drzewo decyzyjne
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@ -293,7 +293,7 @@
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"vege": false,
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"vege": false,
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"size": 50,
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"size": 50,
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"allergens": "tomato",
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"allergens": "tomato",
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"ingridients": [ "cheese", "chicken", "onion", "corn", "toamto" ],
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"ingridients": [ "cheese", "chicken", "onion", "corn", "tomato" ],
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"drink_in": true
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"drink_in": true
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},
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},
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{
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{
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150
tiles.py
150
tiles.py
@ -456,136 +456,8 @@ def evaluate_preferences(preferences):
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return data
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return data
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# decision tree ręcznie
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class GadId3Classifier:
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def fit(self, input, output):
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data = input.copy()
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data[output.name] = output
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self.tree = self.decision_tree(data, data, input.columns, output.name)
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def predict(self, input):
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samples = input.to_dict(orient='records')
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predictions = []
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for sample in samples:
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predictions.append(self.make_prediction(sample, self.tree, 1.0))
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return predictions
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def entropy(self, attribute_column):
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values, counts = np.unique(attribute_column, return_counts=True)
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entropy_list = []
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for i in range(len(values)):
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probability = counts[i] / np.sum(counts)
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entropy_list.append(-probability * np.log2(probability))
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total_entropy = np.sum(entropy_list)
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return total_entropy
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def information_gain(self, data, feature_attribute_name, target_attribute_name):
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total_entropy = self.entropy(data[target_attribute_name])
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values, counts = np.unique(data[feature_attribute_name], return_counts=True)
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weighted_entropy_list = []
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for i in range(len(values)):
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subset_probability = counts[i] / np.sum(counts)
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subset_entropy = self.entropy(
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data.where(data[feature_attribute_name] == values[i]).dropna()[target_attribute_name])
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weighted_entropy_list.append(subset_probability * subset_entropy)
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total_weighted_entropy = np.sum(weighted_entropy_list)
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information_gain = total_entropy - total_weighted_entropy
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return information_gain
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def decision_tree(self, data, orginal_data, feature_attribute_names, target_attribute_name, parent_node_class=None):
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unique_classes = np.unique(data[target_attribute_name])
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if len(unique_classes) <= 1:
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return unique_classes[0]
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elif len(data) == 0:
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majority_class_index = np.argmax(np.unique(original_data[target_attribute_name], return_counts=True)[1])
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return np.unique(original_data[target_attribute_name])[majority_class_index]
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elif len(feature_attribute_names) == 0:
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return parent_node_class
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else:
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majority_class_index = np.argmax(np.unique(data[target_attribute_name], return_counts=True)[1])
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parent_node_class = unique_classes[majority_class_index]
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ig_values = [self.information_gain(data, feature, target_attribute_name) for feature in
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feature_attribute_names]
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best_feature_index = np.argmax(ig_values)
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best_feature = feature_attribute_names[best_feature_index]
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tree = {best_feature: {}}
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feature_attribute_names = [i for i in feature_attribute_names if i != best_feature]
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parent_attribute_values = np.unique(data[best_feature])
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for value in parent_attribute_values:
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sub_data = data.where(data[best_feature] == value).dropna()
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subtree = self.decision_tree(sub_data, orginal_data, feature_attribute_names, target_attribute_name,
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parent_node_class)
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tree[best_feature][value] = subtree
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return tree
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def make_prediction(self, sample, tree, default=1):
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for attribute in list(sample.keys()):
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if attribute in list(tree.keys()):
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try:
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result = tree[attribute][sample[attribute]]
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except:
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return default
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result = tree[attribute][sample[attribute]]
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if isinstance(result, dict):
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return self.make_prediction(sample, result)
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else:
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return result
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def train_id3(prefernce):
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df = pd.read_excel("data.xlsx")
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d = {'low': 30, 'high': 50}
