progress bars conditioned by verbose

Author: Julien Roussel

README.rst

@@ -70,17 +70,18 @@ With just these few lines of code, you can see how easy it is to
   from qolmat.utils import data
 
   # load and prepare csv data
+
   df_data = data.get_data("Beijing")
   columns = ["TEMP", "PRES", "WSPM"]
   df_data = df_data[columns]
   df_with_nan = data.add_holes(df_data, ratio_masked=0.2, mean_size=120)
 
   # impute and compare
-  imputer_mean = imputers.ImputerSimple(strategy="mean", groups=("station",))
+  imputer_median = imputers.ImputerSimple(groups=("station",))
   imputer_interpol = imputers.ImputerInterpolation(method="linear", groups=("station",))
   imputer_var1 = imputers.ImputerEM(model="VAR", groups=("station",), method="mle", max_iter_em=50, n_iter_ou=15, dt=1e-3, p=1)
   dict_imputers = {
-        "mean": imputer_mean,
+        "median": imputer_median,
         "interpolation": imputer_interpol,
         "VAR(1) process": imputer_var1
     }

qolmat/imputations/em_sampler.py

@@ -435,7 +435,9 @@ def fit_X(self, X: NDArray) -> None:
         X = self._maximize_likelihood(X_imp, mask_na)
 
         for iter_em in tqdm(
-            range(self.max_iter_em), desc="EM parameters estimation"
+            range(self.max_iter_em),
+            desc="EM parameters estimation",
+            disable=not self.verbose,
         ):
             X = self._sample_ou(X, mask_na)
 
@@ -477,6 +479,7 @@ def fit(self, X: NDArray) -> "EM":
         if hasattr(self, "p_to_fit") and self.p_to_fit:
             aics: List[float] = []
             for p in range(self.max_lagp + 1):
+                print("p=", p)
                 self.p = p
                 self.fit_X(X)
                 n1, n2 = self.X.shape

qolmat/imputations/rpca/rpca_noisy.py

@@ -201,6 +201,7 @@ def decompose_with_basis(
             max_iterations=self.max_iterations,
             tolerance=self.tolerance,
             norm=self.norm,
+            verbose=self.verbose,
         )
 
         self._check_cost_function_minimized(D, M, A, Omega, tau, lam)
@@ -220,6 +221,7 @@ def minimise_loss(
         max_iterations: int = 10000,
         tolerance: float = 1e-6,
         norm: str = "L2",
+        verbose: bool = False,
     ) -> Tuple:
         """Compute the noisy RPCA with a L2 time penalisation.
 
@@ -256,6 +258,9 @@ def minimise_loss(
             consecutive iterations. Defaults to 1e-6.
         norm : str, optional
             Error norm, can be "L1" or "L2". Defaults to "L2".
+        verbose : bool, optional
+            Verbosity level, if False the warnings are silenced. Defaults to
+            False.
 
         Returns
         -------
@@ -313,7 +318,9 @@ def minimise_loss(
         In = identity(n_rows)
 
         for _ in tqdm(
-            range(max_iterations), desc="Noisy RPCA loss minimization"
+            range(max_iterations),
+            desc="Noisy RPCA loss minimization",
+            disable=not verbose,
         ):
             M_temp = M.copy()
             A_temp = A.copy()

qolmat/imputations/rpca/rpca_pcp.py

@@ -127,7 +127,9 @@ def decompose(self, D: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]:
 
         M: NDArray = D - A
         for iteration in tqdm(
-            range(self.max_iterations), desc="RPCA PCP decomposition"
+            range(self.max_iterations),
+            desc="RPCA PCP decomposition",
+            disable=not self.verbose,
         ):
             M = rpca_utils.svd_thresholding(D - A + Y / mu, 1 / mu)
             A = rpca_utils.soft_thresholding(D - M + Y / mu, lam / mu)

qolmat/imputations/softimpute.py

@@ -148,7 +148,9 @@ def decompose(self, X: NDArray, Omega: NDArray) -> Tuple[NDArray, NDArray]:
         M = A @ B.T
         cost_start = SoftImpute.cost_function(X, M, A, Omega, tau)
         for iter_ in tqdm(
-            range(self.max_iterations), desc="Soft Impute decomposition"
+            range(self.max_iterations),
+            desc="Soft Impute decomposition",
+            disable=not self.verbose,
         ):
             U_old = U
             V_old = V