From 5f9b7affbf8ad26d96405052518a572e070db6d3 Mon Sep 17 00:00:00 2001 From: Aaron Meyer <2065146+aarmey@users.noreply.github.com> Date: Fri, 22 Nov 2024 21:00:00 -0800 Subject: [PATCH] Simplify testing and clean up some code (#41) Co-authored-by: ehung Co-authored-by: Aaron Meyer --- poetry.lock | 562 ++------------------------------------ pyproject.toml | 3 +- timpute/decomposition.py | 145 +++++----- timpute/initialization.py | 56 ++-- timpute/test/test_do.py | 2 - 5 files changed, 135 insertions(+), 633 deletions(-) diff --git a/poetry.lock b/poetry.lock index 184faeb..84b2c34 100644 --- a/poetry.lock +++ b/poetry.lock @@ -28,17 +28,6 @@ doc = ["awkward (>=2.0.7)", "ipython", "myst-parser", "nbsphinx", "readthedocs-s gpu = ["cupy"] test = ["awkward (>=2.3)", "boltons", "dask[array,distributed] (>=2022.09.2,<2024.8.0)", "httpx", "joblib", "loompy (>=3.0.5)", "matplotlib", "openpyxl", "pyarrow", "pytest (>=8.2)", "pytest-cov (>=2.10)", "pytest-memray", "pytest-mock", "scanpy", "scikit-learn", "zarr (<3.0.0a0)"] -[[package]] -name = "appnope" -version = "0.1.4" -description = "Disable App Nap on macOS >= 10.9" -optional = false -python-versions = ">=3.6" -files = [ - 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Capable of handling a single tensor. @@ -30,21 +30,22 @@ def __init__(self, data=np.ndarray([0]), max_rr=5): other methods include: tucker_decomp """ self.data = data.copy() - self.rrs = np.arange(1,max_rr+1) - - def imputation(self, - type:str='chord', - repeat:int=3, - drop:int=0.05, - chord_mode:int=0, - method=perform_CLS, - tol=1e-6, - init='random', - maxiter:int=50, - seed = 1, - callback:Tracker=None, - trackCoreConsistency=False, - printRuntime=False): + self.rrs = np.arange(1, max_rr + 1) + + def imputation( + self, + type: str = "chord", + repeat: int = 3, + drop: float = 0.05, + chord_mode: int = 0, + method=perform_CLS, + tol=1e-6, + init="random", + maxiter: int = 50, + seed=1, + callback: Tracker = None, + printRuntime=False, + ): """ Performs imputation (chord or entry) from the [self.data] using [method] for factor decomposition, comparing each component. Drops in Q2X from one component to the next may signify overfitting. @@ -81,18 +82,18 @@ def imputation(self, Each value in a row represents error of the FITTED (not dropped, not missing) values of the tensor calculated for components 1 to max_rr. """ - assert(chord_mode >= 0 and chord_mode < self.data.ndim) - assert(drop < 1 and drop >= 0) - - error = np.zeros((repeat,self.rrs[-1])) - imputed_error = np.zeros((repeat,self.rrs[-1])) - fitted_error = np.zeros((repeat,self.rrs[-1])) - if trackCoreConsistency is True: - assert drop == 0 - corcon = np.zeros((repeat,self.rrs[-1])) - + assert chord_mode >= 0 and chord_mode < self.data.ndim + assert drop < 1 and drop >= 0 + + error = np.zeros((repeat, self.rrs[-1])) + imputed_error = np.zeros((repeat, self.rrs[-1])) + fitted_error = np.zeros((repeat, self.rrs[-1])) + if printRuntime: - missingpatterns = tqdm(range(repeat), desc=f"Decomposing {repeat} tensors using {method.__name__}") + missingpatterns = tqdm( + range(repeat), + desc=f"Decomposing {repeat} tensors using {method.__name__}", + ) else: missingpatterns = range(repeat) @@ -100,16 +101,16 @@ def imputation(self, - `tImp` is a copy of data, used a reference for imputation accuracy - `missingCube` is where values are dropped """ - tImp = self.data.copy() # avoid editing in-place of data + tImp = self.data.copy() # avoid editing in-place of data if chord_mode != 0: - tImp = np.moveaxis(tImp,chord_mode,0) + tImp = np.moveaxis(tImp, chord_mode, 0) - if type=='entry': - drop = int(drop*np.sum(np.isfinite(tImp))) - elif type=='chord': - drop = int(drop*tImp.size/tImp.shape[0]) + if type == "entry": + drop = int(drop * np.sum(np.isfinite(tImp))) + elif type == "chord": + drop = int(drop * tImp.size / tImp.shape[0]) else: - raise ValueError('invalid imputation type') + raise ValueError("invalid imputation type") for x in missingpatterns: missingCube = tImp.copy() @@ -118,20 +119,20 @@ def imputation(self, - `imputed_vals` has a 1 where values were artifically dropped - `fitted_vals` has a 1 where