dodalem drzewo decyzyjne
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<component name="Black">
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<option name="sdkName" value="Python 3.11 (ai-wozek) (2)" />
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<component name="ProjectRootManager" version="2" project-jdk-name="Python 3.11 (ai-wozek)" project-jdk-type="Python SDK" />
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</project>
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@ -4,7 +4,7 @@
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<content url="file://$MODULE_DIR$">
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<excludeFolder url="file://$MODULE_DIR$/venv" />
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</content>
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<orderEntry type="jdk" jdkName="Python 3.12 (ai-wozek)" jdkType="Python SDK" />
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<orderEntry type="inheritedJdk" />
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<orderEntry type="sourceFolder" forTests="false" />
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</component>
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</module>
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74
ai-wozek/decision_tree
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74
ai-wozek/decision_tree
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digraph {
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root [label=root]
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Label_State [label=Label_State]
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no [label=no]
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Label_State -> no [label=""]
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Height [label=Height]
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no -> Height [label=""]
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no [label=no shape=box]
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Height -> no [label=medium]
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no [label=no shape=box]
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Height -> no [label=big]
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small [label=small]
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Height -> small [label=""]
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Width [label=Width]
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small -> Width [label=""]
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small [label=small]
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Width -> small [label=""]
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Depth [label=Depth]
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small -> Depth [label=""]
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no [label=no shape=box]
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Depth -> no [label=big]
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yes [label=yes shape=box]
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Depth -> yes [label=medium]
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no [label=no shape=box]
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Width -> no [label=big]
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medium [label=medium]
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Width -> medium [label=""]
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Depth [label=Depth]
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medium -> Depth [label=""]
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no [label=no shape=box]
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Depth -> no [label=big]
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yes [label=yes shape=box]
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Depth -> yes [label=medium]
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yes [label=yes shape=box]
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Depth -> yes [label=small]
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yes [label=yes]
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Label_State -> yes [label=""]
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Damage [label=Damage]
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yes -> Damage [label=""]
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yes [label=yes shape=box]
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Damage -> yes [label=no]
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yes [label=yes]
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Damage -> yes [label=""]
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Height [label=Height]
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yes -> Height [label=""]
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no [label=no shape=box]
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Height -> no [label=medium]
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no [label=no shape=box]
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Height -> no [label=big]
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small [label=small]
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Height -> small [label=""]
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Width [label=Width]
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small -> Width [label=""]
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no [label=no shape=box]
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Width -> no [label=big]
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small [label=small]
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Width -> small [label=""]
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Depth [label=Depth]
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small -> Depth [label=""]
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yes [label=yes shape=box]
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Depth -> yes [label=medium]
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no [label=no shape=box]
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Depth -> no [label=big]
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yes [label=yes shape=box]
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Depth -> yes [label=small]
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medium [label=medium]
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Width -> medium [label=""]
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Value [label=Value]
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medium -> Value [label=""]
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yes [label=yes shape=box]
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Value -> yes [label=cheap]
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no [label=no shape=box]
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Value -> no [label=expensive]
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}
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@ -378,13 +378,77 @@ def astar(isstate,final):
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#drzewko
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tree_data_base = pd.read_csv('paczki.csv')
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def entropy(data):
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labels = data.iloc[:, -1] # Ostatnia kolumna zawiera etykiety klas i pomija 1 wiersz bo jest tytulowy
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counts = labels.value_counts() #tu zlicza wszystkie opcje
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counts = labels.value_counts() # tu zlicza wszystkie opcje
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probabilities = counts / len(labels)
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entropy = -sum(probabilities * np.log2(probabilities))
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return entropy
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def information_gain(data, attribute):
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total_entropy = entropy(data)
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values = data[attribute].unique() # przypisujemy wszystkie opcje danego atrybutu np wyoski/niski/sredni
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weighted_entropy = 0
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for value in values:
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subset = data[data[attribute] == value] # przypisujesz wszystkie wiersze danego value do subset
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subset_entropy = entropy(subset)
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weighted_entropy += (len(subset) / len(data)) * subset_entropy
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return total_entropy - weighted_entropy
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def id3(data, attributes, target_attribute):
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unique_targets = data[target_attribute].unique()
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# Jeśli wszystkie przykłady mają tę samą etykietę, zwróć tę etykietę
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if len(unique_targets) == 1:
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return unique_targets[0]
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# Jeśli zbiór atrybutów jest pusty, zwróć najczęstszą etykietę
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if len(attributes) == 0:
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return data[target_attribute].mode()[0]
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# Wybierz atrybut o największym przyroście informacji
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info_gains = [(attr, information_gain(data, attr)) for attr in attributes]
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best_attribute = max(info_gains, key=lambda x: x[1])[0]
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# Tworzymy węzeł drzewa
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tree = {best_attribute: {}}
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# Usuwamy wybrany atrybut z listy atrybutów
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attributes = [attr for attr in attributes if attr != best_attribute]
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# Dla każdej wartości wybranego atrybutu tworzę gałąź drzewa
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for value in data[best_attribute].unique():
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subset = data[data[best_attribute] == value]
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subtree = id3(subset, attributes, target_attribute)
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tree[best_attribute][value] = subtree
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return tree
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# Przygotowanie danych
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data = tree_data_base.iloc[:, :9] # Zakładamy, że ostatnia kolumna to etykieta, a pierwsze osiem kolumn to atrybuty
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attributes = list(data.columns[:-1])
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target_attribute = data.columns[-1]
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# Trenowanie drzewa decyzyjnego
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decision_tree = id3(data, attributes, target_attribute)
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# Opcja podglądu wyuczonego drzewa
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def print_tree(tree, indent=""):
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if isinstance(tree, dict):
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for key, value in tree.items():
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print(f"{indent}{key}")
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print_tree(value, indent + " ")
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else:
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print(f"{indent}{tree}")
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print_tree(decision_tree)
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def information_gain(data, attribute):
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total_entropy = entropy(data)
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values = data[attribute].unique() #przypisujemy wszystkie opcje danego atrybutu np wyoski/niski/sredni
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