104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
import math
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from functools import reduce
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from LabWork01.LabWork6.ConvertorDataFrame import CovertorDataFrame
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# дата-сет
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dfMain = CovertorDataFrame()[0]
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dfTest = CovertorDataFrame()[1]
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cstr = lambda s: [k + ":" + str(v) for k, v in sorted(s.value_counts().items())]
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# Структура данных Decision Tree
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tree = {
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# name: Название этого нода (узла)
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"name": "decision tree " + dfMain.columns[-1] + " " + str(cstr(dfMain.iloc[:, -1])),
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# df: Данные, связанные с этим нодом (узлом)
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"df": dfMain,
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# edges: Список ребер (ветвей), выходящих из этого узла, или пустой массив, если ниже нет листового узла.
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"edges": [],
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}
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# Генерацию дерева, у узлов которого могут быть ветви, сохраняем в open
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open = [tree]
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# Лямба-выражение для вычесления энтропии.
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# Аргумент - pandas.Series、возвращаемое значение - число энтропии
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entropy = lambda s: -reduce(lambda x, y: x + y, map(lambda x: (x / len(s)) * math.log2(x / len(s)), s.value_counts()))
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# Зацикливаем, пока open не станет пустым
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while (len(open) != 0):
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n = open.pop(0)
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df_n = n["df"]
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if 0 == entropy(df_n.iloc[:, -1]):
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continue
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attrs = {}
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for attr in df_n.columns[:-1]:
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attrs[attr] = {"entropy": 0, "dfs": [], "values": []}
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for value in sorted(set(df_n[attr])):
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df_m = df_n.query(attr + "=='" + value + "'")
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attrs[attr]["entropy"] += entropy(df_m.iloc[:, -1]) * df_m.shape[0] / df_n.shape[0]
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attrs[attr]["dfs"] += [df_m]
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attrs[attr]["values"] += [value]
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pass
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pass
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if len(attrs) == 0:
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continue
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attr = min(attrs, key=lambda x: attrs[x]["entropy"])
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for d, v in zip(attrs[attr]["dfs"], attrs[attr]["values"]):
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m = {"name": attr + "=" + v, "edges": [], "df": d.drop(columns=attr)}
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n["edges"].append(m)
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open.append(m)
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pass
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# Выводим дата сет
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print(dfMain, "\n-------------")
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# оценка тестовых данных
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def predict_bp(nodes, target) -> int:
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overlap = None
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for node in nodes:
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check: bool = node["value"] == target[node["attr"]]
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if check:
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overlap = node
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break
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if overlap is None:
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overlap = nodes[-1]
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if len(overlap["edges"]) == 0:
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return int(overlap["df"]["StoreSales"].mean())
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else:
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return predict_bp(overlap["edges"], target)
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def predict_str(count: int):
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predictions = []
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for i in range(count):
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row = dfTest.iloc[i]
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prediction = f"{ {'Age': row['Age'], 'BMI': row['BMI']} }" + \
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f"<br/>predict {predict_bp(tree['edges'], {'Age': row['Age'], 'BMI': row['BMI']})} / fact {row['BloodPressure']}"
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predictions.append(prediction)
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return '<br/>'.join(predictions)
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def tstr(tree, indent=""):
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s = indent + tree["name"] + str(cstr(tree["df"].iloc[:, -1]) if len(tree["edges"]) == 0 else "") + "\n"
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# Зацикливаем все ветви этого узла.
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for e in tree["edges"]:
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s += tstr(e, "\t" + indent + " ")
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pass
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return s
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def getStringTree():
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return tstr(tree)
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# Выводим древо в его символьном представлении.
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print(tstr(tree))
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