nvm

1 files changed, 0 insertions, 369 deletions

diff --git a/ass1_vectorized.py b/ass1_vectorized.py
deleted file mode 100644
index 08a4f8e..0000000
--- a/ass1_vectorized.py
+++ /dev/null
@@ -1,369 +0,0 @@
-import time
-import numpy as np
-
-credit_data = np.genfromtxt('./credit_score.txt',
-                            delimiter=',',
-                            skip_header=True)
-
-# age,married,house,income,gender,class
-# [(22, 0, 0, 28, 1, 0)
-#  (46, 0, 1, 32, 0, 0)
-#  (24, 1, 1, 24, 1, 0)
-#  (25, 0, 0, 27, 1, 0)
-#  (29, 1, 1, 32, 0, 0)
-#  (45, 1, 1, 30, 0, 1)
-#  (63, 1, 1, 58, 1, 1)
-#  (36, 1, 0, 52, 1, 1)
-#  (23, 0, 1, 40, 0, 1)
-#  (50, 1, 1, 28, 0, 1)]
-
-# In the program data points are called rows
-
-# In the program categorical or numerical attributes are called cols for columns
-
-# The last column are the classes and will be called as classes in the program
-
-
-class Tree():
-    """
-    @todo: docstring for Tree
-    """
-    def __init__(self, tree_vec_of_tuples):
-        """@todo: Docstring for init method.
-
-        /root_node_obj/ @todo
-
-        """
-        self.tree_vec_of_tuples = tree_vec_of_tuples
-        self.leaf_nodes = np.where(tree_vec_of_tuples[:,1] == -1)
-        self.classes = [(1, -1), (1, -1)]
-
-    def drop(self, y):
-        """
-        @todo: Docstring for drop
-        """
-        return y * 100
-        
-    def leaf(self, y):
-        """
-        @todo: Docstring for drop
-        """
-        return y
-        
-
-
-
-
-def impurity(array) -> int:
-    """
-    Assumes the argument array is a one dimensional vector of zeroes and ones.
-    Computes the gini index impurity based on the relative frequency of ones in
-    the vector.
-
-    Example:
-
-    >>> array=np.array([1,0,1,1,1,0,0,1,1,0,1])
-    >>> array
-    array([1,0,1,1,1,0,0,1,1,0,1])
-
-    >>> impurity(array)
-    0.23140495867768596
-    """
-    # Total labels
-    n_labels = len(array)
-    if n_labels == 0:
-        # Prevents division by zero, when the potential split does not have any rows
-        n_labels = 1
-    # Number of tuples labeled 1
-    n_labels_1 = array.sum()
-    # Calculate the relative frequency of ones with respect to the total labels
-    rel_freq_1 = n_labels_1 / n_labels
-    # Use the symmetry around the median property to also calculate the
-    # relative frequency of zeroes
-    rel_freq_0 = 1 - rel_freq_1
-    # Multiply the frequencies to get the gini index
-    gini_index = rel_freq_1 * rel_freq_0
-    return gini_index
-
-
-def bestsplit(x, y) -> int:
-    """
-    x = vector of single col
-    y = vector of classes (last col in x)
-
-    Consider splits of type "x <= c" where "c" is the average of two consecutive
-    values of x in the sorted order.
-
-    x and y must be of the same length
-
-    y[i] must be the class label of the i-th observation, and x[i] is the
-    correspnding value of attribute x
-
-    Example (best split on income):
-
-    >>> bestsplit(credit_data[:,3],credit_data[:,5])
-     36
-    """
-    x_sorted = np.sort(np.unique(x))
-    if len(x_sorted) <= 2:
-        # Allows splitting on categorical (0 or 1) cols
-        split_points = [0.5]
-    else:
-        # Take average between consecutive numerical rows in the x col
-        split_points = (x_sorted[:len(x_sorted) - 1] + x_sorted[1:]) / 2
-
-    # De toepassing van bestsplit verdeelt de x col vector in tweeen, twee
-    # arrays van "x rows". Deze moeten we terug krijgen om de in de child nodes
-    # bestsplit toe te passen.
-    #
-    # Deze lus berekent de best split value, en op basis daarvan weten we welke
-    # twee "x rows" arrays we moeten returnen, en welke split value het beste
-    # was natuurlijk.
