Merge pull request #134 from scikit-learn-contrib/regressor_cat

Regressor cat

Author: JulienRoussel77

HISTORY.rst

@@ -10,6 +10,7 @@ History
 * Tutorial plot_tuto_categorical showcasing mixed type imputation
 * Titanic dataset added
 * accuracy metric implemented
+* metrics.py rationalized, and split with algebra.py
 
 0.1.3 (2024-03-07)
 ------------------

docs/imputers.rst

@@ -3,24 +3,28 @@ Imputers
 
 All imputers can be found in the ``qolmat.imputations`` folder.
 
-1. Simple (mean/median/shuffle)
--------------------------------
-Imputes the missing values using the mean/median along each column or with a random value in each column. See the :class:`~qolmat.imputations.imputers.ImputerSimple` and :class:`~qolmat.imputations.imputers.ImputerShuffle` classes.
+1. Simple (mean/median/mode)
+----------------------------
+Imputes the missing values using a basic simple statistics: the mode (most frequent value) for the categorical columns, and the mean,median or mode (depending on the user parameter) for the numerical columns. See :class:`~qolmat.imputations.imputers.ImputerSimple`.
 
-2. LOCF
+2. Shuffle
+----------
+Imputes the missing values using a random value sampled in the same column. See :class:`~qolmat.imputations.imputers.ImputerShuffle`.
+
+3. LOCF
 -------
-Imputes the missing values using the last observation carried forward. See the :class:`~qolmat.imputations.imputers.ImputerLOCF` class.
+Imputes the missing values using the last observation carried forward. See :class:`~qolmat.imputations.imputers.ImputerLOCF`.
 
-3. Time interpolation and TSA decomposition
+4. Time interpolation and TSA decomposition
 -------------------------------------------
-Imputes missing using some interpolation strategies supported by `pd.Series.interpolate <https://pandas.pydata.org/docs/reference/api/pandas.Series.interpolate.html>`_. It is done column by column. See the :class:`~qolmat.imputations.imputers.ImputerInterpolation` class. When data are temporal with clear seasonal decomposition, we can interpolate on the residuals instead of directly interpolate the raw data. Series are de-seasonalised based on `statsmodels.tsa.seasonal.seasonal_decompose <https://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html>`_, residuals are imputed via linear interpolation, then residuals are re-seasonalised. It is also done column by column. See the :class:`~qolmat.imputations.imputers.ImputerResiduals` class.
+Imputes missing using some interpolation strategies supported by `pd.Series.interpolate <https://pandas.pydata.org/docs/reference/api/pandas.Series.interpolate.html>`_. It is done column by column. See the :class:`~qolmat.imputations.imputers.ImputerInterpolation` class. When data are temporal with clear seasonal decomposition, we can interpolate on the residuals instead of directly interpolate the raw data. Series are de-seasonalised based on `statsmodels.tsa.seasonal.seasonal_decompose <https://www.statsmodels.org/stable/generated/statsmodels.tsa.seasonal.seasonal_decompose.html>`_, residuals are imputed via linear interpolation, then residuals are re-seasonalised. It is also done column by column. See :class:`~qolmat.imputations.imputers.ImputerResiduals`.
 
 
-4. MICE
+5. MICE
 -------
 Multiple Imputation by Chained Equation: multiple imputations based on ICE. It uses `IterativeImputer <https://scikit-learn.org/stable/modules/generated/sklearn.impute.IterativeImputer.html#sklearn.impute.IterativeImputer>`_. See the :class:`~qolmat.imputations.imputers.ImputerMICE` class.
 
-5. RPCA
+6. RPCA
 -------
 Robust Principal Component Analysis (RPCA) is a modification of the statistical procedure of PCA which allows to work with a data matrix :math:`\mathbf{D} \in \mathbb{R}^{n \times d}` containing missing values and grossly corrupted observations. We consider here the imputation task alone, but these methods can also tackle anomaly correction.
 
@@ -46,7 +50,7 @@ The class :class:`RpcaNoisy` implements an recommanded improved version, which r
 with :math:`\mathbf{E} = \mathbf{D} - \mathbf{M} - \mathbf{A}`.
 See the :class:`~qolmat.imputations.imputers.ImputerRpcaNoisy` class for implementation details.
 
-6. SoftImpute
+7. SoftImpute
 -------------
 SoftImpute is an iterative method for matrix completion that uses nuclear-norm regularization [11]. It is a faster alternative to RPCA, although it is much less robust due to the quadratic penalization. Given a matrix :math:`\mathbf{D} \in \mathbb{R}^{n \times d}` with observed entries indexed by the set :math:`\Omega`, this algorithm solves the following problem:
 
@@ -56,11 +60,11 @@ SoftImpute is an iterative method for matrix completion that uses nuclear-norm r
 The imputed values are then given by the matrix :math:`M=LQ` on the unobserved data.
 See the :class:`~qolmat.imputations.imputers.ImputerSoftImpute` class for implementation details.
 