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df['level of hunger'] = df['level of hunger'].map(d)
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d = {'none': 0, 'tomato': 1, 'feta': 2, 'olives': 3}
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df['allergy'] = df['allergy'].map(d)
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d = {'none': 0, 'salami': 1, 'mushrooms': 2, 'pineapple': 3, 'shrimps': 4, 'sausage': 5}
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df['favorite ingridient'] = df['favorite ingridient'].map(d)
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d = {'margherita': 0, 'hawajska': 1, 'funghi': 2, 'light': 3, '4 sery': 4, 'pepperoni': 5,
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'salami': 6, 'wegetarianska': 7, 'barbecue': 8, 'miesna': 9, 'paprykowa': 10,
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'jalapeno': 11, 'barbecue wege': 12, 'kebab': 13, 'grecka': 14, 'piekielna': 15,
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'drwala': 16, 'grzybowa': 17, 'staropolska': 18, 'goralska': 19, 'prosciutto': 20,
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'broccoli': 21, 'americana': 22, 'farmerska': 23, 'nachos': 24, 'texas': 25,
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'kurczak': 26, 'zielona': 27, 'mix': 28}
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df['pizza'] = df['pizza'].map(d)
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features = ['budget', 'spiciness', 'vege', 'level of hunger', 'allergy', 'favorite ingridient', 'drink in']
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X = df[features]
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y = df['pizza']
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X_train, X_test, y_train, y_test = train_test_split(X, y)
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model = GadId3Classifier()
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model.fit(X_train, y_train)
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pre = [prefernce]
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df = pd.DataFrame(pre, columns=['budget','spiciness','vege','level of hunger','allergy','favorite ingridient','drink in'])
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return model.predict(df)
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# decision tree z biblioteka
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def choose_pizza(prefernce):
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def choose_pizza(prefernce):
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df = pd.read_excel("data.xlsx")
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df = pd.read_excel("restaurant.xlsx")
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d = {'low': 30, 'high': 50}
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d = {'low': 30, 'high': 50}
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df['level of hunger'] = df['level of hunger'].map(d)
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df['level of hunger'] = df['level of hunger'].map(d)
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@ -610,7 +482,7 @@ def choose_pizza(prefernce):
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y = df['pizza']
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y = df['pizza']
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x_train, x_test, y_train, y_test = train_test_split(x, y)
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x_train, x_test, y_train, y_test = train_test_split(x, y)
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clf = DecisionTreeClassifier(criterion='entropy')
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clf = DecisionTreeClassifier(random_state=400)
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clf = clf.fit(x_train, y_train)
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clf = clf.fit(x_train, y_train)
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return clf.predict([prefernce])
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return clf.predict([prefernce])
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@ -702,25 +574,17 @@ def main():
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route = astar(map.get_arr(), (waiter.loc[1] // 32, waiter.loc[0] // 32), goal)
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route = astar(map.get_arr(), (waiter.loc[1] // 32, waiter.loc[0] // 32), goal)
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direction = [(x[1] - y[1], x[0] - y[0]) for x, y in zip(route[1:], route)]
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direction = [(x[1] - y[1], x[0] - y[0]) for x, y in zip(route[1:], route)]
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break
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break
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print()
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print("Hello Sir, tell me yours preferences")
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print("Hello Sir, tell me yours preferences")
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print("Pass: 'budget', 'spiciness', 'vege', 'level_of_hunger', 'allergy', 'favorite_ingridient', 'drink_in'\n")
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print("Pass: 'budget', 'spiciness', 'vege', 'level_of_hunger', 'allergy', 'favorite_ingridient', 'drink_in'\n")
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print("Here is my list of preferences")
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print("Here is my list of preferences")
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ingridients = tell_preferences()
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ingridients = tell_preferences()
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print(ingridients)
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print(ingridients)
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print()
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print()
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evaluated_ingridients = evaluate_preferences(ingridients)
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pizza = get_pizza(choose_pizza(evaluate_preferences(ingridients)))
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print("Our proposition:")
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print("recznie drzewo")
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print("Name = {}\nprice = {}\nspiciness = {}\nvege = {}\nsize = {}\nallergens = {}\ningridients = {}\ndrink_in = {}\n"
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num = train_id3(evaluated_ingridients)
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.format(pizza.name,pizza.price, pizza.spiciness,pizza.vege,pizza.size,pizza.allergens,pizza.ingridients,pizza.drink_in))
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piz = get_pizza(int(num[0]))
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print("Name = {}, pos_in_card - {}, price = {}, spiciness = {}, vege = {}, size = {}, allergens = {}, ingridients = {}, drink_in = {}\n"
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.format(piz.name, piz.pos_in_card, piz.price, piz.spiciness, piz.vege, piz.size,piz.allergens, piz.ingridients, piz.drink_in))
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number_of_pizza = choose_pizza(evaluated_ingridients)
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pizza = get_pizza(number_of_pizza)
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print("drzewo z biblioteka")
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print("Name = {}, pos_in_card - {}, price = {}, spiciness = {}, vege = {}, size = {}, allergens = {}, ingridients = {}, drink_in = {}\n"
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.format(pizza.name,pizza.pos_in_card,pizza.price, pizza.spiciness,pizza.vege,pizza.size,pizza.allergens,pizza.ingridients,pizza.drink_in))
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if len(direction) > 0:
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if len(direction) > 0:
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