-    best_delta_i = None
-    for split in split_points:
-        # np.index_exp maakt een boolean vector die zegt welke elementen in de
-        # col van x hoger of lager zijn dan split
-        col_slice_boolean_matrices = {
-            "left": np.index_exp[x > split],
-            "right": np.index_exp[x <= split]
-        }
-
-        # delta_i formule met de boolean vector van hierboven
-        delta_i = impurity(
-            y) - (len(y[col_slice_boolean_matrices["left"]]) *
-                  impurity(y[col_slice_boolean_matrices["left"]]) +
-                  len(y[col_slice_boolean_matrices["right"]]) *
-                  impurity(y[col_slice_boolean_matrices["right"]])) / len(y)
-
-        print(f"{split=}, {delta_i=}")
-        #
-        if best_delta_i is not None:
-            if delta_i > best_delta_i:
-                best_delta_i, best_split, best_col_slice_boolean_matrices = delta_i, split, col_slice_boolean_matrices
-        else:
-            best_delta_i, best_split, best_col_slice_boolean_matrices = delta_i, split, col_slice_boolean_matrices
-    return best_delta_i, best_split, best_col_slice_boolean_matrices
-
-def tree_example(x=None, tr=None, **defaults) -> None:
-    """
-    @todo: Docstring for tree_example
-    """
-    tree_vec = []
-    print(tree_vec)
-    print(type(tree_vec))
-    tree_vec.append((36,3))
-    print(tree_vec)
-    tree_vec.append((0,3))
-    print(tree_vec)
-    tree_vec.append((1, -1))
-    print(tree_vec)
-    print(tree_vec[0])
-    print(type(tree_vec[0]))
-    tree_vec = np.array(tree_vec) # , dtype=(int, 2))
-    print(tree_vec)
-    print(type(tree_vec[0]))
-
-    tree = Tree(tree_vec)
-    
-    # Let's show how to predict
-    # 1. maak een vector met root node voor elke row in x waarvoor je een class
-    # wil predicten.
-    y = np.ones(len(x), dtype=int)
-    print(y)
-    print(type(y))
-    print(y.shape)
-    # 2. Herinner recurrence relatie van whatsapp, en pas hem toe met
-    # tree.drop(x) op nodes die geen leaf node zijn
-    # 
-    # Returns indices where not leaf node
-    print(tree.leaf_nodes)
-    # print(tree.classes)
-    # leafs = np.searchsorted(
-    # print(leafs)
-    y = np.where(np.searchsorted(tree.leaf_nodes, y))
-
-    # y = y[:,0]
-    # print(y)
-
-
-
-#
-#
-# Put all helper functions above this comment!
-
-
-def tree_grow(x=None,
-              y=None,
-              n_min=None,
-              min_leaf=None,
-              n_feat=None,
-              **defaults) -> Tree:
-    """
-    @todo: Docstring for tree_grow
-    """
-    # Voor de lus die onze tree growt instantieren we een list die tuples als
-    # elementen zal hebben, het grote voordeel is dat we op deze manier vector
-    # operaties meerdere parallele
-    # prediction drops kunnen doen. (Je kan bijvoorbeeld geen object methodes
-    # broadcasten als je een numpy array van node objecten hebt)
-    #
-    # De tuple moet uiteindelijk de informatie bevatten om voor een row in x
-    # een class te voorspellen. Hier hebben we voor nodig:
-    # 
-    # 1. Het split value, voor lager of hoger test
-    # 2. Het col nummer waar de split bij hoort, anders weten we niet waar we op testen
-    #
-    # (split, col)
-    #
-    # De enige uitzondering hierop zijn leaf nodes. Om de tree data structure
-    # een numpy array te maken moeten dit ook tuples zijn. Dit lossen we op
-    # door een negatieve col aan te duiden. Dit zorgt ervoor dat de prediction
-    # functie hier eindigt.
-    #
-    # (class, negative_int: -1)
-    #
-    # Checkout tree example function for more info
-    tree_vec = []
-    # De nodelist heeft in het begin alleen de alle rows van x, omdat alle rows
-    # altijd in de root in acht worden genomen.
-    #
-    # Dit representeren we met een boolean vector, met lengte het aantal rows in x en elementen True.