values were not artifically dropped (considers non-imputed values) """ - if type=='entry': + if type == "entry": mask = entry_drop(missingCube, drop) - elif type=='chord': + elif type == "chord": mask = chord_drop(missingCube, drop) if callback is not None: callback.set_mask(mask) imputed_vals = np.ones_like(missingCube) - mask fitted_vals = np.ones_like(missingCube) - imputed_vals - + # for each component up to max, run method for rr in self.rrs: if isinstance(init, str): - np.random.seed(int(x*seed)) + np.random.seed(int(x * seed)) CPinit = initialize_fac(missingCube.copy(), rr, init) elif isinstance(init, tl.cp_tensor.CPTensor): CPinit = deepcopy(init) @@ -150,29 +151,37 @@ def imputation(self, callback.begin() callback(CPinit) - tFac = method(missingCube.copy(), rank=rr, n_iter_max=maxiter, mask=mask, init=CPinit, callback=callback, tol=tol) - + tFac = method( + missingCube.copy(), + rank=rr, + n_iter_max=maxiter, + mask=mask, + init=CPinit, + callback=callback, + tol=tol, + ) + # update error metrics - error[x,rr-1] = calcR2X(tFac, tIn=tImp, calcError=True) + error[x, rr - 1] = calcR2X(tFac, tIn=tImp, calcError=True) if drop > 0: - imputed_error[x,rr-1] = calcR2X(tFac, tIn=tImp, mask=imputed_vals, calcError=True) - fitted_error[x,rr-1] = calcR2X(tFac, tIn=tImp, mask=fitted_vals, calcError=True) - + imputed_error[x, rr - 1] = calcR2X( + tFac, tIn=tImp, mask=imputed_vals, calcError=True + ) + fitted_error[x, rr - 1] = calcR2X( + tFac, tIn=tImp, mask=fitted_vals, calcError=True + ) + # save objects - if type == 'entry': + if type == "entry": self.entry_total = error self.entry_imputed = imputed_error self.entry_fitted = fitted_error - elif type == 'chord': + elif type == "chord": self.chord_total = error self.chord_imputed = imputed_error self.chord_fitted = fitted_error - - if trackCoreConsistency is True: - for r in self.rrs: - corcon[r-1] = corcondia_3d(tFac, r) - + def save(self, pfile): with open(pfile, "wb") as output_file: pickle.dump(self.__dict__, output_file) @@ -182,27 +191,32 @@ def load(self, pfile): tmp_dict = pickle.load(input_file) self.__dict__.update(tmp_dict) -class MultiDecomp(): - ''' + +class MultiDecomp: + """ Saves decomposition results for many Decomposition objects. - ''' - def __init__(self, entry = True, chord = True): + """ + + def __init__(self, entry=True, chord=True): assert entry or chord self.hasEntry = entry self.hasChord = chord self.initialized = False - - def __call__(self, decomp:Decomposition): + def __call__(self, decomp: Decomposition): if self.initialized: if self.hasEntry: - self.entry_total = np.vstack((self.entry_total,decomp.entry_total)) - self.entry_imputed = np.vstack((self.entry_imputed,decomp.entry_imputed)) - self.entry_fitted = np.vstack((self.entry_fitted,decomp.entry_fitted)) + self.entry_total = np.vstack((self.entry_total, decomp.entry_total)) + self.entry_imputed = np.vstack( + (self.entry_imputed, decomp.entry_imputed) + ) + self.entry_fitted = np.vstack((self.entry_fitted, decomp.entry_fitted)) if self.hasChord: - self.chord_total = np.vstack((self.chord_total,decomp.chord_total)) - self.chord_imputed = np.vstack((self.chord_imputed,decomp.chord_imputed)) - self.chord_fitted = np.vstack((self.chord_fitted,decomp.chord_fitted)) + self.chord_total = np.vstack((self.chord_total, decomp.chord_total)) + self.chord_imputed = np.vstack( + (self.chord_imputed, decomp.chord_imputed) + ) + self.chord_fitted = np.vstack((self.chord_fitted, decomp.chord_fitted)) else: self.initialized = True self.rr = decomp.rrs[-1] @@ -215,7 +229,6 @@ def __call__(self, decomp:Decomposition): self.chord_imputed = decomp.chord_imputed self.chord_fitted = decomp.chord_fitted - def save(self, pfile): with open(pfile, "wb") as output_file: pickle.dump(self.__dict__, output_file) @@ -223,4 +236,4 @@ def save(self, pfile): def load(self, pfile): with open(pfile, "rb") as input_file: tmp_dict = pickle.load(input_file) - self.__dict__.update(tmp_dict) \ No newline at end of file + self.