notebooks cleaned

Author: Julien Roussel

examples/RPCA.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.3'
-      jupytext_version: 1.14.5
+      jupytext_version: 1.14.4
   kernelspec:
     display_name: Python 3 (ipykernel)
     language: python
@@ -74,7 +74,7 @@ plt.show()
 
 ```python
 %%time
-rpca_pcp = RPCAPCP(period=100, max_iter=5, mu=.5, lam=1)
+rpca_pcp = RPCAPCP(period=100, max_iterations=5, mu=.5, lam=1)
 X, A = rpca_pcp.decompose_rpca_signal(signal)
 imputed = signal - A
 ```
@@ -102,52 +102,3 @@ plt.plot(imputed)
 ```python
 
 ```
-
-```python
-%%time
-signal_toy = np.array([[1, 2], [np.nan, np.nan]])
-rpca_noisy = RPCANoisy(tau=0, lam=1, norm="L2", do_report=True)
-X, A = rpca_noisy.decompose_rpca_signal(signal_toy)
-```
-
-```python
-print(X)
-print(A)
-```
-
-```python
-%%time
-signal_toy = np.array([[1, 2], [np.nan, np.nan]])
-rpca_pcp = RPCAPCP(lam=1e3)
-X, A = rpca_pcp.decompose_rpca_signal(signal_toy)
-```
-
-```python
-X
-```
-
-```python
-A
-```
-
-```python
-np.log(10) / np.log(1.1)
-```
-
-```python
-X = np.array([[1, 2], [4, 4], [4, 3]])
-# Omega = np.array([[True, False], [True, True], [False, True]])
-Omega = np.array([[True, True], [True, True], [True, True]])
-rpca_noisy = RPCANoisy(period=2, max_iter=200, tau=.5, lam=1, do_report=True)
-M_result, A_result, U_result, V_result = rpca_noisy.decompose_rpca_L2(
-    X, Omega=Omega, lam=1, tau=.5, rank=2
-)
-```
-
-```python
-M_result
-```
-
-```python
-
-```

examples/benchmark.md

@@ -57,19 +57,6 @@ from qolmat.utils import data, utils, plot
 
 ```
 
-```python
-from qolmat.imputations.em_sampler import invert_robust
-```
-
-```python
-M[np.array([0, 1]), np.array([0, 1])]
-```
-
-```python
-M = np.array([[0, 0], [0, 1]])
-invert_robust(M)
-```
-
 ### **I. Load data**
 
 
@@ -174,7 +161,7 @@ dict_config_opti = {
     "tau": ho.hp.uniform("tau", low=.5, high=5),
     "lam": ho.hp.uniform("lam", low=.1, high=1),
 }
-imputer_rpca_opti = imputers.ImputerRPCA(groups=("station",), columnwise=False, max_iter=256)
+imputer_rpca_opti = imputers.ImputerRPCA(groups=("station",), columnwise=False, max_iterations=256)
 imputer_rpca_opti = hyperparameters.optimize(
     imputer_rpca_opti,
     df_data,
@@ -193,7 +180,7 @@ dict_config_opti2 = {
     "lam/TEMP": ho.hp.uniform("lam/TEMP", low=.1, high=1),
     "lam/PRES": ho.hp.uniform("lam/PRES", low=.1, high=1),
 }
-imputer_rpca_opti2 = imputers.ImputerRPCA(groups=("station",), columnwise=True, max_iter=256)
+imputer_rpca_opti2 = imputers.ImputerRPCA(groups=("station",), columnwise=True, max_iterations=256)
 imputer_rpca_opti2 = hyperparameters.optimize(
     imputer_rpca_opti2,
     df_data,
@@ -202,7 +189,6 @@ imputer_rpca_opti2 = hyperparameters.optimize(
     max_evals=10,
     dict_spaces=dict_config_opti2
 )
-# imputer_rpca_opti.params_optim = hyperparams_opti
 ```
 
 ```python
@@ -287,10 +273,6 @@ df_plot = df_data[cols_to_impute]
 dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.items()}
 ```
 
-```python
-df_data.mode().iloc[0]
-```
-
 ```python
 station = df_plot.index.get_level_values("station")[0]
 df_station = df_plot.loc[station]