-7. KNN
+8. KNN
 ------
 K-nearest neighbors, based on `KNNImputer <https://scikit-learn.org/stable/modules/generated/sklearn.impute.KNNImputer.html>`_. See the :class:`~qolmat.imputations.imputers.ImputerKNN` class.
 
-8. EM sampler
+9. EM sampler
 -------------
 Imputes missing values via EM algorithm [5], and more precisely via MCEM algorithm [6]. See the :class:`~qolmat.imputations.imputers.ImputerEM` class.
 Suppose the data :math:`\mathbf{X}` has a density :math:`p_\theta` parametrized by some parameter :math:`\theta`. The EM algorithm allows to draw samples from this distribution by alternating between the expectation and maximization steps.
@@ -104,7 +108,7 @@ Two parametric distributions are implemented:
 * :class:`~qolmat.imputations.em_sampler.VARpEM`: [7]: :math:`\mathbf{X} \in \mathbb{R}^{n \times d} \sim VAR_p(\nu, B_1, ..., B_p)` is generated by a VAR(p) process such that :math:`X_t = \nu + B_1 X_{t-1} + ... + B_p X_{t-p} + u_t` where :math:`\nu \in \mathbb{R}^d` is a vector of intercept terms, the :math:`B_i  \in \mathbb{R}^{d \times d}` are the lags coefficient matrices and :math:`u_t` is white noise nonsingular covariance matrix :math:`\Sigma_u \mathbb{R}^{d \times d}`, so that :math:`\theta = (\nu, B_1, ..., B_p, \Sigma_u)`.
 
 
-9. TabDDPM
+10. TabDDPM
 -----------
 
 :class:`~qolmat.imputations.diffusions.ddpms.TabDDPM` is a deep learning imputer based on Denoising Diffusion Probabilistic Models (DDPMs) [8] for handling multivariate tabular data. Our implementation mainly follows the works of [8, 9]. Diffusion models focus on modeling the process of data transitions from noisy and incomplete observations to the underlying true data. They include two main processes:

examples/benchmark.md

@@ -16,9 +16,6 @@ jupyter:
 **This notebook aims to present the Qolmat repo through an example of a multivariate time series.
 In Qolmat, a few data imputation methods are implemented as well as a way to evaluate their performance.**
 
-```python
-
-```
 
 First, import some useful librairies
 
@@ -36,26 +33,18 @@ from IPython.display import Image
 import pandas as pd
 from datetime import datetime
 import numpy as np
-import scipy
 import hyperopt as ho
-from hyperopt.pyll.base import Apply as hoApply
 np.random.seed(1234)
-import pprint
 from matplotlib import pyplot as plt
-import matplotlib.image as mpimg
 import matplotlib.ticker as plticker
 
 tab10 = plt.get_cmap("tab10")
 plt.rcParams.update({'font.size': 18})
 
-from typing import Optional
 
 from sklearn.linear_model import LinearRegression
-from sklearn.ensemble import RandomForestRegressor, ExtraTreesRegressor, HistGradientBoostingRegressor
 
-
-import sys
-from qolmat.benchmark import comparator, missing_patterns, hyperparameters
+from qolmat.benchmark import comparator, missing_patterns
 from qolmat.imputations import imputers
 from qolmat.utils import data, utils, plot
 
@@ -240,12 +229,8 @@ dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.i
 ```
 
 ```python tags=[]
-dfs_imputed["VAR_max"].groupby("station").min()
-```
-
-```python tags=[]
-# station = df_plot.index.get_level_values("station")[0]
-station = "Huairou"
+station = df_plot.index.get_level_values("station")[0]
+# station = "Huairou"
 df_station = df_plot.loc[station]
 dfs_imputed_station = {name: df_plot.loc[station] for name, df_plot in dfs_imputed.items()}
 ```
@@ -362,7 +347,7 @@ comparison = comparator.Comparator(
 )
 ```
 
-```python jupyter={"outputs_hidden": true} tags=[]
+```python tags=[]
 generator_holes = missing_patterns.EmpiricalHoleGenerator(n_splits=3, groups=('station',), subset=cols_to_impute, ratio_masked=ratio_masked)
 
 comparison = comparator.Comparator(
@@ -393,7 +378,7 @@ plt.show()
 df_plot = df_data[cols_to_impute]
 ```
 
-```python jupyter={"outputs_hidden": true} tags=[]
+```python tags=[]
 dfs_imputed = {name: imp.fit_transform(df_plot) for name, imp in dict_imputers.items()}
 ```
 

examples/tutorials/plot_tuto_categorical.py

@@ -57,7 +57,7 @@
 # - manage categorical features though one hot encoding
 # - manage missing features (native to the HistGradientBoosting)
 
-pipestimator = preprocessing.make_robust_MixteHGB(allow_new=False)
+pipestimator = preprocessing.make_robust_MixteHGB(avoid_new=True)
 imputer_hgb = ImputerRegressor(estimator=pipestimator, handler_nan="none")
 imputer_wrap_hgb = preprocessing.WrapperTransformer(imputer_hgb, bt)