__dict__.update(tmp_dict) diff --git a/timpute/initialization.py b/timpute/initialization.py index 98d40ab..0577527 100644 --- a/timpute/initialization.py +++ b/timpute/initialization.py @@ -3,18 +3,20 @@ from tensorly.tenalg import svd_interface from tensorly.random import random_cp + class IterativeSVD(object): def __init__( - self, - rank, - convergence_threshold=1e-7, - max_iters=500, - random_state=None, - min_value=None, - max_value=None, - verbose=False): - self.min_value=min_value - self.max_value=max_value + self, + rank, + convergence_threshold=1e-7, + max_iters=500, + random_state=None, + min_value=None, + max_value=None, + verbose=False, + ): + self.min_value = min_value + self.max_value = max_value self.rank = rank self.max_iters = max_iters self.convergence_threshold = convergence_threshold @@ -70,11 +72,10 @@ def _converged(self, X_old, X_new, missing_mask): old_missing_values = X_old[missing_mask] new_missing_values = X_new[missing_mask] difference = old_missing_values - new_missing_values - ssd = np.sum(difference ** 2) - old_norm_squared = (old_missing_values ** 2).sum() + ssd = np.sum(difference**2) + old_norm_squared = (old_missing_values**2).sum() # edge cases - if old_norm_squared == 0 or \ - (old_norm_squared < F32PREC and ssd > F32PREC): + if old_norm_squared == 0 or (old_norm_squared < F32PREC and ssd > F32PREC): return False else: return (ssd / old_norm_squared) < self.convergence_threshold @@ -84,7 +85,9 @@ def solve(self, X, missing_mask): X_filled = X for i in range(self.max_iters): curr_rank = self.rank - self.U, S, V = svd_interface(matrix=X_filled, n_eigenvecs=curr_rank, random_state=self.random_state) + self.U, S, V = svd_interface( + matrix=X_filled, n_eigenvecs=curr_rank, random_state=self.random_state + ) X_reconstructed = self.U @ np.diag(S) @ V X_reconstructed = self.clip(X_reconstructed) @@ -92,20 +95,17 @@ def solve(self, X, missing_mask): mae = np.mean(np.abs(X[observed_mask] - X_reconstructed[observed_mask])) if self.verbose: - print( - "[IterativeSVD] Iter %d: observed MAE=%0.6f" % ( - i + 1, mae)) + print("[IterativeSVD] Iter %d: observed MAE=%0.6f" % (i + 1, mae)) converged = self._converged( - X_old=X_filled, - X_new=X_reconstructed, - missing_mask=missing_mask) + X_old=X_filled, X_new=X_reconstructed, missing_mask=missing_mask + ) X_filled[missing_mask] = X_reconstructed[missing_mask] if converged: break return X_filled -def initialize_fac(tensor: np.ndarray, rank: int, method='svd'): +def initialize_fac(tensor: np.ndarray, rank: int, method="svd"): """Initialize factors used in `parafac`. Parameters ---------- @@ -116,11 +116,11 @@ def initialize_fac(tensor: np.ndarray, rank: int, method='svd'): factors : CPTensor An initial cp tensor. """ - if method == 'random': + if method == "random": return random_cp(shape=tensor.shape, rank=rank, normalise_factors=False) factors = [np.ones((tensor.shape[i], rank)) for i in range(tensor.ndim)] - contain_missing = (np.sum(~np.isfinite(tensor)) > 0) + contain_missing = np.sum(~np.isfinite(tensor)) > 0 # SVD init mode whose size is larger than rank for mode in range(tensor.ndim): @@ -131,13 +131,13 @@ def initialize_fac(tensor: np.ndarray, rank: int, method='svd'): unfold = si.fit_transform(unfold) factors[mode] = svd_interface(matrix=unfold, n_eigenvecs=rank, flip=True)[0] - else: # tensor.shape[mode] < rank + else: # tensor.shape[mode] < rank unfold = tl.unfold(tensor, mode) if contain_missing: si = IterativeSVD(tensor.shape[mode]) - unfold = si.fit_transform(unfold) # unfold (tensor.shape[mode] x ...) + unfold = si.fit_transform(unfold) # unfold (tensor.shape[mode] x ...) svd_factor = svd_interface(matrix=unfold, n_eigenvecs=rank, flip=True)[0] - factors[mode][:,0:tensor.shape[mode]] = svd_factor + factors[mode][:, 0 : tensor.shape[mode]] = svd_factor - return tl.cp_tensor.CPTensor((None, factors)) \ No newline at end of file + return tl.cp_tensor.CPTensor((None, factors)) diff --git a/timpute/test/test_do.py b/timpute/test/test_do.py index 29f1746..ae10dfa 100644 --- a/timpute/test/test_do.py +++ b/timpute/test/test_do.py @@ -23,8 +23,6 @@ def test_decomp_dopt(plot=False, method=perform_DO): + np.array2string(np.average(decomp.entry_imputed, axis=0)) ) - return decomp, track - def test_unit_dopt(): tensor = generateTensor("known", shape=(10, 10, 10))