cosmetic on tuto mean median

Author: baruch11

docs/api.rst

@@ -103,4 +103,14 @@ Diffusion engine
 
     imputations.imputers_pytorch.ImputerDiffusion
     imputations.diffusions.ddpms.TabDDPM
-    imputations.diffusions.ddpms.TsDDPM
+    imputations.diffusions.ddpms.TsDDPM
+
+
+Utils
+================
+
+.. autosummary::
+    :toctree: generated/
+    :template: function.rst
+    
+    utils.data.add_holes

examples/tutorials/plot_tuto_diffusion_models.py

@@ -7,7 +7,6 @@
 and :class:`~qolmat.imputations.diffusions.ddpms.TsDDPM` classes.
 """
 
-# %%
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt

examples/tutorials/plot_tuto_mean_median.py

@@ -1,6 +1,6 @@
 """
 ========================================================================================
-Tutorial for comparison between mean and median imputations with uniform hole generation
+Comparison of basic imputers
 ========================================================================================
 
 In this tutorial, we show how to use the Qolmat comparator
@@ -31,11 +31,10 @@
 # the 82nd column contains the critical temperature which is used as the
 # target variable.
 # The data does not contain missing values; so for the purpose of this notebook,
-# we corrupt the data, with the ``qolmat.utils.data.add_holes`` function.
+# we corrupt the data, with the :func:`qolmat.utils.data.add_holes` function.
 # In this way, each column has missing values.
 
-df_data = data.get_data("Superconductor")
-df = data.add_holes(df_data, ratio_masked=0.2, mean_size=120)
+df = data.add_holes(data.get_data("Superconductor"), ratio_masked=0.2, mean_size=120)
 
 # %%
 # The dataset contains 82 columns. For simplicity,
@@ -76,10 +75,6 @@
 imputer_median = imputers.ImputerMedian()
 dict_imputers = {"mean": imputer_mean, "median": imputer_median}
 
-generator_holes = missing_patterns.UniformHoleGenerator(
-    n_splits=2, subset=cols_to_impute, ratio_masked=0.1
-)
-
 metrics = ["mae", "wmape", "KL_columnwise"]
 
 # %%
@@ -88,9 +83,7 @@
 # (those previously mentioned),
 # a list with the columns names to impute,
 # a generator of holes specifying the type of holes to create.
-# Just a few words about hole generation.
 # in this example, we have chosen the uniform hole generator.
-# You can see what this looks like.
 # For example, by imposing that 10% of missing data be created
 # ``ratio_masked=0.1`` and creating missing values in columns
 # ``subset=cols_to_impute``: