Merge pull request #185 from 42-AI/162-ml03ex09-confusion-matrix-last…

…-2-examples-uses-`y_true`-instead-of-`y`

isue #162: ML03ex09 Confusion matrix: Last 2 examples uses y_true instead of y

module08/en.subject.tex

@@ -1251,9 +1251,10 @@ \section*{Instructions}
     """
     Compute confusion matrix to evaluate the accuracy of a classification.
     Args:
-        y_true:a numpy.ndarray for the correct labels
-        y_hat:a numpy.ndarray for the predicted labels
-        labels: optional, a list of labels to index the matrix. This may be used to reorder or select a subset of labels. (default=None)
+        y_true: numpy.ndarray for the correct labels
+        y_hat: numpy.ndarray for the predicted labels
+        labels: Optional, a list of labels to index the matrix.
+                This may be used to reorder or select a subset of labels. (default=None)
     Returns: 
         The confusion matrix as a numpy ndarray.
         None on any error.
@@ -1310,17 +1311,18 @@ \subsection{Objective(s):}
 \subsection{Instructions:}
 
 In the \texttt{confusion\_matrix.py} file, write the following function as per the instructions below:
+
 \begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
-def confusion_matrix_(y, y_hat, labels=None, df_option=False):
+def confusion_matrix_(y_true, y_hat, labels=None, df_option=False):
     """
     Compute confusion matrix to evaluate the accuracy of a classification.
     Args:
-        y:a numpy.ndarray for the correct labels
-        y_hat:a numpy.ndarray for the predicted labels
+        y_true: a numpy.ndarray for the correct labels
+        y_hat: a numpy.ndarray for the predicted labels
         labels: optional, a list of labels to index the matrix. This may be used to reorder or select a subset of labels. (default=None)
         df_option: optional, if set to True the function will return a pandas DataFrame instead of a numpy array. (default=False)
     Returns: 
-        The confusion matrix as a numpy ndarray or a pandas DataFrame according to df_option value.
+        Confusion matrix as a numpy ndarray or a pandas DataFrame according to df_option value.
         None on any error.
     Raises:
         This function should not raise any Exception.
@@ -1329,21 +1331,22 @@ \subsection{Instructions:}
 \end{minted}
 
 \section{Examples:}
+
 \begin{minted}[bgcolor=darcula-back,formatcom=\color{lightgrey},fontsize=\scriptsize]{python}
 import numpy as np
-y_hat = np.array([['norminet'], ['dog'], ['norminet'], ['norminet'], ['dog'], ['bird']])
-y_true = np.array([['dog'], ['dog'], ['norminet'], ['norminet'], ['dog'], ['norminet']])
+y_hat = np.array(['norminet', 'dog', 'norminet', 'norminet', 'dog', 'bird'])
+y = np.array(['dog', 'dog', 'norminet', 'norminet', 'dog', 'norminet'])
 
 # Example 1: 
-confusion_matrix_(y_true, y_hat, df_option=True)
+confusion_matrix_(y, y_hat, df_option=True)
 # Output:
            bird  dog  norminet
  bird         0    0         0
  dog          0    2         1
  norminet     1    0         2
 
 # Example 2:
-confusion_matrix_(y_true, y_hat, labels=['bird', 'dog'], df_option=True)
+confusion_matrix_(y, y_hat, labels=['bird', 'dog'], df_option=True)
 # Output:
            bird  dog
  bird         0    0

version

@@ -1 +1 @@
-v4.0.5
+v4.0.6