Merge pull request #154 from scikit-learn-contrib/rpca_patch

Rpca patch

Author: JulienRoussel77

.bumpversion.cfg

@@ -1,5 +1,5 @@
 [bumpversion]
-current_version = 0.1.7
+current_version = 0.1.8
 commit = True
 tag = True
 

docs/conf.py

@@ -27,7 +27,7 @@
 author = "Quantmetry"
 
 # The full version, including alpha/beta/rc tags
-version = "0.1.7"
+version = "0.1.8"
 release = version
 
 # -- General configuration ---------------------------------------------------

examples/RPCA.md

@@ -34,6 +34,10 @@ from qolmat.imputations.rpca import rpca_utils
 from qolmat.utils.data import generate_artificial_ts
 ```
 
+```python
+from qolmat.imputations.imputers import ImputerRpcaNoisy, ImputerRpcaPcp
+```
+
 **Generate synthetic data**
 
 ```python tags=[]
@@ -46,16 +50,33 @@ amp_noise = 0.1
 X_true, A_true, E_true = generate_artificial_ts(n_samples, periods, amp_anomalies, ratio_anomalies, amp_noise)
 
 signal = X_true + A_true + E_true
+signal = 10 + signal * 40
 
 # Adding missing data
 signal[120:180] = np.nan
 signal[:20] = np.nan
+for i in range(10):
+    signal[i::365] = np.nan
 # signal[80:220] = np.nan
 # mask = np.random.choice(len(signal), round(len(signal) / 20))
 # signal[mask] = np.nan
 
 ```
 
+```python
+import pandas as pd
+df = pd.DataFrame({"signal": signal})
+irn = ImputerRpcaPcp(period=100)
+df_imp = irn.fit_transform(df)
+```
+
+```python
+plt.plot(df_imp["signal"])
+plt.plot(df["signal"])
+
+plt.xlim(0, 200)
+```
+
 ```python tags=[]
 fig = plt.figure(figsize=(15, 8))
 ax = fig.add_subplot(4, 1, 1)

qolmat/_version.py

@@ -1 +1 @@
-__version__ = "0.1.7"
+__version__ = "0.1.8"

qolmat/imputations/imputers.py

@@ -1515,13 +1515,10 @@ def _transform_element(
         D_scale = (D - means) / stds
         M, A = model.decompose(D_scale, Omega)
         M = M * stds + means
-        A = A * stds + means
 
         M_final = utils.get_shape_original(M, X.shape)
-        A_final = utils.get_shape_original(A, X.shape)
-        X_imputed = M_final + A_final
 
-        df_imputed = pd.DataFrame(X_imputed, index=df.index, columns=df.columns)
+        df_imputed = pd.DataFrame(M_final, index=df.index, columns=df.columns)
         df_imputed = df.where(~df.isna(), df_imputed)
 
         return df_imputed
@@ -1705,7 +1702,6 @@ def _transform_element(
         D_scale = (D - means) / stds
         M, A = model.decompose_on_basis(D_scale, Omega, Q)
         M = M * stds + means
-        A = A * stds + means
 
         M_final = utils.get_shape_original(M, X.shape)
 

setup.py

@@ -3,7 +3,7 @@
 from setuptools import find_packages, setup
 
 DISTNAME = "qolmat"
-VERSION = "0.1.7"
+VERSION = "0.1.8"
 DESCRIPTION = "A Python library for optimal data imputation."
 LONG_DESCRIPTION_CONTENT_TYPE = "text/x-rst"
 with codecs.open("README.rst", encoding="utf-8-sig") as f: