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Traktorek/Natalia Wiśniewska- drzewo_decyzyjne.py

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+training_data = [
+    #nawodnienie, kiedyNawadniano, coIleDniTrzebaNawadniac, czyMaPadac, kiedyPadalo
+    ['n', 2, 3, 't', 1],
+    ['s', 1, 3, 't', 1],
+    ['s', 5, 2, 'n', 1],
+    ['n', 3, 5, 'n', 1],
+    ['s', 3, 1, 't', 2],
+    ['n', 2, 4, 'n', 2],
+    ['n', 4, 6, 't', 3],
+    ['n', 6, 5, 't', 3],
+    ['s', 1, 2, 't', 4],
+    ['s', 7, 3, 'n', 5],
+    ['n', 4, 4, 'n', 5],
+    ['s', 5, 6, 't', 5],
+    ['n', 2, 7, 't', 1],
+    ['s', 5, 6, 't', 7],
+    ['s', 5, 3, 'n', 7],
+    ['n', 3, 2, 'n', 7],
+    ['s', 3, 5, 't', 4],
+    ['n', 3, 4, 'n', 4],
+    ['n', 4, 3, 't', 6],
+    ['n', 6, 3, 't', 6],
+    ['s', 1, 4, 't', 6],
+    ['s', 7, 5, 'n', 3],
+    ['n', 2, 5, 'n', 3],
+    ['s', 4, 6, 't', 3],
+    ['s', 4, 8, 'n', 4]
+]
+
+header = ["nawodnienie", "kiedyNawadniano", "coIleDni", "czyMaPadac", "kiedyPadalo"]
+
+def class_counts(rows):
+    counts = {}
+    for row in rows:
+        label = row[-1]
+        if label not in counts:
+            counts[label] = 0
+        counts[label] += 1
+    return counts
+
+
+def is_numeric(value):
+    return isinstance(value, int) or isinstance(value, float)
+
+
+class Question:
+    def __init__(self, column, value):
+        self.column = column
+        self.value = value
+
+    def match(self, example):
+        val = example[self.column]
+        if is_numeric(val):
+            return val >= self.value
+        else:
+            return val == self.value
+
+    def __repr__(self):
+        condition = "=="
+        if is_numeric(self.value):
+            condition = ">="
+        return "Czy %s %s %s?" % (
+            header[self.column], condition, str(self.value))
+
+def partition(rows, question):
+    true_rows, false_rows = [], []
+    for row in rows:
+        if question.match(row):
+            true_rows.append(row)
+        else:
+            false_rows.append(row)
+    return true_rows, false_rows
+
+
+def gini(rows):
+    counts = class_counts(rows)
+    impurity = 1
+    for lbl in counts:
+        prob_of_lbl = counts[lbl] / float(len(rows))
+        impurity -= prob_of_lbl**2
+    return impurity
+
+
+def info_gain(left, right, current_uncertainty):
+    p = float(len(left)) / (len(left) + len(right))
+    return current_uncertainty - p * gini(left) - (1 - p) * gini(right)
+
+
+def find_best_split(rows):
+    best_gain = 0
+    best_question = None
+    current_uncertainty = gini(rows)
+    n_features = len(rows[0]) - 1
+    for col in range(n_features):
+        values = set([row[col] for row in rows])
+        for val in values:
+            question = Question(col, val)
+            true_rows, false_rows = partition(rows, question)
+            if len(true_rows) == 0 or len(false_rows) == 0:
+                continue
+            gain = info_gain(true_rows, false_rows, current_uncertainty)
+            if gain >= best_gain:
+                best_gain, best_question = gain, question
+    return best_gain, best_question
+
+
+class Leaf:
+    def __init__(self, rows):
+        self.predictions = class_counts(rows)
+
+class Decision_Node:
+    def __init__(self,
+                 question,
+                 true_branch,
+                 false_branch):
+        self.question = question
+        self.true_branch = true_branch
+        self.false_branch = false_branch
+
+def build_tree(rows):
+    gain, question = find_best_split(rows)
+    if gain == 0:
+        return Leaf(rows)
+    true_rows, false_rows = partition(rows, question)
+    true_branch = build_tree(true_rows)
+    false_branch = build_tree(false_rows)
+    return Decision_Node(question, true_branch, false_branch)
+
+
+def print_tree(node, spacing=""):
+    if isinstance(node, Leaf):
+        print (spacing + "Predict", node.predictions)
+        return
+    print (spacing + str(node.question))
+    print (spacing + '--> True:')
+    print_tree(node.true_branch, spacing + "  ")
+    print (spacing + '--> False:')
+    print_tree(node.false_branch, spacing + "  ")
+
+
+my_tree = build_tree(training_data)
+
+print_tree(my_tree)
+