-    rows = np.full((1,len(x)), True)
-    nodelist = [rows]
-
-    # tree_array = np.empty 
-    # while nodelist:
-    #     current_node = nodelist.pop()
-    #     slices = current_node.value
-    #     node_classes = y[slices]
-    #     # print(node_classes)
-
-    #     # f'Current node will be leaf node if (( (number of data "tuples" in child node) < {n_min=} )) \n'
-    #     # put stopping rules here before making a split
-    #     if len(node_classes) < n_min:
-    #         current_node.value = Leaf(
-    #             np.argmax(np.bincount(node_classes.astype(int))))
-    #         print(f"leaf node has majority clas:\n{current_node.value.value=}")
-    #         continue
-
-    #     if impurity(node_classes) > 0:
-    #         # print(
-    #         #     f"Exhaustive split search says, new node will check these rows for potential spliterinos:\n{x[slices]}"
-    #         # )
-
-    #         # If we arrive here ever we are splitting
-    #         # bestsplit(col, node_labels) ->
-    #         # {"slices": list[int], "split": numpyfloat, "best_delta_i": numpyfloat}
-
-    #         # slices (list) used for knowing which rows (int) to consider in a node
-    #         # best_split saved in current_node.value
-    #         # best_delta_i used to find best split among x_columns
-    #         best_dict = None
-    #         for i, x_col in enumerate(x[slices].transpose()):
-    #             print(
-    #                 "\nExhaustive split search says; \"Entering new column\":")
-    #             col_split_dict = bestsplit(x_col, node_classes, slices)
-
-    #             if best_dict is not None:
-    #                 if col_split_dict["delta_i"] > best_dict["delta_i"]:
-    #                     best_dict = col_split_dict
-    #                     best_dict["col"] = i
-    #             else:
-    #                 best_dict = col_split_dict
-    #                 best_dict["col"] = i
-    #         print("\nThe best split for current node:", best_dict)
-
-    #         # Here we store the splitted data into Node objects
-    #         current_node.value = best_dict["split"]
-    #         current_node.col = best_dict["col"]
-    #         # Split will not happen if (( (number of data "tuples" potential split) < {min_leaf=} ))\n'
-    #         if min([len(x) for x in best_dict["slices"].values()]) < min_leaf:
-    #             continue
-    #         else:
-    #             # Invert left and right because we want left to pop() first
-    #             children = [
-    #                 Node(value=best_dict["slices"]["right"]),
-    #                 Node(value=best_dict["slices"]["left"])
-    #             ]
-    #             current_node.add_split(children[1], children[0])
-    #             nodelist += children
-    #     else:
-    #         current_node.value = Leaf(
-    #             np.argmax(np.bincount(node_classes.astype(int))))
-    #         print(
-    #             f"\n\nLEAF NODE has majority clas:\n{current_node.value.value=}"
-    #         )
-    #         continue
-    # return tree
-
-
-def predict(x, nodes) -> list:
-    """
-    @todo: Docstring for predict
-    """
-    # which row to drop
-    # print(x)
-    drop = 0
-    while not set(nodes).issubset({0, 1}):
-        print(nodes)
-        # print(x[drop])
-        if isinstance(nodes[drop].value, Leaf):
-            nodes[drop] = nodes[drop].value.value
-            drop += 1
-            continue
-
-        print(nodes[drop].value)
-        print(nodes[drop].col)
-        # print(nodes[drop].col)
-        if x[drop, nodes[drop].col] > nodes[drop].value:
-            nodes[drop] = nodes[drop].left
-        else:
-            nodes[drop] = nodes[drop].right
-    return np.array(nodes)
-
-
-def tree_pred(x=None, tr=None, **defaults) -> np.array:
-    """
-    @todo: Docstring for tree_pred
-    """
-    nodes = [tr.tree] * len(x)
-    # y = np.linspace(0, len(x), 0)
-    # y = np.array(ele)
-    y = predict(x, nodes)
-    print(f"\n\nPredicted classes for {x=}\n\n are: {y=}")
-    return y
-
-
-if __name__ == '__main__':
-    #### IMPURITY TEST
-    # array=np.array([1,0,1,1,1,0,0,1,1,0,1])
-    # print(impurity(array))
-    # Should give 0.23....
-
-    #### BESTSPLIT TEST
-    # print(bestsplit(credit_data[:, 3], credit_data[:, 5]))
-    # Should give 36
-
-    #### TREE_GROW TEST
-    tree_grow_defaults = {
-        'x': credit_data[:, :5],
-        'y': credit_data[:, 5],
-        'n_min': 2,
-        'min_leaf': 1,
-        'n_feat': 5
-    }
-
-    # Calling the tree grow, unpacking default as argument
-    # tree_grow(**tree_grow_defaults)
-
-    #### TREE_PRED TEST
-    tree_pred_defaults = {
-        'x': credit_data[:, :5],
-        # 'tr': tree_grow(**tree_grow_defaults)
-    }
-
-    tree_example(**tree_pred_defaults)
-    # tree_pred(**tree_pred_defaults)
-
-# start_time = time.time()
-# print("--- %s seconds ---" % (time.time() - start_time))