From 9bf55bfb852a457f478e35d52afc462b4e6c0363 Mon Sep 17 00:00:00 2001
From: Amara Spano <amara.spano@crem.ch>
Date: Mon, 21 Sep 2020 01:03:58 +0200
Subject: [PATCH] docstring for paper.py

---
 scripts/paper.py | 40 ++++++++++++++++++++++++++++++++++++++++
 1 file changed, 40 insertions(+)

diff --git a/scripts/paper.py b/scripts/paper.py
index 2a98379..39d8a14 100644
--- a/scripts/paper.py
+++ b/scripts/paper.py
@@ -7,6 +7,19 @@
 
 
 def base_test(protocol, variables, setname='csh101', nb_tree_per_forest=50, max_depth=10):
+    """Basic test for the random forest classifier
+
+    Args:
+        protocol (str): protocol to use
+        variables (1d-array): list of desired variables (features)
+        setname (str): name of the dataset to load
+        nb_tree_per_forest: number of decision trees in the forest
+        max_depth: max depth of the trees
+    Returns:
+        numpy.ndarray: A 2D array (with a dtype of int) containing the confusion matrix.
+    Raises:
+        None
+    """
     # get train data
     train = database.get(protocol, 'train', database.CLASSES, variables, setname)
     # make and train model
@@ -23,6 +36,15 @@ def base_test(protocol, variables, setname='csh101', nb_tree_per_forest=50, max_
 
 
 def pretty_confusion_matrix(cm):
+    """Adds labels to confusion matrix
+
+    Args:
+        cm (numpy.ndarray): A 2D array (with a dtype of int) containing the confusion matrix.
+    Returns:
+        str: nicely formatted confusion matrix for printing
+    Raises:
+        None
+    """
     classes = np.array([database.CLASSES])
     table = tabulate(np.vstack((np.hstack(([[""]], classes)),
                                 np.hstack((classes.T, cm)))))
@@ -30,6 +52,15 @@ def pretty_confusion_matrix(cm):
 
 
 def test_impact_nb_trees(tabnum):
+    """Evaluates and print the impact of the number of trees per forest on the classifiers performance
+
+    Args:
+        tabnum (int): first confusion matrix numbering
+    Returns:
+        None
+    Raises:
+        None
+    """
     nb_trees = [1, 5, 10]
     print("\nImpact of number of trees per forest")
     for n, p in enumerate(database.PROTOCOLS):
@@ -43,6 +74,15 @@ def test_impact_nb_trees(tabnum):
             print(pretty_confusion_matrix(cm))
 
 def test_impact_tree_depth(tabnum):
+    """Evaluates and print the impact of the trees depth on the classifiers performance
+
+    Args:
+        tabnum (int): first confusion matrix numbering
+    Returns:
+        None
+    Raises:
+        None
+    """
     depths = [1, 5, 10]
     print("\nImpact of trees maximum depth")
     for n, p in enumerate(database.PROTOCOLS):