++++++++++++++-- src/salamander/nmf_framework/nmf.py | 57 ++-------- 3 files changed, 107 insertions(+), 97 deletions(-) diff --git a/src/salamander/nmf_framework/corrnmf.py b/src/salamander/nmf_framework/corrnmf.py index 2387cea..cf6aa67 100644 --- a/src/salamander/nmf_framework/corrnmf.py +++ b/src/salamander/nmf_framework/corrnmf.py @@ -7,13 +7,7 @@ from ..utils import match_signatures_pair, shape_checker, type_checker from ._utils_klnmf import kl_divergence, poisson_llh, samplewise_kl_divergence -from .initialization import ( - init_custom, - init_flat, - init_nndsvd, - init_random, - init_separableNMF, -) +from .initialization import initialize from .signature_nmf import SignatureNMF EPSILON = np.finfo(np.float32).eps @@ -436,41 +430,9 @@ def _initialize( self.n_given_signatures = 0 init_kwargs = {} if init_kwargs is None else init_kwargs.copy() - - if self.init_method == "custom": - self.W, _ = init_custom(self.X, self.n_signatures, **init_kwargs) - - elif self.init_method == "flat": - self.W, _ = init_flat(self.X, self.n_signatures) - - elif self.init_method in ["nndsvd", "nndsvda", "nndsvdar"]: - self.W, _ = init_nndsvd( - self.X, self.n_signatures, init=self.init_method, **init_kwargs - ) - - elif self.init_method == "random": - self.W, _ = init_random(self.X, self.n_signatures, **init_kwargs) - - else: - self.W = init_separableNMF(self.X, self.n_signatures) - - if given_signatures is not None: - self.W[:, : self.n_given_signatures] = given_signatures.copy().values - given_signatures_names = given_signatures.columns.to_numpy(dtype=" pd.DataFrame: From da456ce2d78bdcc18f18d00265b8161874fabf55 Mon Sep 17 00:00:00 2001 From: BeGeiger Date: Tue, 31 Oct 2023 14:59:50 -0400 Subject: [PATCH 5/5] bump version --- CHANGELOG.md | 10 ++++++++-- pyproject.toml | 2 +- src/salamander/__init__.py | 2 +- 3 files changed, 10 insertions(+), 4 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index edd5f63..4e4c9c9 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -8,6 +8,12 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 --- --- +## 0.3.0 - 2023-10 +### Added + - Support a sample-weigted KL-divergence loss in KL-NMF + - Support a sample-weighted sparsity regularization in KL-NMF + - Support fixing signature and sample biases in (multimodal) CorrNMF during inference + ## 0.2.1 - 2023-10 ### Fixed - Improve CorrNMF model formulation (added signature biases) @@ -15,8 +21,8 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0 ## 0.2.0 - 2023-10 ### Added - - Support fixing arbitrary many a priori known signatures during inference. - - Improved performance with just-in-time compiled update rules. + - Support fixing arbitrary many a priori known signatures during inference + - Improved performance with just-in-time compiled update rules ## 0.1.0 - 2023-10 ### Added diff --git a/pyproject.toml b/pyproject.toml index 54aadb8..67e905e 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "salamander-learn" -version = "0.2.1" +version = "0.3.0" description = "Salamander is a non-negative matrix factorization framework for signature analysis" license = "MIT" authors = ["Benedikt Geiger"] diff --git a/src/salamander/__init__.py b/src/salamander/__init__.py index e0cc792..714d5f6 100644 --- a/src/salamander/__init__.py +++ b/src/salamander/__init__.py @@ -7,5 +7,5 @@ from .nmf_framework.multimodal_corrnmf import MultimodalCorrNMF from .nmf_framework.mvnmf import MvNMF -__version__ = "0.2.1" +__version__ = "0.3.0" __all__ = ["CorrNMFDet", "KLNMF", "MvNMF", "MultimodalCorrNMF"]